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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
alexcadillon/distilbert-base-uncased-finetuned-emotion | 2023-04-17T12:50:07.000Z | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | text-classification | alexcadillon | null | null | alexcadillon/distilbert-base-uncased-finetuned-emotion | 0 | 2 | transformers | 2023-04-17T12:44:01 | ---
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
args: split
metrics:
- name: Accuracy
type: accuracy
value: 0.924
- name: F1
type: f1
value: 0.9239280493153317
---
<!-- 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: 0.2236
- Accuracy: 0.924
- F1: 0.9239
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8552 | 1.0 | 250 | 0.3217 | 0.9045 | 0.9015 |
| 0.2578 | 2.0 | 500 | 0.2236 | 0.924 | 0.9239 |
### Framework versions
- Transformers 4.13.0
- Pytorch 2.0.0+cu118
- Datasets 2.8.0
- Tokenizers 0.10.3
| 1,801 | [
[
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ardaaras99/bert-fine-tuned-cola | 2023-04-17T14:48:30.000Z | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | text-classification | ardaaras99 | null | null | ardaaras99/bert-fine-tuned-cola | 0 | 2 | transformers | 2023-04-17T14:41:20 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- matthews_correlation
model-index:
- name: bert-fine-tuned-cola
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
config: cola
split: validation
args: cola
metrics:
- name: Matthews Correlation
type: matthews_correlation
value: 0.5676609066599885
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-fine-tuned-cola
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8305
- Matthews Correlation: 0.5677
## 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
### Training results
| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.4763 | 1.0 | 1069 | 0.6758 | 0.4428 |
| 0.3229 | 2.0 | 2138 | 0.7177 | 0.5708 |
| 0.1926 | 3.0 | 3207 | 0.8305 | 0.5677 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
| 1,840 | [
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prbocca/distilbert-base-cased-finetuned-squad2 | 2023-04-17T22:13:00.000Z | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:squad_v2",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | question-answering | prbocca | null | null | prbocca/distilbert-base-cased-finetuned-squad2 | 0 | 2 | transformers | 2023-04-17T14:59:44 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad_v2
model-index:
- name: distilbert-base-cased-finetuned-squad2
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-cased-finetuned-squad2
This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on the squad_v2 dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4343
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.6364 | 1.0 | 2532 | 1.4206 |
| 1.2676 | 2.0 | 5064 | 1.3960 |
| 0.9933 | 3.0 | 7596 | 1.4343 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
| 1,431 | [
[
-0.0275726318359375,
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0.0232391357421875,
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0.0189056396484375,
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manucos/test-beto-finetuned-ner | 2023-04-17T15:29:51.000Z | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | manucos | null | null | manucos/test-beto-finetuned-ner | 0 | 2 | transformers | 2023-04-17T15:13:57 | ---
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: test-beto-finetuned-ner
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. -->
# test-beto-finetuned-ner
This model is a fine-tuned version of [dccuchile/bert-base-spanish-wwm-cased](https://huggingface.co/dccuchile/bert-base-spanish-wwm-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4377
- Precision: 0.6861
- Recall: 0.7734
- F1: 0.7271
- Accuracy: 0.8798
## 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
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 206 | 0.4830 | 0.6042 | 0.6918 | 0.6450 | 0.8391 |
| No log | 2.0 | 412 | 0.4348 | 0.6597 | 0.7555 | 0.7044 | 0.8744 |
| 0.3666 | 3.0 | 618 | 0.4377 | 0.6861 | 0.7734 | 0.7271 | 0.8798 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
| 1,706 | [
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nouman-10/bert-base-multilingual-uncased_vaxxstance_spanish | 2023-04-17T15:42:57.000Z | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | nouman-10 | null | null | nouman-10/bert-base-multilingual-uncased_vaxxstance_spanish | 0 | 2 | transformers | 2023-04-17T15:25:49 | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: bert-base-multilingual-uncased_vaxxstance_spanish
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-multilingual-uncased_vaxxstance_spanish
This model is a fine-tuned version of [bert-base-multilingual-uncased](https://huggingface.co/bert-base-multilingual-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6466
- F1: 0.8026
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 1.0 | 126 | 0.6018 | 0.7622 |
| No log | 2.0 | 252 | 0.5443 | 0.7839 |
| No log | 3.0 | 378 | 0.5674 | 0.8055 |
| 0.5137 | 4.0 | 504 | 0.5841 | 0.7954 |
| 0.5137 | 5.0 | 630 | 0.6466 | 0.8026 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
| 1,640 | [
[
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nouman-10/bert-base-multilingual-cased_vaxxstance_spanish | 2023-04-17T15:53:18.000Z | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | nouman-10 | null | null | nouman-10/bert-base-multilingual-cased_vaxxstance_spanish | 0 | 2 | transformers | 2023-04-17T15:43:15 | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: bert-base-multilingual-cased_vaxxstance_spanish
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-multilingual-cased_vaxxstance_spanish
This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7085
- F1: 0.7853
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 1.0 | 126 | 0.6305 | 0.7723 |
| No log | 2.0 | 252 | 0.5441 | 0.7810 |
| No log | 3.0 | 378 | 0.5909 | 0.7954 |
| 0.5161 | 4.0 | 504 | 0.6339 | 0.7853 |
| 0.5161 | 5.0 | 630 | 0.7085 | 0.7853 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
| 1,632 | [
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nouman-10/bert-base-spanish-wwm-uncased_vaxxstance_spanish | 2023-04-17T17:20:26.000Z | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | text-classification | nouman-10 | null | null | nouman-10/bert-base-spanish-wwm-uncased_vaxxstance_spanish | 0 | 2 | transformers | 2023-04-17T16:15:30 | ---
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: bert-base-spanish-wwm-uncased_vaxxstance_spanish
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-spanish-wwm-uncased_vaxxstance_spanish
This model is a fine-tuned version of [dccuchile/bert-base-spanish-wwm-uncased](https://huggingface.co/dccuchile/bert-base-spanish-wwm-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7700
- F1: 0.8040
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 1.0 | 126 | 0.5468 | 0.7795 |
| No log | 2.0 | 252 | 0.5097 | 0.7925 |
| No log | 3.0 | 378 | 0.6515 | 0.7896 |
| 0.4078 | 4.0 | 504 | 0.7010 | 0.8012 |
| 0.4078 | 5.0 | 630 | 0.7700 | 0.8040 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
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nouman-10/bert-base-spanish-wwm-cased_vaxxstance_spanish | 2023-04-17T17:12:31.000Z | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | text-classification | nouman-10 | null | null | nouman-10/bert-base-spanish-wwm-cased_vaxxstance_spanish | 0 | 2 | transformers | 2023-04-17T17:04:50 | ---
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: bert-base-spanish-wwm-cased_vaxxstance_spanish
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-spanish-wwm-cased_vaxxstance_spanish
This model is a fine-tuned version of [dccuchile/bert-base-spanish-wwm-cased](https://huggingface.co/dccuchile/bert-base-spanish-wwm-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6970
- F1: 0.8156
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 1.0 | 126 | 0.5081 | 0.8012 |
| No log | 2.0 | 252 | 0.4909 | 0.8026 |
| No log | 3.0 | 378 | 0.5365 | 0.8084 |
| 0.4075 | 4.0 | 504 | 0.6336 | 0.8141 |
| 0.4075 | 5.0 | 630 | 0.6970 | 0.8156 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
| 1,628 | [
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nouman-10/distilbert-base-uncased_intent_classification | 2023-04-17T17:18:48.000Z | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | nouman-10 | null | null | nouman-10/distilbert-base-uncased_intent_classification | 0 | 2 | transformers | 2023-04-17T17:13:13 | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: distilbert-base-uncased_intent_classification
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_intent_classification
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 10.1998
- F1: 0.0560
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 1.0 | 312 | 7.5574 | 0.058 |
| 0.458 | 2.0 | 624 | 8.9497 | 0.0560 |
| 0.458 | 3.0 | 936 | 9.6656 | 0.0560 |
| 0.0848 | 4.0 | 1248 | 10.0615 | 0.058 |
| 0.0379 | 5.0 | 1560 | 10.1998 | 0.0560 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
| 1,617 | [
[
-0.022735595703125,
-0.037384033203125,
0.0179901123046875,
0.00780487060546875,
-0.0237274169921875,
-0.0292205810546875,
-0.00439453125,
-0.006866455078125,
0.0011548995971679688,
0.0264739990234375,
-0.049285888671875,
-0.052642822265625,
-0.060028076171875,
... |
nouman-10/distilbert-base-uncased_intent | 2023-04-17T17:27:59.000Z | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | nouman-10 | null | null | nouman-10/distilbert-base-uncased_intent | 0 | 2 | transformers | 2023-04-17T17:23:16 | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: distilbert-base-uncased_intent
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_intent
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 8.9202
- F1: 0.176
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:-----:|
| No log | 1.0 | 312 | 6.9264 | 0.168 |
| 0.3875 | 2.0 | 624 | 8.0481 | 0.172 |
| 0.3875 | 3.0 | 936 | 8.5746 | 0.176 |
| 0.057 | 4.0 | 1248 | 8.8202 | 0.176 |
| 0.0264 | 5.0 | 1560 | 8.9202 | 0.176 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
| 1,578 | [
[
-0.0289154052734375,
-0.042755126953125,
0.0180206298828125,
0.01361083984375,
-0.0293426513671875,
-0.029052734375,
-0.00574493408203125,
-0.0031795501708984375,
0.006702423095703125,
0.024139404296875,
-0.05548095703125,
-0.049102783203125,
-0.055145263671875,... |
tsumeone/vicuna-13B-1.1-GPTQ-4bit-128g-cuda | 2023-04-17T19:31:42.000Z | [
"transformers",
"pytorch",
"llama",
"text-generation",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | tsumeone | null | null | tsumeone/vicuna-13B-1.1-GPTQ-4bit-128g-cuda | 5 | 2 | transformers | 2023-04-17T18:57:37 | ---
library_name: transformers
pipeline_tag: text-generation
---
Quant of https://huggingface.co/TheBloke/vicuna-13B-1.1-HF
There's already one located at https://huggingface.co/TheBloke/vicuna-13B-1.1-GPTQ-4bit-128g, but neither version they uploaded works with certain older versions of GPTQ-for-LLaMA (such as 0cc4m's fork that is used with their fork of KoboldAI).
This was quantized with 0cc4m's fork of GPTQ-for-LLaMA.
```python llama.py ./vicuna-13B-1.1-HF c4 --wbits 4 --true-sequential --groupsize 128 --save_safetensors 4bit-128g.safetensors``` | 557 | [
[
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0.047210693359375,
-0.043701171875,
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0.0253448486328125,
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0.0511474609375,
0.0221710205078125,
-0.048858642578125,
-0.031494140625,
-0.019989013671875,
0.02093505... |
thackerhelik/dqn-SpaceInvadersNoFrameskip-v4 | 2023-04-17T19:59:35.000Z | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | thackerhelik | null | null | thackerhelik/dqn-SpaceInvadersNoFrameskip-v4 | 0 | 2 | stable-baselines3 | 2023-04-17T19:59:04 | ---
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: 410.00 +/- 198.05
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 thackerhelik -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 thackerhelik -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 thackerhelik
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 10000),
('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', 10000),
('n_timesteps', 1000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
| 2,701 | [
[
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0.023834228515625,
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0.01346588134765625,
0.0244140625,
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-0... |
GhifSmile/distilbert-base-uncased-PINA-dfnew-insyaallah | 2023-04-18T00:13:11.000Z | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | GhifSmile | null | null | GhifSmile/distilbert-base-uncased-PINA-dfnew-insyaallah | 0 | 2 | transformers | 2023-04-17T20:52:20 | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
model-index:
- name: distilbert-base-uncased-PINA-dfnew-insyaallah
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-PINA-dfnew-insyaallah
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2680
- Accuracy: 0.9431
- Precision: 0.8480
- Recall: 0.8258
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:---------:|:------:|
| 1.1591 | 1.0 | 1436 | 0.4581 | 0.8945 | 0.7871 | 0.7185 |
| 0.3058 | 2.0 | 2872 | 0.2901 | 0.9349 | 0.8307 | 0.8157 |
| 0.1623 | 3.0 | 4308 | 0.2680 | 0.9431 | 0.8480 | 0.8258 |
| 0.0936 | 4.0 | 5744 | 0.2942 | 0.9474 | 0.8758 | 0.8415 |
| 0.0562 | 5.0 | 7180 | 0.2681 | 0.9535 | 0.8730 | 0.8527 |
| 0.034 | 6.0 | 8616 | 0.3010 | 0.9504 | 0.8761 | 0.8474 |
| 0.0193 | 7.0 | 10052 | 0.2971 | 0.9532 | 0.8643 | 0.8507 |
| 0.0115 | 8.0 | 11488 | 0.3139 | 0.9519 | 0.8640 | 0.8489 |
| 0.0078 | 9.0 | 12924 | 0.3056 | 0.9551 | 0.8649 | 0.8529 |
| 0.0056 | 10.0 | 14360 | 0.3062 | 0.9549 | 0.8636 | 0.8531 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
| 2,275 | [
[
-0.03619384765625,
-0.04119873046875,
0.0161590576171875,
0.0134429931640625,
-0.019805908203125,
-0.0149688720703125,
-0.00029277801513671875,
-0.0031604766845703125,
0.0225830078125,
0.0189666748046875,
-0.046417236328125,
-0.04974365234375,
-0.055633544921875... |
Muhsabrys/autotrain-xlmroberta-iuexist-50302120401 | 2023-04-17T21:28:32.000Z | [
"transformers",
"pytorch",
"xlm-roberta",
"text-classification",
"autotrain",
"unk",
"dataset:Muhsabrys/autotrain-data-xlmroberta-iuexist",
"co2_eq_emissions",
"endpoints_compatible",
"region:us"
] | text-classification | Muhsabrys | null | null | Muhsabrys/autotrain-xlmroberta-iuexist-50302120401 | 0 | 2 | transformers | 2023-04-17T21:25:26 | ---
tags:
- autotrain
- text-classification
language:
- unk
widget:
- text: "I love AutoTrain 🤗"
datasets:
- Muhsabrys/autotrain-data-xlmroberta-iuexist
co2_eq_emissions:
emissions: 1.1811615672607385
---
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 50302120401
- CO2 Emissions (in grams): 1.1812
## Validation Metrics
- Loss: 0.637
- Accuracy: 0.772
- Macro F1: 0.541
- Micro F1: 0.772
- Weighted F1: 0.731
- Macro Precision: 0.514
- Micro Precision: 0.772
- Weighted Precision: 0.694
- Macro Recall: 0.571
- Micro Recall: 0.772
- Weighted Recall: 0.772
## 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/Muhsabrys/autotrain-xlmroberta-iuexist-50302120401
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("Muhsabrys/autotrain-xlmroberta-iuexist-50302120401", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("Muhsabrys/autotrain-xlmroberta-iuexist-50302120401", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
``` | 1,325 | [
[
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0.01428985595703125,
0.007110595703125,
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0.007556915283203125,
-0.049591064453125,
-0.0330810546875,
-0.05770874... |
vocabtrimmer/xlm-roberta-base-trimmed-ar-xnli-ar | 2023-04-20T04:22:26.000Z | [
"transformers",
"pytorch",
"xlm-roberta",
"text-classification",
"endpoints_compatible",
"region:us"
] | text-classification | vocabtrimmer | null | null | vocabtrimmer/xlm-roberta-base-trimmed-ar-xnli-ar | 0 | 2 | transformers | 2023-04-17T22:55:27 | # `vocabtrimmer/xlm-roberta-base-trimmed-ar-xnli-ar`
This model is a fine-tuned version of [vocabtrimmer/xlm-roberta-base-trimmed-ar](https://huggingface.co/vocabtrimmer/xlm-roberta-base-trimmed-ar) on the
[xnli](https://huggingface.co/datasets/xnli) (ar).
Following metrics are computed on the `test` split of
[xnli](https://huggingface.co/datasets/xnli)(ar).
| | eval_f1_micro | eval_recall_micro | eval_precision_micro | eval_f1_macro | eval_recall_macro | eval_precision_macro | eval_accuracy |
|---:|----------------:|--------------------:|-----------------------:|----------------:|--------------------:|-----------------------:|----------------:|
| 0 | 73.79 | 73.79 | 73.79 | 73.75 | 73.79 | 75.06 | 73.79 |
Check the result file [here](https://huggingface.co/vocabtrimmer/xlm-roberta-base-trimmed-ar-xnli-ar/raw/main/eval.json). | 953 | [
[
-0.04052734375,
-0.033172607421875,
0.018585205078125,
-0.0120697021484375,
-0.0293121337890625,
0.0112762451171875,
-0.0135650634765625,
-0.0200653076171875,
0.037841796875,
0.046051025390625,
-0.05328369140625,
-0.061981201171875,
-0.042999267578125,
-0.00... |
vocabtrimmer/xlm-roberta-base-trimmed-de-xnli-de | 2023-04-20T09:33:26.000Z | [
"transformers",
"pytorch",
"xlm-roberta",
"text-classification",
"endpoints_compatible",
"region:us"
] | text-classification | vocabtrimmer | null | null | vocabtrimmer/xlm-roberta-base-trimmed-de-xnli-de | 0 | 2 | transformers | 2023-04-17T22:56:48 | # `vocabtrimmer/xlm-roberta-base-trimmed-de-xnli-de`
This model is a fine-tuned version of [vocabtrimmer/xlm-roberta-base-trimmed-de](https://huggingface.co/vocabtrimmer/xlm-roberta-base-trimmed-de) on the
[xnli](https://huggingface.co/datasets/xnli) (de).
Following metrics are computed on the `test` split of
[xnli](https://huggingface.co/datasets/xnli)(de).
| | eval_f1_micro | eval_recall_micro | eval_precision_micro | eval_f1_macro | eval_recall_macro | eval_precision_macro | eval_accuracy |
|---:|----------------:|--------------------:|-----------------------:|----------------:|--------------------:|-----------------------:|----------------:|
| 0 | 78.3 | 78.3 | 78.3 | 78.24 | 78.3 | 78.56 | 78.3 |
Check the result file [here](https://huggingface.co/vocabtrimmer/xlm-roberta-base-trimmed-de-xnli-de/raw/main/eval.json). | 953 | [
[
-0.039520263671875,
-0.036224365234375,
0.022613525390625,
-0.009521484375,
-0.0265655517578125,
0.00942230224609375,
-0.017181396484375,
-0.0200958251953125,
0.039215087890625,
0.046295166015625,
-0.054962158203125,
-0.06524658203125,
-0.0406494140625,
-0.0... |
vocabtrimmer/xlm-roberta-base-trimmed-en-xnli-en | 2023-04-20T08:47:12.000Z | [
"transformers",
"pytorch",
"xlm-roberta",
"text-classification",
"endpoints_compatible",
"region:us"
] | text-classification | vocabtrimmer | null | null | vocabtrimmer/xlm-roberta-base-trimmed-en-xnli-en | 0 | 2 | transformers | 2023-04-17T23:00:13 | # `vocabtrimmer/xlm-roberta-base-trimmed-en-xnli-en`
This model is a fine-tuned version of [vocabtrimmer/xlm-roberta-base-trimmed-en](https://huggingface.co/vocabtrimmer/xlm-roberta-base-trimmed-en) on the
[xnli](https://huggingface.co/datasets/xnli) (en).
Following metrics are computed on the `test` split of
[xnli](https://huggingface.co/datasets/xnli)(en).
| | eval_f1_micro | eval_recall_micro | eval_precision_micro | eval_f1_macro | eval_recall_macro | eval_precision_macro | eval_accuracy |
|---:|----------------:|--------------------:|-----------------------:|----------------:|--------------------:|-----------------------:|----------------:|
| 0 | 70.56 | 70.56 | 70.56 | 70.73 | 70.56 | 72.51 | 70.56 |
Check the result file [here](https://huggingface.co/vocabtrimmer/xlm-roberta-base-trimmed-en-xnli-en/raw/main/eval.json). | 953 | [
[
-0.039642333984375,
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0.0082550048828125,
-0.0189056396484375,
-0.0223388671875,
0.040008544921875,
0.043487548828125,
-0.056793212890625,
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vocabtrimmer/xlm-roberta-base-xnli-es | 2023-04-18T23:28:46.000Z | [
"transformers",
"pytorch",
"xlm-roberta",
"text-classification",
"endpoints_compatible",
"region:us"
] | text-classification | vocabtrimmer | null | null | vocabtrimmer/xlm-roberta-base-xnli-es | 0 | 2 | transformers | 2023-04-17T23:09:50 | # `vocabtrimmer/xlm-roberta-base-xnli-es`
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the
[xnli](https://huggingface.co/datasets/xnli) (es).
Following metrics are computed on the `test` split of
[xnli](https://huggingface.co/datasets/xnli)(es).
| | eval_f1_micro | eval_recall_micro | eval_precision_micro | eval_f1_macro | eval_recall_macro | eval_precision_macro | eval_accuracy |
|---:|----------------:|--------------------:|-----------------------:|----------------:|--------------------:|-----------------------:|----------------:|
| 0 | 79.8 | 79.8 | 79.8 | 79.78 | 79.8 | 80.42 | 79.8 |
Check the result file [here](https://huggingface.co/vocabtrimmer/xlm-roberta-base-xnli-es/raw/main/eval.json). | 883 | [
[
-0.029815673828125,
-0.030242919921875,
0.0271148681640625,
-0.004917144775390625,
-0.0244598388671875,
0.00983428955078125,
-0.0166015625,
-0.019439697265625,
0.036712646484375,
0.04266357421875,
-0.048797607421875,
-0.06719970703125,
-0.04718017578125,
-0.... |
vocabtrimmer/xlm-roberta-base-trimmed-fr-xnli-fr | 2023-04-21T02:51:15.000Z | [
"transformers",
"pytorch",
"xlm-roberta",
"text-classification",
"endpoints_compatible",
"region:us"
] | text-classification | vocabtrimmer | null | null | vocabtrimmer/xlm-roberta-base-trimmed-fr-xnli-fr | 0 | 2 | transformers | 2023-04-17T23:27:33 | # `vocabtrimmer/xlm-roberta-base-trimmed-fr-xnli-fr`
This model is a fine-tuned version of [vocabtrimmer/xlm-roberta-base-trimmed-fr](https://huggingface.co/vocabtrimmer/xlm-roberta-base-trimmed-fr) on the
[xnli](https://huggingface.co/datasets/xnli) (fr).
Following metrics are computed on the `test` split of
[xnli](https://huggingface.co/datasets/xnli)(fr).
| | eval_f1_micro | eval_recall_micro | eval_precision_micro | eval_f1_macro | eval_recall_macro | eval_precision_macro | eval_accuracy |
|---:|----------------:|--------------------:|-----------------------:|----------------:|--------------------:|-----------------------:|----------------:|
| 0 | 79.62 | 79.62 | 79.62 | 79.62 | 79.62 | 79.86 | 79.62 |
Check the result file [here](https://huggingface.co/vocabtrimmer/xlm-roberta-base-trimmed-fr-xnli-fr/raw/main/eval.json). | 953 | [
[
-0.03936767578125,
-0.033843994140625,
0.01873779296875,
-0.0032901763916015625,
-0.028411865234375,
0.00981903076171875,
-0.01751708984375,
-0.0201568603515625,
0.035125732421875,
0.045501708984375,
-0.055938720703125,
-0.0640869140625,
-0.040435791015625,
... |
vocabtrimmer/xlm-roberta-base-trimmed-es-xnli-es | 2023-04-20T04:49:48.000Z | [
"transformers",
"pytorch",
"xlm-roberta",
"text-classification",
"endpoints_compatible",
"region:us"
] | text-classification | vocabtrimmer | null | null | vocabtrimmer/xlm-roberta-base-trimmed-es-xnli-es | 0 | 2 | transformers | 2023-04-17T23:27:47 | # `vocabtrimmer/xlm-roberta-base-trimmed-es-xnli-es`
This model is a fine-tuned version of [vocabtrimmer/xlm-roberta-base-trimmed-es](https://huggingface.co/vocabtrimmer/xlm-roberta-base-trimmed-es) on the
[xnli](https://huggingface.co/datasets/xnli) (es).
Following metrics are computed on the `test` split of
[xnli](https://huggingface.co/datasets/xnli)(es).
| | eval_f1_micro | eval_recall_micro | eval_precision_micro | eval_f1_macro | eval_recall_macro | eval_precision_macro | eval_accuracy |
|---:|----------------:|--------------------:|-----------------------:|----------------:|--------------------:|-----------------------:|----------------:|
| 0 | 67.17 | 67.17 | 67.17 | 67.19 | 67.17 | 69.26 | 67.17 |
Check the result file [here](https://huggingface.co/vocabtrimmer/xlm-roberta-base-trimmed-es-xnli-es/raw/main/eval.json). | 953 | [
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-0.038055419921875,
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0.021026611328125,
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-0.025970458984375,
0.012542724609375,
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0.039520263671875,
0.044952392578125,
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-0.0662841796875,
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quydchope/chope-fine-dishing-distilbert-base-uncased-finetuned-ner-v0.1 | 2023-04-18T03:59:11.000Z | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | quydchope | null | null | quydchope/chope-fine-dishing-distilbert-base-uncased-finetuned-ner-v0.1 | 0 | 2 | transformers | 2023-04-18T03:00:05 | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: chope-fine-dishing-distilbert-base-uncased-finetuned-ner-v0.1
results: []
widget:
- text: "My name is Quy Dinh and I love Fried Chicken with curry sauce and then dessert with coconut panna cotta"
example_title: "Example 1: Random sentence"
- text: "and Scallop Teppanyaki, Steamed Egg Custard,"
example_title: "Example 2: Splitted from menu"
---
<!-- 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. -->
# chope-fine-dishing-distilbert-base-uncased-finetuned-ner-v0.1
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4448
- Precision: 0.6106
- Recall: 0.7842
- F1: 0.6866
- Accuracy: 0.7525
## 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: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 0.46 | 50 | 0.8879 | 0.3658 | 0.2978 | 0.3283 | 0.5883 |
| No log | 0.92 | 100 | 0.7451 | 0.5553 | 0.6995 | 0.6191 | 0.7048 |
| No log | 1.38 | 150 | 0.7378 | 0.5351 | 0.6448 | 0.5849 | 0.7171 |
| No log | 1.83 | 200 | 0.8367 | 0.6037 | 0.6202 | 0.6119 | 0.7012 |
| No log | 2.29 | 250 | 0.7746 | 0.6328 | 0.6639 | 0.6480 | 0.7373 |
| No log | 2.75 | 300 | 0.8077 | 0.5 | 0.5956 | 0.5436 | 0.6939 |
| No log | 3.21 | 350 | 0.8416 | 0.5284 | 0.4836 | 0.5050 | 0.7012 |
| No log | 3.67 | 400 | 0.9220 | 0.5601 | 0.7131 | 0.6274 | 0.7323 |
| No log | 4.13 | 450 | 0.9337 | 0.5419 | 0.5301 | 0.5359 | 0.7113 |
| 0.2476 | 4.59 | 500 | 0.9225 | 0.6387 | 0.6667 | 0.6524 | 0.7323 |
| 0.2476 | 5.05 | 550 | 1.0376 | 0.5296 | 0.5383 | 0.5339 | 0.7033 |
| 0.2476 | 5.5 | 600 | 1.0138 | 0.5820 | 0.7760 | 0.6651 | 0.7496 |
| 0.2476 | 5.96 | 650 | 1.1675 | 0.6184 | 0.6421 | 0.6300 | 0.7366 |
| 0.2476 | 6.42 | 700 | 1.2386 | 0.5563 | 0.7022 | 0.6208 | 0.7272 |
| 0.2476 | 6.88 | 750 | 1.2480 | 0.6233 | 0.7322 | 0.6734 | 0.7330 |
| 0.2476 | 7.34 | 800 | 1.2026 | 0.6077 | 0.6858 | 0.6444 | 0.7287 |
| 0.2476 | 7.8 | 850 | 1.1666 | 0.6176 | 0.7678 | 0.6845 | 0.7482 |
| 0.2476 | 8.26 | 900 | 1.1741 | 0.6119 | 0.7842 | 0.6874 | 0.7518 |
| 0.2476 | 8.72 | 950 | 1.3172 | 0.5584 | 0.6667 | 0.6077 | 0.7214 |
| 0.0227 | 9.17 | 1000 | 1.3335 | 0.5868 | 0.7295 | 0.6504 | 0.7185 |
| 0.0227 | 9.63 | 1050 | 1.2987 | 0.6247 | 0.7459 | 0.6800 | 0.7352 |
| 0.0227 | 10.09 | 1100 | 1.4033 | 0.5391 | 0.5464 | 0.5427 | 0.7041 |
| 0.0227 | 10.55 | 1150 | 1.5544 | 0.5427 | 0.6940 | 0.6091 | 0.7113 |
| 0.0227 | 11.01 | 1200 | 1.5020 | 0.5771 | 0.5519 | 0.5642 | 0.7221 |
| 0.0227 | 11.47 | 1250 | 1.3234 | 0.5983 | 0.7486 | 0.6650 | 0.7381 |
| 0.0227 | 11.93 | 1300 | 1.4603 | 0.6197 | 0.7213 | 0.6667 | 0.7359 |
| 0.0227 | 12.39 | 1350 | 1.5133 | 0.5301 | 0.5301 | 0.5301 | 0.6975 |
| 0.0227 | 12.84 | 1400 | 1.4874 | 0.5671 | 0.7623 | 0.6503 | 0.7366 |
| 0.0227 | 13.3 | 1450 | 1.5313 | 0.5603 | 0.7240 | 0.6317 | 0.7279 |
| 0.0075 | 13.76 | 1500 | 1.4268 | 0.5895 | 0.6749 | 0.6293 | 0.7229 |
| 0.0075 | 14.22 | 1550 | 1.6733 | 0.5190 | 0.5219 | 0.5204 | 0.6939 |
| 0.0075 | 14.68 | 1600 | 1.5003 | 0.5749 | 0.7650 | 0.6565 | 0.7366 |
| 0.0075 | 15.14 | 1650 | 1.5747 | 0.6353 | 0.5902 | 0.6119 | 0.7294 |
| 0.0075 | 15.6 | 1700 | 1.4836 | 0.5484 | 0.5574 | 0.5528 | 0.7048 |
| 0.0075 | 16.06 | 1750 | 1.7085 | 0.5066 | 0.5273 | 0.5167 | 0.6932 |
| 0.0075 | 16.51 | 1800 | 1.6691 | 0.5669 | 0.5328 | 0.5493 | 0.7048 |
| 0.0075 | 16.97 | 1850 | 1.5524 | 0.534 | 0.7295 | 0.6166 | 0.7236 |
| 0.0075 | 17.43 | 1900 | 1.5616 | 0.5484 | 0.6038 | 0.5748 | 0.7156 |
| 0.0075 | 17.89 | 1950 | 1.5597 | 0.5622 | 0.6667 | 0.61 | 0.7192 |
| 0.0044 | 18.35 | 2000 | 1.4448 | 0.6106 | 0.7842 | 0.6866 | 0.7525 |
| 0.0044 | 18.81 | 2050 | 1.5741 | 0.5802 | 0.5137 | 0.5449 | 0.7055 |
| 0.0044 | 19.27 | 2100 | 1.6085 | 0.5842 | 0.6448 | 0.6130 | 0.7192 |
| 0.0044 | 19.72 | 2150 | 1.5787 | 0.6016 | 0.8087 | 0.6900 | 0.7547 |
| 0.0044 | 20.18 | 2200 | 1.6210 | 0.6004 | 0.8169 | 0.6921 | 0.7547 |
| 0.0044 | 20.64 | 2250 | 1.6739 | 0.5246 | 0.5246 | 0.5246 | 0.7026 |
| 0.0044 | 21.1 | 2300 | 1.7852 | 0.5618 | 0.5710 | 0.5664 | 0.6990 |
| 0.0044 | 21.56 | 2350 | 1.6344 | 0.5576 | 0.6612 | 0.605 | 0.7142 |
| 0.0044 | 22.02 | 2400 | 1.8115 | 0.5363 | 0.5847 | 0.5595 | 0.7033 |
| 0.0044 | 22.48 | 2450 | 1.8336 | 0.5294 | 0.6148 | 0.5689 | 0.6968 |
| 0.0034 | 22.94 | 2500 | 1.7901 | 0.5878 | 0.6038 | 0.5957 | 0.7048 |
| 0.0034 | 23.39 | 2550 | 1.7766 | 0.5615 | 0.6858 | 0.6175 | 0.7113 |
| 0.0034 | 23.85 | 2600 | 1.8159 | 0.5531 | 0.6831 | 0.6112 | 0.7084 |
| 0.0034 | 24.31 | 2650 | 1.8307 | 0.6075 | 0.6175 | 0.6125 | 0.7142 |
| 0.0034 | 24.77 | 2700 | 1.8326 | 0.5410 | 0.6667 | 0.5973 | 0.7055 |
### Framework versions
- Transformers 4.27.2
- Pytorch 1.13.1+cu117
- Datasets 2.10.1
- Tokenizers 0.13.2
| 6,743 | [
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0.0237274169921875,
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-0.04547119140625,
-0.010... |
sumitk/dqn-SpaceInvadersNoFrameskip-v4 | 2023-04-18T03:31:26.000Z | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | sumitk | null | null | sumitk/dqn-SpaceInvadersNoFrameskip-v4 | 0 | 2 | stable-baselines3 | 2023-04-18T03:30:54 | ---
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: 392.50 +/- 121.08
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
```
# Download model and save it into the logs/ folder
python -m utils.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga sumitk -f logs/
python enjoy.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python train.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m utils.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga sumitk
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 10000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
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dzrex/test-trainer | 2023-04-18T06:54:54.000Z | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | text-classification | dzrex | null | null | dzrex/test-trainer | 0 | 2 | transformers | 2023-04-18T06:48:05 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
- f1
model-index:
- name: test-trainer
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
config: mrpc
split: validation
args: mrpc
metrics:
- name: Accuracy
type: accuracy
value: 0.8259803921568627
- name: F1
type: f1
value: 0.8773747841105355
---
<!-- 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. -->
# test-trainer
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7618
- Accuracy: 0.8260
- F1: 0.8774
## 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: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log | 1.0 | 459 | 0.5152 | 0.8431 | 0.8869 |
| 0.2996 | 2.0 | 918 | 0.7618 | 0.8260 | 0.8774 |
| 0.1326 | 3.0 | 1377 | 0.7618 | 0.8260 | 0.8774 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
| 1,847 | [
[
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AlanRobotics/ruT5_q_a | 2023-04-27T15:18:04.000Z | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"ru",
"dataset:sberquad",
"arxiv:1910.09700",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text2text-generation | AlanRobotics | null | null | AlanRobotics/ruT5_q_a | 0 | 2 | transformers | 2023-04-18T06:56:13 | ---
license: apache-2.0
datasets:
- sberquad
language:
- ru
library_name: transformers
pipeline_tag: text2text-generation
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [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]
| 5,289 | [
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m3hrdadfi/keysentence-finder | 2023-04-18T07:24:29.000Z | [
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"has_space",
"region:us"
] | sentence-similarity | m3hrdadfi | null | null | m3hrdadfi/keysentence-finder | 0 | 2 | sentence-transformers | 2023-04-18T07:22:15 | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
model = AutoModel.from_pretrained('{MODEL_NAME}')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 13069 with parameters:
```
{'batch_size': 32, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.ContrastiveLoss.ContrastiveLoss` with parameters:
```
{'distance_metric': 'SiameseDistanceMetric.COSINE_DISTANCE', 'margin': 0.5, 'size_average': True}
```
Parameters of the fit()-Method:
```
{
"epochs": 1,
"evaluation_steps": 1000,
"evaluator": "sentence_transformers.evaluation.BinaryClassificationEvaluator.BinaryClassificationEvaluator",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 100,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information --> | 3,905 | [
[
-0.0207977294921875,
-0.061614990234375,
0.021270751953125,
0.0222320556640625,
-0.0203704833984375,
-0.032745361328125,
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philz1337/deliberate | 2023-04-18T08:10:13.000Z | [
"diffusers",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | null | philz1337 | null | null | philz1337/deliberate | 0 | 2 | diffusers | 2023-04-18T07:53:35 | # DELIBERATE
#### All in One / Any Case Version
This model provides you the ability to create anything you want.</br>
The more power of prompt knowledges you have, the better results you'll get.</br>
It basically means that you'll never get a perfect result with just a few words.</br>
You have to fill out your prompt line extremely detailed.
#### Who find this model perfect:
- NSFW masters
- Meticulous anatomy artists
- Creative prompters
- Art designers
Dive into the perfect creations world with [my prompts](https://civitai.com/models/4823/deliberate "my prompts").</br>
Your research will be appreciated, so feel free to show everyone, what you can get with this model
---
license: bigscience-openrail-m
---
| 768 | [
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gguichard/distilbert-base-uncased-finetuned-clinc | 2023-04-18T09:59:53.000Z | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:clinc_oos",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | text-classification | gguichard | null | null | gguichard/distilbert-base-uncased-finetuned-clinc | 0 | 2 | transformers | 2023-04-18T09:55:54 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- clinc_oos
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased-finetuned-clinc
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: clinc_oos
type: clinc_oos
args: plus
metrics:
- name: Accuracy
type: accuracy
value: 0.9190322580645162
---
<!-- 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-clinc
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the clinc_oos dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7746
- Accuracy: 0.9190
## 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: 48
- eval_batch_size: 48
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 4.2868 | 1.0 | 318 | 3.2777 | 0.7381 |
| 2.6154 | 2.0 | 636 | 1.8682 | 0.8332 |
| 1.5373 | 3.0 | 954 | 1.1544 | 0.8948 |
| 1.0081 | 4.0 | 1272 | 0.8570 | 0.91 |
| 0.7895 | 5.0 | 1590 | 0.7746 | 0.9190 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.13.1+cu117
- Datasets 1.16.1
- Tokenizers 0.10.3
| 1,890 | [
[
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nouman-10/bertin-roberta-base-spanish_vaxxstance_spanish | 2023-04-18T10:46:30.000Z | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"license:cc-by-4.0",
"endpoints_compatible",
"region:us"
] | text-classification | nouman-10 | null | null | nouman-10/bertin-roberta-base-spanish_vaxxstance_spanish | 0 | 2 | transformers | 2023-04-18T10:35:28 | ---
license: cc-by-4.0
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: bertin-roberta-base-spanish_vaxxstance_spanish
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. -->
# bertin-roberta-base-spanish_vaxxstance_spanish
This model is a fine-tuned version of [bertin-project/bertin-roberta-base-spanish](https://huggingface.co/bertin-project/bertin-roberta-base-spanish) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6678
- F1: 0.8141
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 1.0 | 126 | 0.8302 | 0.6787 |
| No log | 2.0 | 252 | 0.6248 | 0.7695 |
| No log | 3.0 | 378 | 0.5223 | 0.7997 |
| 0.5799 | 4.0 | 504 | 0.6068 | 0.8084 |
| 0.5799 | 5.0 | 630 | 0.6678 | 0.8141 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
| 1,657 | [
[
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zap-thamm/DQN-SpaceInvadersNoFrameskip-v4 | 2023-04-18T11:28:42.000Z | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | zap-thamm | null | null | zap-thamm/DQN-SpaceInvadersNoFrameskip-v4 | 0 | 2 | stable-baselines3 | 2023-04-18T11:11:06 | ---
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: 613.50 +/- 160.28
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 zap-thamm -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 zap-thamm -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 zap-thamm
```
## 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)])
```
| 2,694 | [
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nouman-10/xlm-roberta-base_vaxxstance_spanish | 2023-04-18T13:50:51.000Z | [
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"license:mit",
"endpoints_compatible",
"region:us"
] | text-classification | nouman-10 | null | null | nouman-10/xlm-roberta-base_vaxxstance_spanish | 0 | 2 | transformers | 2023-04-18T13:23:58 | ---
license: mit
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: xlm-roberta-base_vaxxstance_spanish
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. -->
# xlm-roberta-base_vaxxstance_spanish
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5696
- F1: 0.8314
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 1.0 | 126 | 0.6786 | 0.7824 |
| No log | 2.0 | 252 | 0.5340 | 0.7925 |
| No log | 3.0 | 378 | 0.5578 | 0.7997 |
| 0.6182 | 4.0 | 504 | 0.5223 | 0.8386 |
| 0.6182 | 5.0 | 630 | 0.5696 | 0.8314 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
| 1,577 | [
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gus07ven/distilbert-base-multilingual-cased-distilled-jd | 2023-05-04T19:52:11.000Z | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | text-classification | gus07ven | null | null | gus07ven/distilbert-base-multilingual-cased-distilled-jd | 0 | 2 | transformers | 2023-04-18T13:56:32 | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-multilingual-cased-distilled-jd
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-multilingual-cased-distilled-jd
This model is a fine-tuned version of [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1316
- Accuracy: 0.8715
## 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: 9
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.4909 | 1.0 | 464 | 0.2007 | 0.8531 |
| 0.1345 | 2.0 | 928 | 0.1814 | 0.8650 |
| 0.0888 | 3.0 | 1392 | 0.1670 | 0.8639 |
| 0.0757 | 4.0 | 1856 | 0.1484 | 0.8726 |
| 0.0637 | 5.0 | 2320 | 0.1394 | 0.8683 |
| 0.0577 | 6.0 | 2784 | 0.1379 | 0.8737 |
| 0.0513 | 7.0 | 3248 | 0.1431 | 0.8704 |
| 0.0464 | 8.0 | 3712 | 0.1329 | 0.8704 |
| 0.0449 | 9.0 | 4176 | 0.1316 | 0.8715 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.13.0
- Datasets 1.16.1
- Tokenizers 0.10.3
| 1,911 | [
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minimax123/distilbert-base-uncased-finetuned-emotion | 2023-04-25T07:30:04.000Z | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | text-classification | minimax123 | null | null | minimax123/distilbert-base-uncased-finetuned-emotion | 0 | 2 | transformers | 2023-04-18T14:41:49 | ---
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
args: split
metrics:
- name: Accuracy
type: accuracy
value: 0.9275
- name: F1
type: f1
value: 0.9276292051262903
---
<!-- 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: 0.2160
- Accuracy: 0.9275
- F1: 0.9276
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8614 | 1.0 | 250 | 0.3235 | 0.903 | 0.9000 |
| 0.2542 | 2.0 | 500 | 0.2160 | 0.9275 | 0.9276 |
### Framework versions
- Transformers 4.13.0
- Pytorch 2.0.0+cu118
- Datasets 2.8.0
- Tokenizers 0.10.3
| 1,803 | [
[
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-0.... |
midwinter73/dipterv6 | 2023-04-18T14:54:12.000Z | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | text-classification | midwinter73 | null | null | midwinter73/dipterv6 | 0 | 2 | transformers | 2023-04-18T14:42:05 | ---
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: dipterv6
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. -->
# dipterv6
This model is a fine-tuned version of [ahmedrachid/FinancialBERT-Sentiment-Analysis](https://huggingface.co/ahmedrachid/FinancialBERT-Sentiment-Analysis) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0300
- Accuracy: 0.9907
- F1: 0.9907
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
| 1,174 | [
[
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MarkP1929/oasst-llama-13b-2-epochs-GPTQ-4bit-128g | 2023-04-18T19:48:27.000Z | [
"transformers",
"pytorch",
"llama",
"text-generation",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | text-generation | MarkP1929 | null | null | MarkP1929/oasst-llama-13b-2-epochs-GPTQ-4bit-128g | 3 | 2 | transformers | 2023-04-18T14:47:14 | This is a quantised version in safetensor format of the oasst-llama-13b-2-epochs model from dvruette/oasst-llama-13b-2-epochs
It has a siginficant speed up for inference when used on oobabooga.
Run with..
python server.py --model oasst-llama-13b-2-epochs-GPTQ-4bit-128g --wbits 4 --groupsize 128
| 300 | [
[
0.00461578369140625,
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0.021942138671875,
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0.03131103515625,
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0.036865234375,
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-0.0... |
nouman-10/xlm-roberta-large_vaxxstance_spanish | 2023-04-18T15:58:01.000Z | [
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"license:mit",
"endpoints_compatible",
"region:us"
] | text-classification | nouman-10 | null | null | nouman-10/xlm-roberta-large_vaxxstance_spanish | 0 | 2 | transformers | 2023-04-18T15:26:40 | ---
license: mit
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: xlm-roberta-large_vaxxstance_spanish
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. -->
# xlm-roberta-large_vaxxstance_spanish
This model is a fine-tuned version of [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5186
- F1: 0.8285
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 1.0 | 126 | 0.7648 | 0.6686 |
| No log | 2.0 | 252 | 0.5188 | 0.8127 |
| No log | 3.0 | 378 | 0.5417 | 0.7882 |
| 0.6762 | 4.0 | 504 | 0.4829 | 0.8285 |
| 0.6762 | 5.0 | 630 | 0.5186 | 0.8285 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
| 1,581 | [
[
-0.03289794921875,
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0.0250396728515625,
0.015106201171875,
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0.01041412353515625,
0.03759765625,
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-0.04937744140625,
-0.0552978515625,
-0.00183... |
danushaaditya/distilbert-base-uncased-finetuned-emotion | 2023-05-03T04:45:12.000Z | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | text-classification | danushaaditya | null | null | danushaaditya/distilbert-base-uncased-finetuned-emotion | 0 | 2 | transformers | 2023-04-18T15:30:04 | ---
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: 0.919
- name: F1
type: f1
value: 0.9191245777780953
---
<!-- 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: 0.2272
- Accuracy: 0.919
- F1: 0.9191
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8167 | 1.0 | 250 | 0.3223 | 0.9025 | 0.8991 |
| 0.2503 | 2.0 | 500 | 0.2272 | 0.919 | 0.9191 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,846 | [
[
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0.01412200927734375,
0.02197265625,
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0.0105133056640625,
0.0088653564453125,
-0.056640625,
-0.0521240234375,
-0.06011962890625,
-0.007572174... |
JamesG-337/distilbert-base-uncased-finetuned-emotion | 2023-05-10T10:32:20.000Z | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | text-classification | JamesG-337 | null | null | JamesG-337/distilbert-base-uncased-finetuned-emotion | 0 | 2 | transformers | 2023-04-18T16:06:47 | ---
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: 0.9235
- name: F1
type: f1
value: 0.9236843302640881
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2170
- Accuracy: 0.9235
- F1: 0.9237
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8329 | 1.0 | 250 | 0.3142 | 0.9085 | 0.9057 |
| 0.2503 | 2.0 | 500 | 0.2170 | 0.9235 | 0.9237 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,848 | [
[
-0.038055419921875,
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... |
ar0mant/xlm-roberta-base-finetuned-cola | 2023-04-22T18:33:49.000Z | [
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:mit",
"model-index",
"endpoints_compatible",
"region:us"
] | text-classification | ar0mant | null | null | ar0mant/xlm-roberta-base-finetuned-cola | 0 | 2 | transformers | 2023-04-18T18:27:58 | ---
license: mit
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- matthews_correlation
model-index:
- name: xlm-roberta-base-finetuned-cola
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
config: cola
split: validation
args: cola
metrics:
- name: Matthews Correlation
type: matthews_correlation
value: 0.5391948418977317
---
<!-- 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. -->
# xlm-roberta-base-finetuned-cola
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5179
- Matthews Correlation: 0.5392
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.6017 | 1.0 | 535 | 0.6923 | 0.2203 |
| 0.4807 | 2.0 | 1070 | 0.5651 | 0.4505 |
| 0.3625 | 3.0 | 1605 | 0.5179 | 0.5392 |
| 0.2849 | 4.0 | 2140 | 0.6297 | 0.5294 |
| 0.2217 | 5.0 | 2675 | 0.7300 | 0.5211 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
| 2,007 | [
[
-0.02471923828125,
-0.052642822265625,
0.0188140869140625,
0.006809234619140625,
-0.01715087890625,
-0.0194854736328125,
-0.0121917724609375,
-0.01180267333984375,
0.0218353271484375,
0.0265655517578125,
-0.052581787109375,
-0.041229248046875,
-0.059722900390625... |
jacinthes/cross-encoder-sloberta-si-nli-snli-mnli | 2023-04-20T09:39:57.000Z | [
"transformers",
"pytorch",
"camembert",
"text-classification",
"sl",
"dataset:cjvt/si_nli",
"dataset:jacinthes/slovene_mnli_snli",
"license:cc-by-sa-4.0",
"endpoints_compatible",
"region:us"
] | text-classification | jacinthes | null | null | jacinthes/cross-encoder-sloberta-si-nli-snli-mnli | 0 | 2 | transformers | 2023-04-18T20:05:13 | ---
datasets:
- cjvt/si_nli
- jacinthes/slovene_mnli_snli
language:
- sl
license: cc-by-sa-4.0
---
# CrossEncoder for Slovene NLI
The model was trained using the [SentenceTransformers](https://sbert.net/) [CrossEncoder](https://www.sbert.net/examples/applications/cross-encoder/README.html) class. <br />
It is based on [SloBerta](https://huggingface.co/EMBEDDIA/sloberta), a monolingual Slovene model.
## Training
This model was trained on the [SI-NLI](https://huggingface.co/datasets/cjvt/si_nli) and the [slovene_mnli_snli](https://huggingface.co/datasets/jacinthes/slovene_mnli_snli) datasets.<br />
More details and the training script are available here: [repo](https://github.com/jacinthes/slovene-nli-benchmark)
## Performance
The model achieves the following metrics:
- Test accuracy: 77.15
- Dev accuracy: 77.51
## Usage
The model can be used for inference using the below code:
```python
from sentence_transformers import CrossEncoder
model = CrossEncoder('jacinthes/cross-encoder-sloberta-si-nli-snli-mnli')
premise = 'Pojdi z menoj v toplice.'
hypothesis = 'Bova lepa bova fit.'
prediction = model.predict([premise, hypothesis])
int2label = {0: 'entailment', 1: 'neutral', 2:'contradiction'}
print(int2label[prediction.argmax()])
``` | 1,251 | [
[
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0.015... |
Almondpeanuts/distilbert-base-uncased-finetuned-clinc | 2023-04-19T00:55:54.000Z | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:clinc_oos",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | text-classification | Almondpeanuts | null | null | Almondpeanuts/distilbert-base-uncased-finetuned-clinc | 0 | 2 | transformers | 2023-04-18T21:31:04 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- clinc_oos
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased-finetuned-clinc
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: clinc_oos
type: clinc_oos
config: plus
split: validation
args: plus
metrics:
- name: Accuracy
type: accuracy
value: 0.9180645161290323
---
<!-- 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-clinc
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the clinc_oos dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7720
- Accuracy: 0.9181
## 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: 48
- eval_batch_size: 48
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 318 | 3.2887 | 0.7419 |
| 3.7868 | 2.0 | 636 | 1.8753 | 0.8371 |
| 3.7868 | 3.0 | 954 | 1.1570 | 0.8961 |
| 1.6927 | 4.0 | 1272 | 0.8573 | 0.9129 |
| 0.9056 | 5.0 | 1590 | 0.7720 | 0.9181 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
| 1,932 | [
[
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0.02215576171875,
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-0.... |
vocabtrimmer/xlm-roberta-base-xnli-ar | 2023-04-18T22:06:23.000Z | [
"transformers",
"pytorch",
"xlm-roberta",
"text-classification",
"endpoints_compatible",
"region:us"
] | text-classification | vocabtrimmer | null | null | vocabtrimmer/xlm-roberta-base-xnli-ar | 0 | 2 | transformers | 2023-04-18T22:02:37 | # `vocabtrimmer/xlm-roberta-base-xnli-ar`
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the
[xnli](https://huggingface.co/datasets/xnli) (ar).
Following metrics are computed on the `test` split of
[xnli](https://huggingface.co/datasets/xnli)(ar).
| | eval_f1_micro | eval_recall_micro | eval_precision_micro | eval_f1_macro | eval_recall_macro | eval_precision_macro | eval_accuracy |
|---:|----------------:|--------------------:|-----------------------:|----------------:|--------------------:|-----------------------:|----------------:|
| 0 | 75.73 | 75.73 | 75.73 | 75.73 | 75.73 | 76.22 | 75.73 |
Check the result file [here](https://huggingface.co/vocabtrimmer/xlm-roberta-base-xnli-ar/raw/main/eval.json). | 883 | [
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0.00769805908203125,
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VinsmokeMir/Further_fine_tuning_E9 | 2023-05-06T19:57:26.000Z | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:xnli_bn",
"endpoints_compatible",
"region:us"
] | text-classification | VinsmokeMir | null | null | VinsmokeMir/Further_fine_tuning_E9 | 0 | 2 | transformers | 2023-04-18T22:29:33 | ---
tags:
- generated_from_trainer
datasets:
- xnli_bn
model-index:
- name: Further_fine_tuning_E9
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. -->
# Further_fine_tuning_E9
This model is a fine-tuned version of [rafsankabir/Pretrained_E10](https://huggingface.co/rafsankabir/Pretrained_E10) on the xnli_bn dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-06
- train_batch_size: 32
- eval_batch_size: 32
- seed: 33
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,076 | [
[
-0.043182373046875,
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0.0038127899169921875,
0.00011861324310302734,
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0.0229644775390625,
0.03485107421875,
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cafbr/distilbert-base-uncased-finetuned-clinc | 2023-04-24T14:03:47.000Z | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:clinc_oos",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | text-classification | cafbr | null | null | cafbr/distilbert-base-uncased-finetuned-clinc | 0 | 2 | transformers | 2023-04-18T23:33:31 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- clinc_oos
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased-finetuned-clinc
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: clinc_oos
type: clinc_oos
config: plus
split: validation
args: plus
metrics:
- name: Accuracy
type: accuracy
value: 0.9509677419354838
---
<!-- 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-clinc
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the clinc_oos dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2354
- Accuracy: 0.9510
## 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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.0114 | 1.0 | 1907 | 0.9483 | 0.8577 |
| 0.2978 | 2.0 | 3814 | 0.2961 | 0.9368 |
| 0.097 | 3.0 | 5721 | 0.2422 | 0.9474 |
| 0.0393 | 4.0 | 7628 | 0.2349 | 0.9519 |
| 0.023 | 5.0 | 9535 | 0.2354 | 0.9510 |
### Framework versions
- Transformers 4.28.1
- Pytorch 1.11.0+cu113
- Datasets 2.11.0
- Tokenizers 0.13.3
| 1,931 | [
[
-0.036407470703125,
-0.041107177734375,
0.01641845703125,
0.006969451904296875,
-0.026763916015625,
-0.0248565673828125,
-0.011444091796875,
-0.00811767578125,
0.004425048828125,
0.022552490234375,
-0.047393798828125,
-0.048004150390625,
-0.057708740234375,
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vocabtrimmer/xlm-roberta-base-xnli-en | 2023-04-19T00:21:18.000Z | [
"transformers",
"pytorch",
"xlm-roberta",
"text-classification",
"endpoints_compatible",
"region:us"
] | text-classification | vocabtrimmer | null | null | vocabtrimmer/xlm-roberta-base-xnli-en | 0 | 2 | transformers | 2023-04-19T00:17:26 | # `vocabtrimmer/xlm-roberta-base-xnli-en`
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the
[xnli](https://huggingface.co/datasets/xnli) (en).
Following metrics are computed on the `test` split of
[xnli](https://huggingface.co/datasets/xnli)(en).
| | eval_f1_micro | eval_recall_micro | eval_precision_micro | eval_f1_macro | eval_recall_macro | eval_precision_macro | eval_accuracy |
|---:|----------------:|--------------------:|-----------------------:|----------------:|--------------------:|-----------------------:|----------------:|
| 0 | 84.57 | 84.57 | 84.57 | 84.56 | 84.57 | 84.68 | 84.57 |
Check the result file [here](https://huggingface.co/vocabtrimmer/xlm-roberta-base-xnli-en/raw/main/eval.json). | 883 | [
[
-0.029815673828125,
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0.0271759033203125,
-0.0027828216552734375,
-0.0238037109375,
0.006885528564453125,
-0.0181884765625,
-0.021270751953125,
0.035430908203125,
0.04266357421875,
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... |
davidliu1110/bert-base-chinese-david-ner | 2023-05-12T03:15:11.000Z | [
"transformers",
"pytorch",
"bert",
"token-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | davidliu1110 | null | null | davidliu1110/bert-base-chinese-david-ner | 0 | 2 | transformers | 2023-04-19T02:17:54 | ---
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-base-chinese-david-ner
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-chinese-david-ner
This model is a fine-tuned version of [bert-base-chinese](https://huggingface.co/bert-base-chinese) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0557
- Precision: 0.9424
- Recall: 0.9568
- F1: 0.9496
- Accuracy: 0.9890
## 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
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 1.0617 | 0.1 | 100 | 0.4293 | 0.2681 | 0.2160 | 0.2393 | 0.8405 |
| 0.2546 | 0.2 | 200 | 0.1427 | 0.7154 | 0.8018 | 0.7561 | 0.9523 |
| 0.1644 | 0.3 | 300 | 0.1148 | 0.7712 | 0.8437 | 0.8058 | 0.9628 |
| 0.132 | 0.39 | 400 | 0.0945 | 0.7956 | 0.8704 | 0.8313 | 0.9691 |
| 0.107 | 0.49 | 500 | 0.0839 | 0.8425 | 0.8971 | 0.8689 | 0.9747 |
| 0.0981 | 0.59 | 600 | 0.0971 | 0.8539 | 0.9060 | 0.8792 | 0.9733 |
| 0.098 | 0.69 | 700 | 0.0794 | 0.8832 | 0.9034 | 0.8932 | 0.9777 |
| 0.0955 | 0.79 | 800 | 0.0716 | 0.9012 | 0.9276 | 0.9142 | 0.9821 |
| 0.0824 | 0.89 | 900 | 0.0697 | 0.8848 | 0.9276 | 0.9057 | 0.9789 |
| 0.0774 | 0.99 | 1000 | 0.0631 | 0.8929 | 0.9212 | 0.9068 | 0.9808 |
| 0.0604 | 1.09 | 1100 | 0.0701 | 0.9087 | 0.9238 | 0.9162 | 0.9812 |
| 0.0621 | 1.18 | 1200 | 0.0583 | 0.9126 | 0.9288 | 0.9207 | 0.9841 |
| 0.0446 | 1.28 | 1300 | 0.0652 | 0.9175 | 0.9327 | 0.9250 | 0.9839 |
| 0.0516 | 1.38 | 1400 | 0.0609 | 0.9093 | 0.9301 | 0.9196 | 0.9842 |
| 0.0539 | 1.48 | 1500 | 0.0648 | 0.9179 | 0.9377 | 0.9277 | 0.9858 |
| 0.0546 | 1.58 | 1600 | 0.0676 | 0.9157 | 0.9390 | 0.9272 | 0.9825 |
| 0.0479 | 1.68 | 1700 | 0.0574 | 0.9106 | 0.9314 | 0.9209 | 0.9848 |
| 0.0424 | 1.78 | 1800 | 0.0572 | 0.9228 | 0.9416 | 0.9321 | 0.9862 |
| 0.054 | 1.88 | 1900 | 0.0499 | 0.9195 | 0.9428 | 0.9310 | 0.9866 |
| 0.0397 | 1.97 | 2000 | 0.0542 | 0.9318 | 0.9555 | 0.9435 | 0.9876 |
| 0.0362 | 2.07 | 2100 | 0.0567 | 0.9217 | 0.9428 | 0.9322 | 0.9867 |
| 0.0226 | 2.17 | 2200 | 0.0670 | 0.925 | 0.9403 | 0.9326 | 0.9854 |
| 0.029 | 2.27 | 2300 | 0.0565 | 0.9375 | 0.9530 | 0.9452 | 0.9883 |
| 0.0293 | 2.37 | 2400 | 0.0540 | 0.9254 | 0.9454 | 0.9353 | 0.9866 |
| 0.0265 | 2.47 | 2500 | 0.0551 | 0.9304 | 0.9517 | 0.9410 | 0.9880 |
| 0.0244 | 2.57 | 2600 | 0.0543 | 0.9316 | 0.9517 | 0.9415 | 0.9886 |
| 0.027 | 2.67 | 2700 | 0.0500 | 0.9399 | 0.9543 | 0.9470 | 0.9894 |
| 0.0286 | 2.76 | 2800 | 0.0479 | 0.9282 | 0.9530 | 0.9404 | 0.9890 |
| 0.0206 | 2.86 | 2900 | 0.0549 | 0.9255 | 0.9466 | 0.9359 | 0.9880 |
| 0.0239 | 2.96 | 3000 | 0.0537 | 0.9294 | 0.9530 | 0.9410 | 0.9889 |
| 0.0178 | 3.06 | 3100 | 0.0557 | 0.9424 | 0.9568 | 0.9496 | 0.9890 |
| 0.0131 | 3.16 | 3200 | 0.0627 | 0.9327 | 0.9504 | 0.9415 | 0.9880 |
| 0.0161 | 3.26 | 3300 | 0.0586 | 0.9340 | 0.9530 | 0.9434 | 0.9883 |
| 0.0162 | 3.36 | 3400 | 0.0542 | 0.9303 | 0.9504 | 0.9403 | 0.9887 |
| 0.0212 | 3.46 | 3500 | 0.0562 | 0.9268 | 0.9492 | 0.9379 | 0.9881 |
| 0.02 | 3.55 | 3600 | 0.0551 | 0.9280 | 0.9504 | 0.9391 | 0.9888 |
| 0.0084 | 3.65 | 3700 | 0.0568 | 0.9292 | 0.9504 | 0.9397 | 0.9888 |
| 0.0143 | 3.75 | 3800 | 0.0564 | 0.9363 | 0.9530 | 0.9446 | 0.9892 |
| 0.0162 | 3.85 | 3900 | 0.0560 | 0.9377 | 0.9568 | 0.9472 | 0.9888 |
| 0.0199 | 3.95 | 4000 | 0.0546 | 0.9377 | 0.9568 | 0.9472 | 0.9894 |
### Framework versions
- Transformers 4.29.0.dev0
- Pytorch 1.10.1+cu113
- Datasets 2.11.0
- Tokenizers 0.13.3
| 5,119 | [
[
-0.04217529296875,
-0.041107177734375,
0.015960693359375,
0.007030487060546875,
-0.00009942054748535156,
0.00020384788513183594,
-0.00022542476654052734,
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0.054229736328125,
0.02392578125,
-0.043792724609375,
-0.048797607421875,
-0.04724... |
WilliamWen/unit_cata_IO | 2023-04-19T02:46:58.000Z | [
"transformers",
"pytorch",
"bert",
"token-classification",
"autotrain",
"en",
"dataset:WilliamWen/autotrain-data-unit_cata_io",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | WilliamWen | null | null | WilliamWen/unit_cata_IO | 0 | 2 | transformers | 2023-04-19T02:44:12 | ---
tags:
- autotrain
- token-classification
language:
- en
widget:
- text: "I love AutoTrain 🤗"
datasets:
- WilliamWen/autotrain-data-unit_cata_io
co2_eq_emissions:
emissions: 1.228627476310992
---
# Model Trained Using AutoTrain
- Problem type: Entity Extraction
- Model ID: 50661120907
- CO2 Emissions (in grams): 1.2286
## Validation Metrics
- Loss: 0.014
- Accuracy: 0.997
- Precision: 0.895
- Recall: 0.938
- F1: 0.916
## 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/WilliamWen/autotrain-unit_cata_io-50661120907
```
Or Python API:
```
from transformers import AutoModelForTokenClassification, AutoTokenizer
model = AutoModelForTokenClassification.from_pretrained("WilliamWen/autotrain-unit_cata_io-50661120907", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("WilliamWen/autotrain-unit_cata_io-50661120907", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
``` | 1,131 | [
[
-0.03033447265625,
-0.0350341796875,
0.0220489501953125,
0.0113983154296875,
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-0.003997802734375,
0.002117156982421875,
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0.0031795501708984375,
0.0161590576171875,
-0.053863525390625,
-0.03533935546875,
-0.0581359863281... |
cmilica/ppo-LunarLander-v2 | 2023-04-20T19:45:09.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | cmilica | null | null | cmilica/ppo-LunarLander-v2 | 0 | 2 | stable-baselines3 | 2023-04-19T02:46:37 | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO-mlppolicy
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 290.16 +/- 16.23
name: mean_reward
verified: false
---
# **PPO-mlppolicy** Agent playing **LunarLander-v2**
This is a trained model of a **PPO-mlppolicy** 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
...
```
| 814 | [
[
0.00008022785186767578,
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0.0171661376953125,
0.0305633544921875,
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0.001293182373046875,
0.0299530029296875,
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0.0230712890625,
0.0601806640625,
-0.048370361328125,
-0.032470703125,
-0.0377197265625,
-0... |
code-is-wonderful/pros_cons_pegasus_sum | 2023-04-21T07:02:18.000Z | [
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"summarization",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | summarization | code-is-wonderful | null | null | code-is-wonderful/pros_cons_pegasus_sum | 0 | 2 | transformers | 2023-04-19T03:15:05 | ---
license: apache-2.0
language:
- en
metrics:
- rouge
library_name: transformers
pipeline_tag: summarization
---
Summarize similar sentences for Amazon reviews | 161 | [
[
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0.036041259765625,
0.0189056396484375,
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0.01861572265625,
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0.042022705078125,
0.0755615234375,
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0.01226806640625,
0.0290... |
pigeon-phobia/distilbert-base-uncased_finetuned_olid_a | 2023-04-19T05:05:41.000Z | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | pigeon-phobia | null | null | pigeon-phobia/distilbert-base-uncased_finetuned_olid_a | 0 | 2 | transformers | 2023-04-19T04:59:23 | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased_finetuned_olid_a
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_olid_a
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3681
- Accuracy: 0.8512
- F1-macro: 0.8034
## 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-macro |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|
| 0.4827 | 1.0 | 207 | 0.3716 | 0.8570 | 0.8113 |
| 0.39 | 2.0 | 414 | 0.3681 | 0.8512 | 0.8034 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
| 1,509 | [
[
-0.034332275390625,
-0.04608154296875,
0.014312744140625,
0.0116119384765625,
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0.007167816162109375,
0.021575927734375,
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vocabtrimmer/xlm-roberta-base-xnli-de | 2023-04-19T05:37:16.000Z | [
"transformers",
"pytorch",
"xlm-roberta",
"text-classification",
"endpoints_compatible",
"region:us"
] | text-classification | vocabtrimmer | null | null | vocabtrimmer/xlm-roberta-base-xnli-de | 0 | 2 | transformers | 2023-04-19T05:33:50 | # `vocabtrimmer/xlm-roberta-base-xnli-de`
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the
[xnli](https://huggingface.co/datasets/xnli) (de).
Following metrics are computed on the `test` split of
[xnli](https://huggingface.co/datasets/xnli)(de).
| | eval_f1_micro | eval_recall_micro | eval_precision_micro | eval_f1_macro | eval_recall_macro | eval_precision_macro | eval_accuracy |
|---:|----------------:|--------------------:|-----------------------:|----------------:|--------------------:|-----------------------:|----------------:|
| 0 | 79.9 | 79.9 | 79.9 | 79.89 | 79.9 | 80.04 | 79.9 |
Check the result file [here](https://huggingface.co/vocabtrimmer/xlm-roberta-base-xnli-de/raw/main/eval.json). | 883 | [
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0.00811767578125,
-0.0171966552734375,
-0.020538330078125,
0.035675048828125,
0.04229736328125,
-0.047332763671875,
-0.066650390625,
-0.0467529296875,
-0.0018... |
vocabtrimmer/xlm-roberta-base-xnli-fr | 2023-04-19T05:40:22.000Z | [
"transformers",
"pytorch",
"xlm-roberta",
"text-classification",
"endpoints_compatible",
"region:us"
] | text-classification | vocabtrimmer | null | null | vocabtrimmer/xlm-roberta-base-xnli-fr | 0 | 2 | transformers | 2023-04-19T05:37:05 | # `vocabtrimmer/xlm-roberta-base-xnli-fr`
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the
[xnli](https://huggingface.co/datasets/xnli) (fr).
Following metrics are computed on the `test` split of
[xnli](https://huggingface.co/datasets/xnli)(fr).
| | eval_f1_micro | eval_recall_micro | eval_precision_micro | eval_f1_macro | eval_recall_macro | eval_precision_macro | eval_accuracy |
|---:|----------------:|--------------------:|-----------------------:|----------------:|--------------------:|-----------------------:|----------------:|
| 0 | 80.12 | 80.12 | 80.12 | 80.12 | 80.12 | 80.44 | 80.12 |
Check the result file [here](https://huggingface.co/vocabtrimmer/xlm-roberta-base-xnli-fr/raw/main/eval.json). | 883 | [
[
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0.043792724609375,
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0.0004773139953... |
Umesh/pulf-classifier_roberta_final | 2023-04-19T10:22:29.000Z | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"license:mit",
"endpoints_compatible",
"region:us"
] | text-classification | Umesh | null | null | Umesh/pulf-classifier_roberta_final | 0 | 2 | transformers | 2023-04-19T05:38:29 | ---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
- recall
- precision
model-index:
- name: pulf-classifier_roberta_final
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. -->
# pulf-classifier_roberta_final
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0165
- Accuracy: 0.9954
- F1-score: 0.9909
- Recall: 0.9917
- Precision: 0.9902
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1-score | Recall | Precision |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:|:------:|:---------:|
| 0.0248 | 1.0 | 10746 | 0.0204 | 0.9937 | 0.9875 | 0.9859 | 0.9891 |
| 0.0228 | 2.0 | 21492 | 0.0152 | 0.9963 | 0.9926 | 0.9906 | 0.9946 |
| 0.0201 | 3.0 | 32238 | 0.0165 | 0.9954 | 0.9909 | 0.9917 | 0.9902 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
| 1,699 | [
[
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... |
melobron/ppo-LunarLander-v2 | 2023-04-19T06:49:22.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | melobron | null | null | melobron/ppo-LunarLander-v2 | 0 | 2 | stable-baselines3 | 2023-04-19T06:48:58 | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: MlpPolicy
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 255.48 +/- 19.75
name: mean_reward
verified: false
---
# **MlpPolicy** Agent playing **LunarLander-v2**
This is a trained model of a **MlpPolicy** 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
...
```
| 802 | [
[
-0.002758026123046875,
-0.035491943359375,
0.01108551025390625,
0.03424072265625,
0.0077667236328125,
0.0037975311279296875,
0.0247955322265625,
-0.0157318115234375,
0.0311126708984375,
0.055816650390625,
-0.058380126953125,
-0.034088134765625,
-0.03213500976562... |
DiracUniverse/ppo-LunarLander-v2 | 2023-04-20T08:49:52.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | DiracUniverse | null | null | DiracUniverse/ppo-LunarLander-v2 | 0 | 2 | stable-baselines3 | 2023-04-19T07:11:57 | ---
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: 251.65 +/- 19.06
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
...
```
| 784 | [
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-0.00021457672119140625,
-0.0271453857421875,
0.017059326171875,
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0.034423828125,
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0.06500244140625,
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-0.0343017578125... |
Sunoh/distilbert-base-uncased-finetuned-clinc | 2023-04-19T07:54:34.000Z | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:clinc_oos",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | text-classification | Sunoh | null | null | Sunoh/distilbert-base-uncased-finetuned-clinc | 0 | 2 | transformers | 2023-04-19T07:47:59 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- clinc_oos
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased-finetuned-clinc
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: clinc_oos
type: clinc_oos
config: plus
split: validation
args: plus
metrics:
- name: Accuracy
type: accuracy
value: 0.9180645161290323
---
<!-- 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-clinc
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the clinc_oos dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7720
- Accuracy: 0.9181
## 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: 48
- eval_batch_size: 48
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 4.2896 | 1.0 | 318 | 3.2887 | 0.7419 |
| 2.6282 | 2.0 | 636 | 1.8753 | 0.8371 |
| 1.548 | 3.0 | 954 | 1.1570 | 0.8961 |
| 1.0148 | 4.0 | 1272 | 0.8573 | 0.9129 |
| 0.7952 | 5.0 | 1590 | 0.7720 | 0.9181 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
| 1,932 | [
[
-0.03509521484375,
-0.040985107421875,
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0.00667572021484375,
-0.027587890625,
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0.00324249267578125,
0.0222625732421875,
-0.04681396484375,
-0.048309326171875,
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... |
julio-mm/distilbert-base-uncased | 2023-05-03T17:00:58.000Z | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | text-classification | julio-mm | null | null | julio-mm/distilbert-base-uncased | 0 | 2 | transformers | 2023-04-19T08:04:36 | ---
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased
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
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2059
- Accuracy: 0.9633
## 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: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 490 | 0.2683 | 0.9459 |
| 0.1658 | 2.0 | 980 | 0.2059 | 0.9633 |
### Framework versions
- Transformers 4.27.1
- Pytorch 2.0.0+cu118
- Datasets 2.10.1
- Tokenizers 0.13.2
| 1,316 | [
[
-0.022796630859375,
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0.020233154296875,
0.01502227783203125,
-0.02325439453125,
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0.016632080078125,
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... |
mudasiryasin/roberta-similarity | 2023-04-19T09:10:39.000Z | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"generated_from_trainer",
"license:mit",
"endpoints_compatible",
"region:us"
] | text-classification | mudasiryasin | null | null | mudasiryasin/roberta-similarity | 0 | 2 | transformers | 2023-04-19T08:32:15 | ---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: roberta-similarity
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-similarity
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7067
- Accuracy: 0.832
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6376 | 0.16 | 10 | 0.6287 | 0.672 |
| 0.5909 | 0.32 | 20 | 0.5762 | 0.672 |
| 0.5422 | 0.48 | 30 | 0.6498 | 0.672 |
| 0.5876 | 0.63 | 40 | 0.6411 | 0.672 |
| 0.523 | 0.79 | 50 | 0.7330 | 0.67 |
| 0.5686 | 0.95 | 60 | 0.6911 | 0.672 |
| 0.4743 | 1.11 | 70 | 0.5254 | 0.792 |
| 0.4183 | 1.27 | 80 | 0.4998 | 0.818 |
| 0.3682 | 1.43 | 90 | 0.5912 | 0.816 |
| 0.6203 | 1.59 | 100 | 0.9526 | 0.706 |
| 0.5078 | 1.75 | 110 | 0.5348 | 0.824 |
| 0.3214 | 1.9 | 120 | 0.5120 | 0.816 |
| 0.3352 | 2.06 | 130 | 0.5275 | 0.808 |
| 0.2805 | 2.22 | 140 | 0.5597 | 0.816 |
| 0.2541 | 2.38 | 150 | 0.5253 | 0.83 |
| 0.3769 | 2.54 | 160 | 0.5075 | 0.796 |
| 0.3203 | 2.7 | 170 | 0.4701 | 0.816 |
| 0.2153 | 2.86 | 180 | 0.5483 | 0.814 |
| 0.1822 | 3.02 | 190 | 0.5819 | 0.832 |
| 0.1761 | 3.17 | 200 | 0.6913 | 0.822 |
| 0.301 | 3.33 | 210 | 0.7678 | 0.804 |
| 0.21 | 3.49 | 220 | 0.9464 | 0.798 |
| 0.3224 | 3.65 | 230 | 0.6209 | 0.832 |
| 0.133 | 3.81 | 240 | 0.7540 | 0.818 |
| 0.1826 | 3.97 | 250 | 0.7332 | 0.828 |
| 0.2547 | 4.13 | 260 | 0.6782 | 0.83 |
| 0.1321 | 4.29 | 270 | 0.7430 | 0.824 |
| 0.1661 | 4.44 | 280 | 0.8056 | 0.826 |
| 0.1525 | 4.6 | 290 | 0.6864 | 0.828 |
| 0.2085 | 4.76 | 300 | 0.6900 | 0.832 |
| 0.1201 | 4.92 | 310 | 0.7067 | 0.832 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Tokenizers 0.13.3
| 3,154 | [
[
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-0.039398193359375,
0.0172271728515625,
0.0029449462890625,
-0.004520416259765625,
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0.03662109375,
0.0216522216796875,
-0.051483154296875,
-0.048675537109375,
-0.0525207519531... |
Sunbird/e2m_best_19_4_23 | 2023-04-19T08:39:12.000Z | [
"transformers",
"pytorch",
"marian",
"text2text-generation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | Sunbird | null | null | Sunbird/e2m_best_19_4_23 | 0 | 2 | transformers | 2023-04-19T08:32:21 | ---
tags:
- generated_from_trainer
model-index:
- name: best
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. -->
# Usage
Translates to Acholi, Lugbara, Luganda, Runyankole and Ateso
Make sure to add a target language and dataset tags before a source sentence.
Ex. >>lug_hq<< I want Posho ---> Njagala Posho
For biblical style translations attempt to use the ood tag
Ex. >>lug_ood<< And thus spoke the LORD to the masses on the mountain
We these other tags which you might want to try [ggl, bt, hq, ood]
Language tags [ach, lgg, lug, nyn, teo]
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 5000
- total_train_batch_size: 5000
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
- label_smoothing_factor: 0.1
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Tokenizers 0.13.3
| 1,162 | [
[
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-0.0457763671875,
0.0020656585693359375,
0.0213165283203125,
-0.04705810546875,
-0.0308074951171875,
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-0.0257110595703125,
0.016632080078125,
0.030364990234375,
-0.03814697265625,
-0.044708251953125,
-0.044097900390625,
0.0081... |
jacinthes/cross-encoder-sloberta-si-nli | 2023-04-20T09:40:51.000Z | [
"transformers",
"pytorch",
"camembert",
"text-classification",
"sl",
"dataset:cjvt/si_nli",
"license:cc-by-sa-4.0",
"endpoints_compatible",
"region:us"
] | text-classification | jacinthes | null | null | jacinthes/cross-encoder-sloberta-si-nli | 0 | 2 | transformers | 2023-04-19T09:01:43 | ---
datasets:
- cjvt/si_nli
language:
- sl
license: cc-by-sa-4.0
---
# CrossEncoder for Slovene NLI
The model was trained using the [SentenceTransformers](https://sbert.net/) [CrossEncoder](https://www.sbert.net/examples/applications/cross-encoder/README.html) class. <br />
It is based on [SloBerta](https://huggingface.co/EMBEDDIA/sloberta), a monolingual Slovene model.
## Training
This model was trained on the [SI-NLI](https://huggingface.co/datasets/cjvt/si_nli) dataset.<br />
More details and the training script are available here: [repo](https://github.com/jacinthes/slovene-nli-benchmark)
## Performance
The model achieves the following metrics:
- Test accuracy: 75.95
- Dev accuracy: 75.14
## Usage
The model can be used for inference using the below code:
```python
from sentence_transformers import CrossEncoder
model = CrossEncoder('jacinthes/cross-encoder-sloberta-si-nli')
premise = 'Pojdi z menoj v toplice.'
hypothesis = 'Bova lepa bova fit.'
prediction = model.predict([premise, hypothesis])
int2label = {0: 'entailment', 1: 'neutral', 2:'contradiction'}
print(int2label[prediction.argmax()])
``` | 1,121 | [
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aisquared/chopt-research-125m | 2023-05-04T16:20:58.000Z | [
"transformers",
"pytorch",
"opt",
"text-generation",
"en",
"dataset:tatsu-lab/alpaca",
"license:other",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | text-generation | aisquared | null | null | aisquared/chopt-research-125m | 0 | 2 | transformers | 2023-04-19T14:30:16 | ---
license: other
datasets:
- tatsu-lab/alpaca
language:
- en
library_name: transformers
---
# Model Card for `chopt-research-125m`
<!-- Provide a quick summary of what the model is/does. -->
AI Squared's `chopt-research-125m` is a large language
model which is derived from Meta AI's Open Pre-trained Transformer language modelsand fine-tuned on a single GPU on a corpus of 50k records ([Stanford Alpaca](https://crfm.stanford.edu/2023/03/13/alpaca.html)) to help it exhibit chat-based capabilities.
The ChOPT family of models from AI Squared are licensed under the OPT-175B license, Copyright (c) Meta Platforms, Inc. All Rights Reserved.
While `chopt-research-125m` is **not a state-of-the-art model**, we believe that the level of interactivity that can be achieved on such a small model that is trained so cheaply is important to showcase, as it continues to demonstrate that creating powerful AI capabilities may be much more accessible than previously thought.
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** AI Squared, Inc.
- **Shared by:** AI Squared, Inc.
- **Model type:** Large Language Model
- **Language(s) (NLP):** EN
- **License:** Other
- **Finetuned from model:** OPT
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
**`chopt-research-125m` is not a state-of-the-art language model.** `chopt-research-125m` is an experimental technology and is not designed for use in any
environment other than for research purposes. Furthermore, the model can sometimes exhibit undesired behaviors. Some of these behaviors include,
but are not limited to: factual inaccuracies, biases, offensive responses, toxicity, and hallucinations.
Just as with any other LLM, we advise users of this technology to exercise good judgment when applying this technology.
## Usage
To use the model with the `transformers` library on a machine with GPUs, first make sure you have the `transformers` and `accelerate` libraries installed.
From your terminal, run:
```python
pip install "accelerate>=0.16.0,<1" "transformers[torch]>=4.28.1,<5" "torch>=1.13.1,<2"
```
The instruction following pipeline can be loaded using the `pipeline` function as shown below. This loads a custom `InstructionTextGenerationPipeline`
found in the model repo [here](https://huggingface.co/aisquared/chopt-research-125m/blob/main/instruct_pipeline.py), which is why `trust_remote_code=True` is required.
Including `torch_dtype=torch.bfloat16` is generally recommended if this type is supported in order to reduce memory usage. It does not appear to impact output quality.
It is also fine to remove it if there is sufficient memory.
```python
from transformers import pipeline
import torch
generate_text = pipeline(model="aisquared/chopt-research-125m", torch_dtype=torch.bfloat16, trust_remote_code=True, device_map="auto")
```
You can then use the pipeline to answer instructions:
```python
res = generate_text("Who was George Washington?")
print(res)
```
Alternatively, if you prefer to not use `trust_remote_code=True` you can download [instruct_pipeline.py](https://huggingface.co/aisquared/chopt-research-125m/blob/main/instruct_pipeline.py),
store it alongside your notebook, and construct the pipeline yourself from the loaded model and tokenizer:
```python
from instruct_pipeline import InstructionTextGenerationPipeline
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
tokenizer = AutoTokenizer.from_pretrained("aisquared/chopt-research-125m", padding_side="left")
model = AutoModelForCausalLM.from_pretrained("aisquared/chopt-research-125m", device_map="auto", torch_dtype=torch.bfloat16)
generate_text = InstructionTextGenerationPipeline(model=model, tokenizer=tokenizer)
```
### Model Performance Metrics
We present the results from various model benchmarks on the EleutherAI LLM Evaluation Harness for all models in the DLite family.
Model results are sorted by mean score, ascending, to provide an ordering. These metrics serve to further show that none of the DLite models are
state of the art, but rather further show that chat-like behaviors in LLMs can be trained almost independent of model size.
| Model | openbookqa | arc_easy | winogrande | hellaswag | arc_challenge | piqa | boolq |
|:--------------------|-------------:|-----------:|-------------:|------------:|----------------:|---------:|---------:|
| chopt-125m | 0.178 | 0.443182 | 0.501973 | 0.294165 | 0.197099 | 0.630577 | 0.476758 |
| chopt-research-125m | 0.17 | 0.436027 | 0.503552 | 0.294762 | 0.205631 | 0.62568 | 0.48685 |
| opt-125m | 0.166 | 0.435606 | 0.501973 | 0.291775 | 0.190273 | 0.6284 | 0.554434 |
| chopt-350m | 0.178 | 0.450758 | 0.508287 | 0.325334 | 0.21843 | 0.650707 | 0.559633 |
| opt_350m | 0.176 | 0.441077 | 0.52644 | 0.320056 | 0.207338 | 0.645267 | 0.57737 |
| chopt-research-350m | 0.172 | 0.462542 | 0.514601 | 0.327524 | 0.235495 | 0.643634 | 0.589908 |
| opt-1.3b | 0.234 | 0.569865 | 0.596685 | 0.414957 | 0.232935 | 0.718172 | 0.577676 |
| chopt-research-1_3b | 0.232 | 0.564815 | 0.59116 | 0.424716 | 0.276451 | 0.713275 | 0.634557 |
| chopt-1_3b | 0.236 | 0.569444 | 0.584057 | 0.42621 | 0.268771 | 0.723069 | 0.658104 |
| opt-2.7b | 0.25 | 0.608165 | 0.608524 | 0.458176 | 0.267918 | 0.738303 | 0.603058 |
| chopt-2_7b | 0.276 | 0.616582 | 0.601421 | 0.472615 | 0.288396 | 0.75136 | 0.552294 |
| chopt-research-2_7b | 0.262 | 0.610269 | 0.625099 | 0.458176 | 0.295222 | 0.742111 | 0.636697 | | 5,959 | [
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0.0023288726806640625,
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0.0281982421875,
0.0117340087890625,
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... |
Lakera/autotrain-cancer-lakera-50807121085 | 2023-04-19T15:20:24.000Z | [
"transformers",
"pytorch",
"beit",
"image-classification",
"autotrain",
"vision",
"dataset:Lakera/autotrain-data-cancer-lakera",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-classification | Lakera | null | null | Lakera/autotrain-cancer-lakera-50807121085 | 0 | 2 | transformers | 2023-04-19T15:10:09 | ---
tags:
- autotrain
- vision
- image-classification
datasets:
- Lakera/autotrain-data-cancer-lakera
widget:
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg
example_title: Tiger
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg
example_title: Teapot
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg
example_title: Palace
co2_eq_emissions:
emissions: 0.017341401621589574
---
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 50807121085
- CO2 Emissions (in grams): 0.0173
## Validation Metrics
- Loss: 0.039
- Accuracy: 0.973
- Macro F1: 0.971
- Micro F1: 0.973
- Weighted F1: 0.973
- Macro Precision: 0.974
- Micro Precision: 0.973
- Weighted Precision: 0.973
- Macro Recall: 0.968
- Micro Recall: 0.973
- Weighted Recall: 0.973 | 883 | [
[
-0.024078369140625,
-0.00974273681640625,
0.0162506103515625,
0.0007891654968261719,
0.006267547607421875,
0.01081085205078125,
0.0067596435546875,
-0.0172271728515625,
-0.0204010009765625,
-0.00484466552734375,
-0.0318603515625,
-0.042449951171875,
-0.045623779... |
andli28/a2c-AntBulletEnv-v0 | 2023-04-19T16:36:11.000Z | [
"stable-baselines3",
"AntBulletEnv-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | andli28 | null | null | andli28/a2c-AntBulletEnv-v0 | 0 | 2 | stable-baselines3 | 2023-04-19T16:35:04 | ---
library_name: stable-baselines3
tags:
- AntBulletEnv-v0
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: AntBulletEnv-v0
type: AntBulletEnv-v0
metrics:
- type: mean_reward
value: 1304.30 +/- 31.39
name: mean_reward
verified: false
---
# **A2C** Agent playing **AntBulletEnv-v0**
This is a trained model of a **A2C** agent playing **AntBulletEnv-v0**
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
...
```
| 790 | [
[
-0.02679443359375,
-0.04443359375,
0.0106964111328125,
0.0208892822265625,
-0.0034961700439453125,
0.0018033981323242188,
0.0187530517578125,
-0.0176544189453125,
0.0193939208984375,
0.0265655517578125,
-0.052642822265625,
-0.037506103515625,
-0.04425048828125,
... |
jusancp99/clasificador-reviews-amazon | 2023-04-19T17:27:50.000Z | [
"transformers",
"pytorch",
"bert",
"text-classification",
"classification",
"generated_from_trainer",
"dataset:amazon_polarity",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | text-classification | jusancp99 | null | null | jusancp99/clasificador-reviews-amazon | 0 | 2 | transformers | 2023-04-19T17:22:11 | ---
license: apache-2.0
tags:
- classification
- generated_from_trainer
datasets:
- amazon_polarity
metrics:
- accuracy
model-index:
- name: clasificador-reviews-amazon
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: amazon_polarity
type: amazon_polarity
config: amazon_polarity
split: test
args: amazon_polarity
metrics:
- name: Accuracy
type: accuracy
value: 0.926
---
<!-- 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. -->
# clasificador-reviews-amazon
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the amazon_polarity dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4642
- Accuracy: 0.926
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
Los conjuntos de train y de test se han reducido respecto al dataset original amazon_polarity para mantener unos tiempos de ejecución relativamente cortos.
## 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: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.3674 | 1.0 | 625 | 0.2204 | 0.928 |
| 0.1924 | 2.0 | 1250 | 0.3444 | 0.926 |
| 0.0974 | 3.0 | 1875 | 0.4642 | 0.926 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
| 1,947 | [
[
-0.037353515625,
-0.0312042236328125,
0.005176544189453125,
0.012908935546875,
-0.04119873046875,
-0.021728515625,
-0.010772705078125,
-0.01409149169921875,
0.01523590087890625,
0.03985595703125,
-0.05950927734375,
-0.049163818359375,
-0.041107177734375,
-0.... |
culteejen/PPO-harcodemap-punish-stagnant-RoombaAToB-harcodemap-punish-stagnant | 2023-04-19T17:39:23.000Z | [
"stable-baselines3",
"RoombaAToB-harcodemap-punish-stagnant",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | culteejen | null | null | culteejen/PPO-harcodemap-punish-stagnant-RoombaAToB-harcodemap-punish-stagnant | 0 | 2 | stable-baselines3 | 2023-04-19T17:38:50 | ---
library_name: stable-baselines3
tags:
- RoombaAToB-harcodemap-punish-stagnant
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: RoombaAToB-harcodemap-punish-stagnant
type: RoombaAToB-harcodemap-punish-stagnant
metrics:
- type: mean_reward
value: -219.76 +/- 0.00
name: mean_reward
verified: false
---
# **PPO** Agent playing **RoombaAToB-harcodemap-punish-stagnant**
This is a trained model of a **PPO** agent playing **RoombaAToB-harcodemap-punish-stagnant**
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
...
```
| 899 | [
[
-0.017486572265625,
-0.046417236328125,
0.0144805908203125,
0.031768798828125,
-0.006633758544921875,
-0.01033782958984375,
0.0223541259765625,
-0.005390167236328125,
0.03717041015625,
0.06365966796875,
-0.03631591796875,
-0.04315185546875,
-0.045318603515625,
... |
culteejen/PPO-harcodemap-punish-stagnant-bounds-RoombaAToB-harcodemap-punish-stagnant-bounds | 2023-04-19T17:46:57.000Z | [
"stable-baselines3",
"RoombaAToB-harcodemap-punish-stagnant-bounds",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | culteejen | null | null | culteejen/PPO-harcodemap-punish-stagnant-bounds-RoombaAToB-harcodemap-punish-stagnant-bounds | 0 | 2 | stable-baselines3 | 2023-04-19T17:46:25 | ---
library_name: stable-baselines3
tags:
- RoombaAToB-harcodemap-punish-stagnant-bounds
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: RoombaAToB-harcodemap-punish-stagnant-bounds
type: RoombaAToB-harcodemap-punish-stagnant-bounds
metrics:
- type: mean_reward
value: -278.31 +/- 0.00
name: mean_reward
verified: false
---
# **PPO** Agent playing **RoombaAToB-harcodemap-punish-stagnant-bounds**
This is a trained model of a **PPO** agent playing **RoombaAToB-harcodemap-punish-stagnant-bounds**
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
...
```
| 934 | [
[
-0.0168609619140625,
-0.051849365234375,
0.01861572265625,
0.0274810791015625,
-0.0016021728515625,
-0.01050567626953125,
0.0175628662109375,
-0.0063629150390625,
0.032073974609375,
0.065185546875,
-0.0401611328125,
-0.043670654296875,
-0.0428466796875,
-0.0... |
Katowise/clasificador-sms | 2023-04-19T18:33:34.000Z | [
"transformers",
"pytorch",
"bert",
"text-classification",
"classification",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | Katowise | null | null | Katowise/clasificador-sms | 0 | 2 | transformers | 2023-04-19T18:17:48 | ---
license: apache-2.0
tags:
- classification
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: clasificador-sms
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. -->
# clasificador-sms
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0286
- Accuracy: 0.9964
## Model description
Se cree que arroja un acuraccy tan bueno porque las clases están desbalanceadas, como no era el objetivo de la asignatura no se indagado más sobre este problema
## 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: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.0805 | 1.0 | 627 | 0.0328 | 0.9928 |
| 0.0343 | 2.0 | 1254 | 0.0180 | 0.9964 |
| 0.0132 | 3.0 | 1881 | 0.0286 | 0.9964 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
| 1,602 | [
[
-0.037750244140625,
-0.0394287109375,
0.0082855224609375,
0.0212249755859375,
-0.0305633544921875,
-0.03314208984375,
-0.01433563232421875,
-0.020782470703125,
0.01413726806640625,
0.024871826171875,
-0.057525634765625,
-0.050537109375,
-0.047698974609375,
-... |
culteejen/PPO-left-goal-punish-stagnant-bounds-RoombaAToB-left-goal-punish-stagnant-bounds | 2023-04-19T18:18:55.000Z | [
"stable-baselines3",
"RoombaAToB-left-goal-punish-stagnant-bounds",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | culteejen | null | null | culteejen/PPO-left-goal-punish-stagnant-bounds-RoombaAToB-left-goal-punish-stagnant-bounds | 0 | 2 | stable-baselines3 | 2023-04-19T18:18:34 | ---
library_name: stable-baselines3
tags:
- RoombaAToB-left-goal-punish-stagnant-bounds
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: RoombaAToB-left-goal-punish-stagnant-bounds
type: RoombaAToB-left-goal-punish-stagnant-bounds
metrics:
- type: mean_reward
value: 1211.81 +/- 0.00
name: mean_reward
verified: false
---
# **PPO** Agent playing **RoombaAToB-left-goal-punish-stagnant-bounds**
This is a trained model of a **PPO** agent playing **RoombaAToB-left-goal-punish-stagnant-bounds**
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
...
```
| 929 | [
[
-0.002593994140625,
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0.011474609375,
0.024932861328125,
-0.003353118896484375,
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0.018341064453125,
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0.03851318359375,
0.05328369140625,
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-0.018... |
culteejen/PPO-punish-stagnant-bounds-RoombaAToB-punish-stagnant-bounds | 2023-04-19T18:35:27.000Z | [
"stable-baselines3",
"RoombaAToB-punish-stagnant-bounds",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | culteejen | null | null | culteejen/PPO-punish-stagnant-bounds-RoombaAToB-punish-stagnant-bounds | 0 | 2 | stable-baselines3 | 2023-04-19T18:34:56 | ---
library_name: stable-baselines3
tags:
- RoombaAToB-punish-stagnant-bounds
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: RoombaAToB-punish-stagnant-bounds
type: RoombaAToB-punish-stagnant-bounds
metrics:
- type: mean_reward
value: -300.75 +/- 0.00
name: mean_reward
verified: false
---
# **PPO** Agent playing **RoombaAToB-punish-stagnant-bounds**
This is a trained model of a **PPO** agent playing **RoombaAToB-punish-stagnant-bounds**
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
...
```
| 879 | [
[
-0.01103973388671875,
-0.054962158203125,
0.01177978515625,
0.02520751953125,
0.0015439987182617188,
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0.02337646484375,
-0.0019969940185546875,
0.0276031494140625,
0.055450439453125,
-0.046661376953125,
-0.027313232421875,
-0.03594970703125,
... |
vocabtrimmer/xlm-roberta-base-xnli-en-trimmed-en | 2023-04-19T19:05:17.000Z | [
"transformers",
"pytorch",
"xlm-roberta",
"text-classification",
"endpoints_compatible",
"region:us"
] | text-classification | vocabtrimmer | null | null | vocabtrimmer/xlm-roberta-base-xnli-en-trimmed-en | 0 | 2 | transformers | 2023-04-19T18:57:02 | # Vocabulary Trimmed [vocabtrimmer/xlm-roberta-base-xnli-en](https://huggingface.co/vocabtrimmer/xlm-roberta-base-xnli-en): `vocabtrimmer/xlm-roberta-base-xnli-en-trimmed-en`
This model is a trimmed version of [vocabtrimmer/xlm-roberta-base-xnli-en](https://huggingface.co/vocabtrimmer/xlm-roberta-base-xnli-en) by [`vocabtrimmer`](https://github.com/asahi417/lm-vocab-trimmer), a tool for trimming vocabulary of language models to compress the model size.
Following table shows a summary of the trimming process.
| | vocabtrimmer/xlm-roberta-base-xnli-en | vocabtrimmer/xlm-roberta-base-xnli-en-trimmed-en |
|:---------------------------|:----------------------------------------|:---------------------------------------------------|
| parameter_size_full | 278,045,955 | 219,090,435 |
| parameter_size_embedding | 192,001,536 | 133,046,016 |
| vocab_size | 250,002 | 173,237 |
| compression_rate_full | 100.0 | 78.8 |
| compression_rate_embedding | 100.0 | 69.29 |
Following table shows the parameter used to trim vocabulary.
| language | dataset | dataset_column | dataset_name | dataset_split | target_vocab_size | min_frequency |
|:-----------|:----------------------------|:-----------------|:---------------|:----------------|:--------------------|----------------:|
| en | vocabtrimmer/mc4_validation | text | en | validation | | 2 | | 1,879 | [
[
-0.059844970703125,
-0.045257568359375,
-0.0019350051879882812,
0.006626129150390625,
-0.0294647216796875,
-0.0127716064453125,
-0.0214996337890625,
-0.01007080078125,
0.0390625,
0.044097900390625,
-0.061370849609375,
-0.05279541015625,
-0.033660888671875,
0... |
vocabtrimmer/xlm-roberta-base-xnli-fr-trimmed-fr | 2023-04-19T19:30:34.000Z | [
"transformers",
"pytorch",
"xlm-roberta",
"text-classification",
"endpoints_compatible",
"region:us"
] | text-classification | vocabtrimmer | null | null | vocabtrimmer/xlm-roberta-base-xnli-fr-trimmed-fr | 0 | 2 | transformers | 2023-04-19T19:25:35 | # Vocabulary Trimmed [vocabtrimmer/xlm-roberta-base-xnli-fr](https://huggingface.co/vocabtrimmer/xlm-roberta-base-xnli-fr): `vocabtrimmer/xlm-roberta-base-xnli-fr-trimmed-fr`
This model is a trimmed version of [vocabtrimmer/xlm-roberta-base-xnli-fr](https://huggingface.co/vocabtrimmer/xlm-roberta-base-xnli-fr) by [`vocabtrimmer`](https://github.com/asahi417/lm-vocab-trimmer), a tool for trimming vocabulary of language models to compress the model size.
Following table shows a summary of the trimming process.
| | vocabtrimmer/xlm-roberta-base-xnli-fr | vocabtrimmer/xlm-roberta-base-xnli-fr-trimmed-fr |
|:---------------------------|:----------------------------------------|:---------------------------------------------------|
| parameter_size_full | 278,045,955 | 151,865,091 |
| parameter_size_embedding | 192,001,536 | 65,820,672 |
| vocab_size | 250,002 | 85,704 |
| compression_rate_full | 100.0 | 54.62 |
| compression_rate_embedding | 100.0 | 34.28 |
Following table shows the parameter used to trim vocabulary.
| language | dataset | dataset_column | dataset_name | dataset_split | target_vocab_size | min_frequency |
|:-----------|:----------------------------|:-----------------|:---------------|:----------------|:--------------------|----------------:|
| fr | vocabtrimmer/mc4_validation | text | fr | validation | | 2 | | 1,879 | [
[
-0.05975341796875,
-0.04693603515625,
-0.004131317138671875,
0.00855255126953125,
-0.0312042236328125,
-0.01299285888671875,
-0.0204010009765625,
-0.0089263916015625,
0.03680419921875,
0.043365478515625,
-0.061065673828125,
-0.051483154296875,
-0.034027099609375... |
Nake/a2c-AntBulletEnv-v0 | 2023-04-19T19:53:52.000Z | [
"stable-baselines3",
"AntBulletEnv-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | Nake | null | null | Nake/a2c-AntBulletEnv-v0 | 0 | 2 | stable-baselines3 | 2023-04-19T19:52:41 | ---
library_name: stable-baselines3
tags:
- AntBulletEnv-v0
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: AntBulletEnv-v0
type: AntBulletEnv-v0
metrics:
- type: mean_reward
value: 845.47 +/- 135.68
name: mean_reward
verified: false
---
# **A2C** Agent playing **AntBulletEnv-v0**
This is a trained model of a **A2C** agent playing **AntBulletEnv-v0**
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
...
```
| 790 | [
[
-0.0267791748046875,
-0.044403076171875,
0.0106964111328125,
0.0208740234375,
-0.0035152435302734375,
0.0017948150634765625,
0.0187530517578125,
-0.01763916015625,
0.0193939208984375,
0.0265655517578125,
-0.052581787109375,
-0.037506103515625,
-0.04425048828125,... |
vocabtrimmer/xlm-roberta-base-xnli-de-trimmed-de | 2023-04-19T20:00:10.000Z | [
"transformers",
"pytorch",
"xlm-roberta",
"text-classification",
"endpoints_compatible",
"region:us"
] | text-classification | vocabtrimmer | null | null | vocabtrimmer/xlm-roberta-base-xnli-de-trimmed-de | 0 | 2 | transformers | 2023-04-19T19:54:42 | # Vocabulary Trimmed [vocabtrimmer/xlm-roberta-base-xnli-de](https://huggingface.co/vocabtrimmer/xlm-roberta-base-xnli-de): `vocabtrimmer/xlm-roberta-base-xnli-de-trimmed-de`
This model is a trimmed version of [vocabtrimmer/xlm-roberta-base-xnli-de](https://huggingface.co/vocabtrimmer/xlm-roberta-base-xnli-de) by [`vocabtrimmer`](https://github.com/asahi417/lm-vocab-trimmer), a tool for trimming vocabulary of language models to compress the model size.
Following table shows a summary of the trimming process.
| | vocabtrimmer/xlm-roberta-base-xnli-de | vocabtrimmer/xlm-roberta-base-xnli-de-trimmed-de |
|:---------------------------|:----------------------------------------|:---------------------------------------------------|
| parameter_size_full | 278,045,955 | 156,466,947 |
| parameter_size_embedding | 192,001,536 | 70,422,528 |
| vocab_size | 250,002 | 91,696 |
| compression_rate_full | 100.0 | 56.27 |
| compression_rate_embedding | 100.0 | 36.68 |
Following table shows the parameter used to trim vocabulary.
| language | dataset | dataset_column | dataset_name | dataset_split | target_vocab_size | min_frequency |
|:-----------|:----------------------------|:-----------------|:---------------|:----------------|:--------------------|----------------:|
| de | vocabtrimmer/mc4_validation | text | de | validation | | 2 | | 1,879 | [
[
-0.059783935546875,
-0.048431396484375,
-0.0007543563842773438,
0.0048675537109375,
-0.0298004150390625,
-0.0137176513671875,
-0.020355224609375,
-0.00811004638671875,
0.039031982421875,
0.043701171875,
-0.05841064453125,
-0.0531005859375,
-0.034637451171875,
... |
vocabtrimmer/xlm-roberta-base-xnli-es-trimmed-es | 2023-04-19T20:32:19.000Z | [
"transformers",
"pytorch",
"xlm-roberta",
"text-classification",
"endpoints_compatible",
"region:us"
] | text-classification | vocabtrimmer | null | null | vocabtrimmer/xlm-roberta-base-xnli-es-trimmed-es | 0 | 2 | transformers | 2023-04-19T20:27:10 | # Vocabulary Trimmed [vocabtrimmer/xlm-roberta-base-xnli-es](https://huggingface.co/vocabtrimmer/xlm-roberta-base-xnli-es): `vocabtrimmer/xlm-roberta-base-xnli-es-trimmed-es`
This model is a trimmed version of [vocabtrimmer/xlm-roberta-base-xnli-es](https://huggingface.co/vocabtrimmer/xlm-roberta-base-xnli-es) by [`vocabtrimmer`](https://github.com/asahi417/lm-vocab-trimmer), a tool for trimming vocabulary of language models to compress the model size.
Following table shows a summary of the trimming process.
| | vocabtrimmer/xlm-roberta-base-xnli-es | vocabtrimmer/xlm-roberta-base-xnli-es-trimmed-es |
|:---------------------------|:----------------------------------------|:---------------------------------------------------|
| parameter_size_full | 278,045,955 | 152,921,859 |
| parameter_size_embedding | 192,001,536 | 66,877,440 |
| vocab_size | 250,002 | 87,080 |
| compression_rate_full | 100.0 | 55.0 |
| compression_rate_embedding | 100.0 | 34.83 |
Following table shows the parameter used to trim vocabulary.
| language | dataset | dataset_column | dataset_name | dataset_split | target_vocab_size | min_frequency |
|:-----------|:----------------------------|:-----------------|:---------------|:----------------|:--------------------|----------------:|
| es | vocabtrimmer/mc4_validation | text | es | validation | | 2 | | 1,879 | [
[
-0.058624267578125,
-0.0467529296875,
-0.001903533935546875,
0.0045318603515625,
-0.0300750732421875,
-0.011962890625,
-0.0207366943359375,
-0.0081634521484375,
0.038818359375,
0.04400634765625,
-0.061614990234375,
-0.053985595703125,
-0.0350341796875,
-0.00... |
vocabtrimmer/xlm-roberta-base-xnli-ar-trimmed-ar | 2023-04-19T20:43:04.000Z | [
"transformers",
"pytorch",
"xlm-roberta",
"text-classification",
"endpoints_compatible",
"region:us"
] | text-classification | vocabtrimmer | null | null | vocabtrimmer/xlm-roberta-base-xnli-ar-trimmed-ar | 0 | 2 | transformers | 2023-04-19T20:38:32 | # Vocabulary Trimmed [vocabtrimmer/xlm-roberta-base-xnli-ar](https://huggingface.co/vocabtrimmer/xlm-roberta-base-xnli-ar): `vocabtrimmer/xlm-roberta-base-xnli-ar-trimmed-ar`
This model is a trimmed version of [vocabtrimmer/xlm-roberta-base-xnli-ar](https://huggingface.co/vocabtrimmer/xlm-roberta-base-xnli-ar) by [`vocabtrimmer`](https://github.com/asahi417/lm-vocab-trimmer), a tool for trimming vocabulary of language models to compress the model size.
Following table shows a summary of the trimming process.
| | vocabtrimmer/xlm-roberta-base-xnli-ar | vocabtrimmer/xlm-roberta-base-xnli-ar-trimmed-ar |
|:---------------------------|:----------------------------------------|:---------------------------------------------------|
| parameter_size_full | 278,045,955 | 124,345,347 |
| parameter_size_embedding | 192,001,536 | 38,300,928 |
| vocab_size | 250,002 | 49,871 |
| compression_rate_full | 100.0 | 44.72 |
| compression_rate_embedding | 100.0 | 19.95 |
Following table shows the parameter used to trim vocabulary.
| language | dataset | dataset_column | dataset_name | dataset_split | target_vocab_size | min_frequency |
|:-----------|:----------------------------|:-----------------|:---------------|:----------------|:--------------------|----------------:|
| ar | vocabtrimmer/mc4_validation | text | ar | validation | | 2 | | 1,879 | [
[
-0.05889892578125,
-0.045989990234375,
-0.0045318603515625,
0.0038738250732421875,
-0.0301971435546875,
-0.0118255615234375,
-0.019256591796875,
-0.009552001953125,
0.037353515625,
0.043060302734375,
-0.058685302734375,
-0.051361083984375,
-0.03582763671875,
... |
Nake/a2c-PandaReachDense-v2 | 2023-04-19T21:03:57.000Z | [
"stable-baselines3",
"PandaReachDense-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | Nake | null | null | Nake/a2c-PandaReachDense-v2 | 0 | 2 | stable-baselines3 | 2023-04-19T21:01:26 | ---
library_name: stable-baselines3
tags:
- PandaReachDense-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachDense-v2
type: PandaReachDense-v2
metrics:
- type: mean_reward
value: -4.77 +/- 1.31
name: mean_reward
verified: false
---
# **A2C** Agent playing **PandaReachDense-v2**
This is a trained model of a **A2C** agent playing **PandaReachDense-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
...
```
| 802 | [
[
-0.019744873046875,
-0.04742431640625,
-0.004787445068359375,
0.0469970703125,
-0.00018846988677978516,
-0.006023406982421875,
0.033172607421875,
-0.0249481201171875,
0.028045654296875,
0.042694091796875,
-0.06256103515625,
-0.0289764404296875,
-0.03277587890625... |
charleschen2022/LunarLander-v2 | 2023-04-19T21:08:21.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | charleschen2022 | null | null | charleschen2022/LunarLander-v2 | 0 | 2 | stable-baselines3 | 2023-04-19T21:07:52 | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 247.27 +/- 17.57
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
...
```
| 784 | [
[
-0.00023484230041503906,
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0.017059326171875,
0.023345947265625,
-0.00606536865234375,
0.002735137939453125,
0.034454345703125,
-0.012115478515625,
0.019866943359375,
0.06500244140625,
-0.043212890625,
-0.035247802734375,
-0.0343017578125,
-... |
FelipePasquevich/ppo-Pyramids | 2023-04-19T21:58:00.000Z | [
"ml-agents",
"tensorboard",
"onnx",
"Pyramids",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
] | reinforcement-learning | FelipePasquevich | null | null | FelipePasquevich/ppo-Pyramids | 0 | 2 | ml-agents | 2023-04-19T21:57:55 | ---
library_name: ml-agents
tags:
- Pyramids
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Pyramids
---
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-Pyramids
2. Step 1: Find your model_id: FelipePasquevich/ppo-Pyramids
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
| 959 | [
[
-0.0272216796875,
-0.01983642578125,
0.0007038116455078125,
0.02667236328125,
-0.0097503662109375,
0.0064849853515625,
0.026702880859375,
-0.0033588409423828125,
0.035919189453125,
0.03521728515625,
-0.035430908203125,
-0.052215576171875,
-0.035614013671875,
... |
culteejen/PPO-mid-goal-RoombaAToB-mid-goal | 2023-04-19T23:07:36.000Z | [
"stable-baselines3",
"RoombaAToB-mid-goal",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | culteejen | null | null | culteejen/PPO-mid-goal-RoombaAToB-mid-goal | 0 | 2 | stable-baselines3 | 2023-04-19T22:21:21 | ---
library_name: stable-baselines3
tags:
- RoombaAToB-mid-goal
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: RoombaAToB-mid-goal
type: RoombaAToB-mid-goal
metrics:
- type: mean_reward
value: 595.49 +/- 0.00
name: mean_reward
verified: false
---
# **PPO** Agent playing **RoombaAToB-mid-goal**
This is a trained model of a **PPO** agent playing **RoombaAToB-mid-goal**
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
...
```
| 808 | [
[
-0.00496673583984375,
-0.043853759765625,
0.0152740478515625,
0.027496337890625,
0.0035858154296875,
-0.004878997802734375,
0.02423095703125,
0.001544952392578125,
0.0271759033203125,
0.03985595703125,
-0.04949951171875,
-0.0307769775390625,
-0.03411865234375,
... |
ROGRANMAR/whisper-espanol | 2023-04-20T11:52:55.000Z | [
"transformers",
"pytorch",
"tensorboard",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"es",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | ROGRANMAR | null | null | ROGRANMAR/whisper-espanol | 0 | 2 | transformers | 2023-04-19T23:40:12 | ---
language:
- es
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: Whisper Small spanish - ROGRANMAR
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. -->
# Whisper Small spanish - ROGRANMAR
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the minds dataset.
It achieves the following results on the evaluation set:
- Loss: nan
- Wer: 100.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.1
- train_batch_size: 64
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1
- training_steps: 40
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:---------:|
| No log | 5.0 | 10 | 141.9978 | 1248.1793 |
| No log | 10.0 | 20 | nan | 100.0 |
| 77.0413 | 15.0 | 30 | nan | 100.0 |
| 77.0413 | 20.0 | 40 | nan | 100.0 |
### Framework versions
- Transformers 4.29.0.dev0
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
| 1,635 | [
[
-0.032562255859375,
-0.0391845703125,
0.01297760009765625,
0.012481689453125,
-0.0251617431640625,
-0.041290283203125,
-0.0262908935546875,
-0.0289154052734375,
0.0209197998046875,
0.0179595947265625,
-0.0628662109375,
-0.039276123046875,
-0.0439453125,
-0.0... |
geovanyuribe/platzi-distilroberta-base-mrpc-glue-geovany-uribe | 2023-04-20T04:04:08.000Z | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | text-classification | geovanyuribe | null | null | geovanyuribe/platzi-distilroberta-base-mrpc-glue-geovany-uribe | 0 | 2 | transformers | 2023-04-20T03:35:56 | ---
license: apache-2.0
tags:
- text-classification
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
- f1
model-index:
- name: platzi-distilroberta-base-mrpc-glue-geovany-uribe
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: 0.8650646950092421
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# platzi-distilroberta-base-mrpc-glue-geovany-uribe
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: 0.5912
- Accuracy: 0.8211
- F1: 0.8651
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.5085 | 1.09 | 500 | 0.6381 | 0.7990 | 0.8571 |
| 0.3597 | 2.18 | 1000 | 0.5912 | 0.8211 | 0.8651 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
| 1,879 | [
[
-0.03253173828125,
-0.040863037109375,
0.01448822021484375,
0.017364501953125,
-0.02899169921875,
-0.0307769775390625,
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-0.0012311935424804688,
0.00022077560424804688,
0.01532745361328125,
-0.0509033203125,
-0.05126953125,
-0.056121826171875,... |
chinmayapani/distilBert-base-tag-classification | 2023-04-24T07:46:14.000Z | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"endpoints_compatible",
"region:us"
] | text-classification | chinmayapani | null | null | chinmayapani/distilBert-base-tag-classification | 0 | 2 | transformers | 2023-04-20T05:45:30 | The following model is a Pytorch pre-trained model obtained from converting pytorch checkpoint found in the official distilbert-base-uncased.
This is one of the faster pre-trained BERT variants, that can be used for multiple tasks. This model is trained on stackoverflow data to predict the language tag.
| 307 | [
[
-0.03668212890625,
-0.05657958984375,
0.0189056396484375,
0.00859832763671875,
-0.01012420654296875,
0.01517486572265625,
-0.0021266937255859375,
-0.030181884765625,
0.0038318634033203125,
0.038421630859375,
-0.06011962890625,
-0.00969696044921875,
-0.0484924316... |
Celestinian/SentimentGPT | 2023-04-25T19:19:39.000Z | [
"transformers",
"pytorch",
"safetensors",
"gpt_neox",
"text-generation",
"en",
"license:apache-2.0",
"has_space",
"text-generation-inference",
"region:us"
] | text-generation | Celestinian | null | null | Celestinian/SentimentGPT | 0 | 2 | transformers | 2023-04-20T06:18:01 | ---
license: apache-2.0
language:
- en
inference: False
---
This is a basic general-purpose sentiment classification model that predicts whether a given text has a positive or negative sentiment. The model outputs '0' for negative sentiment and '1' for positive sentiment.
The EOS token utilized for the model is represented by the symbol ">". Thus, in order to ensure proper functionality, it is recommended to conclude inputs with this particular token.
Try out the demo using the following link: https://huggingface.co/spaces/Celestinian/SentimentGPT | 556 | [
[
-0.03497314453125,
-0.044708251953125,
0.0241241455078125,
-0.008148193359375,
-0.053131103515625,
-0.01318359375,
0.019439697265625,
-0.0176239013671875,
0.06341552734375,
0.015411376953125,
-0.052825927734375,
-0.06048583984375,
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-0.000080287... |
Salesforce/codet5-base-codexglue-sum-php | 2023-04-20T06:50:31.000Z | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"license:bsd-3-clause",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | text2text-generation | Salesforce | null | null | Salesforce/codet5-base-codexglue-sum-php | 1 | 2 | transformers | 2023-04-20T06:48:04 | ---
license: bsd-3-clause
---
This is a finetuned CodeT5-base checkpoint on CodeXGLUE code summarization PHP data.
Pretrained model: https://huggingface.co/Salesforce/codet5-base
Finetuning dataset: https://huggingface.co/datasets/code_x_glue_ct_code_to_text (only the PHP split) | 281 | [
[
-0.0301666259765625,
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0.02374267578125,
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0.030487060546875,
0.0692138671875,
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... |
vvsotnikov/stablelm-tuned-alpha-7b-16bit | 2023-04-20T10:07:27.000Z | [
"transformers",
"pytorch",
"gpt_neox",
"text-generation",
"causal-lm",
"en",
"dataset:dmayhem93/ChatCombined",
"dataset:tatsu-lab/alpaca",
"dataset:nomic-ai/gpt4all_prompt_generations",
"dataset:Dahoas/full-hh-rlhf",
"dataset:jeffwan/sharegpt_vicuna",
"dataset:HuggingFaceH4/databricks_dolly_15... | text-generation | vvsotnikov | null | null | vvsotnikov/stablelm-tuned-alpha-7b-16bit | 5 | 2 | transformers | 2023-04-20T09:27:28 | ---
language:
- en
tags:
- causal-lm
license: cc-by-nc-sa-4.0
datasets:
- dmayhem93/ChatCombined
- tatsu-lab/alpaca
- nomic-ai/gpt4all_prompt_generations
- Dahoas/full-hh-rlhf
- jeffwan/sharegpt_vicuna
- HuggingFaceH4/databricks_dolly_15k
---
# StableLM-Tuned-Alpha 16-bit
## Model Description
16-bit version of `StableLM-Tuned-Alpha` compressed for the sake of speed and memory usage. No other changes were made. Original model: https://huggingface.co/stabilityai/stablelm-tuned-alpha-7b
## Usage
Get started chatting with `StableLM-Tuned-Alpha 16-bit` by using the following code snippet:
```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, StoppingCriteria, StoppingCriteriaList
tokenizer = AutoTokenizer.from_pretrained("vvsotnikov/stablelm-tuned-alpha-7b-16bit")
model = AutoModelForCausalLM.from_pretrained("vvsotnikov/stablelm-tuned-alpha-7b-16bit", torch_dtype=torch.float16)
model.cuda()
class StopOnTokens(StoppingCriteria):
def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool:
stop_ids = [50278, 50279, 50277, 1, 0]
for stop_id in stop_ids:
if input_ids[0][-1] == stop_id:
return True
return False
system_prompt = """<|SYSTEM|># StableLM Tuned (Alpha version)
- StableLM is a helpful and harmless open-source AI language model developed by StabilityAI.
- StableLM is excited to be able to help the user, but will refuse to do anything that could be considered harmful to the user.
- StableLM is more than just an information source, StableLM is also able to write poetry, short stories, and make jokes.
- StableLM will refuse to participate in anything that could harm a human.
"""
prompt = f"{system_prompt}<|USER|>What's your mood today?<|ASSISTANT|>"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
tokens = model.generate(
**inputs,
max_new_tokens=64,
temperature=0.7,
do_sample=True,
stopping_criteria=StoppingCriteriaList([StopOnTokens()])
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
``` | 2,080 | [
[
-0.02801513671875,
-0.07305908203125,
0.0025501251220703125,
0.0216217041015625,
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0.00440216064453125,
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0.0096893310546875,
-0.03729248046875,
-0.0362548828125,
-0.03826904296875,
... |
P3ps/distilbert-amazon-shoe-reviews-scaled | 2023-04-20T11:02:37.000Z | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | P3ps | null | null | P3ps/distilbert-amazon-shoe-reviews-scaled | 0 | 2 | transformers | 2023-04-20T11:00:54 | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: distilbert-amazon-shoe-reviews-scaled
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-amazon-shoe-reviews-scaled
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1836
- Accuracy: 0.456
- F1: [0.56281407 0.31088083 0.32608696 0.32142857 0.6640625 ]
- Precision: [0.5045045 0.34090909 0.37037037 0.35064935 0.59440559]
- Recall: [0.63636364 0.28571429 0.29126214 0.2967033 0.75221239]
## 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: 32
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------------------------------------------------------:|:--------------------------------------------------------:|:--------------------------------------------------------:|
| 1.3447 | 1.0 | 141 | 1.1836 | 0.456 | [0.56281407 0.31088083 0.32608696 0.32142857 0.6640625 ] | [0.5045045 0.34090909 0.37037037 0.35064935 0.59440559] | [0.63636364 0.28571429 0.29126214 0.2967033 0.75221239] |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
| 2,173 | [
[
-0.040924072265625,
-0.036346435546875,
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0.0159149169921875,
-0.0184478759765625,
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0.017242431640625,
0.0119781494140625,
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-0.0552978515625,
... |
P3ps/distilbert-amazon-shoe-reviews | 2023-04-20T11:34:31.000Z | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | P3ps | null | null | P3ps/distilbert-amazon-shoe-reviews | 0 | 2 | transformers | 2023-04-20T11:07:39 | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: distilbert-amazon-shoe-reviews
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-amazon-shoe-reviews
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9519
- Accuracy: 0.5757
- F1: [0.63178677 0.45622938 0.50453543 0.55380711 0.73119358]
- Precision: [0.62256809 0.46798542 0.48583569 0.58248799 0.71751969]
- Recall: [0.64128257 0.4450495 0.52473228 0.52781809 0.74539877]
## 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: 32
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------------------------------------------------------:|:--------------------------------------------------------:|:--------------------------------------------------------:|
| 0.9652 | 1.0 | 2813 | 0.9519 | 0.5757 | [0.63178677 0.45622938 0.50453543 0.55380711 0.73119358] | [0.62256809 0.46798542 0.48583569 0.58248799 0.71751969] | [0.64128257 0.4450495 0.52473228 0.52781809 0.74539877] |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
| 2,160 | [
[
-0.044036865234375,
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0.0182647705078125,
0.01302337646484375,
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0.002399444580078125,
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0.01666259765625,
0.0115203857421875,
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-0.04541015625,
-0.053497314453125,
-0.0... |
harithapliyal/autotrain-tatanic-survival-51030121311 | 2023-04-20T11:58:44.000Z | [
"transformers",
"joblib",
"xgboost",
"autotrain",
"tabular",
"classification",
"tabular-classification",
"dataset:harithapliyal/autotrain-data-tatanic-survival",
"co2_eq_emissions",
"endpoints_compatible",
"region:us"
] | tabular-classification | harithapliyal | null | null | harithapliyal/autotrain-tatanic-survival-51030121311 | 0 | 2 | transformers | 2023-04-20T11:56:15 | ---
tags:
- autotrain
- tabular
- classification
- tabular-classification
datasets:
- harithapliyal/autotrain-data-tatanic-survival
co2_eq_emissions:
emissions: 0.004107493848653723
---
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 51030121311
- CO2 Emissions (in grams): 0.0041
## Validation Metrics
- Loss: 0.358
- Accuracy: 0.872
- Precision: 0.859
- Recall: 0.797
- AUC: 0.903
- F1: 0.827
## Usage
```python
import json
import joblib
import pandas as pd
model = joblib.load('model.joblib')
config = json.load(open('config.json'))
features = config['features']
# data = pd.read_csv("data.csv")
data = data[features]
data.columns = ["feat_" + str(col) for col in data.columns]
predictions = model.predict(data) # or model.predict_proba(data)
``` | 797 | [
[
-0.010650634765625,
-0.0253753662109375,
0.0137176513671875,
0.00576019287109375,
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-0.004535675048828125,
0.00469207763671875,
-0.0079193115234375,
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0.01983642578125,
-0.02197265625,
-0.0418701171875,
-0.05511474609375,... |
TheMrguiller/Deberta_context_toxicity | 2023-05-15T13:05:20.000Z | [
"transformers",
"pytorch",
"safetensors",
"deberta",
"text-classification",
"en",
"endpoints_compatible",
"region:us"
] | text-classification | TheMrguiller | null | null | TheMrguiller/Deberta_context_toxicity | 0 | 2 | transformers | 2023-04-20T13:34:01 | ---
language:
- en
pipeline_tag: text-classification
---
It is a model able to predict toxicity given a history and a response to it. It is created for dialog agents. To use it correctly please use the following schematics: [HST]Hi,how are you?`[END]I am doing fine[ANS] I hope you die.
Token [HST] initiates the history of the conversation and every pair turn is separeted by [END]. Token [ANS] indicates start of the response to the last utterance. I will update this card, but right now I am developing a bigger proyect with these,so i dont have the time to indicate all the results. | 586 | [
[
-0.003818511962890625,
-0.06488037109375,
0.03472900390625,
-0.0017337799072265625,
-0.04425048828125,
0.00885009765625,
0.040985107421875,
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0.044769287109375,
0.054107666015625,
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-0.045562744140625,
-0.03082275390625,
-0.... |
nicotaroni/distilbert-multilingual-cased_fine_tuned_ | 2023-04-21T13:33:40.000Z | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"endpoints_compatible",
"region:us"
] | text-classification | nicotaroni | null | null | nicotaroni/distilbert-multilingual-cased_fine_tuned_ | 0 | 2 | transformers | 2023-04-20T15:10:27 | ---
metrics:
- accuracy
library_name: transformers
pipeline_tag: text-classification
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
The model has been fine-tuned for a multilingual classification text task: it recognizes whether a real-estate advertisement is an agency advertisement (label = 1) or a private advertisement (label = 0).
- **Developed by:** [nicotaroni]
- **Model type:** [text-classification]
- **Language(s) (NLP):** [Multilingual]
- **Finetuned from model [optional]:** [distilbert-multilingual-cased]
## How to Get Started with the Model
Use the code below to get started with the model:
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
tokenizer = AutoTokenizer.from_pretrained("nicotaroni/distilbert-multilingual-cased_fine_tuned_", max_length=512,truncation=True)
model = AutoModelForSequenceClassification.from_pretrained("nicotaroni/distilbert-multilingual-cased_fine_tuned_")
classifier = pipeline("text-classification", model=model, tokenizer=tokenizer, truncation = True)
text = " real estate advertisement "
outputs = classifier(text)
print(outputs)
``` | 1,180 | [
[
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0.005779266357421875,
0.0171051025390625,
-0.01531982421875,
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0.0036869049072265625,
0.03106689453125,
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-0.0653076171875,
-0.04534912109375... |
peanutacake/autotrain-historic-fi-51081121367 | 2023-04-20T15:41:11.000Z | [
"transformers",
"pytorch",
"bert",
"token-classification",
"autotrain",
"fi",
"dataset:peanutacake/autotrain-data-historic-fi",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | peanutacake | null | null | peanutacake/autotrain-historic-fi-51081121367 | 0 | 2 | transformers | 2023-04-20T15:40:04 | ---
tags:
- autotrain
- token-classification
language:
- fi
widget:
- text: "I love AutoTrain 🤗"
datasets:
- peanutacake/autotrain-data-historic-fi
co2_eq_emissions:
emissions: 0.41919224521906834
---
# Model Trained Using AutoTrain
- Problem type: Entity Extraction
- Model ID: 51081121367
- CO2 Emissions (in grams): 0.4192
## Validation Metrics
- Loss: 0.189
- Accuracy: 0.951
- Precision: 0.000
- Recall: 0.000
- F1: 0.000
## 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/peanutacake/autotrain-historic-fi-51081121367
```
Or Python API:
```
from transformers import AutoModelForTokenClassification, AutoTokenizer
model = AutoModelForTokenClassification.from_pretrained("peanutacake/autotrain-historic-fi-51081121367", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("peanutacake/autotrain-historic-fi-51081121367", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
``` | 1,133 | [
[
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0.0171051025390625,
0.0130157470703125,
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0.003009796142578125,
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0.006710052490234375,
0.01255035400390625,
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-0.03570556640625,
-0.05828857421875,
... |
Xenova/t5-small | 2023-09-05T14:57:45.000Z | [
"transformers.js",
"onnx",
"t5",
"text2text-generation",
"text-generation-inference",
"region:us",
"has_space"
] | text2text-generation | Xenova | null | null | Xenova/t5-small | 2 | 2 | transformers.js | 2023-04-20T17:03:33 | ---
library_name: "transformers.js"
---
https://huggingface.co/t5-small with ONNX weights to be compatible with Transformers.js.
Note: Having a separate repo for ONNX weights is intended to be a temporary solution until WebML gains more traction. If you would like to make your models web-ready, we recommend converting to ONNX using [🤗 Optimum](https://huggingface.co/docs/optimum/index) and structuring your repo like this one (with ONNX weights located in a subfolder named `onnx`). | 487 | [
[
-0.03533935546875,
0.019073486328125,
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0.044189453125,
-0.00855255126953125,
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0.032684326171875,
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-0.035919189453125,
-0.043731689453125,... |
nikhilanvekar2001/Hindi_asr_with_LM | 2023-04-20T18:09:18.000Z | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"hi",
"arxiv:2107.07402",
"license:mit",
"model-index",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | nikhilanvekar2001 | null | null | nikhilanvekar2001/Hindi_asr_with_LM | 0 | 2 | transformers | 2023-04-20T17:06:31 | ---
language: hi
#datasets:
#- Interspeech 2021
metrics:
- wer
tags:
- audio
- automatic-speech-recognition
- speech
license: mit
model-index:
- name: Wav2Vec2 Vakyansh Hindi Model by Harveen Chadha
results:
- task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice hi
type: common_voice
args: hi
metrics:
- name: Test WER
type: wer
value: 33.17
---
## Spaces Demo
Check the spaces demo [here](https://huggingface.co/spaces/Harveenchadha/wav2vec2-vakyansh-hindi/tree/main)
## Pretrained Model
Fine-tuned on Multilingual Pretrained Model [CLSRIL-23](https://arxiv.org/abs/2107.07402). The original fairseq checkpoint is present [here](https://github.com/Open-Speech-EkStep/vakyansh-models). When using this model, make sure that your speech input is sampled at 16kHz.
**Note: The result from this model is without a language model so you may witness a higher WER in some cases.**
## Dataset
This model was trained on 4200 hours of Hindi Labelled Data. The labelled data is not present in public domain as of now.
## Training Script
Models were trained using experimental platform setup by Vakyansh team at Ekstep. Here is the [training repository](https://github.com/Open-Speech-EkStep/vakyansh-wav2vec2-experimentation).
In case you want to explore training logs on wandb they are [here](https://wandb.ai/harveenchadha/hindi_finetuning_multilingual?workspace=user-harveenchadha).
## [Colab Demo](https://colab.research.google.com/github/harveenchadha/bol/blob/main/demos/hf/hindi/hf_hindi_him_4200_demo.ipynb)
## Usage
The model can be used directly (without a language model) as follows:
```python
import soundfile as sf
import torch
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import argparse
def parse_transcription(wav_file):
# load pretrained model
processor = Wav2Vec2Processor.from_pretrained("Harveenchadha/vakyansh-wav2vec2-hindi-him-4200")
model = Wav2Vec2ForCTC.from_pretrained("Harveenchadha/vakyansh-wav2vec2-hindi-him-4200")
# load audio
audio_input, sample_rate = sf.read(wav_file)
# pad input values and return pt tensor
input_values = processor(audio_input, sampling_rate=sample_rate, return_tensors="pt").input_values
# INFERENCE
# retrieve logits & take argmax
logits = model(input_values).logits
predicted_ids = torch.argmax(logits, dim=-1)
# transcribe
transcription = processor.decode(predicted_ids[0], skip_special_tokens=True)
print(transcription)
```
## Evaluation
The model can be evaluated as follows on the hindi test data of Common Voice.
```python
import torch
import torchaudio
from datasets import load_dataset, load_metric
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import re
test_dataset = load_dataset("common_voice", "hi", split="test")
wer = load_metric("wer")
processor = Wav2Vec2Processor.from_pretrained("Harveenchadha/vakyansh-wav2vec2-hindi-him-4200")
model = Wav2Vec2ForCTC.from_pretrained("Harveenchadha/vakyansh-wav2vec2-hindi-him-4200")
model.to("cuda")
resampler = torchaudio.transforms.Resample(48_000, 16_000)
chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“]'
# Preprocessing the datasets.
# We need to read the aduio files as arrays
def speech_file_to_array_fn(batch):
batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower()
speech_array, sampling_rate = torchaudio.load(batch["path"])
batch["speech"] = resampler(speech_array).squeeze().numpy()
return batch
test_dataset = test_dataset.map(speech_file_to_array_fn)
# Preprocessing the datasets.
# We need to read the aduio files as arrays
def evaluate(batch):
inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
with torch.no_grad():
logits = model(inputs.input_values.to("cuda")).logits
pred_ids = torch.argmax(logits, dim=-1)
batch["pred_strings"] = processor.batch_decode(pred_ids, skip_special_tokens=True)
return batch
result = test_dataset.map(evaluate, batched=True, batch_size=8)
print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"])))
```
**Test Result**: 33.17 %
[**Colab Evaluation**](https://colab.research.google.com/github/harveenchadha/bol/blob/main/demos/hf/hindi/hf_vakyansh_hindi_him_4200_evaluation_common_voice.ipynb)
## Credits
Thanks to Ekstep Foundation for making this possible. The vakyansh team will be open sourcing speech models in all the Indic Languages. | 4,565 | [
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helenai/assemblyai-distilbert-base-uncased-sst2-ov | 2023-04-20T18:14:14.000Z | [
"transformers",
"openvino",
"distilbert",
"text-classification",
"en",
"endpoints_compatible",
"region:us"
] | text-classification | helenai | null | null | helenai/assemblyai-distilbert-base-uncased-sst2-ov | 0 | 2 | transformers | 2023-04-20T18:13:55 | ---
language:
- en
tags:
- openvino
---
# assemblyai/distilbert-base-uncased-sst2
This is the [assemblyai/distilbert-base-uncased-sst2](https://huggingface.co/assemblyai/distilbert-base-uncased-sst2) model converted to [OpenVINO](https://openvino.ai), for accellerated inference.
An example of how to do inference on this model:
```python
from optimum.intel.openvino import OVModelForSequenceClassification
from transformers import AutoTokenizer, pipeline
# model_id should be set to either a local directory or a model available on the HuggingFace hub.
model_id = "helenai/assemblyai-distilbert-base-uncased-sst2-ov"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = OVModelForSequenceClassification.from_pretrained(model_id)
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
result = pipe("I like you. I love you")
print(result)
```
| 873 | [
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-0.025... |
helenai/echarlaix-bert-base-uncased-sst2-acc91.1-d37-hybrid-ov | 2023-04-20T18:15:34.000Z | [
"transformers",
"openvino",
"bert",
"text-classification",
"en",
"endpoints_compatible",
"region:us"
] | text-classification | helenai | null | null | helenai/echarlaix-bert-base-uncased-sst2-acc91.1-d37-hybrid-ov | 0 | 2 | transformers | 2023-04-20T18:15:10 | ---
language:
- en
tags:
- openvino
---
# echarlaix/bert-base-uncased-sst2-acc91.1-d37-hybrid
This is the [echarlaix/bert-base-uncased-sst2-acc91.1-d37-hybrid](https://huggingface.co/echarlaix/bert-base-uncased-sst2-acc91.1-d37-hybrid) model converted to [OpenVINO](https://openvino.ai), for accellerated inference.
An example of how to do inference on this model:
```python
from optimum.intel.openvino import OVModelForSequenceClassification
from transformers import AutoTokenizer, pipeline
# model_id should be set to either a local directory or a model available on the HuggingFace hub.
model_id = "helenai/echarlaix-bert-base-uncased-sst2-acc91.1-d37-hybrid-ov"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = OVModelForSequenceClassification.from_pretrained(model_id)
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
result = pipe("I like you. I love you")
print(result)
```
| 921 | [
[
-0.023223876953125,
-0.045806884765625,
0.0164794921875,
0.00650787353515625,
-0.023040771484375,
-0.01055145263671875,
-0.01206207275390625,
-0.0101318359375,
0.04034423828125,
0.03387451171875,
-0.05523681640625,
-0.03741455078125,
-0.0303497314453125,
-0.... |
helenai/Alireza1044-albert-base-v2-sst2-ov | 2023-04-20T18:16:58.000Z | [
"transformers",
"openvino",
"albert",
"text-classification",
"en",
"endpoints_compatible",
"region:us"
] | text-classification | helenai | null | null | helenai/Alireza1044-albert-base-v2-sst2-ov | 0 | 2 | transformers | 2023-04-20T18:16:46 | ---
language:
- en
tags:
- openvino
---
# Alireza1044/albert-base-v2-sst2
This is the [Alireza1044/albert-base-v2-sst2](https://huggingface.co/Alireza1044/albert-base-v2-sst2) model converted to [OpenVINO](https://openvino.ai), for accellerated inference.
An example of how to do inference on this model:
```python
from optimum.intel.openvino import OVModelForSequenceClassification
from transformers import AutoTokenizer, pipeline
# model_id should be set to either a local directory or a model available on the HuggingFace hub.
model_id = "helenai/Alireza1044-albert-base-v2-sst2-ov"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = OVModelForSequenceClassification.from_pretrained(model_id)
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
result = pipe("I like you. I love you")
print(result)
```
| 841 | [
[
-0.0183258056640625,
-0.0350341796875,
0.020050048828125,
0.018829345703125,
-0.013031005859375,
-0.0288543701171875,
0.0011119842529296875,
-0.005603790283203125,
0.0205078125,
0.0380859375,
-0.0521240234375,
-0.0269012451171875,
-0.0416259765625,
-0.023391... |
helenai/howey-bert-base-uncased-sst2-ov | 2023-04-20T18:18:06.000Z | [
"transformers",
"openvino",
"bert",
"text-classification",
"en",
"endpoints_compatible",
"region:us"
] | text-classification | helenai | null | null | helenai/howey-bert-base-uncased-sst2-ov | 0 | 2 | transformers | 2023-04-20T18:17:34 | ---
language:
- en
tags:
- openvino
---
# howey/bert-base-uncased-sst2
This is the [howey/bert-base-uncased-sst2](https://huggingface.co/howey/bert-base-uncased-sst2) model converted to [OpenVINO](https://openvino.ai), for accellerated inference.
An example of how to do inference on this model:
```python
from optimum.intel.openvino import OVModelForSequenceClassification
from transformers import AutoTokenizer, pipeline
# model_id should be set to either a local directory or a model available on the HuggingFace hub.
model_id = "helenai/howey-bert-base-uncased-sst2-ov"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = OVModelForSequenceClassification.from_pretrained(model_id)
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
result = pipe("I like you. I love you")
print(result)
```
| 829 | [
[
-0.0117950439453125,
-0.045562744140625,
0.0184783935546875,
0.01174163818359375,
-0.0272674560546875,
-0.0250244140625,
-0.0026721954345703125,
0.00020110607147216797,
0.0251922607421875,
0.037811279296875,
-0.053436279296875,
-0.0281524658203125,
-0.0385742187... |
sw0471/distilbert-base-uncased-finetuned-cola | 2023-04-20T19:16:25.000Z | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | sw0471 | null | null | sw0471/distilbert-base-uncased-finetuned-cola | 0 | 2 | transformers | 2023-04-20T19:06:13 | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- matthews_correlation
model-index:
- name: distilbert-base-uncased-finetuned-cola
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-cola
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5619
- Matthews Correlation: 0.5295
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.5238 | 1.0 | 535 | 0.5285 | 0.4003 |
| 0.3493 | 2.0 | 1070 | 0.4934 | 0.4960 |
| 0.2357 | 3.0 | 1605 | 0.5619 | 0.5295 |
| 0.1793 | 4.0 | 2140 | 0.7578 | 0.5189 |
| 0.137 | 5.0 | 2675 | 0.8105 | 0.5199 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Tokenizers 0.13.3
| 1,720 | [
[
-0.02899169921875,
-0.048095703125,
0.015777587890625,
0.017913818359375,
-0.0267333984375,
-0.007183074951171875,
-0.005035400390625,
-0.005901336669921875,
0.0185546875,
0.0150604248046875,
-0.0477294921875,
-0.036376953125,
-0.0623779296875,
-0.0037765502... |
zonghaoyang/BioLinkBERT-base | 2023-05-22T12:05:07.000Z | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | zonghaoyang | null | null | zonghaoyang/BioLinkBERT-base | 0 | 2 | transformers | 2023-04-20T23:36:05 | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: BioLinkBERT-base
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. -->
# BioLinkBERT-base
This model is a fine-tuned version of [michiyasunaga/BioLinkBERT-base](https://huggingface.co/michiyasunaga/BioLinkBERT-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3937
- Accuracy: 0.9025
- F1: 0.6107
- Precision: 0.6765
- Recall: 0.5565
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.2363 | 1.0 | 1626 | 0.2699 | 0.9057 | 0.5991 | 0.7205 | 0.5127 |
| 0.1832 | 2.0 | 3252 | 0.3328 | 0.9038 | 0.6233 | 0.675 | 0.5789 |
| 0.1324 | 3.0 | 4878 | 0.3937 | 0.9025 | 0.6107 | 0.6765 | 0.5565 |
### Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,702 | [
[
-0.0330810546875,
-0.0321044921875,
0.0157928466796875,
0.00830841064453125,
-0.022430419921875,
-0.0204315185546875,
0.00012069940567016602,
-0.01885986328125,
0.021942138671875,
0.01512908935546875,
-0.05609130859375,
-0.04949951171875,
-0.04718017578125,
... |
platzi/platzi-distilroberta-base-mrpc-glue-saul-burgos | 2023-04-21T02:04:40.000Z | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | text-classification | platzi | null | null | platzi/platzi-distilroberta-base-mrpc-glue-saul-burgos | 0 | 2 | transformers | 2023-04-21T01:58:07 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
- f1
model-index:
- name: platzi-distilroberta-base-mrpc-glue-saul-burgos
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
config: mrpc
split: validation
args: mrpc
metrics:
- name: Accuracy
type: accuracy
value: 0.8406862745098039
- name: F1
type: f1
value: 0.8807339449541283
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# platzi-distilroberta-base-mrpc-glue-saul-burgos
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5331
- Accuracy: 0.8407
- F1: 0.8807
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.5207 | 1.09 | 500 | 0.7202 | 0.7941 | 0.8467 |
| 0.3891 | 2.18 | 1000 | 0.5331 | 0.8407 | 0.8807 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
| 1,846 | [
[
-0.02764892578125,
-0.0428466796875,
0.00909423828125,
0.0220184326171875,
-0.029205322265625,
-0.02545166015625,
-0.0105438232421875,
-0.0027027130126953125,
0.00848388671875,
0.00887298583984375,
-0.04827880859375,
-0.0430908203125,
-0.057159423828125,
-0.... |
eggyeggy/bert-fine-tuned-cola | 2023-04-21T06:45:49.000Z | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | text-classification | eggyeggy | null | null | eggyeggy/bert-fine-tuned-cola | 0 | 2 | transformers | 2023-04-21T06:19:55 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- matthews_correlation
model-index:
- name: bert-fine-tuned-cola
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
config: cola
split: validation
args: cola
metrics:
- name: Matthews Correlation
type: matthews_correlation
value: 0.5677348492150284
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-fine-tuned-cola
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8348
- Matthews Correlation: 0.5677
## 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
### Training results
| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.4732 | 1.0 | 1069 | 0.5295 | 0.5495 |
| 0.3089 | 2.0 | 2138 | 0.5929 | 0.5876 |
| 0.1725 | 3.0 | 3207 | 0.8348 | 0.5677 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
| 1,840 | [
[
-0.027130126953125,
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0.0103607177734375,
0.0185394287109375,
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0.02349853515625,
0.01025390625,
-0.054412841796875,
-0.0308380126953125,
-0.05340576171875,
-0.014... |
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