modelId stringlengths 6 107 | label list | readme stringlengths 0 56.2k | readme_len int64 0 56.2k |
|---|---|---|---|
YXHugging/autotrain-xlm-roberta-base-reviews-672119800 | [
"1",
"2",
"3",
"4",
"5"
] | ---
tags: autotrain
language: unk
widget:
- text: "I love AutoTrain 🤗"
datasets:
- YXHugging/autotrain-data-xlm-roberta-base-reviews
co2_eq_emissions: 2011.6528745969179
---
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 672119800
- CO2 Emissions (in grams): 2011.6528745969179
## Validation Metrics
- Loss: 0.9570887088775635
- Accuracy: 0.5830708333333333
- Macro F1: 0.5789149828346194
- Micro F1: 0.5830708333333333
- Weighted F1: 0.5789149828346193
- Macro Precision: 0.5808338093704437
- Micro Precision: 0.5830708333333333
- Weighted Precision: 0.5808338093704437
- Macro Recall: 0.5830708333333334
- Micro Recall: 0.5830708333333333
- Weighted Recall: 0.5830708333333333
## 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/YXHugging/autotrain-xlm-roberta-base-reviews-672119800
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("YXHugging/autotrain-xlm-roberta-base-reviews-672119800", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("YXHugging/autotrain-xlm-roberta-base-reviews-672119800", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
``` | 1,457 |
PaddyP/distilbert-base-uncased-finetuned-emotion | [
"LABEL_0",
"LABEL_1",
"LABEL_2",
"LABEL_3",
"LABEL_4",
"LABEL_5"
] | ---
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: 0.2302
- Accuracy: 0.922
- F1: 0.9218
## 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 | 250 | 0.3344 | 0.903 | 0.9004 |
| No log | 2.0 | 500 | 0.2302 | 0.922 | 0.9218 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.9.1
- Datasets 2.0.0
- Tokenizers 0.10.3
| 1,496 |
ScandinavianMrT/distilbert_ONION_1epoch_3.0 | null | Entry not found | 15 |
jkhan447/sentiment-model-sample-offline-goemotion | [
"LABEL_0",
"LABEL_1",
"LABEL_10",
"LABEL_11",
"LABEL_12",
"LABEL_13",
"LABEL_14",
"LABEL_15",
"LABEL_16",
"LABEL_17",
"LABEL_18",
"LABEL_19",
"LABEL_2",
"LABEL_20",
"LABEL_21",
"LABEL_22",
"LABEL_23",
"LABEL_24",
"LABEL_25",
"LABEL_26",
"LABEL_27",
"LABEL_3",
"LABEL_4",
... | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: sentiment-model-sample-offline-goemotion
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. -->
# sentiment-model-sample-offline-goemotion
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.0183
- Accuracy: 0.7109
## 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
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.11.6
| 1,187 |
okep/distilbert-base-uncased-finetuned-emotion | [
"LABEL_0",
"LABEL_1",
"LABEL_2",
"LABEL_3",
"LABEL_4",
"LABEL_5"
] | ---
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: 0.9245483619750937
---
<!-- 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: 0.2269
- Accuracy: 0.9245
- F1: 0.9245
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.853 | 1.0 | 250 | 0.3507 | 0.8925 | 0.8883 |
| 0.2667 | 2.0 | 500 | 0.2269 | 0.9245 | 0.9245 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.11.0
- Datasets 1.16.1
- Tokenizers 0.10.3
| 1,801 |
princeton-nlp/CoFi-SST2-s60 | null | This is a model checkpoint for "[Structured Pruning Learns Compact and Accurate Models](https://arxiv.org/pdf/2204.00408.pdf)". The model is pruned from `bert-base-uncased` to a 60% sparsity on dataset SST-2. Please go to [our repository](https://github.com/princeton-nlp/CoFiPruning) for more details on how to use the model for inference. Note that you would have to use the model class specified in our repository to load the model.
| 436 |
royam0820/distilbert-base-uncased-finetuned-emotion | [
"LABEL_0",
"LABEL_1",
"LABEL_2",
"LABEL_3",
"LABEL_4",
"LABEL_5"
] | ---
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: 0.9215
- name: F1
type: f1
value: 0.9217461464484151
---
<!-- 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: 0.2320
- Accuracy: 0.9215
- F1: 0.9217
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8452 | 1.0 | 250 | 0.3418 | 0.897 | 0.8933 |
| 0.2596 | 2.0 | 500 | 0.2320 | 0.9215 | 0.9217 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.11.6
| 1,806 |
Cheatham/xlm-roberta-large-finetuned-d1-003 | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | Entry not found | 15 |
novarac23/distilbert-base-uncased-finetuned-emotion | [
"LABEL_0",
"LABEL_1",
"LABEL_2",
"LABEL_3",
"LABEL_4",
"LABEL_5"
] | ---
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: 0.925
- name: F1
type: f1
value: 0.9251919899321654
---
<!-- 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: 0.2234
- Accuracy: 0.925
- F1: 0.9252
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8213 | 1.0 | 250 | 0.3210 | 0.9025 | 0.8989 |
| 0.2463 | 2.0 | 500 | 0.2234 | 0.925 | 0.9252 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu111
- Datasets 1.16.1
- Tokenizers 0.10.3
| 1,805 |
maxhilsdorf/distilbert-base-uncased-finetuned-emotion | [
"LABEL_0",
"LABEL_1",
"LABEL_2",
"LABEL_3",
"LABEL_4",
"LABEL_5"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- emotion
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 the emotion dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.2991
- eval_accuracy: 0.91
- eval_f1: 0.9083
- eval_runtime: 3.258
- eval_samples_per_second: 613.873
- eval_steps_per_second: 9.822
- epoch: 1.0
- step: 250
## 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
### Framework versions
- Transformers 4.14.1
- Pytorch 1.11.0
- Datasets 2.0.0
- Tokenizers 0.10.3
| 1,312 |
Prinernian/distilbert-base-uncased-finetuned-emotion | [
"LABEL_0",
"LABEL_1",
"LABEL_2",
"LABEL_3",
"LABEL_4",
"LABEL_5"
] | ---
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: 0.2208
- Accuracy: 0.924
- F1: 0.9240
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8538 | 1.0 | 250 | 0.3317 | 0.904 | 0.8999 |
| 0.2599 | 2.0 | 500 | 0.2208 | 0.924 | 0.9240 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Tokenizers 0.11.6
| 1,486 |
JB173/distilbert-base-uncased-finetuned-emotion | [
"LABEL_0",
"LABEL_1",
"LABEL_2",
"LABEL_3",
"LABEL_4",
"LABEL_5"
] | Entry not found | 15 |
hackathon-pln-es/readability-es-3class-paragraphs | [
"advanced",
"basic",
"intermediate"
] | ---
language: es
license: cc-by-4.0
tags:
- spanish
- roberta
- bertin
pipeline_tag: text-classification
widget:
- text: Las Líneas de Nazca son una serie de marcas trazadas en el suelo, cuya anchura oscila entre los 40 y los 110 centímetros.
- text: Hace mucho tiempo, en el gran océano que baña las costas del Perú no había peces.
---
# Readability ES Paragraphs for three classes
Model based on the Roberta architecture finetuned on [BERTIN](https://huggingface.co/bertin-project/bertin-roberta-base-spanish) for readability assessment of Spanish texts.
## Description and performance
This version of the model was trained on a mix of datasets, using sentence-level granularity when possible. The model performs classification among three complexity levels:
- Basic.
- Intermediate.
- Advanced.
The relationship of these categories with the Common European Framework of Reference for Languages is described in [our report](https://wandb.ai/readability-es/readability-es/reports/Texts-Readability-Analysis-for-Spanish--VmlldzoxNzU2MDUx).
This model achieves a F1 macro average score of 0.7881, measured on the validation set.
## Model variants
- [`readability-es-sentences`](https://huggingface.co/hackathon-pln-es/readability-es-sentences). Two classes, sentence-based dataset.
- [`readability-es-paragraphs`](https://huggingface.co/hackathon-pln-es/readability-es-paragraphs). Two classes, paragraph-based dataset.
- [`readability-es-3class-sentences`](https://huggingface.co/hackathon-pln-es/readability-es-3class-sentences). Three classes, sentence-based dataset.
- `readability-es-3class-paragraphs` (this model). Three classes, paragraph-based dataset.
## Datasets
- [`readability-es-hackathon-pln-public`](https://huggingface.co/datasets/hackathon-pln-es/readability-es-hackathon-pln-public), composed of:
* coh-metrix-esp corpus.
* Various text resources scraped from websites.
- Other non-public datasets: newsela-es, simplext.
## Training details
Please, refer to [this training run](https://wandb.ai/readability-es/readability-es/runs/22apaysv/overview) for full details on hyperparameters and training regime.
## Biases and Limitations
- Due to the scarcity of data and the lack of a reliable gold test set, performance metrics are reported on the validation set.
- One of the datasets involved is the Spanish version of newsela, which is frequently used as a reference. However, it was created by translating previous datasets, and therefore it may contain somewhat unnatural phrases.
- Some of the datasets used cannot be publicly disseminated, making it more difficult to assess the existence of biases or mistakes.
- Language might be biased towards the Spanish dialect spoken in Spain. Other regional variants might be sub-represented.
- No effort has been performed to alleviate the shortcomings and biases described in the [original implementation of BERTIN](https://huggingface.co/bertin-project/bertin-roberta-base-spanish#bias-examples-spanish).
## Authors
- [Laura Vásquez-Rodríguez](https://lmvasque.github.io/)
- [Pedro Cuenca](https://twitter.com/pcuenq)
- [Sergio Morales](https://www.fireblend.com/)
- [Fernando Alva-Manchego](https://feralvam.github.io/)
| 3,207 |
aswinsson/fake_new_classifier | [
"LABEL_0"
] | ---
license: afl-3.0
---
The fake news classifer built using distillbert uncased. Created for the Fatima Fellowship coding challenge and trained on P100 instance for 3 epochs. The model is a binary classifier which predicts 1 in case of real news.
Library: transformers \
Language: English \
Dataset: https:\/\/www.kaggle.com/datasets/clmentbisaillon/fake-and-real-news-dataset | 387 |
Seethal/Distilbert-base-uncased-fine-tuned-service-bc | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | # Sentiment analysis model | 26 |
Stremie/roberta-base-clickbait | null | This model classifies whether a tweet is clickbait or not. It has been trained using [Webis-Clickbait-17](https://webis.de/data/webis-clickbait-17.html) dataset. Input is composed of 'postText'. Achieved ~0.7 F1-score on test data. | 232 |
Danni/distilbert-base-uncased-finetuned-cola | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- matthews_correlation
model-index:
- name: distilbert-base-uncased-finetuned-cola
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
args: cola
metrics:
- name: Matthews Correlation
type: matthews_correlation
value: 0.44113488112476795
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-cola
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4994
- Matthews Correlation: 0.4411
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.5282 | 1.0 | 535 | 0.4994 | 0.4411 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0
- Datasets 2.0.0
- Tokenizers 0.11.6
| 1,698 |
AmanPriyanshu/fake-news-detector | [
"LABEL_0"
] | Entry not found | 15 |
jackmleitch/distilbert-base-uncased-finetuned-emotion | [
"LABEL_0",
"LABEL_1",
"LABEL_2",
"LABEL_3",
"LABEL_4",
"LABEL_5"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9285
- name: F1
type: f1
value: 0.9284954323264266
---
<!-- 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: 0.2120
- Accuracy: 0.9285
- F1: 0.9285
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8093 | 1.0 | 250 | 0.3064 | 0.908 | 0.9049 |
| 0.2429 | 2.0 | 500 | 0.2120 | 0.9285 | 0.9285 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.11.0
- Datasets 2.0.0
- Tokenizers 0.11.6
| 1,800 |
btjiong/robbert-twitter-sentiment-custom | [
"NEGATIEF",
"NEUTRAAL",
"POSITIEF"
] | ---
license: mit
tags:
- generated_from_trainer
datasets:
- dutch_social
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: robbert-twitter-sentiment-custom
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: dutch_social
type: dutch_social
args: dutch_social
metrics:
- name: Accuracy
type: accuracy
value: 0.788
- name: F1
type: f1
value: 0.7878005279207152
- name: Precision
type: precision
value: 0.7877102066609215
- name: Recall
type: recall
value: 0.788
---
<!-- 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. -->
# robbert-twitter-sentiment-custom
This model is a fine-tuned version of [pdelobelle/robbert-v2-dutch-base](https://huggingface.co/pdelobelle/robbert-v2-dutch-base) on the dutch_social dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6656
- Accuracy: 0.788
- F1: 0.7878
- Precision: 0.7877
- Recall: 0.788
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.8287 | 1.0 | 282 | 0.7178 | 0.7007 | 0.6958 | 0.6973 | 0.7007 |
| 0.4339 | 2.0 | 564 | 0.5873 | 0.7667 | 0.7668 | 0.7681 | 0.7667 |
| 0.2045 | 3.0 | 846 | 0.6656 | 0.788 | 0.7878 | 0.7877 | 0.788 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.11.0+cpu
- Datasets 2.0.0
- Tokenizers 0.11.6
| 2,186 |
caush/TestMeanFraction2 | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | ---
license: mit
tags:
- generated_from_trainer
metrics:
- matthews_correlation
model-index:
- name: TestMeanFraction2
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. -->
# TestMeanFraction2
This model is a fine-tuned version of [cmarkea/distilcamembert-base](https://huggingface.co/cmarkea/distilcamembert-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3967
- Matthews Correlation: 0.2537
## Model description
More information needed
## Intended uses & limitations
"La panique totale" Cette femme trouve une énorme araignée suspendue à sa douche.
## 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: 8
### Training results
| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| No log | 0.13 | 50 | 1.1126 | 0.1589 |
| No log | 0.25 | 100 | 1.0540 | 0.1884 |
| No log | 0.38 | 150 | 1.1533 | 0.0818 |
| No log | 0.51 | 200 | 1.0676 | 0.1586 |
| No log | 0.64 | 250 | 0.9949 | 0.2280 |
| No log | 0.76 | 300 | 1.0343 | 0.2629 |
| No log | 0.89 | 350 | 1.0203 | 0.2478 |
| No log | 1.02 | 400 | 1.0041 | 0.2752 |
| No log | 1.15 | 450 | 1.0808 | 0.2256 |
| 1.023 | 1.27 | 500 | 1.0029 | 0.2532 |
| 1.023 | 1.4 | 550 | 1.0204 | 0.2508 |
| 1.023 | 1.53 | 600 | 1.1377 | 0.1689 |
| 1.023 | 1.65 | 650 | 1.0499 | 0.2926 |
| 1.023 | 1.78 | 700 | 1.0441 | 0.2474 |
| 1.023 | 1.91 | 750 | 1.0279 | 0.2611 |
| 1.023 | 2.04 | 800 | 1.1511 | 0.2804 |
| 1.023 | 2.16 | 850 | 1.2381 | 0.2512 |
| 1.023 | 2.29 | 900 | 1.3340 | 0.2385 |
| 1.023 | 2.42 | 950 | 1.4372 | 0.2842 |
| 0.7325 | 2.54 | 1000 | 1.3967 | 0.2537 |
| 0.7325 | 2.67 | 1050 | 1.4272 | 0.2624 |
| 0.7325 | 2.8 | 1100 | 1.3869 | 0.1941 |
| 0.7325 | 2.93 | 1150 | 1.4983 | 0.2063 |
| 0.7325 | 3.05 | 1200 | 1.4959 | 0.2409 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.10.0a0+0aef44c
- Datasets 2.0.0
- Tokenizers 0.11.6
| 3,163 |
Cheltone/DistilRoBERTa-C19-Vax-Fine-tuned | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- precision
- accuracy
- f1
model-index:
- name: DistilRoberta
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# DistilRoberta
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1246
- Precision: 0.9633
- Accuracy: 0.9697
- F1: 0.9705
## 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
- lr_scheduler_warmup_steps: 500
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:--------:|:------:|
| 0.5894 | 0.4 | 500 | 0.4710 | 0.8381 | 0.7747 | 0.7584 |
| 0.3863 | 0.8 | 1000 | 0.3000 | 0.8226 | 0.8737 | 0.8858 |
| 0.2272 | 1.2 | 1500 | 0.1973 | 0.9593 | 0.9333 | 0.9329 |
| 0.1639 | 1.6 | 2000 | 0.1694 | 0.9067 | 0.9367 | 0.9403 |
| 0.1263 | 2.0 | 2500 | 0.1128 | 0.9657 | 0.9597 | 0.9603 |
| 0.0753 | 2.4 | 3000 | 0.1305 | 0.9614 | 0.967 | 0.9679 |
| 0.0619 | 2.8 | 3500 | 0.1246 | 0.9633 | 0.9697 | 0.9705 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.11.6
| 1,966 |
cathen/test_model_car | null | Entry not found | 15 |
raquiba/finetuning-sentiment-model-3000-samples | null | ---
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: 0.8633333333333333
- name: F1
type: f1
value: 0.8690095846645367
---
<!-- 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: 0.3242
- Accuracy: 0.8633
- F1: 0.8690
## 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 4.18.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.11.6
| 1,521 |
Giyaseddin/distilbert-base-uncased-finetuned-short-answer-assessment | [
"LABEL_0",
"LABEL_1",
"LABEL_2",
"LABEL_3"
] | ---
license: apache-2.0
language: en
library: transformers
other: distilbert
datasets:
- Short Question Answer Assessment Dataset
---
# DistilBERT base uncased model for Short Question Answer Assessment
## Model description
DistilBERT is a transformers model, smaller and faster than BERT, which was pretrained on the same corpus in a
self-supervised fashion, using the BERT base model as a teacher. This means it was pretrained on the raw texts only,
with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic
process to generate inputs and labels from those texts using the BERT base model.
This is a classification model that solves Short Question Answer Assessment task, finetuned [pretrained DistilBERT model](https://huggingface.co/distilbert-base-uncased) on
[Question Answer Assessment dataset](#)
## Intended uses & limitations
This can only be used for the kind of questions and answers provided by that are similar to the ones in the dataset of [Banjade et al.](https://aclanthology.org/W16-0520.pdf).
### How to use
You can use this model directly with a :
```python
>>> from transformers import pipeline
>>> classifier = pipeline("text-classification", model="Giyaseddin/distilbert-base-uncased-finetuned-short-answer-assessment", return_all_scores=True)
>>> context = "To rescue a child who has fallen down a well, rescue workers fasten him to a rope, the other end of which is then reeled in by a machine. The rope pulls the child straight upward at steady speed."
>>> question = "How does the amount of tension in the rope compare to the downward force of gravity acting on the child?"
>>> ref_answer = "Since the child is being raised straight upward at a constant speed, the net force on the child is zero and all the forces balance. That means that the tension in the rope balances the downward force of gravity."
>>> student_answer = "The tension force is higher than the force of gravity."
>>>
>>> body = " [SEP] ".join([context, question, ref_answer, student_answer])
>>> raw_results = classifier([body])
>>> raw_results
[[{'label': 'LABEL_0', 'score': 0.0004029414849355817},
{'label': 'LABEL_1', 'score': 0.0005476847873069346},
{'label': 'LABEL_2', 'score': 0.998059093952179},
{'label': 'LABEL_3', 'score': 0.0009902542224153876}]]
>>> _LABELS_ID2NAME = {0: "correct", 1: "correct_but_incomplete", 2: "contradictory", 3: "incorrect"}
>>> results = []
>>> for result in raw_results:
for score in result:
results.append([
{_LABELS_ID2NAME[int(score["label"][-1:])]: "%.2f" % score["score"]}
])
>>> results
[[{'correct': '0.00'}],
[{'correct_but_incomplete': '0.00'}],
[{'contradictory': '1.00'}],
[{'incorrect': '0.00'}]]
```
### Limitations and bias
Even if the training data used for this model could be characterized as fairly neutral, this model can have biased
predictions. It also inherits some of
[the bias of its teacher model](https://huggingface.co/bert-base-uncased#limitations-and-bias).
This bias will also affect all fine-tuned versions of this model.
Also one of the limiations of this model is the length, longer sequences would lead to wrong predictions, due to the pre-processing phase (after concatentating the input sequences, the important student answer might be pruned!)
## Pre-training data
DistilBERT pretrained on the same data as BERT, which is [BookCorpus](https://yknzhu.wixsite.com/mbweb), a dataset
consisting of 11,038 unpublished books and [English Wikipedia](https://en.wikipedia.org/wiki/English_Wikipedia)
(excluding lists, tables and headers).
## Fine-tuning data
The annotated dataset consists of 900 students’ short constructed answers and their correctness in the given context. Four qualitative levels of correctness are defined, correct, correct-but-incomplete, contradictory and Incorrect.
## Training procedure
### Preprocessing
In the preprocessing phase, the following parts are concatenated: _question context_, _question_, _reference_answer_, and _student_answer_ using the separator `[SEP]`.
This makes the full text as:
```
[CLS] Context Sentence [SEP] Question Sentence [SEP] Reference Answer Sentence [SEP] Student Answer Sentence [CLS]
```
The data are splitted according to the following ratio:
- Training set 80%.
- Test set 20%.
Lables are mapped as: `{0: "correct", 1: "correct_but_incomplete", 2: "contradictory", 3: "incorrect"}`
### Fine-tuning
The model was finetuned on GeForce GTX 960M for 20 minuts. The parameters are:
| Parameter | Value |
|:-------------------:|:-----:|
| Learning rate | 5e-5 |
| Weight decay | 0.01 |
| Training batch size | 8 |
| Epochs | 4 |
Here is the scores during the training:
| Epoch | Training Loss | Validation Loss | Accuracy | F1 | Precision | Recall |
|:----------:|:-------------:|:-----------------:|:----------:|:---------:|:----------:|:--------:|
| 1 | No log | 0.665765 | 0.755330 | 0.743574 | 0.781210 | 0.755330 |
| 2 | 0.932100 | 0.362124 | 0.890355 | 0.889875 | 0.891407 | 0.890355 |
| 3 | 0.364900 | 0.226225 | 0.942132 | 0.941802 | 0.942458 | 0.942132 |
| 3 | 0.176900 | 0.193660 | 0.954315 | 0.954175 | 0.954985 | 0.954315 |
## Evaluation results
When fine-tuned on downstream task of Question Answer Assessment, 4 class classification, this model achieved the following results:
(scores are rounded to 2 floating points)
| | precision | recall | f1-score | support |
|:------------------------:|:----------:|:-------:|:--------:|:-------:|
| _correct_ | 0.938 | 0.989 | 0.963 | 366 |
| _correct_but_incomplete_ | 0.975 | 0.922 | 0.948 | 257 |
| _contradictory_ | 0.946 | 0.938 | 0.942 | 113 |
| _incorrect_ | 0.963 | 0.944 | 0.953 | 249 |
| accuracy | - | - | 0.954 | 985 |
| macro avg | 0.956 | 0.948 | 0.952 | 985 |
| weighted avg | 0.955 | 0.954 | 0.954 | 985 |
Confusion matrix:
| Actual \ Predicted | _correct_ | _correct_but_incomplete_ | _contradictory_ | _incorrect_ |
|:------------------------:|:---------:|:------------------------:|:---------------:|:-----------:|
| _correct_ | 362 | 4 | 0 | 0 |
| _correct_but_incomplete_ | 13 | 237 | 0 | 7 |
| _contradictory_ | 4 | 1 | 106 | 2 |
| _incorrect_ | 7 | 1 | 6 | 235 |
The AUC score is: 'micro'= **0.9695** and 'macro': **0.9659**
| 6,738 |
Jatin-WIAI/tamil_relevance_clf | null | Entry not found | 15 |
Xuan-Rui/pet-10-all | null | Entry not found | 15 |
Xuan-Rui/pet-1000-p4 | null | Entry not found | 15 |
lucaordronneau/twitter-roberta-base-sentiment-latest-finetuned-FG-SINGLE_SENTENCE-NEWS | [
"fear",
"greed",
"neutral"
] | ---
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: twitter-roberta-base-sentiment-latest-finetuned-FG-SINGLE_SENTENCE-NEWS
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. -->
# twitter-roberta-base-sentiment-latest-finetuned-FG-SINGLE_SENTENCE-NEWS
This model is a fine-tuned version of [cardiffnlp/twitter-roberta-base-sentiment-latest](https://huggingface.co/cardiffnlp/twitter-roberta-base-sentiment-latest) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 3.2822
- Accuracy: 0.6305
- F1: 0.6250
## 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: 6e-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
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log | 1.0 | 321 | 0.9646 | 0.5624 | 0.4048 |
| 0.9537 | 2.0 | 642 | 0.9474 | 0.5644 | 0.4176 |
| 0.9537 | 3.0 | 963 | 0.9008 | 0.5903 | 0.5240 |
| 0.858 | 4.0 | 1284 | 0.9939 | 0.5999 | 0.5846 |
| 0.5908 | 5.0 | 1605 | 1.0947 | 0.6108 | 0.6026 |
| 0.5908 | 6.0 | 1926 | 1.2507 | 0.5740 | 0.5823 |
| 0.3676 | 7.0 | 2247 | 1.4717 | 0.6128 | 0.6017 |
| 0.2246 | 8.0 | 2568 | 1.6726 | 0.5965 | 0.6003 |
| 0.2246 | 9.0 | 2889 | 1.8041 | 0.6380 | 0.6298 |
| 0.1468 | 10.0 | 3210 | 1.9796 | 0.6053 | 0.6026 |
| 0.1161 | 11.0 | 3531 | 2.0988 | 0.6237 | 0.6202 |
| 0.1161 | 12.0 | 3852 | 2.4171 | 0.5944 | 0.5989 |
| 0.0916 | 13.0 | 4173 | 2.3326 | 0.6374 | 0.6288 |
| 0.0916 | 14.0 | 4494 | 2.5472 | 0.6360 | 0.6245 |
| 0.0661 | 15.0 | 4815 | 2.9127 | 0.6176 | 0.6187 |
| 0.0454 | 16.0 | 5136 | 2.9133 | 0.6326 | 0.6276 |
| 0.0454 | 17.0 | 5457 | 3.1299 | 0.6210 | 0.6162 |
| 0.0337 | 18.0 | 5778 | 3.1828 | 0.6224 | 0.6188 |
| 0.0223 | 19.0 | 6099 | 3.2655 | 0.6299 | 0.6223 |
| 0.0223 | 20.0 | 6420 | 3.2822 | 0.6305 | 0.6250 |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.9.1
- Datasets 1.18.4
- Tokenizers 0.11.6
| 2,867 |
maretamasaeva/thesis-freeform-yesno | [
"LABEL_0",
"LABEL_1",
"LABEL_10",
"LABEL_11",
"LABEL_12",
"LABEL_13",
"LABEL_14",
"LABEL_15",
"LABEL_16",
"LABEL_17",
"LABEL_18",
"LABEL_19",
"LABEL_2",
"LABEL_3",
"LABEL_4",
"LABEL_5",
"LABEL_6",
"LABEL_7",
"LABEL_8",
"LABEL_9"
] | ---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: thesis-freeform-yesno
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. -->
# thesis-freeform-yesno
This model is a fine-tuned version of [maretamasaeva/thesis-freeform](https://huggingface.co/maretamasaeva/thesis-freeform) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.4547
- Accuracy: 0.0194
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 2.5001 | 1.0 | 9052 | 2.4600 | 0.0194 |
| 2.4921 | 2.0 | 18104 | 2.4595 | 0.0194 |
| 2.4879 | 3.0 | 27156 | 2.4576 | 0.0194 |
| 2.4793 | 4.0 | 36208 | 2.4547 | 0.0194 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.10.0+cu111
- Datasets 2.1.0
- Tokenizers 0.12.1
| 1,542 |
ASCCCCCCCC/PENGMENGJIE-finetuned-sms | null | ---
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: PENGMENGJIE-finetuned-sms
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. -->
# PENGMENGJIE-finetuned-sms
This model is a fine-tuned version of [bert-base-chinese](https://huggingface.co/bert-base-chinese) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0000
- Accuracy: 1.0
- F1: 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: 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 | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.0116 | 1.0 | 1250 | 0.0060 | 0.999 | 0.9990 |
| 0.003 | 2.0 | 2500 | 0.0000 | 1.0 | 1.0 |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.9.1
- Datasets 1.18.4
- Tokenizers 0.11.6
| 1,426 |
achyut/patronizing_detection | null | This model is fine tuned for Patronizing and Condescending Language Classification task. Have fun. | 98 |
MartinoMensio/racism-models-regression-w-m-vote-epoch-1 | [
"LABEL_0"
] | ---
language: es
license: mit
widget:
- text: "y porqué es lo que hay que hacer con los menas y con los adultos también!!!! NO a los inmigrantes ilegales!!!!"
---
### Description
This model is a fine-tuned version of [BETO (spanish bert)](https://huggingface.co/dccuchile/bert-base-spanish-wwm-uncased) that has been trained on the *Datathon Against Racism* dataset (2022)
We performed several experiments that will be described in the upcoming paper "Estimating Ground Truth in a Low-labelled Data Regime:A Study of Racism Detection in Spanish" (NEATClasS 2022)
We applied 6 different methods ground-truth estimations, and for each one we performed 4 epochs of fine-tuning. The result is made of 24 models:
| method | epoch 1 | epoch 3 | epoch 3 | epoch 4 |
|--- |--- |--- |--- |--- |
| raw-label | [raw-label-epoch-1](https://huggingface.co/MartinoMensio/racism-models-raw-label-epoch-1) | [raw-label-epoch-2](https://huggingface.co/MartinoMensio/racism-models-raw-label-epoch-2) | [raw-label-epoch-3](https://huggingface.co/MartinoMensio/racism-models-raw-label-epoch-3) | [raw-label-epoch-4](https://huggingface.co/MartinoMensio/racism-models-raw-label-epoch-4) |
| m-vote-strict | [m-vote-strict-epoch-1](https://huggingface.co/MartinoMensio/racism-models-m-vote-strict-epoch-1) | [m-vote-strict-epoch-2](https://huggingface.co/MartinoMensio/racism-models-m-vote-strict-epoch-2) | [m-vote-strict-epoch-3](https://huggingface.co/MartinoMensio/racism-models-m-vote-strict-epoch-3) | [m-vote-strict-epoch-4](https://huggingface.co/MartinoMensio/racism-models-m-vote-strict-epoch-4) |
| m-vote-nonstrict | [m-vote-nonstrict-epoch-1](https://huggingface.co/MartinoMensio/racism-models-m-vote-nonstrict-epoch-1) | [m-vote-nonstrict-epoch-2](https://huggingface.co/MartinoMensio/racism-models-m-vote-nonstrict-epoch-2) | [m-vote-nonstrict-epoch-3](https://huggingface.co/MartinoMensio/racism-models-m-vote-nonstrict-epoch-3) | [m-vote-nonstrict-epoch-4](https://huggingface.co/MartinoMensio/racism-models-m-vote-nonstrict-epoch-4) |
| regression-w-m-vote | [regression-w-m-vote-epoch-1](https://huggingface.co/MartinoMensio/racism-models-regression-w-m-vote-epoch-1) | [regression-w-m-vote-epoch-2](https://huggingface.co/MartinoMensio/racism-models-regression-w-m-vote-epoch-2) | [regression-w-m-vote-epoch-3](https://huggingface.co/MartinoMensio/racism-models-regression-w-m-vote-epoch-3) | [regression-w-m-vote-epoch-4](https://huggingface.co/MartinoMensio/racism-models-regression-w-m-vote-epoch-4) |
| w-m-vote-strict | [w-m-vote-strict-epoch-1](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-strict-epoch-1) | [w-m-vote-strict-epoch-2](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-strict-epoch-2) | [w-m-vote-strict-epoch-3](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-strict-epoch-3) | [w-m-vote-strict-epoch-4](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-strict-epoch-4) |
| w-m-vote-nonstrict | [w-m-vote-nonstrict-epoch-1](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-nonstrict-epoch-1) | [w-m-vote-nonstrict-epoch-2](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-nonstrict-epoch-2) | [w-m-vote-nonstrict-epoch-3](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-nonstrict-epoch-3) | [w-m-vote-nonstrict-epoch-4](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-nonstrict-epoch-4) |
This model is `regression-w-m-vote-epoch-1`
### Usage
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
from transformers.pipelines import TextClassificationPipeline
class TextRegressionPipeline(TextClassificationPipeline):
"""
Class based on the TextClassificationPipeline from transformers.
The difference is that instead of being based on a classifier, it is based on a regressor.
You can specify the regression threshold when you call the pipeline or when you instantiate the pipeline.
"""
def __init__(self, **kwargs):
"""
Builds a new Pipeline based on regression.
regression_threshold: Optional(float). If None, the pipeline will simply output the score. If set to a specific value, the output will be both the score and the label.
"""
self.regression_threshold = kwargs.pop("regression_threshold", None)
super().__init__(**kwargs)
def __call__(self, *args, **kwargs):
"""
You can also specify the regression threshold when you call the pipeline.
regression_threshold: Optional(float). If None, the pipeline will simply output the score. If set to a specific value, the output will be both the score and the label.
"""
self.regression_threshold_call = kwargs.pop("regression_threshold", None)
result = super().__call__(*args, **kwargs)
return result
def postprocess(self, model_outputs, function_to_apply=None, return_all_scores=False):
outputs = model_outputs["logits"][0]
outputs = outputs.numpy()
scores = outputs
score = scores[0]
regression_threshold = self.regression_threshold
# override the specific threshold if it is specified in the call
if self.regression_threshold_call:
regression_threshold = self.regression_threshold_call
if regression_threshold:
return {"label": 'racist' if score > regression_threshold else 'non-racist', "score": score}
else:
return {"score": score}
model_name = 'regression-w-m-vote-epoch-1'
tokenizer = AutoTokenizer.from_pretrained("dccuchile/bert-base-spanish-wwm-uncased")
full_model_path = f'MartinoMensio/racism-models-{model_name}'
model = AutoModelForSequenceClassification.from_pretrained(full_model_path)
pipe = TextRegressionPipeline(model=model, tokenizer=tokenizer)
texts = [
'y porqué es lo que hay que hacer con los menas y con los adultos también!!!! NO a los inmigrantes ilegales!!!!',
'Es que los judíos controlan el mundo'
]
# just get the score of regression
print(pipe(texts))
# [{'score': 0.8378907}, {'score': 0.33399782}]
# or also specify a threshold to cut racist/non-racist
print(pipe(texts, regression_threshold=0.9))
# [{'label': 'non-racist', 'score': 0.8378907}, {'label': 'non-racist', 'score': 0.33399782}]
```
For more details, see https://github.com/preyero/neatclass22
| 6,364 |
MartinoMensio/racism-models-m-vote-strict-epoch-3 | null | ---
language: es
license: mit
widget:
- text: "y porqué es lo que hay que hacer con los menas y con los adultos también!!!! NO a los inmigrantes ilegales!!!!"
---
### Description
This model is a fine-tuned version of [BETO (spanish bert)](https://huggingface.co/dccuchile/bert-base-spanish-wwm-uncased) that has been trained on the *Datathon Against Racism* dataset (2022)
We performed several experiments that will be described in the upcoming paper "Estimating Ground Truth in a Low-labelled Data Regime:A Study of Racism Detection in Spanish" (NEATClasS 2022)
We applied 6 different methods ground-truth estimations, and for each one we performed 4 epochs of fine-tuning. The result is made of 24 models:
| method | epoch 1 | epoch 3 | epoch 3 | epoch 4 |
|--- |--- |--- |--- |--- |
| raw-label | [raw-label-epoch-1](https://huggingface.co/MartinoMensio/racism-models-raw-label-epoch-1) | [raw-label-epoch-2](https://huggingface.co/MartinoMensio/racism-models-raw-label-epoch-2) | [raw-label-epoch-3](https://huggingface.co/MartinoMensio/racism-models-raw-label-epoch-3) | [raw-label-epoch-4](https://huggingface.co/MartinoMensio/racism-models-raw-label-epoch-4) |
| m-vote-strict | [m-vote-strict-epoch-1](https://huggingface.co/MartinoMensio/racism-models-m-vote-strict-epoch-1) | [m-vote-strict-epoch-2](https://huggingface.co/MartinoMensio/racism-models-m-vote-strict-epoch-2) | [m-vote-strict-epoch-3](https://huggingface.co/MartinoMensio/racism-models-m-vote-strict-epoch-3) | [m-vote-strict-epoch-4](https://huggingface.co/MartinoMensio/racism-models-m-vote-strict-epoch-4) |
| m-vote-nonstrict | [m-vote-nonstrict-epoch-1](https://huggingface.co/MartinoMensio/racism-models-m-vote-nonstrict-epoch-1) | [m-vote-nonstrict-epoch-2](https://huggingface.co/MartinoMensio/racism-models-m-vote-nonstrict-epoch-2) | [m-vote-nonstrict-epoch-3](https://huggingface.co/MartinoMensio/racism-models-m-vote-nonstrict-epoch-3) | [m-vote-nonstrict-epoch-4](https://huggingface.co/MartinoMensio/racism-models-m-vote-nonstrict-epoch-4) |
| regression-w-m-vote | [regression-w-m-vote-epoch-1](https://huggingface.co/MartinoMensio/racism-models-regression-w-m-vote-epoch-1) | [regression-w-m-vote-epoch-2](https://huggingface.co/MartinoMensio/racism-models-regression-w-m-vote-epoch-2) | [regression-w-m-vote-epoch-3](https://huggingface.co/MartinoMensio/racism-models-regression-w-m-vote-epoch-3) | [regression-w-m-vote-epoch-4](https://huggingface.co/MartinoMensio/racism-models-regression-w-m-vote-epoch-4) |
| w-m-vote-strict | [w-m-vote-strict-epoch-1](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-strict-epoch-1) | [w-m-vote-strict-epoch-2](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-strict-epoch-2) | [w-m-vote-strict-epoch-3](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-strict-epoch-3) | [w-m-vote-strict-epoch-4](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-strict-epoch-4) |
| w-m-vote-nonstrict | [w-m-vote-nonstrict-epoch-1](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-nonstrict-epoch-1) | [w-m-vote-nonstrict-epoch-2](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-nonstrict-epoch-2) | [w-m-vote-nonstrict-epoch-3](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-nonstrict-epoch-3) | [w-m-vote-nonstrict-epoch-4](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-nonstrict-epoch-4) |
This model is `m-vote-strict-epoch-3`
### Usage
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
model_name = 'm-vote-strict-epoch-3'
tokenizer = AutoTokenizer.from_pretrained("dccuchile/bert-base-spanish-wwm-uncased")
full_model_path = f'MartinoMensio/racism-models-{model_name}'
model = AutoModelForSequenceClassification.from_pretrained(full_model_path)
pipe = pipeline("text-classification", model = model, tokenizer = tokenizer)
texts = [
'y porqué es lo que hay que hacer con los menas y con los adultos también!!!! NO a los inmigrantes ilegales!!!!',
'Es que los judíos controlan el mundo'
]
print(pipe(texts))
# [{'label': 'racist', 'score': 0.9929012656211853}, {'label': 'non-racist', 'score': 0.5616322159767151}]
```
For more details, see https://github.com/preyero/neatclass22
| 4,260 |
MartinoMensio/racism-models-w-m-vote-strict-epoch-2 | null | ---
language: es
license: mit
widget:
- text: "y porqué es lo que hay que hacer con los menas y con los adultos también!!!! NO a los inmigrantes ilegales!!!!"
---
### Description
This model is a fine-tuned version of [BETO (spanish bert)](https://huggingface.co/dccuchile/bert-base-spanish-wwm-uncased) that has been trained on the *Datathon Against Racism* dataset (2022)
We performed several experiments that will be described in the upcoming paper "Estimating Ground Truth in a Low-labelled Data Regime:A Study of Racism Detection in Spanish" (NEATClasS 2022)
We applied 6 different methods ground-truth estimations, and for each one we performed 4 epochs of fine-tuning. The result is made of 24 models:
| method | epoch 1 | epoch 3 | epoch 3 | epoch 4 |
|--- |--- |--- |--- |--- |
| raw-label | [raw-label-epoch-1](https://huggingface.co/MartinoMensio/racism-models-raw-label-epoch-1) | [raw-label-epoch-2](https://huggingface.co/MartinoMensio/racism-models-raw-label-epoch-2) | [raw-label-epoch-3](https://huggingface.co/MartinoMensio/racism-models-raw-label-epoch-3) | [raw-label-epoch-4](https://huggingface.co/MartinoMensio/racism-models-raw-label-epoch-4) |
| m-vote-strict | [m-vote-strict-epoch-1](https://huggingface.co/MartinoMensio/racism-models-m-vote-strict-epoch-1) | [m-vote-strict-epoch-2](https://huggingface.co/MartinoMensio/racism-models-m-vote-strict-epoch-2) | [m-vote-strict-epoch-3](https://huggingface.co/MartinoMensio/racism-models-m-vote-strict-epoch-3) | [m-vote-strict-epoch-4](https://huggingface.co/MartinoMensio/racism-models-m-vote-strict-epoch-4) |
| m-vote-nonstrict | [m-vote-nonstrict-epoch-1](https://huggingface.co/MartinoMensio/racism-models-m-vote-nonstrict-epoch-1) | [m-vote-nonstrict-epoch-2](https://huggingface.co/MartinoMensio/racism-models-m-vote-nonstrict-epoch-2) | [m-vote-nonstrict-epoch-3](https://huggingface.co/MartinoMensio/racism-models-m-vote-nonstrict-epoch-3) | [m-vote-nonstrict-epoch-4](https://huggingface.co/MartinoMensio/racism-models-m-vote-nonstrict-epoch-4) |
| regression-w-m-vote | [regression-w-m-vote-epoch-1](https://huggingface.co/MartinoMensio/racism-models-regression-w-m-vote-epoch-1) | [regression-w-m-vote-epoch-2](https://huggingface.co/MartinoMensio/racism-models-regression-w-m-vote-epoch-2) | [regression-w-m-vote-epoch-3](https://huggingface.co/MartinoMensio/racism-models-regression-w-m-vote-epoch-3) | [regression-w-m-vote-epoch-4](https://huggingface.co/MartinoMensio/racism-models-regression-w-m-vote-epoch-4) |
| w-m-vote-strict | [w-m-vote-strict-epoch-1](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-strict-epoch-1) | [w-m-vote-strict-epoch-2](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-strict-epoch-2) | [w-m-vote-strict-epoch-3](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-strict-epoch-3) | [w-m-vote-strict-epoch-4](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-strict-epoch-4) |
| w-m-vote-nonstrict | [w-m-vote-nonstrict-epoch-1](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-nonstrict-epoch-1) | [w-m-vote-nonstrict-epoch-2](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-nonstrict-epoch-2) | [w-m-vote-nonstrict-epoch-3](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-nonstrict-epoch-3) | [w-m-vote-nonstrict-epoch-4](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-nonstrict-epoch-4) |
This model is `w-m-vote-strict-epoch-2`
### Usage
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
model_name = 'w-m-vote-strict-epoch-2'
tokenizer = AutoTokenizer.from_pretrained("dccuchile/bert-base-spanish-wwm-uncased")
full_model_path = f'MartinoMensio/racism-models-{model_name}'
model = AutoModelForSequenceClassification.from_pretrained(full_model_path)
pipe = pipeline("text-classification", model = model, tokenizer = tokenizer)
texts = [
'y porqué es lo que hay que hacer con los menas y con los adultos también!!!! NO a los inmigrantes ilegales!!!!',
'Es que los judíos controlan el mundo'
]
print(pipe(texts))
# [{'label': 'racist', 'score': 0.8647435903549194}, {'label': 'non-racist', 'score': 0.9660486578941345}]
```
For more details, see https://github.com/preyero/neatclass22
| 4,264 |
MartinoMensio/racism-models-w-m-vote-strict-epoch-3 | null | ---
language: es
license: mit
widget:
- text: "y porqué es lo que hay que hacer con los menas y con los adultos también!!!! NO a los inmigrantes ilegales!!!!"
---
### Description
This model is a fine-tuned version of [BETO (spanish bert)](https://huggingface.co/dccuchile/bert-base-spanish-wwm-uncased) that has been trained on the *Datathon Against Racism* dataset (2022)
We performed several experiments that will be described in the upcoming paper "Estimating Ground Truth in a Low-labelled Data Regime:A Study of Racism Detection in Spanish" (NEATClasS 2022)
We applied 6 different methods ground-truth estimations, and for each one we performed 4 epochs of fine-tuning. The result is made of 24 models:
| method | epoch 1 | epoch 3 | epoch 3 | epoch 4 |
|--- |--- |--- |--- |--- |
| raw-label | [raw-label-epoch-1](https://huggingface.co/MartinoMensio/racism-models-raw-label-epoch-1) | [raw-label-epoch-2](https://huggingface.co/MartinoMensio/racism-models-raw-label-epoch-2) | [raw-label-epoch-3](https://huggingface.co/MartinoMensio/racism-models-raw-label-epoch-3) | [raw-label-epoch-4](https://huggingface.co/MartinoMensio/racism-models-raw-label-epoch-4) |
| m-vote-strict | [m-vote-strict-epoch-1](https://huggingface.co/MartinoMensio/racism-models-m-vote-strict-epoch-1) | [m-vote-strict-epoch-2](https://huggingface.co/MartinoMensio/racism-models-m-vote-strict-epoch-2) | [m-vote-strict-epoch-3](https://huggingface.co/MartinoMensio/racism-models-m-vote-strict-epoch-3) | [m-vote-strict-epoch-4](https://huggingface.co/MartinoMensio/racism-models-m-vote-strict-epoch-4) |
| m-vote-nonstrict | [m-vote-nonstrict-epoch-1](https://huggingface.co/MartinoMensio/racism-models-m-vote-nonstrict-epoch-1) | [m-vote-nonstrict-epoch-2](https://huggingface.co/MartinoMensio/racism-models-m-vote-nonstrict-epoch-2) | [m-vote-nonstrict-epoch-3](https://huggingface.co/MartinoMensio/racism-models-m-vote-nonstrict-epoch-3) | [m-vote-nonstrict-epoch-4](https://huggingface.co/MartinoMensio/racism-models-m-vote-nonstrict-epoch-4) |
| regression-w-m-vote | [regression-w-m-vote-epoch-1](https://huggingface.co/MartinoMensio/racism-models-regression-w-m-vote-epoch-1) | [regression-w-m-vote-epoch-2](https://huggingface.co/MartinoMensio/racism-models-regression-w-m-vote-epoch-2) | [regression-w-m-vote-epoch-3](https://huggingface.co/MartinoMensio/racism-models-regression-w-m-vote-epoch-3) | [regression-w-m-vote-epoch-4](https://huggingface.co/MartinoMensio/racism-models-regression-w-m-vote-epoch-4) |
| w-m-vote-strict | [w-m-vote-strict-epoch-1](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-strict-epoch-1) | [w-m-vote-strict-epoch-2](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-strict-epoch-2) | [w-m-vote-strict-epoch-3](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-strict-epoch-3) | [w-m-vote-strict-epoch-4](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-strict-epoch-4) |
| w-m-vote-nonstrict | [w-m-vote-nonstrict-epoch-1](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-nonstrict-epoch-1) | [w-m-vote-nonstrict-epoch-2](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-nonstrict-epoch-2) | [w-m-vote-nonstrict-epoch-3](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-nonstrict-epoch-3) | [w-m-vote-nonstrict-epoch-4](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-nonstrict-epoch-4) |
This model is `w-m-vote-strict-epoch-3`
### Usage
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
model_name = 'w-m-vote-strict-epoch-3'
tokenizer = AutoTokenizer.from_pretrained("dccuchile/bert-base-spanish-wwm-uncased")
full_model_path = f'MartinoMensio/racism-models-{model_name}'
model = AutoModelForSequenceClassification.from_pretrained(full_model_path)
pipe = pipeline("text-classification", model = model, tokenizer = tokenizer)
texts = [
'y porqué es lo que hay que hacer con los menas y con los adultos también!!!! NO a los inmigrantes ilegales!!!!',
'Es que los judíos controlan el mundo'
]
print(pipe(texts))
# [{'label': 'racist', 'score': 0.9619585871696472}, {'label': 'non-racist', 'score': 0.9396700859069824}]
```
For more details, see https://github.com/preyero/neatclass22
| 4,264 |
MartinoMensio/racism-models-w-m-vote-nonstrict-epoch-2 | null | ---
language: es
license: mit
widget:
- text: "y porqué es lo que hay que hacer con los menas y con los adultos también!!!! NO a los inmigrantes ilegales!!!!"
---
### Description
This model is a fine-tuned version of [BETO (spanish bert)](https://huggingface.co/dccuchile/bert-base-spanish-wwm-uncased) that has been trained on the *Datathon Against Racism* dataset (2022)
We performed several experiments that will be described in the upcoming paper "Estimating Ground Truth in a Low-labelled Data Regime:A Study of Racism Detection in Spanish" (NEATClasS 2022)
We applied 6 different methods ground-truth estimations, and for each one we performed 4 epochs of fine-tuning. The result is made of 24 models:
| method | epoch 1 | epoch 3 | epoch 3 | epoch 4 |
|--- |--- |--- |--- |--- |
| raw-label | [raw-label-epoch-1](https://huggingface.co/MartinoMensio/racism-models-raw-label-epoch-1) | [raw-label-epoch-2](https://huggingface.co/MartinoMensio/racism-models-raw-label-epoch-2) | [raw-label-epoch-3](https://huggingface.co/MartinoMensio/racism-models-raw-label-epoch-3) | [raw-label-epoch-4](https://huggingface.co/MartinoMensio/racism-models-raw-label-epoch-4) |
| m-vote-strict | [m-vote-strict-epoch-1](https://huggingface.co/MartinoMensio/racism-models-m-vote-strict-epoch-1) | [m-vote-strict-epoch-2](https://huggingface.co/MartinoMensio/racism-models-m-vote-strict-epoch-2) | [m-vote-strict-epoch-3](https://huggingface.co/MartinoMensio/racism-models-m-vote-strict-epoch-3) | [m-vote-strict-epoch-4](https://huggingface.co/MartinoMensio/racism-models-m-vote-strict-epoch-4) |
| m-vote-nonstrict | [m-vote-nonstrict-epoch-1](https://huggingface.co/MartinoMensio/racism-models-m-vote-nonstrict-epoch-1) | [m-vote-nonstrict-epoch-2](https://huggingface.co/MartinoMensio/racism-models-m-vote-nonstrict-epoch-2) | [m-vote-nonstrict-epoch-3](https://huggingface.co/MartinoMensio/racism-models-m-vote-nonstrict-epoch-3) | [m-vote-nonstrict-epoch-4](https://huggingface.co/MartinoMensio/racism-models-m-vote-nonstrict-epoch-4) |
| regression-w-m-vote | [regression-w-m-vote-epoch-1](https://huggingface.co/MartinoMensio/racism-models-regression-w-m-vote-epoch-1) | [regression-w-m-vote-epoch-2](https://huggingface.co/MartinoMensio/racism-models-regression-w-m-vote-epoch-2) | [regression-w-m-vote-epoch-3](https://huggingface.co/MartinoMensio/racism-models-regression-w-m-vote-epoch-3) | [regression-w-m-vote-epoch-4](https://huggingface.co/MartinoMensio/racism-models-regression-w-m-vote-epoch-4) |
| w-m-vote-strict | [w-m-vote-strict-epoch-1](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-strict-epoch-1) | [w-m-vote-strict-epoch-2](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-strict-epoch-2) | [w-m-vote-strict-epoch-3](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-strict-epoch-3) | [w-m-vote-strict-epoch-4](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-strict-epoch-4) |
| w-m-vote-nonstrict | [w-m-vote-nonstrict-epoch-1](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-nonstrict-epoch-1) | [w-m-vote-nonstrict-epoch-2](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-nonstrict-epoch-2) | [w-m-vote-nonstrict-epoch-3](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-nonstrict-epoch-3) | [w-m-vote-nonstrict-epoch-4](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-nonstrict-epoch-4) |
This model is `w-m-vote-nonstrict-epoch-2`
### Usage
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
model_name = 'w-m-vote-nonstrict-epoch-2'
tokenizer = AutoTokenizer.from_pretrained("dccuchile/bert-base-spanish-wwm-uncased")
full_model_path = f'MartinoMensio/racism-models-{model_name}'
model = AutoModelForSequenceClassification.from_pretrained(full_model_path)
pipe = pipeline("text-classification", model = model, tokenizer = tokenizer)
texts = [
'y porqué es lo que hay que hacer con los menas y con los adultos también!!!! NO a los inmigrantes ilegales!!!!',
'Es que los judíos controlan el mundo'
]
print(pipe(texts))
# [{'label': 'racist', 'score': 0.9680026173591614}, {'label': 'non-racist', 'score': 0.9936750531196594}]
```
For more details, see https://github.com/preyero/neatclass22
| 4,270 |
EandrewJones/distilbert-base-uncased-finetuned-mediations | null | Entry not found | 15 |
ttwj-sutd/finetuning-sentiment-model-3000-samples-5pm | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
metrics:
- accuracy
model-index:
- name: finetuning-sentiment-model-3000-samples-5pm
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: imdb
type: imdb
args: plain_text
metrics:
- name: Accuracy
type: accuracy
value: 0.88
---
<!-- 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-5pm
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: 0.4325
- Accuracy: 0.88
## 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: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 188 | 0.3858 | 0.84 |
| No log | 2.0 | 376 | 0.3146 | 0.8833 |
| 0.2573 | 3.0 | 564 | 0.3938 | 0.8833 |
| 0.2573 | 4.0 | 752 | 0.4325 | 0.88 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.10.0+cu111
- Datasets 2.1.0
- Tokenizers 0.12.1
| 1,805 |
xysmalobia/distilbert-base-uncased-finetuned-emotion | [
"LABEL_0",
"LABEL_1",
"LABEL_2",
"LABEL_3",
"LABEL_4",
"LABEL_5"
] | ---
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: 0.923
- name: F1
type: f1
value: 0.9227457538297092
---
<!-- 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: 0.2161
- Accuracy: 0.923
- F1: 0.9227
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8365 | 1.0 | 250 | 0.3102 | 0.9075 | 0.9051 |
| 0.246 | 2.0 | 500 | 0.2161 | 0.923 | 0.9227 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0+cu102
- Datasets 2.1.0
- Tokenizers 0.12.1
| 1,804 |
Cheltone/TESTING | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- precision
- accuracy
- f1
model-index:
- name: TESTING
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. -->
# TESTING
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1167
- Precision: 0.9561
- Accuracy: 0.9592
- F1: 0.9592
## 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
- lr_scheduler_warmup_steps: 500
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:--------:|:------:|
| 0.5903 | 0.4 | 500 | 0.4695 | 0.7342 | 0.7728 | 0.7890 |
| 0.3986 | 0.8 | 1000 | 0.3469 | 0.8144 | 0.8596 | 0.8684 |
| 0.2366 | 1.2 | 1500 | 0.1939 | 0.9313 | 0.9260 | 0.9253 |
| 0.1476 | 1.6 | 2000 | 0.1560 | 0.9207 | 0.9452 | 0.9465 |
| 0.1284 | 2.0 | 2500 | 0.1167 | 0.9561 | 0.9592 | 0.9592 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.10.0+cu111
- Datasets 2.1.0
- Tokenizers 0.12.1
| 1,788 |
SimoC/distilbert-base-uncased-finetuned-emotion | [
"LABEL_0",
"LABEL_1",
"LABEL_2",
"LABEL_3",
"LABEL_4",
"LABEL_5"
] | Entry not found | 15 |
crcb/dvs_f | [
"0",
"1"
] | ---
tags: autotrain
language: en
widget:
- text: "I love AutoTrain 🤗"
datasets:
- crcb/autotrain-data-dvs
co2_eq_emissions: 8.758858538967111
---
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 753223045
- CO2 Emissions (in grams): 8.758858538967111
## Validation Metrics
- Loss: 0.14833936095237732
- Accuracy: 0.9471454508775469
- Precision: 0.5045871559633027
- Recall: 0.4166666666666667
- AUC: 0.8806422686270332
- F1: 0.4564315352697096
## 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/crcb/autotrain-dvs-753223045
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("crcb/autotrain-dvs-753223045", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("crcb/autotrain-dvs-753223045", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
``` | 1,136 |
Gunulhona/tbnymodel_v2 | [
"Negative",
"Non Related",
"Positive"
] | Entry not found | 15 |
waboucay/camembert-base-finetuned-xnli_fr-finetuned-nli-repnum_wl-rua_wl | [
"contradiction",
"non-contradiction"
] | ---
language:
- fr
tags:
- nli
metrics:
- f1
---
## Eval results
We obtain the following results on ```validation``` and ```test``` sets:
| Set | F1<sub>micro</sub> | F1<sub>macro</sub> |
|------------|--------------------|--------------------|
| validation | 72.3 | 71.9 |
| test | 72.5 | 72.1 | | 367 |
Aldraz/distilbert-base-uncased-finetuned-emotion | [
"LABEL_0",
"LABEL_1",
"LABEL_2",
"LABEL_3",
"LABEL_4",
"LABEL_5"
] | ---
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: 0.2319
- Accuracy: 0.921
- F1: 0.9214
## 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 | 250 | 0.3369 | 0.8985 | 0.8947 |
| No log | 2.0 | 500 | 0.2319 | 0.921 | 0.9214 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.9.1+cpu
- Datasets 2.1.0
- Tokenizers 0.11.6
| 1,500 |
V0ltron/layoutLMTesting-different-labels | [
"LABEL_0",
"LABEL_1",
"LABEL_10",
"LABEL_100",
"LABEL_101",
"LABEL_102",
"LABEL_103",
"LABEL_104",
"LABEL_105",
"LABEL_106",
"LABEL_107",
"LABEL_108",
"LABEL_109",
"LABEL_11",
"LABEL_110",
"LABEL_111",
"LABEL_112",
"LABEL_113",
"LABEL_114",
"LABEL_115",
"LABEL_116",
"LABEL_... | Entry not found | 15 |
Jeevesh8/feather_berts_28 | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | Entry not found | 15 |
afbudiman/indobert-distilled-optimized-for-classification | [
"negative",
"neutral",
"positive"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- indonlu
metrics:
- accuracy
- f1
model-index:
- name: indobert-distilled-optimized-for-classification
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: indonlu
type: indonlu
args: smsa
metrics:
- name: Accuracy
type: accuracy
value: 0.9023809523809524
- name: F1
type: f1
value: 0.9020516403647337
---
<!-- 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. -->
# indobert-distilled-optimized-for-classification
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the indonlu dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5991
- Accuracy: 0.9024
- F1: 0.9021
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5.262995179171344e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 33
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 1.2938 | 1.0 | 688 | 0.8433 | 0.8484 | 0.8513 |
| 0.711 | 2.0 | 1376 | 0.6408 | 0.8881 | 0.8878 |
| 0.4416 | 3.0 | 2064 | 0.7964 | 0.8794 | 0.8793 |
| 0.2907 | 4.0 | 2752 | 0.7559 | 0.8897 | 0.8900 |
| 0.2065 | 5.0 | 3440 | 0.6892 | 0.8968 | 0.8974 |
| 0.1574 | 6.0 | 4128 | 0.6881 | 0.8913 | 0.8906 |
| 0.1131 | 7.0 | 4816 | 0.6224 | 0.8984 | 0.8982 |
| 0.0865 | 8.0 | 5504 | 0.6312 | 0.8976 | 0.8970 |
| 0.0678 | 9.0 | 6192 | 0.6187 | 0.8992 | 0.8989 |
| 0.0526 | 10.0 | 6880 | 0.5991 | 0.9024 | 0.9021 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.10.0+cu111
- Datasets 2.1.0
- Tokenizers 0.12.1
| 2,412 |
Jeevesh8/feather_berts_67 | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | Entry not found | 15 |
Jeevesh8/feather_berts_75 | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | Entry not found | 15 |
Jeevesh8/feather_berts_77 | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | Entry not found | 15 |
Jeevesh8/feather_berts_85 | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | Entry not found | 15 |
Jeevesh8/feather_berts_93 | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | Entry not found | 15 |
Plaban81/results | [
"LABEL_0",
"LABEL_1",
"LABEL_2",
"LABEL_3",
"LABEL_4",
"LABEL_5"
] | Entry not found | 15 |
rdchambers/distilbert-base-uncased-finetuned-emotion | [
"LABEL_0",
"LABEL_1",
"LABEL_2",
"LABEL_3",
"LABEL_4",
"LABEL_5"
] | ---
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: 0.922
- name: F1
type: f1
value: 0.9221171029763118
---
<!-- 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: 0.2238
- Accuracy: 0.922
- F1: 0.9221
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.829 | 1.0 | 250 | 0.3173 | 0.9005 | 0.8980 |
| 0.247 | 2.0 | 500 | 0.2238 | 0.922 | 0.9221 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu111
- Datasets 1.16.1
- Tokenizers 0.10.3
| 1,805 |
PrasunMishra/prasun | null | Entry not found | 15 |
dapang/distilroberta-base-mic-nlp | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: distilroberta-base-mic-nlp
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilroberta-base-mic-nlp
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0049
- Accuracy: 0.9993
- F1: 0.9993
## 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: 2.740146306575944e-05
- train_batch_size: 128
- eval_batch_size: 128
- 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 | 188 | 0.0027 | 0.9997 | 0.9997 |
| No log | 2.0 | 376 | 0.0049 | 0.9993 | 0.9993 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.12.0.dev20220422+cu116
- Datasets 2.1.0
- Tokenizers 0.12.1
| 1,494 |
dapang/distilroberta-base-etc-nlp | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: distilroberta-base-etc-nlp
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilroberta-base-etc-nlp
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0039
- Accuracy: 0.9993
- F1: 0.9993
## 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: 2.740146306575944e-05
- train_batch_size: 128
- eval_batch_size: 128
- 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 | 262 | 0.0025 | 0.9997 | 0.9997 |
| No log | 2.0 | 524 | 0.0039 | 0.9993 | 0.9993 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.12.0.dev20220422+cu116
- Datasets 2.1.0
- Tokenizers 0.12.1
| 1,494 |
dapang/distilroberta-base-etc-sym | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: distilroberta-base-etc-sym
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilroberta-base-etc-sym
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0005
- Accuracy: 0.9997
- F1: 0.9997
## 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: 2.740146306575944e-05
- train_batch_size: 128
- eval_batch_size: 128
- 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 | 262 | 0.0068 | 0.9987 | 0.9987 |
| No log | 2.0 | 524 | 0.0005 | 0.9997 | 0.9997 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.12.0.dev20220422+cu116
- Datasets 2.1.0
- Tokenizers 0.12.1
| 1,494 |
dapang/distilroberta-base-mrl-sym | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: distilroberta-base-mrl-sym
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilroberta-base-mrl-sym
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0001
- Accuracy: 1.0
- F1: 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: 2.740146306575944e-05
- train_batch_size: 128
- eval_batch_size: 128
- 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 | 150 | 0.0001 | 1.0 | 1.0 |
| No log | 2.0 | 300 | 0.0001 | 1.0 | 1.0 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.12.0.dev20220422+cu116
- Datasets 2.1.0
- Tokenizers 0.12.1
| 1,476 |
dapang/distilroberta-base-etc | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: distilroberta-base-etc
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilroberta-base-etc
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3382
- Accuracy: 0.919
- F1: 0.9190
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 4.969790133269121e-05
- train_batch_size: 400
- eval_batch_size: 400
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 7
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log | 1.0 | 84 | 0.2372 | 0.907 | 0.9070 |
| No log | 2.0 | 168 | 0.2358 | 0.9083 | 0.9083 |
| No log | 3.0 | 252 | 0.2430 | 0.9137 | 0.9137 |
| No log | 4.0 | 336 | 0.2449 | 0.919 | 0.9190 |
| No log | 5.0 | 420 | 0.2884 | 0.9193 | 0.9193 |
| No log | 6.0 | 504 | 0.3179 | 0.9167 | 0.9167 |
| No log | 7.0 | 588 | 0.3382 | 0.919 | 0.9190 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0+cu113
- Datasets 2.1.0
- Tokenizers 0.12.1
| 1,828 |
brad1141/bertBasev2 | [
"LABEL_0",
"LABEL_1",
"LABEL_2",
"LABEL_3",
"LABEL_4",
"LABEL_5",
"LABEL_6",
"LABEL_7"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bertBasev2
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. -->
# bertBasev2
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0328
- Precision: 0.9539
- Recall: 0.9707
- F1: 0.9622
- Accuracy: 0.9911
## 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: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 7
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 1.2004 | 1.0 | 1012 | 0.9504 | 0.2620 | 0.3519 | 0.3004 | 0.6856 |
| 1.0265 | 2.0 | 2024 | 0.6205 | 0.4356 | 0.5161 | 0.4725 | 0.7956 |
| 0.6895 | 3.0 | 3036 | 0.3269 | 0.6694 | 0.7302 | 0.6985 | 0.9044 |
| 0.44 | 4.0 | 4048 | 0.1325 | 0.8356 | 0.9091 | 0.8708 | 0.9667 |
| 0.2585 | 5.0 | 5060 | 0.0717 | 0.9259 | 0.9531 | 0.9393 | 0.9844 |
| 0.1722 | 6.0 | 6072 | 0.0382 | 0.9480 | 0.9619 | 0.9549 | 0.99 |
| 0.0919 | 7.0 | 7084 | 0.0328 | 0.9539 | 0.9707 | 0.9622 | 0.9911 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.10.0+cu111
- Datasets 2.1.0
- Tokenizers 0.12.1
| 2,085 |
naomiyjchen/distilbert-base-uncased-finetuned-emotion | [
"LABEL_0",
"LABEL_1",
"LABEL_2",
"LABEL_3",
"LABEL_4",
"LABEL_5"
] | ---
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: 0.9215
- name: F1
type: f1
value: 0.9217262923032896
---
<!-- 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: 0.2208
- Accuracy: 0.9215
- F1: 0.9217
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8381 | 1.0 | 250 | 0.3167 | 0.8995 | 0.8960 |
| 0.2493 | 2.0 | 500 | 0.2208 | 0.9215 | 0.9217 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu111
- Datasets 1.16.1
- Tokenizers 0.10.3
| 1,807 |
Cheatham/xlm-roberta-large-finetuned-dA-001 | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | Entry not found | 15 |
Cheatham/xlm-roberta-large-finetuned-dAB-001 | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | Entry not found | 15 |
James-kc-min/SE_Roberta2 | null | Entry not found | 15 |
crcb/isear_bert | [
"anger",
"disgust",
"fear",
"guilt",
"joy",
"sadness",
"shame"
] | ---
tags: autotrain
language: unk
widget:
- text: "I love AutoTrain 🤗"
datasets:
- crcb/autotrain-data-isear_bert
co2_eq_emissions: 0.026027055434994496
---
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 786224257
- CO2 Emissions (in grams): 0.026027055434994496
## Validation Metrics
- Loss: 0.8348872065544128
- Accuracy: 0.7272727272727273
- Macro F1: 0.7230931630686932
- Micro F1: 0.7272727272727273
- Weighted F1: 0.7236599456423468
- Macro Precision: 0.7328252157220334
- Micro Precision: 0.7272727272727273
- Weighted Precision: 0.7336599708829821
- Macro Recall: 0.7270448163292604
- Micro Recall: 0.7272727272727273
- Weighted Recall: 0.7272727272727273
## 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/crcb/autotrain-isear_bert-786224257
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("crcb/autotrain-isear_bert-786224257", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("crcb/autotrain-isear_bert-786224257", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
``` | 1,385 |
manueltonneau/bert-twitter-en-job-offer | null | ---
language: en # <-- my language
widget:
- text: "Software Engineer job at Amazon in Seattle, WA"
---
# Detection of employment status disclosures on Twitter
## Model main characteristics:
- class: Job Offer (1), else (0)
- country: US
- language: English
- architecture: BERT base
## Model description
This model is a version of `DeepPavlov/bert-base-cased-conversational` finetuned to recognize English tweets containing a job offer. It was trained on English tweets from US-based users. The task is framed as a binary classification problem with:
- the positive class referring to tweets containing a job offer (label=1)
- the negative class referring to all other tweets (label=0)
## Resources
The dataset of English tweets on which this classifier was trained is open-sourced [here](https://github.com/manueltonneau/twitter-unemployment).
Details on the performance can be found in our [ACL 2022 paper](https://arxiv.org/abs/2203.09178).
## Citation
If you find this model useful, please cite our paper (citation to come soon). | 1,052 |
manueltonneau/bert-twitter-en-lost-job | null | ---
language: en # <-- my language
widget:
- text: "Just lost my job..."
---
# Detection of employment status disclosures on Twitter
## Model main characteristics:
- class: Lost Job (1), else (0)
- country: US
- language: English
- architecture: BERT base
## Model description
This model is a version of `DeepPavlov/bert-base-cased-conversational` finetuned to recognize English tweets where a user mentions that she lost her job in the past month. It was trained on English tweets from US-based users. The task is framed as a binary classification problem with:
- the positive class referring to tweets mentioning that a user recently lost her job (label=1)
- the negative class referring to all other tweets (label=0)
## Resources
The dataset of English tweets on which this classifier was trained is open-sourced [here](https://github.com/manueltonneau/twitter-unemployment).
Details on the performance can be found in our [ACL 2022 paper](https://arxiv.org/abs/2203.09178).
## Citation
If you find this model useful, please cite our paper (citation to come soon). | 1,085 |
ml6team/cross-encoder-mmarco-german-distilbert-base | [
"LABEL_0"
] | ---
language:
- de
tags:
- cross-encoder
widget:
- text: "Was sind Lamas. Das Lama (Lama glama) ist eine Art der Kamele. Es ist in den südamerikanischen Anden verbreitet und eine vom Guanako abstammende Haustierform."
example_title: "Example Query / Paragraph"
license: apache-2.0
metrics:
- Rouge-Score
---
# cross-encoder-mmarco-german-distilbert-base
## Model description:
This model is a fine-tuned [cross-encoder](https://www.sbert.net/examples/training/cross-encoder/README.html) on the [MMARCO dataset](https://huggingface.co/datasets/unicamp-dl/mmarco) which is the machine translated version of the MS MARCO dataset.
As base model for the fine-tuning we use [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased)
Model input samples are tuples of the following format, either
`<query, positive_paragraph>` assigned to 1 or `<query, negative_paragraph>` assigned to 0.
The model was trained for 1 epoch.
## Model usage
The cross-encoder model can be used like this:
```
from sentence_transformers import CrossEncoder
model = CrossEncoder('model_name')
scores = model.predict([('Query 1', 'Paragraph 1'), ('Query 2', 'Paragraph 2')])
```
The model will predict scores for the pairs `('Query 1', 'Paragraph 1')` and `('Query 2', 'Paragraph 2')`.
For more details on the usage of the cross-encoder models have a look into the [Sentence-Transformers](https://www.sbert.net/)
## Model Performance:
Model evaluation was done on 2000 evaluation paragraphs of the dataset.
| Accuracy | F1-Score | Precision | Recall |
| --- | --- | --- | --- |
| 89.70 | 86.82 | 86.82 | 93.50 | | 1,632 |
luccazen/finetuning-sentiment-model-3000-samples | null | ---
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: 0.3026
- 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 4.18.0
- Pytorch 1.11.0+cu113
- Datasets 2.1.0
- Tokenizers 0.12.1
| 1,521 |
caush/Clickbait1 | [
"LABEL_0"
] | ---
license: mit
tags:
- generated_from_trainer
model-index:
- name: Clickbait1
results: []
---
# Clickbait1
This model is a fine-tuned version of [microsoft/Multilingual-MiniLM-L12-H384](https://huggingface.co/microsoft/Multilingual-MiniLM-L12-H384) on the [Webis-Clickbait-17](https://zenodo.org/record/5530410) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0257
## Model description
MiniLM is a distilled model from the paper "MiniLM: Deep Self-Attention Distillation for Task-Agnostic Compression of Pre-Trained Transformers".
We fine tune this model to evaluate (regression) the clickbait level of title news.
## Intended uses & limitations
Model looks like the model described in the paper [Predicting Clickbait Strength in Online Social Media](https://aclanthology.org/2020.coling-main.425/) by Indurthi Vijayasaradhi, Syed Bakhtiyar, Gupta Manish, Varma Vasudeva.
The model was trained with english titles.
## Training and evaluation data
We trained the model with the official training data for the chalenge (clickbait17-train-170630.zip (894 MiB, 19538 posts), plus another set that was just available after the end of the challenge (clickbait17-train-170331.zip (157 MiB, 2459 posts).
## Training procedure
Code can be find in [Github](https://github.com/caush/Clickbait).
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 0.05 | 50 | 0.0571 |
| No log | 0.09 | 100 | 0.0448 |
| No log | 0.14 | 150 | 0.0391 |
| No log | 0.18 | 200 | 0.0326 |
| No log | 0.23 | 250 | 0.0343 |
| No log | 0.27 | 300 | 0.0343 |
| No log | 0.32 | 350 | 0.0343 |
| No log | 0.36 | 400 | 0.0346 |
| No log | 0.41 | 450 | 0.0343 |
| 0.0388 | 0.46 | 500 | 0.0297 |
| 0.0388 | 0.5 | 550 | 0.0293 |
| 0.0388 | 0.55 | 600 | 0.0301 |
| 0.0388 | 0.59 | 650 | 0.0290 |
| 0.0388 | 0.64 | 700 | 0.0326 |
| 0.0388 | 0.68 | 750 | 0.0285 |
| 0.0388 | 0.73 | 800 | 0.0285 |
| 0.0388 | 0.77 | 850 | 0.0275 |
| 0.0388 | 0.82 | 900 | 0.0314 |
| 0.0388 | 0.87 | 950 | 0.0309 |
| 0.0297 | 0.91 | 1000 | 0.0277 |
| 0.0297 | 0.96 | 1050 | 0.0281 |
| 0.0297 | 1.0 | 1100 | 0.0273 |
| 0.0297 | 1.05 | 1150 | 0.0270 |
| 0.0297 | 1.09 | 1200 | 0.0291 |
| 0.0297 | 1.14 | 1250 | 0.0293 |
| 0.0297 | 1.18 | 1300 | 0.0269 |
| 0.0297 | 1.23 | 1350 | 0.0276 |
| 0.0297 | 1.28 | 1400 | 0.0279 |
| 0.0297 | 1.32 | 1450 | 0.0267 |
| 0.0265 | 1.37 | 1500 | 0.0270 |
| 0.0265 | 1.41 | 1550 | 0.0300 |
| 0.0265 | 1.46 | 1600 | 0.0274 |
| 0.0265 | 1.5 | 1650 | 0.0274 |
| 0.0265 | 1.55 | 1700 | 0.0266 |
| 0.0265 | 1.59 | 1750 | 0.0267 |
| 0.0265 | 1.64 | 1800 | 0.0267 |
| 0.0265 | 1.68 | 1850 | 0.0280 |
| 0.0265 | 1.73 | 1900 | 0.0274 |
| 0.0265 | 1.78 | 1950 | 0.0272 |
| 0.025 | 1.82 | 2000 | 0.0261 |
| 0.025 | 1.87 | 2050 | 0.0268 |
| 0.025 | 1.91 | 2100 | 0.0268 |
| 0.025 | 1.96 | 2150 | 0.0259 |
| 0.025 | 2.0 | 2200 | 0.0257 |
| 0.025 | 2.05 | 2250 | 0.0260 |
| 0.025 | 2.09 | 2300 | 0.0263 |
| 0.025 | 2.14 | 2350 | 0.0262 |
| 0.025 | 2.19 | 2400 | 0.0269 |
| 0.025 | 2.23 | 2450 | 0.0262 |
| 0.0223 | 2.28 | 2500 | 0.0262 |
| 0.0223 | 2.32 | 2550 | 0.0267 |
| 0.0223 | 2.37 | 2600 | 0.0260 |
| 0.0223 | 2.41 | 2650 | 0.0260 |
| 0.0223 | 2.46 | 2700 | 0.0259 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0a0+17540c5
- Datasets 2.1.0
- Tokenizers 0.12.1
| 4,588 |
caush/Clickbait2 | [
"LABEL_0"
] | ---
tags:
- generated_from_trainer
model-index:
- name: Clickbait2
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. -->
# Clickbait2
This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0212
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 0.05 | 50 | 0.0213 |
| No log | 0.09 | 100 | 0.0213 |
| No log | 0.14 | 150 | 0.0213 |
| No log | 0.18 | 200 | 0.0216 |
| No log | 0.23 | 250 | 0.0214 |
| No log | 0.27 | 300 | 0.0212 |
| No log | 0.32 | 350 | 0.0214 |
| No log | 0.36 | 400 | 0.0212 |
| No log | 0.41 | 450 | 0.0218 |
| 0.0219 | 0.46 | 500 | 0.0219 |
| 0.0219 | 0.5 | 550 | 0.0214 |
| 0.0219 | 0.55 | 600 | 0.0216 |
| 0.0219 | 0.59 | 650 | 0.0217 |
| 0.0219 | 0.64 | 700 | 0.0214 |
| 0.0219 | 0.68 | 750 | 0.0214 |
| 0.0219 | 0.73 | 800 | 0.0214 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0a0+17540c5
- Datasets 2.1.0
- Tokenizers 0.12.1
| 1,925 |
jason9693/KcELECTRA-small-v2022-apeach | [
"Default",
"Spoiled"
] | ---
language: ko
widget:
- text: "코딩을 🐶🍾👟같이 하니까 맨날 장애나잖아 이 🧑🦽아"
datasets:
- jason9693/APEACH
--- | 97 |
manueltonneau/bert-twitter-pt-is-hired | null | ---
language: pt # <-- my language
widget:
- text: "Primeiro dia do novo emprego!"
---
# Detection of employment status disclosures on Twitter
## Model main characteristics:
- class: Is Hired (1), else (0)
- country: BR
- language: Portuguese
- architecture: BERT base
## Model description
This model is a version of `neuralmind/bert-base-portuguese-cased` finetuned to recognize Portuguese tweets where a user mentions that she was hired in the past month. It was trained on Portuguese tweets from users based in Brazil. The task is framed as a binary classification problem with:
- the positive class referring to tweets mentioning that a user was recently hired (label=1)
- the negative class referring to all other tweets (label=0)
## Resources
The dataset of Portuguese tweets on which this classifier was trained is open-sourced [here](https://github.com/manueltonneau/twitter-unemployment).
Details on the performance can be found in our [ACL 2022 paper](https://arxiv.org/abs/2203.09178).
## Citation
If you find this model useful, please cite our paper (citation to come soon). | 1,104 |
cassiepowell/LaBSE-for-similarity | [
"LABEL_0"
] | Entry not found | 15 |
LiYuan/amazon-cross-encoder | [
"LABEL_0"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased-finetuned-mnli
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-mnli
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8244
- Accuracy: 0.6617
## 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
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.8981 | 1.0 | 35702 | 0.8662 | 0.6371 |
| 0.7837 | 2.0 | 71404 | 0.8244 | 0.6617 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0+cu113
- Datasets 2.1.0
- Tokenizers 0.12.1
| 1,448 |
Mim/pro-cell-expert | [
"accept",
"reject"
] | ---
tags: autotrain
language: unk
widget:
- text: "ACE2 overexpression in AAV cell lines"
datasets:
- Mim/autotrain-data-procell-expert
co2_eq_emissions: 0.004814823138367317
---
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 800724769
- CO2 Emissions (in grams): 0.004814823138367317
## Validation Metrics
- Loss: 0.4749071002006531
- Accuracy: 0.9
- Precision: 0.8928571428571429
- Recall: 0.9615384615384616
- AUC: 0.9065934065934066
- F1: 0.9259259259259259
## 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/Mim/autotrain-procell-expert-800724769
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("Mim/autotrain-procell-expert-800724769", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("Mim/autotrain-procell-expert-800724769", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
``` | 1,186 |
TehranNLP-org/electra-base-hateXplain | [
"hatespeech",
"normal",
"offensive"
] | ---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: SEED0042
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: HATEXPLAIN
type: ''
args: hatexplain
metrics:
- name: Accuracy
type: accuracy
value: 0.4162330905306972
---
<!-- 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. -->
# SEED0042
This model is a fine-tuned version of [google/electra-base-discriminator](https://huggingface.co/google/electra-base-discriminator) on the HATEXPLAIN dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7667
- Accuracy: 0.4162
- Accuracy 0: 0.8145
- Accuracy 1: 0.1895
- Accuracy 2: 0.3084
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- distributed_type: not_parallel
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 150
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Accuracy 0 | Accuracy 1 | Accuracy 2 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:----------:|:----------:|:----------:|
| No log | 1.0 | 481 | 0.7431 | 0.4152 | 0.7707 | 0.1805 | 0.3650 |
| No log | 2.0 | 962 | 0.7346 | 0.4152 | 0.8010 | 0.2190 | 0.2774 |
| No log | 3.0 | 1443 | 0.7667 | 0.4162 | 0.8145 | 0.1895 | 0.3084 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.0+cu113
- Datasets 2.1.0
- Tokenizers 0.11.6
| 2,041 |
dineshmane/bert-finetuned-mrpc | null | Entry not found | 15 |
DioLiu/distilbert-base-uncased-finetuned-sst2-newdata | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased-finetuned-sst2-newdata
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-sst2-newdata
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0588
- Accuracy: 0.9911
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.0543 | 1.0 | 1116 | 0.0307 | 0.9911 |
| 0.0235 | 2.0 | 2232 | 0.0372 | 0.9911 |
| 0.0102 | 3.0 | 3348 | 0.0486 | 0.9914 |
| 0.0003 | 4.0 | 4464 | 0.0563 | 0.9914 |
| 0.0008 | 5.0 | 5580 | 0.0588 | 0.9911 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0+cu113
- Datasets 2.1.0
- Tokenizers 0.12.1
| 1,646 |
ali2066/DistilBERT_FINAL_ctxSentence_TRAIN_all_TEST_NULL_second_train_set_null_False | [
"NEGATIVE",
"POSITIVE"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: DistilBERT_FINAL_ctxSentence_TRAIN_all_TEST_NULL_second_train_set_null_False
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_FINAL_ctxSentence_TRAIN_all_TEST_NULL_second_train_set_null_False
This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0699
- Precision: 0.9942
- Recall: 0.9773
- F1: 0.9857
- Accuracy: 0.9725
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 479 | 0.4036 | 0.8333 | 0.9326 | 0.8802 | 0.8054 |
| 0.5047 | 2.0 | 958 | 0.3749 | 0.8635 | 0.9339 | 0.8973 | 0.8361 |
| 0.3336 | 3.0 | 1437 | 0.3789 | 0.8862 | 0.9184 | 0.9020 | 0.8471 |
| 0.2644 | 4.0 | 1916 | 0.4024 | 0.8762 | 0.9171 | 0.8962 | 0.8371 |
| 0.2233 | 5.0 | 2395 | 0.4195 | 0.8784 | 0.9171 | 0.8973 | 0.8391 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.1+cu113
- Datasets 1.18.0
- Tokenizers 0.10.3
| 2,039 |
henry931007/mfma | [
"entailment",
"not_entailment"
] | ## Pre-trained factual consistency checking model for abstractive summaries introduced in the following NAACL-22 paper.
from transformers import AutoModelforSequenceClassification
model = AutoModelforSequenceClassification("henry931007/mfma")
```
@inproceedings{lee2022mfma,
title={Masked Summarization to Generate Factually Inconsistent Summaries for Improved Factual Consistency Checking},
author={Hwanhee Lee and Kang Min Yoo and Joonsuk Park and Hwaran Lee and Kyomin Jung},
year={2022},
month={july},
booktitle={Findings of the Association for Computational Linguistics: NAACL 2022},
}
``` | 629 |
Lauler/motions-classifier | [
"C",
"KD",
"L",
"M",
"MP",
"S",
"SD",
"V",
"independent"
] | ## Swedish parliamentary motions party classifier
A model trained on Swedish parliamentary motions from 2018 to 2021. Outputs the probabilities for different parties being the originator of a given text. | 204 |
DioLiu/distilbert-base-uncased-finetuned-sst2-shake-wiki | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased-finetuned-sst2-shake-wiki
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-sst2-shake-wiki
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0096
- Accuracy: 0.9994
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.001 | 1.0 | 5029 | 0.0120 | 0.9988 |
| 0.0017 | 2.0 | 10058 | 0.0028 | 0.9996 |
| 0.0 | 3.0 | 15087 | 0.0094 | 0.9992 |
| 0.0 | 4.0 | 20116 | 0.0091 | 0.9994 |
| 0.0 | 5.0 | 25145 | 0.0096 | 0.9994 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0+cu113
- Datasets 2.1.0
- Tokenizers 0.12.1
| 1,657 |
YeRyeongLee/bert-base-uncased-finetuned-0505-2 | [
"LABEL_0",
"LABEL_1",
"LABEL_2",
"LABEL_3",
"LABEL_4",
"LABEL_5"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: bert-base-uncased-finetuned-0505-2
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. -->
# bert-base-uncased-finetuned-0505-2
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4277
- Accuracy: 0.9206
- F1: 0.9205
## 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: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log | 1.0 | 1373 | 0.3634 | 0.9025 | 0.9012 |
| No log | 2.0 | 2746 | 0.3648 | 0.9066 | 0.9060 |
| No log | 3.0 | 4119 | 0.3978 | 0.9189 | 0.9183 |
| No log | 4.0 | 5492 | 0.4277 | 0.9206 | 0.9205 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.9.0
- Datasets 1.16.1
- Tokenizers 0.10.3
| 1,612 |
cradle-bio/thermo-predictor-thermo-evotuning-prot_bert | [
"LABEL_0"
] | ---
tags:
- generated_from_trainer
metrics:
- spearmanr
model-index:
- name: thermo-predictor-thermo-evotuning-prot_bert
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. -->
# thermo-predictor-thermo-evotuning-prot_bert
This model is a fine-tuned version of [thundaa/thermo-evotuning-prot_bert](https://huggingface.co/thundaa/thermo-evotuning-prot_bert) on the cradle-bio/tape-thermostability dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1617
- Spearmanr: 0.6914
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 4e-05
- train_batch_size: 256
- eval_batch_size: 256
- seed: 42
- gradient_accumulation_steps: 64
- total_train_batch_size: 16384
- 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 | Spearmanr |
|:-------------:|:-----:|:----:|:---------------:|:---------:|
| 0.4734 | 0.68 | 2 | 0.3146 | 0.3359 |
| 0.4392 | 1.68 | 4 | 0.2936 | 0.3407 |
| 0.4034 | 2.68 | 6 | 0.2633 | 0.3696 |
| 0.3669 | 3.68 | 8 | 0.2437 | 0.3903 |
| 0.3496 | 4.68 | 10 | 0.2377 | 0.4102 |
| 0.3351 | 5.68 | 12 | 0.2285 | 0.4204 |
| 0.3289 | 6.68 | 14 | 0.2267 | 0.4180 |
| 0.3267 | 7.68 | 16 | 0.2258 | 0.4242 |
| 0.3177 | 8.68 | 18 | 0.2206 | 0.4295 |
| 0.3116 | 9.68 | 20 | 0.2150 | 0.4365 |
| 0.3039 | 10.68 | 22 | 0.2115 | 0.4365 |
| 0.2985 | 11.68 | 24 | 0.2062 | 0.4469 |
| 0.2927 | 12.68 | 26 | 0.2045 | 0.4531 |
| 0.2885 | 13.68 | 28 | 0.2005 | 0.4603 |
| 0.2838 | 14.68 | 30 | 0.1987 | 0.4690 |
| 0.2806 | 15.68 | 32 | 0.1975 | 0.4744 |
| 0.2772 | 16.68 | 34 | 0.1970 | 0.4765 |
| 0.2728 | 17.68 | 36 | 0.1939 | 0.4845 |
| 0.2684 | 18.68 | 38 | 0.1931 | 0.4858 |
| 0.2641 | 19.68 | 40 | 0.1925 | 0.4936 |
| 0.2608 | 20.68 | 42 | 0.1905 | 0.4929 |
| 0.2566 | 21.68 | 44 | 0.1886 | 0.5049 |
| 0.2518 | 22.68 | 46 | 0.1875 | 0.5095 |
| 0.2467 | 23.68 | 48 | 0.1869 | 0.5141 |
| 0.2424 | 24.68 | 50 | 0.1859 | 0.5161 |
| 0.2375 | 25.68 | 52 | 0.1850 | 0.5223 |
| 0.2329 | 26.68 | 54 | 0.1851 | 0.5210 |
| 0.2279 | 27.68 | 56 | 0.1850 | 0.5294 |
| 0.2226 | 28.68 | 58 | 0.1837 | 0.5310 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.12.1
| 3,289 |
heranm/finetuning-sentiment-model-3000-samples | null | ---
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: 0.8733333333333333
- name: F1
type: f1
value: 0.8766233766233766
---
<!-- 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: 0.3131
- Accuracy: 0.8733
- F1: 0.8766
## 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 4.18.0
- Pytorch 1.11.0+cpu
- Datasets 2.1.0
- Tokenizers 0.12.1
| 1,519 |
EAST/autotrain-maysix-828926405 | [
"0",
"1"
] | ---
tags: autotrain
language: zh
widget:
- text: "I love AutoTrain 🤗"
datasets:
- EAST/autotrain-data-maysix
co2_eq_emissions: 0.00258669198292644
---
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 828926405
- CO2 Emissions (in grams): 0.00258669198292644
## Validation Metrics
- Loss: 0.1797131597995758
- Accuracy: 0.9318181818181818
- Precision: 0.9047619047619048
- Recall: 0.95
- AUC: 0.9875
- F1: 0.9268292682926829
## 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/EAST/autotrain-maysix-828926405
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("EAST/autotrain-maysix-828926405", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("EAST/autotrain-maysix-828926405", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
``` | 1,125 |
chrishistewandb/hugging-face | null | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: hugging-face
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. -->
# hugging-face
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) 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: 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: 4
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0+cu113
- Datasets 2.2.1
- Tokenizers 0.12.1
| 1,009 |
deepgai/finetuned-tweet_eval-sentiment | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | 0 | |
neal49/distilbert-yelp | null | Entry not found | 15 |
Jeevesh8/bert_ft_cola-4 | null | Entry not found | 15 |
Jeevesh8/bert_ft_cola-11 | null | Entry not found | 15 |
Jeevesh8/bert_ft_cola-12 | null | Entry not found | 15 |
Jeevesh8/bert_ft_cola-20 | null | Entry not found | 15 |
Jeevesh8/bert_ft_cola-21 | null | Entry not found | 15 |
Jeevesh8/bert_ft_cola-31 | null | Entry not found | 15 |
Jeevesh8/bert_ft_cola-39 | null | Entry not found | 15 |
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