index int64 0 22.3k | modelId stringlengths 8 111 | label list | readme stringlengths 0 385k |
|---|---|---|---|
748 | aychang/distilbert-base-cased-trec-coarse | [
"ABBR",
"DESC",
"ENTY",
"HUM",
"LOC",
"NUM"
] | ---
language:
- en
license: mit
tags:
- text-classification
datasets:
- trec
model-index:
- name: aychang/distilbert-base-cased-trec-coarse
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: trec
type: trec
config: default
split: test
metr... |
749 | aychang/roberta-base-imdb | [
"neg",
"pos"
] | ---
language:
- en
thumbnail:
tags:
- text-classification
license: mit
datasets:
- imdb
metrics:
---
# IMDB Sentiment Task: roberta-base
## Model description
A simple base roBERTa model trained on the "imdb" dataset.
## Intended uses & limitations
#### How to use
##### Transformers
```python
# Load model and ... |
753 | batterydata/batterybert-cased-abstract | [
"battery",
"non-battery"
] | ---
language: en
tags: Text Classification
license: apache-2.0
datasets:
- batterydata/paper-abstracts
metrics: glue
---
# BatteryBERT-cased for Battery Abstract Classification
**Language model:** batterybert-cased
**Language:** English
**Downstream-task:** Text Classification
**Training data:** train... |
754 | batterydata/batterybert-uncased-abstract | [
"battery",
"non-battery"
] | ---
language: en
tags: Text Classification
license: apache-2.0
datasets:
- batterydata/paper-abstracts
metrics: glue
---
# BatteryBERT-uncased for Battery Abstract Classification
**Language model:** batterybert-uncased
**Language:** English
**Downstream-task:** Text Classification
**Training data:** t... |
755 | batterydata/batteryonlybert-cased-abstract | [
"battery",
"non-battery"
] | ---
language: en
tags: Text Classification
license: apache-2.0
datasets:
- batterydata/paper-abstracts
metrics: glue
---
# BatteryOnlyBERT-cased for Battery Abstract Classification
**Language model:** batteryonlybert-cased
**Language:** English
**Downstream-task:** Text Classification
**Training data:... |
756 | batterydata/batteryonlybert-uncased-abstract | [
"battery",
"non-battery"
] | ---
language: en
tags: Text Classification
license: apache-2.0
datasets:
- batterydata/paper-abstracts
metrics: glue
---
# BatteryOnlyBERT-uncased for Battery Abstract Classification
**Language model:** batteryonlybert-uncased
**Language:** English
**Downstream-task:** Text Classification
**Training d... |
757 | batterydata/batteryscibert-cased-abstract | [
"battery",
"non-battery"
] | ---
language: en
tags: Text Classification
license: apache-2.0
datasets:
- batterydata/paper-abstracts
metrics: glue
---
# BatterySciBERT-cased for Battery Abstract Classification
**Language model:** batteryscibert-cased
**Language:** English
**Downstream-task:** Text Classification
**Training data:**... |
758 | batterydata/batteryscibert-uncased-abstract | [
"battery",
"non-battery"
] | ---
language: en
tags: Text Classification
license: apache-2.0
datasets:
- batterydata/paper-abstracts
metrics: glue
---
# BatterySciBERT-uncased for Battery Abstract Classification
**Language model:** batteryscibert-uncased
**Language:** English
**Downstream-task:** Text Classification
**Training dat... |
759 | batterydata/bert-base-cased-abstract | [
"battery",
"non-battery"
] | ---
language: en
tags: Text Classification
license: apache-2.0
datasets:
- batterydata/paper-abstracts
metrics: glue
---
# BERT-base-cased for Battery Abstract Classification
**Language model:** bert-base-cased
**Language:** English
**Downstream-task:** Text Classification
**Training data:** training\... |
760 | batterydata/bert-base-uncased-abstract | [
"battery",
"non-battery"
] | ---
language: en
tags: Text Classification
license: apache-2.0
datasets:
- batterydata/paper-abstracts
metrics: glue
---
# BERT-base-uncased for Battery Abstract Classification
**Language model:** bert-base-uncased
**Language:** English
**Downstream-task:** Text Classification
**Training data:** train... |
761 | 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 remov... |
762 | benjaminbeilharz/bert-base-uncased-empatheticdialogues-sentiment-classifier | [
"LABEL_0",
"LABEL_1",
"LABEL_10",
"LABEL_11",
"LABEL_12",
"LABEL_13",
"LABEL_14",
"LABEL_15",
"LABEL_16",
"LABEL_17",
"LABEL_18",
"LABEL_19",
"LABEL_2",
"LABEL_20",
"LABEL_21",
"LABEL_22",
"LABEL_23",
"LABEL_24",
"LABEL_25",
"LABEL_26",
"LABEL_27",
"LABEL_28",
"LABEL_29",... | ---
dataset: empathetic_dialogues
---
|
763 | beomi/distilbert-base-uncased-finetuned-cola | [
"unacceptable",
"acceptable"
] | ---
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
met... |
764 | bergum/xtremedistil-emotion | [
"sadness",
"joy",
"love",
"anger",
"fear",
"surprise"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
model-index:
- name: xtremedistil-emotion
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: emotion
type: emotion
args: default
metrics:
- type: ... |
765 | bergum/xtremedistil-l6-h384-emotion | [
"sadness",
"joy",
"love",
"anger",
"fear",
"surprise"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
model-index:
- name: xtremedistil-l6-h384-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
args: default
metrics:
... |
766 | bergum/xtremedistil-l6-h384-go-emotion | [
"admiration ๐",
"amusement ๐",
"anger ๐ก",
"annoyance ๐",
"approval ๐",
"caring ๐ค",
"confusion ๐",
"curiosity ๐ค",
"desire ๐",
"disappointment ๐",
"disapproval ๐",
"disgust ๐คฎ",
"embarrassment ๐ณ",
"excitement ๐คฉ",
"fear ๐จ",
"gratitude ๐",
"grief ๐ข",
"joy ๐",
"love โค... | ---
license: apache-2.0
datasets:
- go_emotions
metrics:
- accuracy
model-index:
- name: xtremedistil-emotion
results:
- task:
name: Multi Label Text Classification
type: multi_label_classification
dataset:
name: go_emotions
type: emotion
args: default
metrics:
- name: Acc... |
768 | bertin-project/bertin-base-xnli-es | [
"entailment",
"neutral",
"contradiction"
] | ---
language: es
license: cc-by-4.0
tags:
- spanish
- roberta
- xnli
---
This checkpoint has been trained for the XNLI 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](htt... |
769 | bespin-global/klue-roberta-small-3i4k-intent-classification | [
"command",
"fragment",
"intonation-depedent utterance",
"question",
"rhetorical command",
"rhetorical question",
"statement"
] | ---
language: ko
tags:
- intent-classification
datasets:
- kor_3i4k
license: cc-by-nc-4.0
---
## Finetuning
- Pretrain Model : [klue/roberta-small](https://github.com/KLUE-benchmark/KLUE)
- Dataset for fine-tuning : [3i4k](https://github.com/warnikchow/3i4k)
- Train : 46,863
- Validation : 8,271 (15% of Train)
... |
770 | 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)
|
771 | bgoel4132/tweet-disaster-classifier | [
"accident",
"cyclone",
"earthquake",
"explosion",
"fire",
"flood",
"hurricane",
"medical",
"other",
"pollution",
"tornado",
"typhoon",
"volcano"
] | ---
tags: autonlp
language: en
widget:
- text: "I love AutoNLP ๐ค"
datasets:
- bgoel4132/autonlp-data-tweet-disaster-classifier
co2_eq_emissions: 27.22397099134103
---
# Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 28716412
- CO2 Emissions (in grams): 27.22397099134103
## Valida... |
772 | bgoel4132/twitter-sentiment | [
"cyclone",
"earthquake",
"explosion",
"fire",
"flood",
"hurricane",
"medical",
"pollution",
"tornado",
"typhoon",
"volcano"
] | ---
tags: autonlp
language: en
widget:
- text: "I love AutoNLP ๐ค"
datasets:
- bgoel4132/autonlp-data-twitter-sentiment
co2_eq_emissions: 186.8637425115097
---
# Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 35868888
- CO2 Emissions (in grams): 186.8637425115097
## Validation Met... |
773 | bhadresh-savani/albert-base-v2-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
---
# Albert-base-v2-emotion
## Model description:
[Albert](https:/... |
774 | bhadresh-savani/bert-base-go-emotion | [
"admiration",
"amusement",
"anger",
"annoyance",
"approval",
"caring",
"confusion",
"curiosity",
"desire",
"disappointment",
"disapproval",
"disgust",
"embarrassment",
"excitement",
"fear",
"gratitude",
"grief",
"joy",
"love",
"nervousness",
"neutral",
"optimism",
"pride"... | ---
language:
- en
thumbnail: https://avatars3.githubusercontent.com/u/32437151?s=460&u=4ec59abc8d21d5feea3dab323d23a5860e6996a4&v=4
tags:
- text-classification
- go-emotion
- pytorch
license: apache-2.0
datasets:
- go_emotions
metrics:
- Accuracy
---
# Bert-Base-Uncased-Go-Emotion
## Model description:
## Training ... |
775 | bhadresh-savani/bert-base-uncased-emotion | [
"anger",
"fear",
"joy",
"love",
"sadness",
"surprise"
] | ---
language:
- en
license: apache-2.0
tags:
- text-classification
- emotion
- pytorch
datasets:
- emotion
metrics:
- Accuracy, F1 Score
thumbnail: https://avatars3.githubusercontent.com/u/32437151?s=460&u=4ec59abc8d21d5feea3dab323d23a5860e6996a4&v=4
model-index:
- name: bhadresh-savani/bert-base-uncased-emotion
resu... |
776 | bhadresh-savani/distilbert-base-uncased-emotion | [
"anger",
"fear",
"joy",
"love",
"sadness",
"surprise"
] | ---
language:
- en
license: apache-2.0
tags:
- text-classification
- emotion
- pytorch
datasets:
- emotion
metrics:
- Accuracy, F1 Score
thumbnail: https://avatars3.githubusercontent.com/u/32437151?s=460&u=4ec59abc8d21d5feea3dab323d23a5860e6996a4&v=4
model-index:
- name: bhadresh-savani/distilbert-base-uncased-emotion
... |
777 | bhadresh-savani/distilbert-base-uncased-go-emotion | [
"admiration",
"amusement",
"anger",
"annoyance",
"approval",
"caring",
"confusion",
"curiosity",
"desire",
"disappointment",
"disapproval",
"disgust",
"embarrassment",
"excitement",
"fear",
"gratitude",
"grief",
"joy",
"love",
"nervousness",
"neutral",
"optimism",
"pride"... | ---
language:
- en
thumbnail: https://avatars3.githubusercontent.com/u/32437151?s=460&u=4ec59abc8d21d5feea3dab323d23a5860e6996a4&v=4
tags:
- text-classification
- go-emotion
- pytorch
license: apache-2.0
datasets:
- go_emotions
metrics:
- Accuracy
---
# Distilbert-Base-Uncased-Go-Emotion
## Model description:
**Not ... |
778 | 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
... |
779 | bhadresh-savani/roberta-base-emotion | [
"anger",
"fear",
"joy",
"love",
"sadness",
"surprise"
] | ---
language:
- en
license: apache-2.0
tags:
- text-classification
- emotion
- pytorch
datasets:
- emotion
metrics:
- Accuracy, F1 Score
thumbnail: https://avatars3.githubusercontent.com/u/32437151?s=460&u=4ec59abc8d21d5feea3dab323d23a5860e6996a4&v=4
model-index:
- name: bhadresh-savani/roberta-base-emotion
results:
... |
780 | bioformers/bioformer-8L-mnli | [
"contradiction",
"entailment",
"neutral"
] | [bioformer-cased-v1.0](https://huggingface.co/bioformers/bioformer-cased-v1.0) fined-tuned on the [MNLI](https://cims.nyu.edu/~sbowman/multinli/) dataset for 2 epochs.
The fine-tuning process was performed on two NVIDIA GeForce GTX 1080 Ti GPUs (11GB). The parameters are:
```
max_seq_length=512
per_device_train_batch... |
781 | bioformers/bioformer-8L-qnli | [
"entailment",
"not_entailment"
] | ---
license: apache-2.0
language:
- en
---
[bioformer-8L](https://huggingface.co/bioformers/bioformer-8L) fined-tuned on the [QNLI](https://huggingface.co/datasets/glue) dataset for 2 epochs.
The fine-tuning process was performed on two NVIDIA GeForce GTX 1080 Ti GPUs (11GB). The parameters are:
```
max_seq_length=51... |
782 | bipin/malayalam-news-classifier | [
"business",
"entertainment",
"sports"
] | ---
license: mit
tags:
- text-classification
- roberta
- malayalam
- pytorch
widget:
- text: "2032 เดเดณเดฟเดฎเตเดชเดฟเดเตโเดธเดฟเดจเต เดฌเตเดฐเดฟเดธเตโเดฌเตเดฏเตเดจเตโ เดตเตเดฆเดฟเดฏเดพเดเตเด; เดเตเดฏเดฟเดเดธเดฟเดจเต เดตเตเดฆเดฟเดฏเดพเดเตเดจเตเดจ เดฎเตเดจเตเดจเดพเดฎเดคเตเดคเต เดเดธเตโเดเตเดฐเตเดฒเดฟเดฏเดจเตโ เดจเดเดฐเด"
---
## Malayalam news classifier
### Overview
This model is trained on top of [MalayalamBert](https://huggingface.co/... |
783 | bitmorse/autonlp-ks-530615016 | [
"canceled",
"failed",
"live",
"successful"
] | ---
tags: autonlp
language: en
widget:
- text: "I love AutoNLP ๐ค"
datasets:
- bitmorse/autonlp-data-ks
co2_eq_emissions: 2.2247356264808964
---
# Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 530615016
- CO2 Emissions (in grams): 2.2247356264808964
## Validation Metrics
- Loss:... |
784 | biu-nlp/superpal | [
"aligned",
"not_aligned"
] | ---
widget:
- text: "Prime Minister Hun Sen insisted that talks take place in Cambodia. </s><s> Cambodian leader Hun Sen rejected opposition parties' demands for talks outside the country."
---
# SuperPAL model
Summary-Source Proposition-level Alignment: Task, Datasets and Supervised Baseline
Ori Ernst, Ori Shapira, ... |
785 | blackbird/alberta-base-mnli-v1 | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | |
786 | blackbird/bert-base-uncased-MNLI-v1 | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | BERT based model finetuned on MNLI with our custom training routine.
Yields 60% accuraqcy on adversarial HANS dataset. |
787 | blanchefort/rubert-base-cased-sentiment-med | [
"NEUTRAL",
"POSITIVE",
"NEGATIVE"
] | ---
language:
- ru
tags:
- sentiment
- text-classification
---
# RuBERT for Sentiment Analysis of Medical Reviews
This is a [DeepPavlov/rubert-base-cased-conversational](https://huggingface.co/DeepPavlov/rubert-base-cased-conversational) model trained on corpus of medical reviews.
## Labels
0: NEUTRAL
1: POS... |
789 | blanchefort/rubert-base-cased-sentiment-rurewiews | [
"NEUTRAL",
"POSITIVE",
"NEGATIVE"
] | ---
language:
- ru
tags:
- sentiment
- text-classification
datasets:
- RuReviews
---
# RuBERT for Sentiment Analysis of Product Reviews
This is a [DeepPavlov/rubert-base-cased-conversational](https://huggingface.co/DeepPavlov/rubert-base-cased-conversational) model trained on [RuReviews](https://github.com/sismetanin... |
790 | blanchefort/rubert-base-cased-sentiment-rusentiment | [
"NEUTRAL",
"POSITIVE",
"NEGATIVE"
] | ---
language:
- ru
tags:
- sentiment
- text-classification
datasets:
- RuSentiment
---
# RuBERT for Sentiment Analysis
This is a [DeepPavlov/rubert-base-cased-conversational](https://huggingface.co/DeepPavlov/rubert-base-cased-conversational) model trained on [RuSentiment](http://text-machine.cs.uml.edu/projects/ruse... |
791 | blanchefort/rubert-base-cased-sentiment | [
"NEGATIVE",
"NEUTRAL",
"POSITIVE"
] | ---
language:
- ru
tags:
- sentiment
- text-classification
---
# RuBERT for Sentiment Analysis
Short Russian texts sentiment classification
This is a [DeepPavlov/rubert-base-cased-conversational](https://huggingface.co/DeepPavlov/rubert-base-cased-conversational) model trained on aggregated corpus of 351.797 texts.
... |
792 | blizrys/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext-finetuned-pubmedqa-1 | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | ---
license: mit
tags:
- generated_from_trainer
datasets:
- null
metrics:
- accuracy
model-index:
- name: BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext-finetuned-pubmedqa-1
results:
- task:
name: Text Classification
type: text-classification
metrics:
- name: Accuracy
type: accuracy
... |
793 | blizrys/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext-finetuned-pubmedqa-2 | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | ---
license: mit
tags:
- generated_from_trainer
datasets:
- null
metrics:
- accuracy
model-index:
- name: BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext-finetuned-pubmedqa-2
results:
- task:
name: Text Classification
type: text-classification
metrics:
- name: Accuracy
type: accuracy
... |
794 | blizrys/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext-finetuned-pubmedqa | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | ---
license: mit
tags:
- generated_from_trainer
datasets:
- null
metrics:
- accuracy
model-index:
- name: BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext-finetuned-pubmedqa
results:
- task:
name: Text Classification
type: text-classification
metrics:
- name: Accuracy
type: accuracy
... |
795 | blizrys/biobert-base-cased-v1.1-finetuned-pubmedqa | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | ---
tags:
- generated_from_trainer
datasets:
- null
metrics:
- accuracy
model-index:
- name: biobert-base-cased-v1.1-finetuned-pubmedqa
results:
- task:
name: Text Classification
type: text-classification
metrics:
- name: Accuracy
type: accuracy
value: 0.5
---
<!-- This model card h... |
796 | blizrys/biobert-v1.1-finetuned-pubmedqa | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | ---
tags:
- generated_from_trainer
datasets:
- null
metrics:
- accuracy
model-index:
- name: biobert-v1.1-finetuned-pubmedqa
results:
- task:
name: Text Classification
type: text-classification
metrics:
- name: Accuracy
type: accuracy
value: 0.7
---
<!-- This model card has been gen... |
798 | blizrys/distilbert-base-uncased-finetuned-mnli | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased-finetuned-mnli
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
args: mnli
metrics:
- ... |
799 | bobo/bobo_classification_function | [
"NOT_RITNING",
"RITNING"
] | |
800 | bowipawan/bert-sentimental | [
"negative",
"neutral",
"positive"
] | For studying only |
801 | world-wide/sent-sci-irrelevance | [
"False",
"True"
] | ---
tags: autonlp
language: en
widget:
- text: "I love AutoNLP ๐ค"
datasets:
- bozelosp/autonlp-data-sci-relevance
co2_eq_emissions: 3.667033499762825
---
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 33199029
- CO2 Emissions (in grams): 3.667033499762825
## Validation Metrics
- Lo... |
802 | bshlgrs/autonlp-classification-9522090 | [
"No",
"Unsure",
"Yes"
] | ---
tags: autonlp
language: en
widget:
- text: "I love AutoNLP ๐ค"
datasets:
- bshlgrs/autonlp-data-classification
---
# Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 9522090
## Validation Metrics
- Loss: 0.3541755676269531
- Accuracy: 0.8759671179883946
- Macro F1: 0.5330133182... |
803 | bshlgrs/autonlp-classification_with_all_labellers-9532137 | [
"No",
"Unsure",
"Yes"
] | ---
tags: autonlp
language: en
widget:
- text: "I love AutoNLP ๐ค"
datasets:
- bshlgrs/autonlp-data-classification_with_all_labellers
---
# Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 9532137
## Validation Metrics
- Loss: 0.34556105732917786
- Accuracy: 0.8749890724713699
- Ma... |
804 | bshlgrs/autonlp-old-data-trained-10022181 | [
"No",
"Unsure",
"Yes"
] | ---
tags: autonlp
language: en
widget:
- text: "I love AutoNLP ๐ค"
datasets:
- bshlgrs/autonlp-data-old-data-trained
---
# Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 10022181
## Validation Metrics
- Loss: 0.369505375623703
- Accuracy: 0.8706206896551724
- Macro F1: 0.54102266... |
805 | bsingh/roberta_goEmotion | [
"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_3",
"LABEL_4",
... | ---
language: en
tags:
- text-classification
- pytorch
- roberta
- emotions
datasets:
- go_emotions
license: mit
widget:
- text: "I am not feeling well today."
---
## This model is trained for GoEmotions dataset which contains labeled 58k Reddit comments with 28 emotions
- admiration, amusement, anger, annoyance, appr... |
806 | DATEXIS/CORe-clinical-diagnosis-prediction | [
"003",
"0030",
"0031",
"0038",
"0039",
"004",
"0041",
"0048",
"0049",
"005",
"0051",
"0058",
"0059",
"007",
"0071",
"0074",
"008",
"0080",
"0084",
"0085",
"0086",
"0088",
"009",
"0090",
"0091",
"0092",
"0093",
"010",
"0108",
"011",
"0112",
"0113",
"011... | ---
language: "en"
tags:
- bert
- medical
- clinical
- diagnosis
- text-classification
thumbnail: "https://core.app.datexis.com/static/paper.png"
widget:
- text: "Patient with hypertension presents to ICU."
---
# CORe Model - Clinical Diagnosis Prediction
## Model description
The CORe (_Clinical Outcome Representat... |
807 | DATEXIS/CORe-clinical-mortality-prediction | [
"0",
"1"
] | ---
language: "en"
tags:
- bert
- medical
- clinical
- mortality
thumbnail: "https://core.app.datexis.com/static/paper.png"
---
# CORe Model - Clinical Mortality Risk Prediction
## Model description
The CORe (_Clinical Outcome Representations_) model is introduced in the paper [Clinical Outcome Predictions from Admi... |
808 | bvanaken/clinical-assertion-negation-bert | [
"PRESENT",
"ABSENT",
"POSSIBLE"
] | ---
language: "en"
tags:
- bert
- medical
- clinical
- assertion
- negation
- text-classification
widget:
- text: "Patient denies [entity] SOB [entity]."
---
# Clinical Assertion / Negation Classification BERT
## Model description
The Clinical Assertion and Negation Classification BERT is introduced in the paper [A... |
811 | cardiffnlp/bertweet-base-emoji | [
"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_3",
"LABEL_4",
"LABEL_5",
"LABEL_6",
"LABEL_7",
"LABEL_8",
"LABEL_9"
] | |
812 | cardiffnlp/bertweet-base-emotion | [
"LABEL_0",
"LABEL_1",
"LABEL_2",
"LABEL_3"
] | |
816 | cardiffnlp/bertweet-base-sentiment | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | |
817 | cardiffnlp/bertweet-base-stance-abortion | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | |
818 | cardiffnlp/bertweet-base-stance-atheism | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | |
819 | cardiffnlp/bertweet-base-stance-climate | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | |
820 | cardiffnlp/bertweet-base-stance-feminist | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | |
821 | cardiffnlp/bertweet-base-stance-hillary | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | |
822 | cardiffnlp/twitter-roberta-base-emoji | [
"โค",
"๐",
"๐",
"๐",
"๐ฅ",
"๐",
"๐",
"โจ",
"๐",
"๐",
"๐ท",
"๐บ๐ธ",
"โ",
"๐",
"๐",
"๐ฏ",
"๐",
"๐",
"๐ธ",
"๐"
] | # Twitter-roBERTa-base for Emoji prediction
This is a roBERTa-base model trained on ~58M tweets and finetuned for emoji prediction with the TweetEval benchmark.
- Paper: [_TweetEval_ benchmark (Findings of EMNLP 2020)](https://arxiv.org/pdf/2010.12421.pdf).
- Git Repo: [Tweeteval official repository](https://github.... |
823 | cardiffnlp/twitter-roberta-base-emotion | [
"joy",
"optimism",
"anger",
"sadness"
] | # Twitter-roBERTa-base for Emotion Recognition
This is a RoBERTa-base model trained on ~58M tweets and finetuned for emotion recognition with the TweetEval benchmark.
- Paper: [_TweetEval_ benchmark (Findings of EMNLP 2020)](https://arxiv.org/pdf/2010.12421.pdf).
- Git Repo: [Tweeteval official repository](https://g... |
824 | cardiffnlp/twitter-roberta-base-hate | [
"non-hate",
"hate"
] | # Twitter-roBERTa-base for Hate Speech Detection
This is a roBERTa-base model trained on ~58M tweets and finetuned for hate speech detection with the TweetEval benchmark.
This model is specialized to detect hate speech against women and immigrants.
**NEW!** We have made available a more recent and robust hate speech... |
825 | cardiffnlp/twitter-roberta-base-irony | [
"non_irony",
"irony"
] | # Twitter-roBERTa-base for Irony Detection
This is a roBERTa-base model trained on ~58M tweets and finetuned for irony detection with the TweetEval benchmark.
- Paper: [_TweetEval_ benchmark (Findings of EMNLP 2020)](https://arxiv.org/pdf/2010.12421.pdf).
- Git Repo: [Tweeteval official repository](https://github.co... |
826 | cardiffnlp/twitter-roberta-base-offensive | [
"non-offensive",
"offensive"
] | # Twitter-roBERTa-base for Offensive Language Identification
This is a roBERTa-base model trained on ~58M tweets and finetuned for offensive language identification with the TweetEval benchmark.
- Paper: [_TweetEval_ benchmark (Findings of EMNLP 2020)](https://arxiv.org/pdf/2010.12421.pdf).
- Git Repo: [Tweeteval of... |
827 | cardiffnlp/twitter-roberta-base-sentiment | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | ---
datasets:
- tweet_eval
language:
- en
---
# Twitter-roBERTa-base for Sentiment Analysis
This is a roBERTa-base model trained on ~58M tweets and finetuned for sentiment analysis with the TweetEval benchmark. This model is suitable for English (for a similar multilingual model, see [XLM-T](https://huggingface.co/car... |
828 | cardiffnlp/twitter-roberta-base-stance-abortion | [
"none",
"against",
"favor"
] | |
829 | cardiffnlp/twitter-roberta-base-stance-atheism | [
"none",
"against",
"favor"
] | |
830 | cardiffnlp/twitter-roberta-base-stance-climate | [
"none",
"against",
"favor"
] | |
831 | cardiffnlp/twitter-roberta-base-stance-feminist | [
"none",
"against",
"favor"
] | |
832 | cardiffnlp/twitter-roberta-base-stance-hillary | [
"none",
"against",
"favor"
] | |
833 | cardiffnlp/twitter-xlm-roberta-base-sentiment | [
"negative",
"neutral",
"positive"
] | ---
language: multilingual
widget:
- text: "๐ค"
- text: "T'estimo! โค๏ธ"
- text: "I love you!"
- text: "I hate you ๐คฎ"
- text: "Mahal kita!"
- text: "์ฌ๋ํด!"
- text: "๋ ๋๊ฐ ์ซ์ด"
- text: "๐๐๐"
---
# twitter-XLM-roBERTa-base for Sentiment Analysis
This is a multilingual XLM-roBERTa-base model trained on ~198M tweets and ... |
834 | carlosaguayo/distilbert-base-uncased-finetuned-emotion | [
"sadness",
"joy",
"love",
"anger",
"fear",
"surprise"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
args: default... |
836 | celine/emotion-detection_indobenchmark-indobert-lite-base-p1 | [
"anger",
"fear",
"joy",
"sadness"
] | |
837 | celine/hate-speech_indobenchmark-indobert-lite-base-p1 | [
"hs",
"non_hs"
] | |
838 | celtics1863/env-bert-cls-chinese | [
"็ฏๅขๅฝฑๅ่ฏไปทไธ็ฎก็",
"็ขณๆๆพๆงๅถ",
"ๆฐดๆฑกๆไธๆงๅถ",
"ๅคงๆฐๆฑกๆไธๆงๅถ",
"ๅๅฃคๆฑกๆไธๆงๅถ",
"็ฏๅข็ๆ",
"ๅบไฝๅบ็ฉ",
"็ฏๅขๆฏ็ไธๅฅๅบท",
"็ฏๅขๅพฎ็็ฉ",
"็ฏๅขๆฟ็ญไธ็ปๆต"
] | ---
language:
- zh
tags:
- bert
- pytorch
- environment
- multi-class
- classification
---
ไธญๆ็ฏๅขๆๆฌๅ็ฑปๆจกๅ๏ผ1.6M็ๆฐๆฎ้๏ผๅจenv-bert-chineseไธ่ฟ่กfine-tuningใ
ๅไธบ็ฏๅขๅฝฑๅ่ฏไปทไธๆงๅถใ็ขณๆๆพๆงๅถใๆฐดๆฑกๆๆงๅถใๅคงๆฐๆฑกๆๆงๅถใๅๅฃคๆฑกๆๆงๅถใ็ฏๅข็ๆใๅบไฝๅบ็ฉใ็ฏๅขๆฏ็ไธๅฅๅบทใ็ฏๅขๅพฎ็็ฉใ็ฏๅขๆฟ็ญไธ็ปๆต10็ฑปใ
้กน็ฎๆญฃๅจ่ฟ่กไธญ๏ผๅ็ปญไผ้็ปญๆดๆฐ็ธๅ
ณๅ
ๅฎนใ
ๆธ
ๅๅคงๅญฆ็ฏๅขๅญฆ้ข่ฏพ้ข็ป
ๆ็ธๅ
ณ้ๆฑใๅปบ่ฎฎ๏ผ่็ณปbi.huaibin@foxmail.com |
839 | celtics1863/env-bert-topic | [
"็ๆ็ฏๅข",
"ๆฐดๆฑกๆ",
"้็ๅจ็ฉไฟๆค",
"ๅคช้ณ่ฝ",
"็ฏไฟ็ปๆต",
"ๆฑกๆฐดๅค็",
"็ปฟ่ฒๅปบ็ญ",
"ๆฐดๅค็",
"ๅช้ณๆฑกๆ",
"ๆธฉๅฎคๆๅบ",
"ๅๆฐด่ฎพๅค",
"ๅๆฐดๅจ",
"่ชๆฅๆฐด",
"็ๆดป",
"็ฏๅข่ฏไผฐ",
"็ฉบๆฐๆฑกๆ",
"็ฏๅข่ฏไปท",
"ๅทฅไธๆฑกๆ",
"้พ้พ",
"ๆคๆ ",
"็ฏไฟ่กไธ",
"ๆฐดๅค็ๅทฅ็จ",
"ๆฒๆผ ๆฒป็",
"ๅทด้ปๅๅฎ",
"ๆ ธ่ฝ",
"ๅช้ณ",
"็ฏ่ฏๅทฅ็จๅธ",
"ไบๆฐงๅ็ขณ",
"ไฝ็ขณ",
"่ช็ถ็ฏๅข",
"ๆฒๅฐๆด",
"็ฏๅขๅทฅ็จ",
"็งธ็ง็็ง",
... | ---
language: zh
widget:
- text: "็พๅฝ้ๅบใๅทด้ปๅๅฎใ"
- text: "ๆฑกๆฐดๅค็ๅไธญ็ๅ่้่ฆๅๅฐ"
tags:
- pretrain
- pytorch
- environment
- classification
- topic classification
---
่ฏ้ขๅ็ฑปๆจกๅ๏ผไฝฟ็จๆไน"็ฏๅข"่ฏ้ขไธๆๆๅญ่ฏ้ข๏ผ่ฟๆปคๅๅพ69็ฑปใ
top1 acc 60.7,
top3 acc 81.6๏ผ
ๅฏไปฅ็จไบไธญๆ็ฏๅขๆๆฌๆๆ็้ขๅค็ๆญฅ้ชคใ
ๆ ็ญพ๏ผ
"็ๆ็ฏๅข","ๆฐดๆฑกๆ", "้็ๅจ็ฉไฟๆค", "ๅคช้ณ่ฝ", "็ฏไฟ็ปๆต", "ๆฑกๆฐดๅค็", "็ปฟ่ฒๅปบ็ญ", "ๆฐดๅค... |
840 | chisadi/nice-distilbert-v2 | [
"NICE_1",
"NICE_10",
"NICE_11",
"NICE_12",
"NICE_13",
"NICE_14",
"NICE_15",
"NICE_16",
"NICE_17",
"NICE_18",
"NICE_19",
"NICE_2",
"NICE_20",
"NICE_21",
"NICE_22",
"NICE_23",
"NICE_24",
"NICE_25",
"NICE_26",
"NICE_27",
"NICE_28",
"NICE_29",
"NICE_3",
"NICE_30",
"NICE_3... | ### Distibert model finetuned on the task of classifying product descriptions to one of 45 broad [NICE classifications](https://www.wipo.int/classifications/nice/en/)
|
843 | chkla/roberta-argument | [
"NON-ARGUMENT",
"ARGUMENT"
] | ---
language: en
widget:
- text: "It has been determined that the amount of greenhouse gases have decreased by almost half because of the prevalence in the utilization of nuclear power."
---
### Welcome to RoBERTArg!
๐ค **Model description**
This model was trained on ~25k heterogeneous manually annotated sentences (... |
844 | chrommium/bert-base-multilingual-cased-finetuned-news-headlines | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model_index:
- name: bert-base-multilingual-cased-finetuned-cola
results:
- task:
name: Text Classification
type: text-classification
metric:
name: Accuracy
type: accuracy
value: 0.9755
---
<!-- This model ... |
845 | chrommium/rubert-base-cased-sentence-finetuned-headlines_X | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | ---
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: rubert-base-cased-sentence-finetuned-headlines_X
results:
- task:
name: Text Classification
type: text-classification
metrics:
- name: Accuracy
type: accuracy
value: 0.952
---
<!-- This model card has been g... |
846 | chrommium/rubert-base-cased-sentence-finetuned-sent_in_news_sents | [
"LABEL_0",
"LABEL_1",
"LABEL_2",
"LABEL_3",
"LABEL_4",
"LABEL_5",
"LABEL_6"
] | ---
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: rubert-base-cased-sentence-finetuned-sent_in_news_sents
results:
- task:
name: Text Classification
type: text-classification
metrics:
- name: Accuracy
type: accuracy
value: 0.7224199288256228
- name:... |
847 | chrommium/rubert-base-cased-sentence-finetuned-sent_in_ru | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | ---
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: rubert-base-cased-sentence-finetuned-sent_in_ru
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 thi... |
848 | chrommium/sbert_large-finetuned-sent_in_news_sents | [
"LABEL_-3",
"LABEL_-2",
"LABEL_-1",
"LABEL_0",
"LABEL_1",
"LABEL_2",
"LABEL_3"
] | ---
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: sbert_large-finetuned-sent_in_news_sents
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 comme... |
849 | chrommium/sbert_large-finetuned-sent_in_news_sents_3lab | [
"LABEL_-1",
"LABEL_0",
"LABEL_1"
] | ---
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: sbert_large-finetuned-sent_in_news_sents_3lab
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 ... |
850 | 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 remo... |
855 | clem/autonlp-test3-2101779 | [
"not_urgent",
"urgent"
] | ---
tags: autonlp
language: en
widget:
- text: "I love AutoNLP ๐ค"
datasets:
- clem/autonlp-data-test3
---
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 2101779
## Validation Metrics
- Loss: 0.282466858625412
- Accuracy: 1.0
- Precision: 1.0
- Recall: 1.0
- AUC: 1.0
- F1: 1.0
## U... |
856 | clem/autonlp-test3-2101782 | [
"not_urgent",
"urgent"
] | ---
tags: autonlp
language: en
widget:
- text: "I love AutoNLP ๐ค"
datasets:
- clem/autonlp-data-test3
---
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 2101782
## Validation Metrics
- Loss: 0.015991805121302605
- Accuracy: 1.0
- Precision: 1.0
- Recall: 1.0
- AUC: 1.0
- F1: 1.0
#... |
857 | 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... |
858 | climatebert/distilroberta-base-climate-commitment | [
"no",
"yes"
] | ---
license: apache-2.0
datasets:
- climatebert/climate_commitments_actions
language:
- en
metrics:
- accuracy
---
# Model Card for distilroberta-base-climate-commitment
## Model Description
This is the fine-tuned ClimateBERT language model with a classification head for classifying climate-related paragraphs into p... |
859 | climatebert/distilroberta-base-climate-detector | [
"no",
"yes"
] | ---
license: apache-2.0
datasets:
- climatebert/climate_detection
language:
- en
metrics:
- accuracy
---
# Model Card for distilroberta-base-climate-detector
## Model Description
This is the fine-tuned ClimateBERT language model with a classification head for detecting climate-related paragraphs.
Using the [climate... |
860 | climatebert/distilroberta-base-climate-sentiment | [
"neutral",
"opportunity",
"risk"
] | ---
license: apache-2.0
datasets:
- climatebert/climate_sentiment
language:
- en
metrics:
- accuracy
---
# Model Card for distilroberta-base-climate-sentiment
## Model Description
This is the fine-tuned ClimateBERT language model with a classification head for classifying climate-related paragraphs into the climate-... |
861 | climatebert/distilroberta-base-climate-specificity | [
"non",
"spec"
] | ---
license: apache-2.0
datasets:
- climatebert/climate_specificity
language:
- en
metrics:
- accuracy
tags:
- climate
---
# Model Card for distilroberta-base-climate-specificity
## Model Description
This is the fine-tuned ClimateBERT language model with a classification head for classifying climate-related paragrap... |
862 | climatebert/distilroberta-base-climate-tcfd | [
"governance",
"metrics",
"risk",
"strategy"
] | ---
license: apache-2.0
datasets:
- climatebert/tcfd_recommendations
language:
- en
metrics:
- accuracy
tags:
- climate
---
# Model Card for distilroberta-base-climate-tcfd
## Model Description
This is the fine-tuned ClimateBERT language model with a classification head for classifying climate-related paragraphs int... |
863 | 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 parallรจle, miroir du nรดtre ou relevant de ce que l'on appelle la thรฉorie des branes, autoriserait des neutrons ร sortir de notre Unive... |
864 | cmarkea/distilcamembert-base-sentiment | [
"1 star",
"2 stars",
"3 stars",
"4 stars",
"5 stars"
] | ---
language: fr
license: mit
datasets:
- amazon_reviews_multi
- allocine
widget:
- text: "Je pensais lire un livre nul, mais finalement je l'ai trouvรฉ super !"
- text: "Cette banque est trรจs bien, mais elle n'offre pas les services de paiements sans contact."
- text: "Cette banque est trรจs bien et elle offre en plus l... |
868 | cointegrated/rubert-base-cased-dp-paraphrase-detection | [
"entailment",
"not_entailment"
] | ---
language: ["ru"]
tags:
- sentence-similarity
- text-classification
datasets:
- merionum/ru_paraphraser
---
This is a version of paraphrase detector by DeepPavlov ([details in the documentation](http://docs.deeppavlov.ai/en/master/features/overview.html#ranking-model-docs)) ported to the `Transformers` format.
Al... |
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