modelId stringlengths 6 107 | label list | readme stringlengths 0 56.2k | readme_len int64 0 56.2k |
|---|---|---|---|
ahmedrachid/FinancialBERT-Sentiment-Analysis | [
"negative",
"neutral",
"positive"
] | ---
language: en
tags:
- financial-sentiment-analysis
- sentiment-analysis
datasets:
- financial_phrasebank
widget:
- text: Operating profit rose to EUR 13.1 mn from EUR 8.7 mn in the corresponding period in 2007 representing 7.7 % of net sales.
- text: Bids or offers include at least 1,000 shares and the value of the ... | 3,448 |
textattack/albert-base-v2-imdb | null | ## TextAttack Model Card
This `albert-base-v2` model was fine-tuned for sequence classification using TextAttack
and the imdb dataset loaded using the `nlp` library. The model was fine-tuned
for 5 epochs with a batch size of 32, a learning
rate of 2e-05, and a maximum sequence length of 128.
Since this was a classi... | 609 |
textattack/roberta-base-STS-B | [
"LABEL_0"
] | ## TextAttack Model Card
This `roberta-base` model was fine-tuned for sequence classification using TextAttack
and the glue dataset loaded using the `nlp` library. The model was fine-tuned
for 5 epochs with a batch size of 8, a learning
rate of 2e-05, and a maximum sequence length of 128.
Since this was a regressio... | 629 |
yoshitomo-matsubara/bert-base-uncased-mnli | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | ---
language: en
tags:
- bert
- mnli
- ax
- glue
- torchdistill
license: apache-2.0
datasets:
- mnli
- ax
metrics:
- accuracy
---
`bert-base-uncased` fine-tuned on MNLI dataset, using [***torchdistill***](https://github.com/yoshitomo-matsubara/torchdistill) and [Google Colab](https://colab.research.google.com/github/y... | 836 |
philschmid/distilbert-base-multilingual-cased-sentiment-2 | [
"negative",
"neutral",
"positive"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- amazon_reviews_multi
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-multilingual-cased-sentiment-2
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: amazon_reviews_multi
ty... | 2,316 |
fnlp/cpt-large | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | ---
tags:
- fill-mask
- text2text-generation
- fill-mask
- text-classification
- Summarization
- Chinese
- CPT
- BART
- BERT
- seq2seq
language: zh
---
# Chinese CPT-Large
## Model description
This is an implementation of CPT-Large. To use CPT, please import the file `modeling_cpt.py` (**Download** [Here](https://g... | 1,687 |
Sigma/financial-sentiment-analysis | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | ---
tags:
- generated_from_trainer
datasets:
- financial_phrasebank
metrics:
- accuracy
- f1
model-index:
- name: financial-sentiment-analysis
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: financial_phrasebank
type: financial_phrasebank
args: s... | 1,555 |
Jeevesh8/std_0pnt2_bert_ft_cola-0 | null | Entry not found | 15 |
NbAiLab/nb-bert-base-mnli | [
"contradiction",
"neutral",
"entailment"
] | ---
language: no
license: cc-by-4.0
thumbnail: https://raw.githubusercontent.com/NBAiLab/notram/master/images/nblogo_2.png
pipeline_tag: zero-shot-classification
tags:
- nb-bert
- zero-shot-classification
- pytorch
- tensorflow
- norwegian
- bert
datasets:
- mnli
- multi_nli
- xnli
widget:
- example_title: Nyhetsartikk... | 2,939 |
cffl/bert-base-styleclassification-subjective-neutral | [
"NEUTRAL",
"SUBJECTIVE"
] | ---
license: apache-2.0
---
# bert-base-styleclassification-subjective-neutral
## Model description
This [bert-base-uncased](https://huggingface.co/bert-base-uncased) model has been fine-tuned on the [Wiki Neutrality Corpus (WNC)](https://arxiv.org/pdf/1911.09709.pdf) - a parallel corpus of 180,000 biased and neutra... | 4,530 |
huggingface/CodeBERTa-language-id | [
"go",
"java",
"javascript",
"php",
"python",
"ruby"
] | ---
language: code
thumbnail: https://cdn-media.huggingface.co/CodeBERTa/CodeBERTa.png
datasets:
- code_search_net
---
# CodeBERTa-language-id: The World’s fanciest programming language identification algo 🤯
To demonstrate the usefulness of our CodeBERTa pretrained model on downstream tasks beyond language modeling... | 8,962 |
textattack/xlnet-base-cased-SST-2 | null | Entry not found | 15 |
mdraw/german-news-sentiment-bert | [
"negative",
"neutral",
"positive"
] | # German sentiment BERT finetuned on news data
Sentiment analysis model based on https://huggingface.co/oliverguhr/german-sentiment-bert, with additional training on German news texts about migration.
This model is part of the project https://github.com/text-analytics-20/news-sentiment-development, which explores sen... | 1,454 |
Jeevesh8/std_0pnt2_bert_ft_cola-1 | null | Entry not found | 15 |
textattack/roberta-base-ag-news | [
"LABEL_0",
"LABEL_1",
"LABEL_2",
"LABEL_3"
] | ## TextAttack Model CardThis `roberta-base` model was fine-tuned for sequence classification using TextAttack
and the ag_news dataset loaded using the `nlp` library. The model was fine-tuned
for 5 epochs with a batch size of 16, a learning
rate of 5e-05, and a maximum sequence length of 128.
Since this was a classi... | 620 |
Jeevesh8/std_0pnt2_bert_ft_cola-2 | null | Entry not found | 15 |
Jeevesh8/std_0pnt2_bert_ft_cola-6 | null | Entry not found | 15 |
Jeevesh8/std_0pnt2_bert_ft_cola-3 | null | Entry not found | 15 |
SkolkovoInstitute/russian_toxicity_classifier | [
"neutral",
"toxic"
] | ---
language:
- ru
tags:
- toxic comments classification
licenses:
- cc-by-nc-sa
---
Bert-based classifier (finetuned from [Conversational Rubert](https://huggingface.co/DeepPavlov/rubert-base-cased-conversational)) trained on merge of Russian Language Toxic Comments [dataset](https://www.kaggle.com/blackmoon/russia... | 1,819 |
Jeevesh8/std_0pnt2_bert_ft_cola-7 | null | Entry not found | 15 |
Jeevesh8/std_0pnt2_bert_ft_cola-4 | null | Entry not found | 15 |
cross-encoder/stsb-TinyBERT-L-4 | [
"LABEL_0"
] | ---
license: apache-2.0
---
# Cross-Encoder for Quora Duplicate Questions Detection
This model was trained using [SentenceTransformers](https://sbert.net) [Cross-Encoder](https://www.sbert.net/examples/applications/cross-encoder/README.html) class.
## Training Data
This model was trained on the [STS benchmark dataset]... | 941 |
Jeevesh8/std_0pnt2_bert_ft_cola-9 | null | Entry not found | 15 |
Jeevesh8/std_0pnt2_bert_ft_cola-5 | null | Entry not found | 15 |
Hate-speech-CNERG/dehatebert-mono-english | [
"NON_HATE",
"HATE"
] | ---
language: en
license: apache-2.0
---
This model is used detecting **hatespeech** in **English language**. The mono in the name refers to the monolingual setting, where the model is trained using only English language data. It is finetuned on multilingual bert model.
The model is trained with different learning rate... | 1,047 |
Jeevesh8/std_0pnt2_bert_ft_cola-8 | null | Entry not found | 15 |
Jeevesh8/std_0pnt2_bert_ft_cola-10 | null | Entry not found | 15 |
Jeevesh8/std_0pnt2_bert_ft_cola-11 | null | Entry not found | 15 |
Jeevesh8/std_0pnt2_bert_ft_cola-14 | null | Entry not found | 15 |
textattack/distilbert-base-uncased-CoLA | null | ## TextAttack Model Cardand the glue dataset loaded using the `nlp` library. The model was fine-tuned
for 5 epochs with a batch size of 64, a learning
rate of 3e-05, and a maximum sequence length of 128.
Since this was a classification task, the model was trained with a cross-entropy loss function.
The best score t... | 530 |
Jeevesh8/std_0pnt2_bert_ft_cola-12 | null | Entry not found | 15 |
bhadresh-savani/roberta-base-emotion | [
"anger",
"fear",
"joy",
"love",
"sadness",
"surprise"
] | ---
language:
- en
thumbnail: https://avatars3.githubusercontent.com/u/32437151?s=460&u=4ec59abc8d21d5feea3dab323d23a5860e6996a4&v=4
tags:
- text-classification
- emotion
- pytorch
license: apache-2.0
datasets:
- emotion
metrics:
- Accuracy, F1 Score
model-index:
- name: bhadresh-savani/roberta-base-emotion
results:
... | 3,928 |
SkolkovoInstitute/rubert-base-corruption-detector | [
"unnatural",
"natural"
] | ---
language:
- ru
tags:
- fluency
---
This is a model for evaluation of naturalness of short Russian texts. It has been trained to distinguish human-written texts from their corrupted versions.
Corruption sources: random replacement, deletion, addition, shuffling, and re-inflection of words and characters, ran... | 1,110 |
Jeevesh8/std_0pnt2_bert_ft_cola-13 | null | Entry not found | 15 |
microsoft/deberta-base-mnli | [
"CONTRADICTION",
"NEUTRAL",
"ENTAILMENT"
] | ---
language: en
tags:
- deberta-v1
- deberta-mnli
tasks: mnli
thumbnail: https://huggingface.co/front/thumbnails/microsoft.png
license: mit
widget:
- text: "[CLS] I love you. [SEP] I like you. [SEP]"
---
## DeBERTa: Decoding-enhanced BERT with Disentangled Attention
[DeBERTa](https://arxiv.org/abs/2006.03654) impro... | 1,441 |
Jeevesh8/std_0pnt2_bert_ft_cola-17 | null | Entry not found | 15 |
svalabs/cross-electra-ms-marco-german-uncased | [
"LABEL_0"
] | # SVALabs - German Uncased Electra Cross-Encoder
In this repository, we present our german, uncased cross-encoder for Passage Retrieval.
This model was trained on the basis of the german electra uncased model from the [german-nlp-group](https://huggingface.co/german-nlp-group/electra-base-german-uncased) and finetune... | 9,543 |
Jeevesh8/std_0pnt2_bert_ft_cola-15 | null | Entry not found | 15 |
DaNLP/da-bert-emotion-binary | [
"emotional",
"no emotion"
] | ---
language:
- da
tags:
- bert
- pytorch
- emotion
license: cc-by-sa-4.0
datasets:
- social media
metrics:
- f1
widget:
- text: Der er et træ i haven.
---
# Danish BERT for emotion detection
The BERT Emotion model detects whether a Danish text is emotional or not.
It is based on the pretrained [Danish BERT](https:/... | 1,022 |
Jeevesh8/std_0pnt2_bert_ft_cola-16 | null | Entry not found | 15 |
cardiffnlp/tweet-topic-21-multi | [
"arts_&_culture",
"business_&_entrepreneurs",
"celebrity_&_pop_culture",
"diaries_&_daily_life",
"family",
"fashion_&_style",
"film_tv_&_video",
"fitness_&_health",
"food_&_dining",
"gaming",
"learning_&_educational",
"music",
"news_&_social_concern",
"other_hobbies",
"relationships",
... | # tweet-topic-21-multi
This is a roBERTa-base model trained on ~124M tweets from January 2018 to December 2021 (see [here](https://huggingface.co/cardiffnlp/twitter-roberta-base-2021-124m)), and finetuned for multi-label topic classification on a corpus of 11,267 tweets.
The original roBERTa-base model can be found [h... | 2,708 |
Jeevesh8/std_0pnt2_bert_ft_cola-18 | null | Entry not found | 15 |
Jeevesh8/std_0pnt2_bert_ft_cola-19 | null | Entry not found | 15 |
MoritzLaurer/policy-distilbert-7d | [
"external relations",
"freedom and democracy",
"political system",
"economy",
"welfare and quality of life",
"fabric of society",
"social groups"
] | ---
language:
- en
tags:
- text-classification
metrics:
- accuracy (balanced)
- F1 (weighted)
widget:
- text: "70-85% of the population needs to get vaccinated against the novel coronavirus to achieve herd immunity."
---
# Policy-DistilBERT-7d
## Model description
This model was trained on 129.669 manually annota... | 5,215 |
cross-encoder/quora-roberta-large | [
"LABEL_0"
] | ---
license: apache-2.0
---
# Cross-Encoder for Quora Duplicate Questions Detection
This model was trained using [SentenceTransformers](https://sbert.net) [Cross-Encoder](https://www.sbert.net/examples/applications/cross-encoder/README.html) class.
## Training Data
This model was trained on the [Quora Duplicate Questi... | 1,070 |
monologg/koelectra-base-v3-hate-speech | [
"hate",
"none",
"offensive"
] | Entry not found | 15 |
fnlp/cpt-base | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | ---
tags:
- fill-mask
- text2text-generation
- fill-mask
- text-classification
- Summarization
- Chinese
- CPT
- BART
- BERT
- seq2seq
language: zh
---
# Chinese CPT-Base
## Model description
This is an implementation of CPT-Base. To use CPT, please import the file `modeling_cpt.py` (**Download** [Here](https://gi... | 1,679 |
Jeevesh8/std_0pnt2_bert_ft_cola-20 | null | Entry not found | 15 |
Jeevesh8/std_0pnt2_bert_ft_cola-21 | null | Entry not found | 15 |
zhayunduo/roberta-base-stocktwits-finetuned | null | ---
license: apache-2.0
---
## **Sentiment Inferencing model for stock related commments**
#### *A project by NUS ISS students Frank Cao, Gerong Zhang, Jiaqi Yao, Sikai Ni, Yunduo Zhang*
<br />
### Description
This model is fine tuned with roberta-base model on 3200000 comments from stocktwits, with the user labe... | 2,610 |
Jeevesh8/std_0pnt2_bert_ft_cola-22 | null | Entry not found | 15 |
Rostlab/prot_bert_bfd_membrane | [
"Soluble",
"Membrane"
] | Entry not found | 15 |
Jeevesh8/std_0pnt2_bert_ft_cola-27 | null | Entry not found | 15 |
ynie/xlnet-large-cased-snli_mnli_fever_anli_R1_R2_R3-nli | [
"entailment",
"neutral",
"contradiction"
] | Entry not found | 15 |
Jeevesh8/std_0pnt2_bert_ft_cola-23 | null | Entry not found | 15 |
textattack/albert-base-v2-ag-news | [
"LABEL_0",
"LABEL_1",
"LABEL_2",
"LABEL_3"
] | ## TextAttack Model CardThis `albert-base-v2` model was fine-tuned for sequence classification using TextAttack
and the ag_news dataset loaded using the `nlp` library. 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 128.
Since this was a clas... | 622 |
IDEA-CCNL/Erlangshen-Roberta-110M-NLI | [
"CONTRADICTION",
"NEUTRAL",
"ENTAILMENT"
] | ---
language:
- zh
license: apache-2.0
tags:
- bert
- NLU
- NLI
inference: true
widget:
- text: "今天心情不好[SEP]今天很开心"
---
# Erlangshen-Roberta-110M-NLI, model (Chinese)，one model of [Fengshenbang-LM](https://github.com/IDEA-CCNL/Fengshenbang-LM).
We collect 4 NLI（Natural Language Inference） datasets in the Chinese ... | 1,571 |
Jeevesh8/std_0pnt2_bert_ft_cola-24 | null | Entry not found | 15 |
Skoltech/russian-sensitive-topics | [
"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
tags:
- toxic comments classification
licenses:
- cc-by-nc-sa
---
## General concept of the model
This model is trained on the dataset of sensitive topics of the Russian language. The concept of sensitive topics is described [in this article ](https://www.aclweb.org/anthology/2021.bsnlp-1.4/) pre... | 5,040 |
roberta-large-openai-detector | null | ---
language: en
license: mit
tags:
- exbert
datasets:
- bookcorpus
- wikipedia
---
# RoBERTa Large OpenAI Detector
## Table of Contents
- [Model Details](#model-details)
- [Uses](#uses)
- [Risks, Limitations and Biases](#risks-limitations-and-biases)
- [Training](#training)
- [Evaluation](#evaluation)
- [Environmen... | 9,182 |
Jeevesh8/std_0pnt2_bert_ft_cola-25 | null | Entry not found | 15 |
Jeevesh8/std_0pnt2_bert_ft_cola-26 | null | Entry not found | 15 |
eleldar/theme-classification | [
"contradiction",
"entailment",
"neutral"
] | ---
license: mit
thumbnail: https://huggingface.co/front/thumbnails/facebook.png
pipeline_tag: zero-shot-classification
datasets:
- multi_nli
---
# Clone from [https://huggingface.co/facebook/bart-large-mnli](bart-large-mnli)
This is the checkpoint for [bart-large](https://huggingface.co/facebook/bart-large) after be... | 3,855 |
Jeevesh8/std_0pnt2_bert_ft_cola-31 | null | Entry not found | 15 |
microsoft/DialogRPT-human-vs-machine | null | # Demo
Please try this [➤➤➤ Colab Notebook Demo (click me!)](https://colab.research.google.com/drive/1cAtfkbhqsRsT59y3imjR1APw3MHDMkuV?usp=sharing)
| Context | Response | `human_vs_machine` score |
| :------ | :------- | :------------: |
| I love NLP! | I'm not sure if it's a good idea. | 0.000 |
| I love NLP!... | 2,636 |
cross-encoder/mmarco-mMiniLMv2-L12-H384-v1 | [
"LABEL_0"
] | ---
license: apache-2.0
language:
- en
- ar
- zh
- nl
- fr
- de
- hi
- in
- it
- ja
- pt
- ru
- es
- vi
- multilingual
datasets:
- unicamp-dl/mmarco
---
# Cross-Encoder for multilingual MS Marco
This model was trained on the [MMARCO](https://hf.co/unicamp-dl/mmarco) dataset. It is a machine translated version of MS MA... | 2,130 |
Jeevesh8/std_0pnt2_bert_ft_cola-28 | null | Entry not found | 15 |
Jeevesh8/std_0pnt2_bert_ft_cola-29 | null | Entry not found | 15 |
Jeevesh8/std_0pnt2_bert_ft_cola-30 | null | Entry not found | 15 |
Jeevesh8/std_0pnt2_bert_ft_cola-35 | null | Entry not found | 15 |
Jeevesh8/std_0pnt2_bert_ft_cola-32 | null | Entry not found | 15 |
Jeevesh8/std_0pnt2_bert_ft_cola-33 | null | Entry not found | 15 |
Jeevesh8/std_0pnt2_bert_ft_cola-34 | null | Entry not found | 15 |
Jeevesh8/std_0pnt2_bert_ft_cola-36 | null | Entry not found | 15 |
Jeevesh8/std_0pnt2_bert_ft_cola-37 | null | Entry not found | 15 |
Jeevesh8/std_0pnt2_bert_ft_cola-38 | null | Entry not found | 15 |
Jeevesh8/std_0pnt2_bert_ft_cola-39 | null | Entry not found | 15 |
Jeevesh8/std_0pnt2_bert_ft_cola-40 | null | Entry not found | 15 |
Jeevesh8/std_0pnt2_bert_ft_cola-41 | null | Entry not found | 15 |
Jeevesh8/std_0pnt2_bert_ft_cola-43 | null | Entry not found | 15 |
howey/roberta-large-qqp | null | Entry not found | 15 |
Jeevesh8/std_0pnt2_bert_ft_cola-42 | null | Entry not found | 15 |
Jeevesh8/std_0pnt2_bert_ft_cola-44 | null | Entry not found | 15 |
ptaszynski/yacis-electra-small-japanese-cyberbullying | null | ---
language: ja
license: cc-by-sa-4.0
datasets:
- YACIS corpus
- Harmful BBS Japanese comments dataset
- Twitter Japanese cyberbullying dataset
---
# yacis-electra-small-cyberbullying
This is an [ELECTRA](https://github.com/google-research/electra) Small model for the Japanese language finetuned for automatic c... | 3,795 |
cmarkea/distilcamembert-base-nli | [
"contradiction",
"entailment",
"neutral"
] | ---
language: fr
license: mit
tags:
- zero-shot-classification
- sentence-similarity
- nli
pipeline_tag: zero-shot-classification
widget:
- text: "Selon certains physiciens, un univers parallèle, miroir du nôtre ou relevant de ce que l'on appelle la théorie des branes, autoriserait des neutrons à sortir de notre Unive... | 8,208 |
Jeevesh8/std_0pnt2_bert_ft_cola-45 | null | Entry not found | 15 |
bhadresh-savani/distilbert-base-uncased-sentiment-sst2 | [
"NEGATIVE",
"POSITIVE"
] | ---
language: en
license: apache-2.0
datasets:
- sst2
---
# distilbert-base-uncased-sentiment-sst2
This model will be able to identify positivity or negativity present in the sentence
## Dataset:
The Stanford Sentiment Treebank from GLUE
## Results:
```
***** eval metrics *****
epoch = 3.0
... | 557 |
Jeevesh8/std_0pnt2_bert_ft_cola-48 | null | Entry not found | 15 |
techthiyanes/chinese_sentiment | [
"star 1",
"star 2",
"star 3",
"star 4",
"star 5"
] | Entry not found | 15 |
Jeevesh8/std_0pnt2_bert_ft_cola-47 | null | Entry not found | 15 |
Jeevesh8/std_0pnt2_bert_ft_cola-46 | null | Entry not found | 15 |
geckos/bart-fined-tuned-on-entailment-classification | [
"contradiction",
"entailment",
"neutral"
] | Entry not found | 15 |
howey/roberta-large-qnli | null | Entry not found | 15 |
navteca/bart-large-mnli | [
"contradiction",
"neutral",
"entailment"
] | ---
datasets:
- multi_nli
language: en
license: mit
pipeline_tag: zero-shot-classification
tags:
- bart
- zero-shot-classification
---
# Bart large model for NLI-based Zero Shot Text Classification
This model uses [bart-large](https://huggingface.co/facebook/bart-large).
## Training Data
This model was trained on the... | 1,463 |
Jeevesh8/std_0pnt2_bert_ft_cola-49 | null | Entry not found | 15 |
prajjwal1/bert-tiny-mnli | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | The following model is a Pytorch pre-trained model obtained from converting Tensorflow checkpoint found in the [official Google BERT repository](https://github.com/google-research/bert). These BERT variants were introduced in the paper [Well-Read Students Learn Better: On the Importance of Pre-training Compact Models](... | 992 |
Jeevesh8/std_0pnt2_bert_ft_cola-50 | null | Entry not found | 15 |
wietsedv/bert-base-dutch-cased-finetuned-sentiment | [
"neg",
"pos"
] | Entry not found | 15 |
Jeevesh8/std_0pnt2_bert_ft_cola-51 | null | Entry not found | 15 |
Jeevesh8/std_0pnt2_bert_ft_cola-52 | null | Entry not found | 15 |
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