modelId stringlengths 6 107 | label list | readme stringlengths 0 56.2k | readme_len int64 0 56.2k |
|---|---|---|---|
responsibility-framing/predict-perception-xlmr-focus-assassin | [
"LABEL_0"
] | ---
license: mit
tags:
- generated_from_trainer
model-index:
- name: predict-perception-xlmr-focus-assassin
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. -->
# predict-perception-xlmr-focus-assassin
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3264
- Rmse: 0.9437
- Rmse Focus::a Sull'assassino: 0.9437
- Mae: 0.7093
- Mae Focus::a Sull'assassino: 0.7093
- R2: 0.6145
- R2 Focus::a Sull'assassino: 0.6145
- Cos: 0.7391
- Pair: 0.0
- Rank: 0.5
- Neighbors: 0.6131
- Rsa: nan
## 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: 1e-05
- train_batch_size: 20
- eval_batch_size: 8
- seed: 1996
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rmse | Rmse Focus::a Sull'assassino | Mae | Mae Focus::a Sull'assassino | R2 | R2 Focus::a Sull'assassino | Cos | Pair | Rank | Neighbors | Rsa |
|:-------------:|:-----:|:----:|:---------------:|:------:|:----------------------------:|:------:|:---------------------------:|:-------:|:--------------------------:|:-------:|:----:|:----:|:---------:|:---:|
| 1.0403 | 1.0 | 15 | 1.1576 | 1.7771 | 1.7771 | 1.6028 | 1.6028 | -0.3670 | -0.3670 | -0.2174 | 0.0 | 0.5 | 0.2379 | nan |
| 0.9818 | 2.0 | 30 | 0.8916 | 1.5596 | 1.5596 | 1.4136 | 1.4136 | -0.0529 | -0.0529 | 0.3913 | 0.0 | 0.5 | 0.3793 | nan |
| 0.9276 | 3.0 | 45 | 0.9277 | 1.5909 | 1.5909 | 1.4560 | 1.4560 | -0.0955 | -0.0955 | 0.3913 | 0.0 | 0.5 | 0.3742 | nan |
| 0.8395 | 4.0 | 60 | 0.7958 | 1.4734 | 1.4734 | 1.3032 | 1.3032 | 0.0603 | 0.0603 | 0.5652 | 0.0 | 0.5 | 0.4598 | nan |
| 0.7587 | 5.0 | 75 | 0.4647 | 1.1259 | 1.1259 | 0.9316 | 0.9316 | 0.4513 | 0.4513 | 0.6522 | 0.0 | 0.5 | 0.5087 | nan |
| 0.696 | 6.0 | 90 | 0.5368 | 1.2101 | 1.2101 | 1.0847 | 1.0847 | 0.3661 | 0.3661 | 0.7391 | 0.0 | 0.5 | 0.5302 | nan |
| 0.548 | 7.0 | 105 | 0.3110 | 0.9211 | 0.9211 | 0.7896 | 0.7896 | 0.6328 | 0.6328 | 0.6522 | 0.0 | 0.5 | 0.5261 | nan |
| 0.4371 | 8.0 | 120 | 0.3392 | 0.9619 | 0.9619 | 0.8132 | 0.8132 | 0.5995 | 0.5995 | 0.6522 | 0.0 | 0.5 | 0.5261 | nan |
| 0.355 | 9.0 | 135 | 0.3938 | 1.0366 | 1.0366 | 0.8153 | 0.8153 | 0.5349 | 0.5349 | 0.7391 | 0.0 | 0.5 | 0.6131 | nan |
| 0.2919 | 10.0 | 150 | 0.3484 | 0.9749 | 0.9749 | 0.7487 | 0.7487 | 0.5886 | 0.5886 | 0.7391 | 0.0 | 0.5 | 0.6131 | nan |
| 0.2595 | 11.0 | 165 | 0.2812 | 0.8759 | 0.8759 | 0.6265 | 0.6265 | 0.6679 | 0.6679 | 0.7391 | 0.0 | 0.5 | 0.6131 | nan |
| 0.2368 | 12.0 | 180 | 0.2534 | 0.8314 | 0.8314 | 0.6402 | 0.6402 | 0.7008 | 0.7008 | 0.7391 | 0.0 | 0.5 | 0.6131 | nan |
| 0.227 | 13.0 | 195 | 0.2878 | 0.8861 | 0.8861 | 0.6769 | 0.6769 | 0.6601 | 0.6601 | 0.7391 | 0.0 | 0.5 | 0.6131 | nan |
| 0.1979 | 14.0 | 210 | 0.2405 | 0.8100 | 0.8100 | 0.6113 | 0.6113 | 0.7160 | 0.7160 | 0.7391 | 0.0 | 0.5 | 0.6131 | nan |
| 0.1622 | 15.0 | 225 | 0.2575 | 0.8382 | 0.8382 | 0.6017 | 0.6017 | 0.6959 | 0.6959 | 0.8261 | 0.0 | 0.5 | 0.6622 | nan |
| 0.1575 | 16.0 | 240 | 0.2945 | 0.8963 | 0.8963 | 0.6741 | 0.6741 | 0.6523 | 0.6523 | 0.8261 | 0.0 | 0.5 | 0.6622 | nan |
| 0.1479 | 17.0 | 255 | 0.3563 | 0.9859 | 0.9859 | 0.7367 | 0.7367 | 0.5792 | 0.5792 | 0.8261 | 0.0 | 0.5 | 0.6622 | nan |
| 0.1269 | 18.0 | 270 | 0.2806 | 0.8750 | 0.8750 | 0.6665 | 0.6665 | 0.6686 | 0.6686 | 0.8261 | 0.0 | 0.5 | 0.6622 | nan |
| 0.1257 | 19.0 | 285 | 0.3267 | 0.9441 | 0.9441 | 0.6739 | 0.6739 | 0.6142 | 0.6142 | 0.8261 | 0.0 | 0.5 | 0.6622 | nan |
| 0.134 | 20.0 | 300 | 0.3780 | 1.0155 | 1.0155 | 0.7331 | 0.7331 | 0.5536 | 0.5536 | 0.7391 | 0.0 | 0.5 | 0.5302 | nan |
| 0.1171 | 21.0 | 315 | 0.3890 | 1.0301 | 1.0301 | 0.7444 | 0.7444 | 0.5406 | 0.5406 | 0.8261 | 0.0 | 0.5 | 0.6622 | nan |
| 0.0934 | 22.0 | 330 | 0.3131 | 0.9242 | 0.9242 | 0.6923 | 0.6923 | 0.6303 | 0.6303 | 0.8261 | 0.0 | 0.5 | 0.6622 | nan |
| 0.1112 | 23.0 | 345 | 0.2912 | 0.8913 | 0.8913 | 0.6610 | 0.6610 | 0.6561 | 0.6561 | 0.8261 | 0.0 | 0.5 | 0.6622 | nan |
| 0.1038 | 24.0 | 360 | 0.3109 | 0.9209 | 0.9209 | 0.7019 | 0.7019 | 0.6329 | 0.6329 | 0.8261 | 0.0 | 0.5 | 0.6622 | nan |
| 0.085 | 25.0 | 375 | 0.3469 | 0.9728 | 0.9728 | 0.7383 | 0.7383 | 0.5904 | 0.5904 | 0.8261 | 0.0 | 0.5 | 0.6622 | nan |
| 0.0843 | 26.0 | 390 | 0.3017 | 0.9073 | 0.9073 | 0.6848 | 0.6848 | 0.6437 | 0.6437 | 0.7391 | 0.0 | 0.5 | 0.6131 | nan |
| 0.093 | 27.0 | 405 | 0.3269 | 0.9443 | 0.9443 | 0.7042 | 0.7042 | 0.6140 | 0.6140 | 0.7391 | 0.0 | 0.5 | 0.6131 | nan |
| 0.0846 | 28.0 | 420 | 0.3161 | 0.9286 | 0.9286 | 0.6937 | 0.6937 | 0.6267 | 0.6267 | 0.7391 | 0.0 | 0.5 | 0.6131 | nan |
| 0.0764 | 29.0 | 435 | 0.3244 | 0.9408 | 0.9408 | 0.7079 | 0.7079 | 0.6169 | 0.6169 | 0.7391 | 0.0 | 0.5 | 0.6131 | nan |
| 0.0697 | 30.0 | 450 | 0.3264 | 0.9437 | 0.9437 | 0.7093 | 0.7093 | 0.6145 | 0.6145 | 0.7391 | 0.0 | 0.5 | 0.6131 | nan |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.2+cu113
- Datasets 1.18.3
- Tokenizers 0.11.0
| 8,116 |
responsibility-framing/predict-perception-xlmr-focus-object | [
"LABEL_0"
] | ---
license: mit
tags:
- generated_from_trainer
model-index:
- name: predict-perception-xlmr-focus-object
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. -->
# predict-perception-xlmr-focus-object
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1927
- Rmse: 0.5495
- Rmse Focus::a Su un oggetto: 0.5495
- Mae: 0.4174
- Mae Focus::a Su un oggetto: 0.4174
- R2: 0.5721
- R2 Focus::a Su un oggetto: 0.5721
- Cos: 0.5652
- Pair: 0.0
- Rank: 0.5
- Neighbors: 0.5518
- Rsa: nan
## 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: 1e-05
- train_batch_size: 20
- eval_batch_size: 8
- seed: 1996
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rmse | Rmse Focus::a Su un oggetto | Mae | Mae Focus::a Su un oggetto | R2 | R2 Focus::a Su un oggetto | Cos | Pair | Rank | Neighbors | Rsa |
|:-------------:|:-----:|:----:|:---------------:|:------:|:---------------------------:|:------:|:--------------------------:|:-------:|:-------------------------:|:-------:|:----:|:----:|:---------:|:---:|
| 1.0316 | 1.0 | 15 | 0.6428 | 1.0035 | 1.0035 | 0.8806 | 0.8806 | -0.4272 | -0.4272 | -0.4783 | 0.0 | 0.5 | 0.5302 | nan |
| 1.0005 | 2.0 | 30 | 0.4564 | 0.8456 | 0.8456 | 0.7078 | 0.7078 | -0.0134 | -0.0134 | 0.4783 | 0.0 | 0.5 | 0.4440 | nan |
| 0.9519 | 3.0 | 45 | 0.4151 | 0.8063 | 0.8063 | 0.6797 | 0.6797 | 0.0784 | 0.0784 | 0.1304 | 0.0 | 0.5 | 0.4888 | nan |
| 0.92 | 4.0 | 60 | 0.3982 | 0.7898 | 0.7898 | 0.6516 | 0.6516 | 0.1159 | 0.1159 | 0.2174 | 0.0 | 0.5 | 0.5036 | nan |
| 0.8454 | 5.0 | 75 | 0.2739 | 0.6550 | 0.6550 | 0.5292 | 0.5292 | 0.3919 | 0.3919 | 0.6522 | 0.0 | 0.5 | 0.4160 | nan |
| 0.7247 | 6.0 | 90 | 0.2413 | 0.6148 | 0.6148 | 0.5347 | 0.5347 | 0.4642 | 0.4642 | 0.4783 | 0.0 | 0.5 | 0.3453 | nan |
| 0.6055 | 7.0 | 105 | 0.3109 | 0.6978 | 0.6978 | 0.6115 | 0.6115 | 0.3098 | 0.3098 | 0.4783 | 0.0 | 0.5 | 0.4154 | nan |
| 0.5411 | 8.0 | 120 | 0.3932 | 0.7848 | 0.7848 | 0.6712 | 0.6712 | 0.1271 | 0.1271 | 0.4783 | 0.0 | 0.5 | 0.4154 | nan |
| 0.4784 | 9.0 | 135 | 0.1316 | 0.4540 | 0.4540 | 0.3750 | 0.3750 | 0.7079 | 0.7079 | 0.5652 | 0.0 | 0.5 | 0.6247 | nan |
| 0.4039 | 10.0 | 150 | 0.2219 | 0.5896 | 0.5896 | 0.4954 | 0.4954 | 0.5074 | 0.5074 | 0.5652 | 0.0 | 0.5 | 0.4838 | nan |
| 0.3415 | 11.0 | 165 | 0.1935 | 0.5505 | 0.5505 | 0.4443 | 0.4443 | 0.5704 | 0.5704 | 0.5652 | 0.0 | 0.5 | 0.6247 | nan |
| 0.3369 | 12.0 | 180 | 0.2118 | 0.5761 | 0.5761 | 0.4554 | 0.4554 | 0.5296 | 0.5296 | 0.5652 | 0.0 | 0.5 | 0.6247 | nan |
| 0.3083 | 13.0 | 195 | 0.1928 | 0.5496 | 0.5496 | 0.4368 | 0.4368 | 0.5718 | 0.5718 | 0.5652 | 0.0 | 0.5 | 0.6247 | nan |
| 0.2678 | 14.0 | 210 | 0.2205 | 0.5877 | 0.5877 | 0.4472 | 0.4472 | 0.5105 | 0.5105 | 0.5652 | 0.0 | 0.5 | 0.6247 | nan |
| 0.2199 | 15.0 | 225 | 0.2118 | 0.5760 | 0.5760 | 0.4689 | 0.4689 | 0.5297 | 0.5297 | 0.5652 | 0.0 | 0.5 | 0.6247 | nan |
| 0.2238 | 16.0 | 240 | 0.2461 | 0.6209 | 0.6209 | 0.5047 | 0.5047 | 0.4537 | 0.4537 | 0.5652 | 0.0 | 0.5 | 0.6247 | nan |
| 0.2233 | 17.0 | 255 | 0.2307 | 0.6011 | 0.6011 | 0.4618 | 0.4618 | 0.4879 | 0.4879 | 0.5652 | 0.0 | 0.5 | 0.6247 | nan |
| 0.1903 | 18.0 | 270 | 0.2207 | 0.5880 | 0.5880 | 0.4432 | 0.4432 | 0.5100 | 0.5100 | 0.6522 | 0.0 | 0.5 | 0.6622 | nan |
| 0.1714 | 19.0 | 285 | 0.2146 | 0.5798 | 0.5798 | 0.4368 | 0.4368 | 0.5236 | 0.5236 | 0.5652 | 0.0 | 0.5 | 0.6247 | nan |
| 0.1759 | 20.0 | 300 | 0.1745 | 0.5228 | 0.5228 | 0.4152 | 0.4152 | 0.6126 | 0.6126 | 0.5652 | 0.0 | 0.5 | 0.6247 | nan |
| 0.1505 | 21.0 | 315 | 0.1944 | 0.5519 | 0.5519 | 0.4170 | 0.4170 | 0.5684 | 0.5684 | 0.5652 | 0.0 | 0.5 | 0.6247 | nan |
| 0.1467 | 22.0 | 330 | 0.1802 | 0.5313 | 0.5313 | 0.3910 | 0.3910 | 0.5999 | 0.5999 | 0.6522 | 0.0 | 0.5 | 0.6622 | nan |
| 0.1441 | 23.0 | 345 | 0.2360 | 0.6081 | 0.6081 | 0.4755 | 0.4755 | 0.4760 | 0.4760 | 0.4783 | 0.0 | 0.5 | 0.4938 | nan |
| 0.1553 | 24.0 | 360 | 0.2129 | 0.5774 | 0.5774 | 0.4539 | 0.4539 | 0.5274 | 0.5274 | 0.5652 | 0.0 | 0.5 | 0.5518 | nan |
| 0.1163 | 25.0 | 375 | 0.1780 | 0.5281 | 0.5281 | 0.3952 | 0.3952 | 0.6048 | 0.6048 | 0.6522 | 0.0 | 0.5 | 0.6622 | nan |
| 0.1266 | 26.0 | 390 | 0.2163 | 0.5821 | 0.5821 | 0.4569 | 0.4569 | 0.5198 | 0.5198 | 0.5652 | 0.0 | 0.5 | 0.5518 | nan |
| 0.1416 | 27.0 | 405 | 0.1829 | 0.5352 | 0.5352 | 0.4082 | 0.4082 | 0.5939 | 0.5939 | 0.5652 | 0.0 | 0.5 | 0.5518 | nan |
| 0.1576 | 28.0 | 420 | 0.1930 | 0.5498 | 0.5498 | 0.4126 | 0.4126 | 0.5716 | 0.5716 | 0.6522 | 0.0 | 0.5 | 0.6622 | nan |
| 0.118 | 29.0 | 435 | 0.2070 | 0.5694 | 0.5694 | 0.4378 | 0.4378 | 0.5405 | 0.5405 | 0.5652 | 0.0 | 0.5 | 0.5518 | nan |
| 0.1179 | 30.0 | 450 | 0.1927 | 0.5495 | 0.5495 | 0.4174 | 0.4174 | 0.5721 | 0.5721 | 0.5652 | 0.0 | 0.5 | 0.5518 | nan |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.2+cu113
- Datasets 1.18.3
- Tokenizers 0.11.0
| 8,013 |
responsibility-framing/predict-perception-xlmr-cause-none | [
"LABEL_0"
] | ---
license: mit
tags:
- generated_from_trainer
model-index:
- name: predict-perception-xlmr-cause-none
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. -->
# predict-perception-xlmr-cause-none
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8639
- Rmse: 1.3661
- Rmse Cause::a Spontanea, priva di un agente scatenante: 1.3661
- Mae: 1.0795
- Mae Cause::a Spontanea, priva di un agente scatenante: 1.0795
- R2: -1.7872
- R2 Cause::a Spontanea, priva di un agente scatenante: -1.7872
- Cos: -0.3043
- Pair: 0.0
- Rank: 0.5
- Neighbors: 0.3501
- Rsa: nan
## 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: 1e-05
- train_batch_size: 20
- eval_batch_size: 8
- seed: 1996
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rmse | Rmse Cause::a Spontanea, priva di un agente scatenante | Mae | Mae Cause::a Spontanea, priva di un agente scatenante | R2 | R2 Cause::a Spontanea, priva di un agente scatenante | Cos | Pair | Rank | Neighbors | Rsa |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------------------------------------------------------:|:------:|:-----------------------------------------------------:|:-------:|:----------------------------------------------------:|:-------:|:----:|:----:|:---------:|:---:|
| 1.0626 | 1.0 | 15 | 0.6787 | 0.8244 | 0.8244 | 0.7453 | 0.7453 | -0.0149 | -0.0149 | 0.0435 | 0.0 | 0.5 | 0.2515 | nan |
| 1.0186 | 2.0 | 30 | 0.6769 | 0.8233 | 0.8233 | 0.7457 | 0.7457 | -0.0122 | -0.0122 | 0.0435 | 0.0 | 0.5 | 0.2515 | nan |
| 1.0346 | 3.0 | 45 | 0.6812 | 0.8259 | 0.8259 | 0.7489 | 0.7489 | -0.0187 | -0.0187 | 0.0435 | 0.0 | 0.5 | 0.2515 | nan |
| 0.9481 | 4.0 | 60 | 1.0027 | 1.0020 | 1.0020 | 0.8546 | 0.8546 | -0.4994 | -0.4994 | -0.3043 | 0.0 | 0.5 | 0.2579 | nan |
| 0.8838 | 5.0 | 75 | 0.9352 | 0.9677 | 0.9677 | 0.8463 | 0.8463 | -0.3985 | -0.3985 | -0.2174 | 0.0 | 0.5 | 0.2966 | nan |
| 0.7971 | 6.0 | 90 | 0.9396 | 0.9700 | 0.9700 | 0.8608 | 0.8608 | -0.4050 | -0.4050 | -0.2174 | 0.0 | 0.5 | 0.3156 | nan |
| 0.8182 | 7.0 | 105 | 0.9485 | 0.9746 | 0.9746 | 0.8509 | 0.8509 | -0.4184 | -0.4184 | -0.1304 | 0.0 | 0.5 | 0.2788 | nan |
| 0.696 | 8.0 | 120 | 1.1396 | 1.0682 | 1.0682 | 0.9309 | 0.9309 | -0.7041 | -0.7041 | -0.1304 | 0.0 | 0.5 | 0.2899 | nan |
| 0.6337 | 9.0 | 135 | 1.3064 | 1.1437 | 1.1437 | 0.9612 | 0.9612 | -0.9536 | -0.9536 | -0.3913 | 0.0 | 0.5 | 0.4018 | nan |
| 0.5308 | 10.0 | 150 | 1.2403 | 1.1144 | 1.1144 | 0.9359 | 0.9359 | -0.8547 | -0.8547 | -0.3913 | 0.0 | 0.5 | 0.4018 | nan |
| 0.5226 | 11.0 | 165 | 1.3433 | 1.1597 | 1.1597 | 0.9542 | 0.9542 | -1.0087 | -1.0087 | -0.3913 | 0.0 | 0.5 | 0.4018 | nan |
| 0.474 | 12.0 | 180 | 1.5321 | 1.2386 | 1.2386 | 1.0340 | 1.0340 | -1.2910 | -1.2910 | -0.3043 | 0.0 | 0.5 | 0.3205 | nan |
| 0.3899 | 13.0 | 195 | 1.6322 | 1.2784 | 1.2784 | 1.0083 | 1.0083 | -1.4408 | -1.4408 | -0.3043 | 0.0 | 0.5 | 0.3590 | nan |
| 0.3937 | 14.0 | 210 | 1.7519 | 1.3244 | 1.3244 | 1.0540 | 1.0540 | -1.6197 | -1.6197 | -0.3913 | 0.0 | 0.5 | 0.4018 | nan |
| 0.4128 | 15.0 | 225 | 1.8588 | 1.3643 | 1.3643 | 1.0765 | 1.0765 | -1.7797 | -1.7797 | -0.3913 | 0.0 | 0.5 | 0.4018 | nan |
| 0.3424 | 16.0 | 240 | 1.7211 | 1.3128 | 1.3128 | 1.0217 | 1.0217 | -1.5737 | -1.5737 | -0.3913 | 0.0 | 0.5 | 0.4018 | nan |
| 0.3307 | 17.0 | 255 | 1.7802 | 1.3351 | 1.3351 | 1.0790 | 1.0790 | -1.6621 | -1.6621 | -0.3043 | 0.0 | 0.5 | 0.3205 | nan |
| 0.2972 | 18.0 | 270 | 1.5272 | 1.2366 | 1.2366 | 0.9945 | 0.9945 | -1.2837 | -1.2837 | -0.3043 | 0.0 | 0.5 | 0.3501 | nan |
| 0.2862 | 19.0 | 285 | 1.7213 | 1.3128 | 1.3128 | 1.0574 | 1.0574 | -1.5740 | -1.5740 | -0.3913 | 0.0 | 0.5 | 0.3815 | nan |
| 0.2844 | 20.0 | 300 | 1.8999 | 1.3793 | 1.3793 | 1.0930 | 1.0930 | -1.8411 | -1.8411 | -0.3043 | 0.0 | 0.5 | 0.3501 | nan |
| 0.2404 | 21.0 | 315 | 1.9806 | 1.4082 | 1.4082 | 1.1221 | 1.1221 | -1.9617 | -1.9617 | -0.3913 | 0.0 | 0.5 | 0.3815 | nan |
| 0.2349 | 22.0 | 330 | 1.8649 | 1.3665 | 1.3665 | 1.0953 | 1.0953 | -1.7888 | -1.7888 | -0.3913 | 0.0 | 0.5 | 0.3815 | nan |
| 0.2323 | 23.0 | 345 | 1.8256 | 1.3520 | 1.3520 | 1.0694 | 1.0694 | -1.7299 | -1.7299 | -0.3913 | 0.0 | 0.5 | 0.4018 | nan |
| 0.2217 | 24.0 | 360 | 1.9150 | 1.3847 | 1.3847 | 1.1017 | 1.1017 | -1.8636 | -1.8636 | -0.3043 | 0.0 | 0.5 | 0.3501 | nan |
| 0.2262 | 25.0 | 375 | 1.8536 | 1.3624 | 1.3624 | 1.0667 | 1.0667 | -1.7719 | -1.7719 | -0.3043 | 0.0 | 0.5 | 0.3501 | nan |
| 0.2052 | 26.0 | 390 | 1.7727 | 1.3323 | 1.3323 | 1.0475 | 1.0475 | -1.6508 | -1.6508 | -0.3043 | 0.0 | 0.5 | 0.3501 | nan |
| 0.2121 | 27.0 | 405 | 1.8088 | 1.3458 | 1.3458 | 1.0588 | 1.0588 | -1.7048 | -1.7048 | -0.3043 | 0.0 | 0.5 | 0.3501 | nan |
| 0.1723 | 28.0 | 420 | 1.8283 | 1.3530 | 1.3530 | 1.0628 | 1.0628 | -1.7340 | -1.7340 | -0.3043 | 0.0 | 0.5 | 0.3501 | nan |
| 0.1932 | 29.0 | 435 | 1.8566 | 1.3635 | 1.3635 | 1.0763 | 1.0763 | -1.7764 | -1.7764 | -0.3043 | 0.0 | 0.5 | 0.3501 | nan |
| 0.2157 | 30.0 | 450 | 1.8639 | 1.3661 | 1.3661 | 1.0795 | 1.0795 | -1.7872 | -1.7872 | -0.3043 | 0.0 | 0.5 | 0.3501 | nan |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.2+cu113
- Datasets 1.18.3
- Tokenizers 0.11.0
| 10,685 |
Jeevesh8/goog_bert_ft_cola-40 | null | Entry not found | 15 |
valurank/distilroberta-clickbait | [
"CLICKBAIT",
"NOT_CLICKBAIT"
] | ---
license: other
tags:
- generated_from_trainer
model-index:
- name: distilroberta-clickbait
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. -->
# distilroberta-clickbait
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on a dataset of headlines.
It achieves the following results on the evaluation set:
- Loss: 0.0268
- Acc: 0.9963
## Training and evaluation data
The following data sources were used:
* 32k headlines classified as clickbait/not-clickbait from [kaggle](https://www.kaggle.com/amananandrai/clickbait-dataset)
* A dataset of headlines from https://github.com/MotiBaadror/Clickbait-Detection
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 12345
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 16
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Acc |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.0195 | 1.0 | 981 | 0.0192 | 0.9954 |
| 0.0026 | 2.0 | 1962 | 0.0172 | 0.9963 |
| 0.0031 | 3.0 | 2943 | 0.0275 | 0.9945 |
| 0.0003 | 4.0 | 3924 | 0.0268 | 0.9963 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.1
- Datasets 1.17.0
- Tokenizers 0.10.3
| 1,674 |
Jeevesh8/goog_bert_ft_cola-41 | null | Entry not found | 15 |
elozano/bert-base-cased-news-category | [
"Automobile",
"Entertainment",
"Politics",
"Science",
"Sports",
"Technology",
"World"
] | Entry not found | 15 |
Jeevesh8/goog_bert_ft_cola-42 | null | Entry not found | 15 |
Jeevesh8/goog_bert_ft_cola-44 | null | Entry not found | 15 |
Jeevesh8/goog_bert_ft_cola-43 | null | Entry not found | 15 |
FourthBrain/bert_model_reddit_tsla | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: bert_model_reddit_tsla
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert_model_reddit_tsla
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: 0.4678
- Accuracy: 0.7914
- F1: 0.7832
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
| 1,182 |
oigele/Fb_improved_zeroshot | [
"contradiction",
"entailment",
"neutral"
] | ---
pipeline_tag: zero-shot-classification
datasets:
- multi_nli
widget:
- text: "natural language processing"
candidate_labels: "Location & Address, Employment, Organizational, Name, Service, Studies, Science"
hypothesis_template: "This is {}."
---
# Fb_improved_zeroshot
Zero-Shot Model designed to classify academic search logs in German and English. Developed by students at ETH Zürich.
This model was trained using the [bart-large-mnli](https://huggingface.co/facebook/bart-large-mnli/) checkpoint provided by Meta on Huggingface. It was then fine-tuned to suit the needs of this project.
## NLI-based Zero-Shot Text Classification
This method is based on Natural Language Inference (NLI), see [Yin et al.](https://arxiv.org/abs/1909.00161).
The following tutorials are taken from the model card of [bart-large-mnli](https://huggingface.co/facebook/bart-large-mnli/).
#### With the zero-shot classification pipeline
The model can be loaded with the `zero-shot-classification` pipeline like so:
```python
from transformers import pipeline
classifier = pipeline("zero-shot-classification",
model="oigele/Fb_improved_zeroshot")
```
You can then use this pipeline to classify sequences into any of the class names you specify.
```python
sequence_to_classify = "natural language processing"
candidate_labels = ['Location & Address', 'Employment', 'Organizational', 'Name', 'Service', 'Studies', 'Science']
classifier(sequence_to_classify, candidate_labels)
```
If more than one candidate label can be correct, pass `multi_class=True` to calculate each class independently:
```python
candidate_labels = ['Location & Address', 'Employment', 'Organizational', 'Name', 'Service', 'Studies', 'Science']
classifier(sequence_to_classify, candidate_labels, multi_class=True)
```
#### With manual PyTorch
```python
# pose sequence as a NLI premise and label as a hypothesis
from transformers import AutoModelForSequenceClassification, AutoTokenizer
nli_model = AutoModelForSequenceClassification.from_pretrained('oigele/Fb_improved_zeroshot/')
tokenizer = AutoTokenizer.from_pretrained('facebook/bart-large-mnli')
premise = sequence
hypothesis = f'This is {label}.'
# run through model pre-trained on MNLI
x = tokenizer.encode(premise, hypothesis, return_tensors='pt',
truncation_strategy='only_first')
logits = nli_model(x.to(device))[0]
# we throw away "neutral" (dim 1) and take the probability of
# "entailment" (2) as the probability of the label being true
entail_contradiction_logits = logits[:,[0,2]]
probs = entail_contradiction_logits.softmax(dim=1)
prob_label_is_true = probs[:,1]
| 2,638 |
Jeevesh8/goog_bert_ft_cola-45 | null | Entry not found | 15 |
textattack/distilbert-base-cased-MRPC | null | Entry not found | 15 |
ynie/roberta-large_conv_contradiction_detector_v0 | null | Entry not found | 15 |
valurank/distilroberta-current | [
"Current",
"Not_current"
] | ---
license: other
tags:
- generated_from_trainer
model-index:
- name: distilroberta-current
results: []
---
# distilroberta-current
This model classifies articles as current (covering or discussing current events) or not current (not relating to current events).
The model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on a dataset of articles labeled using weak-supervision and manual labeling
It achieves the following results on the evaluation set:
- Loss: 0.1745
- Acc: 0.9355
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 12345
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 16
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Acc |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 1.0 | 11 | 0.6559 | 0.7097 |
| 0.6762 | 2.0 | 22 | 0.5627 | 0.7097 |
| 0.5432 | 3.0 | 33 | 0.4606 | 0.7097 |
| 0.5432 | 4.0 | 44 | 0.3651 | 0.8065 |
| 0.411 | 5.0 | 55 | 0.2512 | 0.9194 |
| 0.269 | 6.0 | 66 | 0.2774 | 0.9355 |
| 0.269 | 7.0 | 77 | 0.2062 | 0.8710 |
| 0.2294 | 8.0 | 88 | 0.2598 | 0.9355 |
| 0.1761 | 9.0 | 99 | 0.1745 | 0.9355 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.1
- Datasets 1.17.0
- Tokenizers 0.10.3
| 1,747 |
IlyaGusev/rubertconv_toxic_clf | [
"neutral",
"toxic"
] | ---
language:
- ru
tags:
- text-classification
license: apache-2.0
---
# RuBERTConv Toxic Classifier
## Model description
Based on [rubert-base-cased-conversational](https://huggingface.co/DeepPavlov/rubert-base-cased-conversational) model
## Intended uses & limitations
#### How to use
Colab: [link](https://colab.research.google.com/drive/1veKO9hke7myxKigZtZho_F-UM2fD9kp8)
```python
from transformers import pipeline
model_name = "IlyaGusev/rubertconv_toxic_clf"
pipe = pipeline("text-classification", model=model_name, tokenizer=model_name, framework="pt")
text = "Ты придурок из интернета"
pipe([text])
```
## Training data
Datasets:
- [2ch]( https://www.kaggle.com/blackmoon/russian-language-toxic-comments)
- [Odnoklassniki](https://www.kaggle.com/alexandersemiletov/toxic-russian-comments)
- [Toloka Persona Chat Rus](https://toloka.ai/ru/datasets)
- [Koziev's Conversations](https://github.com/Koziev/NLP_Datasets/blob/master/Conversations/Data) with [toxic words vocabulary](https://www.dropbox.com/s/ou6lx03b10yhrfl/bad_vocab.txt.tar.gz)
Augmentations:
- ё -> е
- Remove or add "?" or "!"
- Fix CAPS
- Concatenate toxic and non-toxic texts
- Concatenate two non-toxic texts
- Add toxic words from vocabulary
- Add typos
- Mask toxic words with "*", "@", "$"
## Training procedure
TBA | 1,312 |
textattack/albert-base-v2-QQP | null | ## TextAttack Model Card
This `albert-base-v2` model was fine-tuned for sequence classification using TextAttack
and the glue dataset loaded using the `nlp` library. The model was fine-tuned
for 5 epochs with a batch size of 32, a learning
rate of 5e-05, and a maximum sequence length of 128.
Since this was a classification task, the model was trained with a cross-entropy loss function.
The best score the model achieved on this task was 0.9073707642839476, as measured by the
eval set accuracy, found after 3 epochs.
For more information, check out [TextAttack on Github](https://github.com/QData/TextAttack).
| 620 |
valurank/finetuned-distilbert-news-article-categorization | [
"LABEL_0",
"LABEL_1",
"LABEL_2",
"LABEL_3",
"LABEL_4",
"LABEL_5",
"LABEL_6",
"LABEL_7"
] | ---
license: other
tags:
- generated_from_trainer
model-index:
- name: finetuned-distilbert-news-article-categorization
results: []
---
### finetuned-distilbert-news-article-catgorization
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the news_article_categorization dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1548
- F1_score(weighted): 0.96
### Model description
More information needed
### Intended uses & limitations
More information needed
### Training and evaluation data
The model was trained on some subset of the news_article_categorization dataset and it was validated on the remaining subset of the data
### Training procedure
More information needed
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-5
- train_batch_size: 3
- eval_batch_size: 3
- seed: 17
- optimizer: AdamW(lr=1e-5 and epsilon=1e-08)
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 0
- num_epochs: 2
### Training results
| Training Loss | Epoch | Validation Loss | f1 score |
|:-------------:|:-----:|:---------------: |:------:|
| 0.6359 | 1.0 | 0.1739 | 0.9619 |
| 0.1548 | 2.0 | 0.1898 | 0.9648 |
| 1,301 |
Jeevesh8/goog_bert_ft_cola-46 | null | Entry not found | 15 |
Jeevesh8/goog_bert_ft_cola-49 | null | Entry not found | 15 |
Jeevesh8/goog_bert_ft_cola-47 | null | Entry not found | 15 |
Jeevesh8/goog_bert_ft_cola-48 | null | Entry not found | 15 |
transformersbook/distilbert-base-uncased-distilled-clinc | [
"accept_reservations",
"account_blocked",
"alarm",
"application_status",
"apr",
"are_you_a_bot",
"balance",
"bill_balance",
"bill_due",
"book_flight",
"book_hotel",
"calculator",
"calendar",
"calendar_update",
"calories",
"cancel",
"cancel_reservation",
"car_rental",
"card_declined",
"carry_on",
"change_accent",
"change_ai_name",
"change_language",
"change_speed",
"change_user_name",
"change_volume",
"confirm_reservation",
"cook_time",
"credit_limit",
"credit_limit_change",
"credit_score",
"current_location",
"damaged_card",
"date",
"definition",
"direct_deposit",
"directions",
"distance",
"do_you_have_pets",
"exchange_rate",
"expiration_date",
"find_phone",
"flight_status",
"flip_coin",
"food_last",
"freeze_account",
"fun_fact",
"gas",
"gas_type",
"goodbye",
"greeting",
"how_busy",
"how_old_are_you",
"improve_credit_score",
"income",
"ingredient_substitution",
"ingredients_list",
"insurance",
"insurance_change",
"interest_rate",
"international_fees",
"international_visa",
"jump_start",
"last_maintenance",
"lost_luggage",
"make_call",
"maybe",
"meal_suggestion",
"meaning_of_life",
"measurement_conversion",
"meeting_schedule",
"min_payment",
"mpg",
"new_card",
"next_holiday",
"next_song",
"no",
"nutrition_info",
"oil_change_how",
"oil_change_when",
"oos",
"order",
"order_checks",
"order_status",
"pay_bill",
"payday",
"pin_change",
"play_music",
"plug_type",
"pto_balance",
"pto_request",
"pto_request_status",
"pto_used",
"recipe",
"redeem_rewards",
"reminder",
"reminder_update",
"repeat",
"replacement_card_duration",
"report_fraud",
"report_lost_card",
"reset_settings",
"restaurant_reservation",
"restaurant_reviews",
"restaurant_suggestion",
"rewards_balance",
"roll_dice",
"rollover_401k",
"routing",
"schedule_maintenance",
"schedule_meeting",
"share_location",
"shopping_list",
"shopping_list_update",
"smart_home",
"spelling",
"spending_history",
"sync_device",
"taxes",
"tell_joke",
"text",
"thank_you",
"time",
"timer",
"timezone",
"tire_change",
"tire_pressure",
"todo_list",
"todo_list_update",
"traffic",
"transactions",
"transfer",
"translate",
"travel_alert",
"travel_notification",
"travel_suggestion",
"uber",
"update_playlist",
"user_name",
"vaccines",
"w2",
"weather",
"what_are_your_hobbies",
"what_can_i_ask_you",
"what_is_your_name",
"what_song",
"where_are_you_from",
"whisper_mode",
"who_do_you_work_for",
"who_made_you",
"yes"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- clinc_oos
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased-distilled-clinc
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: clinc_oos
type: clinc_oos
args: plus
metrics:
- name: Accuracy
type: accuracy
value: 0.9393548387096774
---
<!-- 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-distilled-clinc
This model is a fine-tuned with knowledge distillation version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the clinc_oos dataset. The model is used in Chapter 8: Making Transformers Efficient in Production in the [NLP with Transformers book](https://learning.oreilly.com/library/view/natural-language-processing/9781098103231/). You can find the full code in the accompanying [Github repository](https://github.com/nlp-with-transformers/notebooks/blob/main/08_model-compression.ipynb).
It achieves the following results on the evaluation set:
- Loss: 0.1005
- Accuracy: 0.9394
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 48
- eval_batch_size: 48
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.9031 | 1.0 | 318 | 0.5745 | 0.7365 |
| 0.4481 | 2.0 | 636 | 0.2856 | 0.8748 |
| 0.2528 | 3.0 | 954 | 0.1798 | 0.9187 |
| 0.176 | 4.0 | 1272 | 0.1398 | 0.9294 |
| 0.1416 | 5.0 | 1590 | 0.1211 | 0.9348 |
| 0.1243 | 6.0 | 1908 | 0.1116 | 0.9348 |
| 0.1133 | 7.0 | 2226 | 0.1062 | 0.9377 |
| 0.1075 | 8.0 | 2544 | 0.1035 | 0.9387 |
| 0.1039 | 9.0 | 2862 | 0.1014 | 0.9381 |
| 0.1018 | 10.0 | 3180 | 0.1005 | 0.9394 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.9.1+cu102
- Datasets 1.13.0
- Tokenizers 0.10.3
| 2,585 |
Jeevesh8/goog_bert_ft_cola-51 | null | Entry not found | 15 |
Jeevesh8/goog_bert_ft_cola-50 | null | Entry not found | 15 |
Jeevesh8/goog_bert_ft_cola-52 | null | Entry not found | 15 |
textattack/distilbert-base-uncased-imdb | null | ## TextAttack Model Card
This `distilbert-base-uncased` model was fine-tuned for sequence classification using TextAttack
and the imdb dataset loaded using the `nlp` library. The model was fine-tuned
for 5 epochs with a batch size of 16, a learning
rate of 2e-05, and a maximum sequence length of 128.
Since this was a classification task, the model was trained with a cross-entropy loss function.
The best score the model achieved on this task was 0.88, as measured by the
eval set accuracy, found after 2 epochs.
For more information, check out [TextAttack on Github](https://github.com/QData/TextAttack).
| 615 |
Jeevesh8/goog_bert_ft_cola-53 | null | Entry not found | 15 |
Jeevesh8/goog_bert_ft_cola-55 | null | Entry not found | 15 |
coderpotter/adversarial-paraphrasing-detector | null | This model is a paraphrase detector trained on the Adversarial Paraphrasing datasets described and used in this paper: https://aclanthology.org/2021.acl-long.552/.
Github repository: https://github.com/Advancing-Machine-Human-Reasoning-Lab/apt.git
Please cite the following if you use this model:
```bib
@inproceedings{nighojkar-licato-2021-improving,
title = "Improving Paraphrase Detection with the Adversarial Paraphrasing Task",
author = "Nighojkar, Animesh and
Licato, John",
booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.acl-long.552",
pages = "7106--7116",
abstract = "If two sentences have the same meaning, it should follow that they are equivalent in their inferential properties, i.e., each sentence should textually entail the other. However, many paraphrase datasets currently in widespread use rely on a sense of paraphrase based on word overlap and syntax. Can we teach them instead to identify paraphrases in a way that draws on the inferential properties of the sentences, and is not over-reliant on lexical and syntactic similarities of a sentence pair? We apply the adversarial paradigm to this question, and introduce a new adversarial method of dataset creation for paraphrase identification: the Adversarial Paraphrasing Task (APT), which asks participants to generate semantically equivalent (in the sense of mutually implicative) but lexically and syntactically disparate paraphrases. These sentence pairs can then be used both to test paraphrase identification models (which get barely random accuracy) and then improve their performance. To accelerate dataset generation, we explore automation of APT using T5, and show that the resulting dataset also improves accuracy. We discuss implications for paraphrase detection and release our dataset in the hope of making paraphrase detection models better able to detect sentence-level meaning equivalence.",
}
``` | 2,231 |
fhamborg/roberta-targeted-sentiment-classification-newsarticles | null | ---
language:
- en
tags:
- text-classification
- sentiment-analysis
- sentiment-classification
- targeted-sentiment-classification
- target-depentent-sentiment-classification
license: "apache-2.0"
datasets: "fhamborg/news_sentiment_newsmtsc"
---
# NewsSentiment: easy-to-use, high-quality target-dependent sentiment classification for news articles
## Important: [use our PyPI package](https://pypi.org/project/NewsSentiment/) instead of this model on the Hub
The Huggingface Hub architecture currently [does not support](https://github.com/huggingface/transformers/issues/14785) target-dependent sentiment classification since you cannot provide the required inputs, i.e., sentence and target. Thus, we recommend that you use our easy-to-use [PyPI package NewsSentiment](https://pypi.org/project/NewsSentiment/).
## Description
This model is the currently [best performing](https://aclanthology.org/2021.eacl-main.142.pdf)
targeted sentiment classifier for news articles. In contrast to regular sentiment
classification, targeted sentiment classification allows you to provide a target in a sentence.
Only for this target, the sentiment is then predicted. This is more reliable in many
cases, as demonstrated by the following simplistic example: "I like Bert, but I hate Robert."
This model is also available as an easy-to-use PyPI package named [`NewsSentiment`](https://pypi.org/project/NewsSentiment/) and
in its original GitHub repository named [`NewsMTSC`](https://github.com/fhamborg/NewsMTSC), where you will find the dataset the model was trained on, other models for sentiment classification, and a training and testing framework. More information on the model and the dataset (consisting of more than 10k sentences sampled from news articles, each
labeled and agreed upon by at least 5 annotators) can be found in our [EACL paper](https://aclanthology.org/2021.eacl-main.142.pdf). The
dataset, the model, and its source code can be viewed in our [GitHub repository](https://github.com/fhamborg/NewsMTSC).
We recommend to use our [PyPI package](https://pypi.org/project/NewsSentiment/) for sentiment classification since the Huggingface Hub platform seems to [not support](https://github.com/huggingface/transformers/issues/14785) target-dependent sentiment classification.
# How to cite
If you use the dataset or model, please cite our [paper](https://www.aclweb.org/anthology/2021.eacl-main.142/) ([PDF](https://www.aclweb.org/anthology/2021.eacl-main.142.pdf)):
```
@InProceedings{Hamborg2021b,
author = {Hamborg, Felix and Donnay, Karsten},
title = {NewsMTSC: (Multi-)Target-dependent Sentiment Classification in News Articles},
booktitle = {Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics (EACL 2021)},
year = {2021},
month = {Apr.},
location = {Virtual Event},
}
```
| 2,893 |
Jeevesh8/goog_bert_ft_cola-54 | null | Entry not found | 15 |
CAMeL-Lab/bert-base-arabic-camelbert-mix-did-madar-corpus26 | [
"ALE",
"ALG",
"ALX",
"AMM",
"ASW",
"BAG",
"BAS",
"BEI",
"BEN",
"CAI",
"DAM",
"DOH",
"FES",
"JED",
"JER",
"KHA",
"MOS",
"MSA",
"MUS",
"RAB",
"RIY",
"SAL",
"SAN",
"SFX",
"TRI",
"TUN"
] | ---
language:
- ar
license: apache-2.0
widget:
- text: "عامل ايه ؟"
---
# CAMeLBERT-Mix DID Madar Corpus26 Model
## Model description
**CAMeLBERT-Mix DID Madar Corpus26 Model** is a dialect identification (DID) model that was built by fine-tuning the [CAMeLBERT-Mix](https://huggingface.co/CAMeL-Lab/bert-base-arabic-camelbert-mix/) model.
For the fine-tuning, we used the [MADAR Corpus 26](https://camel.abudhabi.nyu.edu/madar-shared-task-2019/) dataset, which includes 26 labels.
Our fine-tuning procedure and the hyperparameters we used can be found in our paper *"[The Interplay of Variant, Size, and Task Type in Arabic Pre-trained Language Models](https://arxiv.org/abs/2103.06678)."* Our fine-tuning code can be found [here](https://github.com/CAMeL-Lab/CAMeLBERT).
## Intended uses
You can use the CAMeLBERT-Mix DID Madar Corpus26 model as part of the transformers pipeline.
This model will also be available in [CAMeL Tools](https://github.com/CAMeL-Lab/camel_tools) soon.
#### How to use
To use the model with a transformers pipeline:
```python
>>> from transformers import pipeline
>>> did = pipeline('text-classification', model='CAMeL-Lab/bert-base-arabic-camelbert-mix-did-madar26')
>>> sentences = ['عامل ايه ؟', 'شلونك ؟ شخبارك ؟']
>>> did(sentences)
[{'label': 'CAI', 'score': 0.8751305937767029},
{'label': 'DOH', 'score': 0.9867215156555176}]
```
*Note*: to download our models, you would need `transformers>=3.5.0`.
Otherwise, you could download the models manually.
## Citation
```bibtex
@inproceedings{inoue-etal-2021-interplay,
title = "The Interplay of Variant, Size, and Task Type in {A}rabic Pre-trained Language Models",
author = "Inoue, Go and
Alhafni, Bashar and
Baimukan, Nurpeiis and
Bouamor, Houda and
Habash, Nizar",
booktitle = "Proceedings of the Sixth Arabic Natural Language Processing Workshop",
month = apr,
year = "2021",
address = "Kyiv, Ukraine (Online)",
publisher = "Association for Computational Linguistics",
abstract = "In this paper, we explore the effects of language variants, data sizes, and fine-tuning task types in Arabic pre-trained language models. To do so, we build three pre-trained language models across three variants of Arabic: Modern Standard Arabic (MSA), dialectal Arabic, and classical Arabic, in addition to a fourth language model which is pre-trained on a mix of the three. We also examine the importance of pre-training data size by building additional models that are pre-trained on a scaled-down set of the MSA variant. We compare our different models to each other, as well as to eight publicly available models by fine-tuning them on five NLP tasks spanning 12 datasets. Our results suggest that the variant proximity of pre-training data to fine-tuning data is more important than the pre-training data size. We exploit this insight in defining an optimized system selection model for the studied tasks.",
}
``` | 2,953 |
textattack/bert-base-uncased-QNLI | null | Entry not found | 15 |
Jeevesh8/goog_bert_ft_cola-56 | null | Entry not found | 15 |
textattack/distilbert-base-uncased-MNLI | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | Entry not found | 15 |
Jeevesh8/goog_bert_ft_cola-57 | null | Entry not found | 15 |
Wi/arxiv-topics-distilbert-base-cased | [
"Astrophysics",
"Computer Science",
"Condensed Matter",
"Economics",
"Electrical Engineering and Systems Science",
"General Relativity and Quantum Cosmology",
"High Energy Physics - Experiment",
"High Energy Physics - Lattice",
"High Energy Physics - Phenomenology",
"High Energy Physics - Theory",
"Mathematical Physics",
"Mathematics",
"Nonlinear Sciences",
"Nuclear Experiment",
"Nuclear Theory",
"Other",
"Physics",
"Quantitative Biology",
"Quantitative Finance",
"Quantum Physics",
"Statistics"
] | ---
language: en
license: apache-2.0
tags:
- arxiv
- topic-classification
- distilbert
widget:
- text: "Title: The Design of Radio Telescope Array Configurations using Multiobjective\n\
\ Optimization: Imaging Performance versus Cable Length\nAbstract: The next generation\
\ of radio telescope interferometric arrays requires\ncareful design of the array\
\ configuration to optimize the performance of the\noverall system. We have developed\
\ a framework, based on a genetic algorithm,\nfor rapid exploration and optimization\
\ of the objective space pertaining to\nmultiple objectives. We have evaluated\
\ a large space of possible designs for\n27-, 60-, 100-, and 160-station arrays.\
\ The 27-station optimizations can be\ncompared to the well-known VLA case, and\
\ the larger array designs apply to\narrays currently under design such as LOFAR,\
\ ATA, and the SKA. In the initial\nimplementation of our framework we evaluate\
\ designs with respect to two\nmetrics, array imaging performance and the length\
\ of cable necessary to connect\nthe stations. Imaging performance is measured\
\ by the degree to which the\nsampling of the uv plane is uniform. For the larger\
\ arrays we find that\nwell-known geometric designs perform well and occupy the\
\ Pareto front of\noptimum solutions. For the 27-element case we find designs,\
\ combining features\nof the well-known designs, that are more optimal as measured\
\ by these two\nmetrics. The results obtained by the multiobjective genetic optimization\
\ are\ncorroborated by simulated annealing, which also reveals the role of entropy\
\ in\narray optimization. Our framework is general, and may be applied to other\n\
design goals and issues, such as particular schemes for sampling the uv plane,\n\
array robustness, and phased deployment of arrays.\nAuthors: Babak E. Cohanim,\
\ Jacqueline N. Hewitt, Olivier de Weck"
- text: "Title: Evidence for a Neutron Star in the non-pulsating massive X-ray binary\n\
\ 4U2206+54\nAbstract: We present an analysis of archival RXTE and BeppoSAX data\
\ of the X-ray source\n4U2206+54 . For the first time, high energy data (> 30\
\ kev) are analyzed for\nthis source. The data are well described by comptonization\
\ models (CompTT and\nBMC) in which seed photons with temperatures between 1.1\
\ kev and 1.5 kev are\ncomptonized by a hot plasma at 50 kev thereby producing\
\ a hard tail which\nextends up to, at least, 100 kev. We offer a new method of\
\ identification of\nneutron star systems using a temperature - luminosity relation.\
\ If a given\nX-ray source is characterized by a low bolometric luminosity and\
\ a relatively\nhigh color blackbody temperature (>1 kev) it has necessarily to\
\ be a neutron\nstar rather than a black hole. From these arguments it is shown\
\ that the area\nof the soft photon source must be small (r ~ 1 km) and that the\
\ accretion disk,\nif present, must be truncated very far from the compact object.\
\ Here we report\non the possible existence of a cyclotron line around 30 kev.\
\ The presence of a\nneutron star in the system is strongly favored by the available\
\ data.\nAuthors: J. M. Torrej\xF3n, I. Kreykenbohm, A. Orr, L. Titarchuk, I.\
\ Negueruela"
- text: "Title: Solving the Schrodinger Equation for a Quantum Well\n with a Non-Uniform\
\ Potential\nAbstract: We present a numerical solution to the Schrodinger equation\
\ for a\nquantum well with a non-uniform potential. The potential is a Gaussian\n\
with a non-uniform distribution of energies. The solution is a solution to the\n\
Schrodinger equation with a non-uniform potential. The solution is a\nnon-uniform\
\ solution to the Schrodinger equation with a non-uniform potential.\nAuthors:\
\ George K. Kostopoulos, John A. Kostopoulos, and John C. Kostopoulos"
- text: "Title: Inverting Black-Scholes Model for Option Pricing\n with a Non-Uniformly\
\ Distributed Risk\nAbstract: We present a numerical solution to the Black-Scholes\
\ model for\noption pricing with a non-uniformly distributed risk. The solution\
\ is a\nnon-uniform solution to the Black-Scholes model with a non-uniformly\n\
distributed risk. The solution is a non-uniform solution to the\nBlack-Scholes\
\ model with a non-uniformly distributed risk.\nAuthors: Z. Starosov, L. Randhawa"
---
# DistilBERT on ArXiv
This model was developed to predict the top-level category of a paper, given the
paper's abstract, title, and list of authors. It was trained over a subset of
data pulled from the ArXiv API.
| 4,702 |
Tejas3/distillbert_base_uncased_80_equal | [
"NEGATIVE",
"NEUTRAL",
"POSITIVE"
] | Entry not found | 15 |
Jeevesh8/goog_bert_ft_cola-58 | null | Entry not found | 15 |
Jeevesh8/goog_bert_ft_cola-59 | null | Entry not found | 15 |
Huffon/klue-roberta-base-nli | [
"ENTAILMENT",
"NEUTRAL",
"CONTRADICTION"
] | ---
language: ko
tags:
- roberta
- nli
datasets:
- klue
---
| 60 |
Jeevesh8/goog_bert_ft_cola-60 | null | Entry not found | 15 |
Jeevesh8/goog_bert_ft_cola-61 | null | Entry not found | 15 |
google/tapas-base-finetuned-tabfact | null | ---
language: en
tags:
- tapas
- sequence-classification
license: apache-2.0
datasets:
- tab_fact
---
# TAPAS base model fine-tuned on Tabular Fact Checking (TabFact)
This model has 2 versions which can be used. The latest version, which is the default one, corresponds to the `tapas_tabfact_inter_masklm_base_reset` checkpoint of the [original Github repository](https://github.com/google-research/tapas).
This model was pre-trained on MLM and an additional step which the authors call intermediate pre-training, and then fine-tuned on [TabFact](https://github.com/wenhuchen/Table-Fact-Checking). It uses relative position embeddings by default (i.e. resetting the position index at every cell of the table).
The other (non-default) version which can be used is the one with absolute position embeddings:
- `no_reset`, which corresponds to `tapas_tabfact_inter_masklm_base`
Disclaimer: The team releasing TAPAS did not write a model card for this model so this model card has been written by
the Hugging Face team and contributors.
## Model description
TAPAS is a BERT-like transformers model pretrained on a large corpus of English data from Wikipedia in a self-supervised fashion.
This means it was pretrained on the raw tables and associated texts only, with no humans labelling them in any way (which is why it
can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely, it
was pretrained with two objectives:
- Masked language modeling (MLM): taking a (flattened) table and associated context, the model randomly masks 15% of the words in
the input, then runs the entire (partially masked) sequence through the model. The model then has to predict the masked words.
This is different from traditional recurrent neural networks (RNNs) that usually see the words one after the other,
or from autoregressive models like GPT which internally mask the future tokens. It allows the model to learn a bidirectional
representation of a table and associated text.
- Intermediate pre-training: to encourage numerical reasoning on tables, the authors additionally pre-trained the model by creating
a balanced dataset of millions of syntactically created training examples. Here, the model must predict (classify) whether a sentence
is supported or refuted by the contents of a table. The training examples are created based on synthetic as well as counterfactual statements.
This way, the model learns an inner representation of the English language used in tables and associated texts, which can then be used
to extract features useful for downstream tasks such as answering questions about a table, or determining whether a sentence is entailed
or refuted by the contents of a table. Fine-tuning is done by adding a classification head on top of the pre-trained model, and then
jointly train this randomly initialized classification head with the base model on TabFact.
## Intended uses & limitations
You can use this model for classifying whether a sentence is supported or refuted by the contents of a table.
For code examples, we refer to the documentation of TAPAS on the HuggingFace website.
## Training procedure
### Preprocessing
The texts are lowercased and tokenized using WordPiece and a vocabulary size of 30,000. The inputs of the model are
then of the form:
```
[CLS] Sentence [SEP] Flattened table [SEP]
```
### Fine-tuning
The model was fine-tuned on 32 Cloud TPU v3 cores for 80,000 steps with maximum sequence length 512 and batch size of 512.
In this setup, fine-tuning takes around 14 hours. The optimizer used is Adam with a learning rate of 2e-5, and a warmup
ratio of 0.05. See the [paper](https://arxiv.org/abs/2010.00571) for more details (appendix A2).
### BibTeX entry and citation info
```bibtex
@misc{herzig2020tapas,
title={TAPAS: Weakly Supervised Table Parsing via Pre-training},
author={Jonathan Herzig and Paweł Krzysztof Nowak and Thomas Müller and Francesco Piccinno and Julian Martin Eisenschlos},
year={2020},
eprint={2004.02349},
archivePrefix={arXiv},
primaryClass={cs.IR}
}
```
```bibtex
@misc{eisenschlos2020understanding,
title={Understanding tables with intermediate pre-training},
author={Julian Martin Eisenschlos and Syrine Krichene and Thomas Müller},
year={2020},
eprint={2010.00571},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
```bibtex
@inproceedings{2019TabFactA,
title={TabFact : A Large-scale Dataset for Table-based Fact Verification},
author={Wenhu Chen, Hongmin Wang, Jianshu Chen, Yunkai Zhang, Hong Wang, Shiyang Li, Xiyou Zhou and William Yang Wang},
booktitle = {International Conference on Learning Representations (ICLR)},
address = {Addis Ababa, Ethiopia},
month = {April},
year = {2020}
}
``` | 4,867 |
Intel/roberta-base-mrpc | [
"equivalent",
"not_equivalent"
] | ---
language:
- en
license: mit
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
- f1
model-index:
- name: roberta-base-mrpc
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE MRPC
type: glue
args: mrpc
metrics:
- name: Accuracy
type: accuracy
value: 0.8774509803921569
- name: F1
type: f1
value: 0.9137931034482758
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-base-mrpc
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the GLUE MRPC dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5565
- Accuracy: 0.8775
- F1: 0.9138
- Combined Score: 0.8956
## 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: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5.0
### Training results
### Framework versions
- Transformers 4.18.0
- Pytorch 1.10.0+cu102
- Datasets 2.1.0
- Tokenizers 0.11.6
| 1,493 |
Guscode/DKbert-hatespeech-detection | null | ---
language:
- da
tags:
- Hatespeech
- Danish
- BERT
license: mit
datasets:
- DKHate - OffensEval2020
Classes:
- Hateful
- Not Hateful
---
# DKbert-hatespeech-classification
Use this model to detect hatespeech in Danish. For details, guide and command line tool see [DK hate github](https://github.com/Guscode/DKbert-hatespeech-detection)
## Training data
Training data is from OffensEval2020 which can be found [here]( https://figshare.com/articles/dataset/Danish_Hate_Speech_Abusive_Language_data/12220805)
## Performance
The model achieves a macro F1-score of 0.78
Precision hateful: 0.77
Recall hateful: 0.49
See more on [DK hate github](https://github.com/Guscode/DKbert-hatespeech-detection)
## Training procedure
- [BOTXO Nordic Bert](https://huggingface.co/DJSammy/bert-base-danish-uncased_BotXO,ai)
- Learning rate: 1e-5,
- Batch size: 16
- Max sequence length: 128
## Project information
This model was made in collaboration between [Johan Horsmans](https://github.com/JohanHorsmans) and [Gustav Aarup Lauridsen](https://github.com/Guscode) for their Cultural Data Science Exam.
| 1,109 |
ganeshkharad/gk-hinglish-sentiment | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | ---
language:
- hi-en
tags:
- sentiment
- multilingual
- hindi codemix
- hinglish
license: apache-2.0
datasets:
- sail
---
# Sentiment Classification for hinglish text: `gk-hinglish-sentiment`
## Model description
Trained small amount of reviews dataset
## Intended uses & limitations
I wanted something to work well with hinglish data as it is being used in India mostly.
The training data was not much as expected
#### How to use
```python
#sample code
from transformers import BertTokenizer, BertForSequenceClassification
tokenizerg = BertTokenizer.from_pretrained("/content/model")
modelg = BertForSequenceClassification.from_pretrained("/content/model")
text = "kuch bhi type karo hinglish mai"
encoded_input = tokenizerg(text, return_tensors='pt')
output = modelg(**encoded_input)
print(output)
#output contains 3 lables LABEL_0 = Negative ,LABEL_1 = Nuetral ,LABEL_2 = Positive
```
#### Limitations and bias
The data contains only hinglish codemixed text it and was very much limited may be I will Update this model if I can get good amount of data
## Training data
Training data contains labeled data for 3 labels
link to the pre-trained model card with description of the pre-training data.
I have Tuned below model
https://huggingface.co/rohanrajpal/bert-base-multilingual-codemixed-cased-sentiment
### BibTeX entry and citation info
```@inproceedings{khanuja-etal-2020-gluecos,
title = "{GLUEC}o{S}: An Evaluation Benchmark for Code-Switched {NLP}",
author = "Khanuja, Simran and
Dandapat, Sandipan and
Srinivasan, Anirudh and
Sitaram, Sunayana and
Choudhury, Monojit",
booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.acl-main.329",
pages = "3575--3585"
}
```
| 1,960 |
Jeevesh8/goog_bert_ft_cola-62 | null | Entry not found | 15 |
Jeevesh8/goog_bert_ft_cola-63 | null | Entry not found | 15 |
howey/electra-base-sst2 | null | Entry not found | 15 |
Jeevesh8/goog_bert_ft_cola-64 | null | Entry not found | 15 |
Jeevesh8/goog_bert_ft_cola-65 | null | Entry not found | 15 |
Qiaozhen/fake-news-detector | [
"fake",
"real"
] | Entry not found | 15 |
Jeevesh8/goog_bert_ft_cola-66 | null | Entry not found | 15 |
Jeevesh8/goog_bert_ft_cola-67 | null | Entry not found | 15 |
Newtral/xlm-r-finetuned-toxic-political-tweets-es | null | ---
language: es
license: apache-2.0
---
# xlm-r-finetuned-toxic-political-tweets-es
This model is based on the pre-trained model [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) and was fine-tuned on a dataset of tweets from members of the [Spanish Congress of the Deputies](https://www.congreso.es/) annotated regarding the level of political toxicity they generate.
### Inputs
The model has been trained on the text of Spanish tweets authored by politicians in 2021, so this is the input expected and its performance can degrade when applied to texts from other domains.
### Outputs
The model predicts 2 signals of political toxicity:
* Toxic: the tweet has at least some degree of toxicity.
* Very Toxic: the tweet has a strong degree of toxicity.
A value between 0 and 1 is predicted for each signal.
### Intended uses & limitations
The model was created to be used as a toxicity detector of spanish tweets from Spanish Congress Deputies. If the intended use is other one, for instance; toxicity detection on films reviews, the results won't be reliable and you might look for another model with this concrete purpose.
### How to use
The model can be used directly with a text-classification pipeline:
```python
>>> from transformers import pipeline
>>> text = "Es usted un auténtico impresentable, su señoría."
>>> pipe = pipeline("text-classification", model="Newtral/xlm-r-finetuned-toxic-political-tweets-es")
>>> pipe(text, return_all_scores=True)
[[{'label': 'toxic', 'score': 0.92560875415802},
{'label': 'very toxic', 'score': 0.8310967683792114}]]
```
### Training procedure
The pre-trained model was fine-tuned for sequence classification using the following hyperparameters, which were selected from a validation set:
* Batch size = 32
* Learning rate = 2e-5
* Epochs = 5
* Max length = 64
The optimizer used was AdamW and the loss optimized was binary cross-entropy with class weights proportional to the class imbalance. | 1,970 |
nateraw/bert-base-uncased-imdb | [
"NEGATIVE",
"POSITIVE"
] | Entry not found | 15 |
Jeevesh8/goog_bert_ft_cola-68 | null | Entry not found | 15 |
pollner/dnabertregressor | [
"LABEL_0"
] | ---
tags:
- generated_from_trainer
model-index:
- name: dnabertregressor
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. -->
# dnabertregressor
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1368
- Mae: 0.0812
- R2: 0.7815
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 6
### Training results
| Training Loss | Epoch | Step | Validation Loss | Mae | R2 |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|
| No log | 1.0 | 402 | 0.2486 | 0.1346 | 0.4418 |
| 0.3662 | 2.0 | 804 | 0.1716 | 0.0969 | 0.7091 |
| 0.1746 | 3.0 | 1206 | 0.1509 | 0.0884 | 0.7573 |
| 0.1305 | 4.0 | 1608 | 0.1443 | 0.0850 | 0.7752 |
| 0.1088 | 5.0 | 2010 | 0.1403 | 0.0830 | 0.7740 |
| 0.1088 | 6.0 | 2412 | 0.1368 | 0.0812 | 0.7815 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0
- Datasets 2.3.2
- Tokenizers 0.12.1
| 1,623 |
textattack/distilbert-base-uncased-rotten-tomatoes | null | ## TextAttack Model Card
This `distilbert-base-uncased` model was fine-tuned for sequence classificationusing TextAttack
and the rotten_tomatoes dataset loaded using the `nlp` library. The model was fine-tuned
for 3 epochs with a batch size of 128, a learning
rate of 1e-05, and a maximum sequence length of 128.
Since this was a classification task, the model was trained with a cross-entropy loss function.
The best score the model achieved on this task was 0.8395872420262664, as measured by the
eval set accuracy, found after 2 epochs.
For more information, check out [TextAttack on Github](https://github.com/QData/TextAttack).
| 680 |
allenai/multicite-multilabel-roberta-large | [
"LABEL_0",
"LABEL_1",
"LABEL_2",
"LABEL_3",
"LABEL_4",
"LABEL_5",
"LABEL_6"
] | ---
language: en
tags:
- Roberta
license: mit
---
# MultiCite: Multi-label Citation Intent Classification with Roberta-large (NAACL 2022)
This model has been trained on the data available here: https://github.com/allenai/multicite. | 235 |
Jeevesh8/goog_bert_ft_cola-69 | null | Entry not found | 15 |
Jeevesh8/goog_bert_ft_cola-70 | null | Entry not found | 15 |
matthewburke/korean_sentiment | null | ```
from transformers import pipeline
classifier = pipeline("text-classification", model="matthewburke/korean_sentiment")
custom_tweet = "영화 재밌다."
preds = classifier(custom_tweet, return_all_scores=True)
is_positive = preds[0][1]['score'] > 0.5
```
| 249 |
Jeevesh8/goog_bert_ft_cola-71 | null | Entry not found | 15 |
Jeevesh8/goog_bert_ft_cola-72 | null | Entry not found | 15 |
Jeevesh8/goog_bert_ft_cola-73 | null | Entry not found | 15 |
Jeevesh8/goog_bert_ft_cola-74 | null | Entry not found | 15 |
emrecan/bert-base-turkish-cased-allnli_tr | [
"contradiction",
"entailment",
"neutral"
] | ---
language:
- tr
tags:
- zero-shot-classification
- nli
- pytorch
pipeline_tag: zero-shot-classification
license: mit
datasets:
- nli_tr
metrics:
- accuracy
widget:
- text: "Dolar yükselmeye devam ediyor."
candidate_labels: "ekonomi, siyaset, spor"
- text: "Senaryo çok saçmaydı, beğendim diyemem."
candidate_labels: "olumlu, olumsuz"
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-turkish-cased_allnli_tr
This model is a fine-tuned version of [dbmdz/bert-base-turkish-cased](https://huggingface.co/dbmdz/bert-base-turkish-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5771
- Accuracy: 0.7978
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.8559 | 0.03 | 1000 | 0.7577 | 0.6798 |
| 0.6612 | 0.07 | 2000 | 0.7263 | 0.6958 |
| 0.6115 | 0.1 | 3000 | 0.6431 | 0.7364 |
| 0.5916 | 0.14 | 4000 | 0.6347 | 0.7407 |
| 0.5719 | 0.17 | 5000 | 0.6317 | 0.7483 |
| 0.5575 | 0.2 | 6000 | 0.6034 | 0.7544 |
| 0.5521 | 0.24 | 7000 | 0.6148 | 0.7568 |
| 0.5393 | 0.27 | 8000 | 0.5931 | 0.7610 |
| 0.5382 | 0.31 | 9000 | 0.5866 | 0.7665 |
| 0.5306 | 0.34 | 10000 | 0.5881 | 0.7594 |
| 0.5295 | 0.37 | 11000 | 0.6120 | 0.7632 |
| 0.5225 | 0.41 | 12000 | 0.5620 | 0.7759 |
| 0.5112 | 0.44 | 13000 | 0.5641 | 0.7769 |
| 0.5133 | 0.48 | 14000 | 0.5571 | 0.7798 |
| 0.5023 | 0.51 | 15000 | 0.5719 | 0.7722 |
| 0.5017 | 0.54 | 16000 | 0.5482 | 0.7844 |
| 0.5111 | 0.58 | 17000 | 0.5503 | 0.7800 |
| 0.4929 | 0.61 | 18000 | 0.5502 | 0.7836 |
| 0.4923 | 0.65 | 19000 | 0.5424 | 0.7843 |
| 0.4894 | 0.68 | 20000 | 0.5417 | 0.7851 |
| 0.4877 | 0.71 | 21000 | 0.5514 | 0.7841 |
| 0.4818 | 0.75 | 22000 | 0.5494 | 0.7848 |
| 0.4898 | 0.78 | 23000 | 0.5450 | 0.7859 |
| 0.4823 | 0.82 | 24000 | 0.5417 | 0.7878 |
| 0.4806 | 0.85 | 25000 | 0.5354 | 0.7875 |
| 0.4779 | 0.88 | 26000 | 0.5338 | 0.7848 |
| 0.4744 | 0.92 | 27000 | 0.5277 | 0.7934 |
| 0.4678 | 0.95 | 28000 | 0.5507 | 0.7871 |
| 0.4727 | 0.99 | 29000 | 0.5603 | 0.7789 |
| 0.4243 | 1.02 | 30000 | 0.5626 | 0.7894 |
| 0.3955 | 1.05 | 31000 | 0.5324 | 0.7939 |
| 0.4022 | 1.09 | 32000 | 0.5322 | 0.7925 |
| 0.3976 | 1.12 | 33000 | 0.5450 | 0.7920 |
| 0.3913 | 1.15 | 34000 | 0.5464 | 0.7948 |
| 0.406 | 1.19 | 35000 | 0.5406 | 0.7958 |
| 0.3875 | 1.22 | 36000 | 0.5489 | 0.7878 |
| 0.4024 | 1.26 | 37000 | 0.5427 | 0.7925 |
| 0.3988 | 1.29 | 38000 | 0.5335 | 0.7904 |
| 0.393 | 1.32 | 39000 | 0.5415 | 0.7923 |
| 0.3988 | 1.36 | 40000 | 0.5385 | 0.7962 |
| 0.3912 | 1.39 | 41000 | 0.5383 | 0.7950 |
| 0.3949 | 1.43 | 42000 | 0.5415 | 0.7931 |
| 0.3902 | 1.46 | 43000 | 0.5438 | 0.7893 |
| 0.3948 | 1.49 | 44000 | 0.5348 | 0.7906 |
| 0.3921 | 1.53 | 45000 | 0.5361 | 0.7890 |
| 0.3944 | 1.56 | 46000 | 0.5419 | 0.7953 |
| 0.3959 | 1.6 | 47000 | 0.5402 | 0.7967 |
| 0.3926 | 1.63 | 48000 | 0.5429 | 0.7925 |
| 0.3854 | 1.66 | 49000 | 0.5346 | 0.7959 |
| 0.3864 | 1.7 | 50000 | 0.5241 | 0.7979 |
| 0.385 | 1.73 | 51000 | 0.5149 | 0.8002 |
| 0.3871 | 1.77 | 52000 | 0.5325 | 0.8002 |
| 0.3819 | 1.8 | 53000 | 0.5332 | 0.8022 |
| 0.384 | 1.83 | 54000 | 0.5419 | 0.7873 |
| 0.3899 | 1.87 | 55000 | 0.5225 | 0.7974 |
| 0.3894 | 1.9 | 56000 | 0.5358 | 0.7977 |
| 0.3838 | 1.94 | 57000 | 0.5264 | 0.7988 |
| 0.3881 | 1.97 | 58000 | 0.5280 | 0.7956 |
| 0.3756 | 2.0 | 59000 | 0.5601 | 0.7969 |
| 0.3156 | 2.04 | 60000 | 0.5936 | 0.7925 |
| 0.3125 | 2.07 | 61000 | 0.5898 | 0.7938 |
| 0.3179 | 2.11 | 62000 | 0.5591 | 0.7981 |
| 0.315 | 2.14 | 63000 | 0.5853 | 0.7970 |
| 0.3122 | 2.17 | 64000 | 0.5802 | 0.7979 |
| 0.3105 | 2.21 | 65000 | 0.5758 | 0.7979 |
| 0.3076 | 2.24 | 66000 | 0.5685 | 0.7980 |
| 0.3117 | 2.28 | 67000 | 0.5799 | 0.7944 |
| 0.3108 | 2.31 | 68000 | 0.5742 | 0.7988 |
| 0.3047 | 2.34 | 69000 | 0.5907 | 0.7921 |
| 0.3114 | 2.38 | 70000 | 0.5723 | 0.7937 |
| 0.3035 | 2.41 | 71000 | 0.5944 | 0.7955 |
| 0.3129 | 2.45 | 72000 | 0.5838 | 0.7928 |
| 0.3071 | 2.48 | 73000 | 0.5929 | 0.7949 |
| 0.3061 | 2.51 | 74000 | 0.5794 | 0.7967 |
| 0.3068 | 2.55 | 75000 | 0.5892 | 0.7954 |
| 0.3053 | 2.58 | 76000 | 0.5796 | 0.7962 |
| 0.3117 | 2.62 | 77000 | 0.5763 | 0.7981 |
| 0.3062 | 2.65 | 78000 | 0.5852 | 0.7964 |
| 0.3004 | 2.68 | 79000 | 0.5793 | 0.7966 |
| 0.3146 | 2.72 | 80000 | 0.5693 | 0.7985 |
| 0.3146 | 2.75 | 81000 | 0.5788 | 0.7982 |
| 0.3079 | 2.79 | 82000 | 0.5726 | 0.7978 |
| 0.3058 | 2.82 | 83000 | 0.5677 | 0.7988 |
| 0.3055 | 2.85 | 84000 | 0.5701 | 0.7982 |
| 0.3049 | 2.89 | 85000 | 0.5809 | 0.7970 |
| 0.3044 | 2.92 | 86000 | 0.5741 | 0.7986 |
| 0.3057 | 2.96 | 87000 | 0.5743 | 0.7980 |
| 0.3081 | 2.99 | 88000 | 0.5771 | 0.7978 |
### Framework versions
- Transformers 4.12.3
- Pytorch 1.10.0+cu102
- Datasets 1.15.1
- Tokenizers 0.10.3
| 7,064 |
moussaKam/frugalscore_medium_bert-base_mover-score | [
"LABEL_0"
] | # FrugalScore
FrugalScore is an approach to learn a fixed, low cost version of any expensive NLG metric, while retaining most of its original performance
Paper: https://arxiv.org/abs/2110.08559?context=cs
Project github: https://github.com/moussaKam/FrugalScore
The pretrained checkpoints presented in the paper :
| FrugalScore | Student | Teacher | Method |
|----------------------------------------------------|-------------|----------------|------------|
| [moussaKam/frugalscore_tiny_bert-base_bert-score](https://huggingface.co/moussaKam/frugalscore_tiny_bert-base_bert-score) | BERT-tiny | BERT-Base | BERTScore |
| [moussaKam/frugalscore_small_bert-base_bert-score](https://huggingface.co/moussaKam/frugalscore_small_bert-base_bert-score) | BERT-small | BERT-Base | BERTScore |
| [moussaKam/frugalscore_medium_bert-base_bert-score](https://huggingface.co/moussaKam/frugalscore_medium_bert-base_bert-score) | BERT-medium | BERT-Base | BERTScore |
| [moussaKam/frugalscore_tiny_roberta_bert-score](https://huggingface.co/moussaKam/frugalscore_tiny_roberta_bert-score) | BERT-tiny | RoBERTa-Large | BERTScore |
| [moussaKam/frugalscore_small_roberta_bert-score](https://huggingface.co/moussaKam/frugalscore_small_roberta_bert-score) | BERT-small | RoBERTa-Large | BERTScore |
| [moussaKam/frugalscore_medium_roberta_bert-score](https://huggingface.co/moussaKam/frugalscore_medium_roberta_bert-score) | BERT-medium | RoBERTa-Large | BERTScore |
| [moussaKam/frugalscore_tiny_deberta_bert-score](https://huggingface.co/moussaKam/frugalscore_tiny_deberta_bert-score) | BERT-tiny | DeBERTa-XLarge | BERTScore |
| [moussaKam/frugalscore_small_deberta_bert-score](https://huggingface.co/moussaKam/frugalscore_small_deberta_bert-score) | BERT-small | DeBERTa-XLarge | BERTScore |
| [moussaKam/frugalscore_medium_deberta_bert-score](https://huggingface.co/moussaKam/frugalscore_medium_deberta_bert-score) | BERT-medium | DeBERTa-XLarge | BERTScore |
| [moussaKam/frugalscore_tiny_bert-base_mover-score](https://huggingface.co/moussaKam/frugalscore_tiny_bert-base_mover-score) | BERT-tiny | BERT-Base | MoverScore |
| [moussaKam/frugalscore_small_bert-base_mover-score](https://huggingface.co/moussaKam/frugalscore_small_bert-base_mover-score) | BERT-small | BERT-Base | MoverScore |
| [moussaKam/frugalscore_medium_bert-base_mover-score](https://huggingface.co/moussaKam/frugalscore_medium_bert-base_mover-score) | BERT-medium | BERT-Base | MoverScore | | 2,592 |
clapika2010/adult_finetuned | null | Entry not found | 15 |
textattack/roberta-base-imdb | null | ## TextAttack Model Card
This `roberta-base` model was fine-tuned for sequence classification using TextAttack
and the imdb dataset loaded using the `nlp` library. The model was fine-tuned
for 5 epochs with a batch size of 64, a learning
rate of 3e-05, and a maximum sequence length of 128.
Since this was a classification task, the model was trained with a cross-entropy loss function.
The best score the model achieved on this task was 0.91436, as measured by the
eval set accuracy, found after 2 epochs.
For more information, check out [TextAttack on Github](https://github.com/QData/TextAttack).
| 607 |
sismetanin/sbert-ru-sentiment-rusentiment | [
"LABEL_0",
"LABEL_1",
"LABEL_2",
"LABEL_3",
"LABEL_4"
] | ---
language:
- ru
tags:
- sentiment analysis
- Russian
---
## SBERT-Large-Base-ru-sentiment-RuSentiment
SBERT-Large-ru-sentiment-RuSentiment is a [SBERT-Large](https://huggingface.co/sberbank-ai/sbert_large_nlu_ru) model fine-tuned on [RuSentiment dataset](https://github.com/text-machine-lab/rusentiment) of general-domain Russian-language posts from the largest Russian social network, VKontakte.
<table>
<thead>
<tr>
<th rowspan="4">Model</th>
<th rowspan="4">Score<br></th>
<th rowspan="4">Rank</th>
<th colspan="12">Dataset</th>
</tr>
<tr>
<td colspan="6">SentiRuEval-2016<br></td>
<td colspan="2" rowspan="2">RuSentiment</td>
<td rowspan="2">KRND</td>
<td rowspan="2">LINIS Crowd</td>
<td rowspan="2">RuTweetCorp</td>
<td rowspan="2">RuReviews</td>
</tr>
<tr>
<td colspan="3">TC</td>
<td colspan="3">Banks</td>
</tr>
<tr>
<td>micro F1</td>
<td>macro F1</td>
<td>F1</td>
<td>micro F1</td>
<td>macro F1</td>
<td>F1</td>
<td>wighted</td>
<td>F1</td>
<td>F1</td>
<td>F1</td>
<td>F1</td>
<td>F1</td>
</tr>
</thead>
<tbody>
<tr>
<td>SOTA</td>
<td>n/s</td>
<td></td>
<td>76.71</td>
<td>66.40</td>
<td>70.68</td>
<td>67.51</td>
<td>69.53</td>
<td>74.06</td>
<td>78.50</td>
<td>n/s</td>
<td>73.63</td>
<td>60.51</td>
<td>83.68</td>
<td>77.44</td>
</tr>
<tr>
<td>XLM-RoBERTa-Large</td>
<td>76.37</td>
<td>1</td>
<td>82.26</td>
<td>76.36</td>
<td>79.42</td>
<td>76.35</td>
<td>76.08</td>
<td>80.89</td>
<td>78.31</td>
<td>75.27</td>
<td>75.17</td>
<td>60.03</td>
<td>88.91</td>
<td>78.81</td>
</tr>
<tr>
<td>SBERT-Large</td>
<td>75.43</td>
<td>2</td>
<td>78.40</td>
<td>71.36</td>
<td>75.14</td>
<td>72.39</td>
<td>71.87</td>
<td>77.72</td>
<td>78.58</td>
<td>75.85</td>
<td>74.20</td>
<td>60.64</td>
<td>88.66</td>
<td>77.41</td>
</tr>
<tr>
<td>MBARTRuSumGazeta</td>
<td>74.70</td>
<td>3</td>
<td>76.06</td>
<td>68.95</td>
<td>73.04</td>
<td>72.34</td>
<td>71.93</td>
<td>77.83</td>
<td>76.71</td>
<td>73.56</td>
<td>74.18</td>
<td>60.54</td>
<td>87.22</td>
<td>77.51</td>
</tr>
<tr>
<td>Conversational RuBERT</td>
<td>74.44</td>
<td>4</td>
<td>76.69</td>
<td>69.09</td>
<td>73.11</td>
<td>69.44</td>
<td>68.68</td>
<td>75.56</td>
<td>77.31</td>
<td>74.40</td>
<td>73.10</td>
<td>59.95</td>
<td>87.86</td>
<td>77.78</td>
</tr>
<tr>
<td>LaBSE</td>
<td>74.11</td>
<td>5</td>
<td>77.00</td>
<td>69.19</td>
<td>73.55</td>
<td>70.34</td>
<td>69.83</td>
<td>76.38</td>
<td>74.94</td>
<td>70.84</td>
<td>73.20</td>
<td>59.52</td>
<td>87.89</td>
<td>78.47</td>
</tr>
<tr>
<td>XLM-RoBERTa-Base</td>
<td>73.60</td>
<td>6</td>
<td>76.35</td>
<td>69.37</td>
<td>73.42</td>
<td>68.45</td>
<td>67.45</td>
<td>74.05</td>
<td>74.26</td>
<td>70.44</td>
<td>71.40</td>
<td>60.19</td>
<td>87.90</td>
<td>78.28</td>
</tr>
<tr>
<td>RuBERT</td>
<td>73.45</td>
<td>7</td>
<td>74.03</td>
<td>66.14</td>
<td>70.75</td>
<td>66.46</td>
<td>66.40</td>
<td>73.37</td>
<td>75.49</td>
<td>71.86</td>
<td>72.15</td>
<td>60.55</td>
<td>86.99</td>
<td>77.41</td>
</tr>
<tr>
<td>MBART-50-Large-Many-to-Many</td>
<td>73.15</td>
<td>8</td>
<td>75.38</td>
<td>67.81</td>
<td>72.26</td>
<td>67.13</td>
<td>66.97</td>
<td>73.85</td>
<td>74.78</td>
<td>70.98</td>
<td>71.98</td>
<td>59.20</td>
<td>87.05</td>
<td>77.24</td>
</tr>
<tr>
<td>SlavicBERT</td>
<td>71.96</td>
<td>9</td>
<td>71.45</td>
<td>63.03</td>
<td>68.44</td>
<td>64.32</td>
<td>63.99</td>
<td>71.31</td>
<td>72.13</td>
<td>67.57</td>
<td>72.54</td>
<td>58.70</td>
<td>86.43</td>
<td>77.16</td>
</tr>
<tr>
<td>EnRuDR-BERT</td>
<td>71.51</td>
<td>10</td>
<td>72.56</td>
<td>64.74</td>
<td>69.07</td>
<td>61.44</td>
<td>60.21</td>
<td>68.34</td>
<td>74.19</td>
<td>69.94</td>
<td>69.33</td>
<td>56.55</td>
<td>87.12</td>
<td>77.95</td>
</tr>
<tr>
<td>RuDR-BERT</td>
<td>71.14</td>
<td>11</td>
<td>72.79</td>
<td>64.23</td>
<td>68.36</td>
<td>61.86</td>
<td>60.92</td>
<td>68.48</td>
<td>74.65</td>
<td>70.63</td>
<td>68.74</td>
<td>54.45</td>
<td>87.04</td>
<td>77.91</td>
</tr>
<tr>
<td>MBART-50-Large</td>
<td>69.46</td>
<td>12</td>
<td>70.91</td>
<td>62.67</td>
<td>67.24</td>
<td>61.12</td>
<td>60.25</td>
<td>68.41</td>
<td>72.88</td>
<td>68.63</td>
<td>70.52</td>
<td>46.39</td>
<td>86.48</td>
<td>77.52</td>
</tr>
</tbody>
</table>
The table shows per-task scores and a macro-average of those scores to determine a models’s position on the leaderboard. For datasets with multiple evaluation metrics (e.g., macro F1 and weighted F1 for RuSentiment), we use an unweighted average of the metrics as the score for the task when computing the overall macro-average. The same strategy for comparing models’ results was applied in the GLUE benchmark.
## Citation
If you find this repository helpful, feel free to cite our publication:
```
@article{Smetanin2021Deep,
author = {Sergey Smetanin and Mikhail Komarov},
title = {Deep transfer learning baselines for sentiment analysis in Russian},
journal = {Information Processing & Management},
volume = {58},
number = {3},
pages = {102484},
year = {2021},
issn = {0306-4573},
doi = {0.1016/j.ipm.2020.102484}
}
```
Dataset:
```
@inproceedings{rogers2018rusentiment,
title={RuSentiment: An enriched sentiment analysis dataset for social media in Russian},
author={Rogers, Anna and Romanov, Alexey and Rumshisky, Anna and Volkova, Svitlana and Gronas, Mikhail and Gribov, Alex},
booktitle={Proceedings of the 27th international conference on computational linguistics},
pages={755--763},
year={2018}
}
``` | 6,350 |
Jeevesh8/goog_bert_ft_cola-75 | null | Entry not found | 15 |
HooshvareLab/bert-fa-base-uncased-sentiment-digikala | [
"no_idea",
"not_recommended",
"recommended"
] | ---
language: fa
license: apache-2.0
---
# ParsBERT (v2.0)
A Transformer-based Model for Persian Language Understanding
We reconstructed the vocabulary and fine-tuned the ParsBERT v1.1 on the new Persian corpora in order to provide some functionalities for using ParsBERT in other scopes!
Please follow the [ParsBERT](https://github.com/hooshvare/parsbert) repo for the latest information about previous and current models.
## Persian Sentiment [Digikala, SnappFood, DeepSentiPers]
It aims to classify text, such as comments, based on their emotional bias. We tested three well-known datasets for this task: `Digikala` user comments, `SnappFood` user comments, and `DeepSentiPers` in two binary-form and multi-form types.
### Digikala
Digikala user comments provided by [Open Data Mining Program (ODMP)](https://www.digikala.com/opendata/). This dataset contains 62,321 user comments with three labels:
| Label | # |
|:---------------:|:------:|
| no_idea | 10394 |
| not_recommended | 15885 |
| recommended | 36042 |
**Download**
You can download the dataset from [here](https://www.digikala.com/opendata/)
## Results
The following table summarizes the F1 score obtained by ParsBERT as compared to other models and architectures.
| Dataset | ParsBERT v2 | ParsBERT v1 | mBERT | DeepSentiPers |
|:------------------------:|:-----------:|:-----------:|:-----:|:-------------:|
| Digikala User Comments | 81.72 | 81.74* | 80.74 | - |
## How to use :hugs:
| Task | Notebook |
|---------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| Sentiment Analysis | [](https://colab.research.google.com/github/hooshvare/parsbert/blob/master/notebooks/Taaghche_Sentiment_Analysis.ipynb) |
### BibTeX entry and citation info
Please cite in publications as the following:
```bibtex
@article{ParsBERT,
title={ParsBERT: Transformer-based Model for Persian Language Understanding},
author={Mehrdad Farahani, Mohammad Gharachorloo, Marzieh Farahani, Mohammad Manthouri},
journal={ArXiv},
year={2020},
volume={abs/2005.12515}
}
```
## Questions?
Post a Github issue on the [ParsBERT Issues](https://github.com/hooshvare/parsbert/issues) repo. | 2,674 |
howey/electra-small-mnli | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | Entry not found | 15 |
Rostlab/prot_bert_bfd_localization | [
"Cell.membrane",
"Cytoplasm",
"Endoplasmic.reticulum",
"Extracellular",
"Golgi.apparatus",
"Lysosome/Vacuole",
"Mitochondrion",
"Nucleus",
"Peroxisome",
"Plastid"
] | Entry not found | 15 |
Jeevesh8/goog_bert_ft_cola-76 | null | Entry not found | 15 |
edumunozsala/beto_sentiment_analysis_es | [
"Negativo",
"Positivo"
] | ---
language: es
tags:
- sagemaker
- beto
- TextClassification
- SentimentAnalysis
license: apache-2.0
datasets:
- IMDbreviews_es
metrics:
- accuracy
model-index:
- name: beto_sentiment_analysis_es
results:
- task:
name: Sentiment Analysis
type: sentiment-analysis
dataset:
name: "IMDb Reviews in Spanish"
type: IMDbreviews_es
metrics:
- name: Accuracy,
type: accuracy,
value: 0.9101333333333333
- name: F1 Score,
type: f1,
value: 0.9088450094671354
- name: Precision,
type: precision,
value: 0.9105691056910569
- name: Recall,
type: recall,
value: 0.9071274298056156
widget:
- text: "Se trata de una película interesante, con un solido argumento y un gran interpretación de su actor principal"
---
# Model beto_sentiment_analysis_es
## **A finetuned model for Sentiment analysis in Spanish**
This model was trained using Amazon SageMaker and the new Hugging Face Deep Learning container,
The base model is **BETO** which is a BERT-base model pre-trained on a spanish corpus. BETO is of size similar to a BERT-Base and was trained with the Whole Word Masking technique.
**BETO Citation**
[Spanish Pre-Trained BERT Model and Evaluation Data](https://users.dcc.uchile.cl/~jperez/papers/pml4dc2020.pdf)
```
@inproceedings{CaneteCFP2020,
title={Spanish Pre-Trained BERT Model and Evaluation Data},
author={Cañete, José and Chaperon, Gabriel and Fuentes, Rodrigo and Ho, Jou-Hui and Kang, Hojin and Pérez, Jorge},
booktitle={PML4DC at ICLR 2020},
year={2020}
}
```
## Dataset
The dataset is a collection of movie reviews in Spanish, about 50,000 reviews. The dataset is balanced and provides every review in english, in spanish and the label in both languages.
Sizes of datasets:
- Train dataset: 42,500
- Validation dataset: 3,750
- Test dataset: 3,750
## Intended uses & limitations
This model is intented for Sentiment Analysis for spanish corpus and finetuned specially for movie reviews but it can be applied to other kind of reviews.
## Hyperparameters
{
"epochs": "4",
"train_batch_size": "32",
"eval_batch_size": "8",
"fp16": "true",
"learning_rate": "3e-05",
"model_name": "\"dccuchile/bert-base-spanish-wwm-uncased\"",
"sagemaker_container_log_level": "20",
"sagemaker_program": "\"train.py\"",
}
## Evaluation results
- Accuracy = 0.9101333333333333
- F1 Score = 0.9088450094671354
- Precision = 0.9105691056910569
- Recall = 0.9071274298056156
## Test results
## Model in action
### Usage for Sentiment Analysis
```python
import torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("edumunozsala/beto_sentiment_analysis_es")
model = AutoModelForSequenceClassification.from_pretrained("edumunozsala/beto_sentiment_analysis_es")
text ="Se trata de una película interesante, con un solido argumento y un gran interpretación de su actor principal"
input_ids = torch.tensor(tokenizer.encode(text)).unsqueeze(0)
outputs = model(input_ids)
output = outputs.logits.argmax(1)
```
Created by [Eduardo Muñoz/@edumunozsala](https://github.com/edumunozsala)
| 3,246 |
Jeevesh8/goog_bert_ft_cola-77 | null | Entry not found | 15 |
VictorSanh/roberta-base-finetuned-yelp-polarity | null | ---
language: en
datasets:
- yelp_polarity
---
# RoBERTa-base-finetuned-yelp-polarity
This is a [RoBERTa-base](https://huggingface.co/roberta-base) checkpoint fine-tuned on binary sentiment classifcation from [Yelp polarity](https://huggingface.co/nlp/viewer/?dataset=yelp_polarity).
It gets **98.08%** accuracy on the test set.
## Hyper-parameters
We used the following hyper-parameters to train the model on one GPU:
```python
num_train_epochs = 2.0
learning_rate = 1e-05
weight_decay = 0.0
adam_epsilon = 1e-08
max_grad_norm = 1.0
per_device_train_batch_size = 32
gradient_accumulation_steps = 1
warmup_steps = 3500
seed = 42
```
| 744 |
Jeevesh8/goog_bert_ft_cola-78 | null | Entry not found | 15 |
ivanlau/language-detection-fine-tuned-on-xlm-roberta-base | [
"Arabic",
"Basque",
"Breton",
"Catalan",
"Chinese_China",
"Chinese_Hongkong",
"Chinese_Taiwan",
"Chuvash",
"Czech",
"Dhivehi",
"Dutch",
"English",
"Esperanto",
"Estonian",
"French",
"Frisian",
"Georgian",
"German",
"Greek",
"Hakha_Chin",
"Indonesian",
"Interlingua",
"Italian",
"Japanese",
"Kabyle",
"Kinyarwanda",
"Kyrgyz",
"Latvian",
"Maltese",
"Mangolian",
"Persian",
"Polish",
"Portuguese",
"Romanian",
"Romansh_Sursilvan",
"Russian",
"Sakha",
"Slovenian",
"Spanish",
"Swedish",
"Tamil",
"Tatar",
"Turkish",
"Ukranian",
"Welsh"
] | ---
license: mit
tags:
- generated_from_trainer
datasets:
- common_language
metrics:
- accuracy
model-index:
- name: language-detection-fine-tuned-on-xlm-roberta-base
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: common_language
type: common_language
args: full
metrics:
- name: Accuracy
type: accuracy
value: 0.9738386718094919
---
<!-- 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. -->
# language-detection-fine-tuned-on-xlm-roberta-base
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the [common_language](https://huggingface.co/datasets/common_language) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1886
- Accuracy: 0.9738
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.1 | 1.0 | 22194 | 0.1886 | 0.9738 |
### Framework versions
- Transformers 4.12.5
- Pytorch 1.10.0+cu111
- Datasets 1.15.1
- Tokenizers 0.10.3
### Notebook
[notebook](https://github.com/IvanLauLinTiong/language-detector/blob/main/xlm_roberta_base_commonlanguage_language_detector.ipynb) | 1,748 |
Jeevesh8/goog_bert_ft_cola-79 | null | Entry not found | 15 |
MoritzLaurer/DeBERTa-v3-base-mnli-fever-docnli-ling-2c | [
"entailment",
"not_entailment"
] | ---
language:
- en
tags:
- text-classification
- zero-shot-classification
metrics:
- accuracy
widget:
- text: "I first thought that I liked the movie, but upon second thought it was actually disappointing. [SEP] The movie was good."
---
# DeBERTa-v3-base-mnli-fever-docnli-ling-2c
## Model description
This model was trained on 1.279.665 hypothesis-premise pairs from 8 NLI datasets: [MultiNLI](https://huggingface.co/datasets/multi_nli), [Fever-NLI](https://github.com/easonnie/combine-FEVER-NSMN/blob/master/other_resources/nli_fever.md), [LingNLI](https://arxiv.org/abs/2104.07179) and [DocNLI](https://arxiv.org/pdf/2106.09449.pdf) (which includes [ANLI](https://github.com/facebookresearch/anli), QNLI, DUC, CNN/DailyMail, Curation).
It is the only model in the model hub trained on 8 NLI datasets, including DocNLI with very long texts to learn long range reasoning. Note that the model was trained on binary NLI to predict either "entailment" or "not-entailment". The DocNLI merges the classes "neural" and "contradiction" into "not-entailment" to enable the inclusion of the DocNLI dataset.
The base model is [DeBERTa-v3-base from Microsoft](https://huggingface.co/microsoft/deberta-v3-base). The v3 variant of DeBERTa substantially outperforms previous versions of the model by including a different pre-training objective, see annex 11 of the original [DeBERTa paper](https://arxiv.org/pdf/2006.03654.pdf) as well as the [DeBERTa-V3 paper](https://arxiv.org/abs/2111.09543).
## Intended uses & limitations
#### How to use the model
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
model_name = "MoritzLaurer/DeBERTa-v3-base-mnli-fever-docnli-ling-2c"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
premise = "I first thought that I liked the movie, but upon second thought it was actually disappointing."
hypothesis = "The movie was good."
input = tokenizer(premise, hypothesis, truncation=True, return_tensors="pt")
output = model(input["input_ids"].to(device)) # device = "cuda:0" or "cpu"
prediction = torch.softmax(output["logits"][0], -1).tolist()
label_names = ["entailment", "not_entailment"]
prediction = {name: round(float(pred) * 100, 1) for pred, name in zip(prediction, label_names)}
print(prediction)
```
### Training data
This model was trained on 1.279.665 hypothesis-premise pairs from 8 NLI datasets: [MultiNLI](https://huggingface.co/datasets/multi_nli), [Fever-NLI](https://github.com/easonnie/combine-FEVER-NSMN/blob/master/other_resources/nli_fever.md), [LingNLI](https://arxiv.org/abs/2104.07179) and [DocNLI](https://arxiv.org/pdf/2106.09449.pdf) (which includes [ANLI](https://github.com/facebookresearch/anli), QNLI, DUC, CNN/DailyMail, Curation).
### Training procedure
DeBERTa-v3-small-mnli-fever-docnli-ling-2c was trained using the Hugging Face trainer with the following hyperparameters.
```
training_args = TrainingArguments(
num_train_epochs=3, # total number of training epochs
learning_rate=2e-05,
per_device_train_batch_size=32, # batch size per device during training
per_device_eval_batch_size=32, # batch size for evaluation
warmup_ratio=0.1, # number of warmup steps for learning rate scheduler
weight_decay=0.06, # strength of weight decay
fp16=True # mixed precision training
)
```
### Eval results
The model was evaluated using the binary test sets for MultiNLI and ANLI and the binary dev set for Fever-NLI (two classes instead of three). The metric used is accuracy.
mnli-m-2c | mnli-mm-2c | fever-nli-2c | anli-all-2c | anli-r3-2c | lingnli-2c
---------|----------|---------|----------|----------|------
0.935 | 0.933 | 0.897 | 0.710 | 0.678 | 0.895
## Limitations and bias
Please consult the original DeBERTa paper and literature on different NLI datasets for potential biases.
### BibTeX entry and citation info
If you want to cite this model, please cite the original DeBERTa paper, the respective NLI datasets and include a link to this model on the Hugging Face hub.
### Ideas for cooperation or questions?
If you have questions or ideas for cooperation, contact me at m{dot}laurer{at}vu{dot}nl or [LinkedIn](https://www.linkedin.com/in/moritz-laurer/)
### Debugging and issues
Note that DeBERTa-v3 was released recently and older versions of HF Transformers seem to have issues running the model (e.g. resulting in an issue with the tokenizer). Using Transformers==4.13 might solve some issues. | 4,603 |
Jeevesh8/goog_bert_ft_cola-80 | null | Entry not found | 15 |
sampathkethineedi/industry-classification-api | [
"Advertising",
"Aerospace & Defense",
"Apparel Retail",
"Apparel, Accessories & Luxury Goods",
"Application Software",
"Asset Management & Custody Banks",
"Auto Parts & Equipment",
"Biotechnology",
"Building Products",
"Casinos & Gaming",
"Commodity Chemicals",
"Communications Equipment",
"Construction & Engineering",
"Construction Machinery & Heavy Trucks",
"Consumer Finance",
"Data Processing & Outsourced Services",
"Diversified Metals & Mining",
"Diversified Support Services",
"Electric Utilities",
"Electrical Components & Equipment",
"Electronic Equipment & Instruments",
"Environmental & Facilities Services",
"Gold",
"Health Care Equipment",
"Health Care Facilities",
"Health Care Services",
"Health Care Supplies",
"Health Care Technology",
"Homebuilding",
"Hotels, Resorts & Cruise Lines",
"Human Resource & Employment Services",
"IT Consulting & Other Services",
"Industrial Machinery",
"Integrated Telecommunication Services",
"Interactive Media & Services",
"Internet & Direct Marketing Retail",
"Internet Services & Infrastructure",
"Investment Banking & Brokerage",
"Leisure Products",
"Life Sciences Tools & Services",
"Movies & Entertainment",
"Oil & Gas Equipment & Services",
"Oil & Gas Exploration & Production",
"Oil & Gas Refining & Marketing",
"Oil & Gas Storage & Transportation",
"Packaged Foods & Meats",
"Personal Products",
"Pharmaceuticals",
"Property & Casualty Insurance",
"Real Estate Operating Companies",
"Regional Banks",
"Research & Consulting Services",
"Restaurants",
"Semiconductors",
"Specialty Chemicals",
"Specialty Stores",
"Steel",
"Systems Software",
"Technology Distributors",
"Technology Hardware, Storage & Peripherals",
"Thrifts & Mortgage Finance",
"Trading Companies & Distributors"
] | ---
language: "en"
thumbnail: "https://huggingface.co/sampathkethineedi"
widget:
- text: "3rd Rock Multimedia Limited is an India-based event management company. The Company conducts film promotions, international events, corporate events and cultural events. The Company's entertainment properties include 3rd Rock Fashion Fiesta and 3rd Rock Calendar. The Company's association with various events in Mumbai includes Bryan Adam's Live in Concert, Michael Learns to Rock (MLTR) Eternity Concert, 3rd Rock's Calendar Launch 2011-2012, Airtel I Phone 4 Launch and ISPL Cricket Tournament 2012."
- text: "Stellar Capital Services Limited is an India-based non-banking financial company. The Company is mainly engaged in the business of providing loans and advances and investing in shares, both quoted and unquoted. The Company's segments are trading in share and securities, and advancing of loans. The trading in share and securities segment includes trading in quoted equity shares, mutual funds, bonds, futures and options, and currency. The Company's financial services include inter corporate deposits, financial consultancy, retail initial public offering (IPO) funding, loan against property, management consultancy, personal loans and unsecured loans."
- text: "Chemcrux Enterprises Ltd is a manufacturer of intermediates for bulk drugs, and dyes and pigments. The Company's products include 2 Chloro Benzoic Acid; 3 Chloro Benzoic Acid; 4 Chloro Benzoic Acid; 4 Nitro Benzoic Acid; 2,4 Dichloro Benzoic Acid; 4 Chloro 3 Nitro Benzoic Acid; 2 Chloro 5 Nitro Benzoic Acid; Meta Nitro Benzoic Acid; Lassamide, and Meta Chloro Per Benzoic Acid. The Company also offers various products on custom requirements, including Aceturic Acid; Meta Chloro Benzoyl Chloride; 3-Nitro-4-Methoxy Benzoic Acid; 2 Amino 5 Sulfonamide Benzoic Acid; 3,4 Dichloro Benzoic Acid; 5-Nitro Salycylic Acid, and 4-Chloro Benzoic Acid -3-Sulfonamide. The Company's plant has a capacity of 120 metric tons per month. The Company exports to Europe, Japan, the Middle East and East Africa. It is engaged in development and execution of various processes, such as High Pressure Oxidation, Nitration and Chloro Sulfonation."
tags:
- bert
- pytorch
- text-classification
- industry tags
- buisiness description
- multi-label
- classification
- inference
liscence: "mit"
---
# industry-classification-api
## Model description
BERT Model to classify a business description into one of **62 industry tags**.
Trained on 7000 samples of Business Descriptions and associated labels of companies in India.
## How to use
PyTorch only
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
tokenizer = AutoTokenizer.from_pretrained("sampathkethineedi/industry-classification")
model = AutoModelForSequenceClassification.from_pretrained("industry-classification")
industry_tags = pipeline('sentiment-analysis', model=model, tokenizer=tokenizer)
industry_tags("Stellar Capital Services Limited is an India-based non-banking financial company ... loan against property, management consultancy, personal loans and unsecured loans.")
'''Ouput'''
[{'label': 'Consumer Finance', 'score': 0.9841355681419373}]
```
## Limitations and bias
Training data is only for Indian companies
| 3,294 |
Jeevesh8/goog_bert_ft_cola-81 | null | Entry not found | 15 |
dhpollack/distilbert-dummy-sentiment | [
"negative",
"positive"
] | ---
language:
- "multilingual"
- "en"
tags:
- "sentiment-analysis"
- "testing"
- "unit tests"
---
# DistilBert Dummy Sentiment Model
## Purpose
This is a dummy model that can be used for testing the transformers `pipeline` with the task `sentiment-analysis`. It should always give random results (i.e. `{"label": "negative", "score": 0.5}`).
## How to use
```python
classifier = pipeline("sentiment-analysis", "dhpollack/distilbert-dummy-sentiment")
results = classifier(["this is a test", "another test"])
```
## Notes
This was created as follows:
1. Create a vocab.txt file (in /tmp/vocab.txt in this example).
```
[UNK]
[SEP]
[PAD]
[CLS]
[MASK]
```
2. Open a python shell:
```python
import transformers
config = transformers.DistilBertConfig(vocab_size=5, n_layers=1, n_heads=1, dim=1, hidden_dim=4 * 1, num_labels=2, id2label={0: "negative", 1: "positive"}, label2id={"negative": 0, "positive": 1})
model = transformers.DistilBertForSequenceClassification(config)
tokenizer = transformers.DistilBertTokenizer("/tmp/vocab.txt", model_max_length=512)
config.save_pretrained(".")
model.save_pretrained(".")
tokenizer.save_pretrained(".")
```
| 1,155 |
saattrupdan/verdict-classifier | [
"factual",
"misinformation",
"other"
] | ---
license: mit
language:
- am
- ar
- hy
- eu
- bn
- bs
- bg
- my
- hr
- ca
- cs
- da
- nl
- en
- et
- fi
- fr
- ka
- de
- el
- gu
- ht
- iw
- hi
- hu
- is
- in
- it
- ja
- kn
- km
- ko
- lo
- lv
- lt
- ml
- mr
- ne
- no
- or
- pa
- ps
- fa
- pl
- pt
- ro
- ru
- sr
- zh
- sd
- si
- sk
- sl
- es
- sv
- tl
- ta
- te
- th
- tr
- uk
- ur
- ug
- vi
- cy
tags:
- generated_from_trainer
model-index:
- name: verdict-classifier-en
results:
- task:
type: text-classification
name: Verdict Classification
widget:
- "本文已断章取义。"
---
# Multilingual Verdict Classifier
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on 2,500 deduplicated multilingual verdicts from [Google Fact Check Tools API](https://developers.google.com/fact-check/tools/api/reference/rest/v1alpha1/claims/search), translated into 65 languages with the [Google Cloud Translation API](https://cloud.google.com/translate/docs/reference/rest/).
It achieves the following results on the evaluation set, being 1,000 such verdicts, but here including duplicates to represent the true distribution:
- Loss: 0.2238
- F1 Macro: 0.8540
- F1 Misinformation: 0.9798
- F1 Factual: 0.9889
- F1 Other: 0.5934
- Prec Macro: 0.8348
- Prec Misinformation: 0.9860
- Prec Factual: 0.9889
- Prec Other: 0.5294
## 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
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 162525
- num_epochs: 1000
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 Macro | F1 Misinformation | F1 Factual | F1 Other | Prec Macro | Prec Misinformation | Prec Factual | Prec Other |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:-----------------:|:----------:|:--------:|:----------:|:-------------------:|:------------:|:----------:|
| 1.1109 | 0.1 | 2000 | 1.2166 | 0.0713 | 0.1497 | 0.0 | 0.0640 | 0.2451 | 0.7019 | 0.0 | 0.0334 |
| 0.9551 | 0.2 | 4000 | 0.7801 | 0.3611 | 0.8889 | 0.0 | 0.1943 | 0.3391 | 0.8915 | 0.0 | 0.1259 |
| 0.9275 | 0.3 | 6000 | 0.7712 | 0.3468 | 0.9123 | 0.0 | 0.1282 | 0.3304 | 0.9051 | 0.0 | 0.0862 |
| 0.8881 | 0.39 | 8000 | 0.5386 | 0.3940 | 0.9524 | 0.0 | 0.2297 | 0.3723 | 0.9748 | 0.0 | 0.1420 |
| 0.7851 | 0.49 | 10000 | 0.3298 | 0.6886 | 0.9626 | 0.7640 | 0.3393 | 0.6721 | 0.9798 | 0.7727 | 0.2639 |
| 0.639 | 0.59 | 12000 | 0.2156 | 0.7847 | 0.9633 | 0.9355 | 0.4554 | 0.7540 | 0.9787 | 0.9062 | 0.3770 |
| 0.5677 | 0.69 | 14000 | 0.1682 | 0.7877 | 0.9694 | 0.9667 | 0.4270 | 0.7763 | 0.9745 | 0.9667 | 0.3878 |
| 0.5218 | 0.79 | 16000 | 0.1475 | 0.8037 | 0.9692 | 0.9667 | 0.4752 | 0.7804 | 0.9812 | 0.9667 | 0.3934 |
| 0.4682 | 0.89 | 18000 | 0.1458 | 0.8097 | 0.9734 | 0.9667 | 0.4889 | 0.7953 | 0.9791 | 0.9667 | 0.44 |
| 0.4188 | 0.98 | 20000 | 0.1416 | 0.8370 | 0.9769 | 0.9724 | 0.5618 | 0.8199 | 0.9826 | 0.9670 | 0.5102 |
| 0.3735 | 1.08 | 22000 | 0.1624 | 0.8094 | 0.9698 | 0.9368 | 0.5217 | 0.7780 | 0.9823 | 0.89 | 0.4615 |
| 0.3242 | 1.18 | 24000 | 0.1648 | 0.8338 | 0.9769 | 0.9727 | 0.5517 | 0.8167 | 0.9826 | 0.9570 | 0.5106 |
| 0.2785 | 1.28 | 26000 | 0.1843 | 0.8261 | 0.9739 | 0.9780 | 0.5263 | 0.8018 | 0.9836 | 0.9674 | 0.4545 |
| 0.25 | 1.38 | 28000 | 0.1975 | 0.8344 | 0.9744 | 0.9834 | 0.5455 | 0.8072 | 0.9859 | 0.9780 | 0.4576 |
| 0.2176 | 1.48 | 30000 | 0.1849 | 0.8209 | 0.9691 | 0.9889 | 0.5047 | 0.7922 | 0.9846 | 0.9889 | 0.4030 |
| 0.1966 | 1.58 | 32000 | 0.2119 | 0.8194 | 0.9685 | 0.9944 | 0.4954 | 0.7920 | 0.9846 | 1.0 | 0.3913 |
| 0.1738 | 1.67 | 34000 | 0.2110 | 0.8352 | 0.9708 | 0.9944 | 0.5405 | 0.8035 | 0.9881 | 1.0 | 0.4225 |
| 0.1625 | 1.77 | 36000 | 0.2152 | 0.8165 | 0.9709 | 0.9834 | 0.4950 | 0.7905 | 0.9835 | 0.9780 | 0.4098 |
| 0.1522 | 1.87 | 38000 | 0.2300 | 0.8097 | 0.9697 | 0.9832 | 0.4762 | 0.7856 | 0.9835 | 0.9888 | 0.3846 |
| 0.145 | 1.97 | 40000 | 0.1955 | 0.8519 | 0.9774 | 0.9889 | 0.5895 | 0.8280 | 0.9860 | 0.9889 | 0.5091 |
| 0.1248 | 2.07 | 42000 | 0.2308 | 0.8149 | 0.9703 | 0.9889 | 0.4854 | 0.7897 | 0.9835 | 0.9889 | 0.3968 |
| 0.1186 | 2.17 | 44000 | 0.2368 | 0.8172 | 0.9733 | 0.9834 | 0.4948 | 0.7942 | 0.9836 | 0.9780 | 0.4211 |
| 0.1122 | 2.26 | 46000 | 0.2401 | 0.7968 | 0.9804 | 0.8957 | 0.5143 | 0.8001 | 0.9849 | 1.0 | 0.4154 |
| 0.1099 | 2.36 | 48000 | 0.2290 | 0.8119 | 0.9647 | 0.9834 | 0.4874 | 0.7777 | 0.9880 | 0.9780 | 0.3671 |
| 0.1093 | 2.46 | 50000 | 0.2256 | 0.8247 | 0.9745 | 0.9889 | 0.5106 | 0.8053 | 0.9825 | 0.9889 | 0.4444 |
| 0.1053 | 2.56 | 52000 | 0.2416 | 0.8456 | 0.9799 | 0.9889 | 0.5679 | 0.8434 | 0.9805 | 0.9889 | 0.5610 |
| 0.1049 | 2.66 | 54000 | 0.2850 | 0.7585 | 0.9740 | 0.8902 | 0.4112 | 0.7650 | 0.9802 | 0.9865 | 0.3284 |
| 0.098 | 2.76 | 56000 | 0.2828 | 0.8049 | 0.9642 | 0.9889 | 0.4615 | 0.7750 | 0.9856 | 0.9889 | 0.3506 |
| 0.0962 | 2.86 | 58000 | 0.2238 | 0.8540 | 0.9798 | 0.9889 | 0.5934 | 0.8348 | 0.9860 | 0.9889 | 0.5294 |
| 0.0975 | 2.95 | 60000 | 0.2494 | 0.8249 | 0.9715 | 0.9889 | 0.5143 | 0.7967 | 0.9858 | 0.9889 | 0.4154 |
| 0.0877 | 3.05 | 62000 | 0.2464 | 0.8274 | 0.9733 | 0.9889 | 0.5200 | 0.8023 | 0.9847 | 0.9889 | 0.4333 |
| 0.0848 | 3.15 | 64000 | 0.2338 | 0.8263 | 0.9740 | 0.9889 | 0.5161 | 0.8077 | 0.9814 | 0.9889 | 0.4528 |
| 0.0859 | 3.25 | 66000 | 0.2335 | 0.8365 | 0.9750 | 0.9889 | 0.5455 | 0.8108 | 0.9859 | 0.9889 | 0.4576 |
| 0.084 | 3.35 | 68000 | 0.2067 | 0.8343 | 0.9763 | 0.9889 | 0.5376 | 0.8148 | 0.9837 | 0.9889 | 0.4717 |
| 0.0837 | 3.45 | 70000 | 0.2516 | 0.8249 | 0.9746 | 0.9889 | 0.5111 | 0.8097 | 0.9803 | 0.9889 | 0.46 |
| 0.0809 | 3.54 | 72000 | 0.2948 | 0.8258 | 0.9728 | 0.9944 | 0.5102 | 0.8045 | 0.9824 | 1.0 | 0.4310 |
| 0.0833 | 3.64 | 74000 | 0.2457 | 0.8494 | 0.9744 | 0.9944 | 0.5794 | 0.8173 | 0.9893 | 1.0 | 0.4627 |
| 0.0796 | 3.74 | 76000 | 0.3188 | 0.8277 | 0.9733 | 0.9889 | 0.5208 | 0.8059 | 0.9825 | 0.9889 | 0.4464 |
| 0.0821 | 3.84 | 78000 | 0.2642 | 0.8343 | 0.9714 | 0.9944 | 0.5370 | 0.8045 | 0.9870 | 1.0 | 0.4265 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.9.0+cu102
- Datasets 1.9.0
- Tokenizers 0.10.2 | 8,809 |
Jeevesh8/goog_bert_ft_cola-82 | null | Entry not found | 15 |
clampert/multilingual-sentiment-covid19 | null | ---
pipeline_tag: text-classification
language: multilingual
license: apache-2.0
tags:
- "sentiment-analysis"
- "multilingual"
widget:
- text: "I am very happy."
example_title: "English"
- text: "Heute bin ich schlecht drauf."
example_title: "Deutsch"
- text: "Quel cauchemard!"
example_title: "Francais"
- text: "ฉันรักฤดูใบไม้ผลิ"
example_title: "ภาษาไทย"
---
# Multi-lingual sentiment prediction trained from COVID19-related tweets
Repository: [https://github.com/clampert/multilingual-sentiment-analysis/](https://github.com/clampert/multilingual-sentiment-analysis/)
Model trained on a large-scale (18437530 examples) dataset of
multi-lingual tweets that was collected between March 2020
and November 2021 using Twitter’s Streaming API with varying
COVID19-related keywords. Labels were auto-general based on
the presence of positive and negative emoticons. For details
on the dataset, see our IEEE BigData 2021 publication.
Base model is [sentence-transformers/stsb-xlm-r-multilingual](https://huggingface.co/sentence-transformers/stsb-xlm-r-multilingual).
It was finetuned for sequence classification with `positive`
and `negative` labels for two epochs (48 hours on 8xP100 GPUs).
## Citation
If you use our model your work, please cite:
```
@inproceedings{lampert2021overcoming,
title={Overcoming Rare-Language Discrimination in Multi-Lingual Sentiment Analysis},
author={Jasmin Lampert and Christoph H. Lampert},
booktitle={IEEE International Conference on Big Data (BigData)},
year={2021},
note={Special Session: Machine Learning on Big Data},
}
```
Enjoy!
| 1,600 |
Jeevesh8/goog_bert_ft_cola-83 | null | Entry not found | 15 |
monologg/koelectra-small-finetuned-nsmc | [
"negative",
"positive"
] | Entry not found | 15 |
roberta-base-openai-detector | null | ---
language: en
license: mit
tags:
- exbert
datasets:
- bookcorpus
- wikipedia
---
# RoBERTa Base OpenAI Detector
## Table of Contents
- [Model Details](#model-details)
- [Uses](#uses)
- [Risks, Limitations and Biases](#risks-limitations-and-biases)
- [Training](#training)
- [Evaluation](#evaluation)
- [Environmental Impact](#environmental-impact)
- [Technical Specifications](#technical-specifications)
- [Citation Information](#citation-information)
- [Model Card Authors](#model-card-author)
- [How To Get Started With the Model](#how-to-get-started-with-the-model)
## Model Details
**Model Description:** RoBERTa base OpenAI Detector is the GPT-2 output detector model, obtained by fine-tuning a RoBERTa base model with the outputs of the 1.5B-parameter GPT-2 model. The model can be used to predict if text was generated by a GPT-2 model. This model was released by OpenAI at the same time as OpenAI released the weights of the [largest GPT-2 model](https://huggingface.co/gpt2-xl), the 1.5B parameter version.
- **Developed by:** OpenAI, see [GitHub Repo](https://github.com/openai/gpt-2-output-dataset/tree/master/detector) and [associated paper](https://d4mucfpksywv.cloudfront.net/papers/GPT_2_Report.pdf) for full author list
- **Model Type:** Fine-tuned transformer-based language model
- **Language(s):** English
- **License:** MIT
- **Related Models:** [RoBERTa base](https://huggingface.co/roberta-base), [GPT-XL (1.5B parameter version)](https://huggingface.co/gpt2-xl), [GPT-Large (the 774M parameter version)](https://huggingface.co/gpt2-large), [GPT-Medium (the 355M parameter version)](https://huggingface.co/gpt2-medium) and [GPT-2 (the 124M parameter version)](https://huggingface.co/gpt2)
- **Resources for more information:**
- [Research Paper](https://d4mucfpksywv.cloudfront.net/papers/GPT_2_Report.pdf) (see, in particular, the section beginning on page 12 about Automated ML-based detection).
- [GitHub Repo](https://github.com/openai/gpt-2-output-dataset/tree/master/detector)
- [OpenAI Blog Post](https://openai.com/blog/gpt-2-1-5b-release/)
- [Explore the detector model here](https://huggingface.co/openai-detector )
## Uses
#### Direct Use
The model is a classifier that can be used to detect text generated by GPT-2 models.
#### Downstream Use
The model's developers have stated that they developed and released the model to help with research related to synthetic text generation, so the model could potentially be used for downstream tasks related to synthetic text generation. See the [associated paper](https://d4mucfpksywv.cloudfront.net/papers/GPT_2_Report.pdf) for further discussion.
#### Misuse and Out-of-scope Use
The model should not be used to intentionally create hostile or alienating environments for people. In addition, the model developers discuss the risk of adversaries using the model to better evade detection in their [associated paper](https://d4mucfpksywv.cloudfront.net/papers/GPT_2_Report.pdf), suggesting that using the model for evading detection or for supporting efforts to evade detection would be a misuse of the model.
## Risks, Limitations and Biases
**CONTENT WARNING: Readers should be aware this section may contain content that is disturbing, offensive, and can propagate historical and current stereotypes.**
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model.
#### Risks and Limitations
In their [associated paper](https://d4mucfpksywv.cloudfront.net/papers/GPT_2_Report.pdf), the model developers discuss the risk that the model may be used by bad actors to develop capabilities for evading detection, though one purpose of releasing the model is to help improve detection research.
In a related [blog post](https://openai.com/blog/gpt-2-1-5b-release/), the model developers also discuss the limitations of automated methods for detecting synthetic text and the need to pair automated detection tools with other, non-automated approaches. They write:
> We conducted in-house detection research and developed a detection model that has detection rates of ~95% for detecting 1.5B GPT-2-generated text. We believe this is not high enough accuracy for standalone detection and needs to be paired with metadata-based approaches, human judgment, and public education to be more effective.
The model developers also [report](https://openai.com/blog/gpt-2-1-5b-release/) finding that classifying content from larger models is more difficult, suggesting that detection with automated tools like this model will be increasingly difficult as model sizes increase. The authors find that training detector models on the outputs of larger models can improve accuracy and robustness.
#### Bias
Significant research has explored bias and fairness issues with language models (see, e.g., [Sheng et al. (2021)](https://aclanthology.org/2021.acl-long.330.pdf) and [Bender et al. (2021)](https://dl.acm.org/doi/pdf/10.1145/3442188.3445922)). Predictions generated by RoBERTa base and GPT-2 1.5B (which this model is built/fine-tuned on) can include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups (see the [RoBERTa base](https://huggingface.co/roberta-base) and [GPT-2 XL](https://huggingface.co/gpt2-xl) model cards for more information). The developers of this model discuss these issues further in their [paper](https://d4mucfpksywv.cloudfront.net/papers/GPT_2_Report.pdf).
## Training
#### Training Data
The model is a sequence classifier based on RoBERTa base (see the [RoBERTa base model card](https://huggingface.co/roberta-base) for more details on the RoBERTa base training data) and then fine-tuned using the outputs of the 1.5B GPT-2 model (available [here](https://github.com/openai/gpt-2-output-dataset)).
#### Training Procedure
The model developers write that:
> We based a sequence classifier on RoBERTaBASE (125 million parameters) and fine-tuned it to classify the outputs from the 1.5B GPT-2 model versus WebText, the dataset we used to train the GPT-2 model.
They later state:
> To develop a robust detector model that can accurately classify generated texts regardless of the sampling method, we performed an analysis of the model’s transfer performance.
See the [associated paper](https://d4mucfpksywv.cloudfront.net/papers/GPT_2_Report.pdf) for further details on the training procedure.
## Evaluation
The following evaluation information is extracted from the [associated paper](https://d4mucfpksywv.cloudfront.net/papers/GPT_2_Report.pdf).
#### Testing Data, Factors and Metrics
The model is intended to be used for detecting text generated by GPT-2 models, so the model developers test the model on text datasets, measuring accuracy by:
> testing 510-token test examples comprised of 5,000 samples from the WebText dataset and 5,000 samples generated by a GPT-2 model, which were not used during the training.
#### Results
The model developers [find](https://d4mucfpksywv.cloudfront.net/papers/GPT_2_Report.pdf):
> Our classifier is able to detect 1.5 billion parameter GPT-2-generated text with approximately 95% accuracy...The model’s accuracy depends on sampling methods used when generating outputs, like temperature, Top-K, and nucleus sampling ([Holtzman et al., 2019](https://arxiv.org/abs/1904.09751). Nucleus sampling outputs proved most difficult to correctly classify, but a detector trained using nucleus sampling transfers well across other sampling methods. As seen in Figure 1 [in the paper], we found consistently high accuracy when trained on nucleus sampling.
See the [associated paper](https://d4mucfpksywv.cloudfront.net/papers/GPT_2_Report.pdf), Figure 1 (on page 14) and Figure 2 (on page 16) for full results.
## Environmental Impact
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** Unknown
- **Hours used:** Unknown
- **Cloud Provider:** Unknown
- **Compute Region:** Unknown
- **Carbon Emitted:** Unknown
## Technical Specifications
The model developers write that:
See the [associated paper](https://d4mucfpksywv.cloudfront.net/papers/GPT_2_Report.pdf) for further details on the modeling architecture and training details.
## Citation Information
```bibtex
@article{solaiman2019release,
title={Release strategies and the social impacts of language models},
author={Solaiman, Irene and Brundage, Miles and Clark, Jack and Askell, Amanda and Herbert-Voss, Ariel and Wu, Jeff and Radford, Alec and Krueger, Gretchen and Kim, Jong Wook and Kreps, Sarah and others},
journal={arXiv preprint arXiv:1908.09203},
year={2019}
}
```
APA:
- Solaiman, I., Brundage, M., Clark, J., Askell, A., Herbert-Voss, A., Wu, J., ... & Wang, J. (2019). Release strategies and the social impacts of language models. arXiv preprint arXiv:1908.09203.
## Model Card Authors
This model card was written by the team at Hugging Face.
## How to Get Started with the Model
More information needed.
| 9,170 |
Jeevesh8/goog_bert_ft_cola-84 | null | Entry not found | 15 |
ElKulako/cryptobert | [
"Bearish",
"Bullish",
"Neutral"
] | ---
datasets:
- ElKulako/stocktwits-crypto
language:
- en
tags:
- cryptocurrency
- crypto
- BERT
- sentiment classification
- NLP
- bitcoin
- ethereum
- shib
- social media
- sentiment analysis
- cryptocurrency sentiment analysis
---
# CryptoBERT
CryptoBERT is a pre-trained NLP model to analyse the language and sentiments of cryptocurrency-related social media posts and messages. It was built by further training the [cardiffnlp's Twitter-roBERTa-base](https://huggingface.co/cardiffnlp/twitter-roberta-base) language model on the cryptocurrency domain, using a corpus of over 3.2M unique cryptocurrency-related social media posts.
(A research paper with more details will follow soon.)
## Classification Training
The model was trained on the following labels: "Bearish" : 0, "Neutral": 1, "Bullish": 2
CryptoBERT's sentiment classification head was fine-tuned on a balanced dataset of 2M labelled StockTwits posts, sampled from [ElKulako/stocktwits-crypto](https://huggingface.co/datasets/ElKulako/stocktwits-crypto).
CryptoBERT was trained with a max sequence length of 128. Technically, it can handle sequences of up to 514 tokens, however, going beyond 128 is not recommended.
# Classification Example
```python
from transformers import TextClassificationPipeline, AutoModelForSequenceClassification, AutoTokenizer
model_name = "ElKulako/cryptobert"
tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=True)
model = AutoModelForSequenceClassification.from_pretrained(model_name, num_labels = 3)
pipe = TextClassificationPipeline(model=model, tokenizer=tokenizer, max_length=64, truncation=True, padding = 'max_length')
# post_1 & post_3 = bullish, post_2 = bearish
post_1 = " see y'all tomorrow and can't wait to see ada in the morning, i wonder what price it is going to be at. 😎🐂🤠💯😴, bitcoin is looking good go for it and flash by that 45k. "
post_2 = " alright racers, it’s a race to the bottom! good luck today and remember there are no losers (minus those who invested in currency nobody really uses) take your marks... are you ready? go!!"
post_3 = " i'm never selling. the whole market can bottom out. i'll continue to hold this dumpster fire until the day i die if i need to."
df_posts = [post_1, post_2, post_3]
preds = pipe(df_posts)
print(preds)
```
```
[{'label': 'Bullish', 'score': 0.8734585642814636}, {'label': 'Bearish', 'score': 0.9889495372772217}, {'label': 'Bullish', 'score': 0.6595883965492249}]
```
## Training Corpus
CryptoBERT was trained on 3.2M social media posts regarding various cryptocurrencies. Only non-duplicate posts of length above 4 words were considered. The following communities were used as sources for our corpora:
(1) StockTwits - 1.875M posts about the top 100 cryptos by trading volume. Posts were collected from the 1st of November 2021 to the 16th of June 2022. [ElKulako/stocktwits-crypto](https://huggingface.co/datasets/ElKulako/stocktwits-crypto)
(2) Telegram - 664K posts from top 5 telegram groups: [Binance](https://t.me/binanceexchange), [Bittrex](https://t.me/BittrexGlobalEnglish), [huobi global](https://t.me/huobiglobalofficial), [Kucoin](https://t.me/Kucoin_Exchange), [OKEx](https://t.me/OKExOfficial_English).
Data from 16.11.2020 to 30.01.2021. Courtesy of [Anton](https://www.kaggle.com/datasets/aagghh/crypto-telegram-groups).
(3) Reddit - 172K comments from various crypto investing threads, collected from May 2021 to May 2022
(4) Twitter - 496K posts with hashtags XBT, Bitcoin or BTC. Collected for May 2018. Courtesy of [Paul](https://www.kaggle.com/datasets/paul92s/bitcoin-tweets-14m). | 3,598 |
Jeevesh8/goog_bert_ft_cola-85 | null | Entry not found | 15 |
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