modelId stringlengths 6 107 | label list | readme stringlengths 0 56.2k | readme_len int64 0 56.2k |
|---|---|---|---|
Stremie/bert-base-uncased-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 |
GioReg/AlbertoBertrecensioni | [
"LABEL_0",
"LABEL_1",
"LABEL_2",
"LABEL_3",
"LABEL_4"
] | ---
tags:
- generated_from_trainer
model-index:
- name: AlbertoBertrecensioni
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. -->
# AlbertoBertrecensioni
This model is a fine-tuned version of [m-polignano-uniba/bert_uncased_L-12_H-768_A-12_italian_alb3rt0](https://huggingface.co/m-polignano-uniba/bert_uncased_L-12_H-768_A-12_italian_alb3rt0) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 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 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.11.6
| 1,107 |
Stremie/xlm-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 |
raileymontalan/distilbert-base-casedfinetuned-fake-news-detection | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- f1
- accuracy
model-index:
- name: distilbert-base-casedfinetuned-fake-news-detection
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-casedfinetuned-fake-news-detection
This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on the [Fake and Reals News](https://www.kaggle.com/datasets/clmentbisaillon/fake-and-real-news-dataset) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0019
- F1: 0.9998
- Accuracy: 0.9998
The [Fake and Reals News](https://www.kaggle.com/datasets/clmentbisaillon/fake-and-real-news-dataset) dataset was used. It was stratified split into a train-val-test set (60/20/20).
## 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 | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:------:|:--------:|
| No log | 1.0 | 1684 | 0.0021 | 0.9998 | 0.9998 |
| No log | 2.0 | 3368 | 0.0019 | 0.9998 | 0.9998 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.11.6
| 1,630 |
Shadman-Rohan/results | [
"LABEL_0"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: results
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# results
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0002
- Accuracy: 0.8923
- F1: 0.9167
- Precision: 0.8462
- Recall: 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: 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
- lr_scheduler_warmup_steps: 500
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.0026 | 1.0 | 1956 | 0.0003 | 0.9552 | 0.9636 | 0.9298 | 1.0 |
| 0.0015 | 2.0 | 3912 | 0.0003 | 0.6688 | 0.7815 | 0.6416 | 0.9996 |
| 0.0011 | 3.0 | 5868 | 0.0002 | 0.8923 | 0.9167 | 0.8462 | 1.0 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.11.6
| 1,700 |
Cheatham/xlm-roberta-large-finetuned-d12-006 | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | Entry not found | 15 |
Shadman-Rohan/FakevsRealNews | [
"Fake News",
"Real News"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: FakevsRealNews
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. -->
# Coding challenge
The challenge involved building a fake news classifier using the huggingface library.
This final model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an fake-and-real-news dataset. The link to the dataset is https://www.kaggle.com/datasets/clmentbisaillon/fake-and-real-news-dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0000
- Accuracy: 1.0
- F1: 1.0
- Precision: 1.0
- Recall: 1.0
## Model description
Finetuned Distilbert
## Training and evaluation data
The training data was split into train-dev-test in the ratio 80-10-10.
## Training procedure
The title and text of each news story was concatenated to form each datapoint. Then a model was finetuned to perform single label classification on each datapoint. The final prediction is the class with the highest probability.
### 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
- lr_scheduler_warmup_steps: 500
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.0503 | 1.0 | 1956 | 0.0025 | 0.9995 | 0.9995 | 0.9995 | 0.9995 |
| 0.001 | 2.0 | 3912 | 0.0001 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0007 | 3.0 | 5868 | 0.0000 | 1.0 | 1.0 | 1.0 | 1.0 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.10.0+cu111
- Datasets 2.1.0
- Tokenizers 0.12.1
| 2,131 |
abdelrahman-alkhodary/distilbert-base-uncased-finetuned-emotion | [
"LABEL_0",
"LABEL_1",
"LABEL_2",
"LABEL_3",
"LABEL_4",
"LABEL_5"
] | Entry not found | 15 |
ChrisZeng/electra-large-discriminator-nli-efl-hateval | [
"contradiction",
"entailment",
"neutral"
] | ---
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: electra-large-discriminator-nli-efl-hateval
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# electra-large-discriminator-nli-efl-hateval
This model is a fine-tuned version of [ynie/electra-large-discriminator-snli_mnli_fever_anli_R1_R2_R3-nli](https://huggingface.co/ynie/electra-large-discriminator-snli_mnli_fever_anli_R1_R2_R3-nli) on the None dataset.
It achieves the following results on the evaluation set:
- Accuracy: 0.798
- F1: 0.7968
- Loss: 0.4166
## 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-06
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 128
- 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 | Accuracy | F1 | Validation Loss |
|:-------------:|:-----:|:----:|:--------:|:------:|:---------------:|
| 0.4175 | 1.0 | 210 | 0.7317 | 0.7305 | 0.4020 |
| 0.3061 | 2.0 | 420 | 0.768 | 0.7675 | 0.3520 |
| 0.2588 | 3.0 | 630 | 0.79 | 0.7888 | 0.3253 |
| 0.234 | 4.0 | 840 | 0.788 | 0.7877 | 0.3373 |
| 0.2116 | 5.0 | 1050 | 0.804 | 0.8033 | 0.3247 |
| 0.1974 | 6.0 | 1260 | 0.793 | 0.7928 | 0.3400 |
| 0.1807 | 7.0 | 1470 | 0.7973 | 0.7969 | 0.3511 |
| 0.1715 | 8.0 | 1680 | 0.7993 | 0.7989 | 0.3496 |
| 0.1577 | 9.0 | 1890 | 0.8043 | 0.8032 | 0.3507 |
| 0.1469 | 10.0 | 2100 | 0.798 | 0.7970 | 0.3604 |
| 0.1394 | 11.0 | 2310 | 0.7967 | 0.7957 | 0.3734 |
| 0.1322 | 12.0 | 2520 | 0.7913 | 0.7906 | 0.3929 |
| 0.1231 | 13.0 | 2730 | 0.795 | 0.7941 | 0.3954 |
| 0.1189 | 14.0 | 2940 | 0.7977 | 0.7963 | 0.3994 |
| 0.1143 | 15.0 | 3150 | 0.7993 | 0.7980 | 0.3995 |
| 0.1083 | 16.0 | 3360 | 0.7927 | 0.7918 | 0.4125 |
| 0.1079 | 17.0 | 3570 | 0.7993 | 0.7979 | 0.4036 |
| 0.1055 | 18.0 | 3780 | 0.7967 | 0.7956 | 0.4121 |
| 0.1006 | 19.0 | 3990 | 0.7973 | 0.7961 | 0.4152 |
| 0.101 | 20.0 | 4200 | 0.798 | 0.7968 | 0.4166 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.11.0+cu113
- Datasets 2.0.0
- Tokenizers 0.11.6
| 2,912 |
Jatin-WIAI/kannada_relevance_clf | null | Entry not found | 15 |
fmesa/mi-modelo-bacan-test | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
metrics:
- accuracy
- f1
model-index:
- name: mi-modelo-bacan-test
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: imdb
type: imdb
args: plain_text
metrics:
- name: Accuracy
type: accuracy
value: 0.8766666666666667
- name: F1
type: f1
value: 0.8825396825396825
---
<!-- 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. -->
# mi-modelo-bacan-test
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: 0.3318
- Accuracy: 0.8767
- F1: 0.8825
## 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
| 1,483 |
conviette/korPolBERT | null |
---
license: apache-2.0
---
This model is a binary classifier developed to analyze comment authorship patterns on Korean news articles.
For further details, refer to our paper on Journalism: [News comment sections and online echo chambers: The ideological alignment between partisan news stories and their user comments](https://journals.sagepub.com/doi/full/10.1177/14648849211069241)
* This model is a BERT classification model to classify Korean user generated comments into binary labels of liberal or conservative.
* This model was trained on approximately 37,000 user generated comments collected from NAVER\'s news portal. The dataset was collected in 2019; as such, note that comments related to recent political topics might not be classified correctly.
* This model is a finetuned model based on ETRI\'s KorBERT.
### How to use
* The model requires an edited version of the transformers class `BertTokenizer`, which can be found in the file `KorBertTokenizer.py`.
* Usage example:
~~~python
from KorBertTokenizer import KorBertTokenizer
from transformers import BertForSequenceClassification
import torch
tokenizer = KorBertTokenizer.from_pretrained('conviette/korPolBERT')
model = BertForSequenceClassification.from_pretrained('conviette/korPolBERT')
def classify(text):
inputs = tokenizer(text, padding='max_length', max_length=70, return_tensors='pt')
with torch.no_grad():
logits=model(**inputs).logits
predicted_class_id = logits.argmax().item()
return model.config.id2label[predicted_class_id]
input_strings = ['좌파가 나라 경제 안보 말아먹는다',
'수꼴들은 나라 일본한테 팔아먹었냐']
for input_string in input_strings:
print('===\n입력 텍스트: {}\n분류 결과: {}\n==='.format(input_string, classify(input_string)))
~~~
### Model performance
* Accuracy: 0.8322
* F1-Score: 0.8322
* For further technical details on the model, refer to our paper for the W-NUT workshop (EMNLP 2019), [The Fallacy of Echo Chambers: Analyzing the Political Slants of User-Generated News Comments in Korean Media](https://aclanthology.org/D19-5548/).
| 2,079 |
Xuan-Rui/pet-10-p0 | null | Entry not found | 15 |
Xuan-Rui/pet-10-p1 | null | Entry not found | 15 |
Xuan-Rui/pet-10-p2 | null | Entry not found | 15 |
Xuan-Rui/pet-10-p3 | null | Entry not found | 15 |
Xuan-Rui/pet-10-p4 | null | Entry not found | 15 |
Xuan-Rui/pet-100-p0 | null | Entry not found | 15 |
Xuan-Rui/pet-100-p1 | null | Entry not found | 15 |
Xuan-Rui/pet-100-p2 | null | Entry not found | 15 |
Xuan-Rui/pet-100-p3 | null | Entry not found | 15 |
Xuan-Rui/pet-100-p4 | null | Entry not found | 15 |
Xuan-Rui/pet-100-all | null | Entry not found | 15 |
Xuan-Rui/pet-1000-p0 | null | Entry not found | 15 |
Xuan-Rui/pet-1000-p1 | null | Entry not found | 15 |
Xuan-Rui/pet-1000-p2 | null | Entry not found | 15 |
Xuan-Rui/pet-1000-p3 | null | Entry not found | 15 |
Xuan-Rui/pet-1000-all | null | Entry not found | 15 |
Xuan-Rui/ipet-10-all | null | Entry not found | 15 |
Xuan-Rui/ipet-100-all | null | Entry not found | 15 |
SiriusRen/my-rubbish-model2 | [
"LABEL_0"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: my-rubbish-model2
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. -->
# my-rubbish-model2
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 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
### Framework versions
- Transformers 4.18.0.dev0
- Pytorch 1.10.0
- Datasets 2.0.1.dev0
- Tokenizers 0.11.6
| 1,045 |
dpazmino/finetuning-sentiment-model_duke_final_two | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: finetuning-sentiment-model_duke_final_two
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuning-sentiment-model_duke_final_two
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: 0.3381
- F1: 0.8801
## 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 2.1.0
- Tokenizers 0.12.1
| 1,188 |
Raychanan/COVID_RandomOver | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: results
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# results
This model is a fine-tuned version of [hfl/chinese-bert-wwm-ext](https://huggingface.co/hfl/chinese-bert-wwm-ext) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4235
- F1: 0.9546
## 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
- 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 | F1 |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 1.1307 | 1.0 | 3268 | 0.9040 | 0.0 |
| 0.8795 | 2.0 | 6536 | 0.5532 | 0.9546 |
| 0.8183 | 3.0 | 9804 | 0.3641 | 0.9546 |
| 1.0074 | 4.0 | 13072 | 0.3998 | 0.9546 |
| 0.7947 | 5.0 | 16340 | 0.4235 | 0.9546 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.10.0+cu111
- Datasets 2.1.0
- Tokenizers 0.12.1
| 1,549 |
MartinoMensio/racism-models-raw-label-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 `raw-label-epoch-2`
### Usage
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
model_name = 'raw-label-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': 0.8982619643211365}, {'label': 'non-racist', 'score': 0.6703745126724243}]
```
For more details, see https://github.com/preyero/neatclass22
| 4,252 |
MartinoMensio/racism-models-regression-w-m-vote-epoch-3 | [
"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-3`
### 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-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 = 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': 0.7393736}, {'score': 0.44301373}]
# or also specify a threshold to cut racist/non-racist
print(pipe(texts, regression_threshold=0.9))
# [{'label': 'non-racist', 'score': 0.7393736}, {'label': 'non-racist', 'score': 0.44301373}]
```
For more details, see https://github.com/preyero/neatclass22
| 6,364 |
MartinoMensio/racism-models-m-vote-strict-epoch-1 | 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-1`
### Usage
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
model_name = 'm-vote-strict-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 = 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': 0.6074065566062927}, {'label': 'non-racist', 'score': 0.8047575950622559}]
```
For more details, see https://github.com/preyero/neatclass22
| 4,260 |
MartinoMensio/racism-models-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 `m-vote-strict-epoch-2`
### Usage
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
model_name = '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': 0.923829972743988}, {'label': 'non-racist', 'score': 0.8673009872436523}]
```
For more details, see https://github.com/preyero/neatclass22
| 4,259 |
MartinoMensio/racism-models-m-vote-strict-epoch-4 | 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-4`
### Usage
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
model_name = 'm-vote-strict-epoch-4'
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': 0.9965864419937134}, {'label': 'racist', 'score': 0.6058831214904785}]
```
For more details, see https://github.com/preyero/neatclass22
| 4,256 |
MartinoMensio/racism-models-m-vote-nonstrict-epoch-1 | 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-nonstrict-epoch-1`
### Usage
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
model_name = 'm-vote-nonstrict-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 = 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': 0.9265261888504028}, {'label': 'non-racist', 'score': 0.802951991558075}]
```
For more details, see https://github.com/preyero/neatclass22
| 4,265 |
MartinoMensio/racism-models-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 `m-vote-nonstrict-epoch-2`
### Usage
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
model_name = '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': 0.8650100827217102}, {'label': 'non-racist', 'score': 0.9674995541572571}]
```
For more details, see https://github.com/preyero/neatclass22
| 4,266 |
MartinoMensio/racism-models-m-vote-nonstrict-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-nonstrict-epoch-3`
### Usage
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
model_name = 'm-vote-nonstrict-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': 0.9642159342765808}, {'label': 'non-racist', 'score': 0.9484726786613464}]
```
For more details, see https://github.com/preyero/neatclass22
| 4,266 |
MartinoMensio/racism-models-m-vote-nonstrict-epoch-4 | 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-nonstrict-epoch-4`
### Usage
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
model_name = 'm-vote-nonstrict-epoch-4'
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': 0.9791656136512756}, {'label': 'non-racist', 'score': 0.996966540813446}]
```
For more details, see https://github.com/preyero/neatclass22
| 4,265 |
MartinoMensio/racism-models-w-m-vote-nonstrict-epoch-1 | 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-1`
### Usage
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
model_name = 'w-m-vote-nonstrict-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 = 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': 0.8460916876792908}, {'label': 'non-racist', 'score': 0.9714874029159546}]
```
For more details, see https://github.com/preyero/neatclass22
| 4,270 |
MartinoMensio/racism-models-w-m-vote-nonstrict-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-nonstrict-epoch-3`
### Usage
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
model_name = 'w-m-vote-nonstrict-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': 0.9937393665313721}, {'label': 'non-racist', 'score': 0.9902436137199402}]
```
For more details, see https://github.com/preyero/neatclass22
| 4,270 |
jason9693/koelectra-base-v3-discriminator-apeach | [
"Default",
"Spoiled"
] | ---
language: ko
widget:
- text: "응 어쩔티비~~"
datasets:
- jason9693/APEACH
--- | 76 |
theta/MBTI-ckiplab-albert | [
"LABEL_0",
"LABEL_1",
"LABEL_2",
"LABEL_3"
] | ---
language:
- zh
tags:
- MBTI
- zh
- zh-tw
- generated_from_trainer
model-index:
- name: MBTI-ckiplab-albert
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. -->
# MBTI-ckiplab-albert
This model was trained from scratch on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Framework versions
- Transformers 4.19.1
- Pytorch 1.11.0+cu113
- Datasets 2.2.1
- Tokenizers 0.12.1
| 962 |
crcb/goemos | [
"0",
"1",
"2",
"3",
"4",
"5"
] | ---
tags: autotrain
language: en
widget:
- text: "I love AutoTrain 🤗"
datasets:
- crcb/autotrain-data-go_emo
co2_eq_emissions: 31.11935827749309
---
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 748922872
- CO2 Emissions (in grams): 31.11935827749309
## Validation Metrics
- Loss: 0.17039568722248077
- Accuracy: 0.93625
- Macro F1: 0.9075787460059076
- Micro F1: 0.93625
- Weighted F1: 0.9371621543264445
- Macro Precision: 0.8945117620407296
- Micro Precision: 0.93625
- Weighted Precision: 0.9433589433926076
- Macro Recall: 0.9323604226458176
- Micro Recall: 0.93625
- Weighted Recall: 0.93625
## 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-go_emo-748922872
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("crcb/autotrain-go_emo-748922872", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("crcb/autotrain-go_emo-748922872", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
``` | 1,308 |
clapika2010/hospital_detection | null | Entry not found | 15 |
ttwj-sutd/finetuning-sentiment-model-3000-samples-6pm | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: finetuning-sentiment-model-3000-samples-6pm
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: imdb
type: imdb
args: plain_text
metrics:
- name: Precision
type: precision
value: 0.875
- name: Recall
type: recall
value: 0.8866666666666667
- name: F1
type: f1
value: 0.880794701986755
- 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-6pm
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: 0.2896
- Precision: 0.875
- Recall: 0.8867
- F1: 0.8808
- 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: 1e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 11
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 188 | 0.3436 | 0.8633 | 0.8 | 0.8304 | 0.8367 |
| No log | 2.0 | 376 | 0.2896 | 0.875 | 0.8867 | 0.8808 | 0.88 |
| 0.3 | 3.0 | 564 | 0.3330 | 0.8693 | 0.8867 | 0.8779 | 0.8767 |
| 0.3 | 4.0 | 752 | 0.4378 | 0.8766 | 0.9 | 0.8882 | 0.8867 |
| 0.3 | 5.0 | 940 | 0.5198 | 0.8284 | 0.9333 | 0.8777 | 0.87 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.10.0+cu111
- Datasets 2.1.0
- Tokenizers 0.12.1
| 2,347 |
crcb/carer_2 | [
"anger",
"fear",
"sadness",
"surprise"
] | ---
tags: autotrain
language: en
widget:
- text: "I love AutoTrain 🤗"
datasets:
- crcb/autotrain-data-emo_carer_nojoylove
co2_eq_emissions: 2.370895196595982
---
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 751422974
- CO2 Emissions (in grams): 2.370895196595982
## Validation Metrics
- Loss: 0.15362708270549774
- Accuracy: 0.9345549738219895
- Macro F1: 0.9016011681330569
- Micro F1: 0.9345549738219895
- Weighted F1: 0.9345413976263288
- Macro Precision: 0.9032333514618506
- Micro Precision: 0.9345549738219895
- Weighted Precision: 0.9345804677958041
- Macro Recall: 0.9001021129974442
- Micro Recall: 0.9345549738219895
- Weighted Recall: 0.9345549738219895
## 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-emo_carer_nojoylove-751422974
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("crcb/autotrain-emo_carer_nojoylove-751422974", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("crcb/autotrain-emo_carer_nojoylove-751422974", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
``` | 1,415 |
crcb/hateval_re | [
"0",
"1"
] | ---
tags: autotrain
language: en
widget:
- text: "I love AutoTrain 🤗"
datasets:
- crcb/autotrain-data-hate_speech
co2_eq_emissions: 5.301132895184483
---
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 752122994
- CO2 Emissions (in grams): 5.301132895184483
## Validation Metrics
- Loss: 0.7107211351394653
- Accuracy: 0.7529411764705882
- Precision: 0.7502287282708143
- Recall: 0.9177392277560157
- AUC: 0.8358316393336287
- F1: 0.8255726151522779
## 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-hate_speech-752122994
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("crcb/autotrain-hate_speech-752122994", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("crcb/autotrain-hate_speech-752122994", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
``` | 1,167 |
Jatin-WIAI/marathi_relevance_clf | null | Entry not found | 15 |
crcb/hs_dvs | [
"0",
"1"
] | ---
tags: autotrain
language: en
widget:
- text: "I love AutoTrain 🤗"
datasets:
- crcb/autotrain-data-dvs
co2_eq_emissions: 5.1746636998598445
---
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 753223051
- CO2 Emissions (in grams): 5.1746636998598445
## Validation Metrics
- Loss: 0.14639143645763397
- Accuracy: 0.9493645350010087
- Precision: 0.5460992907801419
- Recall: 0.2916666666666667
- AUC: 0.8843542768404266
- F1: 0.3802469135802469
## 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-753223051
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("crcb/autotrain-dvs-753223051", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("crcb/autotrain-dvs-753223051", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
``` | 1,138 |
crcb/imp_hatred_f | [
"0",
"1",
"2"
] | ---
tags: autotrain
language: en
widget:
- text: "I love AutoTrain 🤗"
datasets:
- crcb/autotrain-data-imp_hs
co2_eq_emissions: 0.05286505617263864
---
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 753423076
- CO2 Emissions (in grams): 0.05286505617263864
## Validation Metrics
- Loss: 0.539419412612915
- Accuracy: 0.7616387337057728
- Macro F1: 0.6428050387135232
- Micro F1: 0.761638733705773
- Weighted F1: 0.7592341595725172
- Macro Precision: 0.6606534010647378
- Micro Precision: 0.7616387337057728
- Weighted Precision: 0.7575825822976101
- Macro Recall: 0.6293404928847536
- Micro Recall: 0.7616387337057728
- Weighted Recall: 0.7616387337057728
## 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-imp_hs-753423076
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("crcb/autotrain-imp_hs-753423076", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("crcb/autotrain-imp_hs-753423076", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
``` | 1,364 |
ndavid/binary-question-classifier-bert | null | Entry not found | 15 |
ShihTing/QA_Leave | [
"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_3",
"LABEL_4",
"LABEL_5",
"LABEL_6",
"LABEL_7",
"LABEL_8",
"LABEL_9"
] | # Title 自製QA請假版
---
tags: autonlp
language: unk
widget:
- text: "如果我想請特休，要怎麼使用"
- text: "我想請事假"
---
自製QA請假版
訓練與驗證分開
訓練筆67驗證筆23，總類別23，也就是驗證資料每一類各一測試
驗證acc=1.0
| 161 |
migueladarlo/distilbert-depression-mixed | null | ---
language:
- en
license: mit # Example: apache-2.0 or any license from https://huggingface.co/docs/hub/model-repos#list-of-license-identifiers
tags:
- text # Example: audio
- Twitter
datasets:
- CLPsych 2015 # Example: common_voice. Use dataset id from https://hf.co/datasets
metrics:
- accuracy, f1, precision, recall, AUC # Example: wer. Use metric id from https://hf.co/metrics
model-index:
- name: distilbert-depression-mixed
results: []
---
# distilbert-depression-mixed
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) trained on CLPsych 2015 and a scraped dataset, and evaluated on a scraped dataset from Twitter to detect potential users in Twitter for depression.
It achieves the following results on the evaluation set:
- Evaluation Loss: 0.71
- Accuracy: 0.63
- F1: 0.59
- Precision: 0.66
- Recall: 0.53
- AUC: 0.63
## Intended uses & limitations
Feed a corpus of tweets to the model to generate label if input is indicative of a depressed user or not. Label 1 is depressed, Label 0 is not depressed.
Limitation: All token sequences longer than 512 are automatically truncated. Also, training and test data may be contaminated with mislabeled users.
### How to use
You can use this model directly with a pipeline for sentiment analysis:
```python
>>> from transformers import DistilBertTokenizerFast, AutoTokenizer
>>> tokenizer = AutoTokenizer.from_pretrained('distilbert-base-uncased')
>>> from transformers import DistilBertForSequenceClassification
>>> model = DistilBertForSequenceClassification.from_pretrained(r"distilbert-depression-mixed")
>>> from transformers import pipeline
>>> classifier = pipeline("sentiment-analysis", model=model, tokenizer=tokenizer)
>>> tokenizer_kwargs = {'padding':True,'truncation':True,'max_length':512}
>>> result=classifier('pain peko',**tokenizer_kwargs) #For truncation to apply in the pipeline
>>> #Should note that the string passed as the input can be a corpus of tweets concatenated together into one document.
[{'label': 'LABEL_1', 'score': 0.5048992037773132}]
```
Otherwise, download the files and specify within the pipeline the path to the folder that contains the config.json, pytorch_model.bin, and training_args.bin
## Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 4.19e-05
- train_batch_size: 16
- eval_batch_size: 16
- weight_decay: 0.06
- num_epochs: 5.0
## Training results
| Epoch | Training Loss | Validation Loss | Accuracy | F1 | Precision | Recall | AUC |
|:-----:|:-------------:|:---------------:|:--------:|:--------:|:---------:|:--------:|:--------:|
| 1.0 | 0.68 | 0.66 | 0.61 | 0.54 | 0.60 | 0.50 | 0.60 |
| 2.0 | 0.65 | 0.65 | 0.63 | 0.49 | 0.70 | 0.37 | 0.62 |
| 3.0 | 0.53 | 0.63 | 0.66 | 0.58 | 0.69 | 0.50 | 0.65 |
| 4.0 | 0.39 | 0.66 | 0.67 | 0.61 | 0.69 | 0.54 | 0.67 |
| 5.0 | 0.27 | 0.72 | 0.65 | 0.61 | 0.63 | 0.60 | 0.64 | | 3,186 |
stevenlx96/distilbert-base-uncased-finetuned-hated | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-hated
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-hated
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: 0.5042
- Accuracy: 0.8135
- F1: 0.8127
## 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 | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.7267 | 1.0 | 215 | 0.5443 | 0.7832 | 0.7833 |
| 0.4548 | 2.0 | 430 | 0.5042 | 0.8135 | 0.8127 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.10.0+cu111
- Datasets 2.1.0
- Tokenizers 0.12.1
| 1,498 |
James-kc-min/AGT_Roberta | null | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: AGT_Roberta
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. -->
# AGT_Roberta
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.18.0
- Pytorch 1.10.0+cu111
- Datasets 1.16.0
- Tokenizers 0.12.1
| 1,033 |
mwong/albert-base-fever-claim-related | null | ---
language: en
license: mit
tags:
- text classification
- fact checking
datasets:
- mwong/fever-claim-related
widget:
- text: "Earth’s changing climate is a critical issue and poses the risk of significant environmental, social and economic disruptions around the globe.</s></s>Because of fears of climate change and adverse effects of drilling explosions and oil spills in the Gulf of Mexico, legislation has been considered, and governmental regulations and orders have been issued, which, combined with the local economic and employment conditions caused by both, could materially adversely impact the oil and gas industries and the economic health of areas in which a significant number of our stores are located."
example_title: "Evidence related to claim"
metrics: f1
---
# FeverAlbert
FeverAlbert is a classifier model that predicts if evidence is related to query claim. The model achieved F1 score of 88.33% with test dataset "mwong/fever-claim-related". Using pretrained albert-base-v2 model, the classifier head is trained on Fever dataset. | 1,057 |
Jeevesh8/feather_berts_0 | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | Entry not found | 15 |
Jeevesh8/feather_berts_1 | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | Entry not found | 15 |
Jeevesh8/feather_berts_2 | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | Entry not found | 15 |
Jeevesh8/feather_berts_3 | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | Entry not found | 15 |
Jeevesh8/feather_berts_4 | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | Entry not found | 15 |
Jeevesh8/feather_berts_5 | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | Entry not found | 15 |
Jeevesh8/feather_berts_6 | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | Entry not found | 15 |
Jeevesh8/feather_berts_7 | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | Entry not found | 15 |
Jeevesh8/feather_berts_8 | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | Entry not found | 15 |
Jeevesh8/feather_berts_9 | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | Entry not found | 15 |
Jeevesh8/feather_berts_10 | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | Entry not found | 15 |
Jeevesh8/feather_berts_11 | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | Entry not found | 15 |
Jeevesh8/feather_berts_12 | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | Entry not found | 15 |
Jeevesh8/feather_berts_13 | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | Entry not found | 15 |
Jeevesh8/feather_berts_14 | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | Entry not found | 15 |
Jeevesh8/feather_berts_15 | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | Entry not found | 15 |
Jeevesh8/feather_berts_16 | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | Entry not found | 15 |
Jeevesh8/feather_berts_17 | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | Entry not found | 15 |
Jeevesh8/feather_berts_18 | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | Entry not found | 15 |
Jeevesh8/feather_berts_20 | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | Entry not found | 15 |
Jeevesh8/feather_berts_22 | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | Entry not found | 15 |
Jeevesh8/feather_berts_23 | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | Entry not found | 15 |
Jeevesh8/feather_berts_24 | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | Entry not found | 15 |
Jeevesh8/feather_berts_25 | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | Entry not found | 15 |
Jeevesh8/feather_berts_26 | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | Entry not found | 15 |
Jeevesh8/feather_berts_27 | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | Entry not found | 15 |
Jeevesh8/feather_berts_29 | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | Entry not found | 15 |
Jeevesh8/feather_berts_30 | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | Entry not found | 15 |
Jeevesh8/feather_berts_31 | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | Entry not found | 15 |
Jeevesh8/feather_berts_32 | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | Entry not found | 15 |
Jeevesh8/feather_berts_33 | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | Entry not found | 15 |
Jeevesh8/feather_berts_34 | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | Entry not found | 15 |
Jeevesh8/feather_berts_36 | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | Entry not found | 15 |
Jeevesh8/feather_berts_37 | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | Entry not found | 15 |
Jeevesh8/feather_berts_38 | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | Entry not found | 15 |
Jeevesh8/feather_berts_39 | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | Entry not found | 15 |
Jeevesh8/feather_berts_40 | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | Entry not found | 15 |
Jeevesh8/feather_berts_41 | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | Entry not found | 15 |
Jeevesh8/feather_berts_42 | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | Entry not found | 15 |
Jeevesh8/feather_berts_43 | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | Entry not found | 15 |
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