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null | transformers |
# KcELECTRA: Korean comments ELECTRA
** Updates on 2022.10.08 **
- KcELECTRA-base-v2022 (๊ตฌ v2022-dev) ๋ชจ๋ธ ์ด๋ฆ์ด ๋ณ๊ฒฝ๋์์ต๋๋ค. --> KcELECTRA-base ๋ ํฌ์ `v2022`๋ก ํตํฉ๋์์ต๋๋ค.
- ์ ๋ชจ๋ธ์ ์ธ๋ถ ์ค์ฝ์ด๋ฅผ ์ถ๊ฐํ์์ต๋๋ค.
- ๊ธฐ์กด KcELECTRA-base(v2021) ๋๋น ๋๋ถ๋ถ์ downstream task์์ ~1%p ์์ค์ ์ฑ๋ฅ ํฅ์์ด ์์ต๋๋ค.
---
๊ณต๊ฐ๋ ํ๊ตญ์ด Transformer ๊ณ์ด ๋ชจ๋ธ๋ค์ ๋๋ถ๋ถ ํ๊ตญ์ด ์ํค, ๋ด์ค ๊ธฐ์ฌ, ์ฑ
๋ฑ ... | {"language": ["ko", "en"], "license": "mit", "tags": ["electra", "korean"]} | beomi/KcELECTRA-base | null | [
"transformers",
"pytorch",
"electra",
"pretraining",
"korean",
"ko",
"en",
"doi:10.57967/hf/0017",
"license:mit",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ko",
"en"
] | TAGS
#transformers #pytorch #electra #pretraining #korean #ko #en #doi-10.57967/hf/0017 #license-mit #endpoints_compatible #has_space #region-us
| KcELECTRA: Korean comments ELECTRA
==================================
Updates on 2022.10.08
* KcELECTRA-base-v2022 (๊ตฌ v2022-dev) ๋ชจ๋ธ ์ด๋ฆ์ด ๋ณ๊ฒฝ๋์์ต๋๋ค. --> KcELECTRA-base ๋ ํฌ์ 'v2022'๋ก ํตํฉ๋์์ต๋๋ค.
* ์ ๋ชจ๋ธ์ ์ธ๋ถ ์ค์ฝ์ด๋ฅผ ์ถ๊ฐํ์์ต๋๋ค.
* ๊ธฐ์กด KcELECTRA-base(v2021) ๋๋น ๋๋ถ๋ถ์ downstream task์์ ~1%p ์์ค์ ์ฑ๋ฅ ํฅ์์ด ์์ต๋๋ค.
---
๊ณต๊ฐ๋ ํ๊ตญ์ด Transformer ... | [
"### Requirements\n\n\n* 'pytorch ~= 1.8.0'\n* 'transformers ~= 4.11.3'\n* 'emoji ~= 0.6.0'\n* 'soynlp ~= 0.0.493'",
"### Default usage\n\n\n\n> \n> ์ด์ KcBERT ๊ด๋ จ ์ฝ๋๋ค์์ 'AutoTokenizer', 'AutoModel' ์ ์ฌ์ฉํ ๊ฒฝ์ฐ '.from\\_pretrained(\"beomi/kcbert-base\")' ๋ถ๋ถ์ '.from\\_pretrained(\"beomi/KcELECTRA-base\")' ๋ก๋ง ๋ณ๊ฒฝํด์ฃผ์๋ฉด ์ฆ์ ... | [
"TAGS\n#transformers #pytorch #electra #pretraining #korean #ko #en #doi-10.57967/hf/0017 #license-mit #endpoints_compatible #has_space #region-us \n",
"### Requirements\n\n\n* 'pytorch ~= 1.8.0'\n* 'transformers ~= 4.11.3'\n* 'emoji ~= 0.6.0'\n* 'soynlp ~= 0.0.493'",
"### Default usage\n\n\n\n> \n> ์ด์ KcBERT ๊ด... |
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... | beomi/distilbert-base-uncased-finetuned-cola | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| 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: 0.7525
* Matthews Correlation: 0.5553
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",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning... |
fill-mask | transformers |
# KcBERT: Korean comments BERT
** Updates on 2021.04.07 **
- KcELECTRA๊ฐ ๋ฆด๋ฆฌ์ฆ ๋์์ต๋๋ค!๐ค
- KcELECTRA๋ ๋ณด๋ค ๋ ๋ง์ ๋ฐ์ดํฐ์
, ๊ทธ๋ฆฌ๊ณ ๋ ํฐ General vocab์ ํตํด KcBERT ๋๋น **๋ชจ๋ ํ์คํฌ์์ ๋ ๋์ ์ฑ๋ฅ**์ ๋ณด์
๋๋ค.
- ์๋ ๊นํ ๋งํฌ์์ ์ง์ ์ฌ์ฉํด๋ณด์ธ์!
- https://github.com/Beomi/KcELECTRA
** Updates on 2021.03.14 **
- KcBERT Paper ์ธ์ฉ ํ๊ธฐ๋ฅผ ์ถ๊ฐํ์์ต๋๋ค.(bibtex)
- KcBERT-fi... | {"language": "ko", "license": "apache-2.0", "tags": ["korean"]} | beomi/kcbert-base | null | [
"transformers",
"pytorch",
"jax",
"safetensors",
"bert",
"fill-mask",
"korean",
"ko",
"arxiv:1810.04805",
"doi:10.57967/hf/0016",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1810.04805"
] | [
"ko"
] | TAGS
#transformers #pytorch #jax #safetensors #bert #fill-mask #korean #ko #arxiv-1810.04805 #doi-10.57967/hf/0016 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| KcBERT: Korean comments BERT
============================
Updates on 2021.04.07
* KcELECTRA๊ฐ ๋ฆด๋ฆฌ์ฆ ๋์์ต๋๋ค!
* KcELECTRA๋ ๋ณด๋ค ๋ ๋ง์ ๋ฐ์ดํฐ์
, ๊ทธ๋ฆฌ๊ณ ๋ ํฐ General vocab์ ํตํด KcBERT ๋๋น ๋ชจ๋ ํ์คํฌ์์ ๋ ๋์ ์ฑ๋ฅ์ ๋ณด์
๋๋ค.
* ์๋ ๊นํ ๋งํฌ์์ ์ง์ ์ฌ์ฉํด๋ณด์ธ์!
* URL
Updates on 2021.03.14
* KcBERT Paper ์ธ์ฉ ํ๊ธฐ๋ฅผ ์ถ๊ฐํ์์ต๋๋ค.(bibtex)
* KcBERT-finetune Performance ... | [
"### Requirements\n\n\n* 'pytorch <= 1.8.0'\n* 'transformers ~= 3.0.1'\n\t+ 'transformers ~= 4.0.0' ๋ ํธํ๋ฉ๋๋ค.\n* 'emoji ~= 0.6.0'\n* 'soynlp ~= 0.0.493'",
"### Pretrain & Finetune Colab ๋งํฌ ๋ชจ์",
"#### Pretrain Data\n\n\n* ๋ฐ์ดํฐ์
๋ค์ด๋ก๋(Kaggle, ๋จ์ผํ์ผ, ๋ก๊ทธ์ธ ํ์)\n* ๋ฐ์ดํฐ์
๋ค์ด๋ก๋(Github, ์์ถ ์ฌ๋ฌํ์ผ, ๋ก๊ทธ์ธ ๋ถํ์)",
"#### Pretrain Co... | [
"TAGS\n#transformers #pytorch #jax #safetensors #bert #fill-mask #korean #ko #arxiv-1810.04805 #doi-10.57967/hf/0016 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### Requirements\n\n\n* 'pytorch <= 1.8.0'\n* 'transformers ~= 3.0.1'\n\t+ 'transformers ~= 4.0.0' ๋ ํธํ๋ฉ๋... |
text-generation | transformers |
# Bert base model for Korean
## Update
- Update at 2021.11.17 : Add Native Support for BERT Tokenizer (works with AutoTokenizer, pipeline)
---
* 70GB Korean text dataset and 42000 lower-cased subwords are used
* Check the model performance and other language models for Korean in [github](https://github.com/kiyoung... | {"language": "ko"} | beomi/kykim-gpt3-kor-small_based_on_gpt2 | null | [
"transformers",
"pytorch",
"tf",
"jax",
"gpt2",
"text-generation",
"ko",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ko"
] | TAGS
#transformers #pytorch #tf #jax #gpt2 #text-generation #ko #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Bert base model for Korean
## Update
- Update at 2021.11.17 : Add Native Support for BERT Tokenizer (works with AutoTokenizer, pipeline)
---
* 70GB Korean text dataset and 42000 lower-cased subwords are used
* Check the model performance and other language models for Korean in github
| [
"# Bert base model for Korean",
"## Update\n\n- Update at 2021.11.17 : Add Native Support for BERT Tokenizer (works with AutoTokenizer, pipeline)\n\n---\n\n* 70GB Korean text dataset and 42000 lower-cased subwords are used\n* Check the model performance and other language models for Korean in github"
] | [
"TAGS\n#transformers #pytorch #tf #jax #gpt2 #text-generation #ko #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Bert base model for Korean",
"## Update\n\n- Update at 2021.11.17 : Add Native Support for BERT Tokenizer (works with AutoTokenizer, pipeline)\n\n---\n\n* 7... |
token-classification | transformers | # LayoutXLM finetuned on XFUN.ja
```python
import torch
import numpy as np
from PIL import Image, ImageDraw, ImageFont
from pathlib import Path
from itertools import chain
from tqdm.notebook import tqdm
from pdf2image import convert_from_path
from transformers import LayoutXLMProcessor, LayoutLMv2ForTokenClassificatio... | {} | beomus/layoutxlm | null | [
"transformers",
"pytorch",
"layoutlmv2",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #layoutlmv2 #token-classification #autotrain_compatible #endpoints_compatible #region-us
| # LayoutXLM finetuned on URL
| [
"# LayoutXLM finetuned on URL"
] | [
"TAGS\n#transformers #pytorch #layoutlmv2 #token-classification #autotrain_compatible #endpoints_compatible #region-us \n",
"# LayoutXLM finetuned on URL"
] |
text-classification | transformers |
# xtremedistil-emotion
This model is a fine-tuned version of [microsoft/xtremedistil-l6-h256-uncased](https://huggingface.co/microsoft/xtremedistil-l6-h256-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Accuracy: 0.9265
### Training hyperparameters
The following hyperpara... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["emotion"], "metrics": ["accuracy"], "model-index": [{"name": "xtremedistil-emotion", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "emotion", "type": "emotion", "args": "default"}, "met... | bergum/xtremedistil-emotion | null | [
"transformers",
"pytorch",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #safetensors #bert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# xtremedistil-emotion
This model is a fine-tuned version of microsoft/xtremedistil-l6-h256-uncased on the emotion dataset.
It achieves the following results on the evaluation set:
- Accuracy: 0.9265
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_... | [
"# xtremedistil-emotion\nThis model is a fine-tuned version of microsoft/xtremedistil-l6-h256-uncased on the emotion dataset.\nIt achieves the following results on the evaluation set:\n- Accuracy: 0.9265",
"### Training hyperparameters\nThe following hyperparameters were used during training:\n- learning_rate: 3e... | [
"TAGS\n#transformers #pytorch #safetensors #bert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# xtremedistil-emotion\nThis model is a fine-tuned version of microsoft/xtremedistil-l6-h256-uncased on the e... |
text-classification | transformers | # xtremedistil-l6-h384-emotion
This model is a fine-tuned version of [microsoft/xtremedistil-l6-h384-uncased](https://huggingface.co/microsoft/xtremedistil-l6-h384-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Accuracy: 0.928
This model can be quantized to int8 and retain ... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["emotion"], "metrics": ["accuracy"], "model-index": [{"name": "xtremedistil-l6-h384-emotion", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "emotion", "type": "emotion", "args": "default... | bergum/xtremedistil-l6-h384-emotion | null | [
"transformers",
"pytorch",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #safetensors #bert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| # xtremedistil-l6-h384-emotion
This model is a fine-tuned version of microsoft/xtremedistil-l6-h384-uncased on the emotion dataset.
It achieves the following results on the evaluation set:
- Accuracy: 0.928
This model can be quantized to int8 and retain accuracy
- Accuracy 0.912
<pre>
import transformers
import tran... | [
"# xtremedistil-l6-h384-emotion\nThis model is a fine-tuned version of microsoft/xtremedistil-l6-h384-uncased on the emotion dataset.\nIt achieves the following results on the evaluation set:\n- Accuracy: 0.928\n\nThis model can be quantized to int8 and retain accuracy \n- Accuracy 0.912\n\n<pre>\nimport transforme... | [
"TAGS\n#transformers #pytorch #safetensors #bert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# xtremedistil-l6-h384-emotion\nThis model is a fine-tuned version of microsoft/xtremedistil-l6-h384-uncased ... |
text-classification | transformers | # xtremedistil-l6-h384-go-emotion
This model is a fine-tuned version of [microsoft/xtremedistil-l6-h384-uncased](https://huggingface.co/microsoft/xtremedistil-l6-h384-uncased) on the
[go_emotions dataset](https://huggingface.co/datasets/go_emotions).
See notebook for how the model was trained and converted to ONNX f... | {"license": "apache-2.0", "datasets": ["go_emotions"], "metrics": ["accuracy"], "model-index": [{"name": "xtremedistil-emotion", "results": [{"task": {"type": "multi_label_classification", "name": "Multi Label Text Classification"}, "dataset": {"name": "go_emotions", "type": "emotion", "args": "default"}, "metrics": [{... | bergum/xtremedistil-l6-h384-go-emotion | null | [
"transformers",
"pytorch",
"safetensors",
"bert",
"text-classification",
"dataset:go_emotions",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #safetensors #bert #text-classification #dataset-go_emotions #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| # xtremedistil-l6-h384-go-emotion
This model is a fine-tuned version of microsoft/xtremedistil-l6-h384-uncased on the
go_emotions dataset.
See notebook for how the model was trained and converted to ONNX format ![Training Notebook](URL
This model is deployed to URL for live demo of the model.
See URL for how to r... | [
"# xtremedistil-l6-h384-go-emotion\nThis model is a fine-tuned version of microsoft/xtremedistil-l6-h384-uncased on the \ngo_emotions dataset. \n\nSee notebook for how the model was trained and converted to ONNX format ![Training Notebook](URL\n\nThis model is deployed to URL for live demo of the model. \n\nSee URL... | [
"TAGS\n#transformers #pytorch #safetensors #bert #text-classification #dataset-go_emotions #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# xtremedistil-l6-h384-go-emotion\nThis model is a fine-tuned version of microsoft/xtremedistil-l6-h384-uncased on the \ngo_emoti... |
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. -->
# IceBERT-finetuned-ner
This model is a fine-tuned version of [vesteinn/IceBERT](https://huggingface.co/vesteinn/IceBERT) on the m... | {"license": "gpl-3.0", "tags": ["generated_from_trainer"], "datasets": ["mim_gold_ner"], "metrics": ["precision", "recall", "f1", "accuracy"], "widget": [{"text": "Bob Dillan beit Mar\u00edu Markan \u00e1 barkann."}], "model-index": [{"name": "IceBERT-finetuned-ner", "results": [{"task": {"type": "token-classification"... | bergurth/IceBERT-finetuned-ner | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"token-classification",
"generated_from_trainer",
"dataset:mim_gold_ner",
"license:gpl-3.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #token-classification #generated_from_trainer #dataset-mim_gold_ner #license-gpl-3.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| IceBERT-finetuned-ner
=====================
This model is a fine-tuned version of vesteinn/IceBERT on the mim\_gold\_ner dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0783
* Precision: 0.8873
* Recall: 0.8627
* F1: 0.8748
* Accuracy: 0.9848
Model description
-----------------
More ... | [
"### 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",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #roberta #token-classification #generated_from_trainer #dataset-mim_gold_ner #license-gpl-3.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learn... |
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. -->
# XLMR-ENIS-finetuned-ner
This model is a fine-tuned version of [vesteinn/XLMR-ENIS](https://huggingface.co/vesteinn/XLMR-ENIS) on... | {"license": "agpl-3.0", "tags": ["generated_from_trainer"], "datasets": ["mim_gold_ner"], "metrics": ["precision", "recall", "f1", "accuracy"], "widget": [{"text": "B\u00f3nus fe\u00f0garnir J\u00f3hannes J\u00f3nsson og J\u00f3n \u00c1sgeir J\u00f3hannesson opnu\u00f0u fyrstu B\u00f3nusb\u00fa\u00f0ina \u00ed 400 ferm... | bergurth/XLMR-ENIS-finetuned-ner | null | [
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:mim_gold_ner",
"license:agpl-3.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #xlm-roberta #token-classification #generated_from_trainer #dataset-mim_gold_ner #license-agpl-3.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| XLMR-ENIS-finetuned-ner
=======================
This model is a fine-tuned version of vesteinn/XLMR-ENIS on the mim\_gold\_ner dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0938
* Precision: 0.8619
* Recall: 0.8384
* F1: 0.8500
* Accuracy: 0.9831
Model description
-----------------
... | [
"### 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",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #xlm-roberta #token-classification #generated_from_trainer #dataset-mim_gold_ner #license-agpl-3.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* ... |
fill-mask | transformers |
This is a **RoBERTa-base** model trained from scratch in Spanish.
The training dataset is [mc4](https://huggingface.co/datasets/bertin-project/mc4-es-sampled ) subsampling documents to a total of about 50 million examples. Sampling is biased towards average perplexity values (using a Gaussian function), discarding mo... | {"language": "es", "license": "cc-by-4.0", "tags": ["spanish", "roberta"], "pipeline_tag": "fill-mask", "widget": [{"text": "Fui a la librer\u00eda a comprar un <mask>."}]} | bertin-project/bertin-base-gaussian-exp-512seqlen | null | [
"transformers",
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"jax",
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"joblib",
"roberta",
"fill-mask",
"spanish",
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"license:cc-by-4.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"es"
] | TAGS
#transformers #pytorch #jax #tensorboard #joblib #roberta #fill-mask #spanish #es #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
This is a RoBERTa-base model trained from scratch in Spanish.
The training dataset is mc4 subsampling documents to a total of about 50 million examples. Sampling is biased towards average perplexity values (using a Gaussian function), discarding more often documents with very large values (poor quality) of very small... | [
"## Team members\n\n- Eduardo Gonzรกlez (edugp)\n- Javier de la Rosa (versae)\n- Manu Romero (mrm8488)\n- Marรญa Grandury (mariagrandury)\n- Pablo Gonzรกlez de Prado (Pablogps)\n- Paulo Villegas (paulo)"
] | [
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"## Team members\n\n- Eduardo Gonzรกlez (edugp)\n- Javier de la Rosa (versae)\n- Manu Romero (mrm8488)\n- Marรญa Grandury (mariagrandury)... |
fill-mask | transformers |
This is a **RoBERTa-base** model trained from scratch in Spanish.
The training dataset is [mc4](https://huggingface.co/datasets/bertin-project/mc4-es-sampled ) subsampling documents to a total of about 50 million examples. Sampling is biased towards average perplexity values (using a Gaussian function), discarding mo... | {"language": "es", "license": "cc-by-4.0", "tags": ["spanish", "roberta"], "pipeline_tag": "fill-mask", "widget": [{"text": "Fui a la librer\u00eda a comprar un <mask>."}]} | bertin-project/bertin-base-gaussian | null | [
"transformers",
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"fill-mask",
"spanish",
"es",
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"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"es"
] | TAGS
#transformers #pytorch #jax #tensorboard #joblib #roberta #fill-mask #spanish #es #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
This is a RoBERTa-base model trained from scratch in Spanish.
The training dataset is mc4 subsampling documents to a total of about 50 million examples. Sampling is biased towards average perplexity values (using a Gaussian function), discarding more often documents with very large values (poor quality) of very small... | [
"## Team members\n\n- Eduardo Gonzรกlez (edugp)\n- Javier de la Rosa (versae)\n- Manu Romero (mrm8488)\n- Marรญa Grandury (mariagrandury)\n- Pablo Gonzรกlez de Prado (Pablogps)\n- Paulo Villegas (paulo)"
] | [
"TAGS\n#transformers #pytorch #jax #tensorboard #joblib #roberta #fill-mask #spanish #es #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"## Team members\n\n- Eduardo Gonzรกlez (edugp)\n- Javier de la Rosa (versae)\n- Manu Romero (mrm8488)\n- Marรญa Grandury (mariagrandury)... |
token-classification | transformers |
This checkpoint has been trained for the NER task using the CoNLL2002-es dataset.
This is a NER checkpoint created from **Bertin Gaussian 512**, which is a **RoBERTa-base** model trained from scratch in Spanish. Information on this base model may be found at [its own card](https://huggingface.co/bertin-project/bertin... | {"language": "es", "license": "cc-by-4.0", "tags": ["spanish", "roberta", "ner"]} | bertin-project/bertin-base-ner-conll2002-es | null | [
"transformers",
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"spanish",
"ner",
"es",
"license:cc-by-4.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"es"
] | TAGS
#transformers #pytorch #safetensors #roberta #token-classification #spanish #ner #es #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
This checkpoint has been trained for the NER task using the CoNLL2002-es dataset.
This is a NER checkpoint created from Bertin Gaussian 512, which is a RoBERTa-base model trained from scratch in Spanish. Information on this base model may be found at its own card and at deeper detail on the main project card.
The t... | [
"## Team members\n\n- Eduardo Gonzรกlez (edugp)\n- Javier de la Rosa (versae)\n- Manu Romero (mrm8488)\n- Marรญa Grandury (mariagrandury)\n- Pablo Gonzรกlez de Prado (Pablogps)\n- Paulo Villegas (paulo)"
] | [
"TAGS\n#transformers #pytorch #safetensors #roberta #token-classification #spanish #ner #es #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"## Team members\n\n- Eduardo Gonzรกlez (edugp)\n- Javier de la Rosa (versae)\n- Manu Romero (mrm8488)\n- Marรญa Grandury (mariagrandu... |
text-classification | transformers |
This checkpoint has been trained for the PAWS-X task using the CoNLL 2002-es dataset.
This checkpoint was created from **Bertin Gaussian 512**, which is a **RoBERTa-base** model trained from scratch in Spanish. Information on this base model may be found at [its own card](https://huggingface.co/bertin-project/bertin-... | {"language": "es", "license": "cc-by-4.0", "tags": ["spanish", "roberta", "paws-x"]} | bertin-project/bertin-base-paws-x-es | null | [
"transformers",
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"spanish",
"paws-x",
"es",
"license:cc-by-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"es"
] | TAGS
#transformers #pytorch #safetensors #roberta #text-classification #spanish #paws-x #es #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #region-us
|
This checkpoint has been trained for the PAWS-X task using the CoNLL 2002-es dataset.
This checkpoint was created from Bertin Gaussian 512, which is a RoBERTa-base model trained from scratch in Spanish. Information on this base model may be found at its own card and at deeper detail on the main project card.
The tr... | [
"## Team members\n\n- Eduardo Gonzรกlez (edugp)\n- Javier de la Rosa (versae)\n- Manu Romero (mrm8488)\n- Marรญa Grandury (mariagrandury)\n- Pablo Gonzรกlez de Prado (Pablogps)\n- Paulo Villegas (paulo)"
] | [
"TAGS\n#transformers #pytorch #safetensors #roberta #text-classification #spanish #paws-x #es #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"## Team members\n\n- Eduardo Gonzรกlez (edugp)\n- Javier de la Rosa (versae)\n- Manu Romero (mrm8488)\n- Marรญa Grandury (mariagrandury)\n- Pa... |
token-classification | transformers |
This checkpoint has been trained for the POS task using the CoNLL 2002-es dataset.
This checkpoint was created from **Bertin Gaussian 512**, which is a **RoBERTa-base** model trained from scratch in Spanish. Information on this base model may be found at [its own card](https://huggingface.co/bertin-project/bertin-bas... | {"language": "es", "license": "cc-by-4.0", "tags": ["spanish", "roberta", "ner"]} | bertin-project/bertin-base-pos-conll2002-es | null | [
"transformers",
"pytorch",
"safetensors",
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"token-classification",
"spanish",
"ner",
"es",
"license:cc-by-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"es"
] | TAGS
#transformers #pytorch #safetensors #roberta #token-classification #spanish #ner #es #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #region-us
|
This checkpoint has been trained for the POS task using the CoNLL 2002-es dataset.
This checkpoint was created from Bertin Gaussian 512, which is a RoBERTa-base model trained from scratch in Spanish. Information on this base model may be found at its own card and at deeper detail on the main project card.
The train... | [
"## Team members\n\n- Eduardo Gonzรกlez (edugp)\n- Javier de la Rosa (versae)\n- Manu Romero (mrm8488)\n- Marรญa Grandury (mariagrandury)\n- Pablo Gonzรกlez de Prado (Pablogps)\n- Paulo Villegas (paulo)"
] | [
"TAGS\n#transformers #pytorch #safetensors #roberta #token-classification #spanish #ner #es #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"## Team members\n\n- Eduardo Gonzรกlez (edugp)\n- Javier de la Rosa (versae)\n- Manu Romero (mrm8488)\n- Marรญa Grandury (mariagrandury)\n- Pabl... |
fill-mask | transformers |
This is a **RoBERTa-base** model trained from scratch in Spanish.
The training dataset is [mc4](https://huggingface.co/datasets/bertin-project/mc4-es-sampled ) subsampling documents to a total of about 50 million examples. Sampling is random.
This model continued training from [sequence length 128](https://huggingfac... | {"language": "es", "license": "cc-by-4.0", "tags": ["spanish", "roberta"], "pipeline_tag": "fill-mask", "widget": [{"text": "Fui a la librer\u00eda a comprar un <mask>."}]} | bertin-project/bertin-base-random-exp-512seqlen | null | [
"transformers",
"pytorch",
"jax",
"tensorboard",
"joblib",
"roberta",
"fill-mask",
"spanish",
"es",
"license:cc-by-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"es"
] | TAGS
#transformers #pytorch #jax #tensorboard #joblib #roberta #fill-mask #spanish #es #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #region-us
|
This is a RoBERTa-base model trained from scratch in Spanish.
The training dataset is mc4 subsampling documents to a total of about 50 million examples. Sampling is random.
This model continued training from sequence length 128 using 20.000 steps for length 512.
Please see our main card for more information.
This i... | [
"## Team members\n\n- Eduardo Gonzรกlez (edugp)\n- Javier de la Rosa (versae)\n- Manu Romero (mrm8488)\n- Marรญa Grandury (mariagrandury)\n- Pablo Gonzรกlez de Prado (Pablogps)\n- Paulo Villegas (paulo)"
] | [
"TAGS\n#transformers #pytorch #jax #tensorboard #joblib #roberta #fill-mask #spanish #es #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"## Team members\n\n- Eduardo Gonzรกlez (edugp)\n- Javier de la Rosa (versae)\n- Manu Romero (mrm8488)\n- Marรญa Grandury (mariagrandury)\n- Pablo G... |
fill-mask | transformers |
This is a **RoBERTa-base** model trained from scratch in Spanish.
The training dataset is [mc4](https://huggingface.co/datasets/bertin-project/mc4-es-sampled ) subsampling documents to a total of about 50 million examples. Sampling is random.
This model has been trained for 230.000 steps (early stopped before the 25... | {"language": "es", "license": "cc-by-4.0", "tags": ["spanish", "roberta"], "pipeline_tag": "fill-mask", "widget": [{"text": "Fui a la librer\u00eda a comprar un <mask>."}]} | bertin-project/bertin-base-random | null | [
"transformers",
"pytorch",
"jax",
"tensorboard",
"joblib",
"roberta",
"fill-mask",
"spanish",
"es",
"license:cc-by-4.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"es"
] | TAGS
#transformers #pytorch #jax #tensorboard #joblib #roberta #fill-mask #spanish #es #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
This is a RoBERTa-base model trained from scratch in Spanish.
The training dataset is mc4 subsampling documents to a total of about 50 million examples. Sampling is random.
This model has been trained for 230.000 steps (early stopped before the 250k intended steps).
Please see our main card for more information.
T... | [
"## Team members\n\n- Eduardo Gonzรกlez (edugp)\n- Javier de la Rosa (versae)\n- Manu Romero (mrm8488)\n- Marรญa Grandury (mariagrandury)\n- Pablo Gonzรกlez de Prado (Pablogps)\n- Paulo Villegas (paulo)"
] | [
"TAGS\n#transformers #pytorch #jax #tensorboard #joblib #roberta #fill-mask #spanish #es #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"## Team members\n\n- Eduardo Gonzรกlez (edugp)\n- Javier de la Rosa (versae)\n- Manu Romero (mrm8488)\n- Marรญa Grandury (mariagrandury)... |
fill-mask | transformers |
This is a **RoBERTa-base** model trained from scratch in Spanish.
The training dataset is [mc4](https://huggingface.co/datasets/bertin-project/mc4-es-sampled ) subsampling documents to a total of about 50 million examples. Sampling is biased towards average perplexity values (using a Gaussian function), discarding mo... | {"language": "es", "license": "cc-by-4.0", "tags": ["spanish", "roberta"], "pipeline_tag": "fill-mask", "widget": [{"text": "Fui a la librer\u00eda a comprar un <mask>."}]} | bertin-project/bertin-base-stepwise-exp-512seqlen | null | [
"transformers",
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"jax",
"tensorboard",
"joblib",
"roberta",
"fill-mask",
"spanish",
"es",
"license:cc-by-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"es"
] | TAGS
#transformers #pytorch #jax #tensorboard #joblib #roberta #fill-mask #spanish #es #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #region-us
|
This is a RoBERTa-base model trained from scratch in Spanish.
The training dataset is mc4 subsampling documents to a total of about 50 million examples. Sampling is biased towards average perplexity values (using a Gaussian function), discarding more often documents with very large values (poor quality) of very small... | [
"## Team members\n\n- Eduardo Gonzรกlez (edugp)\n- Javier de la Rosa (versae)\n- Manu Romero (mrm8488)\n- Marรญa Grandury (mariagrandury)\n- Pablo Gonzรกlez de Prado (Pablogps)\n- Paulo Villegas (paulo)"
] | [
"TAGS\n#transformers #pytorch #jax #tensorboard #joblib #roberta #fill-mask #spanish #es #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"## Team members\n\n- Eduardo Gonzรกlez (edugp)\n- Javier de la Rosa (versae)\n- Manu Romero (mrm8488)\n- Marรญa Grandury (mariagrandury)\n- Pablo G... |
fill-mask | transformers |
This is a **RoBERTa-base** model trained from scratch in Spanish.
The training dataset is [mc4](https://huggingface.co/datasets/bertin-project/mc4-es-sampled ) subsampling documents to a total of about 50 million examples. Sampling is biased towards average perplexity values (defining perplexity boundaries based on q... | {"language": "es", "license": "cc-by-4.0", "tags": ["spanish", "roberta"], "pipeline_tag": "fill-mask", "widget": [{"text": "Fui a la librer\u00eda a comprar un <mask>."}]} | bertin-project/bertin-base-stepwise | null | [
"transformers",
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"roberta",
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"spanish",
"es",
"license:cc-by-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"es"
] | TAGS
#transformers #pytorch #jax #tensorboard #joblib #roberta #fill-mask #spanish #es #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #region-us
|
This is a RoBERTa-base model trained from scratch in Spanish.
The training dataset is mc4 subsampling documents to a total of about 50 million examples. Sampling is biased towards average perplexity values (defining perplexity boundaries based on quartiles), discarding more often documents with very large values (Q4,... | [
"## Team members\n\n- Eduardo Gonzรกlez (edugp)\n- Javier de la Rosa (versae)\n- Manu Romero (mrm8488)\n- Marรญa Grandury (mariagrandury)\n- Pablo Gonzรกlez de Prado (Pablogps)\n- Paulo Villegas (paulo)"
] | [
"TAGS\n#transformers #pytorch #jax #tensorboard #joblib #roberta #fill-mask #spanish #es #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"## Team members\n\n- Eduardo Gonzรกlez (edugp)\n- Javier de la Rosa (versae)\n- Manu Romero (mrm8488)\n- Marรญa Grandury (mariagrandury)\n- Pablo G... |
text-classification | transformers |
This checkpoint has been trained for the XNLI dataset.
This checkpoint was created from **Bertin Gaussian 512**, which is a **RoBERTa-base** model trained from scratch in Spanish. Information on this base model may be found at [its own card](https://huggingface.co/bertin-project/bertin-base-gaussian-exp-512seqlen) an... | {"language": "es", "license": "cc-by-4.0", "tags": ["spanish", "roberta", "xnli"]} | bertin-project/bertin-base-xnli-es | null | [
"transformers",
"pytorch",
"safetensors",
"roberta",
"text-classification",
"spanish",
"xnli",
"es",
"license:cc-by-4.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"es"
] | TAGS
#transformers #pytorch #safetensors #roberta #text-classification #spanish #xnli #es #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
This checkpoint has been trained for the XNLI dataset.
This checkpoint was created from Bertin Gaussian 512, which is a RoBERTa-base model trained from scratch in Spanish. Information on this base model may be found at its own card and at deeper detail on the main project card.
The training dataset for the base mod... | [
"## Team members\n\n- Eduardo Gonzรกlez (edugp)\n- Javier de la Rosa (versae)\n- Manu Romero (mrm8488)\n- Marรญa Grandury (mariagrandury)\n- Pablo Gonzรกlez de Prado (Pablogps)\n- Paulo Villegas (paulo)"
] | [
"TAGS\n#transformers #pytorch #safetensors #roberta #text-classification #spanish #xnli #es #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"## Team members\n\n- Eduardo Gonzรกlez (edugp)\n- Javier de la Rosa (versae)\n- Manu Romero (mrm8488)\n- Marรญa Grandury (mariagrandu... |
fill-mask | transformers |
- [Version v2](https://huggingface.co/bertin-project/bertin-roberta-base-spanish/tree/v2) (default): April 28th, 2022
- [Version v1](https://huggingface.co/bertin-project/bertin-roberta-base-spanish/tree/v1): July 26th, 2021
- [Version v1-512](https://huggingface.co/bertin-project/bertin-roberta-base-spanish/tree/v1-5... | {"language": "es", "license": "cc-by-4.0", "tags": ["spanish", "roberta"], "datasets": ["bertin-project/mc4-es-sampled"], "pipeline_tag": "fill-mask", "widget": [{"text": "Fui a la librer\u00eda a comprar un <mask>."}]} | bertin-project/bertin-roberta-base-spanish | null | [
"transformers",
"pytorch",
"jax",
"tensorboard",
"safetensors",
"roberta",
"fill-mask",
"spanish",
"es",
"dataset:bertin-project/mc4-es-sampled",
"arxiv:2107.07253",
"arxiv:1907.11692",
"license:cc-by-4.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2107.07253",
"1907.11692"
] | [
"es"
] | TAGS
#transformers #pytorch #jax #tensorboard #safetensors #roberta #fill-mask #spanish #es #dataset-bertin-project/mc4-es-sampled #arxiv-2107.07253 #arxiv-1907.11692 #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| * Version v2 (default): April 28th, 2022
* Version v1: July 26th, 2021
* Version v1-512: July 26th, 2021
* Version beta: July 15th, 2021
BERTIN
======
BERTIN is a series of BERT-based models for Spanish. The current model hub points to the best of all RoBERTa-base models trained from sc... | [
"### Training details\n\n\nWe then used the same setup and hyperparameters as Liu et al. (2019) but trained only for half the steps (250k) on a sequence length of 128. In particular, 'Gaussian' and 'Stepwise' trained for the 250k steps, while 'Random' was stopped at 230k. 'Stepwise' needed to be initially stopped a... | [
"TAGS\n#transformers #pytorch #jax #tensorboard #safetensors #roberta #fill-mask #spanish #es #dataset-bertin-project/mc4-es-sampled #arxiv-2107.07253 #arxiv-1907.11692 #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### Training details\n\n\nWe then used the same setup ... |
question-answering | transformers | ## Demo
- [https://huggingface.co/spaces/bespin-global/Bespin-QuestionAnswering](https://huggingface.co/spaces/bespin-global/Bespin-QuestionAnswering)
## Finetuning
- Pretrain Model : [klue/bert-base](https://github.com/KLUE-benchmark/KLUE)
- Dataset for fine-tuning : [AIHub ๊ธฐ๊ณ๋
ํด ๋ฐ์ดํฐ์
](https://aihub.or.kr/aidata/86)... | {"language": "ko", "license": "cc-by-nc-4.0", "tags": ["bert", "mrc"], "datasets": ["aihub"]} | bespin-global/klue-bert-base-aihub-mrc | null | [
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"bert",
"question-answering",
"mrc",
"ko",
"dataset:aihub",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ko"
] | TAGS
#transformers #pytorch #bert #question-answering #mrc #ko #dataset-aihub #license-cc-by-nc-4.0 #endpoints_compatible #has_space #region-us
| ## Demo
- URL
## Finetuning
- Pretrain Model : klue/bert-base
- Dataset for fine-tuning : AIHub ๊ธฐ๊ณ๋
ํด ๋ฐ์ดํฐ์
- ํ์ค ๋ฐ์ดํฐ ์
(25m) + ์ค๋ช
๊ฐ๋ฅ ๋ฐ์ดํฐ ์
(10m)
- Random Sampling (random_seed: 1234)
- Train : 30m
- Test : 5m
- Parameters of Training
## Usage
## Citing & Authors
Jaehyeong at Bespin Global | [
"## Demo\n - URL",
"## Finetuning\n- Pretrain Model : klue/bert-base\n- Dataset for fine-tuning : AIHub ๊ธฐ๊ณ๋
ํด ๋ฐ์ดํฐ์
\n - ํ์ค ๋ฐ์ดํฐ ์
(25m) + ์ค๋ช
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ํด ๋ฐ์ดํฐ์
\n - ํ์ค ๋ฐ์ดํฐ ์
(25m) + ์ค๋ช
๊ฐ๋ฅ ๋ฐ์ดํฐ ์
(10m)\n - Ran... |
text-classification | transformers |
## Finetuning
- Pretrain Model : [klue/roberta-small](https://github.com/KLUE-benchmark/KLUE)
- Dataset for fine-tuning : [3i4k](https://github.com/warnikchow/3i4k)
- Train : 46,863
- Validation : 8,271 (15% of Train)
- Test : 6,121
- Label info
- 0: "fragment",
- 1: "statement",
- 2: "question",
- 3: ... | {"language": "ko", "license": "cc-by-nc-4.0", "tags": ["intent-classification"], "datasets": ["kor_3i4k"]} | bespin-global/klue-roberta-small-3i4k-intent-classification | null | [
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"text-classification",
"intent-classification",
"ko",
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"license:cc-by-nc-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ko"
] | TAGS
#transformers #pytorch #tf #safetensors #roberta #text-classification #intent-classification #ko #dataset-kor_3i4k #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #region-us
|
## Finetuning
- Pretrain Model : klue/roberta-small
- Dataset for fine-tuning : 3i4k
- Train : 46,863
- Validation : 8,271 (15% of Train)
- Test : 6,121
- Label info
- 0: "fragment",
- 1: "statement",
- 2: "question",
- 3: "command",
- 4: "rhetorical question",
- 5: "rhetorical command",
- 6: "in... | [
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"## Finetuning\n- Pretrain Model : klue/roberta-small\n- Dataset for fine-tuning : 3i4k \n - Train : 46,863\n... |
sentence-similarity | sentence-transformers |
# bespin-global/klue-sentence-roberta-kornlu
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using... | {"license": "cc-by-nc-4.0", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "datasets": ["kor_nlu"], "pipeline_tag": "sentence-similarity"} | bespin-global/klue-sentence-roberta-base-kornlu | null | [
"sentence-transformers",
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"roberta",
"feature-extraction",
"sentence-similarity",
"transformers",
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"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #roberta #feature-extraction #sentence-similarity #transformers #dataset-kor_nlu #license-cc-by-nc-4.0 #endpoints_compatible #region-us
|
# bespin-global/klue-sentence-roberta-kornlu
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformer... | [
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sentence-similarity | sentence-transformers |
# bespin-global/klue-sentence-roberta-base
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using t... | {"license": "cc-by-nc-4.0", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "datasets": ["klue"], "pipeline_tag": "sentence-similarity"} | bespin-global/klue-sentence-roberta-base | null | [
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"roberta",
"feature-extraction",
"sentence-similarity",
"transformers",
"dataset:klue",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #roberta #feature-extraction #sentence-similarity #transformers #dataset-klue #license-cc-by-nc-4.0 #endpoints_compatible #region-us
|
# bespin-global/klue-sentence-roberta-base
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers ... | [
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"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-tra... | [
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text-generation | transformers |
# The Tenth Doctor DialoGPT Model | {"tags": ["conversational"]} | bestminerevah/DialoGPT-small-thetenthdoctor | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# The Tenth Doctor DialoGPT Model | [
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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_paraphrase_generator_en_de_v2
This model was trained from scratch on an unknown dataset.
## Model description
More ... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "bart_large_paraphrase_generator_en_de_v2", "results": []}]} | bettertextapp/bart_large_paraphrase_generator_en_de_v2 | null | [
"transformers",
"pytorch",
"tensorboard",
"mbart",
"text2text-generation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #mbart #text2text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
|
# bart_large_paraphrase_generator_en_de_v2
This model was trained from scratch on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
{'eval_loss': 0.9200083613395691, 'eval_score': 49.97448884411352, 'eval_counts': [100712, 72963, 57055, 4157... | [
"# bart_large_paraphrase_generator_en_de_v2\n\nThis model was trained from scratch on an unknown dataset.",
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"## Model description\n\nMore information needed... |
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_teaser_de_v2
This model was trained from scratch on an unknown dataset.
## Model description
More information neede... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "bart_large_teaser_de_v2", "results": []}]} | bettertextapp/bart_large_teaser_de_v2 | null | [
"transformers",
"pytorch",
"tensorboard",
"mbart",
"text2text-generation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #mbart #text2text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
|
# bart_large_teaser_de_v2
This model was trained from scratch on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
{'eval_loss': 0.2028738558292389, 'eval_score': 80.750962016922, 'eval_counts': [342359, 3160... | [
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"## Intended uses & limitations\n\nMore information needed",
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"## Model description\n\nMore information needed",
"## Intended... |
text-classification | transformers |
## bart-large-mnli
Trained by Facebook, [original source](https://github.com/pytorch/fairseq/tree/master/examples/bart)
| {"widget": [{"text": "I like you. </s></s> I love you."}]} | bewgle/bart-large-mnli-bewgle | null | [
"transformers",
"pytorch",
"bart",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bart #text-classification #autotrain_compatible #endpoints_compatible #region-us
|
## bart-large-mnli
Trained by Facebook, original source
| [
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] |
question-answering | null |
# Performance
This ensemble was evaluated on [SQuAD 2.0](https://huggingface.co/datasets/squad_v2) with the following results:
```
{'HasAns_exact': 52.5472334682861,
'HasAns_f1': 67.94939813758602,
'HasAns_total': 5928,
'NoAns_exact': 91.75777964676199,
'NoAns_f1': 91.75777964676199,
'NoAns_total': 5945,
'best_... | {"language": "en", "license": "cc-by-4.0", "tags": ["pytorch", "question-answering"], "datasets": ["squad_v2", "squad2"], "metrics": ["squad_v2", "exact", "f1"], "widget": [{"text": "By what main attribute are computational problems classified utilizing computational complexity theory?", "context": "Computational compl... | bgfruna/double-bart-ensemble-squad2 | null | [
"pytorch",
"question-answering",
"en",
"dataset:squad_v2",
"dataset:squad2",
"license:cc-by-4.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#pytorch #question-answering #en #dataset-squad_v2 #dataset-squad2 #license-cc-by-4.0 #region-us
|
# Performance
This ensemble was evaluated on SQuAD 2.0 with the following results:
| [
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] |
text-classification | transformers |
# Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 28716412
- CO2 Emissions (in grams): 27.22397099134103
## Validation Metrics
- Loss: 0.4146720767021179
- Accuracy: 0.8066924731182795
- Macro F1: 0.7835463282531184
- Micro F1: 0.8066924731182795
- Weighted F1: 0.7974252447208724
... | {"language": "en", "tags": "autonlp", "datasets": ["bgoel4132/autonlp-data-tweet-disaster-classifier"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}], "co2_eq_emissions": 27.22397099134103} | bgoel4132/tweet-disaster-classifier | null | [
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"safetensors",
"distilbert",
"text-classification",
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"en",
"dataset:bgoel4132/autonlp-data-tweet-disaster-classifier",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
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|
# Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 28716412
- CO2 Emissions (in grams): 27.22397099134103
## Validation Metrics
- Loss: 0.4146720767021179
- Accuracy: 0.8066924731182795
- Macro F1: 0.7835463282531184
- Micro F1: 0.8066924731182795
- Weighted F1: 0.7974252447208724
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"# Model Trained Using AutoNLP\n\n- Problem type: Multi-class Classification\n- Model ID:... |
text-classification | transformers |
# Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 35868888
- CO2 Emissions (in grams): 186.8637425115097
## Validation Metrics
- Loss: 0.2020547091960907
- Accuracy: 0.9233253193796257
- Macro F1: 0.9240407542958707
- Micro F1: 0.9233253193796257
- Weighted F1: 0.921800586774046
-... | {"language": "en", "tags": "autonlp", "datasets": ["bgoel4132/autonlp-data-twitter-sentiment"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}], "co2_eq_emissions": 186.8637425115097} | bgoel4132/twitter-sentiment | null | [
"transformers",
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"en",
"dataset:bgoel4132/autonlp-data-twitter-sentiment",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
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|
# Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 35868888
- CO2 Emissions (in grams): 186.8637425115097
## Validation Metrics
- Loss: 0.2020547091960907
- Accuracy: 0.9233253193796257
- Macro F1: 0.9240407542958707
- Micro F1: 0.9233253193796257
- Weighted F1: 0.921800586774046
-... | [
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"# Model Trained Using AutoNLP\n\n- Problem type: Multi-class Classification\n- Model ID: 35868888\n- CO2 Emissions ... |
text-generation | transformers |
# Loki GPT Dialog Bot | {"tags": ["conversational"]} | bhaden94/LokiDiscordBot-medium | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Loki GPT Dialog Bot | [
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"# Loki GPT Dialog Bot"
] |
text-classification | transformers | # Albert-base-v2-emotion
## Model description:
[Albert](https://arxiv.org/pdf/1909.11942v6.pdf) is A Lite BERT architecture that has significantly fewer parameters than a traditional BERT architecture.
[Albert-base-v2](https://huggingface.co/albert-base-v2) finetuned on the emotion dataset using HuggingFace Trainer w... | {"language": ["en"], "license": "apache-2.0", "tags": ["text-classification", "emotion", "pytorch"], "datasets": ["emotion"], "metrics": ["Accuracy, F1 Score"], "thumbnail": "https://avatars3.githubusercontent.com/u/32437151?s=460&u=4ec59abc8d21d5feea3dab323d23a5860e6996a4&v=4"} | bhadresh-savani/albert-base-v2-emotion | null | [
"transformers",
"pytorch",
"tf",
"jax",
"albert",
"text-classification",
"emotion",
"en",
"dataset:emotion",
"arxiv:1909.11942",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1909.11942"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #jax #albert #text-classification #emotion #en #dataset-emotion #arxiv-1909.11942 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| Albert-base-v2-emotion
======================
Model description:
------------------
Albert is A Lite BERT architecture that has significantly fewer parameters than a traditional BERT architecture.
Albert-base-v2 finetuned on the emotion dataset using HuggingFace Trainer with below Hyperparameters
Model Performa... | [] | [
"TAGS\n#transformers #pytorch #tf #jax #albert #text-classification #emotion #en #dataset-emotion #arxiv-1909.11942 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
text-classification | transformers | # Bert-Base-Uncased-Go-Emotion
## Model description:
## Training Parameters:
```
Num examples = 169208
Num Epochs = 3
Instantaneous batch size per device = 16
Total train batch size (w. parallel, distributed & accumulation) = 16
Gradient Accumulation steps = 1
Total optimization steps = 31728
```
## TrainOutput:
```... | {"language": ["en"], "license": "apache-2.0", "tags": ["text-classification", "go-emotion", "pytorch"], "datasets": ["go_emotions"], "metrics": ["Accuracy"], "thumbnail": "https://avatars3.githubusercontent.com/u/32437151?s=460&u=4ec59abc8d21d5feea3dab323d23a5860e6996a4&v=4"} | bhadresh-savani/bert-base-go-emotion | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"go-emotion",
"en",
"dataset:go_emotions",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bert #text-classification #go-emotion #en #dataset-go_emotions #license-apache-2.0 #endpoints_compatible #has_space #region-us
| # Bert-Base-Uncased-Go-Emotion
## Model description:
## Training Parameters:
## TrainOutput:
## Evalution Output:
## Colab Notebook:
Notebook | [
"# Bert-Base-Uncased-Go-Emotion",
"## Model description:",
"## Training Parameters:",
"## TrainOutput:",
"## Evalution Output:",
"## Colab Notebook:\nNotebook"
] | [
"TAGS\n#transformers #pytorch #bert #text-classification #go-emotion #en #dataset-go_emotions #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"# Bert-Base-Uncased-Go-Emotion",
"## Model description:",
"## Training Parameters:",
"## TrainOutput:",
"## Evalution Output:",
"## Colab No... |
text-classification | transformers | # bert-base-uncased-emotion
## Model description:
[Bert](https://arxiv.org/abs/1810.04805) is a Transformer Bidirectional Encoder based Architecture trained on MLM(Mask Language Modeling) objective
[bert-base-uncased](https://huggingface.co/bert-base-uncased) finetuned on the emotion dataset using HuggingFace Traine... | {"language": ["en"], "license": "apache-2.0", "tags": ["text-classification", "emotion", "pytorch"], "datasets": ["emotion"], "metrics": ["Accuracy, F1 Score"], "thumbnail": "https://avatars3.githubusercontent.com/u/32437151?s=460&u=4ec59abc8d21d5feea3dab323d23a5860e6996a4&v=4", "model-index": [{"name": "bhadresh-savan... | bhadresh-savani/bert-base-uncased-emotion | null | [
"transformers",
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"text-classification",
"emotion",
"en",
"dataset:emotion",
"arxiv:1810.04805",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1810.04805"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #jax #safetensors #bert #text-classification #emotion #en #dataset-emotion #arxiv-1810.04805 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
| bert-base-uncased-emotion
=========================
Model description:
------------------
Bert is a Transformer Bidirectional Encoder based Architecture trained on MLM(Mask Language Modeling) objective
bert-base-uncased finetuned on the emotion dataset using HuggingFace Trainer with below training parameters
Mo... | [] | [
"TAGS\n#transformers #pytorch #tf #jax #safetensors #bert #text-classification #emotion #en #dataset-emotion #arxiv-1810.04805 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
text-classification | transformers | # Distilbert-base-uncased-emotion
## Model description:
[Distilbert](https://arxiv.org/abs/1910.01108) is created with knowledge distillation during the pre-training phase which reduces the size of a BERT model by 40%, while retaining 97% of its language understanding. It's smaller, faster than Bert and any other Bert... | {"language": ["en"], "license": "apache-2.0", "tags": ["text-classification", "emotion", "pytorch"], "datasets": ["emotion"], "metrics": ["Accuracy, F1 Score"], "thumbnail": "https://avatars3.githubusercontent.com/u/32437151?s=460&u=4ec59abc8d21d5feea3dab323d23a5860e6996a4&v=4", "model-index": [{"name": "bhadresh-savan... | bhadresh-savani/distilbert-base-uncased-emotion | null | [
"transformers",
"pytorch",
"tf",
"jax",
"distilbert",
"text-classification",
"emotion",
"en",
"dataset:emotion",
"arxiv:1910.01108",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1910.01108"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #jax #distilbert #text-classification #emotion #en #dataset-emotion #arxiv-1910.01108 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
| Distilbert-base-uncased-emotion
===============================
Model description:
------------------
Distilbert is created with knowledge distillation during the pre-training phase which reduces the size of a BERT model by 40%, while retaining 97% of its language understanding. It's smaller, faster than Bert and a... | [] | [
"TAGS\n#transformers #pytorch #tf #jax #distilbert #text-classification #emotion #en #dataset-emotion #arxiv-1910.01108 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
text-classification | transformers | # Distilbert-Base-Uncased-Go-Emotion
## Model description:
**Not working fine**
## Training Parameters:
```
Num Epochs = 3
Instantaneous batch size per device = 32
Total train batch size (w. parallel, distributed & accumulation) = 32
Gradient Accumulation steps = 1
Total optimization steps = 15831
```
## ... | {"language": ["en"], "license": "apache-2.0", "tags": ["text-classification", "go-emotion", "pytorch"], "datasets": ["go_emotions"], "metrics": ["Accuracy"], "thumbnail": "https://avatars3.githubusercontent.com/u/32437151?s=460&u=4ec59abc8d21d5feea3dab323d23a5860e6996a4&v=4"} | bhadresh-savani/distilbert-base-uncased-go-emotion | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"go-emotion",
"en",
"dataset:go_emotions",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #distilbert #text-classification #go-emotion #en #dataset-go_emotions #license-apache-2.0 #endpoints_compatible #region-us
| # Distilbert-Base-Uncased-Go-Emotion
## Model description:
Not working fine
## Training Parameters:
## TrainOutput:
## Evalution Output:
## Colab Notebook:
Notebook | [
"# Distilbert-Base-Uncased-Go-Emotion",
"## Model description:\n\nNot working fine",
"## Training Parameters:",
"## TrainOutput:",
"## Evalution Output:",
"## Colab Notebook:\nNotebook"
] | [
"TAGS\n#transformers #pytorch #distilbert #text-classification #go-emotion #en #dataset-go_emotions #license-apache-2.0 #endpoints_compatible #region-us \n",
"# Distilbert-Base-Uncased-Go-Emotion",
"## Model description:\n\nNot working fine",
"## Training Parameters:",
"## TrainOutput:",
"## Evalution Out... |
text-classification | transformers |
# distilbert-base-uncased-sentiment-sst2
This model will be able to identify positivity or negativity present in the sentence
## Dataset:
The Stanford Sentiment Treebank from GLUE
## Results:
```
***** eval metrics *****
epoch = 3.0
eval_accuracy = 0.9094
eval_loss ... | {"language": "en", "license": "apache-2.0", "datasets": ["sst2"]} | bhadresh-savani/distilbert-base-uncased-sentiment-sst2 | null | [
"transformers",
"pytorch",
"tf",
"jax",
"distilbert",
"text-classification",
"en",
"dataset:sst2",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #tf #jax #distilbert #text-classification #en #dataset-sst2 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# distilbert-base-uncased-sentiment-sst2
This model will be able to identify positivity or negativity present in the sentence
## Dataset:
The Stanford Sentiment Treebank from GLUE
## Results:
| [
"# distilbert-base-uncased-sentiment-sst2\nThis model will be able to identify positivity or negativity present in the sentence",
"## Dataset:\nThe Stanford Sentiment Treebank from GLUE",
"## Results:"
] | [
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"## Dataset:... |
text-classification | transformers | # robert-base-emotion
## Model description:
[roberta](https://arxiv.org/abs/1907.11692) is Bert with better hyperparameter choices so they said it's Robustly optimized Bert during pretraining.
[roberta-base](https://huggingface.co/roberta-base) finetuned on the emotion dataset using HuggingFace Trainer with below Hyp... | {"language": ["en"], "license": "apache-2.0", "tags": ["text-classification", "emotion", "pytorch"], "datasets": ["emotion"], "metrics": ["Accuracy, F1 Score"], "thumbnail": "https://avatars3.githubusercontent.com/u/32437151?s=460&u=4ec59abc8d21d5feea3dab323d23a5860e6996a4&v=4", "model-index": [{"name": "bhadresh-savan... | bhadresh-savani/roberta-base-emotion | null | [
"transformers",
"pytorch",
"tf",
"jax",
"safetensors",
"roberta",
"text-classification",
"emotion",
"en",
"dataset:emotion",
"arxiv:1907.11692",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1907.11692"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #jax #safetensors #roberta #text-classification #emotion #en #dataset-emotion #arxiv-1907.11692 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
| robert-base-emotion
===================
Model description:
------------------
roberta is Bert with better hyperparameter choices so they said it's Robustly optimized Bert during pretraining.
roberta-base finetuned on the emotion dataset using HuggingFace Trainer with below Hyperparameters
Model Performance Comp... | [] | [
"TAGS\n#transformers #pytorch #tf #jax #safetensors #roberta #text-classification #emotion #en #dataset-emotion #arxiv-1907.11692 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
null | null | added readme | {} | bhagvanarch/test | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| added readme | [] | [
"TAGS\n#region-us \n"
] |
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-uncased-finetuned-squad
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/d... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "distilbert-base-uncased-finetuned-squad", "results": []}]} | bhan/distilbert-base-uncased-finetuned-squad | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-squad
=======================================
This model is a fine-tuned version of distilbert-base-uncased on the squad dataset.
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Trai... | [
"### 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: 1",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_s... |
automatic-speech-recognition | 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. -->
# Tamil-Wav2Vec-xls-r-300m-Tamil-colab
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co... | {"license": "apache-2.0", "tags": ["generated_from_trainer", "ta", "robust-speech-event"], "datasets": ["common_voice"], "model-index": [{"name": "Tamil-Wav2Vec-xls-r-300m-Tamil-colab", "results": []}]} | bharat-raghunathan/Tamil-Wav2Vec-xls-r-300m-Tamil-colab | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"ta",
"robust-speech-event",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #ta #robust-speech-event #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
|
# Tamil-Wav2Vec-xls-r-300m-Tamil-colab
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common_voice dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training proced... | [
"# Tamil-Wav2Vec-xls-r-300m-Tamil-colab\n\nThis model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common_voice dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information need... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #ta #robust-speech-event #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us \n",
"# Tamil-Wav2Vec-xls-r-300m-Tamil-colab\n\nThis model is a fine-tuned version of facebook/wav2vec2-xls... |
question-answering | transformers | # BibTeX entry and citation info
```
@misc{pandya2021cascading,
title={Cascading Adaptors to Leverage English Data to Improve Performance of Question Answering for Low-Resource Languages},
author={Hariom A. Pandya and Bhavik Ardeshna and Dr. Brijesh S. Bhatt},
year={2021},
eprint={2112.09866},... | {} | bhavikardeshna/multilingual-bert-base-cased-arabic | null | [
"transformers",
"pytorch",
"bert",
"question-answering",
"arxiv:2112.09866",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2112.09866"
] | [] | TAGS
#transformers #pytorch #bert #question-answering #arxiv-2112.09866 #endpoints_compatible #region-us
| # BibTeX entry and citation info
| [
"# BibTeX entry and citation info"
] | [
"TAGS\n#transformers #pytorch #bert #question-answering #arxiv-2112.09866 #endpoints_compatible #region-us \n",
"# BibTeX entry and citation info"
] |
question-answering | transformers | # BibTeX entry and citation info
```
@misc{pandya2021cascading,
title={Cascading Adaptors to Leverage English Data to Improve Performance of Question Answering for Low-Resource Languages},
author={Hariom A. Pandya and Bhavik Ardeshna and Dr. Brijesh S. Bhatt},
year={2021},
eprint={2112.09866},... | {} | bhavikardeshna/multilingual-bert-base-cased-chinese | null | [
"transformers",
"pytorch",
"bert",
"question-answering",
"arxiv:2112.09866",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2112.09866"
] | [] | TAGS
#transformers #pytorch #bert #question-answering #arxiv-2112.09866 #endpoints_compatible #region-us
| # BibTeX entry and citation info
| [
"# BibTeX entry and citation info"
] | [
"TAGS\n#transformers #pytorch #bert #question-answering #arxiv-2112.09866 #endpoints_compatible #region-us \n",
"# BibTeX entry and citation info"
] |
question-answering | transformers | # BibTeX entry and citation info
```
@misc{pandya2021cascading,
title={Cascading Adaptors to Leverage English Data to Improve Performance of Question Answering for Low-Resource Languages},
author={Hariom A. Pandya and Bhavik Ardeshna and Dr. Brijesh S. Bhatt},
year={2021},
eprint={2112.09866},... | {} | bhavikardeshna/multilingual-bert-base-cased-english | null | [
"transformers",
"pytorch",
"bert",
"question-answering",
"arxiv:2112.09866",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2112.09866"
] | [] | TAGS
#transformers #pytorch #bert #question-answering #arxiv-2112.09866 #endpoints_compatible #region-us
| # BibTeX entry and citation info
| [
"# BibTeX entry and citation info"
] | [
"TAGS\n#transformers #pytorch #bert #question-answering #arxiv-2112.09866 #endpoints_compatible #region-us \n",
"# BibTeX entry and citation info"
] |
question-answering | transformers | # BibTeX entry and citation info
```
@misc{pandya2021cascading,
title={Cascading Adaptors to Leverage English Data to Improve Performance of Question Answering for Low-Resource Languages},
author={Hariom A. Pandya and Bhavik Ardeshna and Dr. Brijesh S. Bhatt},
year={2021},
eprint={2112.09866},... | {} | bhavikardeshna/multilingual-bert-base-cased-german | null | [
"transformers",
"pytorch",
"bert",
"question-answering",
"arxiv:2112.09866",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2112.09866"
] | [] | TAGS
#transformers #pytorch #bert #question-answering #arxiv-2112.09866 #endpoints_compatible #region-us
| # BibTeX entry and citation info
| [
"# BibTeX entry and citation info"
] | [
"TAGS\n#transformers #pytorch #bert #question-answering #arxiv-2112.09866 #endpoints_compatible #region-us \n",
"# BibTeX entry and citation info"
] |
question-answering | transformers | # BibTeX entry and citation info
```
@misc{pandya2021cascading,
title={Cascading Adaptors to Leverage English Data to Improve Performance of Question Answering for Low-Resource Languages},
author={Hariom A. Pandya and Bhavik Ardeshna and Dr. Brijesh S. Bhatt},
year={2021},
eprint={2112.09866},... | {} | bhavikardeshna/multilingual-bert-base-cased-hindi | null | [
"transformers",
"pytorch",
"bert",
"question-answering",
"arxiv:2112.09866",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2112.09866"
] | [] | TAGS
#transformers #pytorch #bert #question-answering #arxiv-2112.09866 #endpoints_compatible #region-us
| # BibTeX entry and citation info
| [
"# BibTeX entry and citation info"
] | [
"TAGS\n#transformers #pytorch #bert #question-answering #arxiv-2112.09866 #endpoints_compatible #region-us \n",
"# BibTeX entry and citation info"
] |
question-answering | transformers | # BibTeX entry and citation info
```
@misc{pandya2021cascading,
title={Cascading Adaptors to Leverage English Data to Improve Performance of Question Answering for Low-Resource Languages},
author={Hariom A. Pandya and Bhavik Ardeshna and Dr. Brijesh S. Bhatt},
year={2021},
eprint={2112.09866},... | {} | bhavikardeshna/multilingual-bert-base-cased-spanish | null | [
"transformers",
"pytorch",
"bert",
"question-answering",
"arxiv:2112.09866",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2112.09866"
] | [] | TAGS
#transformers #pytorch #bert #question-answering #arxiv-2112.09866 #endpoints_compatible #region-us
| # BibTeX entry and citation info
| [
"# BibTeX entry and citation info"
] | [
"TAGS\n#transformers #pytorch #bert #question-answering #arxiv-2112.09866 #endpoints_compatible #region-us \n",
"# BibTeX entry and citation info"
] |
question-answering | transformers | # BibTeX entry and citation info
```
@misc{pandya2021cascading,
title={Cascading Adaptors to Leverage English Data to Improve Performance of Question Answering for Low-Resource Languages},
author={Hariom A. Pandya and Bhavik Ardeshna and Dr. Brijesh S. Bhatt},
year={2021},
eprint={2112.09866},... | {} | bhavikardeshna/multilingual-bert-base-cased-vietnamese | null | [
"transformers",
"pytorch",
"bert",
"question-answering",
"arxiv:2112.09866",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2112.09866"
] | [] | TAGS
#transformers #pytorch #bert #question-answering #arxiv-2112.09866 #endpoints_compatible #region-us
| # BibTeX entry and citation info
| [
"# BibTeX entry and citation info"
] | [
"TAGS\n#transformers #pytorch #bert #question-answering #arxiv-2112.09866 #endpoints_compatible #region-us \n",
"# BibTeX entry and citation info"
] |
question-answering | transformers | # BibTeX entry and citation info
```
@misc{pandya2021cascading,
title={Cascading Adaptors to Leverage English Data to Improve Performance of Question Answering for Low-Resource Languages},
author={Hariom A. Pandya and Bhavik Ardeshna and Dr. Brijesh S. Bhatt},
year={2021},
eprint={2112.09866},... | {} | bhavikardeshna/xlm-roberta-base-arabic | null | [
"transformers",
"pytorch",
"xlm-roberta",
"question-answering",
"arxiv:2112.09866",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2112.09866"
] | [] | TAGS
#transformers #pytorch #xlm-roberta #question-answering #arxiv-2112.09866 #endpoints_compatible #region-us
| # BibTeX entry and citation info
| [
"# BibTeX entry and citation info"
] | [
"TAGS\n#transformers #pytorch #xlm-roberta #question-answering #arxiv-2112.09866 #endpoints_compatible #region-us \n",
"# BibTeX entry and citation info"
] |
question-answering | transformers | # BibTeX entry and citation info
```
@misc{pandya2021cascading,
title={Cascading Adaptors to Leverage English Data to Improve Performance of Question Answering for Low-Resource Languages},
author={Hariom A. Pandya and Bhavik Ardeshna and Dr. Brijesh S. Bhatt},
year={2021},
eprint={2112.09866},... | {} | bhavikardeshna/xlm-roberta-base-chinese | null | [
"transformers",
"pytorch",
"xlm-roberta",
"question-answering",
"arxiv:2112.09866",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2112.09866"
] | [] | TAGS
#transformers #pytorch #xlm-roberta #question-answering #arxiv-2112.09866 #endpoints_compatible #region-us
| # BibTeX entry and citation info
| [
"# BibTeX entry and citation info"
] | [
"TAGS\n#transformers #pytorch #xlm-roberta #question-answering #arxiv-2112.09866 #endpoints_compatible #region-us \n",
"# BibTeX entry and citation info"
] |
question-answering | transformers | # BibTeX entry and citation info
```
@misc{pandya2021cascading,
title={Cascading Adaptors to Leverage English Data to Improve Performance of Question Answering for Low-Resource Languages},
author={Hariom A. Pandya and Bhavik Ardeshna and Dr. Brijesh S. Bhatt},
year={2021},
eprint={2112.09866},... | {} | bhavikardeshna/xlm-roberta-base-german | null | [
"transformers",
"pytorch",
"xlm-roberta",
"question-answering",
"arxiv:2112.09866",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2112.09866"
] | [] | TAGS
#transformers #pytorch #xlm-roberta #question-answering #arxiv-2112.09866 #endpoints_compatible #region-us
| # BibTeX entry and citation info
| [
"# BibTeX entry and citation info"
] | [
"TAGS\n#transformers #pytorch #xlm-roberta #question-answering #arxiv-2112.09866 #endpoints_compatible #region-us \n",
"# BibTeX entry and citation info"
] |
question-answering | transformers | # BibTeX entry and citation info
```
@misc{pandya2021cascading,
title={Cascading Adaptors to Leverage English Data to Improve Performance of Question Answering for Low-Resource Languages},
author={Hariom A. Pandya and Bhavik Ardeshna and Dr. Brijesh S. Bhatt},
year={2021},
eprint={2112.09866},... | {} | bhavikardeshna/xlm-roberta-base-hindi | null | [
"transformers",
"pytorch",
"xlm-roberta",
"question-answering",
"arxiv:2112.09866",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2112.09866"
] | [] | TAGS
#transformers #pytorch #xlm-roberta #question-answering #arxiv-2112.09866 #endpoints_compatible #region-us
| # BibTeX entry and citation info
| [
"# BibTeX entry and citation info"
] | [
"TAGS\n#transformers #pytorch #xlm-roberta #question-answering #arxiv-2112.09866 #endpoints_compatible #region-us \n",
"# BibTeX entry and citation info"
] |
question-answering | transformers | # BibTeX entry and citation info
```
@misc{pandya2021cascading,
title={Cascading Adaptors to Leverage English Data to Improve Performance of Question Answering for Low-Resource Languages},
author={Hariom A. Pandya and Bhavik Ardeshna and Dr. Brijesh S. Bhatt},
year={2021},
eprint={2112.09866},... | {} | bhavikardeshna/xlm-roberta-base-spanish | null | [
"transformers",
"pytorch",
"xlm-roberta",
"question-answering",
"arxiv:2112.09866",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2112.09866"
] | [] | TAGS
#transformers #pytorch #xlm-roberta #question-answering #arxiv-2112.09866 #endpoints_compatible #region-us
| # BibTeX entry and citation info
| [
"# BibTeX entry and citation info"
] | [
"TAGS\n#transformers #pytorch #xlm-roberta #question-answering #arxiv-2112.09866 #endpoints_compatible #region-us \n",
"# BibTeX entry and citation info"
] |
question-answering | transformers | # BibTeX entry and citation info
```
@misc{pandya2021cascading,
title={Cascading Adaptors to Leverage English Data to Improve Performance of Question Answering for Low-Resource Languages},
author={Hariom A. Pandya and Bhavik Ardeshna and Dr. Brijesh S. Bhatt},
year={2021},
eprint={2112.09866},... | {} | bhavikardeshna/xlm-roberta-base-vietnamese | null | [
"transformers",
"pytorch",
"xlm-roberta",
"question-answering",
"arxiv:2112.09866",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2112.09866"
] | [] | TAGS
#transformers #pytorch #xlm-roberta #question-answering #arxiv-2112.09866 #endpoints_compatible #region-us
| # BibTeX entry and citation info
| [
"# BibTeX entry and citation info"
] | [
"TAGS\n#transformers #pytorch #xlm-roberta #question-answering #arxiv-2112.09866 #endpoints_compatible #region-us \n",
"# BibTeX entry and citation info"
] |
text-generation | transformers | #Chandler DialoGPT model | {"tags": ["conversational"]} | bhavya689/DialoGPT-large-chandler | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| #Chandler DialoGPT model | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
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. -->
# t5-small-text_summarization
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the xsum datase... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["xsum"], "metrics": ["rouge"], "model-index": [{"name": "t5-small-text_summarization", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "xsum", "type": "xsum", "args": "... | bhuvaneswari/t5-small-text_summarization | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:xsum",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-xsum #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| t5-small-text\_summarization
============================
This model is a fine-tuned version of t5-small on the xsum dataset.
It achieves the following results on the evaluation set:
* Loss: 2.4591
* Rouge1: 28.6917
* Rouge2: 7.976
* Rougel: 22.6383
* Rougelsum: 22.6353
* Gen Len: 18.8185
Model description
------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 25\n* eval\\_batch\\_size: 25\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1\n* mixed\\_prec... | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-xsum #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during train... |
text-generation | transformers |
# ๐ธ ๐ฅ Rockbot ๐ค ๐ง
A [GPT-2](https://openai.com/blog/better-language-models/) based lyrics generator fine-tuned on the writing styles of 16000 songs by 270 artists across MANY genres (not just rock).
**Instructions:** Type in a fake song title, pick an artist, click "Generate".
Most language models are imprecise... | {} | bigjoedata/rockbot-scratch | null | [
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Rockbot
A GPT-2 based lyrics generator fine-tuned on the writing styles of 16000 songs by 270 artists across MANY genres (not just rock).
Instructions: Type in a fake song title, pick an artist, click "Generate".
Most language models are imprecise and Rockbot is no exception. You may see NSFW lyrics unexpecte... | [
"# Rockbot \nA GPT-2 based lyrics generator fine-tuned on the writing styles of 16000 songs by 270 artists across MANY genres (not just rock).\n\nInstructions: Type in a fake song title, pick an artist, click \"Generate\".\n\nMost language models are imprecise and Rockbot is no exception. You may see NSFW lyric... | [
"TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Rockbot \nA GPT-2 based lyrics generator fine-tuned on the writing styles of 16000 songs by 270 artists across MANY genres (not just rock).\n\nInstructions: Type ... |
text-generation | transformers |
# ๐ธ ๐ฅ Rockbot ๐ค ๐ง
A [GPT-2](https://openai.com/blog/better-language-models/) based lyrics generator fine-tuned on the writing styles of 16000 songs by 270 artists across MANY genres (not just rock).
**Instructions:** Type in a fake song title, pick an artist, click "Generate".
Most language models are imprecise... | {} | bigjoedata/rockbot | null | [
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Rockbot
A GPT-2 based lyrics generator fine-tuned on the writing styles of 16000 songs by 270 artists across MANY genres (not just rock).
Instructions: Type in a fake song title, pick an artist, click "Generate".
Most language models are imprecise and Rockbot is no exception. You may see NSFW lyrics unexpecte... | [
"# Rockbot \nA GPT-2 based lyrics generator fine-tuned on the writing styles of 16000 songs by 270 artists across MANY genres (not just rock).\n\nInstructions: Type in a fake song title, pick an artist, click \"Generate\".\n\nMost language models are imprecise and Rockbot is no exception. You may see NSFW lyric... | [
"TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Rockbot \nA GPT-2 based lyrics generator fine-tuned on the writing styles of 16000 songs by 270 artists across MANY genres (not just rock).\n\nInstructions: Type ... |
text-generation | transformers |
# ๐ธ ๐ฅ Rockbot ๐ค ๐ง
A [GPT-2](https://openai.com/blog/better-language-models/) based lyrics generator fine-tuned on the writing styles of 16000 songs by 270 artists across MANY genres (not just rock).
**Instructions:** Type in a fake song title, pick an artist, click "Generate".
Most language models are imprecise... | {} | bigjoedata/rockbot355M | null | [
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Rockbot
A GPT-2 based lyrics generator fine-tuned on the writing styles of 16000 songs by 270 artists across MANY genres (not just rock).
Instructions: Type in a fake song title, pick an artist, click "Generate".
Most language models are imprecise and Rockbot is no exception. You may see NSFW lyrics unexpecte... | [
"# Rockbot \nA GPT-2 based lyrics generator fine-tuned on the writing styles of 16000 songs by 270 artists across MANY genres (not just rock).\n\nInstructions: Type in a fake song title, pick an artist, click \"Generate\".\n\nMost language models are imprecise and Rockbot is no exception. You may see NSFW lyric... | [
"TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Rockbot \nA GPT-2 based lyrics generator fine-tuned on the writing styles of 16000 songs by 270 artists across MANY genres (not just rock).\n\nInstructions: Type ... |
text2text-generation | transformers |
**How do I pronounce the name of the model?** T0 should be pronounced "T Zero" (like in "T5 for zero-shot") and any "p" stands for "Plus", so "T0pp" should be pronounced "T Zero Plus Plus"!
**Official repository**: [bigscience-workshop/t-zero](https://github.com/bigscience-workshop/t-zero)
# Model Description
T0* s... | {"language": "en", "license": "apache-2.0", "datasets": ["bigscience/P3"], "widget": [{"text": "A is the son's of B's uncle. What is the family relationship between A and B?"}, {"text": "Reorder the words in this sentence: justin and name bieber years is my am I 27 old."}, {"text": "Task: copy but say the opposite.\n P... | bigscience/T0 | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"en",
"dataset:bigscience/P3",
"arxiv:2110.08207",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2110.08207"
] | [
"en"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #en #dataset-bigscience/P3 #arxiv-2110.08207 #license-apache-2.0 #autotrain_compatible #text-generation-inference #region-us
| How do I pronounce the name of the model? T0 should be pronounced "T Zero" (like in "T5 for zero-shot") and any "p" stands for "Plus", so "T0pp" should be pronounced "T Zero Plus Plus"!
Official repository: bigscience-workshop/t-zero
Model Description
=================
T0\* shows zero-shot task generalization on ... | [] | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #en #dataset-bigscience/P3 #arxiv-2110.08207 #license-apache-2.0 #autotrain_compatible #text-generation-inference #region-us \n"
] |
text2text-generation | transformers |
**How do I pronounce the name of the model?** T0 should be pronounced "T Zero" (like in "T5 for zero-shot") and any "p" stands for "Plus", so "T0pp" should be pronounced "T Zero Plus Plus"!
**Official repository**: [bigscience-workshop/t-zero](https://github.com/bigscience-workshop/t-zero)
# Model Description
T0* s... | {"language": "en", "license": "apache-2.0", "datasets": ["bigscience/P3"], "widget": [{"text": "A is the son's of B's uncle. What is the family relationship between A and B?"}, {"text": "Reorder the words in this sentence: justin and name bieber years is my am I 27 old."}, {"text": "Task: copy but say the opposite.\n P... | bigscience/T0_3B | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"en",
"dataset:bigscience/P3",
"arxiv:2110.08207",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2110.08207"
] | [
"en"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #en #dataset-bigscience/P3 #arxiv-2110.08207 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| How do I pronounce the name of the model? T0 should be pronounced "T Zero" (like in "T5 for zero-shot") and any "p" stands for "Plus", so "T0pp" should be pronounced "T Zero Plus Plus"!
Official repository: bigscience-workshop/t-zero
Model Description
=================
T0\* shows zero-shot task generalization on ... | [] | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #en #dataset-bigscience/P3 #arxiv-2110.08207 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n"
] |
text2text-generation | transformers |
**How do I pronounce the name of the model?** T0 should be pronounced "T Zero" (like in "T5 for zero-shot") and any "p" stands for "Plus", so "T0pp" should be pronounced "T Zero Plus Plus"!
**Official repository**: [bigscience-workshop/t-zero](https://github.com/bigscience-workshop/t-zero)
# Model Description
T0* s... | {"language": "en", "license": "apache-2.0", "datasets": ["bigscience/P3"], "widget": [{"text": "A is the son's of B's uncle. What is the family relationship between A and B?"}, {"text": "Reorder the words in this sentence: justin and name bieber years is my am I 27 old."}, {"text": "Task: copy but say the opposite.\n P... | bigscience/T0_original_task_only | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"en",
"dataset:bigscience/P3",
"arxiv:2110.08207",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2110.08207"
] | [
"en"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #en #dataset-bigscience/P3 #arxiv-2110.08207 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| How do I pronounce the name of the model? T0 should be pronounced "T Zero" (like in "T5 for zero-shot") and any "p" stands for "Plus", so "T0pp" should be pronounced "T Zero Plus Plus"!
Official repository: bigscience-workshop/t-zero
Model Description
=================
T0\* shows zero-shot task generalization on ... | [] | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #en #dataset-bigscience/P3 #arxiv-2110.08207 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n"
] |
text2text-generation | transformers |
**How do I pronounce the name of the model?** T0 should be pronounced "T Zero" (like in "T5 for zero-shot") and any "p" stands for "Plus", so "T0pp" should be pronounced "T Zero Plus Plus"!
**Official repository**: [bigscience-workshop/t-zero](https://github.com/bigscience-workshop/t-zero)
# Model Description
T0* s... | {"language": "en", "license": "apache-2.0", "datasets": ["bigscience/P3"], "widget": [{"text": "A is the son's of B's uncle. What is the family relationship between A and B?"}, {"text": "Reorder the words in this sentence: justin and name bieber years is my am I 27 old."}, {"text": "Task: copy but say the opposite.\n P... | bigscience/T0_single_prompt | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"en",
"dataset:bigscience/P3",
"arxiv:2110.08207",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2110.08207"
] | [
"en"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #en #dataset-bigscience/P3 #arxiv-2110.08207 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| How do I pronounce the name of the model? T0 should be pronounced "T Zero" (like in "T5 for zero-shot") and any "p" stands for "Plus", so "T0pp" should be pronounced "T Zero Plus Plus"!
Official repository: bigscience-workshop/t-zero
Model Description
=================
T0\* shows zero-shot task generalization on ... | [] | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #en #dataset-bigscience/P3 #arxiv-2110.08207 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n"
] |
text2text-generation | transformers |
**How do I pronounce the name of the model?** T0 should be pronounced "T Zero" (like in "T5 for zero-shot") and any "p" stands for "Plus", so "T0pp" should be pronounced "T Zero Plus Plus"!
**Official repository**: [bigscience-workshop/t-zero](https://github.com/bigscience-workshop/t-zero)
# Model Description
T0* s... | {"language": "en", "license": "apache-2.0", "datasets": ["bigscience/P3"], "widget": [{"text": "A is the son's of B's uncle. What is the family relationship between A and B?"}, {"text": "Reorder the words in this sentence: justin and name bieber years is my am I 27 old."}, {"text": "Task: copy but say the opposite.\n P... | bigscience/T0p | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"en",
"dataset:bigscience/P3",
"arxiv:2110.08207",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2110.08207"
] | [
"en"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #en #dataset-bigscience/P3 #arxiv-2110.08207 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| How do I pronounce the name of the model? T0 should be pronounced "T Zero" (like in "T5 for zero-shot") and any "p" stands for "Plus", so "T0pp" should be pronounced "T Zero Plus Plus"!
Official repository: bigscience-workshop/t-zero
Model Description
=================
T0\* shows zero-shot task generalization on ... | [] | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #en #dataset-bigscience/P3 #arxiv-2110.08207 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n"
] |
text2text-generation | transformers |
**How do I pronounce the name of the model?** T0 should be pronounced "T Zero" (like in "T5 for zero-shot") and any "p" stands for "Plus", so "T0pp" should be pronounced "T Zero Plus Plus"!
**Official repository**: [bigscience-workshop/t-zero](https://github.com/bigscience-workshop/t-zero)
# Model Description
T0* s... | {"language": "en", "license": "apache-2.0", "datasets": ["bigscience/P3"], "widget": [{"text": "A is the son's of B's uncle. What is the family relationship between A and B?"}, {"text": "Reorder the words in this sentence: justin and name bieber years is my am I 27 old."}, {"text": "Task: copy but say the opposite.\n P... | bigscience/T0pp | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"en",
"dataset:bigscience/P3",
"arxiv:2110.08207",
"license:apache-2.0",
"autotrain_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2110.08207"
] | [
"en"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #en #dataset-bigscience/P3 #arxiv-2110.08207 #license-apache-2.0 #autotrain_compatible #has_space #text-generation-inference #region-us
| How do I pronounce the name of the model? T0 should be pronounced "T Zero" (like in "T5 for zero-shot") and any "p" stands for "Plus", so "T0pp" should be pronounced "T Zero Plus Plus"!
Official repository: bigscience-workshop/t-zero
Model Description
=================
T0\* shows zero-shot task generalization on ... | [] | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #en #dataset-bigscience/P3 #arxiv-2110.08207 #license-apache-2.0 #autotrain_compatible #has_space #text-generation-inference #region-us \n"
] |
null | null | This is for sharing various data files used for testing and script development with those without access to JeanZay - feel free to create a sub-folder with your username to keep things a bit organized. | {} | bigscience/misc-test-data | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| This is for sharing various data files used for testing and script development with those without access to JeanZay - feel free to create a sub-folder with your username to keep things a bit organized. | [] | [
"TAGS\n#region-us \n"
] |
null | null | 160 intermediary checkpoints from the tr1-13B training
these models have a bug in them. While we are fixing things if you try to use any of these please run it through this script:
```
python -c '
import sys, torch
f=sys.argv[1]
sd=torch.load(f)
d=2048
for k in sd.keys():
if k.endswith(".attn.bias"):
sd[k... | {} | bigscience/tr1-13B-checkpoints | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| 160 intermediary checkpoints from the tr1-13B training
these models have a bug in them. While we are fixing things if you try to use any of these please run it through this script:
| [] | [
"TAGS\n#region-us \n"
] |
null | null | CodeCarbon wasn't ready until the training was over so we only did an additional 10h run to measure with and then we can extrapolate to the whole training.
This set of records captures the startup time and 2499 iterations in 2 records per gpu, since there was also an intermediary checkpoint saved half-way and we flush... | {} | bigscience/tr1-13B-codecarbon | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| CodeCarbon wasn't ready until the training was over so we only did an additional 10h run to measure with and then we can extrapolate to the whole training.
This set of records captures the startup time and 2499 iterations in 2 records per gpu, since there was also an intermediary checkpoint saved half-way and we flush... | [] | [
"TAGS\n#region-us \n"
] |
null | null | This data is from [13B-en training](https://github.com/bigscience-workshop/bigscience/tree/master/train/tr1-13B-base)
- indices - these are Megatron-LM shuffled indices that the training was using. They were generated the first time the training started. So the order is the same if one replays them via the dataloade... | {} | bigscience/tr1-13B-data | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| This data is from 13B-en training
- indices - these are Megatron-LM shuffled indices that the training was using. They were generated the first time the training started. So the order is the same if one replays them via the dataloader w/o actually doing the training steps.
- the corresponding dataset is oscar-en th... | [] | [
"TAGS\n#region-us \n"
] |
null | null | These are tensorboard logs for https://github.com/bigscience-workshop/bigscience/tree/master/train/tr1-13B-base | {} | bigscience/tr1-13B-tensorboard | null | [
"tensorboard",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#tensorboard #region-us
| These are tensorboard logs for URL | [] | [
"TAGS\n#tensorboard #region-us \n"
] |
null | null | You need a custom version of the `tokenizers` library to use this tokenizer.
To install this custom version you can:
```bash
pip install transformers
git clone https://github.com/huggingface/tokenizers.git
cd tokenizers
git checkout bigscience_fork
cd bindings/python
pip install setuptools_rust
pip install -e .
```
a... | {} | bigscience-catalogue-data-dev/byte-level-bpe-tokenizer-no-norm-250k-whitespace-and-eos-regex-alpha-v3-dedup-lines-articles | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| You need a custom version of the 'tokenizers' library to use this tokenizer.
To install this custom version you can:
and then to load it, do:
| [] | [
"TAGS\n#region-us \n"
] |
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. -->
# sapbert-from-pubmedbert-squad2
This model is a fine-tuned version of [cambridgeltl/SapBERT-from-PubMedBERT-fulltext](https://hug... | {"datasets": ["squad_v2"], "model_index": [{"name": "sapbert-from-pubmedbert-squad2", "results": [{"task": {"name": "Question Answering", "type": "question-answering"}, "dataset": {"name": "squad_v2", "type": "squad_v2", "args": "squad_v2"}}]}]} | bigwiz83/sapbert-from-pubmedbert-squad2 | null | [
"transformers",
"pytorch",
"bert",
"question-answering",
"dataset:squad_v2",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #question-answering #dataset-squad_v2 #endpoints_compatible #region-us
| sapbert-from-pubmedbert-squad2
==============================
This model is a fine-tuned version of cambridgeltl/SapBERT-from-PubMedBERT-fulltext on the squad\_v2 dataset.
It achieves the following results on the evaluation set:
* Loss: 1.2582
Model description
-----------------
More information needed
Intend... | [
"### 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",
"### Traini... | [
"TAGS\n#transformers #pytorch #bert #question-answering #dataset-squad_v2 #endpoints_compatible #region-us \n",
"### 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:... |
null | null | test1 | {} | bingzhen/test1 | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| test1 | [] | [
"TAGS\n#region-us \n"
] |
text-generation | transformers | This model is pre-trained **XLNET** with 12 layers.
It comes with paper: SBERT-WK: A Sentence Embedding Method By Dissecting BERT-based Word Models
Project Page: [SBERT-WK](https://github.com/BinWang28/SBERT-WK-Sentence-Embedding)
| {} | binwang/xlnet-base-cased | null | [
"transformers",
"pytorch",
"safetensors",
"xlnet",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #safetensors #xlnet #text-generation #autotrain_compatible #endpoints_compatible #region-us
| This model is pre-trained XLNET with 12 layers.
It comes with paper: SBERT-WK: A Sentence Embedding Method By Dissecting BERT-based Word Models
Project Page: SBERT-WK
| [] | [
"TAGS\n#transformers #pytorch #safetensors #xlnet #text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
token-classification | transformers | [bioformer-8L](https://huggingface.co/bioformers/bioformer-8L) fined-tuned on the [BC2GM](https://doi.org/10.1186/gb-2008-9-s2-s2) dataset for 10 epochs.
This fine-tuned model can be used for NER for genes/proteins. | {"language": ["en"], "license": "apache-2.0", "pipeline_tag": "token-classification"} | bioformers/bioformer-8L-bc2gm | null | [
"transformers",
"pytorch",
"safetensors",
"bert",
"token-classification",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #safetensors #bert #token-classification #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| bioformer-8L fined-tuned on the BC2GM dataset for 10 epochs.
This fine-tuned model can be used for NER for genes/proteins. | [] | [
"TAGS\n#transformers #pytorch #safetensors #bert #token-classification #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-classification | transformers | [bioformer-cased-v1.0](https://huggingface.co/bioformers/bioformer-cased-v1.0) fined-tuned on the [MNLI](https://cims.nyu.edu/~sbowman/multinli/) dataset for 2 epochs.
The fine-tuning process was performed on two NVIDIA GeForce GTX 1080 Ti GPUs (11GB). The parameters are:
```
max_seq_length=512
per_device_train_batch... | {} | bioformers/bioformer-8L-mnli | null | [
"transformers",
"pytorch",
"safetensors",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #safetensors #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us
| bioformer-cased-v1.0 fined-tuned on the MNLI dataset for 2 epochs.
The fine-tuning process was performed on two NVIDIA GeForce GTX 1080 Ti GPUs (11GB). The parameters are:
## Evaluation results
eval_accuracy = 0.803973
## Speed
In our experiments, the inference speed of Bioformer is 3x as fast as BERT-base/BioB... | [
"## Evaluation results\n\neval_accuracy = 0.803973",
"## Speed\n\nIn our experiments, the inference speed of Bioformer is 3x as fast as BERT-base/BioBERT/PubMedBERT, and is 40% faster than DistilBERT.",
"## More information\nThe Multi-Genre Natural Language Inference Corpus is a crowdsourced collection of sente... | [
"TAGS\n#transformers #pytorch #safetensors #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us \n",
"## Evaluation results\n\neval_accuracy = 0.803973",
"## Speed\n\nIn our experiments, the inference speed of Bioformer is 3x as fast as BERT-base/BioBERT/PubMedBERT, and is 40% faste... |
token-classification | transformers |
[bioformer-8L](https://huggingface.co/bioformers/bioformer-8L) fined-tuned on the [NCBI Disease](https://doi.org/10.1016/j.jbi.2013.12.006) dataset for 10 epochs.
This fine-tuned model can be used for NER for diseases.
| {"language": ["en"], "license": "apache-2.0"} | bioformers/bioformer-8L-ncbi-disease | null | [
"transformers",
"pytorch",
"safetensors",
"bert",
"token-classification",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #safetensors #bert #token-classification #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
bioformer-8L fined-tuned on the NCBI Disease dataset for 10 epochs.
This fine-tuned model can be used for NER for diseases.
| [] | [
"TAGS\n#transformers #pytorch #safetensors #bert #token-classification #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-classification | transformers | [bioformer-8L](https://huggingface.co/bioformers/bioformer-8L) fined-tuned on the [QNLI](https://huggingface.co/datasets/glue) dataset for 2 epochs.
The fine-tuning process was performed on two NVIDIA GeForce GTX 1080 Ti GPUs (11GB). The parameters are:
```
max_seq_length=512
per_device_train_batch_size=16
total trai... | {"language": ["en"], "license": "apache-2.0"} | bioformers/bioformer-8L-qnli | null | [
"transformers",
"pytorch",
"safetensors",
"bert",
"text-classification",
"en",
"arxiv:1804.07461",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1804.07461"
] | [
"en"
] | TAGS
#transformers #pytorch #safetensors #bert #text-classification #en #arxiv-1804.07461 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| bioformer-8L fined-tuned on the QNLI dataset for 2 epochs.
The fine-tuning process was performed on two NVIDIA GeForce GTX 1080 Ti GPUs (11GB). The parameters are:
## Evaluation results
eval_accuracy = 0.883397
## More information
The QNLI (Question-answering NLI) dataset is a Natural Language Inference dataset au... | [
"## Evaluation results\neval_accuracy = 0.883397",
"## More information\nThe QNLI (Question-answering NLI) dataset is a Natural Language Inference dataset automatically derived from the Stanford Question Answering Dataset v1.1 (SQuAD). SQuAD v1.1 consists of question-paragraph pairs, where one of the sentences in... | [
"TAGS\n#transformers #pytorch #safetensors #bert #text-classification #en #arxiv-1804.07461 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"## Evaluation results\neval_accuracy = 0.883397",
"## More information\nThe QNLI (Question-answering NLI) dataset is a Natural Language Inf... |
question-answering | transformers | [bioformer-8L](https://huggingface.co/bioformers/bioformer-8L) fined-tuned on the [SQuAD1](https://rajpurkar.github.io/SQuAD-explorer) dataset for 3 epochs.
The fine-tuning process was performed on a single P100 GPUs (16GB). The hyperparameters are:
```
max_seq_length=512
per_device_train_batch_size=16
gradient_accum... | {"language": ["en"], "license": "apache-2.0", "pipeline_tag": "question-answering"} | bioformers/bioformer-8L-squad1 | null | [
"transformers",
"pytorch",
"safetensors",
"bert",
"question-answering",
"en",
"arxiv:1910.01108",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1910.01108"
] | [
"en"
] | TAGS
#transformers #pytorch #safetensors #bert #question-answering #en #arxiv-1910.01108 #license-apache-2.0 #endpoints_compatible #region-us
| bioformer-8L fined-tuned on the SQuAD1 dataset for 3 epochs.
The fine-tuning process was performed on a single P100 GPUs (16GB). The hyperparameters are:
## Evaluation results
Bioformer's performance is on par with DistilBERT (EM/F1: 77.7/85.8),
although Bioformer was pretrained only on biomedical texts.
## ... | [
"## Evaluation results\n\n\n\nBioformer's performance is on par with DistilBERT (EM/F1: 77.7/85.8), \nalthough Bioformer was pretrained only on biomedical texts.",
"## Speed\nIn our experiments, the inference speed of Bioformer is 3x as fast as BERT-base/BioBERT/PubMedBERT, and is 40% faster than DistilBERT."
] | [
"TAGS\n#transformers #pytorch #safetensors #bert #question-answering #en #arxiv-1910.01108 #license-apache-2.0 #endpoints_compatible #region-us \n",
"## Evaluation results\n\n\n\nBioformer's performance is on par with DistilBERT (EM/F1: 77.7/85.8), \nalthough Bioformer was pretrained only on biomedical texts.",
... |
fill-mask | transformers |
**_NOTE: `bioformer-cased-v1.0` has been renamed to `bioformer-8L`. All links to `bioformer-cased-v1.0` will automatically redirect to `bioformer-8L`, including git operations. However, to avoid confusion, we recommend updating any existing local clones to point to the new repository URL._**
Bioformer-8L is a lightwe... | {"language": ["en"], "license": "apache-2.0", "pipeline_tag": "fill-mask"} | bioformers/bioformer-8L | null | [
"transformers",
"pytorch",
"tf",
"safetensors",
"bert",
"fill-mask",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #tf #safetensors #bert #fill-mask #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
_NOTE: 'bioformer-cased-v1.0' has been renamed to 'bioformer-8L'. All links to 'bioformer-cased-v1.0' will automatically redirect to 'bioformer-8L', including git operations. However, to avoid confusion, we recommend updating any existing local clones to point to the new repository URL._
Bioformer-8L is a lightweight... | [
"## Vocabulary of Bioformer-8L\nBioformer-8L uses a cased WordPiece vocabulary trained from a biomedical corpus, which included all PubMed abstracts (33 million, as of Feb 1, 2021) and 1 million PMC full-text articles. PMC has 3.6 million articles but we down-sampled them to 1 million such that the total size of Pu... | [
"TAGS\n#transformers #pytorch #tf #safetensors #bert #fill-mask #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"## Vocabulary of Bioformer-8L\nBioformer-8L uses a cased WordPiece vocabulary trained from a biomedical corpus, which included all PubMed abstracts (33 million, as o... |
null | transformers |
# BlueBert-Base, Uncased, PubMed and MIMIC-III
## Model description
A BERT model pre-trained on PubMed abstracts and clinical notes ([MIMIC-III](https://mimic.physionet.org/)).
## Intended uses & limitations
#### How to use
Please see https://github.com/ncbi-nlp/bluebert
## Training data
We provide [preprocesse... | {"language": ["en"], "license": "cc0-1.0", "tags": ["bert", "bluebert"], "datasets": ["PubMed", "MIMIC-III"]} | bionlp/bluebert_pubmed_mimic_uncased_L-12_H-768_A-12 | null | [
"transformers",
"pytorch",
"jax",
"bert",
"bluebert",
"en",
"dataset:PubMed",
"dataset:MIMIC-III",
"license:cc0-1.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #jax #bert #bluebert #en #dataset-PubMed #dataset-MIMIC-III #license-cc0-1.0 #endpoints_compatible #region-us
|
# BlueBert-Base, Uncased, PubMed and MIMIC-III
## Model description
A BERT model pre-trained on PubMed abstracts and clinical notes (MIMIC-III).
## Intended uses & limitations
#### How to use
Please see URL
## Training data
We provide preprocessed PubMed texts that were used to pre-train the BlueBERT models.
T... | [
"# BlueBert-Base, Uncased, PubMed and MIMIC-III",
"## Model description\n\nA BERT model pre-trained on PubMed abstracts and clinical notes (MIMIC-III).",
"## Intended uses & limitations",
"#### How to use\n\nPlease see URL",
"## Training data\n\nWe provide preprocessed PubMed texts that were used to pre-tra... | [
"TAGS\n#transformers #pytorch #jax #bert #bluebert #en #dataset-PubMed #dataset-MIMIC-III #license-cc0-1.0 #endpoints_compatible #region-us \n",
"# BlueBert-Base, Uncased, PubMed and MIMIC-III",
"## Model description\n\nA BERT model pre-trained on PubMed abstracts and clinical notes (MIMIC-III).",
"## Intende... |
null | transformers |
# BlueBert-Base, Uncased, PubMed and MIMIC-III
## Model description
A BERT model pre-trained on PubMed abstracts and clinical notes ([MIMIC-III](https://mimic.physionet.org/)).
## Intended uses & limitations
#### How to use
Please see https://github.com/ncbi-nlp/bluebert
## Training data
We provide [preprocesse... | {"language": ["en"], "license": "cc0-1.0", "tags": ["bert", "bluebert"], "datasets": ["PubMed", "MIMIC-III"]} | bionlp/bluebert_pubmed_mimic_uncased_L-24_H-1024_A-16 | null | [
"transformers",
"pytorch",
"jax",
"bert",
"bluebert",
"en",
"dataset:PubMed",
"dataset:MIMIC-III",
"license:cc0-1.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #jax #bert #bluebert #en #dataset-PubMed #dataset-MIMIC-III #license-cc0-1.0 #endpoints_compatible #region-us
|
# BlueBert-Base, Uncased, PubMed and MIMIC-III
## Model description
A BERT model pre-trained on PubMed abstracts and clinical notes (MIMIC-III).
## Intended uses & limitations
#### How to use
Please see URL
## Training data
We provide preprocessed PubMed texts that were used to pre-train the BlueBERT models.
T... | [
"# BlueBert-Base, Uncased, PubMed and MIMIC-III",
"## Model description\n\nA BERT model pre-trained on PubMed abstracts and clinical notes (MIMIC-III).",
"## Intended uses & limitations",
"#### How to use\n\nPlease see URL",
"## Training data\n\nWe provide preprocessed PubMed texts that were used to pre-tra... | [
"TAGS\n#transformers #pytorch #jax #bert #bluebert #en #dataset-PubMed #dataset-MIMIC-III #license-cc0-1.0 #endpoints_compatible #region-us \n",
"# BlueBert-Base, Uncased, PubMed and MIMIC-III",
"## Model description\n\nA BERT model pre-trained on PubMed abstracts and clinical notes (MIMIC-III).",
"## Intende... |
null | transformers |
# BlueBert-Base, Uncased, PubMed
## Model description
A BERT model pre-trained on PubMed abstracts
## Intended uses & limitations
#### How to use
Please see https://github.com/ncbi-nlp/bluebert
## Training data
We provide [preprocessed PubMed texts](https://ftp.ncbi.nlm.nih.gov/pub/lu/Suppl/NCBI-BERT/pubmed_unc... | {"language": ["en"], "license": "cc0-1.0", "tags": ["bluebert"], "datasets": ["pubmed"]} | bionlp/bluebert_pubmed_uncased_L-12_H-768_A-12 | null | [
"transformers",
"pytorch",
"bluebert",
"en",
"dataset:pubmed",
"license:cc0-1.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bluebert #en #dataset-pubmed #license-cc0-1.0 #endpoints_compatible #region-us
|
# BlueBert-Base, Uncased, PubMed
## Model description
A BERT model pre-trained on PubMed abstracts
## Intended uses & limitations
#### How to use
Please see URL
## Training data
We provide preprocessed PubMed texts that were used to pre-train the BlueBERT models.
The corpus contains ~4000M words extracted from... | [
"# BlueBert-Base, Uncased, PubMed",
"## Model description\n\nA BERT model pre-trained on PubMed abstracts",
"## Intended uses & limitations",
"#### How to use\n\nPlease see URL",
"## Training data\n\nWe provide preprocessed PubMed texts that were used to pre-train the BlueBERT models. \nThe corpus contains ... | [
"TAGS\n#transformers #pytorch #bluebert #en #dataset-pubmed #license-cc0-1.0 #endpoints_compatible #region-us \n",
"# BlueBert-Base, Uncased, PubMed",
"## Model description\n\nA BERT model pre-trained on PubMed abstracts",
"## Intended uses & limitations",
"#### How to use\n\nPlease see URL",
"## Training... |
null | transformers |
# BlueBert-Base, Uncased, PubMed
## Model description
A BERT model pre-trained on PubMed abstracts.
## Intended uses & limitations
#### How to use
Please see https://github.com/ncbi-nlp/bluebert
## Training data
We provide [preprocessed PubMed texts](https://ftp.ncbi.nlm.nih.gov/pub/lu/Suppl/NCBI-BERT/pubmed_un... | {"language": ["en"], "license": "cc0-1.0", "tags": ["bert", "bluebert"], "datasets": ["PubMed"]} | bionlp/bluebert_pubmed_uncased_L-24_H-1024_A-16 | null | [
"transformers",
"pytorch",
"jax",
"bert",
"bluebert",
"en",
"dataset:PubMed",
"license:cc0-1.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #jax #bert #bluebert #en #dataset-PubMed #license-cc0-1.0 #endpoints_compatible #region-us
|
# BlueBert-Base, Uncased, PubMed
## Model description
A BERT model pre-trained on PubMed abstracts.
## Intended uses & limitations
#### How to use
Please see URL
## Training data
We provide preprocessed PubMed texts that were used to pre-train the BlueBERT models.
The corpus contains ~4000M words extracted fro... | [
"# BlueBert-Base, Uncased, PubMed",
"## Model description\n\nA BERT model pre-trained on PubMed abstracts.",
"## Intended uses & limitations",
"#### How to use\n\nPlease see URL",
"## Training data\n\nWe provide preprocessed PubMed texts that were used to pre-train the BlueBERT models. \nThe corpus contains... | [
"TAGS\n#transformers #pytorch #jax #bert #bluebert #en #dataset-PubMed #license-cc0-1.0 #endpoints_compatible #region-us \n",
"# BlueBert-Base, Uncased, PubMed",
"## Model description\n\nA BERT model pre-trained on PubMed abstracts.",
"## Intended uses & limitations",
"#### How to use\n\nPlease see URL",
... |
text-classification | transformers | ## Malayalam news classifier
### Overview
This model is trained on top of [MalayalamBert](https://huggingface.co/eliasedwin7/MalayalamBERT) for the task of classifying malayalam news headlines. Presently, the following news categories are supported:
* Business
* Sports
* Entertainment
### Dataset
The dataset used ... | {"license": "mit", "tags": ["text-classification", "roberta", "malayalam", "pytorch"], "widget": [{"text": "2032 \u0d12\u0d33\u0d3f\u0d2e\u0d4d\u0d2a\u0d3f\u0d15\u0d4d\u200c\u0d38\u0d3f\u0d28\u0d4d \u0d2c\u0d4d\u0d30\u0d3f\u0d38\u0d4d\u200c\u0d2c\u0d46\u0d2f\u0d4d\u0d28\u0d4d\u200d \u0d35\u0d47\u0d26\u0d3f\u0d2f\u0d3e\... | bipin/malayalam-news-classifier | null | [
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"pytorch",
"roberta",
"text-classification",
"malayalam",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #text-classification #malayalam #license-mit #autotrain_compatible #endpoints_compatible #region-us
| ## Malayalam news classifier
### Overview
This model is trained on top of MalayalamBert for the task of classifying malayalam news headlines. Presently, the following news categories are supported:
* Business
* Sports
* Entertainment
### Dataset
The dataset used for training this model can be found here.
### Usin... | [
"## Malayalam news classifier",
"### Overview\n\nThis model is trained on top of MalayalamBert for the task of classifying malayalam news headlines. Presently, the following news categories are supported:\n\n* Business\n* Sports\n* Entertainment",
"### Dataset\n\nThe dataset used for training this model can be ... | [
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"## Malayalam news classifier",
"### Overview\n\nThis model is trained on top of MalayalamBert for the task of classifying malayalam news headlines. Presently, the foll... |
automatic-speech-recognition | transformers | # Wav2vec 2.0 large VoxRex Swedish (C)
Experiment with LM model.
**Disclaimer:** This is a work in progress. See [VoxRex](https://huggingface.co/KBLab/wav2vec2-large-voxrex) for more details.
**Update 2022-01-10:** Updated to VoxRex-C version.
Finetuned version of KBs [VoxRex large](https://huggingface.co/KBLab/wa... | {"language": "sv", "license": "cc0-1.0", "tags": ["audio", "automatic-speech-recognition", "speech"], "datasets": ["common_voice", "NST Swedish ASR Database", "P4"], "metrics": ["wer"], "model-index": [{"name": "Wav2vec 2.0 large VoxRex Swedish", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Sp... | birgermoell/lm-swedish | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"sv",
"license:cc0-1.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"sv"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #speech #sv #license-cc0-1.0 #model-index #endpoints_compatible #region-us
| # Wav2vec 2.0 large VoxRex Swedish (C)
Experiment with LM model.
Disclaimer: This is a work in progress. See VoxRex for more details.
Update 2022-01-10: Updated to VoxRex-C version.
Finetuned version of KBs VoxRex large model using Swedish radio broadcasts, NST and Common Voice data. Evalutation without a language... | [
"# Wav2vec 2.0 large VoxRex Swedish (C)\n\nExperiment with LM model. \n\nDisclaimer: This is a work in progress. See VoxRex for more details.\n\nUpdate 2022-01-10: Updated to VoxRex-C version.\n\nFinetuned version of KBs VoxRex large model using Swedish radio broadcasts, NST and Common Voice data. Evalutation witho... | [
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"# Wav2vec 2.0 large VoxRex Swedish (C)\n\nExperiment with LM model. \n\nDisclaimer: This is a work in progress. See VoxRex for more details.\n\nUpdate 2022-... |
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. -->
# ner-swedish-wikiann
This model is a fine-tuned version of [nordic-roberta-wiki](hhttps://huggingface.co/flax-community/nordic-r... | {"license": "apache-2.0", "tags": ["token-classification"], "datasets": ["wikiann"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "ner-swedish-wikiann", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "wikiann", "type": "wikian... | birgermoell/ner-swedish-wikiann | null | [
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"roberta",
"token-classification",
"dataset:wikiann",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #token-classification #dataset-wikiann #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# ner-swedish-wikiann
This model is a fine-tuned version of nordic-roberta-wiki trained for NER on the wikiann dataset.
eval F1-Score: 83,78
test F1-Score: 83,76
## Model Usage
| [
"# ner-swedish-wikiann\n\nThis model is a fine-tuned version of nordic-roberta-wiki trained for NER on the wikiann dataset.\n\neval F1-Score: 83,78 \n\ntest F1-Score: 83,76",
"## Model Usage"
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"# ner-swedish-wikiann\n\nThis model is a fine-tuned version of nordic-roberta-wiki trained for NER on the wikiann dataset.\n\neval F1-Score: 8... |
feature-extraction | transformers | # Svensk Roberta
## Description
Swedish Roberta model trained on the MC4 dataset. The model performance needs to be assessed
## Model series
This model is part of a series of models training on TPU with Flax Jax during Huggingface Flax/Jax challenge.
## Gpt models
## Swedish Gpt
https://huggingface.co/birgermoell/s... | {"language": "sv", "license": "cc-by-4.0", "tags": ["translate"], "datasets": ["mc4"], "widget": [{"text": "Meningen med livet \u00e4r <mask>"}]} | birgermoell/roberta-swedish-scandi | null | [
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"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"sv"
] | TAGS
#transformers #pytorch #jax #tensorboard #roberta #feature-extraction #translate #sv #dataset-mc4 #license-cc-by-4.0 #endpoints_compatible #region-us
| # Svensk Roberta
## Description
Swedish Roberta model trained on the MC4 dataset. The model performance needs to be assessed
## Model series
This model is part of a series of models training on TPU with Flax Jax during Huggingface Flax/Jax challenge.
## Gpt models
## Swedish Gpt
URL
## Swedish gpt wiki
URL
# Nord... | [
"# Svensk Roberta",
"## Description\nSwedish Roberta model trained on the MC4 dataset. The model performance needs to be assessed",
"## Model series\nThis model is part of a series of models training on TPU with Flax Jax during Huggingface Flax/Jax challenge.",
"## Gpt models",
"## Swedish Gpt\nURL",
"## ... | [
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"# Svensk Roberta",
"## Description\nSwedish Roberta model trained on the MC4 dataset. The model performance needs to be assessed",
"## Model series... |
fill-mask | transformers |
Swedish RoBERTa
## Model series
This model is part of a series of models training on TPU with Flax Jax during Huggingface Flax/Jax challenge.
## Gpt models
## Swedish Gpt
https://huggingface.co/birgermoell/swedish-gpt/
## Swedish gpt wiki
https://huggingface.co/flax-community/swe-gpt-wiki
# Nordic gpt wiki
https... | {"widget": [{"text": "Var kan jag hitta n\u00e5gon <mask> talar engelska?"}]} | birgermoell/roberta-swedish | null | [
"transformers",
"pytorch",
"jax",
"tensorboard",
"roberta",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #tensorboard #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us
|
Swedish RoBERTa
## Model series
This model is part of a series of models training on TPU with Flax Jax during Huggingface Flax/Jax challenge.
## Gpt models
## Swedish Gpt
URL
## Swedish gpt wiki
URL
# Nordic gpt wiki
URL
## Dansk gpt wiki
URL
## Norsk gpt wiki
URL
## Roberta models
## Nordic Roberta Wiki
URL... | [
"## Model series\nThis model is part of a series of models training on TPU with Flax Jax during Huggingface Flax/Jax challenge.",
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"## Swedish Gpt\nURL",
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"# Nordic gpt wiki\nURL",
"## Dansk gpt wiki\nURL",
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"## Roberta models",
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"## Gpt models",
"## Swedish Gpt\nURL",
"## Swedis... |
automatic-speech-recognition | transformers |
# common-voice-vox-populi-swedish
Fine-tuned [facebook/wav2vec2-large-sv-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) in Swedish using the [Common Voice](https://huggingface.co/datasets/common_voice)
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The m... | {"language": "et", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice"], "model-index": [{"name": "common-voice-vox-populi-swedish by Birger Moell", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech Recogn... | birgermoell/swedish-common-voice-vox-voxpopuli | null | [
"transformers",
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] | null | 2022-03-02T23:29:05+00:00 | [] | [
"et"
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|
# common-voice-vox-populi-swedish
Fine-tuned facebook/wav2vec2-large-sv-voxpopuli in Swedish using the Common Voice
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used directly (without a language model) as follows:
## Evaluation
The model can be evalu... | [
"# common-voice-vox-populi-swedish\n\nFine-tuned facebook/wav2vec2-large-sv-voxpopuli in Swedish using the Common Voice\nWhen using this model, make sure that your speech input is sampled at 16kHz.",
"## Usage\n\nThe model can be used directly (without a language model) as follows:",
"## Evaluation\n\nThe model... | [
"TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #et #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"# common-voice-vox-populi-swedish\n\nFine-tuned facebook/wav2vec2-large-sv-voxpopuli in Swedish using t... |
text-generation | transformers |
## Model series
This model is part of a series of models training on TPU with Flax Jax during Huggingface Flax/Jax challenge.
## Gpt models
## Swedish Gpt
https://huggingface.co/birgermoell/swedish-gpt/
## Swedish gpt wiki
https://huggingface.co/flax-community/swe-gpt-wiki
# Nordic gpt wiki
https://huggingface.co/... | {"language": "sv", "widget": [{"text": "Jag \u00e4r en svensk spr\u00e5kmodell."}]} | birgermoell/swedish-gpt | null | [
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] | null | 2022-03-02T23:29:05+00:00 | [] | [
"sv"
] | TAGS
#transformers #pytorch #jax #tensorboard #gpt2 #text-generation #sv #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
## Model series
This model is part of a series of models training on TPU with Flax Jax during Huggingface Flax/Jax challenge.
## Gpt models
## Swedish Gpt
URL
## Swedish gpt wiki
URL
# Nordic gpt wiki
URL
## Dansk gpt wiki
URL
## Norsk gpt wiki
URL
## Roberta models
## Nordic Roberta Wiki
URL
## Swe Roberta W... | [
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"# Nordic gpt wiki\nURL",
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"## Roberta models",
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"## Gpt m... |
translation | transformers | [Google's T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html)
Pretraining Dataset: [C4](https://huggingface.co/datasets/oscar)
Paper: [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://arxiv.org/pdf/1910.10683.pdf)
Authors: *Colin Raffel, Noam Shazee... | {"language": ["sv"], "license": "apache-2.0", "tags": ["summarization", "translation"], "datasets": ["oscar"]} | birgermoell/t5-base-swedish | null | [
"transformers",
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"sv",
"dataset:oscar",
"arxiv:1910.10683",
"license:apache-2.0",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1910.10683"
] | [
"sv"
] | TAGS
#transformers #pytorch #jax #tensorboard #t5 #feature-extraction #summarization #translation #sv #dataset-oscar #arxiv-1910.10683 #license-apache-2.0 #endpoints_compatible #text-generation-inference #region-us
| Google's T5
Pretraining Dataset: C4
Paper: Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
Authors: *Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, Peter J. Liu*
## Abstract
Transfer learning, where a model is first pre-tra... | [
"## Abstract\nTransfer learning, where a model is first pre-trained on a data-rich task before being fine-tuned on a downstream task, has emerged as a powerful technique in natural language processing (NLP). The effectiveness of transfer learning has given rise to a diversity of approaches, methodology, and practic... | [
"TAGS\n#transformers #pytorch #jax #tensorboard #t5 #feature-extraction #summarization #translation #sv #dataset-oscar #arxiv-1910.10683 #license-apache-2.0 #endpoints_compatible #text-generation-inference #region-us \n",
"## Abstract\nTransfer learning, where a model is first pre-trained on a data-rich task befo... |
automatic-speech-recognition | 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. -->
# wav2vec2-common_voice-tr-demo
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/fac... | {"language": ["sv-SE"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "common_voice", "generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-common_voice-tr-demo", "results": []}]} | birgermoell/wav2vec2-common_voice-tr-demo | null | [
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"automatic-speech-recognition",
"common_voice",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"sv-SE"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #common_voice #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-common\_voice-tr-demo
==============================
This model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the COMMON\_VOICE - SV-SE dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5528
* Wer: 0.3811
Model description
-----------------
More information nee... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* ... |
automatic-speech-recognition | transformers |
# Wav2Vec2-Large-XLSR-53-Estonian
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) in Luganda using the [Common Voice](https://huggingface.co/datasets/common_voice)
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can... | {"language": "et", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice"], "model-index": [{"name": "XLSR Wav2Vec2 Estonian by Birger Moell", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech Recognition"}, ... | birgermoell/wav2vec2-large-xlrs-estonian | null | [
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"model-index",
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"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"et"
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|
# Wav2Vec2-Large-XLSR-53-Estonian
Fine-tuned facebook/wav2vec2-large-xlsr-53 in Luganda using the Common Voice
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used directly (without a language model) as follows:
## Evaluation
The model can be evaluated as ... | [
"# Wav2Vec2-Large-XLSR-53-Estonian\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 in Luganda using the Common Voice\nWhen using this model, make sure that your speech input is sampled at 16kHz.",
"## Usage\n\nThe model can be used directly (without a language model) as follows:",
"## Evaluation\n\nThe model can ... | [
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"# Wav2Vec2-Large-XLSR-53-Estonian\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 in Luganda using the Co... |
automatic-speech-recognition | transformers |
# Wav2Vec2-Large-XLSR-53-Finnish
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) in Finnish using the [Common Voice](https://huggingface.co/datasets/common_voice)
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can ... | {"language": "fi", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice"], "model-index": [{"name": "XLSR Wav2Vec2 Finnish by Birger Moell", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech Recognition"}, "... | birgermoell/wav2vec2-large-xlsr-finnish | null | [
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"fi"
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#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #fi #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
# Wav2Vec2-Large-XLSR-53-Finnish
Fine-tuned facebook/wav2vec2-large-xlsr-53 in Finnish using the Common Voice
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used directly (without a language model) as follows:
## Evaluation
The model can be evaluated as f... | [
"# Wav2Vec2-Large-XLSR-53-Finnish\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 in Finnish using the Common Voice\nWhen using this model, make sure that your speech input is sampled at 16kHz.",
"## Usage\n\nThe model can be used directly (without a language model) as follows:",
"## Evaluation\n\nThe model can b... | [
"TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #fi #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"# Wav2Vec2-Large-XLSR-53-Finnish\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 in Finnish using the Com... |
automatic-speech-recognition | transformers |
# Wav2Vec2-Large-XLSR-53-Hungarian
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) in Hungarian using the [Common Voice](https://huggingface.co/datasets/common_voice)
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model ... | {"language": "hu", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice"], "model-index": [{"name": "XLSR Wav2Vec2 Hugarian by Birger Moell", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech Recognition"}, ... | birgermoell/wav2vec2-large-xlsr-hungarian | null | [
"transformers",
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"hu",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"hu"
] | TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #hu #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
# Wav2Vec2-Large-XLSR-53-Hungarian
Fine-tuned facebook/wav2vec2-large-xlsr-53 in Hungarian using the Common Voice
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used directly (without a language model) as follows:
## Evaluation
The model can be evaluated ... | [
"# Wav2Vec2-Large-XLSR-53-Hungarian\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 in Hungarian using the Common Voice\nWhen using this model, make sure that your speech input is sampled at 16kHz.",
"## Usage\n\nThe model can be used directly (without a language model) as follows:",
"## Evaluation\n\nThe model c... | [
"TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #hu #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"# Wav2Vec2-Large-XLSR-53-Hungarian\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 in Hungarian using the... |
automatic-speech-recognition | transformers |
# Wav2Vec2-Large-XLSR-53-Luganda
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) in Luganda using the [Common Voice](https://huggingface.co/datasets/common_voice)
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can ... | {"language": "lg", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice"], "model-index": [{"name": "XLSR Wav2Vec2 Luganda by Birger Moell", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech Recognition"}, "... | birgermoell/wav2vec2-luganda | null | [
"transformers",
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"lg",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"lg"
] | TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #lg #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
# Wav2Vec2-Large-XLSR-53-Luganda
Fine-tuned facebook/wav2vec2-large-xlsr-53 in Luganda using the Common Voice
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used directly (without a language model) as follows:
## Evaluation
The model can be evaluated as f... | [
"# Wav2Vec2-Large-XLSR-53-Luganda\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 in Luganda using the Common Voice\nWhen using this model, make sure that your speech input is sampled at 16kHz.",
"## Usage\n\nThe model can be used directly (without a language model) as follows:",
"## Evaluation\n\nThe model can b... | [
"TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #lg #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"# Wav2Vec2-Large-XLSR-53-Luganda\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 in Luganda using the Com... |
automatic-speech-recognition | 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. -->
# wav2vec2-speechdat
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2v... | {"language": ["sv-SE"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "common_voice", "generated_from_trainer"], "model-index": [{"name": "wav2vec2-speechdat", "results": []}]} | birgermoell/wav2vec2-speechdat | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"common_voice",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"sv-SE"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #common_voice #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-speechdat
==================
This model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the COMMON\_VOICE - SV-SE dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4578
* Wer: 0.2927
Model description
-----------------
More information needed
Intended uses & li... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #common_voice #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: ... |
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