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echarlaix/distilbert-sst2-inc-dynamic-quantization-magnitude-pruning-0.1 | d568bc2520a81b6017a152db2fa0ef80b611dcd3 | 2022-06-06T11:25:48.000Z | [
"pytorch",
"distilbert",
"text-classification",
"transformers",
"license:apache-2.0"
]
| text-classification | false | echarlaix | null | echarlaix/distilbert-sst2-inc-dynamic-quantization-magnitude-pruning-0.1 | 10 | null | transformers | 11,900 | ---
license: apache-2.0
---
|
nboudad/Maghriberta0.0 | 75ce27119a6b44ab753eb448b22938562e90c2f6 | 2022-06-07T12:05:50.000Z | [
"pytorch",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
]
| fill-mask | false | nboudad | null | nboudad/Maghriberta0.0 | 10 | null | transformers | 11,901 | ---
widget:
- text: "جاب ليا <mask> ."
example_title: "example1"
- text: "مشيت نجيب <mask> فالفرماسيان ."
example_title: "example2"
--- |
ahmeddbahaa/mT5_multilingual_XLSum-finetuned-en-cnn | c2316e37b3a236cb95559cf40aa7c9673fd2428a | 2022-06-09T06:28:23.000Z | [
"pytorch",
"tensorboard",
"mt5",
"text2text-generation",
"dataset:cnn_dailymail",
"transformers",
"summarization",
"en",
"Abstractive Summarization",
"generated_from_trainer",
"model-index",
"autotrain_compatible"
]
| summarization | false | ahmeddbahaa | null | ahmeddbahaa/mT5_multilingual_XLSum-finetuned-en-cnn | 10 | null | transformers | 11,902 | ---
tags:
- summarization
- en
- mt5
- Abstractive Summarization
- generated_from_trainer
datasets:
- cnn_dailymail
model-index:
- name: mT5_multilingual_XLSum-finetuned-en-cnn
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. -->
# mT5_multilingual_XLSum-finetuned-en-cnn
This model is a fine-tuned version of [csebuetnlp/mT5_multilingual_XLSum](https://huggingface.co/csebuetnlp/mT5_multilingual_XLSum) on the cnn_dailymail dataset.
It achieves the following results on the evaluation set:
- Loss: 3.0025
- Rouge-1: 36.87
- Rouge-2: 15.31
- Rouge-l: 33.74
- Gen Len: 77.93
- Bertscore: 88.28
## 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: 0.0005
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 250
- num_epochs: 4
- label_smoothing_factor: 0.1
### Training results
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
hossay/distilbert-base-uncased-finetuned-ner | 965bf2583488faa9ec90335b11bb5af7e655dcb9 | 2022-07-13T13:32:51.000Z | [
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"dataset:conll2003",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
]
| token-classification | false | hossay | null | hossay/distilbert-base-uncased-finetuned-ner | 10 | null | transformers | 11,903 | ---
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: 0.9263064854712186
- name: Recall
type: recall
value: 0.9379125181787672
- name: F1
type: f1
value: 0.9320733740967203
- name: Accuracy
type: accuracy
value: 0.9838117781625813
---
<!-- 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: 0.0614
- Precision: 0.9263
- Recall: 0.9379
- F1: 0.9321
- Accuracy: 0.9838
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2418 | 1.0 | 878 | 0.0709 | 0.9168 | 0.9242 | 0.9204 | 0.9806 |
| 0.0514 | 2.0 | 1756 | 0.0622 | 0.9175 | 0.9338 | 0.9255 | 0.9826 |
| 0.0306 | 3.0 | 2634 | 0.0614 | 0.9263 | 0.9379 | 0.9321 | 0.9838 |
### Framework versions
- Transformers 4.19.3
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
ghpkishore/distilbert-base-uncased-finetuned-emotion | e73e8fc09e8234b3b82021ff51ffa300c22f95e2 | 2022-07-22T10:09:57.000Z | [
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"dataset:emotion",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
]
| text-classification | false | ghpkishore | null | ghpkishore/distilbert-base-uncased-finetuned-emotion | 10 | null | transformers | 11,904 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9285
- name: F1
type: f1
value: 0.9285439912301902
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2183
- Accuracy: 0.9285
- F1: 0.9285
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8381 | 1.0 | 250 | 0.3165 | 0.9075 | 0.9040 |
| 0.2524 | 2.0 | 500 | 0.2183 | 0.9285 | 0.9285 |
### Framework versions
- Transformers 4.19.3
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
mmillet/distilrubert-tiny-cased-conversational-v1_single_finetuned_on_cedr_augmented | 7f7f00822d1ff99e71e01bc674b06b127db040c2 | 2022-06-10T20:27:38.000Z | [
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"transformers",
"generated_from_trainer",
"model-index"
]
| text-classification | false | mmillet | null | mmillet/distilrubert-tiny-cased-conversational-v1_single_finetuned_on_cedr_augmented | 10 | null | transformers | 11,905 | ---
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: distilrubert-tiny-cased-conversational-v1_single_finetuned_on_cedr_augmented
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_single_finetuned_on_cedr_augmented
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: 0.5908
- Accuracy: 0.8653
- F1: 0.8656
- Precision: 0.8665
- Recall: 0.8653
## 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: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-06
- lr_scheduler_type: linear
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.9172 | 1.0 | 69 | 0.5124 | 0.8246 | 0.8220 | 0.8271 | 0.8246 |
| 0.4709 | 2.0 | 138 | 0.4279 | 0.8528 | 0.8505 | 0.8588 | 0.8528 |
| 0.3194 | 3.0 | 207 | 0.3770 | 0.8737 | 0.8727 | 0.8740 | 0.8737 |
| 0.2459 | 4.0 | 276 | 0.3951 | 0.8685 | 0.8682 | 0.8692 | 0.8685 |
| 0.1824 | 5.0 | 345 | 0.4005 | 0.8831 | 0.8834 | 0.8841 | 0.8831 |
| 0.1515 | 6.0 | 414 | 0.4356 | 0.8800 | 0.8797 | 0.8801 | 0.8800 |
| 0.1274 | 7.0 | 483 | 0.4642 | 0.8727 | 0.8726 | 0.8731 | 0.8727 |
| 0.0833 | 8.0 | 552 | 0.5226 | 0.8633 | 0.8627 | 0.8631 | 0.8633 |
| 0.073 | 9.0 | 621 | 0.5327 | 0.8695 | 0.8686 | 0.8692 | 0.8695 |
| 0.0575 | 10.0 | 690 | 0.5908 | 0.8653 | 0.8656 | 0.8665 | 0.8653 |
### Framework versions
- Transformers 4.19.3
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
sasuke/bert-base-uncased-finetuned-sst2 | 68b034e693b214a4d0d89c3e24e13b120f69c869 | 2022-06-16T03:58:09.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"dataset:glue",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
]
| text-classification | false | sasuke | null | sasuke/bert-base-uncased-finetuned-sst2 | 10 | null | transformers | 11,906 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: bert-base-uncased-finetuned-sst2
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
args: sst2
metrics:
- name: Accuracy
type: accuracy
value: 0.9323394495412844
---
<!-- 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-uncased-finetuned-sst2
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2982
- Accuracy: 0.9323
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.1817 | 1.0 | 4210 | 0.2920 | 0.9186 |
| 0.1297 | 2.0 | 8420 | 0.3069 | 0.9209 |
| 0.0978 | 3.0 | 12630 | 0.2982 | 0.9323 |
| 0.062 | 4.0 | 16840 | 0.3278 | 0.9312 |
| 0.0303 | 5.0 | 21050 | 0.3642 | 0.9323 |
### Framework versions
- Transformers 4.20.0.dev0
- Pytorch 1.11.0
- Datasets 2.2.2
- Tokenizers 0.12.1
|
orkg/orkgnlp-tdm-extraction | 733949b6dda81ceeb35dfb22db9490c90d68bcc8 | 2022-06-13T16:00:47.000Z | [
"pytorch",
"xlnet",
"text-classification",
"transformers",
"license:mit"
]
| text-classification | false | orkg | null | orkg/orkgnlp-tdm-extraction | 10 | null | transformers | 11,907 | ---
license: mit
---
This Repository includes the files required to run the `TDM Extraction` ORKG-NLP service.
Please check [this article](https://orkg-nlp-pypi.readthedocs.io/en/latest/services/services.html) for more details about the service. |
Deborah/bertimbau-finetuned-pos-accelerate2 | 1cbebcaa02c5322df8b2cd20bd8c3c30d88b138b | 2022-06-13T13:21:47.000Z | [
"pytorch",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
]
| token-classification | false | Deborah | null | Deborah/bertimbau-finetuned-pos-accelerate2 | 10 | null | transformers | 11,908 | Entry not found |
ghadeermobasher/BioNLP13-Modified-PubMedBERT-384 | ba9c741c2983a8819bed47b55025652666238c92 | 2022-06-13T21:45:39.000Z | [
"pytorch",
"tensorboard",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
]
| token-classification | false | ghadeermobasher | null | ghadeermobasher/BioNLP13-Modified-PubMedBERT-384 | 10 | null | transformers | 11,909 | Entry not found |
ghadeermobasher/BC5CDR-Chem-Original-SciBERT-512 | e931ebb74243f4debc1e1c3808e7a74978a8a105 | 2022-06-14T00:13:07.000Z | [
"pytorch",
"tensorboard",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
]
| token-classification | false | ghadeermobasher | null | ghadeermobasher/BC5CDR-Chem-Original-SciBERT-512 | 10 | null | transformers | 11,910 | Entry not found |
ghadeermobasher/BC5CDR-Chem-Original-BlueBERT-512 | 49515824527c4c1d8ba3273085046c16621d8480 | 2022-06-14T00:22:24.000Z | [
"pytorch",
"tensorboard",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
]
| token-classification | false | ghadeermobasher | null | ghadeermobasher/BC5CDR-Chem-Original-BlueBERT-512 | 10 | null | transformers | 11,911 | Entry not found |
ghadeermobasher/BC5CDR-Chem-Original-BioBERT-512 | bcadd56d47e3356db8056c3020fc1dd7a75afa84 | 2022-06-14T00:24:48.000Z | [
"pytorch",
"tensorboard",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
]
| token-classification | false | ghadeermobasher | null | ghadeermobasher/BC5CDR-Chem-Original-BioBERT-512 | 10 | null | transformers | 11,912 | Entry not found |
ahmeddbahaa/xlmroberta2xlmroberta-finetune-summarization-ar | c872b9ce57c13fc105b6929c6017587a21ebe69a | 2022-06-14T16:05:58.000Z | [
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"dataset:xlsum",
"transformers",
"summarization",
"ar",
"xlm-roberta",
"Abstractive Summarization",
"roberta",
"generated_from_trainer",
"model-index",
"autotrain_compatible"
]
| summarization | false | ahmeddbahaa | null | ahmeddbahaa/xlmroberta2xlmroberta-finetune-summarization-ar | 10 | null | transformers | 11,913 | ---
tags:
- summarization
- ar
- encoder-decoder
- xlm-roberta
- Abstractive Summarization
- roberta
- generated_from_trainer
datasets:
- xlsum
model-index:
- name: xlmroberta2xlmroberta-finetune-summarization-ar
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. -->
# xlmroberta2xlmroberta-finetune-summarization-ar
This model is a fine-tuned version of [](https://huggingface.co/) on the xlsum dataset.
It achieves the following results on the evaluation set:
- Loss: 4.1298
- Rouge-1: 21.69
- Rouge-2: 8.73
- Rouge-l: 19.52
- Gen Len: 19.96
- Bertscore: 71.0
## 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: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 250
- num_epochs: 10
- label_smoothing_factor: 0.1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge-1 | Rouge-2 | Rouge-l | Gen Len | Bertscore |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:-------:|:---------:|
| 8.0645 | 1.0 | 1172 | 7.3567 | 8.22 | 0.66 | 7.94 | 20.0 | 58.18 |
| 7.2042 | 2.0 | 2344 | 6.6058 | 12.12 | 2.19 | 11.4 | 20.0 | 63.24 |
| 6.4168 | 3.0 | 3516 | 5.8784 | 16.46 | 4.31 | 15.15 | 20.0 | 66.3 |
| 5.4622 | 4.0 | 4688 | 4.7931 | 17.6 | 5.87 | 15.9 | 19.99 | 69.21 |
| 4.7829 | 5.0 | 5860 | 4.4418 | 19.17 | 6.74 | 17.22 | 19.98 | 70.23 |
| 4.4829 | 6.0 | 7032 | 4.2950 | 19.8 | 7.11 | 17.74 | 19.98 | 70.38 |
| 4.304 | 7.0 | 8204 | 4.2155 | 20.71 | 7.59 | 18.56 | 19.98 | 70.66 |
| 4.1778 | 8.0 | 9376 | 4.1632 | 21.1 | 7.94 | 18.99 | 19.98 | 70.86 |
| 4.0886 | 9.0 | 10548 | 4.1346 | 21.44 | 8.03 | 19.28 | 19.98 | 70.93 |
| 4.0294 | 10.0 | 11720 | 4.1298 | 21.51 | 8.14 | 19.33 | 19.98 | 71.02 |
### Framework versions
- Transformers 4.19.4
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
erickfm/true-sweep-1 | c3a167d88698b45c5fecd651fc122eab31f8603c | 2022-06-15T03:51:23.000Z | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
]
| text2text-generation | false | erickfm | null | erickfm/true-sweep-1 | 10 | 1 | transformers | 11,914 | Entry not found |
hossay/biobert-base-cased-v1.2-finetuned-ner | 55ed1aa19bd4d757ca31bf868ff82b86c7687047 | 2022-06-15T07:38:51.000Z | [
"pytorch",
"tensorboard",
"bert",
"token-classification",
"dataset:ncbi_disease",
"transformers",
"generated_from_trainer",
"model-index",
"autotrain_compatible"
]
| token-classification | false | hossay | null | hossay/biobert-base-cased-v1.2-finetuned-ner | 10 | null | transformers | 11,915 | ---
tags:
- generated_from_trainer
datasets:
- ncbi_disease
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: biobert-base-cased-v1.2-finetuned-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: ncbi_disease
type: ncbi_disease
args: ncbi_disease
metrics:
- name: Precision
type: precision
value: 0.8396334478808706
- name: Recall
type: recall
value: 0.8731387730792138
- name: F1
type: f1
value: 0.856058394160584
- name: Accuracy
type: accuracy
value: 0.9824805769647444
---
<!-- 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. -->
# biobert-base-cased-v1.2-finetuned-ner
This model is a fine-tuned version of [dmis-lab/biobert-base-cased-v1.2](https://huggingface.co/dmis-lab/biobert-base-cased-v1.2) on the ncbi_disease dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0706
- Precision: 0.8396
- Recall: 0.8731
- F1: 0.8561
- Accuracy: 0.9825
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 340 | 0.0691 | 0.8190 | 0.7868 | 0.8026 | 0.9777 |
| 0.101 | 2.0 | 680 | 0.0700 | 0.8334 | 0.8553 | 0.8442 | 0.9807 |
| 0.0244 | 3.0 | 1020 | 0.0706 | 0.8396 | 0.8731 | 0.8561 | 0.9825 |
### Framework versions
- Transformers 4.19.4
- Pytorch 1.11.0+cu113
- Datasets 2.3.0
- Tokenizers 0.12.1
|
AnyaSchen/rugpt3_tyutchev | 015f7ab0d83bd9eff4ce5bf7a92ef7d6d5009e0d | 2022-06-15T11:33:16.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
]
| text-generation | false | AnyaSchen | null | AnyaSchen/rugpt3_tyutchev | 10 | null | transformers | 11,916 | This model was created as a fine-tuned GPT-3 medium model, which is tuned to the style of Tyutchev's poetry in Russian. You can give her a word, a phrase, or just an empty line as an input, and she will give out a poem in the style of Tyutchev.
 |
ghadeermobasher/BC5CDR-Chem-Original-BioBERT-384 | 5ada33335ad7eee029a4e76b61c5176479387bd0 | 2022-06-15T13:01:09.000Z | [
"pytorch",
"tensorboard",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
]
| token-classification | false | ghadeermobasher | null | ghadeermobasher/BC5CDR-Chem-Original-BioBERT-384 | 10 | null | transformers | 11,917 | Entry not found |
adamlin/question-paraphraser | e7eff5db2f0a792d680c1cd41fc30282862cd32c | 2022-06-16T00:35:09.000Z | [
"pytorch",
"mt5",
"text2text-generation",
"dataset:adamlin/question_augmentation",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
]
| text2text-generation | false | adamlin | null | adamlin/question-paraphraser | 10 | null | transformers | 11,918 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- adamlin/question_augmentation
metrics:
- rouge
model-index:
- name: question-paraphraser
results:
- task:
name: Summarization
type: summarization
dataset:
name: adamlin/question_augmentation
type: adamlin/question_augmentation
metrics:
- name: Rouge1
type: rouge
value: 0.5385
---
<!-- 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. -->
# question-paraphraser
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the adamlin/question_augmentation dataset.
It achieves the following results on the evaluation set:
- Loss: 3.5901
- Rouge1: 0.5385
- Rouge2: 0.0769
- Rougel: 0.5586
- Rougelsum: 0.5586
- Gen Len: 7.6712
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
### Framework versions
- Transformers 4.19.4
- Pytorch 1.11.0
- Datasets 2.3.2
- Tokenizers 0.12.1
|
Afework/t5_boolq | db94925977ed39c03432ff5d76bf21c9c42ca221 | 2022-06-16T16:23:35.000Z | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
]
| text2text-generation | false | Afework | null | Afework/t5_boolq | 10 | null | transformers | 11,919 | Entry not found |
anantoj/T5-summarizer-simple-wiki | 8e211ba089abee6549308df8745a97f23f434ea7 | 2022-06-16T10:47:42.000Z | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
]
| text2text-generation | false | anantoj | null | anantoj/T5-summarizer-simple-wiki | 10 | null | transformers | 11,920 | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: results
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. -->
# results
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.0868
## 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
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 2.2583 | 1.0 | 14719 | 2.1164 |
| 2.2649 | 2.0 | 29438 | 2.0925 |
| 2.209 | 3.0 | 44157 | 2.0868 |
### Framework versions
- Transformers 4.19.4
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
Afework/t5-mcq | 49027311979b5ef9fc6fdfcf807ec8176fb6d711 | 2022-06-16T18:52:15.000Z | [
"pytorch",
"jax",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
]
| text2text-generation | false | Afework | null | Afework/t5-mcq | 10 | null | transformers | 11,921 | Entry not found |
mariolinml/roberta-large-finetuned-chunking | b1422508caa99472f24359dc96b0f6b611e5303f | 2022-06-18T20:09:57.000Z | [
"pytorch",
"bert",
"token-classification",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
]
| token-classification | false | mariolinml | null | mariolinml/roberta-large-finetuned-chunking | 10 | null | transformers | 11,922 | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: roberta-large-finetuned-chunking
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-large-finetuned-chunking
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: 1.4192
- Precision: 0.3222
- Recall: 0.3161
- F1: 0.3191
- Accuracy: 0.8632
## 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: 6
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0373 | 1.0 | 2498 | 0.9545 | 0.3166 | 0.2545 | 0.2822 | 0.8656 |
| 0.0045 | 2.0 | 4996 | 1.1324 | 0.2667 | 0.3142 | 0.2885 | 0.8525 |
| 0.0022 | 3.0 | 7494 | 1.3138 | 0.3349 | 0.3097 | 0.3218 | 0.8672 |
| 0.0015 | 4.0 | 9992 | 1.3454 | 0.3261 | 0.3260 | 0.3260 | 0.8647 |
| 0.0014 | 5.0 | 12490 | 1.3640 | 0.3064 | 0.3126 | 0.3095 | 0.8603 |
| 0.0008 | 6.0 | 14988 | 1.4192 | 0.3222 | 0.3161 | 0.3191 | 0.8632 |
### Framework versions
- Transformers 4.20.0
- Pytorch 1.11.0
- Datasets 2.3.2
- Tokenizers 0.12.1
|
camilag/bertimbau-finetuned-pos-accelerate-5 | 39c7908b15dc5ad877ed20319671f1b349cad0f7 | 2022-06-19T02:24:11.000Z | [
"pytorch",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
]
| token-classification | false | camilag | null | camilag/bertimbau-finetuned-pos-accelerate-5 | 10 | null | transformers | 11,923 | Entry not found |
camilag/bertimbau-finetuned-pos-accelerate-6 | 87e70efc2e3be25947e5cdb2b3ad06f25457fc1c | 2022-06-20T23:14:00.000Z | [
"pytorch",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
]
| token-classification | false | camilag | null | camilag/bertimbau-finetuned-pos-accelerate-6 | 10 | null | transformers | 11,924 | Entry not found |
scjones/distilbert-base-uncased-finetuned-emotion | f872858c1ae527b2c7a4f6fe1eaeb49fb6c7917f | 2022-06-21T00:16:41.000Z | [
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"dataset:emotion",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
]
| text-classification | false | scjones | null | scjones/distilbert-base-uncased-finetuned-emotion | 10 | null | transformers | 11,925 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9315
- name: F1
type: f1
value: 0.9317528216385311
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1630
- Accuracy: 0.9315
- F1: 0.9318
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.2115 | 1.0 | 250 | 0.1696 | 0.93 | 0.9295 |
| 0.1376 | 2.0 | 500 | 0.1630 | 0.9315 | 0.9318 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.11.0+cu113
- Datasets 1.16.1
- Tokenizers 0.10.3
|
SISLab/amc-opt-msmd | 5373bac5bc2f17905e0a4c51edfe772109220cf1 | 2022-06-21T11:43:57.000Z | [
"pytorch",
"bert",
"it",
"transformers",
"text-classification",
"sentiment-analysis"
]
| text-classification | false | SISLab | null | SISLab/amc-opt-msmd | 10 | null | transformers | 11,926 | ---
tags:
- text-classification
- sentiment-analysis
language:
- "it"
--- |
kktoto/tiny_focal_ckpt | 30688a382ec3f287363f3b2976d2466c5d335bf8 | 2022-06-21T15:05:00.000Z | [
"pytorch",
"tensorboard",
"bert",
"token-classification",
"transformers",
"generated_from_trainer",
"model-index",
"autotrain_compatible"
]
| token-classification | false | kktoto | null | kktoto/tiny_focal_ckpt | 10 | null | transformers | 11,927 | ---
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: tiny_focal_ckpt
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. -->
# tiny_focal_ckpt
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0561
- Precision: 0.6529
- Recall: 0.6366
- F1: 0.6446
- Accuracy: 0.9516
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.058 | 1.0 | 5561 | 0.0583 | 0.6327 | 0.5945 | 0.6130 | 0.9484 |
| 0.0566 | 2.0 | 11122 | 0.0570 | 0.6401 | 0.5985 | 0.6186 | 0.9492 |
| 0.0564 | 3.0 | 16683 | 0.0567 | 0.6364 | 0.6241 | 0.6302 | 0.9496 |
| 0.053 | 4.0 | 22244 | 0.0561 | 0.6416 | 0.6312 | 0.6364 | 0.9503 |
| 0.052 | 5.0 | 27805 | 0.0558 | 0.6501 | 0.6239 | 0.6367 | 0.9510 |
| 0.0507 | 6.0 | 33366 | 0.0555 | 0.6555 | 0.6208 | 0.6377 | 0.9514 |
| 0.0497 | 7.0 | 38927 | 0.0552 | 0.6559 | 0.6256 | 0.6404 | 0.9515 |
| 0.0485 | 8.0 | 44488 | 0.0561 | 0.6485 | 0.6397 | 0.6440 | 0.9513 |
| 0.0481 | 9.0 | 50049 | 0.0558 | 0.6531 | 0.6344 | 0.6436 | 0.9515 |
| 0.0469 | 10.0 | 55610 | 0.0561 | 0.6529 | 0.6366 | 0.6446 | 0.9516 |
### Framework versions
- Transformers 4.20.0
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
Jeevesh8/std_0pnt2_bert_ft_cola-35 | 35dd3a62eee243aa1b92c8f499888473f3491b8e | 2022-06-21T13:27:41.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
]
| text-classification | false | Jeevesh8 | null | Jeevesh8/std_0pnt2_bert_ft_cola-35 | 10 | null | transformers | 11,928 | Entry not found |
Jeevesh8/std_0pnt2_bert_ft_cola-18 | 19e6a8ec6913e87c16066f347e0812f46cc22cdf | 2022-06-21T13:30:08.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
]
| text-classification | false | Jeevesh8 | null | Jeevesh8/std_0pnt2_bert_ft_cola-18 | 10 | null | transformers | 11,929 | Entry not found |
Jeevesh8/std_0pnt2_bert_ft_cola-26 | d7189315c8182cad18a7839d73b79e6e45b88388 | 2022-06-21T13:28:10.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
]
| text-classification | false | Jeevesh8 | null | Jeevesh8/std_0pnt2_bert_ft_cola-26 | 10 | null | transformers | 11,930 | Entry not found |
Jeevesh8/std_0pnt2_bert_ft_cola-21 | b9170bc88e91639025982b7d11d176230909fad5 | 2022-06-21T13:28:22.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
]
| text-classification | false | Jeevesh8 | null | Jeevesh8/std_0pnt2_bert_ft_cola-21 | 10 | null | transformers | 11,931 | Entry not found |
Jeevesh8/std_0pnt2_bert_ft_cola-9 | d061aac8cd1be45073ca1f788d599e515ae7e35d | 2022-06-21T13:31:10.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
]
| text-classification | false | Jeevesh8 | null | Jeevesh8/std_0pnt2_bert_ft_cola-9 | 10 | null | transformers | 11,932 | Entry not found |
Jeevesh8/std_0pnt2_bert_ft_cola-27 | 9663f06dabf0df15da178dadd13cd4196819953b | 2022-06-21T13:28:11.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
]
| text-classification | false | Jeevesh8 | null | Jeevesh8/std_0pnt2_bert_ft_cola-27 | 10 | null | transformers | 11,933 | Entry not found |
Jeevesh8/std_0pnt2_bert_ft_cola-8 | 7e161c58e2bac0432ace20a33b94c92c83424f0b | 2022-06-21T13:30:49.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
]
| text-classification | false | Jeevesh8 | null | Jeevesh8/std_0pnt2_bert_ft_cola-8 | 10 | null | transformers | 11,934 | Entry not found |
Sayan01/tiny-bert-mrpc-distilled | a6848fc64ab85be64b0b18b25d0b541e67c8027a | 2022-07-15T19:32:31.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
]
| text-classification | false | Sayan01 | null | Sayan01/tiny-bert-mrpc-distilled | 10 | null | transformers | 11,935 | Entry not found |
deepesh0x/autotrain-GlueFineTunedModel-1013533786 | 0a640f183640259de3e9460a674e34f186b3c468 | 2022-06-21T18:05:40.000Z | [
"pytorch",
"bert",
"text-classification",
"unk",
"dataset:deepesh0x/autotrain-data-GlueFineTunedModel",
"transformers",
"autotrain",
"co2_eq_emissions"
]
| text-classification | false | deepesh0x | null | deepesh0x/autotrain-GlueFineTunedModel-1013533786 | 10 | 1 | transformers | 11,936 | ---
tags: autotrain
language: unk
widget:
- text: "I love AutoTrain 🤗"
datasets:
- deepesh0x/autotrain-data-GlueFineTunedModel
co2_eq_emissions: 57.79463560530838
---
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 1013533786
- CO2 Emissions (in grams): 57.79463560530838
## Validation Metrics
- Loss: 0.18257243931293488
- Accuracy: 0.9261538461538461
- Precision: 0.9244319632371713
- Recall: 0.9282235324275827
- AUC: 0.9800523984255356
- F1: 0.92632386799693
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/deepesh0x/autotrain-GlueFineTunedModel-1013533786
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("deepesh0x/autotrain-GlueFineTunedModel-1013533786", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("deepesh0x/autotrain-GlueFineTunedModel-1013533786", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
``` |
camilag/bertimbau-finetuned-pos-accelerate-7 | 563bd6b793aeca222dbd8f73881c4c1e1c8c545b | 2022-06-21T21:36:12.000Z | [
"pytorch",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
]
| token-classification | false | camilag | null | camilag/bertimbau-finetuned-pos-accelerate-7 | 10 | null | transformers | 11,937 | Entry not found |
bsenker/autotrain-sentanaly-1016134101 | 30617ddcf51d1485037cbb5380d45ae3fc0c3bd4 | 2022-06-22T03:34:19.000Z | [
"pytorch",
"bert",
"text-classification",
"tr",
"dataset:bsenker/autotrain-data-sentanaly",
"transformers",
"autotrain",
"co2_eq_emissions"
]
| text-classification | false | bsenker | null | bsenker/autotrain-sentanaly-1016134101 | 10 | null | transformers | 11,938 | ---
tags: autotrain
language: tr
widget:
- text: "I love AutoTrain 🤗"
datasets:
- bsenker/autotrain-data-sentanaly
co2_eq_emissions: 2.4274113973426568
---
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 1016134101
- CO2 Emissions (in grams): 2.4274113973426568
## Validation Metrics
- Loss: 0.8357052803039551
- Accuracy: 0.6425438596491229
- Macro F1: 0.6449751139113629
- Micro F1: 0.6425438596491229
- Weighted F1: 0.644975113911363
- Macro Precision: 0.6642782595845687
- Micro Precision: 0.6425438596491229
- Weighted Precision: 0.6642782595845685
- Macro Recall: 0.6425438596491229
- Micro Recall: 0.6425438596491229
- Weighted Recall: 0.6425438596491229
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/bsenker/autotrain-sentanaly-1016134101
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("bsenker/autotrain-sentanaly-1016134101", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("bsenker/autotrain-sentanaly-1016134101", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
``` |
upsalite/bert-base-german-cased-finetuned-emotion-14-labels | 90110bd5b5f4d790a45491e6c969109e64e6f0ba | 2022-06-23T06:25:20.000Z | [
"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-14-labels | 10 | null | transformers | 11,939 | ---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: bert-base-german-cased-finetuned-emotion-14-labels
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-14-labels
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: 1.1072
- Accuracy: 0.7304
- F1: 0.7302
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 1.9084 | 1.0 | 128 | 1.3889 | 0.5735 | 0.5631 |
| 1.0961 | 2.0 | 256 | 1.0875 | 0.6422 | 0.6379 |
| 0.7211 | 3.0 | 384 | 0.9900 | 0.6873 | 0.6859 |
| 0.4556 | 4.0 | 512 | 0.9495 | 0.7137 | 0.7166 |
| 0.2916 | 5.0 | 640 | 0.9807 | 0.7069 | 0.7054 |
| 0.1784 | 6.0 | 768 | 0.9956 | 0.7196 | 0.7199 |
| 0.1134 | 7.0 | 896 | 1.0471 | 0.7167 | 0.7169 |
| 0.0759 | 8.0 | 1024 | 1.0822 | 0.7235 | 0.7225 |
| 0.0502 | 9.0 | 1152 | 1.1048 | 0.7157 | 0.7173 |
| 0.041 | 10.0 | 1280 | 1.1072 | 0.7304 | 0.7302 |
### Framework versions
- Transformers 4.19.0
- Pytorch 1.11.0+cu113
- Datasets 1.16.1
- Tokenizers 0.12.1
|
Zamachi/distillbert-for-multilabel-sentence-classification | be57db01255f3bfc7664131c2893ba34af4d0f59 | 2022-07-12T02:13:34.000Z | [
"pytorch",
"distilbert",
"text-classification",
"transformers"
]
| text-classification | false | Zamachi | null | Zamachi/distillbert-for-multilabel-sentence-classification | 10 | null | transformers | 11,940 | Entry not found |
truongxl/NER_SucKhoe | 7ece9ef590f6b30b695fb3e663a0dd65fbbef07a | 2022-06-23T07:46:38.000Z | [
"pytorch",
"roberta",
"token-classification",
"transformers",
"autotrain_compatible"
]
| token-classification | false | truongxl | null | truongxl/NER_SucKhoe | 10 | null | transformers | 11,941 | Entry not found |
alk/distilbert-base-uncased-finetuned-header-classifier | 02408556170bfdd599a2e73e71810ac2fe285779 | 2022-06-24T15:26:42.000Z | [
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
]
| text-classification | false | alk | null | alk/distilbert-base-uncased-finetuned-header-classifier | 10 | null | transformers | 11,942 | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: distilbert-base-uncased-finetuned-header-classifier
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-header-classifier
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-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: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 64
- 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
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
emekaboris/codetrans_t5_small_mt_ft_git_diff | 6e407c1cf7fb9b3f9396303f1b35b294a2e91850 | 2022-06-26T17:12:55.000Z | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
]
| text2text-generation | false | emekaboris | null | emekaboris/codetrans_t5_small_mt_ft_git_diff | 10 | null | transformers | 11,943 | Entry not found |
alk/roberta-large-mnli-finetuned-header-classifier | e840b52be3c00cf00916111ae6f4a76d5a33649c | 2022-06-28T00:28:10.000Z | [
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index"
]
| text-classification | false | alk | null | alk/roberta-large-mnli-finetuned-header-classifier | 10 | null | transformers | 11,944 | ---
license: mit
tags:
- generated_from_trainer
model-index:
- name: roberta-large-mnli-finetuned-header-classifier
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-large-mnli-finetuned-header-classifier
This model is a fine-tuned version of [roberta-large-mnli](https://huggingface.co/roberta-large-mnli) 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: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 64
- 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
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
Yanjie/message-intent-220628 | 130430f94cb316e11cce0c24b2c4b35493ff88fe | 2022-06-28T18:18:11.000Z | [
"pytorch",
"distilbert",
"text-classification",
"transformers"
]
| text-classification | false | Yanjie | null | Yanjie/message-intent-220628 | 10 | null | transformers | 11,945 | Entry not found |
elliotthwang/mt5-small-finetuned-tradition-zh | fd8a3eacff61a45548bd7948b9bbe359ffd6ad7a | 2022-07-18T16:44:21.000Z | [
"pytorch",
"tensorboard",
"mt5",
"text2text-generation",
"dataset:xlsum",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
]
| text2text-generation | false | elliotthwang | null | elliotthwang/mt5-small-finetuned-tradition-zh | 10 | null | transformers | 11,946 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- xlsum
metrics:
- rouge
model-index:
- name: mt5-small-finetuned-tradition-zh
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: xlsum
type: xlsum
args: chinese_traditional
metrics:
- name: Rouge1
type: rouge
value: 5.7806
---
<!-- 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-small-finetuned-tradition-zh
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the xlsum dataset.
It achieves the following results on the evaluation set:
- Loss: 2.9218
- Rouge1: 5.7806
- Rouge2: 1.266
- Rougel: 5.761
- Rougelsum: 5.7833
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5.6e-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: 6
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:---------:|
| 4.542 | 1.0 | 2336 | 3.1979 | 4.8334 | 1.025 | 4.8142 | 4.8326 |
| 3.7542 | 2.0 | 4672 | 3.0662 | 5.2155 | 1.0978 | 5.2025 | 5.2158 |
| 3.5706 | 3.0 | 7008 | 3.0070 | 5.5471 | 1.3397 | 5.5386 | 5.5391 |
| 3.4668 | 4.0 | 9344 | 2.9537 | 5.5865 | 1.1558 | 5.5816 | 5.5964 |
| 3.4082 | 5.0 | 11680 | 2.9391 | 5.8061 | 1.3462 | 5.7944 | 5.812 |
| 3.375 | 6.0 | 14016 | 2.9218 | 5.7806 | 1.266 | 5.761 | 5.7833 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.12.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
Jeevesh8/goog_bert_ft_cola-7 | 66a8aa4e6d06b54bd1a491789379f50fddf18b9d | 2022-06-29T17:31:48.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
]
| text-classification | false | Jeevesh8 | null | Jeevesh8/goog_bert_ft_cola-7 | 10 | null | transformers | 11,947 | Entry not found |
Jeevesh8/goog_bert_ft_cola-6 | 1a68ef290e72f49843b9150bdcb76b2d0d62e9d6 | 2022-06-29T17:31:38.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
]
| text-classification | false | Jeevesh8 | null | Jeevesh8/goog_bert_ft_cola-6 | 10 | null | transformers | 11,948 | Entry not found |
Jeevesh8/goog_bert_ft_cola-8 | f1070a84051eb63412d0e33cae466237b3c33717 | 2022-06-29T17:32:13.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
]
| text-classification | false | Jeevesh8 | null | Jeevesh8/goog_bert_ft_cola-8 | 10 | null | transformers | 11,949 | Entry not found |
Jeevesh8/goog_bert_ft_cola-10 | 87d3d8fa6981ea8e3328010d2355c619a2b64d69 | 2022-06-29T17:32:18.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
]
| text-classification | false | Jeevesh8 | null | Jeevesh8/goog_bert_ft_cola-10 | 10 | null | transformers | 11,950 | Entry not found |
Jeevesh8/goog_bert_ft_cola-9 | 9e2e9c03f07bce387581536cf8df69cfbd52c75b | 2022-06-29T17:32:17.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
]
| text-classification | false | Jeevesh8 | null | Jeevesh8/goog_bert_ft_cola-9 | 10 | null | transformers | 11,951 | Entry not found |
Jeevesh8/goog_bert_ft_cola-12 | 596d4a373bde6d85b424569ab704cb1ba986e6ac | 2022-06-29T17:33:27.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
]
| text-classification | false | Jeevesh8 | null | Jeevesh8/goog_bert_ft_cola-12 | 10 | null | transformers | 11,952 | Entry not found |
Jeevesh8/goog_bert_ft_cola-13 | f5cb60504d27224fa472e31d31f152c5049f19f2 | 2022-06-29T17:33:33.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
]
| text-classification | false | Jeevesh8 | null | Jeevesh8/goog_bert_ft_cola-13 | 10 | null | transformers | 11,953 | Entry not found |
Jeevesh8/goog_bert_ft_cola-17 | 8ef6b9dc80d167b6be50878015d7e758dfc989a1 | 2022-06-29T17:37:37.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
]
| text-classification | false | Jeevesh8 | null | Jeevesh8/goog_bert_ft_cola-17 | 10 | null | transformers | 11,954 | Entry not found |
Jeevesh8/goog_bert_ft_cola-14 | dd45bf27f031e4f5b7585576d55d12c55de43dda | 2022-06-29T17:33:41.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
]
| text-classification | false | Jeevesh8 | null | Jeevesh8/goog_bert_ft_cola-14 | 10 | null | transformers | 11,955 | Entry not found |
Jeevesh8/goog_bert_ft_cola-18 | dbb7db9c5b2849188c246b8f5b75f705338d4979 | 2022-06-29T17:33:45.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
]
| text-classification | false | Jeevesh8 | null | Jeevesh8/goog_bert_ft_cola-18 | 10 | null | transformers | 11,956 | Entry not found |
Jeevesh8/goog_bert_ft_cola-11 | bb316f001706a9fbca697468b6d25838c084ca59 | 2022-06-29T17:37:53.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
]
| text-classification | false | Jeevesh8 | null | Jeevesh8/goog_bert_ft_cola-11 | 10 | null | transformers | 11,957 | Entry not found |
Jeevesh8/goog_bert_ft_cola-15 | 29dc5ef3c279e61bbc1d1a2610f1cd580cfbfe51 | 2022-06-29T17:33:44.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
]
| text-classification | false | Jeevesh8 | null | Jeevesh8/goog_bert_ft_cola-15 | 10 | null | transformers | 11,958 | Entry not found |
Jeevesh8/goog_bert_ft_cola-19 | 5c6a378347af81cdbe7a47b10faff392b3229d6e | 2022-06-29T17:33:04.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
]
| text-classification | false | Jeevesh8 | null | Jeevesh8/goog_bert_ft_cola-19 | 10 | null | transformers | 11,959 | Entry not found |
Jeevesh8/goog_bert_ft_cola-25 | 7fa43f20fdc97fbd165020530f36f1641e6f928d | 2022-06-29T17:33:47.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
]
| text-classification | false | Jeevesh8 | null | Jeevesh8/goog_bert_ft_cola-25 | 10 | null | transformers | 11,960 | Entry not found |
Jeevesh8/goog_bert_ft_cola-27 | a2435f85c89342fccf6c0e4e8813cf1478fa3247 | 2022-06-29T17:33:52.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
]
| text-classification | false | Jeevesh8 | null | Jeevesh8/goog_bert_ft_cola-27 | 10 | null | transformers | 11,961 | Entry not found |
Jeevesh8/goog_bert_ft_cola-26 | 20d68c14493b5373f1a535e888c8eb6557b4514f | 2022-06-29T17:34:01.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
]
| text-classification | false | Jeevesh8 | null | Jeevesh8/goog_bert_ft_cola-26 | 10 | null | transformers | 11,962 | Entry not found |
Jeevesh8/goog_bert_ft_cola-44 | 305b4d1e8e1290ed0168f8ee061e77b29271ae63 | 2022-06-29T17:34:06.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
]
| text-classification | false | Jeevesh8 | null | Jeevesh8/goog_bert_ft_cola-44 | 10 | null | transformers | 11,963 | Entry not found |
Jeevesh8/goog_bert_ft_cola-23 | 605ac934ddc1f09791cf72ed45dac88cda4f00de | 2022-06-29T17:33:06.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
]
| text-classification | false | Jeevesh8 | null | Jeevesh8/goog_bert_ft_cola-23 | 10 | null | transformers | 11,964 | Entry not found |
Jeevesh8/goog_bert_ft_cola-45 | 2e26c5fa1f40d305549fd6307187c85a83af03b7 | 2022-06-29T17:34:04.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
]
| text-classification | false | Jeevesh8 | null | Jeevesh8/goog_bert_ft_cola-45 | 10 | null | transformers | 11,965 | Entry not found |
Jeevesh8/goog_bert_ft_cola-51 | b37a7d3489fa1a382a90aa3d03d41c16aca7a0f8 | 2022-06-29T17:34:28.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
]
| text-classification | false | Jeevesh8 | null | Jeevesh8/goog_bert_ft_cola-51 | 10 | null | transformers | 11,966 | Entry not found |
Jeevesh8/goog_bert_ft_cola-55 | 818acc260c90ccf72dbe52d3fd1f4e540b16f390 | 2022-06-29T17:34:26.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
]
| text-classification | false | Jeevesh8 | null | Jeevesh8/goog_bert_ft_cola-55 | 10 | null | transformers | 11,967 | Entry not found |
Jeevesh8/goog_bert_ft_cola-65 | 58bced61e2f2086632303ddf408108bd1b45dc99 | 2022-06-29T17:35:34.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
]
| text-classification | false | Jeevesh8 | null | Jeevesh8/goog_bert_ft_cola-65 | 10 | null | transformers | 11,968 | Entry not found |
Jeevesh8/goog_bert_ft_cola-61 | c0c07f02e0b60486a31d87aa006f5e6a3a2c8761 | 2022-06-29T17:33:17.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
]
| text-classification | false | Jeevesh8 | null | Jeevesh8/goog_bert_ft_cola-61 | 10 | null | transformers | 11,969 | Entry not found |
Jeevesh8/goog_bert_ft_cola-91 | 2cf88589e2b755bbb1e038a357db8150d15207e4 | 2022-06-29T17:34:06.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
]
| text-classification | false | Jeevesh8 | null | Jeevesh8/goog_bert_ft_cola-91 | 10 | null | transformers | 11,970 | Entry not found |
Jeevesh8/goog_bert_ft_cola-89 | 74fbd5939f5977c5a11ad0595d272045dcb5f320 | 2022-06-29T17:35:51.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
]
| text-classification | false | Jeevesh8 | null | Jeevesh8/goog_bert_ft_cola-89 | 10 | null | transformers | 11,971 | Entry not found |
Jeevesh8/goog_bert_ft_cola-90 | df9c407117482542e78b8c053b4fcc6e9ea0c026 | 2022-06-29T17:35:55.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
]
| text-classification | false | Jeevesh8 | null | Jeevesh8/goog_bert_ft_cola-90 | 10 | null | transformers | 11,972 | Entry not found |
Jeevesh8/goog_bert_ft_cola-88 | 226ea54a4932f2559f4b5b776378fe9665485ad6 | 2022-06-29T17:33:47.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
]
| text-classification | false | Jeevesh8 | null | Jeevesh8/goog_bert_ft_cola-88 | 10 | null | transformers | 11,973 | Entry not found |
Jeevesh8/goog_bert_ft_cola-93 | 251cd09a7a88fbb99e332bc3e75dfa207be396d6 | 2022-06-29T17:35:52.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
]
| text-classification | false | Jeevesh8 | null | Jeevesh8/goog_bert_ft_cola-93 | 10 | null | transformers | 11,974 | Entry not found |
Jeevesh8/goog_bert_ft_cola-85 | eefb3b2225abbb0bee6981e301cfa8384da2be73 | 2022-06-29T17:34:00.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
]
| text-classification | false | Jeevesh8 | null | Jeevesh8/goog_bert_ft_cola-85 | 10 | null | transformers | 11,975 | Entry not found |
Jeevesh8/goog_bert_ft_cola-99 | edb78e09f80b4ea8a008a370635bc74f7386c197 | 2022-06-29T17:38:19.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
]
| text-classification | false | Jeevesh8 | null | Jeevesh8/goog_bert_ft_cola-99 | 10 | null | transformers | 11,976 | Entry not found |
Jeevesh8/goog_bert_ft_cola-95 | 4b4bd5e1ffaab8fd01a70c6e2d1ac0c104994737 | 2022-06-29T17:36:03.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
]
| text-classification | false | Jeevesh8 | null | Jeevesh8/goog_bert_ft_cola-95 | 10 | null | transformers | 11,977 | Entry not found |
Jeevesh8/goog_bert_ft_cola-98 | 1ba5408f6145fa7649ec325988018911d888f2ca | 2022-06-29T17:38:32.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
]
| text-classification | false | Jeevesh8 | null | Jeevesh8/goog_bert_ft_cola-98 | 10 | null | transformers | 11,978 | Entry not found |
Jeevesh8/goog_bert_ft_cola-94 | 0ba5e20f1461331e1076eeb66a1b27837cdd2ec3 | 2022-06-29T17:38:33.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
]
| text-classification | false | Jeevesh8 | null | Jeevesh8/goog_bert_ft_cola-94 | 10 | null | transformers | 11,979 | Entry not found |
luffycodes/t5_small_v51 | 3b411e163d899e8136e375991b435d10363d29eb | 2022-07-11T10:15:07.000Z | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
]
| text2text-generation | false | luffycodes | null | luffycodes/t5_small_v51 | 10 | null | transformers | 11,980 | Entry not found |
Luojike/autotrain-test_3-1071537591 | edeced2fb9f06d276bf38d024f7ebebc64f6da04 | 2022-07-01T15:04:07.000Z | [
"pytorch",
"bert",
"text-classification",
"unk",
"dataset:Luojike/autotrain-data-test_3",
"transformers",
"autotrain",
"co2_eq_emissions"
]
| text-classification | false | Luojike | null | Luojike/autotrain-test_3-1071537591 | 10 | null | transformers | 11,981 | ---
tags: autotrain
language: unk
widget:
- text: "I love AutoTrain 🤗"
datasets:
- Luojike/autotrain-data-test_3
co2_eq_emissions: 0.03985401798934018
---
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 1071537591
- CO2 Emissions (in grams): 0.03985401798934018
## Validation Metrics
- Loss: 0.5283975601196289
- Accuracy: 0.7389705882352942
- Precision: 0.5032894736842105
- Recall: 0.3574766355140187
- AUC: 0.7135599403856304
- F1: 0.41803278688524587
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/Luojike/autotrain-test_3-1071537591
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("Luojike/autotrain-test_3-1071537591", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("Luojike/autotrain-test_3-1071537591", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
``` |
amasi/wikineural-multilingual-ner | 88a0bd54b5a1594a9972ba3c063057784678e957 | 2022-07-03T19:40:00.000Z | [
"pytorch",
"tensorboard",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
]
| token-classification | false | amasi | null | amasi/wikineural-multilingual-ner | 10 | null | transformers | 11,982 | Entry not found |
Kayvane/distilbert-complaints-wandb-product | b362343b328d34dc7ee6a41a51ee6f76a6bc4085 | 2022-07-04T10:52:27.000Z | [
"pytorch",
"distilbert",
"text-classification",
"dataset:consumer-finance-complaints",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
]
| text-classification | false | Kayvane | null | Kayvane/distilbert-complaints-wandb-product | 10 | null | transformers | 11,983 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- consumer-finance-complaints
metrics:
- accuracy
- f1
- recall
- precision
model-index:
- name: distilbert-complaints-wandb-product
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: consumer-finance-complaints
type: consumer-finance-complaints
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.8690996641956535
- name: F1
type: f1
value: 0.8645310918904254
- name: Recall
type: recall
value: 0.8690996641956535
- name: Precision
type: precision
value: 0.8629318199420283
---
<!-- 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-complaints-wandb-product
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the consumer-finance-complaints dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4431
- Accuracy: 0.8691
- F1: 0.8645
- Recall: 0.8691
- Precision: 0.8629
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Recall | Precision |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:------:|:---------:|
| 0.562 | 0.51 | 2000 | 0.5107 | 0.8452 | 0.8346 | 0.8452 | 0.8252 |
| 0.4548 | 1.01 | 4000 | 0.4628 | 0.8565 | 0.8481 | 0.8565 | 0.8466 |
| 0.3439 | 1.52 | 6000 | 0.4519 | 0.8605 | 0.8544 | 0.8605 | 0.8545 |
| 0.2626 | 2.03 | 8000 | 0.4412 | 0.8678 | 0.8618 | 0.8678 | 0.8626 |
| 0.2717 | 2.53 | 10000 | 0.4431 | 0.8691 | 0.8645 | 0.8691 | 0.8629 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
ghadeermobasher/BioRed-Chem-Original-PubMedBERT-320-8 | e89458523e371403321ab4e2e3bf163510768a4a | 2022-07-04T13:20:01.000Z | [
"pytorch",
"tensorboard",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
]
| token-classification | false | ghadeermobasher | null | ghadeermobasher/BioRed-Chem-Original-PubMedBERT-320-8 | 10 | null | transformers | 11,984 | Entry not found |
ghadeermobasher/BioRed-Chem-Modified-PubMedBERT-320-8 | cb4eadfec36514be8bede9ef665f56d8a6333466 | 2022-07-04T13:20:40.000Z | [
"pytorch",
"tensorboard",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
]
| token-classification | false | ghadeermobasher | null | ghadeermobasher/BioRed-Chem-Modified-PubMedBERT-320-8 | 10 | null | transformers | 11,985 | Entry not found |
ghadeermobasher/BioRed-Chem-Original-PubMedBERT-128-32 | 6dd0076c4c22bc154acbda14578d079ce6275df5 | 2022-07-04T13:25:34.000Z | [
"pytorch",
"tensorboard",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
]
| token-classification | false | ghadeermobasher | null | ghadeermobasher/BioRed-Chem-Original-PubMedBERT-128-32 | 10 | null | transformers | 11,986 | Entry not found |
sepidmnorozy/finetuned-sentiment-withGPU | dadb4575d330a784969b0335a36ba0ae38c3eee6 | 2022-07-04T14:01:11.000Z | [
"pytorch",
"xlm-roberta",
"text-classification",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index"
]
| text-classification | false | sepidmnorozy | null | sepidmnorozy/finetuned-sentiment-withGPU | 10 | null | transformers | 11,987 | ---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: finetuning-sentiment-model-10-samples_withGPU
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-model-10-samples_withGPU
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3893
- Accuracy: 0.8744
- F1: 0.8684
- Precision: 0.9126
- Recall: 0.8283
## 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 | Accuracy | F1 | Precision | Recall |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.3631 | 1.0 | 7088 | 0.3622 | 0.8638 | 0.8519 | 0.9334 | 0.7835 |
| 0.35 | 2.0 | 14176 | 0.3875 | 0.8714 | 0.8622 | 0.9289 | 0.8044 |
| 0.3262 | 3.0 | 21264 | 0.3893 | 0.8744 | 0.8684 | 0.9126 | 0.8283 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.10.0
- Datasets 2.0.0
- Tokenizers 0.11.6
|
Chirayu/subject-generator-t5-base | a84f4bf6aaf7103d762800b185a6edb5a559c173 | 2022-07-12T10:33:42.000Z | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
]
| text2text-generation | false | Chirayu | null | Chirayu/subject-generator-t5-base | 10 | null | transformers | 11,988 | # What does this model do?
This model generates a subject line for the email, given the whole email as input. It is fine-tuned T5-Base
Here is how to use this model
```python
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch
model = AutoModelForSeq2SeqLM.from_pretrained("Chirayu/subject-generator-t5-base")
tokenizer = AutoTokenizer.from_pretrained("Chirayu/subject-generator-t5-base")
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = model.to(device)
def get_subject(content, num_beams=5,max_length=512, repetition_penalty=2.5, length_penalty=1, early_stopping=True,top_p=.95, top_k=50, num_return_sequences=3):
text = "title: " + content + " </s>"
input_ids = tokenizer.encode(
text, return_tensors="pt", add_special_tokens=True
)
input_ids = input_ids.to(device)
generated_ids = model.generate(
input_ids=input_ids,
num_beams=num_beams,
max_length=max_length,
repetition_penalty=repetition_penalty,
length_penalty=length_penalty,
early_stopping=early_stopping,
top_p=top_p,
top_k=top_k,
num_return_sequences=num_return_sequences,
)
subjects = [tokenizer.decode(generated_id,skip_special_tokens=True,clean_up_tokenization_spaces=True,) for generated_id in generated_ids]
return subjects
```
|
ghadeermobasher/BioRed-Chem-Original-PubMedBERT-128-10 | 3ccf9e1c318233aed127262cf8ac8b2d72579ae8 | 2022-07-04T14:31:53.000Z | [
"pytorch",
"tensorboard",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
]
| token-classification | false | ghadeermobasher | null | ghadeermobasher/BioRed-Chem-Original-PubMedBERT-128-10 | 10 | null | transformers | 11,989 | Entry not found |
ghadeermobasher/BioRed-Chem-Original-PubMedBERT-256-40 | 662503660a17570b86707f956c96d8492d292113 | 2022-07-05T12:14:57.000Z | [
"pytorch",
"tensorboard",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
]
| token-classification | false | ghadeermobasher | null | ghadeermobasher/BioRed-Chem-Original-PubMedBERT-256-40 | 10 | null | transformers | 11,990 | Entry not found |
domenicrosati/deberta-v3-xsmall-with-biblio-context-finetuned-review_classifier | 04ef7381857ac5e8cca24ec397b2f05476647fec | 2022-07-07T05:12:58.000Z | [
"pytorch",
"tensorboard",
"deberta-v2",
"transformers",
"text-classification",
"generated_from_trainer",
"license:mit",
"model-index"
]
| text-classification | false | domenicrosati | null | domenicrosati/deberta-v3-xsmall-with-biblio-context-finetuned-review_classifier | 10 | null | transformers | 11,991 | ---
license: mit
tags:
- text-classification
- generated_from_trainer
metrics:
- accuracy
- f1
- recall
- precision
model-index:
- name: deberta-v3-xsmall-with-biblio-context-finetuned-review_classifier
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. -->
# deberta-v3-xsmall-with-biblio-context-finetuned-review_classifier
This model is a fine-tuned version of [microsoft/deberta-v3-xsmall](https://huggingface.co/microsoft/deberta-v3-xsmall) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0979
- Accuracy: 0.9682
- F1: 0.8332
- Recall: 0.8466
- Precision: 0.8202
## 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: 4.5e-05
- train_batch_size: 12
- eval_batch_size: 12
- 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: 2
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Recall | Precision |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:------:|:---------:|
| 0.1539 | 1.0 | 6667 | 0.1237 | 0.9584 | 0.7668 | 0.7307 | 0.8067 |
| 0.1271 | 2.0 | 13334 | 0.0979 | 0.9682 | 0.8332 | 0.8466 | 0.8202 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.12.0+cu102
- Datasets 2.3.2
- Tokenizers 0.12.1
|
f00d/distilroberta-base-finetuned-wikitext2 | a2a9b78dd3a8b524b930dcd71cea17db50863084 | 2022-07-06T10:02:54.000Z | [
"pytorch",
"tensorboard",
"roberta",
"fill-mask",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
]
| fill-mask | false | f00d | null | f00d/distilroberta-base-finetuned-wikitext2 | 10 | null | transformers | 11,992 | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: distilroberta-base-finetuned-wikitext2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilroberta-base-finetuned-wikitext2
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8343
## 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: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.0842 | 1.0 | 2406 | 1.9219 |
| 1.9913 | 2.0 | 4812 | 1.8822 |
| 1.9596 | 3.0 | 7218 | 1.8215 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
mbshr/urt5-base-finetuned | c19132e2e1d37ab0307220f560cdba3388e64188 | 2022-07-06T19:49:23.000Z | [
"pytorch",
"mt5",
"text2text-generation",
"transformers",
"autotrain_compatible"
]
| text2text-generation | false | mbshr | null | mbshr/urt5-base-finetuned | 10 | null | transformers | 11,993 | Entry not found |
ghadeermobasher/BioRed-Chem-Original-PubMedBERT-512-5-30 | 74d53f6fecfa1c948d71c0723233fea48b3bc113 | 2022-07-07T14:20:09.000Z | [
"pytorch",
"tensorboard",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
]
| token-classification | false | ghadeermobasher | null | ghadeermobasher/BioRed-Chem-Original-PubMedBERT-512-5-30 | 10 | null | transformers | 11,994 | Entry not found |
dminiotas05/distilbert-base-uncased-finetuned-ft500_6class600 | 836385f37f9ec92cb23d0452601bf675b9ecca79 | 2022-07-07T13:23:59.000Z | [
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
]
| text-classification | false | dminiotas05 | null | dminiotas05/distilbert-base-uncased-finetuned-ft500_6class600 | 10 | null | transformers | 11,995 | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-ft500_6class600
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-ft500_6class600
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6317
- Accuracy: 0.35
- F1: 0.3327
## 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: 8
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 1.5717 | 1.0 | 188 | 1.5375 | 0.3067 | 0.2820 |
| 1.4338 | 2.0 | 376 | 1.5354 | 0.3207 | 0.2824 |
| 1.3516 | 3.0 | 564 | 1.4852 | 0.3573 | 0.3287 |
| 1.2722 | 4.0 | 752 | 1.4997 | 0.366 | 0.3534 |
| 1.1923 | 5.0 | 940 | 1.5474 | 0.362 | 0.3454 |
| 1.1156 | 6.0 | 1128 | 1.5998 | 0.3547 | 0.3387 |
| 1.0522 | 7.0 | 1316 | 1.6154 | 0.3473 | 0.3316 |
| 1.0148 | 8.0 | 1504 | 1.6317 | 0.35 | 0.3327 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
akhisreelibra/mt5-small-finetuned-oneindia | 8690764e4b598b2aa6d94d4af20b86df043fe06c | 2022-07-08T01:05:04.000Z | [
"pytorch",
"tensorboard",
"mt5",
"text2text-generation",
"transformers",
"summarization",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
]
| summarization | false | akhisreelibra | null | akhisreelibra/mt5-small-finetuned-oneindia | 10 | null | transformers | 11,996 | |
kwmr/wav2vec2_japanese | f7b020bbe975d54cc9e767a2880695121fd82d97 | 2022-07-07T20:33:05.000Z | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"transformers"
]
| automatic-speech-recognition | false | kwmr | null | kwmr/wav2vec2_japanese | 10 | 2 | transformers | 11,997 | ## Wav2Vec2.0 XLSR-53 large model の日本語 Fine Tuning モデル
[facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53)を日本語用にFine Tuningしたモデル
## 使用データセット
- [Common Voice](https://commonvoice.mozilla.org/ja)
## 使い方
```python
from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC
from datasets import load_dataset
import torch
# load model and processor
processor = Wav2Vec2Processor.from_pretrained("kwmr/wav2vec2_japanese")
model = Wav2Vec2ForCTC.from_pretrained("kwmr/wav2vec2_japanese")
``` |
dminiotas05/distilbert-base-uncased-finetuned-ft650_6class | 1f59f4e2e98fa2960c0291cd7654806a33f72425 | 2022-07-08T12:11:05.000Z | [
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
]
| text-classification | false | dminiotas05 | null | dminiotas05/distilbert-base-uncased-finetuned-ft650_6class | 10 | null | transformers | 11,998 | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-ft650_6class
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-ft650_6class
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4555
- Accuracy: 0.3707
- F1: 0.3625
## 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: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 1.5838 | 1.0 | 188 | 1.5235 | 0.3253 | 0.2947 |
| 1.4521 | 2.0 | 376 | 1.4744 | 0.3467 | 0.3234 |
| 1.3838 | 3.0 | 564 | 1.4565 | 0.358 | 0.3483 |
| 1.323 | 4.0 | 752 | 1.4555 | 0.3707 | 0.3625 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
steven123/Check_Missing_Teeth | c185ff48cfeabbe5cd88fb79c509782264c058a2 | 2022-07-08T22:59:30.000Z | [
"pytorch",
"tensorboard",
"vit",
"image-classification",
"transformers",
"huggingpics",
"model-index"
]
| image-classification | false | steven123 | null | steven123/Check_Missing_Teeth | 10 | null | transformers | 11,999 | ---
tags:
- image-classification
- pytorch
- huggingpics
metrics:
- accuracy
model-index:
- name: Check_Missing_Teeth
results:
- task:
name: Image Classification
type: image-classification
metrics:
- name: Accuracy
type: accuracy
value: 0.9375
---
# Check_Missing_Teeth
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics).
## Example Images
#### Missing Teeth
#### Non-Missing Teeth
 |
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