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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
connectivity/bert_ft_qqp-40 | abf026bd67ed4b613aa276c0a22b7edcad228e5c | 2022-05-21T16:34:04.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
] | text-classification | false | connectivity | null | connectivity/bert_ft_qqp-40 | 5 | null | transformers | 17,300 | Entry not found |
connectivity/bert_ft_qqp-44 | 906eb3c0ece122a7f1b1c7b074d8bf766b438222 | 2022-05-21T16:34:20.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
] | text-classification | false | connectivity | null | connectivity/bert_ft_qqp-44 | 5 | null | transformers | 17,301 | Entry not found |
connectivity/bert_ft_qqp-45 | 93ffed2aa5df12c57936295d6afa28923598cc2d | 2022-05-21T16:34:24.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
] | text-classification | false | connectivity | null | connectivity/bert_ft_qqp-45 | 5 | null | transformers | 17,302 | Entry not found |
connectivity/bert_ft_qqp-48 | 6ae8f8025c8f81d4ab1b0d1b87e1fc0de641b3fb | 2022-05-21T16:34:40.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
] | text-classification | false | connectivity | null | connectivity/bert_ft_qqp-48 | 5 | null | transformers | 17,303 | Entry not found |
connectivity/bert_ft_qqp-66 | 21a916f1b46a0a74e95c40528efd9ed6a9569c1b | 2022-05-21T16:36:07.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
] | text-classification | false | connectivity | null | connectivity/bert_ft_qqp-66 | 5 | null | transformers | 17,304 | Entry not found |
connectivity/bert_ft_qqp-70 | 591019ccdeab0f7568e9d2ee2699da86b5ee3504 | 2022-05-21T16:36:22.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
] | text-classification | false | connectivity | null | connectivity/bert_ft_qqp-70 | 5 | null | transformers | 17,305 | Entry not found |
connectivity/cola_6ep_ft-0 | caecbf251c584748c352c8dbd246058c1480f1cc | 2022-05-21T16:43:32.000Z | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | false | connectivity | null | connectivity/cola_6ep_ft-0 | 5 | null | transformers | 17,306 | Entry not found |
connectivity/bert_ft_qqp-80 | fef608e9fd1a539c35b59abc2e83cb4a76abc498 | 2022-05-21T16:37:12.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
] | text-classification | false | connectivity | null | connectivity/bert_ft_qqp-80 | 5 | null | transformers | 17,307 | Entry not found |
connectivity/bert_ft_qqp-81 | bff58c96bdd52fe67de97ecf9b11605d6570fc08 | 2022-05-21T16:37:18.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
] | text-classification | false | connectivity | null | connectivity/bert_ft_qqp-81 | 5 | null | transformers | 17,308 | Entry not found |
connectivity/bert_ft_qqp-83 | c556fc5ca0520a74ffd3bc8fb352fb000a5eb14e | 2022-05-21T16:37:27.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
] | text-classification | false | connectivity | null | connectivity/bert_ft_qqp-83 | 5 | null | transformers | 17,309 | Entry not found |
connectivity/bert_ft_qqp-86 | e0ea3e7ecec717d45ce11f92983a44aedbe18316 | 2022-05-21T16:37:39.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
] | text-classification | false | connectivity | null | connectivity/bert_ft_qqp-86 | 5 | null | transformers | 17,310 | Entry not found |
connectivity/bert_ft_qqp-90 | 01a81817890d156efbe7d7527f43adfaa996e7c5 | 2022-05-21T16:37:53.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
] | text-classification | false | connectivity | null | connectivity/bert_ft_qqp-90 | 5 | null | transformers | 17,311 | Entry not found |
connectivity/bert_ft_qqp-96 | 793e9dc23322e32a45a98ab973647744d4bff979 | 2022-05-21T16:38:22.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
] | text-classification | false | connectivity | null | connectivity/bert_ft_qqp-96 | 5 | null | transformers | 17,312 | Entry not found |
connectivity/bert_ft_qqp-97 | 50dbbc1cea57ab6f4383292d0182597835e3376d | 2022-05-21T16:38:25.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
] | text-classification | false | connectivity | null | connectivity/bert_ft_qqp-97 | 5 | null | transformers | 17,313 | Entry not found |
prodm93/rn_gpt2_customdata_model.json | 278806bd4e25689766f8c57dffc022a1e05967c9 | 2022-05-21T17:26:52.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
] | text-generation | false | prodm93 | null | prodm93/rn_gpt2_customdata_model.json | 5 | null | transformers | 17,314 | Entry not found |
prodm93/T5Dynamic_title_model_v2 | ec9e93db9cc0bafa689f8c86cfaad1cd21917446 | 2022-05-21T22:25:48.000Z | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | prodm93 | null | prodm93/T5Dynamic_title_model_v2 | 5 | null | transformers | 17,315 | Entry not found |
SamuelMiller/qa_squad | 3bdc643db90ae549fa231820d631d316056f89ec | 2022-05-22T03:13:46.000Z | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | SamuelMiller | null | SamuelMiller/qa_squad | 5 | null | transformers | 17,316 | Entry not found |
ocm/finetuning-sentiment-model-3000-samples | 372361761b6e257d6d1a351c16c9d72958b37bfa | 2022-05-22T16:12:20.000Z | [
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"dataset:imdb",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | text-classification | false | ocm | null | ocm/finetuning-sentiment-model-3000-samples | 5 | null | transformers | 17,317 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
metrics:
- accuracy
- f1
model-index:
- name: finetuning-sentiment-model-3000-samples
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: imdb
type: imdb
args: plain_text
metrics:
- name: Accuracy
type: accuracy
value: 0.8766666666666667
- name: F1
type: f1
value: 0.877887788778878
---
<!-- 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-sentiment-model-3000-samples
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3107
- Accuracy: 0.8767
- F1: 0.8779
## 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 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
viviastaari/finetuning-sentiment-analysis-en | c1eaf3837e74d5ac4cbe9b884424eee762dac4da | 2022-05-23T03:04:12.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | text-classification | false | viviastaari | null | viviastaari/finetuning-sentiment-analysis-en | 5 | null | transformers | 17,318 | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: finetuning-sentiment-analysis-en
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-sentiment-analysis-en
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0792
- Accuracy: 0.9803
- F1: 0.9856
- Precision: 0.9875
- Recall: 0.9837
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.426 | 1.0 | 1408 | 0.2718 | 0.8910 | 0.9201 | 0.9251 | 0.9151 |
| 0.3247 | 2.0 | 2816 | 0.1552 | 0.9540 | 0.9665 | 0.9656 | 0.9674 |
| 0.1582 | 3.0 | 4224 | 0.0792 | 0.9803 | 0.9856 | 0.9875 | 0.9837 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
roschmid/distilbert-base-uncased-finetuned-ner | 1b1cdae335f687be6c4073109d65fa16aaf8886a | 2022-05-23T09:23:51.000Z | [
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"dataset:conll2003",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] | token-classification | false | roschmid | null | roschmid/distilbert-base-uncased-finetuned-ner | 5 | null | transformers | 17,319 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: distilbert-base-uncased-finetuned-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: conll2003
type: conll2003
args: conll2003
metrics:
- name: Precision
type: precision
value: 0.920704845814978
- name: Recall
type: recall
value: 0.9352276541000112
- name: F1
type: f1
value: 0.927909428936123
- name: Accuracy
type: accuracy
value: 0.9831604365577391
---
<!-- 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-ner
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0631
- Precision: 0.9207
- Recall: 0.9352
- F1: 0.9279
- Accuracy: 0.9832
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2399 | 1.0 | 878 | 0.0678 | 0.9097 | 0.9211 | 0.9154 | 0.9804 |
| 0.0502 | 2.0 | 1756 | 0.0628 | 0.9152 | 0.9320 | 0.9235 | 0.9820 |
| 0.0299 | 3.0 | 2634 | 0.0631 | 0.9207 | 0.9352 | 0.9279 | 0.9832 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
coreybrady/bert-emotion | 5f1f35fe124f41db1d14e0659f94bf03b5d4bffc | 2022-05-23T15:16:26.000Z | [
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"dataset:tweet_eval",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | text-classification | false | coreybrady | null | coreybrady/bert-emotion | 5 | null | transformers | 17,320 | ---
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: 0.7262254187805659
- name: Recall
type: recall
value: 0.725549671319356
---
<!-- 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: 1.1670
- Precision: 0.7262
- Recall: 0.7255
- Fscore: 0.7253
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|
| 0.8561 | 1.0 | 815 | 0.7844 | 0.7575 | 0.6081 | 0.6253 |
| 0.5337 | 2.0 | 1630 | 0.9080 | 0.7567 | 0.7236 | 0.7325 |
| 0.2573 | 3.0 | 2445 | 1.1670 | 0.7262 | 0.7255 | 0.7253 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
SI2M-Lab/DarijaBERT-mix | 56bcb42b9054ea5a69f3d1af964bb7eefe0bc1fb | 2022-05-24T09:04:15.000Z | [
"pytorch",
"bert",
"transformers"
] | null | false | SI2M-Lab | null | SI2M-Lab/DarijaBERT-mix | 5 | null | transformers | 17,321 | Entry not found |
Sebabrata/lmv2-2022-05-24 | f1dd3425e8be23a24ab2f2ef8b102f84ae53c1e0 | 2022-05-24T10:15:14.000Z | [
"pytorch",
"tensorboard",
"layoutlmv2",
"token-classification",
"transformers",
"generated_from_trainer",
"license:cc-by-nc-sa-4.0",
"model-index",
"autotrain_compatible"
] | token-classification | false | Sebabrata | null | Sebabrata/lmv2-2022-05-24 | 5 | null | transformers | 17,322 | ---
license: cc-by-nc-sa-4.0
tags:
- generated_from_trainer
model-index:
- name: lmv2-2022-05-24
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. -->
# lmv2-2022-05-24
This model is a fine-tuned version of [microsoft/layoutlmv2-base-uncased](https://huggingface.co/microsoft/layoutlmv2-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0484
- Address Precision: 0.9474
- Address Recall: 1.0
- Address F1: 0.9730
- Address Number: 18
- Business Name Precision: 1.0
- Business Name Recall: 1.0
- Business Name F1: 1.0
- Business Name Number: 13
- City State Zip Code Precision: 0.8947
- City State Zip Code Recall: 0.8947
- City State Zip Code F1: 0.8947
- City State Zip Code Number: 19
- Ein Precision: 1.0
- Ein Recall: 1.0
- Ein F1: 1.0
- Ein Number: 4
- List Account Number Precision: 0.6
- List Account Number Recall: 0.75
- List Account Number F1: 0.6667
- List Account Number Number: 4
- Name Precision: 1.0
- Name Recall: 0.9444
- Name F1: 0.9714
- Name Number: 18
- Ssn Precision: 1.0
- Ssn Recall: 1.0
- Ssn F1: 1.0
- Ssn Number: 8
- Overall Precision: 0.9412
- Overall Recall: 0.9524
- Overall F1: 0.9467
- Overall Accuracy: 0.9979
## 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: 4e-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: constant
- num_epochs: 30
### Training results
| Training Loss | Epoch | Step | Validation Loss | Address Precision | Address Recall | Address F1 | Address Number | Business Name Precision | Business Name Recall | Business Name F1 | Business Name Number | City State Zip Code Precision | City State Zip Code Recall | City State Zip Code F1 | City State Zip Code Number | Ein Precision | Ein Recall | Ein F1 | Ein Number | List Account Number Precision | List Account Number Recall | List Account Number F1 | List Account Number Number | Name Precision | Name Recall | Name F1 | Name Number | Ssn Precision | Ssn Recall | Ssn F1 | Ssn Number | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:-----------------:|:--------------:|:----------:|:--------------:|:-----------------------:|:--------------------:|:----------------:|:--------------------:|:-----------------------------:|:--------------------------:|:----------------------:|:--------------------------:|:-------------:|:----------:|:------:|:----------:|:-----------------------------:|:--------------------------:|:----------------------:|:--------------------------:|:--------------:|:-----------:|:-------:|:-----------:|:-------------:|:----------:|:------:|:----------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 1.9388 | 1.0 | 79 | 1.5568 | 0.0 | 0.0 | 0.0 | 18 | 0.0 | 0.0 | 0.0 | 13 | 0.0 | 0.0 | 0.0 | 19 | 0.0 | 0.0 | 0.0 | 4 | 0.0 | 0.0 | 0.0 | 4 | 0.0 | 0.0 | 0.0 | 18 | 0.0 | 0.0 | 0.0 | 8 | 0.0 | 0.0 | 0.0 | 0.9465 |
| 1.3777 | 2.0 | 158 | 1.1259 | 0.0 | 0.0 | 0.0 | 18 | 0.0 | 0.0 | 0.0 | 13 | 0.0 | 0.0 | 0.0 | 19 | 0.0 | 0.0 | 0.0 | 4 | 0.0 | 0.0 | 0.0 | 4 | 0.0 | 0.0 | 0.0 | 18 | 0.0 | 0.0 | 0.0 | 8 | 0.0 | 0.0 | 0.0 | 0.9465 |
| 0.9629 | 3.0 | 237 | 0.7497 | 0.0 | 0.0 | 0.0 | 18 | 0.0 | 0.0 | 0.0 | 13 | 0.0 | 0.0 | 0.0 | 19 | 0.0 | 0.0 | 0.0 | 4 | 0.0 | 0.0 | 0.0 | 4 | 0.0 | 0.0 | 0.0 | 18 | 0.0 | 0.0 | 0.0 | 8 | 0.0 | 0.0 | 0.0 | 0.9465 |
| 0.6292 | 4.0 | 316 | 0.4818 | 0.0 | 0.0 | 0.0 | 18 | 0.0 | 0.0 | 0.0 | 13 | 0.0 | 0.0 | 0.0 | 19 | 0.0 | 0.0 | 0.0 | 4 | 0.0 | 0.0 | 0.0 | 4 | 0.0 | 0.0 | 0.0 | 18 | 0.1944 | 0.875 | 0.3182 | 8 | 0.1944 | 0.0833 | 0.1167 | 0.9523 |
| 0.3952 | 5.0 | 395 | 0.2982 | 0.2424 | 0.8889 | 0.3810 | 18 | 0.0 | 0.0 | 0.0 | 13 | 0.1111 | 0.1053 | 0.1081 | 19 | 0.0 | 0.0 | 0.0 | 4 | 0.0 | 0.0 | 0.0 | 4 | 0.0 | 0.0 | 0.0 | 18 | 0.6364 | 0.875 | 0.7368 | 8 | 0.2632 | 0.2976 | 0.2793 | 0.9660 |
| 0.2675 | 6.0 | 474 | 0.2183 | 1.0 | 0.9444 | 0.9714 | 18 | 0.0 | 0.0 | 0.0 | 13 | 0.8824 | 0.7895 | 0.8333 | 19 | 0.0 | 0.0 | 0.0 | 4 | 0.6 | 0.75 | 0.6667 | 4 | 0.1905 | 0.4444 | 0.2667 | 18 | 0.5714 | 1.0 | 0.7273 | 8 | 0.5204 | 0.6071 | 0.5604 | 0.9810 |
| 0.2095 | 7.0 | 553 | 0.1990 | 1.0 | 0.9444 | 0.9714 | 18 | 0.0833 | 0.0769 | 0.08 | 13 | 0.9375 | 0.7895 | 0.8571 | 19 | 0.0 | 0.0 | 0.0 | 4 | 0.75 | 0.75 | 0.75 | 4 | 0.2647 | 0.5 | 0.3462 | 18 | 0.1739 | 1.0 | 0.2963 | 8 | 0.4109 | 0.6310 | 0.4977 | 0.9762 |
| 0.1928 | 8.0 | 632 | 0.1704 | 1.0 | 0.9444 | 0.9714 | 18 | 0.3158 | 0.4615 | 0.3750 | 13 | 0.9412 | 0.8421 | 0.8889 | 19 | 0.0 | 0.0 | 0.0 | 4 | 1.0 | 0.75 | 0.8571 | 4 | 0.3214 | 0.5 | 0.3913 | 18 | 0.5385 | 0.875 | 0.6667 | 8 | 0.5979 | 0.6905 | 0.6409 | 0.9849 |
| 0.159 | 9.0 | 711 | 0.1339 | 1.0 | 0.9444 | 0.9714 | 18 | 0.45 | 0.6923 | 0.5455 | 13 | 0.9444 | 0.8947 | 0.9189 | 19 | 0.0 | 0.0 | 0.0 | 4 | 0.25 | 0.75 | 0.375 | 4 | 0.375 | 0.5 | 0.4286 | 18 | 0.2308 | 0.375 | 0.2857 | 8 | 0.5577 | 0.6905 | 0.6170 | 0.9871 |
| 0.1314 | 10.0 | 790 | 0.1199 | 0.9444 | 0.9444 | 0.9444 | 18 | 0.8571 | 0.9231 | 0.8889 | 13 | 0.9444 | 0.8947 | 0.9189 | 19 | 0.0 | 0.0 | 0.0 | 4 | 0.6 | 0.75 | 0.6667 | 4 | 0.7895 | 0.8333 | 0.8108 | 18 | 0.6667 | 1.0 | 0.8 | 8 | 0.8372 | 0.8571 | 0.8471 | 0.9897 |
| 0.1143 | 11.0 | 869 | 0.1127 | 0.9444 | 0.9444 | 0.9444 | 18 | 1.0 | 1.0 | 1.0 | 13 | 0.9444 | 0.8947 | 0.9189 | 19 | 0.0 | 0.0 | 0.0 | 4 | 0.6 | 0.75 | 0.6667 | 4 | 1.0 | 0.9444 | 0.9714 | 18 | 0.6667 | 1.0 | 0.8 | 8 | 0.9036 | 0.8929 | 0.8982 | 0.9903 |
| 0.1037 | 12.0 | 948 | 0.1039 | 0.85 | 0.9444 | 0.8947 | 18 | 0.9167 | 0.8462 | 0.8800 | 13 | 0.9444 | 0.8947 | 0.9189 | 19 | 0.0 | 0.0 | 0.0 | 4 | 0.6 | 0.75 | 0.6667 | 4 | 0.8889 | 0.8889 | 0.8889 | 18 | 0.6667 | 1.0 | 0.8 | 8 | 0.8471 | 0.8571 | 0.8521 | 0.9901 |
| 0.0925 | 13.0 | 1027 | 0.1124 | 1.0 | 0.9444 | 0.9714 | 18 | 1.0 | 1.0 | 1.0 | 13 | 0.9444 | 0.8947 | 0.9189 | 19 | 0.0 | 0.0 | 0.0 | 4 | 0.75 | 0.75 | 0.75 | 4 | 1.0 | 0.9444 | 0.9714 | 18 | 0.5833 | 0.875 | 0.7000 | 8 | 0.9136 | 0.8810 | 0.8970 | 0.9904 |
| 0.0863 | 14.0 | 1106 | 0.1077 | 0.9444 | 0.9444 | 0.9444 | 18 | 0.7333 | 0.8462 | 0.7857 | 13 | 0.9444 | 0.8947 | 0.9189 | 19 | 0.0 | 0.0 | 0.0 | 4 | 0.6 | 0.75 | 0.6667 | 4 | 1.0 | 0.9444 | 0.9714 | 18 | 0.6154 | 1.0 | 0.7619 | 8 | 0.8488 | 0.8690 | 0.8588 | 0.9916 |
| 0.0845 | 15.0 | 1185 | 0.1035 | 0.9444 | 0.9444 | 0.9444 | 18 | 1.0 | 1.0 | 1.0 | 13 | 0.9412 | 0.8421 | 0.8889 | 19 | 0.0 | 0.0 | 0.0 | 4 | 0.6 | 0.75 | 0.6667 | 4 | 1.0 | 0.9444 | 0.9714 | 18 | 0.5833 | 0.875 | 0.7000 | 8 | 0.8902 | 0.8690 | 0.8795 | 0.9921 |
| 0.0735 | 16.0 | 1264 | 0.0866 | 0.6667 | 0.8889 | 0.7619 | 18 | 1.0 | 1.0 | 1.0 | 13 | 0.9444 | 0.8947 | 0.9189 | 19 | 0.0 | 0.0 | 0.0 | 4 | 0.6 | 0.75 | 0.6667 | 4 | 1.0 | 0.9444 | 0.9714 | 18 | 0.6667 | 1.0 | 0.8 | 8 | 0.8315 | 0.8810 | 0.8555 | 0.9918 |
| 0.0714 | 17.0 | 1343 | 0.0781 | 0.9444 | 0.9444 | 0.9444 | 18 | 1.0 | 0.9231 | 0.9600 | 13 | 0.9412 | 0.8421 | 0.8889 | 19 | 0.0 | 0.0 | 0.0 | 4 | 0.6 | 0.75 | 0.6667 | 4 | 1.0 | 0.9444 | 0.9714 | 18 | 0.6667 | 1.0 | 0.8 | 8 | 0.9012 | 0.8690 | 0.8848 | 0.9921 |
| 0.0656 | 18.0 | 1422 | 0.0816 | 0.8947 | 0.9444 | 0.9189 | 18 | 1.0 | 1.0 | 1.0 | 13 | 0.9444 | 0.8947 | 0.9189 | 19 | 0.0 | 0.0 | 0.0 | 4 | 0.6 | 0.75 | 0.6667 | 4 | 0.9444 | 0.9444 | 0.9444 | 18 | 0.6667 | 1.0 | 0.8 | 8 | 0.8824 | 0.8929 | 0.8876 | 0.9919 |
| 0.0602 | 19.0 | 1501 | 0.0770 | 0.8 | 0.8889 | 0.8421 | 18 | 0.8667 | 1.0 | 0.9286 | 13 | 0.9444 | 0.8947 | 0.9189 | 19 | 0.0 | 0.0 | 0.0 | 4 | 0.6 | 0.75 | 0.6667 | 4 | 0.9444 | 0.9444 | 0.9444 | 18 | 0.6667 | 1.0 | 0.8 | 8 | 0.8409 | 0.8810 | 0.8605 | 0.9912 |
| 0.0516 | 20.0 | 1580 | 0.0710 | 0.8095 | 0.9444 | 0.8718 | 18 | 1.0 | 1.0 | 1.0 | 13 | 0.9444 | 0.8947 | 0.9189 | 19 | 0.0 | 0.0 | 0.0 | 4 | 0.6 | 0.75 | 0.6667 | 4 | 1.0 | 0.9444 | 0.9714 | 18 | 0.6667 | 1.0 | 0.8 | 8 | 0.8721 | 0.8929 | 0.8824 | 0.9919 |
| 0.0475 | 21.0 | 1659 | 0.0686 | 0.6667 | 1.0 | 0.8 | 18 | 0.5 | 0.6154 | 0.5517 | 13 | 0.9412 | 0.8421 | 0.8889 | 19 | 0.0 | 0.0 | 0.0 | 4 | 0.6 | 0.75 | 0.6667 | 4 | 0.9412 | 0.8889 | 0.9143 | 18 | 0.6667 | 1.0 | 0.8 | 8 | 0.7340 | 0.8214 | 0.7753 | 0.9904 |
| 0.0431 | 22.0 | 1738 | 0.0715 | 0.8095 | 0.9444 | 0.8718 | 18 | 0.9286 | 1.0 | 0.9630 | 13 | 0.8421 | 0.8421 | 0.8421 | 19 | 0.0 | 0.0 | 0.0 | 4 | 0.75 | 0.75 | 0.75 | 4 | 0.9444 | 0.9444 | 0.9444 | 18 | 0.3529 | 0.75 | 0.48 | 8 | 0.7273 | 0.8571 | 0.7869 | 0.9933 |
| 0.0383 | 23.0 | 1817 | 0.0627 | 0.8947 | 0.9444 | 0.9189 | 18 | 0.9231 | 0.9231 | 0.9231 | 13 | 0.8947 | 0.8947 | 0.8947 | 19 | 0.0 | 0.0 | 0.0 | 4 | 0.75 | 0.75 | 0.75 | 4 | 1.0 | 0.8889 | 0.9412 | 18 | 0.5714 | 1.0 | 0.7273 | 8 | 0.8111 | 0.8690 | 0.8391 | 0.9961 |
| 0.0327 | 24.0 | 1896 | 0.0683 | 0.8095 | 0.9444 | 0.8718 | 18 | 0.6 | 0.9231 | 0.7273 | 13 | 0.8095 | 0.8947 | 0.8500 | 19 | 0.6 | 0.75 | 0.6667 | 4 | 0.75 | 0.75 | 0.75 | 4 | 0.9412 | 0.8889 | 0.9143 | 18 | 0.8889 | 1.0 | 0.9412 | 8 | 0.7835 | 0.9048 | 0.8398 | 0.9942 |
| 0.0292 | 25.0 | 1975 | 0.0674 | 0.8947 | 0.9444 | 0.9189 | 18 | 1.0 | 1.0 | 1.0 | 13 | 0.85 | 0.8947 | 0.8718 | 19 | 1.0 | 1.0 | 1.0 | 4 | 0.6 | 0.75 | 0.6667 | 4 | 1.0 | 0.9444 | 0.9714 | 18 | 1.0 | 1.0 | 1.0 | 8 | 0.9186 | 0.9405 | 0.9294 | 0.9975 |
| 0.0269 | 26.0 | 2054 | 0.0691 | 0.85 | 0.9444 | 0.8947 | 18 | 1.0 | 1.0 | 1.0 | 13 | 0.9444 | 0.8947 | 0.9189 | 19 | 1.0 | 1.0 | 1.0 | 4 | 0.6 | 0.75 | 0.6667 | 4 | 1.0 | 0.9444 | 0.9714 | 18 | 1.0 | 1.0 | 1.0 | 8 | 0.9294 | 0.9405 | 0.9349 | 0.9976 |
| 0.024 | 27.0 | 2133 | 0.0484 | 0.9474 | 1.0 | 0.9730 | 18 | 1.0 | 1.0 | 1.0 | 13 | 0.8947 | 0.8947 | 0.8947 | 19 | 1.0 | 1.0 | 1.0 | 4 | 0.6 | 0.75 | 0.6667 | 4 | 1.0 | 0.9444 | 0.9714 | 18 | 1.0 | 1.0 | 1.0 | 8 | 0.9412 | 0.9524 | 0.9467 | 0.9979 |
| 0.0221 | 28.0 | 2212 | 0.0619 | 0.85 | 0.9444 | 0.8947 | 18 | 1.0 | 1.0 | 1.0 | 13 | 0.9444 | 0.8947 | 0.9189 | 19 | 1.0 | 1.0 | 1.0 | 4 | 0.6 | 0.75 | 0.6667 | 4 | 1.0 | 0.9444 | 0.9714 | 18 | 1.0 | 1.0 | 1.0 | 8 | 0.9294 | 0.9405 | 0.9349 | 0.9976 |
| 0.0216 | 29.0 | 2291 | 0.0810 | 0.85 | 0.9444 | 0.8947 | 18 | 1.0 | 1.0 | 1.0 | 13 | 0.9444 | 0.8947 | 0.9189 | 19 | 1.0 | 1.0 | 1.0 | 4 | 0.6 | 0.75 | 0.6667 | 4 | 1.0 | 0.9444 | 0.9714 | 18 | 1.0 | 0.875 | 0.9333 | 8 | 0.9286 | 0.9286 | 0.9286 | 0.9960 |
| 0.0175 | 30.0 | 2370 | 0.0646 | 0.85 | 0.9444 | 0.8947 | 18 | 1.0 | 1.0 | 1.0 | 13 | 0.9444 | 0.8947 | 0.9189 | 19 | 1.0 | 1.0 | 1.0 | 4 | 0.6 | 0.75 | 0.6667 | 4 | 1.0 | 0.9444 | 0.9714 | 18 | 1.0 | 1.0 | 1.0 | 8 | 0.9294 | 0.9405 | 0.9349 | 0.9976 |
### Framework versions
- Transformers 4.20.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
kimcando/final_projects | ac80897b435c8632ce4126027941dd7b72f7141b | 2022-05-24T12:57:22.000Z | [
"pytorch",
"roberta",
"text-classification",
"transformers"
] | text-classification | false | kimcando | null | kimcando/final_projects | 5 | null | transformers | 17,323 | Entry not found |
ulyanaisaeva/udmurt-bert-base-uncased | 804606c2ec23a3ad509bc3f633e91bc7337cc397 | 2022-05-30T18:18:07.000Z | [
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"transformers",
"generated_from_trainer",
"model-index",
"autotrain_compatible"
] | fill-mask | false | ulyanaisaeva | null | ulyanaisaeva/udmurt-bert-base-uncased | 5 | null | transformers | 17,324 | ---
tags:
- generated_from_trainer
model-index:
- name: vocab2-bert-base-multilingual-uncased-udm-tsa
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. -->
# vocab2-bert-base-multilingual-uncased-udm-tsa
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 4.8497
## 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: 20
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 7.3112 | 1.0 | 6419 | 6.1814 |
| 5.8524 | 2.0 | 12838 | 5.4075 |
| 5.3392 | 3.0 | 19257 | 5.0810 |
| 5.0958 | 4.0 | 25676 | 4.9015 |
| 4.9897 | 5.0 | 32095 | 4.8497 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
anablasi/finqa_model | 97c67dba169cfe91315c073d7c509e2107f29fe7 | 2022-05-24T21:54:39.000Z | [
"pytorch",
"roberta",
"question-answering",
"transformers",
"autotrain_compatible"
] | question-answering | false | anablasi | null | anablasi/finqa_model | 5 | null | transformers | 17,325 | Entry not found |
leander/bert-finetuned-ner | 36f7572661dd18b3d43a6e75f792cced5ac7de85 | 2022-05-25T09:53:48.000Z | [
"pytorch",
"tensorboard",
"bert",
"token-classification",
"dataset:conll2003",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] | token-classification | false | leander | null | leander/bert-finetuned-ner | 5 | null | transformers | 17,326 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-finetuned-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: conll2003
type: conll2003
args: conll2003
metrics:
- name: Precision
type: precision
value: 0.9329479768786128
- name: Recall
type: recall
value: 0.9506900033658701
- name: F1
type: f1
value: 0.9417354338584647
- name: Accuracy
type: accuracy
value: 0.987048919762171
---
<!-- 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-finetuned-ner
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0589
- Precision: 0.9329
- Recall: 0.9507
- F1: 0.9417
- Accuracy: 0.9870
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0867 | 1.0 | 1756 | 0.0639 | 0.9140 | 0.9386 | 0.9261 | 0.9831 |
| 0.0398 | 2.0 | 3512 | 0.0586 | 0.9326 | 0.9480 | 0.9402 | 0.9858 |
| 0.0212 | 3.0 | 5268 | 0.0589 | 0.9329 | 0.9507 | 0.9417 | 0.9870 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
aliosm/sha3bor-footer-101-arabertv02-base | 8e9f13b976fa811c89157b30e88a9f57d7de897a | 2022-05-28T09:35:18.000Z | [
"pytorch",
"bert",
"text-classification",
"ar",
"transformers",
"license:mit"
] | text-classification | false | aliosm | null | aliosm/sha3bor-footer-101-arabertv02-base | 5 | null | transformers | 17,327 | ---
language: ar
license: mit
widget:
- text: "إن العيون التي في طرفها حور"
- text: "إذا ما فعلت الخير ضوعف شرهم"
- text: "واحر قلباه ممن قلبه شبم"
---
|
rainbow/distilbert-base-uncased-finetuned-emotion | 22977cc44f85cbed6df36e75d5a8b7aafcf65c9c | 2022-05-27T11:00:53.000Z | [
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"transformers"
] | text-classification | false | rainbow | null | rainbow/distilbert-base-uncased-finetuned-emotion | 5 | null | transformers | 17,328 | Entry not found |
Abdelrahman-Rezk/bert-base-arabic-camelbert-mix-poetry-finetuned-qawaf | f3de8d4523971335dea5c163c307b31c5a727e86 | 2022-05-27T21:12:10.000Z | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | false | Abdelrahman-Rezk | null | Abdelrahman-Rezk/bert-base-arabic-camelbert-mix-poetry-finetuned-qawaf | 5 | null | transformers | 17,329 | Entry not found |
bookpanda/wangchanberta-base-att-spm-uncased-finetuned-imdb | 506ebc0ae4c54800cefafedd4434d4c1c68d098d | 2022-06-09T18:17:16.000Z | [
"pytorch",
"tensorboard",
"camembert",
"fill-mask",
"transformers",
"generated_from_trainer",
"model-index",
"autotrain_compatible"
] | fill-mask | false | bookpanda | null | bookpanda/wangchanberta-base-att-spm-uncased-finetuned-imdb | 5 | null | transformers | 17,330 | ---
tags:
- generated_from_trainer
model-index:
- name: wangchanberta-base-att-spm-uncased-finetuned-imdb
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wangchanberta-base-att-spm-uncased-finetuned-imdb
This model is a fine-tuned version of [airesearch/wangchanberta-base-att-spm-uncased](https://huggingface.co/airesearch/wangchanberta-base-att-spm-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0810
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 0.1831 | 1.0 | 4826 | 0.1542 |
| 0.1 | 2.0 | 9652 | 0.1075 |
| 0.0946 | 3.0 | 14478 | 0.0443 |
| 0.0618 | 4.0 | 19304 | 0.0830 |
| 0.0783 | 5.0 | 24130 | 0.0810 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.11.0+cu113
- Datasets 1.17.0
- Tokenizers 0.10.3
|
Nithiwat/soda-berta | f871c30ca12b4c14bff46229c1b23f81900f1d24 | 2022-05-29T17:07:20.000Z | [
"pytorch",
"camembert",
"text-classification",
"transformers"
] | text-classification | false | Nithiwat | null | Nithiwat/soda-berta | 5 | null | transformers | 17,331 | Entry not found |
GENG/hubert_ls_2500 | 58a0fbea17d8aa35e8cac6ddc1b1e9cc34007626 | 2022-05-29T23:48:43.000Z | [
"pytorch",
"hubert",
"automatic-speech-recognition",
"transformers"
] | automatic-speech-recognition | false | GENG | null | GENG/hubert_ls_2500 | 5 | null | transformers | 17,332 | Entry not found |
GENG/hubert_ls_4500 | 63214ec194dc3d3c181d7973df3db0c018bc16b7 | 2022-05-30T00:51:48.000Z | [
"pytorch",
"hubert",
"automatic-speech-recognition",
"transformers"
] | automatic-speech-recognition | false | GENG | null | GENG/hubert_ls_4500 | 5 | null | transformers | 17,333 | Entry not found |
shafin/distilbert-similarity-b32 | 7b20763fe972f06e7a5c07d1d52dd9402741c260 | 2022-05-30T08:56:46.000Z | [
"pytorch",
"distilbert",
"feature-extraction",
"sentence-transformers",
"sentence-similarity"
] | sentence-similarity | false | shafin | null | shafin/distilbert-similarity-b32 | 5 | null | sentence-transformers | 17,334 | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
---
# shafin/distilbert-similarity-b32
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 32 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('shafin/distilbert-similarity-b32')
embeddings = model.encode(sentences)
print(embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=shafin/distilbert-similarity-b32)
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 9375 with parameters:
```
{'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.OnlineContrastiveLoss.OnlineContrastiveLoss`
Parameters of the fit()-Method:
```
{
"epochs": 15,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'transformers.optimization.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 3000,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: DistilBertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
(2): Dense({'in_features': 768, 'out_features': 256, 'bias': True, 'activation_function': 'torch.nn.modules.activation.Tanh'})
(3): Dense({'in_features': 256, 'out_features': 32, 'bias': True, 'activation_function': 'torch.nn.modules.activation.Tanh'})
)
```
## Citing & Authors
<!--- Describe where people can find more information --> |
VanessaSchenkel/mbart-large-50-finetuned-opus-en-pt-translation-finetuned-en-to-pt-dataset-opus-books | 2a05c65c692041ababd2b6851d60c97bd7419bd6 | 2022-05-30T16:38:08.000Z | [
"pytorch",
"tensorboard",
"mbart",
"text2text-generation",
"dataset:opus_books",
"transformers",
"generated_from_trainer",
"model-index",
"autotrain_compatible"
] | text2text-generation | false | VanessaSchenkel | null | VanessaSchenkel/mbart-large-50-finetuned-opus-en-pt-translation-finetuned-en-to-pt-dataset-opus-books | 5 | null | transformers | 17,335 | ---
tags:
- generated_from_trainer
datasets:
- opus_books
model-index:
- name: mbart-large-50-finetuned-opus-en-pt-translation-finetuned-en-to-pt-dataset-opus-books
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. -->
# mbart-large-50-finetuned-opus-en-pt-translation-finetuned-en-to-pt-dataset-opus-books
This model is a fine-tuned version of [Narrativa/mbart-large-50-finetuned-opus-en-pt-translation](https://huggingface.co/Narrativa/mbart-large-50-finetuned-opus-en-pt-translation) on the opus_books dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|
| No log | 1.0 | 79 | 1.5854 | 31.2219 | 26.9149 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
theojolliffe/bart-cnn-science-v3-e2 | 2504418b3f40457c11c14fad4f5a80c7f25c52fc | 2022-05-30T21:42:44.000Z | [
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index",
"autotrain_compatible"
] | text2text-generation | false | theojolliffe | null | theojolliffe/bart-cnn-science-v3-e2 | 5 | null | transformers | 17,336 | ---
license: mit
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: bart-cnn-science-v3-e2
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-cnn-science-v3-e2
This model is a fine-tuned version of [theojolliffe/bart-cnn-science](https://huggingface.co/theojolliffe/bart-cnn-science) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9352
- Rouge1: 52.5497
- Rouge2: 32.5507
- Rougel: 35.0014
- Rougelsum: 50.0575
- Gen Len: 141.5741
## 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: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:--------:|
| No log | 1.0 | 398 | 1.0023 | 52.0744 | 31.917 | 33.2804 | 49.6569 | 142.0 |
| 1.1851 | 2.0 | 796 | 0.9352 | 52.5497 | 32.5507 | 35.0014 | 50.0575 | 141.5741 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
YeRyeongLee/electra-base-discriminator-finetuned-removed-0530 | d220014b57fc8e789e0186f2ff0fdff9b903ac24 | 2022-05-31T10:46:25.000Z | [
"pytorch",
"electra",
"text-classification",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | text-classification | false | YeRyeongLee | null | YeRyeongLee/electra-base-discriminator-finetuned-removed-0530 | 5 | null | transformers | 17,337 | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: electra-base-discriminator-finetuned-removed-0530
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. -->
# electra-base-discriminator-finetuned-removed-0530
This model is a fine-tuned version of [google/electra-base-discriminator](https://huggingface.co/google/electra-base-discriminator) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9713
- Accuracy: 0.8824
- F1: 0.8824
## 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
- lr_scheduler_warmup_steps: 1000
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|
| No log | 1.0 | 3180 | 0.6265 | 0.8107 | 0.8128 |
| No log | 2.0 | 6360 | 0.5158 | 0.8544 | 0.8541 |
| No log | 3.0 | 9540 | 0.6686 | 0.8563 | 0.8567 |
| No log | 4.0 | 12720 | 0.6491 | 0.8711 | 0.8709 |
| No log | 5.0 | 15900 | 0.8048 | 0.8660 | 0.8672 |
| No log | 6.0 | 19080 | 0.8110 | 0.8708 | 0.8710 |
| No log | 7.0 | 22260 | 1.0082 | 0.8651 | 0.8640 |
| 0.2976 | 8.0 | 25440 | 0.8343 | 0.8811 | 0.8814 |
| 0.2976 | 9.0 | 28620 | 0.9366 | 0.8780 | 0.8780 |
| 0.2976 | 10.0 | 31800 | 0.9713 | 0.8824 | 0.8824 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.9.0
- Datasets 1.16.1
- Tokenizers 0.12.1
|
upsalite/bert-base-german-cased-finetuned-emotion | 826a182b35d62165dfe2ddb2ec3af72007fec43f | 2022-06-22T12:51:41.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index"
] | text-classification | false | upsalite | null | upsalite/bert-base-german-cased-finetuned-emotion | 5 | null | transformers | 17,338 | ---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: bert-base-german-cased-finetuned-emotion
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-german-cased-finetuned-emotion
This model is a fine-tuned version of [bert-base-german-cased](https://huggingface.co/bert-base-german-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2345
- Accuracy: 0.6937
- F1: 0.6929
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 1.9412 | 1.0 | 140 | 1.4481 | 0.5402 | 0.5229 |
| 1.1779 | 2.0 | 280 | 1.1625 | 0.6375 | 0.6350 |
| 0.7914 | 3.0 | 420 | 1.0541 | 0.6732 | 0.6700 |
| 0.5264 | 4.0 | 560 | 1.0504 | 0.6821 | 0.6803 |
| 0.344 | 5.0 | 700 | 1.0638 | 0.6884 | 0.6853 |
| 0.2187 | 6.0 | 840 | 1.1309 | 0.6964 | 0.6945 |
| 0.1387 | 7.0 | 980 | 1.1504 | 0.7009 | 0.6986 |
| 0.0988 | 8.0 | 1120 | 1.2012 | 0.6964 | 0.6944 |
| 0.0705 | 9.0 | 1260 | 1.2153 | 0.7009 | 0.7003 |
| 0.0571 | 10.0 | 1400 | 1.2345 | 0.6937 | 0.6929 |
### Framework versions
- Transformers 4.19.0
- Pytorch 1.11.0+cu113
- Datasets 1.16.1
- Tokenizers 0.12.1
|
dexay/reDs | 0b591fbec56f01b965243bdeb326dc38d8b2f951 | 2022-05-31T13:02:48.000Z | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | false | dexay | null | dexay/reDs | 5 | null | transformers | 17,339 | Entry not found |
StanKrewinkel/finetuning-sentiment-model-3000-samples | 38e4186e992978c3a8c7bd8313e91e88a33dcce7 | 2022-06-02T07:06:19.000Z | [
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"transformers"
] | text-classification | false | StanKrewinkel | null | StanKrewinkel/finetuning-sentiment-model-3000-samples | 5 | null | transformers | 17,340 | Entry not found |
wuxiaofei/finetuning-sentiment-model-3000-samples | 7dee0f8f4aa4e9a597822709fe5264b6a0fc0949 | 2022-05-31T15:12:52.000Z | [
"pytorch",
"distilbert",
"text-classification",
"dataset:imdb",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | text-classification | false | wuxiaofei | null | wuxiaofei/finetuning-sentiment-model-3000-samples | 5 | null | transformers | 17,341 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
metrics:
- accuracy
- f1
model-index:
- name: finetuning-sentiment-model-3000-samples
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: imdb
type: imdb
args: plain_text
metrics:
- name: Accuracy
type: accuracy
value: 0.86
- name: F1
type: f1
value: 0.8636363636363636
---
<!-- 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-sentiment-model-3000-samples
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6787
- Accuracy: 0.86
- F1: 0.8636
## 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: 2
- eval_batch_size: 2
- 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 4.19.2
- Pytorch 1.11.0+cu102
- Datasets 2.2.2
- Tokenizers 0.12.1
|
oliverguhr/wav2vec2-large-xlsr-53-german | 3bc9db99ad960e4be76eaa003f5b0e749591efca | 2022-06-01T10:48:43.000Z | [
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"de",
"dataset:mozilla-foundation/common_voice_8_0",
"transformers",
"mozilla-foundation/common_voice_8_0",
"generated_from_trainer",
"model-index"
] | automatic-speech-recognition | false | oliverguhr | null | oliverguhr/wav2vec2-large-xlsr-53-german | 5 | null | transformers | 17,342 | ---
language:
- de
tags:
- automatic-speech-recognition
- mozilla-foundation/common_voice_8_0
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_8_0
model-index:
- name: XLSR Wav2Vec2 Large German by Oliver Guhr
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 8
type: mozilla-foundation/common_voice_8_0
args: de
metrics:
- name: Test WER
type: wer
value: 10.29
- name: Test CER
type: cer
value: 2.51
---
<!-- 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-large-xlsr-53-german-cv8-dropout-30epoch
This model is a fine-tuned version of [./wav2vec2-large-xlsr-53-german-cv8-dropout-30epoch](https://huggingface.co/./wav2vec2-large-xlsr-53-german-cv8-dropout-30epoch) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - DE dataset.
It achieves the following results on the test set:
- Wer: 10.29%
- CER: 2.51%
## 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: 16
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 20.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:------:|:---------------:|:------:|
| 0.2081 | 1.0 | 6815 | 0.1784 | 0.1910 |
| 0.1686 | 2.0 | 13630 | 0.1621 | 0.1725 |
| 0.1515 | 3.0 | 20445 | 0.1569 | 0.1649 |
| 0.1426 | 4.0 | 27260 | 0.1466 | 0.1681 |
| 0.135 | 5.0 | 34075 | 0.1357 | 0.1410 |
| 0.1093 | 6.0 | 40890 | 0.1313 | 0.1436 |
| 0.1 | 7.0 | 47705 | 0.1242 | 0.1250 |
| 0.0999 | 8.0 | 54520 | 0.1191 | 0.1218 |
| 0.084 | 9.0 | 61335 | 0.1134 | 0.1164 |
| 0.0752 | 10.0 | 68150 | 0.1111 | 0.1117 |
| 0.0724 | 11.0 | 6815 | 0.1222 | 0.1206 |
| 0.0726 | 12.0 | 13630 | 0.1241 | 0.1247 |
| 0.0816 | 13.0 | 20445 | 0.1235 | 0.1174 |
| 0.0814 | 14.0 | 27260 | 0.1231 | 0.1238 |
| 0.063 | 15.0 | 34075 | 0.1171 | 0.1159 |
| 0.0793 | 16.0 | 40890 | 0.1158 | 0.1168 |
| 0.0686 | 17.0 | 47705 | 0.1187 | 0.1151 |
| 0.071 | 18.0 | 54520 | 0.1170 | 0.1182 |
| 0.0629 | 19.0 | 61335 | 0.1160 | 0.1085 |
| 0.0558 | 20.0 | 68150 | 0.1154 | 0.1093 |
| 0.0531 | 21.0 | 74965 | 0.1175 | 0.1044 |
| 0.0648 | 22.0 | 81780 | 0.1172 | 0.1056 |
| 0.0513 | 23.0 | 88595 | 0.1180 | 0.1048 |
| 0.0496 | 24.0 | 95410 | 0.1197 | 0.1025 |
| 0.0549 | 25.0 | 102225 | 0.1184 | 0.0991 |
| 0.0493 | 26.0 | 109040 | 0.1176 | 0.0977 |
| 0.0445 | 27.0 | 115855 | 0.1178 | 0.0989 |
| 0.0451 | 28.0 | 122670 | 0.1188 | 0.0992 |
| 0.045 | 29.0 | 129485 | 0.1182 | 0.0990 |
| 0.0452 | 30.0 | 136300 | 0.1190 | 0.0980 |
### Framework versions
- Transformers 4.19.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.0.0
- Tokenizers 0.11.6
|
ShoneRan/bert-emotion | 0c11c1f65eeff0572218f6dcdb3e5985b24678f8 | 2022-06-02T05:15:37.000Z | [
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"dataset:tweet_eval",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | text-classification | false | ShoneRan | null | ShoneRan/bert-emotion | 5 | null | transformers | 17,343 | ---
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: 0.7262254187805659
- name: Recall
type: recall
value: 0.725549671319356
---
<!-- 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: 1.1670
- Precision: 0.7262
- Recall: 0.7255
- Fscore: 0.7253
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|
| 0.8561 | 1.0 | 815 | 0.7844 | 0.7575 | 0.6081 | 0.6253 |
| 0.5337 | 2.0 | 1630 | 0.9080 | 0.7567 | 0.7236 | 0.7325 |
| 0.2573 | 3.0 | 2445 | 1.1670 | 0.7262 | 0.7255 | 0.7253 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
etch/distilbert-base-uncased-finetuned-sst-2-english-finetuned-sst2 | b10369f234ed7caae10b163e6916abda2350ac4a | 2022-06-02T19:36:13.000Z | [
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"dataset:glue",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | text-classification | false | etch | null | etch/distilbert-base-uncased-finetuned-sst-2-english-finetuned-sst2 | 5 | null | transformers | 17,344 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased-finetuned-sst-2-english-finetuned-sst2
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
args: sst2
metrics:
- name: Accuracy
type: accuracy
value: 0.9059633027522935
---
<!-- 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-sst-2-english-finetuned-sst2
This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3950
- Accuracy: 0.9060
## 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: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.0818 | 1.0 | 4210 | 0.3950 | 0.9060 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
Umer4/UrduAudio2Text | 5ec0f811c9b78ba7dc95af5277c5e4c5300b5e00 | 2022-06-04T16:17:45.000Z | [
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"dataset:common_voice",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | automatic-speech-recognition | false | Umer4 | null | Umer4/UrduAudio2Text | 5 | null | transformers | 17,345 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- common_voice
model-index:
- name: UrduAudio2Text
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. -->
# UrduAudio2Text
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4978
- Wer: 0.8376
## 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: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- 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: 500
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 5.5558 | 15.98 | 400 | 1.4978 | 0.8376 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu113
- Datasets 2.2.2
- Tokenizers 0.10.3
|
Edric111/distilbert-base-uncased-finetuned-ner | 117a8fa42fdb2ebd457272b95b127488ace6972f | 2022-06-05T16:32:56.000Z | [
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"dataset:conll2003",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] | token-classification | false | Edric111 | null | Edric111/distilbert-base-uncased-finetuned-ner | 5 | null | transformers | 17,346 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: distilbert-base-uncased-finetuned-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: conll2003
type: conll2003
args: conll2003
metrics:
- name: Precision
type: precision
value: 0.9273854328093868
- name: Recall
type: recall
value: 0.9372413021590782
- name: F1
type: f1
value: 0.9322873198686918
- name: Accuracy
type: accuracy
value: 0.9840341874910639
---
<!-- 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-ner
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0599
- Precision: 0.9274
- Recall: 0.9372
- F1: 0.9323
- Accuracy: 0.9840
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2378 | 1.0 | 878 | 0.0719 | 0.9107 | 0.9200 | 0.9154 | 0.9801 |
| 0.0509 | 2.0 | 1756 | 0.0620 | 0.9156 | 0.9311 | 0.9233 | 0.9821 |
| 0.0307 | 3.0 | 2634 | 0.0599 | 0.9274 | 0.9372 | 0.9323 | 0.9840 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
enteramine/distilbert-base-uncased-finetuned-new-imdb | 69e7405229025f7a5a5022f6f22fa85d13ce45c9 | 2022-06-03T17:38:46.000Z | [
"pytorch",
"tensorboard",
"distilbert",
"fill-mask",
"dataset:imdb",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] | fill-mask | false | enteramine | null | enteramine/distilbert-base-uncased-finetuned-new-imdb | 5 | null | transformers | 17,347 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
model-index:
- name: distilbert-base-uncased-finetuned-new-imdb
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-new-imdb
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 2.4367
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.6828 | 1.0 | 157 | 2.5231 |
| 2.5621 | 2.0 | 314 | 2.4732 |
| 2.5255 | 3.0 | 471 | 2.4367 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
roshnir/mBert-finetuned-mlqa-dev-zh-hi | 0e30d6dd2064178c9f5caadf8dfae27830506b79 | 2022-06-03T20:43:49.000Z | [
"pytorch",
"bert",
"question-answering",
"transformers",
"autotrain_compatible"
] | question-answering | false | roshnir | null | roshnir/mBert-finetuned-mlqa-dev-zh-hi | 5 | null | transformers | 17,348 | Entry not found |
Jeevesh8/lecun_feather_berts-69 | 7bba220362f89745c10c57712c95734b8243ed0c | 2022-06-04T06:50:39.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
] | text-classification | false | Jeevesh8 | null | Jeevesh8/lecun_feather_berts-69 | 5 | null | transformers | 17,349 | Entry not found |
Jeevesh8/lecun_feather_berts-55 | 16329d3b324476a3a4789aed79d1346dad5f9d6b | 2022-06-04T06:50:51.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
] | text-classification | false | Jeevesh8 | null | Jeevesh8/lecun_feather_berts-55 | 5 | null | transformers | 17,350 | Entry not found |
Jeevesh8/lecun_feather_berts-34 | b774daaba9d58659732f19399cbb5736518cb8eb | 2022-06-04T06:51:04.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
] | text-classification | false | Jeevesh8 | null | Jeevesh8/lecun_feather_berts-34 | 5 | null | transformers | 17,351 | Entry not found |
Jeevesh8/lecun_feather_berts-73 | c8b90ccb7db876a179b6f118928d0afeb37aad28 | 2022-06-04T06:50:54.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
] | text-classification | false | Jeevesh8 | null | Jeevesh8/lecun_feather_berts-73 | 5 | null | transformers | 17,352 | Entry not found |
Jeevesh8/lecun_feather_berts-59 | 6d3783a8cb5d5e395c60b870eb12b0d0432665a5 | 2022-06-04T06:51:06.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
] | text-classification | false | Jeevesh8 | null | Jeevesh8/lecun_feather_berts-59 | 5 | null | transformers | 17,353 | Entry not found |
Jeevesh8/lecun_feather_berts-57 | f0d405dba0fb87667d5fb6eac193fd38be26485b | 2022-06-04T06:50:58.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
] | text-classification | false | Jeevesh8 | null | Jeevesh8/lecun_feather_berts-57 | 5 | null | transformers | 17,354 | Entry not found |
Jeevesh8/lecun_feather_berts-60 | a7797823d13077ed8df483bdf259bf07610a8d2b | 2022-06-04T06:50:57.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
] | text-classification | false | Jeevesh8 | null | Jeevesh8/lecun_feather_berts-60 | 5 | null | transformers | 17,355 | Entry not found |
Jeevesh8/lecun_feather_berts-25 | e7721cba6e1645d5c15042383eb6a4aee64293d1 | 2022-06-04T06:51:47.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
] | text-classification | false | Jeevesh8 | null | Jeevesh8/lecun_feather_berts-25 | 5 | null | transformers | 17,356 | Entry not found |
Jeevesh8/lecun_feather_berts-23 | 75f3a18507a798ba4f01f12e51a8500859285c2f | 2022-06-04T06:51:51.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
] | text-classification | false | Jeevesh8 | null | Jeevesh8/lecun_feather_berts-23 | 5 | null | transformers | 17,357 | Entry not found |
Jeevesh8/lecun_feather_berts-28 | cae86909ec7e7a7cd6c2a28e0bde42b0c69483e0 | 2022-06-04T06:51:55.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
] | text-classification | false | Jeevesh8 | null | Jeevesh8/lecun_feather_berts-28 | 5 | null | transformers | 17,358 | Entry not found |
Jeevesh8/lecun_feather_berts-32 | 23fc3c17edf9bd0adc9da5037aac68aed2bc46d6 | 2022-06-04T06:53:24.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
] | text-classification | false | Jeevesh8 | null | Jeevesh8/lecun_feather_berts-32 | 5 | null | transformers | 17,359 | Entry not found |
Jeevesh8/lecun_feather_berts-6 | cd5bd58b50efb589251da8643ed7061fc7e97706 | 2022-06-04T06:52:17.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
] | text-classification | false | Jeevesh8 | null | Jeevesh8/lecun_feather_berts-6 | 5 | null | transformers | 17,360 | Entry not found |
Jeevesh8/lecun_feather_berts-13 | 247c000b1fbc2d2126655bd2b7c73c07fe26be49 | 2022-06-04T06:51:39.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
] | text-classification | false | Jeevesh8 | null | Jeevesh8/lecun_feather_berts-13 | 5 | null | transformers | 17,361 | Entry not found |
Jeevesh8/lecun_feather_berts-17 | 598f081cfab0589085c3bd6a713818449fa957bf | 2022-06-04T06:52:10.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
] | text-classification | false | Jeevesh8 | null | Jeevesh8/lecun_feather_berts-17 | 5 | null | transformers | 17,362 | Entry not found |
Jeevesh8/lecun_feather_berts-15 | 42c948b587dc556dbabe144be8c8e668eba88999 | 2022-06-04T06:52:22.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
] | text-classification | false | Jeevesh8 | null | Jeevesh8/lecun_feather_berts-15 | 5 | null | transformers | 17,363 | Entry not found |
Jeevesh8/lecun_feather_berts-16 | e2a77409ad4d0be6d9e4974e8dfb613b833de1eb | 2022-06-04T06:52:01.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
] | text-classification | false | Jeevesh8 | null | Jeevesh8/lecun_feather_berts-16 | 5 | null | transformers | 17,364 | Entry not found |
Jeevesh8/lecun_feather_berts-94 | 05d4cb4386f74afdde1f7d94cca485954e1f02f5 | 2022-06-04T06:51:02.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
] | text-classification | false | Jeevesh8 | null | Jeevesh8/lecun_feather_berts-94 | 5 | null | transformers | 17,365 | Entry not found |
Jeevesh8/lecun_feather_berts-74 | e665870ee38b556af9a3471aeafbf5f2c9ca3eb1 | 2022-06-04T06:51:09.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
] | text-classification | false | Jeevesh8 | null | Jeevesh8/lecun_feather_berts-74 | 5 | null | transformers | 17,366 | Entry not found |
Jeevesh8/lecun_feather_berts-87 | 510c10695a63a4e5cbf00968e1152bd2bfebe6bc | 2022-06-04T06:53:07.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
] | text-classification | false | Jeevesh8 | null | Jeevesh8/lecun_feather_berts-87 | 5 | null | transformers | 17,367 | Entry not found |
lbw/distilbert-base-uncased-finetuned-ner | 623fa25c4a440e12399d0bcd4cca82e55af92e30 | 2022-06-04T07:44:07.000Z | [
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"dataset:conll2003",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] | token-classification | false | lbw | null | lbw/distilbert-base-uncased-finetuned-ner | 5 | null | transformers | 17,368 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: distilbert-base-uncased-finetuned-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: conll2003
type: conll2003
args: conll2003
metrics:
- name: Precision
type: precision
value: 0.9279388974983396
- name: Recall
type: recall
value: 0.9378006488421524
- name: F1
type: f1
value: 0.9328437100094585
- name: Accuracy
type: accuracy
value: 0.9839706419686403
---
<!-- 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-ner
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0596
- Precision: 0.9279
- Recall: 0.9378
- F1: 0.9328
- Accuracy: 0.9840
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2377 | 1.0 | 878 | 0.0717 | 0.9140 | 0.9205 | 0.9172 | 0.9800 |
| 0.0498 | 2.0 | 1756 | 0.0609 | 0.9168 | 0.9332 | 0.9249 | 0.9827 |
| 0.0301 | 3.0 | 2634 | 0.0596 | 0.9279 | 0.9378 | 0.9328 | 0.9840 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
bubblecookie/samsum_trained_t5_model | d19be31791c1e61ad5b7c428a0a519b80d65fada | 2022-06-04T13:32:18.000Z | [
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | bubblecookie | null | bubblecookie/samsum_trained_t5_model | 5 | null | transformers | 17,369 | Entry not found |
yanekyuk/bert-cased-keyword-discriminator | fc2e64f87118dc006117d59bf6a877ca64cda0f3 | 2022-06-04T20:24:14.000Z | [
"pytorch",
"bert",
"token-classification",
"en",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] | token-classification | false | yanekyuk | null | yanekyuk/bert-cased-keyword-discriminator | 5 | null | transformers | 17,370 | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
- accuracy
- f1
language:
- en
widget:
- text: "Broadcom agreed to acquire cloud computing company VMware in a $61 billion (€57bn) cash-and stock deal, massively diversifying the chipmaker’s business and almost tripling its software-related revenue to about 45% of its total sales. By the numbers: VMware shareholders will receive either $142.50 in cash or 0.2520 of a Broadcom share for each VMware stock. Broadcom will also assume $8 billion of VMware's net debt."
- text: "Canadian Natural Resources Minister Jonathan Wilkinson told Bloomberg that the country could start supplying Europe with liquefied natural gas (LNG) in as soon as three years by converting an existing LNG import facility on Canada’s Atlantic coast into an export terminal. Bottom line: Wilkinson said what Canada cares about is that the new LNG facility uses a low-emission process for the gas and is capable of transitioning to exporting hydrogen later on."
- text: "Google is being investigated by the UK’s antitrust watchdog for its dominance in the \"ad tech stack,\" the set of services that facilitate the sale of online advertising space between advertisers and sellers. Google has strong positions at various levels of the ad tech stack and charges fees to both publishers and advertisers. A step back: UK Competition and Markets Authority has also been investigating whether Google and Meta colluded over ads, probing into the advertising agreement between the two companies, codenamed Jedi Blue."
- text: "Shares in Twitter closed 6.35% up after an SEC 13D filing revealed that Elon Musk pledged to put up an additional $6.25 billion of his own wealth to fund the $44 billion takeover deal, lifting the total to $33.5 billion from an initial $27.25 billion. In other news: Former Twitter CEO Jack Dorsey announced he's stepping down, but would stay on Twitter’s board \\“until his term expires at the 2022 meeting of stockholders.\""
model-index:
- name: bert-keyword-discriminator
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-keyword-discriminator
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1310
- Precision: 0.8522
- Recall: 0.8868
- Accuracy: 0.9732
- F1: 0.8692
- Ent/precision: 0.8874
- Ent/accuracy: 0.9246
- Ent/f1: 0.9056
- Con/precision: 0.8011
- Con/accuracy: 0.8320
- Con/f1: 0.8163
## 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: 8
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | Accuracy | F1 | Ent/precision | Ent/accuracy | Ent/f1 | Con/precision | Con/accuracy | Con/f1 |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:--------:|:------:|:-------------:|:------------:|:------:|:-------------:|:------------:|:------:|
| 0.1744 | 1.0 | 1875 | 0.1261 | 0.7176 | 0.7710 | 0.9494 | 0.7433 | 0.7586 | 0.8503 | 0.8018 | 0.6514 | 0.6561 | 0.6537 |
| 0.1261 | 2.0 | 3750 | 0.1041 | 0.7742 | 0.8057 | 0.9600 | 0.7896 | 0.8083 | 0.8816 | 0.8433 | 0.7185 | 0.6957 | 0.7070 |
| 0.0878 | 3.0 | 5625 | 0.0979 | 0.8176 | 0.8140 | 0.9655 | 0.8158 | 0.8518 | 0.8789 | 0.8651 | 0.7634 | 0.7199 | 0.7410 |
| 0.0625 | 4.0 | 7500 | 0.0976 | 0.8228 | 0.8643 | 0.9696 | 0.8430 | 0.8515 | 0.9182 | 0.8836 | 0.7784 | 0.7862 | 0.7823 |
| 0.0456 | 5.0 | 9375 | 0.1047 | 0.8304 | 0.8758 | 0.9704 | 0.8525 | 0.8758 | 0.9189 | 0.8968 | 0.7655 | 0.8133 | 0.7887 |
| 0.0342 | 6.0 | 11250 | 0.1207 | 0.8363 | 0.8887 | 0.9719 | 0.8617 | 0.8719 | 0.9274 | 0.8988 | 0.7846 | 0.8327 | 0.8080 |
| 0.0256 | 7.0 | 13125 | 0.1241 | 0.848 | 0.8892 | 0.9731 | 0.8681 | 0.8791 | 0.9299 | 0.9038 | 0.8019 | 0.8302 | 0.8158 |
| 0.0205 | 8.0 | 15000 | 0.1310 | 0.8522 | 0.8868 | 0.9732 | 0.8692 | 0.8874 | 0.9246 | 0.9056 | 0.8011 | 0.8320 | 0.8163 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
helliun/distilbert-gaydar | 5fa91e4d5895c01639338a85ac72ab6d38f57f34 | 2022-06-04T21:44:20.000Z | [
"pytorch",
"distilbert",
"text-classification",
"transformers"
] | text-classification | false | helliun | null | helliun/distilbert-gaydar | 5 | null | transformers | 17,371 | Entry not found |
nestoralvaro/mT5_multilingual_XLSum-finetuned-xsum-xsum | a3f6bb5f3c8c5241a3096b0670726ab89668c4c0 | 2022-06-05T19:30:12.000Z | [
"pytorch",
"tensorboard",
"mt5",
"text2text-generation",
"dataset:xsum",
"transformers",
"generated_from_trainer",
"model-index",
"autotrain_compatible"
] | text2text-generation | false | nestoralvaro | null | nestoralvaro/mT5_multilingual_XLSum-finetuned-xsum-xsum | 5 | null | transformers | 17,372 | ---
tags:
- generated_from_trainer
datasets:
- xsum
metrics:
- rouge
model-index:
- name: mT5_multilingual_XLSum-finetuned-xsum-xsum
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: xsum
type: xsum
args: default
metrics:
- name: Rouge1
type: rouge
value: 0.0
---
<!-- 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. -->
# mT5_multilingual_XLSum-finetuned-xsum-xsum
This model is a fine-tuned version of [csebuetnlp/mT5_multilingual_XLSum](https://huggingface.co/csebuetnlp/mT5_multilingual_XLSum) on the xsum dataset.
It achieves the following results on the evaluation set:
- Loss: nan
- Rouge1: 0.0
- Rouge2: 0.0
- Rougel: 0.0
- Rougelsum: 0.0
- Gen Len: 1.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 2
- eval_batch_size: 2
- 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 |
|:-------------:|:-----:|:------:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| 0.0 | 1.0 | 102023 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
bondi/bert-semaphore-prediction-w8 | 2a8204861ddb1578b69ea2ed67a16e2cbcb460b3 | 2022-06-06T02:36:31.000Z | [
"pytorch",
"bert",
"text-classification",
"transformers",
"generated_from_trainer",
"model-index"
] | text-classification | false | bondi | null | bondi/bert-semaphore-prediction-w8 | 5 | null | transformers | 17,373 | ---
tags:
- generated_from_trainer
model-index:
- name: bert-semaphore-prediction-w8
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-semaphore-prediction-w8
This model was trained from scratch 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: 2e-05
- train_batch_size: 6
- eval_batch_size: 6
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0
- Datasets 2.2.2
- Tokenizers 0.12.1
|
seonghee/bert-base-uncased-emotion | e5ab47314fd88d61c6c82f491f80d851c26ae0c6 | 2022-06-06T05:33:54.000Z | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | false | seonghee | null | seonghee/bert-base-uncased-emotion | 5 | null | transformers | 17,374 | Entry not found |
bekirbakar/wav2vec2-large-xls-r-300m-finnish | 2709d9fa3126059a424c43aa7ea511d8cbfbaf51 | 2022-06-16T13:34:45.000Z | [
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"dataset:common_voice",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | automatic-speech-recognition | false | bekirbakar | null | bekirbakar/wav2vec2-large-xls-r-300m-finnish | 5 | null | transformers | 17,375 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- common_voice
model-index:
- name: wav2vec2-large-xls-r-300m-finnish
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-large-xls-r-300m-finnish
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4747
- Wer: 0.5143
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- 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: 500
- num_epochs: 30
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.1666 | 14.8 | 400 | 0.4747 | 0.5143 |
| 0.0875 | 29.62 | 800 | 0.4747 | 0.5143 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu113
- Datasets 1.18.3
- Tokenizers 0.10.3
|
logo-data-science/mt5-logo-qg-qa-turkish | f6c8539ccf517caddf95152f7af6beb09a933673 | 2022-06-06T14:55:17.000Z | [
"pytorch",
"mt5",
"text2text-generation",
"transformers",
"license:gpl",
"autotrain_compatible"
] | text2text-generation | false | logo-data-science | null | logo-data-science/mt5-logo-qg-qa-turkish | 5 | null | transformers | 17,376 | ---
license: gpl
---
|
bondi/bert-clean-semaphore-prediction-w0 | 775537aea2b848a93caa88601e72ff48b41e968a | 2022-06-07T05:54:44.000Z | [
"pytorch",
"bert",
"text-classification",
"transformers",
"generated_from_trainer",
"model-index"
] | text-classification | false | bondi | null | bondi/bert-clean-semaphore-prediction-w0 | 5 | null | transformers | 17,377 | ---
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: bert-clean-semaphore-prediction-w0
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-clean-semaphore-prediction-w0
This model is a fine-tuned version of [dccuchile/bert-base-spanish-wwm-cased](https://huggingface.co/dccuchile/bert-base-spanish-wwm-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0680
- Accuracy: 0.9693
- F1: 0.9694
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0
- Datasets 2.2.2
- Tokenizers 0.12.1
|
DanielSM/1444Test | ebc5f32fee2a59cb2b0703f4e101f4ab69743cc6 | 2022-06-07T06:24:02.000Z | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | false | DanielSM | null | DanielSM/1444Test | 5 | null | transformers | 17,378 | Entry not found |
bondi/bert-clean-semaphore-prediction-w4 | 1dca3c92c9c418f2da8ddfe188fd40829bc1047e | 2022-06-07T07:55:16.000Z | [
"pytorch",
"bert",
"text-classification",
"transformers",
"generated_from_trainer",
"model-index"
] | text-classification | false | bondi | null | bondi/bert-clean-semaphore-prediction-w4 | 5 | null | transformers | 17,379 | ---
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: bert-clean-semaphore-prediction-w4
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-clean-semaphore-prediction-w4
This model is a fine-tuned version of [dccuchile/bert-base-spanish-wwm-cased](https://huggingface.co/dccuchile/bert-base-spanish-wwm-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0747
- Accuracy: 0.9652
- F1: 0.9651
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0
- Datasets 2.2.2
- Tokenizers 0.12.1
|
bondi/bert-clean-semaphore-prediction-w8 | 6b22353f2bc3644d9976ac0b76c1958107503d6d | 2022-06-07T08:55:38.000Z | [
"pytorch",
"bert",
"text-classification",
"transformers",
"generated_from_trainer",
"model-index"
] | text-classification | false | bondi | null | bondi/bert-clean-semaphore-prediction-w8 | 5 | null | transformers | 17,380 | ---
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: bert-clean-semaphore-prediction-w8
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-clean-semaphore-prediction-w8
This model is a fine-tuned version of [dccuchile/bert-base-spanish-wwm-cased](https://huggingface.co/dccuchile/bert-base-spanish-wwm-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0669
- Accuracy: 0.9671
- F1: 0.9672
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0
- Datasets 2.2.2
- Tokenizers 0.12.1
|
Matthijs/ane-distilbert-test | e4c33a637122614bcd26939f1236fe87f853faa2 | 2022-06-07T14:14:12.000Z | [
"pytorch",
"distilbert",
"text-classification",
"transformers"
] | text-classification | false | Matthijs | null | Matthijs/ane-distilbert-test | 5 | null | transformers | 17,381 | Entry not found |
mmillet/distilrubert-tiny-cased-conversational-v1_finetuned_emotion_experiment_augmented_anger_fear | 3af6c59503cdf5538a4845029329e9e9e9cdd1b7 | 2022-06-08T16:10:06.000Z | [
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"transformers",
"generated_from_trainer",
"model-index"
] | text-classification | false | mmillet | null | mmillet/distilrubert-tiny-cased-conversational-v1_finetuned_emotion_experiment_augmented_anger_fear | 5 | null | transformers | 17,382 | ---
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: distilrubert-tiny-cased-conversational-v1_finetuned_emotion_experiment_augmented_anger_fear
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. -->
# distilrubert-tiny-cased-conversational-v1_finetuned_emotion_experiment_augmented_anger_fear
This model is a fine-tuned version of [DeepPavlov/distilrubert-tiny-cased-conversational-v1](https://huggingface.co/DeepPavlov/distilrubert-tiny-cased-conversational-v1) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3760
- Accuracy: 0.8758
- F1: 0.8750
- Precision: 0.8753
- Recall: 0.8758
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=0.0001
- lr_scheduler_type: linear
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 1.2636 | 1.0 | 69 | 1.0914 | 0.6013 | 0.5599 | 0.5780 | 0.6013 |
| 1.029 | 2.0 | 138 | 0.9180 | 0.6514 | 0.6344 | 0.6356 | 0.6514 |
| 0.904 | 3.0 | 207 | 0.8235 | 0.6827 | 0.6588 | 0.6904 | 0.6827 |
| 0.8084 | 4.0 | 276 | 0.7272 | 0.7537 | 0.7477 | 0.7564 | 0.7537 |
| 0.7242 | 5.0 | 345 | 0.6435 | 0.7860 | 0.7841 | 0.7861 | 0.7860 |
| 0.6305 | 6.0 | 414 | 0.5543 | 0.8173 | 0.8156 | 0.8200 | 0.8173 |
| 0.562 | 7.0 | 483 | 0.4860 | 0.8392 | 0.8383 | 0.8411 | 0.8392 |
| 0.5042 | 8.0 | 552 | 0.4474 | 0.8528 | 0.8514 | 0.8546 | 0.8528 |
| 0.4535 | 9.0 | 621 | 0.4213 | 0.8580 | 0.8579 | 0.8590 | 0.8580 |
| 0.4338 | 10.0 | 690 | 0.4106 | 0.8591 | 0.8578 | 0.8605 | 0.8591 |
| 0.4026 | 11.0 | 759 | 0.4064 | 0.8622 | 0.8615 | 0.8632 | 0.8622 |
| 0.3861 | 12.0 | 828 | 0.3874 | 0.8737 | 0.8728 | 0.8733 | 0.8737 |
| 0.3709 | 13.0 | 897 | 0.3841 | 0.8706 | 0.8696 | 0.8701 | 0.8706 |
| 0.3592 | 14.0 | 966 | 0.3841 | 0.8716 | 0.8709 | 0.8714 | 0.8716 |
| 0.3475 | 15.0 | 1035 | 0.3834 | 0.8737 | 0.8728 | 0.8732 | 0.8737 |
| 0.3537 | 16.0 | 1104 | 0.3805 | 0.8727 | 0.8717 | 0.8722 | 0.8727 |
| 0.3317 | 17.0 | 1173 | 0.3775 | 0.8747 | 0.8739 | 0.8741 | 0.8747 |
| 0.323 | 18.0 | 1242 | 0.3759 | 0.8727 | 0.8718 | 0.8721 | 0.8727 |
| 0.3327 | 19.0 | 1311 | 0.3776 | 0.8758 | 0.8750 | 0.8756 | 0.8758 |
| 0.3339 | 20.0 | 1380 | 0.3760 | 0.8758 | 0.8750 | 0.8753 | 0.8758 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
huggingtweets/makimasdoggy | 3fddcd9fa61916b8ca12206908de94800f45665c | 2022-06-08T19:17:06.000Z | [
"pytorch",
"gpt2",
"text-generation",
"en",
"transformers",
"huggingtweets"
] | text-generation | false | huggingtweets | null | huggingtweets/makimasdoggy | 5 | null | transformers | 17,383 | ---
language: en
thumbnail: http://www.huggingtweets.com/makimasdoggy/1654715821978/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1534537330014445569/ql3I-npY_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Vanser</div>
<div style="text-align: center; font-size: 14px;">@makimasdoggy</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from Vanser.
| Data | Vanser |
| --- | --- |
| Tweets downloaded | 3249 |
| Retweets | 1548 |
| Short tweets | 346 |
| Tweets kept | 1355 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/66wk3fyw/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @makimasdoggy's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2di8hgps) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2di8hgps/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/makimasdoggy')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
huggingtweets/verizon | d1a63d4f7bc835e46ebb9303b380230d4aef3d21 | 2022-06-09T00:33:36.000Z | [
"pytorch",
"gpt2",
"text-generation",
"en",
"transformers",
"huggingtweets"
] | text-generation | false | huggingtweets | null | huggingtweets/verizon | 5 | null | transformers | 17,384 | ---
language: en
thumbnail: https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1496892874276880389/ndAolYWm_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Verizon</div>
<div style="text-align: center; font-size: 14px;">@verizon</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from Verizon.
| Data | Verizon |
| --- | --- |
| Tweets downloaded | 3246 |
| Retweets | 408 |
| Short tweets | 188 |
| Tweets kept | 2650 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2rssnlth/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @verizon's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/17qcsqw6) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/17qcsqw6/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/verizon')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
Skil-Internal/bart-paraphrase-finetuned-xsum-v4 | 4702e43bb02ebe990ce525bf5a8028508a436d21 | 2022-06-09T08:52:10.000Z | [
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] | text2text-generation | false | Skil-Internal | null | Skil-Internal/bart-paraphrase-finetuned-xsum-v4 | 5 | null | transformers | 17,385 | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: bart-paraphrase-finetuned-xsum-v4
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-v4
This model is a fine-tuned version of [eugenesiow/bart-paraphrase](https://huggingface.co/eugenesiow/bart-paraphrase) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1765
- Rouge1: 49.972
- Rouge2: 49.85
- Rougel: 49.9165
- Rougelsum: 49.7819
- Gen Len: 8.3061
## 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: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| No log | 1.0 | 263 | 0.5050 | 47.9628 | 47.7085 | 47.8625 | 47.772 | 6.9639 |
| 0.676 | 2.0 | 526 | 0.5793 | 49.6085 | 49.3495 | 49.5196 | 49.4173 | 7.4715 |
| 0.676 | 3.0 | 789 | 0.7011 | 49.8635 | 49.6937 | 49.8155 | 49.6604 | 7.576 |
| 0.322 | 4.0 | 1052 | 0.7585 | 49.8851 | 49.7578 | 49.8526 | 49.6977 | 7.6654 |
| 0.322 | 5.0 | 1315 | 0.6615 | 49.861 | 49.7185 | 49.7978 | 49.6669 | 8.3023 |
| 0.2828 | 6.0 | 1578 | 0.6233 | 49.916 | 49.7819 | 49.8861 | 49.7384 | 7.6084 |
| 0.2828 | 7.0 | 1841 | 0.9380 | 49.916 | 49.7819 | 49.8861 | 49.7384 | 8.2433 |
| 0.2073 | 8.0 | 2104 | 0.8497 | 49.9624 | 49.8355 | 49.91 | 49.7666 | 7.6331 |
| 0.2073 | 9.0 | 2367 | 0.7715 | 49.972 | 49.85 | 49.9165 | 49.7819 | 7.9772 |
| 0.1744 | 10.0 | 2630 | 1.1765 | 49.972 | 49.85 | 49.9165 | 49.7819 | 8.3061 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
ghadeermobasher/WLT-PubMedBERT-NCBI | 4fcf51cc796410aff255f7557c8224a76a8746ed | 2022-06-09T10:28:35.000Z | [
"pytorch",
"tensorboard",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | false | ghadeermobasher | null | ghadeermobasher/WLT-PubMedBERT-NCBI | 5 | null | transformers | 17,386 | Entry not found |
radiogroup-crits/voxpopuli_base_it_2_5_gram_doc4lm | 47526bc9ae9e990587cb12de8746ba2643713be1 | 2022-06-15T09:31:23.000Z | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"it",
"dataset:voxpopuli-v2",
"transformers",
"audio",
"hf-asr-leaderboard",
"voxpopuli-v2",
"speech",
"license:apache-2.0"
] | automatic-speech-recognition | false | radiogroup-crits | null | radiogroup-crits/voxpopuli_base_it_2_5_gram_doc4lm | 5 | null | transformers | 17,387 | ---
language:
- it
license: apache-2.0
datasets:
- voxpopuli-v2
metrics:
- wer
- cer
tags:
- audio
- automatic-speech-recognition
- hf-asr-leaderboard
- it
- voxpopuli-v2
- speech
- wav2vec2
---
# VOXPOLULI_BASE_IT_2_5_GRAM_DOC4LM
## Language model information
Our language model was generated using a dataset of Italian wikipedia articles and manual transcriptions of radio newspapers and television programs.
## Citation
If you want to cite this model you can use this:
```bibtex
@misc{crits2022voxpopuli_base_it_2_5_gram_doc4lm,
title={Wav2Vec2 with LM Italian by radiogroup crits},
author={Teraoni Prioletti Raffaele, Casagranda Paolo and Russo Francesco},
publisher={Hugging Face},
journal={Hugging Face Hub},
howpublished={\url{https://huggingface.co/radiogroup-crits/voxpopuli_base_it_2_5_gram_doc4lm}},
year={2022}
}
``` |
ghadeermobasher/WLT-BlueBERT-BC4CHEMD | d8deaf41766b9e670468d3d7a9f851c8fcfb4e9d | 2022-06-09T16:47:23.000Z | [
"pytorch",
"tensorboard",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | false | ghadeermobasher | null | ghadeermobasher/WLT-BlueBERT-BC4CHEMD | 5 | null | transformers | 17,388 | Entry not found |
ghadeermobasher/WLT-SciBERT-BC4CHEMD-O | b58115af8bce48d6edd3963daab31976bcf9b8b9 | 2022-06-09T13:45:04.000Z | [
"pytorch",
"tensorboard",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | false | ghadeermobasher | null | ghadeermobasher/WLT-SciBERT-BC4CHEMD-O | 5 | null | transformers | 17,389 | Entry not found |
Peltarion/dnabert-minilm-small | 88883e0c412bcccefd2846c4cda67e5eaa06b578 | 2022-07-02T11:29:00.000Z | [
"pytorch",
"bert",
"transformers",
"DNA",
"license:mit"
] | null | false | Peltarion | null | Peltarion/dnabert-minilm-small | 5 | null | transformers | 17,390 | ---
tags:
- DNA
license: mit
---
## MiniDNA small model
This is a distilled version of [DNABERT](https://github.com/jerryji1993/DNABERT) by using MiniLM technique. It has a BERT architecture with 6 layers and 384 hidden units, pre-trained on 6-mer DNA sequences. For more details on the pre-training scheme and methods, please check the original [thesis report](http://www.diva-portal.org/smash/record.jsf?dswid=846&pid=diva2%3A1676068&c=1&searchType=SIMPLE&language=en&query=joana+palés&af=%5B%5D&aq=%5B%5B%5D%5D&aq2=%5B%5B%5D%5D&aqe=%5B%5D&noOfRows=50&sortOrder=author_sort_asc&sortOrder2=title_sort_asc&onlyFullText=false&sf=all)..
## How to Use
The model can be used to fine-tune on a downstream genomic task, e.g. promoter identification.
```python
import torch
from transformers import BertForSequenceClassification
model = BertForSequenceClassification.from_pretrained('Peltarion/dnabert-minilm-small')
```
More details on how to fine-tune the model, dataset and additional source codes are available on [github.com/joanaapa/Distillation-DNABERT-Promoter](https://github.com/joanaapa/Distillation-DNABERT-Promoter). |
roshnir/xlmr-finetuned-mlqa-dev-en-zh-hi | 1e3705e14ed2d6bf8e7bdaa74fd0435b882899fb | 2022-06-09T19:02:16.000Z | [
"pytorch",
"xlm-roberta",
"question-answering",
"transformers",
"autotrain_compatible"
] | question-answering | false | roshnir | null | roshnir/xlmr-finetuned-mlqa-dev-en-zh-hi | 5 | null | transformers | 17,391 | Entry not found |
mfreihaut/finetuned-audio-transcriber | 3df044cd2b0bd466d35d571c38ca24a6bd95499d | 2022-06-10T05:24:30.000Z | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"transformers"
] | automatic-speech-recognition | false | mfreihaut | null | mfreihaut/finetuned-audio-transcriber | 5 | null | transformers | 17,392 | Entry not found |
titi7242229/roberta-base-bne-finetuned_personality_multi | 0ab729cc14001f2bd3fbd26d0b9d0b93c534cf4f | 2022-06-10T14:19:54.000Z | [
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | text-classification | false | titi7242229 | null | titi7242229/roberta-base-bne-finetuned_personality_multi | 5 | null | transformers | 17,393 | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: roberta-base-bne-finetuned_personality_multi
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-base-bne-finetuned_personality_multi
This model is a fine-tuned version of [BSC-TeMU/roberta-base-bne](https://huggingface.co/BSC-TeMU/roberta-base-bne) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.3709
- Accuracy: 0.5130
## 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: 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: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.2576 | 1.0 | 125 | 2.2755 | 0.2340 |
| 2.0409 | 2.0 | 250 | 2.1425 | 0.2974 |
| 1.6358 | 3.0 | 375 | 1.8730 | 0.4403 |
| 1.3553 | 4.0 | 500 | 1.7443 | 0.5032 |
| 0.9201 | 5.0 | 625 | 1.7165 | 0.5055 |
| 0.5199 | 6.0 | 750 | 1.7476 | 0.5107 |
| 0.5588 | 7.0 | 875 | 1.7758 | 0.5153 |
| 0.2079 | 8.0 | 1000 | 1.7964 | 0.5251 |
| 0.2685 | 9.0 | 1125 | 1.8886 | 0.5187 |
| 0.1261 | 10.0 | 1250 | 1.9463 | 0.5199 |
| 0.1105 | 11.0 | 1375 | 2.0337 | 0.5222 |
| 0.1572 | 12.0 | 1500 | 2.1206 | 0.5084 |
| 0.0643 | 13.0 | 1625 | 2.1815 | 0.5182 |
| 0.0174 | 14.0 | 1750 | 2.2412 | 0.5176 |
| 0.0266 | 15.0 | 1875 | 2.2741 | 0.5112 |
| 0.0447 | 16.0 | 2000 | 2.3089 | 0.5159 |
| 0.02 | 17.0 | 2125 | 2.3401 | 0.5135 |
| 0.0414 | 18.0 | 2250 | 2.3504 | 0.5159 |
| 0.0122 | 19.0 | 2375 | 2.3661 | 0.5130 |
| 0.0154 | 20.0 | 2500 | 2.3709 | 0.5130 |
### Framework versions
- Transformers 4.19.3
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
edumunozsala/vit_base-224-in21k-ft-cifar10 | 115eb6c2394aaf071ae8a52a22366e137cb5fb29 | 2022-07-21T10:54:09.000Z | [
"pytorch",
"vit",
"image-classification",
"es",
"dataset:cifar10",
"arxiv:2006.03677",
"transformers",
"sagemaker",
"ImageClassification",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | image-classification | false | edumunozsala | null | edumunozsala/vit_base-224-in21k-ft-cifar10 | 5 | null | transformers | 17,394 | ---
language: es
tags:
- sagemaker
- vit
- ImageClassification
- generated_from_trainer
license: apache-2.0
datasets:
- cifar10
metrics:
- accuracy
model-index:
- name: vit_base-224-in21k-ft-cifar10
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: "Cifar10"
type: cifar10
metrics:
- name: Accuracy
type: accuracy
value: 0.97
---
# Model vit_base-224-in21k-ft-cifar10
## **A finetuned model for Image classification in Spanish**
This model was trained using Amazon SageMaker and the Hugging Face Deep Learning container,
The base model is **Vision Transformer (base-sized model)** which is a transformer encoder model (BERT-like) pretrained on a large collection of images in a supervised fashion, namely ImageNet-21k, at a resolution of 224x224 pixels.[Link to base model](https://huggingface.co/google/vit-base-patch16-224-in21k)
## Base model citation
### BibTeX entry and citation info
```bibtex
@misc{wu2020visual,
title={Visual Transformers: Token-based Image Representation and Processing for Computer Vision},
author={Bichen Wu and Chenfeng Xu and Xiaoliang Dai and Alvin Wan and Peizhao Zhang and Zhicheng Yan and Masayoshi Tomizuka and Joseph Gonzalez and Kurt Keutzer and Peter Vajda},
year={2020},
eprint={2006.03677},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```
## Dataset
[Link to dataset description](http://www.cs.toronto.edu/~kriz/cifar.html)
The CIFAR-10 and CIFAR-100 are labeled subsets of the 80 million tiny images dataset. They were collected by Alex Krizhevsky, Vinod Nair, and Geoffrey Hinton
The CIFAR-10 dataset consists of 60000 32x32 colour images in 10 classes, with 6000 images per class. There are 50000 training images and 10000 test images.
The dataset is divided into five training batches and one test batch, each with 10000 images. The test batch contains exactly 1000 randomly-selected images from each class. The training batches contain the remaining images in random order, but some training batches may contain more images from one class than another. Between them, the training batches contain exactly 5000 images from each class.
Sizes of datasets:
- Train dataset: 50,000
- Test dataset: 10,000
## Intended uses & limitations
This model is intented for Image Classification.
## Hyperparameters
{
"epochs": "5",
"train_batch_size": "32",
"eval_batch_size": "8",
"fp16": "true",
"learning_rate": "1e-05",
}
## Test results
- Accuracy = 0.97
## Model in action
### Usage for Image Classification
```python
from transformers import ViTFeatureExtractor, ViTModel
from PIL import Image
import requests
url = 'http://images.cocodataset.org/val2017/000000039769.jpg'
image = Image.open(requests.get(url, stream=True).raw)
feature_extractor = ViTFeatureExtractor.from_pretrained('google/vit-base-patch16-224-in21k')
model = ViTModel.from_pretrained('edumunozsala/vit_base-224-in21k-ft-cifar10')
inputs = feature_extractor(images=image, return_tensors="pt")
outputs = model(**inputs)
last_hidden_states = outputs.last_hidden_state
```
Created by [Eduardo Muñoz/@edumunozsala](https://github.com/edumunozsala)
|
titi7242229/roberta-base-bne-finetuned_personality_multi_4 | 4b97cf1711b016e37aef57d5ab9938275a4e20a2 | 2022-06-11T19:13:27.000Z | [
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | text-classification | false | titi7242229 | null | titi7242229/roberta-base-bne-finetuned_personality_multi_4 | 5 | null | transformers | 17,395 | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: roberta-base-bne-finetuned_personality_multi_4
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-base-bne-finetuned_personality_multi_4
This model is a fine-tuned version of [BSC-TeMU/roberta-base-bne](https://huggingface.co/BSC-TeMU/roberta-base-bne) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 4.1709
- Accuracy: 0.3470
## 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: 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: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.1759 | 1.0 | 125 | 2.1873 | 0.2548 |
| 1.8651 | 2.0 | 250 | 2.2285 | 0.2680 |
| 1.8619 | 3.0 | 375 | 2.1732 | 0.2951 |
| 1.7224 | 4.0 | 500 | 2.0688 | 0.3925 |
| 1.6432 | 5.0 | 625 | 2.1094 | 0.3735 |
| 1.3599 | 6.0 | 750 | 2.1732 | 0.3631 |
| 1.0623 | 7.0 | 875 | 2.4785 | 0.3579 |
| 1.0504 | 8.0 | 1000 | 2.4598 | 0.3844 |
| 0.7662 | 9.0 | 1125 | 2.8081 | 0.3573 |
| 0.9167 | 10.0 | 1250 | 2.9385 | 0.3452 |
| 0.6391 | 11.0 | 1375 | 2.9933 | 0.3320 |
| 0.3893 | 12.0 | 1500 | 3.1037 | 0.3579 |
| 0.673 | 13.0 | 1625 | 3.4369 | 0.3631 |
| 0.3498 | 14.0 | 1750 | 3.6396 | 0.3383 |
| 0.3891 | 15.0 | 1875 | 3.8332 | 0.3556 |
| 0.0818 | 16.0 | 2000 | 3.9451 | 0.3401 |
| 0.1438 | 17.0 | 2125 | 3.9271 | 0.3458 |
| 0.0634 | 18.0 | 2250 | 4.1564 | 0.3481 |
| 0.0121 | 19.0 | 2375 | 4.1405 | 0.3499 |
| 0.0071 | 20.0 | 2500 | 4.1709 | 0.3470 |
### Framework versions
- Transformers 4.19.4
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
Lindeberg/distilbert-base-uncased-finetuned-cola | c1bfd0d5b5ad2589e8dfa06731d1b7b47c1972cd | 2022-06-11T21:10:06.000Z | [
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"dataset:glue",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | text-classification | false | Lindeberg | null | Lindeberg/distilbert-base-uncased-finetuned-cola | 5 | null | transformers | 17,396 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- matthews_correlation
model-index:
- name: distilbert-base-uncased-finetuned-cola
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
args: cola
metrics:
- name: Matthews Correlation
type: matthews_correlation
value: 0.4496664370323995
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-cola
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4949
- Matthews Correlation: 0.4497
## 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: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.5231 | 1.0 | 535 | 0.4949 | 0.4497 |
### Framework versions
- Transformers 4.19.4
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
MyMild/finetune_iapp_thaiqa | 46b6f7f0cbfbc9f52dd7fc3a9839fe4ea39ece55 | 2022-06-12T07:52:39.000Z | [
"pytorch",
"tensorboard",
"camembert",
"question-answering",
"transformers",
"generated_from_trainer",
"model-index",
"autotrain_compatible"
] | question-answering | false | MyMild | null | MyMild/finetune_iapp_thaiqa | 5 | null | transformers | 17,397 | ---
tags:
- generated_from_trainer
model-index:
- name: finetune_iapp_thaiqa
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. -->
# finetune_iapp_thaiqa
This model is a fine-tuned version of [airesearch/wangchanberta-base-att-spm-uncased](https://huggingface.co/airesearch/wangchanberta-base-att-spm-uncased) on the 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: 2e-05
- train_batch_size: 16
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.2
- num_epochs: 2
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.15.0
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.10.3
|
Jeevesh8/std_pnt_04_feather_berts-14 | 604e1fe60a2355cbd8aeda9738e39ea23b0777bd | 2022-06-12T06:03:23.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
] | text-classification | false | Jeevesh8 | null | Jeevesh8/std_pnt_04_feather_berts-14 | 5 | null | transformers | 17,398 | Entry not found |
Jeevesh8/std_pnt_04_feather_berts-10 | ebdea55a64e3a92bc88663ea7b72e8800eab8e49 | 2022-06-12T06:04:23.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
] | text-classification | false | Jeevesh8 | null | Jeevesh8/std_pnt_04_feather_berts-10 | 5 | null | transformers | 17,399 | Entry not found |
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