index int64 0 22.3k | modelId stringlengths 8 111 | label list | readme stringlengths 0 385k |
|---|---|---|---|
436 | SetFit/distilbert-base-uncased__hate_speech_offensive__train-8-9 | [
"hate speech",
"neither",
"offensive language"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased__hate_speech_offensive__train-8-9
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and com... |
437 | SetFit/distilbert-base-uncased__sst2__all-train | [
"negative",
"positive"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased__sst2__all-train
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 re... |
438 | SetFit/distilbert-base-uncased__sst2__train-16-0 | [
"negative",
"positive"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased__sst2__train-16-0
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 r... |
439 | SetFit/distilbert-base-uncased__sst2__train-16-1 | [
"negative",
"positive"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased__sst2__train-16-1
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 r... |
440 | SetFit/distilbert-base-uncased__sst2__train-16-2 | [
"negative",
"positive"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased__sst2__train-16-2
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 r... |
441 | SetFit/distilbert-base-uncased__sst2__train-16-3 | [
"negative",
"positive"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased__sst2__train-16-3
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 r... |
442 | SetFit/distilbert-base-uncased__sst2__train-16-4 | [
"negative",
"positive"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased__sst2__train-16-4
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 r... |
443 | SetFit/distilbert-base-uncased__sst2__train-16-5 | [
"negative",
"positive"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased__sst2__train-16-5
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 r... |
444 | SetFit/distilbert-base-uncased__sst2__train-16-6 | [
"negative",
"positive"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased__sst2__train-16-6
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 r... |
445 | SetFit/distilbert-base-uncased__sst2__train-16-7 | [
"negative",
"positive"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased__sst2__train-16-7
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 r... |
446 | SetFit/distilbert-base-uncased__sst2__train-16-8 | [
"negative",
"positive"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased__sst2__train-16-8
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 r... |
447 | SetFit/distilbert-base-uncased__sst2__train-16-9 | [
"negative",
"positive"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased__sst2__train-16-9
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 r... |
448 | SetFit/distilbert-base-uncased__sst2__train-32-0 | [
"negative",
"positive"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased__sst2__train-32-0
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 r... |
449 | SetFit/distilbert-base-uncased__sst2__train-32-1 | [
"negative",
"positive"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased__sst2__train-32-1
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 r... |
450 | SetFit/distilbert-base-uncased__sst2__train-32-2 | [
"negative",
"positive"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased__sst2__train-32-2
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 r... |
451 | SetFit/distilbert-base-uncased__sst2__train-32-3 | [
"negative",
"positive"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased__sst2__train-32-3
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 r... |
452 | SetFit/distilbert-base-uncased__sst2__train-32-4 | [
"negative",
"positive"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased__sst2__train-32-4
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 r... |
453 | SetFit/distilbert-base-uncased__sst2__train-32-5 | [
"negative",
"positive"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased__sst2__train-32-5
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 r... |
454 | SetFit/distilbert-base-uncased__sst2__train-32-6 | [
"negative",
"positive"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased__sst2__train-32-6
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 r... |
455 | SetFit/distilbert-base-uncased__sst2__train-32-7 | [
"negative",
"positive"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased__sst2__train-32-7
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 r... |
456 | SetFit/distilbert-base-uncased__sst2__train-32-8 | [
"negative",
"positive"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased__sst2__train-32-8
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 r... |
457 | SetFit/distilbert-base-uncased__sst2__train-32-9 | [
"negative",
"positive"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased__sst2__train-32-9
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 r... |
458 | SetFit/distilbert-base-uncased__sst2__train-8-0 | [
"negative",
"positive"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased__sst2__train-8-0
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 re... |
459 | SetFit/distilbert-base-uncased__sst2__train-8-1 | [
"negative",
"positive"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased__sst2__train-8-1
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 re... |
460 | SetFit/distilbert-base-uncased__sst2__train-8-2 | [
"negative",
"positive"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased__sst2__train-8-2
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 re... |
461 | SetFit/distilbert-base-uncased__sst2__train-8-3 | [
"negative",
"positive"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased__sst2__train-8-3
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 re... |
462 | SetFit/distilbert-base-uncased__sst2__train-8-4 | [
"negative",
"positive"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased__sst2__train-8-4
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 re... |
463 | SetFit/distilbert-base-uncased__sst2__train-8-5 | [
"negative",
"positive"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased__sst2__train-8-5
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 re... |
464 | SetFit/distilbert-base-uncased__sst2__train-8-6 | [
"negative",
"positive"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased__sst2__train-8-6
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 re... |
465 | SetFit/distilbert-base-uncased__sst2__train-8-7 | [
"negative",
"positive"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased__sst2__train-8-7
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 re... |
466 | SetFit/distilbert-base-uncased__sst2__train-8-8 | [
"negative",
"positive"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased__sst2__train-8-8
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 re... |
467 | SetFit/distilbert-base-uncased__sst2__train-8-9 | [
"negative",
"positive"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased__sst2__train-8-9
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 re... |
468 | SetFit/distilbert-base-uncased__sst5__all-train | [
"negative",
"neutral",
"positive",
"very negative",
"very positive"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased__sst5__all-train
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 re... |
469 | SetFit/distilbert-base-uncased__subj__all-train | [
"objective",
"subjective"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased__subj__all-train
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 re... |
470 | SetFit/distilbert-base-uncased__subj__train-8-0 | [
"objective",
"subjective"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased__subj__train-8-0
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 re... |
471 | SetFit/distilbert-base-uncased__subj__train-8-1 | [
"objective",
"subjective"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased__subj__train-8-1
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 re... |
472 | SetFit/distilbert-base-uncased__subj__train-8-2 | [
"objective",
"subjective"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased__subj__train-8-2
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 re... |
473 | SetFit/distilbert-base-uncased__subj__train-8-3 | [
"objective",
"subjective"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased__subj__train-8-3
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 re... |
474 | SetFit/distilbert-base-uncased__subj__train-8-4 | [
"objective",
"subjective"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased__subj__train-8-4
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 re... |
475 | SetFit/distilbert-base-uncased__subj__train-8-5 | [
"objective",
"subjective"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased__subj__train-8-5
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 re... |
476 | SetFit/distilbert-base-uncased__subj__train-8-6 | [
"objective",
"subjective"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased__subj__train-8-6
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 re... |
477 | SetFit/distilbert-base-uncased__subj__train-8-7 | [
"objective",
"subjective"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased__subj__train-8-7
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 re... |
478 | SetFit/distilbert-base-uncased__subj__train-8-8 | [
"objective",
"subjective"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased__subj__train-8-8
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 re... |
479 | SetFit/distilbert-base-uncased__subj__train-8-9 | [
"objective",
"subjective"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased__subj__train-8-9
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 re... |
480 | SharanSMenon/22-languages-bert-base-cased | [
"Arabic",
"Chinese",
"Latin",
"Persian",
"Portugese",
"Pushto",
"Romanian",
"Russian",
"Spanish",
"Swedish",
"Tamil",
"Thai",
"Dutch",
"Turkish",
"Urdu",
"English",
"Estonian",
"French",
"Hindi",
"Indonesian",
"Japanese",
"Korean"
] | ---
metrics:
- accuracy
widget:
- text: "In war resolution, in defeat defiance, in victory magnanimity"
- text: "en la guerra resolución en la derrota desafío en la victoria magnanimidad"
---
[](https://colab.research.google.com/drive/1dqeUwS_DZ... |
482 | Shuvam/autonlp-college_classification-164469 | [
"0",
"1"
] | ---
tags: autonlp
language: en
widget:
- text: "I love AutoNLP 🤗"
datasets:
- Shuvam/autonlp-data-college_classification
---
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 164469
## Validation Metrics
- Loss: 0.05527503043413162
- Accuracy: 0.9853049228508449
- Precision: 0.9910447... |
483 | s-nlp/roberta-base-formality-ranker | [
"formal",
"informal"
] | ---
language:
- en
tags:
- formality
datasets:
- GYAFC
- Pavlick-Tetreault-2016
---
The model has been trained to predict for English sentences, whether they are formal or informal.
Base model: `roberta-base`
Datasets: [GYAFC](https://github.com/raosudha89/GYAFC-corpus) from [Rao and Tetreault, 2018](https... |
485 | s-nlp/roberta_toxicity_classifier | [
"neutral",
"toxic"
] | ---
language:
- en
tags:
- toxic comments classification
licenses:
- cc-by-nc-sa
---
## Toxicity Classification Model
This model is trained for toxicity classification task. The dataset used for training is the merge of the English parts of the three datasets by **Jigsaw** ([Jigsaw 2018](https://www.kaggle.com/c/jigs... |
487 | s-nlp/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... |
488 | s-nlp/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... |
489 | s-nlp/xlmr_formality_classifier | [
"formal",
"informal"
] | ---
language:
- en
- fr
- it
- pt
tags:
- formal or informal classification
licenses:
- cc-by-nc-sa
---
XLMRoberta-based classifier trained on XFORMAL.
all
| | precision | recall | f1-score | support |
|--------------|-----------|----------|----------|---------|
| 0 | 0.744912 | 0.92779... |
491 | apanc/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... |
492 | Smone55/autonlp-au_topics-452311620 | [
"-1",
"0",
"1",
"10",
"100",
"101",
"102",
"103",
"104",
"105",
"106",
"107",
"108",
"109",
"11",
"110",
"111",
"112",
"113",
"114",
"115",
"116",
"117",
"118",
"119",
"12",
"120",
"121",
"122",
"123",
"124",
"125",
"13",
"14",
"15",
"16",
"17"... | ---
tags: autonlp
language: en
widget:
- text: "I love AutoNLP 🤗"
datasets:
- Smone55/autonlp-data-au_topics
co2_eq_emissions: 208.0823957145878
---
# Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 452311620
- CO2 Emissions (in grams): 208.0823957145878
## Validation Metrics
- L... |
494 | SparkBeyond/roberta-large-sts-b | [
"LABEL_0"
] |
# Roberta Large STS-B
This model is a fine tuned RoBERTA model over STS-B.
It was trained with these params:
!python /content/transformers/examples/text-classification/run_glue.py \
--model_type roberta \
--model_name_or_path roberta-large \
--task_name STS-B \
--do_train \
--do_eval \
--do_l... |
495 | StevenLimcorn/indo-roberta-indonli | [
"c",
"e",
"n"
] | ---
language: id
tags:
- roberta
license: mit
datasets:
- indonli
widget:
- text: "Amir Sjarifoeddin Harahap lahir di Kota Medan, Sumatera Utara, 27 April 1907. Ia meninggal di Surakarta, Jawa Tengah, pada 19 Desember 1948 dalam usia 41 tahun. </s></s> Amir Sjarifoeddin Harahap masih hidup."
---
## Indo-roberta-indonl... |
496 | StevenLimcorn/indonesian-roberta-base-emotion-classifier | [
"anger",
"fear",
"happy",
"love",
"sadness"
] | ---
language: id
tags:
- roberta
license: mit
datasets:
- indonlu
widget:
- text: "Hal-hal baik akan datang."
---
# Indo RoBERTa Emotion Classifier
Indo RoBERTa Emotion Classifier is emotion classifier based on [Indo-roberta](https://huggingface.co/flax-community/indonesian-roberta-base) model. It was trained on the ... |
497 | Tahsin/distilbert-base-uncased-finetuned-emotion | [
"anger",
"fear",
"joy",
"love",
"sadness",
"surprise"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased-finetuned-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
args: default
... |
498 | MonoHime/rubert-base-cased-sentiment-new | [
"NEGATIVE",
"NEUTRAL",
"POSITIVE"
] | ---
language:
- ru
tags:
- sentiment
- text-classification
datasets:
- Tatyana/ru_sentiment_dataset
---
# Model Card for RuBERT for Sentiment Analysis
# Model Details
## Model Description
Russian texts sentiment classification.
- **Developed by:** Tatyana Voloshina
- **Shared by [Optional]:** Tatyana Volo... |
503 | Theivaprakasham/bert-base-cased-twitter_sentiment | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: bert-base-cased-twitter_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 th... |
504 | Theivaprakasham/sentence-transformers-msmarco-distilbert-base-tas-b-twitter_sentiment | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: sentence-transformers-msmarco-distilbert-base-tas-b-twitter_sentiment
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proof... |
505 | TomO/xlm-roberta-base-finetuned-marc-en | [
"good",
"great",
"ok",
"poor",
"terrible"
] | ---
license: mit
tags:
- generated_from_trainer
datasets:
- amazon_reviews_multi
model-index:
- name: xlm-roberta-base-finetuned-marc-en
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 re... |
506 | TomW/TOMFINSEN | [
"negative",
"neutral",
"positive"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- financial_phrasebank
metrics:
- recall
- accuracy
- precision
model-index:
- name: TOMFINSEN
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: financial_phrasebank
type: financial_phraseb... |
507 | Tommy930/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... |
508 | TransQuest/monotransquest-da-any_en | [
"LABEL_0"
] | ---
language: multilingual-en
tags:
- Quality Estimation
- monotransquest
- DA
license: apache-2.0
---
# TransQuest: Translation Quality Estimation with Cross-lingual Transformers
The goal of quality estimation (QE) is to evaluate the quality of a translation without having access to a reference translation. High-acc... |
509 | TransQuest/monotransquest-da-en_any | [
"LABEL_0"
] | ---
language: en-multilingual
tags:
- Quality Estimation
- monotransquest
- DA
license: apache-2.0
---
# TransQuest: Translation Quality Estimation with Cross-lingual Transformers
The goal of quality estimation (QE) is to evaluate the quality of a translation without having access to a reference translation. High-acc... |
510 | TransQuest/monotransquest-da-en_de-wiki | [
"LABEL_0"
] | ---
language: en-de
tags:
- Quality Estimation
- monotransquest
- DA
license: apache-2.0
---
# TransQuest: Translation Quality Estimation with Cross-lingual Transformers
The goal of quality estimation (QE) is to evaluate the quality of a translation without having access to a reference translation. High-accuracy QE t... |
511 | TransQuest/monotransquest-da-en_zh-wiki | [
"LABEL_0"
] | ---
language: en-zh
tags:
- Quality Estimation
- monotransquest
- DA
license: apache-2.0
---
# TransQuest: Translation Quality Estimation with Cross-lingual Transformers
The goal of quality estimation (QE) is to evaluate the quality of a translation without having access to a reference translation. High-accuracy QE t... |
512 | TransQuest/monotransquest-da-et_en-wiki | [
"LABEL_0"
] | ---
language: et-en
tags:
- Quality Estimation
- monotransquest
- DA
license: apache-2.0
---
# TransQuest: Translation Quality Estimation with Cross-lingual Transformers
The goal of quality estimation (QE) is to evaluate the quality of a translation without having access to a reference translation. High-accuracy QE t... |
513 | TransQuest/monotransquest-da-multilingual | [
"LABEL_0"
] | ---
language: multilingual-multilingual
tags:
- Quality Estimation
- monotransquest
- DA
license: apache-2.0
---
# TransQuest: Translation Quality Estimation with Cross-lingual Transformers
The goal of quality estimation (QE) is to evaluate the quality of a translation without having access to a reference translation... |
514 | TransQuest/monotransquest-da-ne_en-wiki | [
"LABEL_0"
] | ---
language: ne-en
tags:
- Quality Estimation
- monotransquest
- DA
license: apache-2.0
---
# TransQuest: Translation Quality Estimation with Cross-lingual Transformers
The goal of quality estimation (QE) is to evaluate the quality of a translation without having access to a reference translation. High-accuracy QE t... |
515 | TransQuest/monotransquest-da-ro_en-wiki | [
"LABEL_0"
] | ---
language: ro-en
tags:
- Quality Estimation
- monotransquest
- DA
license: apache-2.0
---
# TransQuest: Translation Quality Estimation with Cross-lingual Transformers
The goal of quality estimation (QE) is to evaluate the quality of a translation without having access to a reference translation. High-accuracy QE t... |
516 | TransQuest/monotransquest-da-ru_en-reddit_wikiquotes | [
"LABEL_0"
] | ---
language: ru-en
tags:
- Quality Estimation
- monotransquest
- DA
license: apache-2.0
---
# TransQuest: Translation Quality Estimation with Cross-lingual Transformers
The goal of quality estimation (QE) is to evaluate the quality of a translation without having access to a reference translation. High-accuracy QE t... |
517 | TransQuest/monotransquest-da-si_en-wiki | [
"LABEL_0"
] | ---
language: si-en
tags:
- Quality Estimation
- monotransquest
- DA
license: apache-2.0
---
# TransQuest: Translation Quality Estimation with Cross-lingual Transformers
The goal of quality estimation (QE) is to evaluate the quality of a translation without having access to a reference translation. High-accuracy QE t... |
518 | TransQuest/monotransquest-hter-de_en-pharmaceutical | [
"LABEL_0"
] | ---
language: de-en
tags:
- Quality Estimation
- monotransquest
- hter
license: apache-2.0
---
# TransQuest: Translation Quality Estimation with Cross-lingual Transformers
The goal of quality estimation (QE) is to evaluate the quality of a translation without having access to a reference translation. High-accuracy QE... |
519 | TransQuest/monotransquest-hter-en_any | [
"LABEL_0"
] | ---
language: en-multilingual
tags:
- Quality Estimation
- monotransquest
- HTER
license: apache-2.0
---
# TransQuest: Translation Quality Estimation with Cross-lingual Transformers
The goal of quality estimation (QE) is to evaluate the quality of a translation without having access to a reference translation. High-a... |
520 | TransQuest/monotransquest-hter-en_cs-pharmaceutical | [
"LABEL_0"
] | ---
language: en-cs
tags:
- Quality Estimation
- monotransquest
- hter
license: apache-2.0
---
# TransQuest: Translation Quality Estimation with Cross-lingual Transformers
The goal of quality estimation (QE) is to evaluate the quality of a translation without having access to a reference translation. High-accuracy QE... |
521 | TransQuest/monotransquest-hter-en_de-it-nmt | [
"LABEL_0"
] | ---
language: en-de
tags:
- Quality Estimation
- monotransquest
- hter
license: apache-2.0
---
# TransQuest: Translation Quality Estimation with Cross-lingual Transformers
The goal of quality estimation (QE) is to evaluate the quality of a translation without having access to a reference translation. High-accuracy QE... |
522 | TransQuest/monotransquest-hter-en_de-it-smt | [
"LABEL_0"
] | ---
language: en-de
tags:
- Quality Estimation
- monotransquest
- hter
license: apache-2.0
---
# TransQuest: Translation Quality Estimation with Cross-lingual Transformers
The goal of quality estimation (QE) is to evaluate the quality of a translation without having access to a reference translation. High-accuracy QE... |
523 | TransQuest/monotransquest-hter-en_de-wiki | [
"LABEL_0"
] | ---
language: en-de
tags:
- Quality Estimation
- monotransquest
- hter
license: apache-2.0
---
# TransQuest: Translation Quality Estimation with Cross-lingual Transformers
The goal of quality estimation (QE) is to evaluate the quality of a translation without having access to a reference translation. High-accuracy QE... |
524 | TransQuest/monotransquest-hter-en_lv-it-nmt | [
"LABEL_0"
] | ---
language: en-lv
tags:
- Quality Estimation
- monotransquest
- hter
license: apache-2.0
---
# TransQuest: Translation Quality Estimation with Cross-lingual Transformers
The goal of quality estimation (QE) is to evaluate the quality of a translation without having access to a reference translation. High-accuracy QE... |
525 | TransQuest/monotransquest-hter-en_lv-it-smt | [
"LABEL_0"
] | ---
language: en-lv
tags:
- Quality Estimation
- monotransquest
- hter
license: apache-2.0
---
# TransQuest: Translation Quality Estimation with Cross-lingual Transformers
The goal of quality estimation (QE) is to evaluate the quality of a translation without having access to a reference translation. High-accuracy QE... |
526 | TransQuest/monotransquest-hter-en_zh-wiki | [
"LABEL_0"
] | ---
language: en-zh
tags:
- Quality Estimation
- monotransquest
- hter
license: apache-2.0
---
# TransQuest: Translation Quality Estimation with Cross-lingual Transformers
The goal of quality estimation (QE) is to evaluate the quality of a translation without having access to a reference translation. High-accuracy QE... |
528 | Vasanth/tamil-sentiment-distilbert | [
"LABEL_0",
"LABEL_1",
"LABEL_2",
"LABEL_3",
"LABEL_4"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- tamilmixsentiment
metrics:
- accuracy
model_index:
- name: tamil-sentiment-distilbert
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: tamilmixsentiment
type: tamilmixsentiment
arg... |
529 | Vassilis/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, t... |
532 | Wellcome/WellcomeBertMesh | [
"LABEL_0",
"LABEL_1",
"LABEL_10",
"LABEL_100",
"LABEL_1000",
"LABEL_10000",
"LABEL_10001",
"LABEL_10002",
"LABEL_10003",
"LABEL_10004",
"LABEL_10005",
"LABEL_10006",
"LABEL_10007",
"LABEL_10008",
"LABEL_10009",
"LABEL_1001",
"LABEL_10010",
"LABEL_10011",
"LABEL_10012",
"LABEL_1... | ---
license: apache-2.0
pipeline_tag: text-classification
---
# WellcomeBertMesh
WellcomeBertMesh is build from the data science team at the WellcomeTrust to tag biomedical grants with Medical Subject Headings ([Mesh](https://www.nlm.nih.gov/mesh/meshhome.html)). Even though developed with the intention to be used to... |
533 | Worldman/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... |
538 | XYHY/autonlp-123-478412765 | [
"0",
"1"
] | ---
tags: autonlp
language: unk
widget:
- text: "I love AutoNLP 🤗"
datasets:
- XYHY/autonlp-data-123
co2_eq_emissions: 69.86520391863117
---
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 478412765
- CO2 Emissions (in grams): 69.86520391863117
## Validation Metrics
- Loss: 0.186362... |
540 | Yah216/Sentiment_Analysis_CAMelBERT_msa_sixteenth_HARD | [
"NEGATIVE",
"NEUTRAL",
"POSITIVE"
] | ---
language: ar
widget:
- text: "ممتاز"
- text: "أنا حزين"
- text: "لا شيء"
---
# Model description
This model is an Arabic language sentiment analysis pretrained model.
The model is built on top of the CAMelBERT_msa_sixteenth BERT-based model.
We used the HARD dataset of hotels review to fine tune the model.
The... |
541 | Yaia/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... |
542 | Yanjie/message-intent | [
"goodbye",
"discount",
"can_i_help",
"other",
"escalation",
"goodbye|purchase",
"restock",
"subscription",
"discount|other",
"subscription|removal",
"goodbye|anything_else",
"issue|query_clarification",
"order|query_order_number",
"shipping|policy",
"shopping|query_link_item",
"shoppin... | This is the concierge intent model. Fined tuned on DistilBert uncased model. |
543 | Yanjie/message-preamble | [
"blank",
"great",
"welcome",
"no_worries",
"thanks",
"sorry",
"sure",
"got_it",
"alright",
"no_rush",
"confirmation",
"disagreement",
"will_do",
"understand",
"funny"
] | This is the concierge preamble model. Fined tuned on DistilBert uncased model. |
544 | Yuri/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... |
591 | abdelkader/distilbert-base-uncased-distilled-clinc | [
"accept_reservations",
"account_blocked",
"alarm",
"application_status",
"apr",
"are_you_a_bot",
"balance",
"bill_balance",
"bill_due",
"book_flight",
"book_hotel",
"calculator",
"calendar",
"calendar_update",
"calories",
"cancel",
"cancel_reservation",
"car_rental",
"card_declin... | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- clinc_oos
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased-distilled-clinc
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: clinc_oos
type: clinc_oos
args: plus
... |
592 | abdelkader/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
... |
593 | abdelkader/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... |
594 | abhishek/autonlp-bbc-news-classification-37229289 | [
"business",
"entertainment",
"politics",
"sport",
"tech"
] | ---
tags: autonlp
language: en
widget:
- text: "I love AutoNLP 🤗"
datasets:
- abhishek/autonlp-data-bbc-news-classification
co2_eq_emissions: 5.448567309047846
---
# Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 37229289
- CO2 Emissions (in grams): 5.448567309047846
## Validatio... |
595 | abhishek/autonlp-bbc-roberta-37249301 | [
"business",
"entertainment",
"politics",
"sport",
"tech"
] | ---
tags: autonlp
language: unk
widget:
- text: "I love AutoNLP 🤗"
datasets:
- abhishek/autonlp-data-bbc-roberta
co2_eq_emissions: 1.9859980179658823
---
# Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 37249301
- CO2 Emissions (in grams): 1.9859980179658823
## Validation Metrics... |
596 | abhishek/autonlp-ferd1-2652021 | [
"0",
"1"
] | ---
tags: autonlp
language: en
widget:
- text: "I love AutoNLP 🤗"
datasets:
- abhishek/autonlp-data-ferd1
---
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 2652021
## Validation Metrics
- Loss: 0.3934604227542877
- Accuracy: 0.8411030860144452
- Precision: 0.8201550387596899
- Rec... |
597 | abhishek/autonlp-fred2-2682064 | [
"0",
"1"
] | ---
tags: autonlp
language: en
widget:
- text: "I love AutoNLP 🤗"
datasets:
- abhishek/autonlp-data-fred2
---
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 2682064
## Validation Metrics
- Loss: 0.4454168379306793
- Accuracy: 0.8188976377952756
- Precision: 0.8442028985507246
- Rec... |
598 | abhishek/autonlp-imdb-roberta-base-3662644 | [
"neg",
"pos"
] | ---
tags: autonlp
language: unk
widget:
- text: "I love AutoNLP 🤗"
datasets:
- abhishek/autonlp-data-imdb-roberta-base
co2_eq_emissions: 25.894117734124272
---
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 3662644
- CO2 Emissions (in grams): 25.894117734124272
## Validation Metrics... |
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