modelId stringlengths 6 107 | label list | readme stringlengths 0 56.2k | readme_len int64 0 56.2k |
|---|---|---|---|
boronbrown48/wangchanberta-sentiment-504-v3 | [
"LABEL_0",
"LABEL_1",
"LABEL_2",
"LABEL_3",
"LABEL_4",
"LABEL_5",
"LABEL_6"
] | Entry not found | 15 |
bshlgrs/autonlp-classification-9522090 | [
"No",
"Unsure",
"Yes"
] | ---
tags: autonlp
language: en
widget:
- text: "I love AutoNLP 🤗"
datasets:
- bshlgrs/autonlp-data-classification
---
# Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 9522090
## Validation Metrics
- Loss: 0.3541755676269531
- Accuracy: 0.8759671179883946
- Macro F1: 0.5330133182738012
- Micro F1: 0.8759671179883946
- Weighted F1: 0.8482773065757196
- Macro Precision: 0.537738108882869
- Micro Precision: 0.8759671179883946
- Weighted Precision: 0.8241048710814852
- Macro Recall: 0.5316621214820499
- Micro Recall: 0.8759671179883946
- Weighted Recall: 0.8759671179883946
## 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 AutoNLP"}' https://api-inference.huggingface.co/models/bshlgrs/autonlp-classification-9522090
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("bshlgrs/autonlp-classification-9522090", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("bshlgrs/autonlp-classification-9522090", use_auth_token=True)
inputs = tokenizer("I love AutoNLP", return_tensors="pt")
outputs = model(**inputs)
``` | 1,297 |
celential/erc | null | Entry not found | 15 |
clem/autonlp-test3-2101779 | [
"not_urgent",
"urgent"
] | ---
tags: autonlp
language: en
widget:
- text: "I love AutoNLP 🤗"
datasets:
- clem/autonlp-data-test3
---
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 2101779
## Validation Metrics
- Loss: 0.282466858625412
- Accuracy: 1.0
- Precision: 1.0
- Recall: 1.0
- AUC: 1.0
- F1: 1.0
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoNLP"}' https://api-inference.huggingface.co/models/clem/autonlp-test3-2101779
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("clem/autonlp-test3-2101779", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("clem/autonlp-test3-2101779", use_auth_token=True)
inputs = tokenizer("I love AutoNLP", return_tensors="pt")
outputs = model(**inputs)
``` | 959 |
damlab/HIV_V3_Coreceptor | [
"CCR5",
"CXCR4"
] | ---
license: mit
widget:
- text: 'C T R P N N N T R K S I R I Q R G P G R A F V T I G K I G N M R Q A H C'
- text: 'C T R P N N N T R K S I H I G P G R A F Y T T G Q I I G D I R Q A Y C'
- text: 'C T R P N N N T R R S I R I G P G Q A F Y A T G D I I G D I R Q A H C'
- text: 'C G R P N N H R I K G L R I G P G R A F F A M G A I G G G E I R Q A H C'
---
# HIV_V3_coreceptor model
## Table of Contents
- [Summary](#model-summary)
- [Model Description](#model-description)
- [Intended Uses & Limitations](#intended-uses-&-limitations)
- [How to Use](#how-to-use)
- [Training Data](#training-data)
- [Training Procedure](#training-procedure)
- [Preprocessing](#preprocessing)
- [Training](#training)
- [Evaluation Results](#evaluation-results)
- [BibTeX Entry and Citation Info](#bibtex-entry-and-citation-info)
## Summary
The HIV-BERT-Coreceptor model was trained as a refinement of the [HIV-BERT model](https://huggingface.co/damlab/HIV_BERT) and serves to better predict HIV V3 coreceptor tropism. HIV-BERT is a model refined from the [ProtBert-BFD model](https://huggingface.co/Rostlab/prot_bert_bfd) to better fulfill HIV-centric tasks. This model was then trained using HIV V3 sequences from the [Los Alamos HIV Sequence Database](https://www.hiv.lanl.gov/content/sequence/HIV/mainpage.html), allowing even more precise prediction of V3 coreceptor tropism than the HIV-BERT model can provide.
## Model Description
The HIV-BERT-Coreceptor model is intended to predict the Co-receptor tropism of HIV from a segment of the envelope protein. These envelope proteins encapsulate the virus and interact with the host cell through the human CD4 receptor. HIV then requires the interaction of one, of two, co-receptors: CCR5 or CXCR4. The availability of these co-receptors on different cell types allows the virus to invade different areas of the body and evade antiretroviral therapy. The 3rd variable loop of the envelope protein, the V3 loop, is responsible for this interaction. Given a V3 loop sequence, the HIV-BERT-Coreceptor model will predict the likelihood of binding to each of these co-receptors.
## Intended Uses & Limitations
This tool can be used as a predictor of HIV tropism from the Env-V3 loop. It can recognize both R5, X4, and dual tropic viruses natively. It should not be considered a clinical diagnostic tool.
This tool was trained using the [Los Alamos HIV sequence dataset](https://www.hiv.lanl.gov/content/sequence/HIV/mainpage.html). Due to the sampling nature of this database, it is predominantly composed of subtype B sequences from North America and Europe with only minor contributions of Subtype C, A, and D. Currently, there was no effort made to balance the performance across these classes. As such, one should consider refinement with additional sequences to perform well on non-B sequences.
## How to use
*Need to add*
## Training Data
This model was trained using the [damlab/HIV_V3_coreceptor dataset](https://huggingface.co/datasets/damlab/HIV_V3_coreceptor) using the 0th fold. The dataset consists of 2935 V3 sequences (approximately 35 tokens each) extracted from the [Los Alamos HIV Sequence database](https://www.hiv.lanl.gov/content/sequence/HIV/mainpage.html).
## Training Procedure
### Preprocessing
As with the [rostlab/Prot-bert-bfd model](https://huggingface.co/Rostlab/prot_bert_bfd), the rare amino acids U, Z, O, and B were converted to X and spaces were added between each amino acid. All strings were concatenated and chunked into 256 token chunks for training. A random 20% of chunks were held for validation.
### Training
The [damlab/HIV-BERT model](https://huggingface.co/damlab/HIV_BERT) was used as the initial weights for an AutoModelforClassificiation. The model was trained with a learning rate of 1E-5, 50K warm-up steps, and a cosine_with_restarts learning rate schedule and continued until 3 consecutive epochs did not improve the loss on the held-out dataset. As this is a multiple classification task (a protein can bind to CCR5, CXCR4, neither, or both) the loss was calculated as the Binary Cross Entropy for each category. The BCE was weighted by the inverse of the class ratio to balance the weight across the class imbalance.
## Evaluation Results
*Need to add*
## BibTeX Entry and Citation Info
[More Information Needed]
| 4,393 |
danlou/distilbert-base-uncased-finetuned-rte | null | Testing | 7 |
deeq/dbert-eth2 | [
"0",
"1"
] | Entry not found | 15 |
diegozs97/finetuned-chemprot-seed-1-2000k | [
"CPR:3",
"CPR:4",
"CPR:5",
"CPR:6",
"CPR:9",
"false"
] | Entry not found | 15 |
diegozs97/finetuned-sciie-seed-3-1000k | [
"COMPARE",
"CONJUNCTION",
"EVALUATE-FOR",
"FEATURE-OF",
"HYPONYM-OF",
"PART-OF",
"USED-FOR"
] | Entry not found | 15 |
diegozs97/finetuned-sciie-seed-4-2000k | [
"COMPARE",
"CONJUNCTION",
"EVALUATE-FOR",
"FEATURE-OF",
"HYPONYM-OF",
"PART-OF",
"USED-FOR"
] | Entry not found | 15 |
diwank/maptask-deberta-pair | [
"LABEL_0",
"LABEL_1",
"LABEL_10",
"LABEL_11",
"LABEL_2",
"LABEL_3",
"LABEL_4",
"LABEL_5",
"LABEL_6",
"LABEL_7",
"LABEL_8",
"LABEL_9"
] | ---
license: mit
---
# maptask-deberta-pair
Deberta-based Daily MapTask style dialog-act annotations classification model
## Example
```python
from simpletransformers.classification import (
ClassificationModel, ClassificationArgs
)
model = ClassificationModel("deberta", "diwank/maptask-deberta-pair")
predictions, raw_outputs = model.predict([["Say what is the meaning of life?", "I dont know"]])
convert_to_label = lambda n: ["acknowledge (0), align (1), check (2), clarify (3), explain (4), instruct (5), query_w (6), query_yn (7), ready (8), reply_n (9), reply_w (10), reply_y (11)".split(', ')[i] for i in n]
convert_to_label(predictions) # reply_n (9)
``` | 694 |
echarlaix/bert-base-uncased-sst2-static-quant-test | null | Entry not found | 15 |
echarlaix/distilbert-base-uncased-sst2-magnitude-pruning-test | [
"0",
"1"
] | Entry not found | 15 |
edbeeching/test-trainer-to-hub | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
- f1
model-index:
- name: test-trainer-to-hub
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
args: mrpc
metrics:
- name: Accuracy
type: accuracy
value: 0.8455882352941176
- name: F1
type: f1
value: 0.893760539629005
---
<!-- 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. -->
# test-trainer-to-hub
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7352
- Accuracy: 0.8456
- F1: 0.8938
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log | 1.0 | 459 | 0.4489 | 0.8235 | 0.8792 |
| 0.5651 | 2.0 | 918 | 0.4885 | 0.8260 | 0.8811 |
| 0.3525 | 3.0 | 1377 | 0.7352 | 0.8456 | 0.8938 |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.2+cu102
- Datasets 1.18.3
- Tokenizers 0.11.0
| 1,818 |
emekaboris/autonlp-txc-17923129 | [
"1.0",
"10.0",
"11.0",
"12.0",
"13.0",
"14.0",
"15.0",
"16.0",
"17.0",
"18.0",
"19.0",
"2.0",
"20.0",
"21.0",
"22.0",
"23.0",
"24.0",
"3.0",
"4.0",
"5.0",
"6.0",
"7.0",
"8.0",
"9.0"
] | ---
tags: autonlp
language: en
widget:
- text: "I love AutoNLP 🤗"
datasets:
- emekaboris/autonlp-data-txc
co2_eq_emissions: 610.861733873082
---
# Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 17923129
- CO2 Emissions (in grams): 610.861733873082
## Validation Metrics
- Loss: 0.2319454699754715
- Accuracy: 0.9264228741381642
- Macro F1: 0.6730537318152493
- Micro F1: 0.9264228741381642
- Weighted F1: 0.9251493598895151
- Macro Precision: 0.7767479491141245
- Micro Precision: 0.9264228741381642
- Weighted Precision: 0.9277971545757154
- Macro Recall: 0.6617262519071917
- Micro Recall: 0.9264228741381642
- Weighted Recall: 0.9264228741381642
## 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 AutoNLP"}' https://api-inference.huggingface.co/models/emekaboris/autonlp-txc-17923129
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("emekaboris/autonlp-txc-17923129", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("emekaboris/autonlp-txc-17923129", use_auth_token=True)
inputs = tokenizer("I love AutoNLP", return_tensors="pt")
outputs = model(**inputs)
``` | 1,350 |
frahman/distilbert-base-uncased-finetuned-emotion | [
"sadness",
"joy",
"love",
"anger",
"fear",
"surprise"
] | ---
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: 0.9205
- name: F1
type: f1
value: 0.9206660865871332
---
<!-- 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: 0.2202
- Accuracy: 0.9205
- F1: 0.9207
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8234 | 1.0 | 250 | 0.3185 | 0.9025 | 0.8992 |
| 0.2466 | 2.0 | 500 | 0.2202 | 0.9205 | 0.9207 |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.11.0
| 1,807 |
guilhermedrud/bert-large-portuguese-socioambiental | null | Entry not found | 15 |
hadxu/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: 0.92
- name: F1
type: f1
value: 0.9202797627524772
---
<!-- 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: 0.2307
- Accuracy: 0.92
- F1: 0.9203
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8397 | 1.0 | 250 | 0.3345 | 0.9045 | 0.9007 |
| 0.2544 | 2.0 | 500 | 0.2307 | 0.92 | 0.9203 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu111
- Datasets 1.16.1
- Tokenizers 0.10.3
| 1,803 |
howey/electra-large-qqp | null | Entry not found | 15 |
hyunwoongko/brainbert-base-ko-kornli | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | Entry not found | 15 |
jonc/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: 0.9230733583303665
---
<!-- 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: 0.2159
- Accuracy: 0.923
- F1: 0.9231
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8494 | 1.0 | 250 | 0.3134 | 0.907 | 0.9051 |
| 0.2504 | 2.0 | 500 | 0.2159 | 0.923 | 0.9231 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu111
- Datasets 1.16.1
- Tokenizers 0.10.3
| 1,805 |
julien-c/distilbert-sagemaker-1609802168 | [
"neg",
"pos"
] |
---
tags:
- sagemaker
datasets:
- imdb
---
## distilbert-sagemaker-1609802168
Trained from SageMaker HuggingFace extension.
Fine-tuned from [distilbert-base-uncased](/distilbert-base-uncased) on [imdb](/datasets/imdb) 🔥
#### Eval
| key | value |
| --- | ----- |
| eval_loss | 0.19187863171100616 |
| eval_accuracy | 0.9259 |
| eval_f1 | 0.9272173656811707 |
| eval_precision | 0.9147286821705426 |
| eval_recall | 0.9400517825134436 |
| epoch | 1.0 |
| 458 |
k-partha/extrabert_bio | [
"Introvert",
"Extravert"
] | Classifies Twitter biographies as either introverts or extroverts.
Go to your Twitter profile, copy your biography and paste in the inference widget, remove any URLs and press hit!
Trained on self-described personality labels. Interpret as a continuous score, not as a discrete label. Have fun!
Barack Obama: Extrovert; Ellen DeGeneres: Extrovert; Naomi Osaka: Introvert
Note: Performance on inputs other than Twitter biographies [the training data source] is not verified.
For further details and expected performance, read the [paper](https://arxiv.org/abs/2109.06402). | 580 |
l3cube-pune/hate-bert-hasoc-marathi | null | ---
language: mr
tags:
- albert
license: cc-by-4.0
datasets:
- HASOC 2021
widget:
- text: "I like you. </s></s> I love you."
---
## hate-bert-hasoc-marathi
hate-bert-hasoc-marathi is a binary hate speech model fine-tuned on Marathi Hasoc Hate Speech Dataset 2021.
The label mappings are 0 -> None, 1 -> Hate.
More details on the dataset, models, and baseline results can be found in our [paper] (https://arxiv.org/abs/2110.12200)
A new version of Marathi Hate Speech Detection models can be found here: <br>
binary: https://huggingface.co/l3cube-pune/mahahate-bert <br>
multi label: https://huggingface.co/l3cube-pune/mahahate-multi-roberta <br>
```
@article{velankar2021hate,
title={Hate and Offensive Speech Detection in Hindi and Marathi},
author={Velankar, Abhishek and Patil, Hrushikesh and Gore, Amol and Salunke, Shubham and Joshi, Raviraj},
journal={arXiv preprint arXiv:2110.12200},
year={2021}
}
``` | 923 |
lewtun/distilbert-base-uncased-finetuned-emotion-test-01 | [
"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-test-01
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.39
- name: F1
type: f1
value: 0.21884892086330932
---
<!-- 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-test-01
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: 1.7510
- Accuracy: 0.39
- F1: 0.2188
## 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 | 2 | 1.7634 | 0.39 | 0.2188 |
| No log | 2.0 | 4 | 1.7510 | 0.39 | 0.2188 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu111
- Datasets 1.16.1
- Tokenizers 0.10.3
| 1,820 |
liam168/c4-zh-distilbert-base-uncased | [
"Female",
"Sports",
"Literature",
"Campus"
] | ---
language: zh
tags:
- exbert
license: apache-2.0
widget:
- text: "女人做得越纯粹，皮肤和身材就越好"
- text: "我喜欢篮球"
---
# liam168/c4-zh-distilbert-base-uncased
## Model description
用 ["女性","体育","文学","校园"]4类数据训练的分类模型。
## Overview
- **Language model**: DistilBERT
- **Model size**: 280M
- **Language**: Chinese
## Example
```python
>>> from transformers import DistilBertForSequenceClassification , AutoTokenizer, pipeline
>>> model_name = "liam168/c4-zh-distilbert-base-uncased"
>>> class_num = 4
>>> ts_texts = ["女人做得越纯粹，皮肤和身材就越好", "我喜欢篮球"]
>>> model = DistilBertForSequenceClassification.from_pretrained(model_name, num_labels=class_num)
>>> tokenizer = AutoTokenizer.from_pretrained(model_name)
>>> classifier = pipeline('sentiment-analysis', model=model, tokenizer=tokenizer)
>>> classifier(ts_texts[0])
>>> classifier(ts_texts[1])
[{'label': 'Female', 'score': 0.9137857556343079}]
[{'label': 'Sports', 'score': 0.8206522464752197}]
```
| 939 |
mlkorra/OGBV-gender-bert-hi-en | [
"NGEN",
"GEN"
] | ## BERT Model for OGBV gendered text classification
## How to use
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("mlkorra/OGBV-gender-bert-hi-en")
model = AutoModelForSequenceClassification.from_pretrained("mlkorra/OGBV-gender-bert-hi-en")
```
## Model Performance
|Metric|dev|test|
|---|--|--|
|Accuracy|0.88|0.81|
|F1(weighted)|0.86|0.80|
| 431 |
mohsenfayyaz/distilbert-fa-description-classifier | [
"LABEL_0",
"LABEL_1",
"LABEL_2",
"LABEL_3",
"LABEL_4",
"LABEL_5",
"LABEL_6",
"LABEL_7",
"LABEL_8",
"LABEL_9"
] | Entry not found | 15 |
monologg/koelectra-base-bias | [
"gender",
"none",
"others"
] | Entry not found | 15 |
monologg/koelectra-base-gender-bias | [
"False",
"True"
] | Entry not found | 15 |
monologg/koelectra-base-v3-gender-bias | [
"False",
"True"
] | Entry not found | 15 |
ncduy/bert-base-cased-finetuned-emotion | [
"anger",
"fear",
"joy",
"love",
"sadness",
"surprise"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- f1
model-index:
- name: bert-base-cased-finetuned-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
args: default
metrics:
- name: F1
type: f1
value: 0.9365323747830425
---
<!-- 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-cased-finetuned-emotion
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1342
- F1: 0.9365
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.7357 | 1.0 | 250 | 0.2318 | 0.9224 |
| 0.1758 | 2.0 | 500 | 0.1679 | 0.9349 |
| 0.1228 | 3.0 | 750 | 0.1385 | 0.9382 |
| 0.0961 | 4.0 | 1000 | 0.1452 | 0.9340 |
| 0.0805 | 5.0 | 1250 | 0.1342 | 0.9365 |
### Framework versions
- Transformers 4.12.5
- Pytorch 1.10.0+cu111
- Datasets 1.16.1
- Tokenizers 0.10.3
| 1,858 |
nepp1d0/Bert-pretrained-proteinBindingDB | [
"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 |
pmthangk09/bert-base-uncased-esnli | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | Entry not found | 15 |
reatiny/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: 0.9217811693486851
---
<!-- 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: 0.2226
- Accuracy: 0.9215
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8235 | 1.0 | 250 | 0.3190 | 0.901 | 0.8979 |
| 0.2497 | 2.0 | 500 | 0.2226 | 0.9215 | 0.9218 |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.0
- Datasets 1.15.1
- Tokenizers 0.11.0
| 1,801 |
rohanrajpal/bert-base-en-es-codemix-cased | [
"negative",
"neutral",
"positive"
] | ---
language:
- es
- en
tags:
- es
- en
- codemix
license: "apache-2.0"
datasets:
- SAIL 2017
metrics:
- fscore
- accuracy
- precision
- recall
---
# BERT codemixed base model for spanglish (cased)
This model was built using [lingualytics](https://github.com/lingualytics/py-lingualytics), an open-source library that supports code-mixed analytics.
## Model description
Input for the model: Any codemixed spanglish text
Output for the model: Sentiment. (0 - Negative, 1 - Neutral, 2 - Positive)
I took a bert-base-multilingual-cased model from Huggingface and finetuned it on [CS-EN-ES-CORPUS](http://www.grupolys.org/software/CS-CORPORA/cs-en-es-corpus-wassa2015.txt) dataset.
Performance of this model on the dataset
| metric | score |
|------------|----------|
| acc | 0.718615 |
| f1 | 0.71759 |
| acc_and_f1 | 0.718103 |
| precision | 0.719302 |
| recall | 0.718615 |
## Intended uses & limitations
Make sure to preprocess your data using [these methods](https://github.com/microsoft/GLUECoS/blob/master/Data/Preprocess_Scripts/preprocess_sent_en_es.py) before using this model.
#### How to use
Here is how to use this model to get the features of a given text in *PyTorch*:
```python
# You can include sample code which will be formatted
from transformers import BertTokenizer, BertModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained('rohanrajpal/bert-base-en-es-codemix-cased')
model = AutoModelForSequenceClassification.from_pretrained('rohanrajpal/bert-base-en-es-codemix-cased')
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
```
and in *TensorFlow*:
```python
from transformers import BertTokenizer, TFBertModel
tokenizer = BertTokenizer.from_pretrained('rohanrajpal/bert-base-en-es-codemix-cased')
model = TFBertModel.from_pretrained('rohanrajpal/bert-base-en-es-codemix-cased')
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='tf')
output = model(encoded_input)
```
#### Limitations and bias
Since I dont know spanish, I cant verify the quality of annotations or the dataset itself. This is a very simple transfer learning approach and I'm open to discussions to improve upon this.
## Training data
I trained on the dataset on the [bert-base-multilingual-cased model](https://huggingface.co/bert-base-multilingual-cased).
## Training procedure
Followed the preprocessing techniques followed [here](https://github.com/microsoft/GLUECoS/blob/master/Data/Preprocess_Scripts/preprocess_sent_en_es.py)
## Eval results
### BibTeX entry and citation info
```bibtex
@inproceedings{khanuja-etal-2020-gluecos,
title = "{GLUEC}o{S}: An Evaluation Benchmark for Code-Switched {NLP}",
author = "Khanuja, Simran and
Dandapat, Sandipan and
Srinivasan, Anirudh and
Sitaram, Sunayana and
Choudhury, Monojit",
booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.acl-main.329",
pages = "3575--3585"
}
```
| 3,262 |
shiyue/roberta-large-realsumm | [
"contradiction",
"entailment",
"neutral"
] | Entry not found | 15 |
sismetanin/xlm_roberta_large-financial_phrasebank | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | Entry not found | 15 |
spencerh/leftpartisan | null | # Text classifier using DistilBERT to determine Partisanship
## This is one of many single-class partisanship models
label_0 refers to "left" while label_1 refers to "other".
This model was trained on 40,000 articles.
### Best Practices
This model was optimized for 512 token-length text. Any text below 150 tokens will result in inaccurate results. | 357 |
staceythompson/autonlp-new-text-classification-38319698 | [
"Negative",
"Outofscope",
"Positive",
"Price",
"WhoIsThis"
] | ---
tags: autonlp
language: unk
widget:
- text: "I love AutoNLP 🤗"
datasets:
- staceythompson/autonlp-data-new-text-classification
co2_eq_emissions: 2.0318857468309206
---
# Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 38319698
- CO2 Emissions (in grams): 2.0318857468309206
## Validation Metrics
- Loss: 0.04461582377552986
- Accuracy: 0.9909255898366606
- Macro F1: 0.9951842095089771
- Micro F1: 0.9909255898366606
- Weighted F1: 0.9909493945587176
- Macro Precision: 0.9942196531791907
- Micro Precision: 0.9909255898366606
- Weighted Precision: 0.9911878560263526
- Macro Recall: 0.9962686567164181
- Micro Recall: 0.9909255898366606
- Weighted Recall: 0.9909255898366606
## 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 AutoNLP"}' https://api-inference.huggingface.co/models/staceythompson/autonlp-new-text-classification-38319698
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("staceythompson/autonlp-new-text-classification-38319698", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("staceythompson/autonlp-new-text-classification-38319698", use_auth_token=True)
inputs = tokenizer("I love AutoNLP", return_tensors="pt")
outputs = model(**inputs)
``` | 1,452 |
textattack/albert-base-v2-snli | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | ## TextAttack Model Card
This `albert-base-v2` model was fine-tuned for sequence classification using TextAttack
and the snli dataset loaded using the `nlp` library. The model was fine-tuned
for 5 epochs with a batch size of 64, a learning
rate of 2e-05, and a maximum sequence length of 64.
Since this was a classification task, the model was trained with a cross-entropy loss function.
The best score the model achieved on this task was 0.9060150375939849, as measured by the
eval set accuracy, found after 2 epochs.
For more information, check out [TextAttack on Github](https://github.com/QData/TextAttack).
| 619 |
trtd56/autonlp-wrime_joy_only-117396 | [
"0",
"1"
] | ---
tags: autonlp
language: ja
widget:
- text: "I love AutoNLP 🤗"
datasets:
- trtd56/autonlp-data-wrime_joy_only
---
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 117396
## Validation Metrics
- Loss: 0.4094310998916626
- Accuracy: 0.8201678240740741
- Precision: 0.6750303520841765
- Recall: 0.7912713472485768
- AUC: 0.8927167943538512
- F1: 0.728543350076436
## 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 AutoNLP"}' https://api-inference.huggingface.co/models/trtd56/autonlp-wrime_joy_only-117396
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("trtd56/autonlp-wrime_joy_only-117396", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("trtd56/autonlp-wrime_joy_only-117396", use_auth_token=True)
inputs = tokenizer("I love AutoNLP", return_tensors="pt")
outputs = model(**inputs)
``` | 1,074 |
yoshitomo-matsubara/bert-base-uncased-wnli_from_bert-large-uncased-wnli | null | ---
language: en
tags:
- bert
- wnli
- glue
- kd
- torchdistill
license: apache-2.0
datasets:
- wnli
metrics:
- accuracy
---
`bert-base-uncased` fine-tuned on WNLI dataset, using fine-tuned `bert-large-uncased` as a teacher model, [***torchdistill***](https://github.com/yoshitomo-matsubara/torchdistill) and [Google Colab](https://colab.research.google.com/github/yoshitomo-matsubara/torchdistill/blob/master/demo/glue_kd_and_submission.ipynb) for knowledge distillation.
The training configuration (including hyperparameters) is available [here](https://github.com/yoshitomo-matsubara/torchdistill/blob/main/configs/sample/glue/wnli/kd/bert_base_uncased_from_bert_large_uncased.yaml).
I submitted prediction files to [the GLUE leaderboard](https://gluebenchmark.com/leaderboard), and the overall GLUE score was **78.9**.
| 828 |
yoshitomo-matsubara/bert-large-uncased-rte | null | ---
language: en
tags:
- bert
- rte
- glue
- torchdistill
license: apache-2.0
datasets:
- rte
metrics:
- accuracy
---
`bert-large-uncased` fine-tuned on RTE dataset, using [***torchdistill***](https://github.com/yoshitomo-matsubara/torchdistill) and [Google Colab](https://colab.research.google.com/github/yoshitomo-matsubara/torchdistill/blob/master/demo/glue_finetuning_and_submission.ipynb).
The hyperparameters are the same as those in Hugging Face's example and/or the paper of BERT, and the training configuration (including hyperparameters) is available [here](https://github.com/yoshitomo-matsubara/torchdistill/blob/main/configs/sample/glue/rte/ce/bert_large_uncased.yaml).
I submitted prediction files to [the GLUE leaderboard](https://gluebenchmark.com/leaderboard), and the overall GLUE score was **80.2**.
| 824 |
yoshitomo-matsubara/bert-large-uncased-stsb | [
"LABEL_0"
] | ---
language: en
tags:
- bert
- stsb
- glue
- torchdistill
license: apache-2.0
datasets:
- stsb
metrics:
- pearson correlation
- spearman correlation
---
`bert-large-uncased` fine-tuned on STS-B dataset, using [***torchdistill***](https://github.com/yoshitomo-matsubara/torchdistill) and [Google Colab](https://colab.research.google.com/github/yoshitomo-matsubara/torchdistill/blob/master/demo/glue_finetuning_and_submission.ipynb).
The hyperparameters are the same as those in Hugging Face's example and/or the paper of BERT, and the training configuration (including hyperparameters) is available [here](https://github.com/yoshitomo-matsubara/torchdistill/blob/main/configs/sample/glue/stsb/mse/bert_large_uncased.yaml).
I submitted prediction files to [the GLUE leaderboard](https://gluebenchmark.com/leaderboard), and the overall GLUE score was **80.2**.
| 864 |
DoyyingFace/bert-asian-hate-tweets-asonam-unclean | null | Entry not found | 15 |
saptarshidatta96/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: 0.879746835443038
---
<!-- 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: 0.3209
- Accuracy: 0.8733
- F1: 0.8797
## 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 4.16.2
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.11.0
| 1,521 |
MhF/distilbert-base-uncased-distilled-clinc | [
"accept_reservations",
"account_blocked",
"alarm",
"application_status",
"apr",
"are_you_a_bot",
"balance",
"bill_balance",
"bill_due",
"book_flight",
"book_hotel",
"calculator",
"calendar",
"calendar_update",
"calories",
"cancel",
"cancel_reservation",
"car_rental",
"card_declin... | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- clinc_oos
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased-distilled-clinc
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: clinc_oos
type: clinc_oos
args: plus
metrics:
- name: Accuracy
type: accuracy
value: 0.9461290322580646
---
<!-- 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-distilled-clinc
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the clinc_oos dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2663
- Accuracy: 0.9461
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 48
- eval_batch_size: 48
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 9
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 4.1991 | 1.0 | 318 | 3.1495 | 0.7523 |
| 2.4112 | 2.0 | 636 | 1.5868 | 0.8510 |
| 1.1887 | 3.0 | 954 | 0.7975 | 0.9203 |
| 0.5952 | 4.0 | 1272 | 0.4870 | 0.9319 |
| 0.3275 | 5.0 | 1590 | 0.3571 | 0.9419 |
| 0.2066 | 6.0 | 1908 | 0.3070 | 0.9429 |
| 0.1456 | 7.0 | 2226 | 0.2809 | 0.9448 |
| 0.1154 | 8.0 | 2544 | 0.2697 | 0.9468 |
| 0.1011 | 9.0 | 2862 | 0.2663 | 0.9461 |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.11.0
| 2,138 |
ali2066/finetuned_sentence_itr4_2e-05_all_26_02_2022-04_20_09 | [
"NEGATIVE",
"POSITIVE"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: finetuned_sentence_itr4_2e-05_all_26_02_2022-04_20_09
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. -->
# finetuned_sentence_itr4_2e-05_all_26_02_2022-04_20_09
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: 0.4676
- Accuracy: 0.8299
- F1: 0.8892
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log | 1.0 | 195 | 0.4087 | 0.8073 | 0.8754 |
| No log | 2.0 | 390 | 0.3952 | 0.8159 | 0.8803 |
| 0.4084 | 3.0 | 585 | 0.4183 | 0.8195 | 0.8831 |
| 0.4084 | 4.0 | 780 | 0.4596 | 0.8280 | 0.8867 |
| 0.4084 | 5.0 | 975 | 0.4919 | 0.8280 | 0.8873 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.1+cu113
- Datasets 1.18.0
- Tokenizers 0.10.3
| 1,788 |
DoyyingFace/bert-asian-hate-tweets-self-clean-small-warmup-50 | null | Entry not found | 15 |
DoyyingFace/bert-asian-hate-tweets-self-clean-small-discriminate | null | Entry not found | 15 |
batterydata/batterybert-uncased-abstract | [
"battery",
"non-battery"
] | ---
language: en
tags: Text Classification
license: apache-2.0
datasets:
- batterydata/paper-abstracts
metrics: glue
---
# BatteryBERT-uncased for Battery Abstract Classification
**Language model:** batterybert-uncased
**Language:** English
**Downstream-task:** Text Classification
**Training data:** training\_data.csv
**Eval data:** val\_data.csv
**Code:** See [example](https://github.com/ShuHuang/batterybert)
**Infrastructure**: 8x DGX A100
## Hyperparameters
```
batch_size = 32
n_epochs = 11
base_LM_model = "batterybert-uncased"
learning_rate = 2e-5
```
## Performance
```
"Validation accuracy": 97.10,
"Test accuracy": 96.94,
```
## Usage
### In Transformers
```python
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model_name = "batterydata/batterybert-uncased-abstract"
# a) Get predictions
nlp = pipeline('text-classification', model=model_name, tokenizer=model_name)
input = {'The typical non-aqueous electrolyte for commercial Li-ion cells is a solution of LiPF6 in linear and cyclic carbonates.'}
res = nlp(input)
# b) Load model & tokenizer
model = AutoModelForSequenceClassification.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
```
## Authors
Shu Huang: `sh2009 [at] cam.ac.uk`
Jacqueline Cole: `jmc61 [at] cam.ac.uk`
## Citation
BatteryBERT: A Pre-trained Language Model for Battery Database Enhancement | 1,460 |
DoyyingFace/bert-asian-hate-tweets-self-unclean-focus_epoch5 | null | Entry not found | 15 |
DoyyingFace/bert-asian-hate-tweets-self-unclean-with-asian | null | Entry not found | 15 |
DoyyingFace/bert-asian-hate-tweets-self-unclean-with-asian-epoch5 | null | Entry not found | 15 |
Narshion/mWACH_mBERT_System | null | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: model
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. -->
# model
This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on mWACH NEO dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6344
## 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: 5.0
### Training results
### Framework versions
- Transformers 4.12.4
- Pytorch 1.10.0+cu111
- Datasets 1.15.1
- Tokenizers 0.10.3
| 1,101 |
Someshfengde/autonlp-kaggledays-625717986 | [
"association",
"disagreement",
"unbiased"
] | ---
tags: autonlp
language: en
widget:
- text: "I love AutoNLP 🤗"
datasets:
- Someshfengde/autonlp-data-kaggledays
co2_eq_emissions: 68.73074770596023
---
# Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 625717986
- CO2 Emissions (in grams): 68.73074770596023
## Validation Metrics
- Loss: 0.859463632106781
- Accuracy: 0.6118427330852181
- Macro F1: 0.6112554383858383
- Micro F1: 0.6118427330852181
- Weighted F1: 0.6112706859556324
- Macro Precision: 0.6121119616189625
- Micro Precision: 0.6118427330852181
- Weighted Precision: 0.6121068719118146
- Macro Recall: 0.6118067898609261
- Micro Recall: 0.6118427330852181
- Weighted Recall: 0.6118427330852181
## 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 AutoNLP"}' https://api-inference.huggingface.co/models/Someshfengde/autonlp-kaggledays-625717986
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("Someshfengde/autonlp-kaggledays-625717986", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("Someshfengde/autonlp-kaggledays-625717986", use_auth_token=True)
inputs = tokenizer("I love AutoNLP", return_tensors="pt")
outputs = model(**inputs)
``` | 1,391 |
keerthisaran/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: 0.92
- name: F1
type: f1
value: 0.920435758296201
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2183
- Accuracy: 0.92
- F1: 0.9204
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8464 | 1.0 | 250 | 0.3125 | 0.9085 | 0.9061 |
| 0.2476 | 2.0 | 500 | 0.2183 | 0.92 | 0.9204 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu111
- Datasets 1.16.1
- Tokenizers 0.10.3
| 1,802 |
dennishauser/distilbert-base-uncased-finetuned-emotion | [
"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_28",
"LABEL_29",... | ---
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: 1.2128
- Accuracy: 0.7597
- F1: 0.6574
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 1.3846 | 1.0 | 243 | 1.2627 | 0.7598 | 0.6561 |
| 1.0463 | 2.0 | 486 | 1.2128 | 0.7597 | 0.6574 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.2+cu102
- Datasets 2.0.0
- Tokenizers 0.11.6
| 1,504 |
swetava/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: 0.924792312369614
---
<!-- 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: 0.2259
- Accuracy: 0.9245
- F1: 0.9248
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8432 | 1.0 | 250 | 0.3353 | 0.8975 | 0.8939 |
| 0.2571 | 2.0 | 500 | 0.2259 | 0.9245 | 0.9248 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu111
- Datasets 1.16.1
- Tokenizers 0.10.3
| 1,806 |
Ketzu/koelectra-sts-v0.5 | [
"LABEL_0"
] | ---
tags:
- generated_from_trainer
metrics:
- spearmanr
model-index:
- name: koelectra-sts-v0.5
results:
- task:
name: Text Classification
type: text-classification
metrics:
- name: Spearmanr
type: spearmanr
value: 0.87026647480689
---
<!-- 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. -->
# koelectra-sts-v0.5
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0213
- Pearson: 0.9958
- Spearmanr: 0.8703
## 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 | Pearson | Spearmanr |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:---------:|
| 0.058 | 1.0 | 6250 | 0.0428 | 0.9915 | 0.8702 |
| 0.0433 | 2.0 | 12500 | 0.0448 | 0.9911 | 0.8685 |
| 0.0362 | 3.0 | 18750 | 0.0261 | 0.9950 | 0.8705 |
| 0.0107 | 4.0 | 25000 | 0.0234 | 0.9953 | 0.8702 |
| 0.0075 | 5.0 | 31250 | 0.0213 | 0.9958 | 0.8703 |
### Framework versions
- Transformers 4.10.0
- Pytorch 1.10.1+cu113
- Datasets 1.17.0
- Tokenizers 0.10.3
| 1,827 |
EALeon16/results | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
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 [BSC-TeMU/roberta-base-bne](https://huggingface.co/BSC-TeMU/roberta-base-bne) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9229
- Accuracy: 0.7586
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.9119 | 1.0 | 258 | 0.8750 | 0.7241 |
| 0.8307 | 2.0 | 516 | 0.9229 | 0.7586 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.11.6
| 1,384 |
loulou/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: 0.9221931901873676
---
<!-- 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: 0.2285
- Accuracy: 0.922
- F1: 0.9222
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8366 | 1.0 | 250 | 0.3212 | 0.9025 | 0.8990 |
| 0.2588 | 2.0 | 500 | 0.2285 | 0.922 | 0.9222 |
### Framework versions
- Transformers 4.18.0.dev0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.11.6
| 1,809 |
saattrupdan/job-listing-relevance-model | [
"LABEL_0"
] | ---
license: mit
tags:
- generated_from_trainer
model-index:
- name: job-listing-relevance-model
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. -->
# job-listing-relevance-model
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1649
## 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
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- 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 |
|:-------------:|:-----:|:----:|:---------------:|
| 0.7435 | 0.43 | 50 | 0.6889 |
| 0.3222 | 0.87 | 100 | 0.2906 |
| 0.2573 | 1.3 | 150 | 0.1937 |
| 0.1205 | 1.74 | 200 | 0.1411 |
| 0.1586 | 2.17 | 250 | 0.2008 |
| 0.0755 | 2.61 | 300 | 0.1926 |
| 0.062 | 3.04 | 350 | 0.2257 |
| 0.0644 | 3.48 | 400 | 0.1497 |
| 0.1034 | 3.91 | 450 | 0.1561 |
| 0.008 | 4.35 | 500 | 0.2067 |
| 0.0616 | 4.78 | 550 | 0.2067 |
| 0.0766 | 5.22 | 600 | 0.1494 |
| 0.0029 | 5.65 | 650 | 0.2078 |
| 0.1076 | 6.09 | 700 | 0.1669 |
| 0.0025 | 6.52 | 750 | 0.1564 |
| 0.0498 | 6.95 | 800 | 0.2355 |
| 0.0011 | 7.39 | 850 | 0.1652 |
| 0.0271 | 7.82 | 900 | 0.1731 |
| 0.012 | 8.26 | 950 | 0.1590 |
| 0.0257 | 8.69 | 1000 | 0.1638 |
| 0.0009 | 9.13 | 1050 | 0.1851 |
| 0.0013 | 9.56 | 1100 | 0.1613 |
| 0.0015 | 10.0 | 1150 | 0.1649 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.11.0+cu113
- Datasets 2.0.0
- Tokenizers 0.11.6
| 2,448 |
krishnayogik/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: 0.9247696388302888
---
<!-- 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: 0.2258
- Accuracy: 0.9245
- F1: 0.9248
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8359 | 1.0 | 250 | 0.3316 | 0.901 | 0.8967 |
| 0.2584 | 2.0 | 500 | 0.2258 | 0.9245 | 0.9248 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu111
- Datasets 1.16.1
- Tokenizers 0.10.3
| 1,807 |
ScandinavianMrT/distilbert_ONION_1epoch_2.0 | null | Entry not found | 15 |
Supreeth/BioBERT | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | Entry not found | 15 |
hackathon-pln-es/electricidad-small-discriminator-finetuned-clasificacion-comentarios-suicidas | null | ---
license: apache-2.0
language: "es"
tags:
- generated_from_trainer
- sentiment
- emotion
- suicide
- depresión
- suicidio
- español
- es
- spanish
- depression
widget:
- text: "La vida no merece la pena"
example_title: "Ejemplo 1"
- text: "Para vivir así lo mejor es estar muerto"
example_title: "Ejemplo 2"
- text: "me siento triste por no poder viajar"
example_title: "Ejemplo 3"
- text: "Quiero terminar con todo"
example_title: "Ejemplo 4"
- text: "Disfruto de la vista"
example_title: "Ejemplo 5"
metrics:
- accuracy
model-index:
- name: electricidad-small-discriminator-finetuned-clasificacion-comentarios-suicidas
results: []
---
# electricidad-small-discriminator-finetuned-clasificacion-comentarios-suicidas
El presente modelo se encentra basado en una versión mejorada de [mrm8488/electricidad-small-discriminator](https://huggingface.co/mrm8488/electricidad-small-discriminator), y con el uso de la base de datos [hackathon-pln-es/comentarios_depresivos](https://huggingface.co/datasets/hackathon-pln-es/comentarios_depresivos).
Siendo de esta manera los resultados obtenidos en la evaluación del modelo:
- Pérdida 0.0458
- Precisión: 0.9916
## Autores
- Danny Vásquez
- César Salazar
- Alexis Cañar
- Yannela Castro
- Daniel Patiño
## Descripción del Modelo
electricidad-small-discriminator-finetuned-clasificacion-comentarios-suicidas es un modelo Transformers pre-entrenado bajo un largo corpus de comentarios obtenidos de REDDIT traducidos al español, con el fin de poder predecir si un comentario tiene una tendencia suicida en base al contexto. Por ende, recibirá una ENTRADA en la cuál se ingresará el texto a comprobar, para posteriormente obtener como única SALIDA de igual manera dos posibles opciones: “Suicida” o “No Suicida”.
## Motivación
Siendo la principal inspiración del modelo que sea utilizado para futuros proyectos que busquen detectar los casos de depresión a tiempo mediante el procesamiento del lenguaje natural, para poder prevenir los casos de suicido en niños, jóvenes y adultos.
## ¿Cómo usarlo?
El modelo puede ser utilizado de manera directa mediante la importación de la librería pipeline de transformers:
```python
>>> from transformers import pipeline
>>> model_name= 'hackathon-pln-es/electricidad-small-discriminator-finetuned-clasificacion-comentarios-suicidas'
>>> cls= pipeline("text-classification", model=model_name)
>>> cls(“Estoy feliz”)[0]['label']
[{'resultado': "No Suicida"
}]
>>> cls(“Quiero acabar con todo”)[0]['label']
[{'resultado': " Suicida"
}]
```
## Proceso de entrenamiento
### Datos de entrenamiento
Como se declaró anteriormente, el modelo se pre-entrenó basándose en la base de datos [comentarios_depresivos]( https://huggingface.co/datasets/hackathon-pln-es/comentarios_depresivos), el cuál posee una cantidad de 192 347 filas de datos para el entrenamiento, 33 944 para las pruebas y 22630 para la validación.
### Hiper parámetros de entrenamiento
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- lr_scheduler_type: linear
- num_epochs: 15
### Resultados del entrenamiento
| Pérdida_entrenamiento | Epoch | Pérdida_Validación | Presición |
|:-------------:|:-----:|:---------------:|:--------:|
| 0.161100 | 1.0 | 0.133057 | 0.952718 |
| 0.134500 | 2.0 | 0.110966 | 0.960804 |
| 0.108500 | 3.0 | 0.086417 | 0.970835 |
| 0.099400 | 4.0 | 0.073618 | 0.974856 |
| 0.090500 | 5.0 | 0.065231 | 0.979629 |
| 0.080700 | 6.0 | 0.060849 | 0.982324 |
| 0.069200 | 7.0 | 0.054718 | 0.986125 |
| 0.060400 | 8.0 | 0.051153 | 0.985948 |
| 0.048200 | 9.0 | 0.045747 | 0.989748 |
| 0.045500 | 10.0 | 0.049992 | 0.988069 |
| 0.043400 | 11.0 | 0.046325 | 0.990234 |
| 0.034300 | 12.0 | 0.050746 | 0.989792 |
| 0.032900 | 13.0 | 0.043434 | 0.991737 |
| 0.028400 | 14.0 | 0.045003 | 0.991869 |
| 0.022300 | 15.0 | 0.045819 | 0.991648 |
### Versiones del Framework
- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.11.6
## Citación BibTeX
```bibtex
@article{ccs_2022,
author = {Danny Vásquez and
César Salazar and
Alexis Cañar and
Yannela Castro and
Daniel Patiño},
title = {Modelo Electricidad-small-discriminator-finetuned-clasificacion-comentarios-suicidas},
journal = {Huggingface},
year = {2022},
}
```
<h3>Visualizar en GRADIO:</h3>
<a href="https://huggingface.co/spaces/hackathon-pln-es/clasificador-comentarios-suicidas">
<img width="300px" src="https://hf.space/embed/hackathon-pln-es/clasificador-comentarios-suicidas/static/img/logo.svg">
</a>
---
| 4,937 |
lkm2835/distilbert-imdb | [
"neg",
"pos"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
model-index:
- name: distilbert-imdb
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-imdb
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb 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: 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: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 391 | 0.1849 | 0.9281 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.11.0+cu113
- Datasets 2.0.0
- Tokenizers 0.11.6
| 1,240 |
princeton-nlp/CoFi-SST2-s95 | 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 95% 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 |
Cheatham/xlm-roberta-large-finetuned-d1-001 | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | Entry not found | 15 |
Tahsin-Mayeesha/distilbert-finetuned-fakenews | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: distilbert-finetuned-fakenews
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-finetuned-fakenews
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: 0.0049
- Accuracy: 0.9995
- F1: 0.9995
## 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 | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.0392 | 1.0 | 500 | 0.0059 | 0.999 | 0.999 |
| 0.002 | 2.0 | 1000 | 0.0047 | 0.9995 | 0.9995 |
| 0.0001 | 3.0 | 1500 | 0.0047 | 0.9995 | 0.9995 |
| 0.0001 | 4.0 | 2000 | 0.0049 | 0.9995 | 0.9995 |
| 0.0 | 5.0 | 2500 | 0.0049 | 0.9995 | 0.9995 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.12.0
| 1,691 |
AnnaBabaie/ms-marco-MiniLM-L-12-v2-news | [
"LABEL_0"
] | This model is fined tuned for the Fake news classifier: Train a text classification model to detect fake news articles. Base on the Kaggle dataset(https://www.kaggle.com/clmentbisaillon/fake-and-real-news-dataset).
| 215 |
Sleoruiz/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: 0.5396261051709696
---
<!-- 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: 0.7663
- Matthews Correlation: 0.5396
## 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 | Matthews Correlation |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.5281 | 1.0 | 535 | 0.5268 | 0.4071 |
| 0.3503 | 2.0 | 1070 | 0.5074 | 0.5126 |
| 0.2399 | 3.0 | 1605 | 0.6440 | 0.4977 |
| 0.1807 | 4.0 | 2140 | 0.7663 | 0.5396 |
| 0.1299 | 5.0 | 2675 | 0.8786 | 0.5192 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.11.6
| 1,999 |
yj2773/hinglish11k-sentiment-analysis | [
"Positive",
"Neutral",
"Negative"
] | ---
license: afl-3.0
language:
- en
- ur
- hi
widget:
- text: "Tum bohot badiya ho."
---
## Hinglish-Bert-Class fine-tuned on Hinglish11K dataset.
# MCC= 0.69
### Citation info
```bibtex
@model{
contributors= {Mohammad Yusuf Jamal Aziz Azmi and
Ayush Aggarwal
},
year = {2022},
timestamp = {Sun, 08 May 2022},
}
``` | 361 |
palakagl/Roberta_Multiclass_TextClassification | [
"alarm_query",
"alarm_remove",
"alarm_set",
"audio_volume_down",
"audio_volume_mute",
"audio_volume_up",
"calendar_query",
"calendar_remove",
"calendar_set",
"cooking_recipe",
"datetime_convert",
"datetime_query",
"email_addcontact",
"email_query",
"email_querycontact",
"email_sendemai... | ---
tags: autotrain
language: en
widget:
- text: "I love AutoTrain 🤗"
datasets:
- palakagl/autotrain-data-PersonalAssitant
co2_eq_emissions: 0.014567637985425905
---
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 717221783
- CO2 Emissions (in grams): 0.014567637985425905
## Validation Metrics
- Loss: 0.38848456740379333
- Accuracy: 0.9180509413067552
- Macro F1: 0.9157418163085091
- Micro F1: 0.9180509413067552
- Weighted F1: 0.9185290137253468
- Macro Precision: 0.9189981206383326
- Micro Precision: 0.9180509413067552
- Weighted Precision: 0.9221607328493303
- Macro Recall: 0.9158232837734661
- Micro Recall: 0.9180509413067552
- Weighted Recall: 0.9180509413067552
## 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/palakagl/autotrain-PersonalAssitant-717221783
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("palakagl/autotrain-PersonalAssitant-717221783", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("palakagl/autotrain-PersonalAssitant-717221783", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
``` | 1,425 |
palakagl/bert_TextClassification | [
"alarm_query",
"alarm_remove",
"alarm_set",
"audio_volume_down",
"audio_volume_mute",
"audio_volume_up",
"calendar_query",
"calendar_remove",
"calendar_set",
"cooking_recipe",
"datetime_convert",
"datetime_query",
"email_addcontact",
"email_query",
"email_querycontact",
"email_sendemai... | ---
tags: autotrain
language: en
widget:
- text: "I love AutoTrain 🤗"
datasets:
- palakagl/autotrain-data-PersonalAssitant
co2_eq_emissions: 7.025108874009706
---
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 717221787
- CO2 Emissions (in grams): 7.025108874009706
## Validation Metrics
- Loss: 0.35467109084129333
- Accuracy: 0.9186046511627907
- Macro F1: 0.9202890631142154
- Micro F1: 0.9186046511627907
- Weighted F1: 0.9185859051606837
- Macro Precision: 0.921802482563032
- Micro Precision: 0.9186046511627907
- Weighted Precision: 0.9210238644296779
- Macro Recall: 0.9218155764486292
- Micro Recall: 0.9186046511627907
- Weighted Recall: 0.9186046511627907
## 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/palakagl/autotrain-PersonalAssitant-717221787
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("palakagl/autotrain-PersonalAssitant-717221787", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("palakagl/autotrain-PersonalAssitant-717221787", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
``` | 1,418 |
dapang/distilbert-base-uncased-finetuned-mic | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-mic
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-mic
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: 0.5640
- Accuracy: 0.7809
- F1: 0.8769
## 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: 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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log | 1.0 | 18 | 0.7080 | 0.7232 | 0.8394 |
| No log | 2.0 | 36 | 0.4768 | 0.8443 | 0.9156 |
| No log | 3.0 | 54 | 0.5714 | 0.7866 | 0.8806 |
| No log | 4.0 | 72 | 0.7035 | 0.7151 | 0.8339 |
| No log | 5.0 | 90 | 0.5640 | 0.7809 | 0.8769 |
### Framework versions
- Transformers 4.18.0.dev0
- Pytorch 1.10.1
- Datasets 2.0.0
- Tokenizers 0.11.0
| 1,726 |
JminJ/tunibElectra_base_Bad_Sentence_Classifier | [
"bad_sen",
"ok_sen"
] | # Bad_text_classifier
## Model 소개
인터넷 상에 퍼져있는 여러 댓글, 채팅이 민감한 내용인지 아닌지를 판별하는 모델을 공개합니다. 해당 모델은 공개데이터를 사용해 label을 수정하고 데이터들을 합쳐 구성해 finetuning을 진행하였습니다. 해당 모델이 언제나 모든 문장을 정확히 판단이 가능한 것은 아니라는 점 양해해 주시면 감사드리겠습니다.
```
NOTE)
공개 데이터의 저작권 문제로 인해 모델 학습에 사용된 변형된 데이터는 공개 불가능하다는 점을 밝힙니다.
또한 해당 모델의 의견은 제 의견과 무관하다는 점을 미리 밝힙니다.
```
## Dataset
### data label
* **0 : bad sentence**
* **1 : not bad sentence**
### 사용한 dataset
* [smilegate-ai/Korean Unsmile Dataset](https://github.com/smilegate-ai/korean_unsmile_dataset)
* [kocohub/Korean HateSpeech Dataset](https://github.com/kocohub/korean-hate-speech)
### dataset 가공 방법
기존 이진 분류가 아니였던 두 데이터를 이진 분류 형태로 labeling을 다시 해준 뒤, Korean HateSpeech Dataset중 label 1(not bad sentence)만을 추려 가공된 Korean Unsmile Dataset에 합쳐 주었습니다.
</br>
**Korean Unsmile Dataset에 clean으로 labeling 되어있던 데이터 중 몇개의 데이터를 0 (bad sentence)으로 수정하였습니다.**
* "~노"가 포함된 문장 중, "이기", "노무"가 포함된 데이터는 0 (bad sentence)으로 수정
* "좆", "봊" 등 성 관련 뉘앙스가 포함된 데이터는 0 (bad sentence)으로 수정
</br>
## Model Training
* huggingface transformers의 ElectraForSequenceClassification를 사용해 finetuning을 수행하였습니다.
* 한국어 공개 Electra 모델 중 3가지 모델을 사용해 각각 학습시켜주었습니다.
### use model
* [Beomi/KcELECTRA](https://github.com/Beomi/KcELECTRA)
* [monologg/koELECTRA](https://github.com/monologg/KoELECTRA)
* [tunib/electra-ko-base](https://huggingface.co/tunib/electra-ko-base)
## How to use model?
```PYTHON
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained('JminJ/tunibElectra_base_Bad_Sentence_Classifier')
tokenizer = AutoTokenizer.from_pretrained('JminJ/tunibElectra_base_Bad_Sentence_Classifier')
```
## Model Valid Accuracy
| mdoel | accuracy |
| ---------- | ---------- |
| kcElectra_base_fp16_wd_custom_dataset | 0.8849 |
| tunibElectra_base_fp16_wd_custom_dataset | 0.8726 |
| koElectra_base_fp16_wd_custom_dataset | 0.8434 |
```
Note)
모든 모델은 동일한 seed, learning_rate(3e-06), weight_decay lambda(0.001), batch_size(128)로 학습되었습니다.
```
## Contact
* jminju254@gmail.com
</br></br>
## Github
* https://github.com/JminJ/Bad_text_classifier
</br></br>
## Reference
* [Beomi/KcELECTRA](https://github.com/Beomi/KcELECTRA)
* [monologg/koELECTRA](https://github.com/monologg/KoELECTRA)
* [tunib/electra-ko-base](https://huggingface.co/tunib/electra-ko-base)
* [smilegate-ai/Korean Unsmile Dataset](https://github.com/smilegate-ai/korean_unsmile_dataset)
* [kocohub/Korean HateSpeech Dataset](https://github.com/kocohub/korean-hate-speech)
* [ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators](https://arxiv.org/abs/2003.10555)
| 2,604 |
SiriusRen/my-awesome-model2 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: my-awesome-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-awesome-model2
This model is a fine-tuned version of [SiriusRen/my-awesome-model](https://huggingface.co/SiriusRen/my-awesome-model) 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
| 1,051 |
flood/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:
- 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: F1
type: f1
value: 0.9334621346059612
---
<!-- 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: 0.1698
- Accuracy : 0.933
- F1: 0.9335
## 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: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|
| 0.6265 | 1.0 | 500 | 0.2137 | 0.926 | 0.9256 |
| 0.1795 | 2.0 | 1000 | 0.1698 | 0.933 | 0.9335 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu102
- Datasets 2.2.2
- Tokenizers 0.12.1
| 1,737 |
brad1141/oldData_BERT | [
"LABEL_0",
"LABEL_1",
"LABEL_2",
"LABEL_3",
"LABEL_4",
"LABEL_5",
"LABEL_6",
"LABEL_7"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: oldData_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. -->
# oldData_BERT
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: 0.0616
## 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 |
|:-------------:|:-----:|:----:|:---------------:|
| 1.2348 | 1.0 | 1125 | 1.0185 |
| 1.0082 | 2.0 | 2250 | 0.7174 |
| 0.699 | 3.0 | 3375 | 0.3657 |
| 0.45 | 4.0 | 4500 | 0.1880 |
| 0.2915 | 5.0 | 5625 | 0.1140 |
| 0.2056 | 6.0 | 6750 | 0.0708 |
| 0.1312 | 7.0 | 7875 | 0.0616 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.10.0+cu111
- Datasets 2.1.0
- Tokenizers 0.12.1
| 1,605 |
GioReg/bertdbmdzIhate | null | ---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: bertdbmdzIhate
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. -->
# bertdbmdzIhate
This model is a fine-tuned version of [dbmdz/bert-base-italian-xxl-cased](https://huggingface.co/dbmdz/bert-base-italian-xxl-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6880
- Accuracy: 0.726
- F1: 0.4170
## 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
| 1,176 |
MartinoMensio/racism-models-regression-w-m-vote-epoch-2 | [
"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-2`
### 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-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 = 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': 0.8367272}, {'score': 0.4402479}]
# or also specify a threshold to cut racist/non-racist
print(pipe(texts, regression_threshold=0.9))
# [{'label': 'non-racist', 'score': 0.8367272}, {'label': 'non-racist', 'score': 0.4402479}]
```
For more details, see https://github.com/preyero/neatclass22
| 6,362 |
paulagarciaserrano/roberta-depression-detection | [
"not depression",
"moderate",
"severe"
] | ---
language: "en"
datasets:
- Shared task on Detecting Signs of Depression from Social Media Text at LT-EDI 2022-ACL 2022
metrics:
- Macro F1-Score
---
# Roberta for depression signs detection
This model is a fine-tuned version the <a href="https://huggingface.co/cardiffnlp/twitter-roberta-base">cardiffnlp/twitter-roberta-base</a> model. It has been trained using a recently published corpus: <a href="https://competitions.codalab.org/competitions/36410#learn_the_details">Shared task on Detecting Signs of Depression from Social Media Text at LT-EDI 2022-ACL 2022</a>.
The obtained macro f1-score is 0.54, on the development set of the competition.
# Intended uses
This model is trained to classify the given text into one of the following classes: *moderate*, *severe*, or *not depression*.
It corresponds to a **multiclass classification** task.
# How to use
You can use this model directly with a pipeline for text classification:
```python
>>> from transformers import pipeline
>>> classifier = pipeline("text-classification", model="paulagarciaserrano/roberta-depression-detection")
>>> your_text = "I am very sad."
>>> classifier (your_text)
```
# Training and evaluation data
The **train** dataset characteristics are:
<table>
<tr>
<th>Class</th>
<th>Nº sentences</th>
<th>Avg. document length (in sentences)</th>
<th>Nº words</th>
<th>Avg. sentence length (in words)</th>
</tr>
<tr>
<th>not depression</th>
<td>7,884</td>
<td>4</td>
<td>153,738</td>
<td>78</td>
</tr>
<tr>
<th>moderate</th>
<td>36,114</td>
<td>6</td>
<td>601,900</td>
<td>100</td>
</tr>
<tr>
<th>severe</th>
<td>9,911</td>
<td>11</td>
<td>126,140</td>
<td>140</td>
</tr>
</table>
Similarly, the **evaluation** dataset characteristics are:
<table>
<tr>
<th>Class</th>
<th>Nº sentences</th>
<th>Avg. document length (in sentences)</th>
<th>Nº words</th>
<th>Avg. sentence length (in words)</th>
</tr>
<tr>
<th>not depression</th>
<td>3,660</td>
<td>2</td>
<td>10,980</td>
<td>6</td>
</tr>
<tr>
<th>moderate</th>
<td>66,874</td>
<td>29</td>
<td>804,794</td>
<td>349</td>
</tr>
<tr>
<th>severe</th>
<td>2,880</td>
<td>8</td>
<td>75,240</td>
<td>209</td>
</tr>
</table>
# Training hyperparameters
The following hyperparameters were used during training:
* learning_rate: 2e-05
* evaluation_strategy: epoch
* save_strategy: epoch
* per_device_train_batch_size: 8
* per_device_eval_batch_size: 8
* num_train_epochs: 5
* seed: 10
* weight_decay: 0.01
* metric_for_best_model: macro-f1 | 2,661 |
Raychanan/bert-bert-cased-first512-Conflict | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- f1
- accuracy
- precision
- recall
model-index:
- name: bert-bert-cased-first512-Conflict
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-bert-cased-first512-Conflict
`conv_text = '\n'.join([utt.text for utt in conv.get_chronological_utterance_list()])`
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6932
- F1: 0.6667
- Accuracy: 0.5
- Precision: 0.5
- 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 | Accuracy | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:------:|:--------:|:---------:|:------:|
| 0.7098 | 1.0 | 685 | 0.6945 | 0.0 | 0.5 | 0.0 | 0.0 |
| 0.7046 | 2.0 | 1370 | 0.6997 | 0.6667 | 0.5 | 0.5 | 1.0 |
| 0.7013 | 3.0 | 2055 | 0.6949 | 0.6667 | 0.5 | 0.5 | 1.0 |
| 0.7027 | 4.0 | 2740 | 0.6931 | 0.6667 | 0.5 | 0.5 | 1.0 |
| 0.702 | 5.0 | 3425 | 0.6932 | 0.6667 | 0.5 | 0.5 | 1.0 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.10.0+cu111
- Datasets 2.1.0
- Tokenizers 0.12.1
| 1,968 |
Raychanan/bert-bert-cased-first512-Conflict-SEP | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- f1
- accuracy
- precision
- recall
model-index:
- name: bert-bert-cased-first512-Conflict-SEP
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-bert-cased-first512-Conflict-SEP
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6806
- F1: 0.6088
- Accuracy: 0.5914
- Precision: 0.5839
- Recall: 0.6360
## 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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 | Accuracy | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:------:|:--------:|:---------:|:------:|
| 0.7027 | 1.0 | 685 | 0.6956 | 0.6018 | 0.5365 | 0.5275 | 0.7003 |
| 0.7009 | 2.0 | 1370 | 0.6986 | 0.6667 | 0.5 | 0.5 | 1.0 |
| 0.7052 | 3.0 | 2055 | 0.6983 | 0.6667 | 0.5 | 0.5 | 1.0 |
| 0.6987 | 4.0 | 2740 | 0.6830 | 0.5235 | 0.5636 | 0.5764 | 0.4795 |
| 0.6761 | 5.0 | 3425 | 0.6806 | 0.6088 | 0.5914 | 0.5839 | 0.6360 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.10.0+cu111
- Datasets 2.1.0
- Tokenizers 0.12.1
| 1,896 |
nbhimte/tiny-bert-mnli-distilled | [
"contradiction",
"entailment",
"neutral"
] | ---
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: tiny-bert-mnli-distilled
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
args: mnli
metrics:
- name: Accuracy
type: accuracy
value: 0.5818644931227712
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# tiny-bert-mnli-distilled
It achieves the following results on the evaluation set:
- Loss: 1.5018
- Accuracy: 0.5819
- F1 score: 0.5782
- Precision score: 0.6036
- Metric recall: 0.5819
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 64
- eval_batch_size: 32
- seed: 33
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 score | Precision score | Metric recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:---------------:|:-------------:|
| 1.4475 | 1.0 | 614 | 1.4296 | 0.4521 | 0.4070 | 0.5621 | 0.4521 |
| 1.3354 | 2.0 | 1228 | 1.4320 | 0.4805 | 0.4579 | 0.5276 | 0.4805 |
| 1.2244 | 3.0 | 1842 | 1.4786 | 0.5699 | 0.5602 | 0.5865 | 0.5699 |
| 1.1416 | 4.0 | 2456 | 1.5018 | 0.5819 | 0.5782 | 0.6036 | 0.5819 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.9.1
- Datasets 2.1.0
- Tokenizers 0.11.6
| 1,996 |
ardallie/distilbert-base-uncased-finetuned-emotion | [
"LABEL_0",
"LABEL_1",
"LABEL_2",
"LABEL_3",
"LABEL_4",
"LABEL_5"
] | Entry not found | 15 |
Jeevesh8/feather_berts_21 | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | Entry not found | 15 |
Jeevesh8/feather_berts_35 | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | Entry not found | 15 |
Jeevesh8/feather_berts_73 | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | Entry not found | 15 |
thanawan/bert-base-uncased-finetuned-humordetection | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: bert-base-uncased-finetuned-humordetection
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-humordetection
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: 0.3136
- F1: 0.9586
## 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 | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 1.0 | 375 | 0.1768 | 0.9507 |
| 0.2266 | 2.0 | 750 | 0.1910 | 0.9553 |
| 0.08 | 3.0 | 1125 | 0.2822 | 0.9529 |
| 0.0194 | 4.0 | 1500 | 0.2989 | 0.9560 |
| 0.0194 | 5.0 | 1875 | 0.3136 | 0.9586 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.10.0+cu111
- Datasets 2.1.0
- Tokenizers 0.12.1
| 1,600 |
Raychanan/bert-base-cased-last500-SEP | null | Entry not found | 15 |
Intel/bert-base-uncased-mrpc-int8-dynamic | [
"0",
"1"
] | ---
language: en
license: apache-2.0
tags:
- text-classfication
- int8
- Intel® Neural Compressor
- PostTrainingDynamic
datasets:
- mrpc
metrics:
- f1
---
# INT8 BERT base uncased finetuned MRPC
### Post-training dynamic quantization
This is an INT8 PyTorch model quantized with [Intel® Neural Compressor](https://github.com/intel/neural-compressor).
The original fp32 model comes from the fine-tuned model [Intel/bert-base-uncased-mrpc](https://huggingface.co/Intel/bert-base-uncased-mrpc).
### Test result
| |INT8|FP32|
|---|:---:|:---:|
| **Accuracy (eval-f1)** |0.8997|0.9042|
| **Model size (MB)** |174|418|
### Load with Intel® Neural Compressor:
```python
from neural_compressor.utils.load_huggingface import OptimizedModel
int8_model = OptimizedModel.from_pretrained(
'Intel/bert-base-uncased-mrpc-int8-dynamic',
)
```
| 845 |
dapang/distilroberta-base-mrl | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: distilroberta-base-mrl
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
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: 0.0170
- Accuracy: 0.9967
- F1: 0.9967
## 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: 2.1821851463909416e-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: 9
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log | 1.0 | 48 | 0.0265 | 0.9946 | 0.9946 |
| No log | 2.0 | 96 | 0.0180 | 0.9962 | 0.9962 |
| No log | 3.0 | 144 | 0.0163 | 0.9962 | 0.9962 |
| No log | 4.0 | 192 | 0.0194 | 0.9946 | 0.9946 |
| No log | 5.0 | 240 | 0.0193 | 0.9942 | 0.9942 |
| No log | 6.0 | 288 | 0.0172 | 0.9967 | 0.9967 |
| No log | 7.0 | 336 | 0.0206 | 0.9954 | 0.9954 |
| No log | 8.0 | 384 | 0.0183 | 0.9962 | 0.9962 |
| No log | 9.0 | 432 | 0.0170 | 0.9967 | 0.9967 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0+cu113
- Datasets 2.1.0
- Tokenizers 0.12.1
| 1,972 |
dapang/distilroberta-base-mic | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: distilroberta-base-mic
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
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: 0.3435
- Accuracy: 0.9104
- F1: 0.9103
## 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: 8.748413056668156e-05
- train_batch_size: 200
- eval_batch_size: 200
- 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 | 120 | 0.2830 | 0.8804 | 0.8797 |
| No log | 2.0 | 240 | 0.2398 | 0.9046 | 0.9046 |
| No log | 3.0 | 360 | 0.3474 | 0.8959 | 0.8954 |
| No log | 4.0 | 480 | 0.3435 | 0.9104 | 0.9103 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0+cu113
- Datasets 2.1.0
- Tokenizers 0.12.1
| 1,616 |
mrosinski/distilbert-base-uncased-finetuned-emotion | [
"sadness",
"joy",
"love",
"anger",
"fear",
"surprise"
] | ---
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: 0.923306902377617
---
<!-- 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: 0.2317
- Accuracy: 0.923
- F1: 0.9233
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8669 | 1.0 | 250 | 0.3344 | 0.9025 | 0.9004 |
| 0.2607 | 2.0 | 500 | 0.2317 | 0.923 | 0.9233 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu111
- Datasets 1.16.1
- Tokenizers 0.10.3
| 1,804 |
avacaondata/maria-exist22-task1 | null | Entry not found | 15 |
Hate-speech-CNERG/kannada-codemixed-abusive-MuRIL | null | ---
language: ka-en
license: afl-3.0
---
This model is used to detect **abusive speech** in **Code-Mixed Kannada**. It is finetuned on MuRIL model using Code-Mixed Kannada abusive speech dataset.
The model is trained with learning rates of 2e-5. Training code can be found at this [url](https://github.com/hate-alert/IndicAbusive)
LABEL_0 :-> Normal
LABEL_1 :-> Abusive
### For more details about our paper
Mithun Das, Somnath Banerjee and Animesh Mukherjee. "[Data Bootstrapping Approaches to Improve Low Resource Abusive Language Detection for Indic Languages](https://arxiv.org/abs/2204.12543)". Accepted at ACM HT 2022.
***Please cite our paper in any published work that uses any of these resources.***
~~~
@article{das2022data,
title={Data Bootstrapping Approaches to Improve Low Resource Abusive Language Detection for Indic Languages},
author={Das, Mithun and Banerjee, Somnath and Mukherjee, Animesh},
journal={arXiv preprint arXiv:2204.12543},
year={2022}
}
~~~ | 986 |
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