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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-base-timit-demo-colab
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wa... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-base-timit-demo-colab", "results": []}]} | juanhebert/wav2vec2-base-timit-demo-colab | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"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 #license-apache-2.0 #endpoints_compatible #region-us
|
# wav2vec2-base-timit-demo-colab
This model is a fine-tuned version of facebook/wav2vec2-base on the None dataset.
It achieves the following results on the evaluation set:
- eval_loss: 3.2385
- eval_wer: 1.0
- eval_runtime: 145.9952
- eval_samples_per_second: 11.507
- eval_steps_per_second: 1.438
- epoch: 0.25
- st... | [
"# wav2vec2-base-timit-demo-colab\n\nThis model is a fine-tuned version of facebook/wav2vec2-base on the None dataset.\nIt achieves the following results on the evaluation set:\n- eval_loss: 3.2385\n- eval_wer: 1.0\n- eval_runtime: 145.9952\n- eval_samples_per_second: 11.507\n- eval_steps_per_second: 1.438\n- epoch... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"# wav2vec2-base-timit-demo-colab\n\nThis model is a fine-tuned version of facebook/wav2vec2-base on the None dataset.\nIt achieves the following res... |
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-indonesia
This model is a fine-tuned version of [juanhebert/wav2vec2-indonesia](https://huggingface.co/juanhebert/wav2v... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-indonesia", "results": []}]} | juanhebert/wav2vec2-indonesia | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"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 #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-indonesia
==================
This model is a fine-tuned version of juanhebert/wav2vec2-indonesia on the commonvoice "id" dataset.
It achieves the following results on the evaluation set:
* Loss: 3.0727
* Wer: 1.0
Model description
-----------------
More information needed
Intended uses & limitations
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 5\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps:... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #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: 0.0001\n* train\\_batch\\_size: 5... |
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-large-xls-r-thai-test
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/fa... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-large-xls-r-thai-test", "results": []}]} | juierror/wav2vec2-large-xls-r-thai-test | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"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 #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
|
# wav2vec2-large-xls-r-thai-test
This model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the common_voice dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.7728
- eval_wer: 0.9490
- eval_runtime: 678.2819
- eval_samples_per_second: 3.226
- eval_steps_per_second: 0.404... | [
"# wav2vec2-large-xls-r-thai-test\n\nThis model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the common_voice dataset.\nIt achieves the following results on the evaluation set:\n- eval_loss: 0.7728\n- eval_wer: 0.9490\n- eval_runtime: 678.2819\n- eval_samples_per_second: 3.226\n- eval_steps_per_sec... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us \n",
"# wav2vec2-large-xls-r-thai-test\n\nThis model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the common_voice ... |
text-generation | transformers |
# Harry Potter DialogGPT Model | {"tags": ["conversational"]} | julianolf/DialoGPT-small-harrypotter | 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
|
# Harry Potter DialogGPT Model | [
"# Harry Potter DialogGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Harry Potter DialogGPT Model"
] |
audio-to-audio | asteroid |
## Asteroid model `mpariente/DPRNNTasNet(ks=16)_WHAM!_sepclean`
♻️ Imported from https://zenodo.org/record/3903795#.X8pMBRNKjUI
This model was trained by Manuel Pariente using the wham/DPRNN recipe in [Asteroid](https://github.com/asteroid-team/asteroid). It was trained on the sep_clean task of the WHAM! dataset.
... | {"license": "cc-by-sa-4.0", "tags": ["audio-to-audio", "asteroid", "audio", "audio-source-separation"], "datasets": ["wham", "sep_clean"]} | julien-c/DPRNNTasNet-ks16_WHAM_sepclean | null | [
"asteroid",
"pytorch",
"audio-to-audio",
"audio",
"audio-source-separation",
"dataset:wham",
"dataset:sep_clean",
"arxiv:2005.04132",
"license:cc-by-sa-4.0",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2005.04132"
] | [] | TAGS
#asteroid #pytorch #audio-to-audio #audio #audio-source-separation #dataset-wham #dataset-sep_clean #arxiv-2005.04132 #license-cc-by-sa-4.0 #has_space #region-us
|
## Asteroid model 'mpariente/DPRNNTasNet(ks=16)_WHAM!_sepclean'
️ Imported from URL
This model was trained by Manuel Pariente using the wham/DPRNN recipe in Asteroid. It was trained on the sep_clean task of the WHAM! dataset.
### Demo: How to use in Asteroid
### Training config
- data:
- mode: min
- nondefa... | [
"## Asteroid model 'mpariente/DPRNNTasNet(ks=16)_WHAM!_sepclean'\n\n️ Imported from URL\n\nThis model was trained by Manuel Pariente using the wham/DPRNN recipe in Asteroid. It was trained on the sep_clean task of the WHAM! dataset.",
"### Demo: How to use in Asteroid",
"### Training config\n\n- data:\n\t- mode... | [
"TAGS\n#asteroid #pytorch #audio-to-audio #audio #audio-source-separation #dataset-wham #dataset-sep_clean #arxiv-2005.04132 #license-cc-by-sa-4.0 #has_space #region-us \n",
"## Asteroid model 'mpariente/DPRNNTasNet(ks=16)_WHAM!_sepclean'\n\n️ Imported from URL\n\nThis model was trained by Manuel Pariente using t... |
token-classification | transformers |
# EsperBERTo: RoBERTa-like Language model trained on Esperanto
**Companion model to blog post https://huggingface.co/blog/how-to-train** 🔥
## Training Details
- current checkpoint: 566000
- machine name: `galinette`
## Example p... | {"language": "eo", "thumbnail": "https://huggingface.co/blog/assets/01_how-to-train/EsperBERTo-thumbnail-v2.png", "widget": [{"text": "Mi estas viro kej estas tago varma."}]} | julien-c/EsperBERTo-small-pos | null | [
"transformers",
"pytorch",
"jax",
"onnx",
"safetensors",
"roberta",
"token-classification",
"eo",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"eo"
] | TAGS
#transformers #pytorch #jax #onnx #safetensors #roberta #token-classification #eo #autotrain_compatible #endpoints_compatible #region-us
|
# EsperBERTo: RoBERTa-like Language model trained on Esperanto
Companion model to blog post URL
## Training Details
- current checkpoint: 566000
- machine name: 'galinette'
![](URL
## Example pipeline
| [
"# EsperBERTo: RoBERTa-like Language model trained on Esperanto\n\nCompanion model to blog post URL",
"## Training Details\n\n- current checkpoint: 566000\n- machine name: 'galinette'\n\n\n![](URL",
"## Example pipeline"
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"## Training Details\n\n- current checkpoint: 566000\n- machi... |
fill-mask | transformers |
# EsperBERTo: RoBERTa-like Language model trained on Esperanto
**Companion model to blog post https://huggingface.co/blog/how-to-train** 🔥
## Training Details
- current checkpoint: 566000
- machine name: `galinette`
## Example p... | {"language": "eo", "thumbnail": "https://huggingface.co/blog/assets/01_how-to-train/EsperBERTo-thumbnail-v2.png", "widget": [{"text": "Jen la komenco de bela <mask>."}, {"text": "Uno du <mask>"}, {"text": "Jen fini\u011das bela <mask>."}]} | julien-c/EsperBERTo-small | null | [
"transformers",
"pytorch",
"jax",
"safetensors",
"roberta",
"fill-mask",
"eo",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"eo"
] | TAGS
#transformers #pytorch #jax #safetensors #roberta #fill-mask #eo #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# EsperBERTo: RoBERTa-like Language model trained on Esperanto
Companion model to blog post URL
## Training Details
- current checkpoint: 566000
- machine name: 'galinette'
![](URL
## Example pipeline
| [
"# EsperBERTo: RoBERTa-like Language model trained on Esperanto\n\nCompanion model to blog post URL",
"## Training Details\n\n- current checkpoint: 566000\n- machine name: 'galinette'\n\n\n![](URL",
"## Example pipeline"
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"## Training Details\n\n- current checkpoint: 566000\n- machine nam... |
fill-mask | transformers | ## How to build a dummy model
```python
from transformers BertConfig, BertForMaskedLM, BertTokenizer, TFBertForMaskedLM
SMALL_MODEL_IDENTIFIER = "julien-c/bert-xsmall-dummy"
DIRNAME = "./bert-xsmall-dummy"
config = BertConfig(10, 20, 1, 1, 40)
model = BertForMaskedLM(config)
model.save_pretrained(DIRNAME)
tf_mode... | {} | julien-c/bert-xsmall-dummy | null | [
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #jax #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| ## How to build a dummy model
| [
"## How to build a dummy model"
] | [
"TAGS\n#transformers #pytorch #tf #jax #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n",
"## How to build a dummy model"
] |
feature-extraction | transformers |
# Distilbert, used as a Feature Extractor
| {"tags": ["feature-extraction"], "widget": [{"text": "Hello world"}]} | julien-c/distilbert-feature-extraction | null | [
"transformers",
"pytorch",
"distilbert",
"feature-extraction",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #feature-extraction #endpoints_compatible #has_space #region-us
|
# Distilbert, used as a Feature Extractor
| [
"# Distilbert, used as a Feature Extractor"
] | [
"TAGS\n#transformers #pytorch #distilbert #feature-extraction #endpoints_compatible #has_space #region-us \n",
"# Distilbert, used as a Feature Extractor"
] |
text-classification | transformers |
## distilbert-sagemaker-1609802168
Trained from SageMaker HuggingFace extension.
Fine-tuned from [distilbert-base-uncased](/distilbert-base-uncased) on [imdb](/datasets/imdb) 🔥
#### Eval
| key | value |
| --- | ----- |
| eval_loss | 0.19187863171100616 |
| eval_accuracy | 0.9259 |
| eval_f1 | 0.9272173656811707 ... | {"tags": ["sagemaker"], "datasets": ["imdb"]} | julien-c/distilbert-sagemaker-1609802168 | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"sagemaker",
"dataset:imdb",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #text-classification #sagemaker #dataset-imdb #autotrain_compatible #endpoints_compatible #region-us
| distilbert-sagemaker-1609802168
-------------------------------
Trained from SageMaker HuggingFace extension.
Fine-tuned from distilbert-base-uncased on imdb
#### Eval
| [
"#### Eval"
] | [
"TAGS\n#transformers #pytorch #distilbert #text-classification #sagemaker #dataset-imdb #autotrain_compatible #endpoints_compatible #region-us \n",
"#### Eval"
] |
null | null | in the editor i only change this line
Example of a hf.co repo containing signed commits.
hello tabs
| {} | julien-c/dummy-for-flat | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| in the editor i only change this line
Example of a URL repo containing signed commits.
hello tabs
| [] | [
"TAGS\n#region-us \n"
] |
fill-mask | transformers |
## Dummy model used for unit testing and CI
```python
import json
import os
from transformers import RobertaConfig, RobertaForMaskedLM, TFRobertaForMaskedLM
DIRNAME = "./dummy-unknown"
config = RobertaConfig(10, 20, 1, 1, 40)
model = RobertaForMaskedLM(config)
model.save_pretrained(DIRNAME)
tf_model = TFRoberta... | {"tags": ["ci"]} | julien-c/dummy-unknown | null | [
"transformers",
"pytorch",
"tf",
"jax",
"roberta",
"fill-mask",
"ci",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #jax #roberta #fill-mask #ci #autotrain_compatible #endpoints_compatible #region-us
|
## Dummy model used for unit testing and CI
| [
"## Dummy model used for unit testing and CI"
] | [
"TAGS\n#transformers #pytorch #tf #jax #roberta #fill-mask #ci #autotrain_compatible #endpoints_compatible #region-us \n",
"## Dummy model used for unit testing and CI"
] |
null | fasttext |
## FastText model for language identification
#### ♻️ Imported from https://fasttext.cc/docs/en/language-identification.html
> [1] A. Joulin, E. Grave, P. Bojanowski, T. Mikolov, Bag of Tricks for Efficient Text Classification
```bibtex
@article{joulin2016bag,
title={Bag of Tricks for Efficient Text Classificatio... | {"language": "multilingual", "license": "cc-by-sa-4.0", "library_name": "fasttext", "tags": ["fasttext"], "datasets": ["wikipedia", "tatoeba", "setimes"], "inference": false} | julien-c/fasttext-language-id | null | [
"fasttext",
"multilingual",
"dataset:wikipedia",
"dataset:tatoeba",
"dataset:setimes",
"license:cc-by-sa-4.0",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"multilingual"
] | TAGS
#fasttext #multilingual #dataset-wikipedia #dataset-tatoeba #dataset-setimes #license-cc-by-sa-4.0 #has_space #region-us
|
## FastText model for language identification
#### ️ Imported from URL
> [1] A. Joulin, E. Grave, P. Bojanowski, T. Mikolov, Bag of Tricks for Efficient Text Classification
> [2] A. Joulin, E. Grave, P. Bojanowski, M. Douze, H. Jégou, T. Mikolov, URL: Compressing text classification models
| [
"## FastText model for language identification",
"#### ️ Imported from URL\n\n> [1] A. Joulin, E. Grave, P. Bojanowski, T. Mikolov, Bag of Tricks for Efficient Text Classification\n\n\n\n> [2] A. Joulin, E. Grave, P. Bojanowski, M. Douze, H. Jégou, T. Mikolov, URL: Compressing text classification models"
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"#### ️ Imported from URL\n\n> [1] A. Joulin, E. Grave, P. Bojanowski, T. Mikolov, Bag of Tricks for Efficient Text Classification... |
token-classification | flair |
## Flair NER model `de-ner-conll03-v0.4.pt`
Imported from https://nlp.informatik.hu-berlin.de/resources/models/de-ner/
### Demo: How to use in Flair
```python
from flair.data import Sentence
from flair.models import SequenceTagger
sentence = Sentence(
"Mein Name ist Julien, ich lebe zurzeit in Paris, ich arbeite ... | {"language": "de", "tags": ["flair", "token-classification", "sequence-tagger-model"], "datasets": ["conll2003"], "inference": false} | julien-c/flair-de-ner | null | [
"flair",
"pytorch",
"token-classification",
"sequence-tagger-model",
"de",
"dataset:conll2003",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"de"
] | TAGS
#flair #pytorch #token-classification #sequence-tagger-model #de #dataset-conll2003 #region-us
|
## Flair NER model 'de-ner-conll03-v0.4.pt'
Imported from URL
### Demo: How to use in Flair
yields the following output:
> 'Mein Name ist Julien <S-PER> , ich lebe zurzeit in Paris <S-LOC> , ich arbeite bei Hugging <B-ORG> Face <E-ORG> , Inc <S-ORG> .'
### Thanks @stefan-it for the Flair integration ️
| [
"## Flair NER model 'de-ner-conll03-v0.4.pt'\n\nImported from URL",
"### Demo: How to use in Flair\n\n\n\nyields the following output:\n\n> 'Mein Name ist Julien <S-PER> , ich lebe zurzeit in Paris <S-LOC> , ich arbeite bei Hugging <B-ORG> Face <E-ORG> , Inc <S-ORG> .'",
"### Thanks @stefan-it for the Flair int... | [
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"### Demo: How to use in Flair\n\n\n\nyields the following output:\n\n> 'Mein Name ist Julien <S-PER> , ich lebe zurzeit in Paris <S-LOC>... |
token-classification | flair |
## Flair NER model `en-ner-conll03-v0.4.pt`
Imported from https://nlp.informatik.hu-berlin.de/resources/models/ner/
### Demo: How to use in Flair
```python
from flair.data import Sentence
from flair.models import SequenceTagger
sentence = Sentence(
"My name is Julien, I currently live in Paris, I work at Hugging ... | {"language": "en", "tags": ["flair", "token-classification", "sequence-tagger-model"], "datasets": ["conll2003"], "inference": false} | julien-c/flair-ner | null | [
"flair",
"pytorch",
"token-classification",
"sequence-tagger-model",
"en",
"dataset:conll2003",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#flair #pytorch #token-classification #sequence-tagger-model #en #dataset-conll2003 #region-us
|
## Flair NER model 'en-ner-conll03-v0.4.pt'
Imported from URL
### Demo: How to use in Flair
yields the following output:
> 'My name is Julien <S-PER> , I currently live in Paris <S-LOC> , I work at Hugging <B-LOC> Face <E-LOC> .'
### Thanks @stefan-it for the Flair integration ️
| [
"## Flair NER model 'en-ner-conll03-v0.4.pt'\n\nImported from URL",
"### Demo: How to use in Flair\n\n\n\nyields the following output:\n\n> 'My name is Julien <S-PER> , I currently live in Paris <S-LOC> , I work at Hugging <B-LOC> Face <E-LOC> .'",
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] | [
"TAGS\n#flair #pytorch #token-classification #sequence-tagger-model #en #dataset-conll2003 #region-us \n",
"## Flair NER model 'en-ner-conll03-v0.4.pt'\n\nImported from URL",
"### Demo: How to use in Flair\n\n\n\nyields the following output:\n\n> 'My name is Julien <S-PER> , I currently live in Paris <S-LOC> , ... |
image-classification | transformers |
# hotdog-not-hotdog
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https://github.com/nateraw/hu... | {"tags": ["image-classification", "huggingpics"], "metrics": ["accuracy"]} | julien-c/hotdog-not-hotdog | null | [
"transformers",
"pytorch",
"tensorboard",
"coreml",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #coreml #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# hotdog-not-hotdog
Autogenerated by HuggingPics️
Create your own image classifier for anything by running the demo on Google Colab.
Report any issues with the demo at the github repo.
## Example Images
#### hot dog
!hot dog
#### not hot dog
!miscellaneous | [
"# hotdog-not-hotdog\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nReport any issues with the demo at the github repo.",
"## Example Images",
"#### hot dog\n\n!hot dog",
"#### not hot dog\n\n!miscellaneous"
] | [
"TAGS\n#transformers #pytorch #tensorboard #coreml #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# hotdog-not-hotdog\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Cola... |
text-to-speech | espnet |
## Example ESPnet2 TTS model
### `kan-bayashi/jsut_tts_train_tacotron2_raw_phn_jaconv_pyopenjtalk_accent_train.loss.ave`
♻️ Imported from https://zenodo.org/record/4381098/
This model was trained by kan-bayashi using jsut/tts1 recipe in [espnet](https://github.com/espnet/espnet/).
### Training
![](./exp/tts_trai... | {"language": "ja", "license": "cc-by-4.0", "tags": ["espnet", "audio", "text-to-speech"], "datasets": ["jsut"], "inference": false} | julien-c/kan-bayashi-jsut_tts_train_tacotron2 | null | [
"espnet",
"audio",
"text-to-speech",
"ja",
"dataset:jsut",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1804.00015"
] | [
"ja"
] | TAGS
#espnet #audio #text-to-speech #ja #dataset-jsut #arxiv-1804.00015 #license-cc-by-4.0 #region-us
|
## Example ESPnet2 TTS model
### 'kan-bayashi/jsut_tts_train_tacotron2_raw_phn_jaconv_pyopenjtalk_accent_train.URL'
️ Imported from URL
This model was trained by kan-bayashi using jsut/tts1 recipe in espnet.
### Training
### C... | [
"## Example ESPnet2 TTS model",
"### 'kan-bayashi/jsut_tts_train_tacotron2_raw_phn_jaconv_pyopenjtalk_accent_train.URL'\n\n️ Imported from URL\n\nThis model was trained by kan-bayashi using jsut/tts1 recipe in espnet.",
"### Training\n\n![](./exp/tts_train_tacotron2_raw_phn_jaconv_pyopenjtalk_accent/images/attn... | [
"TAGS\n#espnet #audio #text-to-speech #ja #dataset-jsut #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"## Example ESPnet2 TTS model",
"### 'kan-bayashi/jsut_tts_train_tacotron2_raw_phn_jaconv_pyopenjtalk_accent_train.URL'\n\n️ Imported from URL\n\nThis model was trained by kan-bayashi using jsut/tts1 rec... |
text-to-speech | espnet |
## Example ESPnet2 TTS model
♻️ Imported from https://zenodo.org/record/3963886/
This model was trained by kan-bayashi using jsut/tts1 recipe in [espnet](https://github.com/espnet/espnet/).
Model id:
`kan-bayashi/jsut_tts_train_tacotron2_raw_phn_jaconv_pyopenjtalk_train.loss.best`
### Citing ESPnet
```BibTex
@i... | {"language": "ja", "license": "cc-by-4.0", "tags": ["espnet", "audio", "text-to-speech"], "datasets": ["jsut"], "inference": false} | julien-c/kan-bayashi-jsut_tts_train_tacotron2_ja | null | [
"espnet",
"audio",
"text-to-speech",
"ja",
"dataset:jsut",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1804.00015"
] | [
"ja"
] | TAGS
#espnet #audio #text-to-speech #ja #dataset-jsut #arxiv-1804.00015 #license-cc-by-4.0 #region-us
|
## Example ESPnet2 TTS model
️ Imported from URL
This model was trained by kan-bayashi using jsut/tts1 recipe in espnet.
Model id:
'kan-bayashi/jsut_tts_train_tacotron2_raw_phn_jaconv_pyopenjtalk_train.URL'
### Citing ESPnet
or arXiv:
| [
"## Example ESPnet2 TTS model \n\n️ Imported from URL\n\nThis model was trained by kan-bayashi using jsut/tts1 recipe in espnet.\n\nModel id: \n'kan-bayashi/jsut_tts_train_tacotron2_raw_phn_jaconv_pyopenjtalk_train.URL'",
"### Citing ESPnet\n\n\n\nor arXiv:"
] | [
"TAGS\n#espnet #audio #text-to-speech #ja #dataset-jsut #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"## Example ESPnet2 TTS model \n\n️ Imported from URL\n\nThis model was trained by kan-bayashi using jsut/tts1 recipe in espnet.\n\nModel id: \n'kan-bayashi/jsut_tts_train_tacotron2_raw_phn_jaconv_pyopenjt... |
text-to-speech | espnet |
## ESPnet2 TTS model
### `kan-bayashi/csmsc_tacotron2`
♻️ Imported from https://zenodo.org/record/3969118
This model was trained by kan-bayashi using csmsc/tts1 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```python
# coming soon
```
### Citing ESPnet
```BibTex
@inpr... | {"language": "zh", "license": "cc-by-4.0", "tags": ["espnet", "audio", "text-to-speech"], "datasets": ["csmsc"], "widget": [{"text": "\u8bf7\u60a8\u8bf4\u5f97\u6162\u4e9b\u597d\u5417"}]} | julien-c/kan-bayashi_csmsc_tacotron2 | null | [
"espnet",
"audio",
"text-to-speech",
"zh",
"dataset:csmsc",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1804.00015"
] | [
"zh"
] | TAGS
#espnet #audio #text-to-speech #zh #dataset-csmsc #arxiv-1804.00015 #license-cc-by-4.0 #region-us
|
## ESPnet2 TTS model
### 'kan-bayashi/csmsc_tacotron2'
️ Imported from URL
This model was trained by kan-bayashi using csmsc/tts1 recipe in espnet.
### Demo: How to use in ESPnet2
### Citing ESPnet
or arXiv:
| [
"## ESPnet2 TTS model",
"### 'kan-bayashi/csmsc_tacotron2'\n\n️ Imported from URL\n\nThis model was trained by kan-bayashi using csmsc/tts1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\n\n\nor arXiv:"
] | [
"TAGS\n#espnet #audio #text-to-speech #zh #dataset-csmsc #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"## ESPnet2 TTS model",
"### 'kan-bayashi/csmsc_tacotron2'\n\n️ Imported from URL\n\nThis model was trained by kan-bayashi using csmsc/tts1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"##... |
text-to-speech | espnet |
## Example ESPnet2 TTS model
### `kan-bayashi/ljspeech_tts_train_tacotron2_raw_phn_tacotron_g2p_en_no_space_train.loss.best`
♻️ Imported from https://zenodo.org/record/3989498#.X90RlOlKjkM
This model was trained by kan-bayashi using ljspeech/tts1 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: ... | {"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "text-to-speech"], "datasets": ["ljspeech"], "widget": [{"text": "Hello, how are you doing?"}]} | julien-c/ljspeech_tts_train_tacotron2_raw_phn_tacotron_g2p_en_no_space_train | null | [
"espnet",
"audio",
"text-to-speech",
"en",
"dataset:ljspeech",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1804.00015"
] | [
"en"
] | TAGS
#espnet #audio #text-to-speech #en #dataset-ljspeech #arxiv-1804.00015 #license-cc-by-4.0 #region-us
|
## Example ESPnet2 TTS model
### 'kan-bayashi/ljspeech_tts_train_tacotron2_raw_phn_tacotron_g2p_en_no_space_train.URL'
️ Imported from URL
This model was trained by kan-bayashi using ljspeech/tts1 recipe in espnet.
### Demo: How to use in ESPnet2
### Citing ESPnet
or arXiv:
### Training config
See ful... | [
"## Example ESPnet2 TTS model",
"### 'kan-bayashi/ljspeech_tts_train_tacotron2_raw_phn_tacotron_g2p_en_no_space_train.URL'\n\n️ Imported from URL\n\nThis model was trained by kan-bayashi using ljspeech/tts1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\n\n\nor arXiv:",
"### Tra... | [
"TAGS\n#espnet #audio #text-to-speech #en #dataset-ljspeech #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"## Example ESPnet2 TTS model",
"### 'kan-bayashi/ljspeech_tts_train_tacotron2_raw_phn_tacotron_g2p_en_no_space_train.URL'\n\n️ Imported from URL\n\nThis model was trained by kan-bayashi using ljspee... |
automatic-speech-recognition | espnet |
## Example ESPnet2 ASR model
### `kamo-naoyuki/mini_an4_asr_train_raw_bpe_valid.acc.best`
♻️ Imported from https://zenodo.org/record/3957940#.X90XNelKjkM
This model was trained by kamo-naoyuki using mini_an4 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```python
# comi... | {"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "automatic-speech-recognition"], "datasets": ["ljspeech"]} | julien-c/mini_an4_asr_train_raw_bpe_valid | null | [
"espnet",
"audio",
"automatic-speech-recognition",
"en",
"dataset:ljspeech",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1804.00015"
] | [
"en"
] | TAGS
#espnet #audio #automatic-speech-recognition #en #dataset-ljspeech #arxiv-1804.00015 #license-cc-by-4.0 #region-us
|
## Example ESPnet2 ASR model
### 'kamo-naoyuki/mini_an4_asr_train_raw_bpe_valid.URL'
️ Imported from URL
This model was trained by kamo-naoyuki using mini_an4 recipe in espnet.
### Demo: How to use in ESPnet2
### Citing ESPnet
or arXiv:
| [
"## Example ESPnet2 ASR model",
"### 'kamo-naoyuki/mini_an4_asr_train_raw_bpe_valid.URL'\n\n️ Imported from URL\n\nThis model was trained by kamo-naoyuki using mini_an4 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\n\n\nor arXiv:"
] | [
"TAGS\n#espnet #audio #automatic-speech-recognition #en #dataset-ljspeech #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"## Example ESPnet2 ASR model",
"### 'kamo-naoyuki/mini_an4_asr_train_raw_bpe_valid.URL'\n\n️ Imported from URL\n\nThis model was trained by kamo-naoyuki using mini_an4 recipe in espnet... |
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. -->
# model
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on an unkown dataset... | {"license": "apache-2.0", "tags": ["generated-from-trainer"], "datasets": ["julien-c/reactiongif"], "metrics": ["accuracy"]} | julien-c/reactiongif-roberta | null | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"roberta",
"text-classification",
"generated-from-trainer",
"dataset:julien-c/reactiongif",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #safetensors #roberta #text-classification #generated-from-trainer #dataset-julien-c/reactiongif #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| model
=====
This model is a fine-tuned version of distilroberta-base on an unkown dataset.
It achieves the following results on the evaluation set:
* Loss: 2.9150
* Accuracy: 0.2662
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More infor... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #safetensors #roberta #text-classification #generated-from-trainer #dataset-julien-c/reactiongif #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during... |
null | null | <style>
@import url('https://fonts.googleapis.com/css2?family=Roboto+Slab:wght@900&family=Rokkitt:wght@900&display=swap');
.text1 {
position: absolute;
top: 3vh;
left: calc(50% - 50vh);
}
.text2 {
position: absolute;
bottom: 4vh;
left: 50%;
}
.retro {
font-family: "Roboto Slab";
font-size: 13vh;
displ... | {} | julien-c/roberta-threejs | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| <style>
@import url('URL
.text1 {
position: absolute;
top: 3vh;
left: calc(50% - 50vh);
}
.text2 {
position: absolute;
bottom: 4vh;
left: 50%;
}
.retro {
font-family: "Roboto Slab";
font-size: 13vh;
display: block;
color: #000;
text-shadow: -0.5vh 0 #8800aa, 0 0.5vh #8800aa, 0.5vh 0 #aa0088, 0 -0.... | [] | [
"TAGS\n#region-us \n"
] |
null | null | ## Dummy model containing only Tensorboard traces
from multiple different experiments | {} | julien-c/tensorboard-traces | null | [
"tensorboard",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#tensorboard #region-us
| ## Dummy model containing only Tensorboard traces
from multiple different experiments | [
"## Dummy model containing only Tensorboard traces\n\nfrom multiple different experiments"
] | [
"TAGS\n#tensorboard #region-us \n",
"## Dummy model containing only Tensorboard traces\n\nfrom multiple different experiments"
] |
image-classification | timm |
# `dpn92` from `rwightman/pytorch-image-models`
From [`rwightman/pytorch-image-models`](https://github.com/rwightman/pytorch-image-models):
```
""" PyTorch implementation of DualPathNetworks
Based on original MXNet implementation https://github.com/cypw/DPNs with
many ideas from another PyTorch implementation https:... | {"license": "apache-2.0", "tags": ["image-classification", "timm", "dpn"], "datasets": ["imagenet"]} | julien-c/timm-dpn92 | null | [
"timm",
"pytorch",
"image-classification",
"dpn",
"dataset:imagenet",
"arxiv:1707.01629",
"arxiv:1906.02659",
"arxiv:2010.15052",
"license:apache-2.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1707.01629",
"1906.02659",
"2010.15052"
] | [] | TAGS
#timm #pytorch #image-classification #dpn #dataset-imagenet #arxiv-1707.01629 #arxiv-1906.02659 #arxiv-2010.15052 #license-apache-2.0 #region-us
|
# 'dpn92' from 'rwightman/pytorch-image-models'
From 'rwightman/pytorch-image-models':
## Model description
Dual Path Networks
## Intended uses & limitations
You can use the raw model to classify images along the 1,000 ImageNet labels, but you can also change its head
to fine-tune it on a downstream task (anoth... | [
"# 'dpn92' from 'rwightman/pytorch-image-models'\n\nFrom 'rwightman/pytorch-image-models':",
"## Model description\n\nDual Path Networks",
"## Intended uses & limitations\n\nYou can use the raw model to classify images along the 1,000 ImageNet labels, but you can also change its head\nto fine-tune it on a downs... | [
"TAGS\n#timm #pytorch #image-classification #dpn #dataset-imagenet #arxiv-1707.01629 #arxiv-1906.02659 #arxiv-2010.15052 #license-apache-2.0 #region-us \n",
"# 'dpn92' from 'rwightman/pytorch-image-models'\n\nFrom 'rwightman/pytorch-image-models':",
"## Model description\n\nDual Path Networks",
"## Intended u... |
voice-activity-detection | null |
## Example pyannote-audio Voice Activity Detection model
### `pyannote.audio.models.segmentation.PyanNet`
♻️ Imported from https://github.com/pyannote/pyannote-audio-hub
This model was trained by @hbredin.
### Demo: How to use in pyannote-audio
```python
from pyannote.audio.core.inference import Inference
mode... | {"license": "mit", "tags": ["pyannote", "audio", "voice-activity-detection"], "datasets": ["dihard"], "inference": false} | julien-c/voice-activity-detection | null | [
"pytorch",
"pyannote",
"audio",
"voice-activity-detection",
"dataset:dihard",
"arxiv:1910.10655",
"license:mit",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1910.10655"
] | [] | TAGS
#pytorch #pyannote #audio #voice-activity-detection #dataset-dihard #arxiv-1910.10655 #license-mit #region-us
|
## Example pyannote-audio Voice Activity Detection model
### 'URL.segmentation.PyanNet'
️ Imported from URL
This model was trained by @hbredin.
### Demo: How to use in pyannote-audio
### Citing pyannote-audio
or
| [
"## Example pyannote-audio Voice Activity Detection model",
"### 'URL.segmentation.PyanNet'\n\n️ Imported from URL\n\nThis model was trained by @hbredin.",
"### Demo: How to use in pyannote-audio",
"### Citing pyannote-audio\n\n\n\nor"
] | [
"TAGS\n#pytorch #pyannote #audio #voice-activity-detection #dataset-dihard #arxiv-1910.10655 #license-mit #region-us \n",
"## Example pyannote-audio Voice Activity Detection model",
"### 'URL.segmentation.PyanNet'\n\n️ Imported from URL\n\nThis model was trained by @hbredin.",
"### Demo: How to use in pyannot... |
tabular-classification | sklearn |
## Wine Quality classification
### A Simple Example of Scikit-learn Pipeline
> Inspired by https://towardsdatascience.com/a-simple-example-of-pipeline-in-machine-learning-with-scikit-learn-e726ffbb6976 by Saptashwa Bhattacharyya
### How to use
```python
from huggingface_hub import hf_hub_url, cached_download
impo... | {"tags": ["tabular-classification", "sklearn"], "datasets": ["wine-quality", "lvwerra/red-wine"], "widget": [{"structuredData": {"fixed_acidity": [7.4, 7.8, 10.3], "volatile_acidity": [0.7, 0.88, 0.32], "citric_acid": [0, 0, 0.45], "residual_sugar": [1.9, 2.6, 6.4], "chlorides": [0.076, 0.098, 0.073], "free_sulfur_diox... | julien-c/wine-quality | null | [
"sklearn",
"joblib",
"tabular-classification",
"dataset:wine-quality",
"dataset:lvwerra/red-wine",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#sklearn #joblib #tabular-classification #dataset-wine-quality #dataset-lvwerra/red-wine #has_space #region-us
| Wine Quality classification
---------------------------
### A Simple Example of Scikit-learn Pipeline
>
> Inspired by URL by Saptashwa Bhattacharyya
>
>
>
### How to use
#### Get sample data from this repo
#### Get your prediction
#### Eval
### Disclaimer
No red wine was drunk (unfortunately) whil... | [
"### A Simple Example of Scikit-learn Pipeline\n\n\n\n> \n> Inspired by URL by Saptashwa Bhattacharyya\n> \n> \n>",
"### How to use",
"#### Get sample data from this repo",
"#### Get your prediction",
"#### Eval",
"### Disclaimer\n\n\nNo red wine was drunk (unfortunately) while training this model"
] | [
"TAGS\n#sklearn #joblib #tabular-classification #dataset-wine-quality #dataset-lvwerra/red-wine #has_space #region-us \n",
"### A Simple Example of Scikit-learn Pipeline\n\n\n\n> \n> Inspired by URL by Saptashwa Bhattacharyya\n> \n> \n>",
"### How to use",
"#### Get sample data from this repo",
"#### Get yo... |
text-classification | transformers |
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 16622767
## Validation Metrics
- Loss: 0.20029613375663757
- Accuracy: 0.9256
- Precision: 0.9090909090909091
- Recall: 0.9466984884645983
- AUC: 0.979257749523025
- F1: 0.9275136399064692
## Usage
You can use cURL to access this mode... | {"language": "en", "tags": "autonlp", "datasets": ["juliensimon/autonlp-data-imdb-demo-hf"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}]} | juliensimon/autonlp-imdb-demo-hf-16622767 | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"autonlp",
"en",
"dataset:juliensimon/autonlp-data-imdb-demo-hf",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #distilbert #text-classification #autonlp #en #dataset-juliensimon/autonlp-data-imdb-demo-hf #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 16622767
## Validation Metrics
- Loss: 0.20029613375663757
- Accuracy: 0.9256
- Precision: 0.9090909090909091
- Recall: 0.9466984884645983
- AUC: 0.979257749523025
- F1: 0.9275136399064692
## Usage
You can use cURL to access this mode... | [
"# Model Trained Using AutoNLP\n\n- Problem type: Binary Classification\n- Model ID: 16622767",
"## Validation Metrics\n\n- Loss: 0.20029613375663757\n- Accuracy: 0.9256\n- Precision: 0.9090909090909091\n- Recall: 0.9466984884645983\n- AUC: 0.979257749523025\n- F1: 0.9275136399064692",
"## Usage\n\nYou can use ... | [
"TAGS\n#transformers #pytorch #distilbert #text-classification #autonlp #en #dataset-juliensimon/autonlp-data-imdb-demo-hf #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoNLP\n\n- Problem type: Binary Classification\n- Model ID: 16622767",
"## Validation Metrics\n\n- Loss:... |
text-classification | transformers |
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 16622775
## Validation Metrics
- Loss: 0.18653589487075806
- Accuracy: 0.9408
- Precision: 0.9537643207855974
- Recall: 0.9272076372315036
- AUC: 0.985847396174344
- F1: 0.9402985074626865
## Usage
You can use cURL to access this mode... | {"language": "en", "tags": "autonlp", "datasets": ["juliensimon/autonlp-data-imdb-demo-hf"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}]} | juliensimon/autonlp-imdb-demo-hf-16622775 | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"autonlp",
"en",
"dataset:juliensimon/autonlp-data-imdb-demo-hf",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #roberta #text-classification #autonlp #en #dataset-juliensimon/autonlp-data-imdb-demo-hf #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 16622775
## Validation Metrics
- Loss: 0.18653589487075806
- Accuracy: 0.9408
- Precision: 0.9537643207855974
- Recall: 0.9272076372315036
- AUC: 0.985847396174344
- F1: 0.9402985074626865
## Usage
You can use cURL to access this mode... | [
"# Model Trained Using AutoNLP\n\n- Problem type: Binary Classification\n- Model ID: 16622775",
"## Validation Metrics\n\n- Loss: 0.18653589487075806\n- Accuracy: 0.9408\n- Precision: 0.9537643207855974\n- Recall: 0.9272076372315036\n- AUC: 0.985847396174344\n- F1: 0.9402985074626865",
"## Usage\n\nYou can use ... | [
"TAGS\n#transformers #pytorch #roberta #text-classification #autonlp #en #dataset-juliensimon/autonlp-data-imdb-demo-hf #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# Model Trained Using AutoNLP\n\n- Problem type: Binary Classification\n- Model ID: 16622775",
"## Validation Metrics\n\... |
text2text-generation | transformers |
# Model Trained Using AutoNLP
- Problem type: Summarization
- Model ID: 31447312
- CO2 Emissions (in grams): 206.46626351359515
## Validation Metrics
- Loss: 1.1907752752304077
- Rouge1: 55.9215
- Rouge2: 30.7724
- RougeL: 53.185
- RougeLsum: 53.3353
- Gen Len: 15.1236
## Usage
You can use cURL to access this mod... | {"language": "en", "tags": "autonlp", "datasets": ["juliensimon/autonlp-data-reuters-summarization"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}], "co2_eq_emissions": 206.46626351359515} | juliensimon/autonlp-reuters-summarization-31447312 | null | [
"transformers",
"pytorch",
"safetensors",
"pegasus",
"text2text-generation",
"autonlp",
"en",
"dataset:juliensimon/autonlp-data-reuters-summarization",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #safetensors #pegasus #text2text-generation #autonlp #en #dataset-juliensimon/autonlp-data-reuters-summarization #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoNLP
- Problem type: Summarization
- Model ID: 31447312
- CO2 Emissions (in grams): 206.46626351359515
## Validation Metrics
- Loss: 1.1907752752304077
- Rouge1: 55.9215
- Rouge2: 30.7724
- RougeL: 53.185
- RougeLsum: 53.3353
- Gen Len: 15.1236
## Usage
You can use cURL to access this mod... | [
"# Model Trained Using AutoNLP\n\n- Problem type: Summarization\n- Model ID: 31447312\n- CO2 Emissions (in grams): 206.46626351359515",
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"## Usage\n\nYou can us... | [
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"# Model Trained Using AutoNLP\n\n- Problem type: Summarization\n- Model ID: 31447312\n- CO2 ... |
text-classification | transformers |
# Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 18753417
- CO2 Emissions (in grams): 112.75546781635975
## Validation Metrics
- Loss: 0.9065971970558167
- Accuracy: 0.6680274633512711
- Macro F1: 0.5384854358272774
- Micro F1: 0.6680274633512711
- Weighted F1: 0.6414749238882866... | {"language": "en", "tags": ["autonlp"], "datasets": ["juliensimon/autonlp-data-song-lyrics"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}], "co2_eq_emissions": 112.75546781635975} | juliensimon/autonlp-song-lyrics-18753417 | null | [
"transformers",
"pytorch",
"safetensors",
"bert",
"text-classification",
"autonlp",
"en",
"dataset:juliensimon/autonlp-data-song-lyrics",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #safetensors #bert #text-classification #autonlp #en #dataset-juliensimon/autonlp-data-song-lyrics #co2_eq_emissions #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 18753417
- CO2 Emissions (in grams): 112.75546781635975
## Validation Metrics
- Loss: 0.9065971970558167
- Accuracy: 0.6680274633512711
- Macro F1: 0.5384854358272774
- Micro F1: 0.6680274633512711
- Weighted F1: 0.6414749238882866... | [
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"# Model Trained Using AutoNLP\n\n- Problem type: Multi-class Classification\n- Model ID: 187534... |
text-classification | transformers |
# Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 18753423
- CO2 Emissions (in grams): 55.552987716859484
## Validation Metrics
- Loss: 0.913820743560791
- Accuracy: 0.654110224531453
- Macro F1: 0.5327761649415296
- Micro F1: 0.654110224531453
- Weighted F1: 0.6339481529454227
- ... | {"language": "en", "tags": "autonlp", "datasets": ["juliensimon/autonlp-data-song-lyrics"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}], "co2_eq_emissions": 55.552987716859484} | juliensimon/autonlp-song-lyrics-18753423 | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"autonlp",
"en",
"dataset:juliensimon/autonlp-data-song-lyrics",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #distilbert #text-classification #autonlp #en #dataset-juliensimon/autonlp-data-song-lyrics #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 18753423
- CO2 Emissions (in grams): 55.552987716859484
## Validation Metrics
- Loss: 0.913820743560791
- Accuracy: 0.654110224531453
- Macro F1: 0.5327761649415296
- Micro F1: 0.654110224531453
- Weighted F1: 0.6339481529454227
- ... | [
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"# Model Trained Using AutoNLP\n\n- Problem type: Multi-class Classification\n- Model ID: 18753423\n- CO2 Emission... |
text-classification | transformers |
Distilbert model fine-tuned on English language product reviews
A notebook for Amazon SageMaker is available in the 'code' subfolder.
| {"language": ["en"], "tags": ["distilbert", "sentiment-analysis"], "datasets": ["generated_reviews_enth"]} | juliensimon/reviews-sentiment-analysis | null | [
"transformers",
"pytorch",
"safetensors",
"distilbert",
"text-classification",
"sentiment-analysis",
"en",
"dataset:generated_reviews_enth",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #safetensors #distilbert #text-classification #sentiment-analysis #en #dataset-generated_reviews_enth #autotrain_compatible #endpoints_compatible #has_space #region-us
|
Distilbert model fine-tuned on English language product reviews
A notebook for Amazon SageMaker is available in the 'code' subfolder.
| [] | [
"TAGS\n#transformers #pytorch #safetensors #distilbert #text-classification #sentiment-analysis #en #dataset-generated_reviews_enth #autotrain_compatible #endpoints_compatible #has_space #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. -->
# biobert-base-cased-v1.1-squad-finetuned-covbiobert
This model is a fine-tuned version of [dmis-lab/biobert-base-cased-v1.1-squad... | {"tags": ["generated_from_trainer"], "datasets": ["covid_qa_deepset"], "model-index": [{"name": "biobert-base-cased-v1.1-squad-finetuned-covbiobert", "results": []}]} | juliusco/biobert-base-cased-v1.1-squad-finetuned-covbiobert | null | [
"transformers",
"pytorch",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:covid_qa_deepset",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #question-answering #generated_from_trainer #dataset-covid_qa_deepset #endpoints_compatible #region-us
| biobert-base-cased-v1.1-squad-finetuned-covbiobert
==================================================
This model is a fine-tuned version of dmis-lab/biobert-base-cased-v1.1-squad on the covid\_qa\_deepset dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3959
Model description
----------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Trai... | [
"TAGS\n#transformers #pytorch #bert #question-answering #generated_from_trainer #dataset-covid_qa_deepset #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: 128\n* eval\\_batch\\_s... |
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. -->
# biobert-base-cased-v1.1-squad-finetuned-covdrobert
This model is a fine-tuned version of [dmis-lab/biobert-base-cased-v1.1-squad... | {"tags": ["generated_from_trainer"], "datasets": ["covid_qa_deepset"], "model-index": [{"name": "biobert-base-cased-v1.1-squad-finetuned-covdrobert", "results": []}]} | juliusco/biobert-base-cased-v1.1-squad-finetuned-covdrobert | null | [
"transformers",
"pytorch",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:covid_qa_deepset",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #question-answering #generated_from_trainer #dataset-covid_qa_deepset #endpoints_compatible #region-us
| biobert-base-cased-v1.1-squad-finetuned-covdrobert
==================================================
This model is a fine-tuned version of dmis-lab/biobert-base-cased-v1.1-squad on the covid\_qa\_deepset dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3959
Model description
----------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Trai... | [
"TAGS\n#transformers #pytorch #bert #question-answering #generated_from_trainer #dataset-covid_qa_deepset #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: 128\n* eval\\_batch\\_s... |
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-covdistilbert
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingf... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["covid_qa_deepset"], "model-index": [{"name": "distilbert-base-uncased-finetuned-covdistilbert", "results": []}]} | juliusco/distilbert-base-uncased-finetuned-covdistilbert | null | [
"transformers",
"pytorch",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:covid_qa_deepset",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #question-answering #generated_from_trainer #dataset-covid_qa_deepset #license-apache-2.0 #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-covdistilbert
===============================================
This model is a fine-tuned version of distilbert-base-uncased on the covid\_qa\_deepset dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4844
Model description
-----------------
More inform... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\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",
"### Trai... | [
"TAGS\n#transformers #pytorch #distilbert #question-answering #generated_from_trainer #dataset-covid_qa_deepset #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\\_siz... |
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": []}]} | juliusco/distilbert-base-uncased-finetuned-squad | null | [
"transformers",
"pytorch",
"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 #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.
It achieves the following results on the evaluation set:
* Loss: 1.3672
Model description
-----------------
More information needed
Intended uses ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 4",
"### Training... | [
"TAGS\n#transformers #pytorch #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\\_size: 8\n* eva... |
fill-mask | transformers |
# https://github.com/JunnYu/ChineseBert_pytorch
# ChineseBert_pytorch
本项目主要自定义了tokenization_chinesebert_fast.py文件中的ChineseBertTokenizerFast代码。从而可以从huggingface.co调用。
```python
pretrained_tokenizer_name = "junnyu/ChineseBERT-base"
tokenizer = ChineseBertTokenizerFast.from_pretrained(pretrained_tokenizer_name)
```
# Pa... | {"language": "zh", "tags": ["glycebert"], "inference": false} | junnyu/ChineseBERT-base | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"glycebert",
"zh",
"arxiv:2106.16038",
"autotrain_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2106.16038"
] | [
"zh"
] | TAGS
#transformers #pytorch #bert #fill-mask #glycebert #zh #arxiv-2106.16038 #autotrain_compatible #region-us
|
# URL
# ChineseBert_pytorch
本项目主要自定义了tokenization_chinesebert_fast.py文件中的ChineseBertTokenizerFast代码。从而可以从huggingface.co调用。
# Paper
ChineseBERT: Chinese Pretraining Enhanced by Glyph and Pinyin Information
*Zijun Sun, Xiaoya Li, Xiaofei Sun, Yuxian Meng, Xiang Ao, Qing He, Fei Wu and Jiwei Li*
# Install
# Usage... | [
"# URL",
"# ChineseBert_pytorch\n本项目主要自定义了tokenization_chinesebert_fast.py文件中的ChineseBertTokenizerFast代码。从而可以从huggingface.co调用。",
"# Paper\nChineseBERT: Chinese Pretraining Enhanced by Glyph and Pinyin Information \n*Zijun Sun, Xiaoya Li, Xiaofei Sun, Yuxian Meng, Xiang Ao, Qing He, Fei Wu and Jiwei Li*",
"#... | [
"TAGS\n#transformers #pytorch #bert #fill-mask #glycebert #zh #arxiv-2106.16038 #autotrain_compatible #region-us \n",
"# URL",
"# ChineseBert_pytorch\n本项目主要自定义了tokenization_chinesebert_fast.py文件中的ChineseBertTokenizerFast代码。从而可以从huggingface.co调用。",
"# Paper\nChineseBERT: Chinese Pretraining Enhanced by Glyph a... |
fill-mask | transformers |
# https://github.com/JunnYu/ChineseBert_pytorch
# ChineseBert_pytorch
本项目主要自定义了tokenization_chinesebert_fast.py文件中的ChineseBertTokenizerFast代码。从而可以从huggingface.co调用。
```python
pretrained_tokenizer_name = "junnyu/ChineseBERT-large"
tokenizer = ChineseBertTokenizerFast.from_pretrained(pretrained_tokenizer_name)
```
# P... | {"language": "zh", "tags": ["glycebert"], "inference": false} | junnyu/ChineseBERT-large | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"glycebert",
"zh",
"arxiv:2106.16038",
"autotrain_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2106.16038"
] | [
"zh"
] | TAGS
#transformers #pytorch #bert #fill-mask #glycebert #zh #arxiv-2106.16038 #autotrain_compatible #region-us
|
# URL
# ChineseBert_pytorch
本项目主要自定义了tokenization_chinesebert_fast.py文件中的ChineseBertTokenizerFast代码。从而可以从huggingface.co调用。
# Paper
ChineseBERT: Chinese Pretraining Enhanced by Glyph and Pinyin Information
*Zijun Sun, Xiaoya Li, Xiaofei Sun, Yuxian Meng, Xiang Ao, Qing He, Fei Wu and Jiwei Li*
# Install
# Usage... | [
"# URL",
"# ChineseBert_pytorch\n本项目主要自定义了tokenization_chinesebert_fast.py文件中的ChineseBertTokenizerFast代码。从而可以从huggingface.co调用。",
"# Paper\nChineseBERT: Chinese Pretraining Enhanced by Glyph and Pinyin Information \n*Zijun Sun, Xiaoya Li, Xiaofei Sun, Yuxian Meng, Xiang Ao, Qing He, Fei Wu and Jiwei Li*",
"#... | [
"TAGS\n#transformers #pytorch #bert #fill-mask #glycebert #zh #arxiv-2106.16038 #autotrain_compatible #region-us \n",
"# URL",
"# ChineseBert_pytorch\n本项目主要自定义了tokenization_chinesebert_fast.py文件中的ChineseBertTokenizerFast代码。从而可以从huggingface.co调用。",
"# Paper\nChineseBERT: Chinese Pretraining Enhanced by Glyph a... |
fill-mask | transformers | https://github.com/alibaba-research/ChineseBLUE | {} | junnyu/bert_chinese_mc_base | null | [
"transformers",
"pytorch",
"jax",
"bert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| URL | [] | [
"TAGS\n#transformers #pytorch #jax #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n"
] |
null | transformers | https://github.com/PaddlePaddle/Research/tree/master/KG/eHealth | {} | junnyu/eHealth_pytorch | null | [
"transformers",
"pytorch",
"bert",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #endpoints_compatible #region-us
| URL | [] | [
"TAGS\n#transformers #pytorch #bert #endpoints_compatible #region-us \n"
] |
null | transformers | # 一、 个人在openwebtext数据集上训练得到的electra-small模型
# 二、 复现结果(dev dataset)
|Model|CoLA|SST|MRPC|STS|QQP|MNLI|QNLI|RTE|Avg.|
|---|---|---|---|---|---|---|---|---|---|
|Metrics|MCC|Acc|Acc|Spearman|Acc|Acc|Acc|Acc||
|ELECTRA-Small-OWT(original)|56.8|88.3|87.4|86.8|88.3|78.9|87.9|68.5|80.36|
|**ELECTRA-Small-OWT (this)**| 55.82 ... | {"language": "en", "license": "mit", "tags": ["pytorch", "electra"], "datasets": ["openwebtext"], "thumbnail": "https://github.com/junnyu"} | junnyu/electra_small_discriminator | null | [
"transformers",
"pytorch",
"electra",
"pretraining",
"en",
"dataset:openwebtext",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #electra #pretraining #en #dataset-openwebtext #license-mit #endpoints_compatible #region-us
| 一、 个人在openwebtext数据集上训练得到的electra-small模型
=========================================
二、 复现结果(dev dataset)
====================
三、 训练细节
=======
* 数据集 openwebtext
* 训练batch\_size 256
* 学习率lr 5e-4
* 最大句子长度max\_seqlen 128
* 训练total step 62.5W
* GPU RTX3090
* 训练时间总共耗费2.5天
四、 使用
=====
| [] | [
"TAGS\n#transformers #pytorch #electra #pretraining #en #dataset-openwebtext #license-mit #endpoints_compatible #region-us \n"
] |
fill-mask | transformers | # 一、 个人在openwebtext数据集上训练得到的electra-small模型
# 二、 复现结果(dev dataset)
|Model|CoLA|SST|MRPC|STS|QQP|MNLI|QNLI|RTE|Avg.|
|---|---|---|---|---|---|---|---|---|---|
|ELECTRA-Small-OWT(original)|56.8|88.3|87.4|86.8|88.3|78.9|87.9|68.5|80.36|
|**ELECTRA-Small-OWT (this)**| 55.82 |89.67|87.0|86.96|89.28|80.08|87.50|66.07|80.30|... | {"language": "en", "license": "mit", "tags": ["pytorch", "electra", "masked-lm"], "datasets": ["openwebtext"], "thumbnail": "https://github.com/junnyu"} | junnyu/electra_small_generator | null | [
"transformers",
"pytorch",
"electra",
"fill-mask",
"masked-lm",
"en",
"dataset:openwebtext",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #electra #fill-mask #masked-lm #en #dataset-openwebtext #license-mit #autotrain_compatible #endpoints_compatible #region-us
| 一、 个人在openwebtext数据集上训练得到的electra-small模型
=========================================
二、 复现结果(dev dataset)
====================
三、 训练细节
=======
* 数据集 openwebtext
* 训练batch\_size 256
* 学习率lr 5e-4
* 最大句子长度max\_seqlen 128
* 训练total step 62.5W
* GPU RTX3090
* 训练时间总共耗费2.5天
四、 使用
=====
| [] | [
"TAGS\n#transformers #pytorch #electra #fill-mask #masked-lm #en #dataset-openwebtext #license-mit #autotrain_compatible #endpoints_compatible #region-us \n"
] |
fill-mask | transformers | ## 介绍
Pretrained model on 13G Chinese corpus(clue corpus small). Masked language modeling(MLM) and sentence order prediction(SOP) are used as training task.
在13g的clue corpus small数据集上进行的预训练，使用了`Whole Mask LM` 和 `SOP` 任务
训练逻辑参考了这里。https://github.com/PaddlePaddle/PaddleNLP/tree/develop/examples/language_model/ernie-1.0
... | {"language": "zh", "tags": ["roformer", "pytorch", "tf2.0", "paddlepaddle"], "widget": [{"text": "\u4eca\u5929[MASK]\u5f88\u597d\uff0c\u6211\u60f3\u53bb\u516c\u56ed\u73a9\uff01"}]} | junnyu/roformer_base_wwm_cluecorpussmall | null | [
"transformers",
"pytorch",
"roformer",
"fill-mask",
"tf2.0",
"paddlepaddle",
"zh",
"arxiv:2104.09864",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2104.09864"
] | [
"zh"
] | TAGS
#transformers #pytorch #roformer #fill-mask #tf2.0 #paddlepaddle #zh #arxiv-2104.09864 #autotrain_compatible #endpoints_compatible #region-us
| ## 介绍
Pretrained model on 13G Chinese corpus(clue corpus small). Masked language modeling(MLM) and sentence order prediction(SOP) are used as training task.
在13g的clue corpus small数据集上进行的预训练，使用了'Whole Mask LM' 和 'SOP' 任务
训练逻辑参考了这里。URL
## 训练细节：
- paddlepaddle+paddlenlp
- V100 x 4
- batch size 256
- max_seq_len 512
- m... | [
"## 介绍\nPretrained model on 13G Chinese corpus(clue corpus small). Masked language modeling(MLM) and sentence order prediction(SOP) are used as training task.\n在13g的clue corpus small数据集上进行的预训练，使用了'Whole Mask LM' 和 'SOP' 任务\n\n训练逻辑参考了这里。URL",
"## 训练细节：\n- paddlepaddle+paddlenlp\n- V100 x 4\n- batch size 256\n- max... | [
"TAGS\n#transformers #pytorch #roformer #fill-mask #tf2.0 #paddlepaddle #zh #arxiv-2104.09864 #autotrain_compatible #endpoints_compatible #region-us \n",
"## 介绍\nPretrained model on 13G Chinese corpus(clue corpus small). Masked language modeling(MLM) and sentence order prediction(SOP) are used as training task.\n... |
null | paddlenlp | ## 介绍
### tf版本
https://github.com/ZhuiyiTechnology/roformer
### pytorch版本+tf2.0版本
https://github.com/JunnYu/RoFormer_pytorch
## pytorch使用
```python
import torch
from transformers import RoFormerForMaskedLM, RoFormerTokenizer
text = "今天[MASK]很好，我想去公园玩！"
tokenizer = RoFormerTokenizer.from_pretrained("junnyu/roformer_... | {"language": "zh", "tags": ["roformer", "pytorch", "tf2.0"], "widget": [{"text": "\u4eca\u5929[MASK]\u5f88\u597d\uff0c\u6211\u60f3\u53bb\u516c\u56ed\u73a9\uff01"}]} | junnyu/roformer_chinese_base | null | [
"paddlenlp",
"pytorch",
"tf",
"jax",
"paddlepaddle",
"roformer",
"tf2.0",
"zh",
"arxiv:2104.09864",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2104.09864"
] | [
"zh"
] | TAGS
#paddlenlp #pytorch #tf #jax #paddlepaddle #roformer #tf2.0 #zh #arxiv-2104.09864 #has_space #region-us
| ## 介绍
### tf版本
URL
### pytorch版本+tf2.0版本
URL
## pytorch使用
## tensorflow2.0使用
## 引用
Bibtex：
| [
"## 介绍",
"### tf版本 \nURL",
"### pytorch版本+tf2.0版本\nURL",
"## pytorch使用",
"## tensorflow2.0使用",
"## 引用\n\nBibtex："
] | [
"TAGS\n#paddlenlp #pytorch #tf #jax #paddlepaddle #roformer #tf2.0 #zh #arxiv-2104.09864 #has_space #region-us \n",
"## 介绍",
"### tf版本 \nURL",
"### pytorch版本+tf2.0版本\nURL",
"## pytorch使用",
"## tensorflow2.0使用",
"## 引用\n\nBibtex："
] |
null | paddlenlp | ## 介绍
### tf版本
https://github.com/ZhuiyiTechnology/roformer
### pytorch版本+tf2.0版本
https://github.com/JunnYu/RoFormer_pytorch
## pytorch使用
```python
import torch
from transformers import RoFormerForMaskedLM, RoFormerTokenizer
text = "今天[MASK]很好，我[MASK]去公园玩。"
tokenizer = RoFormerTokenizer.from_pretrained("junnyu/rofo... | {"language": "zh", "tags": ["roformer", "pytorch", "tf2.0"], "widget": [{"text": "\u4eca\u5929[MASK]\u5f88\u597d\uff0c\u6211\u60f3\u53bb\u516c\u56ed\u73a9\uff01"}]} | junnyu/roformer_chinese_char_base | null | [
"paddlenlp",
"pytorch",
"tf",
"jax",
"paddlepaddle",
"roformer",
"tf2.0",
"zh",
"arxiv:2104.09864",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2104.09864"
] | [
"zh"
] | TAGS
#paddlenlp #pytorch #tf #jax #paddlepaddle #roformer #tf2.0 #zh #arxiv-2104.09864 #has_space #region-us
| ## 介绍
### tf版本
URL
### pytorch版本+tf2.0版本
URL
## pytorch使用
## tensorflow2.0使用
## 引用
Bibtex：
| [
"## 介绍",
"### tf版本 \nURL",
"### pytorch版本+tf2.0版本\nURL",
"## pytorch使用",
"## tensorflow2.0使用",
"## 引用\n\nBibtex："
] | [
"TAGS\n#paddlenlp #pytorch #tf #jax #paddlepaddle #roformer #tf2.0 #zh #arxiv-2104.09864 #has_space #region-us \n",
"## 介绍",
"### tf版本 \nURL",
"### pytorch版本+tf2.0版本\nURL",
"## pytorch使用",
"## tensorflow2.0使用",
"## 引用\n\nBibtex："
] |
fill-mask | transformers | ## 介绍
### tf版本
https://github.com/ZhuiyiTechnology/roformer
### pytorch版本+tf2.0版本
https://github.com/JunnYu/RoFormer_pytorch
## pytorch使用
```python
import torch
from transformers import RoFormerForMaskedLM, RoFormerTokenizer
text = "今天[MASK]很好，我[MASK]去公园玩。"
tokenizer = RoFormerTokenizer.from_pretrained("junnyu/rofo... | {"language": "zh", "tags": ["roformer", "pytorch", "tf2.0"], "widget": [{"text": "\u4eca\u5929[MASK]\u5f88\u597d\uff0c\u6211\u60f3\u53bb\u516c\u56ed\u73a9\uff01"}]} | junnyu/roformer_chinese_char_small | null | [
"transformers",
"pytorch",
"tf",
"jax",
"roformer",
"fill-mask",
"tf2.0",
"zh",
"arxiv:2104.09864",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2104.09864"
] | [
"zh"
] | TAGS
#transformers #pytorch #tf #jax #roformer #fill-mask #tf2.0 #zh #arxiv-2104.09864 #autotrain_compatible #endpoints_compatible #has_space #region-us
| ## 介绍
### tf版本
URL
### pytorch版本+tf2.0版本
URL
## pytorch使用
## tensorflow2.0使用
## 引用
Bibtex：
| [
"## 介绍",
"### tf版本 \nURL",
"### pytorch版本+tf2.0版本\nURL",
"## pytorch使用",
"## tensorflow2.0使用",
"## 引用\n\nBibtex："
] | [
"TAGS\n#transformers #pytorch #tf #jax #roformer #fill-mask #tf2.0 #zh #arxiv-2104.09864 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"## 介绍",
"### tf版本 \nURL",
"### pytorch版本+tf2.0版本\nURL",
"## pytorch使用",
"## tensorflow2.0使用",
"## 引用\n\nBibtex："
] |
text-generation | transformers | # 安装
- pip install roformer==0.4.3
# 使用
```python
import torch
import numpy as np
from roformer import RoFormerForCausalLM, RoFormerConfig
from transformers import BertTokenizer
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
pretrained_model = "junnyu/roformer_chinese_sim_char_base"
tokenizer... | {"language": "zh", "tags": ["roformer", "pytorch", "tf2.0"], "inference": false} | junnyu/roformer_chinese_sim_char_base | null | [
"transformers",
"pytorch",
"roformer",
"text-generation",
"tf2.0",
"zh",
"autotrain_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"zh"
] | TAGS
#transformers #pytorch #roformer #text-generation #tf2.0 #zh #autotrain_compatible #region-us
| # 安装
- pip install roformer==0.4.3
# 使用
| [
"# 安装\n- pip install roformer==0.4.3",
"# 使用"
] | [
"TAGS\n#transformers #pytorch #roformer #text-generation #tf2.0 #zh #autotrain_compatible #region-us \n",
"# 安装\n- pip install roformer==0.4.3",
"# 使用"
] |
text-generation | transformers | # 安装
- pip install roformer==0.4.3
# 使用
```python
import torch
import numpy as np
from roformer import RoFormerForCausalLM, RoFormerConfig
from transformers import BertTokenizer
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
pretrained_model = "junnyu/roformer_chinese_sim_char_base"
tokenizer... | {"language": "zh", "tags": ["roformer", "pytorch", "tf2.0"], "inference": false} | junnyu/roformer_chinese_sim_char_ft_base | null | [
"transformers",
"pytorch",
"roformer",
"text-generation",
"tf2.0",
"zh",
"autotrain_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"zh"
] | TAGS
#transformers #pytorch #roformer #text-generation #tf2.0 #zh #autotrain_compatible #has_space #region-us
| # 安装
- pip install roformer==0.4.3
# 使用
| [
"# 安装\n- pip install roformer==0.4.3",
"# 使用"
] | [
"TAGS\n#transformers #pytorch #roformer #text-generation #tf2.0 #zh #autotrain_compatible #has_space #region-us \n",
"# 安装\n- pip install roformer==0.4.3",
"# 使用"
] |
text-generation | transformers | # 安装
- pip install roformer==0.4.3
# 使用
```python
import torch
import numpy as np
from roformer import RoFormerForCausalLM, RoFormerConfig
from transformers import BertTokenizer
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
pretrained_model = "junnyu/roformer_chinese_sim_char_base"
tokenizer... | {"language": "zh", "tags": ["roformer", "pytorch", "tf2.0"], "inference": false} | junnyu/roformer_chinese_sim_char_ft_small | null | [
"transformers",
"pytorch",
"roformer",
"text-generation",
"tf2.0",
"zh",
"autotrain_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"zh"
] | TAGS
#transformers #pytorch #roformer #text-generation #tf2.0 #zh #autotrain_compatible #region-us
| # 安装
- pip install roformer==0.4.3
# 使用
| [
"# 安装\n- pip install roformer==0.4.3",
"# 使用"
] | [
"TAGS\n#transformers #pytorch #roformer #text-generation #tf2.0 #zh #autotrain_compatible #region-us \n",
"# 安装\n- pip install roformer==0.4.3",
"# 使用"
] |
text-generation | transformers | # 安装
- pip install roformer==0.4.3
# 使用
```python
import torch
import numpy as np
from roformer import RoFormerForCausalLM, RoFormerConfig
from transformers import BertTokenizer
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
pretrained_model = "junnyu/roformer_chinese_sim_char_base"
tokenizer... | {"language": "zh", "tags": ["roformer", "pytorch", "tf2.0"], "inference": false} | junnyu/roformer_chinese_sim_char_small | null | [
"transformers",
"pytorch",
"roformer",
"text-generation",
"tf2.0",
"zh",
"autotrain_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"zh"
] | TAGS
#transformers #pytorch #roformer #text-generation #tf2.0 #zh #autotrain_compatible #region-us
| # 安装
- pip install roformer==0.4.3
# 使用
| [
"# 安装\n- pip install roformer==0.4.3",
"# 使用"
] | [
"TAGS\n#transformers #pytorch #roformer #text-generation #tf2.0 #zh #autotrain_compatible #region-us \n",
"# 安装\n- pip install roformer==0.4.3",
"# 使用"
] |
fill-mask | transformers | ## 介绍
### tf版本
https://github.com/ZhuiyiTechnology/roformer
### pytorch版本+tf2.0版本
https://github.com/JunnYu/RoFormer_pytorch
## pytorch使用
```python
import torch
from transformers import RoFormerForMaskedLM, RoFormerTokenizer
text = "今天[MASK]很好，我[MASK]去公园玩。"
tokenizer = RoFormerTokenizer.from_pretrained("junnyu/rofo... | {"language": "zh", "tags": ["roformer", "pytorch", "tf2.0"], "widget": [{"text": "\u4eca\u5929[MASK]\u5f88\u597d\uff0c\u6211\u60f3\u53bb\u516c\u56ed\u73a9\uff01"}]} | junnyu/roformer_chinese_small | null | [
"transformers",
"pytorch",
"tf",
"jax",
"roformer",
"fill-mask",
"tf2.0",
"zh",
"arxiv:2104.09864",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2104.09864"
] | [
"zh"
] | TAGS
#transformers #pytorch #tf #jax #roformer #fill-mask #tf2.0 #zh #arxiv-2104.09864 #autotrain_compatible #endpoints_compatible #has_space #region-us
| ## 介绍
### tf版本
URL
### pytorch版本+tf2.0版本
URL
## pytorch使用
## tensorflow2.0使用
## 引用
Bibtex：
| [
"## 介绍",
"### tf版本 \nURL",
"### pytorch版本+tf2.0版本\nURL",
"## pytorch使用",
"## tensorflow2.0使用",
"## 引用\n\nBibtex："
] | [
"TAGS\n#transformers #pytorch #tf #jax #roformer #fill-mask #tf2.0 #zh #arxiv-2104.09864 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"## 介绍",
"### tf版本 \nURL",
"### pytorch版本+tf2.0版本\nURL",
"## pytorch使用",
"## tensorflow2.0使用",
"## 引用\n\nBibtex："
] |
null | null | # paddle paddle版本的RoFormer
# 需要安装最新的paddlenlp
`pip install git+https://github.com/PaddlePaddle/PaddleNLP.git`
## 预训练模型转换
预训练模型可以从 huggingface/transformers 转换而来，方法如下（适用于roformer模型，其他模型按情况调整）：
1. 从huggingface.co获取roformer模型权重
2. 设置参数运行convert.py代码
3. 例子：
假设我想转换https://huggingface.co/junnyu/roformer_chinese_base 权重... | {} | junnyu/roformer_paddle | null | [
"paddlepaddle",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#paddlepaddle #region-us
| # paddle paddle版本的RoFormer
# 需要安装最新的paddlenlp
'pip install git+URL
## 预训练模型转换
预训练模型可以从 huggingface/transformers 转换而来，方法如下（适用于roformer模型，其他模型按情况调整）：
1. 从huggingface.co获取roformer模型权重
2. 设置参数运行convert.py代码
3. 例子：
假设我想转换https://URL 权重
- (1)首先下载 URL 中的pytorch_model.bin文件,假设我们存入了'./roformer_chinese_base/pytorch_mod... | [
"# paddle paddle版本的RoFormer",
"# 需要安装最新的paddlenlp\n'pip install git+URL",
"## 预训练模型转换\n\n预训练模型可以从 huggingface/transformers 转换而来，方法如下（适用于roformer模型，其他模型按情况调整）：\n\n1. 从huggingface.co获取roformer模型权重\n2. 设置参数运行convert.py代码\n3. 例子：\n 假设我想转换https://URL 权重\n - (1)首先下载 URL 中的pytorch_model.bin文件,假设我们存入了'./roformer_ch... | [
"TAGS\n#paddlepaddle #region-us \n",
"# paddle paddle版本的RoFormer",
"# 需要安装最新的paddlenlp\n'pip install git+URL",
"## 预训练模型转换\n\n预训练模型可以从 huggingface/transformers 转换而来，方法如下（适用于roformer模型，其他模型按情况调整）：\n\n1. 从huggingface.co获取roformer模型权重\n2. 设置参数运行convert.py代码\n3. 例子：\n 假设我想转换https://URL 权重\n - (1)首先下载 URL 中的py... |
feature-extraction | transformers | # 一、 个人在openwebtext数据集上添加rotary-position-embedding，训练得到的electra-small模型
# 二、 复现结果(dev dataset)
|Model|CoLA|SST|MRPC|STS|QQP|MNLI|QNLI|RTE|Avg.|
|---|---|---|---|---|---|---|---|---|---|
|ELECTRA-Small-OWT(original)|56.8|88.3|87.4|86.8|88.3|78.9|87.9|68.5|80.36|
|**ELECTRA-RoFormer-Small-OWT (this)**|55.76|90.45|87.3|8... | {"language": "en", "license": "mit", "tags": ["pytorch", "electra", "roformer", "rotary position embedding"], "datasets": ["openwebtext"], "thumbnail": "https://github.com/junnyu"} | junnyu/roformer_small_discriminator | null | [
"transformers",
"pytorch",
"roformer",
"feature-extraction",
"electra",
"rotary position embedding",
"en",
"dataset:openwebtext",
"license:mit",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #roformer #feature-extraction #electra #rotary position embedding #en #dataset-openwebtext #license-mit #endpoints_compatible #has_space #region-us
| 一、 个人在openwebtext数据集上添加rotary-position-embedding，训练得到的electra-small模型
=====================================================================
二、 复现结果(dev dataset)
====================
三、 训练细节
=======
* 数据集 openwebtext
* 训练batch\_size 256
* 学习率lr 5e-4
* 最大句子长度max\_seqlen 128
* 训练total step 50W
* GPU RTX3090
* 训练时间总... | [] | [
"TAGS\n#transformers #pytorch #roformer #feature-extraction #electra #rotary position embedding #en #dataset-openwebtext #license-mit #endpoints_compatible #has_space #region-us \n"
] |
fill-mask | transformers | # 一、 个人在openwebtext数据集上添加rotary-position-embedding，训练得到的electra-small模型
# 二、 复现结果(dev dataset)
|Model|CoLA|SST|MRPC|STS|QQP|MNLI|QNLI|RTE|Avg.|
|---|---|---|---|---|---|---|---|---|---|
|ELECTRA-Small-OWT(original)|56.8|88.3|87.4|86.8|88.3|78.9|87.9|68.5|80.36|
|**ELECTRA-RoFormer-Small-OWT (this)**|55.76|90.45|87.3|8... | {"language": "en", "license": "mit", "tags": ["pytorch", "electra", "masked-lm", "rotary position embedding"], "datasets": ["openwebtext"], "thumbnail": "https://github.com/junnyu", "widget": [{"text": "Paris is the [MASK] of France."}]} | junnyu/roformer_small_generator | null | [
"transformers",
"pytorch",
"roformer",
"fill-mask",
"electra",
"masked-lm",
"rotary position embedding",
"en",
"dataset:openwebtext",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #roformer #fill-mask #electra #masked-lm #rotary position embedding #en #dataset-openwebtext #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us
| 一、 个人在openwebtext数据集上添加rotary-position-embedding，训练得到的electra-small模型
=====================================================================
二、 复现结果(dev dataset)
====================
三、 训练细节
=======
* 数据集 openwebtext
* 训练batch\_size 256
* 学习率lr 5e-4
* 最大句子长度max\_seqlen 128
* 训练total step 50W
* GPU RTX3090
* 训练时间总... | [] | [
"TAGS\n#transformers #pytorch #roformer #fill-mask #electra #masked-lm #rotary position embedding #en #dataset-openwebtext #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
fill-mask | transformers | https://github.com/dbiir/UER-py/wiki/Modelzoo 中的
MixedCorpus+BertEncoder(large)+MlmTarget
https://share.weiyun.com/5G90sMJ
Pre-trained on mixed large Chinese corpus. The configuration file is bert_large_config.json
## 引用
```tex
@article{zhao2019uer,
title={UER: An Open-Source Toolkit for Pre-training Models},
... | {"language": "zh", "tags": ["bert", "pytorch"], "widget": [{"text": "\u5df4\u9ece\u662f[MASK]\u56fd\u7684\u9996\u90fd\u3002"}]} | junnyu/uer_large | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"zh",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"zh"
] | TAGS
#transformers #pytorch #bert #fill-mask #zh #autotrain_compatible #endpoints_compatible #region-us
| URL 中的
MixedCorpus+BertEncoder(large)+MlmTarget
URL
Pre-trained on mixed large Chinese corpus. The configuration file is bert_large_config.json
## 引用
| [
"## 引用"
] | [
"TAGS\n#transformers #pytorch #bert #fill-mask #zh #autotrain_compatible #endpoints_compatible #region-us \n",
"## 引用"
] |
fill-mask | transformers | ## 介绍
### tf版本
https://github.com/ZhuiyiTechnology/WoBERT
### pytorch版本
https://github.com/JunnYu/WoBERT_pytorch
## 安装(主要为了安装WoBertTokenizer)
注意：transformers版本需要>=4.7.0
WoBertTokenizer的实现与RoFormerTokenizer是一样的，因此使用RoFormerTokenizer就可以了
## 使用
```python
import torch
from transformers import BertForMaskedLM as WoBertF... | {"language": "zh", "tags": ["wobert"]} | junnyu/wobert_chinese_base | null | [
"transformers",
"pytorch",
"jax",
"bert",
"fill-mask",
"wobert",
"zh",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"zh"
] | TAGS
#transformers #pytorch #jax #bert #fill-mask #wobert #zh #autotrain_compatible #endpoints_compatible #region-us
| ## 介绍
### tf版本
URL
### pytorch版本
URL
## 安装(主要为了安装WoBertTokenizer)
注意：transformers版本需要>=4.7.0
WoBertTokenizer的实现与RoFormerTokenizer是一样的，因此使用RoFormerTokenizer就可以了
## 使用
## 引用
Bibtex：
| [
"## 介绍",
"### tf版本 \nURL",
"### pytorch版本 \nURL",
"## 安装(主要为了安装WoBertTokenizer)\n注意：transformers版本需要>=4.7.0\nWoBertTokenizer的实现与RoFormerTokenizer是一样的，因此使用RoFormerTokenizer就可以了",
"## 使用",
"## 引用\n\nBibtex："
] | [
"TAGS\n#transformers #pytorch #jax #bert #fill-mask #wobert #zh #autotrain_compatible #endpoints_compatible #region-us \n",
"## 介绍",
"### tf版本 \nURL",
"### pytorch版本 \nURL",
"## 安装(主要为了安装WoBertTokenizer)\n注意：transformers版本需要>=4.7.0\nWoBertTokenizer的实现与RoFormerTokenizer是一样的，因此使用RoFormerTokenizer就可以了",
"## ... |
fill-mask | transformers | ## 介绍
### tf版本
https://github.com/ZhuiyiTechnology/WoBERT
### pytorch版本
https://github.com/JunnYu/WoBERT_pytorch
## 安装(主要为了安装WoBertTokenizer)
```bash
pip install git+https://github.com/JunnYu/WoBERT_pytorch.git
```
## 使用
```python
import torch
from transformers import BertForMaskedLM as WoBertForMaskedLM
from wober... | {"language": "zh", "tags": ["wobert"], "inference": false} | junnyu/wobert_chinese_plus_base | null | [
"transformers",
"pytorch",
"jax",
"bert",
"fill-mask",
"wobert",
"zh",
"autotrain_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"zh"
] | TAGS
#transformers #pytorch #jax #bert #fill-mask #wobert #zh #autotrain_compatible #has_space #region-us
| ## 介绍
### tf版本
URL
### pytorch版本
URL
## 安装(主要为了安装WoBertTokenizer)
## 使用
## 引用
Bibtex：
| [
"## 介绍",
"### tf版本 \nURL",
"### pytorch版本 \nURL",
"## 安装(主要为了安装WoBertTokenizer)",
"## 使用",
"## 引用\n\nBibtex："
] | [
"TAGS\n#transformers #pytorch #jax #bert #fill-mask #wobert #zh #autotrain_compatible #has_space #region-us \n",
"## 介绍",
"### tf版本 \nURL",
"### pytorch版本 \nURL",
"## 安装(主要为了安装WoBertTokenizer)",
"## 使用",
"## 引用\n\nBibtex："
] |
null | null | Text Emotion Recognition using RoBERTa-base
| {} | junxtjx/roberta-base_TER | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| Text Emotion Recognition using RoBERTa-base
| [] | [
"TAGS\n#region-us \n"
] |
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. -->
# bert_finetuning_test
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the ... | {"language": ["en"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "bert_finetuning_test", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "GLUE MRPC", "type": "glue", "a... | junzai/demo | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"en",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bert #text-classification #generated_from_trainer #en #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# bert_finetuning_test
This model is a fine-tuned version of bert-base-uncased on the GLUE MRPC dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4023
- Accuracy: 0.8284
- F1: 0.8818
- Combined Score: 0.8551
## Model description
More information needed
## Intended uses & limitations
Mo... | [
"# bert_finetuning_test\n\nThis model is a fine-tuned version of bert-base-uncased on the GLUE MRPC dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.4023\n- Accuracy: 0.8284\n- F1: 0.8818\n- Combined Score: 0.8551",
"## Model description\n\nMore information needed",
"## Intended use... | [
"TAGS\n#transformers #pytorch #bert #text-classification #generated_from_trainer #en #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# bert_finetuning_test\n\nThis model is a fine-tuned version of bert-base-uncased on the GLUE MRPC dataset.\nIt achieves ... |
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. -->
# bert_finetuning_test
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the ... | {"language": ["en"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "bert_finetuning_test", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "GLUE MRPC", "type": "glue", "a... | junzai/demotest | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"en",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bert #text-classification #generated_from_trainer #en #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# bert_finetuning_test
This model is a fine-tuned version of bert-base-uncased on the GLUE MRPC dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4023
- Accuracy: 0.8284
- F1: 0.8818
- Combined Score: 0.8551
## Model description
More information needed
## Intended uses & limitations
Mo... | [
"# bert_finetuning_test\n\nThis model is a fine-tuned version of bert-base-uncased on the GLUE MRPC dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.4023\n- Accuracy: 0.8284\n- F1: 0.8818\n- Combined Score: 0.8551",
"## Model description\n\nMore information needed",
"## Intended use... | [
"TAGS\n#transformers #pytorch #bert #text-classification #generated_from_trainer #en #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# bert_finetuning_test\n\nThis model is a fine-tuned version of bert-base-uncased on the GLUE MRPC dataset.\nIt achieves ... |
text-classification | transformers |
## Model Description
1. Based on the uncased BERT pretrained model with a linear output layer.
2. Added several commonly-used emoji and tokens to the special token list of the tokenizer.
3. Did label smoothing while training.
4. Used weighted loss and focal loss to help the cases which trained badly.
| {"language": "en", "license": "mit", "tags": ["go-emotion", "text-classification", "pytorch"], "datasets": ["go_emotions"], "metrics": ["f1"], "widget": [{"text": "Thanks for giving advice to the people who need it! \ud83d\udc4c\ud83d\ude4f"}]} | justin871030/bert-base-uncased-goemotions-ekman-finetuned | null | [
"transformers",
"pytorch",
"bert",
"go-emotion",
"text-classification",
"en",
"dataset:go_emotions",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bert #go-emotion #text-classification #en #dataset-go_emotions #license-mit #endpoints_compatible #region-us
|
## Model Description
1. Based on the uncased BERT pretrained model with a linear output layer.
2. Added several commonly-used emoji and tokens to the special token list of the tokenizer.
3. Did label smoothing while training.
4. Used weighted loss and focal loss to help the cases which trained badly.
| [
"## Model Description\n1. Based on the uncased BERT pretrained model with a linear output layer.\n2. Added several commonly-used emoji and tokens to the special token list of the tokenizer.\n3. Did label smoothing while training.\n4. Used weighted loss and focal loss to help the cases which trained badly."
] | [
"TAGS\n#transformers #pytorch #bert #go-emotion #text-classification #en #dataset-go_emotions #license-mit #endpoints_compatible #region-us \n",
"## Model Description\n1. Based on the uncased BERT pretrained model with a linear output layer.\n2. Added several commonly-used emoji and tokens to the special token li... |
text-classification | transformers |
## Model Description
1. Based on the uncased BERT pretrained model with a linear output layer.
2. Added several commonly-used emoji and tokens to the special token list of the tokenizer.
3. Did label smoothing while training.
4. Used weighted loss and focal loss to help the cases which trained badly.
## Results
Best ... | {"language": "en", "license": "mit", "tags": ["go-emotion", "text-classification", "pytorch"], "datasets": ["go_emotions"], "metrics": ["f1"], "widget": [{"text": "Thanks for giving advice to the people who need it! \ud83d\udc4c\ud83d\ude4f"}]} | justin871030/bert-base-uncased-goemotions-group-finetuned | null | [
"transformers",
"pytorch",
"bert",
"go-emotion",
"text-classification",
"en",
"dataset:go_emotions",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bert #go-emotion #text-classification #en #dataset-go_emotions #license-mit #endpoints_compatible #region-us
|
## Model Description
1. Based on the uncased BERT pretrained model with a linear output layer.
2. Added several commonly-used emoji and tokens to the special token list of the tokenizer.
3. Did label smoothing while training.
4. Used weighted loss and focal loss to help the cases which trained badly.
## Results
Best ... | [
"## Model Description\n1. Based on the uncased BERT pretrained model with a linear output layer.\n2. Added several commonly-used emoji and tokens to the special token list of the tokenizer.\n3. Did label smoothing while training.\n4. Used weighted loss and focal loss to help the cases which trained badly.",
"## R... | [
"TAGS\n#transformers #pytorch #bert #go-emotion #text-classification #en #dataset-go_emotions #license-mit #endpoints_compatible #region-us \n",
"## Model Description\n1. Based on the uncased BERT pretrained model with a linear output layer.\n2. Added several commonly-used emoji and tokens to the special token li... |
text-classification | transformers |
## Model Description
1. Based on the uncased BERT pretrained model with a linear output layer.
2. Added several commonly-used emoji and tokens to the special token list of the tokenizer.
3. Did label smoothing while training.
4. Used weighted loss and focal loss to help the cases which trained badly.
## Results
Best ... | {"language": "en", "license": "mit", "tags": ["go-emotion", "text-classification", "pytorch"], "datasets": ["go_emotions"], "metrics": ["f1"], "widget": [{"text": "Thanks for giving advice to the people who need it! \ud83d\udc4c\ud83d\ude4f"}]} | justin871030/bert-base-uncased-goemotions-original-finetuned | null | [
"transformers",
"pytorch",
"bert",
"go-emotion",
"text-classification",
"en",
"dataset:go_emotions",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bert #go-emotion #text-classification #en #dataset-go_emotions #license-mit #endpoints_compatible #region-us
|
## Model Description
1. Based on the uncased BERT pretrained model with a linear output layer.
2. Added several commonly-used emoji and tokens to the special token list of the tokenizer.
3. Did label smoothing while training.
4. Used weighted loss and focal loss to help the cases which trained badly.
## Results
Best ... | [
"## Model Description\n1. Based on the uncased BERT pretrained model with a linear output layer.\n2. Added several commonly-used emoji and tokens to the special token list of the tokenizer.\n3. Did label smoothing while training.\n4. Used weighted loss and focal loss to help the cases which trained badly.",
"## R... | [
"TAGS\n#transformers #pytorch #bert #go-emotion #text-classification #en #dataset-go_emotions #license-mit #endpoints_compatible #region-us \n",
"## Model Description\n1. Based on the uncased BERT pretrained model with a linear output layer.\n2. Added several commonly-used emoji and tokens to the special token li... |
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. -->
# bertweet-covid19-base-uncased-pretraining-covid-vaccine-tweets
This model is a fine-tuned version of [justinqbui/bertweet-covid1... | {"model-index": [{"name": "bertweet-covid--vaccine-tweets-finetuned", "results": []}]} | justinqbui/bertweet-covid-vaccine-tweets-finetuned | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #text-classification #autotrain_compatible #endpoints_compatible #has_space #region-us
| bertweet-covid19-base-uncased-pretraining-covid-vaccine-tweets
==============================================================
This model is a fine-tuned version of justinqbui/bertweet-covid19-base-uncased-pretraining-covid-vaccine-tweets which was finetuned by using this google fact check ~3k dataset size and webscra... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-5\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0\n*",
"### ... | [
"TAGS\n#transformers #pytorch #roberta #text-classification #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-5\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\n* se... |
fill-mask | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bertweet-covid19-base-uncased-pretraining-covid-vaccine-tweets
This model is a further pre-trained version of [vinai/bertweet-co... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "bertweet-covid19-base-uncased-pretraining-covid-vaccine-tweets", "results": []}]} | justinqbui/bertweet-covid19-base-uncased-pretraining-covid-vaccine-tweets | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"fill-mask",
"generated_from_trainer",
"arxiv:1907.11692",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1907.11692"
] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #fill-mask #generated_from_trainer #arxiv-1907.11692 #autotrain_compatible #endpoints_compatible #region-us
| bertweet-covid19-base-uncased-pretraining-covid-vaccine-tweets
==============================================================
This model is a further pre-trained version of vinai/bertweet-covid19-base-uncased on masked language modeling using a kaggle dataset with tweets up until early December.
It achieves the follo... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0\n* mixed\\_prec... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\... |
text-classification | transformers |
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 27366103
- CO2 Emissions (in grams): 32.912881644048
## Validation Metrics
- Loss: 0.18175844848155975
- Accuracy: 0.9437683592110785
- Precision: 0.9416809605488851
- Recall: 0.8459167950693375
- AUC: 0.9815242330050846
- F1: 0.8912337... | {"language": "unk", "tags": "autonlp", "datasets": ["jwuthri/autonlp-data-shipping_status_2"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}], "co2_eq_emissions": 32.912881644048} | jwuthri/autonlp-shipping_status_2-27366103 | null | [
"transformers",
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"dataset:jwuthri/autonlp-data-shipping_status_2",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"unk"
] | TAGS
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|
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 27366103
- CO2 Emissions (in grams): 32.912881644048
## Validation Metrics
- Loss: 0.18175844848155975
- Accuracy: 0.9437683592110785
- Precision: 0.9416809605488851
- Recall: 0.8459167950693375
- AUC: 0.9815242330050846
- F1: 0.8912337... | [
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"# Model Trained Using AutoNLP\n\n- Problem type: Binary Classification\n- Model ID: 27366103\n- CO2 Emissions ... |
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. -->
# xlm-roberta-base-finetuned-marc-en-j-run
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-rob... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["amazon_reviews_multi"], "model-index": [{"name": "xlm-roberta-base-finetuned-marc-en-j-run", "results": []}]} | jx88/xlm-roberta-base-finetuned-marc-en-j-run | null | [
"transformers",
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"tensorboard",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"dataset:amazon_reviews_multi",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #xlm-roberta #text-classification #generated_from_trainer #dataset-amazon_reviews_multi #license-mit #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-marc-en-j-run
========================================
This model is a fine-tuned version of xlm-roberta-base on the amazon\_reviews\_multi dataset.
It achieves the following results on the evaluation set:
* Loss: 0.9189
* Mae: 0.4634
Model description
-----------------
Trained follow... | [
"### 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: 2",
"### Traini... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_... |
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. -->
# roberta-base-finetuned-cola
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the glu... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["matthews_correlation"], "model_index": [{"name": "roberta-base-finetuned-cola", "results": [{"task": {"name": "Text Classification", "type": "text-classification"}, "dataset": {"name": "glue", "type": "glue", "args": "cola"}, "met... | jxuhf/roberta-base-finetuned-cola | null | [
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"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #dataset-glue #license-mit #autotrain_compatible #endpoints_compatible #region-us
| roberta-base-finetuned-cola
===========================
This model is a fine-tuned version of roberta-base on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4716
* Matthews Correlation: 0.5579
Model description
-----------------
More information needed
Intended uses & lim... | [
"### 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: 2",
"### Traini... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train... |
text-classification | transformers | Labels Twitter biographies on [Openness](https://en.wikipedia.org/wiki/Openness_to_experience), strongly related to intellectual curiosity.
Intuitive: Associated with higher intellectual curiosity
Sensing: Associated with lower intellectual curiosity
Go to your Twitter profile, copy your biography and paste in... | {} | k-partha/curiosity_bert_bio | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"arxiv:2109.06402",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2109.06402"
] | [] | TAGS
#transformers #pytorch #bert #text-classification #arxiv-2109.06402 #autotrain_compatible #endpoints_compatible #region-us
| Labels Twitter biographies on Openness, strongly related to intellectual curiosity.
Intuitive: Associated with higher intellectual curiosity
Sensing: Associated with lower intellectual curiosity
Go to your Twitter profile, copy your biography and paste in the inference widget, remove any URLs and press hit!
T... | [] | [
"TAGS\n#transformers #pytorch #bert #text-classification #arxiv-2109.06402 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-classification | transformers | Rates Twitter biographies on decision-making preference: Thinking or Feeling. Roughly corresponds to [agreeableness.](https://en.wikipedia.org/wiki/Agreeableness)
Go to your Twitter profile, copy your biography and paste in the inference widget, remove any URLs and press hit!
Trained on self-described personality lab... | {} | k-partha/decision_bert_bio | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"arxiv:2109.06402",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2109.06402"
] | [] | TAGS
#transformers #pytorch #bert #text-classification #arxiv-2109.06402 #autotrain_compatible #endpoints_compatible #region-us
| Rates Twitter biographies on decision-making preference: Thinking or Feeling. Roughly corresponds to agreeableness.
Go to your Twitter profile, copy your biography and paste in the inference widget, remove any URLs and press hit!
Trained on self-described personality labels. Interpret as a continuous score, not as a ... | [] | [
"TAGS\n#transformers #pytorch #bert #text-classification #arxiv-2109.06402 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-classification | transformers | Rates Twitter biographies on decision-making preference: Judging (focused, goal-oriented decision strategy) or Prospecting (open-ended, explorative strategy). Roughly corresponds to [conscientiousness](https://en.wikipedia.org/wiki/Conscientiousness)
Go to your Twitter profile, copy your biography and paste in the inf... | {} | k-partha/decision_style_bert_bio | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"arxiv:2109.06402",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2109.06402"
] | [] | TAGS
#transformers #pytorch #bert #text-classification #arxiv-2109.06402 #autotrain_compatible #endpoints_compatible #region-us
| Rates Twitter biographies on decision-making preference: Judging (focused, goal-oriented decision strategy) or Prospecting (open-ended, explorative strategy). Roughly corresponds to conscientiousness
Go to your Twitter profile, copy your biography and paste in the inference widget, remove any URLs and press hit!
Trai... | [] | [
"TAGS\n#transformers #pytorch #bert #text-classification #arxiv-2109.06402 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-classification | transformers | Classifies Twitter biographies as either introverts or extroverts.
Go to your Twitter profile, copy your biography and paste in the inference widget, remove any URLs and press hit!
Trained on self-described personality labels. Interpret as a continuous score, not as a discrete label. Have fun!
Barack Obama: Extrove... | {} | k-partha/extrabert_bio | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"arxiv:2109.06402",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2109.06402"
] | [] | TAGS
#transformers #pytorch #bert #text-classification #arxiv-2109.06402 #autotrain_compatible #endpoints_compatible #region-us
| Classifies Twitter biographies as either introverts or extroverts.
Go to your Twitter profile, copy your biography and paste in the inference widget, remove any URLs and press hit!
Trained on self-described personality labels. Interpret as a continuous score, not as a discrete label. Have fun!
Barack Obama: Extrove... | [] | [
"TAGS\n#transformers #pytorch #bert #text-classification #arxiv-2109.06402 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
fill-mask | transformers | Копия модели https://huggingface.co/cointegrated/rubert-tiny. Чисто для теста!
| {"language": ["ru", "en"], "license": "mit", "tags": ["russian", "fill-mask", "pretraining", "embeddings", "masked-lm", "tiny"], "widget": [{"text": "\u041c\u0438\u043d\u0438\u0430\u0442\u044e\u0440\u043d\u0430\u044f \u043c\u043e\u0434\u0435\u043b\u044c \u0434\u043b\u044f [MASK] \u0440\u0430\u0437\u043d\u044b\u0445 \u0... | k0t1k/test | null | [
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"embeddings",
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"tiny",
"ru",
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"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ru",
"en"
] | TAGS
#transformers #pytorch #bert #pretraining #russian #fill-mask #embeddings #masked-lm #tiny #ru #en #license-mit #endpoints_compatible #region-us
| Копия модели URL Чисто для теста!
| [] | [
"TAGS\n#transformers #pytorch #bert #pretraining #russian #fill-mask #embeddings #masked-lm #tiny #ru #en #license-mit #endpoints_compatible #region-us \n"
] |
null | null | >tr|Q8ZR27|Q8ZR27_SALTY Putative glycerol dehydrogenase OS=Salmonella typhimurium (strain LT2 / SGSC1412 / ATCC 700720) OX=99287 GN=ybdH PE=3 SV=1
MNHTEIRVVTGPANYFSHAGSLERLTDFFTPEQLSHAVWVYGERAIAAARPYLPEAFERA
GAKHLPFTGHCSERHVAQLAHACNDDRQVVIGVGGGALLDTAKALARRLALPFVAIPTIA
ATCAAWTPLSVWYNDAGQALQFEIFDDANFLVLVEPRIILQAPDDYLLAGI... | {} | k948181/ybdH-1 | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| >tr|Q8ZR27|Q8ZR27_SALTY Putative glycerol dehydrogenase OS=Salmonella typhimurium (strain LT2 / SGSC1412 / ATCC 700720) OX=99287 GN=ybdH PE=3 SV=1
MNHTEIRVVTGPANYFSHAGSLERLTDFFTPEQLSHAVWVYGERAIAAARPYLPEAFERA
GAKHLPFTGHCSERHVAQLAHACNDDRQVVIGVGGGALLDTAKALARRLALPFVAIPTIA
ATCAAWTPLSVWYNDAGQALQFEIFDDANFLVLVEPRIILQAPDDYLLAGI... | [] | [
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token-classification | transformers |
# Model Trained Using AutoNLP
- Problem type: Entity Extraction
- Model ID: 557515810
- CO2 Emissions (in grams): 2.96638567287195
## Validation Metrics
- Loss: 0.12897901237010956
- Accuracy: 0.9713212700580403
- Precision: 0.9475614228089475
- Recall: 0.96274217585693
- F1: 0.9550914803178709
## Usage
You can u... | {"language": "en", "tags": "autonlp", "datasets": ["kSaluja/autonlp-data-tele_new_5k"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}], "co2_eq_emissions": 2.96638567287195} | kSaluja/autonlp-tele_new_5k-557515810 | null | [
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"token-classification",
"autonlp",
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"dataset:kSaluja/autonlp-data-tele_new_5k",
"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: Entity Extraction
- Model ID: 557515810
- CO2 Emissions (in grams): 2.96638567287195
## Validation Metrics
- Loss: 0.12897901237010956
- Accuracy: 0.9713212700580403
- Precision: 0.9475614228089475
- Recall: 0.96274217585693
- F1: 0.9550914803178709
## Usage
You can u... | [
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"# Model Trained Using AutoNLP\n\n- Problem type: Entity Extraction\n- Model ID: 557515810\n- CO2 Emissions (in grams): 2.9... |
token-classification | transformers |
# Model Trained Using AutoNLP
- Problem type: Entity Extraction
- Model ID: 585716433
- CO2 Emissions (in grams): 2.379476355147211
## Validation Metrics
- Loss: 0.15210922062397003
- Accuracy: 0.9724770642201835
- Precision: 0.950836820083682
- Recall: 0.9625838333921638
- F1: 0.9566742676723382
## Usage
You can... | {"language": "en", "tags": "autonlp", "datasets": ["kSaluja/autonlp-data-tele_red_data_model"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}], "co2_eq_emissions": 2.379476355147211} | kSaluja/autonlp-tele_red_data_model-585716433 | null | [
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"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: Entity Extraction
- Model ID: 585716433
- CO2 Emissions (in grams): 2.379476355147211
## Validation Metrics
- Loss: 0.15210922062397003
- Accuracy: 0.9724770642201835
- Precision: 0.950836820083682
- Recall: 0.9625838333921638
- F1: 0.9566742676723382
## Usage
You can... | [
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"# Model Trained Using AutoNLP\n\n- Problem type: Entity Extraction\n- Model ID: 585716433\n- CO2 Emissions (in gra... |
text-generation | transformers |
#wanda bot go reeeeeeeeeeeeeeeeeeeeee | {"tags": ["conversational"]} | kaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaot1k/DialoGPT-small-Wanda | 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
|
#wanda bot go reeeeeeeeeeeeeeeeeeeeee | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
fill-mask | transformers | # Reference extraction in patents
This repository contains a finetuned BERT model that can extract references to scientific literature from patents.
See https://github.com/kaesve/patent-citation-extraction and https://arxiv.org/abs/2101.01039 for more information. | {} | kaesve/BERT_patent_reference_extraction | null | [
"transformers",
"pytorch",
"jax",
"bert",
"fill-mask",
"arxiv:2101.01039",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2101.01039"
] | [] | TAGS
#transformers #pytorch #jax #bert #fill-mask #arxiv-2101.01039 #autotrain_compatible #endpoints_compatible #region-us
| # Reference extraction in patents
This repository contains a finetuned BERT model that can extract references to scientific literature from patents.
See URL and URL for more information. | [
"# Reference extraction in patents\r\n\r\nThis repository contains a finetuned BERT model that can extract references to scientific literature from patents.\r\n\r\nSee URL and URL for more information."
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fill-mask | transformers | # Reference extraction in patents
This repository contains a finetuned BioBERT model that can extract references to scientific literature from patents.
See https://github.com/kaesve/patent-citation-extraction and https://arxiv.org/abs/2101.01039 for more information.
| {} | kaesve/BioBERT_patent_reference_extraction | null | [
"transformers",
"pytorch",
"jax",
"bert",
"fill-mask",
"arxiv:2101.01039",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2101.01039"
] | [] | TAGS
#transformers #pytorch #jax #bert #fill-mask #arxiv-2101.01039 #autotrain_compatible #endpoints_compatible #region-us
| # Reference extraction in patents
This repository contains a finetuned BioBERT model that can extract references to scientific literature from patents.
See URL and URL for more information.
| [
"# Reference extraction in patents\r\n\r\nThis repository contains a finetuned BioBERT model that can extract references to scientific literature from patents.\r\n\r\nSee URL and URL for more information."
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null | transformers | # Reference extraction in patents
This repository contains a finetuned SciBERT model that can extract references to scientific literature from patents.
See https://github.com/kaesve/patent-citation-extraction and https://arxiv.org/abs/2101.01039 for more information.
| {} | kaesve/SciBERT_patent_reference_extraction | null | [
"transformers",
"pytorch",
"arxiv:2101.01039",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2101.01039"
] | [] | TAGS
#transformers #pytorch #arxiv-2101.01039 #endpoints_compatible #region-us
| # Reference extraction in patents
This repository contains a finetuned SciBERT model that can extract references to scientific literature from patents.
See URL and URL for more information.
| [
"# Reference extraction in patents\r\n\r\nThis repository contains a finetuned SciBERT model that can extract references to scientific literature from patents.\r\n\r\nSee URL and URL for more information."
] | [
"TAGS\n#transformers #pytorch #arxiv-2101.01039 #endpoints_compatible #region-us \n",
"# Reference extraction in patents\r\n\r\nThis repository contains a finetuned SciBERT model that can extract references to scientific literature from patents.\r\n\r\nSee URL and URL for more information."
] |
text-generation | transformers |
#Radion DialoGPT Model | {"tags": ["conversational"]} | kagennotsuki/DialoGPT-medium-radion | 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
|
#Radion DialoGPT Model | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #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": []}]} | kaggleodin/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.
It achieves the following results on the evaluation set:
* Loss: 1.1639
Model description
-----------------
More information needed
Intended uses ... | [
"### 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 #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... |
text-classification | transformers |
Welcome! This is the model built for the sentiment analysis on the STEM course reviews at UCLA.
- Author: Kaixin Wang
- Email: kaixinwang@g.ucla.edu
- Time Updated: March 2022 | {"language": ["Python"], "tags": ["sentiment analysis", "STEM", "text classification"], "thumbnail": "url to a thumbnail used in social sharing"} | kaixinwang/NLP | null | [
"transformers",
"tf",
"distilbert",
"text-classification",
"sentiment analysis",
"STEM",
"text classification",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"Python"
] | TAGS
#transformers #tf #distilbert #text-classification #sentiment analysis #STEM #text classification #autotrain_compatible #endpoints_compatible #has_space #region-us
|
Welcome! This is the model built for the sentiment analysis on the STEM course reviews at UCLA.
- Author: Kaixin Wang
- Email: kaixinwang@g.URL
- Time Updated: March 2022 | [] | [
"TAGS\n#transformers #tf #distilbert #text-classification #sentiment analysis #STEM #text classification #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
null | null |
# KakaoBrain project KoGPT
KakaoBrain's Pre-Trained Language Models.
* KakaoBrain project KoGPT (Korean Generative Pre-trained Transformer)
* [https://github.com/kakaobrain/kogpt](https://github.com/kakaobrain/kogpt)
* [https://huggingface.co/kakaobrain/kogpt](https://huggingface.co/kakaobrain/kogpt)
## Model... | {"language": "ko", "license": "cc-by-nc-nd-4.0", "tags": ["KakaoBrain", "KoGPT", "GPT", "GPT3"]} | kakaobrain/kogpt | null | [
"KakaoBrain",
"KoGPT",
"GPT",
"GPT3",
"ko",
"arxiv:2104.09864",
"arxiv:2109.04650",
"license:cc-by-nc-nd-4.0",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2104.09864",
"2109.04650"
] | [
"ko"
] | TAGS
#KakaoBrain #KoGPT #GPT #GPT3 #ko #arxiv-2104.09864 #arxiv-2109.04650 #license-cc-by-nc-nd-4.0 #has_space #region-us
| KakaoBrain project KoGPT
========================
KakaoBrain's Pre-Trained Language Models.
* KakaoBrain project KoGPT (Korean Generative Pre-trained Transformer)
+ URL
+ URL
Model Descriptions
------------------
### KoGPT6B-ryan1.5b
* [[huggingface][kakaobrain/kogpt][KoGPT6B-ryan1.5b]](URL
* [[huggingface]... | [
"### KoGPT6B-ryan1.5b\n\n\n* [[huggingface][kakaobrain/kogpt][KoGPT6B-ryan1.5b]](URL\n* [[huggingface][kakaobrain/kogpt][KoGPT6B-ryan1.5b-float16]](URL\n\n\n\nHardware requirements\n---------------------",
"### KoGPT6B-ryan1.5b",
"#### GPU\n\n\nThe following is the recommended minimum GPU hardware guidance for ... | [
"TAGS\n#KakaoBrain #KoGPT #GPT #GPT3 #ko #arxiv-2104.09864 #arxiv-2109.04650 #license-cc-by-nc-nd-4.0 #has_space #region-us \n",
"### KoGPT6B-ryan1.5b\n\n\n* [[huggingface][kakaobrain/kogpt][KoGPT6B-ryan1.5b]](URL\n* [[huggingface][kakaobrain/kogpt][KoGPT6B-ryan1.5b-float16]](URL\n\n\n\nHardware requirements\n---... |
token-classification | transformers |
BioELECTRA-PICO
Cite our paper using below citation
```
@inproceedings{kanakarajan-etal-2021-bioelectra,
title = "{B}io{ELECTRA}:Pretrained Biomedical text Encoder using Discriminators",
author = "Kanakarajan, Kamal raj and
Kundumani, Bhuvana and
Sankarasubbu, Malaikannan",
booktitle = "Pro... | {"widget": [{"text": "Those in the aspirin group experienced reduced duration of headache compared to those in the placebo arm (P<0.05)"}]} | kamalkraj/BioELECTRA-PICO | null | [
"transformers",
"pytorch",
"safetensors",
"electra",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #safetensors #electra #token-classification #autotrain_compatible #endpoints_compatible #has_space #region-us
|
BioELECTRA-PICO
Cite our paper using below citation
| [] | [
"TAGS\n#transformers #pytorch #safetensors #electra #token-classification #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
null | transformers | ## BioELECTRA:Pretrained Biomedical text Encoder using Discriminators
Recent advancements in pretraining strategies in NLP have shown a significant improvement in the performance of models on various text mining tasks. In this paper, we introduce BioELECTRA, a biomedical domain-specific language encoder model that ada... | {} | kamalkraj/bioelectra-base-discriminator-pubmed-pmc-lt | null | [
"transformers",
"pytorch",
"electra",
"pretraining",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #electra #pretraining #endpoints_compatible #region-us
| ## BioELECTRA:Pretrained Biomedical text Encoder using Discriminators
Recent advancements in pretraining strategies in NLP have shown a significant improvement in the performance of models on various text mining tasks. In this paper, we introduce BioELECTRA, a biomedical domain-specific language encoder model that ada... | [
"## BioELECTRA:Pretrained Biomedical text Encoder using Discriminators\n\nRecent advancements in pretraining strategies in NLP have shown a significant improvement in the performance of models on various text mining tasks. In this paper, we introduce BioELECTRA, a biomedical domain-specific language encoder model t... | [
"TAGS\n#transformers #pytorch #electra #pretraining #endpoints_compatible #region-us \n",
"## BioELECTRA:Pretrained Biomedical text Encoder using Discriminators\n\nRecent advancements in pretraining strategies in NLP have shown a significant improvement in the performance of models on various text mining tasks. I... |
null | transformers | ## BioELECTRA:Pretrained Biomedical text Encoder using Discriminators
Recent advancements in pretraining strategies in NLP have shown a significant improvement in the performance of models on various text mining tasks. In this paper, we introduce BioELECTRA, a biomedical domain-specific language encoder model that ada... | {} | kamalkraj/bioelectra-base-discriminator-pubmed-pmc | null | [
"transformers",
"pytorch",
"electra",
"pretraining",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #electra #pretraining #endpoints_compatible #region-us
| ## BioELECTRA:Pretrained Biomedical text Encoder using Discriminators
Recent advancements in pretraining strategies in NLP have shown a significant improvement in the performance of models on various text mining tasks. In this paper, we introduce BioELECTRA, a biomedical domain-specific language encoder model that ada... | [
"## BioELECTRA:Pretrained Biomedical text Encoder using Discriminators\n\nRecent advancements in pretraining strategies in NLP have shown a significant improvement in the performance of models on various text mining tasks. In this paper, we introduce BioELECTRA, a biomedical domain-specific language encoder model t... | [
"TAGS\n#transformers #pytorch #electra #pretraining #endpoints_compatible #region-us \n",
"## BioELECTRA:Pretrained Biomedical text Encoder using Discriminators\n\nRecent advancements in pretraining strategies in NLP have shown a significant improvement in the performance of models on various text mining tasks. I... |
null | transformers | ## BioELECTRA:Pretrained Biomedical text Encoder using Discriminators
Recent advancements in pretraining strategies in NLP have shown a significant improvement in the performance of models on various text mining tasks. In this paper, we introduce BioELECTRA, a biomedical domain-specific language encoder model that ada... | {} | kamalkraj/bioelectra-base-discriminator-pubmed | null | [
"transformers",
"pytorch",
"electra",
"pretraining",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #electra #pretraining #endpoints_compatible #region-us
| ## BioELECTRA:Pretrained Biomedical text Encoder using Discriminators
Recent advancements in pretraining strategies in NLP have shown a significant improvement in the performance of models on various text mining tasks. In this paper, we introduce BioELECTRA, a biomedical domain-specific language encoder model that ada... | [
"## BioELECTRA:Pretrained Biomedical text Encoder using Discriminators\n\nRecent advancements in pretraining strategies in NLP have shown a significant improvement in the performance of models on various text mining tasks. In this paper, we introduce BioELECTRA, a biomedical domain-specific language encoder model t... | [
"TAGS\n#transformers #pytorch #electra #pretraining #endpoints_compatible #region-us \n",
"## BioELECTRA:Pretrained Biomedical text Encoder using Discriminators\n\nRecent advancements in pretraining strategies in NLP have shown a significant improvement in the performance of models on various text mining tasks. I... |
feature-extraction | transformers |
## DeBERTa: Decoding-enhanced BERT with Disentangled Attention
[DeBERTa](https://arxiv.org/abs/2006.03654) improves the BERT and RoBERTa models using disentangled attention and enhanced mask decoder. It outperforms BERT and RoBERTa on majority of NLU tasks with 80GB training data.
Please check the [official reposi... | {"language": "en", "license": "mit", "tags": "deberta-v1", "thumbnail": "https://huggingface.co/front/thumbnails/microsoft.png"} | kamalkraj/deberta-base | null | [
"transformers",
"tf",
"deberta",
"feature-extraction",
"deberta-v1",
"en",
"arxiv:2006.03654",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2006.03654"
] | [
"en"
] | TAGS
#transformers #tf #deberta #feature-extraction #deberta-v1 #en #arxiv-2006.03654 #license-mit #endpoints_compatible #region-us
| DeBERTa: Decoding-enhanced BERT with Disentangled Attention
-----------------------------------------------------------
DeBERTa improves the BERT and RoBERTa models using disentangled attention and enhanced mask decoder. It outperforms BERT and RoBERTa on majority of NLU tasks with 80GB training data.
Please check ... | [
"#### Fine-tuning on NLU tasks\n\n\nWe present the dev results on SQuAD 1.1/2.0 and MNLI tasks.\n\n\n\nIf you find DeBERTa useful for your work, please cite the following paper:"
] | [
"TAGS\n#transformers #tf #deberta #feature-extraction #deberta-v1 #en #arxiv-2006.03654 #license-mit #endpoints_compatible #region-us \n",
"#### Fine-tuning on NLU tasks\n\n\nWe present the dev results on SQuAD 1.1/2.0 and MNLI tasks.\n\n\n\nIf you find DeBERTa useful for your work, please cite the following pape... |
feature-extraction | transformers |
## DeBERTa: Decoding-enhanced BERT with Disentangled Attention
[DeBERTa](https://arxiv.org/abs/2006.03654) improves the BERT and RoBERTa models using disentangled attention and enhanced mask decoder. It outperforms BERT and RoBERTa on majority of NLU tasks with 80GB training data.
Please check the [official reposit... | {"language": "en", "license": "mit", "tags": "deberta", "thumbnail": "https://huggingface.co/front/thumbnails/microsoft.png"} | kamalkraj/deberta-v2-xlarge | null | [
"transformers",
"tf",
"deberta-v2",
"feature-extraction",
"deberta",
"en",
"arxiv:2006.03654",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2006.03654"
] | [
"en"
] | TAGS
#transformers #tf #deberta-v2 #feature-extraction #deberta #en #arxiv-2006.03654 #license-mit #endpoints_compatible #region-us
| DeBERTa: Decoding-enhanced BERT with Disentangled Attention
-----------------------------------------------------------
DeBERTa improves the BERT and RoBERTa models using disentangled attention and enhanced mask decoder. It outperforms BERT and RoBERTa on majority of NLU tasks with 80GB training data.
Please check ... | [
"### Fine-tuning on NLU tasks\n\n\nWe present the dev results on SQuAD 1.1/2.0 and several GLUE benchmark tasks.\n\n\n\n\n\n---",
"#### Notes.\n\n\n* 1 Following RoBERTa, for RTE, MRPC, STS-B, we fine-tune the tasks based on DeBERTa-Large-MNLI, DeBERTa-XLarge-MNLI, DeBERTa-V2-XLarge-MNLI, DeBERTa-V2-XXLarge-MNLI.... | [
"TAGS\n#transformers #tf #deberta-v2 #feature-extraction #deberta #en #arxiv-2006.03654 #license-mit #endpoints_compatible #region-us \n",
"### Fine-tuning on NLU tasks\n\n\nWe present the dev results on SQuAD 1.1/2.0 and several GLUE benchmark tasks.\n\n\n\n\n\n---",
"#### Notes.\n\n\n* 1 Following RoBERTa, fo... |
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 [bert-large-uncased-whole-word-masking-finetuned-s... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "distilbert-base-uncased-finetuned-squad", "results": []}]} | kamilali/distilbert-base-uncased-finetuned-squad | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"question-answering",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-squad
=======================================
This model is a fine-tuned version of bert-large-uncased-whole-word-masking-finetuned-squad on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1042
Model description
-----------------
More inform... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #question-answering #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: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_bat... |
text-classification | transformers |
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 208681
## Validation Metrics
- Loss: 0.37569838762283325
- Accuracy: 0.8365019011406845
- Precision: 0.8398058252427184
- Recall: 0.9453551912568307
- AUC: 0.9048838797814208
- F1: 0.8894601542416453
## Usage
You can use cURL to acces... | {"language": "en", "tags": "autonlp", "datasets": ["kamivao/autonlp-data-cola_gram"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}]} | kamivao/autonlp-cola_gram-208681 | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autonlp",
"en",
"dataset:kamivao/autonlp-data-cola_gram",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bert #text-classification #autonlp #en #dataset-kamivao/autonlp-data-cola_gram #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 208681
## Validation Metrics
- Loss: 0.37569838762283325
- Accuracy: 0.8365019011406845
- Precision: 0.8398058252427184
- Recall: 0.9453551912568307
- AUC: 0.9048838797814208
- F1: 0.8894601542416453
## Usage
You can use cURL to acces... | [
"# Model Trained Using AutoNLP\n\n- Problem type: Binary Classification\n- Model ID: 208681",
"## Validation Metrics\n\n- Loss: 0.37569838762283325\n- Accuracy: 0.8365019011406845\n- Precision: 0.8398058252427184\n- Recall: 0.9453551912568307\n- AUC: 0.9048838797814208\n- F1: 0.8894601542416453",
"## Usage\n\nY... | [
"TAGS\n#transformers #pytorch #bert #text-classification #autonlp #en #dataset-kamivao/autonlp-data-cola_gram #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoNLP\n\n- Problem type: Binary Classification\n- Model ID: 208681",
"## Validation Metrics\n\n- Loss: 0.375698387622... |
text-classification | transformers |
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 5771228
## Validation Metrics
- Loss: 0.17127291858196259
- Accuracy: 0.9206671174216813
- Precision: 0.9588885738588036
- Recall: 0.9423237670660352
- AUC: 0.9720189638675828
- F1: 0.9505340078695896
## Usage
You can use cURL to acce... | {"language": "en", "tags": "autonlp", "datasets": ["kamivao/autonlp-data-entity_selection"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}]} | kamivao/autonlp-entity_selection-5771228 | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autonlp",
"en",
"dataset:kamivao/autonlp-data-entity_selection",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bert #text-classification #autonlp #en #dataset-kamivao/autonlp-data-entity_selection #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 5771228
## Validation Metrics
- Loss: 0.17127291858196259
- Accuracy: 0.9206671174216813
- Precision: 0.9588885738588036
- Recall: 0.9423237670660352
- AUC: 0.9720189638675828
- F1: 0.9505340078695896
## Usage
You can use cURL to acce... | [
"# Model Trained Using AutoNLP\n\n- Problem type: Binary Classification\n- Model ID: 5771228",
"## Validation Metrics\n\n- Loss: 0.17127291858196259\n- Accuracy: 0.9206671174216813\n- Precision: 0.9588885738588036\n- Recall: 0.9423237670660352\n- AUC: 0.9720189638675828\n- F1: 0.9505340078695896",
"## Usage\n\n... | [
"TAGS\n#transformers #pytorch #bert #text-classification #autonlp #en #dataset-kamivao/autonlp-data-entity_selection #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoNLP\n\n- Problem type: Binary Classification\n- Model ID: 5771228",
"## Validation Metrics\n\n- Loss: 0.1712... |
text-classification | transformers | learning rate: 5e-5
training epochs: 5
batch size: 8
seed: 42
model: bert-base-uncased
trained on CB which is converted into two-way nli classification (predict entailment or not-entailment class) | {} | kangnichaluo/cb | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us
| learning rate: 5e-5
training epochs: 5
batch size: 8
seed: 42
model: bert-base-uncased
trained on CB which is converted into two-way nli classification (predict entailment or not-entailment class) | [] | [
"TAGS\n#transformers #pytorch #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-classification | transformers | learning rate: 2e-5
training epochs: 3
batch size: 64
seed: 42
model: bert-base-uncased
trained on MNLI which is converted into two-way nli classification (predict entailment or not-entailment class) | {} | kangnichaluo/mnli-1 | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us
| learning rate: 2e-5
training epochs: 3
batch size: 64
seed: 42
model: bert-base-uncased
trained on MNLI which is converted into two-way nli classification (predict entailment or not-entailment class) | [] | [
"TAGS\n#transformers #pytorch #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-classification | transformers | learning rate: 3e-5
training epochs: 3
batch size: 64
seed: 0
model: bert-base-uncased
trained on MNLI which is converted into two-way nli classification (predict entailment or not-entailment class) | {} | kangnichaluo/mnli-2 | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us
| learning rate: 3e-5
training epochs: 3
batch size: 64
seed: 0
model: bert-base-uncased
trained on MNLI which is converted into two-way nli classification (predict entailment or not-entailment class) | [] | [
"TAGS\n#transformers #pytorch #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-classification | transformers | learning rate: 2e-5
training epochs: 3
batch size: 64
seed: 13
model: bert-base-uncased
trained on MNLI which is converted into two-way nli classification (predict entailment or not-entailment class) | {} | kangnichaluo/mnli-3 | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us
| learning rate: 2e-5
training epochs: 3
batch size: 64
seed: 13
model: bert-base-uncased
trained on MNLI which is converted into two-way nli classification (predict entailment or not-entailment class) | [] | [
"TAGS\n#transformers #pytorch #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-classification | transformers | learning rate: 2e-5
training epochs: 3
batch size: 64
seed: 87
model: bert-base-uncased
trained on MNLI which is converted into two-way nli classification (predict entailment or not-entailment class) | {} | kangnichaluo/mnli-4 | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us
| learning rate: 2e-5
training epochs: 3
batch size: 64
seed: 87
model: bert-base-uncased
trained on MNLI which is converted into two-way nli classification (predict entailment or not-entailment class) | [] | [
"TAGS\n#transformers #pytorch #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-classification | transformers | learning rate: 2e-5
training epochs: 3
batch size: 64
seed: 111
model: bert-base-uncased
trained on MNLI which is converted into two-way nli classification (predict entailment or not-entailment class) | {} | kangnichaluo/mnli-5 | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us
| learning rate: 2e-5
training epochs: 3
batch size: 64
seed: 111
model: bert-base-uncased
trained on MNLI which is converted into two-way nli classification (predict entailment or not-entailment class) | [] | [
"TAGS\n#transformers #pytorch #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us \n"
] |
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