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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
sagnikrayc/roberta-base-fever | 2023-10-02T14:13:57.000Z | [
"transformers",
"pytorch",
"safetensors",
"roberta",
"text-classification",
"en",
"dataset:copenlu/fever_gold_evidence",
"license:afl-3.0",
"endpoints_compatible",
"region:us"
] | text-classification | sagnikrayc | null | null | sagnikrayc/roberta-base-fever | 0 | 2 | transformers | 2023-05-26T19:47:58 | ---
license: afl-3.0
datasets:
- copenlu/fever_gold_evidence
language:
- en
metrics:
- precision
- recall
- f1
---
```
wandb: Run summary:
wandb: eval/f1 0.8823
wandb: eval/loss 0.55886
wandb: eval/p 0.88088
wandb: eval/r 0.88558
```
**Note**:
1. `[evidence_text][SEP][claim]`
2. Only trained/validated on instances length <= 512 tokens. | 406 | [
[
-0.01418304443359375,
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0.0181884765625,
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0.0281982421875,
0.03802490234375,
-0.022705078125,
-0.022979736328125,
-0.038421630859375,
... |
sagnikrayc/roberta-large-fever | 2023-05-26T20:12:44.000Z | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"en",
"dataset:copenlu/fever_gold_evidence",
"license:afl-3.0",
"endpoints_compatible",
"region:us"
] | text-classification | sagnikrayc | null | null | sagnikrayc/roberta-large-fever | 0 | 2 | transformers | 2023-05-26T19:52:33 | ---
license: afl-3.0
datasets:
- copenlu/fever_gold_evidence
language:
- en
metrics:
- precision
- recall
- f1
---
```
wandb: eval/f1 0.88556
wandb: eval/loss 0.62762
wandb: eval/p 0.88384
wandb: eval/r 0.8891
```
**Note**:
1. `[evidence_text][SEP][claim]`
2. Only trained/validated on instances length <= 512 tokens. | 386 | [
[
-0.006305694580078125,
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0.033203125,
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sagnikrayc/bert-large-cased-fever | 2023-05-26T20:14:34.000Z | [
"transformers",
"pytorch",
"bert",
"text-classification",
"en",
"dataset:copenlu/fever_gold_evidence",
"license:afl-3.0",
"endpoints_compatible",
"region:us"
] | text-classification | sagnikrayc | null | null | sagnikrayc/bert-large-cased-fever | 0 | 2 | transformers | 2023-05-26T20:01:44 | ---
license: afl-3.0
datasets:
- copenlu/fever_gold_evidence
language:
- en
metrics:
- precision
- recall
- f1
---
```
wandb: eval/f1 0.87196
wandb: eval/loss 0.73371
wandb: eval/p 0.87077
wandb: eval/r 0.8753
```
**Note**:
1. `[evidence_text][SEP][claim]`
2. Only trained/validated on instances length <= 512 tokens.
| 387 | [
[
-0.0088043212890625,
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sagnikrayc/bert-large-uncased-fever | 2023-05-26T20:16:19.000Z | [
"transformers",
"pytorch",
"bert",
"text-classification",
"en",
"dataset:copenlu/fever_gold_evidence",
"license:afl-3.0",
"endpoints_compatible",
"region:us"
] | text-classification | sagnikrayc | null | null | sagnikrayc/bert-large-uncased-fever | 0 | 2 | transformers | 2023-05-26T20:12:05 | ---
license: afl-3.0
datasets:
- copenlu/fever_gold_evidence
language:
- en
metrics:
- precision
- recall
- f1
---
```
wandb: eval/f1 0.87565
wandb: eval/loss 0.70447
wandb: eval/p 0.8745
wandb: eval/r 0.87847
```
**Note**:
1. `[evidence_text][SEP][claim]`
2. Only trained/validated on instances length <= 512 tokens. | 386 | [
[
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0.025146484375,
0.02264404296875,
-0.01494598388671875,
-0.025115966796875,
-0.03704833984375,... |
YakovElm/IntelDAOS15Classic_256 | 2023-05-26T20:40:30.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/IntelDAOS15Classic_256 | 0 | 2 | transformers | 2023-05-26T20:39:52 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: IntelDAOS15Classic_256
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_256
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: 0.1959
- Train Accuracy: 0.9460
- Validation Loss: 0.3646
- 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 |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.2340 | 0.9460 | 0.3859 | 0.8859 | 0 |
| 0.2042 | 0.9460 | 0.3765 | 0.8859 | 1 |
| 0.1959 | 0.9460 | 0.3646 | 0.8859 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,788 | [
[
-0.044891357421875,
-0.04254150390625,
0.0201416015625,
0.0013036727905273438,
-0.034576416015625,
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0.0106353759765625,
-0.05523681640625,
-0.04827880859375,
-0.051788330078125,
-0.0... |
Showroom/shoes_subcategory_classifier | 2023-05-26T21:08:59.000Z | [
"transformers",
"pytorch",
"safetensors",
"bert",
"text-classification",
"autotrain",
"en",
"dataset:Showroom/autotrain-data-shoes_categories",
"co2_eq_emissions",
"endpoints_compatible",
"region:us"
] | text-classification | Showroom | null | null | Showroom/shoes_subcategory_classifier | 0 | 2 | transformers | 2023-05-26T21:06:24 | ---
tags:
- autotrain
- text-classification
language:
- en
widget:
- text: "I love AutoTrain"
datasets:
- Showroom/autotrain-data-shoes_categories
co2_eq_emissions:
emissions: 0.19050292538257937
---
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 62075134986
- CO2 Emissions (in grams): 0.1905
## Validation Metrics
- Loss: 0.372
- Accuracy: 0.903
- Macro F1: 0.801
- Micro F1: 0.903
- Weighted F1: 0.902
- Macro Precision: 0.809
- Micro Precision: 0.903
- Weighted Precision: 0.903
- Macro Recall: 0.796
- Micro Recall: 0.903
- Weighted Recall: 0.903
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/Showroom/autotrain-shoes_categories-62075134986
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("Showroom/autotrain-shoes_categories-62075134986", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("Showroom/autotrain-shoes_categories-62075134986", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
``` | 1,311 | [
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YakovElm/IntelDAOS20Classic_256 | 2023-05-26T22:16:28.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/IntelDAOS20Classic_256 | 0 | 2 | transformers | 2023-05-26T22:15:52 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: IntelDAOS20Classic_256
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_256
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: 0.1503
- Train Accuracy: 0.9610
- Validation Loss: 0.3345
- 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 |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.2440 | 0.9300 | 0.3165 | 0.9099 | 0 |
| 0.1550 | 0.9610 | 0.3098 | 0.9099 | 1 |
| 0.1503 | 0.9610 | 0.3345 | 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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0.0108184814453125,
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-0.02... |
YakovElm/Apache5Classic_32 | 2023-05-26T22:37:57.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/Apache5Classic_32 | 0 | 2 | transformers | 2023-05-26T22:37:21 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Apache5Classic_32
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_32
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: 0.2422
- Train Accuracy: 0.9181
- Validation Loss: 0.6553
- Validation Accuracy: 0.8129
- 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 |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.3112 | 0.9049 | 0.4947 | 0.8233 | 0 |
| 0.2848 | 0.9120 | 0.4767 | 0.8233 | 1 |
| 0.2422 | 0.9181 | 0.6553 | 0.8129 | 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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0.01314544677734375,
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-0.0238... |
YakovElm/Apache10Classic_32 | 2023-05-26T23:03:47.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/Apache10Classic_32 | 0 | 2 | transformers | 2023-05-26T23:03:04 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Apache10Classic_32
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_32
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: 0.1864
- Train Accuracy: 0.9398
- Validation Loss: 0.4344
- Validation Accuracy: 0.8631
- 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 |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.2450 | 0.9344 | 0.4172 | 0.8644 | 0 |
| 0.2183 | 0.9383 | 0.4524 | 0.8644 | 1 |
| 0.1864 | 0.9398 | 0.4344 | 0.8631 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,780 | [
[
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YakovElm/Apache15Classic_32 | 2023-05-26T23:27:43.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/Apache15Classic_32 | 0 | 2 | transformers | 2023-05-26T23:27:09 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Apache15Classic_32
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. -->
# Apache15Classic_32
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: 0.1627
- Train Accuracy: 0.9533
- Validation Loss: 0.4178
- Validation Accuracy: 0.8924
- 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 |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.1997 | 0.9498 | 0.3502 | 0.8924 | 0 |
| 0.1803 | 0.9542 | 0.3673 | 0.8924 | 1 |
| 0.1627 | 0.9533 | 0.4178 | 0.8924 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
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YakovElm/Jira5Classic_256 | 2023-05-26T23:47:50.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/Jira5Classic_256 | 0 | 2 | transformers | 2023-05-26T23:47:14 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Jira5Classic_256
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_256
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: 0.4111
- Train Accuracy: 0.8090
- Validation Loss: 1.1085
- Validation Accuracy: 0.5237
- 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 |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.5551 | 0.7429 | 0.8002 | 0.4858 | 0 |
| 0.4860 | 0.7712 | 0.7765 | 0.4890 | 1 |
| 0.4111 | 0.8090 | 1.1085 | 0.5237 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,776 | [
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-0.04876708984375,
-0.05047607421875,... |
YakovElm/Apache20Classic_32 | 2023-05-26T23:51:31.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/Apache20Classic_32 | 0 | 2 | transformers | 2023-05-26T23:50:38 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Apache20Classic_32
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. -->
# Apache20Classic_32
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: 0.1517
- Train Accuracy: 0.9624
- Validation Loss: 0.3060
- Validation Accuracy: 0.9055
- 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 |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.1711 | 0.9581 | 0.4085 | 0.9055 | 0 |
| 0.1568 | 0.9624 | 0.3792 | 0.9055 | 1 |
| 0.1517 | 0.9624 | 0.3060 | 0.9055 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,780 | [
[
-0.044586181640625,
-0.045074462890625,
0.020538330078125,
0.0070343017578125,
-0.035186767578125,
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-0.018524169921875,
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0.010406494140625,
0.01363372802734375,
-0.05419921875,
-0.04791259765625,
-0.0528564453125,
-0.02... |
YakovElm/Apache5Classic_64 | 2023-05-27T00:28:21.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/Apache5Classic_64 | 0 | 2 | transformers | 2023-05-27T00:27:46 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Apache5Classic_64
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_64
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: 0.2482
- Train Accuracy: 0.9136
- Validation Loss: 0.5374
- Validation Accuracy: 0.7947
- 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 |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.3112 | 0.9051 | 0.5143 | 0.8233 | 0 |
| 0.2845 | 0.9116 | 0.4954 | 0.8220 | 1 |
| 0.2482 | 0.9136 | 0.5374 | 0.7947 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,778 | [
[
-0.04473876953125,
-0.043182373046875,
0.0199737548828125,
0.00540924072265625,
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0.01415252685546875,
-0.054046630859375,
-0.048828125,
-0.053497314453125,
-0.02... |
YakovElm/Apache10Classic_64 | 2023-05-27T01:07:04.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/Apache10Classic_64 | 0 | 2 | transformers | 2023-05-27T01:06:30 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Apache10Classic_64
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_64
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: 0.1916
- Train Accuracy: 0.9381
- Validation Loss: 0.4588
- Validation Accuracy: 0.8644
- 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 |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.2421 | 0.9353 | 0.4801 | 0.8644 | 0 |
| 0.2256 | 0.9383 | 0.4038 | 0.8644 | 1 |
| 0.1916 | 0.9381 | 0.4588 | 0.8644 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,780 | [
[
-0.04534912109375,
-0.04522705078125,
0.02056884765625,
0.006397247314453125,
-0.035888671875,
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-0.01751708984375,
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0.01097869873046875,
0.0146636962890625,
-0.053741455078125,
-0.047149658203125,
-0.052825927734375,
-0.023... |
YakovElm/Jira10Classic_256 | 2023-05-27T01:19:24.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/Jira10Classic_256 | 0 | 2 | transformers | 2023-05-27T01:18:48 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Jira10Classic_256
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_256
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: 0.3342
- Train Accuracy: 0.8405
- Validation Loss: 0.7061
- Validation Accuracy: 0.6088
- 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 |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.5118 | 0.7817 | 0.8080 | 0.4921 | 0 |
| 0.4265 | 0.7849 | 0.8772 | 0.4921 | 1 |
| 0.3342 | 0.8405 | 0.7061 | 0.6088 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,778 | [
[
-0.04119873046875,
-0.04144287109375,
0.0198822021484375,
0.0003428459167480469,
-0.033721923828125,
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0.016265869140625,
0.01241302490234375,
-0.0516357421875,
-0.047149658203125,
-0.05096435546875,
... |
YakovElm/Apache15Classic_64 | 2023-05-27T01:46:32.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/Apache15Classic_64 | 0 | 2 | transformers | 2023-05-27T01:45:58 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Apache15Classic_64
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. -->
# Apache15Classic_64
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: 0.1664
- Train Accuracy: 0.9542
- Validation Loss: 0.3210
- Validation Accuracy: 0.8924
- 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 |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.1964 | 0.9533 | 0.3529 | 0.8924 | 0 |
| 0.1834 | 0.9542 | 0.3501 | 0.8924 | 1 |
| 0.1664 | 0.9542 | 0.3210 | 0.8924 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,780 | [
[
-0.04498291015625,
-0.043975830078125,
0.0209808349609375,
0.0065155029296875,
-0.03564453125,
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0.01102447509765625,
0.0137786865234375,
-0.053131103515625,
-0.047637939453125,
-0.052734375,
-0.025... |
raygx/BERT-NepSA-T2 | 2023-07-22T11:28:22.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:mit",
"endpoints_compatible",
"region:us"
] | text-classification | raygx | null | null | raygx/BERT-NepSA-T2 | 0 | 2 | transformers | 2023-05-27T02:03:56 | ---
license: mit
base_model: Shushant/nepaliBERT
tags:
- generated_from_keras_callback
model-index:
- name: BERT-NepSA-T2
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-NepSA-T2
This model is a fine-tuned version of [Shushant/nepaliBERT](https://huggingface.co/Shushant/nepaliBERT) on an unknown dataset.
It achieves the following results on the evaluation set:
## 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': 1e-06, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.0001}
- training_precision: float32
### Training results
### Framework versions
- Transformers 4.31.0
- TensorFlow 2.12.0
- Datasets 2.13.1
- Tokenizers 0.13.3
| 1,133 | [
[
-0.024566650390625,
-0.052215576171875,
0.013763427734375,
0.01471710205078125,
-0.044921875,
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0.0275115966796875,
0.01113128662109375,
-0.049468994140625,
-0.0295257568359375,
-0.06036376953125,
-0.... |
YakovElm/Apache20Classic_64 | 2023-05-27T02:30:58.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/Apache20Classic_64 | 0 | 2 | transformers | 2023-05-27T02:30:21 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Apache20Classic_64
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. -->
# Apache20Classic_64
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: 0.1374
- Train Accuracy: 0.9624
- Validation Loss: 0.3081
- Validation Accuracy: 0.9055
- 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 |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.1664 | 0.9620 | 0.3171 | 0.9055 | 0 |
| 0.1522 | 0.9624 | 0.2966 | 0.9055 | 1 |
| 0.1374 | 0.9624 | 0.3081 | 0.9055 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,780 | [
[
-0.044921875,
-0.044830322265625,
0.0204315185546875,
0.0061798095703125,
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0.0106353759765625,
0.013946533203125,
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-0.048004150390625,
-0.05328369140625,
-0.024... |
YakovElm/Hyperledger5Classic_32 | 2023-05-27T02:47:14.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/Hyperledger5Classic_32 | 0 | 2 | transformers | 2023-05-27T02:46:41 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Hyperledger5Classic_32
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_32
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: 0.3550
- Train Accuracy: 0.8578
- Validation Loss: 0.4350
- Validation Accuracy: 0.8361
- 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 |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.4133 | 0.8547 | 0.4381 | 0.8361 | 0 |
| 0.3935 | 0.8554 | 0.4381 | 0.8361 | 1 |
| 0.3550 | 0.8578 | 0.4350 | 0.8361 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,788 | [
[
-0.047332763671875,
-0.037139892578125,
0.0219573974609375,
0.0030460357666015625,
-0.031280517578125,
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-0.0180511474609375,
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0.00814056396484375,
0.01297760009765625,
-0.053253173828125,
-0.0506591796875,
-0.053619384765625... |
YakovElm/Jira15Classic_256 | 2023-05-27T02:50:35.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/Jira15Classic_256 | 0 | 2 | transformers | 2023-05-27T02:49:59 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Jira15Classic_256
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_256
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: 0.3963
- Train Accuracy: 0.7912
- Validation Loss: 0.6595
- Validation Accuracy: 0.6593
- 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 |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.5200 | 0.7692 | 0.8593 | 0.5205 | 0 |
| 0.4517 | 0.7922 | 0.8734 | 0.5205 | 1 |
| 0.3963 | 0.7912 | 0.6595 | 0.6593 | 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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-0.04150390625,
0.02001953125,
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0.0120849609375,
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-0.048004150390625,
-0.05126953125,
-0.02786254882812... |
YakovElm/Hyperledger10Classic_32 | 2023-05-27T03:02:45.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/Hyperledger10Classic_32 | 0 | 2 | transformers | 2023-05-27T03:02:01 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Hyperledger10Classic_32
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_32
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: 0.2991
- Train Accuracy: 0.8845
- Validation Loss: 0.3973
- Validation Accuracy: 0.8548
- 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 |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.3675 | 0.8779 | 0.3861 | 0.8600 | 0 |
| 0.3449 | 0.8838 | 0.3911 | 0.8600 | 1 |
| 0.2991 | 0.8845 | 0.3973 | 0.8548 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,790 | [
[
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0.003009796142578125,
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-0.02008056640625,
-0.0258636474609375,
0.01168060302734375,
0.01364898681640625,
-0.051544189453125,
-0.04693603515625,
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YakovElm/Hyperledger15Classic_32 | 2023-05-27T03:18:38.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/Hyperledger15Classic_32 | 0 | 2 | transformers | 2023-05-27T03:17:57 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Hyperledger15Classic_32
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. -->
# Hyperledger15Classic_32
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: 0.2991
- Train Accuracy: 0.9028
- Validation Loss: 0.3422
- Validation Accuracy: 0.8807
- 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 |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.3396 | 0.8914 | 0.3557 | 0.8807 | 0 |
| 0.3083 | 0.9035 | 0.3524 | 0.8807 | 1 |
| 0.2991 | 0.9028 | 0.3422 | 0.8807 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,790 | [
[
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0.01024627685546875,
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... |
YakovElm/Hyperledger20Classic_32 | 2023-05-27T03:34:15.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/Hyperledger20Classic_32 | 0 | 2 | transformers | 2023-05-27T03:33:41 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Hyperledger20Classic_32
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_32
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: 0.2565
- Train Accuracy: 0.9149
- Validation Loss: 0.3101
- 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 |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.3031 | 0.9059 | 0.3074 | 0.8983 | 0 |
| 0.2700 | 0.9149 | 0.2988 | 0.8983 | 1 |
| 0.2565 | 0.9149 | 0.3101 | 0.8983 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,790 | [
[
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openbmb/cpm-bee-5b | 2023-07-03T11:34:49.000Z | [
"transformers",
"pytorch",
"cpmbee",
"feature-extraction",
"custom_code",
"en",
"zh",
"region:us"
] | feature-extraction | openbmb | null | null | openbmb/cpm-bee-5b | 6 | 2 | transformers | 2023-05-27T03:59:34 | ---
language:
- en
- zh
---
# CPM-Bee
**CPM-Bee** is a fully open-source, commercially-usable Chinese-English bilingual base model with a capacity of ten billion parameters. It is the second milestone achieved through the training process of [**CPM-live**](https://live.openbmb.org/).
Utilizing the Transformer auto-regressive architecture, CPM-Bee has been pre-trained on an extensive corpus of trillion-scale tokens, thereby possessing remarkable foundational capabilities.
## Model description
- **Open-source and Commercial Usable**：OpenBMB adheres to the spirit of open-source, aiming to make large-scale models accessible to everyone. CPM-Bee, as a foudation model, is fully open-source and available for commercial use, contributing to the advancement of the field of large-scale models.
- **Excellent Performance in Chinese and English**： : CPM-Bee's base model has undergone rigorous selection and balancing of pre-training data, resulting in outstanding performance in both Chinese and English. For detailed information regarding evaluation tasks and results, please refer to the assessment documentation.
- **Vast and High-quality Corpus**： CPM-Bee, as a base model, has been trained on an extensive corpus of over trillion tokens, making it one of the models with the highest volume of training data within the open-source community. Furthermore, we have implemented stringent selection, cleaning, and post-processing procedures on the pre-training corpus to ensure its quality.
- **Support for OpenBMB System**： The OpenBMB system provides a comprehensive ecosystem of tools and scripts for high-performance pre-training, adaptation, compression, deployment, and tool development. CPM-Bee, as a base model, is accompanied by all the necessary tool scripts, enabling developers to efficiently utilize and explore advanced functionalities.
- **Conversational and Tool Usage Capabilities**： Building upon OpenBMB's exploration in instruction-based fine-tuning and tool learning, we have performed fine-tuning on top of the CPM-Bee base model, resulting in an instance model with powerful conversational and tool usage capabilities. The API and beta testing for this model will be made available in the near future.
## Intended uses & limitations
You can use the raw model for many NLP tasks like text generation or fine-tune it to a downstream task.
### How to use
```python
>>> from transformers import AutoModelForCausalLM, AutoTokenizer
>>> tokenizer = AutoTokenizer.from_pretrained("openbmb/cpm-bee-5b", trust_remote_code=True)
>>> model = AutoModelForCausalLM.from_pretrained("openbmb/cpm-bee-5b", trust_remote_code=True).cuda() #
>>> result = model.generate({"input": "今天天气不错，", "<ans>": ""}, tokenizer)
>>> print(result)
```
If you wanna use multi GPUs to inference, you can use `accelerate` as follow:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
from accelerate import dispatch_model
from accelerate.utils import get_balanced_memory, infer_auto_device_map
tokenizer = AutoTokenizer.from_pretrained("openbmb/cpm-bee-5b", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("openbmb/cpm-bee-5b", trust_remote_code=True).cuda()
max_memory = get_balanced_memory(
model,
no_split_module_classes=["CpmBeeTransformerBlock"]
)
device_map = infer_auto_device_map(model, max_memory=max_memory, no_split_module_classes=["CpmBeeTransformerBlock"])
# make sure the data on the same device when projecting hidden states to logits.
device_map["cpmbee.encoder.output_layernorm"] = device_map["cpmbee.input_embedding"] = 0
model = dispatch_model(model, device_map=device_map)
res = model.generate(
[
{"input": "今天天气是真的", "<ans>": ""},
{"input": "NGC 6231是一个位于天蝎座的疏散星团，天球座标为赤经16时54分，赤纬-41度48分，视觉观测大小约45角分，亮度约2.6视星等，距地球5900光年。NGC 6231年龄约为三百二十万年，是一个非常年轻的星团，星团内的最亮星是5等的天蝎座 ζ1星。用双筒望远镜或小型望远镜就能看到个别的行星。NGC 6231在1654年被意大利天文学家乔瓦尼·巴蒂斯特·霍迪尔纳（Giovanni Battista Hodierna）以Luminosae的名字首次纪录在星表中，但是未见记载于夏尔·梅西耶的天体列表和威廉·赫歇尔的深空天体目录。这个天体在1678年被爱德蒙·哈雷（I.7）、1745年被夏西亚科斯（Jean-Phillippe Loys de Cheseaux）（9）、1751年被尼可拉·路易·拉卡伊（II.13）分别再次独立发现。", "question": "NGC 6231的经纬度是多少？", "<ans>": ""}
],
tokenizer,
max_new_tokens=100
)
print(res)
```
We suggest to use `bmtrain` to finetune CPM-Bee. Also, you can use `accelerate` and `deepspeed` to finetune CPM-Bee. Here we will give a brief example of a training loop:
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
from accelerate import Accelerator
from torch.utils.data import Dataset, DataLoader
accelerator = Accelerator()
trainset = Dataset() # Make sure trainset.__getitem__() can get data with correct format like {"input": "...", "<ans>": ""}
# for details, you can read https://github.com/OpenBMB/CPM-Bee/tree/main/tutorials/basic_task_finetune
train_loader = DataLoader(trainset, batch_size=1)
tokenizer = AutoTokenizer.from_pretrained("openbmb/cpm-bee-5b", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("openbmb/cpm-bee-5b", trust_remote_code=True).cuda()
optimizer = torch.optim.Adam(model.parameters())
model, optimizer, train_loader = accelerator.prepare(
model, optimizer, train_loader
)
for iter, data in enumerate(train_loader):
optimizer.zero_grad()
# change the data to a trainable format
input_encoded = tokenizer.prepare_for_finetune(data, max_length=512).to(model.device)
outputs = model(**input_encoded)
loss = outputs.loss
accelerator.backward(loss)
optimizer.step()
```
You should design your own parallel and mix_precision training strategy on the basis of it.
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YakovElm/Hyperledger5Classic_64 | 2023-05-27T04:03:05.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/Hyperledger5Classic_64 | 0 | 2 | transformers | 2023-05-27T04:02:31 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Hyperledger5Classic_64
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_64
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: 0.3683
- Train Accuracy: 0.8561
- Validation Loss: 0.4172
- Validation Accuracy: 0.8351
- 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 |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.4207 | 0.8481 | 0.4357 | 0.8361 | 0 |
| 0.3940 | 0.8547 | 0.4199 | 0.8361 | 1 |
| 0.3683 | 0.8561 | 0.4172 | 0.8351 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,788 | [
[
-0.04608154296875,
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0.00002288818359375,
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0.01424407958984375,
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-0.... |
YakovElm/Jira20Classic_256 | 2023-05-27T04:22:30.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/Jira20Classic_256 | 0 | 2 | transformers | 2023-05-27T04:21:54 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Jira20Classic_256
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_256
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: 0.2410
- Train Accuracy: 0.8972
- Validation Loss: 0.2703
- Validation Accuracy: 0.9338
- 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 |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.3731 | 0.8562 | 0.2694 | 0.9338 | 0 |
| 0.3110 | 0.8772 | 0.2464 | 0.9338 | 1 |
| 0.2410 | 0.8972 | 0.2703 | 0.9338 | 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/Hyperledger10Classic_64 | 2023-05-27T04:27:49.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/Hyperledger10Classic_64 | 0 | 2 | transformers | 2023-05-27T04:27:14 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Hyperledger10Classic_64
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_64
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: 0.2869
- Train Accuracy: 0.8865
- Validation Loss: 0.4772
- Validation Accuracy: 0.8600
- 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 |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.3638 | 0.8831 | 0.3750 | 0.8600 | 0 |
| 0.3325 | 0.8838 | 0.3629 | 0.8600 | 1 |
| 0.2869 | 0.8865 | 0.4772 | 0.8600 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
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YakovElm/Hyperledger15Classic_64 | 2023-05-27T04:53:01.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/Hyperledger15Classic_64 | 0 | 2 | transformers | 2023-05-27T04:52:27 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Hyperledger15Classic_64
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. -->
# Hyperledger15Classic_64
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: 0.2628
- Train Accuracy: 0.9045
- Validation Loss: 0.3526
- Validation Accuracy: 0.8683
- 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 |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.3275 | 0.8942 | 0.3392 | 0.8807 | 0 |
| 0.2991 | 0.9035 | 0.3343 | 0.8807 | 1 |
| 0.2628 | 0.9045 | 0.3526 | 0.8683 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,790 | [
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YakovElm/Hyperledger20Classic_64 | 2023-05-27T05:16:23.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/Hyperledger20Classic_64 | 0 | 2 | transformers | 2023-05-27T05:15:48 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Hyperledger20Classic_64
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_64
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: 0.2229
- Train Accuracy: 0.9198
- Validation Loss: 0.3311
- Validation Accuracy: 0.8963
- 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 |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.2931 | 0.9149 | 0.3059 | 0.8983 | 0 |
| 0.2643 | 0.9142 | 0.2926 | 0.8983 | 1 |
| 0.2229 | 0.9198 | 0.3311 | 0.8963 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,790 | [
[
-0.0467529296875,
-0.040283203125,
0.0222015380859375,
0.002262115478515625,
-0.0307769775390625,
-0.029815673828125,
-0.018096923828125,
-0.0257110595703125,
0.0091400146484375,
0.016510009765625,
-0.05377197265625,
-0.049530029296875,
-0.054779052734375,
-... |
YakovElm/IntelDAOS5Classic_32 | 2023-05-27T05:22:47.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/IntelDAOS5Classic_32 | 0 | 2 | transformers | 2023-05-27T05:22:13 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: IntelDAOS5Classic_32
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_32
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: 0.3605
- Train Accuracy: 0.8740
- Validation Loss: 0.4734
- Validation Accuracy: 0.8438
- 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 |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.3976 | 0.8740 | 0.4505 | 0.8438 | 0 |
| 0.3801 | 0.8740 | 0.4481 | 0.8438 | 1 |
| 0.3605 | 0.8740 | 0.4734 | 0.8438 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,784 | [
[
-0.0440673828125,
-0.038909912109375,
0.0207977294921875,
0.0014715194702148438,
-0.0340576171875,
-0.0281829833984375,
-0.0189971923828125,
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0.01122283935546875,
0.01091766357421875,
-0.05377197265625,
-0.04925537109375,
-0.052490234375,
... |
YakovElm/IntelDAOS10Classic_32 | 2023-05-27T05:28:48.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/IntelDAOS10Classic_32 | 0 | 2 | transformers | 2023-05-27T05:28:15 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: IntelDAOS10Classic_32
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_32
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: 0.2725
- Train Accuracy: 0.9200
- Validation Loss: 0.3952
- 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 |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.3293 | 0.8960 | 0.3865 | 0.8739 | 0 |
| 0.2838 | 0.9200 | 0.4036 | 0.8739 | 1 |
| 0.2725 | 0.9200 | 0.3952 | 0.8739 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,786 | [
[
-0.0440673828125,
-0.03985595703125,
0.0211944580078125,
0.0019702911376953125,
-0.03448486328125,
-0.028533935546875,
-0.0182952880859375,
-0.027618408203125,
0.01212310791015625,
0.01078033447265625,
-0.052520751953125,
-0.04779052734375,
-0.051727294921875,
... |
YakovElm/IntelDAOS15Classic_32 | 2023-05-27T05:34:51.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/IntelDAOS15Classic_32 | 0 | 2 | transformers | 2023-05-27T05:34:17 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: IntelDAOS15Classic_32
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_32
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: 0.2037
- Train Accuracy: 0.9460
- Validation Loss: 0.3651
- 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 |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.2431 | 0.9260 | 0.3967 | 0.8859 | 0 |
| 0.2160 | 0.9460 | 0.4047 | 0.8859 | 1 |
| 0.2037 | 0.9460 | 0.3651 | 0.8859 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,786 | [
[
-0.04364013671875,
-0.040374755859375,
0.0221405029296875,
0.002933502197265625,
-0.033721923828125,
-0.0296630859375,
-0.01812744140625,
-0.027008056640625,
0.01209259033203125,
0.0119171142578125,
-0.05426025390625,
-0.048431396484375,
-0.05169677734375,
-... |
YakovElm/IntelDAOS20Classic_32 | 2023-05-27T05:40:35.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/IntelDAOS20Classic_32 | 0 | 2 | transformers | 2023-05-27T05:40:00 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: IntelDAOS20Classic_32
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_32
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: 0.1464
- Train Accuracy: 0.9610
- Validation Loss: 0.3274
- 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 |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.1971 | 0.9610 | 0.3072 | 0.9099 | 0 |
| 0.1570 | 0.9610 | 0.3179 | 0.9099 | 1 |
| 0.1464 | 0.9610 | 0.3274 | 0.9099 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,786 | [
[
-0.0447998046875,
-0.039947509765625,
0.0207061767578125,
0.002422332763671875,
-0.035369873046875,
-0.0280609130859375,
-0.0184783935546875,
-0.0271453857421875,
0.01284027099609375,
0.0105133056640625,
-0.053497314453125,
-0.04766845703125,
-0.052001953125,
... |
YakovElm/IntelDAOS5Classic_64 | 2023-05-27T05:49:09.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/IntelDAOS5Classic_64 | 0 | 2 | transformers | 2023-05-27T05:48:34 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: IntelDAOS5Classic_64
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_64
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: 0.3585
- Train Accuracy: 0.8740
- Validation Loss: 0.4320
- Validation Accuracy: 0.8438
- 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 |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.4007 | 0.8720 | 0.4336 | 0.8438 | 0 |
| 0.3732 | 0.8740 | 0.4284 | 0.8438 | 1 |
| 0.3585 | 0.8740 | 0.4320 | 0.8438 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,784 | [
[
-0.044464111328125,
-0.03900146484375,
0.021209716796875,
0.0007915496826171875,
-0.034515380859375,
-0.0290985107421875,
-0.018951416015625,
-0.0280914306640625,
0.01091766357421875,
0.01114654541015625,
-0.053558349609375,
-0.04931640625,
-0.052490234375,
... |
YakovElm/IntelDAOS10Classic_64 | 2023-05-27T05:57:58.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/IntelDAOS10Classic_64 | 0 | 2 | transformers | 2023-05-27T05:57:23 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: IntelDAOS10Classic_64
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_64
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: 0.2613
- Train Accuracy: 0.9200
- Validation Loss: 0.3848
- 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 |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.3128 | 0.8920 | 0.3859 | 0.8739 | 0 |
| 0.2678 | 0.9200 | 0.4156 | 0.8739 | 1 |
| 0.2613 | 0.9200 | 0.3848 | 0.8739 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,786 | [
[
-0.04449462890625,
-0.039886474609375,
0.0208892822265625,
0.0006036758422851562,
-0.033935546875,
-0.029144287109375,
-0.0188446044921875,
-0.0278167724609375,
0.0128173828125,
0.0118865966796875,
-0.052764892578125,
-0.04833984375,
-0.05206298828125,
-0.02... |
YakovElm/IntelDAOS15Classic_64 | 2023-05-27T06:07:23.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/IntelDAOS15Classic_64 | 0 | 2 | transformers | 2023-05-27T06:06:48 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: IntelDAOS15Classic_64
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_64
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: 0.1817
- Train Accuracy: 0.9460
- Validation Loss: 0.3953
- 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 |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.2502 | 0.9450 | 0.3577 | 0.8859 | 0 |
| 0.2086 | 0.9460 | 0.3578 | 0.8859 | 1 |
| 0.1817 | 0.9460 | 0.3953 | 0.8859 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,786 | [
[
-0.044342041015625,
-0.0413818359375,
0.0214385986328125,
0.0018548965454101562,
-0.03375244140625,
-0.029571533203125,
-0.0186309814453125,
-0.026702880859375,
0.01273345947265625,
0.0121612548828125,
-0.05389404296875,
-0.048797607421875,
-0.052001953125,
... |
YakovElm/MariaDB5Classic_256 | 2023-05-27T06:15:35.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/MariaDB5Classic_256 | 0 | 2 | transformers | 2023-05-27T06:14:59 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: MariaDB5Classic_256
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_256
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: 0.2607
- Train Accuracy: 0.9088
- Validation Loss: 0.2602
- Validation Accuracy: 0.9322
- 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 |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.3286 | 0.8862 | 0.2445 | 0.9322 | 0 |
| 0.2829 | 0.8954 | 0.2511 | 0.9322 | 1 |
| 0.2607 | 0.9088 | 0.2602 | 0.9322 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,782 | [
[
-0.044342041015625,
-0.041778564453125,
0.0210723876953125,
0.0025691986083984375,
-0.0333251953125,
-0.030853271484375,
-0.0153961181640625,
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0.01509857177734375,
0.0147705078125,
-0.056060791015625,
-0.050628662109375,
-0.05126953125,
-0... |
YakovElm/IntelDAOS20Classic_64 | 2023-05-27T06:15:50.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/IntelDAOS20Classic_64 | 0 | 2 | transformers | 2023-05-27T06:15:16 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: IntelDAOS20Classic_64
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_64
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: 0.1354
- Train Accuracy: 0.9610
- Validation Loss: 0.3272
- 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 |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.2413 | 0.9400 | 0.3377 | 0.9099 | 0 |
| 0.1555 | 0.9610 | 0.3160 | 0.9099 | 1 |
| 0.1354 | 0.9610 | 0.3272 | 0.9099 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,786 | [
[
-0.044921875,
-0.040130615234375,
0.0212249755859375,
0.0014820098876953125,
-0.033172607421875,
-0.0290374755859375,
-0.0186920166015625,
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0.01287078857421875,
0.0114593505859375,
-0.054290771484375,
-0.04840087890625,
-0.052215576171875,
... |
YakovElm/Jira5Classic_32 | 2023-05-27T06:21:26.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/Jira5Classic_32 | 0 | 2 | transformers | 2023-05-27T06:20:51 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Jira5Classic_32
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_32
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: 0.3030
- Train Accuracy: 0.8867
- Validation Loss: 1.0513
- Validation Accuracy: 0.6151
- 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 |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.5139 | 0.7555 | 0.7646 | 0.5047 | 0 |
| 0.4087 | 0.8038 | 0.8291 | 0.5552 | 1 |
| 0.3030 | 0.8867 | 1.0513 | 0.6151 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,774 | [
[
-0.04022216796875,
-0.039794921875,
0.020721435546875,
0.0003685951232910156,
-0.033905029296875,
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0.012725830078125,
0.0120391845703125,
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-0.049407958984375,
-0.05157470703125,
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YakovElm/Jira10Classic_32 | 2023-05-27T06:26:44.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/Jira10Classic_32 | 0 | 2 | transformers | 2023-05-27T06:26:11 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Jira10Classic_32
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_32
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: 0.2559
- Train Accuracy: 0.8972
- Validation Loss: 1.0026
- Validation Accuracy: 0.5994
- 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 |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.5162 | 0.7629 | 0.8404 | 0.4890 | 0 |
| 0.4017 | 0.8122 | 0.8047 | 0.6151 | 1 |
| 0.2559 | 0.8972 | 1.0026 | 0.5994 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,776 | [
[
-0.040313720703125,
-0.04168701171875,
0.0200042724609375,
0.0009250640869140625,
-0.03369140625,
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0.01494598388671875,
0.01290130615234375,
-0.0518798828125,
-0.047271728515625,
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YakovElm/Jira15Classic_32 | 2023-05-27T06:32:14.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/Jira15Classic_32 | 0 | 2 | transformers | 2023-05-27T06:31:38 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Jira15Classic_32
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_32
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: 0.2421
- Train Accuracy: 0.9024
- Validation Loss: 1.1123
- Validation Accuracy: 0.6372
- 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 |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.4984 | 0.7702 | 0.8224 | 0.5205 | 0 |
| 0.3898 | 0.8216 | 0.7801 | 0.6215 | 1 |
| 0.2421 | 0.9024 | 1.1123 | 0.6372 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,776 | [
[
-0.0416259765625,
-0.04180908203125,
0.0208892822265625,
0.0025615692138671875,
-0.033782958984375,
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0.01398468017578125,
0.013153076171875,
-0.0516357421875,
-0.048736572265625,
-0.051055908203125,
... |
YakovElm/Jira20Classic_32 | 2023-05-27T06:38:00.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/Jira20Classic_32 | 0 | 2 | transformers | 2023-05-27T06:37:22 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Jira20Classic_32
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_32
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: 0.1936
- Train Accuracy: 0.9224
- Validation Loss: 0.3181
- Validation Accuracy: 0.9148
- 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 |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.3608 | 0.8741 | 0.3038 | 0.9338 | 0 |
| 0.2758 | 0.8741 | 0.3191 | 0.9306 | 1 |
| 0.1936 | 0.9224 | 0.3181 | 0.9148 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,776 | [
[
-0.04022216796875,
-0.04144287109375,
0.019378662109375,
0.00278472900390625,
-0.03594970703125,
-0.0276336669921875,
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0.014556884765625,
0.01239776611328125,
-0.05194091796875,
-0.04815673828125,
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-0.... |
YakovElm/Jira5Classic_64 | 2023-05-27T06:46:28.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/Jira5Classic_64 | 0 | 2 | transformers | 2023-05-27T06:45:33 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Jira5Classic_64
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_64
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: 0.3482
- Train Accuracy: 0.8562
- Validation Loss: 1.2752
- Validation Accuracy: 0.5457
- 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 |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.5168 | 0.7639 | 0.8426 | 0.4858 | 0 |
| 0.4535 | 0.7922 | 0.9725 | 0.4858 | 1 |
| 0.3482 | 0.8562 | 1.2752 | 0.5457 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,774 | [
[
-0.040069580078125,
-0.03948974609375,
0.0200347900390625,
-0.00020301342010498047,
-0.034393310546875,
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-0.0174713134765625,
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0.01271820068359375,
0.0124053955078125,
-0.052093505859375,
-0.048797607421875,
-0.052124023437... |
YakovElm/Jira10Classic_64 | 2023-05-27T06:54:53.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/Jira10Classic_64 | 0 | 2 | transformers | 2023-05-27T06:54:21 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Jira10Classic_64
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_64
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: 0.3304
- Train Accuracy: 0.8426
- Validation Loss: 0.6563
- Validation Accuracy: 0.6814
- 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 |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.5094 | 0.7807 | 0.8002 | 0.4921 | 0 |
| 0.4211 | 0.7901 | 0.7682 | 0.5205 | 1 |
| 0.3304 | 0.8426 | 0.6563 | 0.6814 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,776 | [
[
-0.040374755859375,
-0.0413818359375,
0.0200347900390625,
0.000021398067474365234,
-0.034393310546875,
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0.01535797119140625,
0.01305389404296875,
-0.050018310546875,
-0.048095703125,
-0.051666259765625,
... |
YakovElm/Jira15Classic_64 | 2023-05-27T07:03:03.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/Jira15Classic_64 | 0 | 2 | transformers | 2023-05-27T07:02:29 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Jira15Classic_64
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_64
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: 0.3055
- Train Accuracy: 0.8678
- Validation Loss: 0.8529
- Validation Accuracy: 0.6530
- 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 |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.4973 | 0.7922 | 0.8065 | 0.5205 | 0 |
| 0.4266 | 0.7849 | 0.8817 | 0.5174 | 1 |
| 0.3055 | 0.8678 | 0.8529 | 0.6530 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,776 | [
[
-0.04052734375,
-0.04217529296875,
0.0202178955078125,
0.00043845176696777344,
-0.034393310546875,
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0.0146636962890625,
0.01287841796875,
-0.05157470703125,
-0.0489501953125,
-0.05157470703125,
-0.02... |
YakovElm/Jira20Classic_64 | 2023-05-27T07:11:37.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/Jira20Classic_64 | 0 | 2 | transformers | 2023-05-27T07:11:04 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Jira20Classic_64
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_64
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: 0.2121
- Train Accuracy: 0.9224
- Validation Loss: 0.3072
- Validation Accuracy: 0.9085
- 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 |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.3687 | 0.8678 | 0.2697 | 0.9338 | 0 |
| 0.2722 | 0.8909 | 0.2871 | 0.9306 | 1 |
| 0.2121 | 0.9224 | 0.3072 | 0.9085 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,776 | [
[
-0.040252685546875,
-0.0408935546875,
0.0200653076171875,
0.000980377197265625,
-0.034454345703125,
-0.0281982421875,
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0.014556884765625,
0.01378631591796875,
-0.0518798828125,
-0.04840087890625,
-0.05084228515625,
-0... |
YakovElm/MariaDB5Classic_32 | 2023-05-27T07:18:20.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/MariaDB5Classic_32 | 0 | 2 | transformers | 2023-05-27T07:17:45 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: MariaDB5Classic_32
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_32
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: 0.2727
- Train Accuracy: 0.8929
- Validation Loss: 0.2534
- Validation Accuracy: 0.9322
- 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 |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.3469 | 0.8787 | 0.2551 | 0.9322 | 0 |
| 0.2924 | 0.8946 | 0.2727 | 0.9322 | 1 |
| 0.2727 | 0.8929 | 0.2534 | 0.9322 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,780 | [
[
-0.04302978515625,
-0.04193115234375,
0.02130126953125,
0.003353118896484375,
-0.0341796875,
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0.01358795166015625,
0.0146484375,
-0.0546875,
-0.050079345703125,
-0.052032470703125,
-0.0264892578125,... |
YakovElm/MariaDB10Classic_32 | 2023-05-27T07:25:01.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/MariaDB10Classic_32 | 0 | 2 | transformers | 2023-05-27T07:24:28 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: MariaDB10Classic_32
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_32
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: 0.1860
- Train Accuracy: 0.9356
- Validation Loss: 0.2225
- Validation Accuracy: 0.9497
- 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 |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.2943 | 0.9121 | 0.2078 | 0.9523 | 0 |
| 0.2359 | 0.9213 | 0.2011 | 0.9497 | 1 |
| 0.1860 | 0.9356 | 0.2225 | 0.9497 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,782 | [
[
-0.04327392578125,
-0.042144775390625,
0.0215301513671875,
0.003757476806640625,
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0.01418304443359375,
0.01416015625,
-0.054229736328125,
-0.049102783203125,
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-0... |
YakovElm/MariaDB15Classic_32 | 2023-05-27T07:31:30.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/MariaDB15Classic_32 | 0 | 2 | transformers | 2023-05-27T07:30:52 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: MariaDB15Classic_32
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_32
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: 0.1739
- Train Accuracy: 0.9347
- Validation Loss: 0.1727
- 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 |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.2658 | 0.9264 | 0.1676 | 0.9598 | 0 |
| 0.2067 | 0.9314 | 0.1605 | 0.9573 | 1 |
| 0.1739 | 0.9347 | 0.1727 | 0.9598 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,782 | [
[
-0.04345703125,
-0.04193115234375,
0.021148681640625,
0.00421142578125,
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0.01409912109375,
-0.05462646484375,
-0.048095703125,
-0.052764892578125,
-0.0258026... |
YakovElm/MariaDB20Classic_32 | 2023-05-27T07:38:04.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/MariaDB20Classic_32 | 0 | 2 | transformers | 2023-05-27T07:37:18 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: MariaDB20Classic_32
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_32
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: 0.2150
- Train Accuracy: 0.9356
- Validation Loss: 0.1324
- 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 |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.2765 | 0.9305 | 0.1945 | 0.9698 | 0 |
| 0.2427 | 0.9356 | 0.1311 | 0.9698 | 1 |
| 0.2150 | 0.9356 | 0.1324 | 0.9698 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,782 | [
[
-0.043182373046875,
-0.042633056640625,
0.02178955078125,
0.003753662109375,
-0.03448486328125,
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0.0140838623046875,
-0.05474853515625,
-0.049774169921875,
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-0... |
YakovElm/MariaDB5Classic_64 | 2023-05-27T07:47:05.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/MariaDB5Classic_64 | 0 | 2 | transformers | 2023-05-27T07:46:25 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: MariaDB5Classic_64
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_64
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: 0.2438
- Train Accuracy: 0.9004
- Validation Loss: 0.2560
- Validation Accuracy: 0.9271
- 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 |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.3297 | 0.8921 | 0.2584 | 0.9322 | 0 |
| 0.2592 | 0.9079 | 0.2489 | 0.9271 | 1 |
| 0.2438 | 0.9004 | 0.2560 | 0.9271 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,780 | [
[
-0.043792724609375,
-0.04180908203125,
0.021209716796875,
0.002925872802734375,
-0.033843994140625,
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0.014068603515625,
0.01491546630859375,
-0.0550537109375,
-0.050048828125,
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-0.... |
YakovElm/MariaDB10Classic_64 | 2023-05-27T07:55:55.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/MariaDB10Classic_64 | 0 | 2 | transformers | 2023-05-27T07:55:18 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: MariaDB10Classic_64
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_64
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: 0.1941
- Train Accuracy: 0.9339
- Validation Loss: 0.1951
- Validation Accuracy: 0.9472
- 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 | 0.9004 | 0.1959 | 0.9523 | 0 |
| 0.2384 | 0.9155 | 0.1959 | 0.9472 | 1 |
| 0.1941 | 0.9339 | 0.1951 | 0.9472 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,782 | [
[
-0.042877197265625,
-0.04388427734375,
0.021209716796875,
0.004474639892578125,
-0.037017822265625,
-0.0305023193359375,
-0.0158538818359375,
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0.0164337158203125,
0.01447296142578125,
-0.053924560546875,
-0.047821044921875,
-0.052154541015625,... |
YakovElm/MariaDB15Classic_64 | 2023-05-27T08:06:04.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/MariaDB15Classic_64 | 0 | 2 | transformers | 2023-05-27T08:05:30 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: MariaDB15Classic_64
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_64
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: 0.1915
- Train Accuracy: 0.9381
- Validation Loss: 0.1826
- Validation Accuracy: 0.9372
- 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 |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.2834 | 0.9096 | 0.1811 | 0.9598 | 0 |
| 0.2120 | 0.9238 | 0.1664 | 0.9598 | 1 |
| 0.1915 | 0.9381 | 0.1826 | 0.9372 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,782 | [
[
-0.043212890625,
-0.042999267578125,
0.020904541015625,
0.0045013427734375,
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-0.048736572265625,
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-... |
YakovElm/MariaDB10Classic_256 | 2023-05-27T08:09:44.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/MariaDB10Classic_256 | 0 | 2 | transformers | 2023-05-27T08:09:08 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: MariaDB10Classic_256
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_256
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: 0.2336
- Train Accuracy: 0.9188
- Validation Loss: 0.1912
- 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 |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.3205 | 0.8996 | 0.1897 | 0.9523 | 0 |
| 0.2709 | 0.9163 | 0.1853 | 0.9523 | 1 |
| 0.2336 | 0.9188 | 0.1912 | 0.9523 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,784 | [
[
-0.042816162109375,
-0.042633056640625,
0.0207366943359375,
0.0038814544677734375,
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0.01436614990234375,
-0.05548095703125,
-0.04864501953125,
-0.052276611328125... |
YakovElm/MariaDB20Classic_64 | 2023-05-27T08:17:03.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/MariaDB20Classic_64 | 0 | 2 | transformers | 2023-05-27T08:16:24 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: MariaDB20Classic_64
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_64
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: 0.2044
- Train Accuracy: 0.9364
- Validation Loss: 0.1367
- 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 |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.3003 | 0.9121 | 0.1490 | 0.9698 | 0 |
| 0.2201 | 0.9356 | 0.1322 | 0.9698 | 1 |
| 0.2044 | 0.9364 | 0.1367 | 0.9698 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,782 | [
[
-0.043304443359375,
-0.04327392578125,
0.0214080810546875,
0.0035552978515625,
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0.0150604248046875,
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-0.049835205078125,
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-0... |
YakovElm/Qt5Classic_32 | 2023-05-27T08:34:58.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/Qt5Classic_32 | 0 | 2 | transformers | 2023-05-27T08:34:21 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Qt5Classic_32
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_32
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: 0.2765
- Train Accuracy: 0.8953
- Validation Loss: 0.2633
- 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 |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.3389 | 0.8937 | 0.2566 | 0.9294 | 0 |
| 0.3223 | 0.8943 | 0.2479 | 0.9294 | 1 |
| 0.2765 | 0.8953 | 0.2633 | 0.9294 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,770 | [
[
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0.022308349609375,
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-0.0504150390625,
... |
YakovElm/Qt10Classic_32 | 2023-05-27T08:52:28.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/Qt10Classic_32 | 0 | 2 | transformers | 2023-05-27T08:51:48 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Qt10Classic_32
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_32
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: 0.2267
- Train Accuracy: 0.9208
- Validation Loss: 0.2144
- 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 |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.2754 | 0.9202 | 0.2156 | 0.9416 | 0 |
| 0.2484 | 0.9210 | 0.2215 | 0.9416 | 1 |
| 0.2267 | 0.9208 | 0.2144 | 0.9416 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,772 | [
[
-0.040863037109375,
-0.036285400390625,
0.0227203369140625,
0.003582000732421875,
-0.034393310546875,
-0.027008056640625,
-0.0131072998046875,
-0.0225372314453125,
0.00908660888671875,
0.012542724609375,
-0.052978515625,
-0.04876708984375,
-0.050628662109375,
... |
YakovElm/Qt15Classic_32 | 2023-05-27T09:10:35.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/Qt15Classic_32 | 0 | 2 | transformers | 2023-05-27T09:10:01 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Qt15Classic_32
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_32
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: 0.2029
- Train Accuracy: 0.9370
- Validation Loss: 0.2012
- 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 |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.2402 | 0.9354 | 0.1920 | 0.9505 | 0 |
| 0.2261 | 0.9367 | 0.1922 | 0.9505 | 1 |
| 0.2029 | 0.9370 | 0.2012 | 0.9505 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,772 | [
[
-0.041107177734375,
-0.03924560546875,
0.0211334228515625,
0.00501251220703125,
-0.03704833984375,
-0.0283355712890625,
-0.0138092041015625,
-0.0231475830078125,
0.01074981689453125,
0.013092041015625,
-0.05377197265625,
-0.048065185546875,
-0.051544189453125,
... |
YakovElm/Apache20Classic_512 | 2023-05-27T09:16:59.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/Apache20Classic_512 | 0 | 2 | transformers | 2023-05-27T09:16:23 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Apache20Classic_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. -->
# Apache20Classic_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: 0.1347
- Train Accuracy: 0.9624
- Validation Loss: 0.3465
- Validation Accuracy: 0.9042
- 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 |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.1676 | 0.9622 | 0.3358 | 0.9055 | 0 |
| 0.1498 | 0.9624 | 0.3097 | 0.9055 | 1 |
| 0.1347 | 0.9624 | 0.3465 | 0.9042 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,782 | [
[
-0.045013427734375,
-0.044830322265625,
0.0204925537109375,
0.0063018798828125,
-0.03436279296875,
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-0.018341064453125,
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0.011444091796875,
0.01355743408203125,
-0.05419921875,
-0.04779052734375,
-0.052978515625,
-0.0243... |
YakovElm/Qt20Classic_32 | 2023-05-27T09:29:05.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/Qt20Classic_32 | 0 | 2 | transformers | 2023-05-27T09:28:31 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Qt20Classic_32
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_32
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: 0.1986
- Train Accuracy: 0.9462
- Validation Loss: 0.1705
- 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 |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.2402 | 0.9365 | 0.1717 | 0.9586 | 0 |
| 0.2074 | 0.9462 | 0.1663 | 0.9586 | 1 |
| 0.1986 | 0.9462 | 0.1705 | 0.9586 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,772 | [
[
-0.040069580078125,
-0.036376953125,
0.022430419921875,
0.0050201416015625,
-0.037750244140625,
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-0.0119171142578125,
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0.0083465576171875,
0.012451171875,
-0.054931640625,
-0.04840087890625,
-0.04974365234375,
-0.0271453857... |
YakovElm/Qt5Classic_64 | 2023-05-27T10:01:26.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/Qt5Classic_64 | 0 | 2 | transformers | 2023-05-27T10:00:49 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Qt5Classic_64
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_64
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: 0.2960
- Train Accuracy: 0.8918
- Validation Loss: 0.2467
- Validation Accuracy: 0.9303
- 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 |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.3437 | 0.8889 | 0.2451 | 0.9294 | 0 |
| 0.3214 | 0.8943 | 0.2529 | 0.9294 | 1 |
| 0.2960 | 0.8918 | 0.2467 | 0.9303 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,770 | [
[
-0.04132080078125,
-0.034820556640625,
0.0228424072265625,
0.0015993118286132812,
-0.035400390625,
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-0.011962890625,
-0.023712158203125,
0.005939483642578125,
0.01322174072265625,
-0.053436279296875,
-0.050140380859375,
-0.0498046875,
-0.025... |
YakovElm/MariaDB15Classic_256 | 2023-05-27T10:03:34.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/MariaDB15Classic_256 | 0 | 2 | transformers | 2023-05-27T10:02:58 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: MariaDB15Classic_256
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_256
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: 0.1888
- Train Accuracy: 0.9364
- Validation Loss: 0.1602
- 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 |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.2885 | 0.9163 | 0.1635 | 0.9598 | 0 |
| 0.2145 | 0.9297 | 0.1719 | 0.9598 | 1 |
| 0.1888 | 0.9364 | 0.1602 | 0.9598 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,784 | [
[
-0.04327392578125,
-0.042999267578125,
0.0209808349609375,
0.0037841796875,
-0.03509521484375,
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0.015777587890625,
0.01373291015625,
-0.0550537109375,
-0.04876708984375,
-0.05169677734375,
-0.025... |
YakovElm/Qt10Classic_64 | 2023-05-27T10:31:07.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/Qt10Classic_64 | 0 | 2 | transformers | 2023-05-27T10:30:34 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Qt10Classic_64
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_64
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: 0.2489
- Train Accuracy: 0.9210
- Validation Loss: 0.2184
- 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 |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.2887 | 0.9191 | 0.2186 | 0.9416 | 0 |
| 0.2710 | 0.9210 | 0.2124 | 0.9416 | 1 |
| 0.2489 | 0.9210 | 0.2184 | 0.9416 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,772 | [
[
-0.040435791015625,
-0.035675048828125,
0.0223388671875,
0.002361297607421875,
-0.034576416015625,
-0.02655029296875,
-0.01242828369140625,
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0.008270263671875,
0.01293182373046875,
-0.05267333984375,
-0.048248291015625,
-0.050018310546875,
... |
YakovElm/Qt15Classic_64 | 2023-05-27T11:01:23.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/Qt15Classic_64 | 0 | 2 | transformers | 2023-05-27T11:00:50 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Qt15Classic_64
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_64
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: 0.9367
- Validation Loss: 0.1941
- 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 |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.2461 | 0.9332 | 0.1899 | 0.9505 | 0 |
| 0.2245 | 0.9367 | 0.1855 | 0.9505 | 1 |
| 0.2040 | 0.9367 | 0.1941 | 0.9505 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,772 | [
[
-0.04132080078125,
-0.0396728515625,
0.021453857421875,
0.004673004150390625,
-0.036834716796875,
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0.01013946533203125,
0.01348114013671875,
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... |
YakovElm/Qt20Classic_64 | 2023-05-27T11:30:50.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/Qt20Classic_64 | 0 | 2 | transformers | 2023-05-27T11:29:55 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Qt20Classic_64
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_64
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: 0.1783
- Train Accuracy: 0.9465
- Validation Loss: 0.1657
- 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 |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.2235 | 0.9440 | 0.1706 | 0.9586 | 0 |
| 0.2009 | 0.9462 | 0.1646 | 0.9586 | 1 |
| 0.1783 | 0.9465 | 0.1657 | 0.9586 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,772 | [
[
-0.0396728515625,
-0.036865234375,
0.02166748046875,
0.0038089752197265625,
-0.03802490234375,
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0.012237548828125,
-0.054718017578125,
-0.049041748046875,
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-... |
YakovElm/MariaDB20Classic_256 | 2023-05-27T11:57:20.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/MariaDB20Classic_256 | 0 | 2 | transformers | 2023-05-27T11:56:44 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: MariaDB20Classic_256
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_256
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: 0.1850
- Train Accuracy: 0.9322
- Validation Loss: 0.1319
- 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 |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.2456 | 0.9356 | 0.1422 | 0.9698 | 0 |
| 0.2090 | 0.9356 | 0.1346 | 0.9698 | 1 |
| 0.1850 | 0.9322 | 0.1319 | 0.9698 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,784 | [
[
-0.04400634765625,
-0.0428466796875,
0.0221099853515625,
0.0026416778564453125,
-0.03350830078125,
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0.0154266357421875,
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myasa/distilbert-base-uncased-finetuned-emotion | 2023-05-27T13:45:46.000Z | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | text-classification | myasa | null | null | myasa/distilbert-base-uncased-finetuned-emotion | 0 | 2 | transformers | 2023-05-27T12:58:31 | ---
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: 0.926
- name: F1
type: f1
value: 0.926001422605883
---
<!-- 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: 0.2124
- Accuracy: 0.926
- F1: 0.9260
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8123 | 1.0 | 250 | 0.2922 | 0.909 | 0.9067 |
| 0.2351 | 2.0 | 500 | 0.2124 | 0.926 | 0.9260 |
### Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1+cu118
- Datasets 2.8.0
- Tokenizers 0.13.3
| 1,844 | [
[
-0.037994384765625,
-0.0416259765625,
0.015380859375,
0.0217742919921875,
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0.01023101806640625,
0.00830841064453125,
-0.05584716796875,
-0.05120849609375,
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-0... |
Inhaexpress/DialoGPT-medium-harrypotter | 2023-05-27T14:21:57.000Z | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | conversational | Inhaexpress | null | null | Inhaexpress/DialoGPT-medium-harrypotter | 1 | 2 | transformers | 2023-05-27T14:15:02 | ---
tags:
- conversational
---
# Harry Potter DialoGPT Model
# He doesn't want to be Harry for some reason | 107 | [
[
-0.029754638671875,
-0.0390625,
0.013671875,
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0.01033782958984375,
0.00878143310546875,
0.0321044921875,
0.034088134765625,
-0.049041748046875,
0.0027332305908203125,
-0.0192413330078125,
0.0344848... |
ShayDuane/distilbert-base-uncased-finetuned-imdb | 2023-05-27T15:10:54.000Z | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"feature-extraction",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | feature-extraction | ShayDuane | null | null | ShayDuane/distilbert-base-uncased-finetuned-imdb | 1 | 2 | transformers | 2023-05-27T14:57:08 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
model-index:
- name: distilbert-base-uncased-finetuned-imdb
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-base-uncased-finetuned-imdb
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 2.4336
## 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
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.5803 | 1.0 | 1250 | 2.5043 |
| 2.4534 | 2.0 | 2500 | 2.4634 |
| 2.4564 | 3.0 | 3750 | 2.4336 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,466 | [
[
-0.04205322265625,
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0.007633209228515625,
0.006694793701171875,
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... |
jonastokoliu/text_cls_bert-base-uncased_imdb_finetune | 2023-06-09T10:12:51.000Z | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | text-classification | jonastokoliu | null | null | jonastokoliu/text_cls_bert-base-uncased_imdb_finetune | 0 | 2 | transformers | 2023-05-27T16:11:59 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
metrics:
- accuracy
model-index:
- name: text_cls_bert-base-uncased_imdb_finetune
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: imdb
type: imdb
config: plain_text
split: test
args: plain_text
metrics:
- name: Accuracy
type: accuracy
value: 0.93672
---
<!-- 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. -->
# text_cls_bert-base-uncased_imdb_finetune
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1784
- Accuracy: 0.9367
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 391 | 0.1758 | 0.9346 |
| 0.2319 | 2.0 | 782 | 0.1784 | 0.9367 |
### Framework versions
- Transformers 4.30.0
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,711 | [
[
-0.0404052734375,
-0.042449951171875,
0.007663726806640625,
0.006938934326171875,
-0.036102294921875,
-0.0273590087890625,
-0.01502227783203125,
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0.0140380859375,
0.0277099609375,
-0.058929443359375,
-0.0347900390625,
-0.051361083984375,
... |
HasinMDG/distil_roberta_SD_country | 2023-05-27T16:36:46.000Z | [
"sentence-transformers",
"pytorch",
"roberta",
"setfit",
"text-classification",
"arxiv:2209.11055",
"license:apache-2.0",
"region:us"
] | text-classification | HasinMDG | null | null | HasinMDG/distil_roberta_SD_country | 0 | 2 | sentence-transformers | 2023-05-27T16:36:34 | ---
license: apache-2.0
tags:
- setfit
- sentence-transformers
- text-classification
pipeline_tag: text-classification
---
# HasinMDG/distil_roberta_SD_country
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.
## Usage
To use this model for inference, first install the SetFit library:
```bash
python -m pip install setfit
```
You can then run inference as follows:
```python
from setfit import SetFitModel
# Download from Hub and run inference
model = SetFitModel.from_pretrained("HasinMDG/distil_roberta_SD_country")
# Run inference
preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"])
```
## BibTeX entry and citation info
```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}
}
```
| 1,557 | [
[
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0.039306640625,
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HasinMDG/distil_roberta_SD_government | 2023-05-27T16:47:53.000Z | [
"sentence-transformers",
"pytorch",
"roberta",
"setfit",
"text-classification",
"arxiv:2209.11055",
"license:apache-2.0",
"region:us"
] | text-classification | HasinMDG | null | null | HasinMDG/distil_roberta_SD_government | 0 | 2 | sentence-transformers | 2023-05-27T16:47:41 | ---
license: apache-2.0
tags:
- setfit
- sentence-transformers
- text-classification
pipeline_tag: text-classification
---
# HasinMDG/distil_roberta_SD_government
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.
## Usage
To use this model for inference, first install the SetFit library:
```bash
python -m pip install setfit
```
You can then run inference as follows:
```python
from setfit import SetFitModel
# Download from Hub and run inference
model = SetFitModel.from_pretrained("HasinMDG/distil_roberta_SD_government")
# Run inference
preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"])
```
## BibTeX entry and citation info
```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}
}
```
| 1,563 | [
[
-0.006591796875,
-0.0604248046875,
0.0369873046875,
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0.0421142578125,
-0.037109375,
-0.0258941650390625,
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0.0... |
HasinMDG/distil_roberta_SD_company | 2023-05-27T16:58:27.000Z | [
"sentence-transformers",
"pytorch",
"roberta",
"setfit",
"text-classification",
"arxiv:2209.11055",
"license:apache-2.0",
"region:us"
] | text-classification | HasinMDG | null | null | HasinMDG/distil_roberta_SD_company | 0 | 2 | sentence-transformers | 2023-05-27T16:58:15 | ---
license: apache-2.0
tags:
- setfit
- sentence-transformers
- text-classification
pipeline_tag: text-classification
---
# HasinMDG/distil_roberta_SD_company
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.
## Usage
To use this model for inference, first install the SetFit library:
```bash
python -m pip install setfit
```
You can then run inference as follows:
```python
from setfit import SetFitModel
# Download from Hub and run inference
model = SetFitModel.from_pretrained("HasinMDG/distil_roberta_SD_company")
# Run inference
preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"])
```
## BibTeX entry and citation info
```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}
}
```
| 1,557 | [
[
-0.00217437744140625,
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0.03082275390625,
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0.03851318359375,
-0.043548583984375,
-0.02325439453125,
-0.043121337890... |
YakovElm/Qt5Classic_256 | 2023-05-27T17:37:56.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/Qt5Classic_256 | 0 | 2 | transformers | 2023-05-27T17:37:19 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Qt5Classic_256
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_256
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: 0.2872
- Train Accuracy: 0.8943
- Validation Loss: 0.2604
- Validation Accuracy: 0.9278
- 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 |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.3403 | 0.8943 | 0.2470 | 0.9294 | 0 |
| 0.3156 | 0.8943 | 0.2504 | 0.9294 | 1 |
| 0.2872 | 0.8943 | 0.2604 | 0.9278 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,772 | [
[
-0.041290283203125,
-0.034698486328125,
0.023345947265625,
0.0013513565063476562,
-0.035308837890625,
-0.0251922607421875,
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0.006374359130859375,
0.01197052001953125,
-0.053985595703125,
-0.050079345703125,
-0.0487976074... |
adityavelusamy/Questy-v1 | 2023-05-27T18:56:44.000Z | [
"transformers",
"pytorch",
"safetensors",
"t5",
"text2text-generation",
"autotrain",
"summarization",
"unk",
"dataset:adityavelusamy/autotrain-data-f",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | summarization | adityavelusamy | null | null | adityavelusamy/Questy-v1 | 0 | 2 | transformers | 2023-05-27T18:48:37 | ---
tags:
- autotrain
- summarization
language:
- unk
widget:
- text: "I love AutoTrain"
datasets:
- adityavelusamy/autotrain-data-f
co2_eq_emissions:
emissions: 0.5793683469903973
---
# Model Trained Using AutoTrain
- Problem type: Summarization
- Model ID: 62230135023
- CO2 Emissions (in grams): 0.5794
## Validation Metrics
- Loss: 0.883
- Rouge1: 52.493
- Rouge2: 33.950
- RougeL: 47.184
- RougeLsum: 47.225
- Gen Len: 15.493
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_HUGGINGFACE_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/adityavelusamy/autotrain-f-62230135023
``` | 710 | [
[
-0.033416748046875,
-0.031585693359375,
0.0271759033203125,
0.01486968994140625,
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-0.06201171875,
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... |
HasinMDG/masked_distil_roberta_SD_country | 2023-05-27T18:55:54.000Z | [
"sentence-transformers",
"pytorch",
"roberta",
"setfit",
"text-classification",
"arxiv:2209.11055",
"license:apache-2.0",
"region:us"
] | text-classification | HasinMDG | null | null | HasinMDG/masked_distil_roberta_SD_country | 0 | 2 | sentence-transformers | 2023-05-27T18:55:42 | ---
license: apache-2.0
tags:
- setfit
- sentence-transformers
- text-classification
pipeline_tag: text-classification
---
# HasinMDG/masked_distil_roberta_SD_country
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.
## Usage
To use this model for inference, first install the SetFit library:
```bash
python -m pip install setfit
```
You can then run inference as follows:
```python
from setfit import SetFitModel
# Download from Hub and run inference
model = SetFitModel.from_pretrained("HasinMDG/masked_distil_roberta_SD_country")
# Run inference
preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"])
```
## BibTeX entry and citation info
```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}
}
```
| 1,571 | [
[
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0.004291534423828125,
0.0438232421875,
-0.042236328125,
-0.031494140625,
-0.052459716796875,... |
HasinMDG/masked_distil_roberta_SD_government | 2023-05-27T19:07:40.000Z | [
"sentence-transformers",
"pytorch",
"roberta",
"setfit",
"text-classification",
"arxiv:2209.11055",
"license:apache-2.0",
"region:us"
] | text-classification | HasinMDG | null | null | HasinMDG/masked_distil_roberta_SD_government | 0 | 2 | sentence-transformers | 2023-05-27T19:07:28 | ---
license: apache-2.0
tags:
- setfit
- sentence-transformers
- text-classification
pipeline_tag: text-classification
---
# HasinMDG/masked_distil_roberta_SD_government
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.
## Usage
To use this model for inference, first install the SetFit library:
```bash
python -m pip install setfit
```
You can then run inference as follows:
```python
from setfit import SetFitModel
# Download from Hub and run inference
model = SetFitModel.from_pretrained("HasinMDG/masked_distil_roberta_SD_government")
# Run inference
preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"])
```
## BibTeX entry and citation info
```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}
}
```
| 1,577 | [
[
-0.0102081298828125,
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0.0338134765625,
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-0.0166015625,
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0.002460479736328125,
0.0458984375,
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-0.030059814453125,
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0.01... |
HasinMDG/masked_distil_roberta_SD_company | 2023-05-27T19:18:44.000Z | [
"sentence-transformers",
"pytorch",
"roberta",
"setfit",
"text-classification",
"arxiv:2209.11055",
"license:apache-2.0",
"region:us"
] | text-classification | HasinMDG | null | null | HasinMDG/masked_distil_roberta_SD_company | 0 | 2 | sentence-transformers | 2023-05-27T19:18:32 | ---
license: apache-2.0
tags:
- setfit
- sentence-transformers
- text-classification
pipeline_tag: text-classification
---
# HasinMDG/masked_distil_roberta_SD_company
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.
## Usage
To use this model for inference, first install the SetFit library:
```bash
python -m pip install setfit
```
You can then run inference as follows:
```python
from setfit import SetFitModel
# Download from Hub and run inference
model = SetFitModel.from_pretrained("HasinMDG/masked_distil_roberta_SD_company")
# Run inference
preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"])
```
## BibTeX entry and citation info
```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}
}
```
| 1,571 | [
[
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0.0028896331787109375,
0.0426025390625,
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-0.0484313964843... |
YakovElm/Hyperledger5Classic_512 | 2023-05-27T19:38:29.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/Hyperledger5Classic_512 | 0 | 2 | transformers | 2023-05-27T19:37:53 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Hyperledger5Classic_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. -->
# Hyperledger5Classic_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: 0.3034
- Train Accuracy: 0.8744
- Validation Loss: 0.4265
- Validation Accuracy: 0.8185
- 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 |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.4068 | 0.8537 | 0.4270 | 0.8361 | 0 |
| 0.3760 | 0.8537 | 0.4053 | 0.8361 | 1 |
| 0.3034 | 0.8744 | 0.4265 | 0.8185 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,790 | [
[
-0.048614501953125,
-0.03778076171875,
0.022003173828125,
0.0026836395263671875,
-0.029693603515625,
-0.02618408203125,
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0.01129913330078125,
0.0136871337890625,
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... |
m33393/llama-65b-gptq-cuda-4bit-32g-safetensors | 2023-05-30T04:17:26.000Z | [
"transformers",
"llama",
"text-generation",
"safetensors",
"license:other",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | m33393 | null | null | m33393/llama-65b-gptq-cuda-4bit-32g-safetensors | 2 | 2 | transformers | 2023-05-27T21:30:36 | ---
license: other
library_name: transformers
tags:
- safetensors
- llama
---
Converted to HF with `transformers 4.30.0.dev0`, then quantized to 4 bit with GPTQ (Group size `32`):
`python llama.py ../llama-65b-hf c4 --wbits 4 --true-sequential --act-order --groupsize 32 --save_safetensors 4bit-32g.safetensors`
PPL should be marginally better than group size 128 at the cost of more VRAM. An A6000 should still be able to fit it all at full 2048 context.
---
Note that this model was quantized under GPTQ's `cuda` branch. Which means it should work with 0cc4m's KoboldAI fork:
https://github.com/0cc4m/KoboldAI | 614 | [
[
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0.037445068359375,
0.021240234375,
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0.01812744140625,
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0.0408935546875,
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-0.0243072509765625,
... |
YakovElm/Qt10Classic_256 | 2023-05-27T23:26:01.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/Qt10Classic_256 | 0 | 2 | transformers | 2023-05-27T23:25:24 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Qt10Classic_256
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_256
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: 0.2176
- Train Accuracy: 0.9205
- Validation Loss: 0.2088
- 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 |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.2773 | 0.9208 | 0.2342 | 0.9416 | 0 |
| 0.2556 | 0.9210 | 0.2074 | 0.9416 | 1 |
| 0.2176 | 0.9205 | 0.2088 | 0.9416 | 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... |
indigorange/dqn-SpaceInvadersNoFrameskip-v4 | 2023-05-28T04:35:59.000Z | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | indigorange | null | null | indigorange/dqn-SpaceInvadersNoFrameskip-v4 | 0 | 2 | stable-baselines3 | 2023-05-28T04:35:23 | ---
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: 645.50 +/- 137.41
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 indigorange -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 indigorange -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 indigorange
```
## 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)])
```
| 2,700 | [
[
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YakovElm/Qt15Classic_256 | 2023-05-28T05:13:17.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/Qt15Classic_256 | 0 | 2 | transformers | 2023-05-28T05:12:41 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Qt15Classic_256
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_256
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: 0.2023
- Train Accuracy: 0.9373
- Validation Loss: 0.2062
- Validation Accuracy: 0.9465
- 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 |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.2403 | 0.9367 | 0.2023 | 0.9505 | 0 |
| 0.2233 | 0.9367 | 0.1936 | 0.9505 | 1 |
| 0.2023 | 0.9373 | 0.2062 | 0.9465 | 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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0.0221099853515625,
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-0.050567626953125,... |
HasinMDG/MLM_distilroberta_SD_government | 2023-05-28T05:21:27.000Z | [
"sentence-transformers",
"pytorch",
"roberta",
"setfit",
"text-classification",
"arxiv:2209.11055",
"license:apache-2.0",
"region:us"
] | text-classification | HasinMDG | null | null | HasinMDG/MLM_distilroberta_SD_government | 0 | 2 | sentence-transformers | 2023-05-28T05:21:15 | ---
license: apache-2.0
tags:
- setfit
- sentence-transformers
- text-classification
pipeline_tag: text-classification
---
# HasinMDG/MLM_distilroberta_SD_government
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.
## Usage
To use this model for inference, first install the SetFit library:
```bash
python -m pip install setfit
```
You can then run inference as follows:
```python
from setfit import SetFitModel
# Download from Hub and run inference
model = SetFitModel.from_pretrained("HasinMDG/MLM_distilroberta_SD_government")
# Run inference
preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"])
```
## BibTeX entry and citation info
```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}
}
```
| 1,569 | [
[
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0.0447998046875,
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HasinMDG/MLM_distilroberta_SD_company | 2023-05-28T05:30:18.000Z | [
"sentence-transformers",
"pytorch",
"roberta",
"setfit",
"text-classification",
"arxiv:2209.11055",
"license:apache-2.0",
"region:us"
] | text-classification | HasinMDG | null | null | HasinMDG/MLM_distilroberta_SD_company | 0 | 2 | sentence-transformers | 2023-05-28T05:30:06 | ---
license: apache-2.0
tags:
- setfit
- sentence-transformers
- text-classification
pipeline_tag: text-classification
---
# HasinMDG/MLM_distilroberta_SD_company
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.
## Usage
To use this model for inference, first install the SetFit library:
```bash
python -m pip install setfit
```
You can then run inference as follows:
```python
from setfit import SetFitModel
# Download from Hub and run inference
model = SetFitModel.from_pretrained("HasinMDG/MLM_distilroberta_SD_company")
# Run inference
preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"])
```
## BibTeX entry and citation info
```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}
}
```
| 1,563 | [
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-0.016845703125,
-0.0036029815673828125,
-0.004772186279296875,
0.04046630859375,
-0.048065185546875,
-0.0267181396484375,
-0.045227050... |
YakovElm/Hyperledger10Classic_512 | 2023-05-28T05:45:24.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/Hyperledger10Classic_512 | 0 | 2 | transformers | 2023-05-28T05:44:46 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Hyperledger10Classic_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. -->
# Hyperledger10Classic_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: 0.2833
- Train Accuracy: 0.8900
- Validation Loss: 0.3935
- Validation Accuracy: 0.8610
- 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 |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.3645 | 0.8731 | 0.3704 | 0.8600 | 0 |
| 0.3302 | 0.8838 | 0.3660 | 0.8600 | 1 |
| 0.2833 | 0.8900 | 0.3935 | 0.8610 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,792 | [
[
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0.0144500732421875,
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HasinMDG/distilroberta_SD_country_v2 | 2023-05-28T06:11:40.000Z | [
"sentence-transformers",
"pytorch",
"roberta",
"setfit",
"text-classification",
"arxiv:2209.11055",
"license:apache-2.0",
"region:us"
] | text-classification | HasinMDG | null | null | HasinMDG/distilroberta_SD_country_v2 | 0 | 2 | sentence-transformers | 2023-05-28T06:11:27 | ---
license: apache-2.0
tags:
- setfit
- sentence-transformers
- text-classification
pipeline_tag: text-classification
---
# HasinMDG/distilroberta_SD_country_v2
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.
## Usage
To use this model for inference, first install the SetFit library:
```bash
python -m pip install setfit
```
You can then run inference as follows:
```python
from setfit import SetFitModel
# Download from Hub and run inference
model = SetFitModel.from_pretrained("HasinMDG/distilroberta_SD_country_v2")
# Run inference
preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"])
```
## BibTeX entry and citation info
```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}
}
```
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HasinMDG/distilroberta_SD_government_v2 | 2023-05-28T06:23:03.000Z | [
"sentence-transformers",
"pytorch",
"roberta",
"setfit",
"text-classification",
"arxiv:2209.11055",
"license:apache-2.0",
"region:us"
] | text-classification | HasinMDG | null | null | HasinMDG/distilroberta_SD_government_v2 | 0 | 2 | sentence-transformers | 2023-05-28T06:22:52 | ---
license: apache-2.0
tags:
- setfit
- sentence-transformers
- text-classification
pipeline_tag: text-classification
---
# HasinMDG/distilroberta_SD_government_v2
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.
## Usage
To use this model for inference, first install the SetFit library:
```bash
python -m pip install setfit
```
You can then run inference as follows:
```python
from setfit import SetFitModel
# Download from Hub and run inference
model = SetFitModel.from_pretrained("HasinMDG/distilroberta_SD_government_v2")
# Run inference
preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"])
```
## BibTeX entry and citation info
```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}
}
```
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[
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BrainRoster/ppo-LunarLander-v2 | 2023-06-09T19:59:15.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | BrainRoster | null | null | BrainRoster/ppo-LunarLander-v2 | 0 | 2 | stable-baselines3 | 2023-05-28T06:49:41 | ---
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: 279.02 +/- 16.41
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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kyo-takano/open-calm-7b-8bit | 2023-05-28T11:41:05.000Z | [
"transformers",
"pytorch",
"gpt_neox",
"text-generation",
"japanese",
"causal-lm",
"quantized",
"ja",
"license:cc-by-sa-4.0",
"has_space",
"text-generation-inference",
"region:us"
] | text-generation | kyo-takano | null | null | kyo-takano/open-calm-7b-8bit | 10 | 2 | transformers | 2023-05-28T10:22:16 | ---
license: cc-by-sa-4.0
language:
- ja
tags:
- japanese
- causal-lm
- quantized
inference: false
---
# OpenCALM-7B - 8bit
[](https://colab.research.google.com/gist/kyo-takano/0c7bf0479158aa137e0ba935dec70461/opencalm-7b-8bit.ipynb)
8-bit quantized version of [OpenCALM-7B by CyberAgent (under CC BY-SA 4.0)](https://huggingface.co/cyberagent/open-calm-7b)
When using this quantized model, please be sure to give credit to the original.
## Setup
```sh
pip install -q -U bitsandbytes
pip install -q -U git+https://github.com/huggingface/transformers.git
pip install -q -U git+https://github.com/huggingface/accelerate.git
```
## Usage
```python
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
MODEL_ID = "kyo-takano/open-calm-7b-8bit"
model = AutoModelForCausalLM.from_pretrained(MODEL_ID)
tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
inputs = tokenizer("AIによって私達の暮らしは、", return_tensors="pt").to(model.device)
with torch.no_grad():
tokens = model.generate(
**inputs,
max_new_tokens=64,
do_sample=True,
temperature=0.7,
top_p=0.9,
repetition_penalty=1.05,
pad_token_id=tokenizer.pad_token_id,
)
output = tokenizer.decode(tokens[0], skip_special_tokens=True)
print(output)
```
## Model Details
- Developed by: CyberAgent, Inc.
- Quantized by: Kyo Takano
- Model type: Transformer-based Language Model
- Language: Japanese
- Library: GPT-NeoX
- License: OpenCALM is licensed under the Creative Commons Attribution-ShareAlike 4.0 International License (CC BY-SA 4.0). When using this model, please provide appropriate credit to **CyberAgent, Inc.**
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YakovElm/Qt20Classic_256 | 2023-05-28T11:01:53.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/Qt20Classic_256 | 0 | 2 | transformers | 2023-05-28T11:01:15 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Qt20Classic_256
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_256
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: 0.1866
- Train Accuracy: 0.9454
- Validation Loss: 0.1784
- 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 |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.2203 | 0.9383 | 0.1651 | 0.9586 | 0 |
| 0.2026 | 0.9462 | 0.1571 | 0.9586 | 1 |
| 0.1866 | 0.9454 | 0.1784 | 0.9586 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
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-0.0... |
YakovElm/Hyperledger15Classic_512 | 2023-05-28T15:51:00.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/Hyperledger15Classic_512 | 0 | 2 | transformers | 2023-05-28T15:50:25 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Hyperledger15Classic_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. -->
# Hyperledger15Classic_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: 0.2806
- Train Accuracy: 0.9035
- Validation Loss: 0.3198
- Validation Accuracy: 0.8807
- 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 |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.3217 | 0.8952 | 0.3253 | 0.8807 | 0 |
| 0.2967 | 0.9035 | 0.3233 | 0.8807 | 1 |
| 0.2806 | 0.9035 | 0.3198 | 0.8807 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
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Abhilashvj/CIRCL_website_classifier_test | 2023-05-28T16:44:04.000Z | [
"transformers",
"pytorch",
"resnet",
"image-classification",
"arxiv:1910.09700",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-classification | Abhilashvj | null | null | Abhilashvj/CIRCL_website_classifier_test | 0 | 2 | transformers | 2023-05-28T16:16:41 | ---
license: apache-2.0
pipeline_tag: image-classification
metrics:
- accuracy
- f1
---
# Model Card for Model ID
<!-- This model can be used to classify website screenshots. -->
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:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Data Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] | 5,253 | [
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Peraboom/SBertV1 | 2023-05-28T16:36:37.000Z | [
"transformers",
"pytorch",
"bert",
"text-classification",
"license:other",
"endpoints_compatible",
"region:us"
] | text-classification | Peraboom | null | null | Peraboom/SBertV1 | 1 | 2 | transformers | 2023-05-28T16:25:24 | ---
license: other
---
This is distilled model from Bert Base uncased. It has 6 layers, 6 heads and 384 hidden Size. It has 29.8M parameter. Performance wise, it has the potential of 87% performance of bert base with has 12 layers and 12 heads with 110M parameters. | 265 | [
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tonirodriguez/roberta-base-bne-finetuned-toxicity-tweets | 2023-05-28T18:36:49.000Z | [
"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 | 0 | 2 | transformers | 2023-05-28T16:45:52 | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: roberta-base-bne-finetuned-toxicity-tweets
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
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: 0.1345
- Accuracy: 0.9604
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.18 | 1.0 | 229 | 0.1270 | 0.9559 |
| 0.0508 | 2.0 | 458 | 0.1345 | 0.9604 |
### Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,454 | [
[
-0.0232696533203125,
-0.046600341796875,
0.0156707763671875,
0.00821685791015625,
-0.0245513916015625,
-0.038909912109375,
-0.00960540771484375,
-0.01274871826171875,
0.00946044921875,
0.032318115234375,
-0.04315185546875,
-0.05816650390625,
-0.05108642578125,
... |
theSOL1/kogrammar-base | 2023-06-08T11:52:16.000Z | [
"transformers",
"pytorch",
"safetensors",
"bart",
"text2text-generation",
"grammar",
"ko",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | theSOL1 | null | null | theSOL1/kogrammar-base | 1 | 2 | transformers | 2023-05-28T17:20:47 | ---
language: ko
license: mit
tags:
- bart
- grammar
---
# kogrammar-base
Dataset: 국립국어원 맞춤법 교정 말뭉치
<br>
<br>
Backbone Model: [kobart-base-v2](https://huggingface.co/gogamza/kobart-base-v2/blob/main/README.md)
<br>
GitHub Repo: [SOL1archive/KoGrammar](https://github.com/SOL1archive/KoGrammar)
## Train Method
전체 데이터셋 중 약 45%를 학습데이터로 활용하여 학습함.
## Metric
|BLEU-2|ROUGE-2 F1|
|-|-|
|77.8 %|55.0 %|
| 404 | [
[
-0.0212554931640625,
-0.0131072998046875,
0.0150909423828125,
0.038604736328125,
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0.0034542083740234375,
0.00319671630859375,
0.014373779296875,
0.039520263671875,
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-0.0452575683593... |
JoseVerutti/uao-distilroberta-base-mrpc-glue-verutti-benjumea-lopez | 2023-05-28T17:26:42.000Z | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | text-classification | JoseVerutti | null | null | JoseVerutti/uao-distilroberta-base-mrpc-glue-verutti-benjumea-lopez | 0 | 2 | transformers | 2023-05-28T17:23:23 | ---
license: apache-2.0
tags:
- text-classification
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
- f1
model-index:
- name: uao-distilroberta-base-mrpc-glue-verutti-benjumea-lopez
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: datasetX
type: glue
config: mrpc
split: validation
args: mrpc
metrics:
- name: Accuracy
type: accuracy
value: 0.821078431372549
- name: F1
type: f1
value: 0.8717047451669596
---
<!-- 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. -->
# uao-distilroberta-base-mrpc-glue-verutti-benjumea-lopez
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the datasetX dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5776
- Accuracy: 0.8211
- F1: 0.8717
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.5197 | 1.09 | 500 | 0.5776 | 0.8211 | 0.8717 |
| 0.35 | 2.18 | 1000 | 0.5931 | 0.8309 | 0.8752 |
### Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,891 | [
[
-0.0292205810546875,
-0.04046630859375,
0.00881195068359375,
0.019989013671875,
-0.0263671875,
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-0.006439208984375,
-0.00885009765625,
0.0005192756652832031,
0.0178985595703125,
-0.04412841796875,
-0.041748046875,
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0.000... |
JoseVerutti/uao-bert-base-uncased-mrpc-glue-verutti-benjumea-lopez | 2023-05-30T22:37:38.000Z | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | text-classification | JoseVerutti | null | null | JoseVerutti/uao-bert-base-uncased-mrpc-glue-verutti-benjumea-lopez | 0 | 2 | transformers | 2023-05-28T17:31:25 | ---
license: apache-2.0
tags:
- text-classification
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
- f1
model-index:
- name: uao-bert-base-uncased-mrpc-glue-verutti-benjumea-lopez
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: datasetX
type: glue
config: mrpc
split: validation
args: mrpc
metrics:
- name: Accuracy
type: accuracy
value: 0.8406862745098039
- name: F1
type: f1
value: 0.8853615520282188
---
<!-- 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. -->
# uao-bert-base-uncased-mrpc-glue-verutti-benjumea-lopez
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the datasetX dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6569
- Accuracy: 0.8407
- F1: 0.8854
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.5352 | 1.09 | 500 | 0.5610 | 0.8064 | 0.8587 |
| 0.3137 | 2.18 | 1000 | 0.6569 | 0.8407 | 0.8854 |
### Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,888 | [
[
-0.033233642578125,
-0.036041259765625,
0.01114654541015625,
0.01451873779296875,
-0.0274658203125,
-0.0287017822265625,
-0.0193023681640625,
-0.018218994140625,
0.00543212890625,
0.02740478515625,
-0.05181884765625,
-0.044342041015625,
-0.046783447265625,
-... |
YakovElm/IntelDAOS5Classic_512 | 2023-05-28T19:12:59.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/IntelDAOS5Classic_512 | 0 | 2 | transformers | 2023-05-28T19:12:26 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: IntelDAOS5Classic_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. -->
# IntelDAOS5Classic_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: 0.3745
- Train Accuracy: 0.8740
- Validation Loss: 0.4273
- Validation Accuracy: 0.8438
- 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 |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.3984 | 0.8710 | 0.4399 | 0.8438 | 0 |
| 0.3811 | 0.8740 | 0.4332 | 0.8438 | 1 |
| 0.3745 | 0.8740 | 0.4273 | 0.8438 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,786 | [
[
-0.04473876953125,
-0.039215087890625,
0.02130126953125,
0.0008177757263183594,
-0.03350830078125,
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0.0124359130859375,
0.0110015869140625,
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-0.048675537109375,
-0.051910400390625,
... |
YakovElm/MariaDB5Classic_512 | 2023-05-28T19:20:55.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/MariaDB5Classic_512 | 0 | 2 | transformers | 2023-05-28T19:20:21 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: MariaDB5Classic_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. -->
# MariaDB5Classic_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: 0.2565
- Train Accuracy: 0.9113
- Validation Loss: 0.2527
- Validation Accuracy: 0.9322
- 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 |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.3291 | 0.8795 | 0.2461 | 0.9322 | 0 |
| 0.2694 | 0.9063 | 0.2598 | 0.9296 | 1 |
| 0.2565 | 0.9113 | 0.2527 | 0.9322 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,782 | [
[
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0.01476287841796875,
0.01439666748046875,
-0.0552978515625,
-0.04998779296875,
-0.051544189453125,
-0... |
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