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SetFit/distilbert-base-uncased__sst2__train-32-5 | 97535708092c66424888ca731b60ff95de298a71 | 2022-02-10T07:32:51.000Z | [
"pytorch",
"distilbert",
"text-classification",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | text-classification | false | SetFit | null | SetFit/distilbert-base-uncased__sst2__train-32-5 | 4 | null | transformers | 18,200 | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased__sst2__train-32-5
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__sst2__train-32-5
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: 0.6248
- Accuracy: 0.6826
## 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: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.7136 | 1.0 | 13 | 0.6850 | 0.5385 |
| 0.6496 | 2.0 | 26 | 0.6670 | 0.6154 |
| 0.5895 | 3.0 | 39 | 0.6464 | 0.7692 |
| 0.4271 | 4.0 | 52 | 0.6478 | 0.7692 |
| 0.2182 | 5.0 | 65 | 0.6809 | 0.6923 |
| 0.103 | 6.0 | 78 | 0.9119 | 0.6923 |
| 0.0326 | 7.0 | 91 | 1.0718 | 0.6923 |
| 0.0154 | 8.0 | 104 | 1.0721 | 0.7692 |
| 0.0087 | 9.0 | 117 | 1.1416 | 0.7692 |
| 0.0067 | 10.0 | 130 | 1.2088 | 0.7692 |
| 0.005 | 11.0 | 143 | 1.2656 | 0.7692 |
| 0.0037 | 12.0 | 156 | 1.3104 | 0.7692 |
| 0.0032 | 13.0 | 169 | 1.3428 | 0.6923 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2
- Tokenizers 0.10.3
|
SetFit/distilbert-base-uncased__sst2__train-32-6 | e8cb84b59241fe4dcf97ff05f0c7b45f7c91e2ca | 2022-02-10T07:33:45.000Z | [
"pytorch",
"distilbert",
"text-classification",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | text-classification | false | SetFit | null | SetFit/distilbert-base-uncased__sst2__train-32-6 | 4 | null | transformers | 18,201 | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased__sst2__train-32-6
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__sst2__train-32-6
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: 0.5072
- Accuracy: 0.7650
## 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: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.7057 | 1.0 | 13 | 0.6704 | 0.6923 |
| 0.6489 | 2.0 | 26 | 0.6228 | 0.8462 |
| 0.5475 | 3.0 | 39 | 0.5079 | 0.8462 |
| 0.4014 | 4.0 | 52 | 0.4203 | 0.8462 |
| 0.1923 | 5.0 | 65 | 0.3872 | 0.8462 |
| 0.1014 | 6.0 | 78 | 0.4909 | 0.8462 |
| 0.0349 | 7.0 | 91 | 0.5460 | 0.8462 |
| 0.0173 | 8.0 | 104 | 0.4867 | 0.8462 |
| 0.0098 | 9.0 | 117 | 0.5274 | 0.8462 |
| 0.0075 | 10.0 | 130 | 0.6086 | 0.8462 |
| 0.0057 | 11.0 | 143 | 0.6604 | 0.8462 |
| 0.0041 | 12.0 | 156 | 0.6904 | 0.8462 |
| 0.0037 | 13.0 | 169 | 0.7164 | 0.8462 |
| 0.0034 | 14.0 | 182 | 0.7368 | 0.8462 |
| 0.0031 | 15.0 | 195 | 0.7565 | 0.8462 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2
- Tokenizers 0.10.3
|
SetFit/distilbert-base-uncased__sst2__train-8-0 | 923d714bb5207fe3abe43562963443b617569f0a | 2022-02-10T07:08:27.000Z | [
"pytorch",
"distilbert",
"text-classification",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | text-classification | false | SetFit | null | SetFit/distilbert-base-uncased__sst2__train-8-0 | 4 | null | transformers | 18,202 | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased__sst2__train-8-0
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__sst2__train-8-0
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: 0.6920
- Accuracy: 0.5189
## 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: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6916 | 1.0 | 3 | 0.7035 | 0.25 |
| 0.6852 | 2.0 | 6 | 0.7139 | 0.25 |
| 0.6533 | 3.0 | 9 | 0.7192 | 0.25 |
| 0.6211 | 4.0 | 12 | 0.7322 | 0.25 |
| 0.5522 | 5.0 | 15 | 0.7561 | 0.25 |
| 0.488 | 6.0 | 18 | 0.7883 | 0.25 |
| 0.48 | 7.0 | 21 | 0.8224 | 0.25 |
| 0.3948 | 8.0 | 24 | 0.8605 | 0.25 |
| 0.3478 | 9.0 | 27 | 0.8726 | 0.25 |
| 0.2723 | 10.0 | 30 | 0.8885 | 0.25 |
| 0.2174 | 11.0 | 33 | 0.8984 | 0.5 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2
- Tokenizers 0.10.3
|
SetFit/distilbert-base-uncased__sst2__train-8-1 | c6957605df54b9de7f50171e29a36b383f162690 | 2022-02-10T07:09:19.000Z | [
"pytorch",
"distilbert",
"text-classification",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | text-classification | false | SetFit | null | SetFit/distilbert-base-uncased__sst2__train-8-1 | 4 | null | transformers | 18,203 | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased__sst2__train-8-1
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased__sst2__train-8-1
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6930
- Accuracy: 0.5047
## 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: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.7082 | 1.0 | 3 | 0.7048 | 0.25 |
| 0.6761 | 2.0 | 6 | 0.7249 | 0.25 |
| 0.6653 | 3.0 | 9 | 0.7423 | 0.25 |
| 0.6212 | 4.0 | 12 | 0.7727 | 0.25 |
| 0.5932 | 5.0 | 15 | 0.8098 | 0.25 |
| 0.5427 | 6.0 | 18 | 0.8496 | 0.25 |
| 0.5146 | 7.0 | 21 | 0.8992 | 0.25 |
| 0.4356 | 8.0 | 24 | 0.9494 | 0.25 |
| 0.4275 | 9.0 | 27 | 0.9694 | 0.25 |
| 0.3351 | 10.0 | 30 | 0.9968 | 0.25 |
| 0.2812 | 11.0 | 33 | 1.0056 | 0.5 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2
- Tokenizers 0.10.3
|
SetFit/distilbert-base-uncased__sst2__train-8-2 | 9915864d5dd92ff1850f653afe083b9e6b9a137a | 2022-02-10T07:10:08.000Z | [
"pytorch",
"distilbert",
"text-classification",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | text-classification | false | SetFit | null | SetFit/distilbert-base-uncased__sst2__train-8-2 | 4 | null | transformers | 18,204 | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased__sst2__train-8-2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased__sst2__train-8-2
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6932
- Accuracy: 0.4931
## 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: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.7081 | 1.0 | 3 | 0.7031 | 0.25 |
| 0.6853 | 2.0 | 6 | 0.7109 | 0.25 |
| 0.6696 | 3.0 | 9 | 0.7211 | 0.25 |
| 0.6174 | 4.0 | 12 | 0.7407 | 0.25 |
| 0.5717 | 5.0 | 15 | 0.7625 | 0.25 |
| 0.5096 | 6.0 | 18 | 0.7732 | 0.25 |
| 0.488 | 7.0 | 21 | 0.7798 | 0.25 |
| 0.4023 | 8.0 | 24 | 0.7981 | 0.25 |
| 0.3556 | 9.0 | 27 | 0.8110 | 0.25 |
| 0.2714 | 10.0 | 30 | 0.8269 | 0.25 |
| 0.2295 | 11.0 | 33 | 0.8276 | 0.25 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2
- Tokenizers 0.10.3
|
SetFit/distilbert-base-uncased__sst2__train-8-4 | fdd3c0f72c6b6815bdfa19e5c1cf548008829fcc | 2022-02-10T07:11:48.000Z | [
"pytorch",
"distilbert",
"text-classification",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | text-classification | false | SetFit | null | SetFit/distilbert-base-uncased__sst2__train-8-4 | 4 | null | transformers | 18,205 | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased__sst2__train-8-4
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__sst2__train-8-4
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: 0.6921
- Accuracy: 0.5107
## 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: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.7163 | 1.0 | 3 | 0.7100 | 0.25 |
| 0.6785 | 2.0 | 6 | 0.7209 | 0.25 |
| 0.6455 | 3.0 | 9 | 0.7321 | 0.25 |
| 0.6076 | 4.0 | 12 | 0.7517 | 0.25 |
| 0.5593 | 5.0 | 15 | 0.7780 | 0.25 |
| 0.5202 | 6.0 | 18 | 0.7990 | 0.25 |
| 0.4967 | 7.0 | 21 | 0.8203 | 0.25 |
| 0.4158 | 8.0 | 24 | 0.8497 | 0.25 |
| 0.3997 | 9.0 | 27 | 0.8638 | 0.25 |
| 0.3064 | 10.0 | 30 | 0.8732 | 0.25 |
| 0.2618 | 11.0 | 33 | 0.8669 | 0.25 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2
- Tokenizers 0.10.3
|
SetFit/distilbert-base-uncased__sst2__train-8-5 | 31c5f0a6ecb77a7eb7b543ce9e2721b0db08bdd7 | 2022-02-10T07:13:25.000Z | [
"pytorch",
"distilbert",
"text-classification",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | text-classification | false | SetFit | null | SetFit/distilbert-base-uncased__sst2__train-8-5 | 4 | null | transformers | 18,206 | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased__sst2__train-8-5
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__sst2__train-8-5
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: 0.8419
- Accuracy: 0.6172
## 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: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.7057 | 1.0 | 3 | 0.6848 | 0.75 |
| 0.6681 | 2.0 | 6 | 0.6875 | 0.5 |
| 0.6591 | 3.0 | 9 | 0.6868 | 0.25 |
| 0.6052 | 4.0 | 12 | 0.6943 | 0.25 |
| 0.557 | 5.0 | 15 | 0.7078 | 0.25 |
| 0.4954 | 6.0 | 18 | 0.7168 | 0.25 |
| 0.4593 | 7.0 | 21 | 0.7185 | 0.25 |
| 0.3936 | 8.0 | 24 | 0.7212 | 0.25 |
| 0.3699 | 9.0 | 27 | 0.6971 | 0.5 |
| 0.2916 | 10.0 | 30 | 0.6827 | 0.5 |
| 0.2511 | 11.0 | 33 | 0.6464 | 0.5 |
| 0.2109 | 12.0 | 36 | 0.6344 | 0.75 |
| 0.1655 | 13.0 | 39 | 0.6377 | 0.75 |
| 0.1412 | 14.0 | 42 | 0.6398 | 0.75 |
| 0.1157 | 15.0 | 45 | 0.6315 | 0.75 |
| 0.0895 | 16.0 | 48 | 0.6210 | 0.75 |
| 0.0783 | 17.0 | 51 | 0.5918 | 0.75 |
| 0.0606 | 18.0 | 54 | 0.5543 | 0.75 |
| 0.0486 | 19.0 | 57 | 0.5167 | 0.75 |
| 0.0405 | 20.0 | 60 | 0.4862 | 0.75 |
| 0.0376 | 21.0 | 63 | 0.4644 | 0.75 |
| 0.0294 | 22.0 | 66 | 0.4497 | 0.75 |
| 0.0261 | 23.0 | 69 | 0.4428 | 0.75 |
| 0.0238 | 24.0 | 72 | 0.4408 | 0.75 |
| 0.0217 | 25.0 | 75 | 0.4392 | 0.75 |
| 0.0187 | 26.0 | 78 | 0.4373 | 0.75 |
| 0.0177 | 27.0 | 81 | 0.4360 | 0.75 |
| 0.0136 | 28.0 | 84 | 0.4372 | 0.75 |
| 0.0144 | 29.0 | 87 | 0.4368 | 0.75 |
| 0.014 | 30.0 | 90 | 0.4380 | 0.75 |
| 0.0137 | 31.0 | 93 | 0.4383 | 0.75 |
| 0.0133 | 32.0 | 96 | 0.4409 | 0.75 |
| 0.013 | 33.0 | 99 | 0.4380 | 0.75 |
| 0.0096 | 34.0 | 102 | 0.4358 | 0.75 |
| 0.012 | 35.0 | 105 | 0.4339 | 0.75 |
| 0.0122 | 36.0 | 108 | 0.4305 | 0.75 |
| 0.0109 | 37.0 | 111 | 0.4267 | 0.75 |
| 0.0121 | 38.0 | 114 | 0.4231 | 0.75 |
| 0.0093 | 39.0 | 117 | 0.4209 | 0.75 |
| 0.0099 | 40.0 | 120 | 0.4199 | 0.75 |
| 0.0091 | 41.0 | 123 | 0.4184 | 0.75 |
| 0.0116 | 42.0 | 126 | 0.4173 | 0.75 |
| 0.01 | 43.0 | 129 | 0.4163 | 0.75 |
| 0.0098 | 44.0 | 132 | 0.4153 | 0.75 |
| 0.0101 | 45.0 | 135 | 0.4155 | 0.75 |
| 0.0088 | 46.0 | 138 | 0.4149 | 0.75 |
| 0.0087 | 47.0 | 141 | 0.4150 | 0.75 |
| 0.0093 | 48.0 | 144 | 0.4147 | 0.75 |
| 0.0081 | 49.0 | 147 | 0.4147 | 0.75 |
| 0.009 | 50.0 | 150 | 0.4150 | 0.75 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2
- Tokenizers 0.10.3
|
SetFit/distilbert-base-uncased__sst2__train-8-6 | 053aa4d02baf3d434ba4cbea619a78e48a6b713b | 2022-02-10T07:14:49.000Z | [
"pytorch",
"distilbert",
"text-classification",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | text-classification | false | SetFit | null | SetFit/distilbert-base-uncased__sst2__train-8-6 | 4 | null | transformers | 18,207 | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased__sst2__train-8-6
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__sst2__train-8-6
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: 0.5336
- Accuracy: 0.7523
## 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: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.7161 | 1.0 | 3 | 0.6941 | 0.5 |
| 0.6786 | 2.0 | 6 | 0.7039 | 0.25 |
| 0.6586 | 3.0 | 9 | 0.7090 | 0.25 |
| 0.6121 | 4.0 | 12 | 0.7183 | 0.25 |
| 0.5696 | 5.0 | 15 | 0.7266 | 0.25 |
| 0.522 | 6.0 | 18 | 0.7305 | 0.25 |
| 0.4899 | 7.0 | 21 | 0.7339 | 0.25 |
| 0.3985 | 8.0 | 24 | 0.7429 | 0.25 |
| 0.3758 | 9.0 | 27 | 0.7224 | 0.25 |
| 0.2876 | 10.0 | 30 | 0.7068 | 0.5 |
| 0.2498 | 11.0 | 33 | 0.6751 | 0.75 |
| 0.1921 | 12.0 | 36 | 0.6487 | 0.75 |
| 0.1491 | 13.0 | 39 | 0.6261 | 0.75 |
| 0.1276 | 14.0 | 42 | 0.6102 | 0.75 |
| 0.0996 | 15.0 | 45 | 0.5964 | 0.75 |
| 0.073 | 16.0 | 48 | 0.6019 | 0.75 |
| 0.0627 | 17.0 | 51 | 0.5933 | 0.75 |
| 0.053 | 18.0 | 54 | 0.5768 | 0.75 |
| 0.0403 | 19.0 | 57 | 0.5698 | 0.75 |
| 0.0328 | 20.0 | 60 | 0.5656 | 0.75 |
| 0.03 | 21.0 | 63 | 0.5634 | 0.75 |
| 0.025 | 22.0 | 66 | 0.5620 | 0.75 |
| 0.0209 | 23.0 | 69 | 0.5623 | 0.75 |
| 0.0214 | 24.0 | 72 | 0.5606 | 0.75 |
| 0.0191 | 25.0 | 75 | 0.5565 | 0.75 |
| 0.0173 | 26.0 | 78 | 0.5485 | 0.75 |
| 0.0175 | 27.0 | 81 | 0.5397 | 0.75 |
| 0.0132 | 28.0 | 84 | 0.5322 | 0.75 |
| 0.0138 | 29.0 | 87 | 0.5241 | 0.75 |
| 0.0128 | 30.0 | 90 | 0.5235 | 0.75 |
| 0.0126 | 31.0 | 93 | 0.5253 | 0.75 |
| 0.012 | 32.0 | 96 | 0.5317 | 0.75 |
| 0.0118 | 33.0 | 99 | 0.5342 | 0.75 |
| 0.0092 | 34.0 | 102 | 0.5388 | 0.75 |
| 0.0117 | 35.0 | 105 | 0.5414 | 0.75 |
| 0.0124 | 36.0 | 108 | 0.5453 | 0.75 |
| 0.0109 | 37.0 | 111 | 0.5506 | 0.75 |
| 0.0112 | 38.0 | 114 | 0.5555 | 0.75 |
| 0.0087 | 39.0 | 117 | 0.5597 | 0.75 |
| 0.01 | 40.0 | 120 | 0.5640 | 0.75 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2
- Tokenizers 0.10.3
|
SetFit/distilbert-base-uncased__sst2__train-8-7 | e5a0510d963ebf750f4893ed8d52747b65a85bdc | 2022-02-10T07:15:41.000Z | [
"pytorch",
"distilbert",
"text-classification",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | text-classification | false | SetFit | null | SetFit/distilbert-base-uncased__sst2__train-8-7 | 4 | null | transformers | 18,208 | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased__sst2__train-8-7
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__sst2__train-8-7
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: 0.6950
- Accuracy: 0.4618
## 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: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.7156 | 1.0 | 3 | 0.6965 | 0.25 |
| 0.6645 | 2.0 | 6 | 0.7059 | 0.25 |
| 0.6368 | 3.0 | 9 | 0.7179 | 0.25 |
| 0.5944 | 4.0 | 12 | 0.7408 | 0.25 |
| 0.5369 | 5.0 | 15 | 0.7758 | 0.25 |
| 0.449 | 6.0 | 18 | 0.8009 | 0.25 |
| 0.4352 | 7.0 | 21 | 0.8209 | 0.5 |
| 0.3462 | 8.0 | 24 | 0.8470 | 0.5 |
| 0.3028 | 9.0 | 27 | 0.8579 | 0.5 |
| 0.2365 | 10.0 | 30 | 0.8704 | 0.5 |
| 0.2023 | 11.0 | 33 | 0.8770 | 0.5 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2
- Tokenizers 0.10.3
|
SetFit/distilbert-base-uncased__sst2__train-8-8 | d04140330b1f481cbbfc95566cab2aaaa8a90a4d | 2022-02-10T07:16:33.000Z | [
"pytorch",
"distilbert",
"text-classification",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | text-classification | false | SetFit | null | SetFit/distilbert-base-uncased__sst2__train-8-8 | 4 | null | transformers | 18,209 | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased__sst2__train-8-8
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__sst2__train-8-8
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: 0.6925
- Accuracy: 0.5200
## 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: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.7061 | 1.0 | 3 | 0.6899 | 0.75 |
| 0.6627 | 2.0 | 6 | 0.7026 | 0.25 |
| 0.644 | 3.0 | 9 | 0.7158 | 0.25 |
| 0.6087 | 4.0 | 12 | 0.7325 | 0.25 |
| 0.5602 | 5.0 | 15 | 0.7555 | 0.25 |
| 0.5034 | 6.0 | 18 | 0.7725 | 0.25 |
| 0.4672 | 7.0 | 21 | 0.7983 | 0.25 |
| 0.403 | 8.0 | 24 | 0.8314 | 0.25 |
| 0.3571 | 9.0 | 27 | 0.8555 | 0.25 |
| 0.2792 | 10.0 | 30 | 0.9065 | 0.25 |
| 0.2373 | 11.0 | 33 | 0.9286 | 0.25 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2
- Tokenizers 0.10.3
|
SetFit/distilbert-base-uncased__sst2__train-8-9 | 1dd1a3db6cdd2fdf14bd0af335af40a4191d4c85 | 2022-02-10T07:17:41.000Z | [
"pytorch",
"distilbert",
"text-classification",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | text-classification | false | SetFit | null | SetFit/distilbert-base-uncased__sst2__train-8-9 | 4 | null | transformers | 18,210 | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased__sst2__train-8-9
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__sst2__train-8-9
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: 0.6925
- Accuracy: 0.5140
## 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: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.7204 | 1.0 | 3 | 0.7025 | 0.5 |
| 0.6885 | 2.0 | 6 | 0.7145 | 0.5 |
| 0.6662 | 3.0 | 9 | 0.7222 | 0.5 |
| 0.6182 | 4.0 | 12 | 0.7427 | 0.25 |
| 0.5707 | 5.0 | 15 | 0.7773 | 0.25 |
| 0.5247 | 6.0 | 18 | 0.8137 | 0.25 |
| 0.5003 | 7.0 | 21 | 0.8556 | 0.25 |
| 0.4195 | 8.0 | 24 | 0.9089 | 0.5 |
| 0.387 | 9.0 | 27 | 0.9316 | 0.25 |
| 0.2971 | 10.0 | 30 | 0.9558 | 0.25 |
| 0.2581 | 11.0 | 33 | 0.9420 | 0.25 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2
- Tokenizers 0.10.3
|
SetFit/distilbert-base-uncased__subj__all-train | 46f584e9c35de22e8d654bc69ac1bfa314b2647e | 2022-01-26T20:54:03.000Z | [
"pytorch",
"distilbert",
"text-classification",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | text-classification | false | SetFit | null | SetFit/distilbert-base-uncased__subj__all-train | 4 | null | transformers | 18,211 | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased__subj__all-train
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__subj__all-train
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: 0.3193
- Accuracy: 0.9485
## 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
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.1992 | 1.0 | 500 | 0.1236 | 0.963 |
| 0.084 | 2.0 | 1000 | 0.1428 | 0.963 |
| 0.0333 | 3.0 | 1500 | 0.1906 | 0.965 |
| 0.0159 | 4.0 | 2000 | 0.3193 | 0.9485 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.1+cu102
- Datasets 1.17.0
- Tokenizers 0.10.3
|
SetFit/distilbert-base-uncased__subj__train-8-1 | 7003ea9848933dc3e3b2a6d3cf3127f16b26c87a | 2022-02-09T20:19:28.000Z | [
"pytorch",
"distilbert",
"text-classification",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | text-classification | false | SetFit | null | SetFit/distilbert-base-uncased__subj__train-8-1 | 4 | null | transformers | 18,212 | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased__subj__train-8-1
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased__subj__train-8-1
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5488
- Accuracy: 0.791
## 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: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.703 | 1.0 | 3 | 0.6906 | 0.5 |
| 0.666 | 2.0 | 6 | 0.6945 | 0.25 |
| 0.63 | 3.0 | 9 | 0.6885 | 0.5 |
| 0.588 | 4.0 | 12 | 0.6888 | 0.25 |
| 0.5181 | 5.0 | 15 | 0.6899 | 0.25 |
| 0.4508 | 6.0 | 18 | 0.6770 | 0.5 |
| 0.4025 | 7.0 | 21 | 0.6579 | 0.5 |
| 0.3361 | 8.0 | 24 | 0.6392 | 0.5 |
| 0.2919 | 9.0 | 27 | 0.6113 | 0.5 |
| 0.2151 | 10.0 | 30 | 0.5774 | 0.75 |
| 0.1728 | 11.0 | 33 | 0.5248 | 0.75 |
| 0.1313 | 12.0 | 36 | 0.4824 | 0.75 |
| 0.1046 | 13.0 | 39 | 0.4456 | 0.75 |
| 0.0858 | 14.0 | 42 | 0.4076 | 0.75 |
| 0.0679 | 15.0 | 45 | 0.3755 | 0.75 |
| 0.0485 | 16.0 | 48 | 0.3422 | 0.75 |
| 0.0416 | 17.0 | 51 | 0.3055 | 0.75 |
| 0.0358 | 18.0 | 54 | 0.2731 | 1.0 |
| 0.0277 | 19.0 | 57 | 0.2443 | 1.0 |
| 0.0234 | 20.0 | 60 | 0.2187 | 1.0 |
| 0.0223 | 21.0 | 63 | 0.1960 | 1.0 |
| 0.0187 | 22.0 | 66 | 0.1762 | 1.0 |
| 0.017 | 23.0 | 69 | 0.1629 | 1.0 |
| 0.0154 | 24.0 | 72 | 0.1543 | 1.0 |
| 0.0164 | 25.0 | 75 | 0.1476 | 1.0 |
| 0.0131 | 26.0 | 78 | 0.1423 | 1.0 |
| 0.0139 | 27.0 | 81 | 0.1387 | 1.0 |
| 0.0107 | 28.0 | 84 | 0.1360 | 1.0 |
| 0.0108 | 29.0 | 87 | 0.1331 | 1.0 |
| 0.0105 | 30.0 | 90 | 0.1308 | 1.0 |
| 0.0106 | 31.0 | 93 | 0.1276 | 1.0 |
| 0.0104 | 32.0 | 96 | 0.1267 | 1.0 |
| 0.0095 | 33.0 | 99 | 0.1255 | 1.0 |
| 0.0076 | 34.0 | 102 | 0.1243 | 1.0 |
| 0.0094 | 35.0 | 105 | 0.1235 | 1.0 |
| 0.0103 | 36.0 | 108 | 0.1228 | 1.0 |
| 0.0086 | 37.0 | 111 | 0.1231 | 1.0 |
| 0.0094 | 38.0 | 114 | 0.1236 | 1.0 |
| 0.0074 | 39.0 | 117 | 0.1240 | 1.0 |
| 0.0085 | 40.0 | 120 | 0.1246 | 1.0 |
| 0.0079 | 41.0 | 123 | 0.1253 | 1.0 |
| 0.0088 | 42.0 | 126 | 0.1248 | 1.0 |
| 0.0082 | 43.0 | 129 | 0.1244 | 1.0 |
| 0.0082 | 44.0 | 132 | 0.1234 | 1.0 |
| 0.0082 | 45.0 | 135 | 0.1223 | 1.0 |
| 0.0071 | 46.0 | 138 | 0.1212 | 1.0 |
| 0.0073 | 47.0 | 141 | 0.1208 | 1.0 |
| 0.0081 | 48.0 | 144 | 0.1205 | 1.0 |
| 0.0067 | 49.0 | 147 | 0.1202 | 1.0 |
| 0.0077 | 50.0 | 150 | 0.1202 | 1.0 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2
- Tokenizers 0.10.3
|
SetFit/distilbert-base-uncased__subj__train-8-2 | 62d27832f1e977f00a43d981b1b879d68f811640 | 2022-02-09T20:21:28.000Z | [
"pytorch",
"distilbert",
"text-classification",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | text-classification | false | SetFit | null | SetFit/distilbert-base-uncased__subj__train-8-2 | 4 | null | transformers | 18,213 | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased__subj__train-8-2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased__subj__train-8-2
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3081
- Accuracy: 0.8755
## 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: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.7146 | 1.0 | 3 | 0.6798 | 0.75 |
| 0.6737 | 2.0 | 6 | 0.6847 | 0.75 |
| 0.6519 | 3.0 | 9 | 0.6783 | 0.75 |
| 0.6105 | 4.0 | 12 | 0.6812 | 0.25 |
| 0.5463 | 5.0 | 15 | 0.6869 | 0.25 |
| 0.4922 | 6.0 | 18 | 0.6837 | 0.5 |
| 0.4543 | 7.0 | 21 | 0.6716 | 0.5 |
| 0.3856 | 8.0 | 24 | 0.6613 | 0.75 |
| 0.3475 | 9.0 | 27 | 0.6282 | 0.75 |
| 0.2717 | 10.0 | 30 | 0.6045 | 0.75 |
| 0.2347 | 11.0 | 33 | 0.5620 | 0.75 |
| 0.1979 | 12.0 | 36 | 0.5234 | 1.0 |
| 0.1535 | 13.0 | 39 | 0.4771 | 1.0 |
| 0.1332 | 14.0 | 42 | 0.4277 | 1.0 |
| 0.1041 | 15.0 | 45 | 0.3785 | 1.0 |
| 0.082 | 16.0 | 48 | 0.3318 | 1.0 |
| 0.0672 | 17.0 | 51 | 0.2885 | 1.0 |
| 0.0538 | 18.0 | 54 | 0.2568 | 1.0 |
| 0.0412 | 19.0 | 57 | 0.2356 | 1.0 |
| 0.0361 | 20.0 | 60 | 0.2217 | 1.0 |
| 0.0303 | 21.0 | 63 | 0.2125 | 1.0 |
| 0.0268 | 22.0 | 66 | 0.2060 | 1.0 |
| 0.0229 | 23.0 | 69 | 0.2015 | 1.0 |
| 0.0215 | 24.0 | 72 | 0.1989 | 1.0 |
| 0.0211 | 25.0 | 75 | 0.1969 | 1.0 |
| 0.0172 | 26.0 | 78 | 0.1953 | 1.0 |
| 0.0165 | 27.0 | 81 | 0.1935 | 1.0 |
| 0.0132 | 28.0 | 84 | 0.1923 | 1.0 |
| 0.0146 | 29.0 | 87 | 0.1914 | 1.0 |
| 0.0125 | 30.0 | 90 | 0.1904 | 1.0 |
| 0.0119 | 31.0 | 93 | 0.1897 | 1.0 |
| 0.0122 | 32.0 | 96 | 0.1886 | 1.0 |
| 0.0118 | 33.0 | 99 | 0.1875 | 1.0 |
| 0.0097 | 34.0 | 102 | 0.1866 | 1.0 |
| 0.0111 | 35.0 | 105 | 0.1861 | 1.0 |
| 0.0111 | 36.0 | 108 | 0.1855 | 1.0 |
| 0.0102 | 37.0 | 111 | 0.1851 | 1.0 |
| 0.0109 | 38.0 | 114 | 0.1851 | 1.0 |
| 0.0085 | 39.0 | 117 | 0.1854 | 1.0 |
| 0.0089 | 40.0 | 120 | 0.1855 | 1.0 |
| 0.0092 | 41.0 | 123 | 0.1863 | 1.0 |
| 0.0105 | 42.0 | 126 | 0.1868 | 1.0 |
| 0.0089 | 43.0 | 129 | 0.1874 | 1.0 |
| 0.0091 | 44.0 | 132 | 0.1877 | 1.0 |
| 0.0096 | 45.0 | 135 | 0.1881 | 1.0 |
| 0.0081 | 46.0 | 138 | 0.1881 | 1.0 |
| 0.0086 | 47.0 | 141 | 0.1883 | 1.0 |
| 0.009 | 48.0 | 144 | 0.1884 | 1.0 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2
- Tokenizers 0.10.3
|
SetFit/distilbert-base-uncased__subj__train-8-3 | 5e0dd7d4d8e9b9ca1b6e6e6cb3c76b6d04d57c03 | 2022-02-09T20:23:31.000Z | [
"pytorch",
"distilbert",
"text-classification",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | text-classification | false | SetFit | null | SetFit/distilbert-base-uncased__subj__train-8-3 | 4 | null | transformers | 18,214 | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased__subj__train-8-3
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__subj__train-8-3
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: 0.3496
- Accuracy: 0.859
## 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: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.7136 | 1.0 | 3 | 0.6875 | 0.75 |
| 0.6702 | 2.0 | 6 | 0.6824 | 0.75 |
| 0.6456 | 3.0 | 9 | 0.6687 | 0.75 |
| 0.5934 | 4.0 | 12 | 0.6564 | 0.75 |
| 0.537 | 5.0 | 15 | 0.6428 | 0.75 |
| 0.4812 | 6.0 | 18 | 0.6180 | 0.75 |
| 0.4279 | 7.0 | 21 | 0.5864 | 0.75 |
| 0.3608 | 8.0 | 24 | 0.5540 | 0.75 |
| 0.3076 | 9.0 | 27 | 0.5012 | 1.0 |
| 0.2292 | 10.0 | 30 | 0.4497 | 1.0 |
| 0.1991 | 11.0 | 33 | 0.3945 | 1.0 |
| 0.1495 | 12.0 | 36 | 0.3483 | 1.0 |
| 0.1176 | 13.0 | 39 | 0.3061 | 1.0 |
| 0.0947 | 14.0 | 42 | 0.2683 | 1.0 |
| 0.0761 | 15.0 | 45 | 0.2295 | 1.0 |
| 0.0584 | 16.0 | 48 | 0.1996 | 1.0 |
| 0.0451 | 17.0 | 51 | 0.1739 | 1.0 |
| 0.0387 | 18.0 | 54 | 0.1521 | 1.0 |
| 0.0272 | 19.0 | 57 | 0.1333 | 1.0 |
| 0.0247 | 20.0 | 60 | 0.1171 | 1.0 |
| 0.0243 | 21.0 | 63 | 0.1044 | 1.0 |
| 0.0206 | 22.0 | 66 | 0.0943 | 1.0 |
| 0.0175 | 23.0 | 69 | 0.0859 | 1.0 |
| 0.0169 | 24.0 | 72 | 0.0799 | 1.0 |
| 0.0162 | 25.0 | 75 | 0.0746 | 1.0 |
| 0.0137 | 26.0 | 78 | 0.0705 | 1.0 |
| 0.0141 | 27.0 | 81 | 0.0674 | 1.0 |
| 0.0107 | 28.0 | 84 | 0.0654 | 1.0 |
| 0.0117 | 29.0 | 87 | 0.0634 | 1.0 |
| 0.0113 | 30.0 | 90 | 0.0617 | 1.0 |
| 0.0107 | 31.0 | 93 | 0.0599 | 1.0 |
| 0.0106 | 32.0 | 96 | 0.0585 | 1.0 |
| 0.0101 | 33.0 | 99 | 0.0568 | 1.0 |
| 0.0084 | 34.0 | 102 | 0.0553 | 1.0 |
| 0.0101 | 35.0 | 105 | 0.0539 | 1.0 |
| 0.0102 | 36.0 | 108 | 0.0529 | 1.0 |
| 0.009 | 37.0 | 111 | 0.0520 | 1.0 |
| 0.0092 | 38.0 | 114 | 0.0511 | 1.0 |
| 0.0073 | 39.0 | 117 | 0.0504 | 1.0 |
| 0.0081 | 40.0 | 120 | 0.0497 | 1.0 |
| 0.0079 | 41.0 | 123 | 0.0492 | 1.0 |
| 0.0092 | 42.0 | 126 | 0.0488 | 1.0 |
| 0.008 | 43.0 | 129 | 0.0483 | 1.0 |
| 0.0087 | 44.0 | 132 | 0.0479 | 1.0 |
| 0.009 | 45.0 | 135 | 0.0474 | 1.0 |
| 0.0076 | 46.0 | 138 | 0.0470 | 1.0 |
| 0.0075 | 47.0 | 141 | 0.0467 | 1.0 |
| 0.008 | 48.0 | 144 | 0.0465 | 1.0 |
| 0.0069 | 49.0 | 147 | 0.0464 | 1.0 |
| 0.0077 | 50.0 | 150 | 0.0464 | 1.0 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2
- Tokenizers 0.10.3
|
SetFit/distilbert-base-uncased__subj__train-8-6 | e54ab8cae10be4e73686399ff59d3e702995b42c | 2022-02-09T20:28:41.000Z | [
"pytorch",
"distilbert",
"text-classification",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | text-classification | false | SetFit | null | SetFit/distilbert-base-uncased__subj__train-8-6 | 4 | null | transformers | 18,215 | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased__subj__train-8-6
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__subj__train-8-6
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: 0.6075
- Accuracy: 0.7485
## 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: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.7163 | 1.0 | 3 | 0.6923 | 0.5 |
| 0.6648 | 2.0 | 6 | 0.6838 | 0.5 |
| 0.6329 | 3.0 | 9 | 0.6747 | 0.75 |
| 0.5836 | 4.0 | 12 | 0.6693 | 0.5 |
| 0.5287 | 5.0 | 15 | 0.6670 | 0.25 |
| 0.4585 | 6.0 | 18 | 0.6517 | 0.5 |
| 0.415 | 7.0 | 21 | 0.6290 | 0.5 |
| 0.3353 | 8.0 | 24 | 0.6019 | 0.5 |
| 0.2841 | 9.0 | 27 | 0.5613 | 0.75 |
| 0.2203 | 10.0 | 30 | 0.5222 | 1.0 |
| 0.1743 | 11.0 | 33 | 0.4769 | 1.0 |
| 0.1444 | 12.0 | 36 | 0.4597 | 1.0 |
| 0.1079 | 13.0 | 39 | 0.4462 | 1.0 |
| 0.0891 | 14.0 | 42 | 0.4216 | 1.0 |
| 0.0704 | 15.0 | 45 | 0.3880 | 1.0 |
| 0.0505 | 16.0 | 48 | 0.3663 | 1.0 |
| 0.0428 | 17.0 | 51 | 0.3536 | 1.0 |
| 0.0356 | 18.0 | 54 | 0.3490 | 1.0 |
| 0.0283 | 19.0 | 57 | 0.3531 | 1.0 |
| 0.025 | 20.0 | 60 | 0.3595 | 1.0 |
| 0.0239 | 21.0 | 63 | 0.3594 | 1.0 |
| 0.0202 | 22.0 | 66 | 0.3521 | 1.0 |
| 0.0168 | 23.0 | 69 | 0.3475 | 1.0 |
| 0.0159 | 24.0 | 72 | 0.3458 | 1.0 |
| 0.0164 | 25.0 | 75 | 0.3409 | 1.0 |
| 0.0132 | 26.0 | 78 | 0.3360 | 1.0 |
| 0.0137 | 27.0 | 81 | 0.3302 | 1.0 |
| 0.0112 | 28.0 | 84 | 0.3235 | 1.0 |
| 0.0113 | 29.0 | 87 | 0.3178 | 1.0 |
| 0.0111 | 30.0 | 90 | 0.3159 | 1.0 |
| 0.0113 | 31.0 | 93 | 0.3108 | 1.0 |
| 0.0107 | 32.0 | 96 | 0.3101 | 1.0 |
| 0.0101 | 33.0 | 99 | 0.3100 | 1.0 |
| 0.0083 | 34.0 | 102 | 0.3110 | 1.0 |
| 0.0092 | 35.0 | 105 | 0.3117 | 1.0 |
| 0.0102 | 36.0 | 108 | 0.3104 | 1.0 |
| 0.0086 | 37.0 | 111 | 0.3086 | 1.0 |
| 0.0092 | 38.0 | 114 | 0.3047 | 1.0 |
| 0.0072 | 39.0 | 117 | 0.3024 | 1.0 |
| 0.0079 | 40.0 | 120 | 0.3014 | 1.0 |
| 0.0079 | 41.0 | 123 | 0.2983 | 1.0 |
| 0.0091 | 42.0 | 126 | 0.2948 | 1.0 |
| 0.0077 | 43.0 | 129 | 0.2915 | 1.0 |
| 0.0085 | 44.0 | 132 | 0.2890 | 1.0 |
| 0.009 | 45.0 | 135 | 0.2870 | 1.0 |
| 0.0073 | 46.0 | 138 | 0.2856 | 1.0 |
| 0.0073 | 47.0 | 141 | 0.2844 | 1.0 |
| 0.0076 | 48.0 | 144 | 0.2841 | 1.0 |
| 0.0065 | 49.0 | 147 | 0.2836 | 1.0 |
| 0.0081 | 50.0 | 150 | 0.2835 | 1.0 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2
- Tokenizers 0.10.3
|
SetFit/distilbert-base-uncased__subj__train-8-8 | 9b69b190892f79f99b88ec5b5ef6f1421438daee | 2022-02-09T20:32:49.000Z | [
"pytorch",
"distilbert",
"text-classification",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | text-classification | false | SetFit | null | SetFit/distilbert-base-uncased__subj__train-8-8 | 4 | null | transformers | 18,216 | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased__subj__train-8-8
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__subj__train-8-8
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: 0.3160
- Accuracy: 0.8735
## 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: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.7187 | 1.0 | 3 | 0.6776 | 1.0 |
| 0.684 | 2.0 | 6 | 0.6608 | 1.0 |
| 0.6532 | 3.0 | 9 | 0.6364 | 1.0 |
| 0.5996 | 4.0 | 12 | 0.6119 | 1.0 |
| 0.5242 | 5.0 | 15 | 0.5806 | 1.0 |
| 0.4612 | 6.0 | 18 | 0.5320 | 1.0 |
| 0.4192 | 7.0 | 21 | 0.4714 | 1.0 |
| 0.3274 | 8.0 | 24 | 0.4071 | 1.0 |
| 0.2871 | 9.0 | 27 | 0.3378 | 1.0 |
| 0.2082 | 10.0 | 30 | 0.2822 | 1.0 |
| 0.1692 | 11.0 | 33 | 0.2271 | 1.0 |
| 0.1242 | 12.0 | 36 | 0.1793 | 1.0 |
| 0.0977 | 13.0 | 39 | 0.1417 | 1.0 |
| 0.0776 | 14.0 | 42 | 0.1117 | 1.0 |
| 0.0631 | 15.0 | 45 | 0.0894 | 1.0 |
| 0.0453 | 16.0 | 48 | 0.0733 | 1.0 |
| 0.0399 | 17.0 | 51 | 0.0617 | 1.0 |
| 0.0333 | 18.0 | 54 | 0.0528 | 1.0 |
| 0.0266 | 19.0 | 57 | 0.0454 | 1.0 |
| 0.0234 | 20.0 | 60 | 0.0393 | 1.0 |
| 0.0223 | 21.0 | 63 | 0.0345 | 1.0 |
| 0.0195 | 22.0 | 66 | 0.0309 | 1.0 |
| 0.0161 | 23.0 | 69 | 0.0281 | 1.0 |
| 0.0167 | 24.0 | 72 | 0.0260 | 1.0 |
| 0.0163 | 25.0 | 75 | 0.0242 | 1.0 |
| 0.0134 | 26.0 | 78 | 0.0227 | 1.0 |
| 0.0128 | 27.0 | 81 | 0.0214 | 1.0 |
| 0.0101 | 28.0 | 84 | 0.0204 | 1.0 |
| 0.0109 | 29.0 | 87 | 0.0194 | 1.0 |
| 0.0112 | 30.0 | 90 | 0.0186 | 1.0 |
| 0.0108 | 31.0 | 93 | 0.0179 | 1.0 |
| 0.011 | 32.0 | 96 | 0.0174 | 1.0 |
| 0.0099 | 33.0 | 99 | 0.0169 | 1.0 |
| 0.0083 | 34.0 | 102 | 0.0164 | 1.0 |
| 0.0096 | 35.0 | 105 | 0.0160 | 1.0 |
| 0.01 | 36.0 | 108 | 0.0156 | 1.0 |
| 0.0084 | 37.0 | 111 | 0.0152 | 1.0 |
| 0.0089 | 38.0 | 114 | 0.0149 | 1.0 |
| 0.0073 | 39.0 | 117 | 0.0146 | 1.0 |
| 0.0082 | 40.0 | 120 | 0.0143 | 1.0 |
| 0.008 | 41.0 | 123 | 0.0141 | 1.0 |
| 0.0093 | 42.0 | 126 | 0.0139 | 1.0 |
| 0.0078 | 43.0 | 129 | 0.0138 | 1.0 |
| 0.0086 | 44.0 | 132 | 0.0136 | 1.0 |
| 0.009 | 45.0 | 135 | 0.0135 | 1.0 |
| 0.0072 | 46.0 | 138 | 0.0134 | 1.0 |
| 0.0075 | 47.0 | 141 | 0.0133 | 1.0 |
| 0.0082 | 48.0 | 144 | 0.0133 | 1.0 |
| 0.0068 | 49.0 | 147 | 0.0132 | 1.0 |
| 0.0074 | 50.0 | 150 | 0.0132 | 1.0 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2
- Tokenizers 0.10.3
|
ShengdingHu/qqp | 3071ab6e9bcb9e22b7b6a36cb5d0c448b504306b | 2022-02-02T15:52:19.000Z | [
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | ShengdingHu | null | ShengdingHu/qqp | 4 | null | transformers | 18,217 | Entry not found |
ShinxisS/DialoGPT-small-Neku | c41fcb35a803d4ca652f1feb82b297b53df0eae3 | 2021-07-22T08:39:19.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
] | text-generation | false | ShinxisS | null | ShinxisS/DialoGPT-small-Neku | 4 | null | transformers | 18,218 |
tags:
- conversational
|
SilentMyuth/stablejen | bcbe6b04e4066a35a4bf81a9a35ce180ece54ed4 | 2021-08-27T22:30:06.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
] | text-generation | false | SilentMyuth | null | SilentMyuth/stablejen | 4 | null | transformers | 18,219 | Hewlo |
Sirinya/wangchanberta-th-squad_test1 | 4d2550afecfbb9d36b45c329f68af8073680872a | 2022-04-24T13:43:51.000Z | [
"pytorch",
"tensorboard",
"camembert",
"question-answering",
"dataset:thaiqa_squad",
"transformers",
"generated_from_trainer",
"model-index",
"autotrain_compatible"
] | question-answering | false | Sirinya | null | Sirinya/wangchanberta-th-squad_test1 | 4 | null | transformers | 18,220 | ---
tags:
- generated_from_trainer
datasets:
- thaiqa_squad
model-index:
- name: wangchanberta-base-att-spm-uncased-finetuned-th-squad
results: []
widget:
- text: "สโมสรเรอัลมาดริดก่อตั้งขึ้นในปีใด"
context: "สโมสรฟุตบอลเรอัลมาดริด (สเปน: Real Madrid Club de Fútbol) เป็นสโมสรฟุตบอลที่มีชื่อเสียงในประเทศสเปน ตั้งอยู่ที่กรุงมาดริด ปัจจุบันเล่นอยู่ในลาลิกา ก่อตั้งขึ้นใน ค.ศ. 1902 โดยเป็นหนึ่งในสโมสรที่ประสบความสำเร็จมากที่สุดในทวีปยุโรป เรอัลมาดริดเป็นสมาชิกของกลุ่ม 14 ซึ่งเป็นกลุ่มสโมสรชั้นนำของยูฟ่า และยังเป็นหนึ่งในสามสโมสรผู้ร่วมก่อตั้งลาลิกาซึ่งไม่เคยตกชั้นจากลีกสูงสุดนับตั้งแต่ ค.ศ. 1929 มีคู่อริคือสโมสรบาร์เซโลนา และ อัตเลติโกเดมาดริด มีสนามเหย้าคือซานเตียโก เบร์นาเบว"
- text: "รักบี้ถือกำเนิดขึ้นในปีใด"
context: "รักบี้ เป็นกีฬาชนิดหนึ่งถือกำเนิดขึ้นจากโรงเรียนรักบี้ (Rugby School) ในเมืองรักบี้ ในเขตวอร์วิกเชียร์ ประเทศอังกฤษ เริ่มต้นจาก ในปี ค.ศ. 1826 ขณะนั้นเป็นการแข่งขัน ฟุตบอล ภายในของโรงเรียนรักบี้ ซึ่งตั้งอยู่ ณ เมืองรักบี้ ประเทศอังกฤษ ผู้เล่นคนหนึ่งชื่อ วิลเลียม เวบบ์ เอลลิส (William Webb Ellis) ได้ทำผิดกติกาการแข่งขันที่วางไว้ โดยวิ่งอุ้มลูกบอลซึ่งตัวเขาเองไม่ได้เป็นผู้เล่นในตำแหน่งผู้รักษาประตู และได้วิ่งอุ้มลูกบอลไปจนถึงเส้นประตูฝ่ายตรงข้าม เขาจะจงใจหรือไม่ก็ตามแต่ แต่การเล่นที่นอกลู่นอกทางของเขาได้เป็นที่พูดถึงอย่างแพร่หลาย ในหมู่ผู้เล่นและผู้ดูจนแพร่กระจายไปตามโรงเรียนต่างๆในอังกฤษ โดยเฉพาะในหมู่นักเรียนของโรงเรียนเคมบริดจ์ ได้นำเอาวิธีการเล่นของ นายเอลลีส ไปจัดการแข่งขันโดยเรียกชื่อเกมชนิดใหม่นี้ว่า รักบี้เกมส์ (Rugby Games) ภายหลังจากนั้นก็เป็นที่นิยมเล่นกันมากขึ้น ทั้งได้มีการเปลี่ยนแปลงแก้ไขการเล่นเรื่อยมาในประเทศอังกฤษ"
---
<!-- 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. -->
# wangchanberta-base-att-spm-uncased-finetuned-th-squad
This model is a fine-tuned version of [airesearch/wangchanberta-base-att-spm-uncased](https://huggingface.co/airesearch/wangchanberta-base-att-spm-uncased) on the thaiqa_squad dataset.
### 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
### Framework versions
- Transformers 4.13.0
- Pytorch 1.10.0+cu111
- Datasets 1.16.1
- Tokenizers 0.10.3
|
SongRb/distilbert-base-uncased-finetuned-cola | c617391ba7ce23cc7aff554684ed680e7b7bc8c4 | 2021-08-31T10:19:57.000Z | [
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"dataset:glue",
"transformers",
"generated_from_trainer",
"license:apache-2.0"
] | text-classification | false | SongRb | null | SongRb/distilbert-base-uncased-finetuned-cola | 4 | null | transformers | 18,221 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- matthews_correlation
model_index:
- name: distilbert-base-uncased-finetuned-cola
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
args: cola
metric:
name: Matthews Correlation
type: matthews_correlation
value: 0.5332198659134496
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-cola
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8549
- Matthews Correlation: 0.5332
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.5213 | 1.0 | 535 | 0.5163 | 0.4183 |
| 0.3479 | 2.0 | 1070 | 0.5351 | 0.5182 |
| 0.231 | 3.0 | 1605 | 0.6271 | 0.5291 |
| 0.166 | 4.0 | 2140 | 0.7531 | 0.5279 |
| 0.1313 | 5.0 | 2675 | 0.8549 | 0.5332 |
### Framework versions
- Transformers 4.10.0.dev0
- Pytorch 1.8.1
- Datasets 1.11.0
- Tokenizers 0.10.3
|
StevenLimcorn/indonesian-roberta-base-bapos-tagger | 59ebfd5863c2f8ae3240aed059a173a3458cf9c1 | 2021-07-11T10:19:22.000Z | [
"pytorch",
"tf",
"roberta",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | false | StevenLimcorn | null | StevenLimcorn/indonesian-roberta-base-bapos-tagger | 4 | null | transformers | 18,222 | Entry not found |
SupriyaArun/squeezebert-uncased-finetuned-squad-finetuned-squad | 3419d58625a123682e83d43e1005c6435f180b63 | 2021-12-11T20:16:19.000Z | [
"pytorch",
"squeezebert",
"question-answering",
"dataset:squad",
"transformers",
"generated_from_trainer",
"model-index",
"autotrain_compatible"
] | question-answering | false | SupriyaArun | null | SupriyaArun/squeezebert-uncased-finetuned-squad-finetuned-squad | 4 | null | transformers | 18,223 | ---
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: squeezebert-uncased-finetuned-squad-finetuned-squad
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. -->
# squeezebert-uncased-finetuned-squad-finetuned-squad
This model is a fine-tuned version of [SupriyaArun/squeezebert-uncased-finetuned-squad](https://huggingface.co/SupriyaArun/squeezebert-uncased-finetuned-squad) on the squad 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: 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
### Framework versions
- Transformers 4.13.0
- Pytorch 1.10.0+cu111
- Datasets 1.16.1
- Tokenizers 0.10.3
|
Suva/uptag-url-model | 88405f4ae280d32a5d6ff2851bd5f636e58e675f | 2022-01-25T04:32:49.000Z | [
"pytorch",
"t5",
"text2text-generation",
"dataset:arxiv",
"transformers",
"license:mit",
"autotrain_compatible"
] | text2text-generation | false | Suva | null | Suva/uptag-url-model | 4 | null | transformers | 18,224 | ---
datasets:
- arxiv
widget:
- text: "summarize: We describe a system called Overton, whose main design goal is to support engineers in building, monitoring, and improving production
machinelearning systems. Key challenges engineers face are monitoring fine-grained quality, diagnosing errors in sophisticated applications, and
handling contradictory or incomplete supervision data. Overton automates the life cycle of model construction, deployment, and monitoring by providing a set of novel high-level, declarative abstractions. Overton's vision is to shift developers to these higher-level tasks instead of lower-level machine learning tasks.
In fact, using Overton, engineers can build deep-learning-based applications without writing any code in frameworks like TensorFlow. For over a year,
Overton has been used in production to support multiple applications in both near-real-time applications and back-of-house processing.
In that time, Overton-based applications have answered billions of queries in multiple languages and processed trillions of records reducing errors
1.7-2.9 times versus production systems."
license: mit
---
## Usage:
```python
abstract = """We describe a system called Overton, whose main design goal is to support engineers in building, monitoring, and improving production
machine learning systems. Key challenges engineers face are monitoring fine-grained quality, diagnosing errors in sophisticated applications, and
handling contradictory or incomplete supervision data. Overton automates the life cycle of model construction, deployment, and monitoring by providing a
set of novel high-level, declarative abstractions. Overton's vision is to shift developers to these higher-level tasks instead of lower-level machine learning tasks.
In fact, using Overton, engineers can build deep-learning-based applications without writing any code in frameworks like TensorFlow. For over a year,
Overton has been used in production to support multiple applications in both near-real-time applications and back-of-house processing. In that time,
Overton-based applications have answered billions of queries in multiple languages and processed trillions of records reducing errors 1.7-2.9 times versus production systems.
"""
```
### Using Transformers🤗
```python
model_name = "Suva/uptag-url-model"
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
input_ids = tokenizer.encode("summarize: " + abstract, return_tensors="pt", add_special_tokens=True)
generated_ids = model.generate(input_ids=input_ids,num_beams=5,max_length=100,repetition_penalty=2.5,length_penalty=1,early_stopping=True,num_return_sequences=3)
preds = [tokenizer.decode(g, skip_special_tokens=True, clean_up_tokenization_spaces=True) for g in generated_ids]
print(preds)
# output
["Overton: Building, Deploying, and Monitoring Machine Learning Systems for Engineers",
"Overton: A System for Building, Monitoring, and Improving Production Machine Learning Systems",
"Overton: Building, Monitoring, and Improving Production Machine Learning Systems"]
``` |
TJMUCH/transcriptome-iseeek | 70cd2c031b325fec84c80951705477b118db59cc | 2021-12-10T09:32:52.000Z | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | TJMUCH | null | TJMUCH/transcriptome-iseeek | 4 | null | transformers | 18,225 |
# iSEEEK
A universal approach for integrating super large-scale single-cell transcriptomes by exploring gene rankings
## An simple pipeline for single-cell analysis
```python
import torch
import gzip
import re
from tqdm import tqdm
import numpy as np
import scanpy as sc
from torch.utils.data import DataLoader, Dataset
from transformers import PreTrainedTokenizerFast, BertForMaskedLM
class LineDataset(Dataset):
def __init__(self, lines):
self.lines = lines
self.regex = re.compile(r'\-|\.')
def __getitem__(self, i):
return self.regex.sub('_', self.lines[i])
def __len__(self):
return len(self.lines)
device = "cuda" if torch.cuda.is_available() else "cpu"
torch.set_num_threads(2)
tokenizer = PreTrainedTokenizerFast.from_pretrained("TJMUCH/transcriptome-iseeek")
model = BertForMaskedLM.from_pretrained("TJMUCH/transcriptome-iseeek").bert
model = model.to(device)
model.eval()
## Data desposited in https://huggingface.co/TJMUCH/transcriptome-iseeek/tree/main
lines = [s.strip().decode() for s in gzip.open("pbmc_ranking.txt.gz")]
labels = [s.strip().decode() for s in gzip.open("pbmc_label.txt.gz")]
labels = np.asarray(labels)
ds = LineDataset(lines)
dl = DataLoader(ds, batch_size=80)
features = []
for a in tqdm(dl, total=len(dl)):
batch = tokenizer(a, max_length=128, truncation=True,
padding=True, return_tensors="pt")
for k, v in batch.items():
batch[k] = v.to(device)
with torch.no_grad():
out = model(**batch)
f = out.last_hidden_state[:,0,:]
features.extend(f.tolist())
features = np.stack(features)
adata = sc.AnnData(features)
adata.obs['celltype'] = labels
adata.obs.celltype = adata.obs.celltype.astype("category")
sc.pp.neighbors(adata, use_rep='X')
sc.tl.umap(adata)
sc.tl.leiden(adata)
sc.pl.umap(adata, color=['celltype','leiden'],save= "UMAP")
```
## Extract token representations
```python
cell_counts = len(lines)
x = np.zeros((cell_counts, len(tokenizer)), dtype=np.float16)
for a in tqdm(dl, total=len(dl)):
batch = tokenizer(a, max_length=128, truncation=True,
padding=True, return_tensors="pt")
for k, v in batch.items():
batch[k] = v.to(device)
with torch.no_grad():
out = model(**batch)
eos_idxs = batch.attention_mask.sum(dim=1) - 1
f = out.last_hidden_state
batch_size = f.shape[0]
input_ids = batch.input_ids
for i in range(batch_size):
##genes = tokenizer.batch_decode(input_ids[i])
token_norms = [f[i][j].norm().item() for j in range(1, eos_idxs[i])]
idxs = input_ids[i].tolist()[1:eos_idxs[i]]
x[counter, idxs] = token_norms
counter = counter + 1
```
|
TehranNLP/albert-base-v2-mnli | 795e3982164f346ef56e071b9a8ce8050a39e70f | 2021-06-03T11:30:55.000Z | [
"pytorch",
"albert",
"text-classification",
"transformers"
] | text-classification | false | TehranNLP | null | TehranNLP/albert-base-v2-mnli | 4 | null | transformers | 18,226 | Entry not found |
TehranNLP/electra-base-mnli | dc6b02f94258c8a7efd30f17ec07c9e71bb8600d | 2021-06-03T11:47:40.000Z | [
"pytorch",
"electra",
"text-classification",
"transformers"
] | text-classification | false | TehranNLP | null | TehranNLP/electra-base-mnli | 4 | null | transformers | 18,227 | Entry not found |
TehranNLP-org/bert-base-uncased-avg-cola-2e-5-21 | 0e8ca1f54ab76bbf767d1a3ab674bb03b367528a | 2021-07-23T17:43:55.000Z | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | false | TehranNLP-org | null | TehranNLP-org/bert-base-uncased-avg-cola-2e-5-21 | 4 | null | transformers | 18,228 | Entry not found |
TehranNLP-org/bert-base-uncased-avg-cola-2e-5-42 | 019fd747c402a831bc2fbb70b20fa2ba78468cc2 | 2021-07-23T17:13:25.000Z | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | false | TehranNLP-org | null | TehranNLP-org/bert-base-uncased-avg-cola-2e-5-42 | 4 | null | transformers | 18,229 | Entry not found |
TehranNLP-org/bert-base-uncased-avg-cola-2e-5-63 | 32aa4f18872dc55743673e10099ab6e1faf36379 | 2021-07-23T18:10:35.000Z | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | false | TehranNLP-org | null | TehranNLP-org/bert-base-uncased-avg-cola-2e-5-63 | 4 | null | transformers | 18,230 | Entry not found |
TehranNLP-org/bert-base-uncased-avg-mnli-2e-5-21 | 96b8589b1037ef39f84347c74f4c49f6f53ba686 | 2021-07-21T16:25:41.000Z | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | false | TehranNLP-org | null | TehranNLP-org/bert-base-uncased-avg-mnli-2e-5-21 | 4 | null | transformers | 18,231 | Entry not found |
TehranNLP-org/bert-base-uncased-avg-mnli-2e-5-63 | ec22e5546dc23704b5c0a482cc0f8690e48a4f15 | 2021-07-23T08:36:12.000Z | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | false | TehranNLP-org | null | TehranNLP-org/bert-base-uncased-avg-mnli-2e-5-63 | 4 | null | transformers | 18,232 | Entry not found |
TehranNLP-org/bert-base-uncased-avg-sst2-2e-5-42 | 0156418f02a4c3013c240736b319393616f3b8c3 | 2021-07-31T19:53:57.000Z | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | false | TehranNLP-org | null | TehranNLP-org/bert-base-uncased-avg-sst2-2e-5-42 | 4 | null | transformers | 18,233 | Entry not found |
TehranNLP-org/bert-base-uncased-cls-mnli | ee915d9be40de5ddbc2fff630532e06e640ac816 | 2022-05-02T20:59:25.000Z | [
"pytorch",
"tf",
"bert",
"text-classification",
"transformers"
] | text-classification | false | TehranNLP-org | null | TehranNLP-org/bert-base-uncased-cls-mnli | 4 | null | transformers | 18,234 | Entry not found |
TehranNLP-org/bert-base-uncased-mrpc-2e-5-42 | b23bbeb20e3aac8be838c7ad633250051b26ad9f | 2021-08-18T18:30:28.000Z | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | false | TehranNLP-org | null | TehranNLP-org/bert-base-uncased-mrpc-2e-5-42 | 4 | null | transformers | 18,235 | Entry not found |
TehranNLP-org/electra-base-ag-news-2e-5-42 | 7eb338f291422aa963c153335b3b8b4391b3e424 | 2021-08-28T19:52:33.000Z | [
"pytorch",
"electra",
"text-classification",
"transformers"
] | text-classification | false | TehranNLP-org | null | TehranNLP-org/electra-base-ag-news-2e-5-42 | 4 | null | transformers | 18,236 | Entry not found |
TehranNLP-org/electra-base-avg-cola-2e-5-63 | 93482c682b64f920bda616d26cfa3cd91381963e | 2021-07-23T18:36:22.000Z | [
"pytorch",
"electra",
"text-classification",
"transformers"
] | text-classification | false | TehranNLP-org | null | TehranNLP-org/electra-base-avg-cola-2e-5-63 | 4 | null | transformers | 18,237 | Entry not found |
TehranNLP-org/electra-base-avg-mnli-2e-5-21 | 074cbdbf686befa115e2444c174e375e30e3cef2 | 2021-07-21T23:24:00.000Z | [
"pytorch",
"electra",
"text-classification",
"transformers"
] | text-classification | false | TehranNLP-org | null | TehranNLP-org/electra-base-avg-mnli-2e-5-21 | 4 | null | transformers | 18,238 | Entry not found |
TehranNLP-org/electra-base-avg-qqp-2e-5-42 | a54bbcbd41c3b472a40580c1951d7774392b1d7e | 2021-08-14T23:32:48.000Z | [
"pytorch",
"electra",
"text-classification",
"transformers"
] | text-classification | false | TehranNLP-org | null | TehranNLP-org/electra-base-avg-qqp-2e-5-42 | 4 | null | transformers | 18,239 | Entry not found |
TehranNLP-org/electra-base-avg-sst2-2e-5-21 | 954b3482f8a3025f99757707f565c4deef4efe0a | 2021-07-31T16:32:45.000Z | [
"pytorch",
"electra",
"text-classification",
"transformers"
] | text-classification | false | TehranNLP-org | null | TehranNLP-org/electra-base-avg-sst2-2e-5-21 | 4 | null | transformers | 18,240 | Entry not found |
TehranNLP-org/electra-base-avg-sst2-2e-5-63 | 9280b9384b7c2e8bf3c7cab9896eb0e6abd30a17 | 2021-07-31T18:03:47.000Z | [
"pytorch",
"electra",
"text-classification",
"transformers"
] | text-classification | false | TehranNLP-org | null | TehranNLP-org/electra-base-avg-sst2-2e-5-63 | 4 | null | transformers | 18,241 | Entry not found |
TehranNLP-org/electra-base-mrpc-2e-5-42 | 3b64b580a0d9423e06c49b596907f251225def1b | 2021-08-18T18:52:55.000Z | [
"pytorch",
"electra",
"text-classification",
"transformers"
] | text-classification | false | TehranNLP-org | null | TehranNLP-org/electra-base-mrpc-2e-5-42 | 4 | null | transformers | 18,242 | Entry not found |
TehranNLP-org/electra-base-qqp-2e-5-42 | d20c2a1736383d1ebfa607a61fc18fb5ef36073e | 2021-08-20T05:39:17.000Z | [
"pytorch",
"electra",
"text-classification",
"transformers"
] | text-classification | false | TehranNLP-org | null | TehranNLP-org/electra-base-qqp-2e-5-42 | 4 | null | transformers | 18,243 | Entry not found |
TehranNLP-org/electra-base-qqp-cls-2e-5-42 | 777b72aee695b5338eddc17d59572a7a365c2313 | 2021-08-29T10:11:43.000Z | [
"pytorch",
"electra",
"text-classification",
"transformers"
] | text-classification | false | TehranNLP-org | null | TehranNLP-org/electra-base-qqp-cls-2e-5-42 | 4 | null | transformers | 18,244 | Entry not found |
TehranNLP-org/xlnet-base-cased-avg-cola-2e-5-21 | 84ae5cc99f9d0bad93000fba23517baa9fa6b0b2 | 2021-07-23T15:21:15.000Z | [
"pytorch",
"xlnet",
"text-classification",
"transformers"
] | text-classification | false | TehranNLP-org | null | TehranNLP-org/xlnet-base-cased-avg-cola-2e-5-21 | 4 | null | transformers | 18,245 | Entry not found |
TehranNLP-org/xlnet-base-cased-avg-cola-2e-5-63 | c7652840d693268173fbc40fef539797b5ae4a8d | 2021-07-23T16:31:30.000Z | [
"pytorch",
"xlnet",
"text-classification",
"transformers"
] | text-classification | false | TehranNLP-org | null | TehranNLP-org/xlnet-base-cased-avg-cola-2e-5-63 | 4 | null | transformers | 18,246 | Entry not found |
TehranNLP-org/xlnet-base-cased-avg-sst2-2e-5-21 | b83c256d402e9f3aa26f31b1d949c40580f8304b | 2021-07-31T22:45:16.000Z | [
"pytorch",
"xlnet",
"text-classification",
"transformers"
] | text-classification | false | TehranNLP-org | null | TehranNLP-org/xlnet-base-cased-avg-sst2-2e-5-21 | 4 | null | transformers | 18,247 | Entry not found |
TehranNLP-org/xlnet-base-cased-avg-sst2-2e-5-42 | 75f399df98f2808f44c440163fee688364c26c12 | 2021-07-31T20:57:06.000Z | [
"pytorch",
"xlnet",
"text-classification",
"transformers"
] | text-classification | false | TehranNLP-org | null | TehranNLP-org/xlnet-base-cased-avg-sst2-2e-5-42 | 4 | null | transformers | 18,248 | Entry not found |
TehranNLP-org/xlnet-base-cased-avg-sst2-2e-5-63 | 60d37af02cd153b39826264ab9a3a721b2d002ba | 2021-08-01T07:06:41.000Z | [
"pytorch",
"xlnet",
"text-classification",
"transformers"
] | text-classification | false | TehranNLP-org | null | TehranNLP-org/xlnet-base-cased-avg-sst2-2e-5-63 | 4 | null | transformers | 18,249 | Entry not found |
Tejas3/distillbert_110_uncased_v1 | 243b9f71ea1075bb4c56b9e06d0868a3cbf1313c | 2021-08-22T18:01:35.000Z | [
"pytorch",
"distilbert",
"text-classification",
"transformers"
] | text-classification | false | Tejas3 | null | Tejas3/distillbert_110_uncased_v1 | 4 | null | transformers | 18,250 | Entry not found |
ThaiUWA/gpt2test | 07ecf659f168bfae24dfeeb3d2b3820ee44c0bf3 | 2021-05-21T11:21:40.000Z | [
"pytorch",
"jax",
"gpt2",
"text-generation",
"transformers"
] | text-generation | false | ThaiUWA | null | ThaiUWA/gpt2test | 4 | null | transformers | 18,251 | Entry not found |
The-Data-Hound/bacteria_lamp_network | d4158691bf37e1c3f6bf194eeabfe47b5f961620 | 2020-12-21T16:33:38.000Z | [
"pytorch",
"distilbert",
"text-classification",
"transformers"
] | text-classification | false | The-Data-Hound | null | The-Data-Hound/bacteria_lamp_network | 4 | null | transformers | 18,252 | Entry not found |
TheCatsMoo/DialoGGPT-small-joshua | d4dc6da1e12c32379b592e19755f114abaf53cfc | 2021-09-18T11:09:22.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | false | TheCatsMoo | null | TheCatsMoo/DialoGGPT-small-joshua | 4 | null | transformers | 18,253 | ---
tags:
- conversational
---
#Joshua |
TrLOX/gpt2-tdk | ff265b2a4b4c1d4548d6bd3a8f66db093c1f3d39 | 2021-12-31T02:18:21.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | text-generation | false | TrLOX | null | TrLOX/gpt2-tdk | 4 | null | transformers | 18,254 | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: dgpt
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. -->
# dgpt
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- distributed_type: tpu
- num_devices: 8
- total_train_batch_size: 16
- total_eval_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10.0
### Training results
### Framework versions
- Transformers 4.14.0.dev0
- Pytorch 1.9.0+cu102
- Datasets 1.16.2.dev0
- Tokenizers 0.10.3
hello
hello
|
TransQuest/microtransquest-de_en-pharmaceutical-smt | a4ebf7d9a26ef6db2c4866bcf82eff030e56ec9e | 2021-06-04T08:18:20.000Z | [
"pytorch",
"xlm-roberta",
"token-classification",
"de-en",
"transformers",
"Quality Estimation",
"microtransquest",
"license:apache-2.0",
"autotrain_compatible"
] | token-classification | false | TransQuest | null | TransQuest/microtransquest-de_en-pharmaceutical-smt | 4 | null | transformers | 18,255 | ---
language: de-en
tags:
- Quality Estimation
- microtransquest
license: apache-2.0
---
# TransQuest: Translation Quality Estimation with Cross-lingual Transformers
The goal of quality estimation (QE) is to evaluate the quality of a translation without having access to a reference translation. High-accuracy QE that can be easily deployed for a number of language pairs is the missing piece in many commercial translation workflows as they have numerous potential uses. They can be employed to select the best translation when several translation engines are available or can inform the end user about the reliability of automatically translated content. In addition, QE systems can be used to decide whether a translation can be published as it is in a given context, or whether it requires human post-editing before publishing or translation from scratch by a human. The quality estimation can be done at different levels: document level, sentence level and word level.
With TransQuest, we have opensourced our research in translation quality estimation which also won the sentence-level direct assessment quality estimation shared task in [WMT 2020](http://www.statmt.org/wmt20/quality-estimation-task.html). TransQuest outperforms current open-source quality estimation frameworks such as [OpenKiwi](https://github.com/Unbabel/OpenKiwi) and [DeepQuest](https://github.com/sheffieldnlp/deepQuest).
## Features
- Sentence-level translation quality estimation on both aspects: predicting post editing efforts and direct assessment.
- Word-level translation quality estimation capable of predicting quality of source words, target words and target gaps.
- Outperform current state-of-the-art quality estimation methods like DeepQuest and OpenKiwi in all the languages experimented.
- Pre-trained quality estimation models for fifteen language pairs are available in [HuggingFace.](https://huggingface.co/TransQuest)
## Installation
### From pip
```bash
pip install transquest
```
### From Source
```bash
git clone https://github.com/TharinduDR/TransQuest.git
cd TransQuest
pip install -r requirements.txt
```
## Using Pre-trained Models
```python
from transquest.algo.word_level.microtransquest.run_model import MicroTransQuestModel
import torch
model = MicroTransQuestModel("xlmroberta", "TransQuest/microtransquest-de_en-pharmaceutical-smt", labels=["OK", "BAD"], use_cuda=torch.cuda.is_available())
source_tags, target_tags = model.predict([["if not , you may not be protected against the diseases . ", "ja tā nav , Jūs varat nepasargāt no slimībām . "]])
```
## Documentation
For more details follow the documentation.
1. **[Installation](https://tharindudr.github.io/TransQuest/install/)** - Install TransQuest locally using pip.
2. **Architectures** - Checkout the architectures implemented in TransQuest
1. [Sentence-level Architectures](https://tharindudr.github.io/TransQuest/architectures/sentence_level_architectures/) - We have released two architectures; MonoTransQuest and SiameseTransQuest to perform sentence level quality estimation.
2. [Word-level Architecture](https://tharindudr.github.io/TransQuest/architectures/word_level_architecture/) - We have released MicroTransQuest to perform word level quality estimation.
3. **Examples** - We have provided several examples on how to use TransQuest in recent WMT quality estimation shared tasks.
1. [Sentence-level Examples](https://tharindudr.github.io/TransQuest/examples/sentence_level_examples/)
2. [Word-level Examples](https://tharindudr.github.io/TransQuest/examples/word_level_examples/)
4. **Pre-trained Models** - We have provided pretrained quality estimation models for fifteen language pairs covering both sentence-level and word-level
1. [Sentence-level Models](https://tharindudr.github.io/TransQuest/models/sentence_level_pretrained/)
2. [Word-level Models](https://tharindudr.github.io/TransQuest/models/word_level_pretrained/)
5. **[Contact](https://tharindudr.github.io/TransQuest/contact/)** - Contact us for any issues with TransQuest
## Citations
If you are using the word-level architecture, please consider citing this paper which is accepted to [ACL 2021](https://2021.aclweb.org/).
```bash
@InProceedings{ranasinghe2021,
author = {Ranasinghe, Tharindu and Orasan, Constantin and Mitkov, Ruslan},
title = {An Exploratory Analysis of Multilingual Word Level Quality Estimation with Cross-Lingual Transformers},
booktitle = {Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics},
year = {2021}
}
```
If you are using the sentence-level architectures, please consider citing these papers which were presented in [COLING 2020](https://coling2020.org/) and in [WMT 2020](http://www.statmt.org/wmt20/) at EMNLP 2020.
```bash
@InProceedings{transquest:2020a,
author = {Ranasinghe, Tharindu and Orasan, Constantin and Mitkov, Ruslan},
title = {TransQuest: Translation Quality Estimation with Cross-lingual Transformers},
booktitle = {Proceedings of the 28th International Conference on Computational Linguistics},
year = {2020}
}
```
```bash
@InProceedings{transquest:2020b,
author = {Ranasinghe, Tharindu and Orasan, Constantin and Mitkov, Ruslan},
title = {TransQuest at WMT2020: Sentence-Level Direct Assessment},
booktitle = {Proceedings of the Fifth Conference on Machine Translation},
year = {2020}
}
```
|
TransQuest/microtransquest-en_lv-pharmaceutical-smt | b952ca8188d1898944b07a2b10821fe9fc02b8e2 | 2021-06-04T08:22:20.000Z | [
"pytorch",
"xlm-roberta",
"token-classification",
"en-lv",
"transformers",
"Quality Estimation",
"microtransquest",
"license:apache-2.0",
"autotrain_compatible"
] | token-classification | false | TransQuest | null | TransQuest/microtransquest-en_lv-pharmaceutical-smt | 4 | null | transformers | 18,256 | ---
language: en-lv
tags:
- Quality Estimation
- microtransquest
license: apache-2.0
---
# TransQuest: Translation Quality Estimation with Cross-lingual Transformers
The goal of quality estimation (QE) is to evaluate the quality of a translation without having access to a reference translation. High-accuracy QE that can be easily deployed for a number of language pairs is the missing piece in many commercial translation workflows as they have numerous potential uses. They can be employed to select the best translation when several translation engines are available or can inform the end user about the reliability of automatically translated content. In addition, QE systems can be used to decide whether a translation can be published as it is in a given context, or whether it requires human post-editing before publishing or translation from scratch by a human. The quality estimation can be done at different levels: document level, sentence level and word level.
With TransQuest, we have opensourced our research in translation quality estimation which also won the sentence-level direct assessment quality estimation shared task in [WMT 2020](http://www.statmt.org/wmt20/quality-estimation-task.html). TransQuest outperforms current open-source quality estimation frameworks such as [OpenKiwi](https://github.com/Unbabel/OpenKiwi) and [DeepQuest](https://github.com/sheffieldnlp/deepQuest).
## Features
- Sentence-level translation quality estimation on both aspects: predicting post editing efforts and direct assessment.
- Word-level translation quality estimation capable of predicting quality of source words, target words and target gaps.
- Outperform current state-of-the-art quality estimation methods like DeepQuest and OpenKiwi in all the languages experimented.
- Pre-trained quality estimation models for fifteen language pairs are available in [HuggingFace.](https://huggingface.co/TransQuest)
## Installation
### From pip
```bash
pip install transquest
```
### From Source
```bash
git clone https://github.com/TharinduDR/TransQuest.git
cd TransQuest
pip install -r requirements.txt
```
## Using Pre-trained Models
```python
from transquest.algo.word_level.microtransquest.run_model import MicroTransQuestModel
import torch
model = MicroTransQuestModel("xlmroberta", "TransQuest/microtransquest-en_lv-pharmaceutical-smt", labels=["OK", "BAD"], use_cuda=torch.cuda.is_available())
source_tags, target_tags = model.predict([["if not , you may not be protected against the diseases . ", "ja tā nav , Jūs varat nepasargāt no slimībām . "]])
```
## Documentation
For more details follow the documentation.
1. **[Installation](https://tharindudr.github.io/TransQuest/install/)** - Install TransQuest locally using pip.
2. **Architectures** - Checkout the architectures implemented in TransQuest
1. [Sentence-level Architectures](https://tharindudr.github.io/TransQuest/architectures/sentence_level_architectures/) - We have released two architectures; MonoTransQuest and SiameseTransQuest to perform sentence level quality estimation.
2. [Word-level Architecture](https://tharindudr.github.io/TransQuest/architectures/word_level_architecture/) - We have released MicroTransQuest to perform word level quality estimation.
3. **Examples** - We have provided several examples on how to use TransQuest in recent WMT quality estimation shared tasks.
1. [Sentence-level Examples](https://tharindudr.github.io/TransQuest/examples/sentence_level_examples/)
2. [Word-level Examples](https://tharindudr.github.io/TransQuest/examples/word_level_examples/)
4. **Pre-trained Models** - We have provided pretrained quality estimation models for fifteen language pairs covering both sentence-level and word-level
1. [Sentence-level Models](https://tharindudr.github.io/TransQuest/models/sentence_level_pretrained/)
2. [Word-level Models](https://tharindudr.github.io/TransQuest/models/word_level_pretrained/)
5. **[Contact](https://tharindudr.github.io/TransQuest/contact/)** - Contact us for any issues with TransQuest
## Citations
If you are using the word-level architecture, please consider citing this paper which is accepted to [ACL 2021](https://2021.aclweb.org/).
```bash
@InProceedings{ranasinghe2021,
author = {Ranasinghe, Tharindu and Orasan, Constantin and Mitkov, Ruslan},
title = {An Exploratory Analysis of Multilingual Word Level Quality Estimation with Cross-Lingual Transformers},
booktitle = {Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics},
year = {2021}
}
```
If you are using the sentence-level architectures, please consider citing these papers which were presented in [COLING 2020](https://coling2020.org/) and in [WMT 2020](http://www.statmt.org/wmt20/) at EMNLP 2020.
```bash
@InProceedings{transquest:2020a,
author = {Ranasinghe, Tharindu and Orasan, Constantin and Mitkov, Ruslan},
title = {TransQuest: Translation Quality Estimation with Cross-lingual Transformers},
booktitle = {Proceedings of the 28th International Conference on Computational Linguistics},
year = {2020}
}
```
```bash
@InProceedings{transquest:2020b,
author = {Ranasinghe, Tharindu and Orasan, Constantin and Mitkov, Ruslan},
title = {TransQuest at WMT2020: Sentence-Level Direct Assessment},
booktitle = {Proceedings of the Fifth Conference on Machine Translation},
year = {2020}
}
```
|
TransQuest/siamesetransquest-da-ru_en-reddit_wikiquotes | 3a58de4db76aa8a09097ab3729a1d49473b93c6c | 2021-07-23T08:16:47.000Z | [
"pytorch",
"xlm-roberta",
"feature-extraction",
"ru-en",
"transformers",
"Quality Estimation",
"siamesetransquest",
"da",
"license:apache-2.0"
] | feature-extraction | false | TransQuest | null | TransQuest/siamesetransquest-da-ru_en-reddit_wikiquotes | 4 | null | transformers | 18,257 | ---
language: ru-en
tags:
- Quality Estimation
- siamesetransquest
- da
license: apache-2.0
---
# TransQuest: Translation Quality Estimation with Cross-lingual Transformers
The goal of quality estimation (QE) is to evaluate the quality of a translation without having access to a reference translation. High-accuracy QE that can be easily deployed for a number of language pairs is the missing piece in many commercial translation workflows as they have numerous potential uses. They can be employed to select the best translation when several translation engines are available or can inform the end user about the reliability of automatically translated content. In addition, QE systems can be used to decide whether a translation can be published as it is in a given context, or whether it requires human post-editing before publishing or translation from scratch by a human. The quality estimation can be done at different levels: document level, sentence level and word level.
With TransQuest, we have opensourced our research in translation quality estimation which also won the sentence-level direct assessment quality estimation shared task in [WMT 2020](http://www.statmt.org/wmt20/quality-estimation-task.html). TransQuest outperforms current open-source quality estimation frameworks such as [OpenKiwi](https://github.com/Unbabel/OpenKiwi) and [DeepQuest](https://github.com/sheffieldnlp/deepQuest).
## Features
- Sentence-level translation quality estimation on both aspects: predicting post editing efforts and direct assessment.
- Word-level translation quality estimation capable of predicting quality of source words, target words and target gaps.
- Outperform current state-of-the-art quality estimation methods like DeepQuest and OpenKiwi in all the languages experimented.
- Pre-trained quality estimation models for fifteen language pairs are available in [HuggingFace.](https://huggingface.co/TransQuest)
## Installation
### From pip
```bash
pip install transquest
```
### From Source
```bash
git clone https://github.com/TharinduDR/TransQuest.git
cd TransQuest
pip install -r requirements.txt
```
## Using Pre-trained Models
```python
import torch
from transquest.algo.sentence_level.siamesetransquest.run_model import SiameseTransQuestModel
model = SiameseTransQuestModel("TransQuest/siamesetransquest-da-ru_en-reddit_wikiquotes")
predictions = model.predict([["Reducerea acestor conflicte este importantă pentru conservare.", "Reducing these conflicts is not important for preservation."]])
print(predictions)
```
## Documentation
For more details follow the documentation.
1. **[Installation](https://tharindudr.github.io/TransQuest/install/)** - Install TransQuest locally using pip.
2. **Architectures** - Checkout the architectures implemented in TransQuest
1. [Sentence-level Architectures](https://tharindudr.github.io/TransQuest/architectures/sentence_level_architectures/) - We have released two architectures; MonoTransQuest and SiameseTransQuest to perform sentence level quality estimation.
2. [Word-level Architecture](https://tharindudr.github.io/TransQuest/architectures/word_level_architecture/) - We have released MicroTransQuest to perform word level quality estimation.
3. **Examples** - We have provided several examples on how to use TransQuest in recent WMT quality estimation shared tasks.
1. [Sentence-level Examples](https://tharindudr.github.io/TransQuest/examples/sentence_level_examples/)
2. [Word-level Examples](https://tharindudr.github.io/TransQuest/examples/word_level_examples/)
4. **Pre-trained Models** - We have provided pretrained quality estimation models for fifteen language pairs covering both sentence-level and word-level
1. [Sentence-level Models](https://tharindudr.github.io/TransQuest/models/sentence_level_pretrained/)
2. [Word-level Models](https://tharindudr.github.io/TransQuest/models/word_level_pretrained/)
5. **[Contact](https://tharindudr.github.io/TransQuest/contact/)** - Contact us for any issues with TransQuest
## Citations
If you are using the word-level architecture, please consider citing this paper which is accepted to [ACL 2021](https://2021.aclweb.org/).
```bash
@InProceedings{ranasinghe2021,
author = {Ranasinghe, Tharindu and Orasan, Constantin and Mitkov, Ruslan},
title = {An Exploratory Analysis of Multilingual Word Level Quality Estimation with Cross-Lingual Transformers},
booktitle = {Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics},
year = {2021}
}
```
If you are using the sentence-level architectures, please consider citing these papers which were presented in [COLING 2020](https://coling2020.org/) and in [WMT 2020](http://www.statmt.org/wmt20/) at EMNLP 2020.
```bash
@InProceedings{transquest:2020a,
author = {Ranasinghe, Tharindu and Orasan, Constantin and Mitkov, Ruslan},
title = {TransQuest: Translation Quality Estimation with Cross-lingual Transformers},
booktitle = {Proceedings of the 28th International Conference on Computational Linguistics},
year = {2020}
}
```
```bash
@InProceedings{transquest:2020b,
author = {Ranasinghe, Tharindu and Orasan, Constantin and Mitkov, Ruslan},
title = {TransQuest at WMT2020: Sentence-Level Direct Assessment},
booktitle = {Proceedings of the Fifth Conference on Machine Translation},
year = {2020}
}
```
|
TransQuest/siamesetransquest-da-si_en-wiki | 0d32a51cd21c389769fd4e8336e97e5dd71cf5b6 | 2021-06-04T11:15:08.000Z | [
"pytorch",
"xlm-roberta",
"feature-extraction",
"si-en",
"transformers",
"Quality Estimation",
"siamesetransquest",
"da",
"license:apache-2.0"
] | feature-extraction | false | TransQuest | null | TransQuest/siamesetransquest-da-si_en-wiki | 4 | null | transformers | 18,258 | ---
language: si-en
tags:
- Quality Estimation
- siamesetransquest
- da
license: apache-2.0
---
# TransQuest: Translation Quality Estimation with Cross-lingual Transformers
The goal of quality estimation (QE) is to evaluate the quality of a translation without having access to a reference translation. High-accuracy QE that can be easily deployed for a number of language pairs is the missing piece in many commercial translation workflows as they have numerous potential uses. They can be employed to select the best translation when several translation engines are available or can inform the end user about the reliability of automatically translated content. In addition, QE systems can be used to decide whether a translation can be published as it is in a given context, or whether it requires human post-editing before publishing or translation from scratch by a human. The quality estimation can be done at different levels: document level, sentence level and word level.
With TransQuest, we have opensourced our research in translation quality estimation which also won the sentence-level direct assessment quality estimation shared task in [WMT 2020](http://www.statmt.org/wmt20/quality-estimation-task.html). TransQuest outperforms current open-source quality estimation frameworks such as [OpenKiwi](https://github.com/Unbabel/OpenKiwi) and [DeepQuest](https://github.com/sheffieldnlp/deepQuest).
## Features
- Sentence-level translation quality estimation on both aspects: predicting post editing efforts and direct assessment.
- Word-level translation quality estimation capable of predicting quality of source words, target words and target gaps.
- Outperform current state-of-the-art quality estimation methods like DeepQuest and OpenKiwi in all the languages experimented.
- Pre-trained quality estimation models for fifteen language pairs are available in [HuggingFace.](https://huggingface.co/TransQuest)
## Installation
### From pip
```bash
pip install transquest
```
### From Source
```bash
git clone https://github.com/TharinduDR/TransQuest.git
cd TransQuest
pip install -r requirements.txt
```
## Using Pre-trained Models
```python
import torch
from transquest.algo.sentence_level.siamesetransquest.run_model import SiameseTransQuestModel
model = SiameseTransQuestModel("TransQuest/siamesetransquest-da-si_en-wiki")
predictions = model.predict([["Reducerea acestor conflicte este importantă pentru conservare.", "Reducing these conflicts is not important for preservation."]])
print(predictions)
```
## Documentation
For more details follow the documentation.
1. **[Installation](https://tharindudr.github.io/TransQuest/install/)** - Install TransQuest locally using pip.
2. **Architectures** - Checkout the architectures implemented in TransQuest
1. [Sentence-level Architectures](https://tharindudr.github.io/TransQuest/architectures/sentence_level_architectures/) - We have released two architectures; MonoTransQuest and SiameseTransQuest to perform sentence level quality estimation.
2. [Word-level Architecture](https://tharindudr.github.io/TransQuest/architectures/word_level_architecture/) - We have released MicroTransQuest to perform word level quality estimation.
3. **Examples** - We have provided several examples on how to use TransQuest in recent WMT quality estimation shared tasks.
1. [Sentence-level Examples](https://tharindudr.github.io/TransQuest/examples/sentence_level_examples/)
2. [Word-level Examples](https://tharindudr.github.io/TransQuest/examples/word_level_examples/)
4. **Pre-trained Models** - We have provided pretrained quality estimation models for fifteen language pairs covering both sentence-level and word-level
1. [Sentence-level Models](https://tharindudr.github.io/TransQuest/models/sentence_level_pretrained/)
2. [Word-level Models](https://tharindudr.github.io/TransQuest/models/word_level_pretrained/)
5. **[Contact](https://tharindudr.github.io/TransQuest/contact/)** - Contact us for any issues with TransQuest
## Citations
If you are using the word-level architecture, please consider citing this paper which is accepted to [ACL 2021](https://2021.aclweb.org/).
```bash
@InProceedings{ranasinghe2021,
author = {Ranasinghe, Tharindu and Orasan, Constantin and Mitkov, Ruslan},
title = {An Exploratory Analysis of Multilingual Word Level Quality Estimation with Cross-Lingual Transformers},
booktitle = {Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics},
year = {2021}
}
```
If you are using the sentence-level architectures, please consider citing these papers which were presented in [COLING 2020](https://coling2020.org/) and in [WMT 2020](http://www.statmt.org/wmt20/) at EMNLP 2020.
```bash
@InProceedings{transquest:2020a,
author = {Ranasinghe, Tharindu and Orasan, Constantin and Mitkov, Ruslan},
title = {TransQuest: Translation Quality Estimation with Cross-lingual Transformers},
booktitle = {Proceedings of the 28th International Conference on Computational Linguistics},
year = {2020}
}
```
```bash
@InProceedings{transquest:2020b,
author = {Ranasinghe, Tharindu and Orasan, Constantin and Mitkov, Ruslan},
title = {TransQuest at WMT2020: Sentence-Level Direct Assessment},
booktitle = {Proceedings of the Fifth Conference on Machine Translation},
year = {2020}
}
```
|
TuhinColumbia/SimileLiteral | 085b1e04780ec0fe107aee59db6961b14b59e0d9 | 2021-09-12T21:18:21.000Z | [
"pytorch",
"roberta",
"transformers"
] | null | false | TuhinColumbia | null | TuhinColumbia/SimileLiteral | 4 | null | transformers | 18,259 | Entry not found |
TuhinColumbia/multiopus | 7ef706c99b5bad7c18244e4c9ed4b9d0daa3589c | 2021-09-27T18:02:55.000Z | [
"pytorch",
"mbart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | TuhinColumbia | null | TuhinColumbia/multiopus | 4 | null | transformers | 18,260 | Entry not found |
TuhinColumbia/spanishpoetrymany | dfbf3f4df7c6116efbc1ed45a6ac742b3122485a | 2021-09-04T09:14:11.000Z | [
"pytorch",
"mbart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | TuhinColumbia | null | TuhinColumbia/spanishpoetrymany | 4 | null | transformers | 18,261 | Entry not found |
Tymoteusz/optics-abstracts-summarization | a26d315907adaf9f1902acf877e9f0c0ad20de79 | 2021-07-12T18:15:10.000Z | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | Tymoteusz | null | Tymoteusz/optics-abstracts-summarization | 4 | null | transformers | 18,262 | Entry not found |
UBC-NLP/AraT5-tweet-small | e2f9309914f1fa21794ccb50fc6f2d12e004beb3 | 2022-05-26T18:29:06.000Z | [
"pytorch",
"tf",
"t5",
"ar",
"transformers",
"Arabic T5",
"MSA",
"Twitter",
"Arabic Dialect",
"Arabic Machine Translation",
"Arabic Text Summarization",
"Arabic News Title and Question Generation",
"Arabic Paraphrasing and Transliteration",
"Arabic Code-Switched Translation"
] | null | false | UBC-NLP | null | UBC-NLP/AraT5-tweet-small | 4 | 1 | transformers | 18,263 | ---
language:
- ar
tags:
- Arabic T5
- MSA
- Twitter
- Arabic Dialect
- Arabic Machine Translation
- Arabic Text Summarization
- Arabic News Title and Question Generation
- Arabic Paraphrasing and Transliteration
- Arabic Code-Switched Translation
---
# AraT5-tweet-small
# AraT5: Text-to-Text Transformers for Arabic Language Generation
<img src="https://huggingface.co/UBC-NLP/AraT5-base/resolve/main/AraT5_CR_new.png" alt="AraT5" width="45%" height="35%" align="right"/>
This is the repository accompanying our paper [AraT5: Text-to-Text Transformers for Arabic Language Understanding and Generation](https://aclanthology.org/2022.acl-long.47/). In this is the repository we Introduce **AraT5<sub>MSA</sub>**, **AraT5<sub>Tweet</sub>**, and **AraT5**: three powerful Arabic-specific text-to-text Transformer based models;
---
# How to use AraT5 models
Below is an example for fine-tuning **AraT5-base** for News Title Generation on the Aranews dataset
``` bash
!python run_trainier_seq2seq_huggingface.py \
--learning_rate 5e-5 \
--max_target_length 128 --max_source_length 128 \
--per_device_train_batch_size 8 --per_device_eval_batch_size 8 \
--model_name_or_path "UBC-NLP/AraT5-base" \
--output_dir "/content/AraT5_FT_title_generation" --overwrite_output_dir \
--num_train_epochs 3 \
--train_file "/content/ARGEn_title_genration_sample_train.tsv" \
--validation_file "/content/ARGEn_title_genration_sample_valid.tsv" \
--task "title_generation" --text_column "document" --summary_column "title" \
--load_best_model_at_end --metric_for_best_model "eval_bleu" --greater_is_better True --evaluation_strategy epoch --logging_strategy epoch --predict_with_generate\
--do_train --do_eval
```
For more details about the fine-tuning example, please read this notebook [](https://github.com/UBC-NLP/araT5/blob/main/examples/Fine_tuning_AraT5.ipynb)
In addition, we release the fine-tuned checkpoint of the News Title Generation (NGT) which is described in the paper. The model available at Huggingface ([UBC-NLP/AraT5-base-title-generation](https://huggingface.co/UBC-NLP/AraT5-base-title-generation)).
For more details, please visit our own [GitHub](https://github.com/UBC-NLP/araT5).
# AraT5 Models Checkpoints
AraT5 Pytorch and TensorFlow checkpoints are available on the Huggingface website for direct download and use ```exclusively for research```. ```For commercial use, please contact the authors via email @ (muhammad.mageed[at]ubc[dot]ca).```
| **Model** | **Link** |
|---------|:------------------:|
| **AraT5-base** | [https://huggingface.co/UBC-NLP/AraT5-base](https://huggingface.co/UBC-NLP/AraT5-base) |
| **AraT5-msa-base** | [https://huggingface.co/UBC-NLP/AraT5-msa-base](https://huggingface.co/UBC-NLP/AraT5-msa-base) |
| **AraT5-tweet-base** | [https://huggingface.co/UBC-NLP/AraT5-tweet-base](https://huggingface.co/UBC-NLP/AraT5-tweet-base) |
| **AraT5-msa-small** | [https://huggingface.co/UBC-NLP/AraT5-msa-small](https://huggingface.co/UBC-NLP/AraT5-msa-small) |
| **AraT5-tweet-small**| [https://huggingface.co/UBC-NLP/AraT5-tweet-small](https://huggingface.co/UBC-NLP/AraT5-tweet-small) |
# BibTex
If you use our models (Arat5-base, Arat5-msa-base, Arat5-tweet-base, Arat5-msa-small, or Arat5-tweet-small ) for your scientific publication, or if you find the resources in this repository useful, please cite our paper as follows (to be updated):
```bibtex
@inproceedings{nagoudi-etal-2022-arat5,
title = "{A}ra{T}5: Text-to-Text Transformers for {A}rabic Language Generation",
author = "Nagoudi, El Moatez Billah and
Elmadany, AbdelRahim and
Abdul-Mageed, Muhammad",
booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = may,
year = "2022",
address = "Dublin, Ireland",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.acl-long.47",
pages = "628--647",
abstract = "Transfer learning with a unified Transformer framework (T5) that converts all language problems into a text-to-text format was recently proposed as a simple and effective transfer learning approach. Although a multilingual version of the T5 model (mT5) was also introduced, it is not clear how well it can fare on non-English tasks involving diverse data. To investigate this question, we apply mT5 on a language with a wide variety of dialects{--}Arabic. For evaluation, we introduce a novel benchmark for ARabic language GENeration (ARGEN), covering seven important tasks. For model comparison, we pre-train three powerful Arabic T5-style models and evaluate them on ARGEN. Although pre-trained with {\textasciitilde}49 less data, our new models perform significantly better than mT5 on all ARGEN tasks (in 52 out of 59 test sets) and set several new SOTAs. Our models also establish new SOTA on the recently-proposed, large Arabic language understanding evaluation benchmark ARLUE (Abdul-Mageed et al., 2021). Our new models are publicly available. We also link to ARGEN datasets through our repository: https://github.com/UBC-NLP/araT5.",
}
```
## Acknowledgments
We gratefully acknowledge support from the Natural Sciences and Engineering Research Council of Canada, the Social Sciences and Humanities Research Council of Canada, Canadian Foundation for Innovation, [ComputeCanada](www.computecanada.ca) and [UBC ARC-Sockeye](https://doi.org/10.14288/SOCKEYE). We also thank the [Google TensorFlow Research Cloud (TFRC)](https://www.tensorflow.org/tfrc) program for providing us with free TPU access.
|
Unbabel/XLM-R-12L | 67a19f8952131398a76dd2b5554c297f2dd7207b | 2022-01-05T20:02:13.000Z | [
"pytorch",
"xlm-roberta",
"feature-extraction",
"transformers"
] | feature-extraction | false | Unbabel | null | Unbabel/XLM-R-12L | 4 | null | transformers | 18,264 | Entry not found |
Unbabel/XLM-R-13L | bab23a72a8ff0e32401a93f43d0853745dac2218 | 2022-01-05T20:09:01.000Z | [
"pytorch",
"xlm-roberta",
"feature-extraction",
"transformers"
] | feature-extraction | false | Unbabel | null | Unbabel/XLM-R-13L | 4 | null | transformers | 18,265 | Entry not found |
Unbabel/XLM-R-14L | 40d6eda5d28c7297e8d2154ca3439c9d2fd0a59c | 2022-01-05T20:17:55.000Z | [
"pytorch",
"xlm-roberta",
"feature-extraction",
"transformers"
] | feature-extraction | false | Unbabel | null | Unbabel/XLM-R-14L | 4 | null | transformers | 18,266 | Entry not found |
Unbabel/XLM-R-20L | 4c5b2ef086250d162b52552af4a8603a5b99443d | 2022-01-05T21:05:19.000Z | [
"pytorch",
"xlm-roberta",
"feature-extraction",
"transformers"
] | feature-extraction | false | Unbabel | null | Unbabel/XLM-R-20L | 4 | null | transformers | 18,267 | Entry not found |
Unbabel/XLM-R-21L | 98160d4d6b97d37b843e1e58256b68da2c7c7d77 | 2022-01-05T21:13:39.000Z | [
"pytorch",
"xlm-roberta",
"feature-extraction",
"transformers"
] | feature-extraction | false | Unbabel | null | Unbabel/XLM-R-21L | 4 | null | transformers | 18,268 | Entry not found |
Unbabel/XLM-R-23L | 8aa380e17f7621186378c11dfa448255ddc14119 | 2022-01-05T21:31:26.000Z | [
"pytorch",
"xlm-roberta",
"feature-extraction",
"transformers"
] | feature-extraction | false | Unbabel | null | Unbabel/XLM-R-23L | 4 | null | transformers | 18,269 | Entry not found |
Unbabel/XLM-R-5L | a98fff19869afee659334d777f8a794864306a23 | 2022-01-05T19:16:19.000Z | [
"pytorch",
"xlm-roberta",
"feature-extraction",
"transformers"
] | feature-extraction | false | Unbabel | null | Unbabel/XLM-R-5L | 4 | null | transformers | 18,270 | Entry not found |
V3RX2000/distilbert-base-uncased-finetuned-cola | 4778129d3be3c9b207430ad7eb044ec4b2535d86 | 2021-10-12T02:10:11.000Z | [
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"dataset:glue",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | text-classification | false | V3RX2000 | null | V3RX2000/distilbert-base-uncased-finetuned-cola | 4 | null | transformers | 18,271 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- matthews_correlation
model-index:
- name: distilbert-base-uncased-finetuned-cola
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
args: cola
metrics:
- name: Matthews Correlation
type: matthews_correlation
value: 0.5396261051709696
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-cola
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8107
- Matthews Correlation: 0.5396
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.5261 | 1.0 | 535 | 0.5509 | 0.3827 |
| 0.3498 | 2.0 | 1070 | 0.4936 | 0.5295 |
| 0.2369 | 3.0 | 1605 | 0.6505 | 0.5248 |
| 0.1637 | 4.0 | 2140 | 0.8107 | 0.5396 |
| 0.1299 | 5.0 | 2675 | 0.8738 | 0.5387 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.9.0+cu111
- Datasets 1.12.1
- Tokenizers 0.10.3
|
Vaibhavbrkn/question-gen | d4af9e6d9efe2d35840c19c5be1cbd7d44a91e76 | 2021-10-03T15:06:24.000Z | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | Vaibhavbrkn | null | Vaibhavbrkn/question-gen | 4 | null | transformers | 18,272 | Entry not found |
Vampiro/DialoGPT-small-dante_b | d787aa4a95203bf9cb73f6f5cd9530f29608c65d | 2021-09-16T04:25:49.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | false | Vampiro | null | Vampiro/DialoGPT-small-dante_b | 4 | null | transformers | 18,273 | ---
tags:
- conversational
---
# Dante (DMC V) DialogGPT Model |
Vasanth/t5-news-summarization | 2c45774a3228ec757e674ac8a6f0858629f2c729 | 2022-01-20T09:17:47.000Z | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | Vasanth | null | Vasanth/t5-news-summarization | 4 | null | transformers | 18,274 | Entry not found |
Vassilis/distilbert-base-uncased-finetuned-emotion | c5310bb3b31b42b0429418ab0d8ada4430aff48c | 2022-01-09T16:41:12.000Z | [
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | text-classification | false | Vassilis | null | Vassilis/distilbert-base-uncased-finetuned-emotion | 4 | null | transformers | 18,275 | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotion
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
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: 0.1628
- Accuracy: 0.9345
- F1: 0.9348
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.1674 | 1.0 | 250 | 0.1718 | 0.9265 | 0.9266 |
| 0.1091 | 2.0 | 500 | 0.1628 | 0.9345 | 0.9348 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.0
- Tokenizers 0.10.3
|
VirenS13117/distilbert-base-uncased-finetuned-cola | d2d0ad4084a1f60ce7fb0c4b14bddd4d7c256481 | 2021-09-21T22:22:02.000Z | [
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"dataset:glue",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | text-classification | false | VirenS13117 | null | VirenS13117/distilbert-base-uncased-finetuned-cola | 4 | null | transformers | 18,276 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- matthews_correlation
model-index:
- name: distilbert-base-uncased-finetuned-cola
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
args: cola
metrics:
- name: Matthews Correlation
type: matthews_correlation
value: 0.5286324175580216
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-cola
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7809
- Matthews Correlation: 0.5286
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.5299 | 1.0 | 535 | 0.5040 | 0.4383 |
| 0.3472 | 2.0 | 1070 | 0.5284 | 0.4911 |
| 0.2333 | 3.0 | 1605 | 0.6633 | 0.5091 |
| 0.1733 | 4.0 | 2140 | 0.7809 | 0.5286 |
| 0.1255 | 5.0 | 2675 | 0.8894 | 0.5282 |
### Framework versions
- Transformers 4.10.2
- Pytorch 1.9.0+cu102
- Datasets 1.12.1
- Tokenizers 0.10.3
|
Wiirin/BERT-finetuned-PubMed-FoodCancer | dad0a41503f573a7cc388434a75424e42b06677a | 2021-11-08T08:52:26.000Z | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | false | Wiirin | null | Wiirin/BERT-finetuned-PubMed-FoodCancer | 4 | null | transformers | 18,277 | Entry not found |
WikinewsSum/t5-base-multi-en-wiki-news | 0366607b27688a2c5291b6ca49f33fe251994c07 | 2021-06-23T10:41:07.000Z | [
"pytorch",
"jax",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | WikinewsSum | null | WikinewsSum/t5-base-multi-en-wiki-news | 4 | null | transformers | 18,278 | Entry not found |
WikinewsSum/t5-base-with-title-multi-en-wiki-news | 9e438d1736f4a205dc6a4badd6ee49dc2e310a7a | 2021-06-23T11:52:12.000Z | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | WikinewsSum | null | WikinewsSum/t5-base-with-title-multi-en-wiki-news | 4 | null | transformers | 18,279 | Entry not found |
Wintermute/Wintermute | bbc5e403b33687eda40da1cf0003659f7261167d | 2021-05-21T11:40:58.000Z | [
"pytorch",
"jax",
"gpt2",
"text-generation",
"transformers"
] | text-generation | false | Wintermute | null | Wintermute/Wintermute | 4 | null | transformers | 18,280 | Entry not found |
XYHY/autonlp-123-478412765 | fef78dd8ce84aaf73ac6652c4b2a96c907150d91 | 2022-01-06T06:22:38.000Z | [
"pytorch",
"roberta",
"text-classification",
"unk",
"dataset:XYHY/autonlp-data-123",
"transformers",
"autonlp",
"co2_eq_emissions"
] | text-classification | false | XYHY | null | XYHY/autonlp-123-478412765 | 4 | null | transformers | 18,281 | ---
tags: autonlp
language: unk
widget:
- text: "I love AutoNLP 🤗"
datasets:
- XYHY/autonlp-data-123
co2_eq_emissions: 69.86520391863117
---
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 478412765
- CO2 Emissions (in grams): 69.86520391863117
## Validation Metrics
- Loss: 0.186362624168396
- Accuracy: 0.9539955699437723
- Precision: 0.9527454242928453
- Recall: 0.9572049481778669
- AUC: 0.9903929997079495
- F1: 0.9549699799866577
## 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 AutoNLP"}' https://api-inference.huggingface.co/models/XYHY/autonlp-123-478412765
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("XYHY/autonlp-123-478412765", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("XYHY/autonlp-123-478412765", use_auth_token=True)
inputs = tokenizer("I love AutoNLP", return_tensors="pt")
outputs = model(**inputs)
``` |
YSKartal/berturk-social-5m | e0ac971efb9ea41af93a2c9c299816ca32be41ce | 2021-05-20T12:32:31.000Z | [
"pytorch",
"jax",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | YSKartal | null | YSKartal/berturk-social-5m | 4 | null | transformers | 18,282 | Entry not found |
ZZDDBBCC/distilbert-base-uncased-finetuned-cola | 940d25f0b23640ab39021f641f6477abf265cf25 | 2021-09-26T07:53:35.000Z | [
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"dataset:glue",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | text-classification | false | ZZDDBBCC | null | ZZDDBBCC/distilbert-base-uncased-finetuned-cola | 4 | null | transformers | 18,283 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- matthews_correlation
model-index:
- name: distilbert-base-uncased-finetuned-cola
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
args: cola
metrics:
- name: Matthews Correlation
type: matthews_correlation
value: 0.5410897632107913
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-cola
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8631
- Matthews Correlation: 0.5411
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.5249 | 1.0 | 535 | 0.5300 | 0.4152 |
| 0.3489 | 2.0 | 1070 | 0.5238 | 0.4940 |
| 0.2329 | 3.0 | 1605 | 0.6447 | 0.5162 |
| 0.1692 | 4.0 | 2140 | 0.7805 | 0.5332 |
| 0.1256 | 5.0 | 2675 | 0.8631 | 0.5411 |
### Framework versions
- Transformers 4.10.3
- Pytorch 1.9.0+cu102
- Datasets 1.12.1
- Tokenizers 0.10.3
|
aware-ai/distilbart-xsum-12-6-squadv2 | 482d3eed5747a8fbb0796d4b01b453085c5503eb | 2020-06-28T11:04:49.000Z | [
"pytorch",
"bart",
"question-answering",
"transformers",
"autotrain_compatible"
] | question-answering | false | aware-ai | null | aware-ai/distilbart-xsum-12-6-squadv2 | 4 | null | transformers | 18,284 | Entry not found |
aXhyra/demo_emotion_31415 | acb4215400041bdfe65c6ea27f043e715977650b | 2021-12-13T18:17:16.000Z | [
"pytorch",
"distilbert",
"text-classification",
"dataset:tweet_eval",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | text-classification | false | aXhyra | null | aXhyra/demo_emotion_31415 | 4 | null | transformers | 18,285 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- tweet_eval
metrics:
- f1
model-index:
- name: demo_emotion_31415
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: tweet_eval
type: tweet_eval
args: emotion
metrics:
- name: F1
type: f1
value: 0.7348035780583043
---
<!-- 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. -->
# demo_emotion_31415
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the tweet_eval dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9818
- F1: 0.7348
## 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: 7.551070618629693e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 0
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 1.0 | 204 | 0.7431 | 0.6530 |
| No log | 2.0 | 408 | 0.6943 | 0.7333 |
| 0.5176 | 3.0 | 612 | 0.8456 | 0.7326 |
| 0.5176 | 4.0 | 816 | 0.9818 | 0.7348 |
### Framework versions
- Transformers 4.12.5
- Pytorch 1.9.1
- Datasets 1.16.1
- Tokenizers 0.10.3
|
aXhyra/demo_hate_42 | 12c4cff3ec86e94331db2245f94476cc5365bdce | 2021-12-13T19:09:34.000Z | [
"pytorch",
"distilbert",
"text-classification",
"dataset:tweet_eval",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | text-classification | false | aXhyra | null | aXhyra/demo_hate_42 | 4 | null | transformers | 18,286 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- tweet_eval
metrics:
- f1
model-index:
- name: demo_hate_42
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: tweet_eval
type: tweet_eval
args: hate
metrics:
- name: F1
type: f1
value: 0.7772939485986298
---
<!-- 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. -->
# demo_hate_42
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the tweet_eval dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8697
- F1: 0.7773
## 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: 7.320702985778492e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 0
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 1.0 | 282 | 0.4850 | 0.7645 |
| 0.3877 | 2.0 | 564 | 0.5160 | 0.7856 |
| 0.3877 | 3.0 | 846 | 0.6927 | 0.7802 |
| 0.1343 | 4.0 | 1128 | 0.8697 | 0.7773 |
### Framework versions
- Transformers 4.12.5
- Pytorch 1.9.1
- Datasets 1.16.1
- Tokenizers 0.10.3
|
aXhyra/emotion_trained_1234567 | 673a897094ba4b992c302fd255373c0e2399a9c6 | 2021-12-12T13:19:19.000Z | [
"pytorch",
"distilbert",
"text-classification",
"dataset:tweet_eval",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | text-classification | false | aXhyra | null | aXhyra/emotion_trained_1234567 | 4 | null | transformers | 18,287 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- tweet_eval
metrics:
- f1
model-index:
- name: emotion_trained_1234567
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: tweet_eval
type: tweet_eval
args: emotion
metrics:
- name: F1
type: f1
value: 0.7301562209701973
---
<!-- 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. -->
# emotion_trained_1234567
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the tweet_eval dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9051
- F1: 0.7302
## 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: 6.961635072722524e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 1234567
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 1.0 | 204 | 0.6480 | 0.7231 |
| No log | 2.0 | 408 | 0.6114 | 0.7403 |
| 0.5045 | 3.0 | 612 | 0.7592 | 0.7311 |
| 0.5045 | 4.0 | 816 | 0.9051 | 0.7302 |
### Framework versions
- Transformers 4.12.5
- Pytorch 1.9.1
- Datasets 1.16.1
- Tokenizers 0.10.3
|
aXhyra/emotion_trained_31415 | e6c5f6c55b1d3d5b9412aa318d71f8ff44d78f5b | 2021-12-12T13:14:50.000Z | [
"pytorch",
"distilbert",
"text-classification",
"dataset:tweet_eval",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | text-classification | false | aXhyra | null | aXhyra/emotion_trained_31415 | 4 | null | transformers | 18,288 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- tweet_eval
metrics:
- f1
model-index:
- name: emotion_trained_31415
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: tweet_eval
type: tweet_eval
args: emotion
metrics:
- name: F1
type: f1
value: 0.719757533529152
---
<!-- 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. -->
# emotion_trained_31415
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the tweet_eval dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9274
- F1: 0.7198
## 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: 6.961635072722524e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 31415
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 1.0 | 204 | 0.6177 | 0.7137 |
| No log | 2.0 | 408 | 0.7489 | 0.6761 |
| 0.5082 | 3.0 | 612 | 0.8233 | 0.7283 |
| 0.5082 | 4.0 | 816 | 0.9274 | 0.7198 |
### Framework versions
- Transformers 4.12.5
- Pytorch 1.9.1
- Datasets 1.16.1
- Tokenizers 0.10.3
|
aXhyra/emotion_trained_42 | 05c109993a52efa6d3e0525d4a32381082931f16 | 2021-12-12T13:11:11.000Z | [
"pytorch",
"distilbert",
"text-classification",
"dataset:tweet_eval",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | text-classification | false | aXhyra | null | aXhyra/emotion_trained_42 | 4 | null | transformers | 18,289 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- tweet_eval
metrics:
- f1
model-index:
- name: emotion_trained_42
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: tweet_eval
type: tweet_eval
args: emotion
metrics:
- name: F1
type: f1
value: 0.7361210540311689
---
<!-- 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. -->
# emotion_trained_42
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the tweet_eval dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9012
- F1: 0.7361
## 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: 6.961635072722524e-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: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 1.0 | 204 | 0.6131 | 0.6955 |
| No log | 2.0 | 408 | 0.5816 | 0.7297 |
| 0.5148 | 3.0 | 612 | 0.8942 | 0.7199 |
| 0.5148 | 4.0 | 816 | 0.9012 | 0.7361 |
### Framework versions
- Transformers 4.12.5
- Pytorch 1.9.1
- Datasets 1.16.1
- Tokenizers 0.10.3
|
aXhyra/emotion_trained_final | 3fc3579f9618a60430bb9fcb91685f4908fa462b | 2021-12-12T10:50:02.000Z | [
"pytorch",
"distilbert",
"text-classification",
"dataset:tweet_eval",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | text-classification | false | aXhyra | null | aXhyra/emotion_trained_final | 4 | null | transformers | 18,290 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- tweet_eval
metrics:
- f1
model-index:
- name: emotion_trained_final
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: tweet_eval
type: tweet_eval
args: emotion
metrics:
- name: F1
type: f1
value: 0.7469065445487402
---
<!-- 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. -->
# emotion_trained_final
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the tweet_eval dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9349
- F1: 0.7469
## 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: 1.502523631581398e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 0
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.9013 | 1.0 | 815 | 0.7822 | 0.6470 |
| 0.5008 | 2.0 | 1630 | 0.7142 | 0.7419 |
| 0.3684 | 3.0 | 2445 | 0.8621 | 0.7443 |
| 0.2182 | 4.0 | 3260 | 0.9349 | 0.7469 |
### Framework versions
- Transformers 4.12.5
- Pytorch 1.9.1
- Datasets 1.16.1
- Tokenizers 0.10.3
|
aXhyra/hate_trained_final | 35250ed1b6f748f52ce3b3f476dca2fdbc4fc5ab | 2021-12-12T11:25:23.000Z | [
"pytorch",
"distilbert",
"text-classification",
"dataset:tweet_eval",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | text-classification | false | aXhyra | null | aXhyra/hate_trained_final | 4 | null | transformers | 18,291 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- tweet_eval
metrics:
- f1
model-index:
- name: hate_trained_final
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: tweet_eval
type: tweet_eval
args: hate
metrics:
- name: F1
type: f1
value: 0.7697890540753396
---
<!-- 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. -->
# hate_trained_final
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the tweet_eval dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5543
- F1: 0.7698
## 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: 5.460503761236833e-06
- train_batch_size: 8
- eval_batch_size: 8
- seed: 0
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.463 | 1.0 | 1125 | 0.5213 | 0.7384 |
| 0.3943 | 2.0 | 2250 | 0.5134 | 0.7534 |
| 0.3407 | 3.0 | 3375 | 0.5400 | 0.7666 |
| 0.3121 | 4.0 | 4500 | 0.5543 | 0.7698 |
### Framework versions
- Transformers 4.12.5
- Pytorch 1.9.1
- Datasets 1.16.1
- Tokenizers 0.10.3
|
aXhyra/test_emotion_trained_test | 306a30f30c2bcfe219215fa0bced665fe5a0d8ce | 2021-12-12T17:23:27.000Z | [
"pytorch",
"distilbert",
"text-classification",
"dataset:tweet_eval",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | text-classification | false | aXhyra | null | aXhyra/test_emotion_trained_test | 4 | null | transformers | 18,292 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- tweet_eval
metrics:
- f1
model-index:
- name: test_emotion_trained_test
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: tweet_eval
type: tweet_eval
args: emotion
metrics:
- name: F1
type: f1
value: 0.7014611518188594
---
<!-- 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_emotion_trained_test
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the tweet_eval dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5866
- F1: 0.7015
## 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: 2.458132814624325e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 0
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 1.0 | 51 | 0.7877 | 0.5569 |
| No log | 2.0 | 102 | 0.6188 | 0.6937 |
| No log | 3.0 | 153 | 0.5969 | 0.7068 |
| No log | 4.0 | 204 | 0.5866 | 0.7015 |
### Framework versions
- Transformers 4.12.5
- Pytorch 1.9.1
- Datasets 1.16.1
- Tokenizers 0.10.3
|
aapot/wav2vec2-xlsr-1b-finnish | 7fdf93ed10481ad558dde6e5862be1433b2d0299 | 2022-03-28T17:46:41.000Z | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"fi",
"dataset:mozilla-foundation/common_voice_7_0",
"arxiv:2111.09296",
"transformers",
"finnish",
"generated_from_trainer",
"hf-asr-leaderboard",
"robust-speech-event",
"license:apache-2.0",
"model-index"
] | automatic-speech-recognition | false | aapot | null | aapot/wav2vec2-xlsr-1b-finnish | 4 | null | transformers | 18,293 | ---
license: apache-2.0
language: fi
metrics:
- wer
- cer
tags:
- automatic-speech-recognition
- fi
- finnish
- generated_from_trainer
- hf-asr-leaderboard
- robust-speech-event
datasets:
- mozilla-foundation/common_voice_7_0
model-index:
- name: wav2vec2-xlsr-1b-finnish
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 7
type: mozilla-foundation/common_voice_7_0
args: fi
metrics:
- name: Test WER
type: wer
value: 13.11
- name: Test CER
type: cer
value: 2.23
---
# Wav2Vec2 XLS-R for Finnish ASR
This acoustic model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) for Finnish ASR. The model has been fine-tuned with 259.57 hours of Finnish transcribed speech data. Wav2Vec2 XLS-R was introduced in
[this paper](https://arxiv.org/abs/2111.09296) and first released at [this page](https://github.com/pytorch/fairseq/tree/main/examples/wav2vec#wav2vec-20).
**Note**: there is a version with KenLM language model used in the decoding phase producing better transcriptions: [Finnish-NLP/wav2vec2-xlsr-1b-finnish-lm](https://huggingface.co/Finnish-NLP/wav2vec2-xlsr-1b-finnish-lm)
**Note**: there is a better V2 version of this model which has been fine-tuned longer with 16 hours of more data: [Finnish-NLP/wav2vec2-xlsr-1b-finnish-lm-v2](https://huggingface.co/Finnish-NLP/wav2vec2-xlsr-1b-finnish-lm-v2)
## Model description
Wav2Vec2 XLS-R is Facebook AI's large-scale multilingual pretrained model for speech. It is pretrained on 436k hours of unlabeled speech, including VoxPopuli, MLS, CommonVoice, BABEL, and VoxLingua107. It uses the wav2vec 2.0 objective, in 128 languages.
You can read more about the pretrained model from [this blog](https://ai.facebook.com/blog/xls-r-self-supervised-speech-processing-for-128-languages) and [this paper](https://arxiv.org/abs/2111.09296).
This model is fine-tuned version of the pretrained model (1 billion parameter variant) for Finnish ASR.
## Intended uses & limitations
You can use this model for Finnish ASR (speech-to-text) task.
### How to use
Check the [run-finnish-asr-models.ipynb](https://huggingface.co/aapot/wav2vec2-xlsr-1b-finnish/blob/main/run-finnish-asr-models.ipynb) notebook in this repository for an detailed example on how to use this model.
### Limitations and bias
This model was fine-tuned with audio samples which maximum length was 20 seconds so this model most likely works the best for quite short audios of similar length. However, you can try this model with a lot longer audios too and see how it works. If you encounter out of memory errors with very long audio files you can use the audio chunking method introduced in [this blog post](https://huggingface.co/blog/asr-chunking).
A vast majority of the data used for fine-tuning was from the Finnish Parliament dataset so this model may not generalize so well to very different domains like common daily spoken Finnish with dialects etc. In addition, audios of the datasets tend to be adult male dominated so this model may not work as well for speeches of children and women, for example.
## Training data
This model was fine-tuned with 259.57 hours of Finnish transcribed speech data from following datasets:
| Dataset | Hours | % of total hours |
|:----------------------------------------------------------------------------------------------------------------------------------|:--------:|:----------------:|
| [Common Voice 7.0 Finnish train + evaluation + other splits](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0) | 9.70 h | 3.74 % |
| [Finnish parliament session 2](https://b2share.eudat.eu/records/4df422d631544ce682d6af1d4714b2d4) | 0.24 h | 0.09 % |
| [VoxPopuli Finnish](https://github.com/facebookresearch/voxpopuli) | 5.94 h | 2.29 % |
| [CSS10 Finnish](https://github.com/kyubyong/css10) | 10.32 h | 3.98 % |
| [Aalto Finnish Parliament ASR Corpus](http://urn.fi/urn:nbn:fi:lb-2021051903) | 228.00 h | 87.84 % |
| [Finnish Broadcast Corpus](http://urn.fi/urn:nbn:fi:lb-2016042502) | 5.37 h | 2.07 % |
Datasets were filtered to include maximum length of 20 seconds long audio samples.
## Training procedure
This model was trained during [Robust Speech Challenge Event](https://discuss.huggingface.co/t/open-to-the-community-robust-speech-recognition-challenge/13614) organized by Hugging Face. Training was done on a Tesla V100 GPU, sponsored by OVHcloud.
Training script was provided by Hugging Face and it is available [here](https://github.com/huggingface/transformers/blob/main/examples/research_projects/robust-speech-event/run_speech_recognition_ctc_bnb.py). We only modified its data loading for our custom datasets.
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: [8-bit Adam](https://github.com/facebookresearch/bitsandbytes) with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 5
- mixed_precision_training: Native AMP
The pretrained `facebook/wav2vec2-xls-r-1b` model was initialized with following hyperparameters:
- attention_dropout: 0.094
- hidden_dropout: 0.047
- feat_proj_dropout: 0.04
- mask_time_prob: 0.082
- layerdrop: 0.041
- activation_dropout: 0.055
- ctc_loss_reduction: "mean"
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 0.968 | 0.18 | 500 | 0.4870 | 0.4720 |
| 0.6557 | 0.36 | 1000 | 0.2450 | 0.2931 |
| 0.647 | 0.54 | 1500 | 0.1818 | 0.2255 |
| 0.5297 | 0.72 | 2000 | 0.1698 | 0.2354 |
| 0.5802 | 0.9 | 2500 | 0.1581 | 0.2355 |
| 0.6351 | 1.07 | 3000 | 0.1689 | 0.2336 |
| 0.4626 | 1.25 | 3500 | 0.1719 | 0.3099 |
| 0.4526 | 1.43 | 4000 | 0.1434 | 0.2069 |
| 0.4692 | 1.61 | 4500 | 0.1645 | 0.2192 |
| 0.4584 | 1.79 | 5000 | 0.1483 | 0.1987 |
| 0.4234 | 1.97 | 5500 | 0.1499 | 0.2178 |
| 0.4243 | 2.15 | 6000 | 0.1345 | 0.2070 |
| 0.4108 | 2.33 | 6500 | 0.1383 | 0.1850 |
| 0.4048 | 2.51 | 7000 | 0.1338 | 0.1811 |
| 0.4085 | 2.69 | 7500 | 0.1290 | 0.1780 |
| 0.4026 | 2.87 | 8000 | 0.1239 | 0.1650 |
| 0.4033 | 3.04 | 8500 | 0.1346 | 0.1657 |
| 0.3986 | 3.22 | 9000 | 0.1310 | 0.1850 |
| 0.3867 | 3.4 | 9500 | 0.1273 | 0.1741 |
| 0.3658 | 3.58 | 10000 | 0.1219 | 0.1672 |
| 0.382 | 3.76 | 10500 | 0.1306 | 0.1698 |
| 0.3847 | 3.94 | 11000 | 0.1230 | 0.1577 |
| 0.3691 | 4.12 | 11500 | 0.1310 | 0.1615 |
| 0.3593 | 4.3 | 12000 | 0.1296 | 0.1622 |
| 0.3619 | 4.48 | 12500 | 0.1285 | 0.1601 |
| 0.3361 | 4.66 | 13000 | 0.1261 | 0.1569 |
| 0.3603 | 4.84 | 13500 | 0.1235 | 0.1533 |
### Framework versions
- Transformers 4.17.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.18.3
- Tokenizers 0.11.0
## Evaluation results
Evaluation was done with the [Common Voice 7.0 Finnish test split](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
To evaluate this model, run the `eval.py` script in this repository:
```bash
python3 eval.py --model_id aapot/wav2vec2-xlsr-1b-finnish --dataset mozilla-foundation/common_voice_7_0 --config fi --split test
```
This model (the second row of the table) achieves the following WER (Word Error Rate) and CER (Character Error Rate) results compared to our other models:
| | WER (with LM) | WER (without LM) | CER (with LM) | CER (without LM) |
|-----------------------------------------|---------------|------------------|---------------|------------------|
|aapot/wav2vec2-xlsr-1b-finnish-lm-v2 |**4.09** |**9.73** |**0.88** |**1.65** |
|aapot/wav2vec2-xlsr-1b-finnish-lm |5.65 |13.11 |1.20 |2.23 |
|aapot/wav2vec2-xlsr-300m-finnish-lm |8.16 |17.92 |1.97 |3.36 |
## Team Members
- Aapo Tanskanen, [Hugging Face profile](https://huggingface.co/aapot), [LinkedIn profile](https://www.linkedin.com/in/aapotanskanen/)
- Rasmus Toivanen, [Hugging Face profile](https://huggingface.co/RASMUS), [LinkedIn profile](https://www.linkedin.com/in/rasmustoivanen/)
Feel free to contact us for more details 🤗 |
abhijithneilabraham/stsb_multi_mt_distilbert-base-uncased | 5105f1362b58842d18c94af603a4815261482ff2 | 2021-11-02T12:23:53.000Z | [
"pytorch",
"distilbert",
"feature-extraction",
"sentence-transformers",
"sentence-similarity",
"transformers"
] | sentence-similarity | false | abhijithneilabraham | null | abhijithneilabraham/stsb_multi_mt_distilbert-base-uncased | 4 | null | sentence-transformers | 18,294 | ---
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('abhijithneilabraham/stsb_multi_mt_distilbert-base-uncased')
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('abhijithneilabraham/stsb_multi_mt_distilbert-base-uncased')
model = AutoModel.from_pretrained('abhijithneilabraham/stsb_multi_mt_distilbert-base-uncased')
# 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 360 with parameters:
```
{'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss`
Parameters of the fit()-Method:
```
{
"epochs": 25,
"evaluation_steps": 1000,
"evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator",
"max_grad_norm": 1,
"optimizer_class": "<class 'transformers.optimization.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 900,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: DistilBertModel
(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 --> |
abhilash1910/albert-squad-v2 | fa11a5785e84f2a49214d386dbe704d9b7155b3e | 2021-09-14T07:20:53.000Z | [
"pytorch",
"albert",
"question-answering",
"en",
"dataset:squad_v2",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | question-answering | false | abhilash1910 | null | abhilash1910/albert-squad-v2 | 4 | 2 | transformers | 18,295 | ## Albert Transformer on SQuAD-v2
Training is done on the [SQuAD_v2](https://rajpurkar.github.io/SQuAD-explorer/) dataset. The model can be accessed via HuggingFace:
## Model Specifications
We have used the following parameters:
- num_train_epochs=0.25,
- per_device_train_batch_size=5,
- per_device_eval_batch_size=10,
- warmup_steps=100,
- weight_decay=0.01,
## Usage Specifications
```python
from transformers import AutoTokenizer,AutoModelForQuestionAnswering
from transformers import pipeline
model=AutoModelForQuestionAnswering.from_pretrained('abhilash1910/albert-squad-v2')
tokenizer=AutoTokenizer.from_pretrained('abhilash1910/albert-squad-v2')
nlp_QA=pipeline('question-answering',model=model,tokenizer=tokenizer)
QA_inp={
'question': 'How many parameters does Bert large have?',
'context': 'Bert large is really big... it has 24 layers, for a total of 340M parameters.Altogether it is 1.34 GB so expect it to take a couple minutes to download to your Colab instance.'
}
result=nlp_QA(QA_inp)
result
```
## Result
The result is:
{'answer': '340M', 'end': 65, 'score': 0.14847151935100555, 'start': 61}
---
language:
- en
license: apache-2.0
datasets:
- squad_v2
---
|
abhishek/autonlp-ferd1-2652021 | c306c92ebda955281ad3b67b1139222b64381e62 | 2021-07-30T12:27:14.000Z | [
"pytorch",
"distilbert",
"text-classification",
"en",
"dataset:abhishek/autonlp-data-ferd1",
"transformers",
"autonlp"
] | text-classification | false | abhishek | null | abhishek/autonlp-ferd1-2652021 | 4 | null | transformers | 18,296 | ---
tags: autonlp
language: en
widget:
- text: "I love AutoNLP 🤗"
datasets:
- abhishek/autonlp-data-ferd1
---
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 2652021
## Validation Metrics
- Loss: 0.3934604227542877
- Accuracy: 0.8411030860144452
- Precision: 0.8201550387596899
- Recall: 0.8076335877862595
- AUC: 0.8946767157983608
- F1: 0.8138461538461538
## 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 AutoNLP"}' https://api-inference.huggingface.co/models/abhishek/autonlp-ferd1-2652021
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("abhishek/autonlp-ferd1-2652021", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("abhishek/autonlp-ferd1-2652021", use_auth_token=True)
inputs = tokenizer("I love AutoNLP", return_tensors="pt")
outputs = model(**inputs)
``` |
abhishek/autonlp-toxic-new-30516963 | 2b37359d0bf76fc87c77cc21dcbfd0c6796934ff | 2021-11-08T19:31:37.000Z | [
"pytorch",
"distilbert",
"text-classification",
"en",
"dataset:abhishek/autonlp-data-toxic-new",
"transformers",
"autonlp",
"co2_eq_emissions"
] | text-classification | false | abhishek | null | abhishek/autonlp-toxic-new-30516963 | 4 | null | transformers | 18,297 | ---
tags: autonlp
language: en
widget:
- text: "I love AutoNLP 🤗"
datasets:
- abhishek/autonlp-data-toxic-new
co2_eq_emissions: 30.684995819386277
---
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 30516963
- CO2 Emissions (in grams): 30.684995819386277
## Validation Metrics
- Loss: 0.08340361714363098
- Accuracy: 0.9688222161294113
- Precision: 0.9102096627164995
- Recall: 0.7692604006163328
- AUC: 0.9859340458715813
- F1: 0.8338204592901879
## 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 AutoNLP"}' https://api-inference.huggingface.co/models/abhishek/autonlp-toxic-new-30516963
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("abhishek/autonlp-toxic-new-30516963", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("abhishek/autonlp-toxic-new-30516963", use_auth_token=True)
inputs = tokenizer("I love AutoNLP", return_tensors="pt")
outputs = model(**inputs)
``` |
adalbertojunior/image_captioning_portuguese | b12e4019565cb2ce3e065e12b120d0077c023ad8 | 2022-02-08T20:26:50.000Z | [
"pytorch",
"jax",
"vision-encoder-decoder",
"pt",
"transformers"
] | null | false | adalbertojunior | null | adalbertojunior/image_captioning_portuguese | 4 | 1 | transformers | 18,298 | ---
language:
- pt
---
Image Captioning in Portuguese trained with ViT and GPT2
[DEMO](https://huggingface.co/spaces/adalbertojunior/image_captioning_portuguese)
Research supported with Cloud TPUs from Google's TPU Research Cloud (TRC) |
adalbertojunior/modular-test | fed596969c99b9a162079631f38e169ff8ac99f3 | 2021-12-23T04:05:02.000Z | [
"pytorch",
"modular",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | adalbertojunior | null | adalbertojunior/modular-test | 4 | null | transformers | 18,299 | Entry not found |
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