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text2text-generation | transformers |
# ke-t5 base
Pretrained T5 Model on Korean and English. See [Github](https://github.com/AIRC-KETI/ke-t5) and [Paper](https://aclanthology.org/2021.findings-emnlp.33/) [Korean paper](https://koreascience.kr/article/CFKO202130060717834.pdf) for more details.
## How to use
```python
from transformers import AutoModel,... | {"language": ["ko", "en"], "license": "apache-2.0", "tags": ["t5"], "eos_token": "</s>", "widget": [{"text": "\uc544\ubc84\uc9c0\uac00 \ubc29\uc5d0 \ub4e4\uc5b4\uac00\uc2e0\ub2e4.</s>"}]} | KETI-AIR/ke-t5-small-newslike | null | [
"transformers",
"pytorch",
"tf",
"jax",
"safetensors",
"t5",
"text2text-generation",
"ko",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"ko",
"en"
] | TAGS
#transformers #pytorch #tf #jax #safetensors #t5 #text2text-generation #ko #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# ke-t5 base
Pretrained T5 Model on Korean and English. See Github and Paper Korean paper for more details.
## How to use
## BibTeX entry and citation info
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text2text-generation | transformers |
# ke-t5 base
Pretrained T5 Model on Korean and English. See [Github](https://github.com/AIRC-KETI/ke-t5) and [Paper](https://aclanthology.org/2021.findings-emnlp.33/) [Korean paper](https://koreascience.kr/article/CFKO202130060717834.pdf) for more details.
## How to use
```python
from transformers import AutoModel,... | {"language": ["en", "ko"], "license": "apache-2.0", "tags": ["t5"], "eos_token": "</s>", "widget": [{"text": "\uc544\ubc84\uc9c0\uac00 \ubc29\uc5d0 \ub4e4\uc5b4\uac00\uc2e0\ub2e4.</s>"}]} | KETI-AIR/ke-t5-small | null | [
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"ko",
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|
# ke-t5 base
Pretrained T5 Model on Korean and English. See Github and Paper Korean paper for more details.
## How to use
## BibTeX entry and citation info
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text-generation | transformers |
# Clever bot DialoGPT Model | {"tags": ["conversational"]} | KOSTAS/DialoGPT-small-Cleverbot | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
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#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
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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"]} | KP2500/KPBot | null | [
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#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
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Make your own here
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text-generation | transformers | # Harry Potter DialoGPT Model | {"tags": ["conversational"]} | Kai0857/DialoGPT-small-harrypotter | null | [
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"text-generation",
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"text-generation-inference",
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text-generation | transformers |
#Peralta DialoGPT Model | {"tags": ["conversational"]} | Kail91/DialoGPT-small-PeraltaBot | null | [
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text-generation | transformers |
# Rick DialoGPT model | {"tags": ["conversational"]} | Kairu/DialoGPT-small-Rick | null | [
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text-generation | transformers |
# Rick bot chat | {"tags": ["conversational"]} | Kairu/RICKBOT | null | [
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"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
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# Rick bot chat | [
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text-generation | transformers | #my awesome model | {"tags": ["conversational"]} | KakoSi/Smolmm3 | null | [
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"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
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"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 | # My Awesome Model | {"tags": ["conversational"]} | KakoSi/opaazzi | null | [
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"text-generation",
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"autotrain_compatible",
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#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
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text-generation | transformers |
# Dona Julia DialoGPT Model | {"tags": ["conversational"]} | Kaledmgo/DialoGPT-small-donajulia | null | [
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"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
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fill-mask | transformers |
### Overview
SinBerto is a small language model trained on a small news corpus. SinBerto is trained on Sinhala Language which is a low resource language compared to other languages.
### Model Specifications.
model : [Roberta](https://arxiv.org/abs/1907.11692)
vocab_size=52_000,
max_position_embeddings=514,
num_att... | {"language": "si", "tags": ["SinBERTo", "Sinhala", "roberta"]} | Kalindu/SinBerto | null | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"SinBERTo",
"Sinhala",
"si",
"arxiv:1907.11692",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [
"1907.11692"
] | [
"si"
] | TAGS
#transformers #pytorch #roberta #fill-mask #SinBERTo #Sinhala #si #arxiv-1907.11692 #autotrain_compatible #endpoints_compatible #region-us
|
### Overview
SinBerto is a small language model trained on a small news corpus. SinBerto is trained on Sinhala Language which is a low resource language compared to other languages.
### Model Specifications.
model : Roberta
vocab_size=52_000,
max_position_embeddings=514,
num_attention_heads=12,
num_hidden_layers=6... | [
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null | null | demo file | {} | KalyanM/demo | null | [
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null | null | Dummy model | {} | KalyanM/dummy | null | [
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token-classification | 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-ner
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/dis... | {"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": "con... | KamSut/distilbert-base-uncased-finetuned-ner | null | [
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"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-ner
=====================================
This model is a fine-tuned version of distilbert-base-uncased on the conll2003 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0604
* Precision: 0.9271
* Recall: 0.9381
* F1: 0.9326
* Accuracy: 0.9836
Model des... | [
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fill-mask | transformers | AIOX Lab and SI2M Lab INSEA have joined forces to offer researchers, industrialists and the NLP (Natural Language Processing) community the first intelligent Open Source system that understands Moroccan dialectal language "Darija".
**DarijaBERT** is the first BERT model for the Moroccan Arabic dialect called “Darija”.... | {"language": "ar", "widget": [{"text": " Mchit njib [MASK] ."}, {"text": " Yak nta li [MASK] lih dik lhedra."}, {"text": " Ach [MASK] daba."}, {"text": " Lmghrib ajmal [MASK] fl3alam."}]} | SI2M-Lab/DarijaBERT-arabizi | null | [
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"bert",
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| AIOX Lab and SI2M Lab INSEA have joined forces to offer researchers, industrialists and the NLP (Natural Language Processing) community the first intelligent Open Source system that understands Moroccan dialectal language "Darija".
DarijaBERT is the first BERT model for the Moroccan Arabic dialect called “Darija”. It ... | [] | [
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fill-mask | transformers |
AIOX Lab and SI2M Lab INSEA have joined forces to offer researchers, industrialists and the NLP (Natural Language Processing) community the first intelligent Open Source system that understands Moroccan dialectal language "Darija".
**DarijaBERT** is the first BERT model for the Moroccan Arabic dialect called “Dari... | {"language": "ar", "widget": [{"text": " \u062c\u0627\u0628 \u0644\u064a\u0627 [MASK] ."}, {"text": "\u0645\u0634\u064a\u062a \u0646\u062c\u064a\u0628[MASK] \u0641\u0627\u0644\u0641\u0631\u0645\u0627\u0633\u064a\u0627\u0646 ."}]} | SI2M-Lab/DarijaBERT | null | [
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"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"ar"
] | TAGS
#transformers #pytorch #bert #fill-mask #ar #autotrain_compatible #endpoints_compatible #region-us
|
AIOX Lab and SI2M Lab INSEA have joined forces to offer researchers, industrialists and the NLP (Natural Language Processing) community the first intelligent Open Source system that understands Moroccan dialectal language "Darija".
DarijaBERT is the first BERT model for the Moroccan Arabic dialect called “Darija”.... | [] | [
"TAGS\n#transformers #pytorch #bert #fill-mask #ar #autotrain_compatible #endpoints_compatible #region-us \n"
] | [
30
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text2text-generation | transformers |
# MArSum: Moroccan Articles Summarization dataset
- [Description](#description)
- [Dataset](#dataset)
- [Citation](#citation)
- [License](#license)
## Description
This dataset contains **19,806** news articles written in Moroccan Arabic dialect along with their titles. The articles were crawled from [Goud.ma](http:/... | {"language": "ar", "widget": [{"text": " \u0643\u0634\u0641 \u0627\u0644\u0645\u0644\u064a\u0627\u0631\u062f\u064a\u0631 \u0627\u0644\u0645\u064a\u0631\u064a\u0643\u0627\u0646\u064a \u0648\u0645\u0624\u0633\u0633 \u0634\u0631\u0643\u0629 \u201c\u0645\u0627\u064a\u0643\u0631\u0648\u0633\u0648\u0641\u062a\u201d\u060c \u0... | Kamel/t5-darija-summarization | null | [
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# MArSum: Moroccan Articles Summarization dataset
- Description
- Dataset
- Citation
- License
## Description
This dataset contains 19,806 news articles written in Moroccan Arabic dialect along with their titles. The articles were crawled from URL website between 01/01/2018 and 12/31/2020.
The articles are written ... | [
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text-classification | transformers | samyarn-bert-base-multilingual-cased
kao | {} | Kao/samyarn-bert-base-multilingual-cased | null | [
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| samyarn-bert-base-multilingual-cased
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text-generation | transformers |
# randombot DialoGPT Model | {"tags": ["conversational"]} | Kargan/DialoGPT-small-randombot | null | [
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null | null | this is a test. How do you write a paper? | {} | Katiejdarby/test1 | null | [
"region:us"
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#region-us
| this is a test. How do you write a paper? | [] | [
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text-classification | 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
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilb... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "distilbert-base-uncased-finetuned", "results": []}]} | Katsiaryna/distilbert-base-uncased-finetuned | null | [
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| distilbert-base-uncased-finetuned
=================================
This model is a fine-tuned version of distilbert-base-uncased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.8229
* Accuracy: 0.54
Model description
-----------------
More information needed
Intended u... | [
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text-classification | 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_9th
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/dis... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "distilbert-base-uncased-finetuned_9th", "results": []}]} | Katsiaryna/distilbert-base-uncased-finetuned_9th | null | [
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| distilbert-base-uncased-finetuned\_9th
======================================
This model is a fine-tuned version of distilbert-base-uncased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2826
* Accuracy: 0.4462
Model description
-----------------
More information needed
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
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text-generation | transformers |
# Joshua Dialogue Model | {"tags": ["conversational"]} | KaydenSou/Joshua | null | [
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text-classification | 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-complaints-product
This model was trained from the [CFBP](https://www.consumerfinance.gov/data-research/consumer-comp... | {"tags": ["generated_from_trainer"], "datasets": ["consumer_complaints"], "model-index": [{"name": "distilbert-complaints-product", "results": []}]} | Kayvane/distilbert-complaints-product | null | [
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|
# distilbert-complaints-product
This model was trained from the CFBP dataset, also made available on the HuggingFace Datasets library. This model predicts the type of financial complaint based on the text provided
## Model description
A DistilBert Text Classification Model, with 18 possible classes to determine t... | [
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text-classification | 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-undersampled-noweights
This model was trained from scratch on the None dataset.
## Model description
More informati... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "distilbert-undersampled-noweights", "results": []}]} | Kayvane/distilbert-undersampled-noweights | null | [
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|
# distilbert-undersampled-noweights
This model was trained from scratch on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The fol... | [
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text-classification | 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-undersampled
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-u... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1", "recall", "precision"], "model-index": [{"name": "distilbert-undersampled", "results": []}]} | Kayvane/distilbert-undersampled | null | [
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| distilbert-undersampled
=======================
This model is a fine-tuned version of distilbert-base-uncased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0826
* Accuracy: 0.9811
* F1: 0.9810
* Recall: 0.9811
* Precision: 0.9812
Model description
-----------------
More... | [
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text-classification | transformers |
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 13522454
## Validation Metrics
- Loss: 0.31450966000556946
- Accuracy: 0.8461538461538461
- Precision: 0.8181818181818182
- Recall: 0.782608695652174
- AUC: 0.9369259032455604
- F1: 0.8
## Usage
You can use cURL to access this model:
... | {"language": "en", "tags": "autonlp", "datasets": ["Kceilord/autonlp-data-tc"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}]} | Kceilord/autonlp-tc-13522454 | null | [
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# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 13522454
## Validation Metrics
- Loss: 0.31450966000556946
- Accuracy: 0.8461538461538461
- Precision: 0.8181818181818182
- Recall: 0.782608695652174
- AUC: 0.9369259032455604
- F1: 0.8
## Usage
You can use cURL to access this model:
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text-generation | transformers |
#Harry Potter DialoGPT Model | {"tags": ["conversational"]} | Keen/DialoGPT-small-potter | null | [
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text-generation | transformers |
# Rick3 DialoGPT Model | {"tags": ["conversational"]} | KekLord/DialoGPT-small-rick3 | null | [
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text-generation | transformers |
# Siesta | {"tags": ["conversational"]} | Keqing/Keqing-Siesta | null | [
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text-generation | transformers |
@ Spamton G. Spamton DialoGPT Model | {"tags": ["conversational"]} | Keqipig/DialoGPT-small-spamton | null | [
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@ Spamton G. Spamton DialoGPT Model | [] | [
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text-classification | 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. -->
# koelectra-sts-v0.4
This model was trained from scratch on an unknown dataset.
It achieves the following results on the ev... | {"tags": ["generated_from_trainer"], "metrics": ["spearmanr"]} | Ketzu/koelectra-sts-v0.4 | null | [
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] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #electra #text-classification #generated_from_trainer #model-index #autotrain_compatible #endpoints_compatible #region-us
| koelectra-sts-v0.4
==================
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3368
* Pearson: 0.9303
* Spearmanr: 0.9287
Model description
-----------------
More information needed
Intended uses & limitations
---------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
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text2text-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. -->
# bart-base-finetuned-pubmed
This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base)... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["pub_med_summarization_dataset"], "metrics": ["rouge"], "model-index": [{"name": "bart-base-finetuned-pubmed", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "pub_med_... | Kevincp560/bart-base-finetuned-pubmed | null | [
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#transformers #pytorch #tensorboard #bart #text2text-generation #generated_from_trainer #dataset-pub_med_summarization_dataset #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| bart-base-finetuned-pubmed
==========================
This model is a fine-tuned version of facebook/bart-base on the pub\_med\_summarization\_dataset dataset.
It achieves the following results on the evaluation set:
* Loss: 2.0277
* Rouge1: 9.3963
* Rouge2: 4.0473
* Rougel: 8.4526
* Rougelsum: 8.9659
* Gen Len: 20... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 2\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5\n* mixed\\_precis... | [
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text2text-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. -->
# bart-large-cnn-finetuned-pubmed
This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/facebook/... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["pub_med_summarization_dataset"], "metrics": ["rouge"], "model-index": [{"name": "bart-large-cnn-finetuned-pubmed", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "pub_med_su... | Kevincp560/bart-large-cnn-finetuned-pubmed | null | [
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"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bart #text2text-generation #generated_from_trainer #dataset-pub_med_summarization_dataset #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| bart-large-cnn-finetuned-pubmed
===============================
This model is a fine-tuned version of facebook/bart-large-cnn on the pub\_med\_summarization\_dataset dataset.
It achieves the following results on the evaluation set:
* Loss: 1.8416
* Rouge1: 40.4866
* Rouge2: 16.7472
* Rougel: 24.9831
* Rougelsum: 36... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 2\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5\n* mixed\\_precis... | [
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text2text-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. -->
# bart-large-finetuned-pubmed
This model is a fine-tuned version of [facebook/bart-large](https://huggingface.co/facebook/bart-lar... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["pub_med_summarization_dataset"], "metrics": ["rouge"], "model-index": [{"name": "bart-large-finetuned-pubmed", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "pub_med... | Kevincp560/bart-large-finetuned-pubmed | null | [
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"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bart #text2text-generation #generated_from_trainer #dataset-pub_med_summarization_dataset #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| bart-large-finetuned-pubmed
===========================
This model is a fine-tuned version of facebook/bart-large on the pub\_med\_summarization\_dataset dataset.
It achieves the following results on the evaluation set:
* Loss: 1.8135
* Rouge1: 10.946
* Rouge2: 5.0933
* Rougel: 9.5608
* Rougelsum: 10.4259
* Gen Len... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 2\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5\n* mixed\\_precis... | [
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text-generation | transformers |
# Model for chat bot to talk like tony stark | {"tags": ["conversational"]} | KhanAdeeb/model-tony-stark | null | [
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] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
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|
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question-answering | 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. -->
# bert-base-multilingual-cased-finetuned-squad
This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggin... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "bert-base-multilingual-cased-finetuned-squad", "results": []}]} | Khanh/bert-base-multilingual-cased-finetuned-squad | null | [
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"tensorboard",
"bert",
"question-answering",
"generated_from_trainer",
"license:apache-2.0",
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"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| bert-base-multilingual-cased-finetuned-squad
============================================
This model is a fine-tuned version of bert-base-multilingual-cased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4919
Model description
-----------------
More information needed
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\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",
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question-answering | 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. -->
# bert-base-multilingual-cased-finetuned-viquad
This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggi... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "bert-base-multilingual-cased-finetuned-viquad", "results": []}]} | Khanh/bert-base-multilingual-cased-finetuned-viquad | null | [
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"generated_from_trainer",
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] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| bert-base-multilingual-cased-finetuned-viquad
=============================================
This model is a fine-tuned version of bert-base-multilingual-cased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 1.9815
Model description
-----------------
More information needed
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\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",
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question-answering | 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-multilingual-cased-finetuned-squad
This model is a fine-tuned version of [distilbert-base-multilingual-cased](ht... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "distilbert-base-multilingual-cased-finetuned-squad", "results": []}]} | Khanh/distilbert-base-multilingual-cased-finetuned-squad | null | [
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"distilbert",
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"generated_from_trainer",
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] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| distilbert-base-multilingual-cased-finetuned-squad
==================================================
This model is a fine-tuned version of distilbert-base-multilingual-cased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6587
Model description
-----------------
More inf... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\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",
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question-answering | 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-multilingual-cased-finetuned-viquad
This model is a fine-tuned version of [distilbert-base-multilingual-cased](h... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "distilbert-base-multilingual-cased-finetuned-viquad", "results": []}]} | Khanh/distilbert-base-multilingual-cased-finetuned-viquad | null | [
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"tensorboard",
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"generated_from_trainer",
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] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| distilbert-base-multilingual-cased-finetuned-viquad
===================================================
This model is a fine-tuned version of distilbert-base-multilingual-cased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 3.4241
Model description
-----------------
More i... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
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question-answering | 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. -->
# xlm-roberta-base-finetuned-squad
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-bas... | {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "xlm-roberta-base-finetuned-squad", "results": []}]} | Khanh/xlm-roberta-base-finetuned-squad | null | [
"transformers",
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"tensorboard",
"xlm-roberta",
"question-answering",
"generated_from_trainer",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #xlm-roberta #question-answering #generated_from_trainer #license-mit #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-squad
================================
This model is a fine-tuned version of xlm-roberta-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5539
Model description
-----------------
More information needed
Intended uses & limitations
--------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
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question-answering | 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. -->
# xlm-roberta-base-finetuned-viquad
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-ba... | {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "xlm-roberta-base-finetuned-viquad", "results": []}]} | Khanh/xlm-roberta-base-finetuned-viquad | null | [
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"question-answering",
"generated_from_trainer",
"license:mit",
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] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #xlm-roberta #question-answering #generated_from_trainer #license-mit #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-viquad
=================================
This model is a fine-tuned version of xlm-roberta-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 2.3761
Model description
-----------------
More information needed
Intended uses & limitations
------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
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null | null | VietnameseQA model based on custom dataset. | {} | KhoiNXM/KhoiNXM_Vietnamese_QA | null | [
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#region-us
| VietnameseQA model based on custom dataset. | [] | [
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text-classification | transformers | # CLOG Assessment generator model
| {} | Khu1998/clog-assessment-model | null | [
"transformers",
"tf",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
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#transformers #tf #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us
| # CLOG Assessment generator model
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text-classification | 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-cola
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/di... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["matthews_correlation"], "model-index": [{"name": "distilbert-base-uncased-finetuned-cola", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "ar... | Kien/distilbert-base-uncased-finetuned-cola | null | [
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"dataset:glue",
"license:apache-2.0",
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"autotrain_compatible",
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"region:us"
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| distilbert-base-uncased-finetuned-cola
======================================
This model is a fine-tuned version of distilbert-base-uncased on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5327
* Matthews Correlation: 0.5233
Model description
-----------------
More informa... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
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text-classification | 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-cola
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/di... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["matthews_correlation"], "model_index": [{"name": "distilbert-base-uncased-finetuned-cola", "results": [{"task": {"name": "Text Classification", "type": "text-classification"}, "metric": {"name": "Matthews Correlation", "type": "matthews_correlat... | Kieran/distilbert-base-uncased-finetuned-cola | null | [
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| distilbert-base-uncased-finetuned-cola
======================================
This model is a fine-tuned version of distilbert-base-uncased on an unkown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1037
* Matthews Correlation: 0.9719
Model description
-----------------
More inform... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
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text-classification | 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-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["emotion"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "distilbert-base-uncased-finetuned-emotion", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "emotion", "type": "emotion... | Kiran146/distilbert-base-uncased-finetuned-emotion | null | [
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| distilbert-base-uncased-finetuned-emotion
=========================================
This model is a fine-tuned version of distilbert-base-uncased on the emotion dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2224
* Accuracy: 0.9225
* F1: 0.9228
Model description
-----------------
Mo... | [
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null | null | this is my ReadMe | {} | KiranM/someNewModel | null | [
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text2text-generation | transformers | ### 📝 Description
MBart for Russian summarization fine-tuned for **dialogues** summarization.
This model was firstly fine-tuned by [Ilya Gusev](https://hf.co/IlyaGusev) on [Gazeta dataset](https://huggingface.co/datasets/IlyaGusev/gazeta). We have **fine tuned** that model on [SamSum dataset](https://huggingface.co... | {"language": ["ru"], "license": "cc", "tags": ["mbart"], "datasets": ["IlyaGusev/gazeta", "samsum", "samsum_(translated_into_Russian)"], "inference": {"parameters": {"no_repeat_ngram_size": "4,", "num_beams": 5}}, "widget": [{"text": "\u0414\u0436\u0435\u0444\u0444: \u041c\u043e\u0433\u0443 \u043b\u0438 \u044f \u043e\u... | Kirili4ik/mbart_ruDialogSum | null | [
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| ### Description
MBart for Russian summarization fine-tuned for dialogues summarization.
This model was firstly fine-tuned by Ilya Gusev on Gazeta dataset. We have fine tuned that model on SamSum dataset translated to Russian using GoogleTranslateAPI
Moreover! We have implemented a ! telegram bot @summarization_bo... | [
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text-generation | transformers | ### 📝 Description
DialoGPT trained on Russian language and fine tuned on my telegram chat.
This model was created by [sberbank-ai](https://hf.co/sberbank-ai) and trained on Russian forums (see [Grossmend's model](https://hf.co/Grossmend/rudialogpt3_medium_based_on_gpt2)). You can find info about how it has been tra... | {"language": ["ru", "ru-RU"], "tags": ["conversational"]} | Kirili4ik/ruDialoGpt3-medium-finetuned-telegram | null | [
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| ### Description
DialoGPT trained on Russian language and fine tuned on my telegram chat.
This model was created by sberbank-ai and trained on Russian forums (see Grossmend's model). You can find info about how it has been trained on habr (in Russian). I have created a simple pipeline and fine tuned that model on my... | [
"### Description\n\nDialoGPT trained on Russian language and fine tuned on my telegram chat.\n\n\nThis model was created by sberbank-ai and trained on Russian forums (see Grossmend's model). You can find info about how it has been trained on habr (in Russian). I have created a simple pipeline and fine tuned that m... | [
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text2text-generation | transformers | T5-base fine-tuned on SQuAD and CoQA datasets for question generation
language:
- en-us
tags:
- question-generation
license:
- MIT
datasets:
- SQuAD 2.0
- CoQA | {} | Kithogue/T5_Question_Generation | null | [
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| T5-base fine-tuned on SQuAD and CoQA datasets for question generation
language:
- en-us
tags:
- question-generation
license:
- MIT
datasets:
- SQuAD 2.0
- CoQA | [] | [
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text-classification | 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. -->
# Wangchanberta-Depress-Finetuned
This model is a fine-tuned version of [airesearch/wangchanberta-base-att-spm-uncased](https://hu... | {"tags": ["generated_from_trainer"], "datasets": ["wisesight_sentiment"], "model-index": [{"name": "Wangchanberta-Depress-Finetuned", "results": []}]} | Kittipot/Wangchanberta-Depress-Finetuned | null | [
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| Wangchanberta-Depress-Finetuned
===============================
This model is a fine-tuned version of airesearch/wangchanberta-base-att-spm-uncased on the wisesight\_sentiment dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5910
Model description
-----------------
More information ne... | [
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text-generation | transformers |
# MORTY!!! | {"tags": ["conversational"]} | KnutZuidema/DialoGPT-small-morty | null | [
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text-generation | transformers | # GPT-J 6B - Janeway
## Model Description
GPT-J 6B-Janeway is a finetune created using EleutherAI's GPT-J 6B model.
## Training data
The training data contains around 2210 ebooks, mostly in the sci-fi and fantasy genres. The dataset is based on the same dataset used by GPT-Neo-2.7B-Picard, with 20% more data in var... | {"language": "en", "license": "mit"} | KoboldAI/GPT-J-6B-Janeway | null | [
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| # GPT-J 6B - Janeway
## Model Description
GPT-J 6B-Janeway is a finetune created using EleutherAI's GPT-J 6B model.
## Training data
The training data contains around 2210 ebooks, mostly in the sci-fi and fantasy genres. The dataset is based on the same dataset used by GPT-Neo-2.7B-Picard, with 20% more data in var... | [
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text-generation | transformers | # GPT-J 6B - Shinen
## Model Description
GPT-J 6B-Shinen is a finetune created using EleutherAI's GPT-J 6B model. Compared to GPT-Neo-2.7-Horni, this model is much heavier on the sexual content.
**Warning: THIS model is NOT suitable for use by minors. The model will output X-rated content.**
## Training data
The t... | {"language": "en", "license": "mit"} | KoboldAI/GPT-J-6B-Shinen | null | [
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| # GPT-J 6B - Shinen
## Model Description
GPT-J 6B-Shinen is a finetune created using EleutherAI's GPT-J 6B model. Compared to GPT-Neo-2.7-Horni, this model is much heavier on the sexual content.
Warning: THIS model is NOT suitable for use by minors. The model will output X-rated content.
## Training data
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text-generation | transformers | # Model Card for GPT-J-6B-Skein
# Model Details
## Model Description
- **Developed by:** KoboldAI
- **Shared by [Optional]:** KoboldAI
- **Model type:** Text Generation
- **Language(s) (NLP):** English
- **License:** Apache License 2.0
- **Related Models:** [GPT-J 6B](https://huggingface.co/EleutherAI/gpt-j-6B... | {"tags": ["text-generation"]} | KoboldAI/GPT-J-6B-Skein | null | [
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| # Model Card for GPT-J-6B-Skein
# Model Details
## Model Description
- Developed by: KoboldAI
- Shared by [Optional]: KoboldAI
- Model type: Text Generation
- Language(s) (NLP): English
- License: Apache License 2.0
- Related Models: GPT-J 6B
- Parent Model: GPT-J
- Resources for more information:
- Gi... | [
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text-generation | transformers | # GPT-Neo-125M-AID
This model was finetuned by Henk717 on Google Colab, it contains text adventure tuning and its the smallest 'Adventure' model of its size.
Because of its limited size the behavior is mostly suitable for testing text adventure gamemodes at fast speeds, for a coherent adventure you are better off using... | {} | KoboldAI/GPT-Neo-125M-AID | null | [
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#transformers #pytorch #gpt_neo #text-generation #autotrain_compatible #endpoints_compatible #region-us
| # GPT-Neo-125M-AID
This model was finetuned by Henk717 on Google Colab, it contains text adventure tuning and its the smallest 'Adventure' model of its size.
Because of its limited size the behavior is mostly suitable for testing text adventure gamemodes at fast speeds, for a coherent adventure you are better off using... | [
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text-generation | transformers | # GPT-Neo 2.7B - Janeway
## Model Description
GPT-Neo 2.7B-Janeway is a finetune created using EleutherAI's GPT-Neo 2.7B model.
## Training data
The training data contains around 2210 ebooks, mostly in the sci-fi and fantasy genres. The dataset is based on the same dataset used by GPT-Neo-2.7B-Picard, with 20% more... | {"language": "en", "license": "mit"} | KoboldAI/GPT-Neo-2.7B-Janeway | null | [
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| # GPT-Neo 2.7B - Janeway
## Model Description
GPT-Neo 2.7B-Janeway is a finetune created using EleutherAI's GPT-Neo 2.7B model.
## Training data
The training data contains around 2210 ebooks, mostly in the sci-fi and fantasy genres. The dataset is based on the same dataset used by GPT-Neo-2.7B-Picard, with 20% more... | [
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text-generation | transformers | # GPT-Neo 2.7B - Picard
## Model Description
GPT-Neo 2.7B-Picard is a finetune created using EleutherAI's GPT-Neo 2.7B model.
## Training data
The training data contains around 1800 ebooks, mostly in the sci-fi and fantasy genres.
### How to use
You can use this model directly with a pipeline for text generation. This ... | {"language": "en", "license": "mit"} | KoboldAI/GPT-Neo-2.7B-Picard | null | [
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#transformers #pytorch #safetensors #gpt_neo #text-generation #en #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us
| # GPT-Neo 2.7B - Picard
## Model Description
GPT-Neo 2.7B-Picard is a finetune created using EleutherAI's GPT-Neo 2.7B model.
## Training data
The training data contains around 1800 ebooks, mostly in the sci-fi and fantasy genres.
### How to use
You can use this model directly with a pipeline for text generation. This ... | [
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text-generation | transformers | # GPT-Neo 2.7B - Shinen
## Model Description
GPT-Neo 2.7B-Shinen is a finetune created using EleutherAI's GPT-Neo 2.7B model. Compared to GPT-Neo-2.7-Horni, this model is much heavier on the sexual content.
**Warning: THIS model is NOT suitable for use by minors. The model will output X-rated content.**
## Training da... | {"language": "en", "license": "mit"} | KoboldAI/GPT-Neo-2.7B-Shinen | null | [
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#transformers #pytorch #gpt_neo #text-generation #en #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us
| # GPT-Neo 2.7B - Shinen
## Model Description
GPT-Neo 2.7B-Shinen is a finetune created using EleutherAI's GPT-Neo 2.7B model. Compared to GPT-Neo-2.7-Horni, this model is much heavier on the sexual content.
Warning: THIS model is NOT suitable for use by minors. The model will output X-rated content.
## Training data
T... | [
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text-generation | transformers | This is a Hugging Face transformers-compatible conversion of the original dense 1.3B-parameter model from the paper "[Efficient Large Scale Language Modeling with Mixtures of Experts](https://arxiv.org/abs/2112.10684)" from Artetxe et al. Please refer to the original model card, which can be found at https://github.com... | {"language": "en"} | KoboldAI/fairseq-dense-1.3B | null | [
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| This is a Hugging Face transformers-compatible conversion of the original dense 1.3B-parameter model from the paper "Efficient Large Scale Language Modeling with Mixtures of Experts" from Artetxe et al. Please refer to the original model card, which can be found at URL
Open LLM Leaderboard Evaluation Results
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text-generation | transformers | This is a Hugging Face transformers-compatible conversion of the original dense 125M-parameter model from the paper "[Efficient Large Scale Language Modeling with Mixtures of Experts](https://arxiv.org/abs/2112.10684)" from Artetxe et al. Please refer to the original model card, which can be found at https://github.com... | {"language": "en"} | KoboldAI/fairseq-dense-125M | null | [
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#transformers #pytorch #safetensors #xglm #text-generation #en #arxiv-2112.10684 #autotrain_compatible #endpoints_compatible #has_space #region-us
| This is a Hugging Face transformers-compatible conversion of the original dense 125M-parameter model from the paper "Efficient Large Scale Language Modeling with Mixtures of Experts" from Artetxe et al. Please refer to the original model card, which can be found at URL
Open LLM Leaderboard Evaluation Results
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text-generation | transformers | This is a Hugging Face transformers-compatible conversion of the original dense 13B-parameter model from the paper "[Efficient Large Scale Language Modeling with Mixtures of Experts](https://arxiv.org/abs/2112.10684)" from Artetxe et al. Please refer to the original model card, which can be found at https://github.com/... | {"language": "en"} | KoboldAI/fairseq-dense-13B | null | [
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#transformers #pytorch #xglm #text-generation #en #arxiv-2112.10684 #autotrain_compatible #endpoints_compatible #has_space #region-us
| This is a Hugging Face transformers-compatible conversion of the original dense 13B-parameter model from the paper "Efficient Large Scale Language Modeling with Mixtures of Experts" from Artetxe et al. Please refer to the original model card, which can be found at URL
Open LLM Leaderboard Evaluation Results
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text-generation | transformers | # Fairseq-dense 2.7B - Janeway
## Model Description
Fairseq-dense 2.7B-Janeway is a finetune created using Fairseq's MoE dense model.
## Training data
The training data contains around 2210 ebooks, mostly in the sci-fi and fantasy genres. The dataset is identical as dataset used by GPT-Neo-2.7B-Janeway.
Some parts... | {"language": "en", "license": "mit"} | KoboldAI/fairseq-dense-2.7B-Janeway | null | [
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#transformers #pytorch #xglm #text-generation #en #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us
| # Fairseq-dense 2.7B - Janeway
## Model Description
Fairseq-dense 2.7B-Janeway is a finetune created using Fairseq's MoE dense model.
## Training data
The training data contains around 2210 ebooks, mostly in the sci-fi and fantasy genres. The dataset is identical as dataset used by GPT-Neo-2.7B-Janeway.
Some parts... | [
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text-generation | transformers | This is a Hugging Face transformers-compatible conversion of the original dense 2.7B-parameter model from the paper "[Efficient Large Scale Language Modeling with Mixtures of Experts](https://arxiv.org/abs/2112.10684)" from Artetxe et al. Please refer to the original model card, which can be found at https://github.com... | {"language": "en"} | KoboldAI/fairseq-dense-2.7B | null | [
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Open LLM Leaderboard Evaluation Results
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text-generation | transformers | This is a Hugging Face transformers-compatible conversion of the original dense 355M-parameter model from the paper "[Efficient Large Scale Language Modeling with Mixtures of Experts](https://arxiv.org/abs/2112.10684)" from Artetxe et al. Please refer to the original model card, which can be found at https://github.com... | {"language": "en"} | KoboldAI/fairseq-dense-355M | null | [
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| This is a Hugging Face transformers-compatible conversion of the original dense 355M-parameter model from the paper "Efficient Large Scale Language Modeling with Mixtures of Experts" from Artetxe et al. Please refer to the original model card, which can be found at URL
Open LLM Leaderboard Evaluation Results
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text-generation | transformers | This is a Hugging Face transformers-compatible conversion of the original dense 6.7B-parameter model from the paper "[Efficient Large Scale Language Modeling with Mixtures of Experts](https://arxiv.org/abs/2112.10684)" from Artetxe et al. Please refer to the original model card, which can be found at https://github.com... | {"language": "en"} | KoboldAI/fairseq-dense-6.7B | null | [
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"2112.10684"
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#transformers #pytorch #xglm #text-generation #en #arxiv-2112.10684 #autotrain_compatible #endpoints_compatible #has_space #region-us
| This is a Hugging Face transformers-compatible conversion of the original dense 6.7B-parameter model from the paper "Efficient Large Scale Language Modeling with Mixtures of Experts" from Artetxe et al. Please refer to the original model card, which can be found at URL
Open LLM Leaderboard Evaluation Results
========... | [] | [
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token-classification | transformers |
[](https://pypi.org/project/suparkanbun/)
# SuPar-Kanbun
Tokenizer, POS-Tagger and Dependency-Parser for Classical Chinese Texts (漢文/文言文) with [spaCy](https://spacy.io), [Transformers](https://huggingface.co/transformers/) and [SuPar](https://github.c... | {"language": ["lzh"], "license": "mit", "tags": ["classical chinese", "literary chinese", "ancient chinese", "token-classification", "pos"], "datasets": ["universal_dependencies"], "pipeline_tag": "token-classification", "widget": [{"text": "\u4e0d\u5165\u864e\u7a74\u4e0d\u5f97\u864e\u5b50"}]} | KoichiYasuoka/SuPar-Kanbun | null | [
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"ancient chinese",
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"lzh",
"dataset:universal_dependencies",
"license:mit",
"autotrain_compatible",
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"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
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|
 with spaCy, Transformers and SuPar.
## Basic usage
'URL()' has two options 'URL(BERT="roberta-classical-chinese-base-char",Danku=False)'. With the option 'Danku=True' the pipeline tries to... | [
"# SuPar-Kanbun\n\nTokenizer, POS-Tagger and Dependency-Parser for Classical Chinese Texts (漢文/文言文) with spaCy, Transformers and SuPar.",
"## Basic usage\n\n\n\n'URL()' has two options 'URL(BERT=\"roberta-classical-chinese-base-char\",Danku=False)'. With the option 'Danku=True' the pipeline tries to segment sente... | [
"TAGS\n#transformers #pytorch #roberta #token-classification #classical chinese #literary chinese #ancient chinese #pos #lzh #dataset-universal_dependencies #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# SuPar-Kanbun\n\nTokenizer, POS-Tagger and Dependency-Parser for Classical Chinese... | [
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fill-mask | transformers |
# bert-base-japanese-char-extended
## Model Description
This is a BERT model pre-trained on Japanese Wikipedia texts, derived from [bert-base-japanese-char-v2](https://huggingface.co/cl-tohoku/bert-base-japanese-char-v2). Character-embeddings are enhanced to include all 常用漢字/人名用漢字 characters using BertTokenizerFast.... | {"language": ["ja"], "license": "cc-by-sa-4.0", "tags": ["japanese", "masked-lm", "wikipedia"], "pipeline_tag": "fill-mask", "mask_token": "[MASK]", "widget": [{"text": "\u9178\u7d20\u30dc\u30f3\u30d9\u3092\u5145[MASK]\u3059\u308b\u3002"}]} | KoichiYasuoka/bert-base-japanese-char-extended | null | [
"transformers",
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"bert",
"fill-mask",
"japanese",
"masked-lm",
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"ja",
"license:cc-by-sa-4.0",
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"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"ja"
] | TAGS
#transformers #pytorch #bert #fill-mask #japanese #masked-lm #wikipedia #ja #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# bert-base-japanese-char-extended
## Model Description
This is a BERT model pre-trained on Japanese Wikipedia texts, derived from bert-base-japanese-char-v2. Character-embeddings are enhanced to include all 常用漢字/人名用漢字 characters using BertTokenizerFast. You can fine-tune 'bert-base-japanese-char-extended' for downs... | [
"# bert-base-japanese-char-extended",
"## Model Description\n\nThis is a BERT model pre-trained on Japanese Wikipedia texts, derived from bert-base-japanese-char-v2. Character-embeddings are enhanced to include all 常用漢字/人名用漢字 characters using BertTokenizerFast. You can fine-tune 'bert-base-japanese-char-extended'... | [
"TAGS\n#transformers #pytorch #bert #fill-mask #japanese #masked-lm #wikipedia #ja #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# bert-base-japanese-char-extended",
"## Model Description\n\nThis is a BERT model pre-trained on Japanese Wikipedia texts, derived fro... | [
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"TAGS\n#transformers #pytorch #bert #fill-mask #japanese #masked-lm #wikipedia #ja #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n# bert-base-japanese-char-extended## Model Description\n\nThis is a BERT model pre-trained on Japanese Wikipedia texts, derived from bert-base-... |
token-classification | transformers |
# bert-base-japanese-luw-upos
## Model Description
This is a BERT model pre-trained on Japanese Wikipedia texts for POS-tagging and dependency-parsing, derived from [bert-base-japanese-char-extended](https://huggingface.co/KoichiYasuoka/bert-base-japanese-char-extended). Every long-unit-word is tagged by [UPOS](http... | {"language": ["ja"], "license": "cc-by-sa-4.0", "tags": ["japanese", "token-classification", "pos", "wikipedia", "dependency-parsing"], "datasets": ["universal_dependencies"], "pipeline_tag": "token-classification", "widget": [{"text": "\u56fd\u5883\u306e\u9577\u3044\u30c8\u30f3\u30cd\u30eb\u3092\u629c\u3051\u308b\u306... | KoichiYasuoka/bert-base-japanese-luw-upos | null | [
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"ja",
"dataset:universal_dependencies",
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"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"ja"
] | TAGS
#transformers #pytorch #bert #token-classification #japanese #pos #wikipedia #dependency-parsing #ja #dataset-universal_dependencies #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us
|
# bert-base-japanese-luw-upos
## Model Description
This is a BERT model pre-trained on Japanese Wikipedia texts for POS-tagging and dependency-parsing, derived from bert-base-japanese-char-extended. Every long-unit-word is tagged by UPOS (Universal Part-Of-Speech) and FEATS.
## How to Use
or
## Reference
安岡孝... | [
"# bert-base-japanese-luw-upos",
"## Model Description\n\nThis is a BERT model pre-trained on Japanese Wikipedia texts for POS-tagging and dependency-parsing, derived from bert-base-japanese-char-extended. Every long-unit-word is tagged by UPOS (Universal Part-Of-Speech) and FEATS.",
"## How to Use\n\n\n\nor",
... | [
"TAGS\n#transformers #pytorch #bert #token-classification #japanese #pos #wikipedia #dependency-parsing #ja #dataset-universal_dependencies #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# bert-base-japanese-luw-upos",
"## Model Description\n\nThis is a BERT model pre-trained... | [
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"TAGS\n#transformers #pytorch #bert #token-classification #japanese #pos #wikipedia #dependency-parsing #ja #dataset-universal_dependencies #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us \n# bert-base-japanese-luw-upos## Model Description\n\nThis is a BERT model pre-trained on Japanese... |
token-classification | transformers |
# bert-base-japanese-unidic-luw-upos
## Model Description
This is a BERT model pre-trained on Japanese Wikipedia texts for POS-tagging and dependency-parsing, derived from [bert-base-japanese-v2](https://huggingface.co/cl-tohoku/bert-base-japanese-v2). Every long-unit-word is tagged by [UPOS](https://universaldepend... | {"language": ["ja"], "license": "cc-by-sa-4.0", "tags": ["japanese", "token-classification", "pos", "wikipedia", "dependency-parsing"], "datasets": ["universal_dependencies"], "pipeline_tag": "token-classification", "widget": [{"text": "\u56fd\u5883\u306e\u9577\u3044\u30c8\u30f3\u30cd\u30eb\u3092\u629c\u3051\u308b\u306... | KoichiYasuoka/bert-base-japanese-unidic-luw-upos | null | [
"transformers",
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"bert",
"token-classification",
"japanese",
"pos",
"wikipedia",
"dependency-parsing",
"ja",
"dataset:universal_dependencies",
"license:cc-by-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"ja"
] | TAGS
#transformers #pytorch #bert #token-classification #japanese #pos #wikipedia #dependency-parsing #ja #dataset-universal_dependencies #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us
|
# bert-base-japanese-unidic-luw-upos
## Model Description
This is a BERT model pre-trained on Japanese Wikipedia texts for POS-tagging and dependency-parsing, derived from bert-base-japanese-v2. Every long-unit-word is tagged by UPOS (Universal Part-Of-Speech).
## How to Use
or
fugashi and unidic-lite are req... | [
"# bert-base-japanese-unidic-luw-upos",
"## Model Description\n\nThis is a BERT model pre-trained on Japanese Wikipedia texts for POS-tagging and dependency-parsing, derived from bert-base-japanese-v2. Every long-unit-word is tagged by UPOS (Universal Part-Of-Speech).",
"## How to Use\n\n\n\nor\n\n\n\nfugashi a... | [
"TAGS\n#transformers #pytorch #bert #token-classification #japanese #pos #wikipedia #dependency-parsing #ja #dataset-universal_dependencies #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# bert-base-japanese-unidic-luw-upos",
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"TAGS\n#transformers #pytorch #bert #token-classification #japanese #pos #wikipedia #dependency-parsing #ja #dataset-universal_dependencies #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us \n# bert-base-japanese-unidic-luw-upos## Model Description\n\nThis is a BERT model pre-trained on J... |
token-classification | transformers |
# bert-base-japanese-upos
## Model Description
This is a BERT model pre-trained on Japanese Wikipedia texts for POS-tagging and dependency-parsing, derived from [bert-base-japanese-char-extended](https://huggingface.co/KoichiYasuoka/bert-base-japanese-char-extended). Every short-unit-word is tagged by [UPOS](https:/... | {"language": ["ja"], "license": "cc-by-sa-4.0", "tags": ["japanese", "token-classification", "pos", "wikipedia", "dependency-parsing"], "datasets": ["universal_dependencies"], "pipeline_tag": "token-classification", "widget": [{"text": "\u56fd\u5883\u306e\u9577\u3044\u30c8\u30f3\u30cd\u30eb\u3092\u629c\u3051\u308b\u306... | KoichiYasuoka/bert-base-japanese-upos | null | [
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"wikipedia",
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"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"ja"
] | TAGS
#transformers #pytorch #bert #token-classification #japanese #pos #wikipedia #dependency-parsing #ja #dataset-universal_dependencies #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# bert-base-japanese-upos
## Model Description
This is a BERT model pre-trained on Japanese Wikipedia texts for POS-tagging and dependency-parsing, derived from bert-base-japanese-char-extended. Every short-unit-word is tagged by UPOS (Universal Part-Of-Speech).
## How to Use
or
## See Also
esupar: Tokenizer... | [
"# bert-base-japanese-upos",
"## Model Description\n\nThis is a BERT model pre-trained on Japanese Wikipedia texts for POS-tagging and dependency-parsing, derived from bert-base-japanese-char-extended. Every short-unit-word is tagged by UPOS (Universal Part-Of-Speech).",
"## How to Use\n\n\n\nor",
"## See Als... | [
"TAGS\n#transformers #pytorch #bert #token-classification #japanese #pos #wikipedia #dependency-parsing #ja #dataset-universal_dependencies #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# bert-base-japanese-upos",
"## Model Description\n\nThis is a BERT model pre-... | [
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token-classification | transformers |
# bert-base-thai-upos
## Model Description
This is a BERT model pre-trained on Thai Wikipedia texts for POS-tagging and dependency-parsing, derived from [bert-base-th-cased](https://huggingface.co/Geotrend/bert-base-th-cased). Every word is tagged by [UPOS](https://universaldependencies.org/u/pos/) (Universal Part-O... | {"language": ["th"], "license": "apache-2.0", "tags": ["thai", "token-classification", "pos", "wikipedia", "dependency-parsing"], "datasets": ["universal_dependencies"], "pipeline_tag": "token-classification", "widget": [{"text": "\u0e2b\u0e25\u0e32\u0e22\u0e2b\u0e31\u0e27\u0e14\u0e35\u0e01\u0e27\u0e48\u0e32\u0e2b\u0e3... | KoichiYasuoka/bert-base-thai-upos | null | [
"transformers",
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"bert",
"token-classification",
"thai",
"pos",
"wikipedia",
"dependency-parsing",
"th",
"dataset:universal_dependencies",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"th"
] | TAGS
#transformers #pytorch #bert #token-classification #thai #pos #wikipedia #dependency-parsing #th #dataset-universal_dependencies #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# bert-base-thai-upos
## Model Description
This is a BERT model pre-trained on Thai Wikipedia texts for POS-tagging and dependency-parsing, derived from bert-base-th-cased. Every word is tagged by UPOS (Universal Part-Of-Speech).
## How to Use
or
## See Also
esupar: Tokenizer POS-tagger and Dependency-parser... | [
"# bert-base-thai-upos",
"## Model Description\n\nThis is a BERT model pre-trained on Thai Wikipedia texts for POS-tagging and dependency-parsing, derived from bert-base-th-cased. Every word is tagged by UPOS (Universal Part-Of-Speech).",
"## How to Use\n\n\n\nor",
"## See Also\n\nesupar: Tokenizer POS-tagger... | [
"TAGS\n#transformers #pytorch #bert #token-classification #thai #pos #wikipedia #dependency-parsing #th #dataset-universal_dependencies #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# bert-base-thai-upos",
"## Model Description\n\nThis is a BERT model pre-trained on Thai Wikip... | [
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"TAGS\n#transformers #pytorch #bert #token-classification #thai #pos #wikipedia #dependency-parsing #th #dataset-universal_dependencies #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n# bert-base-thai-upos## Model Description\n\nThis is a BERT model pre-trained on Thai Wikipedia texts f... |
fill-mask | transformers |
# bert-large-japanese-char-extended
## Model Description
This is a BERT model pre-trained on Japanese Wikipedia texts, derived from [bert-large-japanese-char](https://huggingface.co/cl-tohoku/bert-large-japanese-char). Character-embeddings are enhanced to include all 常用漢字/人名用漢字 characters using BertTokenizerFast. Yo... | {"language": ["ja"], "license": "cc-by-sa-4.0", "tags": ["japanese", "masked-lm", "wikipedia"], "pipeline_tag": "fill-mask", "mask_token": "[MASK]", "widget": [{"text": "\u9178\u7d20\u30dc\u30f3\u30d9\u3092\u5145[MASK]\u3059\u308b\u3002"}]} | KoichiYasuoka/bert-large-japanese-char-extended | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"japanese",
"masked-lm",
"wikipedia",
"ja",
"license:cc-by-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"ja"
] | TAGS
#transformers #pytorch #bert #fill-mask #japanese #masked-lm #wikipedia #ja #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us
|
# bert-large-japanese-char-extended
## Model Description
This is a BERT model pre-trained on Japanese Wikipedia texts, derived from bert-large-japanese-char. Character-embeddings are enhanced to include all 常用漢字/人名用漢字 characters using BertTokenizerFast. You can fine-tune 'bert-large-japanese-char-extended' for downs... | [
"# bert-large-japanese-char-extended",
"## Model Description\n\nThis is a BERT model pre-trained on Japanese Wikipedia texts, derived from bert-large-japanese-char. Character-embeddings are enhanced to include all 常用漢字/人名用漢字 characters using BertTokenizerFast. You can fine-tune 'bert-large-japanese-char-extended'... | [
"TAGS\n#transformers #pytorch #bert #fill-mask #japanese #masked-lm #wikipedia #ja #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# bert-large-japanese-char-extended",
"## Model Description\n\nThis is a BERT model pre-trained on Japanese Wikipedia texts, derived from bert-lar... | [
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"TAGS\n#transformers #pytorch #bert #fill-mask #japanese #masked-lm #wikipedia #ja #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us \n# bert-large-japanese-char-extended## Model Description\n\nThis is a BERT model pre-trained on Japanese Wikipedia texts, derived from bert-large-japanese-... |
token-classification | transformers |
# bert-large-japanese-luw-upos
## Model Description
This is a BERT model pre-trained on Japanese Wikipedia texts for POS-tagging and dependency-parsing, derived from [bert-large-japanese-char-extended](https://huggingface.co/KoichiYasuoka/bert-large-japanese-char-extended). Every long-unit-word is tagged by [UPOS](h... | {"language": ["ja"], "license": "cc-by-sa-4.0", "tags": ["japanese", "token-classification", "pos", "wikipedia", "dependency-parsing"], "datasets": ["universal_dependencies"], "pipeline_tag": "token-classification", "widget": [{"text": "\u56fd\u5883\u306e\u9577\u3044\u30c8\u30f3\u30cd\u30eb\u3092\u629c\u3051\u308b\u306... | KoichiYasuoka/bert-large-japanese-luw-upos | null | [
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"bert",
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"japanese",
"pos",
"wikipedia",
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"ja",
"dataset:universal_dependencies",
"license:cc-by-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"ja"
] | TAGS
#transformers #pytorch #bert #token-classification #japanese #pos #wikipedia #dependency-parsing #ja #dataset-universal_dependencies #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us
|
# bert-large-japanese-luw-upos
## Model Description
This is a BERT model pre-trained on Japanese Wikipedia texts for POS-tagging and dependency-parsing, derived from bert-large-japanese-char-extended. Every long-unit-word is tagged by UPOS (Universal Part-Of-Speech) and FEATS.
## How to Use
or
## Reference
安... | [
"# bert-large-japanese-luw-upos",
"## Model Description\n\nThis is a BERT model pre-trained on Japanese Wikipedia texts for POS-tagging and dependency-parsing, derived from bert-large-japanese-char-extended. Every long-unit-word is tagged by UPOS (Universal Part-Of-Speech) and FEATS.",
"## How to Use\n\n\n\nor"... | [
"TAGS\n#transformers #pytorch #bert #token-classification #japanese #pos #wikipedia #dependency-parsing #ja #dataset-universal_dependencies #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# bert-large-japanese-luw-upos",
"## Model Description\n\nThis is a BERT model pre-traine... | [
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"TAGS\n#transformers #pytorch #bert #token-classification #japanese #pos #wikipedia #dependency-parsing #ja #dataset-universal_dependencies #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us \n# bert-large-japanese-luw-upos## Model Description\n\nThis is a BERT model pre-trained on Japanes... |
token-classification | transformers |
# bert-large-japanese-unidic-luw-upos
## Model Description
This is a BERT model pre-trained on Japanese Wikipedia texts for POS-tagging and dependency-parsing, derived from [bert-large-japanese](https://huggingface.co/cl-tohoku/bert-large-japanese). Every long-unit-word is tagged by [UPOS](https://universaldependenc... | {"language": ["ja"], "license": "cc-by-sa-4.0", "tags": ["japanese", "token-classification", "pos", "wikipedia", "dependency-parsing"], "datasets": ["universal_dependencies"], "pipeline_tag": "token-classification", "widget": [{"text": "\u56fd\u5883\u306e\u9577\u3044\u30c8\u30f3\u30cd\u30eb\u3092\u629c\u3051\u308b\u306... | KoichiYasuoka/bert-large-japanese-unidic-luw-upos | null | [
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"bert",
"token-classification",
"japanese",
"pos",
"wikipedia",
"dependency-parsing",
"ja",
"dataset:universal_dependencies",
"license:cc-by-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"ja"
] | TAGS
#transformers #pytorch #bert #token-classification #japanese #pos #wikipedia #dependency-parsing #ja #dataset-universal_dependencies #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us
|
# bert-large-japanese-unidic-luw-upos
## Model Description
This is a BERT model pre-trained on Japanese Wikipedia texts for POS-tagging and dependency-parsing, derived from bert-large-japanese. Every long-unit-word is tagged by UPOS (Universal Part-Of-Speech).
## How to Use
or
fugashi and unidic-lite are requ... | [
"# bert-large-japanese-unidic-luw-upos",
"## Model Description\n\nThis is a BERT model pre-trained on Japanese Wikipedia texts for POS-tagging and dependency-parsing, derived from bert-large-japanese. Every long-unit-word is tagged by UPOS (Universal Part-Of-Speech).",
"## How to Use\n\n\n\nor\n\n\n\nfugashi an... | [
"TAGS\n#transformers #pytorch #bert #token-classification #japanese #pos #wikipedia #dependency-parsing #ja #dataset-universal_dependencies #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# bert-large-japanese-unidic-luw-upos",
"## Model Description\n\nThis is a BERT model pre... | [
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"TAGS\n#transformers #pytorch #bert #token-classification #japanese #pos #wikipedia #dependency-parsing #ja #dataset-universal_dependencies #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us \n# bert-large-japanese-unidic-luw-upos## Model Description\n\nThis is a BERT model pre-trained on ... |
token-classification | transformers |
# bert-large-japanese-upos
## Model Description
This is a BERT model pre-trained on Japanese Wikipedia texts for POS-tagging and dependency-parsing, derived from [bert-large-japanese-char-extended](https://huggingface.co/KoichiYasuoka/bert-large-japanese-char-extended). Every short-unit-word is tagged by [UPOS](http... | {"language": ["ja"], "license": "cc-by-sa-4.0", "tags": ["japanese", "token-classification", "pos", "wikipedia", "dependency-parsing"], "datasets": ["universal_dependencies"], "pipeline_tag": "token-classification", "widget": [{"text": "\u56fd\u5883\u306e\u9577\u3044\u30c8\u30f3\u30cd\u30eb\u3092\u629c\u3051\u308b\u306... | KoichiYasuoka/bert-large-japanese-upos | null | [
"transformers",
"pytorch",
"bert",
"token-classification",
"japanese",
"pos",
"wikipedia",
"dependency-parsing",
"ja",
"dataset:universal_dependencies",
"license:cc-by-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"ja"
] | TAGS
#transformers #pytorch #bert #token-classification #japanese #pos #wikipedia #dependency-parsing #ja #dataset-universal_dependencies #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us
|
# bert-large-japanese-upos
## Model Description
This is a BERT model pre-trained on Japanese Wikipedia texts for POS-tagging and dependency-parsing, derived from bert-large-japanese-char-extended. Every short-unit-word is tagged by UPOS (Universal Part-Of-Speech).
## How to Use
or
## See Also
esupar: Tokeniz... | [
"# bert-large-japanese-upos",
"## Model Description\n\nThis is a BERT model pre-trained on Japanese Wikipedia texts for POS-tagging and dependency-parsing, derived from bert-large-japanese-char-extended. Every short-unit-word is tagged by UPOS (Universal Part-Of-Speech).",
"## How to Use\n\n\n\nor",
"## See A... | [
"TAGS\n#transformers #pytorch #bert #token-classification #japanese #pos #wikipedia #dependency-parsing #ja #dataset-universal_dependencies #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# bert-large-japanese-upos",
"## Model Description\n\nThis is a BERT model pre-trained on... | [
62,
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"TAGS\n#transformers #pytorch #bert #token-classification #japanese #pos #wikipedia #dependency-parsing #ja #dataset-universal_dependencies #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us \n# bert-large-japanese-upos## Model Description\n\nThis is a BERT model pre-trained on Japanese Wi... |
token-classification | transformers |
# chinese-bert-wwm-ext-upos
## Model Description
This is a BERT model pre-trained on Chinese Wikipedia texts (both simplified and traditional) for POS-tagging and dependency-parsing, derived from [chinese-bert-wwm-ext](https://huggingface.co/hfl/chinese-bert-wwm-ext). Every word is tagged by [UPOS](https://universal... | {"language": ["zh"], "license": "apache-2.0", "tags": ["chinese", "token-classification", "pos", "wikipedia", "dependency-parsing"], "datasets": ["universal_dependencies"], "pipeline_tag": "token-classification"} | KoichiYasuoka/chinese-bert-wwm-ext-upos | null | [
"transformers",
"pytorch",
"bert",
"token-classification",
"chinese",
"pos",
"wikipedia",
"dependency-parsing",
"zh",
"dataset:universal_dependencies",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"zh"
] | TAGS
#transformers #pytorch #bert #token-classification #chinese #pos #wikipedia #dependency-parsing #zh #dataset-universal_dependencies #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# chinese-bert-wwm-ext-upos
## Model Description
This is a BERT model pre-trained on Chinese Wikipedia texts (both simplified and traditional) for POS-tagging and dependency-parsing, derived from chinese-bert-wwm-ext. Every word is tagged by UPOS (Universal Part-Of-Speech).
## How to Use
or
## See Also
esupa... | [
"# chinese-bert-wwm-ext-upos",
"## Model Description\n\nThis is a BERT model pre-trained on Chinese Wikipedia texts (both simplified and traditional) for POS-tagging and dependency-parsing, derived from chinese-bert-wwm-ext. Every word is tagged by UPOS (Universal Part-Of-Speech).",
"## How to Use\n\n\n\nor",
... | [
"TAGS\n#transformers #pytorch #bert #token-classification #chinese #pos #wikipedia #dependency-parsing #zh #dataset-universal_dependencies #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# chinese-bert-wwm-ext-upos",
"## Model Description\n\nThis is a BERT model pre-trained on C... | [
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"TAGS\n#transformers #pytorch #bert #token-classification #chinese #pos #wikipedia #dependency-parsing #zh #dataset-universal_dependencies #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n# chinese-bert-wwm-ext-upos## Model Description\n\nThis is a BERT model pre-trained on Chinese Wikip... |
token-classification | transformers |
# chinese-roberta-base-upos
## Model Description
This is a BERT model pre-trained on Chinese Wikipedia texts (both simplified and traditional) for POS-tagging and dependency-parsing, derived from [chinese-roberta-wwm-ext](https://huggingface.co/hfl/chinese-roberta-wwm-ext). Every word is tagged by [UPOS](https://uni... | {"language": ["zh"], "license": "apache-2.0", "tags": ["chinese", "token-classification", "pos", "wikipedia", "dependency-parsing"], "datasets": ["universal_dependencies"], "pipeline_tag": "token-classification"} | KoichiYasuoka/chinese-roberta-base-upos | null | [
"transformers",
"pytorch",
"bert",
"token-classification",
"chinese",
"pos",
"wikipedia",
"dependency-parsing",
"zh",
"dataset:universal_dependencies",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"zh"
] | TAGS
#transformers #pytorch #bert #token-classification #chinese #pos #wikipedia #dependency-parsing #zh #dataset-universal_dependencies #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# chinese-roberta-base-upos
## Model Description
This is a BERT model pre-trained on Chinese Wikipedia texts (both simplified and traditional) for POS-tagging and dependency-parsing, derived from chinese-roberta-wwm-ext. Every word is tagged by UPOS (Universal Part-Of-Speech).
## How to Use
or
## See Also
es... | [
"# chinese-roberta-base-upos",
"## Model Description\n\nThis is a BERT model pre-trained on Chinese Wikipedia texts (both simplified and traditional) for POS-tagging and dependency-parsing, derived from chinese-roberta-wwm-ext. Every word is tagged by UPOS (Universal Part-Of-Speech).",
"## How to Use\n\n\n\nor"... | [
"TAGS\n#transformers #pytorch #bert #token-classification #chinese #pos #wikipedia #dependency-parsing #zh #dataset-universal_dependencies #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# chinese-roberta-base-upos",
"## Model Description\n\nThis is a BERT model pre-trained on C... | [
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6,
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"TAGS\n#transformers #pytorch #bert #token-classification #chinese #pos #wikipedia #dependency-parsing #zh #dataset-universal_dependencies #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n# chinese-roberta-base-upos## Model Description\n\nThis is a BERT model pre-trained on Chinese Wikip... |
token-classification | transformers |
# chinese-roberta-large-upos
## Model Description
This is a BERT model pre-trained on Chinese Wikipedia texts (both simplified and traditional) for POS-tagging and dependency-parsing, derived from [chinese-roberta-wwm-ext-large](https://huggingface.co/hfl/chinese-roberta-wwm-ext-large). Every word is tagged by [UPOS... | {"language": ["zh"], "license": "apache-2.0", "tags": ["chinese", "token-classification", "pos", "wikipedia", "dependency-parsing"], "datasets": ["universal_dependencies"], "pipeline_tag": "token-classification"} | KoichiYasuoka/chinese-roberta-large-upos | null | [
"transformers",
"pytorch",
"bert",
"token-classification",
"chinese",
"pos",
"wikipedia",
"dependency-parsing",
"zh",
"dataset:universal_dependencies",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"zh"
] | TAGS
#transformers #pytorch #bert #token-classification #chinese #pos #wikipedia #dependency-parsing #zh #dataset-universal_dependencies #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# chinese-roberta-large-upos
## Model Description
This is a BERT model pre-trained on Chinese Wikipedia texts (both simplified and traditional) for POS-tagging and dependency-parsing, derived from chinese-roberta-wwm-ext-large. Every word is tagged by UPOS (Universal Part-Of-Speech).
## How to Use
or
## See A... | [
"# chinese-roberta-large-upos",
"## Model Description\n\nThis is a BERT model pre-trained on Chinese Wikipedia texts (both simplified and traditional) for POS-tagging and dependency-parsing, derived from chinese-roberta-wwm-ext-large. Every word is tagged by UPOS (Universal Part-Of-Speech).",
"## How to Use\n\n... | [
"TAGS\n#transformers #pytorch #bert #token-classification #chinese #pos #wikipedia #dependency-parsing #zh #dataset-universal_dependencies #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# chinese-roberta-large-upos",
"## Model Description\n\nThis is a BERT model pre-trained on ... | [
59,
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6,
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] | [
"TAGS\n#transformers #pytorch #bert #token-classification #chinese #pos #wikipedia #dependency-parsing #zh #dataset-universal_dependencies #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n# chinese-roberta-large-upos## Model Description\n\nThis is a BERT model pre-trained on Chinese Wiki... |
token-classification | transformers |
# roberta-base-english-upos
## Model Description
This is a RoBERTa model pre-trained with [UD_English](https://universaldependencies.org/en/) for POS-tagging and dependency-parsing, derived from [roberta-base](https://huggingface.co/roberta-base). Every word is tagged by [UPOS](https://universaldependencies.org/u/po... | {"language": ["en"], "license": "cc-by-sa-4.0", "tags": ["english", "token-classification", "pos", "dependency-parsing"], "datasets": ["universal_dependencies"], "pipeline_tag": "token-classification"} | KoichiYasuoka/roberta-base-english-upos | null | [
"transformers",
"pytorch",
"roberta",
"token-classification",
"english",
"pos",
"dependency-parsing",
"en",
"dataset:universal_dependencies",
"license:cc-by-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #roberta #token-classification #english #pos #dependency-parsing #en #dataset-universal_dependencies #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us
|
# roberta-base-english-upos
## Model Description
This is a RoBERTa model pre-trained with UD_English for POS-tagging and dependency-parsing, derived from roberta-base. Every word is tagged by UPOS (Universal Part-Of-Speech).
## How to Use
or
## See Also
esupar: Tokenizer POS-tagger and Dependency-parser with... | [
"# roberta-base-english-upos",
"## Model Description\n\nThis is a RoBERTa model pre-trained with UD_English for POS-tagging and dependency-parsing, derived from roberta-base. Every word is tagged by UPOS (Universal Part-Of-Speech).",
"## How to Use\n\n\n\nor",
"## See Also\n\nesupar: Tokenizer POS-tagger and ... | [
"TAGS\n#transformers #pytorch #roberta #token-classification #english #pos #dependency-parsing #en #dataset-universal_dependencies #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# roberta-base-english-upos",
"## Model Description\n\nThis is a RoBERTa model pre-trained with UD... | [
60,
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6,
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"TAGS\n#transformers #pytorch #roberta #token-classification #english #pos #dependency-parsing #en #dataset-universal_dependencies #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us \n# roberta-base-english-upos## Model Description\n\nThis is a RoBERTa model pre-trained with UD_English for... |
fill-mask | transformers |
# roberta-base-japanese-aozora-char
## Model Description
This is a RoBERTa model pre-trained on 青空文庫 texts with character tokenizer. You can fine-tune `roberta-base-japanese-aozora-char` for downstream tasks, such as [POS-tagging](https://huggingface.co/KoichiYasuoka/roberta-base-japanese-char-luw-upos), [dependency... | {"language": ["ja"], "license": "cc-by-sa-4.0", "tags": ["japanese", "masked-lm"], "pipeline_tag": "fill-mask", "mask_token": "[MASK]", "widget": [{"text": "\u65e5\u672c\u306b\u7740\u3044\u305f\u3089[MASK]\u3092\u8a2a\u306d\u306a\u3055\u3044\u3002"}]} | KoichiYasuoka/roberta-base-japanese-aozora-char | null | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"japanese",
"masked-lm",
"ja",
"license:cc-by-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"ja"
] | TAGS
#transformers #pytorch #roberta #fill-mask #japanese #masked-lm #ja #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us
|
# roberta-base-japanese-aozora-char
## Model Description
This is a RoBERTa model pre-trained on 青空文庫 texts with character tokenizer. You can fine-tune 'roberta-base-japanese-aozora-char' for downstream tasks, such as POS-tagging, dependency-parsing, and so on.
## How to Use
## Reference
安岡孝一: Transformersと国語研長単... | [
"# roberta-base-japanese-aozora-char",
"## Model Description\n\nThis is a RoBERTa model pre-trained on 青空文庫 texts with character tokenizer. You can fine-tune 'roberta-base-japanese-aozora-char' for downstream tasks, such as POS-tagging, dependency-parsing, and so on.",
"## How to Use",
"## Reference\n\n安岡孝一: ... | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #japanese #masked-lm #ja #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# roberta-base-japanese-aozora-char",
"## Model Description\n\nThis is a RoBERTa model pre-trained on 青空文庫 texts with character tokenizer. You can fine-tu... | [
49,
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"TAGS\n#transformers #pytorch #roberta #fill-mask #japanese #masked-lm #ja #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us \n# roberta-base-japanese-aozora-char## Model Description\n\nThis is a RoBERTa model pre-trained on 青空文庫 texts with character tokenizer. You can fine-tune 'roberta-... |
fill-mask | transformers |
# roberta-base-japanese-aozora
## Model Description
This is a RoBERTa model pre-trained on 青空文庫 texts with [Japanese-LUW-Tokenizer](https://github.com/KoichiYasuoka/Japanese-LUW-Tokenizer). You can fine-tune `roberta-base-japanese-aozora` for downstream tasks, such as [POS-tagging](https://huggingface.co/KoichiYasuo... | {"language": ["ja"], "license": "cc-by-sa-4.0", "tags": ["japanese", "masked-lm"], "pipeline_tag": "fill-mask", "mask_token": "[MASK]", "widget": [{"text": "\u65e5\u672c\u306b\u7740\u3044\u305f\u3089[MASK]\u3092\u8a2a\u306d\u306a\u3055\u3044\u3002"}]} | KoichiYasuoka/roberta-base-japanese-aozora | null | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"japanese",
"masked-lm",
"ja",
"license:cc-by-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"ja"
] | TAGS
#transformers #pytorch #roberta #fill-mask #japanese #masked-lm #ja #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us
|
# roberta-base-japanese-aozora
## Model Description
This is a RoBERTa model pre-trained on 青空文庫 texts with Japanese-LUW-Tokenizer. You can fine-tune 'roberta-base-japanese-aozora' for downstream tasks, such as POS-tagging, dependency-parsing, and so on.
## How to Use
## Reference
安岡孝一: Transformersと国語研長単位による日本語... | [
"# roberta-base-japanese-aozora",
"## Model Description\n\nThis is a RoBERTa model pre-trained on 青空文庫 texts with Japanese-LUW-Tokenizer. You can fine-tune 'roberta-base-japanese-aozora' for downstream tasks, such as POS-tagging, dependency-parsing, and so on.",
"## How to Use",
"## Reference\n\n安岡孝一: Transfo... | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #japanese #masked-lm #ja #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# roberta-base-japanese-aozora",
"## Model Description\n\nThis is a RoBERTa model pre-trained on 青空文庫 texts with Japanese-LUW-Tokenizer. You can fine-tune... | [
49,
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5,
72
] | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #japanese #masked-lm #ja #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us \n# roberta-base-japanese-aozora## Model Description\n\nThis is a RoBERTa model pre-trained on 青空文庫 texts with Japanese-LUW-Tokenizer. You can fine-tune 'roberta-ba... |
token-classification | transformers |
# roberta-base-japanese-char-luw-upos
## Model Description
This is a RoBERTa model pre-trained on 青空文庫 texts for POS-tagging and dependency-parsing, derived from [roberta-base-japanese-aozora-char](https://huggingface.co/KoichiYasuoka/roberta-base-japanese-aozora-char). Every long-unit-word is tagged by [UPOS](https... | {"language": ["ja"], "license": "cc-by-sa-4.0", "tags": ["japanese", "token-classification", "pos", "dependency-parsing"], "datasets": ["universal_dependencies"], "pipeline_tag": "token-classification", "widget": [{"text": "\u56fd\u5883\u306e\u9577\u3044\u30c8\u30f3\u30cd\u30eb\u3092\u629c\u3051\u308b\u3068\u96ea\u56fd... | KoichiYasuoka/roberta-base-japanese-char-luw-upos | null | [
"transformers",
"pytorch",
"roberta",
"token-classification",
"japanese",
"pos",
"dependency-parsing",
"ja",
"dataset:universal_dependencies",
"license:cc-by-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"ja"
] | TAGS
#transformers #pytorch #roberta #token-classification #japanese #pos #dependency-parsing #ja #dataset-universal_dependencies #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us
|
# roberta-base-japanese-char-luw-upos
## Model Description
This is a RoBERTa model pre-trained on 青空文庫 texts for POS-tagging and dependency-parsing, derived from roberta-base-japanese-aozora-char. Every long-unit-word is tagged by UPOS (Universal Part-Of-Speech) and FEATS.
## How to Use
or
## Reference
安岡孝一:... | [
"# roberta-base-japanese-char-luw-upos",
"## Model Description\n\nThis is a RoBERTa model pre-trained on 青空文庫 texts for POS-tagging and dependency-parsing, derived from roberta-base-japanese-aozora-char. Every long-unit-word is tagged by UPOS (Universal Part-Of-Speech) and FEATS.",
"## How to Use\n\n\n\nor",
... | [
"TAGS\n#transformers #pytorch #roberta #token-classification #japanese #pos #dependency-parsing #ja #dataset-universal_dependencies #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# roberta-base-japanese-char-luw-upos",
"## Model Description\n\nThis is a RoBERTa model pre-trai... | [
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"TAGS\n#transformers #pytorch #roberta #token-classification #japanese #pos #dependency-parsing #ja #dataset-universal_dependencies #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us \n# roberta-base-japanese-char-luw-upos## Model Description\n\nThis is a RoBERTa model pre-trained on 青空文庫 ... |
token-classification | transformers |
# roberta-base-japanese-luw-upos
## Model Description
This is a RoBERTa model pre-trained on 青空文庫 texts for POS-tagging and dependency-parsing, derived from [roberta-base-japanese-aozora](https://huggingface.co/KoichiYasuoka/roberta-base-japanese-aozora). Every long-unit-word is tagged by [UPOS](https://universaldep... | {"language": ["ja"], "license": "cc-by-sa-4.0", "tags": ["japanese", "token-classification", "pos", "dependency-parsing"], "datasets": ["universal_dependencies"], "pipeline_tag": "token-classification", "widget": [{"text": "\u56fd\u5883\u306e\u9577\u3044\u30c8\u30f3\u30cd\u30eb\u3092\u629c\u3051\u308b\u3068\u96ea\u56fd... | KoichiYasuoka/roberta-base-japanese-luw-upos | null | [
"transformers",
"pytorch",
"roberta",
"token-classification",
"japanese",
"pos",
"dependency-parsing",
"ja",
"dataset:universal_dependencies",
"license:cc-by-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"ja"
] | TAGS
#transformers #pytorch #roberta #token-classification #japanese #pos #dependency-parsing #ja #dataset-universal_dependencies #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us
|
# roberta-base-japanese-luw-upos
## Model Description
This is a RoBERTa model pre-trained on 青空文庫 texts for POS-tagging and dependency-parsing, derived from roberta-base-japanese-aozora. Every long-unit-word is tagged by UPOS (Universal Part-Of-Speech).
## How to Use
or
## Reference
安岡孝一: Transformersと国語研長単... | [
"# roberta-base-japanese-luw-upos",
"## Model Description\n\nThis is a RoBERTa model pre-trained on 青空文庫 texts for POS-tagging and dependency-parsing, derived from roberta-base-japanese-aozora. Every long-unit-word is tagged by UPOS (Universal Part-Of-Speech).",
"## How to Use\n\n\n\nor",
"## Reference\n\n安岡孝... | [
"TAGS\n#transformers #pytorch #roberta #token-classification #japanese #pos #dependency-parsing #ja #dataset-universal_dependencies #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# roberta-base-japanese-luw-upos",
"## Model Description\n\nThis is a RoBERTa model pre-trained o... | [
60,
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62,
6,
72,
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"TAGS\n#transformers #pytorch #roberta #token-classification #japanese #pos #dependency-parsing #ja #dataset-universal_dependencies #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us \n# roberta-base-japanese-luw-upos## Model Description\n\nThis is a RoBERTa model pre-trained on 青空文庫 texts... |
token-classification | transformers |
# roberta-base-thai-char-upos
## Model Description
This is a RoBERTa model pre-trained on Thai Wikipedia texts for POS-tagging and dependency-parsing, derived from [roberta-base-thai-char](https://huggingface.co/KoichiYasuoka/roberta-base-thai-char). Every word is tagged by [UPOS](https://universaldependencies.org/u... | {"language": ["th"], "license": "apache-2.0", "tags": ["thai", "token-classification", "pos", "wikipedia", "dependency-parsing"], "datasets": ["universal_dependencies"], "pipeline_tag": "token-classification", "widget": [{"text": "\u0e2b\u0e25\u0e32\u0e22\u0e2b\u0e31\u0e27\u0e14\u0e35\u0e01\u0e27\u0e48\u0e32\u0e2b\u0e3... | KoichiYasuoka/roberta-base-thai-char-upos | null | [
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|
# roberta-base-thai-char-upos
## Model Description
This is a RoBERTa model pre-trained on Thai Wikipedia texts for POS-tagging and dependency-parsing, derived from roberta-base-thai-char. Every word is tagged by UPOS (Universal Part-Of-Speech).
## How to Use
or
## See Also
esupar: Tokenizer POS-tagger and De... | [
"# roberta-base-thai-char-upos",
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"## How to Use\n\n\n\nor",
"## See Also\n\nesupar: Token... | [
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fill-mask | transformers |
# roberta-base-thai-char
## Model Description
This is a RoBERTa model pre-trained on Thai Wikipedia texts with character-wise embeddings to use BertTokenizerFast. You can fine-tune `roberta-base-thai-char` for downstream tasks, such as [POS-tagging](https://huggingface.co/KoichiYasuoka/roberta-base-thai-char-upos), ... | {"language": ["th"], "license": "apache-2.0", "tags": ["thai", "masked-lm", "wikipedia"], "pipeline_tag": "fill-mask", "mask_token": "[MASK]"} | KoichiYasuoka/roberta-base-thai-char | null | [
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|
# roberta-base-thai-char
## Model Description
This is a RoBERTa model pre-trained on Thai Wikipedia texts with character-wise embeddings to use BertTokenizerFast. You can fine-tune 'roberta-base-thai-char' for downstream tasks, such as POS-tagging, dependency-parsing, and so on.
## How to Use
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token-classification | transformers |
# roberta-base-thai-spm-upos
## Model Description
This is a RoBERTa model pre-trained on Thai Wikipedia texts for POS-tagging and dependency-parsing, derived from [roberta-base-thai-spm](https://huggingface.co/KoichiYasuoka/roberta-base-thai-spm). Every word is tagged by [UPOS](https://universaldependencies.org/u/po... | {"language": ["th"], "license": "apache-2.0", "tags": ["thai", "token-classification", "pos", "wikipedia", "dependency-parsing"], "datasets": ["universal_dependencies"], "pipeline_tag": "token-classification", "widget": [{"text": "\u0e2b\u0e25\u0e32\u0e22\u0e2b\u0e31\u0e27\u0e14\u0e35\u0e01\u0e27\u0e48\u0e32\u0e2b\u0e3... | KoichiYasuoka/roberta-base-thai-spm-upos | null | [
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|
# roberta-base-thai-spm-upos
## Model Description
This is a RoBERTa model pre-trained on Thai Wikipedia texts for POS-tagging and dependency-parsing, derived from roberta-base-thai-spm. Every word is tagged by UPOS (Universal Part-Of-Speech).
## How to Use
or
## See Also
esupar: Tokenizer POS-tagger and Depe... | [
"# roberta-base-thai-spm-upos",
"## Model Description\n\nThis is a RoBERTa model pre-trained on Thai Wikipedia texts for POS-tagging and dependency-parsing, derived from roberta-base-thai-spm. Every word is tagged by UPOS (Universal Part-Of-Speech).",
"## How to Use\n\n\n\nor",
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fill-mask | transformers |
# roberta-base-thai-spm
## Model Description
This is a RoBERTa model pre-trained on Thai Wikipedia texts. You can fine-tune `roberta-base-thai-spm` for downstream tasks, such as [POS-tagging](https://huggingface.co/KoichiYasuoka/roberta-base-thai-spm-upos), [dependency-parsing](https://huggingface.co/KoichiYasuoka/r... | {"language": ["th"], "license": "apache-2.0", "tags": ["thai", "masked-lm", "wikipedia"], "pipeline_tag": "fill-mask", "mask_token": "[MASK]"} | KoichiYasuoka/roberta-base-thai-spm | null | [
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] | null | 2022-03-02T23:29:04+00:00 | [] | [
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|
# roberta-base-thai-spm
## Model Description
This is a RoBERTa model pre-trained on Thai Wikipedia texts. You can fine-tune 'roberta-base-thai-spm' for downstream tasks, such as POS-tagging, dependency-parsing, and so on.
## How to Use
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"## How to Use"
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token-classification | transformers |
# roberta-base-thai-syllable-upos
## Model Description
This is a RoBERTa model pre-trained on Thai Wikipedia texts for POS-tagging and dependency-parsing, derived from [roberta-base-thai-syllable](https://huggingface.co/KoichiYasuoka/roberta-base-thai-syllable). Every word is tagged by [UPOS](https://universaldepend... | {"language": ["th"], "license": "apache-2.0", "tags": ["thai", "token-classification", "pos", "wikipedia", "dependency-parsing"], "datasets": ["universal_dependencies"], "pipeline_tag": "token-classification", "widget": [{"text": "\u0e2b\u0e25\u0e32\u0e22\u0e2b\u0e31\u0e27\u0e14\u0e35\u0e01\u0e27\u0e48\u0e32\u0e2b\u0e3... | KoichiYasuoka/roberta-base-thai-syllable-upos | null | [
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"autotrain_compatible",
"endpoints_compatible",
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] | null | 2022-03-02T23:29:04+00:00 | [] | [
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|
# roberta-base-thai-syllable-upos
## Model Description
This is a RoBERTa model pre-trained on Thai Wikipedia texts for POS-tagging and dependency-parsing, derived from roberta-base-thai-syllable. Every word is tagged by UPOS (Universal Part-Of-Speech).
## How to Use
or
## See Also
esupar: Tokenizer POS-tagge... | [
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"## Model Description\n\nThis is a RoBERTa model pre-trained on Thai Wikipedia texts for POS-tagging and dependency-parsing, derived from roberta-base-thai-syllable. Every word is tagged by UPOS (Universal Part-Of-Speech).",
"## How to Use\n\n\n\nor",
"## See Also\n\nesupa... | [
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fill-mask | transformers |
# roberta-base-thai-syllable
## Model Description
This is a RoBERTa model pre-trained on Thai Wikipedia texts, derived from [wangchanberta-base-wiki-syllable](https://huggingface.co/airesearch/wangchanberta-base-wiki-syllable). Character-embeddings are modified to use BertTokenizerFast. You can fine-tune `roberta-ba... | {"language": ["th"], "license": "apache-2.0", "tags": ["thai", "masked-lm", "wikipedia"], "pipeline_tag": "fill-mask", "mask_token": "<mask>", "widget": [{"text": "\u0e41\u0e1c\u0e19\u0e01\u0e19\u0e35\u0e49\u0e01\u0e33\u0e25\u0e31\u0e07<mask>\u0e01\u0e31\u0e1a\u0e04\u0e27\u0e32\u0e21\u0e17\u0e49\u0e32\u0e17\u0e32\u0e22... | KoichiYasuoka/roberta-base-thai-syllable | null | [
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"license:apache-2.0",
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|
# roberta-base-thai-syllable
## Model Description
This is a RoBERTa model pre-trained on Thai Wikipedia texts, derived from wangchanberta-base-wiki-syllable. Character-embeddings are modified to use BertTokenizerFast. You can fine-tune 'roberta-base-thai-syllable' for downstream tasks, such as POS-tagging, dependenc... | [
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"## Model Description\n\nThis is a RoBERTa model pre-trained on Thai Wikipedia texts, derived from wangchanberta-base-wiki-syllable. Character-embeddings are modified to use BertTokenizerFast. You can fine-tune 'roberta-base-thai-syllable' for downstream tasks, such as POS-tagging,... | [
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fill-mask | transformers |
# roberta-classical-chinese-base-char
## Model Description
This is a RoBERTa model pre-trained on Classical Chinese texts, derived from [GuwenBERT-base](https://huggingface.co/ethanyt/guwenbert-base). Character-embeddings are enhanced into traditional/simplified characters. You can fine-tune `roberta-classical-chine... | {"language": ["lzh"], "license": "apache-2.0", "tags": ["classical chinese", "literary chinese", "ancient chinese", "masked-lm"], "pipeline_tag": "fill-mask", "mask_token": "[MASK]", "widget": [{"text": "\u5b5f\u5b50[MASK]\u6881\u60e0\u738b"}]} | KoichiYasuoka/roberta-classical-chinese-base-char | null | [
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"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
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|
# roberta-classical-chinese-base-char
## Model Description
This is a RoBERTa model pre-trained on Classical Chinese texts, derived from GuwenBERT-base. Character-embeddings are enhanced into traditional/simplified characters. You can fine-tune 'roberta-classical-chinese-base-char' for downstream tasks, such as sente... | [
"# roberta-classical-chinese-base-char",
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token-classification | transformers |
# roberta-classical-chinese-base-sentence-segmentation
## Model Description
This is a RoBERTa model pre-trained on Classical Chinese texts for sentence segmentation, derived from [roberta-classical-chinese-base-char](https://huggingface.co/KoichiYasuoka/roberta-classical-chinese-base-char). Every segmented sentence ... | {"language": ["lzh"], "license": "apache-2.0", "tags": ["classical chinese", "literary chinese", "ancient chinese", "sentence segmentation", "token-classification"], "pipeline_tag": "token-classification", "widget": [{"text": "\u5b50\u66f0\u5b78\u800c\u6642\u7fd2\u4e4b\u4e0d\u4ea6\u8aac\u4e4e\u6709\u670b\u81ea\u9060\u6... | KoichiYasuoka/roberta-classical-chinese-base-sentence-segmentation | null | [
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"autotrain_compatible",
"endpoints_compatible",
"region:us"
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"lzh"
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|
# roberta-classical-chinese-base-sentence-segmentation
## Model Description
This is a RoBERTa model pre-trained on Classical Chinese texts for sentence segmentation, derived from roberta-classical-chinese-base-char. Every segmented sentence begins with token-class "B" and ends with token-class "E" (except for single... | [
"# roberta-classical-chinese-base-sentence-segmentation",
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token-classification | transformers |
# roberta-classical-chinese-base-upos
## Model Description
This is a RoBERTa model pre-trained on Classical Chinese texts for POS-tagging and dependency-parsing, derived from [roberta-classical-chinese-base-char](https://huggingface.co/KoichiYasuoka/roberta-classical-chinese-base-char). Every word is tagged by [UPOS... | {"language": ["lzh"], "license": "apache-2.0", "tags": ["classical chinese", "literary chinese", "ancient chinese", "token-classification", "pos", "dependency-parsing"], "datasets": ["universal_dependencies"], "pipeline_tag": "token-classification", "widget": [{"text": "\u5b50\u66f0\u5b78\u800c\u6642\u7fd2\u4e4b\u4e0d\... | KoichiYasuoka/roberta-classical-chinese-base-upos | null | [
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"literary chinese",
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"dataset:universal_dependencies",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"lzh"
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|
# roberta-classical-chinese-base-upos
## Model Description
This is a RoBERTa model pre-trained on Classical Chinese texts for POS-tagging and dependency-parsing, derived from roberta-classical-chinese-base-char. Every word is tagged by UPOS (Universal Part-Of-Speech) and FEATS.
## How to Use
or
## Reference
... | [
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"## How to Use\n\n\n\nor... | [
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fill-mask | transformers |
# roberta-classical-chinese-large-char
## Model Description
This is a RoBERTa model pre-trained on Classical Chinese texts, derived from [GuwenBERT-large](https://huggingface.co/ethanyt/guwenbert-large). Character-embeddings are enhanced into traditional/simplified characters. You can fine-tune `roberta-classical-ch... | {"language": ["lzh"], "license": "apache-2.0", "tags": ["classical chinese", "literary chinese", "ancient chinese", "masked-lm"], "pipeline_tag": "fill-mask", "mask_token": "[MASK]", "widget": [{"text": "\u5b5f\u5b50[MASK]\u6881\u60e0\u738b"}]} | KoichiYasuoka/roberta-classical-chinese-large-char | null | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"classical chinese",
"literary chinese",
"ancient chinese",
"masked-lm",
"lzh",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"lzh"
] | TAGS
#transformers #pytorch #roberta #fill-mask #classical chinese #literary chinese #ancient chinese #masked-lm #lzh #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# roberta-classical-chinese-large-char
## Model Description
This is a RoBERTa model pre-trained on Classical Chinese texts, derived from GuwenBERT-large. Character-embeddings are enhanced into traditional/simplified characters. You can fine-tune 'roberta-classical-chinese-large-char' for downstream tasks, such as se... | [
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"## Model Description\n\nThis is a RoBERTa model pre-trained on Classical Chinese texts, derived from GuwenBERT-large. Character-embeddings are enhanced into traditional/simplified characters. You can fine-tune 'roberta-classical-chinese-large-char' for downstream tasks, ... | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #classical chinese #literary chinese #ancient chinese #masked-lm #lzh #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# roberta-classical-chinese-large-char",
"## Model Description\n\nThis is a RoBERTa model pre-trained on Classi... | [
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token-classification | transformers |
# roberta-classical-chinese-large-sentence-segmentation
## Model Description
This is a RoBERTa model pre-trained on Classical Chinese texts for sentence segmentation, derived from [roberta-classical-chinese-large-char](https://huggingface.co/KoichiYasuoka/roberta-classical-chinese-large-char). Every segmented senten... | {"language": ["lzh"], "license": "apache-2.0", "tags": ["classical chinese", "literary chinese", "ancient chinese", "sentence segmentation", "token-classification"], "pipeline_tag": "token-classification", "widget": [{"text": "\u5b50\u66f0\u5b78\u800c\u6642\u7fd2\u4e4b\u4e0d\u4ea6\u8aac\u4e4e\u6709\u670b\u81ea\u9060\u6... | KoichiYasuoka/roberta-classical-chinese-large-sentence-segmentation | null | [
"transformers",
"pytorch",
"roberta",
"token-classification",
"classical chinese",
"literary chinese",
"ancient chinese",
"sentence segmentation",
"lzh",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"lzh"
] | TAGS
#transformers #pytorch #roberta #token-classification #classical chinese #literary chinese #ancient chinese #sentence segmentation #lzh #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# roberta-classical-chinese-large-sentence-segmentation
## Model Description
This is a RoBERTa model pre-trained on Classical Chinese texts for sentence segmentation, derived from roberta-classical-chinese-large-char. Every segmented sentence begins with token-class "B" and ends with token-class "E" (except for sing... | [
"# roberta-classical-chinese-large-sentence-segmentation",
"## Model Description\n\nThis is a RoBERTa model pre-trained on Classical Chinese texts for sentence segmentation, derived from roberta-classical-chinese-large-char. Every segmented sentence begins with token-class \"B\" and ends with token-class \"E\" (e... | [
"TAGS\n#transformers #pytorch #roberta #token-classification #classical chinese #literary chinese #ancient chinese #sentence segmentation #lzh #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
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token-classification | transformers |
# roberta-classical-chinese-large-upos
## Model Description
This is a RoBERTa model pre-trained on Classical Chinese texts for POS-tagging and dependency-parsing, derived from [roberta-classical-chinese-large-char](https://huggingface.co/KoichiYasuoka/roberta-classical-chinese-large-char). Every word is tagged by [U... | {"language": ["lzh"], "license": "apache-2.0", "tags": ["classical chinese", "literary chinese", "ancient chinese", "token-classification", "pos", "dependency-parsing"], "datasets": ["universal_dependencies"], "pipeline_tag": "token-classification", "widget": [{"text": "\u5b50\u66f0\u5b78\u800c\u6642\u7fd2\u4e4b\u4e0d\... | KoichiYasuoka/roberta-classical-chinese-large-upos | null | [
"transformers",
"pytorch",
"roberta",
"token-classification",
"classical chinese",
"literary chinese",
"ancient chinese",
"pos",
"dependency-parsing",
"lzh",
"dataset:universal_dependencies",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"lzh"
] | TAGS
#transformers #pytorch #roberta #token-classification #classical chinese #literary chinese #ancient chinese #pos #dependency-parsing #lzh #dataset-universal_dependencies #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# roberta-classical-chinese-large-upos
## Model Description
This is a RoBERTa model pre-trained on Classical Chinese texts for POS-tagging and dependency-parsing, derived from roberta-classical-chinese-large-char. Every word is tagged by UPOS (Universal Part-Of-Speech) and FEATS.
## How to Use
or
## Reference
... | [
"# roberta-classical-chinese-large-upos",
"## Model Description\n\nThis is a RoBERTa model pre-trained on Classical Chinese texts for POS-tagging and dependency-parsing, derived from roberta-classical-chinese-large-char. Every word is tagged by UPOS (Universal Part-Of-Speech) and FEATS.",
"## How to Use\n\n\nor... | [
"TAGS\n#transformers #pytorch #roberta #token-classification #classical chinese #literary chinese #ancient chinese #pos #dependency-parsing #lzh #dataset-universal_dependencies #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# roberta-classical-chinese-large-upos",
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token-classification | transformers |
# roberta-large-english-upos
## Model Description
This is a RoBERTa model pre-trained with [UD_English](https://universaldependencies.org/en/) for POS-tagging and dependency-parsing, derived from [roberta-large](https://huggingface.co/roberta-large). Every word is tagged by [UPOS](https://universaldependencies.org/u... | {"language": ["en"], "license": "cc-by-sa-4.0", "tags": ["english", "token-classification", "pos", "dependency-parsing"], "datasets": ["universal_dependencies"], "pipeline_tag": "token-classification"} | KoichiYasuoka/roberta-large-english-upos | null | [
"transformers",
"pytorch",
"roberta",
"token-classification",
"english",
"pos",
"dependency-parsing",
"en",
"dataset:universal_dependencies",
"license:cc-by-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #roberta #token-classification #english #pos #dependency-parsing #en #dataset-universal_dependencies #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us
|
# roberta-large-english-upos
## Model Description
This is a RoBERTa model pre-trained with UD_English for POS-tagging and dependency-parsing, derived from roberta-large. Every word is tagged by UPOS (Universal Part-Of-Speech).
## How to Use
or
## See Also
esupar: Tokenizer POS-tagger and Dependency-parser wi... | [
"# roberta-large-english-upos",
"## Model Description\n\nThis is a RoBERTa model pre-trained with UD_English for POS-tagging and dependency-parsing, derived from roberta-large. Every word is tagged by UPOS (Universal Part-Of-Speech).",
"## How to Use\n\n\n\nor",
"## See Also\n\nesupar: Tokenizer POS-tagger an... | [
"TAGS\n#transformers #pytorch #roberta #token-classification #english #pos #dependency-parsing #en #dataset-universal_dependencies #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# roberta-large-english-upos",
"## Model Description\n\nThis is a RoBERTa model pre-trained with U... | [
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fill-mask | transformers |
# roberta-large-japanese-aozora-char
## Model Description
This is a RoBERTa model pre-trained on 青空文庫 texts with character tokenizer. You can fine-tune `roberta-large-japanese-aozora-char` for downstream tasks, such as [POS-tagging](https://huggingface.co/KoichiYasuoka/roberta-large-japanese-char-luw-upos), [depende... | {"language": ["ja"], "license": "cc-by-sa-4.0", "tags": ["japanese", "masked-lm"], "pipeline_tag": "fill-mask", "mask_token": "[MASK]", "widget": [{"text": "\u65e5\u672c\u306b\u7740\u3044\u305f\u3089[MASK]\u3092\u8a2a\u306d\u306a\u3055\u3044\u3002"}]} | KoichiYasuoka/roberta-large-japanese-aozora-char | null | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"japanese",
"masked-lm",
"ja",
"license:cc-by-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"ja"
] | TAGS
#transformers #pytorch #roberta #fill-mask #japanese #masked-lm #ja #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us
|
# roberta-large-japanese-aozora-char
## Model Description
This is a RoBERTa model pre-trained on 青空文庫 texts with character tokenizer. You can fine-tune 'roberta-large-japanese-aozora-char' for downstream tasks, such as POS-tagging, dependency-parsing, and so on.
## How to Use
## Reference
安岡孝一: Transformersと国語研... | [
"# roberta-large-japanese-aozora-char",
"## Model Description\n\nThis is a RoBERTa model pre-trained on 青空文庫 texts with character tokenizer. You can fine-tune 'roberta-large-japanese-aozora-char' for downstream tasks, such as POS-tagging, dependency-parsing, and so on.",
"## How to Use",
"## Reference\n\n安岡孝一... | [
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"# roberta-large-japanese-aozora-char",
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