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null | null | PatientINF embedding model, derived from ClinicalBERT and retrained with patient forum conversation. See the GitHub for documentation: https://github.com/sap218/PatientINF
model is intended for my PhD thesis and open source for secondary research, thesis will be available soon. | {"license": "mit"} | sap218/PatientINF | null | [
"license:mit",
"region:us"
] | null | 2022-04-20T16:20:08+00:00 | [] | [] | TAGS
#license-mit #region-us
| PatientINF embedding model, derived from ClinicalBERT and retrained with patient forum conversation. See the GitHub for documentation: URL
model is intended for my PhD thesis and open source for secondary research, thesis will be available soon. | [] | [
"TAGS\n#license-mit #region-us \n"
] |
text2text-generation | transformers | language:
- en
tags:
- Table to text
- Data to text
## Dataset:
- [ToTTo](https://github.com/google-research-datasets/ToTTo)
A Controlled Table-to-Text Dataset. Totto is an open-source table-to-text dataset with over 1,20,000 examples in the English language. It defines a controlled generation task as: given a Wikip... | {"license": "apache-2.0"} | Tejas21/Totto_t5_base_BERT_Score_20k_steps | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-20T16:35:32+00:00 | [] | [] | TAGS
#transformers #pytorch #t5 #text2text-generation #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| language:
- en
tags:
- Table to text
- Data to text
## Dataset:
- ToTTo
A Controlled Table-to-Text Dataset. Totto is an open-source table-to-text dataset with over 1,20,000 examples in the English language. It defines a controlled generation task as: given a Wikipedia table and a set of highlighted cells, generate a... | [
"## Dataset:\n- ToTTo\nA Controlled Table-to-Text Dataset. Totto is an open-source table-to-text dataset with over 1,20,000 examples in the English language. It defines a controlled generation task as: given a Wikipedia table and a set of highlighted cells, generate a one-sentence description.",
"## Base Model - ... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"## Dataset:\n- ToTTo\nA Controlled Table-to-Text Dataset. Totto is an open-source table-to-text dataset with over 1,20,000 examples in the English lan... |
null | null |
# ConvBERT
## Model Description
ConvBERT base pre-trained on large_spanish_corpus. The ConvBERT architecture has been presented in the ["ConvBERT: Improving BERT with Span-based Dynamic Convolution"](https://arxiv.org/abs/2008.02496) paper.
## Original implementation
Follow [this link](https://huggingface.co/mrm8... | {"language": ["es"], "license": "mit", "tags": ["ConvBERT"], "datasets": ["large_spanish_corpus"]} | OWG/convbert-base-spanish | null | [
"onnx",
"ConvBERT",
"es",
"dataset:large_spanish_corpus",
"arxiv:2008.02496",
"license:mit",
"region:us"
] | null | 2022-04-20T16:52:31+00:00 | [
"2008.02496"
] | [
"es"
] | TAGS
#onnx #ConvBERT #es #dataset-large_spanish_corpus #arxiv-2008.02496 #license-mit #region-us
|
# ConvBERT
## Model Description
ConvBERT base pre-trained on large_spanish_corpus. The ConvBERT architecture has been presented in the "ConvBERT: Improving BERT with Span-based Dynamic Convolution" paper.
## Original implementation
Follow this link to see the original implementation.
# How to use
Download the m... | [
"# ConvBERT",
"## Model Description\n\nConvBERT base pre-trained on large_spanish_corpus. The ConvBERT architecture has been presented in the \"ConvBERT: Improving BERT with Span-based Dynamic Convolution\" paper.",
"## Original implementation\n\nFollow this link to see the original implementation.",
"# How t... | [
"TAGS\n#onnx #ConvBERT #es #dataset-large_spanish_corpus #arxiv-2008.02496 #license-mit #region-us \n",
"# ConvBERT",
"## Model Description\n\nConvBERT base pre-trained on large_spanish_corpus. The ConvBERT architecture has been presented in the \"ConvBERT: Improving BERT with Span-based Dynamic Convolution\" p... |
text-generation | transformers | ```
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("BigSalmon/InformalToFormalLincoln39")
model = AutoModelForCausalLM.from_pretrained("BigSalmon/InformalToFormalLincoln39")
```
```
How To Make Prompt:
informal english: i am very ready to do that just that.
Tra... | {} | BigSalmon/InformalToFormalLincoln39 | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-04-20T17:29:03+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
Keywords to sentences or sentence.
| [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n"
] |
null | null | # MMTAfrica
[Paper](https://aclanthology.org/2021.wmt-1.48/) - [Installation](#installation) - [Example](#example) - [Model checkpoint](#model-checkpoint) - [Citation](#citation)
This repository contains the official implementation of the MMTAfrica paper ([Emezue & Dossou, WMT 2021](https://aclanthology.org/2021.wmt... | {} | chrisjay/mmtafrica | null | [
"has_space",
"region:us"
] | null | 2022-04-20T17:35:17+00:00 | [] | [] | TAGS
#has_space #region-us
| # MMTAfrica
Paper - Installation - Example - Model checkpoint - Citation
This repository contains the official implementation of the MMTAfrica paper (Emezue & Dossou, WMT 2021).
We focus on the task of multilingual machine translation for African languages in the 2021 WMT Shared Task: Large-Scale Multilingual Machi... | [
"# MMTAfrica\nPaper - Installation - Example - Model checkpoint - Citation\n\n\nThis repository contains the official implementation of the MMTAfrica paper (Emezue & Dossou, WMT 2021).\n\nWe focus on the task of multilingual machine translation for African languages in the 2021 WMT Shared Task: Large-Scale Multili... | [
"TAGS\n#has_space #region-us \n",
"# MMTAfrica\nPaper - Installation - Example - Model checkpoint - Citation\n\n\nThis repository contains the official implementation of the MMTAfrica paper (Emezue & Dossou, WMT 2021).\n\nWe focus on the task of multilingual machine translation for African languages in the 2021 ... |
null | null | Hello! | {} | lberelidze/image-recognition | null | [
"region:us"
] | null | 2022-04-20T17:42:35+00:00 | [] | [] | TAGS
#region-us
| Hello! | [] | [
"TAGS\n#region-us \n"
] |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-uncased-finetuned-humordetection
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["f1"], "model-index": [{"name": "bert-base-uncased-finetuned-humordetection", "results": []}]} | thanawan/bert-base-uncased-finetuned-humordetection | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-20T17:57:09+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| bert-base-uncased-finetuned-humordetection
==========================================
This model is a fine-tuned version of bert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3136
* F1: 0.9586
Model description
-----------------
More information needed
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\... |
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_v5
This model is a fine-tuned version of [allenai/longformer-base-4096](https://huggingface.co/allenai/longformer-base-4096... | {"tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "Bert_v5", "results": []}]} | brad1141/Bert_v5 | null | [
"transformers",
"pytorch",
"tensorboard",
"longformer",
"token-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-20T18:15:19+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #longformer #token-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| Bert\_v5
========
This model is a fine-tuned version of allenai/longformer-base-4096 on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.9191
* Precision: 0.7612
* Recall: 0.8007
* F1: 0.5106
* Accuracy: 0.7357
Model description
-----------------
More information needed
Int... | [
"### 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 #tensorboard #longformer #token-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: 5e-05\n* train\\_batch\\_size: 1\n* e... |
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. -->
# t5-finetuned-parasci
This model is a fine-tuned version of [domenicrosati/t5-finetuned-parasci](https://huggingface.co/domenicro... | {"license": "apache-2.0", "tags": ["summarization", "generated_from_trainer"], "metrics": ["bleu"], "model-index": [{"name": "t5-finetuned-parasci", "results": []}]} | domenicrosati/t5-finetuned-parasci | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"summarization",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-20T19:15:06+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #summarization #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# t5-finetuned-parasci
This model is a fine-tuned version of domenicrosati/t5-finetuned-parasci on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0845
- Bleu: 19.5623
## Model description
More information needed
## Intended uses & limitations
More information needed
## Tra... | [
"# t5-finetuned-parasci\n\nThis model is a fine-tuned version of domenicrosati/t5-finetuned-parasci on the None dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 1.0845\n- Bleu: 19.5623",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore informa... | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #summarization #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# t5-finetuned-parasci\n\nThis model is a fine-tuned version of domenicrosati/t5-finetuned-parasci... |
text-generation | transformers |
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1503591435324563456/foUr... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/elonmusk-nicolebehnam-punk6529/1650487127903/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/elonmusk-nicolebehnam-punk6529 | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-20T19:37:10+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI CYBORG
Elon Musk & 6529 & nic b
@elonmusk-nicolebehnam-punk6529
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 r... | [] | [
"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/1505511419982213126/2Xfm... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true", "widget": [{"text": "My dream is"}]} | huggingtweets/nicolebehnam | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-20T20:05:40+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
nic b
@nicolebehnam
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"
] |
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-large-finetuned-ner
This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on the [... | {"language": ["en"], "license": "mit", "tags": ["generated_from_trainer"], "datasets": ["surrey-nlp/PLOD-unfiltered"], "metrics": ["precision", "recall", "f1", "accuracy"], "model_creators": ["Leonardo Zilio, Hadeel Saadany, Prashant Sharma, Diptesh Kanojia, Constantin Orasan"], "widget": [{"text": "Light dissolved ino... | surrey-nlp/roberta-large-finetuned-abbr | null | [
"transformers",
"pytorch",
"tf",
"roberta",
"token-classification",
"generated_from_trainer",
"en",
"dataset:surrey-nlp/PLOD-unfiltered",
"base_model:roberta-large",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-20T20:16:53+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #tf #roberta #token-classification #generated_from_trainer #en #dataset-surrey-nlp/PLOD-unfiltered #base_model-roberta-large #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| roberta-large-finetuned-ner
===========================
This model is a fine-tuned version of roberta-large on the PLOD-unfiltered dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1393
* Precision: 0.9663
* Recall: 0.9627
* F1: 0.9645
* Accuracy: 0.9608
Model description
---------------... | [
"### 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 #tf #roberta #token-classification #generated_from_trainer #en #dataset-surrey-nlp/PLOD-unfiltered #base_model-roberta-large #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were use... |
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
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-base-timit-demo", "results": []}]} | dlu66061/wav2vec2-base-timit-demo | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-04-20T20:55:56+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-base-timit-demo
========================
This model is a fine-tuned version of facebook/wav2vec2-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4094
* Wer: 0.2825
Model description
-----------------
More information needed
Intended uses & limitations
----... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 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* lr\\_scheduler\\_warmup\\_steps... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 32\n* eval\\_b... |
token-classification | spacy | Bio literature Named Entity Recognition using microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext transformer model. The model recognises the following entities:
**CD**: Chemical/Drugs, **DS**: Diseases, **GP**: Gene/Protein and **OG**: Organism
| Feature | Description |
| --- | --- |
| **Name** | `en_Biomed... | {"language": ["en"], "tags": ["spacy", "token-classification"], "widget": [{"text": "Blood glucose control is the primary strategy to prevent complications in diabetes. At the onset of kidney disease, therapies that inhibit components of the renin angiotensin system (RAS) are also indicated, but these approaches are no... | tsantosh7/en_BiomedNER_EuropePMC | null | [
"spacy",
"token-classification",
"en",
"model-index",
"region:us"
] | null | 2022-04-20T21:12:35+00:00 | [] | [
"en"
] | TAGS
#spacy #token-classification #en #model-index #region-us
| Bio literature Named Entity Recognition using microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext transformer model. The model recognises the following entities:
CD: Chemical/Drugs, DS: Diseases, GP: Gene/Protein and OG: Organism
### Label Scheme
View label scheme (4 labels for 1 components)
### Accur... | [
"### Label Scheme\n\n\n\nView label scheme (4 labels for 1 components)",
"### Accuracy"
] | [
"TAGS\n#spacy #token-classification #en #model-index #region-us \n",
"### Label Scheme\n\n\n\nView label scheme (4 labels for 1 components)",
"### Accuracy"
] |
summarization | transformers |
This model was introduced in [this paper](https://openreview.net/forum?id=BMVq5MELb9). It is an encoder-decoder model that was initialized with [DziriBERT](https://huggingface.co/alger-ia/dziribert) checkpoint. The model is finetuned for text summarization on [Goud dataset](https://huggingface.co/datasets/Goud/Goud-su... | {"language": ["Moroccan Arabic (MA)", "Modern Standard Arabic (MSA)"], "tags": ["summarization"], "datasets": ["Goud/Goud-sum"], "metrics": ["rouge"], "widget": [{"text": "\u062a\u0648\u0635\u0644 \u0627\u0644\u0627\u062a\u062d\u0627\u062f \u0627\u0644\u0623\u0648\u0631\u0648\u0628\u064a\u060c \u0641\u064a \u0648\u0642... | Goud/DziriBERT-summarization-goud | null | [
"transformers",
"pytorch",
"encoder-decoder",
"text2text-generation",
"summarization",
"dataset:Goud/Goud-sum",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-20T21:16:15+00:00 | [] | [
"Moroccan Arabic (MA)",
"Modern Standard Arabic (MSA)"
] | TAGS
#transformers #pytorch #encoder-decoder #text2text-generation #summarization #dataset-Goud/Goud-sum #autotrain_compatible #endpoints_compatible #region-us
|
This model was introduced in this paper. It is an encoder-decoder model that was initialized with DziriBERT checkpoint. The model is finetuned for text summarization on Goud dataset.
## How to use
This is how you can use this model
| [
"## How to use\n\nThis is how you can use this model"
] | [
"TAGS\n#transformers #pytorch #encoder-decoder #text2text-generation #summarization #dataset-Goud/Goud-sum #autotrain_compatible #endpoints_compatible #region-us \n",
"## How to use\n\nThis is how you can use this model"
] |
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-gl-jupyter7
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.c... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-large-xls-r-300m-gl-jupyter7", "results": []}]} | 4m1g0/wav2vec2-large-xls-r-300m-gl-jupyter7 | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-04-20T21:28:22+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-large-xls-r-300m-gl-jupyter7
=====================================
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: 0.1004
* Wer: 0.0647
Model description
-----------------
More information needed
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 1... |
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-53m-gl-jupyter5
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-large-xls-r-53m-gl-jupyter5", "results": []}]} | 4m1g0/wav2vec2-large-xls-r-53m-gl-jupyter5 | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-04-20T21:30:12+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-large-xls-r-53m-gl-jupyter5
====================================
This model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1025
* Wer: 0.0625
Model description
-----------------
More information needed
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 1... |
summarization | transformers |
This model was introduced in [this paper](https://openreview.net/forum?id=BMVq5MELb9). It is an encoder-decoder model that was initialized with [DarijaBERT](https://huggingface.co/Kamel/DarijaBERT) checkpoint. The model is finetuned for text summarization on [Goud dataset](https://huggingface.co/datasets/Goud/Goud-sum... | {"language": ["Moroccan Arabic (MA)", "Modern Standard Arabic (MSA)"], "tags": ["summarization"], "datasets": ["Goud/Goud-sum"], "metrics": ["rouge"], "widget": [{"text": "\u062a\u0648\u0635\u0644 \u0627\u0644\u0627\u062a\u062d\u0627\u062f \u0627\u0644\u0623\u0648\u0631\u0648\u0628\u064a\u060c \u0641\u064a \u0648\u0642... | Goud/DarijaBERT-summarization-goud | null | [
"transformers",
"pytorch",
"encoder-decoder",
"text2text-generation",
"summarization",
"dataset:Goud/Goud-sum",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-20T21:37:47+00:00 | [] | [
"Moroccan Arabic (MA)",
"Modern Standard Arabic (MSA)"
] | TAGS
#transformers #pytorch #encoder-decoder #text2text-generation #summarization #dataset-Goud/Goud-sum #autotrain_compatible #endpoints_compatible #region-us
|
This model was introduced in this paper. It is an encoder-decoder model that was initialized with DarijaBERT checkpoint. The model is finetuned for text summarization on Goud dataset.
## How to use
This is how you can use this model
| [
"## How to use\n\nThis is how you can use this model"
] | [
"TAGS\n#transformers #pytorch #encoder-decoder #text2text-generation #summarization #dataset-Goud/Goud-sum #autotrain_compatible #endpoints_compatible #region-us \n",
"## How to use\n\nThis is how you can use this model"
] |
summarization | transformers |
This model was introduced in [this paper](https://openreview.net/forum?id=BMVq5MELb9). It is an encoder-decoder model that was initialized with [bert-base-arabertv02-twitter](https://huggingface.co/aubmindlab/bert-base-arabertv02-twitter) checkpoint. The model is finetuned for text summarization on [Goud dataset](http... | {"language": ["Moroccan Arabic (MA)", "Modern Standard Arabic (MSA)"], "tags": ["summarization"], "datasets": ["Goud/Goud-sum"], "metrics": ["rouge"], "widget": [{"text": "\u062a\u0648\u0635\u0644 \u0627\u0644\u0627\u062a\u062d\u0627\u062f \u0627\u0644\u0623\u0648\u0631\u0648\u0628\u064a\u060c \u0641\u064a \u0648\u0642... | Goud/AraBERT-summarization-goud | null | [
"transformers",
"pytorch",
"encoder-decoder",
"text2text-generation",
"summarization",
"dataset:Goud/Goud-sum",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-20T22:02:15+00:00 | [] | [
"Moroccan Arabic (MA)",
"Modern Standard Arabic (MSA)"
] | TAGS
#transformers #pytorch #encoder-decoder #text2text-generation #summarization #dataset-Goud/Goud-sum #autotrain_compatible #endpoints_compatible #region-us
|
This model was introduced in this paper. It is an encoder-decoder model that was initialized with bert-base-arabertv02-twitter checkpoint. The model is finetuned for text summarization on Goud dataset.
## How to use
This is how you can use this model
| [
"## How to use\n\nThis is how you can use this model"
] | [
"TAGS\n#transformers #pytorch #encoder-decoder #text2text-generation #summarization #dataset-Goud/Goud-sum #autotrain_compatible #endpoints_compatible #region-us \n",
"## How to use\n\nThis is how you can use this model"
] |
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. -->
# roberta-base-culinary
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "roberta-base-culinary", "results": []}]} | juancavallotti/roberta-base-culinary | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-20T22:48:43+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| roberta-base-culinary
=====================
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: 1.1032
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 16",
"### Trainin... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n... |
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/1575782906/110930-ENMA-1... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/torstenvolk/1650500124030/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/torstenvolk | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-20T23:10:29+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Torsten Volk
@torstenvolk
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-timit-demo-colab3
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/w... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-base-timit-demo-colab3", "results": []}]} | obokkkk/wav2vec2-base-timit-demo-colab3 | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-04-21T00:39:21+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-colab3
===============================
This model is a fine-tuned version of facebook/wav2vec2-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4832
* Wer: 0.3419
Model description
-----------------
More information needed
Intended uses & li... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\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* lr\\_scheduler\\_warmup\\_steps... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 3... |
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": []}]} | ToToKr/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 | 2022-04-21T01:09:41+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-base-timit-demo-colab
==============================
This model is a fine-tuned version of facebook/wav2vec2-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4520
* Wer: 0.2286
Model description
-----------------
More information needed
Intended uses & limi... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\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* lr\\_scheduler\\_warmup\\_steps... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 3... |
text-classification | transformers | # INT8 camembert-base-mrpc
## Post-training dynamic quantization
### PyTorch
This is an INT8 PyTorch model quantized with [Intel® Neural Compressor](https://github.com/intel/neural-compressor).
The original fp32 model comes from the fine-tuned model [camembert-base-mrpc](https://huggingface.co/Intel/camembert-ba... | {"language": ["en"], "license": "mit", "tags": ["text-classfication", "int8", "Intel\u00ae Neural Compressor", "neural-compressor", "PostTrainingDynamic", "onnx"], "datasets": ["glue"], "metrics": ["f1"], "model-index": [{"name": "camembert-base-mrpc-int8-dynamic", "results": [{"task": {"type": "text-classification", "... | Intel/camembert-base-mrpc-int8-dynamic | null | [
"transformers",
"pytorch",
"onnx",
"camembert",
"text-classification",
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"int8",
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"PostTrainingDynamic",
"en",
"dataset:glue",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"... | null | 2022-04-21T01:40:01+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #onnx #camembert #text-classification #text-classfication #int8 #Intel® Neural Compressor #neural-compressor #PostTrainingDynamic #en #dataset-glue #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| INT8 camembert-base-mrpc
========================
Post-training dynamic quantization
----------------------------------
### PyTorch
This is an INT8 PyTorch model quantized with Intel® Neural Compressor.
The original fp32 model comes from the fine-tuned model camembert-base-mrpc.
The linear module URL.6.URL fa... | [
"### PyTorch\n\n\nThis is an INT8 PyTorch model quantized with Intel® Neural Compressor.\n\n\nThe original fp32 model comes from the fine-tuned model camembert-base-mrpc.\n\n\nThe linear module URL.6.URL falls back to fp32 to meet the 1% relative accuracy loss.",
"#### Test result",
"#### Load with Intel® Neura... | [
"TAGS\n#transformers #pytorch #onnx #camembert #text-classification #text-classfication #int8 #Intel® Neural Compressor #neural-compressor #PostTrainingDynamic #en #dataset-glue #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### PyTorch\n\n\nThis is an INT8 PyTorch model qu... |
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. -->
# camembert-base-mrpc
This model is a fine-tuned version of [camembert-base](https://huggingface.co/camembert-base) on the GLUE MR... | {"language": ["en"], "license": "mit", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "camembert-base-mrpc", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "GLUE MRPC", "type": "glue", "args": "m... | Intel/camembert-base-mrpc | null | [
"transformers",
"pytorch",
"camembert",
"text-classification",
"generated_from_trainer",
"en",
"dataset:glue",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-21T01:40:14+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #camembert #text-classification #generated_from_trainer #en #dataset-glue #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# camembert-base-mrpc
This model is a fine-tuned version of camembert-base on the GLUE MRPC dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4286
- Accuracy: 0.8505
- F1: 0.8928
- Combined Score: 0.8716
## Model description
More information needed
## Intended uses & limitations
More i... | [
"# camembert-base-mrpc\n\nThis model is a fine-tuned version of camembert-base on the GLUE MRPC dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.4286\n- Accuracy: 0.8505\n- F1: 0.8928\n- Combined Score: 0.8716",
"## Model description\n\nMore information needed",
"## Intended uses & ... | [
"TAGS\n#transformers #pytorch #camembert #text-classification #generated_from_trainer #en #dataset-glue #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# camembert-base-mrpc\n\nThis model is a fine-tuned version of camembert-base on the GLUE MRPC dataset.\nIt achieves the fo... |
null | fastai |
# Amazing!
Congratulations on hosting your fastai model on the Hugging Face Hub!
# Some next steps
1. Fill out this model card with more information (template below and [documentation here](https://huggingface.co/docs/hub/model-repos))!
2. Create a demo in Gradio or Streamlit using the 🤗Spaces ([documentation here... | {"tags": ["fastai"]} | espejelomar/fastai-dummy-learner | null | [
"fastai",
"region:us"
] | null | 2022-04-21T02:08:52+00:00 | [] | [] | TAGS
#fastai #region-us
|
# Amazing!
Congratulations on hosting your fastai model on the Hugging Face Hub!
# Some next steps
1. Fill out this model card with more information (template below and documentation here)!
2. Create a demo in Gradio or Streamlit using the Spaces (documentation here).
3. Join our fastai community on the Hugging Fa... | [
"# Amazing!\n\nCongratulations on hosting your fastai model on the Hugging Face Hub!",
"# Some next steps\n1. Fill out this model card with more information (template below and documentation here)!\n\n2. Create a demo in Gradio or Streamlit using the Spaces (documentation here).\n\n3. Join our fastai community on... | [
"TAGS\n#fastai #region-us \n",
"# Amazing!\n\nCongratulations on hosting your fastai model on the Hugging Face Hub!",
"# Some next steps\n1. Fill out this model card with more information (template below and documentation here)!\n\n2. Create a demo in Gradio or Streamlit using the Spaces (documentation here).\n... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# focus_sum_mT5_minshi
This model is a fine-tuned version of [csebuetnlp/mT5_multilingual_XLSum](https://huggingface.co/csebuetnlp... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "focus_sum_mT5_minshi", "results": []}]} | eagles/focus_sum_mT5_minshi | null | [
"transformers",
"pytorch",
"tensorboard",
"mt5",
"text2text-generation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-21T02:26:00+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #mt5 #text2text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| focus\_sum\_mT5\_minshi
=======================
This model is a fine-tuned version of csebuetnlp/mT5\_multilingual\_XLSum on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0930
Model description
-----------------
More information needed
Intended uses & limitations
-----... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\n* seed: 42\n* gradient\\_accumulation\\_steps: 16\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=... | [
"TAGS\n#transformers #pytorch #tensorboard #mt5 #text2text-generation #generated_from_trainer #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: 5e-05\n* train\\_... |
text-classification | transformers |
# INT8 BERT base uncased finetuned MRPC
## Post-training dynamic quantization
### PyTorch
This is an INT8 PyTorch model quantized with [huggingface/optimum-intel](https://github.com/huggingface/optimum-intel) through the usage of [Intel® Neural Compressor](https://github.com/intel/neural-compressor).
The origina... | {"language": "en", "license": "apache-2.0", "tags": ["text-classfication", "int8", "Intel\u00ae Neural Compressor", "PostTrainingDynamic", "onnx"], "datasets": ["mrpc"], "metrics": ["f1"]} | Intel/bert-base-uncased-mrpc-int8-dynamic | null | [
"transformers",
"pytorch",
"onnx",
"bert",
"text-classification",
"text-classfication",
"int8",
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"PostTrainingDynamic",
"en",
"dataset:mrpc",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-21T03:20:30+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #onnx #bert #text-classification #text-classfication #int8 #Intel® Neural Compressor #PostTrainingDynamic #en #dataset-mrpc #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| INT8 BERT base uncased finetuned MRPC
=====================================
Post-training dynamic quantization
----------------------------------
### PyTorch
This is an INT8 PyTorch model quantized with huggingface/optimum-intel through the usage of Intel® Neural Compressor.
The original fp32 model comes from t... | [
"### PyTorch\n\n\nThis is an INT8 PyTorch model quantized with huggingface/optimum-intel through the usage of Intel® Neural Compressor.\n\n\nThe original fp32 model comes from the fine-tuned model Intel/bert-base-uncased-mrpc.",
"#### Test result",
"#### Load with optimum:",
"### ONNX\n\n\nThis is an INT8 ONN... | [
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"### PyTorch\n\n\nThis is an INT8 PyTorch model quantized with huggingface/optim... |
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. -->
# GPT2_v5
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the None dataset.
It achieves the following... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "GPT2_v5", "results": []}]} | brad1141/GPT2_v5 | null | [
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"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-21T03:21:08+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #gpt2 #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| GPT2\_v5
========
This model is a fine-tuned version of gpt2 on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.7670
* Precision: 0.7725
* Recall: 0.8367
* F1: 0.4733
* Accuracy: 0.7646
Model description
-----------------
More information needed
Intended uses & limitations... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\n* seed: 42\n* gradient\\_accumulation\\_steps: 8\n* total\\_train\\_batch\\_size: 8\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-... |
text-generation | transformers |
# Japanese GPT2 Lyric Model
## Model description
The model is used to generate Japanese lyrics.
You can try it on my website [https://lyric.fab.moe/](https://lyric.fab.moe/#/)
## How to use
```python
import torch
from transformers import T5Tokenizer, GPT2LMHeadModel
tokenizer = T5Tokenizer.from_pretrained("skytn... | {"language": "ja", "license": "mit", "tags": ["ja", "japanese", "gpt2", "text-generation", "lm", "nlp"], "datasets": ["skytnt/japanese-lyric"], "widget": [{"text": "\u685c\u304c\u54b2\u304f"}]} | skytnt/gpt2-japanese-lyric-small | null | [
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"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-21T03:25:18+00:00 | [] | [
"ja"
] | TAGS
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|
# Japanese GPT2 Lyric Model
## Model description
The model is used to generate Japanese lyrics.
You can try it on my website URL
## How to use
## Training data
Training data contains 143,587 Japanese lyrics which are collected from uta-net by lyric_download | [
"# Japanese GPT2 Lyric Model",
"## Model description\n\nThe model is used to generate Japanese lyrics.\n\nYou can try it on my website URL",
"## How to use",
"## Training data\n\nTraining data contains 143,587 Japanese lyrics which are collected from uta-net by lyric_download"
] | [
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"# Japanese GPT2 Lyric Model",
"## Model description\n\nThe model is used to generate Ja... |
text-classification | transformers |
## Model Card
This model is a fine-tuned version of [MARBERTv2](https://huggingface.co/UBC-NLP/MARBERTv2). We finetuned this model for binary text classification `(Neutral-Hate)` on a custom Egyptian-Arabic hate speech dataset.
## Acknowledgement
Model fine-tuning, data collection, annotation and pre-processing for... | {"language": ["ar", "arz"], "license": "apache-2.0", "library_name": "transformers", "pipeline_tag": "text-classification", "widget": [{"text": "\u0639\u0627\u0645\u0644 \u0627\u064a\u0647 \u064a\u0627 \u0628\u0627\u0634\u0627 \u061f", "output": [{"label": "Neutral", "score": 0.999}, {"label": "Hate", "score": 0.001}]}... | IbrahimAmin/marbertv2-finetuned-egyptian-hate-speech-detection | null | [
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"bert",
"text-classification",
"ar",
"arz",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-21T03:27:17+00:00 | [] | [
"ar",
"arz"
] | TAGS
#transformers #pytorch #bert #text-classification #ar #arz #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
## Model Card
This model is a fine-tuned version of MARBERTv2. We finetuned this model for binary text classification '(Neutral-Hate)' on a custom Egyptian-Arabic hate speech dataset.
## Acknowledgement
Model fine-tuning, data collection, annotation and pre-processing for this work were performed as part of a Gradu... | [
"## Model Card\n\nThis model is a fine-tuned version of MARBERTv2. We finetuned this model for binary text classification '(Neutral-Hate)' on a custom Egyptian-Arabic hate speech dataset.",
"## Acknowledgement\n\nModel fine-tuning, data collection, annotation and pre-processing for this work were performed as par... | [
"TAGS\n#transformers #pytorch #bert #text-classification #ar #arz #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"## Model Card\n\nThis model is a fine-tuned version of MARBERTv2. We finetuned this model for binary text classification '(Neutral-Hate)' on a custom Egyptian-Arabic h... |
text-generation | transformers |
# Technoblade DialoGPT Model | {"tags": ["conversational"]} | Shivierra/DialoGPT-small-technoblade | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-21T03:47:18+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Technoblade DialoGPT Model | [
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"# Technoblade DialoGPT Model"
] |
text-classification | transformers | # INT8 roberta-base-mrpc
## Post-training static quantization
### PyTorch
This is an INT8 PyTorch model quantized with [Intel® Neural Compressor](https://github.com/intel/neural-compressor).
The original fp32 model comes from the fine-tuned model [roberta-base-mrpc](https://huggingface.co/Intel/roberta-base-mrpc... | {"language": ["en"], "license": "mit", "tags": ["text-classfication", "int8", "Intel\u00ae Neural Compressor", "neural-compressor", "PostTrainingStatic"], "datasets": ["glue"], "metrics": ["f1"], "model-index": [{"name": "roberta-base-mrpc-int8-static", "results": [{"task": {"type": "text-classification", "name": "Text... | Intel/roberta-base-mrpc-int8-static | null | [
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"PostTrainingStatic",
"en",
"dataset:glue",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-21T04:08:32+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #onnx #roberta #text-classification #text-classfication #int8 #Intel® Neural Compressor #neural-compressor #PostTrainingStatic #en #dataset-glue #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| INT8 roberta-base-mrpc
======================
Post-training static quantization
---------------------------------
### PyTorch
This is an INT8 PyTorch model quantized with Intel® Neural Compressor.
The original fp32 model comes from the fine-tuned model roberta-base-mrpc.
The calibration dataloader is the trai... | [
"### PyTorch\n\n\nThis is an INT8 PyTorch model quantized with Intel® Neural Compressor.\n\n\nThe original fp32 model comes from the fine-tuned model roberta-base-mrpc.\n\n\nThe calibration dataloader is the train dataloader. The default calibration sampling size 100 isn't divisible exactly by batch size 8, so the ... | [
"TAGS\n#transformers #pytorch #onnx #roberta #text-classification #text-classfication #int8 #Intel® Neural Compressor #neural-compressor #PostTrainingStatic #en #dataset-glue #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### PyTorch\n\n\nThis is an INT8 PyTorch model quant... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-base-mrpc
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the GLUE MRPC dat... | {"language": ["en"], "license": "mit", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "roberta-base-mrpc", "results": [{"task": {"type": "natural-language-inference", "name": "Natural Language Inference"}, "dataset": {"name": "glue", "type": "glue", "co... | Intel/roberta-base-mrpc | null | [
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"generated_from_trainer",
"en",
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"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-21T04:24:11+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #roberta #text-classification #generated_from_trainer #en #dataset-glue #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# roberta-base-mrpc
This model is a fine-tuned version of roberta-base on the GLUE MRPC dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5565
- Accuracy: 0.8775
- F1: 0.9138
- Combined Score: 0.8956
## Model description
More information needed
## Intended uses & limitations
More infor... | [
"# roberta-base-mrpc\n\nThis model is a fine-tuned version of roberta-base on the GLUE MRPC dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.5565\n- Accuracy: 0.8775\n- F1: 0.9138\n- Combined Score: 0.8956",
"## Model description\n\nMore information needed",
"## Intended uses & limi... | [
"TAGS\n#transformers #pytorch #roberta #text-classification #generated_from_trainer #en #dataset-glue #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# roberta-base-mrpc\n\nThis model is a fine-tuned version of roberta-base on the GLUE MRPC dataset.\nIt achieves the followin... |
summarization | fastai |
## Fine-tuned Text Summarization Model - CNNMail (blurr model)
This model is trained as shown in [the link](https://github.com/kurianbenoy/chaloRR/blob/master/TextSummarisation_Seq2Seq.ipynb).
Most of the code is developed based on [blurr tutorial on modelling with mid-level APIs](https://ohmeow.github.io/blurr/text... | {"license": "mit", "tags": ["fastai", "summarization"]} | kurianbenoy/blurr_cnnmail_textsumarrisation | null | [
"fastai",
"summarization",
"license:mit",
"region:us"
] | null | 2022-04-21T05:11:33+00:00 | [] | [] | TAGS
#fastai #summarization #license-mit #region-us
|
## Fine-tuned Text Summarization Model - CNNMail (blurr model)
This model is trained as shown in the link.
Most of the code is developed based on blurr tutorial on modelling with mid-level APIs
| [
"## Fine-tuned Text Summarization Model - CNNMail (blurr model)\n\nThis model is trained as shown in the link.\n\nMost of the code is developed based on blurr tutorial on modelling with mid-level APIs"
] | [
"TAGS\n#fastai #summarization #license-mit #region-us \n",
"## Fine-tuned Text Summarization Model - CNNMail (blurr model)\n\nThis model is trained as shown in the link.\n\nMost of the code is developed based on blurr tutorial on modelling with mid-level APIs"
] |
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. -->
# mbart-large-50-finetuned-ar-wikilingua
This model is a fine-tuned version of [facebook/mbart-large-50](https://huggingface.co/fa... | {"tags": ["summarization", "generated_from_trainer"], "datasets": ["wiki_lingua"], "model-index": [{"name": "mbart-large-50-finetuned-ar-wikilingua", "results": []}]} | ahmeddbahaa/mbart-large-50-finetuned-ar-wikilingua | null | [
"transformers",
"pytorch",
"mbart",
"text2text-generation",
"summarization",
"generated_from_trainer",
"dataset:wiki_lingua",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-21T05:26:05+00:00 | [] | [] | TAGS
#transformers #pytorch #mbart #text2text-generation #summarization #generated_from_trainer #dataset-wiki_lingua #autotrain_compatible #endpoints_compatible #region-us
| mbart-large-50-finetuned-ar-wikilingua
======================================
This model is a fine-tuned version of facebook/mbart-large-50 on the wiki\_lingua dataset.
It achieves the following results on the evaluation set:
* Loss: 4.0001
* Rouge-1: 22.11
* Rouge-2: 7.33
* Rouge-l: 19.75
* Gen Len: 59.4
* Bertsco... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-06\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 #mbart #text2text-generation #summarization #generated_from_trainer #dataset-wiki_lingua #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-06\n* train\\_ba... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-mrpc
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the G... | {"language": ["en"], "license": "mit", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "xlm-roberta-base-mrpc", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "GLUE MRPC", "type": "glue", "args": ... | Intel/xlm-roberta-base-mrpc | null | [
"transformers",
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"xlm-roberta",
"text-classification",
"generated_from_trainer",
"en",
"dataset:glue",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-21T05:35:48+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #xlm-roberta #text-classification #generated_from_trainer #en #dataset-glue #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# xlm-roberta-base-mrpc
This model is a fine-tuned version of xlm-roberta-base on the GLUE MRPC dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3703
- Accuracy: 0.8578
- F1: 0.9010
- Combined Score: 0.8794
## Model description
More information needed
## Intended uses & limitations
Mo... | [
"# xlm-roberta-base-mrpc\n\nThis model is a fine-tuned version of xlm-roberta-base on the GLUE MRPC dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.3703\n- Accuracy: 0.8578\n- F1: 0.9010\n- Combined Score: 0.8794",
"## Model description\n\nMore information needed",
"## Intended use... | [
"TAGS\n#transformers #pytorch #xlm-roberta #text-classification #generated_from_trainer #en #dataset-glue #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# xlm-roberta-base-mrpc\n\nThis model is a fine-tuned version of xlm-roberta-base on the GLUE MRPC dataset.\nIt achieves ... |
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-ner-xtreme-id
This model is a fine-tuned version of [bert-base-multilingual-uncased](https://huggingfac... | {"language": "id", "license": "apache-2.0", "tags": ["generated_from_keras_callback"], "datasets": ["xtreme"], "widget": [{"text": "Nama saya Tono, saya bekerja di Gotot dan tinggal di Mars."}], "model-index": [{"name": "Sultannn/bert-base-ft-ner-xtreme-id", "results": []}]} | Sultannn/bert-base-ft-ner-xtreme-id | null | [
"transformers",
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"token-classification",
"generated_from_keras_callback",
"id",
"dataset:xtreme",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-21T06:01:19+00:00 | [] | [
"id"
] | TAGS
#transformers #tf #bert #token-classification #generated_from_keras_callback #id #dataset-xtreme #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| Sultannn/bert-base-ft-ner-xtreme-id
===================================
This model is a fine-tuned version of bert-base-multilingual-uncased on an xtreme URL for NER downstream task.
Details of the downstream task (NER) - Dataset
----------------------------------------------
Metrics on evaluation set
----------... | [] | [
"TAGS\n#transformers #tf #bert #token-classification #generated_from_keras_callback #id #dataset-xtreme #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
null | fastai |
# Amazing!
Congratulations on hosting your fastai model on the Hugging Face Hub!
# Some next steps
1. Fill out this model card with more information (template below and [documentation here](https://huggingface.co/docs/hub/model-repos))!
2. Create a demo in Gradio or Streamlit using the 🤗Spaces ([documentation here... | {"tags": ["fastai"]} | osanseviero/fastai-dummy-learner | null | [
"fastai",
"region:us"
] | null | 2022-04-21T06:01:37+00:00 | [] | [] | TAGS
#fastai #region-us
|
# Amazing!
Congratulations on hosting your fastai model on the Hugging Face Hub!
# Some next steps
1. Fill out this model card with more information (template below and documentation here)!
2. Create a demo in Gradio or Streamlit using the Spaces (documentation here).
3. Join our fastai community on the Hugging Fa... | [
"# Amazing!\n\nCongratulations on hosting your fastai model on the Hugging Face Hub!",
"# Some next steps\n1. Fill out this model card with more information (template below and documentation here)!\n\n2. Create a demo in Gradio or Streamlit using the Spaces (documentation here).\n\n3. Join our fastai community on... | [
"TAGS\n#fastai #region-us \n",
"# Amazing!\n\nCongratulations on hosting your fastai model on the Hugging Face Hub!",
"# Some next steps\n1. Fill out this model card with more information (template below and documentation here)!\n\n2. Create a demo in Gradio or Streamlit using the Spaces (documentation here).\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-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["emotion"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "distilbert-base-uncased-finetuned-emotion", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "emotion", "type": "emotion... | seongwkim/distilbert-base-uncased-finetuned-emotion | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-21T06:26:46+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-emotion
=========================================
This model is a fine-tuned version of distilbert-base-uncased on the emotion dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2251
* Accuracy: 0.923
* F1: 0.9230
Model description
-----------------
Mor... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learn... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-ner
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/dis... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "distilbert-base-uncased-finetuned-ner", "results": []}]} | SnailPoo/distilbert-base-uncased-finetuned-ner | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-21T06:30:31+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-ner
=====================================
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1079
* Precision: 0.8408
* Recall: 0.8686
* F1: 0.8545
* Accuracy: 0.9638
Model descri... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #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\\_... |
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. -->
# xlnet-base-cased-mrpc
This model is a fine-tuned version of [xlnet-base-cased](https://huggingface.co/xlnet-base-cased) on the G... | {"language": ["en"], "license": "mit", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "xlnet-base-cased-mrpc", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "GLUE MRPC", "type": "glue", "args": ... | Intel/xlnet-base-cased-mrpc | null | [
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"pytorch",
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"generated_from_trainer",
"en",
"dataset:glue",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-21T06:44:55+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #xlnet #text-classification #generated_from_trainer #en #dataset-glue #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# xlnet-base-cased-mrpc
This model is a fine-tuned version of xlnet-base-cased on the GLUE MRPC dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7156
- Accuracy: 0.8456
- F1: 0.8897
- Combined Score: 0.8676
## Model description
More information needed
## Intended uses & limitations
Mo... | [
"# xlnet-base-cased-mrpc\n\nThis model is a fine-tuned version of xlnet-base-cased on the GLUE MRPC dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.7156\n- Accuracy: 0.8456\n- F1: 0.8897\n- Combined Score: 0.8676",
"## Model description\n\nMore information needed",
"## Intended use... | [
"TAGS\n#transformers #pytorch #xlnet #text-classification #generated_from_trainer #en #dataset-glue #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
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text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bart-large-mrpc
This model is a fine-tuned version of [facebook/bart-large](https://huggingface.co/facebook/bart-large) on the G... | {"language": ["en"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["accuracy", "f1"], "base_model": "facebook/bart-large", "model-index": [{"name": "bart-large-mrpc", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name":... | Intel/bart-large-mrpc | null | [
"transformers",
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"text-classification",
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"en",
"dataset:glue",
"base_model:facebook/bart-large",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-21T07:00:01+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bart #text-classification #generated_from_trainer #en #dataset-glue #base_model-facebook/bart-large #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# bart-large-mrpc
This model is a fine-tuned version of facebook/bart-large on the GLUE MRPC dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5684
- Accuracy: 0.8775
- F1: 0.9120
- Combined Score: 0.8947
## Model description
More information needed
## Intended uses & limitations
More ... | [
"# bart-large-mrpc\n\nThis model is a fine-tuned version of facebook/bart-large on the GLUE MRPC dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.5684\n- Accuracy: 0.8775\n- F1: 0.9120\n- Combined Score: 0.8947",
"## Model description\n\nMore information needed",
"## Intended uses &... | [
"TAGS\n#transformers #pytorch #bart #text-classification #generated_from_trainer #en #dataset-glue #base_model-facebook/bart-large #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# bart-large-mrpc\n\nThis model is a fine-tuned version of facebook/bart-large on the GLU... |
text-classification | transformers |
# CTRL44 Classification model
This is a pretrained version of the 4-class simplification operation classifier presented in the NAACL 2022 paper "Controllable Sentence Simplification via Operation Classification". It was trained on the IRSD classification dataset.
Predictions from this model can be used for input int... | {"language": "en"} | liamcripwell/ctrl44-clf | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"en",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-21T07:04:24+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #roberta #text-classification #en #autotrain_compatible #endpoints_compatible #region-us
|
# CTRL44 Classification model
This is a pretrained version of the 4-class simplification operation classifier presented in the NAACL 2022 paper "Controllable Sentence Simplification via Operation Classification". It was trained on the IRSD classification dataset.
Predictions from this model can be used for input int... | [
"# CTRL44 Classification model\n\nThis is a pretrained version of the 4-class simplification operation classifier presented in the NAACL 2022 paper \"Controllable Sentence Simplification via Operation Classification\". It was trained on the IRSD classification dataset.\n\nPredictions from this model can be used for... | [
"TAGS\n#transformers #pytorch #roberta #text-classification #en #autotrain_compatible #endpoints_compatible #region-us \n",
"# CTRL44 Classification model\n\nThis is a pretrained version of the 4-class simplification operation classifier presented in the NAACL 2022 paper \"Controllable Sentence Simplification via... |
text2text-generation | transformers |
# CTRL44 Simplification model
This is a pretrained version of the controllable simplification model presented in the NAACL 2022 paper "Controllable Sentence Simplification via Operation Classification". It was trained on the IRSD simplification dataset.
A control token is expected at the start of input sequences to ... | {"language": "en"} | liamcripwell/ctrl44-simp | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"en",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-21T07:38:41+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bart #text2text-generation #en #autotrain_compatible #endpoints_compatible #region-us
|
# CTRL44 Simplification model
This is a pretrained version of the controllable simplification model presented in the NAACL 2022 paper "Controllable Sentence Simplification via Operation Classification". It was trained on the IRSD simplification dataset.
A control token is expected at the start of input sequences to ... | [
"# CTRL44 Simplification model\n\nThis is a pretrained version of the controllable simplification model presented in the NAACL 2022 paper \"Controllable Sentence Simplification via Operation Classification\". It was trained on the IRSD simplification dataset.\n\nA control token is expected at the start of input seq... | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #en #autotrain_compatible #endpoints_compatible #region-us \n",
"# CTRL44 Simplification model\n\nThis is a pretrained version of the controllable simplification model presented in the NAACL 2022 paper \"Controllable Sentence Simplification via Operation C... |
summarization | transformers | # mBART (large-cc25 model), fine-tuned on the *Dataset for Automatic summarization of Catalan and Spanish newspaper Articles (DACSA)* dataset for Catalan
The mBART model was presented in [Multilingual Denoising Pre-training for Neural Machine Translation](https://arxiv.org/abs/2001.08210) by Yinhan Liu, Jiatao Gu, Nam... | {"language": "ca", "tags": ["summarization"], "widget": [{"text": "La Universitat Polit\u00e8cnica de Val\u00e8ncia (UPV), a trav\u00e9s del projecte Atenea \u201cplataforma de dones, art i tecnologia\u201d i en col\u00b7laboraci\u00f3 amb les companyies tecnol\u00f2giques Metric Salad i Zetalab, ha digitalitzat i mode... | ELiRF/mbart-large-cc25-dacsa-ca | null | [
"transformers",
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"safetensors",
"mbart",
"text2text-generation",
"summarization",
"ca",
"arxiv:2001.08210",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-21T07:50:41+00:00 | [
"2001.08210"
] | [
"ca"
] | TAGS
#transformers #pytorch #safetensors #mbart #text2text-generation #summarization #ca #arxiv-2001.08210 #autotrain_compatible #endpoints_compatible #region-us
| # mBART (large-cc25 model), fine-tuned on the *Dataset for Automatic summarization of Catalan and Spanish newspaper Articles (DACSA)* dataset for Catalan
The mBART model was presented in Multilingual Denoising Pre-training for Neural Machine Translation by Yinhan Liu, Jiatao Gu, Naman Goyal, Xian Li, Sergey Edunov Mar... | [
"# mBART (large-cc25 model), fine-tuned on the *Dataset for Automatic summarization of Catalan and Spanish newspaper Articles (DACSA)* dataset for Catalan\n\nThe mBART model was presented in Multilingual Denoising Pre-training for Neural Machine Translation by Yinhan Liu, Jiatao Gu, Naman Goyal, Xian Li, Sergey Edu... | [
"TAGS\n#transformers #pytorch #safetensors #mbart #text2text-generation #summarization #ca #arxiv-2001.08210 #autotrain_compatible #endpoints_compatible #region-us \n",
"# mBART (large-cc25 model), fine-tuned on the *Dataset for Automatic summarization of Catalan and Spanish newspaper Articles (DACSA)* dataset fo... |
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-gl-jupyter9
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.c... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-large-xls-r-300m-gl-jupyter9", "results": []}]} | 4m1g0/wav2vec2-large-xls-r-300m-gl-jupyter9 | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-04-21T08:07:56+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-large-xls-r-300m-gl-jupyter9
=====================================
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: 0.0970
* Wer: 0.0624
Model description
-----------------
More information needed
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 1... |
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-53m-gl-jupyter7
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-large-xls-r-53m-gl-jupyter7", "results": []}]} | 4m1g0/wav2vec2-large-xls-r-53m-gl-jupyter7 | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-04-21T08:12:17+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-large-xls-r-53m-gl-jupyter7
====================================
This model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1000
* Wer: 0.0639
Model description
-----------------
More information needed
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 1... |
fill-mask | transformers |
# TOD-XLMR
TOD-XLMR is a conversationally specialized multilingual version based on [XLM-RoBERTa](https://huggingface.co/xlm-roberta-base). It is pre-trained on English conversational corpora consisting of nine human-to-human multi-turn task-oriented dialog (TOD) datasets as proposed in the paper [TOD-BERT: Pre-train... | {"language": "multilingual", "license": "mit", "tags": ["exbert"]} | umanlp/TOD-XLMR | null | [
"transformers",
"pytorch",
"xlm-roberta",
"fill-mask",
"exbert",
"multilingual",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-21T08:29:28+00:00 | [] | [
"multilingual"
] | TAGS
#transformers #pytorch #xlm-roberta #fill-mask #exbert #multilingual #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# TOD-XLMR
TOD-XLMR is a conversationally specialized multilingual version based on XLM-RoBERTa. It is pre-trained on English conversational corpora consisting of nine human-to-human multi-turn task-oriented dialog (TOD) datasets as proposed in the paper TOD-BERT: Pre-trained Natural Language Understanding for Task-O... | [
"# TOD-XLMR\n\nTOD-XLMR is a conversationally specialized multilingual version based on XLM-RoBERTa. It is pre-trained on English conversational corpora consisting of nine human-to-human multi-turn task-oriented dialog (TOD) datasets as proposed in the paper TOD-BERT: Pre-trained Natural Language Understanding for ... | [
"TAGS\n#transformers #pytorch #xlm-roberta #fill-mask #exbert #multilingual #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# TOD-XLMR\n\nTOD-XLMR is a conversationally specialized multilingual version based on XLM-RoBERTa. It is pre-trained on English conversational corpora consisting o... |
translation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# model_zu-en_updated
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-mul-en](https://huggingface.co/Helsinki-NLP/opus... | {"license": "apache-2.0", "tags": ["translation", "generated_from_trainer"], "metrics": ["bleu"], "model-index": [{"name": "model_zu-en_updated", "results": []}]} | kabelomalapane/model_zu-en_updated | null | [
"transformers",
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"translation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-21T08:33:12+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #marian #text2text-generation #translation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# model_zu-en_updated
This model is a fine-tuned version of Helsinki-NLP/opus-mt-mul-en on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8306
- Bleu: 27.1218
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training an... | [
"# model_zu-en_updated\n\nThis model is a fine-tuned version of Helsinki-NLP/opus-mt-mul-en on the None dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 1.8306\n- Bleu: 27.1218",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information nee... | [
"TAGS\n#transformers #pytorch #tensorboard #marian #text2text-generation #translation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# model_zu-en_updated\n\nThis model is a fine-tuned version of Helsinki-NLP/opus-mt-mul-en on the None dataset.\nIt achieve... |
text-classification | spacy | | Feature | Description |
| --- | --- |
| **Name** | `en_tenarchexp3` |
| **Version** | `0.0.0` |
| **spaCy** | `>=3.2.4,<3.3.0` |
| **Default Pipeline** | `tok2vec`, `ner`, `textcat_multilabel` |
| **Components** | `tok2vec`, `ner`, `textcat_multilabel` |
| **Vectors** | 0 keys, 0 unique vectors (0 dimensions) |
| **S... | {"language": ["en"], "tags": ["spacy", "token-classification", "text-classification"]} | Valentijn/en_tenarchexp3 | null | [
"spacy",
"token-classification",
"text-classification",
"en",
"model-index",
"region:us"
] | null | 2022-04-21T08:34:38+00:00 | [] | [
"en"
] | TAGS
#spacy #token-classification #text-classification #en #model-index #region-us
|
### Label Scheme
View label scheme (7 labels for 2 components)
### Accuracy
| [
"### Label Scheme\n\n\n\nView label scheme (7 labels for 2 components)",
"### Accuracy"
] | [
"TAGS\n#spacy #token-classification #text-classification #en #model-index #region-us \n",
"### Label Scheme\n\n\n\nView label scheme (7 labels for 2 components)",
"### Accuracy"
] |
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/1469588644088451073/VEu0... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true", "widget": [{"text": "My dream is"}]} | huggingtweets/route2fi | null | [
"transformers",
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"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-21T09:07:34+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Route 2 FI
@route2fi
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"
] |
summarization | transformers | # mT5 (base model), fine-tuned on the *Dataset for Automatic summarization of Catalan and Spanish newspaper Articles (DACSA)* dataset for Catalan
The mT5 model was presented in [mT5: A massively multilingual pre-trained text-to-text transformer](https://arxiv.org/abs/2010.11934) by Linting Xue, Noah Constant, Adam Rob... | {"language": "ca", "tags": ["summarization"], "widget": [{"text": "La Universitat Polit\u00e8cnica de Val\u00e8ncia (UPV), a trav\u00e9s del projecte Atenea \u201cplataforma de dones, art i tecnologia\u201d i en col\u00b7laboraci\u00f3 amb les companyies tecnol\u00f2giques Metric Salad i Zetalab, ha digitalitzat i mode... | ELiRF/mt5-base-dacsa-ca | null | [
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"safetensors",
"mt5",
"text2text-generation",
"summarization",
"ca",
"arxiv:2010.11934",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-21T09:14:21+00:00 | [
"2010.11934"
] | [
"ca"
] | TAGS
#transformers #pytorch #safetensors #mt5 #text2text-generation #summarization #ca #arxiv-2010.11934 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # mT5 (base model), fine-tuned on the *Dataset for Automatic summarization of Catalan and Spanish newspaper Articles (DACSA)* dataset for Catalan
The mT5 model was presented in mT5: A massively multilingual pre-trained text-to-text transformer by Linting Xue, Noah Constant, Adam Roberts, Mihir Kale, Rami Al-Rfou, Adit... | [
"# mT5 (base model), fine-tuned on the *Dataset for Automatic summarization of Catalan and Spanish newspaper Articles (DACSA)* dataset for Catalan\n\nThe mT5 model was presented in mT5: A massively multilingual pre-trained text-to-text transformer by Linting Xue, Noah Constant, Adam Roberts, Mihir Kale, Rami Al-Rfo... | [
"TAGS\n#transformers #pytorch #safetensors #mt5 #text2text-generation #summarization #ca #arxiv-2010.11934 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# mT5 (base model), fine-tuned on the *Dataset for Automatic summarization of Catalan and Spanish newspaper Articles (D... |
null | keras |
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'learning_rate': ... | {"library_name": "keras"} | maretamasaeva/SharedTask_Group5_GloVeEmbeddings | null | [
"keras",
"region:us"
] | null | 2022-04-21T09:26:18+00:00 | [] | [] | TAGS
#keras #region-us
| Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following h... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'learning\\_rate': 1e-04, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}\n* training\\_precision: float32\n\n\nTraining Metrics\n----------------\n\n\... | [
"TAGS\n#keras #region-us \n",
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summarization | transformers | # mBART (large-cc25 model), fine-tuned on the *Dataset for Automatic summarization of Catalan and Spanish newspaper Articles (DACSA)* dataset for Spanish
The mBART model was presented in [Multilingual Denoising Pre-training for Neural Machine Translation](https://arxiv.org/abs/2001.08210) by Yinhan Liu, Jiatao Gu, Nam... | {"language": "es", "tags": ["summarization"], "widget": [{"text": "La Universitat Polit\u00e8cnica de Val\u00e8ncia (UPV), a trav\u00e9s del proyecto Atenea \u201cplataforma de mujeres, arte y tecnolog\u00eda\u201d y en colaboraci\u00f3n con las compa\u00f1\u00edas tecnol\u00f3gicas Metric Salad y Zetalab, ha digitaliz... | ELiRF/mbart-large-cc25-dacsa-es | null | [
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| # mBART (large-cc25 model), fine-tuned on the *Dataset for Automatic summarization of Catalan and Spanish newspaper Articles (DACSA)* dataset for Spanish
The mBART model was presented in Multilingual Denoising Pre-training for Neural Machine Translation by Yinhan Liu, Jiatao Gu, Naman Goyal, Xian Li, Sergey Edunov Mar... | [
"# mBART (large-cc25 model), fine-tuned on the *Dataset for Automatic summarization of Catalan and Spanish newspaper Articles (DACSA)* dataset for Spanish\n\nThe mBART model was presented in Multilingual Denoising Pre-training for Neural Machine Translation by Yinhan Liu, Jiatao Gu, Naman Goyal, Xian Li, Sergey Edu... | [
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summarization | transformers | # mT5 (base model), fine-tuned on the *Dataset for Automatic summarization of Catalan and Spanish newspaper Articles (DACSA)* dataset for Spanish
The mT5 model was presented in [mT5: A massively multilingual pre-trained text-to-text transformer](https://arxiv.org/abs/2010.11934) by Linting Xue, Noah Constant, Adam Rob... | {"language": "es", "tags": ["summarization"], "widget": [{"text": "La Universitat Polit\u00e8cnica de Val\u00e8ncia (UPV), a trav\u00e9s del proyecto Atenea \u201cplataforma de mujeres, arte y tecnolog\u00eda\u201d y en colaboraci\u00f3n con las compa\u00f1\u00edas tecnol\u00f3gicas Metric Salad y Zetalab, ha digitaliz... | ELiRF/mt5-base-dacsa-es | null | [
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| # mT5 (base model), fine-tuned on the *Dataset for Automatic summarization of Catalan and Spanish newspaper Articles (DACSA)* dataset for Spanish
The mT5 model was presented in mT5: A massively multilingual pre-trained text-to-text transformer by Linting Xue, Noah Constant, Adam Roberts, Mihir Kale, Rami Al-Rfou, Adit... | [
"# mT5 (base model), fine-tuned on the *Dataset for Automatic summarization of Catalan and Spanish newspaper Articles (DACSA)* dataset for Spanish\n\nThe mT5 model was presented in mT5: A massively multilingual pre-trained text-to-text transformer by Linting Xue, Noah Constant, Adam Roberts, Mihir Kale, Rami Al-Rfo... | [
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"# mT5 (base model), fine-tuned on the *Dataset for Automatic summarization of Catalan and Spanish newspaper Articles (D... |
automatic-speech-recognition | transformers |
# wav2vec2-common_voice-lithuanian-fairseq
| {"language": ["lt"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "common_voice", "generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-common_voice-lithuanian-fairseq", "results": []}]} | birgermoell/common-voice-lithuanian-fairseq | null | [
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] | null | 2022-04-21T10:29:35+00:00 | [] | [
"lt"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #common_voice #generated_from_trainer #lt #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
|
# wav2vec2-common_voice-lithuanian-fairseq
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"# wav2vec2-common_voice-lithuanian-fairseq"
] |
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. -->
# distilroberta-base-finetuned-wikitext2
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilr... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "distilroberta-base-finetuned-wikitext2", "results": []}]} | lamyae/distilroberta-base-finetuned-wikitext2 | null | [
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] | null | 2022-04-21T10:40:23+00:00 | [] | [] | TAGS
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| distilroberta-base-finetuned-wikitext2
======================================
This model is a fine-tuned version of distilroberta-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 3.0917
Model description
-----------------
More information needed
Intended uses & limita... | [
"### 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... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: ... |
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-few-shot-sentiment-model
This model is a fine-tuned version of [distilbert-base-uncased](https://hugging... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "distilbert-base-uncased-few-shot-sentiment-model", "results": []}]} | okho0653/distilbert-base-uncased-few-shot-sentiment-model | null | [
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"region:us"
] | null | 2022-04-21T11:20:11+00:00 | [] | [] | TAGS
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|
# distilbert-base-uncased-few-shot-sentiment-model
This model is a fine-tuned version of distilbert-base-uncased on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6819
- Accuracy: 0.75
- F1: 0.8
## Model description
More information needed
## Intended uses & limitations
Mor... | [
"# distilbert-base-uncased-few-shot-sentiment-model\n\nThis model is a fine-tuned version of distilbert-base-uncased on the None dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.6819\n- Accuracy: 0.75\n- F1: 0.8",
"## Model description\n\nMore information needed",
"## Intended uses ... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
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image-classification | keras | This model predicts the breed of the dog on the photo. It has been trained on the Stanford Dogs Dataset.
| {"language": ["en", "fr"], "license": "mit", "tags": ["image-classification", "keras", "tf"], "metrics": ["accuracy"]} | JbIPS/DogRace | null | [
"keras",
"tensorboard",
"image-classification",
"tf",
"en",
"fr",
"license:mit",
"has_space",
"region:us"
] | null | 2022-04-21T11:49:47+00:00 | [] | [
"en",
"fr"
] | TAGS
#keras #tensorboard #image-classification #tf #en #fr #license-mit #has_space #region-us
| This model predicts the breed of the dog on the photo. It has been trained on the Stanford Dogs Dataset.
| [] | [
"TAGS\n#keras #tensorboard #image-classification #tf #en #fr #license-mit #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/1062716172418699265/Obup... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/kfc_uki/1650549131420/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/kfc_uki | null | [
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"en",
"autotrain_compatible",
"endpoints_compatible",
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"region:us"
] | null | 2022-04-21T12:50:21+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
KFC UK
@kfc\_uki
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 | # INT8 bart-large-mrpc
## Post-training dynamic quantization
### PyTorch
This is an INT8 PyTorch model quantized with [huggingface/optimum-intel](https://github.com/huggingface/optimum-intel) through the usage of [Intel® Neural Compressor](https://github.com/intel/neural-compressor).
The original fp32 model come... | {"language": ["en"], "license": "apache-2.0", "tags": ["text-classfication", "int8", "Intel\u00ae Neural Compressor", "PostTrainingDynamic", "onnx"], "datasets": ["glue"], "metrics": ["f1"], "model-index": [{"name": "bart-large-mrpc-int8-dynamic", "results": [{"task": {"type": "text-classification", "name": "Text Class... | Intel/bart-large-mrpc-int8-dynamic | null | [
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"en"
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| INT8 bart-large-mrpc
====================
Post-training dynamic quantization
----------------------------------
### PyTorch
This is an INT8 PyTorch model quantized with huggingface/optimum-intel through the usage of Intel® Neural Compressor.
The original fp32 model comes from the fine-tuned model bart-large-mrp... | [
"### PyTorch\n\n\nThis is an INT8 PyTorch model quantized with huggingface/optimum-intel through the usage of Intel® Neural Compressor.\n\n\nThe original fp32 model comes from the fine-tuned model bart-large-mrpc.",
"#### Test result",
"#### Load with optimum:",
"### ONNX\n\n\nThis is an INT8 ONNX model quant... | [
"TAGS\n#transformers #pytorch #onnx #bart #text-classification #text-classfication #int8 #Intel® Neural Compressor #PostTrainingDynamic #en #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### PyTorch\n\n\nThis is an INT8 PyTorch model quantized with hugg... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["emotion"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "distilbert-base-uncased-finetuned-emotion", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "emotion", "type": "emotion... | RajaRang/distilbert-base-uncased-finetuned-emotion | null | [
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] | null | 2022-04-21T13:01:30+00:00 | [] | [] | TAGS
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| distilbert-base-uncased-finetuned-emotion
=========================================
This model is a fine-tuned version of distilbert-base-uncased on the emotion dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2183
* Accuracy: 0.925
* F1: 0.9251
Model description
-----------------
Mor... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learn... |
text-classification | transformers | # INT8 xlnet-base-cased-mrpc
## Post-training static quantization
### PyTorch
This is an INT8 PyTorch model quantized with [Intel® Neural Compressor](https://github.com/intel/neural-compressor).
The original fp32 model comes from the fine-tuned model [xlnet-base-cased-mrpc](https://huggingface.co/Intel/xlnet-base... | {"language": ["en"], "license": "mit", "tags": ["text-classfication", "int8", "neural-compressor", "Intel\u00ae Neural Compressor", "PostTrainingStatic", "onnx"], "datasets": ["glue"], "metrics": ["f1"], "model-index": [{"name": "xlnet-base-cased-mrpc-int8-static", "results": [{"task": {"type": "text-classification", "... | Intel/xlnet-base-cased-mrpc-int8-static | null | [
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] | null | 2022-04-21T13:23:38+00:00 | [] | [
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| INT8 xlnet-base-cased-mrpc
==========================
Post-training static quantization
---------------------------------
### PyTorch
This is an INT8 PyTorch model quantized with Intel® Neural Compressor.
The original fp32 model comes from the fine-tuned model xlnet-base-cased-mrpc.
The calibration dataloader... | [
"### PyTorch\n\n\nThis is an INT8 PyTorch model quantized with Intel® Neural Compressor.\n\n\nThe original fp32 model comes from the fine-tuned model xlnet-base-cased-mrpc.\n\n\nThe calibration dataloader is the train dataloader. The default calibration sampling size 300 isn't divisible exactly by batch size 8, so ... | [
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"### PyTorch\n\n\nThis is an INT8 PyTorch model quantiz... |
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. -->
# electra-small-discriminator-mrpc
This model is a fine-tuned version of [google/electra-small-discriminator](https://huggingface.... | {"language": ["en"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "electra-small-discriminator-mrpc", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "GLUE MRPC", "type"... | Intel/electra-small-discriminator-mrpc | null | [
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"en",
"dataset:glue",
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"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-21T13:32:59+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #electra #text-classification #generated_from_trainer #en #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# electra-small-discriminator-mrpc
This model is a fine-tuned version of google/electra-small-discriminator on the GLUE MRPC dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3909
- Accuracy: 0.8529
- F1: 0.8983
- Combined Score: 0.8756
## Model description
More information needed
## In... | [
"# electra-small-discriminator-mrpc\n\nThis model is a fine-tuned version of google/electra-small-discriminator on the GLUE MRPC dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.3909\n- Accuracy: 0.8529\n- F1: 0.8983\n- Combined Score: 0.8756",
"## Model description\n\nMore informatio... | [
"TAGS\n#transformers #pytorch #electra #text-classification #generated_from_trainer #en #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# electra-small-discriminator-mrpc\n\nThis model is a fine-tuned version of google/electra-small-discriminator on the ... |
text-classification | transformers |
# INT8 electra-small-discriminator-mrpc
## Post-training static quantization
### PyTorch
This is an INT8 PyTorch model quantized with [huggingface/optimum-intel](https://github.com/huggingface/optimum-intel) through the usage of [Intel® Neural Compressor](https://github.com/intel/neural-compressor).
The origina... | {"language": ["en"], "license": "mit", "tags": ["text-classfication", "int8", "Intel\u00ae Neural Compressor", "PostTrainingStatic", "onnx"], "datasets": ["glue"], "metrics": ["f1"], "model-index": [{"name": "electra-small-discriminator-mrpc-int8-static", "results": [{"task": {"type": "text-classification", "name": "Te... | Intel/electra-small-discriminator-mrpc-int8-static | null | [
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"dataset:glue",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-21T13:35:34+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #onnx #electra #text-classification #text-classfication #int8 #Intel® Neural Compressor #PostTrainingStatic #en #dataset-glue #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| INT8 electra-small-discriminator-mrpc
=====================================
Post-training static quantization
---------------------------------
### PyTorch
This is an INT8 PyTorch model quantized with huggingface/optimum-intel through the usage of Intel® Neural Compressor.
The original fp32 model comes from the... | [
"### PyTorch\n\n\nThis is an INT8 PyTorch model quantized with huggingface/optimum-intel through the usage of Intel® Neural Compressor.\n\n\nThe original fp32 model comes from the fine-tuned model electra-small-discriminator-mrpc.\n\n\nThe calibration dataloader is the train dataloader. The default calibration samp... | [
"TAGS\n#transformers #pytorch #onnx #electra #text-classification #text-classfication #int8 #Intel® Neural Compressor #PostTrainingStatic #en #dataset-glue #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### PyTorch\n\n\nThis is an INT8 PyTorch model quantized with huggingfa... |
fill-mask | transformers | # LuxemBERT
LuxemBERT is a BERT model for the Luxembourgish language.
It was trained using 6.1 million Luxembourgish sentences from various sources including the Luxembourgish Wikipedia, the Leipzig Corpora Collection and rtl.lu.
In addition, we partially translated 6.1 million sentences from the German Wikipedia fro... | {} | lothritz/LuxemBERT | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-04-21T13:52:44+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #fill-mask #autotrain_compatible #endpoints_compatible #has_space #region-us
| # LuxemBERT
LuxemBERT is a BERT model for the Luxembourgish language.
It was trained using 6.1 million Luxembourgish sentences from various sources including the Luxembourgish Wikipedia, the Leipzig Corpora Collection and URL.
In addition, we partially translated 6.1 million sentences from the German Wikipedia from G... | [
"# LuxemBERT\n\nLuxemBERT is a BERT model for the Luxembourgish language.\nIt was trained using 6.1 million Luxembourgish sentences from various sources including the Luxembourgish Wikipedia, the Leipzig Corpora Collection and URL.\nIn addition, we partially translated 6.1 million sentences from the German Wikipedi... | [
"TAGS\n#transformers #pytorch #bert #fill-mask #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# LuxemBERT\n\nLuxemBERT is a BERT model for the Luxembourgish language.\nIt was trained using 6.1 million Luxembourgish sentences from various sources including the Luxembourgish Wikipedia, the ... |
text2text-generation | transformers |
# Update Summarization with BART Large and Longformer Encoder Decoder
## Model description
This model is a Transformer-based model that supports long document generative sequence-to-sequence.
Based on [BART Large](https://huggingface.co/transformers/model_doc/bart.html) with [Longformer Encode Decoder](https://hugg... | {"language": ["en"], "tags": ["update summarization", "longformer", "transformers", "BART", "PyTorch", "Tensorboard", "led"], "metrics": ["edit distance", "ROUGE", "BertScore"]} | hyesunyun/update-summarization-led-edit-at-a-time | null | [
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"update summarization",
"longformer",
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"PyTorch",
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"en",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-21T13:55:31+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #tensorboard #led #text2text-generation #update summarization #longformer #BART #PyTorch #Tensorboard #en #autotrain_compatible #endpoints_compatible #region-us
|
# Update Summarization with BART Large and Longformer Encoder Decoder
## Model description
This model is a Transformer-based model that supports long document generative sequence-to-sequence.
Based on BART Large with Longformer Encode Decoder to allow for longer inputs.
The output is one edit operation which inclu... | [
"# Update Summarization with BART Large and Longformer Encoder Decoder",
"## Model description\n\nThis model is a Transformer-based model that supports long document generative sequence-to-sequence.\n\nBased on BART Large with Longformer Encode Decoder to allow for longer inputs.\n\nThe output is one edit operati... | [
"TAGS\n#transformers #pytorch #tensorboard #led #text2text-generation #update summarization #longformer #BART #PyTorch #Tensorboard #en #autotrain_compatible #endpoints_compatible #region-us \n",
"# Update Summarization with BART Large and Longformer Encoder Decoder",
"## Model description\n\nThis model is a Tr... |
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. -->
# aalogan/bert-finetuned-ner
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unkno... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "aalogan/bert-finetuned-ner", "results": []}]} | aalogan/bert-finetuned-ner | null | [
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"bert",
"token-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-21T14:39:21+00:00 | [] | [] | TAGS
#transformers #tf #bert #token-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| aalogan/bert-finetuned-ner
==========================
This model is a fine-tuned version of bert-base-cased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.0170
* Validation Loss: 0.0546
* Epoch: 3
Model description
-----------------
More information needed
Inte... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_steps': 3508, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': ... | [
"TAGS\n#transformers #tf #bert #token-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_... |
summarization | transformers |
# mT5-m2o-arabic-CrossSum
This repository contains the many-to-one (m2o) mT5 checkpoint finetuned on all cross-lingual pairs of the [CrossSum](https://huggingface.co/datasets/csebuetnlp/CrossSum) dataset, where the target summary was in **arabic**, i.e. this model tries to **summarize text written in any language in ... | {"language": ["am", "ar", "az", "bn", "my", "zh", "en", "fr", "gu", "ha", "hi", "ig", "id", "ja", "rn", "ko", "ky", "mr", "ne", "om", "ps", "fa", "pcm", "pt", "pa", "ru", "gd", "sr", "si", "so", "es", "sw", "ta", "te", "th", "ti", "tr", "uk", "ur", "uz", "vi", "cy", "yo"], "tags": ["summarization", "mT5"], "licenses": ... | csebuetnlp/mT5_m2o_arabic_crossSum | null | [
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"uk",
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"uz... | TAGS
#transformers #pytorch #mt5 #text2text-generation #summarization #mT5 #am #ar #az #bn #my #zh #en #fr #gu #ha #hi #ig #id #ja #rn #ko #ky #mr #ne #om #ps #fa #pcm #pt #pa #ru #gd #sr #si #so #es #sw #ta #te #th #ti #tr #uk #ur #uz #vi #cy #yo #arxiv-2112.08804 #autotrain_compatible #endpoints_compatible #text-gene... |
# mT5-m2o-arabic-CrossSum
This repository contains the many-to-one (m2o) mT5 checkpoint finetuned on all cross-lingual pairs of the CrossSum dataset, where the target summary was in arabic, i.e. this model tries to summarize text written in any language in Arabic. For finetuning details and scripts, see the paper and... | [
"# mT5-m2o-arabic-CrossSum\n\nThis repository contains the many-to-one (m2o) mT5 checkpoint finetuned on all cross-lingual pairs of the CrossSum dataset, where the target summary was in arabic, i.e. this model tries to summarize text written in any language in Arabic. For finetuning details and scripts, see the pap... | [
"TAGS\n#transformers #pytorch #mt5 #text2text-generation #summarization #mT5 #am #ar #az #bn #my #zh #en #fr #gu #ha #hi #ig #id #ja #rn #ko #ky #mr #ne #om #ps #fa #pcm #pt #pa #ru #gd #sr #si #so #es #sw #ta #te #th #ti #tr #uk #ur #uz #vi #cy #yo #arxiv-2112.08804 #autotrain_compatible #endpoints_compatible #tex... |
summarization | transformers |
# mT5-m2o-russian-CrossSum
This repository contains the many-to-one (m2o) mT5 checkpoint finetuned on all cross-lingual pairs of the [CrossSum](https://huggingface.co/datasets/csebuetnlp/CrossSum) dataset, where the target summary was in **russian**, i.e. this model tries to **summarize text written in any language i... | {"language": ["am", "ar", "az", "bn", "my", "zh", "en", "fr", "gu", "ha", "hi", "ig", "id", "ja", "rn", "ko", "ky", "mr", "ne", "om", "ps", "fa", "pcm", "pt", "pa", "ru", "gd", "sr", "si", "so", "es", "sw", "ta", "te", "th", "ti", "tr", "uk", "ur", "uz", "vi", "cy", "yo"], "tags": ["summarization", "mT5"], "licenses": ... | csebuetnlp/mT5_m2o_russian_crossSum | null | [
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"mt5",
"text2text-generation",
"summarization",
"mT5",
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"pcm",
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"ru",
"gd",
"sr",... | null | 2022-04-21T15:26:05+00:00 | [
"2112.08804"
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"th",
"ti",
"tr",
"uk",
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"uz... | TAGS
#transformers #pytorch #mt5 #text2text-generation #summarization #mT5 #am #ar #az #bn #my #zh #en #fr #gu #ha #hi #ig #id #ja #rn #ko #ky #mr #ne #om #ps #fa #pcm #pt #pa #ru #gd #sr #si #so #es #sw #ta #te #th #ti #tr #uk #ur #uz #vi #cy #yo #arxiv-2112.08804 #autotrain_compatible #endpoints_compatible #text-gene... |
# mT5-m2o-russian-CrossSum
This repository contains the many-to-one (m2o) mT5 checkpoint finetuned on all cross-lingual pairs of the CrossSum dataset, where the target summary was in russian, i.e. this model tries to summarize text written in any language in Russian. For finetuning details and scripts, see the paper ... | [
"# mT5-m2o-russian-CrossSum\n\nThis repository contains the many-to-one (m2o) mT5 checkpoint finetuned on all cross-lingual pairs of the CrossSum dataset, where the target summary was in russian, i.e. this model tries to summarize text written in any language in Russian. For finetuning details and scripts, see the ... | [
"TAGS\n#transformers #pytorch #mt5 #text2text-generation #summarization #mT5 #am #ar #az #bn #my #zh #en #fr #gu #ha #hi #ig #id #ja #rn #ko #ky #mr #ne #om #ps #fa #pcm #pt #pa #ru #gd #sr #si #so #es #sw #ta #te #th #ti #tr #uk #ur #uz #vi #cy #yo #arxiv-2112.08804 #autotrain_compatible #endpoints_compatible #tex... |
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"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "finetuning-sentiment-model-3000-samples", "results": []}]} | satish860/finetuning-sentiment-model-3000-samples | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-21T15:53:44+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# finetuning-sentiment-model-3000-samples
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0454
- Accuracy: 0.9886
- F1: 0.9571
## Model description
More information needed
## Intended uses & limitations
More ... | [
"# finetuning-sentiment-model-3000-samples\n\nThis model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.0454\n- Accuracy: 0.9886\n- F1: 0.9571",
"## Model description\n\nMore information needed",
"## Intended uses & ... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# finetuning-sentiment-model-3000-samples\n\nThis model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.\nIt... |
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. -->
# KevinForm/bert-finetuned-ner
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unk... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "KevinForm/bert-finetuned-ner", "results": []}]} | KevinForm/bert-finetuned-ner | null | [
"transformers",
"tf",
"bert",
"token-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-21T16:05:16+00:00 | [] | [] | TAGS
#transformers #tf #bert #token-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| KevinForm/bert-finetuned-ner
============================
This model is a fine-tuned version of bert-base-cased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.1831
* Validation Loss: 0.0644
* Epoch: 0
Model description
-----------------
More information needed
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'inner\\_optimizer': {'class\\_name': 'AdamWeightDecay', 'config': {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\... | [
"TAGS\n#transformers #tf #bert #token-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'inner\\_optimizer': {'class\\_name': '... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["emotion"], "metrics": ["accuracy", "f1"], "base_model": "distilbert-base-uncased", "model-index": [{"name": "distilbert-base-uncased-finetuned-emotion", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "datas... | rdchambers/distilbert-base-uncased-finetuned-emotion | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"base_model:distilbert-base-uncased",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-21T16:19:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #base_model-distilbert-base-uncased #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-emotion
=========================================
This model is a fine-tuned version of distilbert-base-uncased on the emotion dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2238
* Accuracy: 0.922
* F1: 0.9221
Model description
-----------------
Mor... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #base_model-distilbert-base-uncased #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters wer... |
token-classification | spacy | COVID 19 Bio Annotations
The dataset was taken from https://github.com/davidcampos/covid19-corpus
Dataset
The dataset was then split into several datasets each one representing one entity. Namely, Disorder, Species, Chemical or Drug, Gene and Protein, Enzyme, Anatomy, Biological Process, Molecular Function, Cellular ... | {"language": ["en"], "tags": ["spacy", "token-classification"], "widget": [{"text": "Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the cause of the coronavirus disease-19 (COVID-19) pandemic, was identified in late 2019 and caused >5 million deaths by February 2022. To date, targeted antiviral intervent... | tsantosh7/en_covid19_ner | null | [
"spacy",
"token-classification",
"en",
"model-index",
"region:us"
] | null | 2022-04-21T16:42:08+00:00 | [] | [
"en"
] | TAGS
#spacy #token-classification #en #model-index #region-us
| COVID 19 Bio Annotations
The dataset was taken from URL
Dataset
The dataset was then split into several datasets each one representing one entity. Namely, Disorder, Species, Chemical or Drug, Gene and Protein, Enzyme, Anatomy, Biological Process, Molecular Function, Cellular Component, Pathway and microRNA. Moreove... | [
"### Label Scheme\n\n\n\nView label scheme (10 labels for 1 components)",
"### Accuracy"
] | [
"TAGS\n#spacy #token-classification #en #model-index #region-us \n",
"### Label Scheme\n\n\n\nView label scheme (10 labels for 1 components)",
"### Accuracy"
] |
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",... | jackmleitch/distilbert-base-uncased-finetuned-clinc | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:clinc_oos",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-21T16:54:52+00:00 | [] | [] | TAGS
#transformers #pytorch #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: 0.7702
* Accuracy: 0.9184
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 #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* learning\\_rate:... |
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-distilled-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-distilled-clinc", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "clinc_oos", "type": "clinc_oos",... | jackmleitch/distilbert-base-uncased-distilled-clinc | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:clinc_oos",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-21T18:48:59+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-clinc_oos #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-distilled-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: 0.1004
* Accuracy: 0.9432
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: 10",
"### Train... | [
"TAGS\n#transformers #pytorch #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* learning\\_rate:... |
automatic-speech-recognition | transformers |
FineTuned wav2vec2 large 960H lv60 self pre-trained facebook model on 72 Hours of MI Diaries Data
WER 13 % -> 9.7% on 20 min test set of MI Diaries audio clips (https://mi-diaries.org/)
### Usage ###
model = Wav2Vec2ForCTC.from_pretrained("caurdy/wav2vec2-large-960h-lv60-self_MIDIARIES_72H_FT")
processor = Wav2Vec... | {"license": "afl-3.0"} | caurdy/wav2vec2-large-960h-lv60-self_MIDIARIES_72H_FT | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"license:afl-3.0",
"endpoints_compatible",
"region:us"
] | null | 2022-04-21T19:03:06+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #license-afl-3.0 #endpoints_compatible #region-us
|
FineTuned wav2vec2 large 960H lv60 self pre-trained facebook model on 72 Hours of MI Diaries Data
WER 13 % -> 9.7% on 20 min test set of MI Diaries audio clips (URL
### Usage ###
model = Wav2Vec2ForCTC.from_pretrained("caurdy/wav2vec2-large-960h-lv60-self_MIDIARIES_72H_FT")
processor = Wav2Vec2Processor.from_pretr... | [
"### Usage ###\nmodel = Wav2Vec2ForCTC.from_pretrained(\"caurdy/wav2vec2-large-960h-lv60-self_MIDIARIES_72H_FT\")\n\nprocessor = Wav2Vec2Processor.from_pretrained(\"caurdy/wav2vec2-large-960h-lv60-self_MIDIARIES_72H_FT\")"
] | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #license-afl-3.0 #endpoints_compatible #region-us \n",
"### Usage ###\nmodel = Wav2Vec2ForCTC.from_pretrained(\"caurdy/wav2vec2-large-960h-lv60-self_MIDIARIES_72H_FT\")\n\nprocessor = Wav2Vec2Processor.from_pretrained(\"caurdy/wav2vec2-large-96... |
null | transformers | This model contains the pre-trained ResNet50 R3M model from the paper "R3M: A Universal Visual Representation for Robot Manipulation" (Nair et al.) The model is trained on the Ego4D dataset using time-contrastive learning, video-language alignment, and sparsity objectives. It is used for efficient downstream robotic le... | {} | surajnair/r3m-50 | null | [
"transformers",
"pytorch",
"r3m",
"endpoints_compatible",
"region:us"
] | null | 2022-04-21T19:07:23+00:00 | [] | [] | TAGS
#transformers #pytorch #r3m #endpoints_compatible #region-us
| This model contains the pre-trained ResNet50 R3M model from the paper "R3M: A Universal Visual Representation for Robot Manipulation" (Nair et al.) The model is trained on the Ego4D dataset using time-contrastive learning, video-language alignment, and sparsity objectives. It is used for efficient downstream robotic le... | [] | [
"TAGS\n#transformers #pytorch #r3m #endpoints_compatible #region-us \n"
] |
null | null | \nhello
| {"license": "apache-2.0"} | julien-c/gpt2-from-colab | null | [
"pytorch",
"license:apache-2.0",
"region:us"
] | null | 2022-04-21T19:07:38+00:00 | [] | [] | TAGS
#pytorch #license-apache-2.0 #region-us
| \nhello
| [] | [
"TAGS\n#pytorch #license-apache-2.0 #region-us \n"
] |
null | transformers | This model contains the pre-trained ResNet34 R3M model from the paper "R3M: A Universal Visual Representation for Robot Manipulation" (Nair et al.) The model is trained on the Ego4D dataset using time-contrastive learning, video-language alignment, and sparsity objectives. It is used for efficient downstream robotic le... | {} | surajnair/r3m-34 | null | [
"transformers",
"pytorch",
"r3m",
"endpoints_compatible",
"region:us"
] | null | 2022-04-21T19:09:11+00:00 | [] | [] | TAGS
#transformers #pytorch #r3m #endpoints_compatible #region-us
| This model contains the pre-trained ResNet34 R3M model from the paper "R3M: A Universal Visual Representation for Robot Manipulation" (Nair et al.) The model is trained on the Ego4D dataset using time-contrastive learning, video-language alignment, and sparsity objectives. It is used for efficient downstream robotic le... | [] | [
"TAGS\n#transformers #pytorch #r3m #endpoints_compatible #region-us \n"
] |
null | null |
# Neural Cellular Automata (Based on https://distill.pub/2020/growing-ca/) implemented in Jax (Flax)
## Installation
from source
```bash
git clone git@github.com:shyamsn97/jax-nca.git
cd jax-nca
python setup.py install
```
from PYPI
```bash
pip install jax-nca
```
## How do NCAs work?
For more information, view ... | {"tags": ["image-generation"]} | shyamsn97/Jax-NCA | null | [
"image-generation",
"region:us"
] | null | 2022-04-21T19:09:15+00:00 | [] | [] | TAGS
#image-generation #region-us
|
# Neural Cellular Automata (Based on URL implemented in Jax (Flax)
## Installation
from source
from PYPI
## How do NCAs work?
For more information, view the awesome article URL -- Mordvintsev, et al., "Growing Neural Cellular Automata", Distill, 2020
Image below describes a single update step: URL
## Why Ja... | [
"# Neural Cellular Automata (Based on URL implemented in Jax (Flax)",
"## Installation\nfrom source\n\n\n\nfrom PYPI",
"## How do NCAs work?\nFor more information, view the awesome article URL -- Mordvintsev, et al., \"Growing Neural Cellular Automata\", Distill, 2020\n\nImage below describes a single update st... | [
"TAGS\n#image-generation #region-us \n",
"# Neural Cellular Automata (Based on URL implemented in Jax (Flax)",
"## Installation\nfrom source\n\n\n\nfrom PYPI",
"## How do NCAs work?\nFor more information, view the awesome article URL -- Mordvintsev, et al., \"Growing Neural Cellular Automata\", Distill, 2020\... |
null | transformers | This model contains the pre-trained ResNet18 R3M model from the paper "R3M: A Universal Visual Representation for Robot Manipulation" (Nair et al.) The model is trained on the Ego4D dataset using time-contrastive learning, video-language alignment, and sparsity objectives. It is used for efficient downstream robotic le... | {} | surajnair/r3m-18 | null | [
"transformers",
"pytorch",
"r3m",
"endpoints_compatible",
"region:us"
] | null | 2022-04-21T19:10:15+00:00 | [] | [] | TAGS
#transformers #pytorch #r3m #endpoints_compatible #region-us
| This model contains the pre-trained ResNet18 R3M model from the paper "R3M: A Universal Visual Representation for Robot Manipulation" (Nair et al.) The model is trained on the Ego4D dataset using time-contrastive learning, video-language alignment, and sparsity objectives. It is used for efficient downstream robotic le... | [] | [
"TAGS\n#transformers #pytorch #r3m #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. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["emotion"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "distilbert-base-uncased-finetuned-emotion", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "emotion", "type": "emotion... | Sarim24/distilbert-base-uncased-finetuned-emotion | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-21T19:58:07+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-emotion
=========================================
This model is a fine-tuned version of distilbert-base-uncased on the emotion dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2135
* Accuracy: 0.9305
* F1: 0.9307
Model description
-----------------
Mo... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learn... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-de
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-b... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["xtreme"], "metrics": ["f1"], "model-index": [{"name": "xlm-roberta-base-finetuned-panx-de", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "xtreme", "type": "xtreme", "args": "PAN-X.de"}, "me... | Sarim24/xlm-roberta-base-finetuned-panx-de | null | [
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-21T21:07:59+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-panx-de
==================================
This model is a fine-tuned version of xlm-roberta-base on the xtreme dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1374
* F1: 0.8627
Model description
-----------------
More information needed
Intended uses & l... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 24\n* eval\\_batch\\_size: 24\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_... |
text-generation | transformers |
# GPT-NEO-Model for Lean Tactics
In the project, we used an HuggingFace GPT-NEO small model and fine-tuned the tactic dataset. The Input should be of the form
```
<GOAL> Goal <PROOFSTEP>
```
The model can easily be accessed using the following code.
```
from transformers import GPT2Tokenizer, GPTNeoForCausalLM
im... | {"license": "apache-2.0"} | Saisam/gpt-neo-math-small | null | [
"transformers",
"pytorch",
"gpt_neo",
"text-generation",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-21T22:46:06+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt_neo #text-generation #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# GPT-NEO-Model for Lean Tactics
In the project, we used an HuggingFace GPT-NEO small model and fine-tuned the tactic dataset. The Input should be of the form
The model can easily be accessed using the following code.
More Information can be found at URL
The current model beats the GPT-F for minif2f benchmark... | [
"# GPT-NEO-Model for Lean Tactics\n\n\nIn the project, we used an HuggingFace GPT-NEO small model and fine-tuned the tactic dataset. The Input should be of the form\n\n\nThe model can easily be accessed using the following code. \n\n\n\nMore Information can be found at URL\n\nThe current model beats the GPT-F for m... | [
"TAGS\n#transformers #pytorch #gpt_neo #text-generation #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# GPT-NEO-Model for Lean Tactics\n\n\nIn the project, we used an HuggingFace GPT-NEO small model and fine-tuned the tactic dataset. The Input should be of the form\n\n\nThe 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"], "model-index": [{"name": "finetuning-sentiment-model-3000-samples", "results": []}]} | PrasunMishra/finetuning-sentiment-model-3000-samples | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-21T23:59:22+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-imdb #license-apache-2.0 #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.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### T... | [
"# finetuning-sentiment-model-3000-samples\n\nThis model is a fine-tuned version of distilbert-base-uncased on the imdb 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 #distilbert #text-classification #generated_from_trainer #dataset-imdb #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# finetuning-sentiment-model-3000-samples\n\nThis model is a fine-tuned version of distilbert-base-uncased on the imdb ... |
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-zero-shot-sentiment-model
This model is a fine-tuned version of [distilbert-base-uncased](https://huggin... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "distilbert-base-uncased-zero-shot-sentiment-model", "results": []}]} | okho0653/distilbert-base-uncased-zero-shot-sentiment-model | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-22T00:28:21+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# distilbert-base-uncased-zero-shot-sentiment-model
This model is a fine-tuned version of distilbert-base-uncased on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training proced... | [
"# distilbert-base-uncased-zero-shot-sentiment-model\n\nThis model is a fine-tuned version of distilbert-base-uncased on the None dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information need... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# distilbert-base-uncased-zero-shot-sentiment-model\n\nThis model is a fine-tuned version of distilbert-base-uncased on the None data... |
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. -->
# sms_spam_detection-manning
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-bas... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "sms_spam_detection-manning", "results": []}]} | satish860/sms_spam_detection-manning | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-22T01:20:36+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# sms_spam_detection-manning
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0512
- Accuracy: 0.9886
- F1: 0.9573
## Model description
More information needed
## Intended uses & limitations
More information n... | [
"# sms_spam_detection-manning\n\nThis model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.0512\n- Accuracy: 0.9886\n- F1: 0.9573",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# sms_spam_detection-manning\n\nThis model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.\nIt achieves the... |
text-generation | transformers |
# Arbiter DialoGPT Model | {"tags": ["conversational"]} | Scaprod/DialoGPT-small-arbiter | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-22T01:45:03+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Arbiter DialoGPT Model | [
"# Arbiter DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Arbiter DialoGPT Model"
] |
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-960h-timit-demo-colab
This model is a fine-tuned version of [facebook/wav2vec2-base-960h](https://huggingface.co/f... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-base-960h-timit-demo-colab", "results": []}]} | obokkkk/wav2vec2-base-960h-timit-demo-colab | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-04-22T01:59:15+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-base-960h-timit-demo-colab
===================================
This model is a fine-tuned version of facebook/wav2vec2-base-960h on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2002
* Wer: 0.2160
Model description
-----------------
More information needed
Inten... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\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* lr\\_scheduler\\_warmup\\_steps... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 3... |
question-answering | transformers |
Question Answering model
| {"license": "osl-3.0"} | AswiN037/xlm-roberta-squad-tamil | null | [
"transformers",
"pytorch",
"xlm-roberta",
"question-answering",
"license:osl-3.0",
"endpoints_compatible",
"region:us"
] | null | 2022-04-22T02:55:42+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #question-answering #license-osl-3.0 #endpoints_compatible #region-us
|
Question Answering model
| [] | [
"TAGS\n#transformers #pytorch #xlm-roberta #question-answering #license-osl-3.0 #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. -->
# hubert-large-ls960-ft-timit
This model is a fine-tuned version of [facebook/hubert-large-ls960-ft](https://huggingface.co/facebo... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "hubert-large-ls960-ft-timit", "results": []}]} | obokkkk/hubert-large-ls960-ft-timit | null | [
"transformers",
"pytorch",
"tensorboard",
"hubert",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-04-22T03:05:24+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #hubert #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| hubert-large-ls960-ft-timit
===========================
This model is a fine-tuned version of facebook/hubert-large-ls960-ft on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1074
* Wer: 0.1708
Model description
-----------------
More information needed
Intended uses & li... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\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* lr\\_scheduler\\_warmup\\_steps... | [
"TAGS\n#transformers #pytorch #tensorboard #hubert #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 32\... |
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. -->
# ctrlv-speechrecognition-model
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "ctrlv-speechrecognition-model", "results": []}]} | proseph/ctrlv-speechrecognition-model | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-04-22T03:30:16+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| ctrlv-speechrecognition-model
=============================
This model is a fine-tuned version of facebook/wav2vec2-base on the TIMIT dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4730
* Wer: 0.3031
Test WER in TIMIT dataset
-------------------------
* Wer: 0.189
Google Colab Not... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\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* lr\\_scheduler\\_warmup\\_steps... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 3... |
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. -->
# filipino-wav2vec2-l-xls-r-300m-test
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["filipino_voice"], "model-index": [{"name": "filipino-wav2vec2-l-xls-r-300m-test", "results": []}]} | Khalsuu/filipino-wav2vec2-l-xls-r-300m-test | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:filipino_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-04-22T03:36:24+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-filipino_voice #license-apache-2.0 #endpoints_compatible #region-us
| filipino-wav2vec2-l-xls-r-300m-test
===================================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the filipino\_voice dataset.
It achieves the following results on the evaluation set:
* Loss: 0.7753
* Wer: 0.4831
Model description
-----------------
More information ne... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.001\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-filipino_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: 0.001\n* ... |
text-classification | transformers | Buchwald-Hartwig-Yield-prediction is a finetuned model based on 'DeepChem/ChemBERTa-77M-MLM' for yield prediction.
For training and testing the model, 'https://tdcommons.ai/single_pred_tasks/yields' data was used with 70/30 random splitting for the train and test dataset.
the R2 score is equal to 97.2879% and val_loss ... | {} | Parsa/Buchwald-Hartwig-Yield-prediction | null | [
"transformers",
"pytorch",
"safetensors",
"roberta",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-22T03:48:36+00:00 | [] | [] | TAGS
#transformers #pytorch #safetensors #roberta #text-classification #autotrain_compatible #endpoints_compatible #region-us
| Buchwald-Hartwig-Yield-prediction is a finetuned model based on 'DeepChem/ChemBERTa-77M-MLM' for yield prediction.
For training and testing the model, 'URL data was used with 70/30 random splitting for the train and test dataset.
the R2 score is equal to 97.2879% and val_loss is equal to 0.0020.
for using it, your inpu... | [] | [
"TAGS\n#transformers #pytorch #safetensors #roberta #text-classification #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-classification | transformers | # BERT base model (uncased)
Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/abs/1810.04805) and first released in
[this repository](https://github.com/google-research/bert). This model is uncased: it does not make a difference
b... | {"language": "en", "license": "apache-2.0", "tags": ["exbert"], "datasets": ["Confidential"]} | sbcBI/sentiment_analysis | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"exbert",
"en",
"dataset:Confidential",
"arxiv:1810.04805",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-04-22T05:31:09+00:00 | [
"1810.04805"
] | [
"en"
] | TAGS
#transformers #pytorch #distilbert #text-classification #exbert #en #dataset-Confidential #arxiv-1810.04805 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| # BERT base model (uncased)
Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
this paper and first released in
this repository. This model is uncased: it does not make a difference
between english and English.
## Model description
BERT is a transformers model... | [
"# BERT base model (uncased)\n\nPretrained model on English language using a masked language modeling (MLM) objective. It was introduced in\nthis paper and first released in\nthis repository. This model is uncased: it does not make a difference\nbetween english and English.",
"## Model description\n\nBERT is a tr... | [
"TAGS\n#transformers #pytorch #distilbert #text-classification #exbert #en #dataset-Confidential #arxiv-1810.04805 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# BERT base model (uncased)\n\nPretrained model on English language using a masked language modeling (MLM) ... |
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. -->
# wolof
This model is a fine-tuned version of [LeBenchmark/wav2vec2-FR-2.6K-base](https://huggingface.co/LeBenchmark/wav2vec2-FR-2... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wolof", "results": []}]} | abdouaziiz/wav2vec2-WOLOF-2.6K-base | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-04-22T06:10:27+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wolof
=====
This model is a fine-tuned version of LeBenchmark/wav2vec2-FR-2.6K-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2816
* Wer: 0.3897
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
M... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 20\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 20\n* eval\\_b... |
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-Ganesh123
This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset.
## Model d... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "bert-base-uncased-Ganesh123", "results": []}]} | stevems1/bert-base-uncased-Ganesh123 | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-22T06:12:32+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
|
# bert-base-uncased-Ganesh123
This model is a fine-tuned version of [](URL on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
Th... | [
"# bert-base-uncased-Ganesh123\n\nThis model is a fine-tuned version of [](URL on an unknown dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"##... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"# bert-base-uncased-Ganesh123\n\nThis model is a fine-tuned version of [](URL on an unknown dataset.",
"## Model description\n\nMore information needed",
"## Intende... |
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-base | null | [
"optimum_habana",
"license:apache-2.0",
"region:us"
] | null | 2022-04-22T06:20:57+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 Base model HPU configuration\n\nThis model only contains the 'GaudiConfig' file for running the roberta-base 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 mixed... | [
"TAGS\n#optimum_habana #license-apache-2.0 #region-us \n",
"## RoBERTa Base model HPU configuration\n\nThis model only contains the 'GaudiConfig' file for running the roberta-base model on Habana's Gaudi processors (HPU).\n\nThis model contains no model weights, only a GaudiConfig.\n\nThis enables to specify:\n- ... |
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