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andrewzolensky/bert-emotion | 3b1a7f9a848ba99d439563d215d21a59b1ec0ee4 | 2022-05-26T01:51:23.000Z | [
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"transformers"
]
| text-classification | false | andrewzolensky | null | andrewzolensky/bert-emotion | 6 | null | transformers | 15,700 | Entry not found |
SamuelMiller/lil_sum_sum | 66bd3eef4daa6a0afef9a67d3178fcd273c011f3 | 2022-05-23T05:04:39.000Z | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
]
| text2text-generation | false | SamuelMiller | null | SamuelMiller/lil_sum_sum | 6 | null | transformers | 15,701 | Entry not found |
SamuelMiller/lil_sumsum | 021ed1b27a589b9d81c513932f69f3119544e704 | 2022-05-23T19:49:44.000Z | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
]
| text2text-generation | false | SamuelMiller | null | SamuelMiller/lil_sumsum | 6 | null | transformers | 15,702 | ## This is the model for the 'Sum_it' app ##
Find it at HuggingFace Spaces!
https://huggingface.co/spaces/SamuelMiller/sum_it |
renjithks/layoutlmv3-er-ner | 23b32a5762766b8571bf033af8420c3118c64c09 | 2022-05-31T17:36:05.000Z | [
"pytorch",
"tensorboard",
"layoutlmv3",
"token-classification",
"transformers",
"generated_from_trainer",
"model-index",
"autotrain_compatible"
]
| token-classification | false | renjithks | null | renjithks/layoutlmv3-er-ner | 6 | null | transformers | 15,703 | ---
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: layoutlmv3-er-ner
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# layoutlmv3-er-ner
This model is a fine-tuned version of [renjithks/layoutlmv3-cord-ner](https://huggingface.co/renjithks/layoutlmv3-cord-ner) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2025
- Precision: 0.6442
- Recall: 0.6761
- F1: 0.6598
- Accuracy: 0.9507
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 22 | 0.2940 | 0.4214 | 0.2956 | 0.3475 | 0.9147 |
| No log | 2.0 | 44 | 0.2487 | 0.4134 | 0.4526 | 0.4321 | 0.9175 |
| No log | 3.0 | 66 | 0.1922 | 0.5399 | 0.5460 | 0.5429 | 0.9392 |
| No log | 4.0 | 88 | 0.1977 | 0.5653 | 0.5813 | 0.5732 | 0.9434 |
| No log | 5.0 | 110 | 0.2018 | 0.6173 | 0.6252 | 0.6212 | 0.9477 |
| No log | 6.0 | 132 | 0.1823 | 0.6232 | 0.6153 | 0.6192 | 0.9485 |
| No log | 7.0 | 154 | 0.1972 | 0.6203 | 0.6238 | 0.6220 | 0.9477 |
| No log | 8.0 | 176 | 0.1952 | 0.6292 | 0.6407 | 0.6349 | 0.9511 |
| No log | 9.0 | 198 | 0.2070 | 0.6331 | 0.6492 | 0.6411 | 0.9489 |
| No log | 10.0 | 220 | 0.2025 | 0.6442 | 0.6761 | 0.6598 | 0.9507 |
### Framework versions
- Transformers 4.20.0.dev0
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.12.1
|
Lucifer-nick/coconut_smiles | 635069d5fe9c16a685cf8a2acb6ac626518efc34 | 2022-07-05T08:59:51.000Z | [
"pytorch",
"jax",
"roberta",
"fill-mask",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
]
| fill-mask | false | Lucifer-nick | null | Lucifer-nick/coconut_smiles | 6 | null | transformers | 15,704 | ---
license: apache-2.0
---
|
luisu0124/Amazon_review | b59c73fb83ac0a246698d0ea5e370087af58642c | 2022-05-26T03:28:01.000Z | [
"pytorch",
"bert",
"text-classification",
"es",
"transformers",
"Text Classification"
]
| text-classification | false | luisu0124 | null | luisu0124/Amazon_review | 6 | null | transformers | 15,705 | ---
language:
- es
tags:
- Text Classification
---
## language:
- es
## tags:
- amazon_reviews_multi
- Text Clasiffication
### Dataset
### Example structure review:
| review_id (string) | product_id (string) | reviewer_id (string) | stars (int) | review_body (string) | review_title (string) | language (string) | product_category (string) |
| ------------- | ------------- | ------------- | ------------- | ------------- | ------------- | ------------- | ------------- |
| de_0203609|product_de_0865382|reviewer_de_0267719|1|Armband ist leider nach 1 Jahr kaputt gegangen|Leider nach 1 Jahr kaputt|de|sports|
### Model
### Model train
| Text | Classification |
| ------------- | ------------- |
| review_body | stars |
### Model test
### Clasiffication reviews in Spanish
Uses `POS`, `NEG` labels. |
Danastos/qacombined_bert_el_4 | 7a223a795d0abae49192226a9136993f2f748128 | 2022-05-24T22:01:23.000Z | [
"pytorch",
"bert",
"question-answering",
"transformers",
"autotrain_compatible"
]
| question-answering | false | Danastos | null | Danastos/qacombined_bert_el_4 | 6 | null | transformers | 15,706 | Entry not found |
OHenry/finetuned-neural-bert-ner | 96299c07a4eb82934c96acfa5b462f72f8020fc0 | 2022-05-25T13:42:27.000Z | [
"pytorch",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
]
| token-classification | false | OHenry | null | OHenry/finetuned-neural-bert-ner | 6 | null | transformers | 15,707 | Entry not found |
chanind/frame-semantic-transformer-large | 348f581f4794e6d7c9be1e6e0a7a6076a77f6a37 | 2022-05-26T08:46:32.000Z | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
]
| text2text-generation | false | chanind | null | chanind/frame-semantic-transformer-large | 6 | null | transformers | 15,708 | Entry not found |
ryan1998/distilbert-base-uncased-finetuned-emotion | 2dcee06e0046c4586bc3a2f8493724f40f73b551 | 2022-05-26T14:32:56.000Z | [
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
]
| text-classification | false | ryan1998 | null | ryan1998/distilbert-base-uncased-finetuned-emotion | 6 | null | transformers | 15,709 | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotion
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.5280
- Accuracy: 0.2886
- F1: 0.2742
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log | 1.0 | 1316 | 2.6049 | 0.2682 | 0.2516 |
| No log | 2.0 | 2632 | 2.5280 | 0.2886 | 0.2742 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
prodm93/GPT2Dynamic_title_model_v1 | bce0035725479c756d6a1b128a82bd539ee1b587 | 2022-05-26T19:01:52.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
]
| text-generation | false | prodm93 | null | prodm93/GPT2Dynamic_title_model_v1 | 6 | null | transformers | 15,710 | Entry not found |
jkhan447/language-detection-RoBerta-base-additional | 9f5c70aace6ade1b2d151bcf6b7ef7ec41d58c06 | 2022-05-30T09:38:00.000Z | [
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index"
]
| text-classification | false | jkhan447 | null | jkhan447/language-detection-RoBerta-base-additional | 6 | null | transformers | 15,711 | ---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: language-detection-RoBerta-base-additional
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. -->
# language-detection-RoBerta-base-additional
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1367
- Accuracy: 0.9874
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
### Training results
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Tokenizers 0.12.1
|
zenkri/autotrain-Arabic_Poetry_by_Subject-920730230 | eb837305e1c60c42357dd69ee3c7ec2a9efc7360 | 2022-05-28T08:41:57.000Z | [
"pytorch",
"bert",
"text-classification",
"ar",
"dataset:zenkri/autotrain-data-Arabic_Poetry_by_Subject-1d8ba412",
"transformers",
"autotrain",
"co2_eq_emissions"
]
| text-classification | false | zenkri | null | zenkri/autotrain-Arabic_Poetry_by_Subject-920730230 | 6 | null | transformers | 15,712 | ---
tags: autotrain
language: ar
widget:
- text: "I love AutoTrain 🤗"
datasets:
- zenkri/autotrain-data-Arabic_Poetry_by_Subject-1d8ba412
co2_eq_emissions: 0.07445219847409645
---
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 920730230
- CO2 Emissions (in grams): 0.07445219847409645
## Validation Metrics
- Loss: 0.5806193351745605
- Accuracy: 0.8785200718993409
- Macro F1: 0.8208042310550474
- Micro F1: 0.8785200718993409
- Weighted F1: 0.8783590365809876
- Macro Precision: 0.8486540338838363
- Micro Precision: 0.8785200718993409
- Weighted Precision: 0.8815185727115001
- Macro Recall: 0.8121110408113442
- Micro Recall: 0.8785200718993409
- Weighted Recall: 0.8785200718993409
## 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/zenkri/autotrain-Arabic_Poetry_by_Subject-920730230
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("zenkri/autotrain-Arabic_Poetry_by_Subject-920730230", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("zenkri/autotrain-Arabic_Poetry_by_Subject-920730230", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
``` |
autoevaluate/translation | a7b2c3ce3e88c03c59c6abae0b14991e11ec4f8e | 2022-05-28T14:31:28.000Z | [
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"dataset:wmt16",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
]
| text2text-generation | false | autoevaluate | null | autoevaluate/translation | 6 | null | transformers | 15,713 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- wmt16
metrics:
- bleu
model-index:
- name: translation
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: wmt16
type: wmt16
args: ro-en
metrics:
- name: Bleu
type: bleu
value: 28.5866
---
<!-- 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. -->
# translation
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-ro](https://huggingface.co/Helsinki-NLP/opus-mt-en-ro) on the wmt16 dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3170
- Bleu: 28.5866
- Gen Len: 33.9575
## 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
- training_steps: 1000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|
| 0.8302 | 0.03 | 1000 | 1.3170 | 28.5866 | 33.9575 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
ashesicsis1/xlsr-english | 4b751f41d013ae6483f5053c2869d616b4d690f4 | 2022-05-29T14:47:54.000Z | [
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"dataset:librispeech_asr",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
]
| automatic-speech-recognition | false | ashesicsis1 | null | ashesicsis1/xlsr-english | 6 | null | transformers | 15,714 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- librispeech_asr
model-index:
- name: xlsr-english
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. -->
# xlsr-english
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the librispeech_asr dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3098
- Wer: 0.1451
## 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: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 3.2453 | 2.37 | 400 | 0.5789 | 0.4447 |
| 0.3736 | 4.73 | 800 | 0.3737 | 0.2850 |
| 0.1712 | 7.1 | 1200 | 0.3038 | 0.2136 |
| 0.117 | 9.47 | 1600 | 0.3016 | 0.2072 |
| 0.0897 | 11.83 | 2000 | 0.3158 | 0.1920 |
| 0.074 | 14.2 | 2400 | 0.3137 | 0.1831 |
| 0.0595 | 16.57 | 2800 | 0.2967 | 0.1745 |
| 0.0493 | 18.93 | 3200 | 0.3192 | 0.1670 |
| 0.0413 | 21.3 | 3600 | 0.3176 | 0.1644 |
| 0.0322 | 23.67 | 4000 | 0.3079 | 0.1598 |
| 0.0296 | 26.04 | 4400 | 0.2978 | 0.1511 |
| 0.0235 | 28.4 | 4800 | 0.3098 | 0.1451 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu113
- Datasets 1.18.3
- Tokenizers 0.10.3
|
Ayush414/distilbert-base-uncased-finetuned-ner | 03280eebc648f4737e7f535fc6edb061cd517bff | 2022-05-30T12:36:18.000Z | [
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"dataset:conll2003",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
]
| token-classification | false | Ayush414 | null | Ayush414/distilbert-base-uncased-finetuned-ner | 6 | null | transformers | 15,715 | ---
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: 0.9253929599291565
- name: Recall
type: recall
value: 0.9352276541000112
- name: F1
type: f1
value: 0.9302843153619317
- name: Accuracy
type: accuracy
value: 0.9835258233116749
---
<!-- 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: 0.0628
- Precision: 0.9254
- Recall: 0.9352
- F1: 0.9303
- Accuracy: 0.9835
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2388 | 1.0 | 878 | 0.0723 | 0.9108 | 0.9186 | 0.9147 | 0.9798 |
| 0.0526 | 2.0 | 1756 | 0.0633 | 0.9176 | 0.9290 | 0.9232 | 0.9817 |
| 0.0303 | 3.0 | 2634 | 0.0628 | 0.9254 | 0.9352 | 0.9303 | 0.9835 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
dunlp/GWW | 37b2823459ed104783827c9742ea2f58f4c659ef | 2022-06-29T09:36:26.000Z | [
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"transformers",
"generated_from_trainer",
"model-index",
"autotrain_compatible"
]
| fill-mask | false | dunlp | null | dunlp/GWW | 6 | null | transformers | 15,716 | ---
tags:
- generated_from_trainer
model-index:
- name: GWW
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. -->
# GWW
This model is a fine-tuned version of [GroNLP/bert-base-dutch-cased](https://huggingface.co/GroNLP/bert-base-dutch-cased) on Dutch civiel works dataset.
It achieves the following results on the evaluation set:
- Loss: 2.7097
## 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: 5.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 3.7179 | 1.0 | 78 | 3.1185 |
| 3.1134 | 2.0 | 156 | 2.8528 |
| 2.9327 | 3.0 | 234 | 2.7249 |
| 2.8377 | 4.0 | 312 | 2.7255 |
| 2.7888 | 5.0 | 390 | 2.6737 |
### Framework versions
- Transformers 4.19.4
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
erfangc/test1 | 20e7cfec04730aff44c89a7bb89a49fa01715e60 | 2022-05-30T18:23:14.000Z | [
"pytorch",
"bert",
"text-classification",
"transformers"
]
| text-classification | false | erfangc | null | erfangc/test1 | 6 | null | transformers | 15,717 | Entry not found |
hhhhzy/roberta-pubhealth | 6f7133efa090591da36a4d75963c6d7d31b0e4a9 | 2022-05-30T23:01:52.000Z | [
"pytorch",
"roberta",
"text-classification",
"transformers"
]
| text-classification | false | hhhhzy | null | hhhhzy/roberta-pubhealth | 6 | null | transformers | 15,718 |
# Roberta-Pubhealth model
This model is a fine-tuned version of [RoBERTa Base](https://huggingface.co/roberta-base) on the health_fact dataset.
It achieves the following results on the evaluation set:
- micro f1 (accuracy): 0.7137
- macro f1: 0.6056
- weighted f1: 0.7106
- samples predicted per second: 9.31
## Dataset desctiption
[PUBHEALTH](https://huggingface.co/datasets/health_fact)is a comprehensive dataset for explainable automated fact-checking of public health claims. Each instance in the PUBHEALTH dataset has an associated veracity label (true, false, unproven, mixture). Furthermore each instance in the dataset has an explanation text field. The explanation is a justification for which the claim has been assigned a particular veracity label.
## Training hyperparameters
The model are trained with the following tuned config:
- model: roberta base
- batch size: 32
- learning rate: 5e-5
- number of epochs: 4
- warmup steps: 0 |
Jiexing/cosql_add_coref_t5_3b_order_0519_ckpt-576 | e2289714e6fbffbe1b55111a1a45f6c7ca2f61b3 | 2022-05-31T02:22:25.000Z | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
]
| text2text-generation | false | Jiexing | null | Jiexing/cosql_add_coref_t5_3b_order_0519_ckpt-576 | 6 | null | transformers | 15,719 | Entry not found |
mccaffary/finetuning-sentiment-model-3000-samples-DM | 644eb1d0baa3d4758427c85666e74b56636c4df6 | 2022-06-01T09:01:21.000Z | [
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"dataset:imdb",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
]
| text-classification | false | mccaffary | null | mccaffary/finetuning-sentiment-model-3000-samples-DM | 6 | null | transformers | 15,720 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
metrics:
- accuracy
- f1
model-index:
- name: finetuning-sentiment-model-3000-samples-DM
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: imdb
type: imdb
args: plain_text
metrics:
- name: Accuracy
type: accuracy
value: 0.8666666666666667
- name: F1
type: f1
value: 0.8734177215189873
---
<!-- 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-DM
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: 0.3248
- Accuracy: 0.8667
- F1: 0.8734
## 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 1.8.0
- Datasets 2.2.2
- Tokenizers 0.12.1
|
Classroom-workshop/assignment1-jack | 890c29ec8d66df7ec2eee93b88456526d0d9ea2f | 2022-06-02T15:22:42.000Z | [
"pytorch",
"tf",
"speech_to_text",
"automatic-speech-recognition",
"en",
"dataset:librispeech_asr",
"arxiv:2010.05171",
"arxiv:1904.08779",
"transformers",
"speech",
"audio",
"hf-asr-leaderboard",
"license:mit",
"model-index"
]
| automatic-speech-recognition | false | Classroom-workshop | null | Classroom-workshop/assignment1-jack | 6 | null | transformers | 15,721 | ---
language: en
datasets:
- librispeech_asr
tags:
- speech
- audio
- automatic-speech-recognition
- hf-asr-leaderboard
license: mit
pipeline_tag: automatic-speech-recognition
widget:
- example_title: Librispeech sample 1
src: https://cdn-media.huggingface.co/speech_samples/sample1.flac
- example_title: Librispeech sample 2
src: https://cdn-media.huggingface.co/speech_samples/sample2.flac
model-index:
- name: s2t-small-librispeech-asr
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: LibriSpeech (clean)
type: librispeech_asr
config: clean
split: test
args:
language: en
metrics:
- name: Test WER
type: wer
value: 4.3
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: LibriSpeech (other)
type: librispeech_asr
config: other
split: test
args:
language: en
metrics:
- name: Test WER
type: wer
value: 9.0
---
# S2T-SMALL-LIBRISPEECH-ASR
`s2t-small-librispeech-asr` is a Speech to Text Transformer (S2T) model trained for automatic speech recognition (ASR).
The S2T model was proposed in [this paper](https://arxiv.org/abs/2010.05171) and released in
[this repository](https://github.com/pytorch/fairseq/tree/master/examples/speech_to_text)
## Model description
S2T is an end-to-end sequence-to-sequence transformer model. It is trained with standard
autoregressive cross-entropy loss and generates the transcripts autoregressively.
## Intended uses & limitations
This model can be used for end-to-end speech recognition (ASR).
See the [model hub](https://huggingface.co/models?filter=speech_to_text) to look for other S2T checkpoints.
### How to use
As this a standard sequence to sequence transformer model, you can use the `generate` method to generate the
transcripts by passing the speech features to the model.
*Note: The `Speech2TextProcessor` object uses [torchaudio](https://github.com/pytorch/audio) to extract the
filter bank features. Make sure to install the `torchaudio` package before running this example.*
*Note: The feature extractor depends on [torchaudio](https://github.com/pytorch/audio) and the tokenizer depends on [sentencepiece](https://github.com/google/sentencepiece)
so be sure to install those packages before running the examples.*
You could either install those as extra speech dependancies with
`pip install transformers"[speech, sentencepiece]"` or install the packages seperatly
with `pip install torchaudio sentencepiece`.
```python
import torch
from transformers import Speech2TextProcessor, Speech2TextForConditionalGeneration
from datasets import load_dataset
model = Speech2TextForConditionalGeneration.from_pretrained("facebook/s2t-small-librispeech-asr")
processor = Speech2TextProcessor.from_pretrained("facebook/s2t-small-librispeech-asr")
ds = load_dataset(
"patrickvonplaten/librispeech_asr_dummy",
"clean",
split="validation"
)
input_features = processor(
ds[0]["audio"]["array"],
sampling_rate=16_000,
return_tensors="pt"
).input_features # Batch size 1
generated_ids = model.generate(input_ids=input_features)
transcription = processor.batch_decode(generated_ids)
```
#### Evaluation on LibriSpeech Test
The following script shows how to evaluate this model on the [LibriSpeech](https://huggingface.co/datasets/librispeech_asr)
*"clean"* and *"other"* test dataset.
```python
from datasets import load_dataset, load_metric
from transformers import Speech2TextForConditionalGeneration, Speech2TextProcessor
librispeech_eval = load_dataset("librispeech_asr", "clean", split="test") # change to "other" for other test dataset
wer = load_metric("wer")
model = Speech2TextForConditionalGeneration.from_pretrained("facebook/s2t-small-librispeech-asr").to("cuda")
processor = Speech2TextProcessor.from_pretrained("facebook/s2t-small-librispeech-asr", do_upper_case=True)
librispeech_eval = librispeech_eval.map(map_to_array)
def map_to_pred(batch):
features = processor(batch["audio"]["array"], sampling_rate=16000, padding=True, return_tensors="pt")
input_features = features.input_features.to("cuda")
attention_mask = features.attention_mask.to("cuda")
gen_tokens = model.generate(input_ids=input_features, attention_mask=attention_mask)
batch["transcription"] = processor.batch_decode(gen_tokens, skip_special_tokens=True)
return batch
result = librispeech_eval.map(map_to_pred, batched=True, batch_size=8, remove_columns=["speech"])
print("WER:", wer(predictions=result["transcription"], references=result["text"]))
```
*Result (WER)*:
| "clean" | "other" |
|:-------:|:-------:|
| 4.3 | 9.0 |
## Training data
The S2T-SMALL-LIBRISPEECH-ASR is trained on [LibriSpeech ASR Corpus](https://www.openslr.org/12), a dataset consisting of
approximately 1000 hours of 16kHz read English speech.
## Training procedure
### Preprocessing
The speech data is pre-processed by extracting Kaldi-compliant 80-channel log mel-filter bank features automatically from
WAV/FLAC audio files via PyKaldi or torchaudio. Further utterance-level CMVN (cepstral mean and variance normalization)
is applied to each example.
The texts are lowercased and tokenized using SentencePiece and a vocabulary size of 10,000.
### Training
The model is trained with standard autoregressive cross-entropy loss and using [SpecAugment](https://arxiv.org/abs/1904.08779).
The encoder receives speech features, and the decoder generates the transcripts autoregressively.
### BibTeX entry and citation info
```bibtex
@inproceedings{wang2020fairseqs2t,
title = {fairseq S2T: Fast Speech-to-Text Modeling with fairseq},
author = {Changhan Wang and Yun Tang and Xutai Ma and Anne Wu and Dmytro Okhonko and Juan Pino},
booktitle = {Proceedings of the 2020 Conference of the Asian Chapter of the Association for Computational Linguistics (AACL): System Demonstrations},
year = {2020},
}
``` |
bradgrimm/patent-cpc-predictor | 4ebf5d51044c5ced9d964956ae2a5de40b669d42 | 2022-06-02T22:33:47.000Z | [
"pytorch",
"deberta-v2",
"feature-extraction",
"en",
"transformers",
"patent",
"deberta",
"license:mit"
]
| feature-extraction | false | bradgrimm | null | bradgrimm/patent-cpc-predictor | 6 | null | transformers | 15,722 | ---
language: en
tags:
- patent
- deberta
license: mit
---
# Patent CPC Predictor
This is a fine-tuned version of microsoft/deberta-v3-small for predicting Patent CPC codes.
# Dataset
Dataset consists of titles and abstracts sampled from granted patent applications:
https://www.kaggle.com/datasets/grimmace/sampled-patent-titles
# Results
| Category | Accuracy |
| --- | ----------- |
| Section | 92% |
| Class | 88% |
| Subclass | 85% | |
ArthurZ/opt-1.3b | 340576fcb4f2edbd6ea82a907fe85a50bb913965 | 2022-06-21T14:34:58.000Z | [
"pytorch",
"tf",
"jax",
"opt",
"text-generation",
"transformers",
"generated_from_keras_callback",
"model-index"
]
| text-generation | false | ArthurZ | null | ArthurZ/opt-1.3b | 6 | null | transformers | 15,723 | ---
tags:
- generated_from_keras_callback
model-index:
- name: opt-1.3b
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# opt-1.3b
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
## 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:
- optimizer: None
- training_precision: float32
### Training results
### Framework versions
- Transformers 4.20.0.dev0
- TensorFlow 2.9.1
- Datasets 2.2.2
- Tokenizers 0.12.1
|
madatnlp/torch-trinity | 16d66ff35d40ec7eefdc4758682812a9e0734379 | 2022-06-03T06:59:20.000Z | [
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"transformers"
]
| text-generation | false | madatnlp | null | madatnlp/torch-trinity | 6 | null | transformers | 15,724 | Entry not found |
Eulaliefy/distilbert-base-uncased-finetuned-ner | c2b6afb7e9771459d736b71a3db335d165869c9f | 2022-06-03T18:21:14.000Z | [
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"dataset:conll2003",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
]
| token-classification | false | Eulaliefy | null | Eulaliefy/distilbert-base-uncased-finetuned-ner | 6 | null | transformers | 15,725 | ---
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: 0.9250691754288877
- name: Recall
type: recall
value: 0.9350039154267815
- name: F1
type: f1
value: 0.9300100144653389
- name: Accuracy
type: accuracy
value: 0.9836052552147044
---
<!-- 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: 0.0620
- Precision: 0.9251
- Recall: 0.9350
- F1: 0.9300
- Accuracy: 0.9836
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2356 | 1.0 | 878 | 0.0699 | 0.9110 | 0.9225 | 0.9167 | 0.9801 |
| 0.0509 | 2.0 | 1756 | 0.0621 | 0.9180 | 0.9314 | 0.9246 | 0.9823 |
| 0.0303 | 3.0 | 2634 | 0.0620 | 0.9251 | 0.9350 | 0.9300 | 0.9836 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
ricardo-filho/bert_base_tcm_0.6 | e6defd66f05a64373ad6c74b7d1eec37637dada1 | 2022-06-09T14:15:12.000Z | [
"pytorch",
"tensorboard",
"bert",
"token-classification",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index",
"autotrain_compatible"
]
| token-classification | false | ricardo-filho | null | ricardo-filho/bert_base_tcm_0.6 | 6 | null | transformers | 15,726 | ---
license: mit
tags:
- generated_from_trainer
model-index:
- name: bert_base_tcm_0.6
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert_base_tcm_0.6
This model is a fine-tuned version of [neuralmind/bert-base-portuguese-cased](https://huggingface.co/neuralmind/bert-base-portuguese-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0193
- Criterio Julgamento Precision: 0.8875
- Criterio Julgamento Recall: 0.8659
- Criterio Julgamento F1: 0.8765
- Criterio Julgamento Number: 82
- Data Sessao Precision: 0.7571
- Data Sessao Recall: 0.9636
- Data Sessao F1: 0.848
- Data Sessao Number: 55
- Modalidade Licitacao Precision: 0.9394
- Modalidade Licitacao Recall: 0.9718
- Modalidade Licitacao F1: 0.9553
- Modalidade Licitacao Number: 319
- Numero Exercicio Precision: 0.9172
- Numero Exercicio Recall: 0.9688
- Numero Exercicio F1: 0.9422
- Numero Exercicio Number: 160
- Objeto Licitacao Precision: 0.4659
- Objeto Licitacao Recall: 0.7069
- Objeto Licitacao F1: 0.5616
- Objeto Licitacao Number: 58
- Valor Objeto Precision: 0.8333
- Valor Objeto Recall: 0.9211
- Valor Objeto F1: 0.875
- Valor Objeto Number: 38
- Overall Precision: 0.8537
- Overall Recall: 0.9340
- Overall F1: 0.8920
- Overall Accuracy: 0.9951
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Criterio Julgamento Precision | Criterio Julgamento Recall | Criterio Julgamento F1 | Criterio Julgamento Number | Data Sessao Precision | Data Sessao Recall | Data Sessao F1 | Data Sessao Number | Modalidade Licitacao Precision | Modalidade Licitacao Recall | Modalidade Licitacao F1 | Modalidade Licitacao Number | Numero Exercicio Precision | Numero Exercicio Recall | Numero Exercicio F1 | Numero Exercicio Number | Objeto Licitacao Precision | Objeto Licitacao Recall | Objeto Licitacao F1 | Objeto Licitacao Number | Valor Objeto Precision | Valor Objeto Recall | Valor Objeto F1 | Valor Objeto Number | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:-----------------------------:|:--------------------------:|:----------------------:|:--------------------------:|:---------------------:|:------------------:|:--------------:|:------------------:|:------------------------------:|:---------------------------:|:-----------------------:|:---------------------------:|:--------------------------:|:-----------------------:|:-------------------:|:-----------------------:|:--------------------------:|:-----------------------:|:-------------------:|:-----------------------:|:----------------------:|:-------------------:|:---------------:|:-------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 0.0252 | 1.0 | 1963 | 0.0202 | 0.8022 | 0.8902 | 0.8439 | 82 | 0.7391 | 0.9273 | 0.8226 | 55 | 0.9233 | 0.9812 | 0.9514 | 319 | 0.8966 | 0.975 | 0.9341 | 160 | 0.4730 | 0.6034 | 0.5303 | 58 | 0.7083 | 0.8947 | 0.7907 | 38 | 0.8327 | 0.9298 | 0.8786 | 0.9948 |
| 0.0191 | 2.0 | 3926 | 0.0226 | 0.8554 | 0.8659 | 0.8606 | 82 | 0.5641 | 0.4 | 0.4681 | 55 | 0.9572 | 0.9812 | 0.9690 | 319 | 0.9273 | 0.9563 | 0.9415 | 160 | 0.3770 | 0.3966 | 0.3866 | 58 | 0.8571 | 0.7895 | 0.8219 | 38 | 0.8620 | 0.8596 | 0.8608 | 0.9951 |
| 0.0137 | 3.0 | 5889 | 0.0193 | 0.8875 | 0.8659 | 0.8765 | 82 | 0.7571 | 0.9636 | 0.848 | 55 | 0.9394 | 0.9718 | 0.9553 | 319 | 0.9172 | 0.9688 | 0.9422 | 160 | 0.4659 | 0.7069 | 0.5616 | 58 | 0.8333 | 0.9211 | 0.875 | 38 | 0.8537 | 0.9340 | 0.8920 | 0.9951 |
| 0.0082 | 4.0 | 7852 | 0.0210 | 0.8780 | 0.8780 | 0.8780 | 82 | 0.7966 | 0.8545 | 0.8246 | 55 | 0.9512 | 0.9781 | 0.9645 | 319 | 0.9023 | 0.9812 | 0.9401 | 160 | 0.5385 | 0.6034 | 0.5691 | 58 | 0.9 | 0.9474 | 0.9231 | 38 | 0.8810 | 0.9256 | 0.9027 | 0.9963 |
| 0.0048 | 5.0 | 9815 | 0.0222 | 0.8261 | 0.9268 | 0.8736 | 82 | 0.7969 | 0.9273 | 0.8571 | 55 | 0.9512 | 0.9781 | 0.9645 | 319 | 0.9231 | 0.975 | 0.9483 | 160 | 0.6515 | 0.7414 | 0.6935 | 58 | 0.875 | 0.9211 | 0.8974 | 38 | 0.8867 | 0.9452 | 0.9150 | 0.9964 |
| 0.0044 | 6.0 | 11778 | 0.0262 | 0.8276 | 0.8780 | 0.8521 | 82 | 0.7681 | 0.9636 | 0.8548 | 55 | 0.9541 | 0.9781 | 0.9659 | 319 | 0.9235 | 0.9812 | 0.9515 | 160 | 0.5263 | 0.6897 | 0.5970 | 58 | 0.9211 | 0.9211 | 0.9211 | 38 | 0.8722 | 0.9396 | 0.9047 | 0.9959 |
| 0.0042 | 7.0 | 13741 | 0.0246 | 0.8523 | 0.9146 | 0.8824 | 82 | 0.7656 | 0.8909 | 0.8235 | 55 | 0.9509 | 0.9718 | 0.9612 | 319 | 0.9118 | 0.9688 | 0.9394 | 160 | 0.5938 | 0.6552 | 0.6230 | 58 | 0.8974 | 0.9211 | 0.9091 | 38 | 0.8815 | 0.9298 | 0.9050 | 0.9960 |
| 0.0013 | 8.0 | 15704 | 0.0294 | 0.8295 | 0.8902 | 0.8588 | 82 | 0.7391 | 0.9273 | 0.8226 | 55 | 0.9543 | 0.9812 | 0.9675 | 319 | 0.9070 | 0.975 | 0.9398 | 160 | 0.6094 | 0.6724 | 0.6393 | 58 | 0.875 | 0.9211 | 0.8974 | 38 | 0.8765 | 0.9368 | 0.9056 | 0.9961 |
| 0.0019 | 9.0 | 17667 | 0.0303 | 0.8690 | 0.8902 | 0.8795 | 82 | 0.8305 | 0.8909 | 0.8596 | 55 | 0.9538 | 0.9718 | 0.9627 | 319 | 0.9290 | 0.9812 | 0.9544 | 160 | 0.6441 | 0.6552 | 0.6496 | 58 | 0.9211 | 0.9211 | 0.9211 | 38 | 0.9019 | 0.9298 | 0.9156 | 0.9961 |
| 0.0007 | 10.0 | 19630 | 0.0295 | 0.8488 | 0.8902 | 0.8690 | 82 | 0.7903 | 0.8909 | 0.8376 | 55 | 0.9571 | 0.9781 | 0.9674 | 319 | 0.9181 | 0.9812 | 0.9486 | 160 | 0.6393 | 0.6724 | 0.6555 | 58 | 0.9211 | 0.9211 | 0.9211 | 38 | 0.8938 | 0.9340 | 0.9135 | 0.9962 |
### Framework versions
- Transformers 4.20.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
haritzpuerto/distilbert-squad | 0dfcf0cb9cc78471945ed00aed6e20df4b6afe4b | 2022-06-03T20:08:44.000Z | [
"pytorch",
"distilbert",
"question-answering",
"transformers",
"autotrain_compatible"
]
| question-answering | false | haritzpuerto | null | haritzpuerto/distilbert-squad | 6 | null | transformers | 15,727 | TrainOutput(global_step=5475, training_loss=1.7323438837756848, metrics={'train_runtime': 4630.6634, 'train_samples_per_second': 18.917, 'train_steps_per_second': 1.182, 'total_flos': 1.1445080909703168e+16, 'train_loss': 1.7323438837756848, 'epoch': 1.0})
|
ssantanag/pasajes_de_la_biblia | 49688f7cbfd2bd46188fd22bd7ed8467bd0bd135 | 2022-06-04T04:32:36.000Z | [
"pytorch",
"tf",
"t5",
"text2text-generation",
"transformers",
"generated_from_keras_callback",
"model-index",
"autotrain_compatible"
]
| text2text-generation | false | ssantanag | null | ssantanag/pasajes_de_la_biblia | 6 | null | transformers | 15,728 | ---
tags:
- generated_from_keras_callback
model-index:
- name: pasajes_de_la_biblia
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# pasajes_de_la_biblia
Este modelo fue entrenado con el dataset publicado en Kaggle de los versiculos de la biblia en el siguiente enlace puede encontrar el dataset https://www.kaggle.com/datasets/camesruiz/biblia-ntv-spanish-bible-ntv.
## Training and evaluation data
la distribución de la data fue la siguiente:
- Training set: 58.20%
- Validation set: 9.65%
- Test set: 32.15%
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: None
- training_precision: float32
### Training results
### Framework versions
- Transformers 4.19.2
- TensorFlow 2.8.2
- Datasets 2.2.2
- Tokenizers 0.12.1
|
Jeevesh8/lecun_feather_berts-63 | b8dd5a2863e750ab6221e7021e66d910b246b2de | 2022-06-04T06:53:03.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
]
| text-classification | false | Jeevesh8 | null | Jeevesh8/lecun_feather_berts-63 | 6 | null | transformers | 15,729 | Entry not found |
Jeevesh8/lecun_feather_berts-12 | 977691e4e4c5705db6de9a0861141b5bd87736ac | 2022-06-04T06:52:11.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
]
| text-classification | false | Jeevesh8 | null | Jeevesh8/lecun_feather_berts-12 | 6 | null | transformers | 15,730 | Entry not found |
gciaffoni/wav2vec2-large-xls-r-300m-it-colab4 | 652ef1fda97e390d10e4c084db5ac42a2908c1aa | 2022-07-20T15:40:53.000Z | [
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"transformers"
]
| automatic-speech-recognition | false | gciaffoni | null | gciaffoni/wav2vec2-large-xls-r-300m-it-colab4 | 6 | null | transformers | 15,731 | R4 checkpoint-16000
|
nitishkumargundapu793/autotrain-chat-bot-responses-949231426 | b6c36ec3efd5e5fc252f3d9f8409ec0cf5f9ae5b | 2022-06-05T03:16:21.000Z | [
"pytorch",
"bert",
"text-classification",
"en",
"dataset:nitishkumargundapu793/autotrain-data-chat-bot-responses",
"transformers",
"autotrain",
"co2_eq_emissions"
]
| text-classification | false | nitishkumargundapu793 | null | nitishkumargundapu793/autotrain-chat-bot-responses-949231426 | 6 | null | transformers | 15,732 | ---
tags: autotrain
language: en
widget:
- text: "I love AutoTrain 🤗"
datasets:
- nitishkumargundapu793/autotrain-data-chat-bot-responses
co2_eq_emissions: 0.01123534537751425
---
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 949231426
- CO2 Emissions (in grams): 0.01123534537751425
## Validation Metrics
- Loss: 0.26922607421875
- Accuracy: 1.0
- Macro F1: 1.0
- Micro F1: 1.0
- Weighted F1: 1.0
- Macro Precision: 1.0
- Micro Precision: 1.0
- Weighted Precision: 1.0
- Macro Recall: 1.0
- Micro Recall: 1.0
- Weighted Recall: 1.0
## 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/nitishkumargundapu793/autotrain-chat-bot-responses-949231426
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("nitishkumargundapu793/autotrain-chat-bot-responses-949231426", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("nitishkumargundapu793/autotrain-chat-bot-responses-949231426", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
``` |
diiogo/caju128k | fd2f78e48076bd7c1c14c688acd285f63fa5c115 | 2022-07-25T21:38:53.000Z | [
"pytorch",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
]
| fill-mask | false | diiogo | null | diiogo/caju128k | 6 | null | transformers | 15,733 | Entry not found |
nestoralvaro/mT5_multilingual_XLSum-finetuned-xsum-mlsum___summary_text | 2fbc2c0e50405cfa97bcf8f295c526cc285cbeee | 2022-06-06T03:26:11.000Z | [
"pytorch",
"tensorboard",
"mt5",
"text2text-generation",
"dataset:mlsum",
"transformers",
"generated_from_trainer",
"model-index",
"autotrain_compatible"
]
| text2text-generation | false | nestoralvaro | null | nestoralvaro/mT5_multilingual_XLSum-finetuned-xsum-mlsum___summary_text | 6 | null | transformers | 15,734 | ---
tags:
- generated_from_trainer
datasets:
- mlsum
metrics:
- rouge
model-index:
- name: mT5_multilingual_XLSum-finetuned-xsum-mlsum___summary_text
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: mlsum
type: mlsum
args: es
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-mlsum___summary_text
This model is a fine-tuned version of [csebuetnlp/mT5_multilingual_XLSum](https://huggingface.co/csebuetnlp/mT5_multilingual_XLSum) on the mlsum 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: 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: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| 0.0 | 1.0 | 66592 | 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-w2 | f3c0d329fc131043b06137505711f56baf2ca66a | 2022-06-06T02:34:15.000Z | [
"pytorch",
"bert",
"text-classification",
"transformers",
"generated_from_trainer",
"model-index"
]
| text-classification | false | bondi | null | bondi/bert-semaphore-prediction-w2 | 6 | null | transformers | 15,735 | ---
tags:
- generated_from_trainer
model-index:
- name: bert-semaphore-prediction-w2
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-w2
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
|
yogeshchandrasekharuni/bart-paraphrase-finetuned-xsum-v3 | da235f3be1584e4e70ba5569579676eb6cbfbc1c | 2022-06-06T09:42:56.000Z | [
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
]
| text2text-generation | false | yogeshchandrasekharuni | null | yogeshchandrasekharuni/bart-paraphrase-finetuned-xsum-v3 | 6 | null | transformers | 15,736 | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: bart-paraphrase-finetuned-xsum-v3
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-v3
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: 0.3377
- Rouge1: 99.9461
- Rouge2: 72.6619
- Rougel: 99.9461
- Rougelsum: 99.9461
- Gen Len: 9.0396
## 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: 20
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| No log | 1.0 | 139 | 0.3653 | 96.4972 | 70.8271 | 96.5252 | 96.5085 | 9.7158 |
| No log | 2.0 | 278 | 0.6624 | 98.3228 | 72.2829 | 98.2598 | 98.2519 | 9.0612 |
| No log | 3.0 | 417 | 0.2880 | 98.2415 | 72.36 | 98.249 | 98.2271 | 9.4496 |
| 0.5019 | 4.0 | 556 | 0.4188 | 98.1123 | 70.8536 | 98.0746 | 98.0465 | 9.4065 |
| 0.5019 | 5.0 | 695 | 0.3718 | 98.8882 | 72.6619 | 98.8997 | 98.8882 | 10.7842 |
| 0.5019 | 6.0 | 834 | 0.4442 | 99.6076 | 72.6619 | 99.6076 | 99.598 | 9.0647 |
| 0.5019 | 7.0 | 973 | 0.2681 | 99.6076 | 72.6619 | 99.598 | 99.598 | 9.1403 |
| 0.2751 | 8.0 | 1112 | 0.3577 | 99.2479 | 72.6619 | 99.2536 | 99.2383 | 9.0612 |
| 0.2751 | 9.0 | 1251 | 0.2481 | 98.8785 | 72.6394 | 98.8882 | 98.8882 | 9.7914 |
| 0.2751 | 10.0 | 1390 | 0.2339 | 99.6076 | 72.6619 | 99.6076 | 99.6076 | 9.1942 |
| 0.2051 | 11.0 | 1529 | 0.2472 | 99.9461 | 72.6619 | 99.9461 | 99.9461 | 9.2338 |
| 0.2051 | 12.0 | 1668 | 0.3948 | 99.6076 | 72.6619 | 99.598 | 99.598 | 9.0468 |
| 0.2051 | 13.0 | 1807 | 0.4756 | 99.6076 | 72.6619 | 99.6076 | 99.6076 | 9.0576 |
| 0.2051 | 14.0 | 1946 | 0.3543 | 99.9461 | 72.6619 | 99.9461 | 99.9461 | 9.0396 |
| 0.1544 | 15.0 | 2085 | 0.2828 | 99.9461 | 72.6619 | 99.9461 | 99.9461 | 9.0576 |
| 0.1544 | 16.0 | 2224 | 0.2456 | 99.9461 | 72.6619 | 99.9461 | 99.9461 | 9.1079 |
| 0.1544 | 17.0 | 2363 | 0.2227 | 99.9461 | 72.6394 | 99.9461 | 99.9461 | 9.5072 |
| 0.1285 | 18.0 | 2502 | 0.3490 | 99.9461 | 72.6619 | 99.9461 | 99.9461 | 9.0396 |
| 0.1285 | 19.0 | 2641 | 0.3736 | 99.9461 | 72.6619 | 99.9461 | 99.9461 | 9.0396 |
| 0.1285 | 20.0 | 2780 | 0.3377 | 99.9461 | 72.6619 | 99.9461 | 99.9461 | 9.0396 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
kabelomalapane/Af-En | 667202b7a92cbc4d0341fd0c31474cabef4a643a | 2022-06-06T13:14:27.000Z | [
"pytorch",
"marian",
"text2text-generation",
"transformers",
"translation",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
]
| translation | false | kabelomalapane | null | kabelomalapane/Af-En | 6 | null | transformers | 15,737 | ---
license: apache-2.0
tags:
- translation
- generated_from_trainer
metrics:
- bleu
model-index:
- name: En-Af
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. -->
# En-Af
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-af-en](https://huggingface.co/Helsinki-NLP/opus-mt-en-af) on the None dataset.
It achieves the following results on the evaluation set:
Before training:
- 'eval_bleu': 46.1522519
- 'eval_loss': 2.5693612
After training:
- Loss: 1.7516168
- Bleu: 55.3924697
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
### Training results
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
mpsb00/ECHR_test_2 | 2cfbcfb35e82870d93454e768195f54840f12c1d | 2022-06-06T11:17:21.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"dataset:lex_glue",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index"
]
| text-classification | false | mpsb00 | null | mpsb00/ECHR_test_2 | 6 | null | transformers | 15,738 | ---
license: mit
tags:
- generated_from_trainer
datasets:
- lex_glue
model-index:
- name: ECHR_test_2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# ECHR_test_2
This model is a fine-tuned version of [prajjwal1/bert-tiny](https://huggingface.co/prajjwal1/bert-tiny) on the lex_glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2487
- Macro-f1: 0.4052
- Micro-f1: 0.5660
## 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: 0.001
- 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: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Macro-f1 | Micro-f1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|
| 0.2056 | 0.44 | 500 | 0.2846 | 0.3335 | 0.4763 |
| 0.1698 | 0.89 | 1000 | 0.2487 | 0.4052 | 0.5660 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
marieke93/BERT-evidence-types | f0f2696c4ca1c7405fe227bcb439fb1fd40b7aac | 2022-06-11T13:32:10.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
]
| text-classification | false | marieke93 | null | marieke93/BERT-evidence-types | 6 | null | transformers | 15,739 | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: BERT-evidence-types
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-evidence-types
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the evidence types dataset.
It achieves the following results on the evaluation set:
- Loss: 2.8008
- Macro f1: 0.4227
- Weighted f1: 0.6976
- Accuracy: 0.7154
- Balanced accuracy: 0.3876
## Training and evaluation data
The data set, as well as the code that was used to fine tune this model can be found in the GitHub repository [BA-Thesis-Information-Science-Persuasion-Strategies](https://github.com/mariekevdh/BA-Thesis-Information-Science-Persuasion-Strategies)
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Macro f1 | Weighted f1 | Accuracy | Balanced accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-----------:|:--------:|:-----------------:|
| 1.1148 | 1.0 | 125 | 1.0531 | 0.2566 | 0.6570 | 0.6705 | 0.2753 |
| 0.7546 | 2.0 | 250 | 0.9725 | 0.3424 | 0.6947 | 0.7002 | 0.3334 |
| 0.4757 | 3.0 | 375 | 1.1375 | 0.3727 | 0.7113 | 0.7184 | 0.3680 |
| 0.2637 | 4.0 | 500 | 1.3585 | 0.3807 | 0.6836 | 0.6910 | 0.3805 |
| 0.1408 | 5.0 | 625 | 1.6605 | 0.3785 | 0.6765 | 0.6872 | 0.3635 |
| 0.0856 | 6.0 | 750 | 1.9703 | 0.3802 | 0.6890 | 0.7047 | 0.3704 |
| 0.0502 | 7.0 | 875 | 2.1245 | 0.4067 | 0.6995 | 0.7169 | 0.3751 |
| 0.0265 | 8.0 | 1000 | 2.2676 | 0.3756 | 0.6816 | 0.6925 | 0.3647 |
| 0.0147 | 9.0 | 1125 | 2.4286 | 0.4052 | 0.6887 | 0.7062 | 0.3803 |
| 0.0124 | 10.0 | 1250 | 2.5773 | 0.4084 | 0.6853 | 0.7040 | 0.3695 |
| 0.0111 | 11.0 | 1375 | 2.5941 | 0.4146 | 0.6915 | 0.7085 | 0.3834 |
| 0.0076 | 12.0 | 1500 | 2.6124 | 0.4157 | 0.6936 | 0.7078 | 0.3863 |
| 0.0067 | 13.0 | 1625 | 2.7050 | 0.4139 | 0.6925 | 0.7108 | 0.3798 |
| 0.0087 | 14.0 | 1750 | 2.6695 | 0.4252 | 0.7009 | 0.7169 | 0.3920 |
| 0.0056 | 15.0 | 1875 | 2.7357 | 0.4257 | 0.6985 | 0.7161 | 0.3868 |
| 0.0054 | 16.0 | 2000 | 2.7389 | 0.4249 | 0.6955 | 0.7116 | 0.3890 |
| 0.0051 | 17.0 | 2125 | 2.7767 | 0.4197 | 0.6967 | 0.7146 | 0.3863 |
| 0.004 | 18.0 | 2250 | 2.7947 | 0.4211 | 0.6977 | 0.7154 | 0.3876 |
| 0.0041 | 19.0 | 2375 | 2.8030 | 0.4204 | 0.6953 | 0.7131 | 0.3855 |
| 0.0042 | 20.0 | 2500 | 2.8008 | 0.4227 | 0.6976 | 0.7154 | 0.3876 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
tzq0301/T5-Pegasus-news-title-generation | 350d5d75eb8f8215e60e40a56ae408e68982d2b3 | 2022-06-09T06:56:58.000Z | [
"pytorch",
"mt5",
"text2text-generation",
"transformers",
"autotrain_compatible"
]
| text2text-generation | false | tzq0301 | null | tzq0301/T5-Pegasus-news-title-generation | 6 | null | transformers | 15,740 | Entry not found |
catofnull/BERT-Pretrain | 5558b20bba5d6b2d67decada899ea47bf2b312d0 | 2022-06-08T16:30:49.000Z | [
"pytorch",
"distilbert",
"fill-mask",
"transformers",
"autotrain_compatible"
]
| fill-mask | false | catofnull | null | catofnull/BERT-Pretrain | 6 | null | transformers | 15,741 | Entry not found |
blenderwang/roberta-base-emotion-32-balanced | 0e11d80f17b2c7cba77f6789eab40881026bcde8 | 2022-06-09T08:34:08.000Z | [
"pytorch",
"roberta",
"text-classification",
"transformers",
"generated_from_trainer",
"model-index"
]
| text-classification | false | blenderwang | null | blenderwang/roberta-base-emotion-32-balanced | 6 | null | transformers | 15,742 | ---
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 was trained from scratch on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 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
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
russellc/bert-finetuned-ner-accelerate | f402fd7e21cc54b2838f57af196e9985fe39bb09 | 2022-06-09T11:22:12.000Z | [
"pytorch",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
]
| token-classification | false | russellc | null | russellc/bert-finetuned-ner-accelerate | 6 | null | transformers | 15,743 | Entry not found |
qualitydatalab/autotrain-car-review-project-966432121 | f28298b06ea8fe9f30ca78fa5a5c57ee7cb08368 | 2022-06-09T13:04:21.000Z | [
"pytorch",
"roberta",
"text-classification",
"en",
"dataset:qualitydatalab/autotrain-data-car-review-project",
"transformers",
"autotrain",
"co2_eq_emissions"
]
| text-classification | false | qualitydatalab | null | qualitydatalab/autotrain-car-review-project-966432121 | 6 | 1 | transformers | 15,744 | ---
tags: autotrain
language: en
widget:
- text: "I love driving this car"
datasets:
- qualitydatalab/autotrain-data-car-review-project
co2_eq_emissions: 0.21529888368377176
---
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 966432121
- CO2 Emissions (in grams): 0.21529888368377176
## Validation Metrics
- Loss: 0.6013365983963013
- Accuracy: 0.737791286727457
- Macro F1: 0.729171012281939
- Micro F1: 0.737791286727457
- Weighted F1: 0.729171012281939
- Macro Precision: 0.7313770127538427
- Micro Precision: 0.737791286727457
- Weighted Precision: 0.7313770127538428
- Macro Recall: 0.737791286727457
- Micro Recall: 0.737791286727457
- Weighted Recall: 0.737791286727457
## 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 driving this car"}' https://api-inference.huggingface.co/models/qualitydatalab/autotrain-car-review-project-966432121
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("qualitydatalab/autotrain-car-review-project-966432121", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("qualitydatalab/autotrain-car-review-project-966432121", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
``` |
HrayrMSint/distilbert-base-uncased-finetuned-clinc | e8dac9ebfb82bc4a7e62eae78373d3d25509d05d | 2022-06-10T01:17:59.000Z | [
"pytorch",
"distilbert",
"text-classification",
"dataset:clinc_oos",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
]
| text-classification | false | HrayrMSint | null | HrayrMSint/distilbert-base-uncased-finetuned-clinc | 6 | null | transformers | 15,745 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- clinc_oos
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased-finetuned-clinc
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: clinc_oos
type: clinc_oos
args: plus
metrics:
- name: Accuracy
type: accuracy
value: 0.9135483870967742
---
<!-- 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-clinc
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the clinc_oos dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7771
- Accuracy: 0.9135
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 48
- eval_batch_size: 48
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 4.2843 | 1.0 | 318 | 3.2793 | 0.7448 |
| 2.6208 | 2.0 | 636 | 1.8750 | 0.8297 |
| 1.5453 | 3.0 | 954 | 1.1565 | 0.8919 |
| 1.0141 | 4.0 | 1272 | 0.8628 | 0.9090 |
| 0.795 | 5.0 | 1590 | 0.7771 | 0.9135 |
### Framework versions
- Transformers 4.13.0
- Pytorch 1.10.0
- Datasets 2.2.2
- Tokenizers 0.10.3
|
juliensimon/distilbert-amazon-shoe-reviews-quantized | e9fc155e18ac3294de8cab3090a00b6f7b07e307 | 2022-06-10T11:24:00.000Z | [
"pytorch",
"distilbert",
"text-classification",
"transformers"
]
| text-classification | false | juliensimon | null | juliensimon/distilbert-amazon-shoe-reviews-quantized | 6 | null | transformers | 15,746 | Entry not found |
Jeevesh8/std_pnt_04_feather_berts-24 | f2765d909e423c944ef90ad16fae304a87215956 | 2022-06-12T06:02:57.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
]
| text-classification | false | Jeevesh8 | null | Jeevesh8/std_pnt_04_feather_berts-24 | 6 | null | transformers | 15,747 | Entry not found |
Jeevesh8/std_pnt_04_feather_berts-47 | 69236732538b82e8d213bd3a47f44c7c83bb676a | 2022-06-12T06:03:10.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
]
| text-classification | false | Jeevesh8 | null | Jeevesh8/std_pnt_04_feather_berts-47 | 6 | null | transformers | 15,748 | Entry not found |
Jeevesh8/std_pnt_04_feather_berts-66 | d742fe6904b663be0c8cb6fd0f5262296f1804d1 | 2022-06-12T06:03:01.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
]
| text-classification | false | Jeevesh8 | null | Jeevesh8/std_pnt_04_feather_berts-66 | 6 | null | transformers | 15,749 | Entry not found |
Jingya/tmpkplizo4c | fd332ea15d2ce4daaf302c4b4fb72a42ca0929a3 | 2022-06-12T22:05:38.000Z | [
"pytorch",
"bert",
"text-classification",
"dataset:glue",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
]
| text-classification | false | Jingya | null | Jingya/tmpkplizo4c | 6 | null | transformers | 15,750 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
model-index:
- name: tmpkplizo4c
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. -->
# tmpkplizo4c
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the glue dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 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
### Framework versions
- Transformers 4.19.3
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
ghadeermobasher/CRAFT-Original-BioBERT-384 | ae4f506b96604622115c7963cb95b5dedc681b24 | 2022-06-13T17:26:35.000Z | [
"pytorch",
"tensorboard",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
]
| token-classification | false | ghadeermobasher | null | ghadeermobasher/CRAFT-Original-BioBERT-384 | 6 | null | transformers | 15,751 | Entry not found |
ghadeermobasher/CRAFT-Original-BioBERT-512 | 305996f49c9e6dbcccfdaab5c2fb2739f2b546f6 | 2022-06-13T18:34:17.000Z | [
"pytorch",
"tensorboard",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
]
| token-classification | false | ghadeermobasher | null | ghadeermobasher/CRAFT-Original-BioBERT-512 | 6 | null | transformers | 15,752 | Entry not found |
ghadeermobasher/CRAFT-Modified-BioBERT-512 | 75676aa0d99b3596376913a7d129db7f8ef34ae1 | 2022-06-13T20:39:14.000Z | [
"pytorch",
"tensorboard",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
]
| token-classification | false | ghadeermobasher | null | ghadeermobasher/CRAFT-Modified-BioBERT-512 | 6 | null | transformers | 15,753 | Entry not found |
ghadeermobasher/CRAFT-Modified-BioBERT-384 | 46832fb8bce26d9f009c216ab0e98e3d3e2b3956 | 2022-06-13T19:31:54.000Z | [
"pytorch",
"tensorboard",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
]
| token-classification | false | ghadeermobasher | null | ghadeermobasher/CRAFT-Modified-BioBERT-384 | 6 | null | transformers | 15,754 | Entry not found |
ghadeermobasher/CRAFT-Original-PubMedBERT-384 | 44a466d97945e45590cd03da6719eb5642eef67a | 2022-06-13T22:54:01.000Z | [
"pytorch",
"tensorboard",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
]
| token-classification | false | ghadeermobasher | null | ghadeermobasher/CRAFT-Original-PubMedBERT-384 | 6 | null | transformers | 15,755 | Entry not found |
ghadeermobasher/CRAFT-Original-BlueBERT-384 | 90b71dd283b9325655bdf8d54f700d7e0e6fbbd9 | 2022-06-13T22:55:00.000Z | [
"pytorch",
"tensorboard",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
]
| token-classification | false | ghadeermobasher | null | ghadeermobasher/CRAFT-Original-BlueBERT-384 | 6 | null | transformers | 15,756 | Entry not found |
ghadeermobasher/CRAFT-Original-SciBERT-512 | 397f81f2b4f35b494c00dfc9df49ba3afcd75c67 | 2022-06-14T00:10:32.000Z | [
"pytorch",
"tensorboard",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
]
| token-classification | false | ghadeermobasher | null | ghadeermobasher/CRAFT-Original-SciBERT-512 | 6 | null | transformers | 15,757 | Entry not found |
ghadeermobasher/CRAFT-Modified-PubMedBERT-384 | 0a5f16233a04fa588bbc0913406d380cd098b02f | 2022-06-13T23:04:15.000Z | [
"pytorch",
"tensorboard",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
]
| token-classification | false | ghadeermobasher | null | ghadeermobasher/CRAFT-Modified-PubMedBERT-384 | 6 | null | transformers | 15,758 | Entry not found |
ghadeermobasher/CRAFT-Modified-PubMedBERT-512 | 0d115bc62259e3d4e3f7639405d51ae023ba7edd | 2022-06-14T00:12:42.000Z | [
"pytorch",
"tensorboard",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
]
| token-classification | false | ghadeermobasher | null | ghadeermobasher/CRAFT-Modified-PubMedBERT-512 | 6 | null | transformers | 15,759 | Entry not found |
ghadeermobasher/CRAFT-Modified-SciBERT-384 | 832fdb32ed960decc5fb2a235e2fbce9ddeb6c6d | 2022-06-13T23:11:55.000Z | [
"pytorch",
"tensorboard",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
]
| token-classification | false | ghadeermobasher | null | ghadeermobasher/CRAFT-Modified-SciBERT-384 | 6 | null | transformers | 15,760 | Entry not found |
ghadeermobasher/BioNLP13-Modified-SciBERT-512 | cc1047c31bf271a791901203aca3a60cb997e4e2 | 2022-06-13T22:05:33.000Z | [
"pytorch",
"tensorboard",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
]
| token-classification | false | ghadeermobasher | null | ghadeermobasher/BioNLP13-Modified-SciBERT-512 | 6 | null | transformers | 15,761 | Entry not found |
ghadeermobasher/BioNLP13-Modified-BioBERT-512 | 8ae13a3ba92ea4433775a641e28cbfde510b34c5 | 2022-06-13T22:13:29.000Z | [
"pytorch",
"tensorboard",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
]
| token-classification | false | ghadeermobasher | null | ghadeermobasher/BioNLP13-Modified-BioBERT-512 | 6 | null | transformers | 15,762 | Entry not found |
ghadeermobasher/BIONLP13CG-CHEM-Chem-Original-SciBERT-512 | dfa0329ddf160b59978a7b940030941989d876a3 | 2022-06-13T23:17:37.000Z | [
"pytorch",
"tensorboard",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
]
| token-classification | false | ghadeermobasher | null | ghadeermobasher/BIONLP13CG-CHEM-Chem-Original-SciBERT-512 | 6 | null | transformers | 15,763 | Entry not found |
ghadeermobasher/BIONLP13CG-CHEM-Chem-Original-BlueBERT-384 | 7c881cdee33a9ea1ac9570389d1d98d5f5cf6197 | 2022-06-14T00:04:04.000Z | [
"pytorch",
"tensorboard",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
]
| token-classification | false | ghadeermobasher | null | ghadeermobasher/BIONLP13CG-CHEM-Chem-Original-BlueBERT-384 | 6 | null | transformers | 15,764 | Entry not found |
ahmeddbahaa/mt5-base-finetuned-fa | 38e05253db3bf24c1f6df812f0735d11ab86c35f | 2022-06-14T17:07:35.000Z | [
"pytorch",
"tensorboard",
"mt5",
"text2text-generation",
"dataset:pn_summary",
"transformers",
"summarization",
"fa",
"Abstractive Summarization",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
]
| summarization | false | ahmeddbahaa | null | ahmeddbahaa/mt5-base-finetuned-fa | 6 | null | transformers | 15,765 | ---
license: apache-2.0
tags:
- summarization
- fa
- mt5
- Abstractive Summarization
- generated_from_trainer
datasets:
- pn_summary
model-index:
- name: mt5-base-finetuned-fa
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-base-finetuned-fa
This model is a fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) on the pn_summary dataset.
It achieves the following results on the evaluation set:
- Loss: 2.6477
- Rouge-1: 33.7
- Rouge-2: 21.28
- Rouge-l: 31.69
- Gen Len: 19.0
- Bertscore: 74.52
## 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: 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: 5
- label_smoothing_factor: 0.1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge-1 | Rouge-2 | Rouge-l | Gen Len | Bertscore |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:-------:|:---------:|
| 3.3828 | 1.0 | 1875 | 2.8114 | 32.17 | 19.47 | 30.12 | 18.99 | 74.25 |
| 2.8204 | 2.0 | 3750 | 2.7080 | 32.67 | 19.92 | 30.56 | 19.0 | 74.31 |
| 2.6907 | 3.0 | 5625 | 2.6724 | 33.22 | 20.44 | 31.11 | 19.0 | 74.47 |
| 2.6029 | 4.0 | 7500 | 2.6513 | 33.46 | 20.75 | 31.38 | 19.0 | 74.54 |
| 2.5414 | 5.0 | 9375 | 2.6477 | 33.68 | 20.91 | 31.62 | 19.0 | 74.58 |
### Framework versions
- Transformers 4.19.4
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
imosnoi/it_sn | 611624a4c96bb7e558ea419f128135c6c0d96180 | 2022-06-14T08:32:27.000Z | [
"pytorch",
"layoutlm",
"token-classification",
"transformers",
"autotrain_compatible"
]
| token-classification | false | imosnoi | null | imosnoi/it_sn | 6 | null | transformers | 15,766 | Entry not found |
erickfm/denim-sweep-2 | 93948e60fdd893a4e3a7695ac1eb5f1595b92f58 | 2022-06-15T03:58:37.000Z | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
]
| text2text-generation | false | erickfm | null | erickfm/denim-sweep-2 | 6 | null | transformers | 15,767 | Entry not found |
roscazo/gpt2-covid | 3d02e3a0c29f605f7e21b4c058d44913851e9ffe | 2022-06-15T09:46:02.000Z | [
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
]
| text-generation | false | roscazo | null | roscazo/gpt2-covid | 6 | null | transformers | 15,768 | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: gpt2-covid
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. -->
# gpt2-covid
This model is a fine-tuned version of [PlanTL-GOB-ES/gpt2-base-bne](https://huggingface.co/PlanTL-GOB-ES/gpt2-base-bne) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 3
### Training results
### Framework versions
- Transformers 4.19.4
- Pytorch 1.11.0+cu113
- Datasets 2.3.0
- Tokenizers 0.12.1
|
Alireza1044/mobilebert_stsb | f2ca96d08df152af065bf2d2ef998bd0566149cf | 2022-06-15T15:37:52.000Z | [
"pytorch",
"tensorboard",
"mobilebert",
"text-classification",
"en",
"dataset:glue",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
]
| text-classification | false | Alireza1044 | null | Alireza1044/mobilebert_stsb | 6 | null | transformers | 15,769 | ---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- spearmanr
model-index:
- name: stsb
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE STSB
type: glue
args: stsb
metrics:
- name: Spearmanr
type: spearmanr
value: 0.8735136732190296
---
<!-- 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. -->
# stsb
This model is a fine-tuned version of [google/mobilebert-uncased](https://huggingface.co/google/mobilebert-uncased) on the GLUE STSB dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5348
- Pearson: 0.8773
- Spearmanr: 0.8735
- Combined Score: 0.8754
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10.0
### Training results
### Framework versions
- Transformers 4.20.0.dev0
- Pytorch 1.11.0
- Datasets 2.2.2
- Tokenizers 0.12.1
|
Willy/bert-base-spanish-wwm-cased-finetuned-emotion | 53907f55f935030188b0ac7a77ef5ab99466aebd | 2022-06-15T23:22:27.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers",
"generated_from_trainer",
"model-index"
]
| text-classification | false | Willy | null | Willy/bert-base-spanish-wwm-cased-finetuned-emotion | 6 | null | transformers | 15,770 | ---
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: bert-base-spanish-wwm-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-spanish-wwm-cased-finetuned-emotion
This model is a fine-tuned version of [dccuchile/bert-base-spanish-wwm-cased](https://huggingface.co/dccuchile/bert-base-spanish-wwm-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5558
- Accuracy: 0.7630
## 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: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.5414 | 1.0 | 67 | 0.5677 | 0.7481 |
| 0.5482 | 2.0 | 134 | 0.5558 | 0.7630 |
### Framework versions
- Transformers 4.19.4
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
microsoft/swinv2-large-patch4-window12to16-192to256-22kto1k-ft | abc4c0b9eda81b52cefde50971da07e3c94f5dcf | 2022-07-09T06:08:46.000Z | [
"pytorch",
"swinv2",
"transformers"
]
| null | false | microsoft | null | microsoft/swinv2-large-patch4-window12to16-192to256-22kto1k-ft | 6 | null | transformers | 15,771 | Entry not found |
erickfm/major-sweep-2 | 55c0aedc4f2b913eeb0bc32dfdc351b7acb4f9f0 | 2022-06-16T21:00:36.000Z | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
]
| text2text-generation | false | erickfm | null | erickfm/major-sweep-2 | 6 | null | transformers | 15,772 | Entry not found |
fanxiao/ext-bart-chinese-cndbpedia | 6a9e65e22de9dfa19a40ad64ed04ca624fe5a954 | 2022-06-17T03:18:22.000Z | [
"pytorch",
"bart",
"text2text-generation",
"transformers",
"autotrain_compatible"
]
| text2text-generation | false | fanxiao | null | fanxiao/ext-bart-chinese-cndbpedia | 6 | null | transformers | 15,773 | rebel-base-chinese-cndbpedia is a generation-based relation extraction model
·a SOTA chinese end-to-end relation extraction model,using bart as backbone.
·using the training method of <REBEL：Relation Extraction By End-to-end Language generation>(EMNLP Findings 2021).
·using the Distant-supervised data from cndbpedia,pretrained from the checkpoint of fnlp/bart-base-chinese.
·can perform SOTA in many chinese relation extraction dataset,such as lic2019,lic2020,HacRED,etc.
·easy to use,just like normal generation task.
·input is sentence,and output is linearlize triples,such as input:姚明是一名NBA篮球运动员 output:[subj]姚明[obj]NBA[rel]公司[obj]篮球运动员[rel]职业(more details can read on REBEL paper)
using model：
from transformers import BertTokenizer, BartForConditionalGeneration
model_name = 'fnlp/bart-base-chinese'
tokenizer_kwargs = {
"use_fast": True,
"additional_special_tokens": ['<rel>', '<obj>', '<subj>'],
} # if cannot see tokens in model card please open readme file
tokenizer = BertTokenizer.from_pretrained(model_name, **tokenizer_kwargs)
model = BartForConditionalGeneration.from_pretrained("fanxiao/rebel-base-chinese-cndbpedia")
|
mariolinml/bert-finetuned-ner_0 | 3c83917bd3a1521133408991613581b23a2743df | 2022-06-17T13:45:51.000Z | [
"pytorch",
"bert",
"token-classification",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
]
| token-classification | false | mariolinml | null | mariolinml/bert-finetuned-ner_0 | 6 | null | transformers | 15,774 | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-finetuned-ner_0
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-finetuned-ner_0
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: 0.2298
- Precision: 0.5119
- Recall: 0.4222
- F1: 0.4627
- Accuracy: 0.9246
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 250 | 0.2364 | 0.4874 | 0.2996 | 0.3711 | 0.9186 |
| 0.2444 | 2.0 | 500 | 0.2219 | 0.5112 | 0.3887 | 0.4416 | 0.9233 |
| 0.2444 | 3.0 | 750 | 0.2298 | 0.5119 | 0.4222 | 0.4627 | 0.9246 |
### Framework versions
- Transformers 4.20.0
- Pytorch 1.11.0
- Datasets 2.3.2
- Tokenizers 0.12.1
|
powerwarez/kindword-klue_bert-base | 7f0a42617d4d11b644125601569898c590137380 | 2022-06-20T00:44:42.000Z | [
"pytorch",
"bert",
"text-classification",
"transformers",
"license:apache-2.0"
]
| text-classification | false | powerwarez | null | powerwarez/kindword-klue_bert-base | 6 | null | transformers | 15,775 | ---
license: apache-2.0
---
klue-bert-base에 스마일게이트 욕설데이터를 FineTune한 모델입니다. |
sasuke/distilbert-base-uncased-finetuned-squad | 24fd759dfffd79de018727da5044159854666774 | 2022-06-20T03:46:26.000Z | [
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"dataset:squad",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
]
| question-answering | false | sasuke | null | sasuke/distilbert-base-uncased-finetuned-squad | 6 | null | transformers | 15,776 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: distilbert-base-uncased-finetuned-squad
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-squad
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1458
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.2997 | 1.0 | 2767 | 1.1918 |
| 1.0491 | 2.0 | 5534 | 1.1328 |
| 0.8768 | 3.0 | 8301 | 1.1458 |
### Framework versions
- Transformers 4.20.0
- Pytorch 1.11.0
- Datasets 2.3.2
- Tokenizers 0.12.1
|
asahi417/lmqg-mt5-small-squad | aa527a72fe59650499cbf57322953479cba1f168 | 2022-06-21T17:01:18.000Z | [
"pytorch",
"mt5",
"text2text-generation",
"transformers",
"autotrain_compatible"
]
| text2text-generation | false | asahi417 | null | asahi417/lmqg-mt5-small-squad | 6 | null | transformers | 15,777 | Entry not found |
Renukswamy/minilm-uncased-squad2-finetuned-squad | ad9f23a96eb37a91c7a25d817ea19be980fa48ff | 2022-06-18T16:29:13.000Z | [
"pytorch",
"tensorboard",
"bert",
"question-answering",
"transformers",
"generated_from_trainer",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible"
]
| question-answering | false | Renukswamy | null | Renukswamy/minilm-uncased-squad2-finetuned-squad | 6 | null | transformers | 15,778 | ---
license: cc-by-4.0
tags:
- generated_from_trainer
model-index:
- name: minilm-uncased-squad2-finetuned-squad
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# minilm-uncased-squad2-finetuned-squad
This model is a fine-tuned version of [deepset/minilm-uncased-squad2](https://huggingface.co/deepset/minilm-uncased-squad2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7239
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|
| 0.7163 | 1.0 | 6941 | 0.6917 |
| 0.5752 | 2.0 | 13882 | 0.7030 |
| 0.4957 | 3.0 | 20823 | 0.7239 |
### Framework versions
- Transformers 4.20.0
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
dibsondivya/distilbert-phmtweets-sutd | 7f8381afdeeb49c1cf21f8873407d0ef25374293 | 2022-06-19T11:40:42.000Z | [
"pytorch",
"distilbert",
"text-classification",
"dataset:custom-phm-tweets",
"arxiv:1802.09130",
"transformers",
"health",
"tweet",
"model-index"
]
| text-classification | false | dibsondivya | null | dibsondivya/distilbert-phmtweets-sutd | 6 | null | transformers | 15,779 | ---
tags:
- distilbert
- health
- tweet
datasets:
- custom-phm-tweets
metrics:
- accuracy
model-index:
- name: distilbert-phmtweets-sutd
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: custom-phm-tweets
type: labelled
metrics:
- name: Accuracy
type: accuracy
value: 0.877
---
# distilbert-phmtweets-sutd
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) for text classification to identify public health events through tweets. The project was based on an [Emory University Study on Detection of Personal Health Mentions in Social Media paper](https://arxiv.org/pdf/1802.09130v2.pdf), that worked with this [custom dataset](https://github.com/emory-irlab/PHM2017).
It achieves the following results on the evaluation set:
- Accuracy: 0.877
## Usage
```Python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("dibsondivya/distilbert-phmtweets-sutd")
model = AutoModelForSequenceClassification.from_pretrained("dibsondivya/distilbert-phmtweets-sutd")
```
### Model Evaluation Results
With Validation Set
- Accuracy: 0.8708661417322835
With Test Set
- Accuracy: 0.8772961058045555
# Reference for distilbert-base-uncased Model
```bibtex
@article{Sanh2019DistilBERTAD,
title={DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter},
author={Victor Sanh and Lysandre Debut and Julien Chaumond and Thomas Wolf},
journal={ArXiv},
year={2019},
volume={abs/1910.01108}
}
```
|
Alireza1044/MobileBERT_Theseus-rte | 9ccd83c743ac3817226fd8d36aa2140e3af54795 | 2022-06-19T12:12:52.000Z | [
"pytorch",
"mobilebert",
"text-classification",
"transformers"
]
| text-classification | false | Alireza1044 | null | Alireza1044/MobileBERT_Theseus-rte | 6 | null | transformers | 15,780 | Entry not found |
jhmin/finetuning-sentiment-model-3000-samples | 429fb1a2dde6d4a4b763a4ee1d8f9cff74571388 | 2022-06-19T13:37:55.000Z | [
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"dataset:imdb",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
]
| text-classification | false | jhmin | null | jhmin/finetuning-sentiment-model-3000-samples | 6 | null | transformers | 15,781 | ---
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: 0.8666666666666667
- name: F1
type: f1
value: 0.8666666666666667
---
<!-- 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: 0.3144
- Accuracy: 0.8667
- F1: 0.8667
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
### Framework versions
- Transformers 4.20.0
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
EventMiner/xlm-roberta-large-en-doc | 2012b3a0db9d48d46c878c6c6f536ef97febf0b6 | 2022-06-19T15:42:30.000Z | [
"pytorch",
"xlm-roberta",
"text-classification",
"multilingual",
"transformers",
"news event detection",
"document level",
"EventMiner",
"license:apache-2.0"
]
| text-classification | false | EventMiner | null | EventMiner/xlm-roberta-large-en-doc | 6 | null | transformers | 15,782 | ---
language: multilingual
tags:
- news event detection
- document level
- EventMiner
license: apache-2.0
---
# EventMiner
EventMiner is designed for multilingual news event detection. The goal of news event detection is the automatic extraction of event details from news articles. This event extraction can be done at different levels: document, sentence and word ranging from coarse-granular information to fine-granular information.
We submitted the best results based on EventMiner to [CASE 2021 shared task 1: *Multilingual Protest News Detection*](https://competitions.codalab.org/competitions/31247). Our approach won first place in English for the document level task while ranking within the top four solutions for other languages: Portuguese, Spanish, and Hindi.
*EventMiner/xlm-roberta-large-en-doc* is an xlm-roberta-large sequence classification model fine-tuned on English document level data of the multilingual version of GLOCON gold standard dataset released with [CASE 2021](https://aclanthology.org/2021.case-1.11/). <br>
Labels:
- Label_0: News article does not contain information about a past or ongoing socio-political event
- Label_1: News article contains information about a past or ongoing socio-political event
More details about the training procedure are available with our [codebase](https://github.com/HHansi/EventMiner).
# How to Use
## Load Model
```python
from transformers import XLMRobertaTokenizer, XLMRobertaForSequenceClassification
model_name = 'EventMiner/xlm-roberta-large-en-doc'
tokenizer = XLMRobertaTokenizer.from_pretrained(model_name)
model = XLMRobertaForSequenceClassification.from_pretrained(model_name)
```
## Classification
```python
from transformers import pipeline
classifier = pipeline("text-classification", model=model, tokenizer=tokenizer)
classifier("Police arrested five more student leaders on Monday when implementing the strike call given by MSU students union as a mark of protest against the decision to introduce payment seats in first-year commerce programme.")
```
# Citation
If you use this model, please consider citing the following paper.
```
@inproceedings{hettiarachchi-etal-2021-daai,
title = "{DAAI} at {CASE} 2021 Task 1: Transformer-based Multilingual Socio-political and Crisis Event Detection",
author = "Hettiarachchi, Hansi and
Adedoyin-Olowe, Mariam and
Bhogal, Jagdev and
Gaber, Mohamed Medhat",
booktitle = "Proceedings of the 4th Workshop on Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE 2021)",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.case-1.16",
doi = "10.18653/v1/2021.case-1.16",
pages = "120--130",
}
``` |
thaidv96/lead-reliability-scoring | 9df9db90b661788b4f07176423c806c741bb5206 | 2022-06-19T16:15:46.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
]
| text-classification | false | thaidv96 | null | thaidv96/lead-reliability-scoring | 6 | null | transformers | 15,783 | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: lead-reliability-scoring
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. -->
# lead-reliability-scoring
This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0123
- F1: 0.9937
## 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 1.0 | 50 | 0.3866 | 0.5761 |
| No log | 2.0 | 100 | 0.3352 | 0.6538 |
| No log | 3.0 | 150 | 0.1786 | 0.8283 |
| No log | 4.0 | 200 | 0.1862 | 0.8345 |
| No log | 5.0 | 250 | 0.1367 | 0.8736 |
| No log | 6.0 | 300 | 0.0642 | 0.9477 |
| No log | 7.0 | 350 | 0.0343 | 0.9748 |
| No log | 8.0 | 400 | 0.0190 | 0.9874 |
| No log | 9.0 | 450 | 0.0123 | 0.9937 |
| 0.2051 | 10.0 | 500 | 0.0058 | 0.9937 |
### Framework versions
- Transformers 4.20.0
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
icelab/cosmicroberta | ff7eb9c95291d3c522fe84b8ba86aff392ebbeec | 2022-06-20T09:14:41.000Z | [
"pytorch",
"roberta",
"fill-mask",
"transformers",
"license:mpl-2.0",
"autotrain_compatible"
]
| fill-mask | false | icelab | null | icelab/cosmicroberta | 6 | null | transformers | 15,784 | ---
license: mpl-2.0
widget:
- text: "The closest planet to earth is <mask>."
- text: "Electrical power is stored on a spacecraft with <mask>."
---
### CosmicRoBERTa
This model is a further pre-trained version of RoBERTa for space science on a domain-specific corpus, which includes abstracts from the NTRS library, abstracts from SCOPUS, ECSS requirements, and other sources from this domain.
The model performs slightly better on a subset (0.6 of total data set) of the CR task presented in our paper [SpaceTransformers: Language Modeling for Space Systems](https://ieeexplore.ieee.org/document/9548078).
| | RoBERTa | CosmiRoBERTa | SpaceRoBERTa |
|-----------------------------------------------|----------------|---------------------|---------------------|
| Parameter | 0.475 | 0.515 | 0.485 |
| GN&C | 0.488 | 0.609 | 0.602 |
| System engineering | 0.523 | 0.559 | 0.555 |
| Propulsion | 0.403 | 0.521 | 0.465 |
| Project Scope | 0.493 | 0.541 | 0.497 |
| OBDH | 0.717 | 0.789 | 0.794 |
| Thermal | 0.432 | 0.509 | 0.491 |
| Quality control | 0.686 | 0.704 | 0.678 |
| Telecom. | 0.360 | 0.614 | 0.557 |
| Measurement | 0.833 | 0.849 | 0.858 |
| Structure & Mechanism | 0.489 | 0.581 | 0.566 |
| Space Environment | 0.543 | 0.681 | 0.605 |
| Cleanliness | 0.616 | 0.621 | 0.651 |
| Project Organisation / Documentation | 0.355 | 0.427 | 0.429 |
| Power | 0.638 | 0.735 | 0.661 |
| Safety / Risk (Control) | 0.647 | 0.727 | 0.676 |
| Materials / EEEs | 0.585 | 0.642 | 0.639 |
| Nonconformity | 0.365 | 0.333 | 0.419 |
| weighted | 0.584 | 0.652(+7%) | 0.633(+5%) |
| Valid. Loss | 0.605 | 0.505 | 0.542 |
### BibTeX entry and citation info
```
@ARTICLE{
9548078,
author={Berquand, Audrey and Darm, Paul and Riccardi, Annalisa},
journal={IEEE Access},
title={SpaceTransformers: Language Modeling for Space Systems},
year={2021},
volume={9},
number={},
pages={133111-133122},
doi={10.1109/ACCESS.2021.3115659}
}
``` |
Splend1dchan/wav2vec2-large-lv60_mt5-base_textdecoderonly_bs64 | 86d13ed630e3597c13d09aec599b34b2501b497c | 2022-06-22T14:38:01.000Z | [
"pytorch",
"speechmix",
"transformers"
]
| null | false | Splend1dchan | null | Splend1dchan/wav2vec2-large-lv60_mt5-base_textdecoderonly_bs64 | 6 | null | transformers | 15,785 | Entry not found |
anjankumar/Anjan-finetuned-iitbombay-en-to-hi | 2edb31a923a8781bab5d9b9fb54188922856f62b | 2022-06-21T11:20:50.000Z | [
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"transformers",
"translation",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
]
| translation | false | anjankumar | null | anjankumar/Anjan-finetuned-iitbombay-en-to-hi | 6 | 1 | transformers | 15,786 | ---
license: apache-2.0
tags:
- translation
- generated_from_trainer
metrics:
- bleu
model-index:
- name: Anjan-finetuned-iitbombay-en-to-hi
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. -->
# Anjan-finetuned-iitbombay-en-to-hi
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-hi](https://huggingface.co/Helsinki-NLP/opus-mt-en-hi) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 3.7924
- Bleu: 6.3001
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 64
- 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
### Framework versions
- Transformers 4.20.0
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
Adapting/dialog_sentiment_classifier | 020f8307e053573abb67bfb4fa63ce6ec58b1c9a | 2022-06-28T20:12:58.000Z | [
"pytorch",
"distilbert",
"text-classification",
"transformers"
]
| text-classification | false | Adapting | null | Adapting/dialog_sentiment_classifier | 6 | null | transformers | 15,787 | colab used to train this model: https://colab.research.google.com/drive/1txlzTh9bdAHVSt229Nbip6dtkYvDbWFj?usp=sharing |
Motahar/clickbait-csebert | 78abd746e12d0884a02b9e8853a37998464ac0a7 | 2022-06-23T17:22:49.000Z | [
"pytorch",
"ganbert",
"transformers"
]
| null | false | Motahar | null | Motahar/clickbait-csebert | 6 | null | transformers | 15,788 | Entry not found |
BigSalmon/InformalToFormalLincoln52 | c8cd12098579fcc20a07ff2796a8af6cc33178ce | 2022-06-23T02:02:34.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
]
| text-generation | false | BigSalmon | null | BigSalmon/InformalToFormalLincoln52 | 6 | null | transformers | 15,789 | ```
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("BigSalmon/InformalToFormalLincoln52")
model = AutoModelForCausalLM.from_pretrained("BigSalmon/InformalToFormalLincoln52")
```
```
How To Make Prompt:
informal english: i am very ready to do that just that.
Translated into the Style of Abraham Lincoln: you can assure yourself of my readiness to work toward this end.
Translated into the Style of Abraham Lincoln: please be assured that i am most ready to undertake this laborious task.
***
informal english: space is huge and needs to be explored.
Translated into the Style of Abraham Lincoln: space awaits traversal, a new world whose boundaries are endless.
Translated into the Style of Abraham Lincoln: space is a ( limitless / boundless ) expanse, a vast virgin domain awaiting exploration.
***
informal english: corn fields are all across illinois, visible once you leave chicago.
Translated into the Style of Abraham Lincoln: corn fields ( permeate illinois / span the state of illinois / ( occupy / persist in ) all corners of illinois / line the horizon of illinois / envelop the landscape of illinois ), manifesting themselves visibly as one ventures beyond chicago.
informal english:
```
```
infill: chrome extensions [MASK] accomplish everyday tasks.
Translated into the Style of Abraham Lincoln: chrome extensions ( expedite the ability to / unlock the means to more readily ) accomplish everyday tasks.
infill: at a time when nintendo has become inflexible, [MASK] consoles that are tethered to a fixed iteration, sega diligently curates its legacy of classic video games on handheld devices.
Translated into the Style of Abraham Lincoln: at a time when nintendo has become inflexible, ( stubbornly [MASK] on / firmly set on / unyielding in its insistence on ) consoles that are tethered to a fixed iteration, sega diligently curates its legacy of classic video games on handheld devices.
infill:
```
```
Essay Intro (Warriors vs. Rockets in Game 7):
text: eagerly anticipated by fans, game 7's are the highlight of the post-season.
text: ever-building in suspense, game 7's have the crowd captivated.
***
Essay Intro (South Korean TV Is Becoming Popular):
text: maturing into a bona fide paragon of programming, south korean television ( has much to offer / entertains without fail / never disappoints ).
text: increasingly held in critical esteem, south korean television continues to impress.
text: at the forefront of quality content, south korea is quickly achieving celebrity status.
***
Essay Intro (
```
```
Search: What is the definition of Checks and Balances?
https://en.wikipedia.org/wiki/Checks_and_balances
Checks and Balances is the idea of having a system where each and every action in government should be subject to one or more checks that would not allow one branch or the other to overly dominate.
https://www.harvard.edu/glossary/Checks_and_Balances
Checks and Balances is a system that allows each branch of government to limit the powers of the other branches in order to prevent abuse of power
https://www.law.cornell.edu/library/constitution/Checks_and_Balances
Checks and Balances is a system of separation through which branches of government can control the other, thus preventing excess power.
***
Search: What is the definition of Separation of Powers?
https://en.wikipedia.org/wiki/Separation_of_powers
The separation of powers is a principle in government, whereby governmental powers are separated into different branches, each with their own set of powers, that are prevent one branch from aggregating too much power.
https://www.yale.edu/tcf/Separation_of_Powers.html
Separation of Powers is the division of governmental functions between the executive, legislative and judicial branches, clearly demarcating each branch's authority, in the interest of ensuring that individual liberty or security is not undermined.
***
Search: What is the definition of Connection of Powers?
https://en.wikipedia.org/wiki/Connection_of_powers
Connection of Powers is a feature of some parliamentary forms of government where different branches of government are intermingled, typically the executive and legislative branches.
https://simple.wikipedia.org/wiki/Connection_of_powers
The term Connection of Powers describes a system of government in which there is overlap between different parts of the government.
***
Search: What is the definition of
```
```
Search: What are phrase synonyms for "second-guess"?
https://www.powerthesaurus.org/second-guess/synonyms
Shortest to Longest:
- feel dubious about
- raise an eyebrow at
- wrinkle their noses at
- cast a jaundiced eye at
- teeter on the fence about
***
Search: What are phrase synonyms for "mean to newbies"?
https://www.powerthesaurus.org/mean_to_newbies/synonyms
Shortest to Longest:
- readiness to balk at rookies
- absence of tolerance for novices
- hostile attitude toward newcomers
***
Search: What are phrase synonyms for "make use of"?
https://www.powerthesaurus.org/make_use_of/synonyms
Shortest to Longest:
- call upon
- glean value from
- reap benefits from
- derive utility from
- seize on the merits of
- draw on the strength of
- tap into the potential of
***
Search: What are phrase synonyms for "hurting itself"?
https://www.powerthesaurus.org/hurting_itself/synonyms
Shortest to Longest:
- erring
- slighting itself
- forfeiting its integrity
- doing itself a disservice
- evincing a lack of backbone
***
Search: What are phrase synonyms for "
```
```
- nebraska
- unicamerical legislature
- different from federal house and senate
text: featuring a unicameral legislature, nebraska's political system stands in stark contrast to the federal model, comprised of a house and senate.
***
-
```
```
original: sports teams are profitable for owners. [MASK], their valuations experience a dramatic uptick.
infill: sports teams are profitable for owners. ( accumulating vast sums / stockpiling treasure / realizing benefits / cashing in / registering robust financials / scoring on balance sheets ), their valuations experience a dramatic uptick.
***
original:
```
```
wordy: classical music is becoming less popular more and more.
Translate into Concise Text: interest in classic music is fading.
***
wordy:
```
```
sweet: savvy voters ousted him.
longer: voters who were informed delivered his defeat.
***
sweet:
```
```
1: commercial space company spacex plans to launch a whopping 52 flights in 2022.
2: spacex, a commercial space company, intends to undertake a total of 52 flights in 2022.
3: in 2022, commercial space company spacex has its sights set on undertaking 52 flights.
4: 52 flights are in the pipeline for 2022, according to spacex, a commercial space company.
5: a commercial space company, spacex aims to conduct 52 flights in 2022.
***
1:
```
Keywords to sentences or sentence.
```
ngos are characterized by:
□ voluntary citizens' group that is organized on a local, national or international level
□ encourage political participation
□ often serve humanitarian functions
□ work for social, economic, or environmental change
***
what are the drawbacks of living near an airbnb?
□ noise
□ parking
□ traffic
□ security
□ strangers
***
```
```
original: musicals generally use spoken dialogue as well as songs to convey the story. operas are usually fully sung.
adapted: musicals generally use spoken dialogue as well as songs to convey the story. ( in a stark departure / on the other hand / in contrast / by comparison / at odds with this practice / far from being alike / in defiance of this standard / running counter to this convention ), operas are usually fully sung.
***
original: akoya and tahitian are types of pearls. akoya pearls are mostly white, and tahitian pearls are naturally dark.
adapted: akoya and tahitian are types of pearls. ( a far cry from being indistinguishable / easily distinguished / on closer inspection / setting them apart / not to be mistaken for one another / hardly an instance of mere synonymy / differentiating the two ), akoya pearls are mostly white, and tahitian pearls are naturally dark.
***
original:
```
```
original: had trouble deciding.
translated into journalism speak: wrestled with the question, agonized over the matter, furrowed their brows in contemplation.
***
original:
```
```
input: not loyal
1800s english: ( two-faced / inimical / perfidious / duplicitous / mendacious / double-dealing / shifty ).
***
input:
``` |
winson/bert-finetuned-ner-accelerate | b6e12a8876448210bda61e5c8728f58cd60badd0 | 2022-06-23T10:49:43.000Z | [
"pytorch",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
]
| token-classification | false | winson | null | winson/bert-finetuned-ner-accelerate | 6 | null | transformers | 15,790 | Nothing, just from tutorial |
kidzy/distilbert-base-uncased-finetuned-emotion | 627312aa2d7ed31f91815c052e08491b97296830 | 2022-06-26T08:19:59.000Z | [
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"dataset:emotion",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
]
| text-classification | false | kidzy | null | kidzy/distilbert-base-uncased-finetuned-emotion | 6 | 1 | transformers | 15,791 | ---
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: 0.9245
- name: F1
type: f1
value: 0.9246037761691881
---
<!-- 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: 0.2240
- Accuracy: 0.9245
- F1: 0.9246
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8521 | 1.0 | 250 | 0.3285 | 0.904 | 0.9017 |
| 0.2546 | 2.0 | 500 | 0.2240 | 0.9245 | 0.9246 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
404E/autotrain-formality-1026434913 | 9422eb7ab20af3ee09786c7c0a4762976cb8d117 | 2022-06-23T15:19:21.000Z | [
"pytorch",
"bert",
"text-classification",
"en",
"dataset:404E/autotrain-data-formality",
"transformers",
"autotrain",
"co2_eq_emissions"
]
| text-classification | false | 404E | null | 404E/autotrain-formality-1026434913 | 6 | null | transformers | 15,792 | ---
tags: autotrain
language: en
widget:
- text: "I love AutoTrain 🤗"
datasets:
- 404E/autotrain-data-formality
co2_eq_emissions: 7.300283563922049
---
# Model Trained Using AutoTrain
- Problem type: Single Column Regression
- Model ID: 1026434913
- CO2 Emissions (in grams): 7.300283563922049
## Validation Metrics
- Loss: 0.5467672348022461
- MSE: 0.5467672944068909
- MAE: 0.5851736068725586
- R2: 0.6883510493648173
- RMSE: 0.7394371628761292
- Explained Variance: 0.6885714530944824
## 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/404E/autotrain-formality-1026434913
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("404E/autotrain-formality-1026434913", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("404E/autotrain-formality-1026434913", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
``` |
enoriega/kw_pubmed_vanilla_document_10000_0.0003_2 | 31e7046e50ca93184cc7b51489a3b5c117bab5b4 | 2022-06-25T16:09:59.000Z | [
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
]
| fill-mask | false | enoriega | null | enoriega/kw_pubmed_vanilla_document_10000_0.0003_2 | 6 | null | transformers | 15,793 | Entry not found |
doraemon1998/distilgpt2-finetuned-wikitext2 | 64f4f7fe750349494f206338cf09b3827e40cd50 | 2022-06-24T09:08:17.000Z | [
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"transformers"
]
| text-generation | false | doraemon1998 | null | doraemon1998/distilgpt2-finetuned-wikitext2 | 6 | null | transformers | 15,794 | Entry not found |
pnichite/QAClassification | 4f272ce6327fd0d3433147d152d989f685dc9d22 | 2022-07-07T07:04:11.000Z | [
"pytorch",
"bert",
"text-classification",
"transformers"
]
| text-classification | false | pnichite | null | pnichite/QAClassification | 6 | null | transformers | 15,795 | Entry not found |
VedantS01/bert-finetuned-custom | 73af18ddf2c29a71e76a568f4745a67e3ad1650a | 2022-07-01T15:35:59.000Z | [
"pytorch",
"tensorboard",
"bert",
"question-answering",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
]
| question-answering | false | VedantS01 | null | VedantS01/bert-finetuned-custom | 6 | null | transformers | 15,796 | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: bert-finetuned-custom
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-finetuned-custom
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-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: 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
- 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
|
sasuke/bert-base-uncased-finetuned-claqua_cqa_predicate | 2f5f6654bf9826c72959d719b65135149c459604 | 2022-06-25T11:36:08.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
]
| text-classification | false | sasuke | null | sasuke/bert-base-uncased-finetuned-claqua_cqa_predicate | 6 | null | transformers | 15,797 | Entry not found |
erickfm/happy-sweep-1 | b214a4bf268e20f3afeff777b537a90aaf6e2358 | 2022-06-25T17:57:39.000Z | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
]
| text2text-generation | false | erickfm | null | erickfm/happy-sweep-1 | 6 | null | transformers | 15,798 | Entry not found |
rpgz31/tiny-nfl | 4a18ca784ccdb5ba3ae0cb98b689d5f37d8f323b | 2022-06-25T18:59:14.000Z | [
"pytorch",
"gpt2",
"text-generation",
"dataset:bittensor",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
]
| text-generation | false | rpgz31 | null | rpgz31/tiny-nfl | 6 | null | transformers | 15,799 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- bittensor
metrics:
- accuracy
model-index:
- name: tiny-nfl
results:
- task:
name: Causal Language Modeling
type: text-generation
dataset:
name: bittensor tiny.json
type: bittensor
args: tiny.json
metrics:
- name: Accuracy
type: accuracy
value: 0.15555555555555556
---
<!-- 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-nfl
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the bittensor tiny.json dataset.
It achieves the following results on the evaluation set:
- Loss: 6.4602
- Accuracy: 0.1556
## 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
### Framework versions
- Transformers 4.21.0.dev0
- Pytorch 1.11.0+cu102
- Datasets 2.3.3.dev0
- Tokenizers 0.12.1
|
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