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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
audio-to-audio | espnet |
## ESPnet2 ENH model
### `Zhaoheng/svoice_wsj0_2mix`
This model was trained by Zhaoheng Ni using wsj0_2mix recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```bash
cd espnet
git checkout 5ae7c9580f85dae5bc81cb1e845366c251d871ac
pip install -e .
cd egs2/wsj0_2mix/enh1
./run.sh... | {"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "audio-to-audio"], "datasets": ["wsj0_2mix"]} | Zhaoheng/svoice_wsj0_2mix | null | [
"espnet",
"audio",
"audio-to-audio",
"en",
"dataset:wsj0_2mix",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | null | 2022-04-14T11:16:35+00:00 | [
"1804.00015"
] | [
"en"
] | TAGS
#espnet #audio #audio-to-audio #en #dataset-wsj0_2mix #arxiv-1804.00015 #license-cc-by-4.0 #region-us
| ESPnet2 ENH model
-----------------
### 'Zhaoheng/svoice\_wsj0\_2mix'
This model was trained by Zhaoheng Ni using wsj0\_2mix recipe in espnet.
### Demo: How to use in ESPnet2
RESULTS
=======
Environments
------------
* date: 'Thu Apr 14 09:47:05 UTC 2022'
* python version: '3.8.12 (default, Oct 12 2021, 13:... | [
"### 'Zhaoheng/svoice\\_wsj0\\_2mix'\n\n\nThis model was trained by Zhaoheng Ni using wsj0\\_2mix recipe in espnet.",
"### Demo: How to use in ESPnet2\n\n\nRESULTS\n=======\n\n\nEnvironments\n------------\n\n\n* date: 'Thu Apr 14 09:47:05 UTC 2022'\n* python version: '3.8.12 (default, Oct 12 2021, 13:49:34) [GCC ... | [
"TAGS\n#espnet #audio #audio-to-audio #en #dataset-wsj0_2mix #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"### 'Zhaoheng/svoice\\_wsj0\\_2mix'\n\n\nThis model was trained by Zhaoheng Ni using wsj0\\_2mix recipe in espnet.",
"### Demo: How to use in ESPnet2\n\n\nRESULTS\n=======\n\n\nEnvironments\n------... |
null | transformers |
<p align="center">
<img src="https://github.com/mindee/doctr/releases/download/v0.3.1/Logo_doctr.gif" width="60%">
</p>
**Optical Character Recognition made seamless & accessible to anyone, powered by TensorFlow 2 & PyTorch**
## Task: detection
https://github.com/mindee/doctr
### Example usage:
```python
>>> from... | {"language": "en"} | Felix92/doctr-dummy-tf-linknet-resnet34 | null | [
"transformers",
"en",
"endpoints_compatible",
"region:us"
] | null | 2022-04-14T11:18:14+00:00 | [] | [
"en"
] | TAGS
#transformers #en #endpoints_compatible #region-us
|
<p align="center">
<img src="URL width="60%">
</p>
Optical Character Recognition made seamless & accessible to anyone, powered by TensorFlow 2 & PyTorch
## Task: detection
URL
### Example usage:
| [
"## Task: detection\n\nURL",
"### Example usage:"
] | [
"TAGS\n#transformers #en #endpoints_compatible #region-us \n",
"## Task: detection\n\nURL",
"### Example usage:"
] |
text-generation | transformers |
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1503591435324563456/foUr... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true", "widget": [{"text": "My dream is"}]} | huggingtweets/elonmusk-joebiden | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-14T11:38:32+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI CYBORG
Elon Musk & Joe Biden
@elonmusk-joebiden
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Trai... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
question-answering | transformers |
# XLM-RoBERTa Large trained on Dravidian Language QA
## Overview
**Language model:** XLM-RoBERTa-lg
**Language:** Multilingual, focussed on Tamil & Hindi
**Downstream-task:** Extractive QA
**Eval data:** K-Fold on Training Data
## Hyperparameters
```
batch_size = 4
base_LM_model = "xlm-roberta-large"
learning_rate ... | {"language": ["multilingual", "ta"], "tags": ["question-answering"], "datasets": ["squad_v2", "chaii", "mlqa", "xquad"], "metrics": ["Exact Match", "F1"], "widget": [{"text": "\u0b9a\u0bc6\u0ba9\u0bcd\u0ba9\u0bc8\u0baf\u0bbf\u0bb2\u0bcd \u0b8e\u0ba4\u0bcd\u0ba4\u0ba9\u0bc8 \u0bae\u0b95\u0bcd\u0b95\u0bb3\u0bcd \u0bb5\u0... | Srini99/TQA | null | [
"transformers",
"pytorch",
"xlm-roberta",
"question-answering",
"multilingual",
"ta",
"dataset:squad_v2",
"dataset:chaii",
"dataset:mlqa",
"dataset:xquad",
"endpoints_compatible",
"region:us"
] | null | 2022-04-14T11:52:37+00:00 | [] | [
"multilingual",
"ta"
] | TAGS
#transformers #pytorch #xlm-roberta #question-answering #multilingual #ta #dataset-squad_v2 #dataset-chaii #dataset-mlqa #dataset-xquad #endpoints_compatible #region-us
|
# XLM-RoBERTa Large trained on Dravidian Language QA
## Overview
Language model: XLM-RoBERTa-lg
Language: Multilingual, focussed on Tamil & Hindi
Downstream-task: Extractive QA
Eval data: K-Fold on Training Data
## Hyperparameters
## Performance
Evaluated on our human annotated dataset with 1000 tamil question-c... | [
"# XLM-RoBERTa Large trained on Dravidian Language QA",
"## Overview\nLanguage model: XLM-RoBERTa-lg\nLanguage: Multilingual, focussed on Tamil & Hindi \nDownstream-task: Extractive QA\nEval data: K-Fold on Training Data",
"## Hyperparameters",
"## Performance\nEvaluated on our human annotated dataset with 10... | [
"TAGS\n#transformers #pytorch #xlm-roberta #question-answering #multilingual #ta #dataset-squad_v2 #dataset-chaii #dataset-mlqa #dataset-xquad #endpoints_compatible #region-us \n",
"# XLM-RoBERTa Large trained on Dravidian Language QA",
"## Overview\nLanguage model: XLM-RoBERTa-lg\nLanguage: Multilingual, focus... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# ClaireV/MLMA_5.3
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dat... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "ClaireV/MLMA_5.3", "results": []}]} | ClaireV/MLMA_5.3 | null | [
"transformers",
"tf",
"bert",
"token-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-14T11:53:10+00:00 | [] | [] | TAGS
#transformers #tf #bert #token-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| ClaireV/MLMA\_5.3
=================
This model is a fine-tuned version of bert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.0246
* Validation Loss: 0.0578
* Epoch: 2
Model description
-----------------
More information needed
Intended uses & limi... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'inner\\_optimizer': {'class\\_name': 'AdamWeightDecay', 'config': {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\... | [
"TAGS\n#transformers #tf #bert #token-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'inner\\_optimizer': {'class\\_name': '... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-de
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-b... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["xtreme"], "metrics": ["f1"], "model-index": [{"name": "xlm-roberta-base-finetuned-panx-de", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "xtreme", "type": "xtreme", "args": "PAN-X.de"}, "me... | Ning-fish/xlm-roberta-base-finetuned-panx-de | null | [
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-14T12:02:31+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-panx-de
==================================
This model is a fine-tuned version of xlm-roberta-base on the xtreme dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1352
* F1: 0.8591
Model description
-----------------
More information needed
Intended uses & l... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 24\n* eval\\_batch\\_size: 24\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### 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-large-finetuned-clinc
This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on the... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["clinc_oos"], "metrics": ["accuracy"], "model-index": [{"name": "roberta-large-finetuned-clinc", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "clinc_oos", "type": "clinc_oos", "args": "plus"},... | philschmid/roberta-large-finetuned-clinc | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"dataset:clinc_oos",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-14T12:11:33+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #dataset-clinc_oos #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| roberta-large-finetuned-clinc
=============================
This model is a fine-tuned version of roberta-large on the clinc\_oos dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2109
* Accuracy: 0.9703
Model description
-----------------
More information needed
Intended uses & limi... | [
"### 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* lr\\_scheduler\\_warmup\\_ste... | [
"TAGS\n#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #dataset-clinc_oos #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_ra... |
null | null |
# Generate face images from the sketch using TediGAN
## Model description
[TediGAN model](https://arxiv.org/abs/2012.03308)
#### How to use
```python
# You can include sample code which will be formatted
```
## Generated Images
![Example image](https://github.com/IIGROUP/TediGAN/raw/main/asserts/results/free-ha... | {"license": "mit", "tags": ["huggan", "gan"]} | huggan/TediGAN_sketch | null | [
"huggan",
"gan",
"arxiv:2012.03308",
"license:mit",
"has_space",
"region:us"
] | null | 2022-04-14T12:27:54+00:00 | [
"2012.03308"
] | [] | TAGS
#huggan #gan #arxiv-2012.03308 #license-mit #has_space #region-us
|
# Generate face images from the sketch using TediGAN
## Model description
TediGAN model
#### How to use
## Generated Images
!Example image
### BibTeX entry and citation info
| [
"# Generate face images from the sketch using TediGAN",
"## Model description\n\nTediGAN model",
"#### How to use",
"## Generated Images\n\n!Example image",
"### BibTeX entry and citation info"
] | [
"TAGS\n#huggan #gan #arxiv-2012.03308 #license-mit #has_space #region-us \n",
"# Generate face images from the sketch using TediGAN",
"## Model description\n\nTediGAN model",
"#### How to use",
"## Generated Images\n\n!Example image",
"### BibTeX entry and citation info"
] |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# annaeze/lab9_1
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown datas... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "annaeze/lab9_1", "results": []}]} | annaeze/lab9_1 | null | [
"transformers",
"tf",
"bert",
"token-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-14T12:43:01+00:00 | [] | [] | TAGS
#transformers #tf #bert #token-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| annaeze/lab9\_1
===============
This model is a fine-tuned version of bert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.0230
* Validation Loss: 0.0572
* Epoch: 2
Model description
-----------------
More information needed
Intended uses & limitati... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_steps': 1017, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': ... | [
"TAGS\n#transformers #tf #bert #token-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_... |
null | transformers | # CirBERTa
### Apply the Circular to the Pretraining Model
| 预训练模型 | 学习率 | batchsize | 设备 | 语料库 | 时间 | 优化器 |
| --------------------- | ------ | --------- | ------ | ------ | ---- | ------ |
| CirBERTa-Chinese-Base | 1e-5 | 256 | 10张3090+3张A100 | 200G | 2月 | AdamW |
使用通用语料（WuDao 200G) 进行无监督预... | {} | WENGSYX/CirBERTa-Chinese-Base | null | [
"transformers",
"pytorch",
"deberta-v2",
"endpoints_compatible",
"region:us"
] | null | 2022-04-14T12:52:29+00:00 | [] | [] | TAGS
#transformers #pytorch #deberta-v2 #endpoints_compatible #region-us
| CirBERTa
========
### Apply the Circular to the Pretraining Model
使用通用语料（WuDao 200G) 进行无监督预训练
在多项中文理解任务上，CirBERTa-Base模型超过MacBERT-Chinese-Large/RoBERTa-Chinese-Large
### 加载与使用
依托于huggingface-transformers
### 引用:
(暂时先引用这个，论文正在撰写...)
| [
"### Apply the Circular to the Pretraining Model\n\n\n\n使用通用语料（WuDao 200G) 进行无监督预训练\n\n\n在多项中文理解任务上，CirBERTa-Base模型超过MacBERT-Chinese-Large/RoBERTa-Chinese-Large",
"### 加载与使用\n\n\n依托于huggingface-transformers",
"### 引用:\n\n\n(暂时先引用这个，论文正在撰写...)"
] | [
"TAGS\n#transformers #pytorch #deberta-v2 #endpoints_compatible #region-us \n",
"### Apply the Circular to the Pretraining Model\n\n\n\n使用通用语料（WuDao 200G) 进行无监督预训练\n\n\n在多项中文理解任务上，CirBERTa-Base模型超过MacBERT-Chinese-Large/RoBERTa-Chinese-Large",
"### 加载与使用\n\n\n依托于huggingface-transformers",
"### 引用:\n\n\n(暂时先引用这... |
fill-mask | transformers |
# mBERTu
A Maltese multilingual model pre-trained on the Korpus Malti v4.0 using multilingual BERT as the initial checkpoint.
## License
This work is licensed under a
[Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License][cc-by-nc-sa].
Permissions beyond the scope of this license may be ... | {"language": ["mt"], "license": "cc-by-nc-sa-4.0", "datasets": ["MLRS/korpus_malti"], "widget": [{"text": "Malta huwa pajji\u017c fl-[MASK]."}], "model-index": [{"name": "mBERTu", "results": [{"task": {"type": "dependency-parsing", "name": "Dependency Parsing"}, "dataset": {"name": "Maltese Universal Dependencies Treeb... | MLRS/mBERTu | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"mt",
"dataset:MLRS/korpus_malti",
"license:cc-by-nc-sa-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-14T12:54:27+00:00 | [] | [
"mt"
] | TAGS
#transformers #pytorch #bert #fill-mask #mt #dataset-MLRS/korpus_malti #license-cc-by-nc-sa-4.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# mBERTu
A Maltese multilingual model pre-trained on the Korpus Malti v4.0 using multilingual BERT as the initial checkpoint.
## License
This work is licensed under a
[Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License][cc-by-nc-sa].
Permissions beyond the scope of this license may be ... | [
"# mBERTu\n\nA Maltese multilingual model pre-trained on the Korpus Malti v4.0 using multilingual BERT as the initial checkpoint.",
"## License\n\nThis work is licensed under a\n[Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License][cc-by-nc-sa].\nPermissions beyond the scope of this li... | [
"TAGS\n#transformers #pytorch #bert #fill-mask #mt #dataset-MLRS/korpus_malti #license-cc-by-nc-sa-4.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# mBERTu\n\nA Maltese multilingual model pre-trained on the Korpus Malti v4.0 using multilingual BERT as the initial checkpoint.",
"## ... |
text-generation | transformers |
# My Awesome Model that talks like Rick but thinks that your name is Morty
| {"tags": ["conversational"]} | florentiino/DialoGPT-small-rick | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-14T12:56:29+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# My Awesome Model that talks like Rick but thinks that your name is Morty
| [
"# My Awesome Model that talks like Rick but thinks that your name is Morty"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# My Awesome Model that talks like Rick but thinks that your name is Morty"
] |
unconditional-image-generation | transformers |
# Hugging NFT: cryptoskulls
## Disclaimer
All rights belong to their owners. Models and datasets can be removed from the site at the request of the copyright
holder.
## Model description
LightWeight GAN model for unconditional generation.
NFT collection available [here](https://opensea.io/collection/cryptoskulls)... | {"license": "mit", "tags": ["huggingnft", "nft", "huggan", "gan", "image", "images", "unconditional-image-generation"], "datasets": ["huggingnft/cryptoskulls"]} | huggingnft/cryptoskulls | null | [
"transformers",
"huggingnft",
"nft",
"huggan",
"gan",
"image",
"images",
"unconditional-image-generation",
"dataset:huggingnft/cryptoskulls",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-04-14T13:19:37+00:00 | [] | [] | TAGS
#transformers #huggingnft #nft #huggan #gan #image #images #unconditional-image-generation #dataset-huggingnft/cryptoskulls #license-mit #endpoints_compatible #region-us
|
# Hugging NFT: cryptoskulls
## Disclaimer
All rights belong to their owners. Models and datasets can be removed from the site at the request of the copyright
holder.
## Model description
LightWeight GAN model for unconditional generation.
NFT collection available here.
Dataset is available here.
Check Space: li... | [
"# Hugging NFT: cryptoskulls",
"## Disclaimer\n\nAll rights belong to their owners. Models and datasets can be removed from the site at the request of the copyright\nholder.",
"## Model description\n\nLightWeight GAN model for unconditional generation.\n\nNFT collection available here.\n\nDataset is available h... | [
"TAGS\n#transformers #huggingnft #nft #huggan #gan #image #images #unconditional-image-generation #dataset-huggingnft/cryptoskulls #license-mit #endpoints_compatible #region-us \n",
"# Hugging NFT: cryptoskulls",
"## Disclaimer\n\nAll rights belong to their owners. Models and datasets can be removed from the si... |
feature-extraction | transformers | # DiffCSE: Difference-based Contrastive Learning for Sentence Embeddings
[](https://github.com/voidism/DiffCSE/)
[](https://colab.research.google.com/github/voidi... | {"license": "apache-2.0"} | voidism/diffcse-bert-base-uncased-trans | null | [
"transformers",
"pytorch",
"bert",
"feature-extraction",
"arxiv:2204.10298",
"arxiv:2104.08821",
"arxiv:2111.00899",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-04-14T14:19:25+00:00 | [
"2204.10298",
"2104.08821",
"2111.00899"
] | [] | TAGS
#transformers #pytorch #bert #feature-extraction #arxiv-2204.10298 #arxiv-2104.08821 #arxiv-2111.00899 #license-apache-2.0 #endpoints_compatible #region-us
| # DiffCSE: Difference-based Contrastive Learning for Sentence Embeddings
![GitHub Stars](URL
![Open In Colab](URL
arXiv link: URL
To be published in NAACL 2022
Authors:
Yung-Sung Chuang,
Rumen Dangovski,
Hongyin Luo,
Yang Zhang,
Shiyu Chang,
Marin Soljačić,
Shang-Wen Li,
Scott Wen-tau Yih,
Yoon Kim,
James Glass... | [
"# DiffCSE: Difference-based Contrastive Learning for Sentence Embeddings \n\n![GitHub Stars](URL\n\n![Open In Colab](URL\n\narXiv link: URL \nTo be published in NAACL 2022\n\nAuthors:\nYung-Sung Chuang, \nRumen Dangovski,\nHongyin Luo,\nYang Zhang,\nShiyu Chang,\nMarin Soljačić,\nShang-Wen Li,\nScott Wen-tau Yih,... | [
"TAGS\n#transformers #pytorch #bert #feature-extraction #arxiv-2204.10298 #arxiv-2104.08821 #arxiv-2111.00899 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# DiffCSE: Difference-based Contrastive Learning for Sentence Embeddings \n\n](https://github.com/voidism/DiffCSE/)
[](https://colab.research.google.com/github/voidi... | {"license": "apache-2.0"} | voidism/diffcse-roberta-base-sts | null | [
"transformers",
"pytorch",
"roberta",
"feature-extraction",
"arxiv:2204.10298",
"arxiv:2104.08821",
"arxiv:2111.00899",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-04-14T14:19:51+00:00 | [
"2204.10298",
"2104.08821",
"2111.00899"
] | [] | TAGS
#transformers #pytorch #roberta #feature-extraction #arxiv-2204.10298 #arxiv-2104.08821 #arxiv-2111.00899 #license-apache-2.0 #endpoints_compatible #has_space #region-us
| # DiffCSE: Difference-based Contrastive Learning for Sentence Embeddings
![GitHub Stars](URL
![Open In Colab](URL
arXiv link: URL
To be published in NAACL 2022
Authors:
Yung-Sung Chuang,
Rumen Dangovski,
Hongyin Luo,
Yang Zhang,
Shiyu Chang,
Marin Soljačić,
Shang-Wen Li,
Scott Wen-tau Yih,
Yoon Kim,
James Glass... | [
"# DiffCSE: Difference-based Contrastive Learning for Sentence Embeddings \n\n![GitHub Stars](URL\n\n![Open In Colab](URL\n\narXiv link: URL \nTo be published in NAACL 2022\n\nAuthors:\nYung-Sung Chuang, \nRumen Dangovski,\nHongyin Luo,\nYang Zhang,\nShiyu Chang,\nMarin Soljačić,\nShang-Wen Li,\nScott Wen-tau Yih,... | [
"TAGS\n#transformers #pytorch #roberta #feature-extraction #arxiv-2204.10298 #arxiv-2104.08821 #arxiv-2111.00899 #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"# DiffCSE: Difference-based Contrastive Learning for Sentence Embeddings \n\n](https://github.com/voidism/DiffCSE/)
[](https://colab.research.google.com/github/voidi... | {"license": "apache-2.0"} | voidism/diffcse-roberta-base-trans | null | [
"transformers",
"pytorch",
"roberta",
"feature-extraction",
"arxiv:2204.10298",
"arxiv:2104.08821",
"arxiv:2111.00899",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-04-14T14:20:39+00:00 | [
"2204.10298",
"2104.08821",
"2111.00899"
] | [] | TAGS
#transformers #pytorch #roberta #feature-extraction #arxiv-2204.10298 #arxiv-2104.08821 #arxiv-2111.00899 #license-apache-2.0 #endpoints_compatible #region-us
| # DiffCSE: Difference-based Contrastive Learning for Sentence Embeddings
![GitHub Stars](URL
![Open In Colab](URL
arXiv link: URL
To be published in NAACL 2022
Authors:
Yung-Sung Chuang,
Rumen Dangovski,
Hongyin Luo,
Yang Zhang,
Shiyu Chang,
Marin Soljačić,
Shang-Wen Li,
Scott Wen-tau Yih,
Yoon Kim,
James Glass... | [
"# DiffCSE: Difference-based Contrastive Learning for Sentence Embeddings \n\n![GitHub Stars](URL\n\n![Open In Colab](URL\n\narXiv link: URL \nTo be published in NAACL 2022\n\nAuthors:\nYung-Sung Chuang, \nRumen Dangovski,\nHongyin Luo,\nYang Zhang,\nShiyu Chang,\nMarin Soljačić,\nShang-Wen Li,\nScott Wen-tau Yih,... | [
"TAGS\n#transformers #pytorch #roberta #feature-extraction #arxiv-2204.10298 #arxiv-2104.08821 #arxiv-2111.00899 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# DiffCSE: Difference-based Contrastive Learning for Sentence Embeddings \n\n is a conditional adversarial networks, a general-purpose solution to image-to-image translation problems. These networks not only learn the mapping from input image to output image, but also learn a loss function to train this mappi... | {"license": "mit", "tags": ["huggan", "gan"], "datasets": ["huggan/night2day"]} | huggan/pix2pix-night2day | null | [
"pytorch",
"huggan",
"gan",
"dataset:huggan/night2day",
"arxiv:1611.07004",
"license:mit",
"has_space",
"region:us"
] | null | 2022-04-14T14:42:14+00:00 | [
"1611.07004"
] | [] | TAGS
#pytorch #huggan #gan #dataset-huggan/night2day #arxiv-1611.07004 #license-mit #has_space #region-us
|
# MyModelName
## Model description
Pix2pix Model is a conditional adversarial networks, a general-purpose solution to image-to-image translation problems. These networks not only learn the mapping from input image to output image, but also learn a loss function to train this mapping. This makes it possible to apply ... | [
"# MyModelName",
"## Model description\n\nPix2pix Model is a conditional adversarial networks, a general-purpose solution to image-to-image translation problems. These networks not only learn the mapping from input image to output image, but also learn a loss function to train this mapping. This makes it possible... | [
"TAGS\n#pytorch #huggan #gan #dataset-huggan/night2day #arxiv-1611.07004 #license-mit #has_space #region-us \n",
"# MyModelName",
"## Model description\n\nPix2pix Model is a conditional adversarial networks, a general-purpose solution to image-to-image translation problems. These networks not only learn the map... |
text-classification | transformers |
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 742522663
- CO2 Emissions (in grams): 0.01856239042036965
## Validation Metrics
- Loss: 0.4798508286476135
- Accuracy: 0.7740053050397878
- Precision: 0.7236622073578596
- Recall: 0.9006243496357961
- AUC: 0.8798210006261515
- F1: 0.8... | {"language": "ko", "tags": "autotrain", "datasets": ["jason9693/APEACH"], "widget": [{"text": "\uac1c\ub150 \uc9d1\uc5d0\ub2e4 ctrl+z\ud5e4\ub193\uace0 \uc654\ub098"}], "co2_eq_emissions": 0.01856239042036965} | jason9693/koelectra-small-v3-generator-apeach | null | [
"transformers",
"pytorch",
"electra",
"text-classification",
"autotrain",
"ko",
"dataset:jason9693/APEACH",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-14T14:44:53+00:00 | [] | [
"ko"
] | TAGS
#transformers #pytorch #electra #text-classification #autotrain #ko #dataset-jason9693/APEACH #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 742522663
- CO2 Emissions (in grams): 0.01856239042036965
## Validation Metrics
- Loss: 0.4798508286476135
- Accuracy: 0.7740053050397878
- Precision: 0.7236622073578596
- Recall: 0.9006243496357961
- AUC: 0.8798210006261515
- F1: 0.8... | [
"# Model Trained Using AutoTrain\n\n- Problem type: Binary Classification\n- Model ID: 742522663\n- CO2 Emissions (in grams): 0.01856239042036965",
"## Validation Metrics\n\n- Loss: 0.4798508286476135\n- Accuracy: 0.7740053050397878\n- Precision: 0.7236622073578596\n- Recall: 0.9006243496357961\n- AUC: 0.87982100... | [
"TAGS\n#transformers #pytorch #electra #text-classification #autotrain #ko #dataset-jason9693/APEACH #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoTrain\n\n- Problem type: Binary Classification\n- Model ID: 742522663\n- CO2 Emissions (in grams): 0.0185623... |
null | null |
# This is a test | {"language": "code", "tags": ["code", "gpt2", "generation"], "datasets": ["lvwerra/codeparrot-clean-train"], "widget": [{"text": "from transformer import", "example_title": "Transformers"}, {"text": "def print_hello_world():\n\t", "example_title": "Hello World!"}, {"text": "def get_file_size(filepath):", "example_title... | lvwerra/test_card | null | [
"code",
"gpt2",
"generation",
"dataset:lvwerra/codeparrot-clean-train",
"model-index",
"region:us"
] | null | 2022-04-14T14:47:07+00:00 | [] | [
"code"
] | TAGS
#code #gpt2 #generation #dataset-lvwerra/codeparrot-clean-train #model-index #region-us
|
# This is a test | [
"# This is a test"
] | [
"TAGS\n#code #gpt2 #generation #dataset-lvwerra/codeparrot-clean-train #model-index #region-us \n",
"# This is a test"
] |
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. -->
# xlnet-base-cased-IUChatbot-ontologyDts-xlnetBaseCased-bertTokenizer-12April2022
This model is a fine-tuned version of [xlnet-bas... | {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "xlnet-base-cased-IUChatbot-ontologyDts-xlnetBaseCased-bertTokenizer-12April2022", "results": []}]} | nntadotzip/xlnet-base-cased-IUChatbot-ontologyDts-xlnetBaseCased-bertTokenizer-12April2022 | null | [
"transformers",
"pytorch",
"tensorboard",
"xlnet",
"question-answering",
"generated_from_trainer",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-04-14T15:11:29+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #xlnet #question-answering #generated_from_trainer #license-mit #endpoints_compatible #region-us
| xlnet-base-cased-IUChatbot-ontologyDts-xlnetBaseCased-bertTokenizer-12April2022
===============================================================================
This model is a fine-tuned version of xlnet-base-cased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4240
Mode... | [
"### 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",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #xlnet #question-answering #generated_from_trainer #license-mit #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* eval\\_batch\\_si... |
null | null |
Here's what I did to export the `pth` to `onnx` (if only for my own future reference):
1. Open the [Colab notebook](https://colab.research.google.com/gist/JingyunLiang/a5e3e54bc9ef8d7bf594f6fee8208533/swinir-demo-on-real-world-image-sr.ipynb#scrollTo=WsqCgH0iqMec) and click Runtime > Run All.
2. Open up and `main_test... | {"license": "apache-2.0"} | rocca/swin-ir-onnx | null | [
"onnx",
"license:apache-2.0",
"region:us"
] | null | 2022-04-14T15:25:21+00:00 | [] | [] | TAGS
#onnx #license-apache-2.0 #region-us
|
Here's what I did to export the 'pth' to 'onnx' (if only for my own future reference):
1. Open the Colab notebook and click Runtime > Run All.
2. Open up and 'main_test_swinir.py' in the Colab editor and placing the following line after 'output = model(img_lq)':
3. Run this:
And the ONNX file that you see in this re... | [] | [
"TAGS\n#onnx #license-apache-2.0 #region-us \n"
] |
text2text-generation | transformers |
# LongT5 (local attention, base-sized model)
LongT5 model pre-trained on English language. The model was introduced in the paper [LongT5: Efficient Text-To-Text Transformer for Long Sequences](https://arxiv.org/pdf/2112.07916.pdf) by Guo et al. and first released in [the LongT5 repository](https://github.com/google-r... | {"language": "en", "license": "apache-2.0"} | google/long-t5-local-base | null | [
"transformers",
"pytorch",
"jax",
"safetensors",
"longt5",
"text2text-generation",
"en",
"arxiv:2112.07916",
"arxiv:1912.08777",
"arxiv:1910.10683",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-04-14T15:53:51+00:00 | [
"2112.07916",
"1912.08777",
"1910.10683"
] | [
"en"
] | TAGS
#transformers #pytorch #jax #safetensors #longt5 #text2text-generation #en #arxiv-2112.07916 #arxiv-1912.08777 #arxiv-1910.10683 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# LongT5 (local attention, base-sized model)
LongT5 model pre-trained on English language. The model was introduced in the paper LongT5: Efficient Text-To-Text Transformer for Long Sequences by Guo et al. and first released in the LongT5 repository. All the model architecture and configuration can be found in Flaxfor... | [
"# LongT5 (local attention, base-sized model)\n\nLongT5 model pre-trained on English language. The model was introduced in the paper LongT5: Efficient Text-To-Text Transformer for Long Sequences by Guo et al. and first released in the LongT5 repository. All the model architecture and configuration can be found in F... | [
"TAGS\n#transformers #pytorch #jax #safetensors #longt5 #text2text-generation #en #arxiv-2112.07916 #arxiv-1912.08777 #arxiv-1910.10683 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# LongT5 (local attention, base-sized model)\n\nLongT5 model pre-trained on English la... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-small-finetuned-cnndm1-wikihow0
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the cnn_... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["cnn_dailymail"], "metrics": ["rouge"], "model-index": [{"name": "t5-small-finetuned-cnndm1-wikihow0", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "cnn_dailymail", ... | Chikashi/t5-small-finetuned-cnndm1-wikihow0 | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:cnn_dailymail",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-14T16:20:21+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-cnn_dailymail #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| t5-small-finetuned-cnndm1-wikihow0
==================================
This model is a fine-tuned version of t5-small on the cnn\_dailymail dataset.
It achieves the following results on the evaluation set:
* Loss: 1.6436
* Rouge1: 24.6116
* Rouge2: 11.8788
* Rougel: 20.3665
* Rougelsum: 23.2474
* Gen Len: 18.9998
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1\n* mixed\\_preci... | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-cnn_dailymail #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used dur... |
text-classification | transformers | This model is fine tuned for Patronizing and Condescending Language Classification task. Have fun. | {} | achyut/patronizing_detection | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-14T16:34:38+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #text-classification #autotrain_compatible #endpoints_compatible #region-us
| This model is fine tuned for Patronizing and Condescending Language Classification task. Have fun. | [] | [
"TAGS\n#transformers #pytorch #distilbert #text-classification #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text2text-generation | transformers |
# LongT5 (local attention, large-sized model)
LongT5 model pre-trained on English language. The model was introduced in the paper [LongT5: Efficient Text-To-Text Transformer for Long Sequences](https://arxiv.org/pdf/2112.07916.pdf) by Guo et al. and first released in [the LongT5 repository](https://github.com/google-... | {"language": "en", "license": "apache-2.0"} | google/long-t5-local-large | null | [
"transformers",
"pytorch",
"jax",
"longt5",
"text2text-generation",
"en",
"arxiv:2112.07916",
"arxiv:1912.08777",
"arxiv:1910.10683",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-04-14T16:41:53+00:00 | [
"2112.07916",
"1912.08777",
"1910.10683"
] | [
"en"
] | TAGS
#transformers #pytorch #jax #longt5 #text2text-generation #en #arxiv-2112.07916 #arxiv-1912.08777 #arxiv-1910.10683 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# LongT5 (local attention, large-sized model)
LongT5 model pre-trained on English language. The model was introduced in the paper LongT5: Efficient Text-To-Text Transformer for Long Sequences by Guo et al. and first released in the LongT5 repository. All the model architecture and configuration can be found in Flaxfo... | [
"# LongT5 (local attention, large-sized model)\n\nLongT5 model pre-trained on English language. The model was introduced in the paper LongT5: Efficient Text-To-Text Transformer for Long Sequences by Guo et al. and first released in the LongT5 repository. All the model architecture and configuration can be found in ... | [
"TAGS\n#transformers #pytorch #jax #longt5 #text2text-generation #en #arxiv-2112.07916 #arxiv-1912.08777 #arxiv-1910.10683 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# LongT5 (local attention, large-sized model)\n\nLongT5 model pre-trained on English language. The ... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Shaopeng/bert-finetuned-ner
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an ... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "Shaopeng/bert-finetuned-ner", "results": []}]} | Shaopeng/bert-finetuned-ner | null | [
"transformers",
"tf",
"bert",
"token-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-14T17:00:12+00:00 | [] | [] | TAGS
#transformers #tf #bert #token-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| Shaopeng/bert-finetuned-ner
===========================
This model is a fine-tuned version of bert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.1009
* Validation Loss: 0.1198
* Epoch: 2
Model description
-----------------
More information needed
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_steps': 1017, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': ... | [
"TAGS\n#transformers #tf #bert #token-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# javilonso/Mex_Rbta_TitleWithOpinion_Augmented_Attraction
This model is a fine-tuned version of [PlanTL-GOB-ES/roberta-base-bne](https:... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "javilonso/Mex_Rbta_TitleWithOpinion_Augmented_Attraction", "results": []}]} | javilonso/Mex_Rbta_TitleWithOpinion_Augmented_Attraction | null | [
"transformers",
"tf",
"roberta",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-14T17:07:02+00:00 | [] | [] | TAGS
#transformers #tf #roberta #text-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| javilonso/Mex\_Rbta\_TitleWithOpinion\_Augmented\_Attraction
============================================================
This model is a fine-tuned version of PlanTL-GOB-ES/roberta-base-bne on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.0059
* Validation Loss: 0.059... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_steps': 11532, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle':... | [
"TAGS\n#transformers #tf #roberta #text-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\... |
image-classification | transformers |
# VIT_Basic
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/huggingpic... | {"tags": ["image-classification", "pytorch", "huggingpics"], "metrics": ["accuracy"]} | AhmedSayeem/VIT_Basic | null | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-14T18:01:13+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# VIT_Basic
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
#### chairs
!chairs
#### hot dog
!hot dog
#### ice cream
!ice cream
#### ladders
!ladders
#### tables
!tabl... | [
"# VIT_Basic\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",
"#### chairs\n\n!chairs",
"#### hot dog\n\n!hot dog",
"#### ice cream\n\n!ice cream",
"#### la... | [
"TAGS\n#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# VIT_Basic\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nReport any issues wit... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# javilonso/Mex_Rbta_TitleWithOpinion_Augmented_Polarity
This model is a fine-tuned version of [PlanTL-GOB-ES/roberta-base-bne](https://... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "javilonso/Mex_Rbta_TitleWithOpinion_Augmented_Polarity", "results": []}]} | javilonso/Mex_Rbta_TitleWithOpinion_Augmented_Polarity | null | [
"transformers",
"tf",
"roberta",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-14T18:21:42+00:00 | [] | [] | TAGS
#transformers #tf #roberta #text-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| javilonso/Mex\_Rbta\_TitleWithOpinion\_Augmented\_Polarity
==========================================================
This model is a fine-tuned version of PlanTL-GOB-ES/roberta-base-bne on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.3830
* Validation Loss: 0.5288
* ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_steps': 7688, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': ... | [
"TAGS\n#transformers #tf #roberta #text-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\... |
text-generation | transformers |
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/669103856106668033/UF3cg... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/jeffbezos/1653651235626/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/jeffbezos | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-14T19:01:23+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Jeff Bezos
@jeffbezos
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
-----------... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
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-300m-chuvash-colab
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-large-xls-r-300m-chuvash-colab", "results": []}]} | mizoru/wav2vec2-large-xls-r-300m-chuvash-colab | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-04-14T19:13:42+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-300m-chuvash-colab
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common_voice dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.6998
- eval_wer: 0.7356
- eval_runtime: 233.6193
- eval_samples_per_second: 3.373
- eval_steps_per_second:... | [
"# wav2vec2-large-xls-r-300m-chuvash-colab\n\nThis model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common_voice dataset.\nIt achieves the following results on the evaluation set:\n- eval_loss: 0.6998\n- eval_wer: 0.7356\n- eval_runtime: 233.6193\n- eval_samples_per_second: 3.373\n- eval_steps_p... | [
"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-300m-chuvash-colab\n\nThis model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common_... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# stog-t5-small
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the web_nlg dataset.
It achie... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["web_nlg"], "model-index": [{"name": "stog-t5-small", "results": []}]} | milyiyo/stog-t5-small | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:web_nlg",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-14T19:15:29+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-web_nlg #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| stog-t5-small
=============
This model is a fine-tuned version of t5-small on the web\_nlg dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1414
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information nee... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.001\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-web_nlg #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n... |
unconditional-image-generation | null |
## Model Description
Generate Art using PyTorch and [DCGAN](https://pytorch.org/tutorials/beginner/dcgan_faces_tutorial.html).
## How To Use
```python
from huggingface_hub import hf_hub_download
import torch
import matplotlib.pyplot as plt
import numpy as np
from torch import nn
class Generator(nn.Module):
d... | {"license": "afl-3.0", "tags": ["PyTorch", "huggan", "gan", "unconditional-image-generation"]} | huggan/ArtGAN | null | [
"PyTorch",
"huggan",
"gan",
"unconditional-image-generation",
"license:afl-3.0",
"has_space",
"region:us"
] | null | 2022-04-14T19:28:54+00:00 | [] | [] | TAGS
#PyTorch #huggan #gan #unconditional-image-generation #license-afl-3.0 #has_space #region-us
|
## Model Description
Generate Art using PyTorch and DCGAN.
## How To Use
## Generate Image
!Example Image
| [
"## Model Description \n\nGenerate Art using PyTorch and DCGAN.",
"## How To Use",
"## Generate Image \n\n!Example Image"
] | [
"TAGS\n#PyTorch #huggan #gan #unconditional-image-generation #license-afl-3.0 #has_space #region-us \n",
"## Model Description \n\nGenerate Art using PyTorch and DCGAN.",
"## How To Use",
"## Generate Image \n\n!Example Image"
] |
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. -->
# oldData_BERT
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the None dataset... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "oldData_BERT", "results": []}]} | brad1141/oldData_BERT | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-14T19:35:11+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| oldData\_BERT
=============
This model is a fine-tuned version of bert-base-cased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0616
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\n* seed: 42\n* gradient\\_accumulation\\_steps: 8\n* total\\_train\\_batch\\_size: 8\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e... | [
"TAGS\n#transformers #pytorch #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 1\n* e... |
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. -->
# hinglish-finetuned
This model is a fine-tuned version of [verloop/Hinglish-Bert](https://huggingface.co/verloop/Hinglish-Bert) o... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "hinglish-finetuned", "results": []}]} | ketan-rmcf/hinglish-finetuned | null | [
"transformers",
"pytorch",
"tf",
"tensorboard",
"bert",
"fill-mask",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-14T20:05:58+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #tensorboard #bert #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| hinglish-finetuned
==================
This model is a fine-tuned version of verloop/Hinglish-Bert on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 2.0786
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 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: 25\n* mixed\\_preci... | [
"TAGS\n#transformers #pytorch #tf #tensorboard #bert #fill-mask #generated_from_trainer #autotrain_compatible #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* eval\\_batch\\... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["emotion"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "distilbert-base-uncased-finetuned-emotion", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "emotion", "type": "emotion... | Adrian/distilbert-base-uncased-finetuned-emotion | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-14T20:58:50+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-emotion
=========================================
This model is a fine-tuned version of distilbert-base-uncased on the emotion dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2071
* Accuracy: 0.9275
* F1: 0.9273
Model description
-----------------
Mo... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\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... | [
"TAGS\n#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2... |
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. -->
# bert-base-uncased-2-finetuned-RRamicus
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-bas... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "bert-base-uncased-2-finetuned-RRamicus", "results": []}]} | repro-rights-amicus-briefs/bert-base-uncased-2-finetuned-RRamicus | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-14T21:04:29+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| bert-base-uncased-2-finetuned-RRamicus
======================================
This model is a fine-tuned version of bert-base-uncased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 1.4784
Model description
-----------------
More information needed
Intended uses & limitat... | [
"### 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: 928\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #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... |
unconditional-image-generation | transformers |
# Hugging NFT: alpacadabraz
## Disclaimer
All rights belong to their owners. Models and datasets can be removed from the site at the request of the copyright
holder.
## Model description
LightWeight GAN model for unconditional generation.
NFT collection available [here](https://opensea.io/collection/alpacadabraz)... | {"license": "mit", "tags": ["huggingnft", "nft", "huggan", "gan", "image", "images", "unconditional-image-generation"], "datasets": ["huggingnft/alpacadabraz"]} | huggingnft/alpacadabraz | null | [
"transformers",
"huggingnft",
"nft",
"huggan",
"gan",
"image",
"images",
"unconditional-image-generation",
"dataset:huggingnft/alpacadabraz",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-04-14T21:08:45+00:00 | [] | [] | TAGS
#transformers #huggingnft #nft #huggan #gan #image #images #unconditional-image-generation #dataset-huggingnft/alpacadabraz #license-mit #endpoints_compatible #region-us
|
# Hugging NFT: alpacadabraz
## Disclaimer
All rights belong to their owners. Models and datasets can be removed from the site at the request of the copyright
holder.
## Model description
LightWeight GAN model for unconditional generation.
NFT collection available here.
Dataset is available here.
Check Space: li... | [
"# Hugging NFT: alpacadabraz",
"## Disclaimer\n\nAll rights belong to their owners. Models and datasets can be removed from the site at the request of the copyright\nholder.",
"## Model description\n\nLightWeight GAN model for unconditional generation.\n\nNFT collection available here.\n\nDataset is available h... | [
"TAGS\n#transformers #huggingnft #nft #huggan #gan #image #images #unconditional-image-generation #dataset-huggingnft/alpacadabraz #license-mit #endpoints_compatible #region-us \n",
"# Hugging NFT: alpacadabraz",
"## Disclaimer\n\nAll rights belong to their owners. Models and datasets can be removed from the si... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# AdwayK/hugging_face_biobert_MLMA
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) o... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "AdwayK/hugging_face_biobert_MLMA", "results": []}]} | AdwayK/hugging_face_biobert_MLMA | null | [
"transformers",
"tf",
"bert",
"token-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-14T21:28:53+00:00 | [] | [] | TAGS
#transformers #tf #bert #token-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| AdwayK/hugging\_face\_biobert\_MLMA
===================================
This model is a fine-tuned version of bert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.0
* Validation Loss: 0.0814
* Epoch: 9
Model description
-----------------
More informat... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_steps': 3390, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': ... | [
"TAGS\n#transformers #tf #bert #token-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# javilonso/Mex_Rbta_TitleWithOpinion_Augmented_Attraction_2epoch
This model is a fine-tuned version of [PlanTL-GOB-ES/roberta-base-bne]... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "javilonso/Mex_Rbta_TitleWithOpinion_Augmented_Attraction_2epoch", "results": []}]} | javilonso/Mex_Rbta_TitleWithOpinion_Augmented_Attraction_2epoch | null | [
"transformers",
"tf",
"roberta",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-14T21:35:36+00:00 | [] | [] | TAGS
#transformers #tf #roberta #text-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| javilonso/Mex\_Rbta\_TitleWithOpinion\_Augmented\_Attraction\_2epoch
====================================================================
This model is a fine-tuned version of PlanTL-GOB-ES/roberta-base-bne on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.0245
* Valida... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_steps': 7688, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': ... | [
"TAGS\n#transformers #tf #roberta #text-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# zhuzhusleepearly/bert-finetuned
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "zhuzhusleepearly/bert-finetuned", "results": []}]} | zhuzhusleepearly/bert-finetuned | null | [
"transformers",
"tf",
"bert",
"token-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-14T22:16:28+00:00 | [] | [] | TAGS
#transformers #tf #bert #token-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| zhuzhusleepearly/bert-finetuned
===============================
This model is a fine-tuned version of bert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.0248
* Validation Loss: 0.0614
* Epoch: 2
Model description
-----------------
More information n... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_steps': 1017, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': ... | [
"TAGS\n#transformers #tf #bert #token-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', '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. -->
# finetuning-sentiment-model_duke_final_two
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["f1"], "model-index": [{"name": "finetuning-sentiment-model_duke_final_two", "results": []}]} | dpazmino/finetuning-sentiment-model_duke_final_two | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-14T22:30:12+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# finetuning-sentiment-model_duke_final_two
This model is a fine-tuned version of distilbert-base-uncased on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3381
- F1: 0.8801
## Model description
More information needed
## Intended uses & limitations
More information needed
... | [
"# finetuning-sentiment-model_duke_final_two\n\nThis model is a fine-tuned version of distilbert-base-uncased on the None dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.3381\n- F1: 0.8801",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore ... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# finetuning-sentiment-model_duke_final_two\n\nThis model is a fine-tuned version of distilbert-base-uncased on the None dataset.\nIt... |
null | null | dataset: https://github.com/cs-chan/ArtGAN/tree/master/WikiArt%20Dataset
trained on the official projected gan github code - you can check out the hfspace to see how to use it to generate images
fun stuff
check out the space demo: https://huggingface.co/spaces/huggan/projected_gan_art
Made by:-<br/>
[Jeronim Matijev... | {} | huggan/projected_gan_impressionism | null | [
"pytorch",
"region:us"
] | null | 2022-04-14T22:32:14+00:00 | [] | [] | TAGS
#pytorch #region-us
| dataset: URL
trained on the official projected gan github code - you can check out the hfspace to see how to use it to generate images
fun stuff
check out the space demo: URL
Made by:-<br/>
Jeronim Matijević<br/>
Massimiliano Pappa<br/>
| [] | [
"TAGS\n#pytorch #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. -->
# results
This model is a fine-tuned version of [hfl/chinese-bert-wwm-ext](https://huggingface.co/hfl/chinese-bert-wwm-ext) on an ... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["f1"], "model-index": [{"name": "results", "results": []}]} | Raychanan/COVID | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-14T22:32:41+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| results
=======
This model is a fine-tuned version of hfl/chinese-bert-wwm-ext on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5193
* F1: 0.9546
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\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",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\... |
null | null | My model is an inverted image detector and can help detect if images are inverted with 99% accuracy. \
I used a dataset containing people with and without masks. I trained my model on ~ 300 images of people without masks and tested it on ~ 60 of the same images distribution: \
author = {Prasoon Kottarathil}, \
title = ... | {"license": "afl-3.0"} | DIANKHA/upside-down | null | [
"license:afl-3.0",
"region:us"
] | null | 2022-04-14T23:06:44+00:00 | [] | [] | TAGS
#license-afl-3.0 #region-us
| My model is an inverted image detector and can help detect if images are inverted with 99% accuracy. \
I used a dataset containing people with and without masks. I trained my model on ~ 300 images of people without masks and tested it on ~ 60 of the same images distribution: \
author = {Prasoon Kottarathil}, \
title = ... | [] | [
"TAGS\n#license-afl-3.0 #region-us \n"
] |
unconditional-image-generation | pytorch |
dataset: https://github.com/cs-chan/ArtGAN/tree/master/WikiArt%20Dataset
trained on the official projected gan github code - you can check out the hfspace to see how to use it to generate images
fun stuff
check out the space demo: https://huggingface.co/spaces/huggan/projected_gan_art
Made by:-<br/>
[Jeronim Matije... | {"library_name": "pytorch", "tags": ["gan", "dcgan", "projected-gan", "huggan", "unconditional-image-generation"]} | huggan/projected_gan_Hana_Hanak | null | [
"pytorch",
"gan",
"dcgan",
"projected-gan",
"huggan",
"unconditional-image-generation",
"region:us"
] | null | 2022-04-14T23:23:33+00:00 | [] | [] | TAGS
#pytorch #gan #dcgan #projected-gan #huggan #unconditional-image-generation #region-us
|
dataset: URL
trained on the official projected gan github code - you can check out the hfspace to see how to use it to generate images
fun stuff
check out the space demo: URL
Made by:-<br/>
Jeronim Matijević<br/>
Massimiliano Pappa<br/>
| [] | [
"TAGS\n#pytorch #gan #dcgan #projected-gan #huggan #unconditional-image-generation #region-us \n"
] |
unconditional-image-generation | pytorch |
dataset: https://github.com/cs-chan/ArtGAN/tree/master/WikiArt%20Dataset
trained on the official projected gan github code - you can check out the hfspace to see how to use it to generate images
fun stuff
check out the space demo: https://huggingface.co/spaces/huggan/projected_gan_art
Made by:-<br/>
[Jeronim Matije... | {"library_name": "pytorch", "tags": ["gan", "dcgan", "projected-gan", "huggan", "unconditional-image-generation"]} | huggan/projected_gan_color_field_hana | null | [
"pytorch",
"gan",
"dcgan",
"projected-gan",
"huggan",
"unconditional-image-generation",
"region:us"
] | null | 2022-04-14T23:30:26+00:00 | [] | [] | TAGS
#pytorch #gan #dcgan #projected-gan #huggan #unconditional-image-generation #region-us
|
dataset: URL
trained on the official projected gan github code - you can check out the hfspace to see how to use it to generate images
fun stuff
check out the space demo: URL
Made by:-<br/>
Jeronim Matijević<br/>
Massimiliano Pappa<br/>
| [] | [
"TAGS\n#pytorch #gan #dcgan #projected-gan #huggan #unconditional-image-generation #region-us \n"
] |
text-generation | transformers | ```
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("BigSalmon/InformalToFormalLincoln36")
model = AutoModelForCausalLM.from_pretrained("BigSalmon/InformalToFormalLincoln36")
```
```
How To Make Prompt:
informal english: i am very ready to do that just that.
Tra... | {} | BigSalmon/InformalToFormalLincoln36 | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-04-14T23:31:32+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
Keywords to sentences or sentence. | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n"
] |
unconditional-image-generation | pytorch |
dataset: https://github.com/cs-chan/ArtGAN/tree/master/WikiArt%20Dataset
trained on the official projected gan github code - you can check out the hfspace to see how to use it to generate images
fun stuff
check out the space demo: https://huggingface.co/spaces/huggan/projected_gan_art
Made by:-<br/>
[Jeronim Matije... | {"library_name": "pytorch", "tags": ["gan", "dcgan", "projected-gan", "huggan", "unconditional-image-generation"]} | huggan/projected_gan_abstract_expressionism_hana | null | [
"pytorch",
"gan",
"dcgan",
"projected-gan",
"huggan",
"unconditional-image-generation",
"region:us"
] | null | 2022-04-14T23:37:59+00:00 | [] | [] | TAGS
#pytorch #gan #dcgan #projected-gan #huggan #unconditional-image-generation #region-us
|
dataset: URL
trained on the official projected gan github code - you can check out the hfspace to see how to use it to generate images
fun stuff
check out the space demo: URL
Made by:-<br/>
Jeronim Matijević<br/>
Massimiliano Pappa<br/>
| [] | [
"TAGS\n#pytorch #gan #dcgan #projected-gan #huggan #unconditional-image-generation #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. -->
# results
This model is a fine-tuned version of [hfl/chinese-bert-wwm-ext](https://huggingface.co/hfl/chinese-bert-wwm-ext) on an ... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["f1"], "model-index": [{"name": "results", "results": []}]} | Raychanan/COVID_RandomOver | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-14T23:42:32+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| results
=======
This model is a fine-tuned version of hfl/chinese-bert-wwm-ext on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4235
* F1: 0.9546
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\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",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\... |
sentence-similarity | sentence-transformers |
# ddobokki/unsup-simcse-klue-roberta-small
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceT... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers", "ko"], "pipeline_tag": "sentence-similarity"} | ddobokki/unsup-simcse-klue-roberta-small | null | [
"sentence-transformers",
"pytorch",
"roberta",
"feature-extraction",
"sentence-similarity",
"transformers",
"ko",
"endpoints_compatible",
"region:us"
] | null | 2022-04-14T23:59:52+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #roberta #feature-extraction #sentence-similarity #transformers #ko #endpoints_compatible #region-us
|
# ddobokki/unsup-simcse-klue-roberta-small
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers installed:
Then you can use the model like this:
(개발중)
git:URL
| [
"# ddobokki/unsup-simcse-klue-roberta-small",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\nThen you can use the model like this:\n\n\n(개발중)\ngit:URL"
] | [
"TAGS\n#sentence-transformers #pytorch #roberta #feature-extraction #sentence-similarity #transformers #ko #endpoints_compatible #region-us \n",
"# ddobokki/unsup-simcse-klue-roberta-small",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\n... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-small-finetuned-cnndm1-wikihow1
This model is a fine-tuned version of [Chikashi/t5-small-finetuned-cnndm1-wikihow0](https://h... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["wikihow"], "metrics": ["rouge"], "model-index": [{"name": "t5-small-finetuned-cnndm1-wikihow1", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "wikihow", "type": "wik... | Chikashi/t5-small-finetuned-cnndm1-wikihow1 | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:wikihow",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-15T00:03:52+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-wikihow #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| t5-small-finetuned-cnndm1-wikihow1
==================================
This model is a fine-tuned version of Chikashi/t5-small-finetuned-cnndm1-wikihow0 on the wikihow dataset.
It achieves the following results on the evaluation set:
* Loss: 2.3727
* Rouge1: 26.6881
* Rouge2: 9.9589
* Rougel: 22.6828
* Rougelsum: 26... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1\n* mixed\\_preci... | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-wikihow #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during tr... |
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. -->
# nick_asr_LID
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "nick_asr_LID", "results": []}]} | ntoldalagi/nick_asr_LID | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | null | 2022-04-15T00:04:50+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #endpoints_compatible #region-us
| nick\_asr\_LID
==============
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: nan
* Wer: 1.0
* Cer: 1.0
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More informa... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 2\n* seed: 42\n* gradient\\_accumulation\\_steps: 12\n* total\\_train\\_batch\\_size: 24\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 2\n* eval\\_batch\\_si... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# nicholasdino/bert-finetuned-ner
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "nicholasdino/bert-finetuned-ner", "results": []}]} | nicholasdino/bert-finetuned-ner | null | [
"transformers",
"tf",
"bert",
"token-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-15T00:28:07+00:00 | [] | [] | TAGS
#transformers #tf #bert #token-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| nicholasdino/bert-finetuned-ner
===============================
This model is a fine-tuned version of bert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.0241
* Validation Loss: 0.0588
* Epoch: 2
Model description
-----------------
More information n... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_steps': 1017, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': ... | [
"TAGS\n#transformers #tf #bert #token-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_... |
text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# chinese-bert-wwm-finetuned-product-1
This model is a fine-tuned version of [hfl/chinese-bert-wwm](https://huggingface.co/hfl/chi... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "chinese-bert-wwm-finetuned-product-1", "results": []}]} | agdsga/chinese-bert-wwm-finetuned-product-1 | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-15T01:08:12+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# chinese-bert-wwm-finetuned-product-1
This model is a fine-tuned version of hfl/chinese-bert-wwm on the None dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.0000
- eval_runtime: 10.6737
- eval_samples_per_second: 362.572
- eval_steps_per_second: 5.715
- epoch: 11.61
- step: 18797
... | [
"# chinese-bert-wwm-finetuned-product-1\n\nThis model is a fine-tuned version of hfl/chinese-bert-wwm on the None dataset.\nIt achieves the following results on the evaluation set:\n- eval_loss: 0.0000\n- eval_runtime: 10.6737\n- eval_samples_per_second: 362.572\n- eval_steps_per_second: 5.715\n- epoch: 11.61\n- st... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# chinese-bert-wwm-finetuned-product-1\n\nThis model is a fine-tuned version of hfl/chinese-bert-wwm on the None dataset.\nIt achieves the foll... |
null | transformers |
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 744122711
- CO2 Emissions (in grams): 0.0006493037575021453
## Validation Metrics
- Loss: 0.09241962407466127
- Accuracy: 0.9666666666666667
- Macro F1: 0.9665831244778613
- Micro F1: 0.9666666666666667
- Weighted F1: 0.966583124... | {"tags": ["autotrain", "tabular", "classification", "structured-data-classification"], "datasets": ["vabadeh213/autotrain-data-iris"], "co2_eq_emissions": 0.0006493037575021453} | vabadeh213/autotrain-iris-744122711 | null | [
"transformers",
"joblib",
"decision_tree",
"autotrain",
"tabular",
"classification",
"structured-data-classification",
"dataset:vabadeh213/autotrain-data-iris",
"co2_eq_emissions",
"endpoints_compatible",
"region:us"
] | null | 2022-04-15T01:08:51+00:00 | [] | [] | TAGS
#transformers #joblib #decision_tree #autotrain #tabular #classification #structured-data-classification #dataset-vabadeh213/autotrain-data-iris #co2_eq_emissions #endpoints_compatible #region-us
|
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 744122711
- CO2 Emissions (in grams): 0.0006493037575021453
## Validation Metrics
- Loss: 0.09241962407466127
- Accuracy: 0.9666666666666667
- Macro F1: 0.9665831244778613
- Micro F1: 0.9666666666666667
- Weighted F1: 0.966583124... | [
"# Model Trained Using AutoTrain\n\n- Problem type: Multi-class Classification\n- Model ID: 744122711\n- CO2 Emissions (in grams): 0.0006493037575021453",
"## Validation Metrics\n\n- Loss: 0.09241962407466127\n- Accuracy: 0.9666666666666667\n- Macro F1: 0.9665831244778613\n- Micro F1: 0.9666666666666667\n- Weight... | [
"TAGS\n#transformers #joblib #decision_tree #autotrain #tabular #classification #structured-data-classification #dataset-vabadeh213/autotrain-data-iris #co2_eq_emissions #endpoints_compatible #region-us \n",
"# Model Trained Using AutoTrain\n\n- Problem type: Multi-class Classification\n- Model ID: 744122711\n- C... |
text-classification | transformers | first 512
training_args = TrainingArguments(
output_dir="./results",
learning_rate=5e-5,
per_device_train_batch_size=16,
per_device_eval_batch_size=16,
num_train_epochs=5,
weight_decay=0.01,
evaluation_strategy="epoch",
push_to_hub=True
) | {} | Raychanan/bert-base-chinese-first512 | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-15T01:10:28+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us
| first 512
training_args = TrainingArguments(
output_dir="./results",
learning_rate=5e-5,
per_device_train_batch_size=16,
per_device_eval_batch_size=16,
num_train_epochs=5,
weight_decay=0.01,
evaluation_strategy="epoch",
push_to_hub=True
) | [] | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us \n"
] |
null | transformers | ERROR: type should be string, got "\nhttps://colab.research.google.com/drive/16rmsJTBelh2vIWVxt9ncFEJmU7cEdUsE?usp=sharing\n\n# Model Trained Using AutoTrain\n\n- Problem type: Binary Classification\n- Model ID: 744222727\n- CO2 Emissions (in grams): 0.00509303545772981\n\n## Validation Metrics\n\n- Loss: 0.40596098709549455\n- Accuracy: 0.8378378378378378\n- Precision: 0.8518518518518519\n- Recall: 0.92\n- AUC: 0.8866666666666667\n- F1: 0.8846153846153846\n\n## Usage\n\n```python\nimport json\nimport joblib\n\nmodel = joblib.load('model.joblib')\nconfig = json.load(open('config.json'))\n\nfeatures = config['features']\n\n# data = pd.read_csv(\"data.csv\")\ndata = data[features]\n\npredictions = model.predict(data) # or model.predict_proba(data)\n\n```" | {"tags": ["autotrain", "tabular", "classification", "structured-data-classification"], "datasets": ["vabadeh213/autotrain-data-titanic"], "co2_eq_emissions": 0.00509303545772981} | vabadeh213/autotrain-titanic-744222727 | null | [
"transformers",
"joblib",
"xgboost",
"autotrain",
"tabular",
"classification",
"structured-data-classification",
"dataset:vabadeh213/autotrain-data-titanic",
"co2_eq_emissions",
"endpoints_compatible",
"region:us"
] | null | 2022-04-15T01:18:16+00:00 | [] | [] | TAGS
#transformers #joblib #xgboost #autotrain #tabular #classification #structured-data-classification #dataset-vabadeh213/autotrain-data-titanic #co2_eq_emissions #endpoints_compatible #region-us
|
URL
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 744222727
- CO2 Emissions (in grams): 0.00509303545772981
## Validation Metrics
- Loss: 0.40596098709549455
- Accuracy: 0.8378378378378378
- Precision: 0.8518518518518519
- Recall: 0.92
- AUC: 0.8866666666666667
- F1: 0.884615384... | [
"# Model Trained Using AutoTrain\n\n- Problem type: Binary Classification\n- Model ID: 744222727\n- CO2 Emissions (in grams): 0.00509303545772981",
"## Validation Metrics\n\n- Loss: 0.40596098709549455\n- Accuracy: 0.8378378378378378\n- Precision: 0.8518518518518519\n- Recall: 0.92\n- AUC: 0.8866666666666667\n- F... | [
"TAGS\n#transformers #joblib #xgboost #autotrain #tabular #classification #structured-data-classification #dataset-vabadeh213/autotrain-data-titanic #co2_eq_emissions #endpoints_compatible #region-us \n",
"# Model Trained Using AutoTrain\n\n- Problem type: Binary Classification\n- Model ID: 744222727\n- CO2 Emiss... |
text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# codeparrot-ds-sample-2ep-14apr
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset.
... | {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "codeparrot-ds-sample-2ep-14apr", "results": []}]} | mimicheng/codeparrot-ds-sample-2ep-14apr | null | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-15T02:25:54+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| codeparrot-ds-sample-2ep-14apr
==============================
This model is a fine-tuned version of gpt2 on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 1.6319
Model description
-----------------
More information needed
Intended uses & limitations
----------------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* distributed\\_type: tpu\n* gradient\\_accumulation\\_steps: 8\n* total\\_train\\_batch\\_size: 512\n* optimizer: Adam with ... | [
"TAGS\n#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# hkayesh/twitter-disaster-nlp
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-un... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "hkayesh/twitter-disaster-nlp", "results": []}]} | hkayesh/twitter-disaster-nlp | null | [
"transformers",
"tf",
"distilbert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-04-15T03:00:02+00:00 | [] | [] | TAGS
#transformers #tf #distilbert #text-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| hkayesh/twitter-disaster-nlp
============================
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.2529
* Train Accuracy: 0.9074
* Validation Loss: 0.4153
* Validation Accuracy: 0.8425
* Epoch: 2
M... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'learning\\_rate': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_steps': 1284, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': False, 'nam... | [
"TAGS\n#transformers #tf #distilbert #text-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'learni... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# zhuzhusleepearly/bert-task5finetuned
This model was trained from scratch on an unknown dataset.
It achieves the following results on t... | {"tags": ["generated_from_keras_callback"], "model-index": [{"name": "zhuzhusleepearly/bert-task5finetuned", "results": []}]} | zhuzhusleepearly/bert-task5finetuned | null | [
"transformers",
"tf",
"bert",
"token-classification",
"generated_from_keras_callback",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-15T03:17:15+00:00 | [] | [] | TAGS
#transformers #tf #bert #token-classification #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #region-us
| zhuzhusleepearly/bert-task5finetuned
====================================
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.0350
* Validation Loss: 0.0775
* Epoch: 2
Model description
-----------------
More information needed
In... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_steps': 669, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': F... | [
"TAGS\n#transformers #tf #bert #token-classification #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_nam... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# qp321/distilbert-base-uncased-finetuned-cola
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/di... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "qp321/distilbert-base-uncased-finetuned-cola", "results": []}]} | qp321/distilbert-base-uncased-finetuned-cola | null | [
"transformers",
"tf",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-15T03:22:54+00:00 | [] | [] | TAGS
#transformers #tf #tensorboard #distilbert #text-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| qp321/distilbert-base-uncased-finetuned-cola
============================================
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.1122
* Validation Loss: 0.6352
* Train Matthews Correlation: 0.5295
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'learning\\_rate': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_steps': 2670, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': False, 'nam... | [
"TAGS\n#transformers #tf #tensorboard #distilbert #text-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'lear... |
text-generation | transformers |
# small-harrypotter model | {"tags": ["conversational"]} | ShibaDeveloper/DialoGPT-small-harrypotter | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-15T03:29:45+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# small-harrypotter model | [
"# small-harrypotter model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# small-harrypotter model"
] |
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. -->
# finetuning-sentiment-model-4000-samples
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/d... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imdb"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "finetuning-sentiment-model-4000-samples", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "imdb", "type": "imdb", "args": ... | Manishkalra/finetuning-sentiment-model-4000-samples | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-15T03:38:56+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-imdb #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# finetuning-sentiment-model-4000-samples
This model is a fine-tuned version of distilbert-base-uncased on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2706
- Accuracy: 0.9
- F1: 0.9038
## Model description
More information needed
## Intended uses & limitations
More infor... | [
"# finetuning-sentiment-model-4000-samples\n\nThis model is a fine-tuned version of distilbert-base-uncased on the imdb dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.2706\n- Accuracy: 0.9\n- F1: 0.9038",
"## Model description\n\nMore information needed",
"## Intended uses & limit... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-imdb #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# finetuning-sentiment-model-4000-samples\n\nThis model is a fine-tuned version of distilbert-base-uncased... |
null | 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. -->
# nick_asr_COMBO
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation se... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "nick_asr_COMBO", "results": []}]} | ntoldalagi/nick_asr_COMBO | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | null | 2022-04-15T03:59:13+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #generated_from_trainer #endpoints_compatible #region-us
| nick\_asr\_COMBO
================
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 1.4313
* Wer: 0.6723
* Cer: 0.2408
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\n* seed: 42\n* gradient\\_accumulation\\_steps: 16\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #generated_from_trainer #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\n* seed: 42\n* gradient\... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mT5_multilingual_XLSum-finetuned-ar
This model is a fine-tuned version of [csebuetnlp/mT5_multilingual_XLSum](https://huggingfac... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "mT5_multilingual_XLSum-finetuned-ar", "results": []}]} | ahmeddbahaa/mT5_multilingual_XLSum-finetuned-ar | null | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-15T04:31:46+00:00 | [] | [] | TAGS
#transformers #pytorch #mt5 #text2text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# mT5_multilingual_XLSum-finetuned-ar
This model is a fine-tuned version of csebuetnlp/mT5_multilingual_XLSum on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedur... | [
"# mT5_multilingual_XLSum-finetuned-ar\n\nThis model is a fine-tuned version of csebuetnlp/mT5_multilingual_XLSum on an unknown dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed... | [
"TAGS\n#transformers #pytorch #mt5 #text2text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# mT5_multilingual_XLSum-finetuned-ar\n\nThis model is a fine-tuned version of csebuetnlp/mT5_multilingual_XLSum on an unknown dataset.",
"## M... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-small-finetuned-cnndm2-wikihow1
This model is a fine-tuned version of [Chikashi/t5-small-finetuned-cnndm1-wikihow1](https://h... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["cnn_dailymail"], "metrics": ["rouge"], "model-index": [{"name": "t5-small-finetuned-cnndm2-wikihow1", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "cnn_dailymail", ... | Chikashi/t5-small-finetuned-cnndm2-wikihow1 | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:cnn_dailymail",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-15T05:14:54+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-cnn_dailymail #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| t5-small-finetuned-cnndm2-wikihow1
==================================
This model is a fine-tuned version of Chikashi/t5-small-finetuned-cnndm1-wikihow1 on the cnn\_dailymail dataset.
It achieves the following results on the evaluation set:
* Loss: 1.6305
* Rouge1: 24.6317
* Rouge2: 11.8655
* Rougel: 20.3598
* Rouge... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1\n* mixed\\_preci... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used dur... |
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. -->
# REA_GenderIdentification_v1
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-ba... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "REA_GenderIdentification_v1", "results": []}]} | malcolm/REA_GenderIdentification_v1 | null | [
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] | null | 2022-04-15T07:23:35+00:00 | [] | [] | TAGS
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|
# REA_GenderIdentification_v1
This model is a fine-tuned version of distilbert-base-uncased on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3366
- Accuracy: 0.8798
- F1: 0.8522
## Model description
More information needed
## Intended uses & limitations
More information ne... | [
"# REA_GenderIdentification_v1\n\nThis model is a fine-tuned version of distilbert-base-uncased on the None dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.3366\n- Accuracy: 0.8798\n- F1: 0.8522",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\... | [
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"# REA_GenderIdentification_v1\n\nThis model is a fine-tuned version of distilbert-base-uncased on the None dataset.\nIt ac... |
text2text-generation | transformers | # Text-Summarizer
## About
An Abstractive text summarizer trained using lstm based sequence to sequence model with attention mechanisim. The attention model is used for generating each word of the summary conditioned on the input sentence.
- Used CNN_DailyMail dataset.
- Code + Deployment : https://www.youtube.com/wa... | {"license": "apache-2.0"} | Saravananofficial/Text_Summarizer | null | [
"transformers",
"tf",
"bart",
"text2text-generation",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-15T08:07:40+00:00 | [] | [] | TAGS
#transformers #tf #bart #text2text-generation #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| # Text-Summarizer
## About
An Abstractive text summarizer trained using lstm based sequence to sequence model with attention mechanisim. The attention model is used for generating each word of the summary conditioned on the input sentence.
- Used CNN_DailyMail dataset.
- Code + Deployment : URL
![IMAGE ALT TEXT HERE]... | [
"# Text-Summarizer",
"## About\n\nAn Abstractive text summarizer trained using lstm based sequence to sequence model with attention mechanisim. The attention model is used for generating each word of the summary conditioned on the input sentence.\n\n- Used CNN_DailyMail dataset.\n- Code + Deployment : URL\n![IMAG... | [
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text-generation | transformers |
# Harry Potter DialoGPT Model | {"tags": ["conversational"]} | sahilnare78/DialogGPT-medium-harrypotter | null | [
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"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-15T08:38:42+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Harry Potter DialoGPT Model | [
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"# Harry Potter DialoGPT Model"
] |
text2text-generation | transformers |
# T5-base-nl36 for Finnish
Pretrained T5 model on Finnish language using a span-based masked language modeling (MLM) objective. T5 was introduced in
[this paper](https://arxiv.org/abs/1910.10683)
and first released at [this page](https://github.com/google-research/text-to-text-transfer-transformer).
**Note:** The Hu... | {"language": ["fi"], "license": "apache-2.0", "tags": ["finnish", "t5", "t5x", "seq2seq"], "datasets": ["Finnish-NLP/mc4_fi_cleaned", "wikipedia"], "inference": false} | Finnish-NLP/t5-base-nl36-finnish | null | [
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"arxiv:2002.05202",
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"license:apache-2.0",
"autotrain_compatible",
"te... | null | 2022-04-15T09:50:33+00:00 | [
"1910.10683",
"2002.05202",
"2109.10686"
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| T5-base-nl36 for Finnish
========================
Pretrained T5 model on Finnish language using a span-based masked language modeling (MLM) objective. T5 was introduced in
this paper
and first released at this page.
Note: The Hugging Face inference widget is deactivated because this model needs a text-to-text fine-... | [
"### How to use\n\n\nHere is how to use this model in PyTorch:\n\n\nand in TensorFlow:",
"### Limitations and bias\n\n\nThe training data used for this model contains a lot of unfiltered content from the internet, which is far from neutral. Therefore, the model can have biased predictions. This bias will also aff... | [
"TAGS\n#transformers #pytorch #jax #tensorboard #t5 #text2text-generation #finnish #t5x #seq2seq #fi #dataset-Finnish-NLP/mc4_fi_cleaned #dataset-wikipedia #arxiv-1910.10683 #arxiv-2002.05202 #arxiv-2109.10686 #license-apache-2.0 #autotrain_compatible #text-generation-inference #region-us \n",
"### How to use\n\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. -->
# bertdbmdzIhate
This model is a fine-tuned version of [dbmdz/bert-base-italian-xxl-cased](https://huggingface.co/dbmdz/bert-base-... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "bertdbmdzIhate", "results": []}]} | GioReg/bertdbmdzIhate | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-15T10:35:59+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# bertdbmdzIhate
This model is a fine-tuned version of dbmdz/bert-base-italian-xxl-cased on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6880
- Accuracy: 0.726
- F1: 0.4170
## Model description
More information needed
## Intended uses & limitations
More information needed... | [
"# bertdbmdzIhate\n\nThis model is a fine-tuned version of dbmdz/bert-base-italian-xxl-cased on the None dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.6880\n- Accuracy: 0.726\n- F1: 0.4170",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMor... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# bertdbmdzIhate\n\nThis model is a fine-tuned version of dbmdz/bert-base-italian-xxl-cased on the None dataset.\nIt achieves the following result... |
unconditional-image-generation | null |
# Generate anime face image using FastGAN
## Model description
[FastGAN model](https://arxiv.org/abs/2101.04775) is a Generative Adversarial Networks (GAN) training on a small amount of high-fidelity images with minimum computing cost. Using a skip-layer channel-wise excitation module and a self-supervised discrimin... | {"license": "mit", "tags": ["huggan", "gan", "unconditional-image-generation"], "datasets": ["huggan/few-shot-anime-face"]} | huggan/fastgan-few-shot-anime-face | null | [
"pytorch",
"huggan",
"gan",
"unconditional-image-generation",
"dataset:huggan/few-shot-anime-face",
"arxiv:2101.04775",
"license:mit",
"has_space",
"region:us"
] | null | 2022-04-15T11:02:27+00:00 | [
"2101.04775"
] | [] | TAGS
#pytorch #huggan #gan #unconditional-image-generation #dataset-huggan/few-shot-anime-face #arxiv-2101.04775 #license-mit #has_space #region-us
|
# Generate anime face image using FastGAN
## Model description
FastGAN model is a Generative Adversarial Networks (GAN) training on a small amount of high-fidelity images with minimum computing cost. Using a skip-layer channel-wise excitation module and a self-supervised discriminator trained as a feature-encoder, t... | [
"# Generate anime face image using FastGAN",
"## Model description\n\nFastGAN model is a Generative Adversarial Networks (GAN) training on a small amount of high-fidelity images with minimum computing cost. Using a skip-layer channel-wise excitation module and a self-supervised discriminator trained as a feature-... | [
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"# Generate anime face image using FastGAN",
"## Model description\n\nFastGAN model is a Generative Adversarial Networks (GAN) training on a small amount of ... |
null | null |
# MyModelName
## Model description
Describe the model here (what it does, what it's used for, etc.)
## Intended uses & limitations
#### How to use
```python
# You can include sample code which will be formatted
```
#### Limitations and bias
Provide examples of latent issues and potential remediations.
## Train... | {"license": "mit", "tags": ["huggan", "gan"]} | Chris1/sim2real-512 | null | [
"pytorch",
"huggan",
"gan",
"license:mit",
"has_space",
"region:us"
] | null | 2022-04-15T11:02:46+00:00 | [] | [] | TAGS
#pytorch #huggan #gan #license-mit #has_space #region-us
|
# MyModelName
## Model description
Describe the model here (what it does, what it's used for, etc.)
## Intended uses & limitations
#### How to use
#### Limitations and bias
Provide examples of latent issues and potential remediations.
## Training data
Describe the data you used to train the model.
If you ini... | [
"# MyModelName",
"## Model description\n\nDescribe the model here (what it does, what it's used for, etc.)",
"## Intended uses & limitations",
"#### How to use",
"#### Limitations and bias\n\nProvide examples of latent issues and potential remediations.",
"## Training data\n\nDescribe the data you used to... | [
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"## Intended uses & limitations",
"#### How to use",
"#### Limitations and bias\n\nProvide examples of latent issues and potenti... |
null | null |
# MyModelName
## Model description
Describe the model here (what it does, what it's used for, etc.)
## Intended uses & limitations
#### How to use
```python
# You can include sample code which will be formatted
```
#### Limitations and bias
Provide examples of latent issues and potential remediations.
## Train... | {"license": "mit", "tags": ["huggan", "gan"]} | Chris1/real2sim-512 | null | [
"pytorch",
"huggan",
"gan",
"license:mit",
"has_space",
"region:us"
] | null | 2022-04-15T11:06:39+00:00 | [] | [] | TAGS
#pytorch #huggan #gan #license-mit #has_space #region-us
|
# MyModelName
## Model description
Describe the model here (what it does, what it's used for, etc.)
## Intended uses & limitations
#### How to use
#### Limitations and bias
Provide examples of latent issues and potential remediations.
## Training data
Describe the data you used to train the model.
If you ini... | [
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"#### How to use",
"#### Limitations and bias\n\nProvide examples of latent issues and potenti... |
unconditional-image-generation | null |
# Generate moon gate image using FastGAN
## Model description
[FastGAN model](https://arxiv.org/abs/2101.04775) is a Generative Adversarial Networks (GAN) training on a small amount of high-fidelity images with minimum computing cost. Using a skip-layer channel-wise excitation module and a self-supervised discrimina... | {"license": "mit", "tags": ["huggan", "gan", "unconditional-image-generation"], "datasets": ["huggan/few-shot-moongate"]} | huggan/fastgan-few-shot-moongate | null | [
"pytorch",
"huggan",
"gan",
"unconditional-image-generation",
"dataset:huggan/few-shot-moongate",
"arxiv:2101.04775",
"license:mit",
"has_space",
"region:us"
] | null | 2022-04-15T11:07:26+00:00 | [
"2101.04775"
] | [] | TAGS
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|
# Generate moon gate image using FastGAN
## Model description
FastGAN model is a Generative Adversarial Networks (GAN) training on a small amount of high-fidelity images with minimum computing cost. Using a skip-layer channel-wise excitation module and a self-supervised discriminator trained as a feature-encoder, th... | [
"# Generate moon gate image using FastGAN",
"## Model description\n\nFastGAN model is a Generative Adversarial Networks (GAN) training on a small amount of high-fidelity images with minimum computing cost. Using a skip-layer channel-wise excitation module and a self-supervised discriminator trained as a feature-e... | [
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"# Generate moon gate image using FastGAN",
"## Model description\n\nFastGAN model is a Generative Adversarial Networks (GAN) training on a small amount of hig... |
image-to-image | null |
# CycleGAN for unpaired image-to-image translation.
## Model description
CycleGAN for unpaired image-to-image translation.
Given two image domains A and B, the following components are trained end2end to translate between such domains:
- A generator A to B, named G_AB conditioned on an image from A
- A g... | {"license": "mit", "tags": ["huggan", "gan", "image-to-image", "huggingnft", "nft", "image", "images"]} | huggingnft/boredapeyachtclub__2__mutant-ape-yacht-club | null | [
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"huggingnft",
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"arxiv:1703.10593",
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"region:us"
] | null | 2022-04-15T11:15:49+00:00 | [
"1703.10593"
] | [] | TAGS
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|
# CycleGAN for unpaired image-to-image translation.
## Model description
CycleGAN for unpaired image-to-image translation.
Given two image domains A and B, the following components are trained end2end to translate between such domains:
- A generator A to B, named G_AB conditioned on an image from A
- A g... | [
"# CycleGAN for unpaired image-to-image translation.",
"## Model description \n\nCycleGAN for unpaired image-to-image translation. \nGiven two image domains A and B, the following components are trained end2end to translate between such domains: \n- A generator A to B, named G_AB conditioned on an image from... | [
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null | null |
# MyModelName
## Model description
Describe the model here (what it does, what it's used for, etc.)
## Intended uses & limitations
#### How to use
```python
# You can include sample code which will be formatted
```
#### Limitations and bias
Provide examples of latent issues and potential remediations.
## Train... | {"license": "mit", "tags": ["huggan", "gan"]} | Chris1/mutant-ape-yacht-club__2__boredapeyachtclub | null | [
"pytorch",
"huggan",
"gan",
"license:mit",
"region:us"
] | null | 2022-04-15T11:17:05+00:00 | [] | [] | TAGS
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|
# MyModelName
## Model description
Describe the model here (what it does, what it's used for, etc.)
## Intended uses & limitations
#### How to use
#### Limitations and bias
Provide examples of latent issues and potential remediations.
## Training data
Describe the data you used to train the model.
If you ini... | [
"# MyModelName",
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"#### How to use",
"#### Limitations and bias\n\nProvide examples of latent issues and potential remediat... |
image-to-image | null |
# CycleGAN for unpaired image-to-image translation.
## Model description
CycleGAN for unpaired image-to-image translation.
Given two image domains A and B, the following components are trained end2end to translate between such domains:
- A generator A to B, named G_AB conditioned on an image from A
- A g... | {"license": "mit", "tags": ["huggan", "gan", "image-to-image", "huggingnft", "nft", "image", "images"]} | huggingnft/mini-mutants__2__boredapeyachtclub | null | [
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"image-to-image",
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"arxiv:1703.10593",
"license:mit",
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] | null | 2022-04-15T11:34:24+00:00 | [
"1703.10593"
] | [] | TAGS
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|
# CycleGAN for unpaired image-to-image translation.
## Model description
CycleGAN for unpaired image-to-image translation.
Given two image domains A and B, the following components are trained end2end to translate between such domains:
- A generator A to B, named G_AB conditioned on an image from A
- A g... | [
"# CycleGAN for unpaired image-to-image translation.",
"## Model description \n\nCycleGAN for unpaired image-to-image translation. \nGiven two image domains A and B, the following components are trained end2end to translate between such domains: \n- A generator A to B, named G_AB conditioned on an image from... | [
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text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-small-finetuned-cnndm2-wikihow2
This model is a fine-tuned version of [Chikashi/t5-small-finetuned-cnndm2-wikihow1](https://h... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["wikihow"], "metrics": ["rouge"], "model-index": [{"name": "t5-small-finetuned-cnndm2-wikihow2", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "wikihow", "type": "wik... | Chikashi/t5-small-finetuned-cnndm2-wikihow2 | null | [
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"dataset:wikihow",
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"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-15T11:41:39+00:00 | [] | [] | TAGS
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| t5-small-finetuned-cnndm2-wikihow2
==================================
This model is a fine-tuned version of Chikashi/t5-small-finetuned-cnndm2-wikihow1 on the wikihow dataset.
It achieves the following results on the evaluation set:
* Loss: 2.3311
* Rouge1: 27.0962
* Rouge2: 10.3575
* Rougel: 23.1099
* Rougelsum: 2... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1\n* mixed\\_preci... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during tr... |
automatic-speech-recognition | transformers |
This model is trained on the PSST Challenge data, with a subset of TIMIT that was augmented using Room Impulse Response (RIR). A file containing the list of TIMIT IDs is in the repository (`timit-ids.txt`)
The model was finetuned on [Wav2vec 2.0 Large, No finetuning](https://github.com/pytorch/fairseq/tree/main/examp... | {"language": ["en"], "license": "apache-2.0", "tags": ["automatic-speech-recognition"]} | birgermoell/psst-fairseq-larger-rir | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-04-15T11:44:14+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #en #license-apache-2.0 #endpoints_compatible #region-us
|
This model is trained on the PSST Challenge data, with a subset of TIMIT that was augmented using Room Impulse Response (RIR). A file containing the list of TIMIT IDs is in the repository ('URL')
The model was finetuned on Wav2vec 2.0 Large, No finetuning, and the results on the validation set were PER: 21\.0%, FER: ... | [] | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #en #license-apache-2.0 #endpoints_compatible #region-us \n"
] |
automatic-speech-recognition | transformers |
This model is trained on the PSST Challenge data, with a subset of TIMIT that was augmented using Room Impulse Response (RIR). A file containing the list of TIMIT IDs is in the repository (`timit-ids.txt`)
The model was finetuned on [Wav2vec 2.0 Base, No finetuning](https://github.com/pytorch/fairseq/tree/main/exampl... | {"language": ["en"], "license": "apache-2.0", "tags": ["automatic-speech-recognition"]} | birgermoell/psst-fairseq-rir | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-04-15T11:46:04+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #en #license-apache-2.0 #endpoints_compatible #region-us
|
This model is trained on the PSST Challenge data, with a subset of TIMIT that was augmented using Room Impulse Response (RIR). A file containing the list of TIMIT IDs is in the repository ('URL')
The model was finetuned on Wav2vec 2.0 Base, No finetuning, and the results on the validation set were PER: 21\.8%, FER: 9... | [] | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #en #license-apache-2.0 #endpoints_compatible #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. -->
# no_need_to_name_this
This model was trained from scratch on an unknown dataset.
## Model description
More information needed
... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "no_need_to_name_this", "results": []}]} | LenaSchmidt/no_need_to_name_this | null | [
"transformers",
"pytorch",
"bert",
"question-answering",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | null | 2022-04-15T12:11:15+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #question-answering #generated_from_trainer #endpoints_compatible #region-us
|
# no_need_to_name_this
This model was trained from scratch on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hype... | [
"# no_need_to_name_this\n\nThis model was trained from scratch on an unknown dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyper... | [
"TAGS\n#transformers #pytorch #bert #question-answering #generated_from_trainer #endpoints_compatible #region-us \n",
"# no_need_to_name_this\n\nThis model was trained from scratch on an unknown dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information n... |
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-xlsr-53_train_data_full
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingfa... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-large-xlsr-53_train_data_full", "results": []}]} | scasutt/wav2vec2-large-xlsr-53_train_data_full | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-04-15T12:23:50+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-large-xlsr-53\_train\_data\_full
=========================================
This model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4168
* Wer: 0.3383
Model description
-----------------
More informatio... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #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: 16\n* eval\\_b... |
unconditional-image-generation | transformers |
# Hugging NFT: cryptoadz-by-gremplin
## Disclaimer
All rights belong to their owners. Models and datasets can be removed from the site at the request of the copyright
holder.
## Model description
LightWeight GAN model for unconditional generation.
NFT collection available [here](https://opensea.io/collection/cryp... | {"license": "mit", "tags": ["huggingnft", "nft", "huggan", "gan", "image", "images", "unconditional-image-generation"], "datasets": ["huggingnft/cryptoadz-by-gremplin"]} | huggingnft/cryptoadz-by-gremplin | null | [
"transformers",
"huggingnft",
"nft",
"huggan",
"gan",
"image",
"images",
"unconditional-image-generation",
"dataset:huggingnft/cryptoadz-by-gremplin",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-04-15T12:29:22+00:00 | [] | [] | TAGS
#transformers #huggingnft #nft #huggan #gan #image #images #unconditional-image-generation #dataset-huggingnft/cryptoadz-by-gremplin #license-mit #endpoints_compatible #region-us
|
# Hugging NFT: cryptoadz-by-gremplin
## Disclaimer
All rights belong to their owners. Models and datasets can be removed from the site at the request of the copyright
holder.
## Model description
LightWeight GAN model for unconditional generation.
NFT collection available here.
Dataset is available here.
Check ... | [
"# Hugging NFT: cryptoadz-by-gremplin",
"## Disclaimer\n\nAll rights belong to their owners. Models and datasets can be removed from the site at the request of the copyright\nholder.",
"## Model description\n\nLightWeight GAN model for unconditional generation.\n\nNFT collection available here.\n\nDataset is av... | [
"TAGS\n#transformers #huggingnft #nft #huggan #gan #image #images #unconditional-image-generation #dataset-huggingnft/cryptoadz-by-gremplin #license-mit #endpoints_compatible #region-us \n",
"# Hugging NFT: cryptoadz-by-gremplin",
"## Disclaimer\n\nAll rights belong to their owners. Models and datasets can be r... |
null | null |
# **CRUST - RELEASED** (Chungus Related Uberduck's Speech toy)
# Welcome to Crust 🍕⭕
Crust is a 168 speaker model based on uberduck's pipeline. We've noticed that having multiple speakers instead of having one speaker, improves the performance of the model and makes it be able to synthesize comparable results with o... | {"language": ["en"], "license": "apache-2.0", "tags": ["synthesis", "speech", "speech synthesis"], "datasets": ["gathered from Uberduck's discord server, put together by Crust."]} | Pikachu/Crust | null | [
"synthesis",
"speech",
"speech synthesis",
"en",
"license:apache-2.0",
"region:us"
] | null | 2022-04-15T12:32:39+00:00 | [] | [
"en"
] | TAGS
#synthesis #speech #speech synthesis #en #license-apache-2.0 #region-us
|
# CRUST - RELEASED (Chungus Related Uberduck's Speech toy)
# Welcome to Crust ⭕
Crust is a 168 speaker model based on uberduck's pipeline. We've noticed that having multiple speakers instead of having one speaker, improves the performance of the model and makes it be able to synthesize comparable results with only 1 ... | [
"# CRUST - RELEASED (Chungus Related Uberduck's Speech toy)",
"# Welcome to Crust ⭕\n\nCrust is a 168 speaker model based on uberduck's pipeline. We've noticed that having multiple speakers instead of having one speaker, improves the performance of the model and makes it be able to synthesize comparable results w... | [
"TAGS\n#synthesis #speech #speech synthesis #en #license-apache-2.0 #region-us \n",
"# CRUST - RELEASED (Chungus Related Uberduck's Speech toy)",
"# Welcome to Crust ⭕\n\nCrust is a 168 speaker model based on uberduck's pipeline. We've noticed that having multiple speakers instead of having one speaker, improve... |
unconditional-image-generation | pytorch |
## Model description
SN-GAN implementation with PyTorch-Lightning to generate Documents.
## Generated samples
<img src="https://raw.githubusercontent.com/ChainYo/docugan/master/documents_samples.png" width="400" height="1200">
Project repository: [DocuGAN](https://github.com/ChainYo/docugan).
## Usage
You can se... | {"license": "mit", "library_name": "pytorch", "tags": ["gan", "sngan", "huggan", "unconditional-image-generation"], "datasets": ["ChainYo/rvl-cdip-invoice"]} | chainyo/DocuGAN | null | [
"pytorch",
"gan",
"sngan",
"huggan",
"unconditional-image-generation",
"dataset:ChainYo/rvl-cdip-invoice",
"license:mit",
"region:us"
] | null | 2022-04-15T12:33:21+00:00 | [] | [] | TAGS
#pytorch #gan #sngan #huggan #unconditional-image-generation #dataset-ChainYo/rvl-cdip-invoice #license-mit #region-us
|
## Model description
SN-GAN implementation with PyTorch-Lightning to generate Documents.
## Generated samples
<img src="URL width="400" height="1200">
Project repository: DocuGAN.
## Usage
You can see the tool to generate document on HuggingFace by trying the space demo.
## Training data
For training, I used t... | [
"## Model description\n\nSN-GAN implementation with PyTorch-Lightning to generate Documents.",
"## Generated samples\n\n<img src=\"URL width=\"400\" height=\"1200\">\n\nProject repository: DocuGAN.",
"## Usage\n\nYou can see the tool to generate document on HuggingFace by trying the space demo.",
"## Training... | [
"TAGS\n#pytorch #gan #sngan #huggan #unconditional-image-generation #dataset-ChainYo/rvl-cdip-invoice #license-mit #region-us \n",
"## Model description\n\nSN-GAN implementation with PyTorch-Lightning to generate Documents.",
"## Generated samples\n\n<img src=\"URL width=\"400\" height=\"1200\">\n\nProject repo... |
fill-mask | transformers |
Conversion script is available at this [link](https://github.com/ccdv-ai/convert_checkpoint_to_lsg).
# LSG model
**Transformers >= 4.36.1**\
**This model relies on a custom modeling file, you need to add trust_remote_code=True**\
**See [\#13467](https://github.com/huggingface/transformers/pull/13467)**
LSG ArXiv [p... | {"language": "fr", "tags": ["camembert", "long context"], "pipeline_tag": "fill-mask"} | ccdv/lsg-distilcamembert-base-4096 | null | [
"transformers",
"pytorch",
"camembert",
"fill-mask",
"long context",
"custom_code",
"fr",
"arxiv:2210.15497",
"autotrain_compatible",
"region:us"
] | null | 2022-04-15T12:45:32+00:00 | [
"2210.15497"
] | [
"fr"
] | TAGS
#transformers #pytorch #camembert #fill-mask #long context #custom_code #fr #arxiv-2210.15497 #autotrain_compatible #region-us
|
Conversion script is available at this link.
# LSG model
Transformers >= 4.36.1\
This model relies on a custom modeling file, you need to add trust_remote_code=True\
See \#13467
LSG ArXiv paper. \
Github/conversion script is available at this link.
* Usage
* Parameters
* Sparse selection type
* Tasks
* Training gl... | [
"# LSG model \nTransformers >= 4.36.1\\\nThis model relies on a custom modeling file, you need to add trust_remote_code=True\\\nSee \\#13467\n\nLSG ArXiv paper. \\\nGithub/conversion script is available at this link.\n\n* Usage\n* Parameters\n* Sparse selection type\n* Tasks\n* Training global tokens\n\nThis model ... | [
"TAGS\n#transformers #pytorch #camembert #fill-mask #long context #custom_code #fr #arxiv-2210.15497 #autotrain_compatible #region-us \n",
"# LSG model \nTransformers >= 4.36.1\\\nThis model relies on a custom modeling file, you need to add trust_remote_code=True\\\nSee \\#13467\n\nLSG ArXiv paper. \\\nGithub/con... |
fill-mask | transformers |
# LSG model
**Transformers >= 4.36.1**\
**This model relies on a custom modeling file, you need to add trust_remote_code=True**\
**See [\#13467](https://github.com/huggingface/transformers/pull/13467)**
LSG ArXiv [paper](https://arxiv.org/abs/2210.15497). \
Github/conversion script is available at this [link](https:... | {"language": "en", "tags": ["roberta", "long context"], "pipeline_tag": "fill-mask"} | ccdv/lsg-distilroberta-base-4096 | null | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"long context",
"custom_code",
"en",
"arxiv:2210.15497",
"autotrain_compatible",
"region:us"
] | null | 2022-04-15T12:50:58+00:00 | [
"2210.15497"
] | [
"en"
] | TAGS
#transformers #pytorch #roberta #fill-mask #long context #custom_code #en #arxiv-2210.15497 #autotrain_compatible #region-us
|
# LSG model
Transformers >= 4.36.1\
This model relies on a custom modeling file, you need to add trust_remote_code=True\
See \#13467
LSG ArXiv paper. \
Github/conversion script is available at this link.
* Usage
* Parameters
* Sparse selection type
* Tasks
* Training global tokens
This model is a small version of ... | [
"# LSG model \nTransformers >= 4.36.1\\\nThis model relies on a custom modeling file, you need to add trust_remote_code=True\\\nSee \\#13467\n\nLSG ArXiv paper. \\\nGithub/conversion script is available at this link.\n\n* Usage\n* Parameters\n* Sparse selection type\n* Tasks\n* Training global tokens\n\nThis model ... | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #long context #custom_code #en #arxiv-2210.15497 #autotrain_compatible #region-us \n",
"# LSG model \nTransformers >= 4.36.1\\\nThis model relies on a custom modeling file, you need to add trust_remote_code=True\\\nSee \\#13467\n\nLSG ArXiv paper. \\\nGithub/conve... |
unconditional-image-generation | null |
# Generate universal image using FastGAN
## Model description
[FastGAN model](https://arxiv.org/abs/2101.04775) is a Generative Adversarial Networks (GAN) training on a small amount of high-fidelity images with minimum computing cost. Using a skip-layer channel-wise excitation module and a self-supervised discrimina... | {"license": "mit", "tags": ["huggan", "gan", "unconditional-image-generation"], "datasets": ["huggan/few-shot-universe"]} | huggan/fastgan-few-shot-universe | null | [
"pytorch",
"huggan",
"gan",
"unconditional-image-generation",
"dataset:huggan/few-shot-universe",
"arxiv:2101.04775",
"license:mit",
"has_space",
"region:us"
] | null | 2022-04-15T12:55:24+00:00 | [
"2101.04775"
] | [] | TAGS
#pytorch #huggan #gan #unconditional-image-generation #dataset-huggan/few-shot-universe #arxiv-2101.04775 #license-mit #has_space #region-us
|
# Generate universal image using FastGAN
## Model description
FastGAN model is a Generative Adversarial Networks (GAN) training on a small amount of high-fidelity images with minimum computing cost. Using a skip-layer channel-wise excitation module and a self-supervised discriminator trained as a feature-encoder, th... | [
"# Generate universal image using FastGAN",
"## Model description\n\nFastGAN model is a Generative Adversarial Networks (GAN) training on a small amount of high-fidelity images with minimum computing cost. Using a skip-layer channel-wise excitation module and a self-supervised discriminator trained as a feature-e... | [
"TAGS\n#pytorch #huggan #gan #unconditional-image-generation #dataset-huggan/few-shot-universe #arxiv-2101.04775 #license-mit #has_space #region-us \n",
"# Generate universal image using FastGAN",
"## Model description\n\nFastGAN model is a Generative Adversarial Networks (GAN) training on a small amount of hig... |
image-classification | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# YKXBCi/vit-base-patch16-224-in21k-euroSat
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingfac... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "YKXBCi/vit-base-patch16-224-in21k-euroSat", "results": []}]} | YKXBCi/vit-base-patch16-224-in21k-euroSat | null | [
"transformers",
"tf",
"tensorboard",
"vit",
"image-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-15T13:35:58+00:00 | [] | [] | TAGS
#transformers #tf #tensorboard #vit #image-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| YKXBCi/vit-base-patch16-224-in21k-euroSat
=========================================
This model is a fine-tuned version of google/vit-base-patch16-224-in21k on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.0495
* Train Accuracy: 0.9948
* Train Top-3-accuracy: 0.9999
* V... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'inner\\_optimizer': {'class\\_name': 'AdamWeightDecay', 'config': {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 3e-05, 'decay\\... | [
"TAGS\n#transformers #tf #tensorboard #vit #image-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'inner\\_optimizer': {'clas... |
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-MIR_ST500_ASR_109
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/w... | {"license": "apache-2.0", "tags": ["automatic-speech-recognition", "/workspace/datasets/datasets/MIR_ST500/MIR_ST500.py", "generated_from_trainer"], "datasets": ["mir_st500"], "model-index": [{"name": "wav2vec2-base-MIR_ST500_ASR_109", "results": []}]} | gary109/wav2vec2-base-MIR_ST500_ASR_109 | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"/workspace/datasets/datasets/MIR_ST500/MIR_ST500.py",
"generated_from_trainer",
"dataset:mir_st500",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-04-15T13:52:50+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #/workspace/datasets/datasets/MIR_ST500/MIR_ST500.py #generated_from_trainer #dataset-mir_st500 #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-base-MIR\_ST500\_ASR\_109
==================================
This model is a fine-tuned version of facebook/wav2vec2-base on the /WORKSPACE/DATASETS/DATASETS/MIR\_ST500/MIR\_ST500.PY - ASR dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6452
* Wer: 0.3732
Model description
---... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* distributed\\_type: multi-GPU\n* num\\_devices: 2\n* total\\_train\\_batch\\_size: 16\n* total\\_eval\\_batch\\_size: 16\n* op... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #/workspace/datasets/datasets/MIR_ST500/MIR_ST500.py #generated_from_trainer #dataset-mir_st500 #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used du... |
text-classification | transformers |
### Description
This model is a fine-tuned version of [BETO (spanish bert)](https://huggingface.co/dccuchile/bert-base-spanish-wwm-uncased) that has been trained on the *Datathon Against Racism* dataset (2022)
We performed several experiments that will be described in the upcoming paper "Estimating Ground Truth in a ... | {"language": "es", "license": "mit", "widget": [{"text": "y porqu\u00e9 es lo que hay que hacer con los menas y con los adultos tambi\u00e9n!!!! NO a los inmigrantes ilegales!!!!"}]} | MartinoMensio/racism-models-raw-label-epoch-1 | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"es",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-15T14:41:29+00:00 | [] | [
"es"
] | TAGS
#transformers #pytorch #bert #text-classification #es #license-mit #autotrain_compatible #endpoints_compatible #region-us
| ### Description
This model is a fine-tuned version of BETO (spanish bert) that has been trained on the *Datathon Against Racism* dataset (2022)
We performed several experiments that will be described in the upcoming paper "Estimating Ground Truth in a Low-labelled Data Regime:A Study of Racism Detection in Spanish"... | [
"### Description\n\n\nThis model is a fine-tuned version of BETO (spanish bert) that has been trained on the *Datathon Against Racism* dataset (2022)\n\n\nWe performed several experiments that will be described in the upcoming paper \"Estimating Ground Truth in a Low-labelled Data Regime:A Study of Racism Detection... | [
"TAGS\n#transformers #pytorch #bert #text-classification #es #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Description\n\n\nThis model is a fine-tuned version of BETO (spanish bert) that has been trained on the *Datathon Against Racism* dataset (2022)\n\n\nWe performed several expe... |
text-classification | transformers |
### Description
This model is a fine-tuned version of [BETO (spanish bert)](https://huggingface.co/dccuchile/bert-base-spanish-wwm-uncased) that has been trained on the *Datathon Against Racism* dataset (2022)
We performed several experiments that will be described in the upcoming paper "Estimating Ground Truth in a ... | {"language": "es", "license": "mit", "widget": [{"text": "y porqu\u00e9 es lo que hay que hacer con los menas y con los adultos tambi\u00e9n!!!! NO a los inmigrantes ilegales!!!!"}]} | MartinoMensio/racism-models-raw-label-epoch-2 | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"es",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-15T15:04:35+00:00 | [] | [
"es"
] | TAGS
#transformers #pytorch #bert #text-classification #es #license-mit #autotrain_compatible #endpoints_compatible #region-us
| ### Description
This model is a fine-tuned version of BETO (spanish bert) that has been trained on the *Datathon Against Racism* dataset (2022)
We performed several experiments that will be described in the upcoming paper "Estimating Ground Truth in a Low-labelled Data Regime:A Study of Racism Detection in Spanish"... | [
"### Description\n\n\nThis model is a fine-tuned version of BETO (spanish bert) that has been trained on the *Datathon Against Racism* dataset (2022)\n\n\nWe performed several experiments that will be described in the upcoming paper \"Estimating Ground Truth in a Low-labelled Data Regime:A Study of Racism Detection... | [
"TAGS\n#transformers #pytorch #bert #text-classification #es #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Description\n\n\nThis model is a fine-tuned version of BETO (spanish bert) that has been trained on the *Datathon Against Racism* dataset (2022)\n\n\nWe performed several expe... |
text-classification | transformers |
### Description
This model is a fine-tuned version of [BETO (spanish bert)](https://huggingface.co/dccuchile/bert-base-spanish-wwm-uncased) that has been trained on the *Datathon Against Racism* dataset (2022)
We performed several experiments that will be described in the upcoming paper "Estimating Ground Truth in a ... | {"language": "es", "license": "mit", "widget": [{"text": "y porqu\u00e9 es lo que hay que hacer con los menas y con los adultos tambi\u00e9n!!!! NO a los inmigrantes ilegales!!!!"}]} | MartinoMensio/racism-models-raw-label-epoch-3 | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"es",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-15T15:10:04+00:00 | [] | [
"es"
] | TAGS
#transformers #pytorch #bert #text-classification #es #license-mit #autotrain_compatible #endpoints_compatible #region-us
| ### Description
This model is a fine-tuned version of BETO (spanish bert) that has been trained on the *Datathon Against Racism* dataset (2022)
We performed several experiments that will be described in the upcoming paper "Estimating Ground Truth in a Low-labelled Data Regime:A Study of Racism Detection in Spanish"... | [
"### Description\n\n\nThis model is a fine-tuned version of BETO (spanish bert) that has been trained on the *Datathon Against Racism* dataset (2022)\n\n\nWe performed several experiments that will be described in the upcoming paper \"Estimating Ground Truth in a Low-labelled Data Regime:A Study of Racism Detection... | [
"TAGS\n#transformers #pytorch #bert #text-classification #es #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Description\n\n\nThis model is a fine-tuned version of BETO (spanish bert) that has been trained on the *Datathon Against Racism* dataset (2022)\n\n\nWe performed several expe... |
unconditional-image-generation | null |
# Generate grumpy cat face using FastGAN
## Model description
[FastGAN model](https://arxiv.org/abs/2101.04775) is a Generative Adversarial Networks (GAN) training on a small amount of high-fidelity images with minimum computing cost. Using a skip-layer channel-wise excitation module and a self-supervised discrimina... | {"license": "mit", "tags": ["huggan", "gan", "unconditional-image-generation"], "datasets": ["huggan/few-shot-grumpy-cat"]} | huggan/fastgan-few-shot-grumpy-cat | null | [
"pytorch",
"huggan",
"gan",
"unconditional-image-generation",
"dataset:huggan/few-shot-grumpy-cat",
"arxiv:2101.04775",
"license:mit",
"has_space",
"region:us"
] | null | 2022-04-15T15:11:14+00:00 | [
"2101.04775"
] | [] | TAGS
#pytorch #huggan #gan #unconditional-image-generation #dataset-huggan/few-shot-grumpy-cat #arxiv-2101.04775 #license-mit #has_space #region-us
|
# Generate grumpy cat face using FastGAN
## Model description
FastGAN model is a Generative Adversarial Networks (GAN) training on a small amount of high-fidelity images with minimum computing cost. Using a skip-layer channel-wise excitation module and a self-supervised discriminator trained as a feature-encoder, th... | [
"# Generate grumpy cat face using FastGAN",
"## Model description\n\nFastGAN model is a Generative Adversarial Networks (GAN) training on a small amount of high-fidelity images with minimum computing cost. Using a skip-layer channel-wise excitation module and a self-supervised discriminator trained as a feature-e... | [
"TAGS\n#pytorch #huggan #gan #unconditional-image-generation #dataset-huggan/few-shot-grumpy-cat #arxiv-2101.04775 #license-mit #has_space #region-us \n",
"# Generate grumpy cat face using FastGAN",
"## Model description\n\nFastGAN model is a Generative Adversarial Networks (GAN) training on a small amount of h... |
text-classification | transformers |
### Description
This model is a fine-tuned version of [BETO (spanish bert)](https://huggingface.co/dccuchile/bert-base-spanish-wwm-uncased) that has been trained on the *Datathon Against Racism* dataset (2022)
We performed several experiments that will be described in the upcoming paper "Estimating Ground Truth in a ... | {"language": "es", "license": "mit", "widget": [{"text": "y porqu\u00e9 es lo que hay que hacer con los menas y con los adultos tambi\u00e9n!!!! NO a los inmigrantes ilegales!!!!"}]} | MartinoMensio/racism-models-raw-label-epoch-4 | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"es",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-15T15:12:31+00:00 | [] | [
"es"
] | TAGS
#transformers #pytorch #bert #text-classification #es #license-mit #autotrain_compatible #endpoints_compatible #region-us
| ### Description
This model is a fine-tuned version of BETO (spanish bert) that has been trained on the *Datathon Against Racism* dataset (2022)
We performed several experiments that will be described in the upcoming paper "Estimating Ground Truth in a Low-labelled Data Regime:A Study of Racism Detection in Spanish"... | [
"### Description\n\n\nThis model is a fine-tuned version of BETO (spanish bert) that has been trained on the *Datathon Against Racism* dataset (2022)\n\n\nWe performed several experiments that will be described in the upcoming paper \"Estimating Ground Truth in a Low-labelled Data Regime:A Study of Racism Detection... | [
"TAGS\n#transformers #pytorch #bert #text-classification #es #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Description\n\n\nThis model is a fine-tuned version of BETO (spanish bert) that has been trained on the *Datathon Against Racism* dataset (2022)\n\n\nWe performed several expe... |
null | null | # Anime2Sketch
- https://github.com/Mukosame/Anime2Sketch
- https://drive.google.com/drive/folders/1Srf-WYUixK0wiUddc9y3pNKHHno5PN6R
| {} | public-data/Anime2Sketch | null | [
"has_space",
"region:us"
] | null | 2022-04-15T15:12:54+00:00 | [] | [] | TAGS
#has_space #region-us
| # Anime2Sketch
- URL
- URL
| [
"# Anime2Sketch\n\n- URL\n - URL"
] | [
"TAGS\n#has_space #region-us \n",
"# Anime2Sketch\n\n- URL\n - URL"
] |
text-classification | transformers |
### Description
This model is a fine-tuned version of [BETO (spanish bert)](https://huggingface.co/dccuchile/bert-base-spanish-wwm-uncased) that has been trained on the *Datathon Against Racism* dataset (2022)
We performed several experiments that will be described in the upcoming paper "Estimating Ground Truth in a ... | {"language": "es", "license": "mit", "widget": [{"text": "y porqu\u00e9 es lo que hay que hacer con los menas y con los adultos tambi\u00e9n!!!! NO a los inmigrantes ilegales!!!!"}]} | MartinoMensio/racism-models-regression-w-m-vote-epoch-1 | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"es",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-15T15:15:44+00:00 | [] | [
"es"
] | TAGS
#transformers #pytorch #bert #text-classification #es #license-mit #autotrain_compatible #endpoints_compatible #region-us
| ### Description
This model is a fine-tuned version of BETO (spanish bert) that has been trained on the *Datathon Against Racism* dataset (2022)
We performed several experiments that will be described in the upcoming paper "Estimating Ground Truth in a Low-labelled Data Regime:A Study of Racism Detection in Spanish"... | [
"### Description\n\n\nThis model is a fine-tuned version of BETO (spanish bert) that has been trained on the *Datathon Against Racism* dataset (2022)\n\n\nWe performed several experiments that will be described in the upcoming paper \"Estimating Ground Truth in a Low-labelled Data Regime:A Study of Racism Detection... | [
"TAGS\n#transformers #pytorch #bert #text-classification #es #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Description\n\n\nThis model is a fine-tuned version of BETO (spanish bert) that has been trained on the *Datathon Against Racism* dataset (2022)\n\n\nWe performed several expe... |
text-classification | transformers |
### Description
This model is a fine-tuned version of [BETO (spanish bert)](https://huggingface.co/dccuchile/bert-base-spanish-wwm-uncased) that has been trained on the *Datathon Against Racism* dataset (2022)
We performed several experiments that will be described in the upcoming paper "Estimating Ground Truth in a ... | {"language": "es", "license": "mit", "widget": [{"text": "y porqu\u00e9 es lo que hay que hacer con los menas y con los adultos tambi\u00e9n!!!! NO a los inmigrantes ilegales!!!!"}]} | MartinoMensio/racism-models-regression-w-m-vote-epoch-2 | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"es",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-15T15:18:45+00:00 | [] | [
"es"
] | TAGS
#transformers #pytorch #bert #text-classification #es #license-mit #autotrain_compatible #endpoints_compatible #region-us
| ### Description
This model is a fine-tuned version of BETO (spanish bert) that has been trained on the *Datathon Against Racism* dataset (2022)
We performed several experiments that will be described in the upcoming paper "Estimating Ground Truth in a Low-labelled Data Regime:A Study of Racism Detection in Spanish"... | [
"### Description\n\n\nThis model is a fine-tuned version of BETO (spanish bert) that has been trained on the *Datathon Against Racism* dataset (2022)\n\n\nWe performed several experiments that will be described in the upcoming paper \"Estimating Ground Truth in a Low-labelled Data Regime:A Study of Racism Detection... | [
"TAGS\n#transformers #pytorch #bert #text-classification #es #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Description\n\n\nThis model is a fine-tuned version of BETO (spanish bert) that has been trained on the *Datathon Against Racism* dataset (2022)\n\n\nWe performed several expe... |
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