modelId stringlengths 6 107 | label list | readme stringlengths 0 56.2k | readme_len int64 0 56.2k |
|---|---|---|---|
dweb/deberta-base-CoLA | null | ---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: deberta-base-CoLA
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# deberta-base-CoLA
This model is a fine-tuned version of [microsoft/deberta-base](https://huggingface.co/microsoft/deberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1655
- Accuracy: 0.8482
- F1: 0.8961
- Roc Auc: 0.8987
- Mcc: 0.6288
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Roc Auc | Mcc |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:-------:|:------:|
| 0.5266 | 1.0 | 535 | 0.4138 | 0.8159 | 0.8698 | 0.8627 | 0.5576 |
| 0.3523 | 2.0 | 1070 | 0.3852 | 0.8387 | 0.8880 | 0.9041 | 0.6070 |
| 0.2479 | 3.0 | 1605 | 0.3981 | 0.8482 | 0.8901 | 0.9120 | 0.6447 |
| 0.1712 | 4.0 | 2140 | 0.4732 | 0.8558 | 0.9008 | 0.9160 | 0.6486 |
| 0.1354 | 5.0 | 2675 | 0.7181 | 0.8463 | 0.8938 | 0.9024 | 0.6250 |
| 0.0876 | 6.0 | 3210 | 0.8453 | 0.8520 | 0.8992 | 0.9123 | 0.6385 |
| 0.0682 | 7.0 | 3745 | 1.0282 | 0.8444 | 0.8938 | 0.9061 | 0.6189 |
| 0.0431 | 8.0 | 4280 | 1.1114 | 0.8463 | 0.8960 | 0.9010 | 0.6239 |
| 0.0323 | 9.0 | 4815 | 1.1663 | 0.8501 | 0.8970 | 0.8967 | 0.6340 |
| 0.0163 | 10.0 | 5350 | 1.1655 | 0.8482 | 0.8961 | 0.8987 | 0.6288 |
### Framework versions
- Transformers 4.11.0
- Pytorch 1.9.0+cu102
- Datasets 1.12.1
- Tokenizers 0.10.3
| 2,310 |
edwardgowsmith/pt-finegrained-one-shot | null | Entry not found | 15 |
eliza-dukim/bert-base-finetuned-sts | [
"LABEL_0"
] | ---
tags:
- generated_from_trainer
datasets:
- klue
metrics:
- pearsonr
- f1
model-index:
- name: bert-base-finetuned-sts
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: klue
type: klue
args: sts
metrics:
- name: Pearsonr
type: pearsonr
value: 0.8756147003619346
- name: F1
type: f1
value: 0.8416666666666667
---
<!-- 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-finetuned-sts
This model is a fine-tuned version of [klue/bert-base](https://huggingface.co/klue/bert-base) on the klue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4115
- Pearsonr: 0.8756
- F1: 0.8417
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Pearsonr | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.7836 | 1.0 | 365 | 0.5507 | 0.8435 | 0.8121 |
| 0.1564 | 2.0 | 730 | 0.4396 | 0.8495 | 0.8136 |
| 0.0989 | 3.0 | 1095 | 0.4115 | 0.8756 | 0.8417 |
| 0.0682 | 4.0 | 1460 | 0.4466 | 0.8746 | 0.8449 |
### Framework versions
- Transformers 4.10.2
- Pytorch 1.7.1
- Datasets 1.12.1
- Tokenizers 0.10.3
| 1,898 |
espejelomar/BETO_Clasificar_Tweets_Mexicano | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | Entry not found | 15 |
gchhablani/fnet-large-finetuned-cola | [
"acceptable",
"unacceptable"
] | ---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- matthews_correlation
model-index:
- name: fnet-large-finetuned-cola
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE COLA
type: glue
args: cola
metrics:
- name: Matthews Correlation
type: matthews_correlation
value: 0.0
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# fnet-large-finetuned-cola
This model is a fine-tuned version of [google/fnet-large](https://huggingface.co/google/fnet-large) on the GLUE COLA dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6243
- Matthews Correlation: 0.0
## 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: 1e-05
- train_batch_size: 4
- 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 | Matthews Correlation |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.6195 | 1.0 | 2138 | 0.6527 | 0.0 |
| 0.6168 | 2.0 | 4276 | 0.6259 | 0.0 |
| 0.616 | 3.0 | 6414 | 0.6243 | 0.0 |
### Framework versions
- Transformers 4.11.0.dev0
- Pytorch 1.9.0
- Datasets 1.12.1
- Tokenizers 0.10.3
| 1,819 |
imzachjohnson/autonlp-spinner-check-16492731 | [
"0",
"1"
] | ---
tags: autonlp
language: en
widget:
- text: "I love AutoNLP 🤗"
datasets:
- imzachjohnson/autonlp-data-spinner-check
---
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 16492731
## Validation Metrics
- Loss: 0.21610039472579956
- Accuracy: 0.9155366722657816
- Precision: 0.9530714194995978
- Recall: 0.944871149164778
- AUC: 0.9553238723676906
- F1: 0.9489535692456846
## 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/imzachjohnson/autonlp-spinner-check-16492731
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("imzachjohnson/autonlp-spinner-check-16492731", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("imzachjohnson/autonlp-spinner-check-16492731", use_auth_token=True)
inputs = tokenizer("I love AutoNLP", return_tensors="pt")
outputs = model(**inputs)
``` | 1,107 |
jaesun/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: 0.51728018358102
---
<!-- 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: 0.8815
- Matthews Correlation: 0.5173
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.5272 | 1.0 | 535 | 0.5099 | 0.4093 |
| 0.3563 | 2.0 | 1070 | 0.5114 | 0.5019 |
| 0.2425 | 3.0 | 1605 | 0.6696 | 0.4898 |
| 0.1726 | 4.0 | 2140 | 0.7715 | 0.5123 |
| 0.132 | 5.0 | 2675 | 0.8815 | 0.5173 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.9.1
- Datasets 1.14.0
- Tokenizers 0.10.3
| 1,991 |
kittinan/exercise-feedback-classification | [
"LABEL_0",
"LABEL_1",
"LABEL_2",
"LABEL_3"
] | # Reddit exercise feedback classification
Model to classify Reddit's comments for exercise feedback. Current classes are good, correction, bad posture, not informative. If you want to use it locally,
### Usage:
```py
from transformers import pipeline
classifier = pipeline("text-classification", "kittinan/exercise-feedback-classification")
classifier("search for alan thrall deadlift video he will explain basic ques")
#[{'label': 'correction', 'score': 0.9998193979263306}]
``` | 481 |
lysandre/dum | [
"NEGATIVE",
"POSITIVE"
] | ---
language: en
license: apache-2.0
datasets:
- sst2
---
# Sentiment Analysis
This is a BERT model fine-tuned for sentiment analysis. | 137 |
mmcquade11/reviews-sentiment-analysis | null | Entry not found | 15 |
serdarakyol/interpress-turkish-news-classification | [
"Culture-Art",
"Economy",
"Politics",
"Education",
"World",
"Sport",
"Technology",
"Magazine",
"Health",
"Agenda"
] | ---
language: tr
Dataset: interpress_news_category_tr
---
# INTERPRESS NEWS CLASSIFICATION
## Dataset
The dataset downloaded from interpress. This dataset is real world data. Actually there are 273K data but I filtered them and used 108K data for this model. For more information about dataset please visit this [link](https://huggingface.co/datasets/interpress_news_category_tr_lite)
## Model
Model accuracy on train data and validation data is %97. The data split as %80 train and %20 validation. The results as shown as below
### Classification report
### Confusion matrix
## Usage for Torch
```sh
pip install transformers or pip install transformers==4.3.3
```
```sh
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("serdarakyol/interpress-turkish-news-classification")
model = AutoModelForSequenceClassification.from_pretrained("serdarakyol/interpress-turkish-news-classification")
```
```sh
import torch
if torch.cuda.is_available():
device = torch.device("cuda")
model = model.cuda()
print('There are %d GPU(s) available.' % torch.cuda.device_count())
print('GPU name is:', torch.cuda.get_device_name(0))
else:
print('No GPU available, using the CPU instead.')
device = torch.device("cpu")
```
```sh
import numpy as np
def prediction(news):
news=[news]
indices=tokenizer.batch_encode_plus(
news,
max_length=512,
add_special_tokens=True,
return_attention_mask=True,
padding='max_length',
truncation=True,
return_tensors='pt')
inputs = indices["input_ids"].clone().detach().to(device)
masks = indices["attention_mask"].clone().detach().to(device)
with torch.no_grad():
output = model(inputs, token_type_ids=None,attention_mask=masks)
logits = output[0]
logits = logits.detach().cpu().numpy()
pred = np.argmax(logits,axis=1)[0]
return pred
```
```sh
news = r"ABD'den Prens Selman'a yaptırım yok Beyaz Saray Sözcüsü Psaki, Muhammed bin Selman'a yaptırım uygulamamanın \"doğru karar\" olduğunu savundu. Psaki, \"Tarihimizde, Demokrat ve Cumhuriyetçi başkanların yönetimlerinde diplomatik ilişki içinde olduğumuz ülkelerin liderlerine yönelik yaptırım getirilmemiştir\" dedi."
```
You can find the news in this [link](https://www.ntv.com.tr/dunya/abdden-prens-selmana-yaptirim-yok,YTeWNv0-oU6Glbhnpjs1JQ) (news date: 02/03/2021)
```sh
labels = {
0 : "Culture-Art",
1 : "Economy",
2 : "Politics",
3 : "Education",
4 : "World",
5 : "Sport",
6 : "Technology",
7 : "Magazine",
8 : "Health",
9 : "Agenda"
}
pred = prediction(news)
print(labels[pred])
# > World
```
## Usage for Tensorflow
```sh
pip install transformers or pip install transformers==4.3.3
import tensorflow as tf
from transformers import BertTokenizer, TFBertForSequenceClassification
import numpy as np
tokenizer = BertTokenizer.from_pretrained('serdarakyol/interpress-turkish-news-classification')
model = TFBertForSequenceClassification.from_pretrained("serdarakyol/interpress-turkish-news-classification")
inputs = tokenizer(news, return_tensors="tf")
inputs["labels"] = tf.reshape(tf.constant(1), (-1, 1)) # Batch size 1
outputs = model(inputs)
loss = outputs.loss
logits = outputs.logits
pred = np.argmax(logits,axis=1)[0]
labels[pred]
# > World
```
Thanks to [@yavuzkomecoglu](https://huggingface.co/yavuzkomecoglu) for contributes
If you have any question, please, don't hesitate to contact with me
[](https://www.linkedin.com/in/serdarakyol55/)
[](https://github.com/serdarakyol) | 3,876 |
sismetanin/sbert-ru-sentiment-rutweetcorp | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | Entry not found | 15 |
wilsontam/bert-base-uncased-dstc10-knowledge-cluster-classifier | [
"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",... | ---
language: "en"
tags:
- dstc10
- knowledge cluster classifier
widget:
- text: "oh and we'll mi thing uh is there bike clo ars or bike crac where i can park my thee"
- text: "oh and one more thing uhhh is there bike lockers or a bike rack where i can park my bike"
- text: "ni yeah that sounds great ummm dold you have the any idea er could you check for me if there's hat three wifie available there"
- text: "nice yeah that sounds great ummm do you have any idea or could you check for me if there's uhhh free wi-fi available there"
- text: "perfect and what is the check kin time for that"
---
This is the model used for knowledge cluster classification for the DSTC10 track2 knowledge selection task, trained with double heads, i.e., classifier head and LM head using ASR error simulator for model training.
For further information, please refer to https://github.com/yctam/dstc10_track2_task2 for the Github repository. You can use this model and use our source code to predict knowledge clusters under ASR errors. AAAI 2022 workshop paper: https://github.com/shanemoon/dstc10/raw/main/papers/dstc10_aaai22_track2_21.pdf
--- | 1,133 |
inovex/multi2convai-logistics-de-bert | [
"details.address",
"tour.postcode.select",
"tour.finish",
"details.safeplace",
"details.preferedNeighbour",
"details.avoidNeighbour",
"tour.job.collected",
"no",
"yes",
"tour.start",
"tour.details",
"tour.job.signature",
"tour.job.delivered",
"select",
"tour.job.safePlace",
"safeplace"... | ---
tags:
- text-classification
widget:
- text: "Wo kann ich das Paket ablegen?"
license: mit
language: de
---
# Multi2ConvAI-Logistics: finetuned Bert for German
This model was developed in the [Multi2ConvAI](https://multi2conv.ai) project:
- domain: Logistics (more details about our use cases: ([en](https://multi2convai/en/blog/use-cases), [de](https://multi2convai/en/blog/use-cases)))
- language: German (de)
- model type: finetuned Bert
## How to run
Requires:
- Huggingface transformers
### Run with Huggingface Transformers
````python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("inovex/multi2convai-logistics-de-bert")
model = AutoModelForSequenceClassification.from_pretrained("inovex/multi2convai-logistics-de-bert")
````
## Further information on Multi2ConvAI:
- https://multi2conv.ai
- https://github.com/inovex/multi2convai
- mailto: info@multi2conv.ai | 984 |
MhF/distilbert-base-uncased-finetuned-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-finetuned-clinc
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: clinc_oos
type: clinc_oos
args: plus
metrics:
- name: Accuracy
type: accuracy
value: 0.9187096774193548
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-clinc
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the clinc_oos dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7703
- Accuracy: 0.9187
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 48
- eval_batch_size: 48
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 4.2896 | 1.0 | 318 | 3.2887 | 0.7419 |
| 2.6309 | 2.0 | 636 | 1.8797 | 0.8310 |
| 1.5443 | 3.0 | 954 | 1.1537 | 0.8974 |
| 1.0097 | 4.0 | 1272 | 0.8560 | 0.9135 |
| 0.7918 | 5.0 | 1590 | 0.7703 | 0.9187 |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.11.0
| 1,890 |
ffalcao/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: 0.9246964318251509
---
<!-- 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: 0.2237
- Accuracy: 0.9245
- F1: 0.9247
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8356 | 1.0 | 250 | 0.3296 | 0.901 | 0.8977 |
| 0.254 | 2.0 | 500 | 0.2237 | 0.9245 | 0.9247 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu111
- Datasets 1.16.1
- Tokenizers 0.10.3
| 1,807 |
ali2066/bert_base_uncased_itr0_0.0001_webDiscourse_01_03_2022-16_08_12 | null | Entry not found | 15 |
adit94/relevancy_classifier | null | {'junk': 0, 'relevant': 1}
| 27 |
gdario/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: 0.8955
- name: F1
type: f1
value: 0.8918003951340884
---
<!-- 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: 0.3662
- Accuracy: 0.8955
- F1: 0.8918
## 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: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log | 1.0 | 125 | 0.5675 | 0.8265 | 0.8067 |
| 0.7565 | 2.0 | 250 | 0.3662 | 0.8955 | 0.8918 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.11.0
- Datasets 1.16.1
- Tokenizers 0.10.3
| 1,803 |
ebrigham/EYY-Topic-Classification | [
"climate change",
"culture",
"democratic values",
"digital",
"education",
"employment and inclusion",
"european learning mobility",
"health and well-being",
"n/a",
"natural sustainability",
"participation and engagement",
"policy dialogues",
"renewable energy",
"research and innovation",
... | Entry not found | 15 |
mrm8488/spanish-TinyBERT-betito-finetuned-xnli-es | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | ---
tags:
- generated_from_trainer
datasets:
- xnli
metrics:
- accuracy
model-index:
- name: spanish-TinyBERT-betito-finetuned-xnli-es
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: xnli
type: xnli
args: es
metrics:
- name: Accuracy
type: accuracy
value: 0.7475049900199601
---
<!-- 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. -->
# spanish-TinyBERT-betito-finetuned-xnli-es
This model is a fine-tuned version of [mrm8488/spanish-TinyBERT-betito](https://huggingface.co/mrm8488/spanish-TinyBERT-betito) on the xnli dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7104
- Accuracy: 0.7475
## 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: 2.50838112218154e-05
- train_batch_size: 8
- eval_batch_size: 64
- seed: 13
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:------:|:---------------:|:--------:|
| 0.7191 | 1.0 | 49399 | 0.6829 | 0.7112 |
| 0.6323 | 2.0 | 98798 | 0.6527 | 0.7305 |
| 0.5727 | 3.0 | 148197 | 0.6531 | 0.7465 |
| 0.4964 | 4.0 | 197596 | 0.7079 | 0.7427 |
| 0.4929 | 5.0 | 246995 | 0.7104 | 0.7475 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 1.18.4
- Tokenizers 0.11.6
| 1,896 |
orzhan/ruroberta-ruatd-binary | null | sberbank-ai/ruRoberta-large fine-tuned for Russian Artificial Text Detection shared task
| 89 |
Kaveh8/autonlp-imdb_rating-625417974 | [
"1",
"2",
"3",
"4",
"5"
] | ---
tags: autonlp
language: en
widget:
- text: "I love AutoNLP 🤗"
datasets:
- Kaveh8/autonlp-data-imdb_rating
co2_eq_emissions: 0.7952957276830314
---
# Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 625417974
- CO2 Emissions (in grams): 0.7952957276830314
## Validation Metrics
- Loss: 1.0167548656463623
- Accuracy: 0.5934065934065934
- Macro F1: 0.5871237509176406
- Micro F1: 0.5934065934065934
- Weighted F1: 0.5905118014752566
- Macro Precision: 0.5959908336094294
- Micro Precision: 0.5934065934065934
- Weighted Precision: 0.5979368174068634
- Macro Recall: 0.5884714803600252
- Micro Recall: 0.5934065934065934
- Weighted Recall: 0.5934065934065934
## 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/Kaveh8/autonlp-imdb_rating-625417974
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("Kaveh8/autonlp-imdb_rating-625417974", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("Kaveh8/autonlp-imdb_rating-625417974", use_auth_token=True)
inputs = tokenizer("I love AutoNLP", return_tensors="pt")
outputs = model(**inputs)
``` | 1,374 |
saattrupdan/job-listing-filtering-model | null | ---
license: mit
tags:
- generated_from_trainer
model-index:
- name: job-listing-filtering-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-filtering-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: 0.1992
## 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: 25
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.4639 | 1.55 | 50 | 0.4343 |
| 0.407 | 3.12 | 100 | 0.3589 |
| 0.3459 | 4.68 | 150 | 0.3110 |
| 0.2871 | 6.25 | 200 | 0.2604 |
| 0.1966 | 7.8 | 250 | 0.2004 |
| 0.0994 | 9.37 | 300 | 0.1766 |
| 0.0961 | 10.92 | 350 | 0.2007 |
| 0.0954 | 12.49 | 400 | 0.1716 |
| 0.0498 | 14.06 | 450 | 0.1642 |
| 0.0419 | 15.62 | 500 | 0.1811 |
| 0.0232 | 17.18 | 550 | 0.1872 |
| 0.0146 | 18.74 | 600 | 0.1789 |
| 0.0356 | 20.31 | 650 | 0.1984 |
| 0.0325 | 21.86 | 700 | 0.1845 |
| 0.0381 | 23.43 | 750 | 0.1994 |
| 0.0063 | 24.98 | 800 | 0.1992 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.11.0+cu113
- Datasets 2.0.0
- Tokenizers 0.11.6
| 2,091 |
FuriouslyAsleep/unhappyZebra100 | [
"False",
"True"
] | ---
tags: autotrain
language: en
widget:
- text: "I love AutoTrain 🤗"
datasets:
- FuriouslyAsleep/autotrain-data-techDataClassifeier
co2_eq_emissions: 0.6969569001670619
---
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 664919631
- CO2 Emissions (in grams): 0.6969569001670619
## Validation Metrics
- Loss: 0.022509008646011353
- 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 AutoTrain"}' https://api-inference.huggingface.co/models/FuriouslyAsleep/autotrain-techDataClassifeier-664919631
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("FuriouslyAsleep/autotrain-techDataClassifeier-664919631", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("FuriouslyAsleep/autotrain-techDataClassifeier-664919631", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
``` | 1,172 |
Aymene/Fake-news-detection-bert-based-uncased | [
"LABEL_0"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: Fake-news-detection-bert-based-uncased
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. -->
# Fake-news-detection-bert-based-uncased
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Tokenizers 0.11.6
| 1,032 |
Giyaseddin/distilbert-base-cased-finetuned-fake-and-real-news-dataset | null | ---
license: gpl-3.0
language: en
library: transformers
other: distilbert
datasets:
- Fake and real news dataset
---
# DistilBERT base cased model for Fake News Classification
## Model description
DistilBERT is a transformers model, smaller and faster than BERT, which was pretrained on the same corpus in a
self-supervised fashion, using the BERT base model as a teacher. This means it was pretrained on the raw texts only,
with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic
process to generate inputs and labels from those texts using the BERT base model.
This is a Fake News classification model finetuned [pretrained DistilBERT model](https://huggingface.co/distilbert-base-cased) on
[Fake and real news dataset](https://www.kaggle.com/datasets/clmentbisaillon/fake-and-real-news-dataset)
## Intended uses & limitations
This can only be used for the kind of news that are similar to the ones in the dataset,
please visit the [dataset's kaggle page](https://www.kaggle.com/datasets/clmentbisaillon/fake-and-real-news-dataset) to see the data.
### How to use
You can use this model directly with a :
```python
>>> from transformers import pipeline
>>> classifier = pipeline("text-classification", model="Giyaseddin/distilbert-base-cased-finetuned-fake-and-real-news-dataset", return_all_scores=True)
>>> examples = ["Yesterday, Speaker Paul Ryan tweeted a video of himself on the Mexican border flying in a helicopter and traveling on horseback with US border agents. RT if you agree It is time for The Wall. pic.twitter.com/s5MO8SG7SL Paul Ryan (@SpeakerRyan) August 1, 2017It makes for great theater to see Republican Speaker Ryan pleading the case for a border wall, but how sincere are the GOP about building the border wall? Even after posting a video that appears to show Ryan s support for the wall, he still seems unsure of himself. It s almost as though he s testing the political winds when he asks Twitter users to retweet if they agree that we need to start building the wall. How committed is the (formerly?) anti-Trump Paul Ryan to building the border wall that would fulfill one of President Trump s most popular campaign promises to the American people? Does he have the what it takes to defy the wishes of corporate donors and the US Chamber of Commerce, and do the right thing for the national security and well-being of our nation?The Last Refuge- Republicans are in control of the House of Representatives, Republicans are in control of the Senate, a Republican President is in the White House, and somehow there s negotiations on how to fund the #1 campaign promise of President Donald Trump, the border wall.Here s the rub.Here s what pundits never discuss.The Republican party doesn t need a single Democrat to fund the border wall.A single spending bill could come from the House of Representatives that fully funds 100% of the border wall. The spending bill then goes to the senate, where again, it doesn t need a single Democrat vote because spending legislation is specifically what reconciliation was designed to facilitate. That House bill can pass the Senate with 51 votes and proceed directly to the President s desk for signature.So, ask yourself: why is this even a point of discussion?The honest answer, for those who are no longer suffering from Battered Conservative Syndrome, is that Republicans don t want to fund or build an actual physical barrier known as the Southern Border Wall.It really is that simple.If one didn t know better, they d almost think Speaker Ryan was attempting to emulate the man he clearly despised during the 2016 presidential campaign."]
>>> classifier(examples)
[[{'label': 'LABEL_0', 'score': 1.0},
{'label': 'LABEL_1', 'score': 1.0119109106199176e-08}]]
```
### Limitations and bias
Even if the training data used for this model could be characterized as fairly neutral, this model can have biased
predictions. It also inherits some of
[the bias of its teacher model](https://huggingface.co/bert-base-uncased#limitations-and-bias).
This bias will also affect all fine-tuned versions of this model.
## Pre-training data
DistilBERT pretrained on the same data as BERT, which is [BookCorpus](https://yknzhu.wixsite.com/mbweb), a dataset
consisting of 11,038 unpublished books and [English Wikipedia](https://en.wikipedia.org/wiki/English_Wikipedia)
(excluding lists, tables and headers).
## Fine-tuning data
[Fake and real news dataset](https://www.kaggle.com/datasets/clmentbisaillon/fake-and-real-news-dataset)
## Training procedure
### Preprocessing
In the preprocessing phase, both the title and the text of the news are concatenated using a separator `[SEP]`.
This makes the full text as:
```
[CLS] Title Sentence [SEP] News text body [SEP]
```
The data are splitted according to the following ratio:
- Training set 60%.
- Validation set 20%.
- Test set 20%.
Lables are mapped as: `{fake: 0, true: 1}`
### Fine-tuning
The model was finetuned on GeForce GTX 960M for 5 hours. The parameters are:
| Parameter | Value |
|:-------------------:|:-----:|
| Learning rate | 5e-5 |
| Weight decay | 0.01 |
| Training batch size | 4 |
| Epochs | 3 |
Here is the scores during the training:
| Epoch | Training Loss | Validation Loss | Accuracy | F1 | Precision | Recall |
|:----------:|:-------------:|:-----------------:|:----------:|:---------:|:-----------:|:---------:|
| 1 | 0.008300 | 0.005783 | 0.998330 | 0.998252 | 0.996511 | 1.000000 |
| 2 | 0.000000 | 0.000161 | 0.999889 | 0.999883 | 0.999767 | 1.000000 |
| 3 | 0.000000 | 0.000122 | 0.999889 | 0.999883 | 0.999767 | 1.000000 |
## Evaluation results
When fine-tuned on downstream task of fake news binary classification, this model achieved the following results:
(scores are rounded to 2 floating points)
| | precision | recall | f1-score | support |
|:------------:|:---------:|:------:|:--------:|:-------:|
| Fake | 1.00 | 1.00 | 1.00 | 4697 |
| True | 1.00 | 1.00 | 1.00 | 4283 |
| accuracy | - | - | 1.00 | 8980 |
| macro avg | 1.00 | 1.00 | 1.00 | 8980 |
| weighted avg | 1.00 | 1.00 | 1.00 | 8980 |
Confision matrix:
| Actual\Predicted | Fake | True |
|:-----------------:|:----:|:----:|
| Fake | 4696 | 1 |
| True | 1 | 4282 |
The AUC score is 0.9997
| 6,720 |
hackathon-pln-es/readability-es-paragraphs | [
"complex",
"simple"
] | ---
language: es
license: cc-by-4.0
tags:
- spanish
- roberta
- bertin
pipeline_tag: text-classification
widget:
- text: La cueva de Zaratustra en el Pretil de los Consejos. Rimeros de libros hacen escombro y cubren las paredes. Empapelan los cuatro vidrios de una puerta cuatro cromos espeluznantes de un novelón por entregas. En la cueva hacen tertulia el gato, el can, el loro y el librero. Zaratustra, abichado y giboso -la cara de tocino rancio y la bufanda de verde serpiente- promueve con su caracterización de fantoche, una aguda y dolorosa disonancia muy emotiva y muy moderna. Encogido en el roto pelote de su silla enana, con los pies entrapados y cepones en la tarima del brasero, guarda la tienda. Un ratón saca el hocico intrigante por un agujero.
---
# Readability ES Paragraphs for two classes
Model based on the Roberta architecture finetuned on [BERTIN](https://huggingface.co/bertin-project/bertin-roberta-base-spanish) for readability assessment of Spanish texts.
## Description and performance
This version of the model was trained on a mix of datasets, using paragraph-level granularity when possible. The model performs binary classification among the following classes:
- Simple.
- Complex.
It achieves a F1 macro average score of 0.8891, measured on the validation set.
## Model variants
- [`readability-es-sentences`](https://huggingface.co/hackathon-pln-es/readability-es-sentences). Two classes, sentence-based dataset.
- `readability-es-paragraphs` (this model). Two classes, paragraph-based dataset.
- [`readability-es-3class-sentences`](https://huggingface.co/hackathon-pln-es/readability-es-3class-sentences). Three classes, sentence-based dataset.
- [`readability-es-3class-paragraphs`](https://huggingface.co/hackathon-pln-es/readability-es-3class-paragraphs). Three classes, paragraph-based dataset.
## Datasets
- [`readability-es-hackathon-pln-public`](https://huggingface.co/datasets/hackathon-pln-es/readability-es-hackathon-pln-public), composed of:
* coh-metrix-esp corpus.
* Various text resources scraped from websites.
- Other non-public datasets: newsela-es, simplext.
## Training details
Please, refer to [this training run](https://wandb.ai/readability-es/readability-es/runs/2z8080pi/overview) for full details on hyperparameters and training regime.
## Biases and Limitations
- Due to the scarcity of data and the lack of a reliable gold test set, performance metrics are reported on the validation set.
- One of the datasets involved is the Spanish version of newsela, which is frequently used as a reference. However, it was created by translating previous datasets, and therefore it may contain somewhat unnatural phrases.
- Some of the datasets used cannot be publicly disseminated, making it more difficult to assess the existence of biases or mistakes.
- Language might be biased towards the Spanish dialect spoken in Spain. Other regional variants might be sub-represented.
- No effort has been performed to alleviate the shortcomings and biases described in the [original implementation of BERTIN](https://huggingface.co/bertin-project/bertin-roberta-base-spanish#bias-examples-spanish).
## Authors
- [Laura Vásquez-Rodríguez](https://lmvasque.github.io/)
- [Pedro Cuenca](https://twitter.com/pcuenq)
- [Sergio Morales](https://www.fireblend.com/)
- [Fernando Alva-Manchego](https://feralvam.github.io/)
| 3,380 |
Stremie/roberta-base-clickbait-keywords | null | This model classifies whether a tweet is clickbait or not. It has been trained using [Webis-Clickbait-17](https://webis.de/data/webis-clickbait-17.html) dataset. Input is composed of 'postText' + '[SEP]' + 'targetKeywords'. Achieved ~0.7 F1-score on test data. | 261 |
dpazmino/finetuning-sentiment-model_duke_final | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: finetuning-sentiment-model_duke_final
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuning-sentiment-model_duke_final
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: 0.4776
- F1: 0.8708
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
### Framework versions
- Transformers 4.18.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.11.6
| 1,180 |
MartinoMensio/racism-models-regression-w-m-vote-epoch-4 | [
"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-4`
### 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-4'
tokenizer = AutoTokenizer.from_pretrained("dccuchile/bert-base-spanish-wwm-uncased")
full_model_path = f'MartinoMensio/racism-models-{model_name}'
model = AutoModelForSequenceClassification.from_pretrained(full_model_path)
pipe = 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': 0.8345461}, {'score': 0.48615143}]
# or also specify a threshold to cut racist/non-racist
print(pipe(texts, regression_threshold=0.9))
# [{'label': 'non-racist', 'score': 0.8345461}, {'label': 'non-racist', 'score': 0.48615143}]
```
For more details, see https://github.com/preyero/neatclass22
| 6,364 |
liamcripwell/ctrl44-clf | [
"ignore",
"rephrase",
"syntax-split",
"discourse-split"
] | ---
language: en
---
# CTRL44 Classification model
This is a pretrained version of the 4-class simplification operation classifier presented in the NAACL 2022 paper "Controllable Sentence Simplification via Operation Classification". It was trained on the IRSD classification dataset.
Predictions from this model can be used for input into the [simplification model](https://huggingface.co/liamcripwell/ctrl44-simp) to reproduce pipeline results seen in the paper.
## How to use
Here is how to use this model in PyTorch:
```python
from transformers import RobertaForSequenceClassification, AutoTokenizer
model = RobertaForSequenceClassification.from_pretrained("liamcripwell/ctrl44-clf")
tokenizer = AutoTokenizer.from_pretrained("liamcripwell/ctrl44-clf")
text = "Barack Hussein Obama II is an American politician who served as the 44th president of the United States from 2009 to 2017."
inputs = tokenizer(text, return_tensors="pt")
with torch.no_grad():
logits = model(**inputs).logits
predicted_class_id = logits.argmax().item()
predicted_class_name = model.config.id2label[predicted_class_id]
``` | 1,112 |
Hate-speech-CNERG/hindi-codemixed-abusive-MuRIL | null | ---
language: hi-en
license: afl-3.0
---
This model is used detecting **abusive speech** in **Code-Mixed Hindi**. It is finetuned on MuRIL model using code-mixed hindi 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}
}
~~~ | 982 |
Hate-speech-CNERG/malayalam-codemixed-abusive-MuRIL | null | ---
language: ma-en
license: afl-3.0
---
This model is used to detect **abusive speech** in **Code-Mixed Malayalam**. It is finetuned on MuRIL model using Code-Mixed Malayalam 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}
}
~~~ | 990 |
Nithiwat/fake-news-debunker | [
"0",
"1"
] | ---
tags: autotrain
language: en
widget:
- text: "Bill Gates wants to use mass Covid-19 vaccination campaign to implant microchips to track people"
datasets:
- Fake and real news datasets by CLÉMENT BISAILLON
co2_eq_emissions: 4.415122243239347
---
# Model Trained Using AutoTrain
- Problem: Fake News Classification
- Problem type: Binary Classification
- Model ID: 785124234
- CO2 Emissions (in grams): 4.415122243239347
## Validation Metrics
- Loss: 0.00012586714001372457
- Accuracy: 0.9998886538247411
- Precision: 1.0
- Recall: 0.9997665732959851
- AUC: 0.9999999999999999
- F1: 0.999883273024396
## 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/Nithiwat/autotrain-fake-news-classifier-785124234
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("Nithiwat/autotrain-fake-news-classifier-785124234", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("Nithiwat/autotrain-fake-news-classifier-785124234", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
``` | 1,326 |
Sie-BERT/glue_sst_classifier | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- f1
- accuracy
model-index:
- name: glue_sst_classifier
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
args: sst2
metrics:
- name: F1
type: f1
value: 0.9033707865168539
- name: Accuracy
type: accuracy
value: 0.9013761467889908
---
<!-- 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. -->
# glue_sst_classifier
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2359
- F1: 0.9034
- Accuracy: 0.9014
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:------:|:--------:|
| 0.3653 | 0.19 | 100 | 0.3213 | 0.8717 | 0.8727 |
| 0.291 | 0.38 | 200 | 0.2662 | 0.8936 | 0.8911 |
| 0.2239 | 0.57 | 300 | 0.2417 | 0.9081 | 0.9060 |
| 0.2306 | 0.76 | 400 | 0.2359 | 0.9105 | 0.9094 |
| 0.2185 | 0.95 | 500 | 0.2371 | 0.9011 | 0.8991 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0+cu113
- Datasets 2.1.0
- Tokenizers 0.12.1
| 1,993 |
cassiepowell/LaBSE-for-agreement | [
"0",
"1",
"2"
] | Entry not found | 15 |
efederici/cross-encoder-distilbert-it | [
"LABEL_0"
] | ---
pipeline_tag: text-classification
license: apache-2.0
language:
- it
tags:
- cross-encoder
- sentence-similarity
- transformers
---
# Cross-Encoder
The model can be used for Information Retrieval: given a query, encode the query will all possible passages. Then sort the passages in a decreasing order.
<p align="center">
<img src="https://www.exibart.com/repository/media/2020/07/bridget-riley-cool-edge.jpg" width="400"> </br>
Bridget Riley, COOL EDGE
</p>
## Training Data
This model was trained on a custom biomedical ranking dataset.
## Usage and Performance
```python
from sentence_transformers import CrossEncoder
model = CrossEncoder('efederici/cross-encoder-distilbert-it')
scores = model.predict([('Sentence 1', 'Sentence 2'), ('Sentence 3', 'Sentence 4')])
```
The model will predict scores for the pairs `('Sentence 1', 'Sentence 2')` and `('Sentence 3', 'Sentence 4')`. | 904 |
charlieoneill/distilbert-base-uncased-finetuned-tweet_eval-offensive | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- tweet_eval
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-tweet_eval-offensive
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: tweet_eval
type: tweet_eval
args: offensive
metrics:
- name: Accuracy
type: accuracy
value: 0.8089123867069486
- name: F1
type: f1
value: 0.8060281168230459
---
<!-- 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-tweet_eval-offensive
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the tweet_eval dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4185
- Accuracy: 0.8089
- F1: 0.8060
## 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 | 187 | 0.4259 | 0.8059 | 0.7975 |
| 0.46 | 2.0 | 374 | 0.4185 | 0.8089 | 0.8060 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.9.1
- Datasets 2.1.0
- Tokenizers 0.12.1
| 1,851 |
LiYouYou/BERT_MRPC | [
"equivalent",
"not_equivalent"
] | Entry not found | 15 |
LiYouYou/bert_finetuning_cn | [
"negative",
"positive"
] | ---
language:
- en
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: bert_finetuning_cn
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE SST2
type: glue
args: sst2
metrics:
- name: Accuracy
type: accuracy
value: 0.8314220183486238
---
<!-- 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_finetuning_cn
This model is a fine-tuned version of [bert-base-chinese](https://huggingface.co/bert-base-chinese) on the GLUE SST2 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5440
- Accuracy: 0.8314
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1.0
### Training results
### Framework versions
- Transformers 4.19.0.dev0
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.12.1
| 1,385 |
JoMart/albert-base-v2 | null | ---
tags:
- generated_from_trainer
model-index:
- name: albert-base-v2
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. -->
# albert-base-v2
This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0075
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 0.024 | 1.0 | 4000 | 0.0300 |
| 0.0049 | 2.0 | 8000 | 0.0075 |
| 0.0 | 3.0 | 12000 | 0.0125 |
| 0.0 | 4.0 | 16000 | 0.0101 |
| 0.0056 | 5.0 | 20000 | 0.0104 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.9.0
- Datasets 2.1.0
- Tokenizers 0.12.1
| 1,366 |
LiYuan/Amazon-Cross-Encoder-Classification | [
"LABEL_0",
"LABEL_1",
"LABEL_2",
"LABEL_3"
] | ---
license: afl-3.0
---
There are two types of Cross-Encoder models. One is the Cross-Encoder Regression model that we fine-tuned and mentioned in the previous section. Next, we have the Cross-Encoder Classification model. These two models are introduced in the same paper https://doi.org/10.48550/arxiv.1908.10084
Both models resolve the issue that the BERT model is too time-consuming and resource-consuming to train in pairwised sentences. These two model weights are initialized as the BERT and RoBERTa networks. We only need to fine-tune them, spending much less time to yield a comparable or even better sentence embedding. The below figure \ref{figure:5} shows the architecture of Cross-Encoder Classification.
Then we evaluated the model performance on the 2,000 held-out test set. We also got a test accuracy **46.05%** that is almost identical to the best validation accuracy, suggesting a good generalization model. | 945 |
Jeevesh8/bert_ft_cola-67 | null | Entry not found | 15 |
JoanTirant/roberta-base-bne-finetuned-amazon_reviews_multi | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- amazon_reviews_multi
metrics:
- accuracy
model-index:
- name: roberta-base-bne-finetuned-amazon_reviews_multi
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: amazon_reviews_multi
type: amazon_reviews_multi
args: es
metrics:
- name: Accuracy
type: accuracy
value: 0.93425
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-base-bne-finetuned-amazon_reviews_multi
This model is a fine-tuned version of [BSC-TeMU/roberta-base-bne](https://huggingface.co/BSC-TeMU/roberta-base-bne) on the amazon_reviews_multi dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2291
- Accuracy: 0.9343
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.1909 | 1.0 | 1250 | 0.1784 | 0.9295 |
| 0.1013 | 2.0 | 2500 | 0.2291 | 0.9343 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0+cu113
- Datasets 2.1.0
- Tokenizers 0.12.1
| 1,754 |
CleveGreen/JobClassifier_v3_gpt | [
"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 |
nikitast/lang-classifier-roberta | [
"az",
"be",
"de",
"en",
"he",
"hy",
"ka",
"kk",
"ru",
"uk"
] | ---
language:
- ru
- uk
- be
- kk
- az
- hy
- ka
- he
- en
- de
tags:
- language classification
datasets:
- open_subtitles
- tatoeba
- oscar
---
# RoBERTa for Single Language Classification
## Training
RoBERTa fine-tuned on small parts of Open Subtitles, Oscar and Tatoeba datasets (~9k samples per language).
| data source | language |
|-----------------|----------------|
| open_subtitles | ka, he, en, de |
| oscar | be, kk, az, hu |
| tatoeba | ru, uk |
## Validation
The metrics obtained from validation on the another part of dataset (~1k samples per language).
|index|class|f1-score|precision|recall|support|
|---|---|---|---|---|---|
|0|az|0\.998|0\.997|1\.0|997|
|1|be|0\.996|0\.998|0\.994|1004|
|2|de|0\.976|0\.966|0\.987|979|
|3|en|0\.976|0\.986|0\.967|1020|
|4|he|1\.0|1\.0|0\.999|1001|
|5|hy|0\.994|0\.991|0\.998|993|
|6|ka|0\.999|0\.999|0\.999|1000|
|7|kk|0\.996|0\.998|0\.993|1005|
|8|uk|0\.982|0\.997|0\.968|1030|
|9|ru|0\.982|0\.968|0\.997|971|
|10|macro\_avg|0\.99|0\.99|0\.99|10000|
|11|weighted avg|0\.99|0\.99|0\.99|10000| | 1,086 |
nikitast/multilang-classifier-roberta | [
"az",
"be",
"de",
"en",
"he",
"hy",
"ka",
"kk",
"ru",
"uk"
] | ---
language:
- ru
- uk
- be
- kk
- az
- hy
- ka
- he
- en
- de
tags:
- language classification
datasets:
- open_subtitles
- tatoeba
- oscar
---
# RoBERTa for Multilabel Language Classification
## Training
RoBERTa fine-tuned on small parts of Open Subtitles, Oscar and Tatoeba datasets (~9k samples per language).
Implemented heuristic algorithm for multilingual training data creation - https://github.com/n1kstep/lang-classifier
| data source | language |
|-----------------|----------------|
| open_subtitles | ka, he, en, de |
| oscar | be, kk, az, hu |
| tatoeba | ru, uk |
## Validation
The metrics obtained from validation on the another part of dataset (~1k samples per language).
| Training Loss | Validation Loss | F1-Score | Roc Auc | Accuracy | Support |
|---------------|-----------------|----------|----------|----------|---------|
| 0.161500 | 0.110949 | 0.947844 | 0.953939 | 0.762063 | 26858 | | 969 |
Jeevesh8/6ep_bert_ft_cola-50 | null | Entry not found | 15 |
Jeevesh8/6ep_bert_ft_cola-54 | null | Entry not found | 15 |
Jeevesh8/6ep_bert_ft_cola-68 | null | Entry not found | 15 |
Jeevesh8/6ep_bert_ft_cola-69 | null | Entry not found | 15 |
Jeevesh8/6ep_bert_ft_cola-71 | null | Entry not found | 15 |
Jeevesh8/6ep_bert_ft_cola-75 | null | Entry not found | 15 |
YeRyeongLee/mental-bert-base-uncased-masked_finetuned-0517 | [
"LABEL_0",
"LABEL_1",
"LABEL_2",
"LABEL_3",
"LABEL_4",
"LABEL_5"
] | ---
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: mental-bert-base-uncased-masked_finetuned-0517
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. -->
# mental-bert-base-uncased-masked_finetuned-0517
This model is a fine-tuned version of [mental/mental-bert-base-uncased](https://huggingface.co/mental/mental-bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5217
- Accuracy: 0.917
- F1: 0.9171
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|
| No log | 1.0 | 3000 | 0.2922 | 0.8993 | 0.8997 |
| No log | 2.0 | 6000 | 0.3964 | 0.9063 | 0.9069 |
| No log | 3.0 | 9000 | 0.4456 | 0.9197 | 0.9197 |
| No log | 4.0 | 12000 | 0.5217 | 0.917 | 0.9171 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.9.0
- Datasets 1.16.1
- Tokenizers 0.10.3
| 1,649 |
Jeevesh8/512seq_len_6ep_bert_ft_cola-68 | null | Entry not found | 15 |
Jeevesh8/512seq_len_6ep_bert_ft_cola-75 | null | Entry not found | 15 |
Jeevesh8/512seq_len_6ep_bert_ft_cola-78 | null | Entry not found | 15 |
connectivity/feather_berts_42 | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | Entry not found | 15 |
connectivity/bert_ft_qqp-10 | null | Entry not found | 15 |
connectivity/feather_berts_0 | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | Entry not found | 15 |
reemalyami/AraRoBERTa_Poem_classification | [
"LABEL_0",
"LABEL_1",
"LABEL_10",
"LABEL_11",
"LABEL_12",
"LABEL_13",
"LABEL_14",
"LABEL_15",
"LABEL_2",
"LABEL_3",
"LABEL_4",
"LABEL_5",
"LABEL_6",
"LABEL_7",
"LABEL_8",
"LABEL_9"
] | Entry not found | 15 |
sahn/distilbert-base-uncased-finetuned-imdb | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased-finetuned-imdb
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: imdb
type: imdb
args: plain_text
metrics:
- name: Accuracy
type: accuracy
value: 0.9294
---
<!-- 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-imdb
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: 0.2214
- Accuracy: 0.9294
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.2435 | 1.0 | 1250 | 0.2186 | 0.917 |
| 0.1495 | 2.0 | 2500 | 0.2214 | 0.9294 |
| 0.0829 | 3.0 | 3750 | 0.4892 | 0.8918 |
| 0.0472 | 4.0 | 5000 | 0.5189 | 0.8976 |
| 0.0268 | 5.0 | 6250 | 0.5478 | 0.8996 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
| 1,861 |
Paoloant/distilbert-base-uncased-finetuned-emotion | [
"LABEL_0",
"LABEL_1",
"LABEL_2",
"LABEL_3",
"LABEL_4",
"LABEL_5"
] | Entry not found | 15 |
Jeevesh8/lecun_feather_berts-34 | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | Entry not found | 15 |
Jeevesh8/lecun_feather_berts-67 | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | Entry not found | 15 |
Jeevesh8/lecun_feather_berts-6 | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | Entry not found | 15 |
Jeevesh8/lecun_feather_berts-18 | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | Entry not found | 15 |
Jeevesh8/lecun_feather_berts-12 | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | Entry not found | 15 |
Jeevesh8/lecun_feather_berts-10 | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | Entry not found | 15 |
Jeevesh8/lecun_feather_berts-7 | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | Entry not found | 15 |
Jeevesh8/lecun_feather_berts-16 | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | Entry not found | 15 |
Jeevesh8/std_pnt_04_feather_berts-10 | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | Entry not found | 15 |
Jeevesh8/std_pnt_04_feather_berts-21 | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | Entry not found | 15 |
Jeevesh8/std_pnt_04_feather_berts-9 | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | Entry not found | 15 |
Jeevesh8/std_pnt_04_feather_berts-22 | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | Entry not found | 15 |
EventMiner/xlm-roberta-large-en-pt-es-doc | null | ---
language: multilingual
tags:
- news event detection
- document level
- EventMiner
license: apache-2.0
---
# EventMiner
EventMiner is designed for multilingual news event detection. The goal of news event detection is the automatic extraction of event details from news articles. This event extraction can be done at different levels: document, sentence and word ranging from coarse-granular information to fine-granular information.
We submitted the best results based on EventMiner to [CASE 2021 shared task 1: *Multilingual Protest News Detection*](https://competitions.codalab.org/competitions/31247). Our approach won first place in English for the document level task while ranking within the top four solutions for other languages: Portuguese, Spanish, and Hindi.
*EventMiner/xlm-roberta-large-en-pt-es-doc* is a xlm-roberta-large sequence classification model fine-tuned on English, Portuguese and Spanish document level data of the multilingual version of GLOCON gold standard dataset released with [CASE 2021](https://aclanthology.org/2021.case-1.11/). <br>
Labels:
- Label_0: News article does not contain information about a past or ongoing socio-political event
- Label_1: News article contains information about a past or ongoing socio-political event
More details about the training procedure are available with our [codebase](https://github.com/HHansi/EventMiner).
# How to Use
## Load Model
```python
from transformers import XLMRobertaTokenizer, XLMRobertaForSequenceClassification
model_name = 'EventMiner/xlm-roberta-large-en-pt-es-doc'
tokenizer = XLMRobertaTokenizer.from_pretrained(model_name)
model = XLMRobertaForSequenceClassification.from_pretrained(model_name)
```
## Classification
```python
from transformers import pipeline
classifier = pipeline("text-classification", model=model, tokenizer=tokenizer)
classifier("Police arrested five more student leaders on Monday when implementing the strike call given by MSU students union as a mark of protest against the decision to introduce payment seats in first-year commerce programme.")
```
# Citation
If you use this model, please consider citing the following paper.
```
@inproceedings{hettiarachchi-etal-2021-daai,
title = "{DAAI} at {CASE} 2021 Task 1: Transformer-based Multilingual Socio-political and Crisis Event Detection",
author = "Hettiarachchi, Hansi and
Adedoyin-Olowe, Mariam and
Bhogal, Jagdev and
Gaber, Mohamed Medhat",
booktitle = "Proceedings of the 4th Workshop on Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE 2021)",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.case-1.16",
doi = "10.18653/v1/2021.case-1.16",
pages = "120--130",
}
``` | 2,857 |
S2312dal/M1_cross | [
"LABEL_0"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- spearmanr
model-index:
- name: M1_cross
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. -->
# M1_cross
This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0066
- Pearson: 0.9828
- Spearmanr: 0.9147
## 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: 25
- optimizer: Adam with betas=(0.9,0.98) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 125.0
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Pearson | Spearmanr |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:---------:|
| 0.0294 | 1.0 | 131 | 0.0457 | 0.8770 | 0.8351 |
| 0.0237 | 2.0 | 262 | 0.0302 | 0.9335 | 0.8939 |
| 0.015 | 3.0 | 393 | 0.0155 | 0.9594 | 0.9054 |
| 0.0177 | 4.0 | 524 | 0.0106 | 0.9778 | 0.9091 |
| 0.0087 | 5.0 | 655 | 0.0066 | 0.9828 | 0.9147 |
### Framework versions
- Transformers 4.20.0
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
| 1,683 |
S2312dal/M6_cross | [
"LABEL_0"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- spearmanr
model-index:
- name: M6_cross
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. -->
# M6_cross
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: 0.0084
- Pearson: 0.9811
- Spearmanr: 0.9075
## 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: 20
- eval_batch_size: 20
- seed: 25
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 6.0
- num_epochs: 5
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Pearson | Spearmanr |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:---------:|
| 0.0059 | 1.0 | 105 | 0.0158 | 0.9633 | 0.9054 |
| 0.001 | 2.0 | 210 | 0.0102 | 0.9770 | 0.9103 |
| 0.0008 | 3.0 | 315 | 0.0083 | 0.9805 | 0.9052 |
| 0.0011 | 4.0 | 420 | 0.0075 | 0.9812 | 0.9082 |
| 0.0017 | 5.0 | 525 | 0.0084 | 0.9811 | 0.9075 |
### Framework versions
- Transformers 4.20.0
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
| 1,727 |
valurank/finetuned-distilbert-adult-content-detection | null | ---
license: other
tags:
- generated_from_trainer
model-index:
- name: finetuned-distilbert-adult-content-detection
results: []
---
### finetuned-distilbert-news-article-catgorization
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the adult_content dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0065
- F1_score(weighted): 0.90
### Model description
More information needed
### Intended uses & limitations
More information needed
### Training and evaluation data
The model was trained on some subset of the adult_content dataset and it was validated on the remaining subset of the data
### Training procedure
More information needed
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-5
- train_batch_size: 5
- eval_batch_size: 5
- seed: 17
- optimizer: AdamW(lr=1e-5 and epsilon=1e-08)
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 0
- num_epochs: 2
### Training results
| Training Loss | Epoch | Validation Loss | f1 score |
|:-------------:|:-----:|:---------------: |:------:|
| 0.1414 | 1.0 | 0.4585 | 0.9058 |
| 0.1410 | 2.0 | 0.4584 | 0.9058 |
| 1,259 |
linuxcoder/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: 0.924
- name: F1
type: f1
value: 0.924047984825329
---
<!-- 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: 0.2294
- Accuracy: 0.924
- F1: 0.9240
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log | 1.0 | 250 | 0.3316 | 0.9025 | 0.8985 |
| No log | 2.0 | 500 | 0.2294 | 0.924 | 0.9240 |
### Framework versions
- Transformers 4.20.0
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
| 1,803 |
dunlp/GWW-finetuned-cola | null | ---
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- matthews_correlation
model-index:
- name: GWW-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: 0.16962352015480656
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# GWW-finetuned-cola
This model is a fine-tuned version of [dunlp/GWW](https://huggingface.co/dunlp/GWW) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6609
- Matthews Correlation: 0.1696
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.6181 | 1.0 | 535 | 0.6585 | 0.0 |
| 0.5938 | 2.0 | 1070 | 0.6276 | 0.0511 |
| 0.5241 | 3.0 | 1605 | 0.6609 | 0.1696 |
| 0.4433 | 4.0 | 2140 | 0.8239 | 0.1432 |
| 0.3492 | 5.0 | 2675 | 0.9236 | 0.1351 |
### Framework versions
- Transformers 4.20.0
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
| 1,912 |
Sayan01/tiny-bert-mnli-distilled | [
"contradiction",
"entailment",
"neutral"
] | Entry not found | 15 |
Elron/deberta-v3-large-emotion | [
"0",
"1",
"2",
"3"
] | ---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: deberta-v3-large
results: []
---
# deberta-v3-large-sentiment
This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) on an [tweet_eval](https://huggingface.co/datasets/tweet_eval) dataset.
## Model description
Test set results:
| Model | Emotion | Hate | Irony | Offensive | Sentiment |
| ------------- | ------------- | ------------- | ------------- | ------------- | ------------- |
| deberta-v3-large | **86.3** | **61.3** | **87.1** | **86.4** | **73.9** |
| BERTweet | 79.3 | - | 82.1 | 79.5 | 73.4 |
| RoB-RT | 79.5 | 52.3 | 61.7 | 80.5 | 69.3 |
[source:papers_with_code](https://paperswithcode.com/sota/sentiment-analysis-on-tweeteval)
## Intended uses & limitations
Classifying attributes of interest on tweeter like data.
## Training and evaluation data
[tweet_eval](https://huggingface.co/datasets/tweet_eval) dataset.
## Training procedure
Fine tuned and evaluated with [run_glue.py]()
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 7e-06
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 50
- num_epochs: 10.0
- label_smoothing_factor: 0.1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.2787 | 0.49 | 100 | 1.1127 | 0.4866 |
| 1.089 | 0.98 | 200 | 0.9668 | 0.7139 |
| 0.9134 | 1.47 | 300 | 0.8720 | 0.7834 |
| 0.8618 | 1.96 | 400 | 0.7726 | 0.7941 |
| 0.686 | 2.45 | 500 | 0.7337 | 0.8209 |
| 0.6333 | 2.94 | 600 | 0.7350 | 0.8235 |
| 0.5765 | 3.43 | 700 | 0.7561 | 0.8235 |
| 0.5502 | 3.92 | 800 | 0.7273 | 0.8476 |
| 0.5049 | 4.41 | 900 | 0.8137 | 0.8102 |
| 0.4695 | 4.9 | 1000 | 0.7581 | 0.8289 |
| 0.4657 | 5.39 | 1100 | 0.8404 | 0.8048 |
| 0.4549 | 5.88 | 1200 | 0.7800 | 0.8369 |
| 0.4305 | 6.37 | 1300 | 0.8575 | 0.8235 |
| 0.4209 | 6.86 | 1400 | 0.8572 | 0.8102 |
| 0.3983 | 7.35 | 1500 | 0.8392 | 0.8316 |
| 0.4139 | 7.84 | 1600 | 0.8152 | 0.8209 |
| 0.393 | 8.33 | 1700 | 0.8261 | 0.8289 |
| 0.3979 | 8.82 | 1800 | 0.8328 | 0.8235 |
| 0.3928 | 9.31 | 1900 | 0.8364 | 0.8209 |
| 0.3848 | 9.8 | 2000 | 0.8322 | 0.8235 |
### Framework versions
- Transformers 4.20.0.dev0
- Pytorch 1.9.0
- Datasets 2.2.2
- Tokenizers 0.11.6
| 3,130 |
Smith123/tiny-bert-sst2-distilled | [
"negative",
"positive"
] | Entry not found | 15 |
climabench/miniLM-cdp-all | [
"LABEL_0"
] | Entry not found | 15 |
ZeyadAhmed/AraElectra-Arabic-SQuADv2-CLS | null | Entry not found | 15 |
eus/testes | [
"LABEL_0",
"LABEL_1",
"LABEL_10",
"LABEL_11",
"LABEL_12",
"LABEL_13",
"LABEL_2",
"LABEL_3",
"LABEL_4",
"LABEL_5",
"LABEL_6",
"LABEL_7",
"LABEL_8",
"LABEL_9"
] | Entry not found | 15 |
PGT/nystromformer-artificial-balanced-max500-490000-1 | [
"LABEL_0",
"LABEL_1",
"LABEL_2",
"LABEL_3",
"LABEL_4",
"LABEL_5",
"LABEL_6"
] | Entry not found | 15 |
jinwooChoi/SKKU_AP_SA_KBT2 | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | Entry not found | 15 |
codeparrot/codeparrot-small-complexity-prediction | [
"LABEL_0",
"LABEL_1",
"LABEL_2",
"LABEL_3",
"LABEL_4",
"LABEL_5",
"LABEL_6"
] | ---
license: apache-2.0
---
This is a fine-tuned version of [codeparrot-small-multi](https://huggingface.co/codeparrot/codeparrot-small-multi), a 110M multilingual model for code generation, on [CodeComplex](https://huggingface.co/datasets/codeparrot/codecomplex), a dataset for complexity prediction of Java code. | 314 |
jinwooChoi/SKKU_AP_SA_KES_trained1 | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | Entry not found | 15 |
jinwooChoi/SKKU_SA_KEB | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | Entry not found | 15 |
ASCCCCCCCC/bert-base-chinese-finetuned-amazon_zh | [
"LABEL_0",
"LABEL_1",
"LABEL_2",
"LABEL_3",
"LABEL_4",
"LABEL_5"
] | Entry not found | 15 |
CLTL/icf-levels-att | [
"LABEL_0"
] | ---
language: nl
license: mit
pipeline_tag: text-classification
inference: false
---
# Regression Model for Attention Functioning Levels (ICF b140)
## Description
A fine-tuned regression model that assigns a functioning level to Dutch sentences describing attention functions. The model is based on a pre-trained Dutch medical language model ([link to be added]()): a RoBERTa model, trained from scratch on clinical notes of the Amsterdam UMC. To detect sentences about attention functions in clinical text in Dutch, use the [icf-domains](https://huggingface.co/CLTL/icf-domains) classification model.
## Functioning levels
Level | Meaning
---|---
4 | No problem with concentrating / directing / holding / dividing attention.
3 | Slight problem with concentrating / directing / holding / dividing attention for a longer period of time or for complex tasks.
2 | Can concentrate / direct / hold / divide attention only for a short time.
1 | Can barely concentrate / direct / hold / divide attention.
0 | Unable to concentrate / direct / hold / divide attention.
The predictions generated by the model might sometimes be outside of the scale (e.g. 4.2); this is normal in a regression model.
## Intended uses and limitations
- The model was fine-tuned (trained, validated and tested) on medical records from the Amsterdam UMC (the two academic medical centers of Amsterdam). It might perform differently on text from a different hospital or text from non-hospital sources (e.g. GP records).
- The model was fine-tuned with the [Simple Transformers](https://simpletransformers.ai/) library. This library is based on Transformers but the model cannot be used directly with Transformers `pipeline` and classes; doing so would generate incorrect outputs. For this reason, the API on this page is disabled.
## How to use
To generate predictions with the model, use the [Simple Transformers](https://simpletransformers.ai/) library:
```
from simpletransformers.classification import ClassificationModel
model = ClassificationModel(
'roberta',
'CLTL/icf-levels-att',
use_cuda=False,
)
example = 'Snel afgeleid, moeite aandacht te behouden.'
_, raw_outputs = model.predict([example])
predictions = np.squeeze(raw_outputs)
```
The prediction on the example is:
```
2.89
```
The raw outputs look like this:
```
[[2.89226103]]
```
## Training data
- The training data consists of clinical notes from medical records (in Dutch) of the Amsterdam UMC. Due to privacy constraints, the data cannot be released.
- The annotation guidelines used for the project can be found [here](https://github.com/cltl/a-proof-zonmw/tree/main/resources/annotation_guidelines).
## Training procedure
The default training parameters of Simple Transformers were used, including:
- Optimizer: AdamW
- Learning rate: 4e-5
- Num train epochs: 1
- Train batch size: 8
## Evaluation results
The evaluation is done on a sentence-level (the classification unit) and on a note-level (the aggregated unit which is meaningful for the healthcare professionals).
| | Sentence-level | Note-level
|---|---|---
mean absolute error | 0.99 | 1.03
mean squared error | 1.35 | 1.47
root mean squared error | 1.16 | 1.21
## Authors and references
### Authors
Jenia Kim, Piek Vossen
### References
TBD
| 3,270 |
CenIA/albert-tiny-spanish-finetuned-pawsx | null | Entry not found | 15 |
DSI/human-directed-sentiment | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | ** Human-Directed Sentiment Analysis in Arabic
A supervised training procedure to classify human-directed-sentiment in a text. We define the human-directed-sentiment as the polarity of one user towards a second person who is involved with him in a discussion. | 260 |
DSI/personal_sentiment | [
"LABEL_0",
"LABEL_1",
"LABEL_2",
"LABEL_3"
] | Entry not found | 15 |
DeadBeast/korscm-mBERT | null | ---
language: korean
license: apache-2.0
datasets:
- Korean-Sarcasm
---
# **Korean-mBERT**
This model is a fine-tune checkpoint of mBERT-base-cased over **Hugging Face Kore_Scm** dataset for Text classification.
### **How to use?**
**Task**: binary-classification
- LABEL_1: Sarcasm (*Sarcasm means tweets contains sarcasm*)
- LABEL_0: Not Sarcasm (*Not Sarcasm means tweets do not contain sarcasm*)
Click on **Use in Transformers**!
| 440 |
EthanChen0418/intent_cls | [
"LABEL_0",
"LABEL_1",
"LABEL_2",
"LABEL_3",
"LABEL_4",
"LABEL_5",
"LABEL_6",
"LABEL_7",
"LABEL_8",
"LABEL_9"
] | Entry not found | 15 |
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