modelId stringlengths 4 81 | tags list | pipeline_tag stringclasses 17
values | config dict | downloads int64 0 59.7M | first_commit timestamp[ns, tz=UTC] | card stringlengths 51 438k | embedding list |
|---|---|---|---|---|---|---|---|
CAMeL-Lab/bert-base-arabic-camelbert-ca-pos-glf | [
"pytorch",
"tf",
"bert",
"token-classification",
"ar",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | token-classification | {
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"no_repeat... | 18 | 2021-11-05T13:48:28Z | ---
pipeline_tag: "fill-mask"
language: en
---
# This repository is a fork of [yiyanghkust/finbert-pretrain](https://huggingface.co/yiyanghkust/finbert-pretrain)
> All credits to [@yiyanghkust](https://huggingface.co/yiyanghkust).
I added the TensorFlow model and a proper `tokenizer.json`
---
`FinBERT` is a BERT ... | [
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CAMeL-Lab/bert-base-arabic-camelbert-ca-pos-msa | [
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"no_repeat... | 71 | null | ---
language:
- de
license: mit
widget:
- text: |
Philipp ist 26 Jahre alt und lebt in Nürnberg, Deutschland. Derzeit arbeitet er als Machine Learning Engineer und Tech Lead bei Hugging Face, um künstliche Intelligenz durch Open Source und Open Science zu demokratisieren.
datasets:
- germaner
metrics:
- precision... | [
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CAMeL-Lab/bert-base-arabic-camelbert-ca-sentiment | [
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"tf",
"bert",
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"ar",
"arxiv:2103.06678",
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"no_rep... | 73 | null | ---
license: apache-2.0
tags:
- summarization
datasets:
- philschmid/prompted-germanquad
widget:
- text: |
Philipp ist 26 Jahre alt und lebt in Nürnberg, Deutschland. Derzeit arbeitet er als Machine Learning Engineer und Tech Lead bei Hugging Face, um künstliche Intelligenz durch Open Source und Open Science zu d... | [
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CAMeL-Lab/bert-base-arabic-camelbert-da-poetry | [
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"text-classification",
"ar",
"arxiv:1905.05700",
"arxiv:2103.06678",
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"no_rep... | 37 | null | ---
language: fr
---
# Pytorch Fork of [tblard/tf-allocine](https://huggingface.co/tblard/tf-allocine)
A french sentiment analysis model, based on [CamemBERT](https://camembert-model.fr/), and finetuned on a large-scale dataset scraped from [Allociné.fr](http://www.allocine.fr/) user reviews.
## Results
| Validation A... | [
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CAMeL-Lab/bert-base-arabic-camelbert-mix-did-madar-corpus26 | [
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"bert",
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"ar",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0"
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"no_rep... | 45 | null | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: philschmid/tf-distilbart-cnn-12-6-tradetheevent
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this co... | [
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CAMeL-Lab/bert-base-arabic-camelbert-mix-did-madar-corpus6 | [
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"tf",
"bert",
"text-classification",
"ar",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0"
] | text-classification | {
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"no_rep... | 34 | null | ---
language: en
tags:
- summarization
license: apache-2.0
datasets:
- cnn_dailymail
- xsum
thumbnail: https://huggingface.co/front/thumbnails/distilbart_medium.png
---
# This is an Tensorflow fork of [sshleifer/distilbart-cnn-12-6](https://huggingface.co/sshleifer/distilbart-cnn-12-6)
### Usage
This checkpoint shou... | [
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CAMeL-Lab/bert-base-arabic-camelbert-msa-poetry | [
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"tf",
"bert",
"text-classification",
"ar",
"arxiv:1905.05700",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0"
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"no_rep... | 25 | 2022-02-23T17:05:54Z | ---
license: apache-2.0
tags:
- automatic-speech-recognition
- phongdtd/VinDataVLSP
- generated_from_trainer
model-index:
- name: fb-vindata-vi-large
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complet... | [
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0.0... |
CLAck/en-vi | [
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"text2text-generation",
"en",
"vi",
"dataset:ALT",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
] | translation | {
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"no_repeat_ngram_size... | 8 | null | ---
language: en
tags:
- BabyBERTa
datasets:
- CHILDES
widget:
- text: "Look here. What is that <mask> ?"
- text: "Do you like your <mask> ?"
---
## BabyBERTA
### Overview
BabyBERTa is a light-weight version of RoBERTa trained on 5M words of American-English child-directed input.
It is intended for language acquisit... | [
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CLAck/indo-mixed | [
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"en",
"id",
"dataset:ALT",
"transformers",
"translation",
"license:apache-2.0",
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"no_repeat_ngram_size... | 15 | null | ---
language: en
tags:
- BabyBERTa
datasets:
- CHILDES
widget:
- text: "Look here. What is that <mask> ?"
- text: "Do you like your <mask> ?"
---
## BabyBERTA
### Overview
BabyBERTa is a light-weight version of RoBERTa trained on 5M words of American-English child-directed input.
It is intended for language acquisit... | [
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CLAck/indo-pure | [
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"no_repeat_ngram_size... | 4 | null | ---
language: en
tags:
- BabyBERTa
license: mit
datasets:
- CHILDES
widget:
- text: "Look here. What is that <mask> ?"
- text: "Do you like your <mask> ?"
---
## BabyBERTA
### Overview
BabyBERTa is a light-weight version of RoBERTa trained on 5M words of American-English child-directed input.
It is intended for lang... | [
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CLTL/MedRoBERTa.nl | [
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"nl",
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] | fill-mask | {
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"no_repeat_ngra... | 2,988 | null | ---
language:
- pt-br
tags:
- question-answering
license: apache-2.0
pipeline_tag: question-answering
metrics:
- em
- f1
---
# BraQuAD BERT
## Model description
This is a question-answering model trained in BraQuAD 2.0, a version of SQuAD 2.0 translated to PT-BR using Google Cloud Translation API.
### Context
Edit... | [
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"... | 31 | null | ---
tags: autonlp
language: fr
widget:
- text: "I love AutoNLP 🤗"
datasets:
- pierreant-p/autonlp-data-jcvd-or-linkedin
---
# Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 3471039
## Validation Metrics
- Loss: 0.6704344749450684
- Accuracy: 0.59375
- Macro F1: 0.372549019607843... | [
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CLTL/icf-levels-fac | [
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"... | 32 | null | ---
language:
- pt
tags:
- generated_from_trainer
datasets:
- pierreguillou/lener_br_finetuning_language_model
model-index:
- name: checkpoints
results:
- task:
name: Fill Mask
type: fill-mask
dataset:
name: pierreguillou/lener_br_finetuning_language_model
type: pierreguillou/lener_br_f... | [
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CLTL/icf-levels-ins | [
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"... | 32 | null | ---
language: pt
license: mit
tags:
- question-answering
- bert
- bert-base
- pytorch
datasets:
- brWaC
- squad
- squad_v1_pt
metrics:
- squad
widget:
- text: "Quando começou a pandemia de Covid-19 no mundo?"
context: "A pandemia de COVID-19, também conhecida como pandemia de coronavírus, é uma pandemia em curso de C... | [
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CLTL/icf-levels-mbw | [
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"... | 30 | null | ---
language:
- pt
tags:
- generated_from_trainer
datasets:
- pierreguillou/lener_br_finetuning_language_model
model-index:
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results:
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name: Fill Mask
type: fill-mask
dataset:
name: pierreguillou/lener_br_finetuning_language_model
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CM-CA/Cartman | [] | null | {
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"num_beams... | 0 | null | ---
language: pt
license: apache-2.0
tags:
- text2text-generation
- byt5
- pytorch
- qa
datasets: squad
metrics: squad
widget:
- text: 'question: "Quando começou a pandemia de Covid-19 no mundo?" context: "A pandemia de COVID-19, também conhecida como pandemia de coronavírus, é uma pandemia em curso de COVID-19, uma d... | [
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CM-CA/DialoGPT-small-cartman | [] | null | {
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language: pt
widget:
- text: "Quem era Jim Henson? Jim Henson era um"
- text: "Em um achado chocante, o cientista descobriu um"
- text: "Barack Hussein Obama II, nascido em 4 de agosto de 1961, é"
- text: "Corrida por vacina contra Covid-19 já tem"
license: mit
datasets:
- wikipedia
---
# GPorTuguese-2: a Langua... | [
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CNT-UPenn/Bio_ClinicalBERT_for_seizureFreedom_classification | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | {
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"BertForSequenceClassification"
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"no_rep... | 28 | null | ---
language:
- pt
tags:
- generated_from_trainer
datasets:
- lener_br
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: checkpoints
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: lener_br
type: lener_br
metrics:
- name: F1... | [
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CSResearcher/TestModel | [
"license:mit"
] | null | {
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"num_beams... | 0 | 2022-01-26T19:11:04Z | ---
language:
- pt
tags:
- text2text-generation
- t5
- pytorch
- qa
datasets:
- squad
- squad_v1_pt
metrics:
- precision
- recall
- f1
- accuracy
- squad
model-index:
- name: checkpoints
results:
- task:
name: text2text-generation
type: text2text-generation
dataset:
name: squad
type: sq... | [
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CSZay/bart | [] | null | {
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"num_beams... | 0 | null | ---
language: en
tags:
- html
license: apache-2.0
datasets:
- squadv2
inference:
parameters:
handle_impossible_answer: true
---
Txt | [
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CTBC/ATS | [] | null | {
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"num_beams... | 0 | null | ---
inference:
parameters:
aggregation_strategy: first
---
. | [
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CZWin32768/xlm-align | [
"pytorch",
"xlm-roberta",
"fill-mask",
"arxiv:2106.06381",
"transformers",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"XLMRobertaForMaskedLM"
],
"model_type": "xlm-roberta",
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"no_repe... | 6 | null | ---
tags: autonlp
language: en
widget:
- text: "I love AutoNLP 🤗"
datasets:
- pierric/autonlp-data-my-own-imdb-sentiment-analysis
---
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 2131817
## Validation Metrics
- Loss: 0.24430708587169647
- Accuracy: 0.9452
- Precision: 0.930394431... | [
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0... |
Caddy/UD | [] | null | {
"architectures": null,
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"num_beams... | 0 | null | ---
tags:
- image-classification
- pytorch
- huggingpics
metrics:
- accuracy
model-index:
- name: ny-cr-fr
results:
- task:
name: Image Classification
type: image-classification
metrics:
- name: Accuracy
type: accuracy
value: 0.9305555820465088
---
# ny-cr-fr
Autogenerated ... | [
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... |
Calamarii/calamari | [] | null | {
"architectures": null,
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"num_beams... | 0 | null | ---
language: eo
thumbnail: https://huggingface.co/blog/assets/EsperBERTo-thumbnail-v2.png
---
## EsperBERTo: RoBERTa-like Language model trained on Esperanto
**Companion model to blog post https://huggingface.co/blog/how-to-train** 🔥
### Training Details
- current checkpoint: 566000
- machine name: `galinette`
| [
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0.02... |
CalvinHuang/mt5-small-finetuned-amazon-en-es | [
"pytorch",
"tensorboard",
"mt5",
"text2text-generation",
"transformers",
"summarization",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible"
] | summarization | {
"architectures": [
"MT5ForConditionalGeneration"
],
"model_type": "mt5",
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},
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"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat... | 16 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- tweet_eval
metrics:
- f1
model-index:
- name: hate_trained
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: tweet_eval
type: tweet_eval
args: hate
metrics:
- name: F1
... | [
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Cameron/BERT-Jigsaw | [
"pytorch",
"jax",
"bert",
"text-classification",
"transformers"
] | text-classification | {
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
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},
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"min_length": null,
"no_rep... | 35 | null | ---
language: da
tags:
- danish
- bert
- sentiment
- analytical
license: cc-by-4.0
widget:
- text: "Jeg synes, det er en elendig film"
---
# Danish BERT fine-tuned for Detecting 'Analytical'
This model detects if a Danish text is 'subjective' or 'objective'.
It is trained and tested on Tweets and texts transcribed fr... | [
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Cameron/BERT-SBIC-offensive | [
"pytorch",
"jax",
"bert",
"text-classification",
"transformers"
] | text-classification | {
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
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},
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"no_rep... | 31 | null | ---
language: da
tags:
- danish
- bert
- sentiment
- polarity
license: cc-by-4.0
widget:
- text: "Sikke en dejlig dag det er i dag"
---
# Danish BERT fine-tuned for Sentiment Analysis with `senda`
This model detects polarity ('positive', 'neutral', 'negative') of Danish texts.
It is trained and tested on Tweets anno... | [
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Cameron/BERT-SBIC-targetcategory | [
"pytorch",
"jax",
"bert",
"text-classification",
"transformers"
] | text-classification | {
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_rep... | 30 | null | # Med-QP Cross Encoder
Demo model for use as part of Augmented SBERT chapters of the [NLP for Semantic Search course](https://www.pinecone.io/learn/nlp). | [
-0.019810108467936516,
0.007877232506871223,
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0.03040357306599617,
0.074... |
Cameron/BERT-eec-emotion | [
"pytorch",
"jax",
"bert",
"text-classification",
"transformers"
] | text-classification | {
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_rep... | 36 | null | # MRPC Cross Encoder
Demo model for use as part of Augmented SBERT chapters of the [NLP for Semantic Search course](https://www.pinecone.io/learn/nlp). | [
-0.039677370339632034,
0.005860840901732445,
-0.0192106980830431,
0.047278571873903275,
0.04282866790890694,
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0.04814901202917099,
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0.005013836082071066,
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0.063... |
Cameron/BERT-jigsaw-identityhate | [
"pytorch",
"jax",
"bert",
"text-classification",
"transformers"
] | text-classification | {
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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"length_penalty": null,
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"min_length": null,
"no_rep... | 37 | null | # Quora-QP Cross Encoder
Demo model for use as part of Augmented SBERT chapters of the [NLP for Semantic Search course](https://www.pinecone.io/learn/nlp). | [
-0.0068384017795324326,
0.005244854837656021,
-0.004033059813082218,
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0.026211027055978775,
0.017245734110474586,
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0.015811555087566376,
... |
Cameron/BERT-mdgender-convai-binary | [
"pytorch",
"jax",
"bert",
"text-classification",
"transformers"
] | text-classification | {
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_rep... | 33 | null | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like cluster... | [
-0.03682786226272583,
-0.017038146033883095,
-0.016540275886654854,
0.0510595329105854,
0.01117929257452488,
0.04447409510612488,
-0.01840854622423649,
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-0.070090651512146,
0.08364398777484894,
0.03946809098124504,
0.013144438154995441,
0.00234610796906054,
0.04092745... |
Cameron/BERT-mdgender-convai-ternary | [
"pytorch",
"jax",
"bert",
"text-classification",
"transformers"
] | text-classification | {
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_rep... | 38 | null | # RTE Cross Encoder
Demo model for use as part of Augmented SBERT chapters of the [NLP for Semantic Search course](https://www.pinecone.io/learn/nlp). | [
-0.013309814967215061,
0.005761236418038607,
0.0015587899833917618,
0.035152558237314224,
0.04595363512635231,
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0.046910159289836884,
0.0003966210060752928,
-0.021376464515924454,
0.022846659645438194,
... |
Cameron/BERT-mdgender-wizard | [
"pytorch",
"jax",
"bert",
"text-classification",
"transformers"
] | text-classification | {
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_rep... | 30 | null | # STSb Cross Encoder
Demo model for use as part of Augmented SBERT chapters of the [NLP for Semantic Search course](https://www.pinecone.io/learn/nlp). | [
-0.02663075365126133,
0.0039133899845182896,
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0.0460791289806366,
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0.041979528963565826,
0.025178685784339905,
0.0004941279767081141,
0.028721636161208153,
0.056... |
Camzure/MaamiBot-test | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 9 | null | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like cluster... | [
-0.03682786226272583,
-0.017038146033883095,
-0.016540275886654854,
0.0510595329105854,
0.01117929257452488,
0.04447409510612488,
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0.08364398777484894,
0.03946809098124504,
0.013144438154995441,
0.00234610796906054,
0.04092745... |
Capreolus/birch-bert-large-car_mb | [
"pytorch",
"tf",
"jax",
"bert",
"next-sentence-prediction",
"transformers"
] | null | {
"architectures": [
"BertForNextSentencePrediction"
],
"model_type": "bert",
"task_specific_params": {
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"max_length": null
},
"summarization": {
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"length_penalty": null,
"max_length": null,
"min_length": null,
"no_rep... | 4 | null | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 512 dimensional dense vector space and can be used for tasks like clustering or semantic... | [
-0.024616500362753868,
-0.022541917860507965,
-0.01864197663962841,
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0.030005697160959244,
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0.08093979954719543,
0.02890029363334179,
0.012176213786005974,
0.009335862472653389,
0.0... |
dccuchile/albert-base-spanish-finetuned-pos | [
"pytorch",
"albert",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | {
"architectures": [
"AlbertForTokenClassification"
],
"model_type": "albert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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"max_length": null,
"min_length": null,
"no_re... | 5 | null | * Install requirements
```
pip install jieba
```
* Generate words.txt
```bash
data_dir=/path/to/wenetspeech
# the data_dir contains:
# tree -L 2 .
# .
# |-- TERMS_OF_ACCESS
# |-- WenetSpeech.json
# |-- audio
# |-- dev
# |-- test_meeting
# |-- test_net
# `-- train
grep "\"text\":" $data_dir/WenetSpeech.json... | [
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dccuchile/albert-tiny-spanish-finetuned-ner | [
"pytorch",
"albert",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | {
"architectures": [
"AlbertForTokenClassification"
],
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},
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"no_re... | 8 | null | ---
language: fr
tags:
- pytorch
- t5
- seq2seq
- summarization
datasets: cnn_dailymail
widget:
- text: "Apollo 11 est une mission du programme spatial américain Apollo au cours de laquelle, pour la première fois, des hommes se sont posés sur la Lune, le lundi 21 juillet 1969. L'agence spatiale américaine, la NASA, rem... | [
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dccuchile/albert-xlarge-spanish-finetuned-qa-mlqa | [
"pytorch",
"albert",
"question-answering",
"transformers",
"autotrain_compatible"
] | question-answering | {
"architectures": [
"AlbertForQuestionAnswering"
],
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},
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"no_repe... | 7 | null | language: en
tags:
- sentiment
- distilbert-
pipeline_tag: text-classification
| [
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dccuchile/albert-xxlarge-spanish-finetuned-mldoc | [
"pytorch",
"albert",
"text-classification",
"transformers"
] | text-classification | {
"architectures": [
"AlbertForSequenceClassification"
],
"model_type": "albert",
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},
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"no... | 26 | null | ---
license: apache-2.0
language:
- es
tags:
- common_voice_8_0
- generated_from_trainer
- hf-asr-leaderboard
- mozilla-foundation/common_voice_8_0
- robust-speech-event
datasets:
- mozilla-foundation/common_voice_8_0
model-index:
- name: wave2vec-xls-r-300m-es
results:
- task:
name: Speech Recognition
... | [
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dccuchile/albert-tiny-spanish | [
"pytorch",
"tf",
"albert",
"pretraining",
"es",
"dataset:large_spanish_corpus",
"transformers",
"spanish",
"OpenCENIA"
] | null | {
"architectures": [
"AlbertForPreTraining"
],
"model_type": "albert",
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},
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"min_length": null,
"no_repeat_ngr... | 393 | null | ---
language:
- th
tags:
- sentiment-analysis
license: apache-2.0
datasets:
- wongnai_reviews
- wisesight_sentiment
- generated_reviews_enth
widget:
- text: "โอโห้ ช่องนี้เปิดโลกเรามากเลยค่ะ คือตอนช่วงหาคำตอบเรานี่อึ้งไปเลย ดูจีเนียสมากๆๆ"
example_title: "Positive"
- text: "เริ่มจากชายเน็ตคนหนึ่งเปิดประเด็นว่าไปพบเจ้... | [
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0.01... |
dccuchile/bert-base-spanish-wwm-cased-finetuned-qa-mlqa | [
"pytorch",
"bert",
"question-answering",
"transformers",
"autotrain_compatible"
] | question-answering | {
"architectures": [
"BertForQuestionAnswering"
],
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},
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"no_repeat_n... | 5 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- matthews_correlation
model-index:
- name: distilbert-base-uncased-finetuned-cola
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
args: cola
met... | [
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0.032... |
dccuchile/bert-base-spanish-wwm-uncased-finetuned-mldoc | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | {
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
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},
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"no_rep... | 39 | null | ---
tags:
- asteroid
- audio
- FasNet-TAC
- audio-to-audio
- multichannel
- beamforming
datasets:
- TACDataset
- sep_noisy
license: cc-by-sa-4.0
---
## Asteroid model `Samuele Cornell/FasNetTAC_TACDataset_separatenoisy`
Imported from [Zenodo](https://zenodo.org/record/4557489)
### Description:
This model was trained ... | [
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dccuchile/bert-base-spanish-wwm-uncased-finetuned-pos | [
"pytorch",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | {
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"no_repeat... | 5 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- xlsum
metrics:
- rouge
model-index:
- name: t5-small-finetuned-xsum
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: xlsum
type: xlsum
args: chinese_traditional... | [
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dccuchile/distilbert-base-spanish-uncased-finetuned-ner | [
"pytorch",
"distilbert",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | {
"architectures": [
"DistilBertForTokenClassification"
],
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... | 28 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-base-timit-demo-colab
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2... | [
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... |
CennetOguz/distilbert-base-uncased-finetuned-recipe-accelerate | [
"pytorch",
"distilbert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"DistilBertForMaskedLM"
],
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"no_repea... | 7 | null | ---
tags:
- conversational
---
# Harry Potter DialoGPT Model | [
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0.03... |
Chaewon/mmnt_decoder_en | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
] | text-generation | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
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},
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"no_repeat_ngram_size... | 12 | null | This model is pre-trained on blog articles from AWS Blogs.
## Pre-training corpora
The input text contains around 3000 blog articles on [AWS Blogs website](https://aws.amazon.com/blogs/) technical subject matter including AWS products, tools and tutorials.
## Pre-training details
I picked a Roberta architecture for ... | [
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Chakita/KNUBert | [
"pytorch",
"tensorboard",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
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],
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"no_repeat_ngra... | 20 | null | If you use the model, please consider citing the paper
```
@misc{bhargava2021generalization,
title={Generalization in NLI: Ways (Not) To Go Beyond Simple Heuristics},
author={Prajjwal Bhargava and Aleksandr Drozd and Anna Rogers},
year={2021},
eprint={2110.01518},
archivePrefix={arXiv},
... | [
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0.0... |
Chakita/Kalbert | [
"pytorch",
"tensorboard",
"albert",
"fill-mask",
"transformers",
"generated_from_trainer",
"license:mit",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"AlbertForMaskedLM"
],
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},
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"no_repeat_ngram_... | 5 | null | 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](... | [
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... |
Chalponkey/DialoGPT-small-Barry | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
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},
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"no_repeat_ngram_size... | 11 | null | ---
language:
- en
license:
- mit
tags:
- BERT
- MNLI
- NLI
- transformer
- pre-training
---
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).
This is one of the smaller ... | [
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Chan/distilgpt2-finetuned-wikitext2 | [] | null | {
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"num_beams... | 0 | null | ---
language:
- en
license:
- mit
tags:
- BERT
- MNLI
- NLI
- transformer
- pre-training
---
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).
This is one of the smaller ... | [
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0.... |
Chandanbhat/distilbert-base-uncased-finetuned-cola | [] | null | {
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"num_beams... | 0 | null | ---
language:
- en
license:
- mit
tags:
- BERT
- MNLI
- NLI
- transformer
- pre-training
---
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).
This is one of the smaller ... | [
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Charlotte/text2dm_models | [] | null | {
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},
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"num_beams... | 0 | null | Please refer to this repository (https://github.com/prajjwal1/discosense) for usage instructions.
---
language:
- en
tags:
- conditional
- text
- generation
license: "mit"
datasets:
- discofuse
- discovery
metrics:
- perplexity
- ppl
--- | [
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0.045... |
Cheatham/xlm-roberta-large-finetuned-d1 | [
"pytorch",
"xlm-roberta",
"text-classification",
"transformers"
] | text-classification | {
"architectures": [
"XLMRobertaForSequenceClassification"
],
"model_type": "xlm-roberta",
"task_specific_params": {
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},
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... | 20 | null | Please refer to this repository (https://github.com/prajjwal1/discosense) for usage instructions.
---
language:
- en
tags:
- conditional
- text
- generation
license: "mit"
datasets:
- discofuse
- discovery
metrics:
- perplexity
- ppl
--- | [
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Cheatham/xlm-roberta-large-finetuned-d12 | [
"pytorch",
"xlm-roberta",
"text-classification",
"transformers"
] | text-classification | {
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... | 20 | null | Please refer to this repository (https://github.com/prajjwal1/discosense) for usage instructions.
---
language:
- en
tags:
- conditional
- text
- generation
license: "mit"
datasets:
- discofuse
- discovery
metrics:
- perplexity
- ppl
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Cheatham/xlm-roberta-large-finetuned-d12_2 | [] | null | {
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---
language:
- en
tags:
- conditional
- text
- generation
license: "mit"
datasets:
- discofuse
- discovery
metrics:
- perplexity
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Cheatham/xlm-roberta-large-finetuned-d1r01 | [
"pytorch",
"xlm-roberta",
"text-classification",
"transformers"
] | text-classification | {
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... | 21 | null | Please refer to this repository (https://github.com/prajjwal1/discosense) for usage instructions.
---
language:
- en
tags:
- conditional
- text
- generation
license: "mit"
datasets:
- discofuse
- discovery
metrics:
- perplexity
- ppl
--- | [
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Check/vaw2tmp | [
"tensorboard"
] | null | {
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"num_beams... | 0 | null | Please refer to this repository (https://github.com/prajjwal1/discosense) for usage instructions.
---
language:
- en
tags:
- conditional
- text
- generation
license: "mit"
datasets:
- discofuse
- discovery
metrics:
- perplexity
- ppl
--- | [
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Ching/negation_detector | [
"pytorch",
"roberta",
"question-answering",
"transformers",
"autotrain_compatible"
] | question-answering | {
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"RobertaForQuestionAnswering"
],
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"no_re... | 9 | null | Roberta-base trained on MNLI.
| Task | Accuracy |
|---------|----------|
| MNLI | 86.32 |
| MNLI-mm | 86.43 |
You can also check out:
- `prajjwal1/roberta-base-mnli`
- `prajjwal1/roberta-large-mnli`
- `prajjwal1/albert-base-v2-mnli`
- `prajjwal1/albert-base-v1-mnli`
- `prajjwal1/albert-large-v2-mnli`
[@... | [
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Chinmay/mlindia | [] | null | {
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"num_beams... | 0 | null | If you use the model, please consider citing the paper
```
@misc{bhargava2021generalization,
title={Generalization in NLI: Ways (Not) To Go Beyond Simple Heuristics},
author={Prajjwal Bhargava and Aleksandr Drozd and Anna Rogers},
year={2021},
eprint={2110.01518},
archivePrefix={arXiv},
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Chiuchiyin/DialoGPT-small-Donald | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | {
"architectures": [
"GPT2LMHeadModel"
],
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"no_repeat_ngram_size... | 7 | null | ---
tags:
- pytorch
- commonsense-reasoning
- sentence-completion
datasets:
- hellaswag
---
`RoBERTa` trained on HellaSwag dataset (`MultipleChoiceModel`). HellaSwag has a multiple choice questions format.
It gets around 74.99% accuracy.
[@prajjwal_1](https://twitter.com/prajjwal_1/)
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Chun/DialoGPT-large-dailydialog | [
"pytorch",
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"transformers"
] | text-generation | {
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"no_repeat_ngram_size... | 6 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- matthews_correlation
model_index:
- name: distilbert-base-uncased-finetuned-cola
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
args: cola
met... | [
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Chun/DialoGPT-small-dailydialog | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
] | text-generation | {
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"no_repeat_ngram_size... | 10 | null |
# GPT2 Genre Based Story Generator
## Model description
GPT2 fine-tuned on genre-based story generation.
## Intended uses
Used to generate stories based on user inputted genre and starting prompts.
## How to use
#### Supported Genres
superhero, action, drama, horror, thriller, sci_fi
#### Input text format
\<BOS... | [
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Chun/w-en2zh-otm | [
"pytorch",
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] | text2text-generation | {
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"no_re... | 7 | null | # Ancient Greek BERT
<img src="https://ichef.bbci.co.uk/images/ic/832xn/p02m4gzb.jpg"/>
The first and only available Ancient Greek sub-word BERT model!
State-of-the-art post fine-tuning on Part-of-Speech Tagging and Morphological Analysis.
Pre-trained weights are made available for a standard 12 layer, 768d BERT-ba... | [
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Chun/w-zh2en-mto | [
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"no_re... | 7 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model_index:
- name: distilbert-base-uncased-finetuned-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: conll2003
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Cinnamon/electra-small-japanese-discriminator | [
"pytorch",
"electra",
"pretraining",
"ja",
"transformers",
"license:apache-2.0"
] | null | {
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"no_repeat_n... | 419 | 2022-02-05T20:52:08Z | ---
language:
- hi
license: apache-2.0
tags:
- automatic-speech-recognition
- mozilla-foundation/common_voice_8_0
- generated_from_trainer
model-index:
- name: ''
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofrea... | [
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Ciruzzo/DialoGPT-medium-harrypotter | [] | null | {
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"num_beams... | 0 | null | ---
tags:
- spacy
- token-classification
language:
- en
model-index:
- name: en_model_ner_skills
results:
- task:
name: NER
type: token-classification
metrics:
- name: NER Precision
type: precision
value: 0.3125
- name: NER Recall
type: recall
value: 0.243902439
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Ciruzzo/DialoGPT-small-harrypotter | [
"pytorch",
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"text-generation",
"transformers",
"conversational"
] | conversational | {
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"no_repeat_ngram_size... | 9 | 2022-02-16T09:23:04Z | ---
tags:
- spacy
- token-classification
language:
- en
model-index:
- name: en_ner_model
results:
- task:
name: NER
type: token-classification
metrics:
- name: NER Precision
type: precision
value: 0.3624161074
- name: NER Recall
type: recall
value: 0.384341637
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Ciruzzo/DialoGPT-small-hattypotter | [] | null | {
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"num_beams... | 0 | 2022-02-16T09:14:14Z | ---
tags:
- spacy
- token-classification
language:
- en
model-index:
- name: en_ner_skills
results:
- task:
name: NER
type: token-classification
metrics:
- name: NER Precision
type: precision
value: 0.3980582524
- name: NER Recall
type: recall
value: 0.3404507711
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ClaudeCOULOMBE/RickBot | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | {
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"no_repeat_ngram_size... | 9 | 2021-12-17T20:23:40Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad_v2
model-index:
- name: distilbert-base-uncased-finetuned-squad
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... | [
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ComCom/gpt2-medium | [
"pytorch",
"gpt2",
"feature-extraction",
"transformers"
] | feature-extraction | {
"architectures": [
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],
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"no_repeat_ngram_size": nul... | 5 | 2021-04-21T03:57:30Z | ---
tags:
- feature-extraction
- bert
---
# Model Card for baikal-sentiment-ball
# Model Details
## Model Description
More information needed
- **Developed by:** Princeton NLP group
- **Shared by [Optional]:** Princeton NLP group
- **Model type:** Feature Extraction
- **Language(s) (NLP):** More information... | [
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ComCom-Dev/gpt2-bible-test | [] | null | {
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"num_beams... | 0 | 2021-04-21T03:57:25Z | ---
tags:
- feature-extraction
---
# Model Card for sup-simcse-roberta-large
# Model Details
## Model Description
- **Developed by:** Princeton-nlp
- **Shared by [Optional]:** More information needed
- **Model type:** Feature Extraction
- **Language(s) (NLP):** More information needed
- **License:** More... | [
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0.051... |
Cometasonmi451/Mine | [] | null | {
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"num_beams... | 0 | null | ---
tags:
- feature-extraction
- bert
---
# Model Card for unsup-simcse-bert-base-uncased
# Model Details
## Model Description
More information needed
- **Developed by:** Princeton NLP group
- **Shared by [Optional]:** Hugging Face
- **Model type:** Feature Extraction
- **Language(s) (NLP):** More information... | [
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CuongLD/wav2vec2-large-xlsr-vietnamese | [
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"vi",
"dataset:common_voice, infore_25h",
"arxiv:2006.11477",
"arxiv:2006.13979",
"transformers",
"audio",
"speech",
"xlsr-fine-tuning-week",
"license:apache-2.0",
"model-index"
] | automatic-speech-recognition | {
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],
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"no_repeat_ngram_s... | 8 | null | ---
language:
- ca
license: apache-2.0
tags:
- "catalan"
- "qa"
datasets:
- "xquad-ca"
- "viquiquad"
metrics:
- "f1"
- "exact match"
widget:
- text: "Quan va començar el Super3?"
context: "El Super3 o Club Super3 és un univers infantil català creat a partir d'un programa emès per Televisió de Catalunya des del 1991... | [
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0.01... |
DTAI-KULeuven/robbertje-1-gb-shuffled | [
"pytorch",
"roberta",
"fill-mask",
"nl",
"dataset:oscar",
"dataset:oscar (NL)",
"dataset:dbrd",
"dataset:lassy-ud",
"dataset:europarl-mono",
"dataset:conll2002",
"arxiv:2101.05716",
"transformers",
"Dutch",
"Flemish",
"RoBERTa",
"RobBERT",
"RobBERTje",
"license:mit",
"autotrain_c... | fill-mask | {
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"RobertaForMaskedLM"
],
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"no_repeat_ngra... | 7 | 2021-11-25T14:50:00Z | ## RoBERTa Latin model
This is a Latin RoBERTa-based LM model.
The data it uses is the same as has been used to compute the text referenced HTR evaluation measures.
The intention of the Transformer-based LM is twofold: on the one hand, it will be used for the evaluation of HTR results, on the other, it should be use... | [
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Daivakai/DialoGPT-small-saitama | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | {
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],
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"no_repeat_ngram_size... | 9 | 2022-02-11T01:57:55Z | ---
language:
- en
tags:
- t5
- qa
- askscience
- lfqa
- information retrieval
datasets:
- eli5
metrics:
- rouge
widget:
- text: "why aren't there more planets in our solar system?"
example_title: "solar system"
- text: "question: what is a probability distribution? context: I am just learning about statistics."
e... | [
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Daltcamalea01/Camaleaodalt | [] | null | {
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"num_beams... | 0 | null | ---
language:
- en
tags:
- t5
- analysis
- book
- notes
datasets:
- kmfoda/booksum
metrics:
- rouge
widget:
- text: I'm just a girl standing in front of a boy asking him to love her.
example_title: Notting Hill
- text: Son, your ego is writing checks your body can't cash.
example_title: top gun
- text: I really lov... | [
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Davlan/bert-base-multilingual-cased-finetuned-wolof | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
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"BertForMaskedLM"
],
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"no_repeat_ngram_size... | 4 | null | Access to model pyannote/embedding is restricted and you are not in the authorized list. Visit https://huggingface.co/pyannote/embedding to ask for access. | [
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Davlan/m2m100_418M-eng-yor-mt | [
"pytorch",
"m2m_100",
"text2text-generation",
"arxiv:2103.08647",
"transformers",
"autotrain_compatible"
] | text2text-generation | {
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"M2M100ForConditionalGeneration"
],
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"no... | 9 | 2021-12-29T00:39:35Z | ---
tags:
- espnet
- audio
- automatic-speech-recognition
language: noinfo
datasets:
- speechcommands
license: cc-by-4.0
---
## ESPnet2 ASR model
### `pyf98/speechcommands_12commands_conformer`
This model was trained by Yifan Peng using speechcommands recipe in [espnet](https://github.com/espnet/espnet/).
### Demo... | [
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Davlan/m2m100_418M-yor-eng-mt | [
"pytorch",
"m2m_100",
"text2text-generation",
"arxiv:2103.08647",
"transformers",
"autotrain_compatible"
] | text2text-generation | {
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"no... | 6 | 2021-12-29T00:59:04Z | ---
tags:
- espnet
- audio
- automatic-speech-recognition
language: noinfo
datasets:
- speechcommands
license: cc-by-4.0
---
## ESPnet2 ASR model
### `pyf98/speechcommands_35commands_conformer`
This model was trained by Yifan Peng using speechcommands recipe in [espnet](https://github.com/espnet/espnet/).
### Demo... | [
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Dazai/Ok | [] | null | {
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"num_beams... | 0 | null | ---
language: ja
datasets:
- common_voice #TODO: remove if you did not use the common voice dataset
- TODO: add more datasets if you have used additional datasets. Make sure to use the exact same
dataset name as the one found [here](https://huggingface.co/datasets). If the dataset can not be found in the official dat... | [
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Dbluciferm3737/Idk | [] | null | {
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"num_beams... | 0 | null | ---
language: ja
datasets:
- common_voice
- jsut
metrics:
- wer
- cer
tags:
- audio
- automatic-speech-recognition
- speech
- xlsr-fine-tuning-week
license: apache-2.0
model-index:
- name: Japanese XLSR Wav2Vec2 Large 53
results:
- task:
name: Speech Recognition
type: aut... | [
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Declan/NPR_model_v2 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
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"BertForMaskedLM"
],
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"no_repeat_ngram_size... | 7 | null | ---
language:
- lu
tags:
- text
- MLM
license: mit
---
## BERT Medium for Luxembourgish
Created from a dataset with 1M Luxembourgish sentences from Wikipedia. Corpus has approx. 16M words.
The MLM objective was trained. The BERT model has parameters `L=8` and `H=512`. Vocabulary has 70K word pieces.
Final loss scor... | [
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Declan/NewYorkTimes_model_v1 | [] | null | {
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"num_beams... | 0 | null | https://twitter.com/i/events/1413870919320104965
https://peatix.com/group/11420372/
https://cmdt-guyane.fr/advert/argentina-vs-brazil-live-stream-final-2021/
https://www.quisqueyapeach.com/advert/argentina-vs-brazil-live-stream-final-2021/
https://www.beauvaissubaquatique.fr/advert/argentina-vs-brazil-live-stream-final... | [
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Declan/NewYorkTimes_model_v6 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
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"no_repeat_ngram_size... | 5 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-base-timit-demo-colab
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2... | [
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DeskDown/MarianMixFT_en-fil | [
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] | text2text-generation | {
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"no_repeat_ngram_size... | 3 | null | ## This is a genre-based Movie plot generator.
For best results, structure the input as follows -
1. Add a `<BOS>` tag in the start.
2. Add a `<genre>` tag (with the genre as a placeholder for lowercased genres such as `<action>`, `<romantic>`, `<thriller>`, `<comedy>` | [
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Doxophobia/DialoGPT-medium-celeste | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | {
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"no_repeat_ngram_size... | 11 | null | ---
license: apache-2.0
language:
- sl
tags:
- generated_from_trainer
- hf-asr-leaderboard
- robust-speech-event
datasets:
- common_voice
model-index:
- name: wav2vec2-large-xls-r-1B-common_voice-sl-ft
results:
- task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: ... | [
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albert-base-v1 | [
"pytorch",
"tf",
"safetensors",
"albert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1909.11942",
"transformers",
"exbert",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] | fill-mask | {
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],
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"no_repeat_ngram_... | 38,156 | 2021-10-08T12:02:48Z | ---
language: "en"
tags:
- agriculture-domain
- agriculture
- fill-mask
widget:
- text: "[MASK] agriculture provides one of the most promising areas for innovation in green and blue infrastructure in cities."
---
# BERT for Agriculture Domain
A BERT-based language model further pre-trained from the checkpoint of [SciBE... | [
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albert-base-v2 | [
"pytorch",
"tf",
"jax",
"rust",
"safetensors",
"albert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1909.11942",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] | fill-mask | {
"architectures": [
"AlbertForMaskedLM"
],
"model_type": "albert",
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"no_repeat_ngram_... | 4,785,283 | 2021-09-06T13:36:29Z | ---
language: "en"
tags:
- buy-intent
- sell-intent
- consumer-intent
widget:
- text: "Flutoprazepam (Restas) is a drug which is a benzodiazepine. It was patented in Japan by Sumitomo."
---
# Chemical vs Pharmaceutical Domain Document Classifier
Chemical domain language model finetuned on 13K Chemical, and 14K Pharma W... | [
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-0.01734272949397564,
0.044037289917469025,
0.029514241963624954,
0.0050957384519279,
0.004831643775105476,
0.0357... |
albert-large-v1 | [
"pytorch",
"tf",
"albert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1909.11942",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] | fill-mask | {
"architectures": [
"AlbertForMaskedLM"
],
"model_type": "albert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_... | 687 | 2021-09-06T05:37:19Z | ---
pipeline_tag: sentence-similarity
license: apache-2.0
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# recobo/chemical-bert-uncased-simcse
```python
from sentence_transformers import SentenceTransformer
model_name = 'recobo/chemical-bert-uncased-simcse'
model = Senten... | [
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0.002705632010474801,
0.041692... |
albert-large-v2 | [
"pytorch",
"tf",
"safetensors",
"albert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1909.11942",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] | fill-mask | {
"architectures": [
"AlbertForMaskedLM"
],
"model_type": "albert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_... | 26,792 | 2021-08-31T20:54:33Z | ```python
from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline
model_name = "recobo/chemical-bert-uncased-squad2"
# a) Get predictions
nlp = pipeline('question-answering', model=model_name, tokenizer=model_name)
QA_input = {
'question': 'Why is model conversion important?',
'context... | [
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0.0... |
albert-xlarge-v1 | [
"pytorch",
"tf",
"albert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1909.11942",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] | fill-mask | {
"architectures": [
"AlbertForMaskedLM"
],
"model_type": "albert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_... | 341 | 2021-09-04T08:31:37Z | ---
pipeline_tag: sentence-similarity
license: apache-2.0
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# recobo/chemical-bert-uncased-tsdae
```python
from sentence_transformers import SentenceTransformer
model_name = 'recobo/chemical-bert-uncased-tsdae'
model = Sentence... | [
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0.028517451137304306,
0.019391711801290512,
0.002140923636034131,
0.042764... |
albert-xlarge-v2 | [
"pytorch",
"tf",
"albert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1909.11942",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] | fill-mask | {
"architectures": [
"AlbertForMaskedLM"
],
"model_type": "albert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_... | 2,973 | 2021-08-31T17:53:46Z | ---
language: "en"
tags:
- chemical-domain
- safety-datasheets
widget:
- text: "The removal of mercaptans, and for drying of gases and [MASK]."
---
# BERT for Chemical Industry
A BERT-based language model further pre-trained from the checkpoint of [SciBERT](https://huggingface.co/allenai/scibert_scivocab_uncased). We u... | [
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0.05272899568080902,
0.024737028405070305,
0.01776622049510479,
0.012760516256093979,
0.... |
albert-xxlarge-v1 | [
"pytorch",
"tf",
"albert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1909.11942",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] | fill-mask | {
"architectures": [
"AlbertForMaskedLM"
],
"model_type": "albert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_... | 7,091 | 2021-12-29T01:42:50Z | ---
tags: autonlp
language: de
widget:
- text: "I love AutoNLP 🤗"
datasets:
- redadmiral/autonlp-data-Headline-Generator
co2_eq_emissions: 651.3545590912366
---
# Model Trained Using AutoNLP
- Problem type: Summarization
- Model ID: 453611714
- CO2 Emissions (in grams): 651.3545590912366
## Validation Metrics
- Lo... | [
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0.01052375603467226,
0.03730... |
albert-xxlarge-v2 | [
"pytorch",
"tf",
"safetensors",
"albert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1909.11942",
"transformers",
"exbert",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] | fill-mask | {
"architectures": [
"AlbertForMaskedLM"
],
"model_type": "albert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_... | 42,640 | null | This Model is a fine-tuned version of T-systems [summarization model v1](https://huggingface.co/deutsche-telekom/mt5-small-sum-de-en-v1).
We used 1000 examples of headline-content pairs from BR24 articles for the fine-tuning process.
Despite the small amount of training data, the tonality of the summarizations has c... | [
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0.04825413599610329,
0.026677902787923813,
0.0024807779118418694,
0.02831743098795414,
0.... |
bert-base-cased | [
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1810.04805",
"transformers",
"exbert",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] | fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 8,621,271 | 2021-12-23T11:06:07Z | ---
tags:
- conversational
---
#Shayo Bot by Shogun
#Ai Chatbot Testing based on GPT2 and DialoGPT-Medium by Microsoft
#shoguπ#9999 | [
-0.018375759944319725,
0.005328182131052017,
0.004002490546554327,
0.014704816043376923,
0.03414413705468178,
0.009876902215182781,
-0.008745811879634857,
0.031119419261813164,
-0.029675696045160294,
0.02723207138478756,
0.04087704420089722,
0.010179530829191208,
0.004326109774410725,
0.03... |
bert-base-chinese | [
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"fill-mask",
"zh",
"arxiv:1810.04805",
"transformers",
"autotrain_compatible",
"has_space"
] | fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 3,377,486 | 2020-04-01T08:55:41Z | ---
language: de
---
# Model description
## Dataset
Trained on fictional and non-fictional German texts written between 1840 and 1920:
* Narrative texts from Digitale Bibliothek (https://textgrid.de/digitale-bibliothek)
* Fairy tales and sagas from Grimm Korpus (https://www1.ids-mannheim.de/kl/projekte/korpora/archiv/... | [
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0.06759873777627945,
0.05333736166357994,
-0.005568948574364185,
-0.01878744177520275,
0.0303... |
bert-base-german-cased | [
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"fill-mask",
"de",
"transformers",
"exbert",
"license:mit",
"autotrain_compatible",
"has_space"
] | fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 175,983 | 2021-08-31T03:18:40Z | This is Korean-TTS model. (based on Tacotron)
Dataset is from Sogang University. | [
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0.038200490176677704,
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0.026994701474905014,
... |
bert-base-german-dbmdz-cased | [
"pytorch",
"jax",
"bert",
"fill-mask",
"de",
"transformers",
"license:mit",
"autotrain_compatible",
"has_space"
] | fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 1,814 | 2021-08-30T10:01:42Z | This is espnet-based korean TTS model.
You should recognize that this is not fisished one.
Dataset is from our university, which is NOT available yet.
| [
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0.02008526213467121,
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0.04222758486866951,
0.0... |
bert-base-german-dbmdz-uncased | [
"pytorch",
"jax",
"safetensors",
"bert",
"fill-mask",
"de",
"transformers",
"license:mit",
"autotrain_compatible",
"has_space"
] | fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 68,305 | 2020-11-16T06:31:55Z | ---
language: "tr"
tags:
- turkish
- tr
- gpt2-tr
- gpt2-turkish
---
# 🇹🇷 Turkish GPT-2 Model
In this repository I release GPT-2 model, that was trained on various texts for Turkish.
The model is meant to be an entry point for fine-tuning on other texts.
## Training corpora
I used a Turkish corpora that is taken ... | [
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bert-large-cased-whole-word-masking-finetuned-squad | [
"pytorch",
"tf",
"jax",
"rust",
"safetensors",
"bert",
"question-answering",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1810.04805",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] | question-answering | {
"architectures": [
"BertForQuestionAnswering"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_n... | 8,214 | 2022-01-12T14:09:26Z | ---
license: apache-2.0
language:
- ar
---
The **AraRoBERTa** models are mono-dialectal Arabic models trained on a country-level dialect. AraRoBERTa uses RoBERTa base config. More details are available in the paper [click](https://aclanthology.org/2022.wanlp-1.24/).
The following are the AraRoBERTa seven dialectal var... | [
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0.005549178924411535,... |
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