modelId stringlengths 4 111 | lastModified stringlengths 24 24 | tags list | pipeline_tag stringlengths 5 30 ⌀ | author stringlengths 2 34 ⌀ | config null | securityStatus null | id stringlengths 4 111 | likes int64 0 9.53k | downloads int64 2 73.6M | library_name stringlengths 2 84 ⌀ | created timestamp[us] | card stringlengths 101 901k | card_len int64 101 901k | embeddings list |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
YakovElm/Jira5Classic_512 | 2023-05-28T19:57:41.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/Jira5Classic_512 | 0 | 2 | transformers | 2023-05-28T19:57:07 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Jira5Classic_512
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Jira5Classic_512
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.4916
- Train Accuracy: 0.7775
- Validation Loss: 0.7670
- Validation Accuracy: 0.4953
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.5471 | 0.7597 | 0.8190 | 0.4858 | 0 |
| 0.4951 | 0.7566 | 0.7401 | 0.5237 | 1 |
| 0.4916 | 0.7775 | 0.7670 | 0.4953 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,776 | [
[
-0.04144287109375,
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-... |
tobro/distilbert-base-uncased-finetuned-emotion | 2023-05-28T20:39:06.000Z | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | text-classification | tobro | null | null | tobro/distilbert-base-uncased-finetuned-emotion | 0 | 2 | transformers | 2023-05-28T20:16:55 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
config: split
split: validation
args: split
metrics:
- name: Accuracy
type: accuracy
value: 0.924
- name: F1
type: f1
value: 0.9240544367354029
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2170
- Accuracy: 0.924
- F1: 0.9241
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8285 | 1.0 | 250 | 0.3046 | 0.905 | 0.9021 |
| 0.2469 | 2.0 | 500 | 0.2170 | 0.924 | 0.9241 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,846 | [
[
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0.00858306884765625,
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-0.051849365234375,
-0.059417724609375... |
YakovElm/IntelDAOS10Classic_512 | 2023-05-28T20:29:54.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/IntelDAOS10Classic_512 | 0 | 2 | transformers | 2023-05-28T20:29:21 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: IntelDAOS10Classic_512
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# IntelDAOS10Classic_512
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.2776
- Train Accuracy: 0.9200
- Validation Loss: 0.3822
- Validation Accuracy: 0.8739
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.3159 | 0.8920 | 0.4005 | 0.8739 | 0 |
| 0.2834 | 0.9200 | 0.3910 | 0.8739 | 1 |
| 0.2776 | 0.9200 | 0.3822 | 0.8739 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,788 | [
[
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0.01404571533203125,
0.01036834716796875,
-0.052886962890625,
-0.048065185546875,
-0.05154418945312... |
YakovElm/MariaDB10Classic_512 | 2023-05-28T20:50:36.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/MariaDB10Classic_512 | 0 | 2 | transformers | 2023-05-28T20:49:40 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: MariaDB10Classic_512
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# MariaDB10Classic_512
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.2262
- Train Accuracy: 0.9180
- Validation Loss: 0.1954
- Validation Accuracy: 0.9523
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.3315 | 0.8787 | 0.1847 | 0.9523 | 0 |
| 0.2425 | 0.9163 | 0.1867 | 0.9523 | 1 |
| 0.2262 | 0.9180 | 0.1954 | 0.9523 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,784 | [
[
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0.021759033203125,
0.0036373138427734375,
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YakovElm/IntelDAOS15Classic_512 | 2023-05-28T21:46:54.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/IntelDAOS15Classic_512 | 0 | 2 | transformers | 2023-05-28T21:46:21 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: IntelDAOS15Classic_512
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# IntelDAOS15Classic_512
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.1928
- Train Accuracy: 0.9460
- Validation Loss: 0.3544
- Validation Accuracy: 0.8859
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.2386 | 0.9410 | 0.3525 | 0.8859 | 0 |
| 0.2091 | 0.9460 | 0.3540 | 0.8859 | 1 |
| 0.1928 | 0.9460 | 0.3544 | 0.8859 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,788 | [
[
-0.044464111328125,
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0.020660400390625,
0.0022735595703125,
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0.01070404052734375,
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-0.0482177734375,
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-0.024963... |
YakovElm/Jira10Classic_512 | 2023-05-28T22:07:50.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/Jira10Classic_512 | 0 | 2 | transformers | 2023-05-28T22:07:15 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Jira10Classic_512
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Jira10Classic_512
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.3196
- Train Accuracy: 0.8730
- Validation Loss: 0.7679
- Validation Accuracy: 0.6278
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.5012 | 0.7880 | 0.6635 | 0.6215 | 0 |
| 0.4297 | 0.8174 | 0.6604 | 0.6562 | 1 |
| 0.3196 | 0.8730 | 0.7679 | 0.6278 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,778 | [
[
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YakovElm/MariaDB15Classic_512 | 2023-05-28T22:18:49.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/MariaDB15Classic_512 | 0 | 2 | transformers | 2023-05-28T22:18:15 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: MariaDB15Classic_512
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# MariaDB15Classic_512
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.2040
- Train Accuracy: 0.9347
- Validation Loss: 0.1593
- Validation Accuracy: 0.9598
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.2696 | 0.9172 | 0.1661 | 0.9598 | 0 |
| 0.2249 | 0.9297 | 0.1700 | 0.9598 | 1 |
| 0.2040 | 0.9347 | 0.1593 | 0.9598 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,784 | [
[
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YakovElm/Qt5Classic_512 | 2023-05-28T22:23:51.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/Qt5Classic_512 | 0 | 2 | transformers | 2023-05-28T22:23:04 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Qt5Classic_512
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Qt5Classic_512
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.2969
- Train Accuracy: 0.8951
- Validation Loss: 0.2446
- Validation Accuracy: 0.9294
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.3406 | 0.8918 | 0.2640 | 0.9294 | 0 |
| 0.3195 | 0.8940 | 0.2617 | 0.9294 | 1 |
| 0.2969 | 0.8951 | 0.2446 | 0.9294 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,772 | [
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YakovElm/IntelDAOS20Classic_512 | 2023-05-28T23:03:42.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/IntelDAOS20Classic_512 | 0 | 2 | transformers | 2023-05-28T23:03:09 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: IntelDAOS20Classic_512
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# IntelDAOS20Classic_512
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.1413
- Train Accuracy: 0.9610
- Validation Loss: 0.3492
- Validation Accuracy: 0.9099
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.2049 | 0.9540 | 0.3516 | 0.9099 | 0 |
| 0.1533 | 0.9610 | 0.3182 | 0.9099 | 1 |
| 0.1413 | 0.9610 | 0.3492 | 0.9099 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,788 | [
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YakovElm/MariaDB20Classic_512 | 2023-05-28T23:49:01.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/MariaDB20Classic_512 | 0 | 2 | transformers | 2023-05-28T23:48:27 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: MariaDB20Classic_512
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# MariaDB20Classic_512
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.2062
- Train Accuracy: 0.9347
- Validation Loss: 0.1332
- Validation Accuracy: 0.9698
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.2920 | 0.9054 | 0.1423 | 0.9698 | 0 |
| 0.2138 | 0.9356 | 0.1391 | 0.9698 | 1 |
| 0.2062 | 0.9347 | 0.1332 | 0.9698 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,784 | [
[
-0.043548583984375,
-0.043121337890625,
0.0213165283203125,
0.0035266876220703125,
-0.0335693359375,
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-0.0165252685546875,
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0.0152587890625,
0.0141448974609375,
-0.05560302734375,
-0.049591064453125,
-0.0517578125,
-0.0... |
YakovElm/Jira15Classic_512 | 2023-05-29T00:33:18.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/Jira15Classic_512 | 0 | 2 | transformers | 2023-05-29T00:32:35 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Jira15Classic_512
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Jira15Classic_512
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.4218
- Train Accuracy: 0.8048
- Validation Loss: 0.8710
- Validation Accuracy: 0.5773
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.5326 | 0.7681 | 0.7742 | 0.5205 | 0 |
| 0.4878 | 0.7870 | 0.7395 | 0.5205 | 1 |
| 0.4218 | 0.8048 | 0.8710 | 0.5773 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,778 | [
[
-0.041748046875,
-0.042633056640625,
0.019989013671875,
0.001056671142578125,
-0.034698486328125,
-0.02923583984375,
-0.017486572265625,
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0.01531219482421875,
0.01255035400390625,
-0.05169677734375,
-0.0479736328125,
-0.051300048828125,
-0... |
YakovElm/Qt10Classic_512 | 2023-05-29T02:45:42.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/Qt10Classic_512 | 0 | 2 | transformers | 2023-05-29T02:40:35 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Qt10Classic_512
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Qt10Classic_512
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.2304
- Train Accuracy: 0.9200
- Validation Loss: 0.2101
- Validation Accuracy: 0.9416
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.2779 | 0.9191 | 0.2090 | 0.9416 | 0 |
| 0.2541 | 0.9210 | 0.2225 | 0.9416 | 1 |
| 0.2304 | 0.9200 | 0.2101 | 0.9416 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,774 | [
[
-0.04052734375,
-0.03631591796875,
0.0232391357421875,
0.0021343231201171875,
-0.033660888671875,
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-0.01177978515625,
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0.0091705322265625,
0.012054443359375,
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-0.0478515625,
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-0.02633... |
YakovElm/Jira20Classic_512 | 2023-05-29T03:02:53.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/Jira20Classic_512 | 0 | 2 | transformers | 2023-05-29T02:58:12 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Jira20Classic_512
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Jira20Classic_512
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.2224
- Train Accuracy: 0.9182
- Validation Loss: 0.2787
- Validation Accuracy: 0.9306
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.3831 | 0.8678 | 0.2569 | 0.9338 | 0 |
| 0.2901 | 0.8793 | 0.2538 | 0.9338 | 1 |
| 0.2224 | 0.9182 | 0.2787 | 0.9306 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,778 | [
[
-0.0408935546875,
-0.041290283203125,
0.0206451416015625,
0.0018434524536132812,
-0.032745361328125,
-0.02801513671875,
-0.01715087890625,
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0.01464080810546875,
0.01265716552734375,
-0.05224609375,
-0.048248291015625,
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-0.... |
cesullivan99/sms-spam-weighted | 2023-05-29T07:07:49.000Z | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | cesullivan99 | null | null | cesullivan99/sms-spam-weighted | 0 | 2 | transformers | 2023-05-29T04:15:10 | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: sms-spam-weighted
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# sms-spam-weighted
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2336
- Accuracy: 0.989
- F1: 0.9575
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.0009 | 1.0 | 125 | 0.1323 | 0.987 | 0.9494 |
| 0.0034 | 2.0 | 250 | 0.1401 | 0.988 | 0.9531 |
| 0.0001 | 3.0 | 375 | 0.2087 | 0.991 | 0.9647 |
| 0.0001 | 4.0 | 500 | 0.2121 | 0.988 | 0.9538 |
| 0.0001 | 5.0 | 625 | 0.2129 | 0.988 | 0.9538 |
| 0.0 | 6.0 | 750 | 0.2242 | 0.99 | 0.9612 |
| 0.0 | 7.0 | 875 | 0.2285 | 0.989 | 0.9575 |
| 0.0 | 8.0 | 1000 | 0.2314 | 0.989 | 0.9575 |
| 0.0 | 9.0 | 1125 | 0.2330 | 0.989 | 0.9575 |
| 0.0 | 10.0 | 1250 | 0.2336 | 0.989 | 0.9575 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
| 2,008 | [
[
-0.03460693359375,
-0.03515625,
0.00971221923828125,
0.0178070068359375,
-0.0152587890625,
-0.0263519287109375,
-0.00641632080078125,
-0.0079803466796875,
0.0229644775390625,
0.024200439453125,
-0.055755615234375,
-0.0506591796875,
-0.057861328125,
-0.016708... |
gokuls/hBERTv1_no_pretrain_cola | 2023-05-29T04:31:54.000Z | [
"transformers",
"pytorch",
"tensorboard",
"hybridbert",
"text-classification",
"generated_from_trainer",
"en",
"dataset:glue",
"model-index",
"endpoints_compatible",
"region:us"
] | text-classification | gokuls | null | null | gokuls/hBERTv1_no_pretrain_cola | 0 | 2 | transformers | 2023-05-29T04:18:06 | ---
language:
- en
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- matthews_correlation
- accuracy
model-index:
- name: hBERTv1_no_pretrain_cola
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE COLA
type: glue
config: cola
split: validation
args: cola
metrics:
- name: Matthews Correlation
type: matthews_correlation
value: 0.0
- name: Accuracy
type: accuracy
value: 0.6912751793861389
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# hBERTv1_no_pretrain_cola
This model is a fine-tuned version of [](https://huggingface.co/) on the GLUE COLA dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6184
- Matthews Correlation: 0.0
- Accuracy: 0.6913
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 128
- eval_batch_size: 128
- seed: 10
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|:--------:|
| 0.8952 | 1.0 | 67 | 0.6664 | 0.0 | 0.6913 |
| 0.6234 | 2.0 | 134 | 0.6184 | 0.0 | 0.6913 |
| 0.6127 | 3.0 | 201 | 0.6197 | 0.0 | 0.6913 |
| 0.6115 | 4.0 | 268 | 0.6209 | 0.0 | 0.6913 |
| 0.6096 | 5.0 | 335 | 0.6237 | 0.0 | 0.6913 |
| 0.6104 | 6.0 | 402 | 0.6209 | 0.0 | 0.6913 |
| 0.6123 | 7.0 | 469 | 0.6185 | 0.0 | 0.6913 |
### Framework versions
- Transformers 4.29.2
- Pytorch 1.14.0a0+410ce96
- Datasets 2.12.0
- Tokenizers 0.13.3
| 2,384 | [
[
-0.026824951171875,
-0.043853759765625,
0.004024505615234375,
0.0176544189453125,
-0.01538848876953125,
-0.005733489990234375,
0.0011692047119140625,
-0.006488800048828125,
0.036468505859375,
0.0096282958984375,
-0.057037353515625,
-0.041168212890625,
-0.0589294... |
gokuls/hBERTv2_new_no_pretrain_cola | 2023-06-14T13:14:18.000Z | [
"transformers",
"pytorch",
"tensorboard",
"hybridbert",
"text-classification",
"generated_from_trainer",
"en",
"dataset:glue",
"model-index",
"endpoints_compatible",
"region:us"
] | text-classification | gokuls | null | null | gokuls/hBERTv2_new_no_pretrain_cola | 0 | 2 | transformers | 2023-05-29T04:29:32 | ---
language:
- en
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- matthews_correlation
- accuracy
model-index:
- name: hBERTv2_new_no_pretrain_cola
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE COLA
type: glue
config: cola
split: validation
args: cola
metrics:
- name: Matthews Correlation
type: matthews_correlation
value: 0.0
- name: Accuracy
type: accuracy
value: 0.6912751793861389
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# hBERTv2_new_no_pretrain_cola
This model is a fine-tuned version of [](https://huggingface.co/) on the GLUE COLA dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6181
- Matthews Correlation: 0.0
- Accuracy: 0.6913
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 4e-05
- train_batch_size: 128
- eval_batch_size: 128
- seed: 10
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|:--------:|
| 0.6421 | 1.0 | 67 | 0.6186 | 0.0 | 0.6913 |
| 0.6181 | 2.0 | 134 | 0.6403 | 0.0 | 0.6913 |
| 0.6176 | 3.0 | 201 | 0.6252 | 0.0 | 0.6913 |
| 0.6185 | 4.0 | 268 | 0.6313 | 0.0 | 0.6913 |
| 0.6163 | 5.0 | 335 | 0.6181 | 0.0 | 0.6913 |
| 0.6118 | 6.0 | 402 | 0.6182 | 0.0 | 0.6913 |
| 0.6516 | 7.0 | 469 | 0.6316 | 0.0 | 0.6913 |
| 0.6363 | 8.0 | 536 | 0.6240 | 0.0 | 0.6913 |
| 0.6235 | 9.0 | 603 | 0.6310 | 0.0 | 0.6913 |
| 0.6152 | 10.0 | 670 | 0.6441 | 0.0 | 0.6913 |
### Framework versions
- Transformers 4.30.2
- Pytorch 1.14.0a0+410ce96
- Datasets 2.12.0
- Tokenizers 0.13.3
| 2,607 | [
[
-0.027008056640625,
-0.04217529296875,
0.005451202392578125,
0.01552581787109375,
-0.0116729736328125,
-0.005462646484375,
0.0009822845458984375,
-0.005123138427734375,
0.03265380859375,
0.00995635986328125,
-0.052490234375,
-0.040252685546875,
-0.0582275390625,... |
gokuls/sa_BERT_no_pretrain_cola | 2023-05-29T04:50:30.000Z | [
"transformers",
"pytorch",
"tensorboard",
"hybridbert",
"text-classification",
"generated_from_trainer",
"en",
"dataset:glue",
"model-index",
"endpoints_compatible",
"region:us"
] | text-classification | gokuls | null | null | gokuls/sa_BERT_no_pretrain_cola | 0 | 2 | transformers | 2023-05-29T04:37:10 | ---
language:
- en
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- matthews_correlation
- accuracy
model-index:
- name: sa_BERT_no_pretrain_cola
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE COLA
type: glue
config: cola
split: validation
args: cola
metrics:
- name: Matthews Correlation
type: matthews_correlation
value: 0.0
- name: Accuracy
type: accuracy
value: 0.6912751793861389
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# sa_BERT_no_pretrain_cola
This model is a fine-tuned version of [](https://huggingface.co/) on the GLUE COLA dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6180
- Matthews Correlation: 0.0
- Accuracy: 0.6913
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 128
- eval_batch_size: 128
- seed: 10
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|:--------:|
| 0.8826 | 1.0 | 67 | 0.6624 | 0.0 | 0.6913 |
| 0.616 | 2.0 | 134 | 0.6358 | 0.0 | 0.6913 |
| 0.6134 | 3.0 | 201 | 0.6195 | 0.0 | 0.6913 |
| 0.6139 | 4.0 | 268 | 0.6285 | 0.0 | 0.6913 |
| 0.6117 | 5.0 | 335 | 0.6180 | 0.0 | 0.6913 |
| 0.6099 | 6.0 | 402 | 0.6183 | 0.0 | 0.6913 |
| 0.6113 | 7.0 | 469 | 0.6232 | 0.0 | 0.6913 |
| 0.6135 | 8.0 | 536 | 0.6182 | 0.0 | 0.6913 |
| 0.6094 | 9.0 | 603 | 0.6221 | 0.0 | 0.6913 |
| 0.6096 | 10.0 | 670 | 0.6310 | 0.0 | 0.6913 |
### Framework versions
- Transformers 4.29.2
- Pytorch 1.14.0a0+410ce96
- Datasets 2.12.0
- Tokenizers 0.13.3
| 2,639 | [
[
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-0.044219970703125,
0.0042724609375,
0.01486968994140625,
-0.01389312744140625,
-0.00823211669921875,
-0.0007786750793457031,
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0.039642333984375,
0.00907135009765625,
-0.056976318359375,
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-0.055755615234375,... |
gokuls/sa_BERT_no_pretrain_mrpc | 2023-06-14T16:04:48.000Z | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"en",
"dataset:glue",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | text-classification | gokuls | null | null | gokuls/sa_BERT_no_pretrain_mrpc | 0 | 2 | transformers | 2023-05-29T04:50:48 | ---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
- f1
model-index:
- name: sa_BERT_no_pretrain_mrpc
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE MRPC
type: glue
config: mrpc
split: validation
args: mrpc
metrics:
- name: Accuracy
type: accuracy
value: 0.6813725490196079
- name: F1
type: f1
value: 0.7781569965870307
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# sa_BERT_no_pretrain_mrpc
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the GLUE MRPC dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6003
- Accuracy: 0.6814
- F1: 0.7782
- Combined Score: 0.7298
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 4e-05
- train_batch_size: 96
- eval_batch_size: 96
- seed: 10
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Combined Score |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------------:|
| 0.6845 | 1.0 | 39 | 0.6307 | 0.6838 | 0.8122 | 0.7480 |
| 0.6398 | 2.0 | 78 | 0.6313 | 0.6838 | 0.8122 | 0.7480 |
| 0.6384 | 3.0 | 117 | 0.6247 | 0.6838 | 0.8122 | 0.7480 |
| 0.6428 | 4.0 | 156 | 0.6467 | 0.6667 | 0.7806 | 0.7237 |
| 0.6021 | 5.0 | 195 | 0.6003 | 0.6814 | 0.7782 | 0.7298 |
| 0.5125 | 6.0 | 234 | 0.6875 | 0.6863 | 0.7874 | 0.7368 |
| 0.3735 | 7.0 | 273 | 0.8672 | 0.6422 | 0.7355 | 0.6888 |
| 0.2662 | 8.0 | 312 | 0.9928 | 0.6765 | 0.7857 | 0.7311 |
| 0.2247 | 9.0 | 351 | 0.9605 | 0.6789 | 0.7798 | 0.7294 |
| 0.1655 | 10.0 | 390 | 1.0684 | 0.6275 | 0.7206 | 0.6740 |
### Framework versions
- Transformers 4.30.2
- Pytorch 1.14.0a0+410ce96
- Datasets 2.12.0
- Tokenizers 0.13.3
| 2,658 | [
[
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0.005889892578125,
0.007793426513671875,
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0.027862548828125,
0.018157958984375,
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gokuls/add_BERT_no_pretrain_cola | 2023-06-14T12:46:24.000Z | [
"transformers",
"pytorch",
"tensorboard",
"hybridbert",
"text-classification",
"generated_from_trainer",
"en",
"dataset:glue",
"model-index",
"endpoints_compatible",
"region:us"
] | text-classification | gokuls | null | null | gokuls/add_BERT_no_pretrain_cola | 0 | 2 | transformers | 2023-05-29T04:55:14 | ---
language:
- en
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- matthews_correlation
- accuracy
model-index:
- name: add_BERT_no_pretrain_cola
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE COLA
type: glue
config: cola
split: validation
args: cola
metrics:
- name: Matthews Correlation
type: matthews_correlation
value: 0.0
- name: Accuracy
type: accuracy
value: 0.6912751793861389
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# add_BERT_no_pretrain_cola
This model is a fine-tuned version of [](https://huggingface.co/) on the GLUE COLA dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6181
- Matthews Correlation: 0.0
- Accuracy: 0.6913
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 4e-05
- train_batch_size: 128
- eval_batch_size: 128
- seed: 10
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|:--------:|
| 0.6339 | 1.0 | 67 | 0.6182 | 0.0 | 0.6913 |
| 0.6177 | 2.0 | 134 | 0.6421 | 0.0 | 0.6913 |
| 0.6204 | 3.0 | 201 | 0.6295 | 0.0 | 0.6913 |
| 0.6182 | 4.0 | 268 | 0.6268 | 0.0 | 0.6913 |
| 0.6149 | 5.0 | 335 | 0.6181 | 0.0 | 0.6913 |
| 0.612 | 6.0 | 402 | 0.6189 | 0.0 | 0.6913 |
| 0.6132 | 7.0 | 469 | 0.6292 | 0.0 | 0.6913 |
| 0.6125 | 8.0 | 536 | 0.6185 | 0.0 | 0.6913 |
| 0.6108 | 9.0 | 603 | 0.6280 | 0.0 | 0.6913 |
| 0.6092 | 10.0 | 670 | 0.6310 | 0.0 | 0.6913 |
### Framework versions
- Transformers 4.30.2
- Pytorch 1.14.0a0+410ce96
- Datasets 2.12.0
- Tokenizers 0.13.3
| 2,601 | [
[
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0.014495849609375,
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0.035980224609375,
0.007320404052734375,
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gokuls/sa_BERT_no_pretrain_qnli | 2023-06-14T18:49:41.000Z | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"en",
"dataset:glue",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | text-classification | gokuls | null | null | gokuls/sa_BERT_no_pretrain_qnli | 0 | 2 | transformers | 2023-05-29T05:01:29 | ---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: sa_BERT_no_pretrain_qnli
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE QNLI
type: glue
config: qnli
split: validation
args: qnli
metrics:
- name: Accuracy
type: accuracy
value: 0.6058941973274757
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# sa_BERT_no_pretrain_qnli
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the GLUE QNLI dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6547
- Accuracy: 0.6059
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 4e-05
- train_batch_size: 96
- eval_batch_size: 96
- seed: 10
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6847 | 1.0 | 1092 | 0.6580 | 0.6068 |
| 0.6491 | 2.0 | 2184 | 0.6547 | 0.6059 |
| 0.6223 | 3.0 | 3276 | 0.6778 | 0.6021 |
| 0.5814 | 4.0 | 4368 | 0.7237 | 0.5843 |
| 0.5176 | 5.0 | 5460 | 0.7387 | 0.5757 |
| 0.4447 | 6.0 | 6552 | 0.8224 | 0.5733 |
| 0.3761 | 7.0 | 7644 | 0.9915 | 0.5598 |
### Framework versions
- Transformers 4.30.2
- Pytorch 1.14.0a0+410ce96
- Datasets 2.12.0
- Tokenizers 0.13.3
| 2,055 | [
[
-0.029266357421875,
-0.0290069580078125,
0.007595062255859375,
0.007686614990234375,
-0.02435302734375,
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0.0185699462890625,
0.01322174072265625,
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... |
YakovElm/Qt15Classic_512 | 2023-05-29T06:41:14.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/Qt15Classic_512 | 0 | 2 | transformers | 2023-05-29T06:40:38 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Qt15Classic_512
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Qt15Classic_512
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.2050
- Train Accuracy: 0.9367
- Validation Loss: 0.2113
- Validation Accuracy: 0.9505
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.2401 | 0.9365 | 0.1849 | 0.9505 | 0 |
| 0.2232 | 0.9367 | 0.1818 | 0.9505 | 1 |
| 0.2050 | 0.9367 | 0.2113 | 0.9505 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,774 | [
[
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0.02154541015625,
0.00391387939453125,
-0.035888671875,
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0.01038360595703125,
0.0124053955078125,
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-0.04888916015625,
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-... |
xavidejuan/dqn-SpaceInvadersNoFrameskip-v4 | 2023-05-29T07:39:43.000Z | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | xavidejuan | null | null | xavidejuan/dqn-SpaceInvadersNoFrameskip-v4 | 0 | 2 | stable-baselines3 | 2023-05-29T07:39:22 | ---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 614.00 +/- 187.23
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga xavidejuan -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga xavidejuan -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga xavidejuan
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 10000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
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gokuls/sa_BERT_no_pretrain_qqp | 2023-06-15T05:40:30.000Z | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"en",
"dataset:glue",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | text-classification | gokuls | null | null | gokuls/sa_BERT_no_pretrain_qqp | 0 | 2 | transformers | 2023-05-29T07:55:56 | ---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
- f1
model-index:
- name: sa_BERT_no_pretrain_qqp
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE QQP
type: glue
config: qqp
split: validation
args: qqp
metrics:
- name: Accuracy
type: accuracy
value: 0.7934207271827851
- name: F1
type: f1
value: 0.6836123948783999
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# sa_BERT_no_pretrain_qqp
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the GLUE QQP dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4355
- Accuracy: 0.7934
- F1: 0.6836
- Combined Score: 0.7385
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 4e-05
- train_batch_size: 96
- eval_batch_size: 96
- seed: 10
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Combined Score |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:--------------:|
| 0.5241 | 1.0 | 3791 | 0.4947 | 0.7638 | 0.6550 | 0.7094 |
| 0.4527 | 2.0 | 7582 | 0.4524 | 0.7853 | 0.7027 | 0.7440 |
| 0.404 | 3.0 | 11373 | 0.4355 | 0.7934 | 0.6836 | 0.7385 |
| 0.3675 | 4.0 | 15164 | 0.4407 | 0.8038 | 0.7438 | 0.7738 |
| 0.3315 | 5.0 | 18955 | 0.4426 | 0.8060 | 0.7368 | 0.7714 |
| 0.3031 | 6.0 | 22746 | 0.4437 | 0.8067 | 0.7444 | 0.7755 |
| 0.2747 | 7.0 | 26537 | 0.4359 | 0.8046 | 0.7523 | 0.7785 |
| 0.2441 | 8.0 | 30328 | 0.4718 | 0.8074 | 0.7547 | 0.7811 |
### Framework versions
- Transformers 4.30.2
- Pytorch 1.14.0a0+410ce96
- Datasets 2.12.0
- Tokenizers 0.13.3
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rzhu/distilbert-base-uncased_emotion_ft_0529 | 2023-05-29T09:01:08.000Z | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | text-classification | rzhu | null | null | rzhu/distilbert-base-uncased_emotion_ft_0529 | 0 | 2 | transformers | 2023-05-29T08:09:23 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
- f1
- precision
model-index:
- name: distilbert-base-uncased_emotion_ft_0529
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
config: split
split: validation
args: split
metrics:
- name: Accuracy
type: accuracy
value: 0.9375
- name: F1
type: f1
value: 0.9378132226886893
- name: Precision
type: precision
value: 0.9124034390576776
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased_emotion_ft_0529
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1485
- Accuracy: 0.9375
- F1: 0.9378
- Precision: 0.9124
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|
| 0.8109 | 1.0 | 250 | 0.2686 | 0.913 | 0.9111 | 0.8958 |
| 0.2078 | 2.0 | 500 | 0.1663 | 0.931 | 0.9309 | 0.9148 |
| 0.1383 | 3.0 | 750 | 0.1562 | 0.9365 | 0.9366 | 0.9170 |
| 0.114 | 4.0 | 1000 | 0.1485 | 0.9375 | 0.9378 | 0.9124 |
### Framework versions
- Transformers 4.27.1
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3
| 2,166 | [
[
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rzhu/distilbert-base-uncased_emotion_ft_0416 | 2023-05-29T08:16:34.000Z | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | text-classification | rzhu | null | null | rzhu/distilbert-base-uncased_emotion_ft_0416 | 0 | 2 | transformers | 2023-05-29T08:13:30 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
- f1
- precision
model-index:
- name: distilbert-base-uncased_emotion_ft_0416
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
config: split
split: validation
args: split
metrics:
- name: Accuracy
type: accuracy
value: 0.9385
- name: F1
type: f1
value: 0.9386825580211653
- name: Precision
type: precision
value: 0.9103398923984992
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased_emotion_ft_0416
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1481
- Accuracy: 0.9385
- F1: 0.9387
- Precision: 0.9103
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|
| 0.7769 | 1.0 | 250 | 0.2467 | 0.9205 | 0.9196 | 0.8974 |
| 0.2029 | 2.0 | 500 | 0.1649 | 0.9325 | 0.9321 | 0.9162 |
| 0.1382 | 3.0 | 750 | 0.1523 | 0.935 | 0.9355 | 0.9023 |
| 0.1121 | 4.0 | 1000 | 0.1481 | 0.9385 | 0.9387 | 0.9103 |
### Framework versions
- Transformers 4.27.1
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3
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L3tsG0/distilbert-base-uncased-finetuned-emotion | 2023-05-29T09:40:23.000Z | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | text-classification | L3tsG0 | null | null | L3tsG0/distilbert-base-uncased-finetuned-emotion | 0 | 2 | transformers | 2023-05-29T08:58:56 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
config: split
split: validation
args: split
metrics:
- name: Accuracy
type: accuracy
value: 0.9415
- name: F1
type: f1
value: 0.9418231040913105
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1351
- Accuracy: 0.9415
- F1: 0.9418
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.5238 | 1.0 | 250 | 0.1800 | 0.928 | 0.9270 |
| 0.141 | 2.0 | 500 | 0.1351 | 0.9415 | 0.9418 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.0+cu117
- Datasets 2.12.0
- Tokenizers 0.13.2
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[
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0.01120758056640625,
0.00791168212890625,
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tonirodriguez/roberta-base-bne-finetuned-toxicity-tweets-balanced-12000 | 2023-05-29T09:16:04.000Z | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | tonirodriguez | null | null | tonirodriguez/roberta-base-bne-finetuned-toxicity-tweets-balanced-12000 | 0 | 2 | transformers | 2023-05-29T09:13:30 | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: roberta-base-bne-finetuned-toxicity-tweets-balanced-12000
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-base-bne-finetuned-toxicity-tweets-balanced-12000
This model is a fine-tuned version of [BSC-TeMU/roberta-base-bne](https://huggingface.co/BSC-TeMU/roberta-base-bne) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2973
- Accuracy: 0.906
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.3404 | 1.0 | 73 | 0.2558 | 0.9 |
| 0.1613 | 2.0 | 146 | 0.2973 | 0.906 |
### Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,483 | [
[
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vsrinivas/ppo-LunarLander-v2-vs-ver3 | 2023-05-31T06:32:32.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | vsrinivas | null | null | vsrinivas/ppo-LunarLander-v2-vs-ver3 | 0 | 2 | stable-baselines3 | 2023-05-29T10:31:52 | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 285.54 +/- 18.52
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
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-... |
bitextor/bicleaner-ai-full-en-sw | 2023-08-24T10:28:26.000Z | [
"transformers",
"tf",
"xlm-roberta",
"bicleaner-ai",
"en",
"sw",
"multilingual",
"license:cc-by-sa-4.0",
"endpoints_compatible",
"region:us"
] | null | bitextor | null | null | bitextor/bicleaner-ai-full-en-sw | 0 | 2 | transformers | 2023-05-29T11:46:21 | ---
language:
- en
- sw
- multilingual
license: cc-by-sa-4.0
tags:
- bicleaner-ai
tasks:
- text-classification
---
# Bicleaner AI full model for en-sw
Bicleaner AI is a tool that aims at detecting noisy sentence pairs in a parallel corpus. It
indicates the likelihood of a pair of sentences being mutual translations (with a value near to 1) or not (with a value near to 0).
Sentence pairs considered very noisy are scored with 0.
Find out at our repository for further instructions on how to use it: https://github.com/bitextor/bicleaner-ai
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vnykr/dqn-SpaceInvadersNoFrameskip-v4 | 2023-05-29T12:07:20.000Z | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | vnykr | null | null | vnykr/dqn-SpaceInvadersNoFrameskip-v4 | 0 | 2 | stable-baselines3 | 2023-05-29T12:06:41 | ---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 508.50 +/- 81.46
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga vnykr -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga vnykr -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga vnykr
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 1000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
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partypress/partypress-monolingual-austria | 2023-09-14T13:51:31.000Z | [
"transformers",
"pytorch",
"tf",
"bert",
"text-classification",
"partypress",
"political science",
"parties",
"press releases",
"de",
"license:cc-by-sa-4.0",
"endpoints_compatible",
"region:us"
] | text-classification | partypress | null | null | partypress/partypress-monolingual-austria | 0 | 2 | transformers | 2023-05-29T12:10:33 | ---
license: cc-by-sa-4.0
language:
- de
metrics:
- accuracy
pipeline_tag: text-classification
tags:
- partypress
- political science
- parties
- press releases
widget:
- text: 'Immissionsschutzgesetz muss ein Klagerecht für BürgerInnen beinhalten: "Es ist seit Jahren bekannt, welche Maßnahmen zur Reduktion der Feinstaubbelastung gesetzt werden müssen. Diese neuerlich bloß aufzuzählen, wie es jetzt Minister Berlakovich tut, hilft den Betroffenen nicht", kritisiert die Grüne Umweltsprecherin Christiane Brunner die jüngsten Aussagen des Umweltministers zur Problematik Feinstaub.'
---
# PARTYPRESS monolingual Austria
Fine-tuned model, based on [dbmdz/bert-base-german-cased](https://huggingface.co/dbmdz/bert-base-german-cased). Used in Erfort et al. (2023), building on the PARTYPRESS database. For the downstream task of classyfing press releases from political parties into 23 unique policy areas we achieve a performance comparable to expert human coders.
## Model description
The PARTYPRESS monolingual model builds on [dbmdz/bert-base-german-cased](https://huggingface.co/dbmdz/bert-base-german-cased) but has a supervised component. This means, it was fine-tuned using texts labeled by humans. The labels indicate 23 different political issue categories derived from the Comparative Agendas Project (CAP):
| Code | Issue |
|--|-------|
| 1 | Macroeconomics |
| 2 | Civil Rights |
| 3 | Health |
| 4 | Agriculture |
| 5 | Labor |
| 6 | Education |
| 7 | Environment |
| 8 | Energy |
| 9 | Immigration |
| 10 | Transportation |
| 12 | Law and Crime |
| 13 | Social Welfare |
| 14 | Housing |
| 15 | Domestic Commerce |
| 16 | Defense |
| 17 | Technology |
| 18 | Foreign Trade |
| 19.1 | International Affairs |
| 19.2 | European Union |
| 20 | Government Operations |
| 23 | Culture |
| 98 | Non-thematic |
| 99 | Other |
## Model variations
There are several monolingual models for different countries, and a multilingual model. The multilingual model can be easily extended to other languages, country contexts, or time periods by fine-tuning it with minimal additional labeled texts.
## Intended uses & limitations
The main use of the model is for text classification of press releases from political parties. It may also be useful for other political texts.
The classification can then be used to measure which issues parties are discussing in their communication.
### How to use
This model can be used directly with a pipeline for text classification:
```python
>>> from transformers import pipeline
>>> tokenizer_kwargs = {'padding':True,'truncation':True,'max_length':512}
>>> partypress = pipeline("text-classification", model = "cornelius/partypress-monolingual-austria", tokenizer = "cornelius/partypress-monolingual-austria", **tokenizer_kwargs)
>>> partypress("Your text here.")
```
### Limitations and bias
The model was trained with data from parties in Austria. For use in other countries, the model may be further fine-tuned. Without further fine-tuning, the performance of the model may be lower.
The model may have biased predictions. We discuss some biases by country, party, and over time in the release paper for the PARTYPRESS database. For example, the performance is highest for press releases from Ireland (75%) and lowest for Poland (55%).
## Training data
The PARTYPRESS multilingual model was fine-tuned with about 3,000 press releases from parties in Austria. The press releases were labeled by two expert human coders.
For the training data of the underlying model, please refer to [dbmdz/bert-base-german-cased](https://huggingface.co/dbmdz/bert-base-german-cased)
## Training procedure
### Preprocessing
For the preprocessing, please refer to [dbmdz/bert-base-german-cased](https://huggingface.co/dbmdz/bert-base-german-cased)
### Pretraining
For the pretraining, please refer to [dbmdz/bert-base-german-cased](https://huggingface.co/dbmdz/bert-base-german-cased)
### Fine-tuning
We fine-tuned the model using about 3,000 labeled press releases from political parties in Austria.
#### Training Hyperparameters
The batch size for training was 12, for testing 2, with four epochs. All other hyperparameters were the standard from the transformers library.
#### Framework versions
- Transformers 4.28.0
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
## Evaluation results
Fine-tuned on our downstream task, this model achieves the following results in a five-fold cross validation that are comparable to the performance of our expert human coders. Please refer to Erfort et al. (2023)
### BibTeX entry and citation info
```bibtex
@article{erfort_partypress_2023,
author = {Cornelius Erfort and
Lukas F. Stoetzer and
Heike Klüver},
title = {The PARTYPRESS Database: A new comparative database of parties’ press releases},
journal = {Research and Politics},
volume = {10},
number = {3},
year = {2023},
doi = {10.1177/20531680231183512},
URL = {https://doi.org/10.1177/20531680231183512}
}
```
Erfort, C., Stoetzer, L. F., & Klüver, H. (2023). The PARTYPRESS Database: A new comparative database of parties’ press releases. Research & Politics, 10(3). [https://doi.org/10.1177/20531680231183512](https://doi.org/10.1177/20531680231183512)
### Further resources
Github: [cornelius-erfort/partypress](https://github.com/cornelius-erfort/partypress)
Research and Politics Dataverse: [Replication Data for: The PARTYPRESS Database: A New Comparative Database of Parties’ Press Releases](https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi%3A10.7910%2FDVN%2FOINX7Q)
## Acknowledgements
Research for this contribution is part of the Cluster of Excellence "Contestations of the Liberal Script" (EXC 2055, Project-ID: 390715649), funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy. Cornelius Erfort is moreover grateful for generous funding provided by the DFG through the Research Training Group DYNAMICS (GRK 2458/1).
## Contact
Cornelius Erfort
Humboldt-Universität zu Berlin
[corneliuserfort.de](corneliuserfort.de)
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gokuls/sa_BERT_no_pretrain_rte | 2023-06-15T05:47:43.000Z | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"en",
"dataset:glue",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | text-classification | gokuls | null | null | gokuls/sa_BERT_no_pretrain_rte | 0 | 2 | transformers | 2023-05-29T13:29:15 | ---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: sa_BERT_no_pretrain_rte
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE RTE
type: glue
config: rte
split: validation
args: rte
metrics:
- name: Accuracy
type: accuracy
value: 0.5306859205776173
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# sa_BERT_no_pretrain_rte
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the GLUE RTE dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6909
- Accuracy: 0.5307
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 4e-05
- train_batch_size: 96
- eval_batch_size: 96
- seed: 10
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.7596 | 1.0 | 26 | 0.6909 | 0.5307 |
| 0.6968 | 2.0 | 52 | 0.6914 | 0.5235 |
| 0.7026 | 3.0 | 78 | 0.6911 | 0.5307 |
| 0.6961 | 4.0 | 104 | 0.6928 | 0.5379 |
| 0.7114 | 5.0 | 130 | 0.6917 | 0.5271 |
| 0.7005 | 6.0 | 156 | 0.7069 | 0.4729 |
### Framework versions
- Transformers 4.30.2
- Pytorch 1.14.0a0+410ce96
- Datasets 2.12.0
- Tokenizers 0.13.3
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gokuls/sa_BERT_no_pretrain_sst2 | 2023-06-15T07:48:32.000Z | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"en",
"dataset:glue",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | text-classification | gokuls | null | null | gokuls/sa_BERT_no_pretrain_sst2 | 0 | 2 | transformers | 2023-05-29T13:35:42 | ---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: sa_BERT_no_pretrain_sst2
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE SST2
type: glue
config: sst2
split: validation
args: sst2
metrics:
- name: Accuracy
type: accuracy
value: 0.8027522935779816
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# sa_BERT_no_pretrain_sst2
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the GLUE SST2 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4637
- Accuracy: 0.8028
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 4e-05
- train_batch_size: 96
- eval_batch_size: 96
- seed: 10
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.4863 | 1.0 | 702 | 0.4747 | 0.7890 |
| 0.2723 | 2.0 | 1404 | 0.4974 | 0.7901 |
| 0.2219 | 3.0 | 2106 | 0.4637 | 0.8028 |
| 0.1848 | 4.0 | 2808 | 0.7501 | 0.7833 |
| 0.1591 | 5.0 | 3510 | 0.5357 | 0.8005 |
| 0.1346 | 6.0 | 4212 | 0.5450 | 0.7833 |
| 0.1148 | 7.0 | 4914 | 0.8002 | 0.7741 |
| 0.1034 | 8.0 | 5616 | 0.8853 | 0.7821 |
### Framework versions
- Transformers 4.30.2
- Pytorch 1.14.0a0+410ce96
- Datasets 2.12.0
- Tokenizers 0.13.3
| 2,117 | [
[
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gokuls/sa_BERT_no_pretrain_stsb | 2023-06-15T08:03:24.000Z | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"en",
"dataset:glue",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | text-classification | gokuls | null | null | gokuls/sa_BERT_no_pretrain_stsb | 0 | 2 | transformers | 2023-05-29T14:26:57 | ---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- spearmanr
model-index:
- name: sa_BERT_no_pretrain_stsb
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE STSB
type: glue
config: stsb
split: validation
args: stsb
metrics:
- name: Spearmanr
type: spearmanr
value: 0.12459536879199183
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# sa_BERT_no_pretrain_stsb
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the GLUE STSB dataset.
It achieves the following results on the evaluation set:
- Loss: 2.5396
- Pearson: 0.1394
- Spearmanr: 0.1246
- Combined Score: 0.1320
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 4e-05
- train_batch_size: 96
- eval_batch_size: 96
- seed: 10
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Pearson | Spearmanr | Combined Score |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:---------:|:--------------:|
| 2.257 | 1.0 | 60 | 3.1111 | 0.0528 | 0.0709 | 0.0619 |
| 2.0476 | 2.0 | 120 | 2.5396 | 0.1394 | 0.1246 | 0.1320 |
| 1.8905 | 3.0 | 180 | 2.5928 | 0.1553 | 0.1593 | 0.1573 |
| 1.5383 | 4.0 | 240 | 3.1130 | 0.1930 | 0.2086 | 0.2008 |
| 1.3384 | 5.0 | 300 | 2.8651 | 0.1788 | 0.2014 | 0.1901 |
| 1.1299 | 6.0 | 360 | 2.9651 | 0.1818 | 0.1947 | 0.1883 |
| 1.0952 | 7.0 | 420 | 2.6404 | 0.2100 | 0.2124 | 0.2112 |
### Framework versions
- Transformers 4.30.2
- Pytorch 1.14.0a0+410ce96
- Datasets 2.12.0
- Tokenizers 0.13.3
| 2,355 | [
[
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gokuls/sa_BERT_no_pretrain_wnli | 2023-06-15T08:08:30.000Z | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"en",
"dataset:glue",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | text-classification | gokuls | null | null | gokuls/sa_BERT_no_pretrain_wnli | 0 | 2 | transformers | 2023-05-29T14:36:04 | ---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: sa_BERT_no_pretrain_wnli
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE WNLI
type: glue
config: wnli
split: validation
args: wnli
metrics:
- name: Accuracy
type: accuracy
value: 0.5633802816901409
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# sa_BERT_no_pretrain_wnli
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the GLUE WNLI dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6866
- Accuracy: 0.5634
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 4e-05
- train_batch_size: 96
- eval_batch_size: 96
- seed: 10
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.0074 | 1.0 | 7 | 0.6958 | 0.4366 |
| 0.6986 | 2.0 | 14 | 0.7035 | 0.4366 |
| 0.7007 | 3.0 | 21 | 0.6866 | 0.5634 |
| 0.7052 | 4.0 | 28 | 0.7037 | 0.4366 |
| 0.7008 | 5.0 | 35 | 0.6951 | 0.4366 |
| 0.7107 | 6.0 | 42 | 0.6908 | 0.5634 |
| 0.6963 | 7.0 | 49 | 0.6945 | 0.4366 |
| 0.7012 | 8.0 | 56 | 0.6894 | 0.5634 |
### Framework versions
- Transformers 4.30.2
- Pytorch 1.14.0a0+410ce96
- Datasets 2.12.0
- Tokenizers 0.13.3
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gokuls/sa_BERT_no_pretrain_mnli | 2023-06-15T22:16:47.000Z | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"en",
"dataset:glue",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | text-classification | gokuls | null | null | gokuls/sa_BERT_no_pretrain_mnli | 0 | 2 | transformers | 2023-05-29T14:41:40 | ---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: sa_BERT_no_pretrain_mnli
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE MNLI
type: glue
config: mnli
split: validation_matched
args: mnli
metrics:
- name: Accuracy
type: accuracy
value: 0.6700569568755086
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# sa_BERT_no_pretrain_mnli
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the GLUE MNLI dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7747
- Accuracy: 0.6701
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 4e-05
- train_batch_size: 96
- eval_batch_size: 96
- seed: 10
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.9765 | 1.0 | 4091 | 0.9090 | 0.5823 |
| 0.8799 | 2.0 | 8182 | 0.8625 | 0.6123 |
| 0.8193 | 3.0 | 12273 | 0.8227 | 0.6362 |
| 0.7551 | 4.0 | 16364 | 0.7929 | 0.6542 |
| 0.6961 | 5.0 | 20455 | 0.7901 | 0.6643 |
| 0.6403 | 6.0 | 24546 | 0.8298 | 0.6687 |
| 0.5831 | 7.0 | 28637 | 0.8135 | 0.6701 |
| 0.5224 | 8.0 | 32728 | 0.8831 | 0.6718 |
| 0.4602 | 9.0 | 36819 | 0.9055 | 0.6652 |
| 0.4003 | 10.0 | 40910 | 0.9812 | 0.6603 |
### Framework versions
- Transformers 4.30.2
- Pytorch 1.14.0a0+410ce96
- Datasets 2.12.0
- Tokenizers 0.13.3
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[
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0.0081329345703125,
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0.02154541015625,
0.01953125,
-0.060791015625,
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-0.0180511474... |
HassanCS/ChemBERTa-77M-MLM-finetuned-4M | 2023-05-29T17:43:23.000Z | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"fill-mask",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | fill-mask | HassanCS | null | null | HassanCS/ChemBERTa-77M-MLM-finetuned-4M | 0 | 2 | transformers | 2023-05-29T15:19:41 | ---
tags:
- generated_from_trainer
model-index:
- name: ChemBERTa-77M-MLM-finetuned-4M
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# ChemBERTa-77M-MLM-finetuned-4M
This model is a fine-tuned version of [DeepChem/ChemBERTa-77M-MLM](https://huggingface.co/DeepChem/ChemBERTa-77M-MLM) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4601
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:------:|:---------------:|
| 0.5648 | 1.0 | 61007 | 0.5632 |
| 0.4649 | 2.0 | 122014 | 0.4801 |
| 0.4338 | 3.0 | 183021 | 0.4601 |
### Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,390 | [
[
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fredymad/distilbert_estricto_2e-5_16_2 | 2023-05-29T16:05:04.000Z | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | fredymad | null | null | fredymad/distilbert_estricto_2e-5_16_2 | 0 | 2 | transformers | 2023-05-29T16:00:19 | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert_estricto_2e-5_16_2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert_estricto_2e-5_16_2
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3345
- Accuracy: 0.8712
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 400 | 0.3638 | 0.8399 |
| 0.4495 | 2.0 | 800 | 0.3345 | 0.8712 |
### Framework versions
- Transformers 4.29.2
- Pytorch 1.13.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,425 | [
[
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sitthichok0230/finetuned-bert | 2023-05-29T19:28:45.000Z | [
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | text-classification | sitthichok0230 | null | null | sitthichok0230/finetuned-bert | 0 | 2 | transformers | 2023-05-29T16:06:14 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
- f1
model-index:
- name: finetuned-bert
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
config: mrpc
split: validation
args: mrpc
metrics:
- name: Accuracy
type: accuracy
value: 0.8627450980392157
- name: F1
type: f1
value: 0.9037800687285222
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuned-bert
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4431
- Accuracy: 0.8627
- F1: 0.9038
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.5331 | 1.0 | 230 | 0.3900 | 0.8333 | 0.8870 |
| 0.2878 | 2.0 | 460 | 0.3675 | 0.8505 | 0.8935 |
| 0.1395 | 3.0 | 690 | 0.4431 | 0.8627 | 0.9038 |
### Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,849 | [
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edata/dqn-SpaceInvadersNoFrameskip-v4 | 2023-05-29T18:00:11.000Z | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | edata | null | null | edata/dqn-SpaceInvadersNoFrameskip-v4 | 0 | 2 | stable-baselines3 | 2023-05-29T17:08:15 | ---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 15.50 +/- 12.54
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga edata -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga edata -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga edata
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 100000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
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0.013427734375,
-0.01337432861328125,
0.01312255859375,
0.0245513916015625,
-0.0706787109375,
-0.035400390625,
-0.0261383056640625,
-0.0046157... |
fredymad/distilbert_laxo_2e-5_16_2 | 2023-05-29T19:06:30.000Z | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | fredymad | null | null | fredymad/distilbert_laxo_2e-5_16_2 | 0 | 2 | transformers | 2023-05-29T18:55:04 | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert_laxo_2e-5_16_2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert_laxo_2e-5_16_2
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2674
- Accuracy: 0.9106
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 400 | 0.2416 | 0.9068 |
| 0.3067 | 2.0 | 800 | 0.2674 | 0.9106 |
### Framework versions
- Transformers 4.29.0
- Pytorch 1.13.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,417 | [
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0.015777587890625,
-0.044036865234375,
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fredymad/bert_estricto_2e-5_16_2 | 2023-05-29T20:18:12.000Z | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | fredymad | null | null | fredymad/bert_estricto_2e-5_16_2 | 0 | 2 | transformers | 2023-05-29T19:41:32 | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: bert_estricto_2e-5_16_2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert_estricto_2e-5_16_2
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3195
- Accuracy: 0.8693
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 400 | 0.3410 | 0.8518 |
| 0.4447 | 2.0 | 800 | 0.3195 | 0.8693 |
### Framework versions
- Transformers 4.29.0
- Pytorch 1.13.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,401 | [
[
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sperera/bert-finetuned-ner | 2023-05-29T22:36:20.000Z | [
"transformers",
"tf",
"bert",
"token-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | sperera | null | null | sperera/bert-finetuned-ner | 0 | 2 | transformers | 2023-05-29T19:56:35 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: sperera/bert-finetuned-ner
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# sperera/bert-finetuned-ner
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.1878
- Validation Loss: 0.0689
- Epoch: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 2634, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: mixed_float16
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 0.1878 | 0.0689 | 0 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,473 | [
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-0.02003479... |
a-grishman/bert-base-banking77-pt2 | 2023-05-30T07:17:48.000Z | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:banking77",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | text-classification | a-grishman | null | null | a-grishman/bert-base-banking77-pt2 | 0 | 2 | transformers | 2023-05-29T20:14:10 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- banking77
metrics:
- f1
model-index:
- name: bert-base-banking77-pt2
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: banking77
type: banking77
config: default
split: test
args: default
metrics:
- name: F1
type: f1
value: 0.9368591300797698
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-banking77-pt2
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the banking77 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2758
- F1: 0.9369
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.7116 | 1.0 | 1251 | 0.5905 | 0.8722 |
| 0.2675 | 2.0 | 2502 | 0.3136 | 0.9229 |
| 0.16 | 3.0 | 3753 | 0.2758 | 0.9369 |
### Framework versions
- Transformers 4.27.1
- Pytorch 2.0.1+cu117
- Datasets 2.9.0
- Tokenizers 0.13.3
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fredymad/bert_laxo_2e-5_16_2 | 2023-05-29T20:46:36.000Z | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | fredymad | null | null | fredymad/bert_laxo_2e-5_16_2 | 0 | 2 | transformers | 2023-05-29T20:23:59 | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: bert_laxo_2e-5_16_2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert_laxo_2e-5_16_2
This model is a fine-tuned version of [fredymad/bert_estricto_2e-5_16_2](https://huggingface.co/fredymad/bert_estricto_2e-5_16_2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2882
- Accuracy: 0.9187
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 400 | 0.2139 | 0.9162 |
| 0.2436 | 2.0 | 800 | 0.2882 | 0.9187 |
### Framework versions
- Transformers 4.29.0
- Pytorch 1.13.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3
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ga21902298/dbert-finetuned-433-1 | 2023-05-29T21:58:42.000Z | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | ga21902298 | null | null | ga21902298/dbert-finetuned-433-1 | 0 | 2 | transformers | 2023-05-29T20:27:14 | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: dbert-finetuned-433-1
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# dbert-finetuned-433-1
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5437
- Accuracy: 0.8438
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.3563 | 1.0 | 6250 | 0.3636 | 0.8400 |
| 0.2989 | 2.0 | 12500 | 0.3517 | 0.8490 |
| 0.2287 | 3.0 | 18750 | 0.3928 | 0.8486 |
| 0.1646 | 4.0 | 25000 | 0.4724 | 0.8458 |
| 0.1383 | 5.0 | 31250 | 0.5437 | 0.8438 |
### Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,601 | [
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platzi/platzi-distilroberta-base-mrpc-glue-luis-rascon | 2023-05-29T21:52:52.000Z | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | text-classification | platzi | null | null | platzi/platzi-distilroberta-base-mrpc-glue-luis-rascon | 0 | 2 | transformers | 2023-05-29T20:29:18 | ---
license: apache-2.0
tags:
- text-classification
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
- f1
widget:
- text: ["Yucaipa owned Dominick 's before selling the chain to Safeway in 1998 for $ 2.5 billion.",
"Yucaipa bought Dominick's in 1995 for $ 693 million and sold it to Safeway for $ 1.8 billion in 1998."]
example_title: Not Equivalent
- text: ["Revenue in the first quarter of the year dropped 15 percent from the same period a year earlier.",
"With the scandal hanging over Stewart's company revenue the first quarter of the year dropped 15 percent from the same period a year earlier."]
example_title: Equivalent
model-index:
- name: platzi-distilroberta-base-mrpc-glue-luis-rascon
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
config: mrpc
split: validation
args: mrpc
metrics:
- name: Accuracy
type: accuracy
value: 0.8235294117647058
- name: F1
type: f1
value: 0.8641509433962264
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# platzi-distilroberta-base-mrpc-glue-luis-rascon
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the glue and the mrpc datasets.
It achieves the following results on the evaluation set:
- Loss: 0.5052
- Accuracy: 0.8235
- F1: 0.8642
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.5201 | 1.09 | 500 | 0.6599 | 0.8382 | 0.8842 |
| 0.3684 | 2.18 | 1000 | 0.5052 | 0.8235 | 0.8642 |
### Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
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pandma/es_billynator_ah | 2023-05-29T20:31:32.000Z | [
"spacy",
"token-classification",
"es",
"model-index",
"region:us"
] | token-classification | pandma | null | null | pandma/es_billynator_ah | 0 | 2 | spacy | 2023-05-29T20:31:07 | ---
tags:
- spacy
- token-classification
language:
- es
model-index:
- name: es_billynator_ah
results:
- task:
name: NER
type: token-classification
metrics:
- name: NER Precision
type: precision
value: 0.9998979071
- name: NER Recall
type: recall
value: 0.9998979071
- name: NER F Score
type: f_score
value: 0.9998979071
---
| Feature | Description |
| --- | --- |
| **Name** | `es_billynator_ah` |
| **Version** | `0.0.0` |
| **spaCy** | `>=3.5.1,<3.6.0` |
| **Default Pipeline** | `transformer`, `ner` |
| **Components** | `transformer`, `ner` |
| **Vectors** | 0 keys, 0 unique vectors (0 dimensions) |
| **Sources** | n/a |
| **License** | n/a |
| **Author** | [n/a]() |
### Label Scheme
<details>
<summary>View label scheme (29 labels for 1 components)</summary>
| Component | Labels |
| --- | --- |
| **`ner`** | `BILLING_PERIOD_END`, `BILLING_PERIOD_START`, `BILL_OWNER`, `COMPANY_NAME`, `CUPS`, `DIRECTION`, `DISCOUNT_TOTAL`, `END_CONTRACT`, `ENERGY_P1_PRICE`, `ENERGY_P2_PRICE`, `ENERGY_P3_PRICE`, `FISCAL_DIRECTION`, `IBAN`, `NIF`, `POWER_EXCESSES_P1`, `POWER_EXCESSES_P2`, `POWER_EXCESSES_P3`, `POWER_P1_PRICE`, `POWER_P2_PRICE`, `POWER_P3_PRICE`, `POWER_P4_PRICE`, `POWER_P5_PRICE`, `POWER_P6_PRICE`, `REACTIVE_P1`, `REACTIVE_P2`, `REACTIVE_P3`, `TOP_GAS_PRICE`, `TOP_GAS_TOTAL`, `TOTAL_IMPORTE` |
</details>
### Accuracy
| Type | Score |
| --- | --- |
| `ENTS_F` | 99.99 |
| `ENTS_P` | 99.99 |
| `ENTS_R` | 99.99 |
| `TRANSFORMER_LOSS` | 282.40 |
| `NER_LOSS` | 18900.94 | | 1,560 | [
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danieliser/ppo-PyramidsRND-v1 | 2023-05-29T21:47:45.000Z | [
"ml-agents",
"tensorboard",
"onnx",
"Pyramids",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
] | reinforcement-learning | danieliser | null | null | danieliser/ppo-PyramidsRND-v1 | 0 | 2 | ml-agents | 2023-05-29T21:47:39 | ---
library_name: ml-agents
tags:
- Pyramids
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Pyramids
---
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-Pyramids
2. Step 1: Find your model_id: danieliser/ppo-PyramidsRND-v1
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
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le1andonly/universityexerciseanothertry | 2023-05-29T23:10:48.000Z | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | le1andonly | null | null | le1andonly/universityexerciseanothertry | 0 | 2 | transformers | 2023-05-29T21:53:09 | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: universityexerciseanothertry
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# universityexerciseanothertry
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4735
- Accuracy: 0.7773
- F1: 0.7992
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
### Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,192 | [
[
-0.0301971435546875,
-0.046112060546875,
0.0207366943359375,
0.0072021484375,
-0.03094482421875,
-0.0240478515625,
-0.01212310791015625,
-0.00926971435546875,
0.00604248046875,
0.016876220703125,
-0.0433349609375,
-0.0450439453125,
-0.044586181640625,
-0.002... |
pedroplanel/bert-base-banking77 | 2023-05-29T22:50:01.000Z | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:banking77",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | text-classification | pedroplanel | null | null | pedroplanel/bert-base-banking77 | 0 | 2 | transformers | 2023-05-29T22:37:08 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- banking77
metrics:
- f1
model-index:
- name: bert-base-banking77
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: banking77
type: banking77
config: default
split: test
args: default
metrics:
- name: F1
type: f1
value: 0.9292916887388843
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-banking77
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the banking77 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3032
- F1: 0.9293
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 1.0309 | 1.0 | 626 | 0.7660 | 0.8508 |
| 0.3691 | 2.0 | 1252 | 0.3553 | 0.9234 |
| 0.1738 | 3.0 | 1878 | 0.3032 | 0.9293 |
### Framework versions
- Transformers 4.27.1
- Pytorch 2.0.0+cu117
- Datasets 2.9.0
- Tokenizers 0.13.3
| 1,720 | [
[
-0.033355712890625,
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0.01251983642578125,
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-0.0257568359375,
-0.01171875,
-0.01611328125,
0.00046706199645996094,
0.04248046875,
-0.04522705078125,
-0.046966552734375,
-0.049041748046875,
-0.025817871... |
neiz/distilbert-base-uncased-finetuned-sst-2-english | 2023-05-29T22:53:08.000Z | [
"transformers",
"onnx",
"distilbert",
"text-classification",
"en",
"dataset:sst2",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | neiz | null | null | neiz/distilbert-base-uncased-finetuned-sst-2-english | 0 | 2 | transformers | 2023-05-29T22:39:04 | ---
language: en
license: apache-2.0
datasets:
- sst2
---
# ONNX convert DistilBERT base uncased finetuned SST-2
## Conversion of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english)
This model is a fine-tune checkpoint of [DistilBERT-base-uncased](https://huggingface.co/distilbert-base-uncased), fine-tuned on SST-2.
This model reaches an accuracy of 91.3 on the dev set (for comparison, Bert bert-base-uncased version reaches an accuracy of 92.7).
For more details about DistilBERT, we encourage users to check out [this model card](https://huggingface.co/distilbert-base-uncased).
# Fine-tuning hyper-parameters
- learning_rate = 1e-5
- batch_size = 32
- warmup = 600
- max_seq_length = 128
- num_train_epochs = 3.0
# Bias
Based on a few experimentations, we observed that this model could produce biased predictions that target underrepresented populations.
For instance, for sentences like `This film was filmed in COUNTRY`, this binary classification model will give radically different probabilities for the positive label depending on the country (0.89 if the country is France, but 0.08 if the country is Afghanistan) when nothing in the input indicates such a strong semantic shift. In this [colab](https://colab.research.google.com/gist/ageron/fb2f64fb145b4bc7c49efc97e5f114d3/biasmap.ipynb), [Aurélien Géron](https://twitter.com/aureliengeron) made an interesting map plotting these probabilities for each country.
<img src="https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english/resolve/main/map.jpeg" alt="Map of positive probabilities per country." width="500"/>
We strongly advise users to thoroughly probe these aspects on their use-cases in order to evaluate the risks of this model. We recommend looking at the following bias evaluation datasets as a place to start: [WinoBias](https://huggingface.co/datasets/wino_bias), [WinoGender](https://huggingface.co/datasets/super_glue), [Stereoset](https://huggingface.co/datasets/stereoset). | 2,053 | [
[
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0.0213470458984375,
-0.03753662109375,
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0.0029582977294921875,
0.036468505859375,
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... |
pszemraj/e5-small-LinkedCringe-setfit-skl-20it-2e | 2023-05-30T18:31:50.000Z | [
"sentence-transformers",
"pytorch",
"bert",
"setfit",
"text-classification",
"LinkedCringe",
"arxiv:2209.11055",
"license:apache-2.0",
"region:us"
] | text-classification | pszemraj | null | null | pszemraj/e5-small-LinkedCringe-setfit-skl-20it-2e | 0 | 2 | sentence-transformers | 2023-05-30T03:11:29 | ---
license: apache-2.0
tags:
- setfit
- sentence-transformers
- text-classification
- LinkedCringe
pipeline_tag: text-classification
thumbnail: https://i.ibb.co/SPVBJrz/model-card.jpg
---
# LinkedCringe v0.2: e5-small
> fine-tuned on LinkedCringe v0.2 from [intfloat/e5-small](https://huggingface.co/intfloat/e5-small)
<a href="https://ibb.co/VMJPTwK"><img src="https://i.ibb.co/XFjvtYw/carbon.png" alt="carbon" border="0"></a>
<!-- alternate -->
<!-- <a href="https://ibb.co/hR49z8Q"><img src="https://i.ibb.co/991g5YK/image.png" alt="image" border="0"></a> -->
<a href="https://colab.research.google.com/gist/pszemraj/0b0c2663aa38f3b5f2d923010cfda5a8/scratchpad.ipynb">
<img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
</a>
This is an initial test/work-in-progress, but not bad thus far.
## Model
This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves:
1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.
### Labels
This model has been trained (_using methods described above_) to predict a single class label for `<text>' from the following:
```
# numeric id: text label
{
1: 'cringe',
2: 'relevant',
3: 'info',
4: 'noise'
}
```
---
## Usage
To use this model for inference, first install the SetFit library:
```bash
python -m pip install setfit
```
### basic inference
You can then run inference as follows:
```python
from setfit import SetFitModel
# Download from Hub and run inference
model = SetFitModel.from_pretrained("pszemraj/e5-small-LinkedCringe-setfit-skl-20it-2e")
# Run inference
preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"])
# manually refer to labels above
preds
```
### Class object with utils
create a"custom" wrapper class with the labels:
```python
from setfit import SetFitModel
from typing import List, Dict
class PostClassifier:
DEFAULT_ID2LABEL = {1: "cringe", 2: "relevant", 3: "info", 4: "noise"}
def __init__(
self,
model_id: str = "pszemraj/e5-small-LinkedCringe-setfit-skl-20it-2e",
id2label: Dict[int, str] = None,
):
"""Initialize PostClassifier with model name and/or label mapping."""
self.model = SetFitModel.from_pretrained(model_id)
self.id2label = id2label if id2label else self.DEFAULT_ID2LABEL
def classify(self, texts: List[str]) -> List[str]:
"""Classify list of texts, return list of corresponding labels."""
preds = self.model(texts)
return [self.id2label[int(pred)] for pred in preds]
def predict_proba(self, texts: List[str]) -> List[Dict[str, float]]:
"""Predict label probabilities for a list of texts, return a list of probability dictionaries."""
proba = self.model.predict_proba(texts)
return [
{self.id2label.get(i + 1, "Unknown"): float(pred) for i, pred in enumerate(pred)}
for pred in proba
]
def __call__(self, texts: List[str]) -> List[str]:
"""Enable class instance to act as a function for text classification."""
return self.classify(texts)
```
instantiate & classify :
```python
# import PostClassifier if you defined it in another script etc
model_name="pszemraj/e5-small-LinkedCringe-setfit-skl-20it-2e"
classifier = PostClassifier(model_name)
# classify some posts (these should all be cringe maaaaybe noise)
posts = [
"🚀 Innovation is our middle name! We're taking synergy to new heights and disrupting the market with our game-changing solutions. Stay tuned for the next paradigm shift! 💥 #CorporateRevolution #SynergisticSolutions",
"🌟 Attention all trailblazers! Our cutting-edge product is the epitome of excellence. It's time to elevate your success and ride the wave of unparalleled achievements. Join us on this journey towards greatness! 🚀 #UnleashYourPotential #SuccessRevolution",
"🌍 We're not just a company, we're a global force for change! Our world-class team is committed to revolutionizing industries and making a lasting impact. Together, let's reshape the future and leave a legacy that will be remembered for ages! 💪 #GlobalTrailblazers #LegacyMakers",
"🔥 Harness the power of synergy and unlock your true potential with our transformative solutions. Together, we'll ignite a fire of success that will radiate across industries. Join the league of winners and conquer new frontiers! 🚀 #SynergyChampions #UnleashThePowerWithin",
"💡 Innovation alert! Our visionary team has cracked the code to redefine excellence. Get ready to be blown away by our mind-boggling breakthroughs that will leave your competitors in the dust. It's time to disrupt the status quo and embrace the future! 🌟 #InnovationRevolution #ExcellenceUnleashed",
"🌐 Welcome to the era of limitless possibilities! Our revolutionary platform will empower you to transcend boundaries and achieve unprecedented success. Together, let's shape a future where dreams become realities and ordinary becomes extraordinary! ✨ #LimitlessSuccess #DreamBig",
"💥 Brace yourselves for a seismic shift in the industry! Our game-changing product is set to revolutionize the way you work, think, and succeed. Say goodbye to mediocrity and join the league of pioneers leading the charge towards a brighter tomorrow! 🚀 #IndustryDisruptors #PioneeringSuccess",
"🚀 Attention all innovators and disruptors! It's time to break free from the chains of convention and rewrite the rulebook of success. Join us on this exhilarating journey as we create a new chapter in the annals of greatness. The sky's not the limit—it's just the beginning! 💫 #BreakingBarriers #UnleashGreatness",
"🌟 Unlock the secret to unprecedented achievements with our exclusive formula for success. Our team of experts has distilled years of wisdom into a powerful elixir that will propel you to the zenith of greatness. It's time to embrace the extraordinary and become a legend in your own right! 💥 #FormulaForSuccess #RiseToGreatness",
"🔑 Step into the realm of infinite possibilities and seize the keys to your success. Our groundbreaking solutions will unlock doors you never knew existed, propelling you towards a future filled with limitless growth and prosperity. Dare to dream big and let us be your catalyst for greatness! 🚀 #UnlockYourPotential #LimitlessSuccess"
]
post_preds = classifier(posts)
print(post_preds)
```
## eval - detailed
```
***** Running evaluation *****
{'accuracy': 0.8,
'based_model_id': 'intfloat/e5-small',
'tuned_model_id': 'e5-small-LinkedCringe-setfit-skl-20it-2e'}
# 10-post results
['cringe',
'cringe',
'info',
'cringe',
'cringe',
'cringe',
'cringe',
'cringe',
'cringe',
'cringe']
```
---
## BibTeX entry and citation info
> Note: this is for `setfit` and not this checkpoint.
```bibtex
@article{https://doi.org/10.48550/arxiv.2209.11055,
doi = {10.48550/ARXIV.2209.11055},
url = {https://arxiv.org/abs/2209.11055},
author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Efficient Few-Shot Learning Without Prompts},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
``` | 7,599 | [
[
-0.02020263671875,
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0.01319122314453125,
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0.0206451416015625,
0.0162811279296875,
-0.04998779296875,
-0.043243408203125,
-0.05902099609375,... |
flashvenom/mpt-7b-base-lora-fix | 2023-05-30T04:56:24.000Z | [
"transformers",
"pytorch",
"mpt",
"text-generation",
"Composer",
"MosaicML",
"llm-foundry",
"StreamingDatasets",
"custom_code",
"dataset:mc4",
"dataset:c4",
"dataset:togethercomputer/RedPajama-Data-1T",
"dataset:bigcode/the-stack",
"dataset:allenai/s2orc",
"arxiv:2108.12409",
"arxiv:23... | text-generation | flashvenom | null | null | flashvenom/mpt-7b-base-lora-fix | 0 | 2 | transformers | 2023-05-30T03:40:57 | ---
license: apache-2.0
tags:
- Composer
- MosaicML
- llm-foundry
- StreamingDatasets
datasets:
- mc4
- c4
- togethercomputer/RedPajama-Data-1T
- bigcode/the-stack
- allenai/s2orc
inference: false
duplicated_from: mosaicml/mpt-7b
---
## Authors Note: This is MPT-7B with some fixes borrowed from https://huggingface.co/Birchlabs/mosaicml-mpt-7b-chat-qlora to allow LoRA fine-tuning
# MPT-7B
MPT-7B is a decoder-style transformer pretrained from scratch on 1T tokens of English text and code.
This model was trained by [MosaicML](https://www.mosaicml.com).
MPT-7B is part of the family of MosaicPretrainedTransformer (MPT) models, which use a modified transformer architecture optimized for efficient training and inference.
These architectural changes include performance-optimized layer implementations and the elimination of context length limits by replacing
positional embeddings with Attention with Linear Biases ([ALiBi](https://arxiv.org/abs/2108.12409)).
Thanks to these modifications, MPT models can be trained with high throughput efficiency and stable convergence.
MPT models can also be served efficiently with both standard HuggingFace pipelines and NVIDIA's [FasterTransformer](https://github.com/NVIDIA/FasterTransformer).
This model uses the MosaicML LLM codebase, which can be found in the [llm-foundry repository](https://github.com/mosaicml/llm-foundry). It was trained by MosaicML’s NLP team on the [MosaicML platform](https://www.mosaicml.com/training) for LLM pretraining, finetuning, and inference.
### How is this model different?
MPT-7B is
* **Licensed for the possibility of commercial use** (unlike [LLaMA](https://arxiv.org/abs/2302.13971)).
* **Trained on a large amount of data** (1T tokens like [LLaMA](https://arxiv.org/abs/2302.13971) vs. 300B for [Pythia](https://github.com/EleutherAI/pythia), 300B for [OpenLLaMA](https://github.com/openlm-research/open_llama), and 800B for [StableLM](https://github.com/Stability-AI/StableLM)).
* **Prepared to handle extremely long inputs** thanks to [ALiBi](https://arxiv.org/abs/2108.12409) (we finetuned [MPT-7B-StoryWriter-65k+](https://huggingface.co/mosaicml/mpt-7b-storywriter) on up to 65k inputs and can handle up to 84k vs. 2k-4k for other open source models).
* **Capable of fast training and inference** (via [FlashAttention](https://arxiv.org/pdf/2205.14135.pdf) and [FasterTransformer](https://github.com/NVIDIA/FasterTransformer))
* **Equipped with highly efficient open-source training code** via the [llm-foundry repository](https://github.com/mosaicml/llm-foundry)
### Models finetuned off MPT-7B:
The following models are finetuned on MPT-7B:
* [MPT-7B-StoryWriter-65k+](https://huggingface.co/mosaicml/mpt-7b-storywriter): a model designed to read and write fictional stories with super long context lengths.
Built by finetuning MPT-7B with a context length of 65k tokens on a filtered fiction subset of the [books3 dataset](https://huggingface.co/datasets/the_pile_books3).
At inference time, thanks to [ALiBi](https://arxiv.org/abs/2108.12409), MPT-7B-StoryWriter-65k+ can extrapolate even beyond 65k tokens.
We demonstrate generations as long as 80k tokens on a single A100-80GB GPU in our [blogpost](www.mosaicml.com/blog/mpt-7b).
* License: Apache 2.0
* [MPT-7B-Instruct](https://huggingface.co/mosaicml/mpt-7b-instruct): a model for short-form instruction following.
Built by finetuning MPT-7B on a [dataset](https://huggingface.co/datasets/mosaicml/dolly_hhrlhf) we also release, derived from the [Databricks Dolly-15k](https://huggingface.co/datasets/databricks/databricks-dolly-15k) and the [Anthropic Helpful and Harmless (HH-RLHF)](https://huggingface.co/datasets/Anthropic/hh-rlhf) datasets.
* License: _CC-By-SA-3.0_
* [Demo on Hugging Face Spaces](https://huggingface.co/spaces/mosaicml/mpt-7b-instruct)
* [MPT-7B-Chat](https://huggingface.co/mosaicml/mpt-7b-chat): a chatbot-like model for dialogue generation.
Built by finetuning MPT-7B on the [ShareGPT-Vicuna](https://huggingface.co/datasets/jeffwan/sharegpt_vicuna), [HC3](https://huggingface.co/datasets/Hello-SimpleAI/HC3),
[Alpaca](https://huggingface.co/datasets/tatsu-lab/alpaca), [HH-RLHF](https://huggingface.co/datasets/Anthropic/hh-rlhf), and [Evol-Instruct](https://huggingface.co/datasets/victor123/evol_instruct_70k) datasets.
* License: _CC-By-NC-SA-4.0_
* [Demo on Hugging Face Spaces](https://huggingface.co/spaces/mosaicml/mpt-7b-chat)
## Model Date
May 5, 2023
## Model License
Apache-2.0
## Documentation
* [Blog post: Introducing MPT-7B: A New Standard for Open-Source, Commercially Usable LLMs](https://www.mosaicml.com/blog/mpt-7b)
* [Codebase (mosaicml/llm-foundry repo)](https://github.com/mosaicml/llm-foundry/)
* Questions: Feel free to contact us via the [MosaicML Community Slack](https://mosaicml.me/slack)!
## How to Use
This model is best used with the MosaicML [llm-foundry repository](https://github.com/mosaicml/llm-foundry) for training and finetuning.
```python
import transformers
model = transformers.AutoModelForCausalLM.from_pretrained(
'mosaicml/mpt-7b',
trust_remote_code=True
)
```
Note: This model requires that `trust_remote_code=True` be passed to the `from_pretrained` method.
This is because we use a custom `MPT` model architecture that is not yet part of the Hugging Face `transformers` package.
`MPT` includes options for many training efficiency features such as [FlashAttention](https://arxiv.org/pdf/2205.14135.pdf), [ALiBi](https://arxiv.org/abs/2108.12409), [QK LayerNorm](https://arxiv.org/abs/2010.04245), and more.
To use the optimized [triton implementation](https://github.com/openai/triton) of FlashAttention, you can load the model with `attn_impl='triton'` and move the model to `bfloat16`:
```python
config = transformers.AutoConfig.from_pretrained(
'mosaicml/mpt-7b',
trust_remote_code=True
)
config.attn_config['attn_impl'] = 'triton'
model = transformers.AutoModelForCausalLM.from_pretrained(
'mosaicml/mpt-7b',
config=config,
torch_dtype=torch.bfloat16,
trust_remote_code=True
)
model.to(device='cuda:0')
```
Although the model was trained with a sequence length of 2048, ALiBi enables users to increase the maximum sequence length during finetuning and/or inference. For example:
```python
config = transformers.AutoConfig.from_pretrained(
'mosaicml/mpt-7b',
trust_remote_code=True
)
config.update({"max_seq_len": 4096})
model = transformers.AutoModelForCausalLM.from_pretrained(
'mosaicml/mpt-7b',
config=config,
trust_remote_code=True
)
```
This model was trained with the [EleutherAI/gpt-neox-20b](https://huggingface.co/EleutherAI/gpt-neox-20b) tokenizer.
```python
from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("EleutherAI/gpt-neox-20b")
```
## Model Description
The architecture is a modification of a standard decoder-only transformer.
The model has been modified from a standard transformer in the following ways:
* It uses [FlashAttention](https://arxiv.org/pdf/2205.14135.pdf)
* It uses [ALiBi (Attention with Linear Biases)](https://arxiv.org/abs/2108.12409) and does not use positional embeddings
* It does not use biases
| Hyperparameter | Value |
|----------------|-------|
|n_parameters | 6.7B |
|n_layers | 32 |
| n_heads | 32 |
| d_model | 4096 |
| vocab size | 50432 |
| sequence length | 2048 |
## Training Data
### Streaming Datasets
Data was formatted using the MosaicML [StreamingDataset](https://github.com/mosaicml/streaming) library to host our data in object storage and efficiently stream it to our compute cluster during training.
StreamingDataset obviates the need to download the whole dataset before starting training, and allows instant resumption of training from any point in the dataset.
### Data Mix
The model was trained for 1T tokens (with batch size 1760 and sequence length 2048). It was trained on the following data mix:
| Data Source | Number of Tokens in Source | Proportion | Effective Number of Tokens | Epochs |
|-------------|----------------------------|------------|----------------------------|--------|
| mC4 3.1.0 - English | 417.99 B | 0.33 | 330 B | 0.14 |
| C4 - English - SemDedup 80% | 100.42 B | 0.299 | 299 B | 2.98 |
| RedPajama - CommonCrawl | 878.45 B | 0.1 | 100 B | 0.11 |
| The Stack - Selected Languages | 463.78 B | 0.1 | 100 B | 0.22 |
| RedPajama - Wikipedia - En | 4.87 B | 0.04 | 40 B | 8.21 |
| The Stack - Markdown | 107.07 B | 0.035 | 35 B | 0.33 |
| S2ORC | 48.85 B | 0.033 | 33 B | 0.68 |
| RedPajama - Books | 26.02 B | 0.03 | 30B | 1.15 |
| RedPajama - arXiv | 28.10 B | 0.019 | 19 B | 0.68 |
| RedPajama - StackExchange | 20.54 B | 0.014 | 14 B |0.68 |
Samples for each batch were selected from one of the datasets with the probability specified above.
The examples were shuffled within each dataset, and each example was constructed from as many sequences from that dataset as were necessary to fill the 2048 sequence length.
The data was tokenized using the [EleutherAI/gpt-neox-20b](https://huggingface.co/EleutherAI/gpt-neox-20b) tokenizer. This BPE tokenizer has a number of desirable characteristics,
most of which are relevant for tokenizing code:
(1) It was trained on a diverse mix of data that includes code (The Pile)
(2) It applies consistent space delimitation, unlike the GPT2 tokenizer which tokenizes inconsistently depending on the presence of prefix spaces
(3) It contains tokens for repeated space characters, which allows superior compression of text with large amounts of repeated space characters.
The model vocabulary size of 50432 was set to be a multiple of 128 (as in [MEGATRON-LM](https://arxiv.org/abs/1909.08053)), model flop utilization (MFU) increased by up to four percentage points.
### Training Configuration
This model was trained on 440 A100-40GBs for about 9.5 days using the [MosaicML Platform](https://www.mosaicml.com/platform).
The model was trained with sharded data parallelism using [FSDP](https://pytorch.org/docs/stable/fsdp.html) and used the [LION](https://arxiv.org/abs/2302.06675) optimizer.
## Limitations and Biases
_The following language is modified from [EleutherAI's GPT-NeoX-20B](https://huggingface.co/EleutherAI/gpt-neox-20b)_
MPT-7B (Base) is **not** intended for deployment without finetuning.
It should not be used for human-facing interactions without further guardrails and user consent.
MPT-7B can produce factually incorrect output, and should not be relied on to produce factually accurate information.
MPT-7B was trained on various public datasets.
While great efforts have been taken to clean the pretraining data, it is possible that this model could generate lewd, biased or otherwise offensive outputs.
## MosaicML Platform
If you're interested in [training](https://www.mosaicml.com/training) and [deploying](https://www.mosaicml.com/inference) your own MPT or LLMs on the MosaicML Platform, [sign up here](https://forms.mosaicml.com/demo?utm_source=huggingface&utm_medium=referral&utm_campaign=mpt-7b).
## Disclaimer
The license on this model does not constitute legal advice. We are not responsible for the actions of third parties who use this model. Please cosult an attorney before using this model for commercial purposes.
## Citation
Please cite this model using the following format:
```
@online{MosaicML2023Introducing,
author = {MosaicML NLP Team},
title = {Introducing MPT-7B: A New Standard for Open-Source,
ly Usable LLMs},
year = {2023},
url = {www.mosaicml.com/blog/mpt-7b},
note = {Accessed: 2023-03-28}, % change this date
urldate = {2023-03-28} % change this date
}
``` | 11,674 | [
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xmj2002/gpt2_tang_poetry | 2023-05-30T06:31:12.000Z | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"zh",
"dataset:xmj2002/tang_poems",
"license:apache-2.0",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | xmj2002 | null | null | xmj2002/gpt2_tang_poetry | 0 | 2 | transformers | 2023-05-30T05:11:49 | ---
license: apache-2.0
datasets:
- xmj2002/tang_poems
language:
- zh
---
使用的预训练模型为[uer/gpt2-chinese-cluecorpussmall](https://huggingface.co/uer/gpt2-chinese-cluecorpussmall)
## Usage
```python
from transformers import AutoModelForCausalLM
from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("xmj2002/gpt2_tang_poetry")
model = AutoModelForCausalLM.from_pretrained("xmj2002/gpt2_tang_poetry")
text = "白居易《远方》"
inputs = tokenizer(text, return_tensors="pt").input_ids
outputs = model.generate(inputs, max_new_tokens=100, do_sample=True, top_k=100, top_p=0.95)
tokenizer.decode(outputs[0], skip_special_tokens=True)
```
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0.00139617919921875,
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fredymad/siebert_laxo_2e-5_16_2 | 2023-05-30T06:01:36.000Z | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | text-classification | fredymad | null | null | fredymad/siebert_laxo_2e-5_16_2 | 0 | 2 | transformers | 2023-05-30T05:43:06 | ---
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: siebert_laxo_2e-5_16_2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# siebert_laxo_2e-5_16_2
This model is a fine-tuned version of [fredymad/siebert_estricto_2e-5_16_2](https://huggingface.co/fredymad/siebert_estricto_2e-5_16_2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2449
- Accuracy: 0.9412
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 400 | 0.1726 | 0.9443 |
| 0.191 | 2.0 | 800 | 0.2449 | 0.9412 |
### Framework versions
- Transformers 4.29.0
- Pytorch 1.13.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,415 | [
[
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fredymad/Financial_laxo_2e-5_16_2 | 2023-05-30T06:33:21.000Z | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | text-classification | fredymad | null | null | fredymad/Financial_laxo_2e-5_16_2 | 0 | 2 | transformers | 2023-05-30T06:26:57 | ---
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: Financial_laxo_2e-5_16_2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Financial_laxo_2e-5_16_2
This model is a fine-tuned version of [fredymad/Financial_estricto_2e-5_16_2](https://huggingface.co/fredymad/Financial_estricto_2e-5_16_2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3601
- Accuracy: 0.8762
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 400 | 0.3033 | 0.8743 |
| 0.3393 | 2.0 | 800 | 0.3601 | 0.8762 |
### Framework versions
- Transformers 4.29.0
- Pytorch 1.13.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3
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casarf/comment_model_test_zucchi | 2023-05-30T10:07:43.000Z | [
"transformers",
"tf",
"distilbert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | casarf | null | null | casarf/comment_model_test_zucchi | 0 | 2 | transformers | 2023-05-30T10:04:00 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: casarf/comment_model_test_zucchi
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# casarf/comment_model_test_zucchi
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.3930
- Validation Loss: 0.4734
- Train Accuracy: 0.7590
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 820, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Train Accuracy | Epoch |
|:----------:|:---------------:|:--------------:|:-----:|
| 0.6538 | 0.5351 | 0.7952 | 0 |
| 0.5180 | 0.4653 | 0.7952 | 1 |
| 0.3930 | 0.4734 | 0.7590 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
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declare-lab/tango-full | 2023-06-17T07:20:32.000Z | [
"transformers",
"music",
"en",
"dataset:declare-lab/TangoPromptBank",
"license:cc-by-nc-sa-4.0",
"endpoints_compatible",
"region:us"
] | null | declare-lab | null | null | declare-lab/tango-full | 4 | 2 | transformers | 2023-05-30T10:27:30 | ---
license: cc-by-nc-sa-4.0
datasets:
- declare-lab/TangoPromptBank
language:
- en
tags:
- music
---
# TANGO: Text to Audio using iNstruction-Guided diffusiOn
**TANGO** is a latent diffusion model for text-to-audio generation. **TANGO** can generate realistic audios including human sounds, animal sounds, natural and artificial sounds and sound effects from textual prompts. We use the frozen instruction-tuned LLM Flan-T5 as the text encoder and train a UNet based diffusion model for audio generation. We outperform current state-of-the-art models for audio generation across both objective and subjective metrics. We release our model, training, inference code and pre-trained checkpoints for the research community.
📣 We are releasing **Tango-Full** which was pre-trained on **TangoPromptBank**.
## Code
Our code is released here: [https://github.com/declare-lab/tango](https://github.com/declare-lab/tango)
We uploaded several **TANGO** generated samples here: [https://tango-web.github.io/](https://tango-web.github.io/)
Please follow the instructions in the repository for installation, usage and experiments.
## Quickstart Guide
Download the **TANGO** model and generate audio from a text prompt:
```python
import IPython
import soundfile as sf
from tango import Tango
tango = Tango("declare-lab/tango-full-ft-audiocaps")
prompt = "An audience cheering and clapping"
audio = tango.generate(prompt)
sf.write(f"{prompt}.wav", audio, samplerate=16000)
IPython.display.Audio(data=audio, rate=16000)
```
[An audience cheering and clapping.webm](https://user-images.githubusercontent.com/13917097/233851915-e702524d-cd35-43f7-93e0-86ea579231a7.webm)
The model will be automatically downloaded and saved in cache. Subsequent runs will load the model directly from cache.
The `generate` function uses 100 steps by default to sample from the latent diffusion model. We recommend using 200 steps for generating better quality audios. This comes at the cost of increased run-time.
```python
prompt = "Rolling thunder with lightning strikes"
audio = tango.generate(prompt, steps=200)
IPython.display.Audio(data=audio, rate=16000)
```
[Rolling thunder with lightning strikes.webm](https://user-images.githubusercontent.com/13917097/233851929-90501e41-911d-453f-a00b-b215743365b4.webm)
<!-- [MachineClicking](https://user-images.githubusercontent.com/25340239/233857834-bfda52b4-4fcc-48de-b47a-6a6ddcb3671b.mp4 "sample 1") -->
Use the `generate_for_batch` function to generate multiple audio samples for a batch of text prompts:
```python
prompts = [
"A car engine revving",
"A dog barks and rustles with some clicking",
"Water flowing and trickling"
]
audios = tango.generate_for_batch(prompts, samples=2)
```
This will generate two samples for each of the three text prompts. | 2,802 | [
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RosyB/distilbert-base-uncased-finetuned-cola | 2023-05-31T09:19:35.000Z | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | text-classification | RosyB | null | null | RosyB/distilbert-base-uncased-finetuned-cola | 0 | 2 | transformers | 2023-05-30T11:51:52 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- matthews_correlation
model-index:
- name: distilbert-base-uncased-finetuned-cola
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
config: cola
split: validation
args: cola
metrics:
- name: Matthews Correlation
type: matthews_correlation
value: 0.5471613867597194
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-cola
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5251
- Matthews Correlation: 0.5472
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.5221 | 1.0 | 535 | 0.5371 | 0.4275 |
| 0.3491 | 2.0 | 1070 | 0.5129 | 0.4946 |
| 0.2382 | 3.0 | 1605 | 0.5251 | 0.5472 |
| 0.1758 | 4.0 | 2140 | 0.7505 | 0.5378 |
| 0.125 | 5.0 | 2675 | 0.7983 | 0.5414 |
### Framework versions
- Transformers 4.26.1
- Pytorch 2.0.1+cpu
- Datasets 1.18.4
- Tokenizers 0.13.2
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fredymad/bert_Pfinal_2e-5_16_2 | 2023-06-02T10:50:02.000Z | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | text-classification | fredymad | null | null | fredymad/bert_Pfinal_2e-5_16_2 | 0 | 2 | transformers | 2023-05-30T12:23:27 | ---
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: bert_Pfinal_2e-5_16_2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert_Pfinal_2e-5_16_2
This model is a fine-tuned version of [dccuchile/bert-base-spanish-wwm-uncased](https://huggingface.co/dccuchile/bert-base-spanish-wwm-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2437
- F1: 0.7464
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2444 | 1.0 | 669 | 0.1785 | 0.7321 |
| 0.1729 | 2.0 | 1338 | 0.2437 | 0.7464 |
### Framework versions
- Transformers 4.28.0
- Pytorch 1.13.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,401 | [
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fredymad/bert_Pfinal_2e-5_16_10 | 2023-06-02T11:41:58.000Z | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | text-classification | fredymad | null | null | fredymad/bert_Pfinal_2e-5_16_10 | 0 | 2 | transformers | 2023-05-30T12:42:33 | ---
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: bert_Pfinal_2e-5_16_10
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert_Pfinal_2e-5_16_10
This model is a fine-tuned version of [fredymad/bert_Pfinal_2e-5_16_2](https://huggingface.co/fredymad/bert_Pfinal_2e-5_16_2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6295
- F1: 0.7355
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.101 | 1.0 | 669 | 0.3000 | 0.7169 |
| 0.1282 | 2.0 | 1338 | 0.2993 | 0.7361 |
| 0.0548 | 3.0 | 2007 | 0.3924 | 0.7308 |
| 0.0278 | 4.0 | 2676 | 0.4989 | 0.7221 |
| 0.0229 | 5.0 | 3345 | 0.6089 | 0.6940 |
| 0.0168 | 6.0 | 4014 | 0.5561 | 0.7361 |
| 0.0082 | 7.0 | 4683 | 0.6112 | 0.7297 |
| 0.008 | 8.0 | 5352 | 0.6101 | 0.7343 |
| 0.0052 | 9.0 | 6021 | 0.6253 | 0.7400 |
| 0.003 | 10.0 | 6690 | 0.6295 | 0.7355 |
### Framework versions
- Transformers 4.28.0
- Pytorch 1.13.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,866 | [
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0.01247406005859375,
-0.0195159912109375,
-0.0309906005859375,
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0.0167236328125,
0.020111083984375,
-0.05615234375,
-0.039703369140625,
-0.045684814453125,
... |
CarnivoraCanis/berturk-cased-tr-fakenews | 2023-06-05T11:06:03.000Z | [
"transformers",
"pytorch",
"bert",
"text-classification",
"Fake News",
"tr",
"endpoints_compatible",
"region:us"
] | text-classification | CarnivoraCanis | null | null | CarnivoraCanis/berturk-cased-tr-fakenews | 0 | 2 | transformers | 2023-05-30T13:28:07 | ---
language:
- tr
metrics:
- accuracy
- f1
pipeline_tag: text-classification
tags:
- Fake News
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** Yakup Haydar Baba, İlkay Yağız Gür, Melih Önol, Hasan Atabey Ayhan, Deniz Bedran Yıldırım
- **Language(s) (NLP):** Turkish
- **Finetuned from model [optional]:** dbmdz/bert-base-turkish-uncased
## Uses
- This model can be used for Turkish fake news detection purposes
### Training Data
Training dataset used from Mertoğlu, U., & Genç, B. (2020). "Automated fake news detection in the age of digital libraries. Information Technology and Libraries, 39(4)" paper.
#### Training Hyperparameters
- All training hyperparameters are used as default
## Model Card Authors
- Yakup Haydar Baba
| 1,137 | [
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YakovElm/Apache5Classic_Balance_DATA_ratio_Half | 2023-05-30T15:14:14.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/Apache5Classic_Balance_DATA_ratio_Half | 0 | 2 | transformers | 2023-05-30T13:55:52 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Apache5Classic_Balance_DATA_ratio_Half
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Apache5Classic_Balance_DATA_ratio_Half
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.5918
- Train Accuracy: 0.6987
- Validation Loss: 0.6202
- Validation Accuracy: 0.6882
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.6351 | 0.6418 | 0.6371 | 0.6426 | 0 |
| 0.6209 | 0.6734 | 0.6201 | 0.6426 | 1 |
| 0.5918 | 0.6987 | 0.6202 | 0.6882 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,820 | [
[
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YakovElm/Hyperledger5Classic_Balance_DATA_ratio_Half | 2023-05-30T17:25:41.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/Hyperledger5Classic_Balance_DATA_ratio_Half | 0 | 2 | transformers | 2023-05-30T14:05:02 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Hyperledger5Classic_Balance_DATA_ratio_Half
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Hyperledger5Classic_Balance_DATA_ratio_Half
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.5204
- Train Accuracy: 0.7552
- Validation Loss: 0.5316
- Validation Accuracy: 0.7478
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.6140 | 0.6549 | 0.5368 | 0.7699 | 0 |
| 0.5649 | 0.7360 | 0.5733 | 0.6903 | 1 |
| 0.5204 | 0.7552 | 0.5316 | 0.7478 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,830 | [
[
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YakovElm/IntelDAOS5Classic_Balance_DATA_ratio_Half | 2023-05-30T22:37:52.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/IntelDAOS5Classic_Balance_DATA_ratio_Half | 0 | 2 | transformers | 2023-05-30T14:09:08 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: IntelDAOS5Classic_Balance_DATA_ratio_Half
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# IntelDAOS5Classic_Balance_DATA_ratio_Half
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.5860
- Train Accuracy: 0.6772
- Validation Loss: 0.6540
- Validation Accuracy: 0.6667
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.6459 | 0.6455 | 0.6410 | 0.6667 | 0 |
| 0.6023 | 0.6825 | 0.6514 | 0.6190 | 1 |
| 0.5860 | 0.6772 | 0.6540 | 0.6667 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,826 | [
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fredymad/roberta_Pfinal_2e-5_16_2 | 2023-06-02T15:47:25.000Z | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | fredymad | null | null | fredymad/roberta_Pfinal_2e-5_16_2 | 0 | 2 | transformers | 2023-05-30T14:15:32 | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: roberta_Pfinal_2e-5_16_2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta_Pfinal_2e-5_16_2
This model is a fine-tuned version of [PlanTL-GOB-ES/roberta-base-bne](https://huggingface.co/PlanTL-GOB-ES/roberta-base-bne) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2494
- F1: 0.7330
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2608 | 1.0 | 669 | 0.2140 | 0.6623 |
| 0.1754 | 2.0 | 1338 | 0.2494 | 0.7330 |
### Framework versions
- Transformers 4.28.0
- Pytorch 1.13.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,409 | [
[
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YakovElm/Jira5Classic_Balance_DATA_ratio_Half | 2023-05-31T00:16:06.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/Jira5Classic_Balance_DATA_ratio_Half | 0 | 2 | transformers | 2023-05-30T14:16:11 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Jira5Classic_Balance_DATA_ratio_Half
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Jira5Classic_Balance_DATA_ratio_Half
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.4710
- Train Accuracy: 0.7959
- Validation Loss: 0.5563
- Validation Accuracy: 0.7239
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.6163 | 0.6918 | 0.5537 | 0.7485 | 0 |
| 0.5128 | 0.7735 | 0.5581 | 0.7485 | 1 |
| 0.4710 | 0.7959 | 0.5563 | 0.7239 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,816 | [
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YakovElm/MariaDB5Classic_Balance_DATA_ratio_Half | 2023-05-31T02:01:46.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/MariaDB5Classic_Balance_DATA_ratio_Half | 0 | 2 | transformers | 2023-05-30T14:19:50 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: MariaDB5Classic_Balance_DATA_ratio_Half
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# MariaDB5Classic_Balance_DATA_ratio_Half
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.5735
- Train Accuracy: 0.7211
- Validation Loss: 0.4513
- Validation Accuracy: 0.8281
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.6439 | 0.6368 | 0.5531 | 0.7188 | 0 |
| 0.5910 | 0.6842 | 0.5049 | 0.7188 | 1 |
| 0.5735 | 0.7211 | 0.4513 | 0.8281 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,822 | [
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YakovElm/Qt5Classic_Balance_DATA_ratio_Half | 2023-05-31T03:49:42.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/Qt5Classic_Balance_DATA_ratio_Half | 0 | 2 | transformers | 2023-05-30T14:27:31 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Qt5Classic_Balance_DATA_ratio_Half
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Qt5Classic_Balance_DATA_ratio_Half
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.5593
- Train Accuracy: 0.7382
- Validation Loss: 0.6399
- Validation Accuracy: 0.6328
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.6323 | 0.6629 | 0.6276 | 0.6610 | 0 |
| 0.6071 | 0.6704 | 0.6235 | 0.6102 | 1 |
| 0.5593 | 0.7382 | 0.6399 | 0.6328 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,812 | [
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rayjyate/bert-emotion | 2023-05-30T14:33:36.000Z | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:tweet_eval",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | text-classification | rayjyate | null | null | rayjyate/bert-emotion | 0 | 2 | transformers | 2023-05-30T14:27:51 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- tweet_eval
metrics:
- precision
- recall
model-index:
- name: bert-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: tweet_eval
type: tweet_eval
config: emotion
split: validation
args: emotion
metrics:
- name: Precision
type: precision
value: 0.7505623807659564
- name: Recall
type: recall
value: 0.7243031825553111
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-emotion
This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on the tweet_eval dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1413
- Precision: 0.7506
- Recall: 0.7243
- Fscore: 0.7340
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | Fscore |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|
| 0.8556 | 1.0 | 815 | 0.7854 | 0.7461 | 0.5929 | 0.6088 |
| 0.5369 | 2.0 | 1630 | 0.9014 | 0.7549 | 0.7278 | 0.7359 |
| 0.2571 | 3.0 | 2445 | 1.1413 | 0.7506 | 0.7243 | 0.7340 |
### Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,970 | [
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YakovElm/Hyperledger20Classic_512 | 2023-05-30T14:31:34.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/Hyperledger20Classic_512 | 0 | 2 | transformers | 2023-05-30T14:30:55 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Hyperledger20Classic_512
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Hyperledger20Classic_512
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.2642
- Train Accuracy: 0.9149
- Validation Loss: 0.2898
- Validation Accuracy: 0.8983
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.3104 | 0.9035 | 0.3020 | 0.8983 | 0 |
| 0.2724 | 0.9149 | 0.2950 | 0.8983 | 1 |
| 0.2642 | 0.9149 | 0.2898 | 0.8983 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,792 | [
[
-0.048431396484375,
-0.0418701171875,
0.0222625732421875,
0.0036525726318359375,
-0.0288848876953125,
-0.0266571044921875,
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0.01296234130859375,
0.0150299072265625,
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fredymad/robertuito_Pfinal | 2023-06-02T12:18:55.000Z | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | text-classification | fredymad | null | null | fredymad/robertuito_Pfinal | 0 | 2 | transformers | 2023-05-30T14:32:10 | ---
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: robertuito_Pfinal
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# robertuito_Pfinal
This model is a fine-tuned version of [pysentimiento/robertuito-base-uncased](https://huggingface.co/pysentimiento/robertuito-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2105
- F1: 0.7639
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2254 | 1.0 | 669 | 0.1746 | 0.7618 |
| 0.1557 | 2.0 | 1338 | 0.2105 | 0.7639 |
### Framework versions
- Transformers 4.28.0
- Pytorch 1.13.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,389 | [
[
-0.029632568359375,
-0.034393310546875,
0.014312744140625,
0.016815185546875,
-0.0316162109375,
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0.0135345458984375,
0.037628173828125,
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kfkas/t5-large-korean-P2G | 2023-06-03T11:55:24.000Z | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"generated_from_keras_callback",
"ko",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text2text-generation | kfkas | null | null | kfkas/t5-large-korean-P2G | 5 | 2 | transformers | 2023-05-30T14:38:58 | ---
language:
- ko
tags:
- generated_from_keras_callback
model-index:
- name: t5-large-korean-P2G
results: []
---
# t5-large-korean-P2G
이 모델은 lcw99 / t5-large-korean-text-summary을 국립 국어원 신문 말뭉치 50만개의 문장을 2021을 g2pK로 훈련시켜 G2P된 데이터를 원본으로 돌립니다.<br>
git : https://github.com/taemin6697<br>
## Usage
```python
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_dir = "kfkas/t5-large-korean-P2G"
tokenizer = AutoTokenizer.from_pretrained(model_dir)
model = AutoModelForSeq2SeqLM.from_pretrained(model_dir)
text = "서규왕국 싸우디 태양광·풍녁 빨쩐 중심지 될 껃"
inputs = tokenizer.encode(text,return_tensors="pt")
output = model.generate(inputs)
decoded_output = tokenizer.batch(output[0], skip_special_tokens=True)
print(decoded_output)#석유왕국 사우디 태양광·풍력 발전 중심지 될 것
```
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: None
- training_precision: float16
### Training results
### Framework versions
- Transformers 4.22.1
- TensorFlow 2.10.0
- Datasets 2.5.1
- Tokenizers 0.12.1 | 1,166 | [
[
-0.0200653076171875,
-0.0347900390625,
0.0157318115234375,
0.0433349609375,
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0.0135498046875,
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0.0... |
HEN10/layoutlmv2_Kb_qa04 | 2023-05-30T14:56:20.000Z | [
"transformers",
"pytorch",
"tensorboard",
"layoutlmv2",
"document-question-answering",
"generated_from_trainer",
"license:cc-by-nc-sa-4.0",
"endpoints_compatible",
"region:us"
] | document-question-answering | HEN10 | null | null | HEN10/layoutlmv2_Kb_qa04 | 0 | 2 | transformers | 2023-05-30T14:45:40 | ---
license: cc-by-nc-sa-4.0
tags:
- generated_from_trainer
model-index:
- name: layoutlmv2_Kb_qa04
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# layoutlmv2_Kb_qa04
This model is a fine-tuned version of [microsoft/layoutlmv2-base-uncased](https://huggingface.co/microsoft/layoutlmv2-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 4.1587
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.9629 | 0.57 | 50 | 3.1486 |
| 2.5694 | 1.14 | 100 | 4.1441 |
| 2.331 | 1.7 | 150 | 3.4756 |
| 1.8442 | 2.27 | 200 | 4.1663 |
| 1.7225 | 2.84 | 250 | 4.1981 |
| 1.5666 | 3.41 | 300 | 4.2186 |
| 1.4984 | 3.98 | 350 | 4.1587 |
### Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.12.1
| 1,597 | [
[
-0.0220947265625,
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0.00971221923828125,
0.0188140869140625,
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0.0034732818603515625,
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-0.0017423629760742188,
0.0284576416015625,
-0.05352783203125,
-0.04571533203125,
-0.03591918945312... |
fredymad/distilbert_Pfinal_4CLASES_2e-5_16_2 | 2023-06-02T10:35:41.000Z | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | text-classification | fredymad | null | null | fredymad/distilbert_Pfinal_4CLASES_2e-5_16_2 | 0 | 2 | transformers | 2023-05-30T15:12:56 | ---
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert_Pfinal_4CLASES_2e-5_16_2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert_Pfinal_4CLASES_2e-5_16_2
This model is a fine-tuned version of [dccuchile/distilbert-base-spanish-uncased](https://huggingface.co/dccuchile/distilbert-base-spanish-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3103
- Accuracy: 0.8987
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.4274 | 1.0 | 669 | 0.3094 | 0.8972 |
| 0.2899 | 2.0 | 1338 | 0.3103 | 0.8987 |
### Framework versions
- Transformers 4.28.0
- Pytorch 1.13.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,453 | [
[
-0.027801513671875,
-0.04638671875,
0.0137176513671875,
0.0254364013671875,
-0.028411865234375,
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-0.01178741455078125,
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0.000988006591796875,
0.011871337890625,
-0.048004150390625,
-0.04986572265625,
-0.05401611328125,
... |
lynxvail/bert-emotion | 2023-05-30T15:30:44.000Z | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:tweet_eval",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | text-classification | lynxvail | null | null | lynxvail/bert-emotion | 0 | 2 | transformers | 2023-05-30T15:22:59 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- tweet_eval
metrics:
- precision
- recall
model-index:
- name: bert-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: tweet_eval
type: tweet_eval
config: emotion
split: validation
args: emotion
metrics:
- name: Precision
type: precision
value: 0.7505623807659564
- name: Recall
type: recall
value: 0.7243031825553111
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-emotion
This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on the tweet_eval dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1413
- Precision: 0.7506
- Recall: 0.7243
- Fscore: 0.7340
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | Fscore |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|
| 0.8556 | 1.0 | 815 | 0.7854 | 0.7461 | 0.5929 | 0.6088 |
| 0.5369 | 2.0 | 1630 | 0.9014 | 0.7549 | 0.7278 | 0.7359 |
| 0.2571 | 3.0 | 2445 | 1.1413 | 0.7506 | 0.7243 | 0.7340 |
### Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,970 | [
[
-0.03363037109375,
-0.045196533203125,
0.018218994140625,
0.022857666015625,
-0.028564453125,
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-0.01788330078125,
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0.01593017578125,
-0.000675201416015625,
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-0.053375244140625,
-0.0584716796875,
-0.0... |
gapvandyaisummer/bert-emotion | 2023-05-30T15:41:09.000Z | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:tweet_eval",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | text-classification | gapvandyaisummer | null | null | gapvandyaisummer/bert-emotion | 0 | 2 | transformers | 2023-05-30T15:23:58 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- tweet_eval
metrics:
- precision
- recall
model-index:
- name: bert-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: tweet_eval
type: tweet_eval
config: emotion
split: validation
args: emotion
metrics:
- name: Precision
type: precision
value: 0.7505623807659564
- name: Recall
type: recall
value: 0.7243031825553111
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-emotion
This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on the tweet_eval dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1413
- Precision: 0.7506
- Recall: 0.7243
- Fscore: 0.7340
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | Fscore |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|
| 0.8556 | 1.0 | 815 | 0.7854 | 0.7461 | 0.5929 | 0.6088 |
| 0.5369 | 2.0 | 1630 | 0.9014 | 0.7549 | 0.7278 | 0.7359 |
| 0.2571 | 3.0 | 2445 | 1.1413 | 0.7506 | 0.7243 | 0.7340 |
### Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,970 | [
[
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0.018218994140625,
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-0.053375244140625,
-0.0584716796875,
-0.0... |
jsilver/bert-emotion | 2023-05-30T15:33:48.000Z | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:tweet_eval",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | text-classification | jsilver | null | null | jsilver/bert-emotion | 0 | 2 | transformers | 2023-05-30T15:27:23 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- tweet_eval
metrics:
- precision
- recall
model-index:
- name: bert-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: tweet_eval
type: tweet_eval
config: emotion
split: validation
args: emotion
metrics:
- name: Precision
type: precision
value: 0.7505623807659564
- name: Recall
type: recall
value: 0.7243031825553111
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-emotion
This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on the tweet_eval dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1413
- Precision: 0.7506
- Recall: 0.7243
- Fscore: 0.7340
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | Fscore |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|
| 0.8556 | 1.0 | 815 | 0.7854 | 0.7461 | 0.5929 | 0.6088 |
| 0.5369 | 2.0 | 1630 | 0.9014 | 0.7549 | 0.7278 | 0.7359 |
| 0.2571 | 3.0 | 2445 | 1.1413 | 0.7506 | 0.7243 | 0.7340 |
### Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,970 | [
[
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-0.053375244140625,
-0.0584716796875,
-0.0... |
YakovElm/Apache5Classic_Balance_DATA_ratio_1 | 2023-05-30T15:29:10.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/Apache5Classic_Balance_DATA_ratio_1 | 0 | 2 | transformers | 2023-05-30T15:28:06 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Apache5Classic_Balance_DATA_ratio_1
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Apache5Classic_Balance_DATA_ratio_1
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.6407
- Train Accuracy: 0.6296
- Validation Loss: 0.6324
- Validation Accuracy: 0.6382
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.6980 | 0.5166 | 0.6806 | 0.5641 | 0 |
| 0.6895 | 0.5470 | 0.6698 | 0.5755 | 1 |
| 0.6407 | 0.6296 | 0.6324 | 0.6382 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,814 | [
[
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0.0126495361328125,
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0.014068603515625,
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-0.040069580078125,
-0.0498046875,
-0.0215... |
FelixHonikker/bert-emotion | 2023-05-30T15:34:36.000Z | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:tweet_eval",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | text-classification | FelixHonikker | null | null | FelixHonikker/bert-emotion | 0 | 2 | transformers | 2023-05-30T15:29:08 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- tweet_eval
metrics:
- precision
- recall
model-index:
- name: bert-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: tweet_eval
type: tweet_eval
config: emotion
split: validation
args: emotion
metrics:
- name: Precision
type: precision
value: 0.7505623807659564
- name: Recall
type: recall
value: 0.7243031825553111
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-emotion
This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on the tweet_eval dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1413
- Precision: 0.7506
- Recall: 0.7243
- Fscore: 0.7340
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | Fscore |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|
| 0.8556 | 1.0 | 815 | 0.7854 | 0.7461 | 0.5929 | 0.6088 |
| 0.5369 | 2.0 | 1630 | 0.9014 | 0.7549 | 0.7278 | 0.7359 |
| 0.2571 | 3.0 | 2445 | 1.1413 | 0.7506 | 0.7243 | 0.7340 |
### Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,970 | [
[
-0.03363037109375,
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0.0182037353515625,
0.0228729248046875,
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0.0159454345703125,
-0.0006780624389648438,
-0.059661865234375,
-0.053375244140625,
-0.0584716796875,
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YuruiGao/bert-emotion | 2023-05-30T15:36:04.000Z | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:tweet_eval",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YuruiGao | null | null | YuruiGao/bert-emotion | 0 | 2 | transformers | 2023-05-30T15:30:01 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- tweet_eval
model-index:
- name: bert-emotion
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-emotion
This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on the tweet_eval dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,030 | [
[
-0.03509521484375,
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0.0300750732421875,
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0.019683837890625,
-0.006031036376953125,
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paku90/bert-emotion | 2023-05-30T15:48:07.000Z | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:tweet_eval",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | text-classification | paku90 | null | null | paku90/bert-emotion | 0 | 2 | transformers | 2023-05-30T15:32:59 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- tweet_eval
metrics:
- precision
- recall
model-index:
- name: bert-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: tweet_eval
type: tweet_eval
config: emotion
split: validation
args: emotion
metrics:
- name: Precision
type: precision
value: 0.7505623807659564
- name: Recall
type: recall
value: 0.7243031825553111
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-emotion
This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on the tweet_eval dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1413
- Precision: 0.7506
- Recall: 0.7243
- Fscore: 0.7340
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | Fscore |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|
| 0.8556 | 1.0 | 815 | 0.7854 | 0.7461 | 0.5929 | 0.6088 |
| 0.5369 | 2.0 | 1630 | 0.9014 | 0.7549 | 0.7278 | 0.7359 |
| 0.2571 | 3.0 | 2445 | 1.1413 | 0.7506 | 0.7243 | 0.7340 |
### Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,970 | [
[
-0.03363037109375,
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0.022857666015625,
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0.01593017578125,
-0.000675201416015625,
-0.059661865234375,
-0.053375244140625,
-0.0584716796875,
-0.0... |
jonglet/mobile_vit | 2023-05-30T17:21:27.000Z | [
"transformers",
"pytorch",
"tensorboard",
"mobilevit",
"image-classification",
"generated_from_trainer",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-classification | jonglet | null | null | jonglet/mobile_vit | 0 | 2 | transformers | 2023-05-30T15:37:09 | ---
license: other
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: mobile_vit
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mobile_vit
This model is a fine-tuned version of [apple/mobilevit-small](https://huggingface.co/apple/mobilevit-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1128
- Accuracy: 0.7615
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 200
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.5859 | 0.78 | 1000 | 0.9741 | 0.7787 |
| 0.1195 | 1.56 | 2000 | 1.1128 | 0.7615 |
### Framework versions
- Transformers 4.27.4
- Pytorch 1.13.0
- Datasets 2.1.0
- Tokenizers 0.13.2
| 1,413 | [
[
-0.031951904296875,
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0.006923675537109375,
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YakovElm/Qt20Classic_512 | 2023-05-30T15:38:16.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/Qt20Classic_512 | 0 | 2 | transformers | 2023-05-30T15:37:34 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Qt20Classic_512
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Qt20Classic_512
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.1699
- Train Accuracy: 0.9462
- Validation Loss: 0.1652
- Validation Accuracy: 0.9586
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.2176 | 0.9448 | 0.1658 | 0.9586 | 0 |
| 0.1966 | 0.9462 | 0.1557 | 0.9586 | 1 |
| 0.1699 | 0.9462 | 0.1652 | 0.9586 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,774 | [
[
-0.039947509765625,
-0.03521728515625,
0.0227813720703125,
0.004642486572265625,
-0.036865234375,
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0.01149749755859375,
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platzi/platzi-distilroberta-base-mrpc-glue-andres_arboleda | 2023-05-30T15:51:56.000Z | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | text-classification | platzi | null | null | platzi/platzi-distilroberta-base-mrpc-glue-andres_arboleda | 0 | 2 | transformers | 2023-05-30T15:43:48 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
- f1
model-index:
- name: platzi-distilroberta-base-mrpc-glue-andres_arboleda
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
config: mrpc
split: validation
args: mrpc
metrics:
- name: Accuracy
type: accuracy
value: 0.8308823529411765
- name: F1
type: f1
value: 0.8685714285714285
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# platzi-distilroberta-base-mrpc-glue-andres_arboleda
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6988
- Accuracy: 0.8309
- F1: 0.8686
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.5239 | 1.09 | 500 | 0.4315 | 0.8186 | 0.8650 |
| 0.3701 | 2.18 | 1000 | 0.6988 | 0.8309 | 0.8686 |
### Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,854 | [
[
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0.01067352294921875,
0.00843048095703125,
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-... |
YakovElm/Apache5Classic_Balance_DATA_ratio_2 | 2023-05-30T15:50:35.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/Apache5Classic_Balance_DATA_ratio_2 | 0 | 2 | transformers | 2023-05-30T15:49:55 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Apache5Classic_Balance_DATA_ratio_2
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Apache5Classic_Balance_DATA_ratio_2
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.5624
- Train Accuracy: 0.7304
- Validation Loss: 0.5657
- Validation Accuracy: 0.7135
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.6344 | 0.6639 | 0.5897 | 0.6926 | 0 |
| 0.6169 | 0.6854 | 0.5808 | 0.6964 | 1 |
| 0.5624 | 0.7304 | 0.5657 | 0.7135 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,814 | [
[
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tcgyver/bert-emotion | 2023-05-30T16:17:22.000Z | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:tweet_eval",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | text-classification | tcgyver | null | null | tcgyver/bert-emotion | 0 | 2 | transformers | 2023-05-30T16:11:48 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- tweet_eval
metrics:
- precision
- recall
model-index:
- name: bert-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: tweet_eval
type: tweet_eval
config: emotion
split: validation
args: emotion
metrics:
- name: Precision
type: precision
value: 0.7505623807659564
- name: Recall
type: recall
value: 0.7243031825553111
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-emotion
This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on the tweet_eval dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1413
- Precision: 0.7506
- Recall: 0.7243
- Fscore: 0.7340
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | Fscore |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|
| 0.8556 | 1.0 | 815 | 0.7854 | 0.7461 | 0.5929 | 0.6088 |
| 0.5369 | 2.0 | 1630 | 0.9014 | 0.7549 | 0.7278 | 0.7359 |
| 0.2571 | 3.0 | 2445 | 1.1413 | 0.7506 | 0.7243 | 0.7340 |
### Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,970 | [
[
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0.018218994140625,
0.0228729248046875,
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0.01593017578125,
-0.0006737709045410156,
-0.059661865234375,
-0.053375244140625,
-0.058502197265625,
-0... |
YakovElm/Apache5Classic_Balance_DATA_ratio_3 | 2023-05-30T16:18:35.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/Apache5Classic_Balance_DATA_ratio_3 | 0 | 2 | transformers | 2023-05-30T16:17:54 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Apache5Classic_Balance_DATA_ratio_3
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Apache5Classic_Balance_DATA_ratio_3
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.4840
- Train Accuracy: 0.7774
- Validation Loss: 0.5341
- Validation Accuracy: 0.7578
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.5576 | 0.7532 | 0.5386 | 0.7507 | 0 |
| 0.5328 | 0.7641 | 0.5329 | 0.7664 | 1 |
| 0.4840 | 0.7774 | 0.5341 | 0.7578 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,814 | [
[
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-0.040863037109375,
-0.049072265625,
-0.0203... |
YakovElm/Apache5Classic_Balance_DATA_ratio_4 | 2023-05-30T16:54:26.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/Apache5Classic_Balance_DATA_ratio_4 | 0 | 2 | transformers | 2023-05-30T16:53:22 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Apache5Classic_Balance_DATA_ratio_4
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Apache5Classic_Balance_DATA_ratio_4
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.4243
- Train Accuracy: 0.8162
- Validation Loss: 0.4969
- Validation Accuracy: 0.8223
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.5149 | 0.7844 | 0.4510 | 0.8200 | 0 |
| 0.4849 | 0.7976 | 0.4359 | 0.8326 | 1 |
| 0.4243 | 0.8162 | 0.4969 | 0.8223 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,814 | [
[
-0.045440673828125,
-0.04345703125,
0.0144500732421875,
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-0.031494140625,
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0.01296234130859375,
0.01387786865234375,
-0.05450439453125,
-0.04107666015625,
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-0.... |
YakovElm/Apache10Classic_Balance_DATA_ratio_Half | 2023-05-31T14:29:57.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/Apache10Classic_Balance_DATA_ratio_Half | 0 | 2 | transformers | 2023-05-30T17:03:19 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Apache10Classic_Balance_DATA_ratio_Half
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Apache10Classic_Balance_DATA_ratio_Half
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.5599
- Train Accuracy: 0.7195
- Validation Loss: 0.6311
- Validation Accuracy: 0.6831
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.6404 | 0.6430 | 0.6082 | 0.6995 | 0 |
| 0.6084 | 0.6885 | 0.6509 | 0.5902 | 1 |
| 0.5599 | 0.7195 | 0.6311 | 0.6831 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,822 | [
[
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0.012969970703125,
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YakovElm/Apache10Classic_Balance_DATA_ratio_1 | 2023-05-31T14:45:02.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/Apache10Classic_Balance_DATA_ratio_1 | 0 | 2 | transformers | 2023-05-30T17:14:25 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Apache10Classic_Balance_DATA_ratio_1
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Apache10Classic_Balance_DATA_ratio_1
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.5633
- Train Accuracy: 0.7077
- Validation Loss: 0.7215
- Validation Accuracy: 0.5287
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.6865 | 0.5519 | 0.6406 | 0.6352 | 0 |
| 0.6470 | 0.6161 | 0.6177 | 0.6434 | 1 |
| 0.5633 | 0.7077 | 0.7215 | 0.5287 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,816 | [
[
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0.0168609619140625,
0.01345062255859375,
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YakovElm/Hyperledger5Classic_Balance_DATA_ratio_1 | 2023-05-30T17:38:56.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/Hyperledger5Classic_Balance_DATA_ratio_1 | 0 | 2 | transformers | 2023-05-30T17:38:20 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Hyperledger5Classic_Balance_DATA_ratio_1
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Hyperledger5Classic_Balance_DATA_ratio_1
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.6162
- Train Accuracy: 0.6626
- Validation Loss: 0.6618
- Validation Accuracy: 0.5894
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.6661 | 0.6018 | 0.6800 | 0.5960 | 0 |
| 0.6524 | 0.6361 | 0.6548 | 0.6325 | 1 |
| 0.6162 | 0.6626 | 0.6618 | 0.5894 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,824 | [
[
-0.04803466796875,
-0.03857421875,
0.01434326171875,
0.0081024169921875,
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0.015655517578125,
0.0144500732421875,
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-0.043121337890625,
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-0.01... |
YakovElm/Hyperledger5Classic_Balance_DATA_ratio_2 | 2023-05-30T17:58:09.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/Hyperledger5Classic_Balance_DATA_ratio_2 | 0 | 2 | transformers | 2023-05-30T17:57:17 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Hyperledger5Classic_Balance_DATA_ratio_2
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Hyperledger5Classic_Balance_DATA_ratio_2
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.5169
- Train Accuracy: 0.7251
- Validation Loss: 0.6507
- Validation Accuracy: 0.6549
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.6223 | 0.6676 | 0.5989 | 0.6460 | 0 |
| 0.5765 | 0.6713 | 0.5891 | 0.6637 | 1 |
| 0.5169 | 0.7251 | 0.6507 | 0.6549 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,824 | [
[
-0.046234130859375,
-0.0382080078125,
0.014862060546875,
0.0075836181640625,
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0.01386260986328125,
0.013427734375,
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YakovElm/Hyperledger5Classic_Balance_DATA_ratio_3 | 2023-05-30T18:22:55.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/Hyperledger5Classic_Balance_DATA_ratio_3 | 0 | 2 | transformers | 2023-05-30T18:22:19 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Hyperledger5Classic_Balance_DATA_ratio_3
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Hyperledger5Classic_Balance_DATA_ratio_3
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.4065
- Train Accuracy: 0.8043
- Validation Loss: 0.5434
- Validation Accuracy: 0.7297
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.5440 | 0.7441 | 0.5403 | 0.7313 | 0 |
| 0.4963 | 0.7523 | 0.5343 | 0.7396 | 1 |
| 0.4065 | 0.8043 | 0.5434 | 0.7297 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,824 | [
[
-0.04705810546875,
-0.039337158203125,
0.016265869140625,
0.00799560546875,
-0.029052734375,
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0.01515960693359375,
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-0.0... |
alozanorius/bert-fine-tuned-cola | 2023-05-30T19:49:40.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | alozanorius | null | null | alozanorius/bert-fine-tuned-cola | 0 | 2 | transformers | 2023-05-30T18:40:48 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: bert-fine-tuned-cola
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# bert-fine-tuned-cola
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.2971
- Validation Loss: 0.4283
- Epoch: 1
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 0.5021 | 0.4639 | 0 |
| 0.2971 | 0.4283 | 1 |
### Framework versions
- Transformers 4.28.0
- TensorFlow 2.10.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,333 | [
[
-0.037750244140625,
-0.059478759765625,
0.01430511474609375,
0.01299285888671875,
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YakovElm/Hyperledger5Classic_Balance_DATA_ratio_4 | 2023-05-30T18:54:03.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/Hyperledger5Classic_Balance_DATA_ratio_4 | 0 | 2 | transformers | 2023-05-30T18:53:00 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Hyperledger5Classic_Balance_DATA_ratio_4
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Hyperledger5Classic_Balance_DATA_ratio_4
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.3930
- Train Accuracy: 0.8280
- Validation Loss: 0.4749
- Validation Accuracy: 0.7865
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.4819 | 0.8015 | 0.5263 | 0.7838 | 0 |
| 0.4474 | 0.8073 | 0.4822 | 0.7812 | 1 |
| 0.3930 | 0.8280 | 0.4749 | 0.7865 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,824 | [
[
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YakovElm/Hyperledger10Classic_Balance_DATA_ratio_Half | 2023-05-30T19:03:15.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/Hyperledger10Classic_Balance_DATA_ratio_Half | 0 | 2 | transformers | 2023-05-30T19:02:40 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Hyperledger10Classic_Balance_DATA_ratio_Half
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Hyperledger10Classic_Balance_DATA_ratio_Half
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.5467
- Train Accuracy: 0.7368
- Validation Loss: 0.5960
- Validation Accuracy: 0.6957
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.6355 | 0.6534 | 0.5992 | 0.7065 | 0 |
| 0.6034 | 0.6824 | 0.6301 | 0.6359 | 1 |
| 0.5467 | 0.7368 | 0.5960 | 0.6957 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,832 | [
[
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0.013275146484375,
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-0.050994873046875,
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YakovElm/Hyperledger10Classic_Balance_DATA_ratio_1 | 2023-05-30T19:13:55.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/Hyperledger10Classic_Balance_DATA_ratio_1 | 0 | 2 | transformers | 2023-05-30T19:13:19 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Hyperledger10Classic_Balance_DATA_ratio_1
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Hyperledger10Classic_Balance_DATA_ratio_1
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.5430
- Train Accuracy: 0.7143
- Validation Loss: 0.6359
- Validation Accuracy: 0.6041
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.6801 | 0.5510 | 0.6161 | 0.6531 | 0 |
| 0.6168 | 0.6327 | 0.5900 | 0.6612 | 1 |
| 0.5430 | 0.7143 | 0.6359 | 0.6041 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,826 | [
[
-0.04693603515625,
-0.042388916015625,
0.0140380859375,
0.00836944580078125,
-0.026397705078125,
-0.030517578125,
-0.0124664306640625,
-0.02142333984375,
0.020721435546875,
0.0147705078125,
-0.053314208984375,
-0.03790283203125,
-0.050323486328125,
-0.021286... |
t12e/instructor-base | 2023-05-30T21:53:29.000Z | [
"sentence-transformers",
"pytorch",
"t5",
"text-embedding",
"embeddings",
"information-retrieval",
"beir",
"text-classification",
"language-model",
"text-clustering",
"text-semantic-similarity",
"text-evaluation",
"prompt-retrieval",
"text-reranking",
"feature-extraction",
"sentence-si... | sentence-similarity | t12e | null | null | t12e/instructor-base | 0 | 2 | sentence-transformers | 2023-05-30T19:22:17 | ---
pipeline_tag: sentence-similarity
tags:
- text-embedding
- embeddings
- information-retrieval
- beir
- text-classification
- language-model
- text-clustering
- text-semantic-similarity
- text-evaluation
- prompt-retrieval
- text-reranking
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
- t5
- English
- Sentence Similarity
- natural_questions
- ms_marco
- fever
- hotpot_qa
- mteb
language: en
inference: false
license: apache-2.0
model-index:
- name: final_base_results
results:
- task:
type: Classification
dataset:
type: mteb/amazon_counterfactual
name: MTEB AmazonCounterfactualClassification (en)
config: en
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 86.2089552238806
- type: ap
value: 55.76273850794966
- type: f1
value: 81.26104211414781
- task:
type: Classification
dataset:
type: mteb/amazon_polarity
name: MTEB AmazonPolarityClassification
config: default
split: test
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
metrics:
- type: accuracy
value: 88.35995000000001
- type: ap
value: 84.18839957309655
- type: f1
value: 88.317619250081
- task:
type: Classification
dataset:
type: mteb/amazon_reviews_multi
name: MTEB AmazonReviewsClassification (en)
config: en
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 44.64
- type: f1
value: 42.48663956478136
- task:
type: Retrieval
dataset:
type: arguana
name: MTEB ArguAna
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 27.383000000000003
- type: map_at_10
value: 43.024
- type: map_at_100
value: 44.023
- type: map_at_1000
value: 44.025999999999996
- type: map_at_3
value: 37.684
- type: map_at_5
value: 40.884
- type: mrr_at_1
value: 28.094
- type: mrr_at_10
value: 43.315
- type: mrr_at_100
value: 44.313
- type: mrr_at_1000
value: 44.317
- type: mrr_at_3
value: 37.862
- type: mrr_at_5
value: 41.155
- type: ndcg_at_1
value: 27.383000000000003
- type: ndcg_at_10
value: 52.032000000000004
- type: ndcg_at_100
value: 56.19499999999999
- type: ndcg_at_1000
value: 56.272
- type: ndcg_at_3
value: 41.166000000000004
- type: ndcg_at_5
value: 46.92
- type: precision_at_1
value: 27.383000000000003
- type: precision_at_10
value: 8.087
- type: precision_at_100
value: 0.989
- type: precision_at_1000
value: 0.099
- type: precision_at_3
value: 17.093
- type: precision_at_5
value: 13.044
- type: recall_at_1
value: 27.383000000000003
- type: recall_at_10
value: 80.868
- type: recall_at_100
value: 98.86200000000001
- type: recall_at_1000
value: 99.431
- type: recall_at_3
value: 51.28
- type: recall_at_5
value: 65.22
- task:
type: Clustering
dataset:
type: mteb/arxiv-clustering-p2p
name: MTEB ArxivClusteringP2P
config: default
split: test
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
metrics:
- type: v_measure
value: 39.68441054431849
- task:
type: Clustering
dataset:
type: mteb/arxiv-clustering-s2s
name: MTEB ArxivClusteringS2S
config: default
split: test
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
metrics:
- type: v_measure
value: 29.188539728343844
- task:
type: Reranking
dataset:
type: mteb/askubuntudupquestions-reranking
name: MTEB AskUbuntuDupQuestions
config: default
split: test
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
metrics:
- type: map
value: 63.173362687519784
- type: mrr
value: 76.18860748362133
- task:
type: STS
dataset:
type: mteb/biosses-sts
name: MTEB BIOSSES
config: default
split: test
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
metrics:
- type: cos_sim_spearman
value: 82.30789953771232
- task:
type: Classification
dataset:
type: mteb/banking77
name: MTEB Banking77Classification
config: default
split: test
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
metrics:
- type: accuracy
value: 77.03571428571428
- type: f1
value: 75.87384305045917
- task:
type: Clustering
dataset:
type: mteb/biorxiv-clustering-p2p
name: MTEB BiorxivClusteringP2P
config: default
split: test
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
metrics:
- type: v_measure
value: 32.98041170516364
- task:
type: Clustering
dataset:
type: mteb/biorxiv-clustering-s2s
name: MTEB BiorxivClusteringS2S
config: default
split: test
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
metrics:
- type: v_measure
value: 25.71652988451154
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackAndroidRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 33.739999999999995
- type: map_at_10
value: 46.197
- type: map_at_100
value: 47.814
- type: map_at_1000
value: 47.934
- type: map_at_3
value: 43.091
- type: map_at_5
value: 44.81
- type: mrr_at_1
value: 41.059
- type: mrr_at_10
value: 52.292
- type: mrr_at_100
value: 52.978
- type: mrr_at_1000
value: 53.015
- type: mrr_at_3
value: 49.976
- type: mrr_at_5
value: 51.449999999999996
- type: ndcg_at_1
value: 41.059
- type: ndcg_at_10
value: 52.608
- type: ndcg_at_100
value: 57.965
- type: ndcg_at_1000
value: 59.775999999999996
- type: ndcg_at_3
value: 48.473
- type: ndcg_at_5
value: 50.407999999999994
- type: precision_at_1
value: 41.059
- type: precision_at_10
value: 9.943
- type: precision_at_100
value: 1.6070000000000002
- type: precision_at_1000
value: 0.20500000000000002
- type: precision_at_3
value: 23.413999999999998
- type: precision_at_5
value: 16.481
- type: recall_at_1
value: 33.739999999999995
- type: recall_at_10
value: 63.888999999999996
- type: recall_at_100
value: 85.832
- type: recall_at_1000
value: 97.475
- type: recall_at_3
value: 51.953
- type: recall_at_5
value: 57.498000000000005
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackEnglishRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 31.169999999999998
- type: map_at_10
value: 41.455
- type: map_at_100
value: 42.716
- type: map_at_1000
value: 42.847
- type: map_at_3
value: 38.568999999999996
- type: map_at_5
value: 40.099000000000004
- type: mrr_at_1
value: 39.427
- type: mrr_at_10
value: 47.818
- type: mrr_at_100
value: 48.519
- type: mrr_at_1000
value: 48.558
- type: mrr_at_3
value: 45.86
- type: mrr_at_5
value: 46.936
- type: ndcg_at_1
value: 39.427
- type: ndcg_at_10
value: 47.181
- type: ndcg_at_100
value: 51.737
- type: ndcg_at_1000
value: 53.74
- type: ndcg_at_3
value: 43.261
- type: ndcg_at_5
value: 44.891
- type: precision_at_1
value: 39.427
- type: precision_at_10
value: 8.847
- type: precision_at_100
value: 1.425
- type: precision_at_1000
value: 0.189
- type: precision_at_3
value: 20.785999999999998
- type: precision_at_5
value: 14.560999999999998
- type: recall_at_1
value: 31.169999999999998
- type: recall_at_10
value: 56.971000000000004
- type: recall_at_100
value: 76.31400000000001
- type: recall_at_1000
value: 88.93900000000001
- type: recall_at_3
value: 45.208
- type: recall_at_5
value: 49.923
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackGamingRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 39.682
- type: map_at_10
value: 52.766000000000005
- type: map_at_100
value: 53.84100000000001
- type: map_at_1000
value: 53.898
- type: map_at_3
value: 49.291000000000004
- type: map_at_5
value: 51.365
- type: mrr_at_1
value: 45.266
- type: mrr_at_10
value: 56.093
- type: mrr_at_100
value: 56.763
- type: mrr_at_1000
value: 56.793000000000006
- type: mrr_at_3
value: 53.668000000000006
- type: mrr_at_5
value: 55.1
- type: ndcg_at_1
value: 45.266
- type: ndcg_at_10
value: 58.836
- type: ndcg_at_100
value: 62.863
- type: ndcg_at_1000
value: 63.912
- type: ndcg_at_3
value: 53.19199999999999
- type: ndcg_at_5
value: 56.125
- type: precision_at_1
value: 45.266
- type: precision_at_10
value: 9.492
- type: precision_at_100
value: 1.236
- type: precision_at_1000
value: 0.13699999999999998
- type: precision_at_3
value: 23.762
- type: precision_at_5
value: 16.414
- type: recall_at_1
value: 39.682
- type: recall_at_10
value: 73.233
- type: recall_at_100
value: 90.335
- type: recall_at_1000
value: 97.452
- type: recall_at_3
value: 58.562000000000005
- type: recall_at_5
value: 65.569
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackGisRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 26.743
- type: map_at_10
value: 34.016000000000005
- type: map_at_100
value: 35.028999999999996
- type: map_at_1000
value: 35.113
- type: map_at_3
value: 31.763
- type: map_at_5
value: 33.013999999999996
- type: mrr_at_1
value: 28.927000000000003
- type: mrr_at_10
value: 36.32
- type: mrr_at_100
value: 37.221
- type: mrr_at_1000
value: 37.281
- type: mrr_at_3
value: 34.105000000000004
- type: mrr_at_5
value: 35.371
- type: ndcg_at_1
value: 28.927000000000003
- type: ndcg_at_10
value: 38.474000000000004
- type: ndcg_at_100
value: 43.580000000000005
- type: ndcg_at_1000
value: 45.64
- type: ndcg_at_3
value: 34.035
- type: ndcg_at_5
value: 36.186
- type: precision_at_1
value: 28.927000000000003
- type: precision_at_10
value: 5.74
- type: precision_at_100
value: 0.8710000000000001
- type: precision_at_1000
value: 0.108
- type: precision_at_3
value: 14.124
- type: precision_at_5
value: 9.74
- type: recall_at_1
value: 26.743
- type: recall_at_10
value: 49.955
- type: recall_at_100
value: 73.904
- type: recall_at_1000
value: 89.133
- type: recall_at_3
value: 38.072
- type: recall_at_5
value: 43.266
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackMathematicaRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 16.928
- type: map_at_10
value: 23.549
- type: map_at_100
value: 24.887
- type: map_at_1000
value: 25.018
- type: map_at_3
value: 21.002000000000002
- type: map_at_5
value: 22.256
- type: mrr_at_1
value: 21.02
- type: mrr_at_10
value: 27.898
- type: mrr_at_100
value: 29.018
- type: mrr_at_1000
value: 29.099999999999998
- type: mrr_at_3
value: 25.456
- type: mrr_at_5
value: 26.625
- type: ndcg_at_1
value: 21.02
- type: ndcg_at_10
value: 28.277
- type: ndcg_at_100
value: 34.54
- type: ndcg_at_1000
value: 37.719
- type: ndcg_at_3
value: 23.707
- type: ndcg_at_5
value: 25.482
- type: precision_at_1
value: 21.02
- type: precision_at_10
value: 5.361
- type: precision_at_100
value: 0.9809999999999999
- type: precision_at_1000
value: 0.13899999999999998
- type: precision_at_3
value: 11.401
- type: precision_at_5
value: 8.209
- type: recall_at_1
value: 16.928
- type: recall_at_10
value: 38.601
- type: recall_at_100
value: 65.759
- type: recall_at_1000
value: 88.543
- type: recall_at_3
value: 25.556
- type: recall_at_5
value: 30.447000000000003
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackPhysicsRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 28.549000000000003
- type: map_at_10
value: 38.426
- type: map_at_100
value: 39.845000000000006
- type: map_at_1000
value: 39.956
- type: map_at_3
value: 35.372
- type: map_at_5
value: 37.204
- type: mrr_at_1
value: 35.034
- type: mrr_at_10
value: 44.041000000000004
- type: mrr_at_100
value: 44.95
- type: mrr_at_1000
value: 44.997
- type: mrr_at_3
value: 41.498000000000005
- type: mrr_at_5
value: 43.077
- type: ndcg_at_1
value: 35.034
- type: ndcg_at_10
value: 44.218
- type: ndcg_at_100
value: 49.958000000000006
- type: ndcg_at_1000
value: 52.019000000000005
- type: ndcg_at_3
value: 39.34
- type: ndcg_at_5
value: 41.892
- type: precision_at_1
value: 35.034
- type: precision_at_10
value: 7.911
- type: precision_at_100
value: 1.26
- type: precision_at_1000
value: 0.16
- type: precision_at_3
value: 18.511
- type: precision_at_5
value: 13.205
- type: recall_at_1
value: 28.549000000000003
- type: recall_at_10
value: 56.035999999999994
- type: recall_at_100
value: 79.701
- type: recall_at_1000
value: 93.149
- type: recall_at_3
value: 42.275
- type: recall_at_5
value: 49.097
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackProgrammersRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 29.391000000000002
- type: map_at_10
value: 39.48
- type: map_at_100
value: 40.727000000000004
- type: map_at_1000
value: 40.835
- type: map_at_3
value: 36.234
- type: map_at_5
value: 37.877
- type: mrr_at_1
value: 35.959
- type: mrr_at_10
value: 44.726
- type: mrr_at_100
value: 45.531
- type: mrr_at_1000
value: 45.582
- type: mrr_at_3
value: 42.047000000000004
- type: mrr_at_5
value: 43.611
- type: ndcg_at_1
value: 35.959
- type: ndcg_at_10
value: 45.303
- type: ndcg_at_100
value: 50.683
- type: ndcg_at_1000
value: 52.818
- type: ndcg_at_3
value: 39.987
- type: ndcg_at_5
value: 42.243
- type: precision_at_1
value: 35.959
- type: precision_at_10
value: 8.241999999999999
- type: precision_at_100
value: 1.274
- type: precision_at_1000
value: 0.163
- type: precision_at_3
value: 18.836
- type: precision_at_5
value: 13.196
- type: recall_at_1
value: 29.391000000000002
- type: recall_at_10
value: 57.364000000000004
- type: recall_at_100
value: 80.683
- type: recall_at_1000
value: 94.918
- type: recall_at_3
value: 42.263
- type: recall_at_5
value: 48.634
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 26.791749999999997
- type: map_at_10
value: 35.75541666666667
- type: map_at_100
value: 37.00791666666667
- type: map_at_1000
value: 37.12408333333333
- type: map_at_3
value: 33.02966666666667
- type: map_at_5
value: 34.56866666666667
- type: mrr_at_1
value: 31.744333333333337
- type: mrr_at_10
value: 39.9925
- type: mrr_at_100
value: 40.86458333333333
- type: mrr_at_1000
value: 40.92175000000001
- type: mrr_at_3
value: 37.68183333333334
- type: mrr_at_5
value: 39.028499999999994
- type: ndcg_at_1
value: 31.744333333333337
- type: ndcg_at_10
value: 40.95008333333334
- type: ndcg_at_100
value: 46.25966666666667
- type: ndcg_at_1000
value: 48.535333333333334
- type: ndcg_at_3
value: 36.43333333333333
- type: ndcg_at_5
value: 38.602333333333334
- type: precision_at_1
value: 31.744333333333337
- type: precision_at_10
value: 7.135166666666666
- type: precision_at_100
value: 1.1535833333333334
- type: precision_at_1000
value: 0.15391666666666665
- type: precision_at_3
value: 16.713
- type: precision_at_5
value: 11.828416666666666
- type: recall_at_1
value: 26.791749999999997
- type: recall_at_10
value: 51.98625
- type: recall_at_100
value: 75.30358333333334
- type: recall_at_1000
value: 91.05433333333333
- type: recall_at_3
value: 39.39583333333333
- type: recall_at_5
value: 45.05925
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackStatsRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 22.219
- type: map_at_10
value: 29.162
- type: map_at_100
value: 30.049999999999997
- type: map_at_1000
value: 30.144
- type: map_at_3
value: 27.204
- type: map_at_5
value: 28.351
- type: mrr_at_1
value: 25.153
- type: mrr_at_10
value: 31.814999999999998
- type: mrr_at_100
value: 32.573
- type: mrr_at_1000
value: 32.645
- type: mrr_at_3
value: 29.934
- type: mrr_at_5
value: 30.946
- type: ndcg_at_1
value: 25.153
- type: ndcg_at_10
value: 33.099000000000004
- type: ndcg_at_100
value: 37.768
- type: ndcg_at_1000
value: 40.331
- type: ndcg_at_3
value: 29.473
- type: ndcg_at_5
value: 31.206
- type: precision_at_1
value: 25.153
- type: precision_at_10
value: 5.183999999999999
- type: precision_at_100
value: 0.8170000000000001
- type: precision_at_1000
value: 0.11100000000000002
- type: precision_at_3
value: 12.831999999999999
- type: precision_at_5
value: 8.895999999999999
- type: recall_at_1
value: 22.219
- type: recall_at_10
value: 42.637
- type: recall_at_100
value: 64.704
- type: recall_at_1000
value: 83.963
- type: recall_at_3
value: 32.444
- type: recall_at_5
value: 36.802
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackTexRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 17.427999999999997
- type: map_at_10
value: 24.029
- type: map_at_100
value: 25.119999999999997
- type: map_at_1000
value: 25.257
- type: map_at_3
value: 22.016
- type: map_at_5
value: 23.143
- type: mrr_at_1
value: 21.129
- type: mrr_at_10
value: 27.750000000000004
- type: mrr_at_100
value: 28.666999999999998
- type: mrr_at_1000
value: 28.754999999999995
- type: mrr_at_3
value: 25.849
- type: mrr_at_5
value: 26.939999999999998
- type: ndcg_at_1
value: 21.129
- type: ndcg_at_10
value: 28.203
- type: ndcg_at_100
value: 33.44
- type: ndcg_at_1000
value: 36.61
- type: ndcg_at_3
value: 24.648999999999997
- type: ndcg_at_5
value: 26.316
- type: precision_at_1
value: 21.129
- type: precision_at_10
value: 5.055
- type: precision_at_100
value: 0.909
- type: precision_at_1000
value: 0.13699999999999998
- type: precision_at_3
value: 11.666
- type: precision_at_5
value: 8.3
- type: recall_at_1
value: 17.427999999999997
- type: recall_at_10
value: 36.923
- type: recall_at_100
value: 60.606
- type: recall_at_1000
value: 83.19
- type: recall_at_3
value: 26.845000000000002
- type: recall_at_5
value: 31.247000000000003
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackUnixRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 26.457000000000004
- type: map_at_10
value: 35.228
- type: map_at_100
value: 36.475
- type: map_at_1000
value: 36.585
- type: map_at_3
value: 32.444
- type: map_at_5
value: 34.046
- type: mrr_at_1
value: 30.784
- type: mrr_at_10
value: 39.133
- type: mrr_at_100
value: 40.11
- type: mrr_at_1000
value: 40.169
- type: mrr_at_3
value: 36.692
- type: mrr_at_5
value: 38.17
- type: ndcg_at_1
value: 30.784
- type: ndcg_at_10
value: 40.358
- type: ndcg_at_100
value: 46.119
- type: ndcg_at_1000
value: 48.428
- type: ndcg_at_3
value: 35.504000000000005
- type: ndcg_at_5
value: 37.864
- type: precision_at_1
value: 30.784
- type: precision_at_10
value: 6.800000000000001
- type: precision_at_100
value: 1.083
- type: precision_at_1000
value: 0.13899999999999998
- type: precision_at_3
value: 15.920000000000002
- type: precision_at_5
value: 11.437
- type: recall_at_1
value: 26.457000000000004
- type: recall_at_10
value: 51.845
- type: recall_at_100
value: 77.046
- type: recall_at_1000
value: 92.892
- type: recall_at_3
value: 38.89
- type: recall_at_5
value: 44.688
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackWebmastersRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 29.378999999999998
- type: map_at_10
value: 37.373
- type: map_at_100
value: 39.107
- type: map_at_1000
value: 39.317
- type: map_at_3
value: 34.563
- type: map_at_5
value: 36.173
- type: mrr_at_1
value: 35.178
- type: mrr_at_10
value: 42.44
- type: mrr_at_100
value: 43.434
- type: mrr_at_1000
value: 43.482
- type: mrr_at_3
value: 39.987
- type: mrr_at_5
value: 41.370000000000005
- type: ndcg_at_1
value: 35.178
- type: ndcg_at_10
value: 42.82
- type: ndcg_at_100
value: 48.935
- type: ndcg_at_1000
value: 51.28
- type: ndcg_at_3
value: 38.562999999999995
- type: ndcg_at_5
value: 40.687
- type: precision_at_1
value: 35.178
- type: precision_at_10
value: 7.945
- type: precision_at_100
value: 1.524
- type: precision_at_1000
value: 0.242
- type: precision_at_3
value: 17.721
- type: precision_at_5
value: 12.925
- type: recall_at_1
value: 29.378999999999998
- type: recall_at_10
value: 52.141999999999996
- type: recall_at_100
value: 79.49000000000001
- type: recall_at_1000
value: 93.782
- type: recall_at_3
value: 39.579
- type: recall_at_5
value: 45.462
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackWordpressRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 19.814999999999998
- type: map_at_10
value: 27.383999999999997
- type: map_at_100
value: 28.483999999999998
- type: map_at_1000
value: 28.585
- type: map_at_3
value: 24.807000000000002
- type: map_at_5
value: 26.485999999999997
- type: mrr_at_1
value: 21.996
- type: mrr_at_10
value: 29.584
- type: mrr_at_100
value: 30.611
- type: mrr_at_1000
value: 30.684
- type: mrr_at_3
value: 27.11
- type: mrr_at_5
value: 28.746
- type: ndcg_at_1
value: 21.996
- type: ndcg_at_10
value: 32.024
- type: ndcg_at_100
value: 37.528
- type: ndcg_at_1000
value: 40.150999999999996
- type: ndcg_at_3
value: 27.016000000000002
- type: ndcg_at_5
value: 29.927999999999997
- type: precision_at_1
value: 21.996
- type: precision_at_10
value: 5.102
- type: precision_at_100
value: 0.856
- type: precision_at_1000
value: 0.117
- type: precision_at_3
value: 11.583
- type: precision_at_5
value: 8.577
- type: recall_at_1
value: 19.814999999999998
- type: recall_at_10
value: 44.239
- type: recall_at_100
value: 69.269
- type: recall_at_1000
value: 89.216
- type: recall_at_3
value: 31.102999999999998
- type: recall_at_5
value: 38.078
- task:
type: Retrieval
dataset:
type: climate-fever
name: MTEB ClimateFEVER
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 11.349
- type: map_at_10
value: 19.436
- type: map_at_100
value: 21.282999999999998
- type: map_at_1000
value: 21.479
- type: map_at_3
value: 15.841
- type: map_at_5
value: 17.558
- type: mrr_at_1
value: 25.863000000000003
- type: mrr_at_10
value: 37.218
- type: mrr_at_100
value: 38.198
- type: mrr_at_1000
value: 38.236
- type: mrr_at_3
value: 33.409
- type: mrr_at_5
value: 35.602000000000004
- type: ndcg_at_1
value: 25.863000000000003
- type: ndcg_at_10
value: 27.953
- type: ndcg_at_100
value: 35.327
- type: ndcg_at_1000
value: 38.708999999999996
- type: ndcg_at_3
value: 21.985
- type: ndcg_at_5
value: 23.957
- type: precision_at_1
value: 25.863000000000003
- type: precision_at_10
value: 8.99
- type: precision_at_100
value: 1.6889999999999998
- type: precision_at_1000
value: 0.232
- type: precision_at_3
value: 16.308
- type: precision_at_5
value: 12.912
- type: recall_at_1
value: 11.349
- type: recall_at_10
value: 34.581
- type: recall_at_100
value: 60.178
- type: recall_at_1000
value: 78.88199999999999
- type: recall_at_3
value: 20.041999999999998
- type: recall_at_5
value: 25.458
- task:
type: Retrieval
dataset:
type: dbpedia-entity
name: MTEB DBPedia
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 7.893
- type: map_at_10
value: 15.457
- type: map_at_100
value: 20.905
- type: map_at_1000
value: 22.116
- type: map_at_3
value: 11.593
- type: map_at_5
value: 13.134
- type: mrr_at_1
value: 57.49999999999999
- type: mrr_at_10
value: 65.467
- type: mrr_at_100
value: 66.022
- type: mrr_at_1000
value: 66.039
- type: mrr_at_3
value: 63.458000000000006
- type: mrr_at_5
value: 64.546
- type: ndcg_at_1
value: 45.875
- type: ndcg_at_10
value: 33.344
- type: ndcg_at_100
value: 36.849
- type: ndcg_at_1000
value: 44.03
- type: ndcg_at_3
value: 37.504
- type: ndcg_at_5
value: 34.892
- type: precision_at_1
value: 57.49999999999999
- type: precision_at_10
value: 25.95
- type: precision_at_100
value: 7.89
- type: precision_at_1000
value: 1.669
- type: precision_at_3
value: 40.333000000000006
- type: precision_at_5
value: 33.050000000000004
- type: recall_at_1
value: 7.893
- type: recall_at_10
value: 20.724999999999998
- type: recall_at_100
value: 42.516
- type: recall_at_1000
value: 65.822
- type: recall_at_3
value: 12.615000000000002
- type: recall_at_5
value: 15.482000000000001
- task:
type: Classification
dataset:
type: mteb/emotion
name: MTEB EmotionClassification
config: default
split: test
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
metrics:
- type: accuracy
value: 51.760000000000005
- type: f1
value: 45.51690565701713
- task:
type: Retrieval
dataset:
type: fever
name: MTEB FEVER
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 53.882
- type: map_at_10
value: 65.902
- type: map_at_100
value: 66.33
- type: map_at_1000
value: 66.348
- type: map_at_3
value: 63.75999999999999
- type: map_at_5
value: 65.181
- type: mrr_at_1
value: 58.041
- type: mrr_at_10
value: 70.133
- type: mrr_at_100
value: 70.463
- type: mrr_at_1000
value: 70.47
- type: mrr_at_3
value: 68.164
- type: mrr_at_5
value: 69.465
- type: ndcg_at_1
value: 58.041
- type: ndcg_at_10
value: 71.84700000000001
- type: ndcg_at_100
value: 73.699
- type: ndcg_at_1000
value: 74.06700000000001
- type: ndcg_at_3
value: 67.855
- type: ndcg_at_5
value: 70.203
- type: precision_at_1
value: 58.041
- type: precision_at_10
value: 9.427000000000001
- type: precision_at_100
value: 1.049
- type: precision_at_1000
value: 0.11
- type: precision_at_3
value: 27.278000000000002
- type: precision_at_5
value: 17.693
- type: recall_at_1
value: 53.882
- type: recall_at_10
value: 85.99
- type: recall_at_100
value: 94.09100000000001
- type: recall_at_1000
value: 96.612
- type: recall_at_3
value: 75.25
- type: recall_at_5
value: 80.997
- task:
type: Retrieval
dataset:
type: fiqa
name: MTEB FiQA2018
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 19.165
- type: map_at_10
value: 31.845000000000002
- type: map_at_100
value: 33.678999999999995
- type: map_at_1000
value: 33.878
- type: map_at_3
value: 27.881
- type: map_at_5
value: 30.049999999999997
- type: mrr_at_1
value: 38.272
- type: mrr_at_10
value: 47.04
- type: mrr_at_100
value: 47.923
- type: mrr_at_1000
value: 47.973
- type: mrr_at_3
value: 44.985
- type: mrr_at_5
value: 46.150000000000006
- type: ndcg_at_1
value: 38.272
- type: ndcg_at_10
value: 39.177
- type: ndcg_at_100
value: 45.995000000000005
- type: ndcg_at_1000
value: 49.312
- type: ndcg_at_3
value: 36.135
- type: ndcg_at_5
value: 36.936
- type: precision_at_1
value: 38.272
- type: precision_at_10
value: 10.926
- type: precision_at_100
value: 1.809
- type: precision_at_1000
value: 0.23700000000000002
- type: precision_at_3
value: 24.331
- type: precision_at_5
value: 17.747
- type: recall_at_1
value: 19.165
- type: recall_at_10
value: 45.103
- type: recall_at_100
value: 70.295
- type: recall_at_1000
value: 90.592
- type: recall_at_3
value: 32.832
- type: recall_at_5
value: 37.905
- task:
type: Retrieval
dataset:
type: hotpotqa
name: MTEB HotpotQA
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 32.397
- type: map_at_10
value: 44.83
- type: map_at_100
value: 45.716
- type: map_at_1000
value: 45.797
- type: map_at_3
value: 41.955999999999996
- type: map_at_5
value: 43.736999999999995
- type: mrr_at_1
value: 64.794
- type: mrr_at_10
value: 71.866
- type: mrr_at_100
value: 72.22
- type: mrr_at_1000
value: 72.238
- type: mrr_at_3
value: 70.416
- type: mrr_at_5
value: 71.304
- type: ndcg_at_1
value: 64.794
- type: ndcg_at_10
value: 54.186
- type: ndcg_at_100
value: 57.623000000000005
- type: ndcg_at_1000
value: 59.302
- type: ndcg_at_3
value: 49.703
- type: ndcg_at_5
value: 52.154999999999994
- type: precision_at_1
value: 64.794
- type: precision_at_10
value: 11.219
- type: precision_at_100
value: 1.394
- type: precision_at_1000
value: 0.16199999999999998
- type: precision_at_3
value: 30.767
- type: precision_at_5
value: 20.397000000000002
- type: recall_at_1
value: 32.397
- type: recall_at_10
value: 56.096999999999994
- type: recall_at_100
value: 69.696
- type: recall_at_1000
value: 80.88499999999999
- type: recall_at_3
value: 46.150999999999996
- type: recall_at_5
value: 50.993
- task:
type: Classification
dataset:
type: mteb/imdb
name: MTEB ImdbClassification
config: default
split: test
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
metrics:
- type: accuracy
value: 81.1744
- type: ap
value: 75.44973697032414
- type: f1
value: 81.09901117955782
- task:
type: Retrieval
dataset:
type: msmarco
name: MTEB MSMARCO
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 19.519000000000002
- type: map_at_10
value: 31.025000000000002
- type: map_at_100
value: 32.275999999999996
- type: map_at_1000
value: 32.329
- type: map_at_3
value: 27.132
- type: map_at_5
value: 29.415999999999997
- type: mrr_at_1
value: 20.115
- type: mrr_at_10
value: 31.569000000000003
- type: mrr_at_100
value: 32.768
- type: mrr_at_1000
value: 32.816
- type: mrr_at_3
value: 27.748
- type: mrr_at_5
value: 29.956
- type: ndcg_at_1
value: 20.115
- type: ndcg_at_10
value: 37.756
- type: ndcg_at_100
value: 43.858000000000004
- type: ndcg_at_1000
value: 45.199
- type: ndcg_at_3
value: 29.818
- type: ndcg_at_5
value: 33.875
- type: precision_at_1
value: 20.115
- type: precision_at_10
value: 6.122
- type: precision_at_100
value: 0.919
- type: precision_at_1000
value: 0.10300000000000001
- type: precision_at_3
value: 12.794
- type: precision_at_5
value: 9.731
- type: recall_at_1
value: 19.519000000000002
- type: recall_at_10
value: 58.62500000000001
- type: recall_at_100
value: 86.99
- type: recall_at_1000
value: 97.268
- type: recall_at_3
value: 37.002
- type: recall_at_5
value: 46.778
- task:
type: Classification
dataset:
type: mteb/mtop_domain
name: MTEB MTOPDomainClassification (en)
config: en
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 93.71865025079799
- type: f1
value: 93.38906173610519
- task:
type: Classification
dataset:
type: mteb/mtop_intent
name: MTEB MTOPIntentClassification (en)
config: en
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 70.2576379388965
- type: f1
value: 49.20405830249464
- task:
type: Classification
dataset:
type: mteb/amazon_massive_intent
name: MTEB MassiveIntentClassification (en)
config: en
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 67.48486886348351
- type: f1
value: 64.92199176095157
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification (en)
config: en
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 72.59246805648958
- type: f1
value: 72.1222026389164
- task:
type: Clustering
dataset:
type: mteb/medrxiv-clustering-p2p
name: MTEB MedrxivClusteringP2P
config: default
split: test
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
metrics:
- type: v_measure
value: 30.887642595096825
- task:
type: Clustering
dataset:
type: mteb/medrxiv-clustering-s2s
name: MTEB MedrxivClusteringS2S
config: default
split: test
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
metrics:
- type: v_measure
value: 28.3764418784054
- task:
type: Reranking
dataset:
type: mteb/mind_small
name: MTEB MindSmallReranking
config: default
split: test
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
metrics:
- type: map
value: 31.81544126336991
- type: mrr
value: 32.82666576268031
- task:
type: Retrieval
dataset:
type: nfcorpus
name: MTEB NFCorpus
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 5.185
- type: map_at_10
value: 11.158
- type: map_at_100
value: 14.041
- type: map_at_1000
value: 15.360999999999999
- type: map_at_3
value: 8.417
- type: map_at_5
value: 9.378
- type: mrr_at_1
value: 44.582
- type: mrr_at_10
value: 53.083999999999996
- type: mrr_at_100
value: 53.787
- type: mrr_at_1000
value: 53.824000000000005
- type: mrr_at_3
value: 51.187000000000005
- type: mrr_at_5
value: 52.379
- type: ndcg_at_1
value: 42.57
- type: ndcg_at_10
value: 31.593
- type: ndcg_at_100
value: 29.093999999999998
- type: ndcg_at_1000
value: 37.909
- type: ndcg_at_3
value: 37.083
- type: ndcg_at_5
value: 34.397
- type: precision_at_1
value: 43.963
- type: precision_at_10
value: 23.498
- type: precision_at_100
value: 7.6160000000000005
- type: precision_at_1000
value: 2.032
- type: precision_at_3
value: 34.572
- type: precision_at_5
value: 29.412
- type: recall_at_1
value: 5.185
- type: recall_at_10
value: 15.234
- type: recall_at_100
value: 29.49
- type: recall_at_1000
value: 62.273999999999994
- type: recall_at_3
value: 9.55
- type: recall_at_5
value: 11.103
- task:
type: Retrieval
dataset:
type: nq
name: MTEB NQ
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 23.803
- type: map_at_10
value: 38.183
- type: map_at_100
value: 39.421
- type: map_at_1000
value: 39.464
- type: map_at_3
value: 33.835
- type: map_at_5
value: 36.327
- type: mrr_at_1
value: 26.68
- type: mrr_at_10
value: 40.439
- type: mrr_at_100
value: 41.415
- type: mrr_at_1000
value: 41.443999999999996
- type: mrr_at_3
value: 36.612
- type: mrr_at_5
value: 38.877
- type: ndcg_at_1
value: 26.68
- type: ndcg_at_10
value: 45.882
- type: ndcg_at_100
value: 51.227999999999994
- type: ndcg_at_1000
value: 52.207
- type: ndcg_at_3
value: 37.511
- type: ndcg_at_5
value: 41.749
- type: precision_at_1
value: 26.68
- type: precision_at_10
value: 7.9750000000000005
- type: precision_at_100
value: 1.0959999999999999
- type: precision_at_1000
value: 0.11900000000000001
- type: precision_at_3
value: 17.449
- type: precision_at_5
value: 12.897
- type: recall_at_1
value: 23.803
- type: recall_at_10
value: 67.152
- type: recall_at_100
value: 90.522
- type: recall_at_1000
value: 97.743
- type: recall_at_3
value: 45.338
- type: recall_at_5
value: 55.106
- task:
type: Retrieval
dataset:
type: quora
name: MTEB QuoraRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 70.473
- type: map_at_10
value: 84.452
- type: map_at_100
value: 85.101
- type: map_at_1000
value: 85.115
- type: map_at_3
value: 81.435
- type: map_at_5
value: 83.338
- type: mrr_at_1
value: 81.19
- type: mrr_at_10
value: 87.324
- type: mrr_at_100
value: 87.434
- type: mrr_at_1000
value: 87.435
- type: mrr_at_3
value: 86.31
- type: mrr_at_5
value: 87.002
- type: ndcg_at_1
value: 81.21000000000001
- type: ndcg_at_10
value: 88.19
- type: ndcg_at_100
value: 89.44
- type: ndcg_at_1000
value: 89.526
- type: ndcg_at_3
value: 85.237
- type: ndcg_at_5
value: 86.892
- type: precision_at_1
value: 81.21000000000001
- type: precision_at_10
value: 13.417000000000002
- type: precision_at_100
value: 1.537
- type: precision_at_1000
value: 0.157
- type: precision_at_3
value: 37.31
- type: precision_at_5
value: 24.59
- type: recall_at_1
value: 70.473
- type: recall_at_10
value: 95.367
- type: recall_at_100
value: 99.616
- type: recall_at_1000
value: 99.996
- type: recall_at_3
value: 86.936
- type: recall_at_5
value: 91.557
- task:
type: Clustering
dataset:
type: mteb/reddit-clustering
name: MTEB RedditClustering
config: default
split: test
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
metrics:
- type: v_measure
value: 59.25776525253911
- task:
type: Clustering
dataset:
type: mteb/reddit-clustering-p2p
name: MTEB RedditClusteringP2P
config: default
split: test
revision: 282350215ef01743dc01b456c7f5241fa8937f16
metrics:
- type: v_measure
value: 63.22135271663078
- task:
type: Retrieval
dataset:
type: scidocs
name: MTEB SCIDOCS
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 4.003
- type: map_at_10
value: 10.062999999999999
- type: map_at_100
value: 11.854000000000001
- type: map_at_1000
value: 12.145999999999999
- type: map_at_3
value: 7.242
- type: map_at_5
value: 8.652999999999999
- type: mrr_at_1
value: 19.7
- type: mrr_at_10
value: 29.721999999999998
- type: mrr_at_100
value: 30.867
- type: mrr_at_1000
value: 30.944
- type: mrr_at_3
value: 26.683
- type: mrr_at_5
value: 28.498
- type: ndcg_at_1
value: 19.7
- type: ndcg_at_10
value: 17.095
- type: ndcg_at_100
value: 24.375
- type: ndcg_at_1000
value: 29.831000000000003
- type: ndcg_at_3
value: 16.305
- type: ndcg_at_5
value: 14.291
- type: precision_at_1
value: 19.7
- type: precision_at_10
value: 8.799999999999999
- type: precision_at_100
value: 1.9349999999999998
- type: precision_at_1000
value: 0.32399999999999995
- type: precision_at_3
value: 15.2
- type: precision_at_5
value: 12.540000000000001
- type: recall_at_1
value: 4.003
- type: recall_at_10
value: 17.877000000000002
- type: recall_at_100
value: 39.217
- type: recall_at_1000
value: 65.862
- type: recall_at_3
value: 9.242
- type: recall_at_5
value: 12.715000000000002
- task:
type: STS
dataset:
type: mteb/sickr-sts
name: MTEB SICK-R
config: default
split: test
revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
metrics:
- type: cos_sim_spearman
value: 80.25888668589654
- task:
type: STS
dataset:
type: mteb/sts12-sts
name: MTEB STS12
config: default
split: test
revision: a0d554a64d88156834ff5ae9920b964011b16384
metrics:
- type: cos_sim_spearman
value: 77.02037527837669
- task:
type: STS
dataset:
type: mteb/sts13-sts
name: MTEB STS13
config: default
split: test
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
metrics:
- type: cos_sim_spearman
value: 86.58432681008449
- task:
type: STS
dataset:
type: mteb/sts14-sts
name: MTEB STS14
config: default
split: test
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
metrics:
- type: cos_sim_spearman
value: 81.31697756099051
- task:
type: STS
dataset:
type: mteb/sts15-sts
name: MTEB STS15
config: default
split: test
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
metrics:
- type: cos_sim_spearman
value: 88.18867599667057
- task:
type: STS
dataset:
type: mteb/sts16-sts
name: MTEB STS16
config: default
split: test
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
metrics:
- type: cos_sim_spearman
value: 84.87853941747623
- task:
type: STS
dataset:
type: mteb/sts17-crosslingual-sts
name: MTEB STS17 (en-en)
config: en-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_spearman
value: 89.46479925383916
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (en)
config: en
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_spearman
value: 66.45272113649146
- task:
type: STS
dataset:
type: mteb/stsbenchmark-sts
name: MTEB STSBenchmark
config: default
split: test
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
metrics:
- type: cos_sim_spearman
value: 86.43357313527851
- task:
type: Reranking
dataset:
type: mteb/scidocs-reranking
name: MTEB SciDocsRR
config: default
split: test
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
metrics:
- type: map
value: 78.82761687254882
- type: mrr
value: 93.46223674655047
- task:
type: Retrieval
dataset:
type: scifact
name: MTEB SciFact
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 44.583
- type: map_at_10
value: 52.978
- type: map_at_100
value: 53.803
- type: map_at_1000
value: 53.839999999999996
- type: map_at_3
value: 50.03300000000001
- type: map_at_5
value: 51.939
- type: mrr_at_1
value: 47.0
- type: mrr_at_10
value: 54.730000000000004
- type: mrr_at_100
value: 55.31399999999999
- type: mrr_at_1000
value: 55.346
- type: mrr_at_3
value: 52.0
- type: mrr_at_5
value: 53.783
- type: ndcg_at_1
value: 47.0
- type: ndcg_at_10
value: 57.82899999999999
- type: ndcg_at_100
value: 61.49400000000001
- type: ndcg_at_1000
value: 62.676
- type: ndcg_at_3
value: 52.373000000000005
- type: ndcg_at_5
value: 55.481
- type: precision_at_1
value: 47.0
- type: precision_at_10
value: 7.867
- type: precision_at_100
value: 0.997
- type: precision_at_1000
value: 0.11
- type: precision_at_3
value: 20.556
- type: precision_at_5
value: 14.066999999999998
- type: recall_at_1
value: 44.583
- type: recall_at_10
value: 71.172
- type: recall_at_100
value: 87.7
- type: recall_at_1000
value: 97.333
- type: recall_at_3
value: 56.511
- type: recall_at_5
value: 64.206
- task:
type: PairClassification
dataset:
type: mteb/sprintduplicatequestions-pairclassification
name: MTEB SprintDuplicateQuestions
config: default
split: test
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
metrics:
- type: cos_sim_accuracy
value: 99.66237623762376
- type: cos_sim_ap
value: 90.35465126226322
- type: cos_sim_f1
value: 82.44575936883628
- type: cos_sim_precision
value: 81.32295719844358
- type: cos_sim_recall
value: 83.6
- type: dot_accuracy
value: 99.66237623762376
- type: dot_ap
value: 90.35464287920453
- type: dot_f1
value: 82.44575936883628
- type: dot_precision
value: 81.32295719844358
- type: dot_recall
value: 83.6
- type: euclidean_accuracy
value: 99.66237623762376
- type: euclidean_ap
value: 90.3546512622632
- type: euclidean_f1
value: 82.44575936883628
- type: euclidean_precision
value: 81.32295719844358
- type: euclidean_recall
value: 83.6
- type: manhattan_accuracy
value: 99.65940594059406
- type: manhattan_ap
value: 90.29220174849843
- type: manhattan_f1
value: 82.4987605354487
- type: manhattan_precision
value: 81.80924287118977
- type: manhattan_recall
value: 83.2
- type: max_accuracy
value: 99.66237623762376
- type: max_ap
value: 90.35465126226322
- type: max_f1
value: 82.4987605354487
- task:
type: Clustering
dataset:
type: mteb/stackexchange-clustering
name: MTEB StackExchangeClustering
config: default
split: test
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
metrics:
- type: v_measure
value: 65.0394225901397
- task:
type: Clustering
dataset:
type: mteb/stackexchange-clustering-p2p
name: MTEB StackExchangeClusteringP2P
config: default
split: test
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
metrics:
- type: v_measure
value: 35.27954189859326
- task:
type: Reranking
dataset:
type: mteb/stackoverflowdupquestions-reranking
name: MTEB StackOverflowDupQuestions
config: default
split: test
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
metrics:
- type: map
value: 50.99055979974896
- type: mrr
value: 51.82745257193787
- task:
type: Summarization
dataset:
type: mteb/summeval
name: MTEB SummEval
config: default
split: test
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
metrics:
- type: cos_sim_pearson
value: 30.21655465344237
- type: cos_sim_spearman
value: 29.853205339630172
- type: dot_pearson
value: 30.216540628083564
- type: dot_spearman
value: 29.868978894753027
- task:
type: Retrieval
dataset:
type: trec-covid
name: MTEB TRECCOVID
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 0.2
- type: map_at_10
value: 1.398
- type: map_at_100
value: 7.406
- type: map_at_1000
value: 18.401
- type: map_at_3
value: 0.479
- type: map_at_5
value: 0.772
- type: mrr_at_1
value: 70.0
- type: mrr_at_10
value: 79.25999999999999
- type: mrr_at_100
value: 79.25999999999999
- type: mrr_at_1000
value: 79.25999999999999
- type: mrr_at_3
value: 77.333
- type: mrr_at_5
value: 78.133
- type: ndcg_at_1
value: 63.0
- type: ndcg_at_10
value: 58.548
- type: ndcg_at_100
value: 45.216
- type: ndcg_at_1000
value: 41.149
- type: ndcg_at_3
value: 60.641999999999996
- type: ndcg_at_5
value: 61.135
- type: precision_at_1
value: 70.0
- type: precision_at_10
value: 64.0
- type: precision_at_100
value: 46.92
- type: precision_at_1000
value: 18.642
- type: precision_at_3
value: 64.667
- type: precision_at_5
value: 66.4
- type: recall_at_1
value: 0.2
- type: recall_at_10
value: 1.6729999999999998
- type: recall_at_100
value: 10.856
- type: recall_at_1000
value: 38.964999999999996
- type: recall_at_3
value: 0.504
- type: recall_at_5
value: 0.852
- task:
type: Retrieval
dataset:
type: webis-touche2020
name: MTEB Touche2020
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 1.6629999999999998
- type: map_at_10
value: 8.601
- type: map_at_100
value: 14.354
- type: map_at_1000
value: 15.927
- type: map_at_3
value: 4.1930000000000005
- type: map_at_5
value: 5.655
- type: mrr_at_1
value: 18.367
- type: mrr_at_10
value: 34.466
- type: mrr_at_100
value: 35.235
- type: mrr_at_1000
value: 35.27
- type: mrr_at_3
value: 28.571
- type: mrr_at_5
value: 31.531
- type: ndcg_at_1
value: 14.285999999999998
- type: ndcg_at_10
value: 20.374
- type: ndcg_at_100
value: 33.532000000000004
- type: ndcg_at_1000
value: 45.561
- type: ndcg_at_3
value: 18.442
- type: ndcg_at_5
value: 18.076
- type: precision_at_1
value: 18.367
- type: precision_at_10
value: 20.204
- type: precision_at_100
value: 7.489999999999999
- type: precision_at_1000
value: 1.5630000000000002
- type: precision_at_3
value: 21.769
- type: precision_at_5
value: 20.408
- type: recall_at_1
value: 1.6629999999999998
- type: recall_at_10
value: 15.549
- type: recall_at_100
value: 47.497
- type: recall_at_1000
value: 84.524
- type: recall_at_3
value: 5.289
- type: recall_at_5
value: 8.035
- task:
type: Classification
dataset:
type: mteb/toxic_conversations_50k
name: MTEB ToxicConversationsClassification
config: default
split: test
revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
metrics:
- type: accuracy
value: 71.8194
- type: ap
value: 14.447702451658554
- type: f1
value: 55.13659412856185
- task:
type: Classification
dataset:
type: mteb/tweet_sentiment_extraction
name: MTEB TweetSentimentExtractionClassification
config: default
split: test
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
metrics:
- type: accuracy
value: 63.310696095076416
- type: f1
value: 63.360434851097814
- task:
type: Clustering
dataset:
type: mteb/twentynewsgroups-clustering
name: MTEB TwentyNewsgroupsClustering
config: default
split: test
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
metrics:
- type: v_measure
value: 51.30677907335145
- task:
type: PairClassification
dataset:
type: mteb/twittersemeval2015-pairclassification
name: MTEB TwitterSemEval2015
config: default
split: test
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
metrics:
- type: cos_sim_accuracy
value: 86.12386004649221
- type: cos_sim_ap
value: 73.99096426215495
- type: cos_sim_f1
value: 68.18416968442834
- type: cos_sim_precision
value: 66.86960933536275
- type: cos_sim_recall
value: 69.55145118733509
- type: dot_accuracy
value: 86.12386004649221
- type: dot_ap
value: 73.99096813038672
- type: dot_f1
value: 68.18416968442834
- type: dot_precision
value: 66.86960933536275
- type: dot_recall
value: 69.55145118733509
- type: euclidean_accuracy
value: 86.12386004649221
- type: euclidean_ap
value: 73.99095984980165
- type: euclidean_f1
value: 68.18416968442834
- type: euclidean_precision
value: 66.86960933536275
- type: euclidean_recall
value: 69.55145118733509
- type: manhattan_accuracy
value: 86.09405734040651
- type: manhattan_ap
value: 73.96825745608601
- type: manhattan_f1
value: 68.13888179729383
- type: manhattan_precision
value: 65.99901088031652
- type: manhattan_recall
value: 70.42216358839049
- type: max_accuracy
value: 86.12386004649221
- type: max_ap
value: 73.99096813038672
- type: max_f1
value: 68.18416968442834
- task:
type: PairClassification
dataset:
type: mteb/twitterurlcorpus-pairclassification
name: MTEB TwitterURLCorpus
config: default
split: test
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
metrics:
- type: cos_sim_accuracy
value: 88.99367407924865
- type: cos_sim_ap
value: 86.19720829843081
- type: cos_sim_f1
value: 78.39889075384951
- type: cos_sim_precision
value: 74.5110278818144
- type: cos_sim_recall
value: 82.71481367416075
- type: dot_accuracy
value: 88.99367407924865
- type: dot_ap
value: 86.19718471454047
- type: dot_f1
value: 78.39889075384951
- type: dot_precision
value: 74.5110278818144
- type: dot_recall
value: 82.71481367416075
- type: euclidean_accuracy
value: 88.99367407924865
- type: euclidean_ap
value: 86.1972021422436
- type: euclidean_f1
value: 78.39889075384951
- type: euclidean_precision
value: 74.5110278818144
- type: euclidean_recall
value: 82.71481367416075
- type: manhattan_accuracy
value: 88.95680521597392
- type: manhattan_ap
value: 86.16659921351506
- type: manhattan_f1
value: 78.39125971550081
- type: manhattan_precision
value: 74.82502799552073
- type: manhattan_recall
value: 82.31444410224823
- type: max_accuracy
value: 88.99367407924865
- type: max_ap
value: 86.19720829843081
- type: max_f1
value: 78.39889075384951
---
# hkunlp/instructor-base
We introduce **Instructor**👨🏫, an instruction-finetuned text embedding model that can generate text embeddings tailored to any task (e.g., classification, retrieval, clustering, text evaluation, etc.) and domains (e.g., science, finance, etc.) ***by simply providing the task instruction, without any finetuning***. Instructor👨 achieves sota on 70 diverse embedding tasks!
The model is easy to use with **our customized** `sentence-transformer` library. For more details, check out [our paper](https://arxiv.org/abs/2212.09741) and [project page](https://instructor-embedding.github.io/)!
**************************** **Updates** ****************************
* 01/21: We released a new [checkpoint](https://huggingface.co/hkunlp/instructor-base) trained with hard negatives, which gives better performance.
* 12/21: We released our [paper](https://arxiv.org/abs/2212.09741), [code](https://github.com/HKUNLP/instructor-embedding), [checkpoint](https://huggingface.co/hkunlp/instructor-base) and [project page](https://instructor-embedding.github.io/)! Check them out!
## Quick start
<hr />
## Installation
```bash
pip install InstructorEmbedding
```
## Compute your customized embeddings
Then you can use the model like this to calculate domain-specific and task-aware embeddings:
```python
from InstructorEmbedding import INSTRUCTOR
model = INSTRUCTOR('hkunlp/instructor-base')
sentence = "3D ActionSLAM: wearable person tracking in multi-floor environments"
instruction = "Represent the Science title:"
embeddings = model.encode([[instruction,sentence]])
print(embeddings)
```
## Use cases
<hr />
## Calculate embeddings for your customized texts
If you want to calculate customized embeddings for specific sentences, you may follow the unified template to write instructions:
Represent the `domain` `text_type` for `task_objective`:
* `domain` is optional, and it specifies the domain of the text, e.g., science, finance, medicine, etc.
* `text_type` is required, and it specifies the encoding unit, e.g., sentence, document, paragraph, etc.
* `task_objective` is optional, and it specifies the objective of embedding, e.g., retrieve a document, classify the sentence, etc.
## Calculate Sentence similarities
You can further use the model to compute similarities between two groups of sentences, with **customized embeddings**.
```python
from sklearn.metrics.pairwise import cosine_similarity
sentences_a = [['Represent the Science sentence: ','Parton energy loss in QCD matter'],
['Represent the Financial statement: ','The Federal Reserve on Wednesday raised its benchmark interest rate.']]
sentences_b = [['Represent the Science sentence: ','The Chiral Phase Transition in Dissipative Dynamics'],
['Represent the Financial statement: ','The funds rose less than 0.5 per cent on Friday']]
embeddings_a = model.encode(sentences_a)
embeddings_b = model.encode(sentences_b)
similarities = cosine_similarity(embeddings_a,embeddings_b)
print(similarities)
```
## Information Retrieval
You can also use **customized embeddings** for information retrieval.
```python
import numpy as np
from sklearn.metrics.pairwise import cosine_similarity
query = [['Represent the Wikipedia question for retrieving supporting documents: ','where is the food stored in a yam plant']]
corpus = [['Represent the Wikipedia document for retrieval: ','Capitalism has been dominant in the Western world since the end of feudalism, but most feel[who?] that the term "mixed economies" more precisely describes most contemporary economies, due to their containing both private-owned and state-owned enterprises. In capitalism, prices determine the demand-supply scale. For example, higher demand for certain goods and services lead to higher prices and lower demand for certain goods lead to lower prices.'],
['Represent the Wikipedia document for retrieval: ',"The disparate impact theory is especially controversial under the Fair Housing Act because the Act regulates many activities relating to housing, insurance, and mortgage loans—and some scholars have argued that the theory's use under the Fair Housing Act, combined with extensions of the Community Reinvestment Act, contributed to rise of sub-prime lending and the crash of the U.S. housing market and ensuing global economic recession"],
['Represent the Wikipedia document for retrieval: ','Disparate impact in United States labor law refers to practices in employment, housing, and other areas that adversely affect one group of people of a protected characteristic more than another, even though rules applied by employers or landlords are formally neutral. Although the protected classes vary by statute, most federal civil rights laws protect based on race, color, religion, national origin, and sex as protected traits, and some laws include disability status and other traits as well.']]
query_embeddings = model.encode(query)
corpus_embeddings = model.encode(corpus)
similarities = cosine_similarity(query_embeddings,corpus_embeddings)
retrieved_doc_id = np.argmax(similarities)
print(retrieved_doc_id)
```
## Clustering
Use **customized embeddings** for clustering texts in groups.
```python
import sklearn.cluster
sentences = [['Represent the Medicine sentence for clustering: ','Dynamical Scalar Degree of Freedom in Horava-Lifshitz Gravity'],
['Represent the Medicine sentence for clustering: ','Comparison of Atmospheric Neutrino Flux Calculations at Low Energies'],
['Represent the Medicine sentence for clustering: ','Fermion Bags in the Massive Gross-Neveu Model'],
['Represent the Medicine sentence for clustering: ',"QCD corrections to Associated t-tbar-H production at the Tevatron"],
['Represent the Medicine sentence for clustering: ','A New Analysis of the R Measurements: Resonance Parameters of the Higher, Vector States of Charmonium']]
embeddings = model.encode(sentences)
clustering_model = sklearn.cluster.MiniBatchKMeans(n_clusters=2)
clustering_model.fit(embeddings)
cluster_assignment = clustering_model.labels_
print(cluster_assignment)
``` | 66,209 | [
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0.0192413330078125,
0.0206146240234375,
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-0.034759521484375... |
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