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fill-mask | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# SimpleDataset
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the None ... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "SimpleDataset", "results": []}]} | DioLiu/SimpleDataset | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-22T06:30:44+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| SimpleDataset
=============
This model is a fine-tuned version of distilroberta-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 3.6762
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More informati... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #roberta #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: ... |
null | null | # StyleGAN-Human
- https://arxiv.org/abs/2204.11823
- https://github.com/stylegan-human/StyleGAN-Human
- weights
- https://drive.google.com/file/d/1h-R-IV-INGdPEzj4P9ml6JTEvihuNgLX/view
- https://drive.google.com/file/d/1FlAb1rYa0r_--Zj_ML8e6shmaF28hQb5/view
- https://drive.google.com/file/d/1dlFEHbu-WzQWJ... | {} | public-data/StyleGAN-Human | null | [
"arxiv:2204.11823",
"has_space",
"region:us"
] | null | 2022-04-22T06:53:56+00:00 | [
"2204.11823"
] | [] | TAGS
#arxiv-2204.11823 #has_space #region-us
| # StyleGAN-Human
- URL
- URL
- weights
- URL
- URL
- URL
| [
"# StyleGAN-Human\n\n- URL\n- URL\n- weights\n - URL\n - URL\n - URL"
] | [
"TAGS\n#arxiv-2204.11823 #has_space #region-us \n",
"# StyleGAN-Human\n\n- URL\n- URL\n- weights\n - URL\n - URL\n - URL"
] |
translation | transformers |
# t5-base-36L-ccmatrix-multi
A [t5-base-36L-dutch-english-cased](https://huggingface.co/yhavinga/t5-base-36L-dutch-english-cased) model finetuned for Dutch to English and English to Dutch translation on the CCMatrix dataset.
Evaluation metrics of this model are listed in the **Translation models** section below.
You... | {"language": ["nl", "en"], "license": "apache-2.0", "tags": ["t5", "translation", "seq2seq"], "datasets": ["yhavinga/mc4_nl_cleaned", "yhavinga/ccmatrix"], "pipeline_tag": "translation", "widget": [{"text": "It is a painful and tragic spectacle that rises before me: I have drawn back the curtain from the rottenness of ... | yhavinga/t5-base-36L-ccmatrix-multi | null | [
"transformers",
"pytorch",
"jax",
"tensorboard",
"t5",
"text2text-generation",
"translation",
"seq2seq",
"nl",
"en",
"dataset:yhavinga/mc4_nl_cleaned",
"dataset:yhavinga/ccmatrix",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inf... | null | 2022-04-22T06:56:31+00:00 | [] | [
"nl",
"en"
] | TAGS
#transformers #pytorch #jax #tensorboard #t5 #text2text-generation #translation #seq2seq #nl #en #dataset-yhavinga/mc4_nl_cleaned #dataset-yhavinga/ccmatrix #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| t5-base-36L-ccmatrix-multi
==========================
A t5-base-36L-dutch-english-cased model finetuned for Dutch to English and English to Dutch translation on the CCMatrix dataset.
Evaluation metrics of this model are listed in the Translation models section below.
You can use this model directly with a pipeline ... | [] | [
"TAGS\n#transformers #pytorch #jax #tensorboard #t5 #text2text-generation #translation #seq2seq #nl #en #dataset-yhavinga/mc4_nl_cleaned #dataset-yhavinga/ccmatrix #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n"
] |
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. -->
# Sultannn/bert-base-ft-pos-xtreme
This model is a fine-tuned version of [indobenchmark/indobert-base-p1](https://huggingface.co/indoben... | {"license": "mit", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "Sultannn/bert-base-ft-pos-xtreme", "results": []}]} | Sultannn/bert-base-ft-pos-xtreme | null | [
"transformers",
"tf",
"bert",
"token-classification",
"generated_from_keras_callback",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-22T06:58:51+00:00 | [] | [] | TAGS
#transformers #tf #bert #token-classification #generated_from_keras_callback #license-mit #autotrain_compatible #endpoints_compatible #region-us
| Sultannn/bert-base-ft-pos-xtreme
================================
This model is a fine-tuned version of indobenchmark/indobert-base-p1 on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.1518
* Validation Loss: 0.2837
* Epoch: 3
Model description
-----------------
Mor... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'WarmUp', 'config': {'initial\\_learning\\_rate': 3e-05, 'decay\\_schedule\\_fn': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_le... | [
"TAGS\n#transformers #tf #bert #token-classification #generated_from_keras_callback #license-mit #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': ... |
token-classification | spacy | Hungarian word vectors for HuSpaCy.
The model is trained on the Hungarian Webcorpus 2.0 using floret with the following hyperparameters: `floret cbow -dim 300 -mode floret -bucket 200000 -minn 4 -maxn 6 -minCount 100 -neg 10 -hashCount 2 -lr 0.01 -thread 70 -epoch 40`
Vectors are published in fasttext and floret for... | {"language": ["hu"], "license": "cc-by-sa-4.0", "tags": ["spacy", "floret", "fasttext", "feature-extraction", "token-classification"]} | huspacy/hu_vectors_web_lg | null | [
"spacy",
"floret",
"fasttext",
"feature-extraction",
"token-classification",
"hu",
"license:cc-by-sa-4.0",
"model-index",
"region:us"
] | null | 2022-04-22T07:01:48+00:00 | [] | [
"hu"
] | TAGS
#spacy #floret #fasttext #feature-extraction #token-classification #hu #license-cc-by-sa-4.0 #model-index #region-us
| Hungarian word vectors for HuSpaCy.
The model is trained on the Hungarian Webcorpus 2.0 using floret with the following hyperparameters: 'floret cbow -dim 300 -mode floret -bucket 200000 -minn 4 -maxn 6 -minCount 100 -neg 10 -hashCount 2 -lr 0.01 -thread 70 -epoch 40'
Vectors are published in fasttext and floret fo... | [
"### Accuracy"
] | [
"TAGS\n#spacy #floret #fasttext #feature-extraction #token-classification #hu #license-cc-by-sa-4.0 #model-index #region-us \n",
"### Accuracy"
] |
fill-mask | transformers |
# pytorch 代码
https://github.com/JunnYu/GAU-alpha-pytorch
# bert4keras代码
https://github.com/ZhuiyiTechnology/GAU-alpha
# Install
```bash
pip install git+https://github.com/JunnYu/GAU-alpha-pytorch.git
or
pip install gau_alpha
```
## 评测对比
### CLUE-dev榜单分类任务结果，base版本。
| | iflytek | tnews | afqmc | cmnli | ocn... | {"language": "zh", "tags": ["gau alpha", "torch"], "inference": false} | junnyu/chinese_GAU-alpha-char_L-24_H-768 | null | [
"transformers",
"pytorch",
"gau_alpha",
"fill-mask",
"gau alpha",
"torch",
"zh",
"autotrain_compatible",
"region:us"
] | null | 2022-04-22T07:03:14+00:00 | [] | [
"zh"
] | TAGS
#transformers #pytorch #gau_alpha #fill-mask #gau alpha #torch #zh #autotrain_compatible #region-us
| pytorch 代码
==========
URL
bert4keras代码
============
URL
Install
=======
评测对比
----
### CLUE-dev榜单分类任务结果，base版本。
### CLUE-test榜单分类任务结果，base版本。
### CLUE-dev集榜单阅读理解和NER结果
### 注：
* 其中RoFormerV2\*表示的是未进行多任务学习的RoFormerV2模型，该模型苏神并未开源，感谢苏神的提醒。
* 其中不带有pytorch后缀结果都是从GAU-alpha仓库复制过来的。
* 其中带有pytorch后缀的结果都是自己训... | [
"### CLUE-dev榜单分类任务结果，base版本。",
"### CLUE-test榜单分类任务结果，base版本。",
"### CLUE-dev集榜单阅读理解和NER结果",
"### 注：\n\n\n* 其中RoFormerV2\\*表示的是未进行多任务学习的RoFormerV2模型，该模型苏神并未开源，感谢苏神的提醒。\n* 其中不带有pytorch后缀结果都是从GAU-alpha仓库复制过来的。\n* 其中带有pytorch后缀的结果都是自己训练得出的。\n\n\nUsage\n=====\n\n\nReference\n=========\n\n\nBibtex："
] | [
"TAGS\n#transformers #pytorch #gau_alpha #fill-mask #gau alpha #torch #zh #autotrain_compatible #region-us \n",
"### CLUE-dev榜单分类任务结果，base版本。",
"### CLUE-test榜单分类任务结果，base版本。",
"### CLUE-dev集榜单阅读理解和NER结果",
"### 注：\n\n\n* 其中RoFormerV2\\*表示的是未进行多任务学习的RoFormerV2模型，该模型苏神并未开源，感谢苏神的提醒。\n* 其中不带有pytorch后缀结果都是从GAU-a... |
null | null |
This model was created using GPT-2 as a base, and fine-tuned upon a dataset of elementary school problems requiring logic and reasoning.
Requires Pytorch
How to use to infer text
```python
from transformers import AutoTokenizer, AutoModelForCasualLM
import torch
type = "gpt2-large"
tokenizer = AutoTokenizer.from_pr... | {"inference": {"parameters": {"temperature": 0.5}}, "widget": {"text": "A courier received 50 packages yesterday and twice as many today. All of these should be delivered tomorrow. How many packages should be delivered tomorrow?"}} | Leli1024/GPT2-ChainOfThought | null | [
"region:us"
] | null | 2022-04-22T07:26:24+00:00 | [] | [] | TAGS
#region-us
|
This model was created using GPT-2 as a base, and fine-tuned upon a dataset of elementary school problems requiring logic and reasoning.
Requires Pytorch
How to use to infer text
| [] | [
"TAGS\n#region-us \n"
] |
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. -->
# bert-base-multilingual-cased-tuned-smartcat
This model is a fine-tuned version of [bert-base-multilingual-cased](https://hugging... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "bert-base-multilingual-cased-tuned-smartcat", "results": []}]} | steysie/bert-base-multilingual-cased-tuned-smartcat | null | [
"transformers",
"pytorch",
"bert",
"text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-22T07:27:20+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| bert-base-multilingual-cased-tuned-smartcat
===========================================
This model is a fine-tuned version of bert-base-multilingual-cased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0000
Model description
-----------------
More information needed
In... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Traini... | [
"TAGS\n#transformers #pytorch #bert #text-generation #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* eval\... |
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. -->
# albert-large-v2-finetuned-ner_with_callbacks
This model is a fine-tuned version of [albert-large-v2](https://huggingface.co/albe... | {"language": ["en"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["surrey-nlp/PLOD-unfiltered"], "metrics": ["precision", "recall", "f1", "accuracy"], "widget": [{"text": "Light dissolved inorganic carbon (DIC) resulting from the oxidation of hydrocarbons."}, {"text": "RAFs are plotted for ... | surrey-nlp/albert-large-v2-finetuned-abbDet | null | [
"transformers",
"pytorch",
"safetensors",
"albert",
"token-classification",
"generated_from_trainer",
"en",
"dataset:surrey-nlp/PLOD-unfiltered",
"base_model:albert-large-v2",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-22T07:32:24+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #safetensors #albert #token-classification #generated_from_trainer #en #dataset-surrey-nlp/PLOD-unfiltered #base_model-albert-large-v2 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| albert-large-v2-finetuned-ner\_with\_callbacks
==============================================
This model is a fine-tuned version of albert-large-v2 on the PLOD-unfiltered dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1235
* Precision: 0.9655
* Recall: 0.9608
* F1: 0.9632
* Accuracy: 0.... | [
"### 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: 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: 6",
"### Training... | [
"TAGS\n#transformers #pytorch #safetensors #albert #token-classification #generated_from_trainer #en #dataset-surrey-nlp/PLOD-unfiltered #base_model-albert-large-v2 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperpa... |
feature-extraction | transformers | This model creates Sanskrit and Tibetan sentence embeddings and can be used for semantic similarity tasks.
Sanskrit needs to be segmented first and converted into internal transliteration (I will upload the according script here soon). The Tibetan needs to be converted into wylie transliteration. | {"license": "lgpl-lr"} | buddhist-nlp/sanstib | null | [
"transformers",
"pytorch",
"roberta",
"feature-extraction",
"license:lgpl-lr",
"endpoints_compatible",
"region:us"
] | null | 2022-04-22T07:35:32+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #feature-extraction #license-lgpl-lr #endpoints_compatible #region-us
| This model creates Sanskrit and Tibetan sentence embeddings and can be used for semantic similarity tasks.
Sanskrit needs to be segmented first and converted into internal transliteration (I will upload the according script here soon). The Tibetan needs to be converted into wylie transliteration. | [] | [
"TAGS\n#transformers #pytorch #roberta #feature-extraction #license-lgpl-lr #endpoints_compatible #region-us \n"
] |
text-classification | transformers |
This is a finetuned PhoBERT model for essay categories classification.
- At primary levels of education in Vietnam, students are introduced to 5 categories of essays:
- Argumentative - Nghị luận
- Expressive - Biểu cảm
- Descriptive - Miêu tả
- Narrative - Tự sự
- Expository - Thuyết... | {"language": ["vi"], "tags": ["essay category", "text-classification"], "widget": [{"text": "C\u00e1i \u0111\u1ed3ng h\u1ed3 c\u1ee7a em cao h\u01a1n 30 cm. \u0110\u1ebf c\u1ee7a n\u00f3 \u0111\u01b0\u1ee3c l\u00e0m b\u1eb1ng i-n\u1ed1c s\u00e1ng lo\u00e1ng h\u00ecnh b\u1ea7u d\u1ee5c. Ch\u1ed7 d\u00e0i nh\u1ea5t c\u1e... | PaulTran/vietnamese_essay_identify | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"essay category",
"vi",
"arxiv:2003.00744",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-22T07:56:56+00:00 | [
"2003.00744"
] | [
"vi"
] | TAGS
#transformers #pytorch #roberta #text-classification #essay category #vi #arxiv-2003.00744 #autotrain_compatible #endpoints_compatible #region-us
|
This is a finetuned PhoBERT model for essay categories classification.
- At primary levels of education in Vietnam, students are introduced to 5 categories of essays:
- Argumentative - Nghị luận
- Expressive - Biểu cảm
- Descriptive - Miêu tả
- Narrative - Tự sự
- Expository - Thuyết... | [] | [
"TAGS\n#transformers #pytorch #roberta #text-classification #essay category #vi #arxiv-2003.00744 #autotrain_compatible #endpoints_compatible #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. -->
# Fine_Tuning_XLSR_300M_on_OpenSLR_model
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "Fine_Tuning_XLSR_300M_on_OpenSLR_model", "results": []}]} | rajat99/Fine_Tuning_XLSR_300M_on_OpenSLR_model | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-04-22T08:02:30+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| Fine\_Tuning\_XLSR\_300M\_on\_OpenSLR\_model
============================================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 3.2669
* Wer: 1.0
Model description
-----------------
More informatio... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 1... |
null | null |
# BigBird base model
BigBird, is a sparse-attention based transformer which extends Transformer based models, such as BERT to much longer sequences. Moreover, BigBird comes along with a theoretical understanding of the capabilities of a complete transformer that the sparse model can handle.
It is a pretrained model ... | {"language": "en", "license": "apache-2.0", "datasets": ["bookcorpus", "wikipedia", "cc_news"]} | OWG/bigbird-roberta-base | null | [
"onnx",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"dataset:cc_news",
"arxiv:2007.14062",
"license:apache-2.0",
"region:us"
] | null | 2022-04-22T09:29:31+00:00 | [
"2007.14062"
] | [
"en"
] | TAGS
#onnx #en #dataset-bookcorpus #dataset-wikipedia #dataset-cc_news #arxiv-2007.14062 #license-apache-2.0 #region-us
|
# BigBird base model
BigBird, is a sparse-attention based transformer which extends Transformer based models, such as BERT to much longer sequences. Moreover, BigBird comes along with a theoretical understanding of the capabilities of a complete transformer that the sparse model can handle.
It is a pretrained model ... | [
"# BigBird base model\n\nBigBird, is a sparse-attention based transformer which extends Transformer based models, such as BERT to much longer sequences. Moreover, BigBird comes along with a theoretical understanding of the capabilities of a complete transformer that the sparse model can handle.\n\nIt is a pretraine... | [
"TAGS\n#onnx #en #dataset-bookcorpus #dataset-wikipedia #dataset-cc_news #arxiv-2007.14062 #license-apache-2.0 #region-us \n",
"# BigBird base model\n\nBigBird, is a sparse-attention based transformer which extends Transformer based models, such as BERT to much longer sequences. Moreover, BigBird comes along with... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-base-timit-demo-colab
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wa... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-base-timit-demo-colab", "results": []}]} | Vishfeb27/wav2vec2-base-timit-demo-colab | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-04-22T09:30:28+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
|
# wav2vec2-base-timit-demo-colab
This model is a fine-tuned version of facebook/wav2vec2-base on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hy... | [
"# wav2vec2-base-timit-demo-colab\n\nThis model is a fine-tuned version of facebook/wav2vec2-base on the None dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training ... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"# wav2vec2-base-timit-demo-colab\n\nThis model is a fine-tuned version of facebook/wav2vec2-base on the None dataset.",
"## Model description\n\nM... |
text-generation | transformers |
# BigScience - testing model
This model aims to test the conversion between Megatron-LM and transformers. It is a small ```GPT-2```-like model that has been used to debug the script. Use it only for integration tests | {"language": ["eng"], "tags": ["integration"], "pipeline_tag": "text-generation"} | bigscience/bigscience-small-testing | null | [
"transformers",
"pytorch",
"safetensors",
"bloom",
"feature-extraction",
"integration",
"text-generation",
"eng",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-04-22T10:04:10+00:00 | [] | [
"eng"
] | TAGS
#transformers #pytorch #safetensors #bloom #feature-extraction #integration #text-generation #eng #endpoints_compatible #has_space #text-generation-inference #region-us
|
# BigScience - testing model
This model aims to test the conversion between Megatron-LM and transformers. It is a small -like model that has been used to debug the script. Use it only for integration tests | [
"# BigScience - testing model\n\nThis model aims to test the conversion between Megatron-LM and transformers. It is a small -like model that has been used to debug the script. Use it only for integration tests"
] | [
"TAGS\n#transformers #pytorch #safetensors #bloom #feature-extraction #integration #text-generation #eng #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"# BigScience - testing model\n\nThis model aims to test the conversion between Megatron-LM and transformers. It is a small -like mod... |
fill-mask | 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. -->
# salihkavaf/distilbert-base-uncased-finetuned-imdb
This model is a fine-tuned version of [salihkavaf/distilbert-base-uncased-finetuned-... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "salihkavaf/distilbert-base-uncased-finetuned-imdb", "results": []}]} | salihkavaf/distilbert-base-uncased-finetuned-imdb | null | [
"transformers",
"tf",
"distilbert",
"fill-mask",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-22T10:19:02+00:00 | [] | [] | TAGS
#transformers #tf #distilbert #fill-mask #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| salihkavaf/distilbert-base-uncased-finetuned-imdb
=================================================
This model is a fine-tuned version of salihkavaf/distilbert-base-uncased-finetuned-imdb on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 2.6769
* Validation Loss: 2.5848
*... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'WarmUp', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_schedule\\_fn': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_le... | [
"TAGS\n#transformers #tf #distilbert #fill-mask #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\\_rate'... |
text-generation | transformers |
# JARVIS DialoGPT Model | {"tags": ["conversational"]} | Tlacaelel/DialoGPT-small-jarvis | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-22T10:22:36+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# JARVIS DialoGPT Model | [
"# JARVIS DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# JARVIS DialoGPT Model"
] |
null | null |
# pytorch 和 paddle代码
https://github.com/JunnYu/GAU-alpha-pytorch
# bert4keras代码
https://github.com/ZhuiyiTechnology/GAU-alpha
# Install
```bash
进入https://github.com/JunnYu/GAU-alpha-pytorch，
下载paddle代码gau_alpha_paddle
```
# Usage
```python
import paddle
from transformers import BertTokenizer as GAUAlphaTokenizer
fr... | {"language": "zh", "tags": ["gau-alpha", "paddlepaddle"], "inference": false} | junnyu/chinese_GAU-alpha-char_L-24_H-768-paddle | null | [
"paddlepaddle",
"gau-alpha",
"zh",
"region:us"
] | null | 2022-04-22T11:13:38+00:00 | [] | [
"zh"
] | TAGS
#paddlepaddle #gau-alpha #zh #region-us
|
# pytorch 和 paddle代码
URL
# bert4keras代码
URL
# Install
# Usage
# Reference
Bibtex：
| [
"# pytorch 和 paddle代码\nURL",
"# bert4keras代码\nURL",
"# Install",
"# Usage",
"# Reference\nBibtex："
] | [
"TAGS\n#paddlepaddle #gau-alpha #zh #region-us \n",
"# pytorch 和 paddle代码\nURL",
"# bert4keras代码\nURL",
"# Install",
"# Usage",
"# Reference\nBibtex："
] |
text2text-generation | transformers |
This model has been trained by the original authors of the paper [(Comet-) Atomic 2020: On Symbolic and Neural Commonsense Knowledge Graphs.](https://www.semanticscholar.org/paper/COMET-ATOMIC-2020%3A-On-Symbolic-and-Neural-Knowledge-Hwang-Bhagavatula/e39503e01ebb108c6773948a24ca798cd444eb62) and has been released [he... | {"license": "afl-3.0"} | mismayil/comet-bart-ai2 | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-22T11:59:37+00:00 | [] | [] | TAGS
#transformers #pytorch #bart #text2text-generation #license-afl-3.0 #autotrain_compatible #endpoints_compatible #region-us
|
This model has been trained by the original authors of the paper (Comet-) Atomic 2020: On Symbolic and Neural Commonsense Knowledge Graphs. and has been released here. Original codebase for training is here | [] | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #license-afl-3.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
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. -->
# ds9_all
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset.
It achieves the followi... | {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "ds9_all", "results": []}]} | Xibanya/DS9Bot | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-04-22T13:52:11+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| ds9\_all
========
This model is a fine-tuned version of gpt2 on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 3.4079
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training a... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1.372e-07\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\n* seed: 3138344630\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warm... | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1.372e-07\... |
zero-shot-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. -->
# clip-vit-large-patch14-336
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluat... | {"tags": ["generated_from_keras_callback"], "widget": [{"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/cat-dog-music.png", "candidate_labels": "playing music, playing sports", "example_title": "Cat & Dog"}], "model-index": [{"name": "clip-vit-large-patch14-336", "results": []}]} | openai/clip-vit-large-patch14-336 | null | [
"transformers",
"pytorch",
"tf",
"clip",
"zero-shot-image-classification",
"generated_from_keras_callback",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-04-22T13:57:43+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #clip #zero-shot-image-classification #generated_from_keras_callback #endpoints_compatible #has_space #region-us
|
# clip-vit-large-patch14-336
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Trai... | [
"# clip-vit-large-patch14-336\n\nThis model was trained from scratch on an unknown dataset.\nIt achieves the following results on the evaluation set:",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore infor... | [
"TAGS\n#transformers #pytorch #tf #clip #zero-shot-image-classification #generated_from_keras_callback #endpoints_compatible #has_space #region-us \n",
"# clip-vit-large-patch14-336\n\nThis model was trained from scratch on an unknown dataset.\nIt achieves the following results on the evaluation set:",
"## Mode... |
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-3000-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-3000-samples", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "imdb", "type": "imdb", "args": ... | praptishadmaan/finetuning-sentiment-model-3000-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 | 2022-04-22T14:11:44+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-3000-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: 0.2345
- Accuracy: 0.9319
- F1: 0.9324
## Model description
More information needed
## Intended uses & limitations
More in... | [
"# finetuning-sentiment-model-3000-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: 0.2345\n- Accuracy: 0.9319\n- F1: 0.9324",
"## Model description\n\nMore information needed",
"## Intended uses & li... | [
"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-3000-samples\n\nThis model is a fine-tuned version of distilbert-base-uncased... |
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/1511292594214551557/4T_z... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/plsnobullywaaa/1650660437516/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/plsnobullywaaa | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-22T15:00:54+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
clementine
@plsnobullywaaa
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"
] |
null | null |
# DeiT
## Model description
DeiT proposed in [this paper](https://arxiv.org/abs/2012.12877) are more efficiently trained transformers for image classification, requiring far less data and far less computing resources compared to the original ViT models.
## Original implementation
Follow [this link](https://huggin... | {"language": "en", "license": "apache-2.0", "tags": ["deit"]} | OWG/DeiT | null | [
"onnx",
"deit",
"en",
"arxiv:2012.12877",
"license:apache-2.0",
"region:us"
] | null | 2022-04-22T15:08:23+00:00 | [
"2012.12877"
] | [
"en"
] | TAGS
#onnx #deit #en #arxiv-2012.12877 #license-apache-2.0 #region-us
|
# DeiT
## Model description
DeiT proposed in this paper are more efficiently trained transformers for image classification, requiring far less data and far less computing resources compared to the original ViT models.
## Original implementation
Follow this link to see the original implementation.
## How to use
| [
"# DeiT",
"## Model description\n\n DeiT proposed in this paper are more efficiently trained transformers for image classification, requiring far less data and far less computing resources compared to the original ViT models.",
"## Original implementation\n\nFollow this link to see the original implementation."... | [
"TAGS\n#onnx #deit #en #arxiv-2012.12877 #license-apache-2.0 #region-us \n",
"# DeiT",
"## Model description\n\n DeiT proposed in this paper are more efficiently trained transformers for image classification, requiring far less data and far less computing resources compared to the original ViT models.",
"## O... |
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/1509040026625224705/B_S4... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/proanatwink/1650648376939/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/proanatwink | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-22T15:43:24+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
God is Love (((they)))/them🇺🇦🇮🇱️️
@proanatwink
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 repor... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
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/1643341916308643841/lCsG... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true", "widget": [{"text": "My dream is"}]} | huggingtweets/charlottefang77 | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-22T16:35:18+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Charlotte Fang @ REMCON TOKYO
@charlottefang77
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.
... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
null | null |
[Optimum Habana](https://github.com/huggingface/optimum-habana) is the interface between the Hugging Face Transformers and Diffusers libraries and Habana's Gaudi processor (HPU).
It provides a set of tools enabling easy and fast model loading, training and inference on single- and multi-HPU settings for different down... | {"license": "apache-2.0"} | Habana/roberta-large | null | [
"optimum_habana",
"license:apache-2.0",
"region:us"
] | null | 2022-04-22T17:03:10+00:00 | [] | [] | TAGS
#optimum_habana #license-apache-2.0 #region-us
|
Optimum Habana is the interface between the Hugging Face Transformers and Diffusers libraries and Habana's Gaudi processor (HPU).
It provides a set of tools enabling easy and fast model loading, training and inference on single- and multi-HPU settings for different downstream tasks.
Learn more about how to take advant... | [
"## RoBERTa Large model HPU configuration\n\nThis model only contains the 'GaudiConfig' file for running the roberta-large model on Habana's Gaudi processors (HPU).\n\nThis model contains no model weights, only a GaudiConfig.\n\nThis enables to specify:\n- 'use_torch_autocast': whether to use PyTorch's autocast mix... | [
"TAGS\n#optimum_habana #license-apache-2.0 #region-us \n",
"## RoBERTa Large model HPU configuration\n\nThis model only contains the 'GaudiConfig' file for running the roberta-large model on Habana's Gaudi processors (HPU).\n\nThis model contains no model weights, only a GaudiConfig.\n\nThis enables to specify:\n... |
null | null |
[Optimum Habana](https://github.com/huggingface/optimum-habana) is the interface between the Hugging Face Transformers and Diffusers libraries and Habana's Gaudi processor (HPU).
It provides a set of tools enabling easy and fast model loading, training and inference on single- and multi-HPU settings for different down... | {"license": "apache-2.0"} | Habana/bert-base-uncased | null | [
"optimum_habana",
"license:apache-2.0",
"region:us"
] | null | 2022-04-22T17:03:54+00:00 | [] | [] | TAGS
#optimum_habana #license-apache-2.0 #region-us
|
Optimum Habana is the interface between the Hugging Face Transformers and Diffusers libraries and Habana's Gaudi processor (HPU).
It provides a set of tools enabling easy and fast model loading, training and inference on single- and multi-HPU settings for different downstream tasks.
Learn more about how to take advant... | [
"## Bert Base model HPU configuration\n\nThis model only contains the 'GaudiConfig' file for running the bert-base-uncased model on Habana's Gaudi processors (HPU).\n\nThis model contains no model weights, only a GaudiConfig.\n\nThis enables to specify:\n- 'use_fused_adam': whether to use Habana's custom AdamW impl... | [
"TAGS\n#optimum_habana #license-apache-2.0 #region-us \n",
"## Bert Base model HPU configuration\n\nThis model only contains the 'GaudiConfig' file for running the bert-base-uncased model on Habana's Gaudi processors (HPU).\n\nThis model contains no model weights, only a GaudiConfig.\n\nThis enables to specify:\n... |
null | null |
[Optimum Habana](https://github.com/huggingface/optimum-habana) is the interface between the Hugging Face Transformers and Diffusers libraries and Habana's Gaudi processor (HPU).
It provides a set of tools enabling easy and fast model loading, training and inference on single- and multi-HPU settings for different down... | {"license": "apache-2.0"} | Habana/bert-large-uncased-whole-word-masking | null | [
"optimum_habana",
"license:apache-2.0",
"region:us"
] | null | 2022-04-22T17:04:29+00:00 | [] | [] | TAGS
#optimum_habana #license-apache-2.0 #region-us
|
Optimum Habana is the interface between the Hugging Face Transformers and Diffusers libraries and Habana's Gaudi processor (HPU).
It provides a set of tools enabling easy and fast model loading, training and inference on single- and multi-HPU settings for different downstream tasks.
Learn more about how to take advant... | [
"## BERT Large model HPU configuration\n\nThis model only contains the 'GaudiConfig' file for running the bert-large-uncased-whole-word-masking model on Habana's Gaudi processors (HPU).\n\nThis model contains no model weights, only a GaudiConfig.\n\nThis enables to specify:\n- 'use_fused_adam': whether to use Haban... | [
"TAGS\n#optimum_habana #license-apache-2.0 #region-us \n",
"## BERT Large model HPU configuration\n\nThis model only contains the 'GaudiConfig' file for running the bert-large-uncased-whole-word-masking model on Habana's Gaudi processors (HPU).\n\nThis model contains no model weights, only a GaudiConfig.\n\nThis ... |
null | null |
[Optimum Habana](https://github.com/huggingface/optimum-habana) is the interface between the Hugging Face Transformers and Diffusers libraries and Habana's Gaudi processor (HPU).
It provides a set of tools enabling easy and fast model loading, training and inference on single- and multi-HPU settings for different down... | {"license": "apache-2.0"} | Habana/albert-large-v2 | null | [
"optimum_habana",
"license:apache-2.0",
"region:us"
] | null | 2022-04-22T17:05:07+00:00 | [] | [] | TAGS
#optimum_habana #license-apache-2.0 #region-us
|
Optimum Habana is the interface between the Hugging Face Transformers and Diffusers libraries and Habana's Gaudi processor (HPU).
It provides a set of tools enabling easy and fast model loading, training and inference on single- and multi-HPU settings for different downstream tasks.
Learn more about how to take advant... | [
"## ALBERT Large model HPU configuration\n\nThis model only contains the 'GaudiConfig' file for running the albert-large-v2 model on Habana's Gaudi processors (HPU).\n\nThis model contains no model weights, only a GaudiConfig.\n\nThis enables to specify:\n- 'use_torch_autocast': whether to use PyTorch's autocast mi... | [
"TAGS\n#optimum_habana #license-apache-2.0 #region-us \n",
"## ALBERT Large model HPU configuration\n\nThis model only contains the 'GaudiConfig' file for running the albert-large-v2 model on Habana's Gaudi processors (HPU).\n\nThis model contains no model weights, only a GaudiConfig.\n\nThis enables to specify:\... |
null | null |
[Optimum Habana](https://github.com/huggingface/optimum-habana) is the interface between the Hugging Face Transformers and Diffusers libraries and Habana's Gaudi processor (HPU).
It provides a set of tools enabling easy and fast model loading, training and inference on single- and multi-HPU settings for different down... | {"license": "apache-2.0"} | Habana/albert-xxlarge-v1 | null | [
"optimum_habana",
"license:apache-2.0",
"region:us"
] | null | 2022-04-22T17:05:35+00:00 | [] | [] | TAGS
#optimum_habana #license-apache-2.0 #region-us
|
Optimum Habana is the interface between the Hugging Face Transformers and Diffusers libraries and Habana's Gaudi processor (HPU).
It provides a set of tools enabling easy and fast model loading, training and inference on single- and multi-HPU settings for different downstream tasks.
Learn more about how to take advant... | [
"## ALBERT XXLarge model HPU configuration\n\nThis model only contains the 'GaudiConfig' file for running the albert-xxlarge-v1 model on Habana's Gaudi processors (HPU).\n\nThis model contains no model weights, only a GaudiConfig.\n\nThis enables to specify:\n- 'use_torch_autocast': whether to use PyTorch's autocas... | [
"TAGS\n#optimum_habana #license-apache-2.0 #region-us \n",
"## ALBERT XXLarge model HPU configuration\n\nThis model only contains the 'GaudiConfig' file for running the albert-xxlarge-v1 model on Habana's Gaudi processors (HPU).\n\nThis model contains no model weights, only a GaudiConfig.\n\nThis enables to speci... |
null | null |
[Optimum Habana](https://github.com/huggingface/optimum-habana) is the interface between the Hugging Face Transformers and Diffusers libraries and Habana's Gaudi processor (HPU).
It provides a set of tools enabling easy and fast model loading, training and inference on single- and multi-HPU settings for different down... | {"license": "apache-2.0"} | Habana/distilbert-base-uncased | null | [
"optimum_habana",
"license:apache-2.0",
"region:us"
] | null | 2022-04-22T17:06:11+00:00 | [] | [] | TAGS
#optimum_habana #license-apache-2.0 #region-us
|
Optimum Habana is the interface between the Hugging Face Transformers and Diffusers libraries and Habana's Gaudi processor (HPU).
It provides a set of tools enabling easy and fast model loading, training and inference on single- and multi-HPU settings for different downstream tasks.
Learn more about how to take advant... | [
"## DistilBERT Base model HPU configuration\n\nThis model only contains the 'GaudiConfig' file for running the distilbert-base-uncased model on Habana's Gaudi processors (HPU).\n\nThis model contains no model weights, only a GaudiConfig.\n\nThis enables to specify:\n- 'use_fused_adam': whether to use Habana's custo... | [
"TAGS\n#optimum_habana #license-apache-2.0 #region-us \n",
"## DistilBERT Base model HPU configuration\n\nThis model only contains the 'GaudiConfig' file for running the distilbert-base-uncased model on Habana's Gaudi processors (HPU).\n\nThis model contains no model weights, only a GaudiConfig.\n\nThis enables t... |
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-issues-128
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased)... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "bert-base-uncased-issues-128", "results": []}]} | cj-mills/bert-base-uncased-issues-128 | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-22T17:10:32+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| bert-base-uncased-issues-128
============================
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: 1.2526
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: 32\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: 16\n* mixed\\_prec... | [
"TAGS\n#transformers #pytorch #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: 5e-05\n* train\\_batch\\_size: 32\n* eval\\_bat... |
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/1400304659688878088/Lbb8... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/miyarepostbot/1650651175106/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/miyarepostbot | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-22T17:11:21+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Miya
@miyarepostbot
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"
] |
text-classification | transformers | WIP, not working yet | {} | kilimandjaro/camembert-base-sentiment | null | [
"transformers",
"pytorch",
"camembert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-22T17:12:36+00:00 | [] | [] | TAGS
#transformers #pytorch #camembert #text-classification #autotrain_compatible #endpoints_compatible #region-us
| WIP, not working yet | [] | [
"TAGS\n#transformers #pytorch #camembert #text-classification #autotrain_compatible #endpoints_compatible #region-us \n"
] |
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/1269411300624363520/-xYW... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/mimpathy/1650652745938/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/mimpathy | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-22T17:38:06+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
𝓗𝓸𝓷𝓸𝓻
@mimpathy
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"
] |
fill-mask | transformers | This is a model checkpoint for ["Should You Mask 15% in Masked Language Modeling"](https://arxiv.org/abs/2202.08005) [(code)](https://github.com/princeton-nlp/DinkyTrain.git). We use pre layer norm, which is not supported by HuggingFace. To use our model, go to our [github repo](https://github.com/princeton-nlp/DinkyTr... | {"inference": false} | princeton-nlp/efficient_mlm_m0.15 | null | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"arxiv:2202.08005",
"autotrain_compatible",
"region:us"
] | null | 2022-04-22T17:44:48+00:00 | [
"2202.08005"
] | [] | TAGS
#transformers #pytorch #roberta #fill-mask #arxiv-2202.08005 #autotrain_compatible #region-us
| This is a model checkpoint for "Should You Mask 15% in Masked Language Modeling" (code). We use pre layer norm, which is not supported by HuggingFace. To use our model, go to our github repo, download our code, and import the RoBERTa class from 'huggingface/modeling_roberta_prelayernorm.py'. For example,
| [] | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #arxiv-2202.08005 #autotrain_compatible #region-us \n"
] |
fill-mask | transformers | This is a model checkpoint for ["Should You Mask 15% in Masked Language Modeling"](https://arxiv.org/abs/2202.08005) [(code)](https://github.com/princeton-nlp/DinkyTrain.git). We use pre layer norm, which is not supported by HuggingFace. To use our model, go to our [github repo](https://github.com/princeton-nlp/DinkyTr... | {"inference": false} | princeton-nlp/efficient_mlm_m0.40 | null | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"arxiv:2202.08005",
"autotrain_compatible",
"region:us"
] | null | 2022-04-22T17:44:55+00:00 | [
"2202.08005"
] | [] | TAGS
#transformers #pytorch #roberta #fill-mask #arxiv-2202.08005 #autotrain_compatible #region-us
| This is a model checkpoint for "Should You Mask 15% in Masked Language Modeling" (code). We use pre layer norm, which is not supported by HuggingFace. To use our model, go to our github repo, download our code, and import the RoBERTa class from 'huggingface/modeling_roberta_prelayernorm.py'. For example,
| [] | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #arxiv-2202.08005 #autotrain_compatible #region-us \n"
] |
fill-mask | transformers | This is a model checkpoint for ["Should You Mask 15% in Masked Language Modeling"](https://arxiv.org/abs/2202.08005) [(code)](https://github.com/princeton-nlp/DinkyTrain.git). We use pre layer norm, which is not supported by HuggingFace. To use our model, go to our [github repo](https://github.com/princeton-nlp/DinkyTr... | {"inference": false} | princeton-nlp/efficient_mlm_m0.15-801010 | null | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"arxiv:2202.08005",
"autotrain_compatible",
"region:us"
] | null | 2022-04-22T17:45:04+00:00 | [
"2202.08005"
] | [] | TAGS
#transformers #pytorch #roberta #fill-mask #arxiv-2202.08005 #autotrain_compatible #region-us
| This is a model checkpoint for "Should You Mask 15% in Masked Language Modeling" (code). We use pre layer norm, which is not supported by HuggingFace. To use our model, go to our github repo, download our code, and import the RoBERTa class from 'huggingface/modeling_roberta_prelayernorm.py'. For example,
| [] | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #arxiv-2202.08005 #autotrain_compatible #region-us \n"
] |
fill-mask | transformers | This is a model checkpoint for ["Should You Mask 15% in Masked Language Modeling"](https://arxiv.org/abs/2202.08005) [(code)](https://github.com/princeton-nlp/DinkyTrain.git). We use pre layer norm, which is not supported by HuggingFace. To use our model, go to our [github repo](https://github.com/princeton-nlp/DinkyTr... | {"inference": false} | princeton-nlp/efficient_mlm_m0.40-801010 | null | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"arxiv:2202.08005",
"autotrain_compatible",
"region:us"
] | null | 2022-04-22T17:45:18+00:00 | [
"2202.08005"
] | [] | TAGS
#transformers #pytorch #roberta #fill-mask #arxiv-2202.08005 #autotrain_compatible #region-us
| This is a model checkpoint for "Should You Mask 15% in Masked Language Modeling" (code). We use pre layer norm, which is not supported by HuggingFace. To use our model, go to our github repo, download our code, and import the RoBERTa class from 'huggingface/modeling_roberta_prelayernorm.py'. For example,
| [] | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #arxiv-2202.08005 #autotrain_compatible #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. -->
# finbert-finetuned-FG-SINGLE_SENTENCE-NEWS
This model is a fine-tuned version of [ProsusAI/finbert](https://huggingface.co/Prosus... | {"tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "finbert-finetuned-FG-SINGLE_SENTENCE-NEWS", "results": []}]} | lucaordronneau/finbert-finetuned-FG-SINGLE_SENTENCE-NEWS | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-22T17:54:48+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| finbert-finetuned-FG-SINGLE\_SENTENCE-NEWS
==========================================
This model is a fine-tuned version of ProsusAI/finbert on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 3.2997
* Accuracy: 0.6414
* F1: 0.6295
Model description
-----------------
More inf... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 6e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 20",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #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: 6e-05\n* train\\_batch\\_size: 32\n* eval\\_... |
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/1376263696389914629/_Fzh... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true", "widget": [{"text": "My dream is"}]} | huggingtweets/it_its_are_are-miyarepostbot-unbridled_id | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-22T18:04:23+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI CYBORG
Sierra Armour 𝔼𝕣𝕚𝕤 & angelicism2727272628 & Miya
@it\_its\_are\_are-miyarepostbot-unbridled\_id
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 h... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
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/1578826930962534400/V7xB... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/unbridled_id/1671037983544/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/unbridled_id | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-22T18:13:11+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
S. Armour 𝔼𝕣𝕚𝕤
@unbridled\_id
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"
] |
null | allennlp |
# TODO: Fill this model card
---
license: cc-by-nc-sa-4.0
--- | {"tags": ["allennlp"]} | emibaylor/ClimateQA | null | [
"allennlp",
"region:us"
] | null | 2022-04-22T18:30:26+00:00 | [] | [] | TAGS
#allennlp #region-us
|
# TODO: Fill this model card
---
license: cc-by-nc-sa-4.0
--- | [
"# TODO: Fill this model card\n\n---\nlicense: cc-by-nc-sa-4.0\n---"
] | [
"TAGS\n#allennlp #region-us \n",
"# TODO: Fill this model card\n\n---\nlicense: cc-by-nc-sa-4.0\n---"
] |
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/1523442545153519616/mYJE... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/propertyexile/1652074114021/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/propertyexile | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-22T19:00:56+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Primo
@propertyexile
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"
] |
audio-to-audio | espnet |
## ESPnet2 ENH model
### `espnet/dns_icassp21_enh_train_enh_tcn_tf_raw`
This model was trained by Yoshiki using dns_icassp21 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```bash
cd espnet
pip install -e .
cd egs2/dns_icassp21/enh1
./run.sh --skip_data_prep false --skip_t... | {"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "audio-to-audio"], "datasets": ["dns_icassp21"]} | espnet/dns_icassp21_enh_train_enh_tcn_tf_raw | null | [
"espnet",
"audio",
"audio-to-audio",
"en",
"dataset:dns_icassp21",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | null | 2022-04-22T19:45:11+00:00 | [
"1804.00015"
] | [
"en"
] | TAGS
#espnet #audio #audio-to-audio #en #dataset-dns_icassp21 #arxiv-1804.00015 #license-cc-by-4.0 #region-us
| ESPnet2 ENH model
-----------------
### 'espnet/dns\_icassp21\_enh\_train\_enh\_tcn\_tf\_raw'
This model was trained by Yoshiki using dns\_icassp21 recipe in espnet.
### Demo: How to use in ESPnet2
RESULTS
=======
Environments
------------
* date: 'Thu Apr 21 21:49:46 UTC 2022'
* python version: '3.7.4 (def... | [
"### 'espnet/dns\\_icassp21\\_enh\\_train\\_enh\\_tcn\\_tf\\_raw'\n\n\nThis model was trained by Yoshiki using dns\\_icassp21 recipe in espnet.",
"### Demo: How to use in ESPnet2\n\n\nRESULTS\n=======\n\n\nEnvironments\n------------\n\n\n* date: 'Thu Apr 21 21:49:46 UTC 2022'\n* python version: '3.7.4 (default, A... | [
"TAGS\n#espnet #audio #audio-to-audio #en #dataset-dns_icassp21 #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"### 'espnet/dns\\_icassp21\\_enh\\_train\\_enh\\_tcn\\_tf\\_raw'\n\n\nThis model was trained by Yoshiki using dns\\_icassp21 recipe in espnet.",
"### Demo: How to use in ESPnet2\n\n\nRESULTS\n==... |
null | null |
# PIFu: Pixel-Aligned Implicit Function for High-Resolution Clothed Human Digitization
<a href="https://github.com/shunsukesaito/PIFu" target="_blank">https://github.com/shunsukesaito/PIFu</a>
This a checkpoint from the original project here are some important <a href="https://github.com/shunsukesaito/PIFu#demo" ta... | {"license": "mit"} | radames/PIFu-upright-standing | null | [
"license:mit",
"has_space",
"region:us"
] | null | 2022-04-22T22:41:52+00:00 | [] | [] | TAGS
#license-mit #has_space #region-us
|
# PIFu: Pixel-Aligned Implicit Function for High-Resolution Clothed Human Digitization
<a href="URL target="_blank">URL
This a checkpoint from the original project here are some important <a href="URL target="_blank">notes</a>:
> Warning: The released model is trained with mostly upright standing scans with weak p... | [
"# PIFu: Pixel-Aligned Implicit Function for High-Resolution Clothed Human Digitization\n\n<a href=\"URL target=\"_blank\">URL\n\nThis a checkpoint from the original project here are some important <a href=\"URL target=\"_blank\">notes</a>:\n\n> Warning: The released model is trained with mostly upright standing sc... | [
"TAGS\n#license-mit #has_space #region-us \n",
"# PIFu: Pixel-Aligned Implicit Function for High-Resolution Clothed Human Digitization\n\n<a href=\"URL target=\"_blank\">URL\n\nThis a checkpoint from the original project here are some important <a href=\"URL target=\"_blank\">notes</a>:\n\n> Warning: The released... |
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/1522032150358511616/83U7... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/newscollected/1675718706662/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/newscollected | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-23T00:06:12+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
del co
@newscollected
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-base-urdu-demo-colab
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/fac... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-base-urdu-demo-colab", "results": []}]} | TahaRazzaq/wav2vec2-base-urdu-demo-colab | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-04-23T00:14:07+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
|
# wav2vec2-base-urdu-demo-colab
This model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Tra... | [
"# wav2vec2-base-urdu-demo-colab\n\nThis model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the None dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## T... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"# wav2vec2-base-urdu-demo-colab\n\nThis model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the None dataset.",
"## Model descript... |
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/1517583783020666881/mmUj... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true", "widget": [{"text": "My dream is"}]} | huggingtweets/angelicism010-propertyexile-wretched_worm | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-23T00:18:51+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI CYBORG
Primo & offlineism010 & wretched worm
@angelicism010-propertyexile-wretched\_worm
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 d... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
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/1585425053541359617/iNim... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/h0uldin/1667944745737/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/h0uldin | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-23T00:56:41+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
H
@h0uldin
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
-------------
The mo... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
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/1383763210314997773/aIID... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/angelicism010/1650756728850/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/angelicism010 | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-23T01:22:53+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
offlineism010
@angelicism010
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"
] |
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. -->
# distilroberta-base-mic-nlp
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base)... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "distilroberta-base-mic-nlp", "results": []}]} | agi-css/distilroberta-base-mic-nlp | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-23T02:12:09+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilroberta-base-mic-nlp
==========================
This model is a fine-tuned version of distilroberta-base on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0049
* Accuracy: 0.9993
* F1: 0.9993
Model description
-----------------
More information needed
Intended use... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2.740146306575944e-05\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs:... | [
"TAGS\n#transformers #pytorch #tensorboard #roberta #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: 2.740146306575944e-05... |
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. -->
# distilroberta-base-mic-sym
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base)... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "distilroberta-base-mic-sym", "results": []}]} | agi-css/distilroberta-base-mic-sym | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-23T02:28:45+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilroberta-base-mic-sym
==========================
This model is a fine-tuned version of distilroberta-base on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0023
* Accuracy: 0.9997
* F1: 0.9997
Model description
-----------------
More information needed
Intended use... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2.740146306575944e-05\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs:... | [
"TAGS\n#transformers #pytorch #tensorboard #roberta #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: 2.740146306575944e-05... |
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/1612123974099472384/MVvI... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true", "widget": [{"text": "My dream is"}]} | huggingtweets/it_its_are_are | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-23T02:58:32+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
angelicism2727272628
@it\_its\_are\_are
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.
Trainin... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #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. -->
# distilroberta-base-etc-nlp
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base)... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "distilroberta-base-etc-nlp", "results": []}]} | agi-css/distilroberta-base-etc-nlp | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-23T03:18:08+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilroberta-base-etc-nlp
==========================
This model is a fine-tuned version of distilroberta-base on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0039
* Accuracy: 0.9993
* F1: 0.9993
Model description
-----------------
More information needed
Intended use... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2.740146306575944e-05\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs:... | [
"TAGS\n#transformers #pytorch #tensorboard #roberta #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: 2.740146306575944e-05... |
summarization | transformers |
Citation
```
@misc{https://doi.org/10.48550/arxiv.2110.07166,
doi = {10.48550/ARXIV.2110.07166},
url = {https://arxiv.org/abs/2110.07166},
author = {Choubey, Prafulla Kumar and Fabbri, Alexander R. and Vig, Jesse and Wu, Chien-Sheng and Liu, Wenhao and Rajani, Nazneen Fatema},
keywords = {Computation and Langu... | {"language": "en", "license": "bsd-3-clause", "tags": ["summarization"], "datasets": ["xsum"]} | praf-choub/bart-CaPE-xsum | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"summarization",
"en",
"dataset:xsum",
"arxiv:2110.07166",
"license:bsd-3-clause",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-23T03:18:51+00:00 | [
"2110.07166"
] | [
"en"
] | TAGS
#transformers #pytorch #bart #text2text-generation #summarization #en #dataset-xsum #arxiv-2110.07166 #license-bsd-3-clause #autotrain_compatible #endpoints_compatible #region-us
|
Citation
| [] | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #summarization #en #dataset-xsum #arxiv-2110.07166 #license-bsd-3-clause #autotrain_compatible #endpoints_compatible #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. -->
# distilroberta-base-etc-sym
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base)... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "distilroberta-base-etc-sym", "results": []}]} | agi-css/distilroberta-base-etc-sym | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-23T03:24:06+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilroberta-base-etc-sym
==========================
This model is a fine-tuned version of distilroberta-base on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0005
* Accuracy: 0.9997
* F1: 0.9997
Model description
-----------------
More information needed
Intended use... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2.740146306575944e-05\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs:... | [
"TAGS\n#transformers #pytorch #tensorboard #roberta #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: 2.740146306575944e-05... |
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. -->
# distilroberta-base-mrl-sym
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base)... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "distilroberta-base-mrl-sym", "results": []}]} | agi-css/distilroberta-base-mrl-sym | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-23T03:28:50+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilroberta-base-mrl-sym
==========================
This model is a fine-tuned version of distilroberta-base on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0001
* Accuracy: 1.0
* F1: 1.0
Model description
-----------------
More information needed
Intended uses & li... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2.740146306575944e-05\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs:... | [
"TAGS\n#transformers #pytorch #tensorboard #roberta #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: 2.740146306575944e-05... |
summarization | transformers |
Citation
```
@misc{https://doi.org/10.48550/arxiv.2110.07166,
doi = {10.48550/ARXIV.2110.07166},
url = {https://arxiv.org/abs/2110.07166},
author = {Choubey, Prafulla Kumar and Fabbri, Alexander R. and Vig, Jesse and Wu, Chien-Sheng and Liu, Wenhao and Rajani, Nazneen Fatema},
keywords = {Computation and Langu... | {"language": "en", "license": "bsd-3-clause", "tags": ["summarization"], "datasets": ["cnn_dailymail"]} | praf-choub/bart-CaPE-cnn | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"summarization",
"en",
"dataset:cnn_dailymail",
"arxiv:2110.07166",
"license:bsd-3-clause",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-23T03:53:57+00:00 | [
"2110.07166"
] | [
"en"
] | TAGS
#transformers #pytorch #bart #text2text-generation #summarization #en #dataset-cnn_dailymail #arxiv-2110.07166 #license-bsd-3-clause #autotrain_compatible #endpoints_compatible #region-us
|
Citation
| [] | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #summarization #en #dataset-cnn_dailymail #arxiv-2110.07166 #license-bsd-3-clause #autotrain_compatible #endpoints_compatible #region-us \n"
] |
summarization | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mt5-base-finetuned-ar-wikilingua
This model is a fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base)... | {"license": "apache-2.0", "tags": ["summarization", "generated_from_trainer"], "datasets": ["wiki_lingua"], "model-index": [{"name": "mt5-base-finetuned-ar-wikilingua", "results": []}]} | ahmeddbahaa/mt5-base-finetuned-ar-wikilingua | null | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"summarization",
"generated_from_trainer",
"dataset:wiki_lingua",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-23T04:58:06+00:00 | [] | [] | TAGS
#transformers #pytorch #mt5 #text2text-generation #summarization #generated_from_trainer #dataset-wiki_lingua #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| mt5-base-finetuned-ar-wikilingua
================================
This model is a fine-tuned version of google/mt5-base on the wiki\_lingua dataset.
It achieves the following results on the evaluation set:
* Loss: 3.6790
* Rouge-1: 19.46
* Rouge-2: 6.82
* Rouge-l: 17.57
* Gen Len: 18.83
* Bertscore: 70.18
Model d... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: ... | [
"TAGS\n#transformers #pytorch #mt5 #text2text-generation #summarization #generated_from_trainer #dataset-wiki_lingua #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... |
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. -->
# distilroberta-base-mrl
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on ... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "distilroberta-base-mrl", "results": []}]} | agi-css/distilroberta-base-mrl | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-23T05:28:03+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilroberta-base-mrl
======================
This model is a fine-tuned version of distilroberta-base on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0170
* Accuracy: 0.9967
* F1: 0.9967
Model description
-----------------
More information needed
Intended uses & limi... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2.1821851463909416e-05\n* train\\_batch\\_size: 400\n* eval\\_batch\\_size: 400\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs... | [
"TAGS\n#transformers #pytorch #tensorboard #roberta #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: 2.1821851463909416e-0... |
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. -->
# distilroberta-base-etc
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on ... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "distilroberta-base-etc", "results": []}]} | agi-css/distilroberta-base-etc | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-23T05:45:15+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilroberta-base-etc
======================
This model is a fine-tuned version of distilroberta-base on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3382
* Accuracy: 0.919
* F1: 0.9190
Model description
-----------------
More information needed
Intended uses & limit... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 4.969790133269121e-05\n* train\\_batch\\_size: 400\n* eval\\_batch\\_size: 400\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs:... | [
"TAGS\n#transformers #pytorch #tensorboard #roberta #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: 4.969790133269121e-05... |
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. -->
# distilroberta-base-mic
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on ... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "distilroberta-base-mic", "results": []}]} | agi-css/distilroberta-base-mic | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-23T06:14:24+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilroberta-base-mic
======================
This model is a fine-tuned version of distilroberta-base on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3435
* Accuracy: 0.9104
* F1: 0.9103
Model description
-----------------
More information needed
Intended uses & limi... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 8.748413056668156e-05\n* train\\_batch\\_size: 200\n* eval\\_batch\\_size: 200\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs:... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 8.748413056668156e-05... |
feature-extraction | sentence-transformers |
# MAGI
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have... | {"tags": ["sentence-transformers", "feature-extraction", "transformers"], "pipeline_tag": "feature-extraction"} | Enoch2090/MAGI | null | [
"sentence-transformers",
"pytorch",
"distilbert",
"feature-extraction",
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2022-04-23T06:14:44+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #distilbert #feature-extraction #transformers #endpoints_compatible #region-us
|
# MAGI
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers installed:
Then you can use the m... | [
"# MAGI\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\nThen you... | [
"TAGS\n#sentence-transformers #pytorch #distilbert #feature-extraction #transformers #endpoints_compatible #region-us \n",
"# MAGI\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## ... |
fill-mask | keras |
# ID G2P BERT
ID G2P BERT is a phoneme de-masking model based on the [BERT](https://arxiv.org/abs/1810.04805) architecture. This model was trained from scratch on a modified [Malay/Indonesian lexicon](https://huggingface.co/datasets/bookbot/id_word2phoneme).
This model was trained using the [Keras](https://keras.io/... | {"language": ["id", "ms"], "license": "apache-2.0", "tags": ["g2p", "fill-mask"], "inference": false} | bookbot/id-g2p-bert | null | [
"keras",
"tensorboard",
"g2p",
"fill-mask",
"id",
"ms",
"arxiv:1810.04805",
"license:apache-2.0",
"region:us"
] | null | 2022-04-23T07:27:04+00:00 | [
"1810.04805"
] | [
"id",
"ms"
] | TAGS
#keras #tensorboard #g2p #fill-mask #id #ms #arxiv-1810.04805 #license-apache-2.0 #region-us
| ID G2P BERT
===========
ID G2P BERT is a phoneme de-masking model based on the BERT architecture. This model was trained from scratch on a modified Malay/Indonesian lexicon.
This model was trained using the Keras framework. All training was done on Google Colaboratory. We adapted the BERT Masked Language Modeling t... | [] | [
"TAGS\n#keras #tensorboard #g2p #fill-mask #id #ms #arxiv-1810.04805 #license-apache-2.0 #region-us \n"
] |
image-classification | transformers |
# rock-challenge-DeiT-solo
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/nat... | {"tags": ["image-classification", "pytorch", "huggingpics"], "metrics": ["accuracy"]} | dimbyTa/rock-challenge-DeiT-solo | null | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-23T08:23:33+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# rock-challenge-DeiT-solo
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
#### fines
!fines
#### large
!large
#### medium
!medium
#### pellets
!pellets | [
"# rock-challenge-DeiT-solo\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",
"#### fines\n\n!fines",
"#### large\n\n!large",
"#### medium\n\n!medium",
"####... | [
"TAGS\n#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# rock-challenge-DeiT-solo\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nReport... |
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. -->
# roberta-base-finetuned-ner
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the [PLO... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["surrey-nlp/PLOD-filtered"], "metrics": ["precision", "recall", "f1", "accuracy"], "model_creators": ["Leonardo Zilio, Hadeel Saadany, Prashant Sharma, Diptesh Kanojia, Constantin Orasan"], "widget": [{"text": "Light dissolved inorganic carbon (DIC) re... | surrey-nlp/roberta-base-finetuned-abbr | null | [
"transformers",
"pytorch",
"tf",
"roberta",
"token-classification",
"generated_from_trainer",
"dataset:surrey-nlp/PLOD-filtered",
"base_model:roberta-base",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-23T08:25:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #roberta #token-classification #generated_from_trainer #dataset-surrey-nlp/PLOD-filtered #base_model-roberta-base #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| roberta-base-finetuned-ner
==========================
This model is a fine-tuned version of roberta-base on the PLOD-filtered dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1148
* Precision: 0.9645
* Recall: 0.9583
* F1: 0.9614
* Accuracy: 0.9576
Model description
-----------------
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 32\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: 6",
"### Trainin... | [
"TAGS\n#transformers #pytorch #tf #roberta #token-classification #generated_from_trainer #dataset-surrey-nlp/PLOD-filtered #base_model-roberta-base #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used durin... |
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. -->
# Xegho.30.4
This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the None dataset.
It achieves the ... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["bleu"], "model-index": [{"name": "Xegho.30.4", "results": []}]} | adityay1221/Xegho.30.4 | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-23T10:50:48+00:00 | [] | [] | TAGS
#transformers #pytorch #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| Xegho.30.4
==========
This model is a fine-tuned version of t5-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1814
* Bleu: 87.4768
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More informatio... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 121\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 30",
"### Traini... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #generated_from_trainer #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* learning\\_rate: 1e-05\n* tr... |
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. -->
# Pixie.30.32
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the None dataset.
It achieves t... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["bleu"], "model-index": [{"name": "Pixie.30.32", "results": []}]} | adityay1221/Pixie.30.32 | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-23T10:52:48+00:00 | [] | [] | TAGS
#transformers #pytorch #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| Pixie.30.32
===========
This model is a fine-tuned version of t5-small on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 1.1623
* Bleu: 47.6437
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: 1e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 121\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 30",
"### Trai... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #generated_from_trainer #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* learning\\_rate: 1e-05\n* tr... |
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. -->
# Xegho.30.2
This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the None dataset.
It achieves the ... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["bleu"], "model-index": [{"name": "Xegho.30.2", "results": []}]} | adityay1221/Xegho.30.2 | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-23T10:56:55+00:00 | [] | [] | TAGS
#transformers #pytorch #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| Xegho.30.2
==========
This model is a fine-tuned version of t5-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1632
* Bleu: 91.1608
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More informatio... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 2\n* seed: 121\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 30",
"### Traini... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #generated_from_trainer #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* learning\\_rate: 1e-05\n* tr... |
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. -->
# Multi-ling-BERT
This model is a fine-tuned version of [bert-base-multilingual-uncased](https://huggingface.co/bert-base-multilin... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "Multi-ling-BERT", "results": []}]} | HankyStyle/Multi-ling-BERT | null | [
"transformers",
"pytorch",
"bert",
"question-answering",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-04-23T12:02:08+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #question-answering #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
|
# Multi-ling-BERT
This model is a fine-tuned version of bert-base-multilingual-uncased on an unknown dataset.
## Usage
### In Transformers
| [
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"### In Transformers"
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"## Usage",
"### In Transformers"
] |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-uncased-finetuned-cola
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-unca... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["matthews_correlation"], "model-index": [{"name": "bert-base-uncased-finetuned-cola", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "args": "... | mofyrt/bert-base-uncased-finetuned-cola | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-23T12:35:27+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| bert-base-uncased-finetuned-cola
================================
This model is a fine-tuned version of bert-base-uncased on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.7445
* Matthews Correlation: 0.5906
Model description
-----------------
More information needed
Inte... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rat... |
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. -->
# speech_processing_project_wav2vec2
This model is a fine-tuned version of [kingabzpro/wav2vec2-urdu](https://huggingface.co/kinga... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "speech_processing_project_wav2vec2", "results": []}]} | Raffay/speech_processing_project_wav2vec2 | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-04-23T12:37:31+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
|
# speech_processing_project_wav2vec2
This model is a fine-tuned version of kingabzpro/wav2vec2-urdu on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Train... | [
"# speech_processing_project_wav2vec2\n\nThis model is a fine-tuned version of kingabzpro/wav2vec2-urdu on the None dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Tra... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"# speech_processing_project_wav2vec2\n\nThis model is a fine-tuned version of kingabzpro/wav2vec2-urdu on the None dataset.",
"## Model descriptio... |
fill-mask | transformers |
This model is the English-targeted version of "UniTE: Unified Translation Evaluation".
| {"license": "apache-2.0", "tags": ["metric", "quality estimation", "translation evaluation"]} | ywan/unite-up | null | [
"transformers",
"pytorch",
"xlm-roberta",
"fill-mask",
"metric",
"quality estimation",
"translation evaluation",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-23T12:40:27+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #fill-mask #metric #quality estimation #translation evaluation #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
This model is the English-targeted version of "UniTE: Unified Translation Evaluation".
| [] | [
"TAGS\n#transformers #pytorch #xlm-roberta #fill-mask #metric #quality estimation #translation evaluation #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
feature-extraction | transformers |
## Overview
Model included in a paper for modeling fine grained similarity between documents:
**Title**: "Multi-Vector Models with Textual Guidance for Fine-Grained Scientific Document Similarity"
**Authors**: Sheshera Mysore, Arman Cohan, Tom Hope
**Paper**: https://arxiv.org/abs/2111.08366
**Github**: https://g... | {"language": "en", "license": "apache-2.0"} | allenai/aspire-biencoder-biomed-scib | null | [
"transformers",
"pytorch",
"bert",
"feature-extraction",
"en",
"arxiv:2111.08366",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-04-23T12:47:04+00:00 | [
"2111.08366"
] | [
"en"
] | TAGS
#transformers #pytorch #bert #feature-extraction #en #arxiv-2111.08366 #license-apache-2.0 #endpoints_compatible #region-us
| Overview
--------
Model included in a paper for modeling fine grained similarity between documents:
Title: "Multi-Vector Models with Textual Guidance for Fine-Grained Scientific Document Similarity"
Authors: Sheshera Mysore, Arman Cohan, Tom Hope
Paper: URL
Github: URL
Note: In the context of the paper, thi... | [
"### Model description\n\n\nThis model is a BERT bi-encoder model trained for similarity of title-abstract pairs in biomedical scientific papers. The model is initialized with the SciBert model. This model inputs the title and abstract of a paper and represents it with a single vector obtained by a scalar mix of th... | [
"TAGS\n#transformers #pytorch #bert #feature-extraction #en #arxiv-2111.08366 #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Model description\n\n\nThis model is a BERT bi-encoder model trained for similarity of title-abstract pairs in biomedical scientific papers. The model is initialized with th... |
fill-mask | transformers |
This model is the multilingual version of "UniTE: Unified Translation Evaluation".
| {"license": "apache-2.0", "tags": ["metric", "quality estimation", "translation evaluation"]} | ywan/unite-mup | null | [
"transformers",
"pytorch",
"xlm-roberta",
"fill-mask",
"metric",
"quality estimation",
"translation evaluation",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-23T12:51:34+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #fill-mask #metric #quality estimation #translation evaluation #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
This model is the multilingual version of "UniTE: Unified Translation Evaluation".
| [] | [
"TAGS\n#transformers #pytorch #xlm-roberta #fill-mask #metric #quality estimation #translation evaluation #license-apache-2.0 #autotrain_compatible #endpoints_compatible #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. -->
# bertBasev2
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"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "bertBasev2", "results": []}]} | brad1141/bertBasev2 | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-23T13:03:03+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| bertBasev2
==========
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: 0.0328
* Precision: 0.9539
* Recall: 0.9707
* F1: 0.9622
* Accuracy: 0.9911
Model description
-----------------
More information needed
Intended use... | [
"### 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... |
image-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. -->
# ConvNeXT (tiny) fine-tuned on EuroSAT
This model is a fine-tuned version of [facebook/convnext-tiny-224](https://huggingface.co/... | {"license": "apache-2.0", "tags": ["generated_from_trainer", "CV", "ConvNeXT", "satellite", "EuroSAT"], "datasets": ["nielsr/eurosat-demo"], "metrics": ["accuracy"], "model-index": [{"name": "convnext-tiny-finetuned-eurosat", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "datase... | mrm8488/convnext-tiny-finetuned-eurosat | null | [
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"tensorboard",
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"convnext",
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"EuroSAT",
"dataset:nielsr/eurosat-demo",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"has_sp... | null | 2022-04-23T13:13:54+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #safetensors #convnext #image-classification #generated_from_trainer #CV #ConvNeXT #satellite #EuroSAT #dataset-nielsr/eurosat-demo #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
| ConvNeXT (tiny) fine-tuned on EuroSAT
=====================================
This model is a fine-tuned version of facebook/convnext-tiny-224 on the EuroSAT dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0549
* Accuracy: 0.9805
#### Drag and drop the following pics in the right widget ... | [
"#### Drag and drop the following pics in the right widget to test the model\n\n\n!image1\n!image2\n\n\nModel description\n-----------------\n\n\nConvNeXT is a pure convolutional model (ConvNet), inspired by the design of Vision Transformers, that claims to outperform them. The authors started from a ResNet and \"m... | [
"TAGS\n#transformers #pytorch #tensorboard #safetensors #convnext #image-classification #generated_from_trainer #CV #ConvNeXT #satellite #EuroSAT #dataset-nielsr/eurosat-demo #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"#### Drag and drop the following p... |
feature-extraction | transformers |
## Overview
Model included in a paper for modeling fine grained similarity between documents:
**Title**: "Multi-Vector Models with Textual Guidance for Fine-Grained Scientific Document Similarity"
**Authors**: Sheshera Mysore, Arman Cohan, Tom Hope
**Paper**: https://arxiv.org/abs/2111.08366
**Github**: https://g... | {"language": "en", "license": "apache-2.0"} | allenai/aspire-biencoder-biomed-spec | null | [
"transformers",
"pytorch",
"bert",
"feature-extraction",
"en",
"arxiv:2111.08366",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-04-23T13:14:35+00:00 | [
"2111.08366"
] | [
"en"
] | TAGS
#transformers #pytorch #bert #feature-extraction #en #arxiv-2111.08366 #license-apache-2.0 #endpoints_compatible #region-us
| Overview
--------
Model included in a paper for modeling fine grained similarity between documents:
Title: "Multi-Vector Models with Textual Guidance for Fine-Grained Scientific Document Similarity"
Authors: Sheshera Mysore, Arman Cohan, Tom Hope
Paper: URL
Github: URL
Note: In the context of the paper, thi... | [
"### Model description\n\n\nThis model is a BERT bi-encoder model trained for similarity of title-abstract pairs in biomedical scientific papers. The model is initialized with the SPECTER encoder. This model inputs the title and abstract of a paper and represents it with a single vector obtained by a scalar mix of ... | [
"TAGS\n#transformers #pytorch #bert #feature-extraction #en #arxiv-2111.08366 #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Model description\n\n\nThis model is a BERT bi-encoder model trained for similarity of title-abstract pairs in biomedical scientific papers. The model is initialized with th... |
feature-extraction | transformers |
## Overview
Model included in a paper for modeling fine grained similarity between documents:
**Title**: "Multi-Vector Models with Textual Guidance for Fine-Grained Scientific Document Similarity"
**Authors**: Sheshera Mysore, Arman Cohan, Tom Hope
**Paper**: https://arxiv.org/abs/2111.08366
**Github**: https://g... | {"language": "en", "license": "apache-2.0"} | allenai/aspire-biencoder-compsci-spec | null | [
"transformers",
"pytorch",
"bert",
"feature-extraction",
"en",
"arxiv:2111.08366",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-04-23T13:15:21+00:00 | [
"2111.08366"
] | [
"en"
] | TAGS
#transformers #pytorch #bert #feature-extraction #en #arxiv-2111.08366 #license-apache-2.0 #endpoints_compatible #region-us
| Overview
--------
Model included in a paper for modeling fine grained similarity between documents:
Title: "Multi-Vector Models with Textual Guidance for Fine-Grained Scientific Document Similarity"
Authors: Sheshera Mysore, Arman Cohan, Tom Hope
Paper: URL
Github: URL
Note: In the context of the paper, thi... | [
"### Model description\n\n\nThis model is a BERT bi-encoder model trained for similarity of title-abstract pairs in biomedical scientific papers. The model is initialized with the SPECTER model. This model inputs the title and abstract of a paper and represents it with a single vector obtained by a scalar mix of th... | [
"TAGS\n#transformers #pytorch #bert #feature-extraction #en #arxiv-2111.08366 #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Model description\n\n\nThis model is a BERT bi-encoder model trained for similarity of title-abstract pairs in biomedical scientific papers. The model is initialized with th... |
feature-extraction | transformers |
## Overview
Model included in a paper for modeling fine grained similarity between documents:
**Title**: "Multi-Vector Models with Textual Guidance for Fine-Grained Scientific Document Similarity"
**Authors**: Sheshera Mysore, Arman Cohan, Tom Hope
**Paper**: https://arxiv.org/abs/2111.08366
**Github**: https://g... | {"language": "en", "license": "apache-2.0"} | allenai/aspire-contextualsentence-multim-biomed | null | [
"transformers",
"pytorch",
"bert",
"feature-extraction",
"en",
"arxiv:2111.08366",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-04-23T13:15:56+00:00 | [
"2111.08366"
] | [
"en"
] | TAGS
#transformers #pytorch #bert #feature-extraction #en #arxiv-2111.08366 #license-apache-2.0 #endpoints_compatible #region-us
| Overview
--------
Model included in a paper for modeling fine grained similarity between documents:
Title: "Multi-Vector Models with Textual Guidance for Fine-Grained Scientific Document Similarity"
Authors: Sheshera Mysore, Arman Cohan, Tom Hope
Paper: URL
Github: URL
Note: In the context of the paper, thi... | [
"### Model description\n\n\nThis model is a BERT based multi-vector model trained for fine-grained similarity of biomedical papers. This model inputs the title and abstract of a paper and represents a paper with a contextual sentence vectors obtained by averaging the token representations of individual sentences - ... | [
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"### Model description\n\n\nThis model is a BERT based multi-vector model trained for fine-grained similarity of biomedical papers. This model inputs the title and abstract of a ... |
feature-extraction | transformers |
## Overview
Model included in a paper for modeling fine grained similarity between documents:
**Title**: "Multi-Vector Models with Textual Guidance for Fine-Grained Scientific Document Similarity"
**Authors**: Sheshera Mysore, Arman Cohan, Tom Hope
**Paper**: https://arxiv.org/abs/2111.08366
**Github**: https://g... | {"language": "en", "license": "apache-2.0"} | allenai/aspire-contextualsentence-multim-compsci | null | [
"transformers",
"pytorch",
"bert",
"feature-extraction",
"en",
"arxiv:2111.08366",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-04-23T13:16:27+00:00 | [
"2111.08366"
] | [
"en"
] | TAGS
#transformers #pytorch #bert #feature-extraction #en #arxiv-2111.08366 #license-apache-2.0 #endpoints_compatible #region-us
| Overview
--------
Model included in a paper for modeling fine grained similarity between documents:
Title: "Multi-Vector Models with Textual Guidance for Fine-Grained Scientific Document Similarity"
Authors: Sheshera Mysore, Arman Cohan, Tom Hope
Paper: URL
Github: URL
Note: In the context of the paper, thi... | [
"### Model description\n\n\nThis model is a BERT based multi-vector model trained for fine-grained similarity of computer science papers. This model inputs the title and abstract of a paper and represents a paper with a contextual sentence vectors obtained by averaging the token representations of individual senten... | [
"TAGS\n#transformers #pytorch #bert #feature-extraction #en #arxiv-2111.08366 #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Model description\n\n\nThis model is a BERT based multi-vector model trained for fine-grained similarity of computer science papers. This model inputs the title and abstract... |
feature-extraction | transformers |
## Overview
Model included in a paper for modeling fine grained similarity between documents:
**Title**: "Multi-Vector Models with Textual Guidance for Fine-Grained Scientific Document Similarity"
**Authors**: Sheshera Mysore, Arman Cohan, Tom Hope
**Paper**: https://arxiv.org/abs/2111.08366
**Github**: https://g... | {"language": "en", "license": "apache-2.0"} | allenai/aspire-contextualsentence-singlem-biomed | null | [
"transformers",
"pytorch",
"bert",
"feature-extraction",
"en",
"arxiv:2111.08366",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-04-23T13:18:20+00:00 | [
"2111.08366"
] | [
"en"
] | TAGS
#transformers #pytorch #bert #feature-extraction #en #arxiv-2111.08366 #license-apache-2.0 #endpoints_compatible #region-us
| Overview
--------
Model included in a paper for modeling fine grained similarity between documents:
Title: "Multi-Vector Models with Textual Guidance for Fine-Grained Scientific Document Similarity"
Authors: Sheshera Mysore, Arman Cohan, Tom Hope
Paper: URL
Github: URL
Note: In the context of the paper, thi... | [
"### Model description\n\n\nThis model is a BERT based multi-vector model trained for fine-grained similarity of biomedical scientific papers. This model inputs the title and abstract of a paper and represents a paper with a contextual sentence vectors obtained by averaging the token representations of individual s... | [
"TAGS\n#transformers #pytorch #bert #feature-extraction #en #arxiv-2111.08366 #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Model description\n\n\nThis model is a BERT based multi-vector model trained for fine-grained similarity of biomedical scientific papers. This model inputs the title and abs... |
feature-extraction | transformers |
## Overview
Model included in a paper for modeling fine grained similarity between documents:
**Title**: "Multi-Vector Models with Textual Guidance for Fine-Grained Scientific Document Similarity"
**Authors**: Sheshera Mysore, Arman Cohan, Tom Hope
**Paper**: https://arxiv.org/abs/2111.08366
**Github**: https://g... | {"language": "en", "license": "apache-2.0"} | allenai/aspire-contextualsentence-singlem-compsci | null | [
"transformers",
"pytorch",
"bert",
"feature-extraction",
"en",
"arxiv:2111.08366",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-04-23T13:18:55+00:00 | [
"2111.08366"
] | [
"en"
] | TAGS
#transformers #pytorch #bert #feature-extraction #en #arxiv-2111.08366 #license-apache-2.0 #endpoints_compatible #region-us
| Overview
--------
Model included in a paper for modeling fine grained similarity between documents:
Title: "Multi-Vector Models with Textual Guidance for Fine-Grained Scientific Document Similarity"
Authors: Sheshera Mysore, Arman Cohan, Tom Hope
Paper: URL
Github: URL
Note: In the context of the paper, thi... | [
"### Model description\n\n\nThis model is a BERT based multi-vector model trained for fine-grained similarity of computer science papers. This model inputs the title and abstract of a paper and represents a paper with a contextual sentence vectors obtained by averaging the token representations of individual senten... | [
"TAGS\n#transformers #pytorch #bert #feature-extraction #en #arxiv-2111.08366 #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Model description\n\n\nThis model is a BERT based multi-vector model trained for fine-grained similarity of computer science papers. This model inputs the title and abstract... |
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. -->
# nbme-deberta-large
This model is a fine-tuned version of [microsoft/deberta-large](https://huggingface.co/microsoft/deberta-larg... | {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "nbme-deberta-large", "results": []}]} | smeoni/nbme-deberta-large | null | [
"transformers",
"pytorch",
"tensorboard",
"deberta",
"fill-mask",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-23T13:40:45+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #deberta #fill-mask #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| nbme-deberta-large
==================
This model is a fine-tuned version of microsoft/deberta-large on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.8806
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: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 8\n* ev... |
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. -->
# local_speech_processing_project_wav2vec2
This model is a fine-tuned version of [kingabzpro/wav2vec2-urdu](https://huggingface.co... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "local_speech_processing_project_wav2vec2", "results": []}]} | Raffay/local_speech_processing_project_wav2vec2 | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-04-23T14:49:07+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
|
# local_speech_processing_project_wav2vec2
This model is a fine-tuned version of kingabzpro/wav2vec2-urdu on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
###... | [
"# local_speech_processing_project_wav2vec2\n\nThis model is a fine-tuned version of kingabzpro/wav2vec2-urdu on the None 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 #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"# local_speech_processing_project_wav2vec2\n\nThis model is a fine-tuned version of kingabzpro/wav2vec2-urdu on the None dataset.",
"## Model desc... |
image-classification | transformers |
# rock-challenge-DeiT-solo-2
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/n... | {"tags": ["image-classification", "pytorch", "huggingpics"], "metrics": ["accuracy"]} | dimbyTa/rock-challenge-DeiT-solo-2 | null | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-23T14:54:23+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# rock-challenge-DeiT-solo-2
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
#### fines
!fines
#### large
!large
#### medium
!medium
#### pellets
!pellets | [
"# rock-challenge-DeiT-solo-2\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",
"#### fines\n\n!fines",
"#### large\n\n!large",
"#### medium\n\n!medium",
"##... | [
"TAGS\n#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# rock-challenge-DeiT-solo-2\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nRepo... |
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. -->
# bert-finetuned-ner
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the None d... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "bert-finetuned-ner", "results": []}]} | rdchambers/bert-finetuned-ner | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-23T15:17:50+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| bert-finetuned-ner
==================
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: 0.0176
* Precision: 0.8418
* Recall: 0.8095
* F1: 0.8253
* Accuracy: 0.9937
Model description
-----------------
More information neede... | [
"### 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 #bert #token-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: 2e-05\n* train\\_batch\... |
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. -->
# org_speech_processing_project_wav2vec2
This model is a fine-tuned version of [kingabzpro/wav2vec2-urdu](https://huggingface.co/k... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "org_speech_processing_project_wav2vec2", "results": []}]} | Raffay/org_speech_processing_project_wav2vec2 | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-04-23T15:46:24+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
|
# org_speech_processing_project_wav2vec2
This model is a fine-tuned version of kingabzpro/wav2vec2-urdu on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### T... | [
"# org_speech_processing_project_wav2vec2\n\nThis model is a fine-tuned version of kingabzpro/wav2vec2-urdu on the None 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 #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"# org_speech_processing_project_wav2vec2\n\nThis model is a fine-tuned version of kingabzpro/wav2vec2-urdu on the None dataset.",
"## Model descri... |
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/1522032150358511616/83U7... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/newscollected-nickmullensgf/1652362865457/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/newscollected-nickmullensgf | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-23T16:13:18+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI CYBORG
del co & kayla
@newscollected-nickmullensgf
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.
T... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
image-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. -->
# ak-vit-base-patch16-224-in21k-image_classification
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](htt... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["image_folder"], "metrics": ["accuracy"], "model-index": [{"name": "ak-vit-base-patch16-224-in21k-image_classification", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "image_folder", "... | amitkayal/ak-vit-base-patch16-224-in21k-image_classification | null | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:image_folder",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-23T16:24:39+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #vit #image-classification #generated_from_trainer #dataset-image_folder #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| ak-vit-base-patch16-224-in21k-image\_classification
===================================================
This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the image\_folder dataset.
It achieves the following results on the evaluation set:
* Loss: 3.1599
* Accuracy: 1.0
Model description
--... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsilo... | [
"TAGS\n#transformers #pytorch #tensorboard #vit #image-classification #generated_from_trainer #dataset-image_folder #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* learni... |
text2text-generation | transformers | ## The T5 base model for the Czech Language
This is the t5 base model for the Czech language that is based on the smaller version of the google/mt5-base model (https://huggingface.co/google/mt5-base).
To make this model, I retained only the Czech and some of the English embeddings from the original multilingual model.
... | {"license": "mit"} | azizbarank/cst5-base | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-23T16:32:50+00:00 | [] | [] | TAGS
#transformers #pytorch #t5 #text2text-generation #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| ## The T5 base model for the Czech Language
This is the t5 base model for the Czech language that is based on the smaller version of the google/mt5-base model (URL
To make this model, I retained only the Czech and some of the English embeddings from the original multilingual model.
# Modifications to the original multi... | [
"## The T5 base model for the Czech Language\nThis is the t5 base model for the Czech language that is based on the smaller version of the google/mt5-base model (URL\nTo make this model, I retained only the Czech and some of the English embeddings from the original multilingual model.",
"# Modifications to the or... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"## The T5 base model for the Czech Language\nThis is the t5 base model for the Czech language that is based on the smaller version of the google/mt5-base mod... |
text2text-generation | transformers |
# MultiIndicSentenceSummarization
This repository contains the [IndicBART](https://huggingface.co/ai4bharat/IndicBART) checkpoint finetuned on the 11 languages of [IndicSentenceSummarization](https://huggingface.co/datasets/ai4bharat/IndicSentenceSummarization) dataset. For finetuning details,
see the [paper](https:/... | {"language": ["as", "bn", "gu", "hi", "kn", "ml", "mr", "or", "pa", "ta", "te"], "license": ["mit"], "tags": ["sentence-summarization", "multilingual", "nlp", "indicnlp"], "datasets": ["ai4bharat/IndicSentenceSummarization"], "widget": ["\u091c\u092e\u094d\u092e\u0942 \u090f\u0935\u0902 \u0915\u0936\u094d\u092e\u0940\u... | ai4bharat/MultiIndicSentenceSummarization | null | [
"transformers",
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"mbart",
"text2text-generation",
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"ta",
"te",
"dataset:ai4bharat/IndicSentenceSummarization",
"arxiv:2203.05437",
"license:mit",
"a... | null | 2022-04-23T16:53:36+00:00 | [
"2203.05437"
] | [
"as",
"bn",
"gu",
"hi",
"kn",
"ml",
"mr",
"or",
"pa",
"ta",
"te"
] | TAGS
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| MultiIndicSentenceSummarization
===============================
This repository contains the IndicBART checkpoint finetuned on the 11 languages of IndicSentenceSummarization dataset. For finetuning details,
see the paper.
* Supported languages: Assamese, Bengali, Gujarati, Hindi, Marathi, Odiya, Punjabi, Kannada, M... | [] | [
"TAGS\n#transformers #pytorch #mbart #text2text-generation #sentence-summarization #multilingual #nlp #indicnlp #as #bn #gu #hi #kn #ml #mr #or #pa #ta #te #dataset-ai4bharat/IndicSentenceSummarization #arxiv-2203.05437 #license-mit #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text2text-generation | transformers |
# MultiIndicSentenceSummarizationSS
This repository contains the [IndicBARTSS](https://huggingface.co/ai4bharat/IndicBARTSS) checkpoint finetuned on the 11 languages of [IndicSentenceSummarization](https://huggingface.co/datasets/ai4bharat/IndicSentenceSummarization) dataset. For finetuning details,
see the [paper](h... | {"language": ["as", "bn", "gu", "hi", "kn", "ml", "mr", "or", "pa", "ta", "te"], "license": ["mit"], "tags": ["sentence-summarization", "multilingual", "nlp", "indicnlp"], "datasets": ["ai4bharat/IndicSentenceSummarization"], "widget": ["\u091c\u092e\u094d\u092e\u0942 \u090f\u0935\u0902 \u0915\u0936\u094d\u092e\u0940\u... | ai4bharat/MultiIndicSentenceSummarizationSS | null | [
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"mbart",
"text2text-generation",
"sentence-summarization",
"multilingual",
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"or",
"pa",
"ta",
"te",
"dataset:ai4bharat/IndicSentenceSummarization",
"arxiv:2203.05437",
"license:mit",
"a... | null | 2022-04-23T16:54:14+00:00 | [
"2203.05437"
] | [
"as",
"bn",
"gu",
"hi",
"kn",
"ml",
"mr",
"or",
"pa",
"ta",
"te"
] | TAGS
#transformers #pytorch #mbart #text2text-generation #sentence-summarization #multilingual #nlp #indicnlp #as #bn #gu #hi #kn #ml #mr #or #pa #ta #te #dataset-ai4bharat/IndicSentenceSummarization #arxiv-2203.05437 #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us
| MultiIndicSentenceSummarizationSS
=================================
This repository contains the IndicBARTSS checkpoint finetuned on the 11 languages of IndicSentenceSummarization dataset. For finetuning details,
see the paper.
* Supported languages: Assamese, Bengali, Gujarati, Hindi, Marathi, Odiya, Punjabi, Kann... | [] | [
"TAGS\n#transformers #pytorch #mbart #text2text-generation #sentence-summarization #multilingual #nlp #indicnlp #as #bn #gu #hi #kn #ml #mr #or #pa #ta #te #dataset-ai4bharat/IndicSentenceSummarization #arxiv-2203.05437 #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
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/1485855322895880192/6tnb... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/dnlklr/1650736963681/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/dnlklr | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-23T17:01:54+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Daniel Keller
@dnlklr
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"
] |
text-generation | transformers |
#Peter from Your Boyfriend Game | {"tags": ["conversational"]} | Coma/Beter | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-23T18:22:36+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
#Peter from Your Boyfriend Game | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #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. -->
# distilbert-base-uncased-finetuned-clinc
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/d... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["clinc_oos"], "metrics": ["accuracy"], "model-index": [{"name": "distilbert-base-uncased-finetuned-clinc", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "clinc_oos", "type": "clinc_oos",... | Sarim24/distilbert-base-uncased-finetuned-clinc | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:clinc_oos",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-23T18:25:00+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-clinc_oos #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-clinc
=======================================
This model is a fine-tuned version of distilbert-base-uncased on the clinc\_oos dataset.
It achieves the following results on the evaluation set:
* Loss: 0.7730
* Accuracy: 0.9116
Model description
-----------------
More information... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 48\n* eval\\_batch\\_size: 48\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-clinc_oos #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* lea... |
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. -->
# nbme-electra-large-discriminator
This model is a fine-tuned version of [google/electra-large-discriminator](https://huggingface.... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "nbme-electra-large-discriminator", "results": []}]} | smeoni/nbme-electra-large-discriminator | null | [
"transformers",
"pytorch",
"tensorboard",
"electra",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-23T19:13:38+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #electra #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| nbme-electra-large-discriminator
================================
This model is a fine-tuned version of google/electra-large-discriminator on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 6.1201
Model description
-----------------
More information needed
Intended uses & ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1... | [
"TAGS\n#transformers #pytorch #tensorboard #electra #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: 5e-05\n* train\\_batch\\_size: ... |
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-urdu-common_voice_8_0
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://hug... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-large-xls-r-300m-urdu-common_voice_8_0", "results": []}]} | omar47/wav2vec2-large-xls-r-300m-urdu-common_voice_8_0 | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-04-23T19:42:12+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-urdu-common\_voice\_8\_0
==================================================
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:
* Loss: 1.3860
* Wer: 0.7546
Model description
-----------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* t... |
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