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token-classification | transformers | # CAMeLBERT-MSA POS-MSA Model
## Model description
**CAMeLBERT-MSA POS-MSA Model** is a Modern Standard Arabic (MSA) POS tagging model that was built by fine-tuning the [CAMeLBERT-MSA](https://huggingface.co/CAMeL-Lab/bert-base-arabic-camelbert-msa/) model.
For the fine-tuning, we used the [PATB](https://dl.acm.org/doi... | {"language": ["ar"], "license": "apache-2.0", "widget": [{"text": "\u0625\u0645\u0627\u0631\u0629 \u0623\u0628\u0648\u0638\u0628\u064a \u0647\u064a \u0625\u062d\u062f\u0649 \u0625\u0645\u0627\u0631\u0627\u062a \u062f\u0648\u0644\u0629 \u0627\u0644\u0625\u0645\u0627\u0631\u0627\u062a \u0627\u0644\u0639\u0631\u0628\u064a... | CAMeL-Lab/bert-base-arabic-camelbert-msa-pos-msa | null | [
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| # CAMeLBERT-MSA POS-MSA Model
## Model description
CAMeLBERT-MSA POS-MSA Model is a Modern Standard Arabic (MSA) POS tagging model that was built by fine-tuning the CAMeLBERT-MSA model.
For the fine-tuning, we used the PATB dataset .
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fill-mask | transformers |
# CAMeLBERT: A collection of pre-trained models for Arabic NLP tasks
## Model description
**CAMeLBERT** is a collection of BERT models pre-trained on Arabic texts with different sizes and variants.
We release pre-trained language models for Modern Standard Arabic (MSA), dialectal Arabic (DA), and classical Arabic (C... | {"language": ["ar"], "license": "apache-2.0", "widget": [{"text": "\u0627\u0644\u0647\u062f\u0641 \u0645\u0646 \u0627\u0644\u062d\u064a\u0627\u0629 \u0647\u0648 [MASK] ."}]} | CAMeL-Lab/bert-base-arabic-camelbert-msa-quarter | null | [
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==================================================================
Model description
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text-classification | transformers | # CAMeLBERT MSA SA Model
## Model description
**CAMeLBERT MSA SA Model** is a Sentiment Analysis (SA) model that was built by fine-tuning the [CAMeLBERT Modern Standard Arabic (MSA)](https://huggingface.co/CAMeL-Lab/bert-base-arabic-camelbert-msa/) model.
For the fine-tuning, we used the [ASTD](https://aclanthology.org... | {"language": ["ar"], "license": "apache-2.0", "widget": [{"text": "\u0623\u0646\u0627 \u0628\u062e\u064a\u0631"}]} | CAMeL-Lab/bert-base-arabic-camelbert-msa-sentiment | null | [
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## Model description
CAMeLBERT MSA SA Model is a Sentiment Analysis (SA) model that was built by fine-tuning the CAMeLBERT Modern Standard Arabic (MSA) model.
For the fine-tuning, we used the ASTD, ArSAS, and SemEval datasets.
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fill-mask | transformers |
# CAMeLBERT: A collection of pre-trained models for Arabic NLP tasks
## Model description
**CAMeLBERT** is a collection of BERT models pre-trained on Arabic texts with different sizes and variants.
We release pre-trained language models for Modern Standard Arabic (MSA), dialectal Arabic (DA), and classical Arabic (C... | {"language": ["ar"], "license": "apache-2.0", "widget": [{"text": "\u0627\u0644\u0647\u062f\u0641 \u0645\u0646 \u0627\u0644\u062d\u064a\u0627\u0629 \u0647\u0648 [MASK] ."}]} | CAMeL-Lab/bert-base-arabic-camelbert-msa-sixteenth | null | [
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| CAMeLBERT: A collection of pre-trained models for Arabic NLP tasks
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fill-mask | transformers |
# CAMeLBERT: A collection of pre-trained models for Arabic NLP tasks
## Model description
**CAMeLBERT** is a collection of BERT models pre-trained on Arabic texts with different sizes and variants.
We release pre-trained language models for Modern Standard Arabic (MSA), dialectal Arabic (DA), and classical Arabic (C... | {"language": ["ar"], "license": "apache-2.0", "widget": [{"text": "\u0627\u0644\u0647\u062f\u0641 \u0645\u0646 \u0627\u0644\u062d\u064a\u0627\u0629 \u0647\u0648 [MASK] ."}]} | CAMeL-Lab/bert-base-arabic-camelbert-msa | null | [
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| CAMeLBERT: A collection of pre-trained models for Arabic NLP tasks
==================================================================
Model description
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fill-mask | transformers | ## JavaBERT
A BERT-like model pretrained on Java software code.
### Training Data
The model was trained on 2,998,345 Java files retrieved from open source projects on GitHub. A ```bert-base-uncased``` tokenizer is used by this model.
### Training Objective
A MLM (Masked Language Model) objective was used to train thi... | {"language": ["java", "code"], "license": "apache-2.0", "widget": [{"text": "public [MASK] isOdd(Integer num){if (num % 2 == 0) {return \"even\";} else {return \"odd\";}}"}]} | CAUKiel/JavaBERT-uncased | null | [
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A BERT-like model pretrained on Java software code.
### Training Data
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### Training Objective
A MLM (Masked Language Model) objective was used to train this model.
### Usage
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# Model Card for JavaBERT
A BERT-like model pretrained on Java software code.
# Model Details
## Model Description
A BERT-like model pretrained on Java software code.
- **Developed by:** Christian-Albrechts-University of Kiel (CAUKiel)
- **Shared by [Optional]:** Hugging Face
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# Model Card for JavaBERT
A BERT-like model pretrained on Java software code.
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A BERT-like model pretrained on Java software code.
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translation | transformers | This model translate from English to Khmer.
It is the pure fine-tuned version of MarianMT model en-zh.
This is the result after 30 epochs of pure fine-tuning of khmer language.
### Example
```
%%capture
!pip install transformers transformers[sentencepiece]
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer... | {"tags": ["translation"]} | CLAck/en-km | null | [
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| This model translate from English to Khmer.
It is the pure fine-tuned version of MarianMT model en-zh.
This is the result after 30 epochs of pure fine-tuning of khmer language.
### Example
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translation | transformers |
This is a finetuning of a MarianMT pretrained on English-Chinese. The target language pair is English-Vietnamese.
The first phase of training (mixed) is performed on a dataset containing both English-Chinese and English-Vietnamese sentences.
The second phase of training (pure) is performed on a dataset containing only... | {"language": ["en", "vi"], "license": "apache-2.0", "tags": ["translation"], "datasets": ["ALT"], "metrics": ["sacrebleu"]} | CLAck/en-vi | null | [
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| This is a finetuning of a MarianMT pretrained on English-Chinese. The target language pair is English-Vietnamese.
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translation | transformers |
This model is pretrained on Chinese and Indonesian languages, and fine-tuned on Indonesian language.
### Example
```
%%capture
!pip install transformers transformers[sentencepiece]
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
# Download the pretrained model for English-Vietnamese available on the hu... | {"language": ["en", "id"], "license": "apache-2.0", "tags": ["translation"], "datasets": ["ALT"], "metrics": ["sacrebleu"]} | CLAck/indo-mixed | null | [
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### Example
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MIXED
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translation | transformers | Pure fine-tuning version of MarianMT en-zh on Indonesian Language
### Example
```
%%capture
!pip install transformers transformers[sentencepiece]
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
# Download the pretrained model for English-Vietnamese available on the hub
model = AutoModelForSeq2SeqLM.from... | {"language": ["en", "id"], "license": "apache-2.0", "tags": ["translation"], "datasets": ["ALT"], "metrics": ["sacrebleu"]} | CLAck/indo-pure | null | [
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translation | transformers |
This is a finetuning of a MarianMT pretrained on Chinese-English. The target language pair is Vietnamese-English.
### Example
```
%%capture
!pip install transformers transformers[sentencepiece]
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
# Download the pretrained model for English-Vietnamese availa... | {"language": ["en", "vi"], "license": "apache-2.0", "tags": ["translation"], "datasets": ["ALT"], "metrics": ["sacrebleu"]} | CLAck/vi-en | null | [
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"dataset:ALT",
"license:apache-2.0",
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#transformers #pytorch #marian #text2text-generation #translation #en #vi #dataset-ALT #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| This is a finetuning of a MarianMT pretrained on Chinese-English. The target language pair is Vietnamese-English.
### Example
### Training results
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] |
fill-mask | transformers |
# MedRoBERTa.nl
## Description
This model is a RoBERTa-based model pre-trained from scratch on Dutch hospital notes sourced from Electronic Health Records. The model is not fine-tuned. All code used for the creation of MedRoBERTa.nl can be found at https://github.com/cltl-students/verkijk_stella_rma_thesis_dutch_medi... | {"language": "nl", "license": "mit"} | CLTL/MedRoBERTa.nl | null | [
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"pytorch",
"roberta",
"fill-mask",
"nl",
"doi:10.57967/hf/0960",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
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|
# URL
## Description
This model is a RoBERTa-based model pre-trained from scratch on Dutch hospital notes sourced from Electronic Health Records. The model is not fine-tuned. All code used for the creation of URL can be found at URL
## Intended use
The model can be fine-tuned on any type of task. Since it is a domai... | [
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token-classification | transformers |
# Early-modern Dutch NER (General Letters)
## Description
This is a fine-tuned NER model for early-modern Dutch United East India Company (VOC) letters based on XLM-R_base [(Conneau et al., 2020)](https://aclanthology.org/2020.acl-main.747/). The model identifies *locations*, *persons*, *organisations*, but also *shi... | {"language": "nl", "license": "apache-2.0", "tags": ["dighum"], "pipeline_tag": "token-classification"} | CLTL/gm-ner-xlmrbase | null | [
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"token-classification",
"dighum",
"nl",
"license:apache-2.0",
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"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"nl"
] | TAGS
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| Early-modern Dutch NER (General Letters)
========================================
Description
-----------
This is a fine-tuned NER model for early-modern Dutch United East India Company (VOC) letters based on XLM-R\_base (Conneau et al., 2020). The model identifies *locations*, *persons*, *organisations*, but also ... | [
"### Metric\n\n\n* entity-level F1",
"### Results\n\n\n\nReference\n---------\n\n\nThe model and fine-tuning data presented here were developed as part of:"
] | [
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] |
text-classification | transformers |
# A-PROOF ICF-domains Classification
## Description
A fine-tuned multi-label classification model that detects 9 [WHO-ICF](https://www.who.int/standards/classifications/international-classification-of-functioning-disability-and-health) domains in clinical text in Dutch. The model is based on a pre-trained Dutch medi... | {"language": "nl", "license": "mit", "pipeline_tag": "text-classification", "inference": false} | CLTL/icf-domains | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"nl",
"license:mit",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"nl"
] | TAGS
#transformers #pytorch #roberta #text-classification #nl #license-mit #region-us
| A-PROOF ICF-domains Classification
==================================
Description
-----------
A fine-tuned multi-label classification model that detects 9 WHO-ICF domains in clinical text in Dutch. The model is based on a pre-trained Dutch medical language model (link to be added), a RoBERTa model, trained from scr... | [
"### Sentence-level",
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] |
text-classification | transformers |
# Regression Model for Respiration Functioning Levels (ICF b440)
## Description
A fine-tuned regression model that assigns a functioning level to Dutch sentences describing respiration functions. The model is based on a pre-trained Dutch medical language model ([link to be added]()): a RoBERTa model, trained from scr... | {"language": "nl", "license": "mit", "pipeline_tag": "text-classification", "inference": false} | CLTL/icf-levels-adm | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"nl",
"license:mit",
"autotrain_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"nl"
] | TAGS
#transformers #pytorch #roberta #text-classification #nl #license-mit #autotrain_compatible #region-us
| Regression Model for Respiration Functioning Levels (ICF b440)
==============================================================
Description
-----------
A fine-tuned regression model that assigns a functioning level to Dutch sentences describing respiration functions. The model is based on a pre-trained Dutch medical ... | [
"### Authors\n\n\nJenia Kim, Piek Vossen",
"### References\n\n\nTBD"
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] |
text-classification | transformers |
# Regression Model for Attention Functioning Levels (ICF b140)
## Description
A fine-tuned regression model that assigns a functioning level to Dutch sentences describing attention functions. The model is based on a pre-trained Dutch medical language model ([link to be added]()): a RoBERTa model, trained from scratch... | {"language": "nl", "license": "mit", "pipeline_tag": "text-classification", "inference": false} | CLTL/icf-levels-att | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"nl",
"license:mit",
"autotrain_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"nl"
] | TAGS
#transformers #pytorch #roberta #text-classification #nl #license-mit #autotrain_compatible #region-us
| Regression Model for Attention Functioning Levels (ICF b140)
============================================================
Description
-----------
A fine-tuned regression model that assigns a functioning level to Dutch sentences describing attention functions. The model is based on a pre-trained Dutch medical langua... | [
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] |
text-classification | transformers |
# Regression Model for Work and Employment Functioning Levels (ICF d840-d859)
## Description
A fine-tuned regression model that assigns a functioning level to Dutch sentences describing work and employment functions. The model is based on a pre-trained Dutch medical language model ([link to be added]()): a RoBERTa mo... | {"language": "nl", "license": "mit", "pipeline_tag": "text-classification", "inference": false} | CLTL/icf-levels-ber | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"nl",
"license:mit",
"autotrain_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"nl"
] | TAGS
#transformers #pytorch #roberta #text-classification #nl #license-mit #autotrain_compatible #region-us
| Regression Model for Work and Employment Functioning Levels (ICF d840-d859)
===========================================================================
Description
-----------
A fine-tuned regression model that assigns a functioning level to Dutch sentences describing work and employment functions. The model is bas... | [
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] |
text-classification | transformers |
# Regression Model for Energy Levels (ICF b1300)
## Description
A fine-tuned regression model that assigns a functioning level to Dutch sentences describing energy level. The model is based on a pre-trained Dutch medical language model ([link to be added]()): a RoBERTa model, trained from scratch on clinical notes of... | {"language": "nl", "license": "mit", "pipeline_tag": "text-classification", "inference": false} | CLTL/icf-levels-enr | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"nl",
"license:mit",
"autotrain_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"nl"
] | TAGS
#transformers #pytorch #roberta #text-classification #nl #license-mit #autotrain_compatible #region-us
| Regression Model for Energy Levels (ICF b1300)
==============================================
Description
-----------
A fine-tuned regression model that assigns a functioning level to Dutch sentences describing energy level. The model is based on a pre-trained Dutch medical language model (link to be added): a RoBE... | [
"### Authors\n\n\nJenia Kim, Piek Vossen",
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] |
text-classification | transformers |
# Regression Model for Eating Functioning Levels (ICF d550)
## Description
A fine-tuned regression model that assigns a functioning level to Dutch sentences describing eating functions. The model is based on a pre-trained Dutch medical language model ([link to be added]()): a RoBERTa model, trained from scratch on cl... | {"language": "nl", "license": "mit", "pipeline_tag": "text-classification", "inference": false} | CLTL/icf-levels-etn | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"nl",
"license:mit",
"autotrain_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"nl"
] | TAGS
#transformers #pytorch #roberta #text-classification #nl #license-mit #autotrain_compatible #region-us
| Regression Model for Eating Functioning Levels (ICF d550)
=========================================================
Description
-----------
A fine-tuned regression model that assigns a functioning level to Dutch sentences describing eating functions. The model is based on a pre-trained Dutch medical language model ... | [
"### Authors\n\n\nJenia Kim, Piek Vossen",
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] |
text-classification | transformers |
# Regression Model for Walking Functioning Levels (ICF d550)
## Description
A fine-tuned regression model that assigns a functioning level to Dutch sentences describing walking functions. The model is based on a pre-trained Dutch medical language model ([link to be added]()): a RoBERTa model, trained from scratch on ... | {"language": "nl", "license": "mit", "pipeline_tag": "text-classification", "inference": false} | CLTL/icf-levels-fac | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"nl",
"license:mit",
"autotrain_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"nl"
] | TAGS
#transformers #pytorch #roberta #text-classification #nl #license-mit #autotrain_compatible #region-us
| Regression Model for Walking Functioning Levels (ICF d550)
==========================================================
Description
-----------
A fine-tuned regression model that assigns a functioning level to Dutch sentences describing walking functions. The model is based on a pre-trained Dutch medical language mod... | [
"### Authors\n\n\nJenia Kim, Piek Vossen",
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] |
text-classification | transformers |
# Regression Model for Exercise Tolerance Functioning Levels (ICF b455)
## Description
A fine-tuned regression model that assigns a functioning level to Dutch sentences describing exercise tolerance functions. The model is based on a pre-trained Dutch medical language model ([link to be added]()): a RoBERTa model, tr... | {"language": "nl", "license": "mit", "pipeline_tag": "text-classification", "inference": false} | CLTL/icf-levels-ins | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"nl",
"license:mit",
"autotrain_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"nl"
] | TAGS
#transformers #pytorch #roberta #text-classification #nl #license-mit #autotrain_compatible #region-us
| Regression Model for Exercise Tolerance Functioning Levels (ICF b455)
=====================================================================
Description
-----------
A fine-tuned regression model that assigns a functioning level to Dutch sentences describing exercise tolerance functions. The model is based on a pre-t... | [
"### Authors\n\n\nJenia Kim, Piek Vossen",
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] |
text-classification | transformers |
# Regression Model for Weight Maintenance Functioning Levels (ICF b530)
## Description
A fine-tuned regression model that assigns a functioning level to Dutch sentences describing weight maintenance functions. The model is based on a pre-trained Dutch medical language model ([link to be added]()): a RoBERTa model, tr... | {"language": "nl", "license": "mit", "pipeline_tag": "text-classification", "inference": false} | CLTL/icf-levels-mbw | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"nl",
"license:mit",
"autotrain_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"nl"
] | TAGS
#transformers #pytorch #roberta #text-classification #nl #license-mit #autotrain_compatible #region-us
| Regression Model for Weight Maintenance Functioning Levels (ICF b530)
=====================================================================
Description
-----------
A fine-tuned regression model that assigns a functioning level to Dutch sentences describing weight maintenance functions. The model is based on a pre-t... | [
"### Authors\n\n\nJenia Kim, Piek Vossen",
"### References\n\n\nTBD"
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] |
text-classification | transformers |
# Regression Model for Emotional Functioning Levels (ICF b152)
## Description
A fine-tuned regression model that assigns a functioning level to Dutch sentences describing emotional functions. The model is based on a pre-trained Dutch medical language model ([link to be added]()): a RoBERTa model, trained from scratch... | {"language": "nl", "license": "mit", "pipeline_tag": "text-classification", "inference": false} | CLTL/icf-levels-stm | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"nl",
"license:mit",
"autotrain_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"nl"
] | TAGS
#transformers #pytorch #roberta #text-classification #nl #license-mit #autotrain_compatible #region-us
| Regression Model for Emotional Functioning Levels (ICF b152)
============================================================
Description
-----------
A fine-tuned regression model that assigns a functioning level to Dutch sentences describing emotional functions. The model is based on a pre-trained Dutch medical langua... | [
"### Authors\n\n\nJenia Kim, Piek Vossen",
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] |
text-classification | transformers | emilyalsentzer/Bio_ClinicalBERT with additional training through the finetuning pipeline described in "Extracting Seizure Frequency From Epilepsy Clinic Notes: A Machine Reading Approach To Natural Language Processing."
Citation: Kevin Xie, Ryan S Gallagher, Erin C Conrad, Chadric O Garrick, Steven N Baldassano, John... | {} | CNT-UPenn/Bio_ClinicalBERT_for_seizureFreedom_classification | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #text-classification #autotrain_compatible #endpoints_compatible #has_space #region-us
| emilyalsentzer/Bio_ClinicalBERT with additional training through the finetuning pipeline described in "Extracting Seizure Frequency From Epilepsy Clinic Notes: A Machine Reading Approach To Natural Language Processing."
Citation: Kevin Xie, Ryan S Gallagher, Erin C Conrad, Chadric O Garrick, Steven N Baldassano, John... | [] | [
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32
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question-answering | transformers | RoBERTa-base with additional training through the finetuning pipeline described in "Extracting Seizure Frequency From Epilepsy Clinic Notes: A Machine Reading Approach To Natural Language Processing."
Citation: Kevin Xie, Ryan S Gallagher, Erin C Conrad, Chadric O Garrick, Steven N Baldassano, John M Bernabei, Peter ... | {} | CNT-UPenn/RoBERTa_for_seizureFrequency_QA | null | [
"transformers",
"pytorch",
"roberta",
"question-answering",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #question-answering #endpoints_compatible #region-us
| RoBERTa-base with additional training through the finetuning pipeline described in "Extracting Seizure Frequency From Epilepsy Clinic Notes: A Machine Reading Approach To Natural Language Processing."
Citation: Kevin Xie, Ryan S Gallagher, Erin C Conrad, Chadric O Garrick, Steven N Baldassano, John M Bernabei, Peter ... | [] | [
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fill-mask | transformers | # XLM-Align
**Improving Pretrained Cross-Lingual Language Models via Self-Labeled Word Alignment** (ACL-2021, [paper](https://arxiv.org/pdf/2106.06381.pdf), [github](https://github.com/CZWin32768/XLM-Align))
XLM-Align is a pretrained cross-lingual language model that supports 94 languages. See details in our [paper](... | {} | CZWin32768/xlm-align | null | [
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| XLM-Align
=========
Improving Pretrained Cross-Lingual Language Models via Self-Labeled Word Alignment (ACL-2021, paper, github)
XLM-Align is a pretrained cross-lingual language model that supports 94 languages. See details in our paper.
Example
-------
Evaluation Results
------------------
XTREME cross-lingu... | [] | [
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summarization | transformers |
# Paper Title Generator
Generates titles for computer science papers given an abstract.
The model is a BERT2BERT Encoder-Decoder using the official `bert-base-uncased` checkpoint as initialization for the encoder and decoder.
It was fine-tuned on 318,500 computer science papers posted on arXiv.org between 2007 and 2... | {"language": ["en"], "license": "apache-2.0", "tags": ["summarization"], "datasets": ["arxiv_dataset"], "metrics": ["rouge"], "widget": [{"text": "The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration. The best performing models als... | Callidior/bert2bert-base-arxiv-titlegen | null | [
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"endpoints_compatible",
"has_space",
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"en"
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|
# Paper Title Generator
Generates titles for computer science papers given an abstract.
The model is a BERT2BERT Encoder-Decoder using the official 'bert-base-uncased' checkpoint as initialization for the encoder and decoder.
It was fine-tuned on 318,500 computer science papers posted on URL between 2007 and 2022 an... | [
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text-generation | transformers | A PyTorch GPT-2 model trained on hansard from 2019-01-01 to 2020-06-01
For more information see: https://github.com/CallumRai/Hansard/ | {} | CallumRai/HansardGPT2 | null | [
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| A PyTorch GPT-2 model trained on hansard from 2019-01-01 to 2020-06-01
For more information see: URL | [] | [
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summarization | transformers |
<!-- 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. -->
# mt5-small-finetuned-amazon-en-es
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-smal... | {"license": "apache-2.0", "tags": ["summarization", "generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "mt5-small-finetuned-amazon-en-es", "results": []}]} | CalvinHuang/mt5-small-finetuned-amazon-en-es | null | [
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| mt5-small-finetuned-amazon-en-es
================================
This model is a fine-tuned version of google/mt5-small on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 3.0393
* Rouge1: 17.2936
* Rouge2: 8.0678
* Rougel: 16.8129
* Rougelsum: 16.9991
Model description
--------... | [
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text-generation | transformers |
# MaamiBot | {"tags": ["conversational"]} | Camzure/MaamiBot-test | null | [
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text-generation | transformers |
# Jesse (Breaking Bad) DialoGPT Model | {"tags": ["conversational"]} | Canadiancaleb/DialoGPT-small-jesse | null | [
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text-generation | transformers |
# Walter (Breaking Bad) DialoGPT Model | {"tags": ["conversational"]} | Canadiancaleb/DialoGPT-small-walter | null | [
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text-classification | transformers | # capreolus/bert-base-msmarco
## Model description
BERT-Base model (`google/bert_uncased_L-12_H-768_A-12`) fine-tuned on the MS MARCO passage classification task. It is intended to be used as a `ForSequenceClassification` model; see the [Capreolus BERT-MaxP implementation](https://github.com/capreolus-ir/capreolus/blo... | {} | Capreolus/bert-base-msmarco | null | [
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| # capreolus/bert-base-msmarco
## Model description
BERT-Base model ('google/bert_uncased_L-12_H-768_A-12') fine-tuned on the MS MARCO passage classification task. It is intended to be used as a 'ForSequenceClassification' model; see the Capreolus BERT-MaxP implementation for a usage example.
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text-classification | transformers | # capreolus/electra-base-msmarco
## Model description
ELECTRA-Base model (`google/electra-base-discriminator`) fine-tuned on the MS MARCO passage classification task. It is intended to be used as a `ForSequenceClassification` model, but requires some modification since it contains a BERT classification head rather tha... | {} | Capreolus/electra-base-msmarco | null | [
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| # capreolus/electra-base-msmarco
## Model description
ELECTRA-Base model ('google/electra-base-discriminator') fine-tuned on the MS MARCO passage classification task. It is intended to be used as a 'ForSequenceClassification' model, but requires some modification since it contains a BERT classification head rather tha... | [
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text-classification | transformers | # Master Thesis
## Predictive Value of Sentiment Analysis from Headlines for Crude Oil Prices
### Understanding and Exploiting Deep Learning-based Sentiment Analysis from News Headlines for Predicting Price Movements of WTI Crude Oil
The focus of this thesis deals with the task of research and development of state-of-... | {} | Captain-1337/CrudeBERT | null | [
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"bert",
"text-classification",
"arxiv:1908.10063",
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"region:us"
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| # Master Thesis
## Predictive Value of Sentiment Analysis from Headlines for Crude Oil Prices
### Understanding and Exploiting Deep Learning-based Sentiment Analysis from News Headlines for Predicting Price Movements of WTI Crude Oil
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text2text-generation | transformers | **mt5-spanish-memmories-analysis**
**// ES**
Este es un trabajo en proceso.
Este modelo aún es solo un punto de control inicial que mejoraré en los próximos meses.
El objetivo es proporcionar un modelo capaz de, utilizando una combinación de tareas del modelo mT5, comprender los recuerdos y proporcionar una interacc... | {} | CarlosPR/mt5-spanish-memmories-analysis | null | [
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#transformers #pytorch #mt5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| mt5-spanish-memmories-analysis
// ES
Este es un trabajo en proceso.
Este modelo aún es solo un punto de control inicial que mejoraré en los próximos meses.
El objetivo es proporcionar un modelo capaz de, utilizando una combinación de tareas del modelo mT5, comprender los recuerdos y proporcionar una interacción útil... | [] | [
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text-generation | transformers |
# Harry potter DialoGPT Model | {"tags": ["conversational"]} | CasualHomie/DialoGPT-small-harrypotter | null | [
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automatic-speech-recognition | transformers |
# Cdial/Hausa_xlsr
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m)
It achieves the following results on the evaluation set (which is 10 percent of train data set merged with invalidated data, reported, other, and dev datasets):
- Loss: 0.27511... | {"language": ["ha"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "mozilla-foundation/common_voice_8_0", "generated_from_trainer", "ha", "robust-speech-event", "model_for_talk", "hf-asr-leaderboard"], "datasets": ["mozilla-foundation/common_voice_8_0"], "model-index": [{"name": "Cdial/Hausa_xlsr", ... | Cdial/hausa-asr | null | [
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| Cdial/Hausa\_xlsr
=================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m
It achieves the following results on the evaluation set (which is 10 percent of train data set merged with invalidated data, reported, other, and dev datasets):
* Loss: 0.275118
* Wer: 0.329955
Model description... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.000096\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 13\n* gradient\\_accumulation\\_steps: 2\n* lr\\_scheduler\\_type: cosine\\_with\\_restarts\n* lr\\_scheduler\\_warmup\\_steps:... | [
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text-generation | transformers |
# Cedille AI
Cedille is a project to bring large language models to non-English languages.
## fr-boris
Boris is a 6B parameter autoregressive language model based on the GPT-J architecture and trained using the [mesh-transformer-jax](https://github.com/kingoflolz/mesh-transformer-jax) codebase.
Boris was trained on ... | {"language": "fr", "license": "mit", "tags": ["pytorch", "causal-lm"], "datasets": ["c4"]} | Cedille/fr-boris | null | [
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"region:us"
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"fr"
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|
# Cedille AI
Cedille is a project to bring large language models to non-English languages.
## fr-boris
Boris is a 6B parameter autoregressive language model based on the GPT-J architecture and trained using the mesh-transformer-jax codebase.
Boris was trained on around 78B tokens of French text from the C4 dataset. ... | [
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null | transformers |
# ALBERT Base Spanish
This is an [ALBERT](https://github.com/google-research/albert) model trained on a [big spanish corpora](https://github.com/josecannete/spanish-corpora).
The model was trained on a single TPU v3-8 with the following hyperparameters and steps/time:
- LR: 0.0008838834765
- Batch Size: 960
- Warmup ... | {"language": ["es"], "tags": ["albert", "spanish", "OpenCENIA"], "datasets": ["large_spanish_corpus"]} | dccuchile/albert-base-spanish | null | [
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|
# ALBERT Base Spanish
This is an ALBERT model trained on a big spanish corpora.
The model was trained on a single TPU v3-8 with the following hyperparameters and steps/time:
- LR: 0.0008838834765
- Batch Size: 960
- Warmup ratio: 0.00625
- Warmup steps: 53333.33333
- Goal steps: 8533333.333
- Total steps: 3650000
- T... | [
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null | transformers |
# ALBERT Large Spanish
This is an [ALBERT](https://github.com/google-research/albert) model trained on a [big spanish corpora](https://github.com/josecannete/spanish-corpora).
The model was trained on a single TPU v3-8 with the following hyperparameters and steps/time:
- LR: 0.000625
- Batch Size: 512
- Warmup ratio:... | {"language": ["es"], "tags": ["albert", "spanish", "OpenCENIA"], "datasets": ["large_spanish_corpus"]} | dccuchile/albert-large-spanish | null | [
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|
# ALBERT Large Spanish
This is an ALBERT model trained on a big spanish corpora.
The model was trained on a single TPU v3-8 with the following hyperparameters and steps/time:
- LR: 0.000625
- Batch Size: 512
- Warmup ratio: 0.003125
- Warmup steps: 12500
- Goal steps: 4000000
- Total steps: 1450000
- Total training t... | [
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null | transformers |
# ALBERT Tiny Spanish
This is an [ALBERT](https://github.com/google-research/albert) model trained on a [big spanish corpora](https://github.com/josecannete/spanish-corpora).
The model was trained on a single TPU v3-8 with the following hyperparameters and steps/time:
- LR: 0.00125
- Batch Size: 2048
- Warmup ratio: ... | {"language": ["es"], "tags": ["albert", "spanish", "OpenCENIA"], "datasets": ["large_spanish_corpus"]} | dccuchile/albert-tiny-spanish | null | [
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|
# ALBERT Tiny Spanish
This is an ALBERT model trained on a big spanish corpora.
The model was trained on a single TPU v3-8 with the following hyperparameters and steps/time:
- LR: 0.00125
- Batch Size: 2048
- Warmup ratio: 0.0125
- Warmup steps: 125000
- Goal steps: 10000000
- Total steps: 8300000
- Total training ti... | [
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null | transformers |
# ALBERT XLarge Spanish
This is an [ALBERT](https://github.com/google-research/albert) model trained on a [big spanish corpora](https://github.com/josecannete/spanish-corpora).
The model was trained on a single TPU v3-8 with the following hyperparameters and steps/time:
- LR: 0.0003125
- Batch Size: 128
- Warmup rati... | {"language": ["es"], "tags": ["albert", "spanish", "OpenCENIA"], "datasets": ["large_spanish_corpus"]} | dccuchile/albert-xlarge-spanish | null | [
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#transformers #pytorch #tf #albert #pretraining #spanish #OpenCENIA #es #dataset-large_spanish_corpus #endpoints_compatible #region-us
|
# ALBERT XLarge Spanish
This is an ALBERT model trained on a big spanish corpora.
The model was trained on a single TPU v3-8 with the following hyperparameters and steps/time:
- LR: 0.0003125
- Batch Size: 128
- Warmup ratio: 0.00078125
- Warmup steps: 6250
- Goal steps: 8000000
- Total steps: 2775000
- Total trainin... | [
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null | transformers |
# ALBERT XXLarge Spanish
This is an [ALBERT](https://github.com/google-research/albert) model trained on a [big spanish corpora](https://github.com/josecannete/spanish-corpora).
The model was trained on a single TPU v3-8 with the following hyperparameters and steps/time:
- LR: 0.0003125
- Batch Size: 128
- Warmup rat... | {"language": ["es"], "tags": ["albert", "spanish", "OpenCENIA"], "datasets": ["large_spanish_corpus"]} | dccuchile/albert-xxlarge-spanish | null | [
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#transformers #pytorch #tf #albert #pretraining #spanish #OpenCENIA #es #dataset-large_spanish_corpus #endpoints_compatible #region-us
|
# ALBERT XXLarge Spanish
This is an ALBERT model trained on a big spanish corpora.
The model was trained on a single TPU v3-8 with the following hyperparameters and steps/time:
- LR: 0.0003125
- Batch Size: 128
- Warmup ratio: 0.00078125
- Warmup steps: 3125
- Goal steps: 4000000
- Total steps: 1650000
- Total traini... | [
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fill-mask | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-recipe-1
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.c... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "distilbert-base-uncased-finetuned-recipe-1", "results": []}]} | CennetOguz/distilbert-base-uncased-finetuned-recipe-1 | null | [
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| distilbert-base-uncased-finetuned-recipe-1
==========================================
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 3.0641
Model description
-----------------
More information needed
Intende... | [
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fill-mask | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-recipe
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "distilbert-base-uncased-finetuned-recipe", "results": []}]} | CennetOguz/distilbert-base-uncased-finetuned-recipe | null | [
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| distilbert-base-uncased-finetuned-recipe
========================================
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 2.9488
Model description
-----------------
More information needed
Intended us... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 256\n* eval\\_batch\\_size: 256\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0\n* mixed\\_... | [
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text-generation | transformers |
# Lego Batman DialoGPT Model | {"tags": ["conversational"]} | Chae/botman | null | [
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text-generation | transformers |
# Model trained on F.R.I.E.N.D.S dialogue | {"tags": ["conversational"]} | Chakita/Friends | null | [
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fill-mask | transformers | Kannada BERT model finetuned on a news corpus
---
language:
- kn
thumbnail:
tags:
- Masked Language model
- Autocomplete
license: mit
datasets:
- custom data set of Kannada news
--- | {} | Chakita/KNUBert | null | [
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"tensorboard",
"roberta",
"fill-mask",
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"endpoints_compatible",
"region:us"
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#transformers #pytorch #tensorboard #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| Kannada BERT model finetuned on a news corpus
---
language:
- kn
thumbnail:
tags:
- Masked Language model
- Autocomplete
license: mit
datasets:
- custom data set of Kannada news
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fill-mask | transformers | RoBERTa model trained on Kannada news corpus. | {"tags": ["masked-lm", "fill-in-the-blanks"]} | Chakita/KROBERT | null | [
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fill-mask | transformers |
<!-- 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. -->
# Kalbert
This model is a fine-tuned version of [ai4bharat/indic-bert](https://huggingface.co/ai4bharat/indic-bert) on a kannada n... | {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "Kalbert", "results": []}]} | Chakita/Kalbert | null | [
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] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
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| Kalbert
=======
This model is a fine-tuned version of ai4bharat/indic-bert on a kannada news dataset.
It achieves the following results on the evaluation set:
* Loss: 1.5324
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information n... | [
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fill-mask | transformers | RoBERTa model trained on OSCAR Kannada corpus. | {"tags": ["masked-lm", "fill-in-the-blanks"]} | Chakita/KannadaBERT | null | [
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#transformers #pytorch #roberta #fill-mask #masked-lm #fill-in-the-blanks #autotrain_compatible #endpoints_compatible #region-us
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text-generation | transformers |
#help why did i feed this bot the bee movie | {"tags": ["conversational"]} | Chalponkey/DialoGPT-small-Barry | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
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|
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text-generation | transformers |
# Harry Potter DialoGPT Model | {"tags": ["conversational"]} | ChaseBread/DialoGPT-small-harrypotter | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
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null | null |
## Model based on
[Ko-GPT-Trinity 1.2B (v0.5)](https://huggingface.co/skt/ko-gpt-trinity-1.2B-v0.5)
## Example
```python
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained(
"CheonggyeMountain-Sherpa/kogpt-trinity-punct-wrapper",
revision="punc... | {"language": ["ko"], "license": "cc-by-nc-sa-4.0", "tags": ["gpt2"]} | CheonggyeMountain-Sherpa/kogpt-trinity-punct-wrapper | null | [
"gpt2",
"ko",
"license:cc-by-nc-sa-4.0",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"ko"
] | TAGS
#gpt2 #ko #license-cc-by-nc-sa-4.0 #region-us
|
## Model based on
Ko-GPT-Trinity 1.2B (v0.5)
## Example
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question-answering | transformers | This question answering model was fine tuned to detect negation expressions
How to use:
question: negation
context: That is not safe!
Answer: not
question: negation
context: Weren't we going to go to the moon?
Answer: Weren't
| {} | Ching/negation_detector | null | [
"transformers",
"pytorch",
"roberta",
"question-answering",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #question-answering #endpoints_compatible #region-us
| This question answering model was fine tuned to detect negation expressions
How to use:
question: negation
context: That is not safe!
Answer: not
question: negation
context: Weren't we going to go to the moon?
Answer: Weren't
| [] | [
"TAGS\n#transformers #pytorch #roberta #question-answering #endpoints_compatible #region-us \n"
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text-generation | transformers |
Donald Trump DialoGPT Model built by following tutorial by [Ruolin Zheng](https://youtu.be/Rk8eM1p_xgM).
The data used for training was 2020 presidential debate.
More work is needed to optimize it. I don't have access to larger VRAM. | {"tags": ["conversational"]} | Chiuchiyin/DialoGPT-small-Donald | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
Donald Trump DialoGPT Model built by following tutorial by Ruolin Zheng.
The data used for training was 2020 presidential debate.
More work is needed to optimize it. I don't have access to larger VRAM. | [] | [
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39
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text-generation | transformers | # CMJS DialoGPT Model | {"tags": ["conversational"]} | ChrisVCB/DialoGPT-medium-cmjs | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
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text-generation | transformers | # Eddie Jones DialoGPT Model | {"tags": ["conversational"]} | ChrisVCB/DialoGPT-medium-ej | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
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depth-estimation | null |
# MADNet Keras
MADNet is a deep stereo depth estimation model. Its key defining features are:
1. It has a light-weight architecture which means it has low latency.
2. It supports self-supervised training, so it can be conveniently adapted in the field with no training data.
3. It's a stereo depth model, whi... | {"license": "apache-2.0", "tags": ["vision", "deep-stereo", "depth-estimation", "Tensorflow2", "Keras"], "datasets": ["flyingthings-3d", "kitti"]} | ChristianOrr/madnet_keras | null | [
"tensorboard",
"vision",
"deep-stereo",
"depth-estimation",
"Tensorflow2",
"Keras",
"dataset:flyingthings-3d",
"dataset:kitti",
"arxiv:1810.05424",
"license:apache-2.0",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [
"1810.05424"
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#tensorboard #vision #deep-stereo #depth-estimation #Tensorflow2 #Keras #dataset-flyingthings-3d #dataset-kitti #arxiv-1810.05424 #license-apache-2.0 #region-us
|
# MADNet Keras
MADNet is a deep stereo depth estimation model. Its key defining features are:
1. It has a light-weight architecture which means it has low latency.
2. It supports self-supervised training, so it can be conveniently adapted in the field with no training data.
3. It's a stereo depth model, whi... | [
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null | transformers | # IndoBERT (Indonesian BERT Model)
## Model description
ELECTRA is a new method for self-supervised language representation learning. This repository contains the pre-trained Electra Base model (tensorflow 1.15.0) trained in a Large Indonesian corpus (~16GB of raw text | ~2B indonesian words).
IndoELECTRA is a pre-tra... | {"language": "id", "datasets": ["oscar"]} | ChristopherA08/IndoELECTRA | null | [
"transformers",
"pytorch",
"electra",
"pretraining",
"id",
"dataset:oscar",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"id"
] | TAGS
#transformers #pytorch #electra #pretraining #id #dataset-oscar #endpoints_compatible #region-us
| # IndoBERT (Indonesian BERT Model)
## Model description
ELECTRA is a new method for self-supervised language representation learning. This repository contains the pre-trained Electra Base model (tensorflow 1.15.0) trained in a Large Indonesian corpus (~16GB of raw text | ~2B indonesian words).
IndoELECTRA is a pre-tra... | [
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text-generation | transformers |
# Harry Potter DialoGPT MOdel | {"tags": ["conversational"]} | Chuah/DialoGPT-small-harrypotter | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
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text-generation | transformers |
# Dr. Fauci DialoGPT Model | {"tags": ["conversational"]} | ChukSamuels/DialoGPT-small-Dr.FauciBot | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
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null | null | copied from boris | {} | Cilan/dalle-knockoff | null | [
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#region-us
| copied from boris | [] | [
"TAGS\n#region-us \n"
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5
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null | transformers |
## Japanese ELECTRA-small
We provide a Japanese **ELECTRA-Small** model, as described in [ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators](https://openreview.net/pdf?id=r1xMH1BtvB).
Our pretraining process employs subword units derived from the [Japanese Wikipedia](https://dumps.wikimedi... | {"language": "ja", "license": "apache-2.0"} | Cinnamon/electra-small-japanese-discriminator | null | [
"transformers",
"pytorch",
"electra",
"pretraining",
"ja",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"ja"
] | TAGS
#transformers #pytorch #electra #pretraining #ja #license-apache-2.0 #endpoints_compatible #region-us
|
## Japanese ELECTRA-small
We provide a Japanese ELECTRA-Small model, as described in ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators.
Our pretraining process employs subword units derived from the Japanese Wikipedia, using the Byte-Pair Encoding method and building on an initial tokeniza... | [
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fill-mask | transformers | ## Japanese ELECTRA-small
We provide a Japanese **ELECTRA-Small** model, as described in [ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators](https://openreview.net/pdf?id=r1xMH1BtvB).
Our pretraining process employs subword units derived from the [Japanese Wikipedia](https://dumps.wikimedia... | {"language": "ja"} | Cinnamon/electra-small-japanese-generator | null | [
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"pytorch",
"electra",
"fill-mask",
"ja",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"ja"
] | TAGS
#transformers #pytorch #electra #fill-mask #ja #autotrain_compatible #endpoints_compatible #region-us
| ## Japanese ELECTRA-small
We provide a Japanese ELECTRA-Small model, as described in ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators.
Our pretraining process employs subword units derived from the Japanese Wikipedia, using the Byte-Pair Encoding method and building on an initial tokenizat... | [
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text-generation | transformers |
# Harry Potter DialoGPT Model | {"tags": ["conversational"]} | Ciruzzo/DialoGPT-small-harrypotter | null | [
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"text-generation",
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"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
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text-generation | transformers | # RickBot built for [Chai](https://chai.ml/)
Make your own [here](https://colab.research.google.com/drive/1o5LxBspm-C28HQvXN-PRQavapDbm5WjG?usp=sharing)
| {"tags": ["conversational"]} | ClaudeCOULOMBE/RickBot | null | [
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#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
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Make your own here
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zero-shot-classification | transformers | ETH Zeroshot | {"datasets": ["multi_nli"], "pipeline_tag": "zero-shot-classification", "widget": [{"text": "ETH", "candidate_labels": "Location & Address, Employment, Organizational, Name, Service, Studies, Science", "hypothesis_template": "This is {}."}]} | ClaudeYang/awesome_fb_model | null | [
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"bart",
"text-classification",
"zero-shot-classification",
"dataset:multi_nli",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #bart #text-classification #zero-shot-classification #dataset-multi_nli #autotrain_compatible #endpoints_compatible #region-us
| ETH Zeroshot | [] | [
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text-generation | null |
# My Awesome Model
| {"tags": ["conversational"]} | ClydeWasTaken/DialoGPT-small-joshua | null | [
"conversational",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#conversational #region-us
|
# My Awesome Model
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text-generation | transformers |
# Cartman DialoGPT Model | {"tags": ["conversational"]} | CodeDanCode/CartmenBot | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
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text-generation | transformers |
# SouthPark Kyle Bot
| {"tags": ["conversational"]} | CodeDanCode/SP-KyleBot | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# SouthPark Kyle Bot
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text-generation | transformers |
# Harry Potter DialoGPT Model | {"tags": ["conversational"]} | CoderBoy432/DialoGPT-small-harrypotter | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
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text-generation | transformers |
Chat with the model:
```python
from transformers import AutoTokenizer, AutoModelWithLMHead
tokenizer = AutoTokenizer.from_pretrained("r3dhummingbird/DialoGPT-marxbot")
model = AutoModelWithLMHead.from_pretrained("r3dhummingbird/DialoGPT-marxbot")
# Let's chat for 4 lines
for step in range(4):
# encode the new u... | {"tags": ["conversational"]} | CoderEFE/DialoGPT-marxbot | null | [
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"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
Chat with the model:
| [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n"
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text-classification | transformers |
# bart-faithful-summary-detector
## Model description
A BART (base) model trained to classify whether a summary is *faithful* to the original article. See our [paper in NAACL'21](https://www.seas.upenn.edu/~sihaoc/static/pdf/CZSR21.pdf) for details.
## Usage
Concatenate a summary and a source document as input (no... | {"language": ["en"], "license": "cc-by-sa-4.0", "tags": ["text-classification", "bart", "xsum"], "datasets": ["xsum"], "thumbnail": "https://cogcomp.seas.upenn.edu/images/logo.png", "widget": [{"text": "<s> Ban Ki-moon was elected for a second term in 2007. </s></s> Ban Ki-Moon was re-elected for a second term by the U... | CogComp/bart-faithful-summary-detector | null | [
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"jax",
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"text-classification",
"xsum",
"en",
"dataset:xsum",
"license:cc-by-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #jax #bart #text-classification #xsum #en #dataset-xsum #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# bart-faithful-summary-detector
## Model description
A BART (base) model trained to classify whether a summary is *faithful* to the original article. See our paper in NAACL'21 for details.
## Usage
Concatenate a summary and a source document as input (note that the summary needs to be the first sentence).
Here'... | [
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fill-mask | transformers | # roberta-temporal-predictor
A RoBERTa-base model that is fine-tuned on the [The New York Times Annotated Corpus](https://catalog.ldc.upenn.edu/LDC2008T19)
to predict temporal precedence of two events. This is used as the ``temporality prediction'' component
in our ROCK framework for reasoning about commonsense caus... | {"license": "mit", "widget": [{"text": "The man turned on the faucet <mask> water flows out."}, {"text": "The woman received her pension <mask> she retired."}]} | CogComp/roberta-temporal-predictor | null | [
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"roberta",
"fill-mask",
"arxiv:2202.00436",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [
"2202.00436"
] | [] | TAGS
#transformers #pytorch #roberta #fill-mask #arxiv-2202.00436 #license-mit #autotrain_compatible #endpoints_compatible #region-us
| # roberta-temporal-predictor
A RoBERTa-base model that is fine-tuned on the The New York Times Annotated Corpus
to predict temporal precedence of two events. This is used as the ''temporality prediction'' component
in our ROCK framework for reasoning about commonsense causality. See our paper for more details.
# ... | [
"# roberta-temporal-predictor\r\nA RoBERTa-base model that is fine-tuned on the The New York Times Annotated Corpus\r\nto predict temporal precedence of two events. This is used as the ''temporality prediction'' component\r\nin our ROCK framework for reasoning about commonsense causality. See our paper for more det... | [
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feature-extraction | transformers | 해당 모델은 [해당 사이트](https://huggingface.co/gpt2-medium)에서 가져온 모델입니다.
해당 모델은 [Teachable NLP](https://ainize.ai/teachable-nlp) 서비스에서 사용됩니다.
| {} | ComCom/gpt2-large | null | [
"transformers",
"pytorch",
"gpt2",
"feature-extraction",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #feature-extraction #endpoints_compatible #text-generation-inference #region-us
| 해당 모델은 해당 사이트에서 가져온 모델입니다.
해당 모델은 Teachable NLP 서비스에서 사용됩니다.
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31
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feature-extraction | transformers | 해당 모델은 [해당 사이트](https://huggingface.co/gpt2-medium)에서 가져온 모델입니다.
해당 모델은 [Teachable NLP](https://ainize.ai/teachable-nlp) 서비스에서 사용됩니다.
| {} | ComCom/gpt2-medium | null | [
"transformers",
"pytorch",
"gpt2",
"feature-extraction",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #feature-extraction #endpoints_compatible #text-generation-inference #region-us
| 해당 모델은 해당 사이트에서 가져온 모델입니다.
해당 모델은 Teachable NLP 서비스에서 사용됩니다.
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feature-extraction | transformers | 해당 모델은 [해당 사이트](https://huggingface.co/gpt2)에서 가져온 모델입니다.
해당 모델은 [Teachable NLP](https://ainize.ai/teachable-nlp) 서비스에서 사용됩니다. | {} | ComCom/gpt2 | null | [
"transformers",
"pytorch",
"gpt2",
"feature-extraction",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #feature-extraction #endpoints_compatible #text-generation-inference #region-us
| 해당 모델은 해당 사이트에서 가져온 모델입니다.
해당 모델은 Teachable NLP 서비스에서 사용됩니다. | [] | [
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text-generation | transformers |
# neurotitle-rugpt3-small
Model based on [ruGPT-3](https://huggingface.co/sberbank-ai) for generating scientific paper titles.
Trained on [All NeurIPS (NIPS) Papers](https://www.kaggle.com/rowhitswami/nips-papers-1987-2019-updated) dataset.
Use exclusively as a crazier alternative to SCIgen.
## Made with Cometrain Al... | {"language": ["ru", "en"], "license": "mit", "tags": ["Cometrain AutoCode", "Cometrain AlphaML"], "datasets": ["All-NeurIPS-Papers-Scraper"], "widget": [{"text": "NIPSE:", "example_title": "NIPS"}, {"text": "Learning CNN", "example_title": "Learning CNN"}, {"text": "ONNX:", "example_title": "ONNX"}, {"text": "BERT:", "... | cometrain/neurotitle-rugpt3-small | null | [
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"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"ru",
"en"
] | TAGS
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|
# neurotitle-rugpt3-small
Model based on ruGPT-3 for generating scientific paper titles.
Trained on All NeurIPS (NIPS) Papers dataset.
Use exclusively as a crazier alternative to SCIgen.
## Made with Cometrain AlphaML & AutoCode
This model was automatically fine-tuned using the Cometrain AlphaML framework and tested ... | [
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text-generation | transformers |
# Rick DialoGPT Model | {"tags": ["conversational"]} | Connor/DialoGPT-small-rick | null | [
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"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
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text-generation | transformers |
#enlightened GPT model | {"tags": ["conversational"]} | Connorvr/BrightBot-small | null | [
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"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
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#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
#enlightened GPT model | [] | [
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text-generation | transformers |
<!-- 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. -->
# model
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset.
## Model description
Mo... | {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "model", "results": []}]} | Connorvr/TeachingGen | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# model
This model is a fine-tuned version of gpt2 on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparamet... | [
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null | null |
@inproceedings{wan2020bringing,
title={Bringing Old Photos Back to Life},
author={Wan, Ziyu and Zhang, Bo and Chen, Dongdong and Zhang, Pan and Chen, Dong and Liao, Jing and Wen, Fang},
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
pages={2747--2757},
year={2020}
}
@art... | {"language": ["en"], "license": "mit", "tags": ["image_restoration", "superresolution"], "thumbnail": "https://github.com/Nick-Harvey/for_my_abuela/blob/master/cuban_large.jpg"} | Coolhand/Abuela | null | [
"image_restoration",
"superresolution",
"en",
"license:mit",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"en"
] | TAGS
#image_restoration #superresolution #en #license-mit #region-us
|
@inproceedings{wan2020bringing,
title={Bringing Old Photos Back to Life},
author={Wan, Ziyu and Zhang, Bo and Chen, Dongdong and Zhang, Pan and Chen, Dong and Liao, Jing and Wen, Fang},
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
pages={2747--2757},
year={2020}
}
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text-generation | transformers |
# Atakan DialoGPT Model | {"tags": ["conversational"]} | CopymySkill/DialoGPT-medium-atakan | null | [
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"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
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text-generation | transformers |
#DiabloGPT Captain Price (Extended) | {"tags": ["conversational"]} | Corvus/DialoGPT-medium-CaptainPrice-Extended | null | [
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"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
#DiabloGPT Captain Price (Extended) | [] | [
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text-generation | transformers |
# Captain Price DialoGPT Model | {"tags": ["conversational"]} | Corvus/DialoGPT-medium-CaptainPrice | null | [
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|
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text-classification | transformers |
### Description
A Multi-label text classification model trained on a customer feedback data using DistilBert.
Possible labels are:
- Delivery (delivery status, time of arrival, etc.)
- Return (return confirmation, return label requests, etc.)
- Product (quality, complaint, etc.)
- Monetary (pending transactions, refun... | {"language": "en", "license": "mit", "tags": ["multi-label"], "widget": [{"text": "I would like to return these pants and shoes"}]} | CouchCat/ma_mlc_v7_distil | null | [
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"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"en"
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|
### Description
A Multi-label text classification model trained on a customer feedback data using DistilBert.
Possible labels are:
- Delivery (delivery status, time of arrival, etc.)
- Return (return confirmation, return label requests, etc.)
- Product (quality, complaint, etc.)
- Monetary (pending transactions, refun... | [
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token-classification | transformers |
### Description
A Named Entity Recognition model trained on a customer feedback data using DistilBert.
Possible labels are:
- PRD: for certain products
- BRND: for brands
### Usage
```
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("CouchCat/ma_n... | {"language": "en", "license": "mit", "tags": ["ner"], "widget": [{"text": "These shoes from Adidas fit quite well"}]} | CouchCat/ma_ner_v6_distil | null | [
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"en",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #distilbert #token-classification #ner #en #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
### Description
A Named Entity Recognition model trained on a customer feedback data using DistilBert.
Possible labels are:
- PRD: for certain products
- BRND: for brands
### Usage
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token-classification | transformers |
### Description
A Named Entity Recognition model trained on a customer feedback data using DistilBert.
Possible labels are in BIO-notation. Performance of the PERS tag could be better because of low data samples:
- PROD: for certain products
- BRND: for brands
- PERS: people names
The following tags are simply in p... | {"language": "en", "license": "mit", "tags": ["ner"], "widget": [{"text": "These shoes I recently bought from Tommy Hilfiger fit quite well. The shirt, however, has got a hole"}]} | CouchCat/ma_ner_v7_distil | null | [
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|
### Description
A Named Entity Recognition model trained on a customer feedback data using DistilBert.
Possible labels are in BIO-notation. Performance of the PERS tag could be better because of low data samples:
- PROD: for certain products
- BRND: for brands
- PERS: people names
The following tags are simply in p... | [
"### Description\n\nA Named Entity Recognition model trained on a customer feedback data using DistilBert.\nPossible labels are in BIO-notation. Performance of the PERS tag could be better because of low data samples:\n\n- PROD: for certain products\n- BRND: for brands\n- PERS: people names\n\nThe following tags ar... | [
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text-classification | transformers |
### Description
A Sentiment Analysis model trained on customer feedback data using DistilBert.
Possible sentiments are:
* negative
* neutral
* positive
### Usage
```
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("CouchCat/ma_sa_v7_distil")
... | {"language": "en", "license": "mit", "tags": ["sentiment-analysis"], "widget": [{"text": "I am disappointed in the terrible quality of my dress"}]} | CouchCat/ma_sa_v7_distil | null | [
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"en",
"license:mit",
"autotrain_compatible",
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"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #distilbert #text-classification #sentiment-analysis #en #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
### Description
A Sentiment Analysis model trained on customer feedback data using DistilBert.
Possible sentiments are:
* negative
* neutral
* positive
### Usage
| [
"### Description\nA Sentiment Analysis model trained on customer feedback data using DistilBert.\nPossible sentiments are:\n* negative\n* neutral\n* positive",
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text-generation | null |
Arthur Morgan DialoGPT Model | {"tags": ["conversational"]} | Coyotl/DialoGPT-test-last-arthurmorgan | null | [
"conversational",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#conversational #region-us
|
Arthur Morgan DialoGPT Model | [] | [
"TAGS\n#conversational #region-us \n"
] | [
8
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text-generation | transformers |
# Arthur Morgan DialoGPT Model | {"tags": ["conversational"]} | Coyotl/DialoGPT-test2-arthurmorgan | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
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|
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] |
text-generation | null |
# DialoGPT Arthur Morgan | {"tags": ["conversational"]} | Coyotl/DialoGPT-test3-arthurmorgan | null | [
"conversational",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#conversational #region-us
|
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] |
text-generation | transformers | @Piglin Talks Harry Potter | {"tags": ["conversational"]} | CracklesCreeper/Piglin-Talks-Harry-Potter | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| @Piglin Talks Harry Potter | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
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39
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"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
feature-extraction | sentence-transformers |
# A model.
| {"license": "apache-2.0", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "feature-extraction"} | Craig/mGqFiPhu | null | [
"sentence-transformers",
"feature-extraction",
"sentence-similarity",
"transformers",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#sentence-transformers #feature-extraction #sentence-similarity #transformers #license-apache-2.0 #endpoints_compatible #region-us
|
# A model.
| [
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feature-extraction | sentence-transformers |
# sentence-transformers/paraphrase-MiniLM-L6-v2
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.
This is a clone of the original model, with `pipeline_tag` metadata chan... | {"license": "apache-2.0", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "feature-extraction"} | Craig/paraphrase-MiniLM-L6-v2 | null | [
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"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"arxiv:1908.10084",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [
"1908.10084"
] | [] | TAGS
#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #arxiv-1908.10084 #license-apache-2.0 #endpoints_compatible #region-us
|
# sentence-transformers/paraphrase-MiniLM-L6-v2
This is a sentence-transformers model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.
This is a clone of the original model, with 'pipeline_tag' metadata changed to 'feature-extractio... | [
"# sentence-transformers/paraphrase-MiniLM-L6-v2\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.\n\nThis is a clone of the original model, with 'pipeline_tag' metadata changed to 'feature-e... | [
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"# sentence-transformers/paraphrase-MiniLM-L6-v2\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 384 dimensi... | [
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"TAGS\n#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #arxiv-1908.10084 #license-apache-2.0 #endpoints_compatible #region-us \n# sentence-transformers/paraphrase-MiniLM-L6-v2\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 384 dimensional d... |
text-classification | transformers |
# Model Finetuned from BERT-base for
- Problem type: Multi-class Classification
- Model ID: 25805800
## Validation Metrics
- Loss: 0.4422711133956909
- Accuracy: 0.8615328555811976
- Macro F1: 0.8642434650461513
- Micro F1: 0.8615328555811976
- Weighted F1: 0.8617743626671308
- Macro Precision: 0.8649112225076049
-... | {"language": "en", "tags": "autonlp", "datasets": ["Crasher222/autonlp-data-kaggle-test"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}], "co2_eq_emissions": 60.744727079482495} | Crasher222/kaggle-comp-test | null | [
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"text-classification",
"autonlp",
"en",
"dataset:Crasher222/autonlp-data-kaggle-test",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"en"
] | TAGS
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|
# Model Finetuned from BERT-base for
- Problem type: Multi-class Classification
- Model ID: 25805800
## Validation Metrics
- Loss: 0.4422711133956909
- Accuracy: 0.8615328555811976
- Macro F1: 0.8642434650461513
- Micro F1: 0.8615328555811976
- Weighted F1: 0.8617743626671308
- Macro Precision: 0.8649112225076049
-... | [
"# Model Finetuned from BERT-base for\n\n- Problem type: Multi-class Classification\n- Model ID: 25805800",
"## Validation Metrics\n\n- Loss: 0.4422711133956909\n- Accuracy: 0.8615328555811976\n- Macro F1: 0.8642434650461513\n- Micro F1: 0.8615328555811976\n- Weighted F1: 0.8617743626671308\n- Macro Precision: 0.... | [
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text-generation | transformers | hello
| {} | CrisLeaf/generador-de-historias-de-tolkien | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| hello
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36
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