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taskydata/DeBERTa-v3-128 | 3402f20625a53e37e841464f36c4d2518c801486 | 2022-05-30T08:40:05.000Z | [
"pytorch",
"deberta-v2",
"text-classification",
"transformers",
"license:apache-2.0"
] | text-classification | false | taskydata | null | taskydata/DeBERTa-v3-128 | 5 | null | transformers | 17,200 | ---
license: apache-2.0
---
**Hyperparameters:**
- learning rate: 2e-5
- weight decay: 0.01
- per_device_train_batch_size: 16
- per_device_eval_batch_size: 16
- gradient_accumulation_steps:1
- eval steps: 5000
- max_length: 128
- num_epochs: 3
**Dataset version:**
- “craffel/tasky_or_not”, “10xp3_10xc4”, “15f88c8”
**Checkpoint:**
- 10000 steps
**Results on Validation set:**
| Step | Training Loss | Validation Loss | Accuracy | Precision | Recall | F1 |
|-------|---------------|-----------------|----------|-----------|----------|----------|
| 5000 | 0.036400 | 0.266518 | 0.926913 | 0.999662 | 0.916934 | 0.956513 |
| 10000 | 0.022500 | 0.222881 | 0.952443 | 0.999494 | 0.946227 | 0.972132 |
| 15000 | 0.016600 | 0.634102 | 0.882638 | 0.999789 | 0.866301 | 0.928270 |
| 20000 | 0.011300 | 1.138026 | 0.849013 | 0.999796 | 0.827928 | 0.905781 |
| 25000 | 0.010300 | 0.623522 | 0.895619 | 0.999728 | 0.881166 | 0.936710 |
| 30000 | 0.006300 | 0.776632 | 0.879492 | 0.999804 | 0.862697 | 0.926204 |
| 35000 | 0.000500 | 0.704599 | 0.899149 | 0.999698 | 0.885220 | 0.938982 |
|
Nurr/wav2vec2-base-finetuned-ks | 8ad4231f56a31c05278959335573972855300dcb | 2022-05-13T04:03:38.000Z | [
"pytorch",
"tensorboard",
"wav2vec2",
"audio-classification",
"dataset:superb",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | audio-classification | false | Nurr | null | Nurr/wav2vec2-base-finetuned-ks | 5 | null | transformers | 17,201 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- superb
model-index:
- name: wav2vec2-base-finetuned-ks
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. -->
# wav2vec2-base-finetuned-ks
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the superb dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
### Framework versions
- Transformers 4.11.3
- Pytorch 1.11.0+cu113
- Datasets 1.14.0
- Tokenizers 0.10.3
|
yogeshchandrasekharuni/bart-paraphrase-finetuned-xsum | 28f79c65c93ac520470b1873f3cc278b0924451d | 2022-05-13T11:12:28.000Z | [
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] | text2text-generation | false | yogeshchandrasekharuni | null | yogeshchandrasekharuni/bart-paraphrase-finetuned-xsum | 5 | null | transformers | 17,202 | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: bart-paraphrase-finetuned-xsum
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. -->
# bart-paraphrase-finetuned-xsum
This model is a fine-tuned version of [eugenesiow/bart-paraphrase](https://huggingface.co/eugenesiow/bart-paraphrase) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-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
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:|
| No log | 1.0 | 61 | 1.1215 | 70.9729 | 60.41 | 70.2648 | 70.2724 | 12.2295 |
### Framework versions
- Transformers 4.19.0
- Pytorch 1.11.0+cu113
- Datasets 2.2.1
- Tokenizers 0.12.1
|
ali-issa/wav2vec2-Arabizi-gpu-colab-similar-to-german-param-more-dataset-more-epochs | b3ebb2d94567579bcaf14cfc61d7e4bcbd92e7cb | 2022-05-13T19:50:44.000Z | [
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | automatic-speech-recognition | false | ali-issa | null | ali-issa/wav2vec2-Arabizi-gpu-colab-similar-to-german-param-more-dataset-more-epochs | 5 | null | transformers | 17,203 | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-Arabizi-gpu-colab-similar-to-german-param-more-dataset-more-epochs
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. -->
# wav2vec2-Arabizi-gpu-colab-similar-to-german-param-more-dataset-more-epochs
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7230
- Wer: 0.4010
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 6
- total_train_batch_size: 12
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 4.7329 | 2.41 | 400 | 2.9049 | 1.0 |
| 1.5315 | 4.82 | 800 | 0.6905 | 0.6211 |
| 0.664 | 7.23 | 1200 | 0.5894 | 0.5038 |
| 0.4908 | 9.64 | 1600 | 0.5510 | 0.4650 |
| 0.4032 | 12.05 | 2000 | 0.5679 | 0.4435 |
| 0.3406 | 14.46 | 2400 | 0.5652 | 0.4273 |
| 0.3049 | 16.86 | 2800 | 0.5747 | 0.4252 |
| 0.2682 | 19.28 | 3200 | 0.5942 | 0.4187 |
| 0.2454 | 21.68 | 3600 | 0.5892 | 0.4171 |
| 0.23 | 24.1 | 4000 | 0.6241 | 0.4160 |
| 0.2113 | 26.5 | 4400 | 0.6336 | 0.4150 |
| 0.1988 | 28.91 | 4800 | 0.6689 | 0.4117 |
| 0.1816 | 31.32 | 5200 | 0.6750 | 0.4117 |
| 0.1779 | 33.73 | 5600 | 0.6783 | 0.3983 |
| 0.1682 | 36.14 | 6000 | 0.6797 | 0.3988 |
| 0.1638 | 38.55 | 6400 | 0.7061 | 0.3988 |
| 0.1548 | 40.96 | 6800 | 0.7083 | 0.3961 |
| 0.152 | 43.37 | 7200 | 0.7151 | 0.4069 |
| 0.1509 | 45.78 | 7600 | 0.7083 | 0.4058 |
| 0.1414 | 48.19 | 8000 | 0.7230 | 0.4010 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu113
- Datasets 1.18.3
- Tokenizers 0.10.3
|
renjithks/distilbert-expense-ner | 4dcaf3a3379b5d795b6f4d615e2c92105df1fe68 | 2022-05-26T05:50:54.000Z | [
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] | token-classification | false | renjithks | null | renjithks/distilbert-expense-ner | 5 | null | transformers | 17,204 | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: distilbert-expense-ner
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-expense-ner
This model is a fine-tuned version of [renjithks/distilbert-cord-ner](https://huggingface.co/renjithks/distilbert-cord-ner) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2930
- Precision: 0.5096
- Recall: 0.4852
- F1: 0.4971
- Accuracy: 0.9275
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 22 | 0.3635 | 0.2888 | 0.0945 | 0.1424 | 0.8866 |
| No log | 2.0 | 44 | 0.2795 | 0.3213 | 0.3018 | 0.3113 | 0.8982 |
| No log | 3.0 | 66 | 0.2432 | 0.4243 | 0.4034 | 0.4136 | 0.9161 |
| No log | 4.0 | 88 | 0.2446 | 0.4615 | 0.4654 | 0.4635 | 0.9193 |
| No log | 5.0 | 110 | 0.2410 | 0.5143 | 0.4810 | 0.4971 | 0.9293 |
| No log | 6.0 | 132 | 0.2598 | 0.5283 | 0.4612 | 0.4925 | 0.9305 |
| No log | 7.0 | 154 | 0.2963 | 0.5230 | 0.4485 | 0.4829 | 0.9268 |
| No log | 8.0 | 176 | 0.2753 | 0.4928 | 0.4838 | 0.4883 | 0.9283 |
| No log | 9.0 | 198 | 0.2897 | 0.5194 | 0.4725 | 0.4948 | 0.9295 |
| No log | 10.0 | 220 | 0.2930 | 0.5096 | 0.4852 | 0.4971 | 0.9275 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.12.1
|
Jeevesh8/6ep_bert_ft_cola-5 | d4c3c853e42af6bfc7819bb48fbd94b38f127da9 | 2022-05-14T11:41:15.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
] | text-classification | false | Jeevesh8 | null | Jeevesh8/6ep_bert_ft_cola-5 | 5 | null | transformers | 17,205 | Entry not found |
Jeevesh8/6ep_bert_ft_cola-23 | 8c830030485f119dd949f94fe71c4ac03dd7ef15 | 2022-05-14T12:36:33.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
] | text-classification | false | Jeevesh8 | null | Jeevesh8/6ep_bert_ft_cola-23 | 5 | null | transformers | 17,206 | Entry not found |
Jeevesh8/6ep_bert_ft_cola-28 | 4a22c0fcbfcfaedcbdf04b92068d22346281eaf5 | 2022-05-14T12:44:56.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
] | text-classification | false | Jeevesh8 | null | Jeevesh8/6ep_bert_ft_cola-28 | 5 | null | transformers | 17,207 | Entry not found |
Jeevesh8/6ep_bert_ft_cola-52 | 3d95b516d1de3fa8ccf8a48c259571eb1270bf4c | 2022-05-14T13:25:40.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
] | text-classification | false | Jeevesh8 | null | Jeevesh8/6ep_bert_ft_cola-52 | 5 | null | transformers | 17,208 | Entry not found |
Jeevesh8/6ep_bert_ft_cola-54 | 22ee9e994badef956f2d829b632d13a367d8e8ad | 2022-05-14T13:29:01.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
] | text-classification | false | Jeevesh8 | null | Jeevesh8/6ep_bert_ft_cola-54 | 5 | null | transformers | 17,209 | Entry not found |
Jeevesh8/6ep_bert_ft_cola-55 | 32a4567119d02d8fabd79018600f2caeb52d4c03 | 2022-05-14T13:30:40.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
] | text-classification | false | Jeevesh8 | null | Jeevesh8/6ep_bert_ft_cola-55 | 5 | null | transformers | 17,210 | Entry not found |
Jeevesh8/6ep_bert_ft_cola-61 | 5d158607889396dd217cb6ed7ae44d9a6ed66835 | 2022-05-14T13:40:40.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
] | text-classification | false | Jeevesh8 | null | Jeevesh8/6ep_bert_ft_cola-61 | 5 | null | transformers | 17,211 | Entry not found |
Jeevesh8/6ep_bert_ft_cola-62 | ded455f6dce2350ee480a036237db5ccd81fe8e1 | 2022-05-14T13:42:20.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
] | text-classification | false | Jeevesh8 | null | Jeevesh8/6ep_bert_ft_cola-62 | 5 | null | transformers | 17,212 | Entry not found |
Jeevesh8/6ep_bert_ft_cola-63 | b5e6de8e63ee8072de1e80339689cf15a0e7433f | 2022-05-14T13:43:59.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
] | text-classification | false | Jeevesh8 | null | Jeevesh8/6ep_bert_ft_cola-63 | 5 | null | transformers | 17,213 | Entry not found |
Jeevesh8/6ep_bert_ft_cola-69 | 8ff359a94514ccc7e8e8d8b1f9e0df24ba455071 | 2022-05-14T13:53:59.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
] | text-classification | false | Jeevesh8 | null | Jeevesh8/6ep_bert_ft_cola-69 | 5 | null | transformers | 17,214 | Entry not found |
Jeevesh8/6ep_bert_ft_cola-75 | 9052450577e0f2c59fcc517677ee76efb73ada16 | 2022-05-14T14:04:01.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
] | text-classification | false | Jeevesh8 | null | Jeevesh8/6ep_bert_ft_cola-75 | 5 | null | transformers | 17,215 | Entry not found |
Jeevesh8/6ep_bert_ft_cola-76 | 6e37f5d5af4afdb00a53a70add00efeda9ead055 | 2022-05-14T14:05:44.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
] | text-classification | false | Jeevesh8 | null | Jeevesh8/6ep_bert_ft_cola-76 | 5 | null | transformers | 17,216 | Entry not found |
Jeevesh8/6ep_bert_ft_cola-77 | 50f18eadcb1276b560de31f6d52e56d7f3678c07 | 2022-05-14T14:07:26.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
] | text-classification | false | Jeevesh8 | null | Jeevesh8/6ep_bert_ft_cola-77 | 5 | null | transformers | 17,217 | Entry not found |
Jeevesh8/6ep_bert_ft_cola-80 | f6fb9282f95ab0330d6596b353a8b31343543b8d | 2022-05-14T14:12:26.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
] | text-classification | false | Jeevesh8 | null | Jeevesh8/6ep_bert_ft_cola-80 | 5 | null | transformers | 17,218 | Entry not found |
Jeevesh8/6ep_bert_ft_cola-81 | 50f5f4504f7afd34c710ef5774a1d2d654dc6f70 | 2022-05-14T14:14:06.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
] | text-classification | false | Jeevesh8 | null | Jeevesh8/6ep_bert_ft_cola-81 | 5 | null | transformers | 17,219 | Entry not found |
Jeevesh8/6ep_bert_ft_cola-87 | e3c0432b304af5e833e880758478c5042426fd5f | 2022-05-14T14:24:07.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
] | text-classification | false | Jeevesh8 | null | Jeevesh8/6ep_bert_ft_cola-87 | 5 | null | transformers | 17,220 | Entry not found |
akreal/mbart-large-50-finetuned-slurp | 0ea0a1a086f3416db016bc134b698088ffed3e5c | 2022-05-14T16:36:01.000Z | [
"pytorch",
"mbart",
"text2text-generation",
"en",
"dataset:SLURP",
"transformers",
"mbart-50",
"license:apache-2.0",
"autotrain_compatible"
] | text2text-generation | false | akreal | null | akreal/mbart-large-50-finetuned-slurp | 5 | null | transformers | 17,221 | ---
language:
- en
tags:
- mbart-50
license: apache-2.0
datasets:
- SLURP
metrics:
- accuracy
- slu-f1
---
This model is `mbart-large-50-many-to-many-mmt` model fine-tuned on the text part of [SLURP](https://github.com/pswietojanski/slurp) spoken language understanding dataset.
The scores on the test set are 85.68% and 79.00% for Intent accuracy and SLU-F1 respectively. |
danieleV9H/hubert-base-libri-clean-ft100h | 0d9392fb07685e8b5a459601ae9a394d0b85974a | 2022-05-15T05:47:23.000Z | [
"pytorch",
"tensorboard",
"hubert",
"automatic-speech-recognition",
"dataset:librispeech_asr",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | automatic-speech-recognition | false | danieleV9H | null | danieleV9H/hubert-base-libri-clean-ft100h | 5 | null | transformers | 17,222 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- librispeech_asr
model-index:
- name: hubert-base-libri-clean-ft100h
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. -->
# hubert-base-libri-clean-ft100h
This model is a fine-tuned version of [facebook/hubert-base-ls960](https://huggingface.co/facebook/hubert-base-ls960) on the librispeech_asr dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1324
- Wer: 0.1597
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 5
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 0.14 | 250 | 4.1508 | 1.0000 |
| 4.4345 | 0.28 | 500 | 3.8766 | 1.0000 |
| 4.4345 | 0.42 | 750 | 3.4376 | 1.0000 |
| 2.8475 | 0.56 | 1000 | 2.7380 | 1.0 |
| 2.8475 | 0.7 | 1250 | 0.8803 | 0.6766 |
| 1.1877 | 0.84 | 1500 | 0.5671 | 0.5102 |
| 1.1877 | 0.98 | 1750 | 0.4537 | 0.4388 |
| 0.5802 | 1.12 | 2000 | 0.3566 | 0.3740 |
| 0.5802 | 1.26 | 2250 | 0.2925 | 0.3209 |
| 0.4301 | 1.4 | 2500 | 0.2613 | 0.2952 |
| 0.4301 | 1.54 | 2750 | 0.2363 | 0.2715 |
| 0.3591 | 1.68 | 3000 | 0.2155 | 0.2552 |
| 0.3591 | 1.82 | 3250 | 0.2062 | 0.2418 |
| 0.3015 | 1.96 | 3500 | 0.1951 | 0.2308 |
| 0.3015 | 2.1 | 3750 | 0.1842 | 0.2207 |
| 0.2698 | 2.24 | 4000 | 0.1900 | 0.2112 |
| 0.2698 | 2.38 | 4250 | 0.1745 | 0.2048 |
| 0.2561 | 2.52 | 4500 | 0.1718 | 0.2040 |
| 0.2561 | 2.66 | 4750 | 0.1625 | 0.1939 |
| 0.2348 | 2.8 | 5000 | 0.1568 | 0.1867 |
| 0.2348 | 2.94 | 5250 | 0.1517 | 0.1855 |
| 0.2278 | 3.08 | 5500 | 0.1501 | 0.1807 |
| 0.2278 | 3.22 | 5750 | 0.1445 | 0.1772 |
| 0.2166 | 3.36 | 6000 | 0.1422 | 0.1752 |
| 0.2166 | 3.5 | 6250 | 0.1418 | 0.1741 |
| 0.2017 | 3.64 | 6500 | 0.1404 | 0.1695 |
| 0.2017 | 3.78 | 6750 | 0.1356 | 0.1674 |
| 0.1922 | 3.92 | 7000 | 0.1350 | 0.1688 |
| 0.1922 | 4.06 | 7250 | 0.1346 | 0.1638 |
| 0.1979 | 4.2 | 7500 | 0.1359 | 0.1638 |
| 0.1979 | 4.34 | 7750 | 0.1336 | 0.1612 |
| 0.1836 | 4.48 | 8000 | 0.1324 | 0.1613 |
| 0.1836 | 4.62 | 8250 | 0.1320 | 0.1606 |
| 0.1891 | 4.76 | 8500 | 0.1325 | 0.1598 |
| 0.1891 | 4.9 | 8750 | 0.1324 | 0.1597 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.11.0+cu113
- Datasets 1.18.3
- Tokenizers 0.12.1
|
anuj55/distilbert-base-uncased-finetuned-mrpc | b4704d564ba0f2d476fade16af9904ead98ae498 | 2022-05-15T10:45:54.000Z | [
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"dataset:glue",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | text-classification | false | anuj55 | null | anuj55/distilbert-base-uncased-finetuned-mrpc | 5 | null | transformers | 17,223 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-mrpc
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
args: mrpc
metrics:
- name: Accuracy
type: accuracy
value: 0.8480392156862745
- name: F1
type: f1
value: 0.8945578231292517
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-mrpc
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6236
- Accuracy: 0.8480
- F1: 0.8946
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log | 1.0 | 230 | 0.4371 | 0.8137 | 0.8746 |
| No log | 2.0 | 460 | 0.4117 | 0.8431 | 0.8940 |
| 0.4509 | 3.0 | 690 | 0.3943 | 0.8431 | 0.8908 |
| 0.4509 | 4.0 | 920 | 0.5686 | 0.8382 | 0.8893 |
| 0.1915 | 5.0 | 1150 | 0.6236 | 0.8480 | 0.8946 |
### Framework versions
- Transformers 4.19.1
- Pytorch 1.8.1+cu102
- Datasets 1.18.4
- Tokenizers 0.12.1
|
aliosm/sha3bor-generator-aragpt2-base | 3f2864cebe1c7fd8b4bf9f23f5dcfce38346b9d3 | 2022-05-28T09:17:13.000Z | [
"pytorch",
"gpt2",
"text-generation",
"ar",
"transformers",
"license:mit"
] | text-generation | false | aliosm | null | aliosm/sha3bor-generator-aragpt2-base | 5 | null | transformers | 17,224 | ---
language: ar
license: mit
widget:
- text: "حبيبي"
example_title: "حبيبي"
- text: "يا"
example_title: "يا"
- text: "رسول الله"
example_title: "رسول الله"
---
|
anuj55/deberta-v3-base-finetuned-polifact | 10d90263444a9aed04c577afb0ec2bf39f5d9d4a | 2022-05-15T17:47:32.000Z | [
"pytorch",
"tensorboard",
"deberta-v2",
"text-classification",
"transformers"
] | text-classification | false | anuj55 | null | anuj55/deberta-v3-base-finetuned-polifact | 5 | null | transformers | 17,225 | Entry not found |
aliosm/sha3bor-metre-detector-arabertv2-base | caf3e348c74cfe4eb79588457bc419c421dafcdd | 2022-05-28T09:33:59.000Z | [
"pytorch",
"bert",
"text-classification",
"ar",
"transformers",
"license:mit"
] | text-classification | false | aliosm | null | aliosm/sha3bor-metre-detector-arabertv2-base | 5 | null | transformers | 17,226 | ---
language: ar
license: mit
widget:
- text: "إن العيون التي في طرفها حور [شطر] قتلننا ثم لم يحيين قتلانا"
- text: "إذا ما فعلت الخير ضوعف شرهم [شطر] وكل إناء بالذي فيه ينضح"
- text: "واحر قلباه ممن قلبه شبم [شطر] ومن بجسمي وحالي عنده سقم"
---
|
IMSyPP/hate_speech_targets_nl | 1df0dbd02175c0d93f598af9e0a515f2d82713d2 | 2022-05-16T04:49:35.000Z | [
"pytorch",
"distilbert",
"text-classification",
"nl",
"transformers",
"license:mit"
] | text-classification | false | IMSyPP | null | IMSyPP/hate_speech_targets_nl | 5 | null | transformers | 17,227 | ---
language:
- nl
license: mit
---
# Hate Speech Target Classifier for Social Media Content in Dutch
A monolingual model for hate speech target classification of social media content in Dutch. The model was trained on 20000 social media posts (youtube, twitter, facebook) and tested on an independent test set of 2000 posts. It is based on the pre-trained language model [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased).
## Tokenizer
During training the text was preprocessed using the Distilbert tokenizer. We suggest the same tokenizer is used for inference.
## Model output
The model classifies each input into one of four distinct classes:
* 0 - HOMOPHOBIA
* 1 - OTHER
* 2 - RELIGION
* 3 - ANTISEMITISM
* 4 - IDEOLOGY
* 5 - MIGRANTS
* 6 - POLITICS
* 7 - RACISM
* 8 - MEDIA
* 9 - ISLAMOPHOBIA
* 10 - INDIVIDUAL
* 11 - SEXISM |
huawei-noah/AutoTinyBERT-KD-S1 | b25716ef98cb0ca8bdb2c2885ecda3e56865facb | 2022-05-16T15:09:32.000Z | [
"pytorch",
"transformers",
"license:other"
] | null | false | huawei-noah | null | huawei-noah/AutoTinyBERT-KD-S1 | 5 | null | transformers | 17,228 | ---
license: other
---
Pre-trained language models (PLMs) have achieved great success in natural language processing. Most of PLMs follow the default setting of architecture hyper-parameters (e.g., the hidden dimension is a quarter of the intermediate dimension in feed-forward sub-networks) in BERT. In this paper, we adopt the one-shot Neural Architecture Search (NAS) to automatically search architecture hyper-parameters for efficient pre-trained language models (at least 6x faster than BERT-base).
AutoTinyBERT provides a model zoo that can meet different latency requirements. |
huawei-noah/AutoTinyBERT-KD-S2 | 45226d3574f75dad94550cb4551ea2421ba2b66c | 2022-05-16T15:11:57.000Z | [
"pytorch",
"transformers"
] | null | false | huawei-noah | null | huawei-noah/AutoTinyBERT-KD-S2 | 5 | null | transformers | 17,229 | Pre-trained language models (PLMs) have achieved great success in natural language processing. Most of PLMs follow the default setting of architecture hyper-parameters (e.g., the hidden dimension is a quarter of the intermediate dimension in feed-forward sub-networks) in BERT. In this paper, we adopt the one-shot Neural Architecture Search (NAS) to automatically search architecture hyper-parameters for efficient pre-trained language models (at least 6x faster than BERT-base).
AutoTinyBERT provides a model zoo that can meet different latency requirements. |
ali-issa/4-wav2vec2-arabiizi-gpu-colab-more-dataset12 | 0d78ae92504aaa82bd8f233eb9e0e89873193fb4 | 2022-05-17T02:28:13.000Z | [
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | automatic-speech-recognition | false | ali-issa | null | ali-issa/4-wav2vec2-arabiizi-gpu-colab-more-dataset12 | 5 | null | transformers | 17,230 | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-arabiizi-gpu-colab-more-dataset12
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. -->
# wav2vec2-arabiizi-gpu-colab-more-dataset12
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6032
- Wer: 0.4112
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 6
- total_train_batch_size: 12
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 40
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 4.842 | 2.41 | 400 | 2.8828 | 1.0 |
| 1.5726 | 4.82 | 800 | 0.6972 | 0.6254 |
| 0.7292 | 7.23 | 1200 | 0.5717 | 0.5280 |
| 0.5706 | 9.64 | 1600 | 0.5367 | 0.4795 |
| 0.483 | 12.05 | 2000 | 0.5773 | 0.4677 |
| 0.418 | 14.46 | 2400 | 0.5391 | 0.4537 |
| 0.3823 | 16.86 | 2800 | 0.6134 | 0.4386 |
| 0.3489 | 19.28 | 3200 | 0.5776 | 0.4360 |
| 0.3227 | 21.68 | 3600 | 0.5890 | 0.4489 |
| 0.2999 | 24.1 | 4000 | 0.5882 | 0.4209 |
| 0.2841 | 26.5 | 4400 | 0.5843 | 0.4150 |
| 0.2729 | 28.91 | 4800 | 0.5793 | 0.4279 |
| 0.2603 | 31.32 | 5200 | 0.6003 | 0.4209 |
| 0.2481 | 33.73 | 5600 | 0.6122 | 0.4128 |
| 0.2405 | 36.14 | 6000 | 0.6137 | 0.4177 |
| 0.24 | 38.55 | 6400 | 0.6032 | 0.4112 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu113
- Datasets 1.18.3
- Tokenizers 0.10.3
|
PSW/cnndm_0.5percent_randomsimdel_seed42 | 5212e545560fc4315d2a9e468e8a097ab093d550 | 2022-05-17T03:26:23.000Z | [
"pytorch",
"bart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | PSW | null | PSW/cnndm_0.5percent_randomsimdel_seed42 | 5 | null | transformers | 17,231 | Entry not found |
PSW/cnndm_0.5percent_minsimins_seed42 | b13d030e6f552ab8f51e1f9854d38752485c0680 | 2022-05-17T06:59:03.000Z | [
"pytorch",
"bart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | PSW | null | PSW/cnndm_0.5percent_minsimins_seed42 | 5 | null | transformers | 17,232 | Entry not found |
Danni/distilbert-base-uncased-finetuned-dbpedia-0517 | c60e5e3865195d4c90c8b65b4d4b66a33a2113ef | 2022-05-17T07:39:35.000Z | [
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"transformers"
] | text-classification | false | Danni | null | Danni/distilbert-base-uncased-finetuned-dbpedia-0517 | 5 | null | transformers | 17,233 | Entry not found |
nielsr/groupvit-gcc-yfcc-old | 3ffdb7c8166864c2fab6534a3c1a871bacdd7805 | 2022-06-08T17:53:52.000Z | [
"pytorch",
"groupvit",
"feature-extraction",
"transformers",
"license:apache-2.0"
] | feature-extraction | false | nielsr | null | nielsr/groupvit-gcc-yfcc-old | 5 | null | transformers | 17,234 | ---
license: apache-2.0
---
|
TrevorAshby/WoW-1hr | aed14bcbe0ccde064fcf7b3f5b0123f3d2dfc24d | 2022-05-18T02:31:20.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
] | text-generation | false | TrevorAshby | null | TrevorAshby/WoW-1hr | 5 | null | transformers | 17,235 | Entry not found |
taskydata/DeBERTa-v3-512 | b9fe6e9e3f46377c2a10ecd9b87f373f1d846197 | 2022-05-30T08:37:18.000Z | [
"pytorch",
"deberta-v2",
"text-classification",
"transformers",
"license:apache-2.0"
] | text-classification | false | taskydata | null | taskydata/DeBERTa-v3-512 | 5 | null | transformers | 17,236 | ---
license: apache-2.0
---
**Hyperparameters:**
- learning rate: 2e-5
- weight decay: 0.01
- per_device_train_batch_size: 8
- per_device_eval_batch_size: 8
- gradient_accumulation_steps:1
- eval steps: 6000
- max_length: 512
- num_epochs: 2
**Dataset version:**
- “craffel/tasky_or_not”, “10xp3_10xc4”, “15f88c8”
**Checkpoint:**
- 48000 steps
**Results on Validation set:**
| Step | Training Loss | Validation Loss | Accuracy | Precision | Recall | F1 |
|-------|---------------|-----------------|----------|-----------|----------|----------|
| 6000 | 0.031900 | 0.163412 | 0.982194 | 0.999211 | 0.980462 | 0.989748 |
| 12000 | 0.014700 | 0.106132 | 0.976666 | 0.999639 | 0.973733 | 0.986516 |
| 18000 | 0.010700 | 0.043012 | 0.995743 | 0.999223 | 0.995918 | 0.997568 |
| 24000 | 0.007400 | 0.095047 | 0.984724 | 0.999857 | 0.982714 | 0.991211 |
| 30000 | 0.004100 | 0.087274 | 0.990400 | 0.999829 | 0.989217 | 0.994495 |
| 36000 | 0.003100 | 0.162909 | 0.981972 | 1.000000 | 0.979434 | 0.989610 |
| 42000 | 0.002200 | 0.148721 | 0.980454 | 0.999986 | 0.977717 | 0.988726 |
| 48000 | 0.001000 | 0.094455 | 0.990437 | 0.999943 | 0.989147 | 0.994516 |
|
khanhnguyen/wav2vec2-base-librispeech-demo-colab | 24e443f6cac7ab3b2ee529d969b1787812924a8d | 2022-05-19T03:39:03.000Z | [
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | automatic-speech-recognition | false | khanhnguyen | null | khanhnguyen/wav2vec2-base-librispeech-demo-colab | 5 | null | transformers | 17,237 | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-base-librispeech-demo-colab
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. -->
# wav2vec2-base-librispeech-demo-colab
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.11.3
- Pytorch 1.11.0+cu113
- Datasets 2.2.1
- Tokenizers 0.10.3
|
badrou1/test_rex_model | f879b86153dd4e4138a4de25fd0cbb4e633e8b02 | 2022-05-18T15:58:28.000Z | [
"pytorch",
"bert",
"text-classification",
"transformers",
"license:other"
] | text-classification | false | badrou1 | null | badrou1/test_rex_model | 5 | null | transformers | 17,238 | ---
license: other
---
|
Jeevesh8/512seq_len_6ep_bert_ft_cola-3 | 3fa16aa5a11c8f167c5d5ed0bfe81aee09b0ebdf | 2022-05-18T18:23:21.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
] | text-classification | false | Jeevesh8 | null | Jeevesh8/512seq_len_6ep_bert_ft_cola-3 | 5 | null | transformers | 17,239 | Entry not found |
Jeevesh8/512seq_len_6ep_bert_ft_cola-72 | fe56dd21a8736c7c57d2e58565ed76ed42656f13 | 2022-05-18T18:56:50.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
] | text-classification | false | Jeevesh8 | null | Jeevesh8/512seq_len_6ep_bert_ft_cola-72 | 5 | null | transformers | 17,240 | Entry not found |
Jeevesh8/512seq_len_6ep_bert_ft_cola-73 | 5651f1bb3ff3e26338418e8b77c7a12d1f158b68 | 2022-05-18T18:58:39.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
] | text-classification | false | Jeevesh8 | null | Jeevesh8/512seq_len_6ep_bert_ft_cola-73 | 5 | null | transformers | 17,241 | Entry not found |
Jeevesh8/512seq_len_6ep_bert_ft_cola-74 | a072ba1e7ee8755c05e024d4615b2b1f14978941 | 2022-05-18T19:00:26.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
] | text-classification | false | Jeevesh8 | null | Jeevesh8/512seq_len_6ep_bert_ft_cola-74 | 5 | null | transformers | 17,242 | Entry not found |
Jeevesh8/512seq_len_6ep_bert_ft_cola-76 | 42babe37ad2fe8440c5c2d0a2646fdd3f8b18838 | 2022-05-18T19:04:06.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
] | text-classification | false | Jeevesh8 | null | Jeevesh8/512seq_len_6ep_bert_ft_cola-76 | 5 | null | transformers | 17,243 | Entry not found |
Jeevesh8/512seq_len_6ep_bert_ft_cola-78 | 979aaf66866963651034a12572c9413c0b8ee37d | 2022-05-18T19:07:43.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
] | text-classification | false | Jeevesh8 | null | Jeevesh8/512seq_len_6ep_bert_ft_cola-78 | 5 | null | transformers | 17,244 | Entry not found |
Jeevesh8/512seq_len_6ep_bert_ft_cola-79 | 74479406f48e6ac5f09f92ea570dd5f76996f9f6 | 2022-05-18T19:09:34.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
] | text-classification | false | Jeevesh8 | null | Jeevesh8/512seq_len_6ep_bert_ft_cola-79 | 5 | null | transformers | 17,245 | Entry not found |
Jeevesh8/512seq_len_6ep_bert_ft_cola-80 | 4a79588c988567aca9ebb567c912b34054906f8d | 2022-05-18T19:11:23.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
] | text-classification | false | Jeevesh8 | null | Jeevesh8/512seq_len_6ep_bert_ft_cola-80 | 5 | null | transformers | 17,246 | Entry not found |
Jeevesh8/512seq_len_6ep_bert_ft_cola-85 | 11d7ca0404f55cced6d0c92e204283b81e34c70f | 2022-05-18T19:20:26.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
] | text-classification | false | Jeevesh8 | null | Jeevesh8/512seq_len_6ep_bert_ft_cola-85 | 5 | null | transformers | 17,247 | Entry not found |
Jeevesh8/512seq_len_6ep_bert_ft_cola-86 | af8b827d91a065900c2b3922d22cf699a411d168 | 2022-05-18T19:22:13.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
] | text-classification | false | Jeevesh8 | null | Jeevesh8/512seq_len_6ep_bert_ft_cola-86 | 5 | null | transformers | 17,248 | Entry not found |
Jeevesh8/512seq_len_6ep_bert_ft_cola-99 | 6824b9615bf5c730857605e7903047c96a93ac49 | 2022-05-18T19:39:33.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
] | text-classification | false | Jeevesh8 | null | Jeevesh8/512seq_len_6ep_bert_ft_cola-99 | 5 | null | transformers | 17,249 | Entry not found |
hungchiayu/distilbert-base-uncased-finetuned-emotion | 32ecbb4dc25754a8903ff5c16fbf85928e6402ea | 2022-05-19T16:59:14.000Z | [
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"transformers"
] | text-classification | false | hungchiayu | null | hungchiayu/distilbert-base-uncased-finetuned-emotion | 5 | null | transformers | 17,250 | Entry not found |
PriaPillai/distilbert-base-uncased-finetuned-query | eb035a21bfc832a0a3c351ba788048fc316e0216 | 2022-06-01T17:44:17.000Z | [
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | text-classification | false | PriaPillai | null | PriaPillai/distilbert-base-uncased-finetuned-query | 5 | null | transformers | 17,251 | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-query
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-query
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: 0.3668
- Accuracy: 0.8936
- F1: 0.8924
## 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: 5
- eval_batch_size: 5
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.6511 | 1.0 | 30 | 0.5878 | 0.7234 | 0.6985 |
| 0.499 | 2.0 | 60 | 0.4520 | 0.8723 | 0.8683 |
| 0.3169 | 3.0 | 90 | 0.3668 | 0.8936 | 0.8924 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
mlnotes/ecnet_seed_42 | ac14cdfe919eaf8ca36eee82a453632ea5ac0e56 | 2022-05-19T20:01:05.000Z | [
"pytorch",
"bert",
"feature-extraction",
"transformers"
] | feature-extraction | false | mlnotes | null | mlnotes/ecnet_seed_42 | 5 | null | transformers | 17,252 | Entry not found |
tamarab/bert-emotion | 22be9f5e4cca71161b273fc7be79650ee48045a6 | 2022-05-20T19:12:14.000Z | [
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"dataset:tweet_eval",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | text-classification | false | tamarab | null | tamarab/bert-emotion | 5 | null | transformers | 17,253 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- tweet_eval
metrics:
- precision
- recall
model-index:
- name: bert-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: tweet_eval
type: tweet_eval
args: emotion
metrics:
- name: Precision
type: precision
value: 0.7462955517135084
- name: Recall
type: recall
value: 0.7095634380533169
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-emotion
This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on the tweet_eval dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1347
- Precision: 0.7463
- Recall: 0.7096
- Fscore: 0.7209
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | Fscore |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|
| 0.8385 | 1.0 | 815 | 0.8366 | 0.7865 | 0.5968 | 0.6014 |
| 0.5451 | 2.0 | 1630 | 0.9301 | 0.7301 | 0.6826 | 0.6947 |
| 0.2447 | 3.0 | 2445 | 1.1347 | 0.7463 | 0.7096 | 0.7209 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.1
- Tokenizers 0.12.1
|
anas-awadalla/albert-xxl-v2-finetuned-squad | ddcda8687ada42f00e4ed1b132f56b06bb381f1e | 2022-05-21T08:02:10.000Z | [
"pytorch",
"albert",
"question-answering",
"dataset:squad",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] | question-answering | false | anas-awadalla | null | anas-awadalla/albert-xxl-v2-finetuned-squad | 5 | 1 | transformers | 17,254 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: albert-xxl-v2-finetuned-squad
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# albert-xxl-v2-finetuned-squad
This model is a fine-tuned version of [albert-xxlarge-v2](https://huggingface.co/albert-xxlarge-v2) on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2.0
### Training results
### Framework versions
- Transformers 4.17.0
- Pytorch 1.11.0+cu113
- Datasets 2.0.0
- Tokenizers 0.11.6
|
connectivity/feather_berts_14 | 21dda72ecce9b507acd48c987cf751f315401788 | 2022-05-21T14:27:52.000Z | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | false | connectivity | null | connectivity/feather_berts_14 | 5 | null | transformers | 17,255 | Entry not found |
connectivity/feather_berts_17 | 458e3af6fe8ee0585b5a5c6511be12c96b7ecdbc | 2022-05-21T14:27:58.000Z | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | false | connectivity | null | connectivity/feather_berts_17 | 5 | null | transformers | 17,256 | Entry not found |
connectivity/feather_berts_18 | 8bfae38a21fc6397933c36cc0e08bbbb0b5cfe24 | 2022-05-21T14:28:00.000Z | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | false | connectivity | null | connectivity/feather_berts_18 | 5 | null | transformers | 17,257 | Entry not found |
connectivity/feather_berts_19 | 7ccf34a23dc444b99e84072fcb6f3bb5b65b6bb6 | 2022-05-21T14:28:01.000Z | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | false | connectivity | null | connectivity/feather_berts_19 | 5 | null | transformers | 17,258 | Entry not found |
connectivity/feather_berts_20 | 9fd6a642e74de5e7bf325251426fa6e0f0fbb07c | 2022-05-21T14:28:04.000Z | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | false | connectivity | null | connectivity/feather_berts_20 | 5 | null | transformers | 17,259 | Entry not found |
connectivity/feather_berts_21 | 6de8226ab99577fd8ea77dbeda4efc6f85606421 | 2022-05-21T14:28:05.000Z | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | false | connectivity | null | connectivity/feather_berts_21 | 5 | null | transformers | 17,260 | Entry not found |
connectivity/feather_berts_22 | 2419db36fd64c8c1e181fc2cadecb9fdcf6e7090 | 2022-05-21T14:28:09.000Z | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | false | connectivity | null | connectivity/feather_berts_22 | 5 | null | transformers | 17,261 | Entry not found |
connectivity/feather_berts_24 | e0b6387a5c05496a401b0d7a9dfad6c09edeb3e4 | 2022-05-21T14:28:15.000Z | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | false | connectivity | null | connectivity/feather_berts_24 | 5 | null | transformers | 17,262 | Entry not found |
connectivity/feather_berts_26 | 7c6b79baad927c399680ab83347a6e8439cb1211 | 2022-05-21T14:28:18.000Z | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | false | connectivity | null | connectivity/feather_berts_26 | 5 | null | transformers | 17,263 | Entry not found |
connectivity/feather_berts_27 | 4bf9fc7a99d9f8530e32832f81af6f6c53622be2 | 2022-05-21T14:28:21.000Z | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | false | connectivity | null | connectivity/feather_berts_27 | 5 | null | transformers | 17,264 | Entry not found |
connectivity/feather_berts_29 | 47f3449823a1fe51674ff6b59da408d4889e4b3d | 2022-05-21T14:28:25.000Z | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | false | connectivity | null | connectivity/feather_berts_29 | 5 | null | transformers | 17,265 | Entry not found |
connectivity/feather_berts_37 | 529f4baeb816fe1498135bd3019453df1aff5ba6 | 2022-05-21T14:28:39.000Z | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | false | connectivity | null | connectivity/feather_berts_37 | 5 | null | transformers | 17,266 | Entry not found |
connectivity/feather_berts_38 | 959b04bf7851a7d6fed225b72e4bc4273b6eb385 | 2022-05-21T14:28:41.000Z | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | false | connectivity | null | connectivity/feather_berts_38 | 5 | null | transformers | 17,267 | Entry not found |
connectivity/feather_berts_42 | 99b0fb3f65443d1998ad075deda8262bae4a13aa | 2022-05-21T14:28:50.000Z | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | false | connectivity | null | connectivity/feather_berts_42 | 5 | null | transformers | 17,268 | Entry not found |
connectivity/feather_berts_45 | a81693fa5793cdfa528d0d4268b7c3c2790e1f84 | 2022-05-21T14:28:55.000Z | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | false | connectivity | null | connectivity/feather_berts_45 | 5 | null | transformers | 17,269 | Entry not found |
connectivity/feather_berts_61 | 3b353ee28648f0805d07dd92e056d265805fc057 | 2022-05-21T14:29:23.000Z | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | false | connectivity | null | connectivity/feather_berts_61 | 5 | null | transformers | 17,270 | Entry not found |
connectivity/feather_berts_64 | 11aad5eeef57a57fed7a5c95e453883dbaa7749a | 2022-05-21T14:29:28.000Z | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | false | connectivity | null | connectivity/feather_berts_64 | 5 | null | transformers | 17,271 | Entry not found |
connectivity/feather_berts_66 | 44ea929e39ea206be3f62e488fbb86133e2677cb | 2022-05-21T14:29:33.000Z | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | false | connectivity | null | connectivity/feather_berts_66 | 5 | null | transformers | 17,272 | Entry not found |
connectivity/feather_berts_67 | 003b90967d16ce444493a0aee048577fcf4fdb78 | 2022-05-21T14:29:35.000Z | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | false | connectivity | null | connectivity/feather_berts_67 | 5 | null | transformers | 17,273 | Entry not found |
connectivity/feather_berts_68 | 670615dc37bc30f2ab602257b859943f356d8af8 | 2022-05-21T14:29:38.000Z | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | false | connectivity | null | connectivity/feather_berts_68 | 5 | null | transformers | 17,274 | Entry not found |
connectivity/feather_berts_69 | c17ba78568cd8e885db40cf7c81ffaa92c8a4b5f | 2022-05-21T14:29:40.000Z | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | false | connectivity | null | connectivity/feather_berts_69 | 5 | null | transformers | 17,275 | Entry not found |
connectivity/feather_berts_70 | 39c17ffcdfc94e012ead07396e18ca296323510c | 2022-05-21T14:29:42.000Z | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | false | connectivity | null | connectivity/feather_berts_70 | 5 | null | transformers | 17,276 | Entry not found |
connectivity/feather_berts_73 | e1dc1ed8c82aaa525f06f036ba2184cd29befb4f | 2022-05-21T14:29:47.000Z | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | false | connectivity | null | connectivity/feather_berts_73 | 5 | null | transformers | 17,277 | Entry not found |
connectivity/feather_berts_74 | e3fffa7e65f591ae56bd0fc33fc33aa0615a3a31 | 2022-05-21T14:30:10.000Z | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | false | connectivity | null | connectivity/feather_berts_74 | 5 | null | transformers | 17,278 | Entry not found |
connectivity/feather_berts_75 | 3f11d1c08b8c7350aa2ba7133db8b1561286ac55 | 2022-05-21T14:30:11.000Z | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | false | connectivity | null | connectivity/feather_berts_75 | 5 | null | transformers | 17,279 | Entry not found |
connectivity/feather_berts_77 | 5c0f2d52f1866c74e4b5416e59c21bd786d111b9 | 2022-05-21T14:30:16.000Z | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | false | connectivity | null | connectivity/feather_berts_77 | 5 | null | transformers | 17,280 | Entry not found |
connectivity/feather_berts_78 | 02359a1671342ec5944a4ecffb9fb603f517279b | 2022-05-21T14:30:18.000Z | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | false | connectivity | null | connectivity/feather_berts_78 | 5 | null | transformers | 17,281 | Entry not found |
connectivity/feather_berts_79 | 00097a832142c869cd1a387aff65a924cf89f2e1 | 2022-05-21T14:30:19.000Z | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | false | connectivity | null | connectivity/feather_berts_79 | 5 | null | transformers | 17,282 | Entry not found |
connectivity/feather_berts_82 | eae7f31d8a4c5b15e582ee1d85a81dceeabbbe9f | 2022-05-21T14:30:26.000Z | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | false | connectivity | null | connectivity/feather_berts_82 | 5 | null | transformers | 17,283 | Entry not found |
connectivity/feather_berts_85 | adbbb9d835befd5eb7fe8d42b3275d4f80cbf03c | 2022-05-21T14:30:35.000Z | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | false | connectivity | null | connectivity/feather_berts_85 | 5 | null | transformers | 17,284 | Entry not found |
connectivity/feather_berts_86 | 53efbd8d385adcd7e8624944e61fcaf5ed5cbdf9 | 2022-05-21T14:30:37.000Z | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | false | connectivity | null | connectivity/feather_berts_86 | 5 | null | transformers | 17,285 | Entry not found |
connectivity/feather_berts_92 | 4fbcee24525a6fe9a106869a676fcbd6fb613f95 | 2022-05-21T14:30:53.000Z | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | false | connectivity | null | connectivity/feather_berts_92 | 5 | null | transformers | 17,286 | Entry not found |
connectivity/feather_berts_94 | 84fd220af0574b976c651a49ca82403a1bed4bba | 2022-05-21T14:30:56.000Z | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | false | connectivity | null | connectivity/feather_berts_94 | 5 | null | transformers | 17,287 | Entry not found |
connectivity/bert_ft_qqp-8 | d48dc43fe738a205bdd2a16324a3062743c16937 | 2022-05-21T16:31:36.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
] | text-classification | false | connectivity | null | connectivity/bert_ft_qqp-8 | 5 | null | transformers | 17,288 | Entry not found |
connectivity/bert_ft_qqp-12 | d8a48e8116604ee8186890934b69c0701cf2ef06 | 2022-05-21T16:31:54.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
] | text-classification | false | connectivity | null | connectivity/bert_ft_qqp-12 | 5 | null | transformers | 17,289 | Entry not found |
connectivity/bert_ft_qqp-13 | d2c9fe28406ffd164843a2f062480bac92eba3fb | 2022-05-21T16:32:00.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
] | text-classification | false | connectivity | null | connectivity/bert_ft_qqp-13 | 5 | null | transformers | 17,290 | Entry not found |
connectivity/bert_ft_qqp-14 | ebed33b45fb33b6db8723c2eeb16c8a0dd347a4c | 2022-05-21T16:32:06.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
] | text-classification | false | connectivity | null | connectivity/bert_ft_qqp-14 | 5 | null | transformers | 17,291 | Entry not found |
connectivity/bert_ft_qqp-15 | deeb6c5cb4ea1f7f878697f766ecd7be98a73ccd | 2022-05-21T16:32:11.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
] | text-classification | false | connectivity | null | connectivity/bert_ft_qqp-15 | 5 | null | transformers | 17,292 | Entry not found |
connectivity/bert_ft_qqp-23 | 781cf0e05f989f03213614a470024236eb60d0a5 | 2022-05-21T16:32:47.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
] | text-classification | false | connectivity | null | connectivity/bert_ft_qqp-23 | 5 | null | transformers | 17,293 | Entry not found |
connectivity/bert_ft_qqp-24 | a0e8b13a69e1fd1c668a0ee14306812a7c67d4bb | 2022-05-21T16:32:50.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
] | text-classification | false | connectivity | null | connectivity/bert_ft_qqp-24 | 5 | null | transformers | 17,294 | Entry not found |
connectivity/bert_ft_qqp-28 | 55ea30fe3fd996c86f6a43741b4cf12bd2250dd8 | 2022-05-21T16:33:10.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
] | text-classification | false | connectivity | null | connectivity/bert_ft_qqp-28 | 5 | null | transformers | 17,295 | Entry not found |
connectivity/bert_ft_qqp-31 | 5fbc1298cae2e3994d75b7590a1824c6c21c1d02 | 2022-05-21T16:33:25.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
] | text-classification | false | connectivity | null | connectivity/bert_ft_qqp-31 | 5 | null | transformers | 17,296 | Entry not found |
connectivity/bert_ft_qqp-35 | 48b3244e97bc2650efd1885f704c0a3a9d5ea73f | 2022-05-21T16:33:40.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
] | text-classification | false | connectivity | null | connectivity/bert_ft_qqp-35 | 5 | null | transformers | 17,297 | Entry not found |
laurens88/finetuning-crypto-tweet-sentiment-test2 | b8d13f4bbcb03c7e040f003e226c76529a7a99af | 2022-05-21T13:19:41.000Z | [
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"transformers",
"generated_from_trainer",
"model-index"
] | text-classification | false | laurens88 | null | laurens88/finetuning-crypto-tweet-sentiment-test2 | 5 | null | transformers | 17,298 | ---
tags:
- generated_from_trainer
model-index:
- name: finetuning-crypto-tweet-sentiment-test2
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. -->
# finetuning-crypto-tweet-sentiment-test2
This model is a fine-tuned version of [finiteautomata/bertweet-base-sentiment-analysis](https://huggingface.co/finiteautomata/bertweet-base-sentiment-analysis) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Tokenizers 0.12.1
|
connectivity/bert_ft_qqp-36 | 3fc3ad4f6eae932352e33f1eb6ab5b3f883a2a5d | 2022-05-21T16:33:43.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
] | text-classification | false | connectivity | null | connectivity/bert_ft_qqp-36 | 5 | null | transformers | 17,299 | Entry not found |
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