modelId stringlengths 6 107 | label list | readme stringlengths 0 56.2k | readme_len int64 0 56.2k |
|---|---|---|---|
begar/xlm-roberta-base-finetuned-marc | [
"good",
"great",
"ok",
"poor",
"terrible"
] | ---
license: mit
tags:
- generated_from_trainer
datasets:
- amazon_reviews_multi
model-index:
- name: xlm-roberta-base-finetuned-marc
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. -->
# xlm-roberta-base-finetuned-marc
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the amazon_reviews_multi dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0276
- Mae: 0.5310
## 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 | Mae |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 1.1582 | 1.0 | 308 | 1.0625 | 0.5221 |
| 1.0091 | 2.0 | 616 | 1.0276 | 0.5310 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.0+cu111
- Datasets 1.17.0
- Tokenizers 0.10.3
| 1,424 |
benjaminbeilharz/bert-base-uncased-sentiment-classifier | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | Entry not found | 15 |
beomi/beep-klue-roberta-base-bias | [
"gender",
"none",
"others"
] | Entry not found | 15 |
bertin-project/bertin-base-paws-x-es | null | ---
language: es
license: cc-by-4.0
tags:
- spanish
- roberta
- paws-x
---
This checkpoint has been trained for the PAWS-X task using the CoNLL 2002-es dataset.
This checkpoint was created from **Bertin Gaussian 512**, which is a **RoBERTa-base** model trained from scratch in Spanish. Information on this base model may be found at [its own card](https://huggingface.co/bertin-project/bertin-base-gaussian-exp-512seqlen) and at deeper detail on [the main project card](https://huggingface.co/bertin-project/bertin-roberta-base-spanish).
The training dataset for the base model is [mc4](https://huggingface.co/datasets/bertin-project/mc4-es-sampled ) subsampling documents to a total of about 50 million examples. Sampling is biased towards average perplexity values (using a Gaussian function), discarding more often documents with very large values (poor quality) of very small values (short, repetitive texts).
This is part of the
[Flax/Jax Community Week](https://discuss.huggingface.co/t/open-to-the-community-community-week-using-jax-flax-for-nlp-cv/7104), organised by [HuggingFace](https://huggingface.co/) and TPU usage sponsored by Google.
## Team members
- Eduardo González ([edugp](https://huggingface.co/edugp))
- Javier de la Rosa ([versae](https://huggingface.co/versae))
- Manu Romero ([mrm8488](https://huggingface.co/))
- María Grandury ([mariagrandury](https://huggingface.co/))
- Pablo González de Prado ([Pablogps](https://huggingface.co/Pablogps))
- Paulo Villegas ([paulo](https://huggingface.co/paulo)) | 1,534 |
bewgle/bart-large-mnli-bewgle | [
"CONTRADICTION",
"NEUTRAL",
"ENTAILMENT"
] | ---
widget :
- text: "I like you. </s></s> I love you."
---
## bart-large-mnli
Trained by Facebook, [original source](https://github.com/pytorch/fairseq/tree/master/examples/bart)
| 182 |
boronbrown48/wangchanberta-sentiment-v2 | [
"LABEL_0",
"LABEL_1",
"LABEL_2",
"LABEL_3",
"LABEL_4",
"LABEL_5",
"LABEL_6"
] | Entry not found | 15 |
boychaboy/MNLI_bert-base-cased_2 | [
"contradiction",
"entailment",
"neutral"
] | Entry not found | 15 |
brcps12/bert-base-finetuned-sts | [
"LABEL_0"
] | Entry not found | 15 |
chitra/finetuned-adversarial-paraphrase-model-test | null | Entry not found | 15 |
chrommium/two-step-finetuning-sbert | [
"LABEL_0",
"LABEL_1",
"LABEL_2",
"LABEL_3",
"LABEL_4",
"LABEL_5",
"LABEL_6"
] | Entry not found | 15 |
chrommium/xlm-roberta-large-finetuned-sent_in_news | [
"LABEL_0",
"LABEL_1",
"LABEL_2",
"LABEL_3",
"LABEL_4",
"LABEL_5",
"LABEL_6"
] | ---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: xlm-roberta-large-finetuned-sent_in_news
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-large-finetuned-sent_in_news
This model is a fine-tuned version of [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8872
- Accuracy: 0.7273
- F1: 0.5125
## Model description
Модель ассиметрична, реагирует на метку X в тексте новости.
Попробуйте следующие примеры:
a) Агентство X понизило рейтинг банка Fitch.
b) Агентство Fitch понизило рейтинг банка X.
a) Компания Финам показала рекордную прибыль, говорят аналитики компании X.
b) Компания X показала рекордную прибыль, говорят аналитики компании Финам.
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 10
- eval_batch_size: 10
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 16
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log | 1.0 | 106 | 1.2526 | 0.6108 | 0.1508 |
| No log | 2.0 | 212 | 1.1553 | 0.6648 | 0.1141 |
| No log | 3.0 | 318 | 1.1150 | 0.6591 | 0.1247 |
| No log | 4.0 | 424 | 1.0007 | 0.6705 | 0.1383 |
| 1.1323 | 5.0 | 530 | 0.9267 | 0.6733 | 0.2027 |
| 1.1323 | 6.0 | 636 | 1.0869 | 0.6335 | 0.4084 |
| 1.1323 | 7.0 | 742 | 1.1224 | 0.6932 | 0.4586 |
| 1.1323 | 8.0 | 848 | 1.2535 | 0.6307 | 0.3424 |
| 1.1323 | 9.0 | 954 | 1.4288 | 0.6932 | 0.4881 |
| 0.5252 | 10.0 | 1060 | 1.5856 | 0.6932 | 0.4739 |
| 0.5252 | 11.0 | 1166 | 1.7101 | 0.6733 | 0.4530 |
| 0.5252 | 12.0 | 1272 | 1.7330 | 0.6903 | 0.4750 |
| 0.5252 | 13.0 | 1378 | 1.8872 | 0.7273 | 0.5125 |
| 0.5252 | 14.0 | 1484 | 1.8797 | 0.7301 | 0.5033 |
| 0.1252 | 15.0 | 1590 | 1.9339 | 0.7330 | 0.5024 |
| 0.1252 | 16.0 | 1696 | 1.9632 | 0.7301 | 0.4967 |
### Framework versions
- Transformers 4.11.2
- Pytorch 1.9.0+cu102
- Datasets 1.12.1
- Tokenizers 0.10.3
| 2,818 |
clem/autonlp-test3-2101787 | [
"not_urgent",
"urgent"
] | ---
tags: autonlp
language: en
widget:
- text: "this can wait"
datasets:
- clem/autonlp-data-test3
---
# Model Trained Using AutoNLP
- Problem type: Binary Classification Urgent/Not Urgent
## Validation Metrics
- Loss: 0.08956164121627808
- 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-test3-2101787
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("clem/autonlp-test3-2101787", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("clem/autonlp-test3-2101787", use_auth_token=True)
inputs = tokenizer("I love AutoNLP", return_tensors="pt")
outputs = model(**inputs)
``` | 956 |
conversify/response-score | null | hello
| 6 |
diegorossi/distilbert-base-uncased-finetuned-sst2 | null | Entry not found | 15 |
diwank/dyda-deberta-pair | [
"LABEL_0",
"LABEL_1",
"LABEL_2",
"LABEL_3",
"LABEL_4"
] | ---
license: mit
---
# diwank/dyda-deberta-pair
Deberta-based Daily Dialog style dialog-act annotations classification model. It takes two sentences as inputs (one previous and one current of a dialog). The previous sentence can be an empty string if this is the first utterance of a speaker in a dialog. Outputs one of four labels (exactly as in the [daily-dialog dataset](https://huggingface.co/datasets/daily_dialog) ): *__dummy__ (0), inform (1), question (2), directive (3), commissive (4)*
## Usage
```python
from simpletransformers.classification import (
ClassificationModel, ClassificationArgs
)
model = ClassificationModel("deberta", "diwank/dyda-deberta-pair")
convert_to_label = lambda n: ["__dummy__ (0), inform (1), question (2), directive (3), commissive (4)".split(', ')[i] for i in n]
predictions, raw_outputs = model.predict([["Say what is the meaning of life?", "I dont know"]])
convert_to_label(predictions) # inform (1)
``` | 975 |
ehdwns1516/klue-roberta-base_sae | [
"yes/no",
"alternative",
"wh- questions",
"prohibitions",
"requirements",
"strong requirements"
] | # klue-roberta-base-sae
* This model trained with Korean dataset.
* Input sentence what you want to grasp intent.
* You can use English, but don't expect accuracy.
klue-roberta-base-kornli DEMO: [Ainize DEMO](https://main-klue-roberta-base-kornli-ehdwns1516.endpoint.ainize.ai/)
klue-roberta-base-kornli API: [Ainize API](https://ainize.web.app/redirect?git_repo=https://github.com/ehdwns1516/KLUE-RoBERTa-base_sae)
## Overview
Language model: [klue/roberta-base](https://huggingface.co/klue/roberta-base)
Language: Korean
Training data: [kor_sae](https://huggingface.co/datasets/kor_sae)
Eval data: [kor_sae](https://huggingface.co/datasets/kor_sae)
Code: See [Ainize Workspace](https://ainize.ai/workspace/create?imageId=hnj95592adzr02xPTqss&git=https://github.com/ehdwns1516/KLUE-RoBERTa-base_sae_notebook)
## Usage
## In Transformers
```
from transformers import AutoTokenizer, pipeline
tokenizer = AutoTokenizer.from_pretrained("ehdwns1516/klue-roberta-base-sae")
classifier = pipeline(
"text-classification",
model="ehdwns1516/klue-roberta-base-kornli",
return_all_scores=True,
)
context = "sentence what you want to grasp intent"
result = dict()
result[0] = classifier(context)[0]
```
| 1,219 |
emfa/danish-bert-botxo-danish-finetuned-hatespeech | null | ---
license: cc-by-4.0
tags:
- generated_from_trainer
model-index:
- name: danish-bert-botxo-danish-finetuned-hatespeech
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. -->
# danish-bert-botxo-danish-finetuned-hatespeech
This model is for a university project and is uploaded for sharing between students. It is training on a danish hate speech labeled training set. Feel free to use it, but as of now, we don't promise any good results ;-)
This model is a fine-tuned version of [Maltehb/danish-bert-botxo](https://huggingface.co/Maltehb/danish-bert-botxo) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3584
## 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: 4.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 315 | 0.3285 |
| 0.2879 | 2.0 | 630 | 0.3288 |
| 0.2879 | 3.0 | 945 | 0.3178 |
| 0.1371 | 4.0 | 1260 | 0.3584 |
### Framework versions
- Transformers 4.12.5
- Pytorch 1.10.0+cu111
- Datasets 1.16.1
- Tokenizers 0.10.3
| 1,701 |
emfa/danish-roberta-botxo-danish-finetuned-hatespeech | null | ---
license: cc-by-4.0
tags:
- generated_from_trainer
model-index:
- name: danish-roberta-botxo-danish-finetuned-hatespeech
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. -->
# danish-roberta-botxo-danish-finetuned-hatespeech
This model is for a university project and is uploaded for sharing between students. It is training on a danish hate speech labeled training set. Feel free to use it, but as of now, we don't promise any good results ;-)
This model is a fine-tuned version of [flax-community/roberta-base-danish](https://huggingface.co/flax-community/roberta-base-danish) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2849
## 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 |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 315 | 0.3074 |
| 0.3016 | 2.0 | 630 | 0.3152 |
| 0.3016 | 3.0 | 945 | 0.2849 |
### Framework versions
- Transformers 4.12.5
- Pytorch 1.10.0+cu111
- Datasets 1.16.1
- Tokenizers 0.10.3
| 1,673 |
fabriceyhc/bert-base-uncased-dbpedia_14 | [
"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"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
- sibyl
datasets:
- dbpedia_14
metrics:
- accuracy
model-index:
- name: bert-base-uncased-dbpedia_14
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: dbpedia_14
type: dbpedia_14
args: dbpedia_14
metrics:
- name: Accuracy
type: accuracy
value: 0.9902857142857143
---
<!-- 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-dbpedia_14
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the dbpedia_14 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0547
- Accuracy: 0.9903
## 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: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 34650
- training_steps: 346500
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.7757 | 0.03 | 2000 | 0.2732 | 0.9880 |
| 0.1002 | 0.06 | 4000 | 0.0620 | 0.9891 |
| 0.0547 | 0.09 | 6000 | 0.0723 | 0.9879 |
| 0.0558 | 0.12 | 8000 | 0.0678 | 0.9875 |
| 0.0534 | 0.14 | 10000 | 0.0554 | 0.9896 |
| 0.0632 | 0.17 | 12000 | 0.0670 | 0.9888 |
| 0.0612 | 0.2 | 14000 | 0.0733 | 0.9873 |
| 0.0667 | 0.23 | 16000 | 0.0623 | 0.9896 |
| 0.0636 | 0.26 | 18000 | 0.0836 | 0.9868 |
| 0.0705 | 0.29 | 20000 | 0.0776 | 0.9855 |
| 0.0726 | 0.32 | 22000 | 0.0805 | 0.9861 |
| 0.0778 | 0.35 | 24000 | 0.0713 | 0.9870 |
| 0.0713 | 0.38 | 26000 | 0.1277 | 0.9805 |
| 0.0965 | 0.4 | 28000 | 0.0810 | 0.9855 |
| 0.0881 | 0.43 | 30000 | 0.0910 | 0.985 |
### Framework versions
- Transformers 4.10.2
- Pytorch 1.7.1
- Datasets 1.6.1
- Tokenizers 0.10.3
| 2,546 |
howey/electra-base-stsb | [
"LABEL_0"
] | Entry not found | 15 |
howey/electra-large-mnli | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | Entry not found | 15 |
llangnickel/long-covid-classification | null | ---
license: mit
---
## long-covid-classification
We fine-tuned bert-base-cased using a [manually curated dataset](https://huggingface.co/llangnickel/long-covid-classification-data) to train a Sequence Classification model able to distinguish between long COVID and non-long COVID-related documents.
## Used hyper parameters
|Parameter|Value|
|---|---|
|Learning rate|3e-5|
|Batch size|16|
|Number of epochs|4|
|Sequence Length|512|
## Metrics
|Precision [%]|Recall [%]|F1-score [%]|
|---|---|---|
|91.18|91.18|91.18|
## How to load the model
```
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("llangnickel/long-covid-classification", use_auth_token=True)
label_dict = {0: "nonLongCOVID", 1: "longCOVID"}
model = AutoModelForSequenceClassification.from_pretrained("llangnickel/long-covid-classification", use_auth_token=True, num_labels=len(label_dict))
```
## Citation
@article{10.1093/database/baac048,
author = {Langnickel, Lisa and Darms, Johannes and Heldt, Katharina and Ducks, Denise and Fluck, Juliane},
title = "{Continuous development of the semantic search engine preVIEW: from COVID-19 to long COVID}",
journal = {Database},
volume = {2022},
year = {2022},
month = {07},
issn = {1758-0463},
doi = {10.1093/database/baac048},
url = {https://doi.org/10.1093/database/baac048},
note = {baac048},
eprint = {https://academic.oup.com/database/article-pdf/doi/10.1093/database/baac048/44371817/baac048.pdf},
} | 1,563 |
mgreenbe/bertlet-base-uncased-for-sequence-classification | null | ---
tags:
- generated_from_trainer
model-index:
- name: bertlet-base-uncased-for-sequence-classification
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. -->
# bertlet-base-uncased-for-sequence-classification
This model is a fine-tuned version of [](https://huggingface.co/) 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: 0
### Framework versions
- Transformers 4.12.5
- Pytorch 1.10.0+cu111
- Datasets 1.15.1
- Tokenizers 0.10.3
| 1,014 |
mofawzy/bert-ajgt | null | ---
language:
- ar
datasets:
- AJGT
tags:
- AJGT
widget:
- text: "يهدي الله من يشاء"
- text: "الاسلوب قذر وقمامه"
---
# BERT-AJGT
Arabic version bert model fine tuned on AJGT dataset
## Data
The model were fine-tuned on ~1800 sentence from twitter for Jordanian dialect.
## Results
| class | precision | recall | f1-score | Support |
|----------|-----------|--------|----------|---------|
| 0 | 0.9462 | 0.9778 | 0.9617 | 90 |
| 1 | 0.9399 | 0.9689 | 0.9542 | 90 |
| Accuracy | | | 0.9611 | 180 |
## How to use
You can use these models by installing `torch` or `tensorflow` and Huggingface library `transformers`. And you can use it directly by initializing it like this:
```python
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model_name="mofawzy/bert-ajgt"
model = AutoModelForSequenceClassification.from_pretrained(model_name,num_labels=2)
tokenizer = AutoTokenizer.from_pretrained(model_name)
```
| 1,003 |
mrm8488/funnel-transformer-intermediate-mnli | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | Entry not found | 15 |
nikunjbjj/jd-resume-model | [
"NEG",
"NEU",
"POS"
] | # Sentiment Analysis in Spanish
## beto-sentiment-analysis
Repository: [https://github.com/finiteautomata/pysentimiento/](https://github.com/finiteautomata/pysentimiento/)
Model trained with TASS 2020 corpus (around ~5k tweets) of several dialects of Spanish. Base model is [BETO](https://github.com/dccuchile/beto), a BERT model trained in Spanish.
Uses `POS`, `NEG`, `NEU` labels.
**Coming soon**: a brief paper describing the model and training.
Enjoy! 🤗
| 458 |
paintingpeter/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: 0.9174193548387096
---
<!-- 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: 0.7713
- Accuracy: 0.9174
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 4.2892 | 1.0 | 318 | 3.2831 | 0.7426 |
| 2.6244 | 2.0 | 636 | 1.8739 | 0.8335 |
| 1.5442 | 3.0 | 954 | 1.1525 | 0.8926 |
| 1.0096 | 4.0 | 1272 | 0.8569 | 0.91 |
| 0.793 | 5.0 | 1590 | 0.7713 | 0.9174 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu111
- Datasets 1.16.1
- Tokenizers 0.10.3
| 1,890 |
philschmid/MiniLMv2-L6-H384-emotion | [
"anger",
"fear",
"joy",
"love",
"sadness",
"surprise"
] | ---
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
model-index:
- name: MiniLMv2-L6-H384-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9215
---
<!-- 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. -->
# MiniLMv2-L6-H384-emotion
This model is a fine-tuned version of [nreimers/MiniLMv2-L6-H384-distilled-from-RoBERTa-Large](https://huggingface.co/nreimers/MiniLMv2-L6-H384-distilled-from-RoBERTa-Large) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2140
- Accuracy: 0.9215
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 8
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.432 | 1.0 | 500 | 0.9992 | 0.6805 |
| 0.8073 | 2.0 | 1000 | 0.5437 | 0.846 |
| 0.4483 | 3.0 | 1500 | 0.3018 | 0.909 |
| 0.2833 | 4.0 | 2000 | 0.2412 | 0.915 |
| 0.2169 | 5.0 | 2500 | 0.2140 | 0.9215 |
| 0.1821 | 6.0 | 3000 | 0.2159 | 0.917 |
| 0.154 | 7.0 | 3500 | 0.2084 | 0.919 |
| 0.1461 | 8.0 | 4000 | 0.2047 | 0.92 |
### Framework versions
- Transformers 4.12.3
- Pytorch 1.9.1
- Datasets 1.15.1
- Tokenizers 0.10.3
| 2,136 |
philschmid/RoBERTa-Banking77 | [
"activate_my_card",
"age_limit",
"apple_pay_or_google_pay",
"atm_support",
"automatic_top_up",
"balance_not_updated_after_bank_transfer",
"balance_not_updated_after_cheque_or_cash_deposit",
"beneficiary_not_allowed",
"cancel_transfer",
"card_about_to_expire",
"card_acceptance",
"card_arrival",... | ---
tags: autonlp
language: en
widget:
- text: "I am still waiting on my card?"
datasets:
- banking77
model-index:
- name: RoBERTa-Banking77
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: "BANKING77"
type: banking77
metrics:
- name: Accuracy
type: accuracy
value: 93.51
- name: Macro F1
type: macro-f1
value: 93.49
- name: Weighted F1
type: weighted-f1
value: 93.49
---
# `RoBERTa-Banking77` trained using autoNLP
- Problem type: Multi-class Classification
## Validation Metrics
- Loss: 0.27382662892341614
- Accuracy: 0.935064935064935
- Macro F1: 0.934939412967268
- Micro F1: 0.935064935064935
- Weighted F1: 0.934939412967268
- Macro Precision: 0.9372295644352715
- Micro Precision: 0.935064935064935
- Weighted Precision: 0.9372295644352717
- Macro Recall: 0.9350649350649349
- Micro Recall: 0.935064935064935
- Weighted Recall: 0.935064935064935
## 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/philschmid/RoBERTa-Banking77
```
Or Python API:
```py
from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
model_id = 'philschmid/RoBERTa-Banking77'
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForSequenceClassification.from_pretrained(model_id)
classifier = pipeline('text-classification', tokenizer=tokenizer, model=model)
classifier('What is the base of the exchange rates?')
``` | 1,673 |
shubh2014shiv/jp_review_sentiments_amzn | null | # Steps to use this model
This model uses tokenizer 'rinna/japanese-roberta-base'. Therefore, below steps are critical to run the model correctly.
1. Create a local root directory on your system and new python environment.
2. Install below requirements
```
transformers==4.12.2
torch==1.10.0
numpy==1.21.3
pandas==1.3.4
sentencepiece==0.1.96
```
3. Go to link: "https://huggingface.co/spaces/shubh2014shiv/Japanese_NLP/tree/main" and download the fine tuned weights "reviewSentiments_jp.pt" in same local root directory.
4. Rename the downloaded weights as "reviewSentiments_jp.pt"
5. Use below code in the newly created environment.
```
from transformers import T5Tokenizer,BertForSequenceClassification
import torch
tokenizer = T5Tokenizer.from_pretrained('rinna/japanese-roberta-base')
japanese_review_text = "履きやすい。タイムセールで購入しました。見た目以上にカッコいいです。(^^)"
encoded_data = tokenizer.batch_encode_plus([japanese_review_text ],
add_special_tokens=True,
return_attention_mask=True,
padding=True,
max_length=200,
return_tensors='pt',
truncation=True)
input_ids = encoded_data['input_ids']
attention_masks = encoded_data['attention_mask']
model = BertForSequenceClassification.from_pretrained("shubh2014shiv/jp_review_sentiments_amzn",
num_labels=2,
output_attentions=False,
output_hidden_states=False)
model.load_state_dict(torch.load('reviewSentiments_jp.pt',map_location=torch.device('cpu')))
inputs = { 'input_ids': input_ids,
'attention_mask': attention_masks}
with torch.no_grad():
outputs = model(**inputs)
logits = outputs.logits
logits = logits.detach().cpu().numpy()
scores = 1 / (1 + np.exp(-1 * logits))
result = {"TEXT (文章)": jp_review_text,'NEGATIVE (ネガティブ)': scores[0][0], 'POSITIVE (ポジティブ)': scores[0][1]}
```
Output:
{'TEXT (文章)': '履きやすい。タイムセールで購入しました。見た目以上にカッコいいです。(^^)', 'NEGATIVE (ネガティブ)': 0.023672901, 'POSITIVE (ポジティブ)': 0.96819043} | 2,383 |
sismetanin/sbert-ru-sentiment-rureviews | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | ---
language:
- ru
tags:
- sentiment analysis
- Russian
---
## SBERT-ru-sentiment-RuReviews
SBERT-ru-sentiment-RuReviews is a [SBERT-Large](https://huggingface.co/sberbank-ai/sbert_large_nlu_ru) model fine-tuned on [RuReviews dataset](https://github.com/sismetanin/rureviews) of Russian-language reviews from the ”Women’s Clothes and Accessories” product category on the primary e-commerce site in Russia.
<table>
<thead>
<tr>
<th rowspan="4">Model</th>
<th rowspan="4">Score<br></th>
<th rowspan="4">Rank</th>
<th colspan="12">Dataset</th>
</tr>
<tr>
<td colspan="6">SentiRuEval-2016<br></td>
<td colspan="2" rowspan="2">RuSentiment</td>
<td rowspan="2">KRND</td>
<td rowspan="2">LINIS Crowd</td>
<td rowspan="2">RuTweetCorp</td>
<td rowspan="2">RuReviews</td>
</tr>
<tr>
<td colspan="3">TC</td>
<td colspan="3">Banks</td>
</tr>
<tr>
<td>micro F1</td>
<td>macro F1</td>
<td>F1</td>
<td>micro F1</td>
<td>macro F1</td>
<td>F1</td>
<td>wighted</td>
<td>F1</td>
<td>F1</td>
<td>F1</td>
<td>F1</td>
<td>F1</td>
</tr>
</thead>
<tbody>
<tr>
<td>SOTA</td>
<td>n/s</td>
<td></td>
<td>76.71</td>
<td>66.40</td>
<td>70.68</td>
<td>67.51</td>
<td>69.53</td>
<td>74.06</td>
<td>78.50</td>
<td>n/s</td>
<td>73.63</td>
<td>60.51</td>
<td>83.68</td>
<td>77.44</td>
</tr>
<tr>
<td>XLM-RoBERTa-Large</td>
<td>76.37</td>
<td>1</td>
<td>82.26</td>
<td>76.36</td>
<td>79.42</td>
<td>76.35</td>
<td>76.08</td>
<td>80.89</td>
<td>78.31</td>
<td>75.27</td>
<td>75.17</td>
<td>60.03</td>
<td>88.91</td>
<td>78.81</td>
</tr>
<tr>
<td>SBERT-Large</td>
<td>75.43</td>
<td>2</td>
<td>78.40</td>
<td>71.36</td>
<td>75.14</td>
<td>72.39</td>
<td>71.87</td>
<td>77.72</td>
<td>78.58</td>
<td>75.85</td>
<td>74.20</td>
<td>60.64</td>
<td>88.66</td>
<td>77.41</td>
</tr>
<tr>
<td>MBARTRuSumGazeta</td>
<td>74.70</td>
<td>3</td>
<td>76.06</td>
<td>68.95</td>
<td>73.04</td>
<td>72.34</td>
<td>71.93</td>
<td>77.83</td>
<td>76.71</td>
<td>73.56</td>
<td>74.18</td>
<td>60.54</td>
<td>87.22</td>
<td>77.51</td>
</tr>
<tr>
<td>Conversational RuBERT</td>
<td>74.44</td>
<td>4</td>
<td>76.69</td>
<td>69.09</td>
<td>73.11</td>
<td>69.44</td>
<td>68.68</td>
<td>75.56</td>
<td>77.31</td>
<td>74.40</td>
<td>73.10</td>
<td>59.95</td>
<td>87.86</td>
<td>77.78</td>
</tr>
<tr>
<td>LaBSE</td>
<td>74.11</td>
<td>5</td>
<td>77.00</td>
<td>69.19</td>
<td>73.55</td>
<td>70.34</td>
<td>69.83</td>
<td>76.38</td>
<td>74.94</td>
<td>70.84</td>
<td>73.20</td>
<td>59.52</td>
<td>87.89</td>
<td>78.47</td>
</tr>
<tr>
<td>XLM-RoBERTa-Base</td>
<td>73.60</td>
<td>6</td>
<td>76.35</td>
<td>69.37</td>
<td>73.42</td>
<td>68.45</td>
<td>67.45</td>
<td>74.05</td>
<td>74.26</td>
<td>70.44</td>
<td>71.40</td>
<td>60.19</td>
<td>87.90</td>
<td>78.28</td>
</tr>
<tr>
<td>RuBERT</td>
<td>73.45</td>
<td>7</td>
<td>74.03</td>
<td>66.14</td>
<td>70.75</td>
<td>66.46</td>
<td>66.40</td>
<td>73.37</td>
<td>75.49</td>
<td>71.86</td>
<td>72.15</td>
<td>60.55</td>
<td>86.99</td>
<td>77.41</td>
</tr>
<tr>
<td>MBART-50-Large-Many-to-Many</td>
<td>73.15</td>
<td>8</td>
<td>75.38</td>
<td>67.81</td>
<td>72.26</td>
<td>67.13</td>
<td>66.97</td>
<td>73.85</td>
<td>74.78</td>
<td>70.98</td>
<td>71.98</td>
<td>59.20</td>
<td>87.05</td>
<td>77.24</td>
</tr>
<tr>
<td>SlavicBERT</td>
<td>71.96</td>
<td>9</td>
<td>71.45</td>
<td>63.03</td>
<td>68.44</td>
<td>64.32</td>
<td>63.99</td>
<td>71.31</td>
<td>72.13</td>
<td>67.57</td>
<td>72.54</td>
<td>58.70</td>
<td>86.43</td>
<td>77.16</td>
</tr>
<tr>
<td>EnRuDR-BERT</td>
<td>71.51</td>
<td>10</td>
<td>72.56</td>
<td>64.74</td>
<td>69.07</td>
<td>61.44</td>
<td>60.21</td>
<td>68.34</td>
<td>74.19</td>
<td>69.94</td>
<td>69.33</td>
<td>56.55</td>
<td>87.12</td>
<td>77.95</td>
</tr>
<tr>
<td>RuDR-BERT</td>
<td>71.14</td>
<td>11</td>
<td>72.79</td>
<td>64.23</td>
<td>68.36</td>
<td>61.86</td>
<td>60.92</td>
<td>68.48</td>
<td>74.65</td>
<td>70.63</td>
<td>68.74</td>
<td>54.45</td>
<td>87.04</td>
<td>77.91</td>
</tr>
<tr>
<td>MBART-50-Large</td>
<td>69.46</td>
<td>12</td>
<td>70.91</td>
<td>62.67</td>
<td>67.24</td>
<td>61.12</td>
<td>60.25</td>
<td>68.41</td>
<td>72.88</td>
<td>68.63</td>
<td>70.52</td>
<td>46.39</td>
<td>86.48</td>
<td>77.52</td>
</tr>
</tbody>
</table>
The table shows per-task scores and a macro-average of those scores to determine a models’s position on the leaderboard. For datasets with multiple evaluation metrics (e.g., macro F1 and weighted F1 for RuSentiment), we use an unweighted average of the metrics as the score for the task when computing the overall macro-average. The same strategy for comparing models’ results was applied in the GLUE benchmark.
## Citation
If you find this repository helpful, feel free to cite our publication:
```
@article{Smetanin2021Deep,
author = {Sergey Smetanin and Mikhail Komarov},
title = {Deep transfer learning baselines for sentiment analysis in Russian},
journal = {Information Processing & Management},
volume = {58},
number = {3},
pages = {102484},
year = {2021},
issn = {0306-4573},
doi = {0.1016/j.ipm.2020.102484}
}
```
Dataset:
```
@INPROCEEDINGS{Smetanin2019Sentiment,
author={Sergey Smetanin and Michail Komarov},
booktitle={2019 IEEE 21st Conference on Business Informatics (CBI)},
title={Sentiment Analysis of Product Reviews in Russian using Convolutional Neural Networks},
year={2019},
volume={01},
pages={482-486},
doi={10.1109/CBI.2019.00062},
ISSN={2378-1963},
month={July}
}
``` | 6,345 |
spencerh/centerpartisan | null | Entry not found | 15 |
sureshs/distilbert-large-sms-spam | [
"not spam",
"spam"
] | # SMS Classifier
Finetuned 'distilbert-large' model for classifying SMS messages. Look at SMS dataset in this hub for your own version. | 136 |
w11wo/javanese-bert-small-imdb-classifier | null | ---
language: jv
tags:
- javanese-bert-small-imdb-classifier
license: mit
datasets:
- w11wo/imdb-javanese
widget:
- text: "Dhuh Gusti, film iki elek banget. Aku getun ndelok !!!"
---
## Javanese BERT Small IMDB Classifier
Javanese BERT Small IMDB Classifier is a movie-classification model based on the [BERT model](https://arxiv.org/abs/1810.04805). It was trained on Javanese IMDB movie reviews.
The model was originally [`w11wo/javanese-bert-small-imdb`](https://huggingface.co/w11wo/javanese-bert-small-imdb) which is then fine-tuned on the [`w11wo/imdb-javanese`](https://huggingface.co/datasets/w11wo/imdb-javanese) dataset consisting of Javanese IMDB movie reviews. It achieved an accuracy of 76.37% on the validation dataset. Many of the techniques used are based on a Hugging Face tutorial [notebook](https://github.com/huggingface/notebooks/blob/master/examples/text_classification.ipynb) written by [Sylvain Gugger](https://github.com/sgugger).
Hugging Face's `Trainer` class from the [Transformers](https://huggingface.co/transformers) library was used to train the model. PyTorch was used as the backend framework during training, but the model remains compatible with TensorFlow nonetheless.
## Model
| Model | #params | Arch. | Training/Validation data (text) |
|---------------------------------------|----------|----------------|---------------------------------|
| `javanese-bert-small-imdb-classifier` | 110M | BERT Small | Javanese IMDB (47.5 MB of text) |
## Evaluation Results
The model was trained for 5 epochs and the following is the final result once the training ended.
| train loss | valid loss | accuracy | total time |
|------------|------------|------------|------------|
| 0.131 | 1.113 | 0.763 | 59:16 |
## How to Use
### As Text Classifier
```python
from transformers import pipeline
pretrained_name = "w11wo/javanese-bert-small-imdb-classifier"
nlp = pipeline(
"sentiment-analysis",
model=pretrained_name,
tokenizer=pretrained_name
)
nlp("Film sing apik banget!")
```
## Disclaimer
Do consider the biases which came from the IMDB review that may be carried over into the results of this model.
## Author
Javanese BERT Small IMDB Classifier was trained and evaluated by [Wilson Wongso](https://w11wo.github.io/). All computation and development are done on Google Colaboratory using their free GPU access.
## Citation
If you use any of our models in your research, please cite:
```bib
@inproceedings{wongso2021causal,
title={Causal and Masked Language Modeling of Javanese Language using Transformer-based Architectures},
author={Wongso, Wilson and Setiawan, David Samuel and Suhartono, Derwin},
booktitle={2021 International Conference on Advanced Computer Science and Information Systems (ICACSIS)},
pages={1--7},
year={2021},
organization={IEEE}
}
```
| 2,913 |
yoshitomo-matsubara/bert-base-uncased-qnli | null | ---
language: en
tags:
- bert
- qnli
- glue
- torchdistill
license: apache-2.0
datasets:
- qnli
metrics:
- accuracy
---
`bert-base-uncased` fine-tuned on QNLI 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/qnli/ce/bert_base_uncased.yaml).
I submitted prediction files to [the GLUE leaderboard](https://gluebenchmark.com/leaderboard), and the overall GLUE score was **77.9**.
| 826 |
inovex/multi2convai-logistics-en-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: "Where can I put the parcel?"
license: mit
language: en
---
# Multi2ConvAI-Logistics: finetuned Bert for English
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: English (en)
- 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-en-bert")
model = AutoModelForSequenceClassification.from_pretrained("inovex/multi2convai-logistics-en-bert")
````
## Further information on Multi2ConvAI:
- https://multi2conv.ai
- https://github.com/inovex/multi2convai
- mailto: info@multi2conv.ai | 983 |
inovex/multi2convai-quality-de-bert | [
"neo.magnetklammern",
"neo.start",
"neo.back",
"neo.gearbox",
"neo.motor.brushcollar",
"neo.motor.worm",
"neo.magnet",
"neo.magnetisierung",
"neo.motor",
"neo.verschaubung",
"neo.zusammenfuehrung",
"neo.zahnradgross",
"neo.zahnradklein",
"neo.yes",
"neo.no",
"neo.einpressen",
"neo.mo... | ---
tags:
- text-classification
widget:
- text: "Starte das Programm"
license: mit
language: de
---
# Multi2ConvAI-Quality: finetuned Bert for German
This model was developed in the [Multi2ConvAI](https://multi2conv.ai) project:
- domain: Quality (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-quality-de-bert")
model = AutoModelForSequenceClassification.from_pretrained("inovex/multi2convai-quality-de-bert")
````
## Further information on Multi2ConvAI:
- https://multi2conv.ai
- https://github.com/inovex/multi2convai
- mailto: info@multi2conv.ai | 965 |
inovex/multi2convai-quality-it-mbert | [
"neo.magnetklammern",
"neo.start",
"neo.back",
"neo.gearbox",
"neo.motor.brushcollar",
"neo.motor.worm",
"neo.magnet",
"neo.magnetisierung",
"neo.motor",
"neo.verschaubung",
"neo.zusammenfuehrung",
"neo.zahnradgross",
"neo.zahnradklein",
"neo.yes",
"neo.no",
"neo.einpressen",
"neo.mo... | ---
tags:
- text-classification
widget:
- text: "Avviare il programma"
license: mit
language: it
---
# Multi2ConvAI-Quality: finetuned MBert for Italian
This model was developed in the [Multi2ConvAI](https://multi2conv.ai) project:
- domain: Quality (more details about our use cases: ([en](https://multi2convai/en/blog/use-cases), [de](https://multi2convai/en/blog/use-cases)))
- language: Italian (it)
- model type: finetuned MBert
## How to run
Requires:
- Huggingface transformers
### Run with Huggingface Transformers
````python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("inovex/multi2convai-quality-it-mbert")
model = AutoModelForSequenceClassification.from_pretrained("inovex/multi2convai-quality-it-mbert")
````
## Further information on Multi2ConvAI:
- https://multi2conv.ai
- https://github.com/inovex/multi2convai
- mailto: info@multi2conv.ai | 972 |
DoyyingFace/bert-asian-hate-tweets-self-clean-small-epoch5-warmup-50 | null | Entry not found | 15 |
nsi319/xlnet-base-cased-finetuned-app | [
"Education",
"Entertainment",
"News & Magazines",
"Photography",
"Productivity",
"Sports"
] | ---
language: "en"
thumbnail: "https://huggingface.co/nsi319"
tags:
- xlnet
- pytorch
- text-classification
- mobile app descriptions
- playstore
license: "mit"
inference: true
---
# Mobile App Classification
## Model description
XLNet is a new unsupervised language representation learning method based on a novel generalized permutation language modeling objective. Additionally, XLNet employs Transformer-XL as the backbone model, exhibiting excellent performance for language tasks involving long context.
The [xlnet-base-cased](https://huggingface.co/xlnet-base-cased) model is fine-tuned to classify an mobile app description into one of **6 play store categories**.
Trained on 9000 samples of English App Descriptions and associated categories of apps available in [Google Play](https://play.google.com/store/apps).
## Fine-tuning
The model was fine-tuned for 5 epochs with a batch size of 16, a learning rate of 2e-05, and a maximum sequence length of 512. Since this was a classification task, the model was trained with a cross-entropy loss function. The best evaluation f1 score achieved by the model was 0.8951433611497919, found after 5 epochs. The accuracy of the model on the test set was 0.895.
## How to use
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
tokenizer = AutoTokenizer.from_pretrained("nsi319/xlnet-base-cased-finetuned-app")
model = AutoModelForSequenceClassification.from_pretrained("nsi319/xlnet-base-cased-finetuned-app")
classifier = pipeline('sentiment-analysis', model=model, tokenizer=tokenizer)
classifier("The official Google Photos app is made for the way you take photos today and includes essential features like shared albums, automatic creations and an advanced editing suite. Additionally every Google Account comes with 15 GB of free storage and you can choose to automatically back up all your photos and videos in High quality or Original quality. You can then access them from any connected device and on photos.google.com.")
'''Output'''
[{'label': 'Photography', 'score': 0.998849630355835}]
```
## Limitations
Training data consists of apps from 6 play store categories namely Education, Entertainment, Productivity, Sports, News & Magazines and Photography.
| 2,278 |
alk/distilbert-base-uncased-finetuned-emotion | [
"LABEL_0",
"LABEL_1",
"LABEL_2",
"LABEL_3",
"LABEL_4",
"LABEL_5"
] | Entry not found | 15 |
zdepablo/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: 0.924
- name: F1
type: f1
value: 0.9241594821961092
---
<!-- 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: 0.2311
- Accuracy: 0.924
- F1: 0.9242
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8868 | 1.0 | 250 | 0.3435 | 0.9005 | 0.8980 |
| 0.2686 | 2.0 | 500 | 0.2311 | 0.924 | 0.9242 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.9.1
- Datasets 1.16.1
- Tokenizers 0.10.3
| 1,798 |
danielbubiola/fine_tuned_text_clf_model | [
"LABEL_0",
"LABEL_1",
"LABEL_2",
"LABEL_3",
"LABEL_4",
"LABEL_5"
] | Entry not found | 15 |
ebrigham/yahoo_answers_topics_classifier | [
"climate change",
"culture",
"democratic values",
"digital",
"education",
"employment and inclusion",
"environmental sustainability",
"european learning mobility",
"health and well-being",
"n/a",
"participation and engagement",
"policy dialogues",
"renewable energy",
"research and innovati... | Entry not found | 15 |
radev/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: 0.8945
- name: F1
type: f1
value: 0.8871610121255439
---
<!-- 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: 0.3645
- Accuracy: 0.8945
- F1: 0.8872
## 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 | 0.5816 | 0.8015 | 0.7597 |
| 0.7707 | 2.0 | 250 | 0.3645 | 0.8945 | 0.8872 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.11.0
- Datasets 1.16.1
- Tokenizers 0.10.3
| 1,803 |
ScandinavianMrT/distilbert_ONION_1epoch | null | Entry not found | 15 |
gui-marra/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: 0.3148
- 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 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.11.6
| 1,520 |
clapika2010/training | null | Entry not found | 15 |
rahulacj/bertweet-base-finetuned-sentiment-analysis | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | ---
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: bertweet-base-finetuned-sentiment-analysis
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. -->
# bertweet-base-finetuned-sentiment-analysis
This model is a fine-tuned version of [cardiffnlp/bertweet-base-sentiment](https://huggingface.co/cardiffnlp/bertweet-base-sentiment) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8458
- Accuracy: 0.6426
- F1: 0.6397
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8904 | 1.0 | 630 | 0.8509 | 0.6381 | 0.6340 |
| 0.7655 | 2.0 | 1260 | 0.8345 | 0.6579 | 0.6559 |
| 0.66 | 3.0 | 1890 | 0.9199 | 0.6548 | 0.6514 |
| 0.447 | 4.0 | 2520 | 1.0324 | 0.6429 | 0.6417 |
| 0.3585 | 5.0 | 3150 | 1.1234 | 0.6452 | 0.6424 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.12.0
| 1,721 |
blacktree/distilbert-base-uncased-finetuned-sst2 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased-finetuned-sst2
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
args: sst2
metrics:
- name: Accuracy
type: accuracy
value: 0.5091743119266054
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-sst2
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: 0.7027
- Accuracy: 0.5092
## 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: 0.01
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6868 | 1.0 | 1053 | 0.7027 | 0.5092 |
| 0.6868 | 2.0 | 2106 | 0.7027 | 0.5092 |
| 0.6867 | 3.0 | 3159 | 0.6970 | 0.5092 |
| 0.687 | 4.0 | 4212 | 0.6992 | 0.5092 |
| 0.6866 | 5.0 | 5265 | 0.6983 | 0.5092 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.11.6
| 1,866 |
Kalaoke/bert-finetuned-sentiment | [
"NEGATIVE",
"NEUTRAL",
"POSITIVE"
] | ---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: bert-finetuned-sentiment
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-finetuned-sentiment
This model is a fine-tuned version of [nlptown/bert-base-multilingual-uncased-sentiment](https://huggingface.co/nlptown/bert-base-multilingual-uncased-sentiment) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4884
- Accuracy: 0.7698
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6778 | 1.0 | 722 | 0.7149 | 0.7482 |
| 0.3768 | 2.0 | 1444 | 0.9821 | 0.7410 |
| 0.1612 | 3.0 | 2166 | 1.4027 | 0.7662 |
| 0.094 | 4.0 | 2888 | 1.4884 | 0.7698 |
| 0.0448 | 5.0 | 3610 | 1.6463 | 0.7590 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.10.0+cu111
- Datasets 2.1.0
- Tokenizers 0.12.1
| 1,643 |
SupritiVijay/fake-news-detector | [
"LABEL_0"
] | Entry not found | 15 |
GioReg/BertMultiHateSpeech | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: BertMultiHateSpeech
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. -->
# BertMultiHateSpeech
This model is a fine-tuned version of [bert-base-multilingual-uncased](https://huggingface.co/bert-base-multilingual-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7496
- Accuracy: 0.74
- F1: 0.4841
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
### Framework versions
- Transformers 4.18.0
- Pytorch 1.10.0+cu111
- Datasets 2.1.0
- Tokenizers 0.12.1
| 1,184 |
ChrisZeng/twitter-roberta-base-efl-hateval | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | ---
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: twitter-roberta-base-efl-hateval
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# twitter-roberta-base-efl-hateval
This model is a fine-tuned version of [cardiffnlp/twitter-roberta-base-2021-124m](https://huggingface.co/cardiffnlp/twitter-roberta-base-2021-124m) on the HatEval dataset.
It achieves the following results on the evaluation set:
- Accuracy: 0.7913
- F1: 0.7899
- Loss: 0.3683
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-06
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30
### Training results
| Training Loss | Epoch | Step | Accuracy | F1 | Validation Loss |
|:-------------:|:-----:|:----:|:--------:|:------:|:---------------:|
| 0.5392 | 1.0 | 211 | 0.7 | 0.6999 | 0.4048 |
| 0.3725 | 2.0 | 422 | 0.759 | 0.7584 | 0.3489 |
| 0.3158 | 3.0 | 633 | 0.7613 | 0.7570 | 0.3287 |
| 0.289 | 4.0 | 844 | 0.769 | 0.7684 | 0.3307 |
| 0.2716 | 5.0 | 1055 | 0.7767 | 0.7750 | 0.3241 |
| 0.2575 | 6.0 | 1266 | 0.7787 | 0.7782 | 0.3272 |
| 0.2441 | 7.0 | 1477 | 0.7783 | 0.7776 | 0.3258 |
| 0.2363 | 8.0 | 1688 | 0.7777 | 0.7773 | 0.3316 |
| 0.2262 | 9.0 | 1899 | 0.7843 | 0.7815 | 0.3150 |
| 0.2191 | 10.0 | 2110 | 0.7813 | 0.7802 | 0.3241 |
| 0.2112 | 11.0 | 2321 | 0.7867 | 0.7860 | 0.3276 |
| 0.2047 | 12.0 | 2532 | 0.7897 | 0.7886 | 0.3266 |
| 0.1973 | 13.0 | 2743 | 0.7893 | 0.7884 | 0.3299 |
| 0.1897 | 14.0 | 2954 | 0.792 | 0.7907 | 0.3301 |
| 0.1862 | 15.0 | 3165 | 0.794 | 0.7925 | 0.3283 |
| 0.1802 | 16.0 | 3376 | 0.7907 | 0.7903 | 0.3465 |
| 0.1764 | 17.0 | 3587 | 0.7937 | 0.7922 | 0.3393 |
| 0.1693 | 18.0 | 3798 | 0.7903 | 0.7893 | 0.3494 |
| 0.1666 | 19.0 | 4009 | 0.7943 | 0.7930 | 0.3486 |
| 0.1631 | 20.0 | 4220 | 0.7927 | 0.7917 | 0.3516 |
| 0.1609 | 21.0 | 4431 | 0.7907 | 0.7893 | 0.3537 |
| 0.1581 | 22.0 | 4642 | 0.7913 | 0.7902 | 0.3586 |
| 0.1548 | 23.0 | 4853 | 0.789 | 0.7884 | 0.3698 |
| 0.1535 | 24.0 | 5064 | 0.7893 | 0.7880 | 0.3622 |
| 0.1522 | 25.0 | 5275 | 0.7923 | 0.7909 | 0.3625 |
| 0.15 | 26.0 | 5486 | 0.7913 | 0.7899 | 0.3632 |
| 0.1479 | 27.0 | 5697 | 0.792 | 0.7909 | 0.3677 |
| 0.1441 | 28.0 | 5908 | 0.792 | 0.7909 | 0.3715 |
| 0.145 | 29.0 | 6119 | 0.792 | 0.7906 | 0.3681 |
| 0.1432 | 30.0 | 6330 | 0.7913 | 0.7899 | 0.3683 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.11.0+cu113
- Datasets 2.0.0
- Tokenizers 0.11.6
| 3,554 |
adache/distilbert-base-uncased-finetuned-emotion | [
"LABEL_0",
"LABEL_1",
"LABEL_2",
"LABEL_3",
"LABEL_4",
"LABEL_5"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotion
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2270
- Accuracy: 0.9245
- F1: 0.9249
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8398 | 1.0 | 250 | 0.3276 | 0.9005 | 0.8966 |
| 0.2541 | 2.0 | 500 | 0.2270 | 0.9245 | 0.9249 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.10.0+cu111
- Tokenizers 0.11.6
| 1,487 |
cj-mills/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: 0.9161290322580645
---
<!-- 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: 0.7796
- Accuracy: 0.9161
## 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
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 4.2938 | 1.0 | 318 | 3.2905 | 0.7410 |
| 2.6346 | 2.0 | 636 | 1.8833 | 0.8326 |
| 1.5554 | 3.0 | 954 | 1.1650 | 0.8926 |
| 1.0189 | 4.0 | 1272 | 0.8636 | 0.9110 |
| 0.8028 | 5.0 | 1590 | 0.7796 | 0.9161 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0
- Datasets 2.0.0
- Tokenizers 0.12.1
| 1,922 |
Raychanan/COVID | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: results
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# results
This model is a fine-tuned version of [hfl/chinese-bert-wwm-ext](https://huggingface.co/hfl/chinese-bert-wwm-ext) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5193
- F1: 0.9546
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.3803 | 1.0 | 1792 | 0.5110 | 0.9546 |
| 0.4129 | 2.0 | 3584 | 0.5256 | 0.9546 |
| 0.4804 | 3.0 | 5376 | 0.5305 | 0.9546 |
| 0.6571 | 4.0 | 7168 | 0.5583 | 0.9546 |
| 0.6605 | 5.0 | 8960 | 0.5193 | 0.9546 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.10.0+cu111
- Datasets 2.1.0
- Tokenizers 0.12.1
| 1,542 |
MartinoMensio/racism-models-raw-label-epoch-1 | null | ---
language: es
license: mit
widget:
- text: "y porqué es lo que hay que hacer con los menas y con los adultos también!!!! NO a los inmigrantes ilegales!!!!"
---
### Description
This model is a fine-tuned version of [BETO (spanish bert)](https://huggingface.co/dccuchile/bert-base-spanish-wwm-uncased) that has been trained on the *Datathon Against Racism* dataset (2022)
We performed several experiments that will be described in the upcoming paper "Estimating Ground Truth in a Low-labelled Data Regime:A Study of Racism Detection in Spanish" (NEATClasS 2022)
We applied 6 different methods ground-truth estimations, and for each one we performed 4 epochs of fine-tuning. The result is made of 24 models:
| method | epoch 1 | epoch 3 | epoch 3 | epoch 4 |
|--- |--- |--- |--- |--- |
| raw-label | [raw-label-epoch-1](https://huggingface.co/MartinoMensio/racism-models-raw-label-epoch-1) | [raw-label-epoch-2](https://huggingface.co/MartinoMensio/racism-models-raw-label-epoch-2) | [raw-label-epoch-3](https://huggingface.co/MartinoMensio/racism-models-raw-label-epoch-3) | [raw-label-epoch-4](https://huggingface.co/MartinoMensio/racism-models-raw-label-epoch-4) |
| m-vote-strict | [m-vote-strict-epoch-1](https://huggingface.co/MartinoMensio/racism-models-m-vote-strict-epoch-1) | [m-vote-strict-epoch-2](https://huggingface.co/MartinoMensio/racism-models-m-vote-strict-epoch-2) | [m-vote-strict-epoch-3](https://huggingface.co/MartinoMensio/racism-models-m-vote-strict-epoch-3) | [m-vote-strict-epoch-4](https://huggingface.co/MartinoMensio/racism-models-m-vote-strict-epoch-4) |
| m-vote-nonstrict | [m-vote-nonstrict-epoch-1](https://huggingface.co/MartinoMensio/racism-models-m-vote-nonstrict-epoch-1) | [m-vote-nonstrict-epoch-2](https://huggingface.co/MartinoMensio/racism-models-m-vote-nonstrict-epoch-2) | [m-vote-nonstrict-epoch-3](https://huggingface.co/MartinoMensio/racism-models-m-vote-nonstrict-epoch-3) | [m-vote-nonstrict-epoch-4](https://huggingface.co/MartinoMensio/racism-models-m-vote-nonstrict-epoch-4) |
| regression-w-m-vote | [regression-w-m-vote-epoch-1](https://huggingface.co/MartinoMensio/racism-models-regression-w-m-vote-epoch-1) | [regression-w-m-vote-epoch-2](https://huggingface.co/MartinoMensio/racism-models-regression-w-m-vote-epoch-2) | [regression-w-m-vote-epoch-3](https://huggingface.co/MartinoMensio/racism-models-regression-w-m-vote-epoch-3) | [regression-w-m-vote-epoch-4](https://huggingface.co/MartinoMensio/racism-models-regression-w-m-vote-epoch-4) |
| w-m-vote-strict | [w-m-vote-strict-epoch-1](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-strict-epoch-1) | [w-m-vote-strict-epoch-2](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-strict-epoch-2) | [w-m-vote-strict-epoch-3](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-strict-epoch-3) | [w-m-vote-strict-epoch-4](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-strict-epoch-4) |
| w-m-vote-nonstrict | [w-m-vote-nonstrict-epoch-1](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-nonstrict-epoch-1) | [w-m-vote-nonstrict-epoch-2](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-nonstrict-epoch-2) | [w-m-vote-nonstrict-epoch-3](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-nonstrict-epoch-3) | [w-m-vote-nonstrict-epoch-4](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-nonstrict-epoch-4) |
This model is `raw-label-epoch-1`
### Usage
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
model_name = 'raw-label-epoch-1'
tokenizer = AutoTokenizer.from_pretrained("dccuchile/bert-base-spanish-wwm-uncased")
full_model_path = f'MartinoMensio/racism-models-{model_name}'
model = AutoModelForSequenceClassification.from_pretrained(full_model_path)
pipe = pipeline("text-classification", model = model, tokenizer = tokenizer)
texts = [
'y porqué es lo que hay que hacer con los menas y con los adultos también!!!! NO a los inmigrantes ilegales!!!!',
'Es que los judíos controlan el mundo'
]
print(pipe(texts))
# [{'label': 'racist', 'score': 0.7924597263336182}, {'label': 'non-racist', 'score': 0.9130864143371582}]
```
For more details, see https://github.com/preyero/neatclass22
| 4,252 |
avacaondata/bertin-exist22-task1 | null | Entry not found | 15 |
UT/BRTW_DEBIAS | null | Entry not found | 15 |
cassiepowell/RoBERTa-large-mnli-for-agreement | [
"0",
"1",
"2"
] | Entry not found | 15 |
Rerare/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: 0.5291140309961344
---
<!-- 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: 0.7643
- Matthews Correlation: 0.5291
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.5288 | 1.0 | 535 | 0.5111 | 0.4154 |
| 0.3546 | 2.0 | 1070 | 0.5285 | 0.4887 |
| 0.235 | 3.0 | 1605 | 0.5950 | 0.5153 |
| 0.1722 | 4.0 | 2140 | 0.7643 | 0.5291 |
| 0.1346 | 5.0 | 2675 | 0.8441 | 0.5185 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0+cu113
- Datasets 2.1.0
- Tokenizers 0.12.1
| 1,999 |
Sathira/autotrain-mbtiNlp-798824628 | [
"ENFJ",
"ENFP",
"ENTJ",
"ENTP",
"ESFJ",
"ESFP",
"ESTJ",
"ESTP",
"INFJ",
"INFP",
"INTJ",
"INTP",
"ISFJ",
"ISFP",
"ISTJ",
"ISTP"
] | ---
tags: autotrain
language: en
widget:
- text: "I love AutoTrain 🤗"
datasets:
- Sathira/autotrain-data-mbtiNlp
co2_eq_emissions: 121.67185089502216
---
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 798824628
- CO2 Emissions (in grams): 121.67185089502216
## Validation Metrics
- Loss: 0.5046824812889099
- Accuracy: 0.8472124039775673
- Macro F1: 0.7812978033330673
- Micro F1: 0.8472124039775673
- Weighted F1: 0.8464983956259307
- Macro Precision: 0.812208631055716
- Micro Precision: 0.8472124039775673
- Weighted Precision: 0.8478968364150775
- Macro Recall: 0.7593223884993787
- Micro Recall: 0.8472124039775673
- Weighted Recall: 0.8472124039775673
## 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/Sathira/autotrain-mbtiNlp-798824628
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("Sathira/autotrain-mbtiNlp-798824628", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("Sathira/autotrain-mbtiNlp-798824628", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
``` | 1,379 |
FremyCompany/tmpxcg_kes9 | null | Entry not found | 15 |
charly/autotrain-sentiment-4-812425472 | [
"mixed",
"negative",
"no_impact",
"positive"
] | ---
tags: autotrain
language: en
widget:
- text: "I love AutoTrain 🤗"
datasets:
- charly/autotrain-data-sentiment-4
co2_eq_emissions: 0.007597570744740809
---
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 812425472
- CO2 Emissions (in grams): 0.007597570744740809
## Validation Metrics
- Loss: 0.5105093121528625
- Accuracy: 0.8268156424581006
- Macro F1: 0.6020923520923521
- Micro F1: 0.8268156424581006
- Weighted F1: 0.8021395116367184
- Macro Precision: 0.5907986111111111
- Micro Precision: 0.8268156424581006
- Weighted Precision: 0.7792248603351954
- Macro Recall: 0.6141625496464206
- Micro Recall: 0.8268156424581006
- Weighted Recall: 0.8268156424581006
## 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/charly/autotrain-sentiment-4-812425472
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("charly/autotrain-sentiment-4-812425472", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("charly/autotrain-sentiment-4-812425472", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
``` | 1,396 |
anshr/distilgpt2_reward_model_final | null | Entry not found | 15 |
svalabs/twitter-xlm-roberta-crypto-spam | null | Entry not found | 15 |
Nakul24/RoBERTa-emotion-classification | [
"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",... | Entry not found | 15 |
Colorful/BureBERT | null | ---
license: mit
---
BureBERT is a pre-trained language model for bug reports. It can be fine-tuned on all kinds of bug report related tasks such as bug report summarization, duplicate bug report detection, bug priority prediction, etc. | 237 |
CleveGreen/FieldClassifier_v3_gpt | [
"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_3",
"LABEL_4",
"LABEL_5",
"LABEL_6",
"... | Entry not found | 15 |
Jeevesh8/6ep_bert_ft_cola-53 | null | Entry not found | 15 |
Jeevesh8/6ep_bert_ft_cola-67 | null | Entry not found | 15 |
Jeevesh8/6ep_bert_ft_cola-70 | null | Entry not found | 15 |
Jeevesh8/6ep_bert_ft_cola-72 | null | Entry not found | 15 |
Jeevesh8/6ep_bert_ft_cola-80 | null | Entry not found | 15 |
Saripudin/distilbert-base-uncased-finetuned-ag-news | [
"LABEL_0",
"LABEL_1",
"LABEL_2",
"LABEL_3"
] | ---
language: en
license: apache-2.0
datasets:
- ag-news
--- | 60 |
CEBaB/lstm.CEBaB.absa.inclusive.seed_99 | [
"0",
"1",
"2"
] | Entry not found | 15 |
Jeevesh8/512seq_len_6ep_bert_ft_cola-72 | null | Entry not found | 15 |
stplgg/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: 0.9230160877762784
---
<!-- 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: 0.2229
- Accuracy: 0.923
- F1: 0.9230
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8655 | 1.0 | 250 | 0.3228 | 0.907 | 0.9038 |
| 0.2625 | 2.0 | 500 | 0.2229 | 0.923 | 0.9230 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.1
- Tokenizers 0.12.1
| 1,804 |
connectivity/feather_berts_15 | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | Entry not found | 15 |
connectivity/feather_berts_16 | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | Entry not found | 15 |
connectivity/feather_berts_17 | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | Entry not found | 15 |
connectivity/feather_berts_26 | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | Entry not found | 15 |
connectivity/feather_berts_27 | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | Entry not found | 15 |
connectivity/cola_6ep_ft-41 | null | Entry not found | 15 |
connectivity/cola_6ep_ft-42 | null | Entry not found | 15 |
GioReg/dbmdzHateSpeech | null | ---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: dbmdzHateSpeech
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. -->
# dbmdzHateSpeech
This model is a fine-tuned version of [dbmdz/bert-base-italian-uncased](https://huggingface.co/dbmdz/bert-base-italian-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7919
- Accuracy: 0.706
- F1: 0.3524
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
| 1,174 |
arize-ai/distilbert_reviews_with_context_drift | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- reviews_with_drift
metrics:
- accuracy
- f1
model-index:
- name: distilbert_finetuned_reviews_with_drift
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: reviews_with_drift
type: reviews_with_drift
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.854780153287616
- name: F1
type: f1
value: 0.8547073010596418
---
<!-- 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_reviews_with_drift
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the reviews_with_drift dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3822
- Accuracy: 0.8548
- F1: 0.8547
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.4173 | 1.0 | 620 | 0.3519 | 0.8511 | 0.8511 |
| 0.259 | 2.0 | 1240 | 0.3822 | 0.8548 | 0.8547 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
| 1,857 |
tartuNLP/mtee-domain-detection | [
"LABEL_0",
"LABEL_1",
"LABEL_2",
"LABEL_3"
] | ---
language:
- et
- en
- ru
- de
tags:
- text-classification
widget:
- text: "Täna lõppes Valgamaa õppuse Siil aktiivne lahingutegevus, mille käigus pidi täielikult formeeritud 2. jalaväebrigaad kaitsma end vastase pealetungi eest."
---
A domain detection model for the MTee machine translation platform. The platform was developed in 2021 as a collaboration between the [TartuNLP](https://tartunlp.ai), the NLP research group at the University of Tartu, and [Tilde](https://tilde.com). More information about the project can be found [here](https://github.com/Project-MTee/mtee-platform/wiki).
#### Model Description
The model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base). It classifies the input sentence into one of the following four domains: `general`, `crisis`, `legal`, `military`. | 850 |
Yah216/Poem_Qafiyah_Detection | [
"ء",
"ؤ",
"ا",
"ب",
"ت",
"ث",
"ج",
"ح",
"خ",
"د",
"ذ",
"ر",
"ز",
"س",
"ش",
"ص",
"ض",
"ط",
"طن",
"ظ",
"ع",
"غ",
"ف",
"ق",
"ك",
"ل",
"لا",
"م",
"ن",
"ه",
"هـ",
"هن",
"و",
"ى",
"ي"
] | ---
language: ar
datasets:
- Yah216/Poem_Rawiy_detection
co2_eq_emissions: 1.8046766441629636
widget:
- "سَلو قَلبي غَداةَ سَلا وَثابا لَعَلَّ عَلى الجَمالِ لَهُ عِتاب"
---
# Model
- Problem type: Multi-class Classification
- CO2 Emissions (in grams): 1.8046766441629636
## Dataset
We used the APCD dataset cited hereafter for pretraining the model. The dataset has been cleaned and only the main text and the Qafiyah column were kept:
```
@Article{Yousef2019LearningMetersArabicEnglish-arxiv,
author = {Yousef, Waleed A. and Ibrahime, Omar M. and Madbouly, Taha M. and Mahmoud,
Moustafa A.},
title = {Learning Meters of Arabic and English Poems With Recurrent Neural Networks: a Step
Forward for Language Understanding and Synthesis},
journal = {arXiv preprint arXiv:1905.05700},
year = 2019,
url = {https://github.com/hci-lab/LearningMetersPoems}
}
```
## Validation Metrics
- Loss: 0.398613303899765
- Accuracy: 0.912351981006084
- Macro F1: 0.717311758991278
- Micro F1: 0.912351981006084
- Weighted F1: 0.9110094798809955
## 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/Yah216/Poem_Rawiy_detection
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("Yah216/Poem_Qafiyah_Detection", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("Yah216/Poem_Qafiyah_Detection", use_auth_token=True)
inputs = tokenizer("text, return_tensors="pt")
outputs = model(**inputs)
``` | 1,759 |
CH0KUN/autotrain-TNC_Data2500_WangchanBERTa-928030564 | [
"Applied Science",
"Arts",
"Belief & Thought",
"Commerce & Finance",
"History",
"Imaginative",
"Natural & Pure Science",
"Social Science "
] | ---
tags: autotrain
language: unk
widget:
- text: "I love AutoTrain 🤗"
datasets:
- CH0KUN/autotrain-data-TNC_Data2500_WangchanBERTa
co2_eq_emissions: 0.07293362913158113
---
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 928030564
- CO2 Emissions (in grams): 0.07293362913158113
## Validation Metrics
- Loss: 0.4989683926105499
- Accuracy: 0.8445845697329377
- Macro F1: 0.8407629450432429
- Micro F1: 0.8445845697329377
- Weighted F1: 0.8407629450432429
- Macro Precision: 0.8390327354531153
- Micro Precision: 0.8445845697329377
- Weighted Precision: 0.8390327354531154
- Macro Recall: 0.8445845697329377
- Micro Recall: 0.8445845697329377
- Weighted Recall: 0.8445845697329377
## 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/CH0KUN/autotrain-TNC_Data2500_WangchanBERTa-928030564
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("CH0KUN/autotrain-TNC_Data2500_WangchanBERTa-928030564", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("CH0KUN/autotrain-TNC_Data2500_WangchanBERTa-928030564", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
``` | 1,455 |
Jeevesh8/lecun_feather_berts-46 | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | Entry not found | 15 |
Jeevesh8/lecun_feather_berts-73 | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | Entry not found | 15 |
Jeevesh8/lecun_feather_berts-20 | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | Entry not found | 15 |
momo/KcELECTRA-base_Hate_speech_Privacy_Detection | null | ---
license: apache-2.0
---
| 28 |
gokuls/tiny-bert-sst2-distilled-model | [
"negative",
"positive"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: tiny-bert-sst2-distilled-model
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
args: sst2
metrics:
- name: Accuracy
type: accuracy
value: 0.838302752293578
---
<!-- 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-sst2-distilled-model
This model is a fine-tuned version of [google/bert_uncased_L-2_H-128_A-2](https://huggingface.co/google/bert_uncased_L-2_H-128_A-2) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2592
- Accuracy: 0.8383
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 6e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 33
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 7
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.5303 | 1.0 | 4210 | 1.2542 | 0.8222 |
| 0.4503 | 2.0 | 8420 | 1.1260 | 0.8211 |
| 0.3689 | 3.0 | 12630 | 1.2325 | 0.8234 |
| 0.3122 | 4.0 | 16840 | 1.2533 | 0.8337 |
| 0.2764 | 5.0 | 21050 | 1.2726 | 0.8337 |
| 0.254 | 6.0 | 25260 | 1.2609 | 0.8337 |
| 0.2358 | 7.0 | 29470 | 1.2592 | 0.8383 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.10.1+cu113
- Datasets 1.15.1
- Tokenizers 0.12.1
| 2,043 |
RogerKam/roberta_fine_tuned_sentiment_financial_news | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | ---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: roberta_fine_tuned_sentiment_financial_news
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta_fine_tuned_sentiment_financial_news
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6362
- Accuracy: 0.8826
- F1 Score: 0.8865
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
### Framework versions
- Transformers 4.19.2
- Pytorch 1.10.0+cu111
- Datasets 2.2.2
- Tokenizers 0.12.1
| 1,192 |
Nehc/FakeMobile | [
"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_... | ---
language:
- ru
widget:
- text: "[CLS] Какая абонентская плата на тарифе Позвони маме? [SEP]"
metrics:
- loss: 0.704381
- accuracy: 1.000000
---
Start from 'DeepPavlov/rubert-base-cased' and finetuning on DUMBOT fake data (http://dumbot.ru/Home/MobileOperatorRate).
100 epoch
on progress...
| 297 |
YeRyeongLee/bertweet-base-finetuned-filtered-0609 | [
"LABEL_0",
"LABEL_1",
"LABEL_2",
"LABEL_3",
"LABEL_4",
"LABEL_5"
] | ---
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: bertweet-base-finetuned-filtered-0609
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. -->
# bertweet-base-finetuned-filtered-0609
This model is a fine-tuned version of [vinai/bertweet-base](https://huggingface.co/vinai/bertweet-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5397
- Accuracy: 0.9299
- Precision: 0.9297
- Recall: 0.9299
- F1: 0.9298
## 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
- lr_scheduler_warmup_steps: 1000
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.331 | 1.0 | 3180 | 0.3687 | 0.9069 | 0.9147 | 0.9069 | 0.9081 |
| 0.2611 | 2.0 | 6360 | 0.3725 | 0.9223 | 0.9227 | 0.9223 | 0.9224 |
| 0.1993 | 3.0 | 9540 | 0.2948 | 0.9336 | 0.9350 | 0.9336 | 0.9339 |
| 0.1648 | 4.0 | 12720 | 0.3563 | 0.9296 | 0.9303 | 0.9296 | 0.9298 |
| 0.1324 | 5.0 | 15900 | 0.4136 | 0.9267 | 0.9279 | 0.9267 | 0.9270 |
| 0.1102 | 6.0 | 19080 | 0.4060 | 0.9352 | 0.9357 | 0.9352 | 0.9353 |
| 0.0568 | 7.0 | 22260 | 0.4653 | 0.9321 | 0.9328 | 0.9321 | 0.9322 |
| 0.0292 | 8.0 | 25440 | 0.4818 | 0.9311 | 0.9310 | 0.9311 | 0.9310 |
| 0.0155 | 9.0 | 28620 | 0.5405 | 0.9286 | 0.9288 | 0.9286 | 0.9286 |
| 0.0095 | 10.0 | 31800 | 0.5397 | 0.9299 | 0.9297 | 0.9299 | 0.9298 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.9.1+cu111
- Datasets 1.16.1
- Tokenizers 0.12.1
| 2,391 |
Jeevesh8/std_pnt_04_feather_berts-85 | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | Entry not found | 15 |
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