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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
translation | transformers | # opus-mt-tc-base-uk-fi
Neural machine translation model for translating from Ukrainian (uk) to Finnish (fi).
This model is part of the [OPUS-MT project](https://github.com/Helsinki-NLP/Opus-MT), an effort to make neural machine translation models widely available and accessible for many languages in the world. All m... | {"language": ["fi", "uk"], "license": "cc-by-4.0", "tags": ["translation", "opus-mt-tc"], "model-index": [{"name": "opus-mt-tc-base-uk-fi", "results": [{"task": {"type": "translation", "name": "Translation ukr-fin"}, "dataset": {"name": "flores101-devtest", "type": "flores_101", "args": "ukr fin devtest"}, "metrics": [... | Helsinki-NLP/opus-mt-tc-base-uk-fi | null | [
"transformers",
"pytorch",
"tf",
"safetensors",
"marian",
"text2text-generation",
"translation",
"opus-mt-tc",
"fi",
"uk",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-24T06:20:21+00:00 | [] | [
"fi",
"uk"
] | TAGS
#transformers #pytorch #tf #safetensors #marian #text2text-generation #translation #opus-mt-tc #fi #uk #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
| opus-mt-tc-base-uk-fi
=====================
Neural machine translation model for translating from Ukrainian (uk) to Finnish (fi).
This model is part of the OPUS-MT project, an effort to make neural machine translation models widely available and accessible for many languages in the world. All models are originally ... | [] | [
"TAGS\n#transformers #pytorch #tf #safetensors #marian #text2text-generation #translation #opus-mt-tc #fi #uk #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
null | null | create environment
```
conda env create -v -f AbstractGenerator.yml
conda activate Recipe-Creator
``` | {} | franz96521/AbstractGenerator | null | [
"tensorboard",
"region:us"
] | null | 2022-03-24T06:54:20+00:00 | [] | [] | TAGS
#tensorboard #region-us
| create environment
| [] | [
"TAGS\n#tensorboard #region-us \n"
] |
translation | transformers | # opus-mt-tc-base-uk-ro
Neural machine translation model for translating from Ukrainian (uk) to Romanian (ro).
This model is part of the [OPUS-MT project](https://github.com/Helsinki-NLP/Opus-MT), an effort to make neural machine translation models widely available and accessible for many languages in the world. All ... | {"language": ["ro", "uk"], "license": "cc-by-4.0", "tags": ["translation", "opus-mt-tc"], "model-index": [{"name": "opus-mt-tc-base-uk-ro", "results": [{"task": {"type": "translation", "name": "Translation ukr-ron"}, "dataset": {"name": "flores101-devtest", "type": "flores_101", "args": "ukr ron devtest"}, "metrics": [... | Helsinki-NLP/opus-mt-tc-base-uk-ro | null | [
"transformers",
"pytorch",
"tf",
"safetensors",
"marian",
"text2text-generation",
"translation",
"opus-mt-tc",
"ro",
"uk",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-24T06:57:51+00:00 | [] | [
"ro",
"uk"
] | TAGS
#transformers #pytorch #tf #safetensors #marian #text2text-generation #translation #opus-mt-tc #ro #uk #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
| opus-mt-tc-base-uk-ro
=====================
Neural machine translation model for translating from Ukrainian (uk) to Romanian (ro).
This model is part of the OPUS-MT project, an effort to make neural machine translation models widely available and accessible for many languages in the world. All models are originally... | [] | [
"TAGS\n#transformers #pytorch #tf #safetensors #marian #text2text-generation #translation #opus-mt-tc #ro #uk #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
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. -->
# daml-t5-pretrain-imdb
This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the imdb dataset.
## M... | {"license": "apache-2.0", "tags": ["translation", "generated_from_trainer"], "datasets": ["imdb"], "model-index": [{"name": "daml-t5-pretrain-imdb", "results": []}]} | buvnswrn/daml-t5-pretrain | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"translation",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-24T07:11:08+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #translation #generated_from_trainer #dataset-imdb #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# daml-t5-pretrain-imdb
This model is a fine-tuned version of t5-base on the imdb dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The follo... | [
"# daml-t5-pretrain-imdb\n\nThis model is a fine-tuned version of t5-base 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",
"## Training procedure",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #translation #generated_from_trainer #dataset-imdb #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# daml-t5-pretrain-imdb\n\nThis model is a fine-tuned version of t5-base on the imdb d... |
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"], "datasets": ["conll2003"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "distilbert-base-uncased-finetuned-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "con... | tiennvcs/distilbert-base-uncased-finetuned-ner | null | [
"transformers",
"pytorch",
"distilbert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-24T07:17:55+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-ner
=====================================
This model is a fine-tuned version of distilbert-base-uncased on the conll2003 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0616
* Precision: 0.9265
* Recall: 0.9361
* F1: 0.9313
* Accuracy: 0.9837
Model des... | [
"### 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 #distilbert #token-classification #generated_from_trainer #dataset-conll2003 #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... |
text2text-generation | transformers |
[Google's T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) fine-tuned on [PAWS](https://github.com/google-research-datasets/paws) for paraphrase generation.
### Details of T5
The T5 model was presented in Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transfo... | {"license": "afl-3.0"} | etomoscow/T5_paraphrase_detector | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-24T07:18:54+00:00 | [] | [] | TAGS
#transformers #pytorch #t5 #text2text-generation #license-afl-3.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
Google's T5 fine-tuned on PAWS for paraphrase generation.
### Details of T5
The T5 model was presented in Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer by Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, Peter J. Liu i... | [
"### Details of T5\r\nThe T5 model was presented in Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer by Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, Peter J. Liu in Here the abstract:\r\n\r\nTransfer learning, where a mo... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #license-afl-3.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Details of T5\r\nThe T5 model was presented in Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer by Colin Raffel, Noa... |
translation | transformers | # opus-mt-tc-base-uk-tr
Neural machine translation model for translating from Ukrainian (uk) to Turkish (tr).
This model is part of the [OPUS-MT project](https://github.com/Helsinki-NLP/Opus-MT), an effort to make neural machine translation models widely available and accessible for many languages in the world. All m... | {"language": ["tr", "uk"], "license": "cc-by-4.0", "tags": ["translation", "opus-mt-tc"], "model-index": [{"name": "opus-mt-tc-base-uk-tr", "results": [{"task": {"type": "translation", "name": "Translation ukr-tur"}, "dataset": {"name": "flores101-devtest", "type": "flores_101", "args": "ukr tur devtest"}, "metrics": [... | Helsinki-NLP/opus-mt-tc-base-uk-tr | null | [
"transformers",
"pytorch",
"safetensors",
"marian",
"text2text-generation",
"translation",
"opus-mt-tc",
"tr",
"uk",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-24T07:33:55+00:00 | [] | [
"tr",
"uk"
] | TAGS
#transformers #pytorch #safetensors #marian #text2text-generation #translation #opus-mt-tc #tr #uk #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
| opus-mt-tc-base-uk-tr
=====================
Neural machine translation model for translating from Ukrainian (uk) to Turkish (tr).
This model is part of the OPUS-MT project, an effort to make neural machine translation models widely available and accessible for many languages in the world. All models are originally ... | [] | [
"TAGS\n#transformers #pytorch #safetensors #marian #text2text-generation #translation #opus-mt-tc #tr #uk #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
translation | transformers | # opus-mt-tc-big-zle-fi
Neural machine translation model for translating from East Slavic languages (zle) to Finnish (fi).
This model is part of the [OPUS-MT project](https://github.com/Helsinki-NLP/Opus-MT), an effort to make neural machine translation models widely available and accessible for many languages in the... | {"language": ["fi", "ru", "uk", "zle"], "license": "cc-by-4.0", "tags": ["translation", "opus-mt-tc"], "model-index": [{"name": "opus-mt-tc-big-zle-fi", "results": [{"task": {"type": "translation", "name": "Translation rus-fin"}, "dataset": {"name": "flores101-devtest", "type": "flores_101", "args": "rus fin devtest"},... | Helsinki-NLP/opus-mt-tc-big-zle-fi | null | [
"transformers",
"pytorch",
"safetensors",
"marian",
"text2text-generation",
"translation",
"opus-mt-tc",
"fi",
"ru",
"uk",
"zle",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-24T07:35:19+00:00 | [] | [
"fi",
"ru",
"uk",
"zle"
] | TAGS
#transformers #pytorch #safetensors #marian #text2text-generation #translation #opus-mt-tc #fi #ru #uk #zle #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
| opus-mt-tc-big-zle-fi
=====================
Neural machine translation model for translating from East Slavic languages (zle) to Finnish (fi).
This model is part of the OPUS-MT project, an effort to make neural machine translation models widely available and accessible for many languages in the world. All models ar... | [] | [
"TAGS\n#transformers #pytorch #safetensors #marian #text2text-generation #translation #opus-mt-tc #fi #ru #uk #zle #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilgpt2-music-search
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None datase... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "distilgpt2-music-search", "results": []}]} | elihoole/distilgpt2-music-search | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-24T07:49:37+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| distilgpt2-music-search
=======================
This model is a fine-tuned version of distilgpt2 on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 4.6516
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
Mo... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Traini... | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-small-finetuned-xsum
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the xsum dataset.
I... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["xsum"], "metrics": ["rouge"], "model-index": [{"name": "t5-small-finetuned-xsum", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "xsum", "type": "xsum", "config": "de... | fanzru/t5-small-finetuned-xsum | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:xsum",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-24T07:58:57+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-xsum #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| t5-small-finetuned-xsum
=======================
This model is a fine-tuned version of t5-small on the xsum dataset.
It achieves the following results on the evaluation set:
* Loss: 2.4786
* Rouge1: 28.2047
* Rouge2: 7.7109
* Rougel: 22.1559
* Rougelsum: 22.1595
* Gen Len: 18.8257
Model description
---------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1\n* mixed\\_prec... | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-xsum #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during train... |
translation | transformers | # opus-mt-tc-base-zle-bat
Neural machine translation model for translating from East Slavic languages (zle) to Baltic languages (bat).
This model is part of the [OPUS-MT project](https://github.com/Helsinki-NLP/Opus-MT), an effort to make neural machine translation models widely available and accessible for many lang... | {"language": ["bat", "lt", "lv", "ru", "zle"], "license": "cc-by-4.0", "tags": ["translation", "opus-mt-tc"], "model-index": [{"name": "opus-mt-tc-base-zle-bat", "results": [{"task": {"type": "translation", "name": "Translation rus-lav"}, "dataset": {"name": "flores101-devtest", "type": "flores_101", "args": "rus lav d... | Helsinki-NLP/opus-mt-tc-base-zle-bat | null | [
"transformers",
"pytorch",
"tf",
"safetensors",
"marian",
"text2text-generation",
"translation",
"opus-mt-tc",
"bat",
"lt",
"lv",
"ru",
"zle",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-24T08:07:23+00:00 | [] | [
"bat",
"lt",
"lv",
"ru",
"zle"
] | TAGS
#transformers #pytorch #tf #safetensors #marian #text2text-generation #translation #opus-mt-tc #bat #lt #lv #ru #zle #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
| opus-mt-tc-base-zle-bat
=======================
Neural machine translation model for translating from East Slavic languages (zle) to Baltic languages (bat).
This model is part of the OPUS-MT project, an effort to make neural machine translation models widely available and accessible for many languages in the world.... | [] | [
"TAGS\n#transformers #pytorch #tf #safetensors #marian #text2text-generation #translation #opus-mt-tc #bat #lt #lv #ru #zle #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
null | transformers |
Russian GPT2-medium model for RDF-triplet to text conversion.
https://github.com/pavel-blinov/ru-rdf2text
```
@inproceedings{blinov-2020-semantic,
title = "Semantic Triples Verbalization with Generative Pre-Training Model",
author = "Blinov, Pavel",
booktitle = "Proceedings of the 3rd International Works... | {"language": ["ru"]} | blinoff/ru-gpt2-medium-rdf-2-text | null | [
"transformers",
"pytorch",
"gpt2",
"ru",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-24T08:11:34+00:00 | [] | [
"ru"
] | TAGS
#transformers #pytorch #gpt2 #ru #endpoints_compatible #text-generation-inference #region-us
|
Russian GPT2-medium model for RDF-triplet to text conversion.
URL
| [] | [
"TAGS\n#transformers #pytorch #gpt2 #ru #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/804464329202409472/_-74e... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/iopred/1648161500488/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/iopred | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-24T08:39:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
diet dr. kit
@iopred
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
------------... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation | transformers |
<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/1506809010988539910/bBCR... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/tariqnasheed/1648112086220/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/tariqnasheed | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-24T08:47:22+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Tariq Nasheed 🇺🇸
@tariqnasheed
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"
] |
translation | transformers | # opus-mt-tc-big-zle-de
Neural machine translation model for translating from East Slavic languages (zle) to German (de).
This model is part of the [OPUS-MT project](https://github.com/Helsinki-NLP/Opus-MT), an effort to make neural machine translation models widely available and accessible for many languages in the ... | {"language": ["be", "de", "ru", "uk", "zle"], "license": "cc-by-4.0", "tags": ["translation", "opus-mt-tc"], "model-index": [{"name": "opus-mt-tc-big-zle-de", "results": [{"task": {"type": "translation", "name": "Translation rus-deu"}, "dataset": {"name": "flores101-devtest", "type": "flores_101", "args": "rus deu devt... | Helsinki-NLP/opus-mt-tc-big-zle-de | null | [
"transformers",
"pytorch",
"safetensors",
"marian",
"text2text-generation",
"translation",
"opus-mt-tc",
"be",
"de",
"ru",
"uk",
"zle",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-24T08:57:20+00:00 | [] | [
"be",
"de",
"ru",
"uk",
"zle"
] | TAGS
#transformers #pytorch #safetensors #marian #text2text-generation #translation #opus-mt-tc #be #de #ru #uk #zle #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
| opus-mt-tc-big-zle-de
=====================
Neural machine translation model for translating from East Slavic languages (zle) to German (de).
This model is part of the OPUS-MT project, an effort to make neural machine translation models widely available and accessible for many languages in the world. All models are... | [] | [
"TAGS\n#transformers #pytorch #safetensors #marian #text2text-generation #translation #opus-mt-tc #be #de #ru #uk #zle #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
translation | transformers | # opus-mt-tc-big-fi-zle
Neural machine translation model for translating from Finnish (fi) to East Slavic languages (zle).
This model is part of the [OPUS-MT project](https://github.com/Helsinki-NLP/Opus-MT), an effort to make neural machine translation models widely available and accessible for many languages in the... | {"language": ["fi", "ru", "uk", "zle"], "license": "cc-by-4.0", "tags": ["translation", "opus-mt-tc"], "model-index": [{"name": "opus-mt-tc-big-fi-zle", "results": [{"task": {"type": "translation", "name": "Translation fin-rus"}, "dataset": {"name": "flores101-devtest", "type": "flores_101", "args": "fin rus devtest"},... | Helsinki-NLP/opus-mt-tc-big-fi-zle | null | [
"transformers",
"pytorch",
"tf",
"safetensors",
"marian",
"text2text-generation",
"translation",
"opus-mt-tc",
"fi",
"ru",
"uk",
"zle",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-24T09:08:13+00:00 | [] | [
"fi",
"ru",
"uk",
"zle"
] | TAGS
#transformers #pytorch #tf #safetensors #marian #text2text-generation #translation #opus-mt-tc #fi #ru #uk #zle #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
| opus-mt-tc-big-fi-zle
=====================
Neural machine translation model for translating from Finnish (fi) to East Slavic languages (zle).
This model is part of the OPUS-MT project, an effort to make neural machine translation models widely available and accessible for many languages in the world. All models ar... | [] | [
"TAGS\n#transformers #pytorch #tf #safetensors #marian #text2text-generation #translation #opus-mt-tc #fi #ru #uk #zle #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
text-generation | transformers |
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1500859213622300673/izXw... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/kytalli-vi0linheart/1648114676311/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/kytalli-vi0linheart | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-24T09:25:29+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI CYBORG
sal & G
@kytalli-vi0linheart
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
--... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation | transformers |
<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/1227670393453936642/6rdB... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/madeleine/1648114714373/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/madeleine | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-24T09:37:42+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Madeleine Albright
@madeleine
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
---... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation | transformers |
<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/1500859213622300673/izXw... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/vi0linheart/1648116634962/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/vi0linheart | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-24T10:09:41+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
sal
@vi0linheart
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 Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# JustAdvanceTechonology/bert-fine-tuned-medical-insurance-ner
This model is a fine-tuned version of [bert-base-cased](https://huggingfa... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "JustAdvanceTechonology/bert-fine-tuned-medical-insurance-ner", "results": []}]} | JustAdvanceTechonology/bert-fine-tuned-medical-insurance-ner | null | [
"transformers",
"tf",
"bert",
"token-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-24T10:20:14+00:00 | [] | [] | TAGS
#transformers #tf #bert #token-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| JustAdvanceTechonology/bert-fine-tuned-medical-insurance-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: 0.0269
* Validation Loss: 0.0551
* Epoch: 2
... | [
"### 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': 2631, '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\\_... |
translation | transformers | # opus-mt-tc-big-zle-fr
Neural machine translation model for translating from East Slavic languages (zle) to French (fr).
This model is part of the [OPUS-MT project](https://github.com/Helsinki-NLP/Opus-MT), an effort to make neural machine translation models widely available and accessible for many languages in the ... | {"language": ["be", "fr", "ru", "uk", "zle"], "license": "cc-by-4.0", "tags": ["translation", "opus-mt-tc"], "model-index": [{"name": "opus-mt-tc-big-zle-fr", "results": [{"task": {"type": "translation", "name": "Translation bel-fra"}, "dataset": {"name": "tatoeba-test-v2020-07-28-v2021-08-07", "type": "tatoeba_mt", "a... | Helsinki-NLP/opus-mt-tc-big-zle-fr | null | [
"transformers",
"pytorch",
"safetensors",
"marian",
"text2text-generation",
"translation",
"opus-mt-tc",
"be",
"fr",
"ru",
"uk",
"zle",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-24T10:22:19+00:00 | [] | [
"be",
"fr",
"ru",
"uk",
"zle"
] | TAGS
#transformers #pytorch #safetensors #marian #text2text-generation #translation #opus-mt-tc #be #fr #ru #uk #zle #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
| opus-mt-tc-big-zle-fr
=====================
Neural machine translation model for translating from East Slavic languages (zle) to French (fr).
This model is part of the OPUS-MT project, an effort to make neural machine translation models widely available and accessible for many languages in the world. All models are... | [] | [
"TAGS\n#transformers #pytorch #safetensors #marian #text2text-generation #translation #opus-mt-tc #be #fr #ru #uk #zle #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
automatic-speech-recognition | transformers |
## Evaluation
The model can be evaluated as follows on the German test data of Common Voice.
```python
import torch
from transformers import AutoModelForCTC, AutoProcessor
from unidecode import unidecode
import re
from datasets import load_dataset, load_metric
import datasets
counter = 0
wer_counter = 0
cer_counter ... | {"language": "de", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "hf-asr-leaderboard"], "datasets": ["common_voice"], "metrics": ["wer", "cer"], "model-index": [{"name": "wav2vec2-xls-r-1b-5gram-german with LM by Florian Zimmermeister @A\\\\Ware", "results": [{"task": {"type": "au... | aware-ai/wav2vec2-xls-r-1b-5gram-german | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"hf-asr-leaderboard",
"de",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-24T10:33:47+00:00 | [] | [
"de"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #speech #hf-asr-leaderboard #de #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
## Evaluation
The model can be evaluated as follows on the German test data of Common Voice.
| [
"## Evaluation\nThe model can be evaluated as follows on the German test data of Common Voice."
] | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #speech #hf-asr-leaderboard #de #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"## Evaluation\nThe model can be evaluated as follows on the German test data of Common Voice."
] |
text-generation | transformers |
#Harry Potter DialoGPT Model | {"tags": ["conversational"]} | LeonLi279/DialoGPT-small-harrypotter | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-24T10:56:35+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
#Harry Potter DialoGPT Model | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Horovod_Tweet_Sentiment_1k_5eps
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "Horovod_Tweet_Sentiment_1k_5eps", "results": []}]} | joe5campbell/Horovod_Tweet_Sentiment_1k_5eps | null | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-24T11:01:49+00:00 | [] | [] | TAGS
#transformers #tf #bert #text-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| Horovod\_Tweet\_Sentiment\_1k\_5eps
===================================
This model is a fine-tuned version of bert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.5216092
* Train Accuracy: 0.784375
* Validation Loss: 0.92405033
* Validation Accuracy: 0.48... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'clipnorm': 1.0, 'learning\\_rate': 0.0003, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}\n* training\\_precision: float32",
"### Training results"... | [
"TAGS\n#transformers #tf #bert #text-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'clipnorm': 1.0, 'learni... |
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. -->
# daml-t5-pretrain-imdb
This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the imdb dataset.
## M... | {"license": "apache-2.0", "tags": ["translation", "generated_from_trainer"], "datasets": ["imdb"], "model-index": [{"name": "daml-t5-pretrain-imdb", "results": []}]} | buvnswrn/daml-t5-pretrain-imdb-accelerate | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"translation",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-24T11:06:02+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #translation #generated_from_trainer #dataset-imdb #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# daml-t5-pretrain-imdb
This model is a fine-tuned version of t5-base on the imdb dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The follo... | [
"# daml-t5-pretrain-imdb\n\nThis model is a fine-tuned version of t5-base 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",
"## Training procedure",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #translation #generated_from_trainer #dataset-imdb #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# daml-t5-pretrain-imdb\n\nThis model is a fine-tuned version of t5-base on the imdb d... |
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-turkish-colab
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-large-xls-r-300m-turkish-colab", "results": []}]} | Khalsuu/wav2vec2-large-xls-r-300m-turkish-colab | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-24T11:10:42+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-large-xls-r-300m-turkish-colab
=======================================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common\_voice dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3631
* Wer: 0.3907
Model description
-----------------
More informat... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 64\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* t... |
translation | transformers | # opus-mt-tc-big-zle-gmq
Neural machine translation model for translating from East Slavic languages (zle) to North Germanic languages (gmq).
This model is part of the [OPUS-MT project](https://github.com/Helsinki-NLP/Opus-MT), an effort to make neural machine translation models widely available and accessible for ma... | {"language": ["da", "gmq", "nb", false, "ru", "sv", "uk", "zle"], "license": "cc-by-4.0", "tags": ["translation", "opus-mt-tc"], "model-index": [{"name": "opus-mt-tc-big-zle-gmq", "results": [{"task": {"type": "translation", "name": "Translation rus-dan"}, "dataset": {"name": "flores101-devtest", "type": "flores_101", ... | Helsinki-NLP/opus-mt-tc-big-zle-gmq | null | [
"transformers",
"pytorch",
"safetensors",
"marian",
"text2text-generation",
"translation",
"opus-mt-tc",
"tc",
"big",
"zle",
"gmq",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-24T11:31:46+00:00 | [] | [
"da",
"gmq",
"nb",
"no",
"ru",
"sv",
"uk",
"zle"
] | TAGS
#transformers #pytorch #safetensors #marian #text2text-generation #translation #opus-mt-tc #tc #big #zle #gmq #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
| opus-mt-tc-big-zle-gmq
======================
Neural machine translation model for translating from East Slavic languages (zle) to North Germanic languages (gmq).
This model is part of the OPUS-MT project, an effort to make neural machine translation models widely available and accessible for many languages in the ... | [] | [
"TAGS\n#transformers #pytorch #safetensors #marian #text2text-generation #translation #opus-mt-tc #tc #big #zle #gmq #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Horovod_Tweet_Sentiment_1k_3eps
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "Horovod_Tweet_Sentiment_1k_3eps", "results": []}]} | joe5campbell/Horovod_Tweet_Sentiment_1k_3eps | null | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-24T11:48:21+00:00 | [] | [] | TAGS
#transformers #tf #bert #text-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| Horovod\_Tweet\_Sentiment\_1k\_3eps
===================================
This model is a fine-tuned version of bert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.6961535
* Train Accuracy: 0.49375
* Validation Loss: 0.6676211
* Validation Accuracy: 0.6437... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'clipnorm': 1.0, 'learning\\_rate': 0.0003, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}\n* training\\_precision: float32",
"### Training results"... | [
"TAGS\n#transformers #tf #bert #text-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'clipnorm': 1.0, 'learni... |
translation | transformers | # opus-mt-tc-big-zle-it
Neural machine translation model for translating from East Slavic languages (zle) to Italian (it).
This model is part of the [OPUS-MT project](https://github.com/Helsinki-NLP/Opus-MT), an effort to make neural machine translation models widely available and accessible for many languages in the... | {"language": ["be", "it", "ru", "uk", "zle"], "license": "cc-by-4.0", "tags": ["translation", "opus-mt-tc"], "model-index": [{"name": "opus-mt-tc-big-zle-it", "results": [{"task": {"type": "translation", "name": "Translation rus-ita"}, "dataset": {"name": "flores101-devtest", "type": "flores_101", "args": "rus ita devt... | Helsinki-NLP/opus-mt-tc-big-zle-it | null | [
"transformers",
"pytorch",
"safetensors",
"marian",
"text2text-generation",
"translation",
"opus-mt-tc",
"be",
"it",
"ru",
"uk",
"zle",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-24T11:59:11+00:00 | [] | [
"be",
"it",
"ru",
"uk",
"zle"
] | TAGS
#transformers #pytorch #safetensors #marian #text2text-generation #translation #opus-mt-tc #be #it #ru #uk #zle #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
| opus-mt-tc-big-zle-it
=====================
Neural machine translation model for translating from East Slavic languages (zle) to Italian (it).
This model is part of the OPUS-MT project, an effort to make neural machine translation models widely available and accessible for many languages in the world. All models ar... | [] | [
"TAGS\n#transformers #pytorch #safetensors #marian #text2text-generation #translation #opus-mt-tc #be #it #ru #uk #zle #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #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... | Thant123/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 | 2022-03-24T12:02:03+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: 0.2270
* Accuracy: 0.924
* F1: 0.9241
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... |
translation | transformers | # opus-mt-tc-big-zle-pt
Neural machine translation model for translating from East Slavic languages (zle) to Portuguese (pt).
This model is part of the [OPUS-MT project](https://github.com/Helsinki-NLP/Opus-MT), an effort to make neural machine translation models widely available and accessible for many languages in ... | {"language": ["pt", "ru", "uk", "zle"], "license": "cc-by-4.0", "tags": ["translation", "opus-mt-tc"], "model-index": [{"name": "opus-mt-tc-big-zle-pt", "results": [{"task": {"type": "translation", "name": "Translation rus-por"}, "dataset": {"name": "flores101-devtest", "type": "flores_101", "args": "rus por devtest"},... | Helsinki-NLP/opus-mt-tc-big-zle-pt | null | [
"transformers",
"pytorch",
"safetensors",
"marian",
"text2text-generation",
"translation",
"opus-mt-tc",
"pt",
"ru",
"uk",
"zle",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-24T12:02:40+00:00 | [] | [
"pt",
"ru",
"uk",
"zle"
] | TAGS
#transformers #pytorch #safetensors #marian #text2text-generation #translation #opus-mt-tc #pt #ru #uk #zle #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
| opus-mt-tc-big-zle-pt
=====================
Neural machine translation model for translating from East Slavic languages (zle) to Portuguese (pt).
This model is part of the OPUS-MT project, an effort to make neural machine translation models widely available and accessible for many languages in the world. All models... | [] | [
"TAGS\n#transformers #pytorch #safetensors #marian #text2text-generation #translation #opus-mt-tc #pt #ru #uk #zle #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
automatic-speech-recognition | espnet | ## ESPnet2 ASR pretrained model
### `espnet/Karthik_DSTC2_asr_train_asr_wav2vec_conformer_2`
This model was trained by Karthik using DSTC2/asr1 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```python
# coming soon
```
### Citing ESPnet
```BibTex
@inproceedings{watanabe2018espn... | {"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "automatic-speech-recognition"], "datasets": ["DSTC2"]} | espnet/Karthik_DSTC2_asr_train_asr_wav2vec_conformer_2 | null | [
"espnet",
"tensorboard",
"audio",
"automatic-speech-recognition",
"en",
"dataset:DSTC2",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | null | 2022-03-24T12:03:09+00:00 | [
"1804.00015"
] | [
"en"
] | TAGS
#espnet #tensorboard #audio #automatic-speech-recognition #en #dataset-DSTC2 #arxiv-1804.00015 #license-cc-by-4.0 #region-us
| ## ESPnet2 ASR pretrained model
### 'espnet/Karthik_DSTC2_asr_train_asr_wav2vec_conformer_2'
This model was trained by Karthik using DSTC2/asr1 recipe in espnet.
### Demo: How to use in ESPnet2
### Citing ESPnet
or arXiv:
| [
"## ESPnet2 ASR pretrained model",
"### 'espnet/Karthik_DSTC2_asr_train_asr_wav2vec_conformer_2'\n\nThis model was trained by Karthik using DSTC2/asr1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] | [
"TAGS\n#espnet #tensorboard #audio #automatic-speech-recognition #en #dataset-DSTC2 #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"## ESPnet2 ASR pretrained model",
"### 'espnet/Karthik_DSTC2_asr_train_asr_wav2vec_conformer_2'\n\nThis model was trained by Karthik using DSTC2/asr1 recipe in espnet.",
"#... |
translation | transformers | # opus-mt-tc-big-zle-es
Neural machine translation model for translating from East Slavic languages (zle) to Spanish (es).
This model is part of the [OPUS-MT project](https://github.com/Helsinki-NLP/Opus-MT), an effort to make neural machine translation models widely available and accessible for many languages in the... | {"language": ["be", "es", "ru", "rue", "uk", "zle"], "license": "cc-by-4.0", "tags": ["translation", "opus-mt-tc"], "model-index": [{"name": "opus-mt-tc-big-zle-es", "results": [{"task": {"type": "translation", "name": "Translation rus-spa"}, "dataset": {"name": "flores101-devtest", "type": "flores_101", "args": "rus s... | Helsinki-NLP/opus-mt-tc-big-zle-es | null | [
"transformers",
"pytorch",
"tf",
"safetensors",
"marian",
"text2text-generation",
"translation",
"opus-mt-tc",
"be",
"es",
"ru",
"rue",
"uk",
"zle",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-24T12:05:31+00:00 | [] | [
"be",
"es",
"ru",
"rue",
"uk",
"zle"
] | TAGS
#transformers #pytorch #tf #safetensors #marian #text2text-generation #translation #opus-mt-tc #be #es #ru #rue #uk #zle #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
| opus-mt-tc-big-zle-es
=====================
Neural machine translation model for translating from East Slavic languages (zle) to Spanish (es).
This model is part of the OPUS-MT project, an effort to make neural machine translation models widely available and accessible for many languages in the world. All models ar... | [] | [
"TAGS\n#transformers #pytorch #tf #safetensors #marian #text2text-generation #translation #opus-mt-tc #be #es #ru #rue #uk #zle #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
translation | transformers | # opus-mt-tc-big-zle-zle
Neural machine translation model for translating from East Slavic languages (zle) to East Slavic languages (zle).
This model is part of the [OPUS-MT project](https://github.com/Helsinki-NLP/Opus-MT), an effort to make neural machine translation models widely available and accessible for many ... | {"language": ["be", "ru", "uk", "zle"], "license": "cc-by-4.0", "tags": ["translation", "opus-mt-tc"], "model-index": [{"name": "opus-mt-tc-big-zle-zle", "results": [{"task": {"type": "translation", "name": "Translation rus-ukr"}, "dataset": {"name": "flores101-devtest", "type": "flores_101", "args": "rus ukr devtest"}... | Helsinki-NLP/opus-mt-tc-big-zle-zle | null | [
"transformers",
"pytorch",
"tf",
"safetensors",
"marian",
"text2text-generation",
"translation",
"opus-mt-tc",
"be",
"ru",
"uk",
"zle",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-24T12:08:16+00:00 | [] | [
"be",
"ru",
"uk",
"zle"
] | TAGS
#transformers #pytorch #tf #safetensors #marian #text2text-generation #translation #opus-mt-tc #be #ru #uk #zle #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
| opus-mt-tc-big-zle-zle
======================
Neural machine translation model for translating from East Slavic languages (zle) to East Slavic languages (zle).
This model is part of the OPUS-MT project, an effort to make neural machine translation models widely available and accessible for many languages in the wor... | [] | [
"TAGS\n#transformers #pytorch #tf #safetensors #marian #text2text-generation #translation #opus-mt-tc #be #ru #uk #zle #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
translation | transformers | # opus-mt-tc-big-zle-zls
Neural machine translation model for translating from East Slavic languages (zle) to South Slavic languages (zls).
This model is part of the [OPUS-MT project](https://github.com/Helsinki-NLP/Opus-MT), an effort to make neural machine translation models widely available and accessible for many... | {"language": ["be", "bg", "hr", "ru", "sh", "sl", "sr_Cyrl", "sr_Latn", "uk", "zle", "zls"], "license": "cc-by-4.0", "tags": ["translation", "opus-mt-tc"], "model-index": [{"name": "opus-mt-tc-big-zle-zls", "results": [{"task": {"type": "translation", "name": "Translation rus-bul"}, "dataset": {"name": "flores101-devte... | Helsinki-NLP/opus-mt-tc-big-zle-zls | null | [
"transformers",
"pytorch",
"tf",
"safetensors",
"marian",
"text2text-generation",
"translation",
"opus-mt-tc",
"tc",
"big",
"zle",
"zls",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-24T12:10:45+00:00 | [] | [
"be",
"bg",
"hr",
"ru",
"sh",
"sl",
"sr_Cyrl",
"sr_Latn",
"uk",
"zle",
"zls"
] | TAGS
#transformers #pytorch #tf #safetensors #marian #text2text-generation #translation #opus-mt-tc #tc #big #zle #zls #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
| opus-mt-tc-big-zle-zls
======================
Neural machine translation model for translating from East Slavic languages (zle) to South Slavic languages (zls).
This model is part of the OPUS-MT project, an effort to make neural machine translation models widely available and accessible for many languages in the wo... | [] | [
"TAGS\n#transformers #pytorch #tf #safetensors #marian #text2text-generation #translation #opus-mt-tc #tc #big #zle #zls #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
translation | transformers | # opus-mt-tc-big-zle-zlw
Neural machine translation model for translating from East Slavic languages (zle) to West Slavic languages (zlw).
This model is part of the [OPUS-MT project](https://github.com/Helsinki-NLP/Opus-MT), an effort to make neural machine translation models widely available and accessible for many ... | {"language": ["be", "cs", "pl", "ru", "uk", "zle", "zlw"], "license": "cc-by-4.0", "tags": ["translation", "opus-mt-tc"], "model-index": [{"name": "opus-mt-tc-big-zle-zlw", "results": [{"task": {"type": "translation", "name": "Translation rus-ces"}, "dataset": {"name": "flores101-devtest", "type": "flores_101", "args":... | Helsinki-NLP/opus-mt-tc-big-zle-zlw | null | [
"transformers",
"pytorch",
"tf",
"safetensors",
"marian",
"text2text-generation",
"translation",
"opus-mt-tc",
"be",
"cs",
"pl",
"ru",
"uk",
"zle",
"zlw",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-24T12:13:49+00:00 | [] | [
"be",
"cs",
"pl",
"ru",
"uk",
"zle",
"zlw"
] | TAGS
#transformers #pytorch #tf #safetensors #marian #text2text-generation #translation #opus-mt-tc #be #cs #pl #ru #uk #zle #zlw #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
| opus-mt-tc-big-zle-zlw
======================
Neural machine translation model for translating from East Slavic languages (zle) to West Slavic languages (zlw).
This model is part of the OPUS-MT project, an effort to make neural machine translation models widely available and accessible for many languages in the wor... | [] | [
"TAGS\n#transformers #pytorch #tf #safetensors #marian #text2text-generation #translation #opus-mt-tc #be #cs #pl #ru #uk #zle #zlw #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
translation | transformers | # opus-mt-tc-base-bat-zle
Neural machine translation model for translating from Baltic languages (bat) to East Slavic languages (zle).
This model is part of the [OPUS-MT project](https://github.com/Helsinki-NLP/Opus-MT), an effort to make neural machine translation models widely available and accessible for many lang... | {"language": ["bat", "lt", "lv", "ru", "zle"], "license": "cc-by-4.0", "tags": ["translation", "opus-mt-tc"], "model-index": [{"name": "opus-mt-tc-base-bat-zle", "results": [{"task": {"type": "translation", "name": "Translation lav-rus"}, "dataset": {"name": "flores101-devtest", "type": "flores_101", "args": "lav rus d... | Helsinki-NLP/opus-mt-tc-base-bat-zle | null | [
"transformers",
"pytorch",
"safetensors",
"marian",
"text2text-generation",
"translation",
"opus-mt-tc",
"bat",
"lt",
"lv",
"ru",
"zle",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-24T12:16:51+00:00 | [] | [
"bat",
"lt",
"lv",
"ru",
"zle"
] | TAGS
#transformers #pytorch #safetensors #marian #text2text-generation #translation #opus-mt-tc #bat #lt #lv #ru #zle #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
| opus-mt-tc-base-bat-zle
=======================
Neural machine translation model for translating from Baltic languages (bat) to East Slavic languages (zle).
This model is part of the OPUS-MT project, an effort to make neural machine translation models widely available and accessible for many languages in the world.... | [] | [
"TAGS\n#transformers #pytorch #safetensors #marian #text2text-generation #translation #opus-mt-tc #bat #lt #lv #ru #zle #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
translation | transformers | # opus-mt-tc-base-ces_slk-uk
Neural machine translation model for translating from Czech and Slovak (cs+sk) to Ukrainian (uk).
This model is part of the [OPUS-MT project](https://github.com/Helsinki-NLP/Opus-MT), an effort to make neural machine translation models widely available and accessible for many languages in... | {"language": ["cs", "sk", "uk"], "license": "cc-by-4.0", "tags": ["translation", "opus-mt-tc"], "model-index": [{"name": "opus-mt-tc-base-ces_slk-uk", "results": [{"task": {"type": "translation", "name": "Translation ces-ukr"}, "dataset": {"name": "flores101-devtest", "type": "flores_101", "args": "ces ukr devtest"}, "... | Helsinki-NLP/opus-mt-tc-base-ces_slk-uk | null | [
"transformers",
"pytorch",
"marian",
"text2text-generation",
"translation",
"opus-mt-tc",
"cs",
"sk",
"uk",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-24T12:18:07+00:00 | [] | [
"cs",
"sk",
"uk"
] | TAGS
#transformers #pytorch #marian #text2text-generation #translation #opus-mt-tc #cs #sk #uk #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
| opus-mt-tc-base-ces\_slk-uk
===========================
Neural machine translation model for translating from Czech and Slovak (cs+sk) to Ukrainian (uk).
This model is part of the OPUS-MT project, an effort to make neural machine translation models widely available and accessible for many languages in the world. Al... | [] | [
"TAGS\n#transformers #pytorch #marian #text2text-generation #translation #opus-mt-tc #cs #sk #uk #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
translation | transformers | # opus-mt-tc-big-de-zle
Neural machine translation model for translating from German (de) to East Slavic languages (zle).
This model is part of the [OPUS-MT project](https://github.com/Helsinki-NLP/Opus-MT), an effort to make neural machine translation models widely available and accessible for many languages in the ... | {"language": ["be", "de", "ru", "uk", "zle"], "license": "cc-by-4.0", "tags": ["translation", "opus-mt-tc"], "model-index": [{"name": "opus-mt-tc-big-de-zle", "results": [{"task": {"type": "translation", "name": "Translation deu-rus"}, "dataset": {"name": "flores101-devtest", "type": "flores_101", "args": "deu rus devt... | Helsinki-NLP/opus-mt-tc-big-de-zle | null | [
"transformers",
"pytorch",
"safetensors",
"marian",
"text2text-generation",
"translation",
"opus-mt-tc",
"be",
"de",
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"uk",
"zle",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-24T12:19:23+00:00 | [] | [
"be",
"de",
"ru",
"uk",
"zle"
] | TAGS
#transformers #pytorch #safetensors #marian #text2text-generation #translation #opus-mt-tc #be #de #ru #uk #zle #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
| opus-mt-tc-big-de-zle
=====================
Neural machine translation model for translating from German (de) to East Slavic languages (zle).
This model is part of the OPUS-MT project, an effort to make neural machine translation models widely available and accessible for many languages in the world. All models are... | [] | [
"TAGS\n#transformers #pytorch #safetensors #marian #text2text-generation #translation #opus-mt-tc #be #de #ru #uk #zle #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
translation | transformers | # opus-mt-tc-big-en-zle
Neural machine translation model for translating from English (en) to East Slavic languages (zle).
This model is part of the [OPUS-MT project](https://github.com/Helsinki-NLP/Opus-MT), an effort to make neural machine translation models widely available and accessible for many languages in the... | {"language": ["be", "en", "ru", "uk", "zle"], "license": "cc-by-4.0", "tags": ["translation", "opus-mt-tc"], "model-index": [{"name": "opus-mt-tc-big-en-zle", "results": [{"task": {"type": "translation", "name": "Translation eng-rus"}, "dataset": {"name": "flores101-devtest", "type": "flores_101", "args": "eng rus devt... | Helsinki-NLP/opus-mt-tc-big-en-zle | null | [
"transformers",
"pytorch",
"tf",
"safetensors",
"marian",
"text2text-generation",
"translation",
"opus-mt-tc",
"be",
"en",
"ru",
"uk",
"zle",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-24T12:23:34+00:00 | [] | [
"be",
"en",
"ru",
"uk",
"zle"
] | TAGS
#transformers #pytorch #tf #safetensors #marian #text2text-generation #translation #opus-mt-tc #be #en #ru #uk #zle #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
| opus-mt-tc-big-en-zle
=====================
Neural machine translation model for translating from English (en) to East Slavic languages (zle).
This model is part of the OPUS-MT project, an effort to make neural machine translation models widely available and accessible for many languages in the world. All models ar... | [] | [
"TAGS\n#transformers #pytorch #tf #safetensors #marian #text2text-generation #translation #opus-mt-tc #be #en #ru #uk #zle #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
translation | transformers | # opus-mt-tc-base-fi-uk
Neural machine translation model for translating from Finnish (fi) to Ukrainian (uk).
This model is part of the [OPUS-MT project](https://github.com/Helsinki-NLP/Opus-MT), an effort to make neural machine translation models widely available and accessible for many languages in the world. All m... | {"language": ["fi", "uk"], "license": "cc-by-4.0", "tags": ["translation", "opus-mt-tc"]} | Helsinki-NLP/opus-mt-tc-base-fi-uk | null | [
"transformers",
"pytorch",
"tf",
"safetensors",
"marian",
"text2text-generation",
"translation",
"opus-mt-tc",
"fi",
"uk",
"license:cc-by-4.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-24T12:28:09+00:00 | [] | [
"fi",
"uk"
] | TAGS
#transformers #pytorch #tf #safetensors #marian #text2text-generation #translation #opus-mt-tc #fi #uk #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| opus-mt-tc-base-fi-uk
=====================
Neural machine translation model for translating from Finnish (fi) to Ukrainian (uk).
This model is part of the OPUS-MT project, an effort to make neural machine translation models widely available and accessible for many languages in the world. All models are originally ... | [] | [
"TAGS\n#transformers #pytorch #tf #safetensors #marian #text2text-generation #translation #opus-mt-tc #fi #uk #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
translation | transformers | # opus-mt-tc-big-fr-zle
Neural machine translation model for translating from French (fr) to East Slavic languages (zle).
This model is part of the [OPUS-MT project](https://github.com/Helsinki-NLP/Opus-MT), an effort to make neural machine translation models widely available and accessible for many languages in the ... | {"language": ["be", "fr", "ru", "uk", "zle"], "license": "cc-by-4.0", "tags": ["translation", "opus-mt-tc"], "model-index": [{"name": "opus-mt-tc-big-fr-zle", "results": [{"task": {"type": "translation", "name": "Translation fra-rus"}, "dataset": {"name": "flores101-devtest", "type": "flores_101", "args": "fra rus devt... | Helsinki-NLP/opus-mt-tc-big-fr-zle | null | [
"transformers",
"pytorch",
"safetensors",
"marian",
"text2text-generation",
"translation",
"opus-mt-tc",
"be",
"fr",
"ru",
"uk",
"zle",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-24T12:29:13+00:00 | [] | [
"be",
"fr",
"ru",
"uk",
"zle"
] | TAGS
#transformers #pytorch #safetensors #marian #text2text-generation #translation #opus-mt-tc #be #fr #ru #uk #zle #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
| opus-mt-tc-big-fr-zle
=====================
Neural machine translation model for translating from French (fr) to East Slavic languages (zle).
This model is part of the OPUS-MT project, an effort to make neural machine translation models widely available and accessible for many languages in the world. All models are... | [] | [
"TAGS\n#transformers #pytorch #safetensors #marian #text2text-generation #translation #opus-mt-tc #be #fr #ru #uk #zle #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
translation | transformers | # opus-mt-tc-big-gmq-zle
Neural machine translation model for translating from North Germanic languages (gmq) to East Slavic languages (zle).
This model is part of the [OPUS-MT project](https://github.com/Helsinki-NLP/Opus-MT), an effort to make neural machine translation models widely available and accessible for ma... | {"language": ["da", "gmq", "is", "nb", false, "ru", "sv", "uk", "zle"], "license": "cc-by-4.0", "tags": ["translation", "opus-mt-tc"], "model-index": [{"name": "opus-mt-tc-big-gmq-zle", "results": [{"task": {"type": "translation", "name": "Translation dan-rus"}, "dataset": {"name": "flores101-devtest", "type": "flores_... | Helsinki-NLP/opus-mt-tc-big-gmq-zle | null | [
"transformers",
"pytorch",
"tf",
"safetensors",
"marian",
"text2text-generation",
"translation",
"opus-mt-tc",
"tc",
"big",
"gmq",
"zle",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-24T12:31:57+00:00 | [] | [
"da",
"gmq",
"is",
"nb",
"no",
"ru",
"sv",
"uk",
"zle"
] | TAGS
#transformers #pytorch #tf #safetensors #marian #text2text-generation #translation #opus-mt-tc #tc #big #gmq #zle #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
| opus-mt-tc-big-gmq-zle
======================
Neural machine translation model for translating from North Germanic languages (gmq) to East Slavic languages (zle).
This model is part of the OPUS-MT project, an effort to make neural machine translation models widely available and accessible for many languages in the ... | [] | [
"TAGS\n#transformers #pytorch #tf #safetensors #marian #text2text-generation #translation #opus-mt-tc #tc #big #gmq #zle #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
translation | transformers | # opus-mt-tc-base-hu-uk
Neural machine translation model for translating from Hungarian (hu) to Ukrainian (uk).
This model is part of the [OPUS-MT project](https://github.com/Helsinki-NLP/Opus-MT), an effort to make neural machine translation models widely available and accessible for many languages in the world. All... | {"language": ["hu", "uk"], "license": "cc-by-4.0", "tags": ["translation", "opus-mt-tc"], "model-index": [{"name": "opus-mt-tc-base-hu-uk", "results": [{"task": {"type": "translation", "name": "Translation hun-ukr"}, "dataset": {"name": "tatoeba-test-v2021-08-07", "type": "tatoeba_mt", "args": "hun-ukr"}, "metrics": [{... | Helsinki-NLP/opus-mt-tc-base-hu-uk | null | [
"transformers",
"pytorch",
"tf",
"safetensors",
"marian",
"text2text-generation",
"translation",
"opus-mt-tc",
"hu",
"uk",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-24T12:34:51+00:00 | [] | [
"hu",
"uk"
] | TAGS
#transformers #pytorch #tf #safetensors #marian #text2text-generation #translation #opus-mt-tc #hu #uk #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
| opus-mt-tc-base-hu-uk
=====================
Neural machine translation model for translating from Hungarian (hu) to Ukrainian (uk).
This model is part of the OPUS-MT project, an effort to make neural machine translation models widely available and accessible for many languages in the world. All models are originall... | [] | [
"TAGS\n#transformers #pytorch #tf #safetensors #marian #text2text-generation #translation #opus-mt-tc #hu #uk #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Horovod_Tweet_Sentiment_1K_4eps
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "Horovod_Tweet_Sentiment_1K_4eps", "results": []}]} | joe5campbell/Horovod_Tweet_Sentiment_1K_4eps | null | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-24T12:35:50+00:00 | [] | [] | TAGS
#transformers #tf #bert #text-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| Horovod\_Tweet\_Sentiment\_1K\_4eps
===================================
This model is a fine-tuned version of bert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.6803332
* Train Accuracy: 0.57187504
* Validation Loss: 0.6883397
* Validation Accuracy: 0.5... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'clipnorm': 1.0, 'learning\\_rate': 0.0003, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}\n* training\\_precision: float32",
"### Training results"... | [
"TAGS\n#transformers #tf #bert #text-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'clipnorm': 1.0, 'learni... |
translation | transformers | # opus-mt-tc-big-it-zle
Neural machine translation model for translating from Italian (it) to East Slavic languages (zle).
This model is part of the [OPUS-MT project](https://github.com/Helsinki-NLP/Opus-MT), an effort to make neural machine translation models widely available and accessible for many languages in the... | {"language": ["be", "it", "ru", "uk", "zle"], "license": "cc-by-4.0", "tags": ["translation", "opus-mt-tc"], "model-index": [{"name": "opus-mt-tc-big-it-zle", "results": [{"task": {"type": "translation", "name": "Translation ita-rus"}, "dataset": {"name": "flores101-devtest", "type": "flores_101", "args": "ita rus devt... | Helsinki-NLP/opus-mt-tc-big-it-zle | null | [
"transformers",
"pytorch",
"tf",
"safetensors",
"marian",
"text2text-generation",
"translation",
"opus-mt-tc",
"be",
"it",
"ru",
"uk",
"zle",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-24T12:36:28+00:00 | [] | [
"be",
"it",
"ru",
"uk",
"zle"
] | TAGS
#transformers #pytorch #tf #safetensors #marian #text2text-generation #translation #opus-mt-tc #be #it #ru #uk #zle #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
| opus-mt-tc-big-it-zle
=====================
Neural machine translation model for translating from Italian (it) to East Slavic languages (zle).
This model is part of the OPUS-MT project, an effort to make neural machine translation models widely available and accessible for many languages in the world. All models ar... | [] | [
"TAGS\n#transformers #pytorch #tf #safetensors #marian #text2text-generation #translation #opus-mt-tc #be #it #ru #uk #zle #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
text-generation | transformers | Hello there , this bot is trained on DialoGTP for an epoch of 45 | {} | Ryukijano/DialoGPT_med_model | null | [
"transformers",
"pytorch",
"safetensors",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-24T12:37:08+00:00 | [] | [] | TAGS
#transformers #pytorch #safetensors #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| Hello there , this bot is trained on DialoGTP for an epoch of 45 | [] | [
"TAGS\n#transformers #pytorch #safetensors #gpt2 #text-generation #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/1251916496307175424/rFil... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/rronigj/1648126016294/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/rronigj | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-24T12:37:39+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Rron Gjinovci
@rronigj
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"
] |
translation | transformers | # opus-mt-tc-big-pt-zle
Neural machine translation model for translating from Portuguese (pt) to East Slavic languages (zle).
This model is part of the [OPUS-MT project](https://github.com/Helsinki-NLP/Opus-MT), an effort to make neural machine translation models widely available and accessible for many languages in ... | {"language": ["pt", "ru", "uk", "zle"], "license": "cc-by-4.0", "tags": ["translation", "opus-mt-tc"], "model-index": [{"name": "opus-mt-tc-big-pt-zle", "results": [{"task": {"type": "translation", "name": "Translation por-rus"}, "dataset": {"name": "flores101-devtest", "type": "flores_101", "args": "por rus devtest"},... | Helsinki-NLP/opus-mt-tc-big-pt-zle | null | [
"transformers",
"pytorch",
"tf",
"safetensors",
"marian",
"text2text-generation",
"translation",
"opus-mt-tc",
"pt",
"ru",
"uk",
"zle",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-24T12:39:11+00:00 | [] | [
"pt",
"ru",
"uk",
"zle"
] | TAGS
#transformers #pytorch #tf #safetensors #marian #text2text-generation #translation #opus-mt-tc #pt #ru #uk #zle #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
| opus-mt-tc-big-pt-zle
=====================
Neural machine translation model for translating from Portuguese (pt) to East Slavic languages (zle).
This model is part of the OPUS-MT project, an effort to make neural machine translation models widely available and accessible for many languages in the world. All models... | [] | [
"TAGS\n#transformers #pytorch #tf #safetensors #marian #text2text-generation #translation #opus-mt-tc #pt #ru #uk #zle #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
translation | transformers | # opus-mt-tc-base-ro-uk
Neural machine translation model for translating from Romanian (ro) to Ukrainian (uk).
This model is part of the [OPUS-MT project](https://github.com/Helsinki-NLP/Opus-MT), an effort to make neural machine translation models widely available and accessible for many languages in the world. All ... | {"language": ["ro", "uk"], "license": "cc-by-4.0", "tags": ["translation", "opus-mt-tc"], "model-index": [{"name": "opus-mt-tc-base-ro-uk", "results": [{"task": {"type": "translation", "name": "Translation ron-ukr"}, "dataset": {"name": "flores101-devtest", "type": "flores_101", "args": "ron ukr devtest"}, "metrics": [... | Helsinki-NLP/opus-mt-tc-base-ro-uk | null | [
"transformers",
"pytorch",
"tf",
"safetensors",
"marian",
"text2text-generation",
"translation",
"opus-mt-tc",
"ro",
"uk",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-24T12:41:59+00:00 | [] | [
"ro",
"uk"
] | TAGS
#transformers #pytorch #tf #safetensors #marian #text2text-generation #translation #opus-mt-tc #ro #uk #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
| opus-mt-tc-base-ro-uk
=====================
Neural machine translation model for translating from Romanian (ro) to Ukrainian (uk).
This model is part of the OPUS-MT project, an effort to make neural machine translation models widely available and accessible for many languages in the world. All models are originally... | [] | [
"TAGS\n#transformers #pytorch #tf #safetensors #marian #text2text-generation #translation #opus-mt-tc #ro #uk #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
translation | transformers | # opus-mt-tc-big-es-zle
Neural machine translation model for translating from Spanish (es) to East Slavic languages (zle).
This model is part of the [OPUS-MT project](https://github.com/Helsinki-NLP/Opus-MT), an effort to make neural machine translation models widely available and accessible for many languages in the... | {"language": ["be", "es", "ru", "uk", "zle"], "license": "cc-by-4.0", "tags": ["translation", "opus-mt-tc"], "model-index": [{"name": "opus-mt-tc-big-es-zle", "results": [{"task": {"type": "translation", "name": "Translation spa-rus"}, "dataset": {"name": "flores101-devtest", "type": "flores_101", "args": "spa rus devt... | Helsinki-NLP/opus-mt-tc-big-es-zle | null | [
"transformers",
"pytorch",
"tf",
"safetensors",
"marian",
"text2text-generation",
"translation",
"opus-mt-tc",
"be",
"es",
"ru",
"uk",
"zle",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-24T12:43:19+00:00 | [] | [
"be",
"es",
"ru",
"uk",
"zle"
] | TAGS
#transformers #pytorch #tf #safetensors #marian #text2text-generation #translation #opus-mt-tc #be #es #ru #uk #zle #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
| opus-mt-tc-big-es-zle
=====================
Neural machine translation model for translating from Spanish (es) to East Slavic languages (zle).
This model is part of the OPUS-MT project, an effort to make neural machine translation models widely available and accessible for many languages in the world. All models ar... | [] | [
"TAGS\n#transformers #pytorch #tf #safetensors #marian #text2text-generation #translation #opus-mt-tc #be #es #ru #uk #zle #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
translation | transformers | # opus-mt-tc-base-tr-uk
Neural machine translation model for translating from Turkish (tr) to Ukrainian (uk).
This model is part of the [OPUS-MT project](https://github.com/Helsinki-NLP/Opus-MT), an effort to make neural machine translation models widely available and accessible for many languages in the world. All m... | {"language": ["tr", "uk"], "license": "cc-by-4.0", "tags": ["translation", "opus-mt-tc"], "model-index": [{"name": "opus-mt-tc-base-tr-uk", "results": [{"task": {"type": "translation", "name": "Translation tur-ukr"}, "dataset": {"name": "tatoeba-test-v2021-08-07", "type": "tatoeba_mt", "args": "tur-ukr"}, "metrics": [{... | Helsinki-NLP/opus-mt-tc-base-tr-uk | null | [
"transformers",
"pytorch",
"tf",
"safetensors",
"marian",
"text2text-generation",
"translation",
"opus-mt-tc",
"tr",
"uk",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-24T12:46:08+00:00 | [] | [
"tr",
"uk"
] | TAGS
#transformers #pytorch #tf #safetensors #marian #text2text-generation #translation #opus-mt-tc #tr #uk #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
| opus-mt-tc-base-tr-uk
=====================
Neural machine translation model for translating from Turkish (tr) to Ukrainian (uk).
This model is part of the OPUS-MT project, an effort to make neural machine translation models widely available and accessible for many languages in the world. All models are originally ... | [] | [
"TAGS\n#transformers #pytorch #tf #safetensors #marian #text2text-generation #translation #opus-mt-tc #tr #uk #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
translation | transformers | # opus-mt-tc-big-zls-zle
Neural machine translation model for translating from South Slavic languages (zls) to East Slavic languages (zle).
This model is part of the [OPUS-MT project](https://github.com/Helsinki-NLP/Opus-MT), an effort to make neural machine translation models widely available and accessible for many... | {"language": ["be", "bg", "hr", "ru", "sh", "sl", "sr_Cyrl", "sr_Latn", "uk", "zle", "zls"], "license": "cc-by-4.0", "tags": ["translation", "opus-mt-tc"], "model-index": [{"name": "opus-mt-tc-big-zls-zle", "results": [{"task": {"type": "translation", "name": "Translation bul-rus"}, "dataset": {"name": "flores101-devte... | Helsinki-NLP/opus-mt-tc-big-zls-zle | null | [
"transformers",
"pytorch",
"safetensors",
"marian",
"text2text-generation",
"translation",
"opus-mt-tc",
"tc",
"big",
"zls",
"zle",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-24T12:47:28+00:00 | [] | [
"be",
"bg",
"hr",
"ru",
"sh",
"sl",
"sr_Cyrl",
"sr_Latn",
"uk",
"zle",
"zls"
] | TAGS
#transformers #pytorch #safetensors #marian #text2text-generation #translation #opus-mt-tc #tc #big #zls #zle #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
| opus-mt-tc-big-zls-zle
======================
Neural machine translation model for translating from South Slavic languages (zls) to East Slavic languages (zle).
This model is part of the OPUS-MT project, an effort to make neural machine translation models widely available and accessible for many languages in the wo... | [] | [
"TAGS\n#transformers #pytorch #safetensors #marian #text2text-generation #translation #opus-mt-tc #tc #big #zls #zle #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #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. -->
# finetuning-sentiment-model-3000-samples
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/d... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imdb"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "finetuning-sentiment-model-3000-samples", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "imdb", "type": "imdb", "args": ... | Luttufuttu/finetuning-sentiment-model-3000-samples | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-24T12:49:11+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-imdb #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# finetuning-sentiment-model-3000-samples
This model is a fine-tuned version of distilbert-base-uncased on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3416
- Accuracy: 0.86
- F1: 0.8679
## Model description
More information needed
## Intended uses & limitations
More info... | [
"# finetuning-sentiment-model-3000-samples\n\nThis model is a fine-tuned version of distilbert-base-uncased on the imdb dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.3416\n- Accuracy: 0.86\n- F1: 0.8679",
"## Model description\n\nMore information needed",
"## Intended uses & limi... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-imdb #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# finetuning-sentiment-model-3000-samples\n\nThis model is a fine-tuned version of distilbert-base-uncased... |
translation | transformers | # opus-mt-tc-big-zlw-zle
Neural machine translation model for translating from West Slavic languages (zlw) to East Slavic languages (zle).
This model is part of the [OPUS-MT project](https://github.com/Helsinki-NLP/Opus-MT), an effort to make neural machine translation models widely available and accessible for many ... | {"language": ["be", "cs", "dsb", "hsb", "pl", "ru", "uk", "zle", "zlw"], "license": "cc-by-4.0", "tags": ["translation", "opus-mt-tc"], "model-index": [{"name": "opus-mt-tc-big-zlw-zle", "results": [{"task": {"type": "translation", "name": "Translation ces-rus"}, "dataset": {"name": "flores101-devtest", "type": "flores... | Helsinki-NLP/opus-mt-tc-big-zlw-zle | null | [
"transformers",
"pytorch",
"safetensors",
"marian",
"text2text-generation",
"translation",
"opus-mt-tc",
"be",
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"dsb",
"hsb",
"pl",
"ru",
"uk",
"zle",
"zlw",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-24T12:50:12+00:00 | [] | [
"be",
"cs",
"dsb",
"hsb",
"pl",
"ru",
"uk",
"zle",
"zlw"
] | TAGS
#transformers #pytorch #safetensors #marian #text2text-generation #translation #opus-mt-tc #be #cs #dsb #hsb #pl #ru #uk #zle #zlw #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
| opus-mt-tc-big-zlw-zle
======================
Neural machine translation model for translating from West Slavic languages (zlw) to East Slavic languages (zle).
This model is part of the OPUS-MT project, an effort to make neural machine translation models widely available and accessible for many languages in the wor... | [] | [
"TAGS\n#transformers #pytorch #safetensors #marian #text2text-generation #translation #opus-mt-tc #be #cs #dsb #hsb #pl #ru #uk #zle #zlw #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #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. -->
# finetuning-sentiment-model-300-samples
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/di... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imdb"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "finetuning-sentiment-model-300-samples", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "imdb", "type": "imdb", "args": "... | Roshan777/finetuning-sentiment-model-300-samples | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-24T13:02:26+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-imdb #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# finetuning-sentiment-model-300-samples
This model is a fine-tuned version of distilbert-base-uncased on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6567
- Accuracy: 0.6833
- F1: 0.6154
## Model description
More information needed
## Intended uses & limitations
More inf... | [
"# finetuning-sentiment-model-300-samples\n\nThis model is a fine-tuned version of distilbert-base-uncased on the imdb dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.6567\n- Accuracy: 0.6833\n- F1: 0.6154",
"## Model description\n\nMore information needed",
"## Intended uses & lim... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-imdb #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# finetuning-sentiment-model-300-samples\n\nThis model is a fine-tuned version of distilbert-base-uncased ... |
text-generation | transformers |
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1054713372845862912/1SR4... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/melindagates/1648128524647/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/melindagates | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-24T13:22:09+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Melinda French Gates
@melindagates
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 dat... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# gpt2-xl_ft_mult_25k
This model is a fine-tuned version of [gpt2-xl](https://huggingface.co/gpt2-xl) on an unknown dataset.
It ac... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "gpt2-xl_ft_mult_25k", "results": []}]} | beston91/gpt2-xl_ft_mult_25k | null | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-24T13:37:10+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| gpt2-xl\_ft\_mult\_25k
======================
This model is a fine-tuned version of gpt2-xl on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5782
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* gradient\\_accumulation\\_steps: 32\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
"TAGS\n#transformers #pytorch #tensorboard #gpt2 #text-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\\_batc... |
null | null |
# LightHuBERT
[**LightHuBERT**](https://arxiv.org/abs/2203.15610): **Lightweight and Configurable Speech Representation Learning with Once-for-All Hidden-Unit BERT**
Authors: Rui Wang, Qibing Bai, Junyi Ao, Long Zhou, Zhixiang Xiong, Zhihua Wei, Yu Zhang, Tom Ko and Haizhou Li
| [**Github**](https://github.c... | {"language": ["en"], "license": "apache-2.0", "tags": ["speech", "self-supervised learning", "model compression", "neural architecture search", "LightHuBERT"], "datasets": ["librispeech_asr", "superb"]} | mechanicalsea/lighthubert | null | [
"speech",
"self-supervised learning",
"model compression",
"neural architecture search",
"LightHuBERT",
"en",
"dataset:librispeech_asr",
"dataset:superb",
"arxiv:2203.15610",
"license:apache-2.0",
"region:us"
] | null | 2022-03-24T14:07:52+00:00 | [
"2203.15610"
] | [
"en"
] | TAGS
#speech #self-supervised learning #model compression #neural architecture search #LightHuBERT #en #dataset-librispeech_asr #dataset-superb #arxiv-2203.15610 #license-apache-2.0 #region-us
| LightHuBERT
===========
LightHuBERT: Lightweight and Configurable Speech Representation Learning with Once-for-All Hidden-Unit BERT
Authors: Rui Wang, Qibing Bai, Junyi Ao, Long Zhou, Zhixiang Xiong, Zhihua Wei, Yu Zhang, Tom Ko and Haizhou Li
| Github | Huggingface |
The authors' PyTorch implementation and pre... | [
"### Profiling LightHuBERT\n\n\nAs mentioned in Profiling Tool for SLT2022 SUPERB Challenge, we profiling the 'lighthubert' in s3prl.",
"### Reference\n\n\nIf you find our work is useful in your research, please cite the following paper:",
"### Contact Information\n\n\nFor help or issues using LightHuBERT model... | [
"TAGS\n#speech #self-supervised learning #model compression #neural architecture search #LightHuBERT #en #dataset-librispeech_asr #dataset-superb #arxiv-2203.15610 #license-apache-2.0 #region-us \n",
"### Profiling LightHuBERT\n\n\nAs mentioned in Profiling Tool for SLT2022 SUPERB Challenge, we profiling the 'lig... |
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. -->
# electra-base-discriminator-yelp-mlm
This model is a fine-tuned version of [google/electra-base-discriminator](https://huggingfac... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["yelp_review_full"], "metrics": ["accuracy"], "model-index": [{"name": "electra-base-discriminator-yelp-mlm", "results": [{"task": {"type": "fill-mask", "name": "Masked Language Modeling"}, "dataset": {"name": "yelp_review_full yelp_review_full"... | Yaxin/electra-base-discriminator-yelp-mlm | null | [
"transformers",
"pytorch",
"electra",
"fill-mask",
"generated_from_trainer",
"dataset:yelp_review_full",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-24T14:18:41+00:00 | [] | [] | TAGS
#transformers #pytorch #electra #fill-mask #generated_from_trainer #dataset-yelp_review_full #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# electra-base-discriminator-yelp-mlm
This model is a fine-tuned version of google/electra-base-discriminator on the yelp_review_full yelp_review_full dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5550
- Accuracy: 0.6783
## Model description
More information needed
## Intended uses ... | [
"# electra-base-discriminator-yelp-mlm\n\nThis model is a fine-tuned version of google/electra-base-discriminator on the yelp_review_full yelp_review_full dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 1.5550\n- Accuracy: 0.6783",
"## Model description\n\nMore information needed",
"... | [
"TAGS\n#transformers #pytorch #electra #fill-mask #generated_from_trainer #dataset-yelp_review_full #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# electra-base-discriminator-yelp-mlm\n\nThis model is a fine-tuned version of google/electra-base-discriminator on the ... |
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. -->
# canine-c-finetuned-sst2
This model is a fine-tuned version of [google/canine-c](https://huggingface.co/google/canine-c) on the g... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["accuracy"], "model-index": [{"name": "canine-c-finetuned-sst2", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "args": "sst2"}, "metrics": [{... | celine98/canine-c-finetuned-sst2 | null | [
"transformers",
"pytorch",
"tensorboard",
"canine",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-24T14:40:39+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #canine #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| canine-c-finetuned-sst2
=======================
This model is a fine-tuned version of google/canine-c on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6025
* Accuracy: 0.8486
Model description
-----------------
More information needed
Intended uses & limitations
--------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 4.9121586874695155e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 16\n* seed: 8\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 4... | [
"TAGS\n#transformers #pytorch #tensorboard #canine #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_r... |
summarization | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mt5-small-finetuned-mt5-en
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on ... | {"license": "apache-2.0", "tags": ["summarization", "generated_from_trainer"], "datasets": ["xlsum"], "metrics": ["rouge"], "model-index": [{"name": "mt5-small-finetuned-mt5-en", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "xlsum", "type":... | ahmeddbahaa/mt5-small-finetuned-mt5-en | null | [
"transformers",
"pytorch",
"tensorboard",
"mt5",
"text2text-generation",
"summarization",
"generated_from_trainer",
"dataset:xlsum",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-24T15:17:24+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #mt5 #text2text-generation #summarization #generated_from_trainer #dataset-xlsum #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| mt5-small-finetuned-mt5-en
==========================
This model is a fine-tuned version of google/mt5-small on the xlsum dataset.
It achieves the following results on the evaluation set:
* Loss: 2.8345
* Rouge1: 23.8952
* Rouge2: 5.8792
* Rougel: 18.6495
* Rougelsum: 18.7057
Model description
-----------------
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* gradient\\_accumulation\\_steps: 10\n* total\\_train\\_batch\\_size: 40\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
"TAGS\n#transformers #pytorch #tensorboard #mt5 #text2text-generation #summarization #generated_from_trainer #dataset-xlsum #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were ... |
question-answering | 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. -->
# Mr-Wick/albert-base-v2
This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on an unknown dat... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "Mr-Wick/albert-base-v2", "results": []}]} | Mr-Wick/albert-base-v2 | null | [
"transformers",
"tf",
"tensorboard",
"albert",
"question-answering",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-24T15:34:30+00:00 | [] | [] | TAGS
#transformers #tf #tensorboard #albert #question-answering #generated_from_keras_callback #license-apache-2.0 #endpoints_compatible #region-us
| Mr-Wick/albert-base-v2
======================
This model is a fine-tuned version of albert-base-v2 on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.6458
* Validation Loss: 0.8180
* Epoch: 1
Model description
-----------------
More information needed
Intended uses... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'learning\\_rate': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_steps': 16494, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': False, 'na... | [
"TAGS\n#transformers #tf #tensorboard #albert #question-answering #generated_from_keras_callback #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'learning\\_rate': {'class\\_nam... |
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/1350186722596974593/lANA... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/untiltrees/1648138126631/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/untiltrees | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-24T15:42:21+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Dancing Box
@untiltrees
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
---------... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation | transformers |
<!-- 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. -->
# try-m
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on an unknown dataset.
It achieves the fol... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "try-m", "results": []}]} | bigmorning/try-m | null | [
"transformers",
"tf",
"gpt2",
"text-generation",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-24T16:02:27+00:00 | [] | [] | TAGS
#transformers #tf #gpt2 #text-generation #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# try-m
This model is a fine-tuned version of distilgpt2 on an unknown dataset.
It achieves the following results on the evaluation set:
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training pro... | [
"# try-m\n\nThis model is a fine-tuned version of distilgpt2 on an unknown dataset.\nIt achieves the following results on the evaluation set:",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information n... | [
"TAGS\n#transformers #tf #gpt2 #text-generation #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# try-m\n\nThis model is a fine-tuned version of distilgpt2 on an unknown dataset.\nIt achieves the following results on the ev... |
text-classification | transformers |
# ONNX convert DistilBERT base uncased finetuned SST-2
## Conversion of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english)
This model is a fine-tune checkpoint of [DistilBERT-base-uncased](https://huggingface.co/distilbert-base-uncased), fine-tun... | {"language": "en", "license": "apache-2.0", "datasets": ["sst2"]} | optimum/distilbert-base-uncased-finetuned-sst-2-english | null | [
"transformers",
"onnx",
"text-classification",
"en",
"dataset:sst2",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-24T16:06:17+00:00 | [] | [
"en"
] | TAGS
#transformers #onnx #text-classification #en #dataset-sst2 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# ONNX convert DistilBERT base uncased finetuned SST-2
## Conversion of distilbert-base-uncased-finetuned-sst-2-english
This model is a fine-tune checkpoint of DistilBERT-base-uncased, fine-tuned on SST-2.
This model reaches an accuracy of 91.3 on the dev set (for comparison, Bert bert-base-uncased version reaches a... | [
"# ONNX convert DistilBERT base uncased finetuned SST-2",
"## Conversion of distilbert-base-uncased-finetuned-sst-2-english\n\nThis model is a fine-tune checkpoint of DistilBERT-base-uncased, fine-tuned on SST-2.\nThis model reaches an accuracy of 91.3 on the dev set (for comparison, Bert bert-base-uncased versio... | [
"TAGS\n#transformers #onnx #text-classification #en #dataset-sst2 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# ONNX convert DistilBERT base uncased finetuned SST-2",
"## Conversion of distilbert-base-uncased-finetuned-sst-2-english\n\nThis model is a fine-tune checkpoint of... |
question-answering | transformers |
# ONNX convert roberta-base for QA
## Conversion of [deepset/roberta-base-squad2](https://huggingface.co/deepset/roberta-base-squad2)
NOTE: This is version 2 of the model. See [this github issue](https://github.com/deepset-ai/FARM/issues/552) from the FARM repository for an explanation of why we updated. If you'd l... | {"language": "en", "license": "cc-by-4.0", "datasets": ["squad_v2"]} | optimum/roberta-base-squad2 | null | [
"transformers",
"onnx",
"question-answering",
"en",
"dataset:squad_v2",
"license:cc-by-4.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-24T16:11:57+00:00 | [] | [
"en"
] | TAGS
#transformers #onnx #question-answering #en #dataset-squad_v2 #license-cc-by-4.0 #endpoints_compatible #region-us
|
# ONNX convert roberta-base for QA
## Conversion of deepset/roberta-base-squad2
NOTE: This is version 2 of the model. See this github issue from the FARM repository for an explanation of why we updated. If you'd like to use version 1, specify 'revision="v1.0"' when loading the model in Transformers 3.5. For exmaple... | [
"# ONNX convert roberta-base for QA",
"## Conversion of deepset/roberta-base-squad2\n\nNOTE: This is version 2 of the model. See this github issue from the FARM repository for an explanation of why we updated. If you'd like to use version 1, specify 'revision=\"v1.0\"' when loading the model in Transformers 3.5. ... | [
"TAGS\n#transformers #onnx #question-answering #en #dataset-squad_v2 #license-cc-by-4.0 #endpoints_compatible #region-us \n",
"# ONNX convert roberta-base for QA",
"## Conversion of deepset/roberta-base-squad2\n\nNOTE: This is version 2 of the model. See this github issue from the FARM repository for an explana... |
token-classification | transformers |
# ONNX convert of bert-base-NER
## Conversion of [bert-base-NER](https://huggingface.co/dslim/bert-base-NER)
## Model description
**bert-base-NER** is a fine-tuned BERT model that is ready to use for **Named Entity Recognition** and achieves **state-of-the-art performance** for the NER task. It has been trained to... | {"language": "en", "license": "mit", "datasets": ["conll2003"]} | optimum/bert-base-NER | null | [
"transformers",
"onnx",
"token-classification",
"en",
"dataset:conll2003",
"arxiv:1810.04805",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-24T16:13:41+00:00 | [
"1810.04805"
] | [
"en"
] | TAGS
#transformers #onnx #token-classification #en #dataset-conll2003 #arxiv-1810.04805 #license-mit #autotrain_compatible #endpoints_compatible #region-us
| ONNX convert of bert-base-NER
=============================
Conversion of bert-base-NER
---------------------------
Model description
-----------------
bert-base-NER is a fine-tuned BERT model that is ready to use for Named Entity Recognition and achieves state-of-the-art performance for the NER task. It has been... | [
"#### How to use\n\n\nYou can use this model with Transformers *pipeline* for NER.",
"#### Limitations and bias\n\n\nThis model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains. Furthermore, the m... | [
"TAGS\n#transformers #onnx #token-classification #en #dataset-conll2003 #arxiv-1810.04805 #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"#### How to use\n\n\nYou can use this model with Transformers *pipeline* for NER.",
"#### Limitations and bias\n\n\nThis model is limited by its tra... |
sentence-similarity | sentence-transformers |
# ONNX convert all-MiniLM-L6-v2
## Conversion of [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2)
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks li... | {"language": "en", "license": "apache-2.0", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity"], "pipeline_tag": "sentence-similarity"} | optimum/all-MiniLM-L6-v2 | null | [
"sentence-transformers",
"onnx",
"feature-extraction",
"sentence-similarity",
"en",
"arxiv:1904.06472",
"arxiv:2102.07033",
"arxiv:2104.08727",
"arxiv:1704.05179",
"arxiv:1810.09305",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-24T16:15:58+00:00 | [
"1904.06472",
"2102.07033",
"2104.08727",
"1704.05179",
"1810.09305"
] | [
"en"
] | TAGS
#sentence-transformers #onnx #feature-extraction #sentence-similarity #en #arxiv-1904.06472 #arxiv-2102.07033 #arxiv-2104.08727 #arxiv-1704.05179 #arxiv-1810.09305 #license-apache-2.0 #endpoints_compatible #has_space #region-us
| ONNX convert all-MiniLM-L6-v2
=============================
Conversion of sentence-transformers/all-MiniLM-L6-v2
----------------------------------------------------
This is a sentence-transformers model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clusterin... | [
"### Pre-training\n\n\nWe use the pretrained 'nreimers/MiniLM-L6-H384-uncased' model. Please refer to the model card for more detailed information about the pre-training procedure.",
"### Fine-tuning\n\n\nWe fine-tune the model using a contrastive objective. Formally, we compute the cosine similarity from each po... | [
"TAGS\n#sentence-transformers #onnx #feature-extraction #sentence-similarity #en #arxiv-1904.06472 #arxiv-2102.07033 #arxiv-2104.08727 #arxiv-1704.05179 #arxiv-1810.09305 #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"### Pre-training\n\n\nWe use the pretrained 'nreimers/MiniLM-L6-H384-unca... |
zero-shot-classification | transformers |
# ONNX convert typeform/distilbert-base-uncased-mnli
## Conversion of [typeform/distilbert-base-uncased-mnli](typeform/distilbert-base-uncased-mnli)
This is the [uncased DistilBERT model](https://huggingface.co/distilbert-base-uncased) fine-tuned on [Multi-Genre Natural Language Inference](https://huggingface.co/d... | {"language": "en", "tags": ["distilbert"], "datasets": ["multi_nli"], "metrics": ["accuracy"], "pipeline_tag": "zero-shot-classification"} | optimum/distilbert-base-uncased-mnli | null | [
"transformers",
"onnx",
"text-classification",
"distilbert",
"zero-shot-classification",
"en",
"dataset:multi_nli",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-24T16:17:54+00:00 | [] | [
"en"
] | TAGS
#transformers #onnx #text-classification #distilbert #zero-shot-classification #en #dataset-multi_nli #autotrain_compatible #endpoints_compatible #has_space #region-us
| ONNX convert typeform/distilbert-base-uncased-mnli
==================================================
Conversion of typeform/distilbert-base-uncased-mnli
---------------------------------------------------
This is the uncased DistilBERT model fine-tuned on Multi-Genre Natural Language Inference (MNLI) dataset for t... | [] | [
"TAGS\n#transformers #onnx #text-classification #distilbert #zero-shot-classification #en #dataset-multi_nli #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
text-generation | transformers |
# ConnerBot DialoGPT Model | {"tags": ["conversational"]} | MolePatrol/DialoGPT-Medium-ConnerBot | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-24T16:23:50+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# ConnerBot DialoGPT Model | [
"# ConnerBot DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# ConnerBot DialoGPT Model"
] |
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/1478043369578266624/vWL3... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/janieclone-wretched_worm/1648140650284/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/janieclone-wretched_worm | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-24T16:50:13+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI CYBORG
wretched worm & Columbine Janie
@janieclone-wretched\_worm
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the ... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-multilingual-cased-finetuned-squad
This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggin... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "bert-base-multilingual-cased-finetuned-squad", "results": []}]} | Paul-Vinh/bert-base-multilingual-cased-finetuned-squad | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-24T19:22:33+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us
| bert-base-multilingual-cased-finetuned-squad
============================================
This model is a fine-tuned version of bert-base-multilingual-cased on the squad dataset.
It achieves the following results on the evaluation set:
* Loss: 1.0122
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: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 1... |
question-answering | 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. -->
# Mr-Wick/Albert
This model is a fine-tuned version of [Mr-Wick/Albert](https://huggingface.co/Mr-Wick/Albert) on an unknown dataset.
It... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "Mr-Wick/Albert", "results": []}]} | Mr-Wick/Albert | null | [
"transformers",
"tf",
"tensorboard",
"albert",
"question-answering",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-24T19:29:49+00:00 | [] | [] | TAGS
#transformers #tf #tensorboard #albert #question-answering #generated_from_keras_callback #license-apache-2.0 #endpoints_compatible #region-us
| Mr-Wick/Albert
==============
This model is a fine-tuned version of Mr-Wick/Albert on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.4248
* Train End Logits Accuracy: 0.3423
* Train Loss Accuracy: 0.0664
* Train Start Logits Accuracy: 0.3437
* Validation Loss: 0.9468
* ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'learning\\_rate': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_steps': 16494, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': False, 'na... | [
"TAGS\n#transformers #tf #tensorboard #albert #question-answering #generated_from_keras_callback #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'learning\\_rate': {'class\\_nam... |
null | null | !pip install -Uqq fastbook
!pip install fastai==2.5
import fastbook
fastbook.setup_book()
from fastbook import *
from fastai.vision.widgets import *
path = Path('/content/gdrive/My Drive/caballos')
modelo = DataBlock(
blocks=(ImageBlock, CategoryBlock),
get_items=get_image_files,
splitter=RandomSplitte... | {} | SergioCabrera/DemoCaballos | null | [
"region:us"
] | null | 2022-03-24T19:34:40+00:00 | [] | [] | TAGS
#region-us
| !pip install -Uqq fastbook
!pip install fastai==2.5
import fastbook
fastbook.setup_book()
from fastbook import *
from URL.widgets import *
path = Path('/content/gdrive/My Drive/caballos')
modelo = DataBlock(
blocks=(ImageBlock, CategoryBlock),
get_items=get_image_files,
splitter=RandomSplitter(valid_pc... | [] | [
"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. -->
# 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"], "model-index": [{"name": "distilbert-base-uncased-finetuned-emotion", "results": []}]} | cdinh2022/distilbert-base-uncased-finetuned-emotion | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-24T21:15:07+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #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.
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
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: 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: 0.1",
"### Trai... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2... |
automatic-speech-recognition | espnet |
## ESPnet2 model
This model was trained by Chaitanya Narisetty using recipe in [espnet](https://github.com/espnet/espnet/).
<!-- Generated by scripts/utils/show_asr_result.sh -->
# RESULTS
## Environments
- date: `Fri Mar 25 04:35:42 EDT 2022`
- python version: `3.9.5 (default, Jun 4 2021, 12:28:51) [GCC 7.5.0]... | {"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "automatic-speech-recognition"], "datasets": ["librispeech_asr", "librispeech 960h"]} | espnet/chai_librispeech_asr_train_rnnt_conformer_raw_en_bpe5000_sp | null | [
"espnet",
"audio",
"automatic-speech-recognition",
"en",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | null | 2022-03-24T21:32:22+00:00 | [
"1804.00015"
] | [
"en"
] | TAGS
#espnet #audio #automatic-speech-recognition #en #arxiv-1804.00015 #license-cc-by-4.0 #region-us
| ESPnet2 model
-------------
This model was trained by Chaitanya Narisetty using recipe in espnet.
RESULTS
=======
Environments
------------
* date: 'Fri Mar 25 04:35:42 EDT 2022'
* python version: '3.9.5 (default, Jun 4 2021, 12:28:51) [GCC 7.5.0]'
* espnet version: 'espnet 0.10.7a1'
* pytorch version: 'pytorch... | [
"### WER",
"### CER",
"### TER\n\n\n\nASR config\n----------\n\n\nexpand",
"### Citing ESPnet\n\n\nor arXiv:"
] | [
"TAGS\n#espnet #audio #automatic-speech-recognition #en #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"### WER",
"### CER",
"### TER\n\n\n\nASR config\n----------\n\n\nexpand",
"### Citing ESPnet\n\n\nor arXiv:"
] |
text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# gpt2-xl-ft-value_it-1k-0_on_1k-1
This model is a fine-tuned version of [newtonkwan/gpt2-xl-ft-0](https://huggingface.co/newtonkw... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "gpt2-xl-ft-value_it-1k-0_on_1k-1", "results": []}]} | IsaacSST/gpt2-xl-ft-value_it-1k-0_on_1k-1 | null | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-24T21:32:58+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| gpt2-xl-ft-value\_it-1k-0\_on\_1k-1
===================================
This model is a fine-tuned version of newtonkwan/gpt2-xl-ft-0 on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 1.8666
Model description
-----------------
More information needed
Intended uses & limit... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 2022\n* gradient\\_accumulation\\_steps: 32\n* total\\_train\\_batch\\_size: 256\n* optimizer: Adam with betas=(0.9,0.999) and epsi... | [
"TAGS\n#transformers #pytorch #tensorboard #gpt2 #text-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: 0.0005\n* train\\_bat... |
translation | transformers |
### eng-spa
* source group: English
* target group: Spanish
* OPUS readme: [eng-spa](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/eng-spa/README.md)
* model: transformer
* source language(s): eng
* target language(s): spa
* model: transformer
* pre-processing: normalization + SentencePiec... | {"language": ["en", "es"], "license": "apache-2.0", "tags": ["translation"]} | JavierIA/es-en | null | [
"transformers",
"pytorch",
"jax",
"marian",
"text2text-generation",
"translation",
"en",
"es",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-24T21:36:02+00:00 | [] | [
"en",
"es"
] | TAGS
#transformers #pytorch #jax #marian #text2text-generation #translation #en #es #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| ### eng-spa
* source group: English
* target group: Spanish
* OPUS readme: eng-spa
* model: transformer
* source language(s): eng
* target language(s): spa
* model: transformer
* pre-processing: normalization + SentencePiece (spm32k,spm32k)
* download original weights: URL
* test set translations: URL
* test set scor... | [
"### eng-spa\n\n\n* source group: English\n* target group: Spanish\n* OPUS readme: eng-spa\n* model: transformer\n* source language(s): eng\n* target language(s): spa\n* model: transformer\n* pre-processing: normalization + SentencePiece (spm32k,spm32k)\n* download original weights: URL\n* test set translations: UR... | [
"TAGS\n#transformers #pytorch #jax #marian #text2text-generation #translation #en #es #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### eng-spa\n\n\n* source group: English\n* target group: Spanish\n* OPUS readme: eng-spa\n* model: transformer\n* source language(s): eng\n* targe... |
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_pt_bleu_10k_steps | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-24T23:20:28+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... |
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. -->
# classification-poems
This model is a fine-tuned version of [BSC-TeMU/roberta-base-bne](https://huggingface.co/BSC-TeMU/roberta-b... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "widget": [{"text": "El amor es una experiencia universal que nos conmueve a todos, pero a veces no hallamos las palabras adecuadas para expresarlo. A lo largo de la historia los poetas han sabido decir aquello que todos sentimos de ... | hackathon-pln-es/class-poems-es | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"base_model:BSC-TeMU/roberta-base-bne",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-24T23:20:31+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #base_model-BSC-TeMU/roberta-base-bne #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| classification-poems
====================
This model is a fine-tuned version of BSC-TeMU/roberta-base-bne on the spanish Poems Dataset dataset.
It achieves the following results on the evaluation set:
* Loss: 0.8228
* Accuracy: 0.7241
Model description
-----------------
The model was trained to classify poems i... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 2\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",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #base_model-BSC-TeMU/roberta-base-bne #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* ... |
text-generation | transformers |
# My Awesome Model
| {"tags": ["conversational"]} | MolePatrol/DialoGPT-Medium-MoleBot | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-25T01:02:48+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# My Awesome Model
| [
"# My Awesome Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# My Awesome Model"
] |
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. -->
# Bertweet-base finetuned on wnut17_ner
This model is a fine-tuned version of [vinai/bertweet-base](https://huggingface.co/vinai/b... | {"license": "apache-2.0", "tags": ["generated_from_trainer", "named-entity-recognition", "token-classification"], "datasets": ["wnut_17"], "metrics": ["precision", "recall", "f1", "accuracy"], "base_model": "vinai/bertweet-base", "model-index": [{"name": "fine_tune_bertweet-base-lp-ft", "results": [{"task": {"type": "t... | socialmediaie/bertweet-base_wnut17_ner | null | [
"transformers",
"pytorch",
"safetensors",
"roberta",
"token-classification",
"generated_from_trainer",
"named-entity-recognition",
"dataset:wnut_17",
"base_model:vinai/bertweet-base",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"regio... | null | 2022-03-25T04:01:52+00:00 | [] | [] | TAGS
#transformers #pytorch #safetensors #roberta #token-classification #generated_from_trainer #named-entity-recognition #dataset-wnut_17 #base_model-vinai/bertweet-base #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
| Bertweet-base finetuned on wnut17\_ner
======================================
This model is a fine-tuned version of vinai/bertweet-base on the wnut\_17 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3376
* Overall Precision: 0.6803
* Overall Recall: 0.6096
* Overall F1: 0.6430
* Overa... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-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: 100",
"### Trai... | [
"TAGS\n#transformers #pytorch #safetensors #roberta #token-classification #generated_from_trainer #named-entity-recognition #dataset-wnut_17 #base_model-vinai/bertweet-base #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### Training hyperparameters\n\n\nTh... |
image-to-text | transformers |
# Manga OCR
Optical character recognition for Japanese text, with the main focus being Japanese manga.
It uses [Vision Encoder Decoder](https://huggingface.co/docs/transformers/model_doc/visionencoderdecoder) framework.
Manga OCR can be used as a general purpose printed Japanese OCR, but its main goal was to provid... | {"language": "ja", "license": "apache-2.0", "tags": ["image-to-text"], "datasets": ["manga109s"]} | TeamFnord/manga-ocr | null | [
"transformers",
"pytorch",
"vision-encoder-decoder",
"image-to-text",
"ja",
"dataset:manga109s",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-25T04:35:09+00:00 | [] | [
"ja"
] | TAGS
#transformers #pytorch #vision-encoder-decoder #image-to-text #ja #dataset-manga109s #license-apache-2.0 #endpoints_compatible #has_space #region-us
|
# Manga OCR
Optical character recognition for Japanese text, with the main focus being Japanese manga.
It uses Vision Encoder Decoder framework.
Manga OCR can be used as a general purpose printed Japanese OCR, but its main goal was to provide a high quality
text recognition, robust against various scenarios specifi... | [
"# Manga OCR\n\nOptical character recognition for Japanese text, with the main focus being Japanese manga.\n\nIt uses Vision Encoder Decoder framework.\n\nManga OCR can be used as a general purpose printed Japanese OCR, but its main goal was to provide a high quality\ntext recognition, robust against various scenar... | [
"TAGS\n#transformers #pytorch #vision-encoder-decoder #image-to-text #ja #dataset-manga109s #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"# Manga OCR\n\nOptical character recognition for Japanese text, with the main focus being Japanese manga.\n\nIt uses Vision Encoder Decoder framework.\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. -->
# sentiment-model-sample-5-emotion
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-unca... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["emotion"], "metrics": ["accuracy"], "model-index": [{"name": "sentiment-model-sample-5-emotion", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "emotion", "type": "emotion", "args": "def... | jkhan447/sentiment-model-sample-5-emotion | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-25T05:26:34+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# sentiment-model-sample-5-emotion
This model is a fine-tuned version of bert-base-uncased on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4360
- Accuracy: 0.925
## Model description
More information needed
## Intended uses & limitations
More information needed
## Tra... | [
"# sentiment-model-sample-5-emotion\n\nThis model is a fine-tuned version of bert-base-uncased on the emotion dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.4360\n- Accuracy: 0.925",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore informa... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# sentiment-model-sample-5-emotion\n\nThis model is a fine-tuned version of bert-base-uncased on the emotion ... |
text-generation | 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. -->
# try-m-e
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on an unknown dataset.
It achieves the f... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "try-m-e", "results": []}]} | bigmorning/try-m-e | null | [
"transformers",
"tf",
"gpt2",
"text-generation",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-25T06:42:40+00:00 | [] | [] | TAGS
#transformers #tf #gpt2 #text-generation #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# try-m-e
This model is a fine-tuned version of distilgpt2 on an unknown dataset.
It achieves the following results on the evaluation set:
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training p... | [
"# try-m-e\n\nThis model is a fine-tuned version of distilgpt2 on an unknown dataset.\nIt achieves the following results on the evaluation set:",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information... | [
"TAGS\n#transformers #tf #gpt2 #text-generation #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# try-m-e\n\nThis model is a fine-tuned version of distilgpt2 on an unknown dataset.\nIt achieves the following results on the ... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-cola
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/di... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["matthews_correlation"], "model-index": [{"name": "distilbert-base-uncased-finetuned-cola", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "ar... | RomanEnikeev/distilbert-base-uncased-finetuned-cola | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-25T06:47:39+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-cola
======================================
This model is a fine-tuned version of distilbert-base-uncased on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.8265
* Matthews Correlation: 0.5671
Model description
-----------------
More informa... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning... |
token-classification | flair |
## Persian Universal Part-of-Speech Tagging in Flair
This is the universal part-of-speech tagging model for Persian that ships with [Flair](https://github.com/flairNLP/flair/).
F1-Score: **97,73** (UD_PERSIAN)
Predicts Universal POS tags:
| **tag** | **meaning** |
|:-------------------------... | {"language": ["fa"], "tags": ["flair", "token-classification", "sequence-tagger-model"], "datasets": ["ontonotes"], "widget": [{"text": "\u0645\u0642\u0627\u0645\u0627\u062a \u0645\u0635\u0631\u06cc \u0628\u0647 \u062e\u0627\u0637\u0631 \u062d\u0641\u0638 \u062b\u0628\u0627\u062a \u06a9\u0634\u0648\u0631 \u062f\u0631 \... | hamedkhaledi/persain-flair-upos | null | [
"flair",
"pytorch",
"token-classification",
"sequence-tagger-model",
"fa",
"dataset:ontonotes",
"region:us"
] | null | 2022-03-25T07:27:51+00:00 | [] | [
"fa"
] | TAGS
#flair #pytorch #token-classification #sequence-tagger-model #fa #dataset-ontonotes #region-us
| Persian Universal Part-of-Speech Tagging in Flair
-------------------------------------------------
This is the universal part-of-speech tagging model for Persian that ships with Flair.
F1-Score: 97,73 (UD\_PERSIAN)
Predicts Universal POS tags:
---
### Demo: How to use in Flair
Requires: Flair ('pip inst... | [
"### Demo: How to use in Flair\n\n\nRequires: Flair ('pip install flair')\n\n\nThis yields the following output:\n\n\n\n\n---",
"### Results\n\n\n* F-score (micro) 0.9773\n* F-score (macro) 0.9461\n* Accuracy 0.9773"
] | [
"TAGS\n#flair #pytorch #token-classification #sequence-tagger-model #fa #dataset-ontonotes #region-us \n",
"### Demo: How to use in Flair\n\n\nRequires: Flair ('pip install flair')\n\n\nThis yields the following output:\n\n\n\n\n---",
"### Results\n\n\n* F-score (micro) 0.9773\n* F-score (macro) 0.9461\n* Accur... |
null | null | Zlewozmywak Nivito to idealny dodatek do każdej luksusowej kuchni. Elegancki, nowoczesny design i wysokiej jakości konstrukcja sprawią, że będzie to ulubione miejsce do mycia naczyń lub przygotowywania posiłków. Ponadto jego głęboka miska pomieści duże garnki i patelnie. Dlaczego więc nie dodać odrobiny luksusu do swoj... | {"license": "afl-3.0"} | DerekCox/Zlewkuchenny | null | [
"license:afl-3.0",
"region:us"
] | null | 2022-03-25T07:44:29+00:00 | [] | [] | TAGS
#license-afl-3.0 #region-us
| Zlewozmywak Nivito to idealny dodatek do każdej luksusowej kuchni. Elegancki, nowoczesny design i wysokiej jakości konstrukcja sprawią, że będzie to ulubione miejsce do mycia naczyń lub przygotowywania posiłków. Ponadto jego głęboka miska pomieści duże garnki i patelnie. Dlaczego więc nie dodać odrobiny luksusu do swoj... | [] | [
"TAGS\n#license-afl-3.0 #region-us \n"
] |
sentence-similarity | sentence-transformers |
# DMetaSoul/sbert-chinese-general-v1
此模型基于 [bert-base-chinese](https://huggingface.co/bert-base-chinese) 版本 BERT 模型，在 NLI、PAWS-X、PKU-Paraphrase-Bank、STS 等语义相似数据集上进行训练，适用于**通用语义匹配**场景（此模型在 Chinese-STS 任务上效果较好，但在其它任务上效果并非最优，存在一定过拟合风险），比如文本特征抽取、文本向量聚类、文本语义搜索等业务场景。
注：此模型的[轻量化版本](https://huggingface.co/DMetaSoul/sbert-ch... | {"language": ["zh"], "license": "apache-2.0", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers", "semantic-search", "chinese", "mteb"], "pipeline_tag": "sentence-similarity", "model-index": [{"name": "sbert-chinese-general-v1", "results": [{"task": {"type": "STS"}, "dataset":... | DMetaSoul/sbert-chinese-general-v1 | null | [
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"semantic-search",
"chinese",
"mteb",
"zh",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-25T08:49:55+00:00 | [] | [
"zh"
] | TAGS
#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #semantic-search #chinese #mteb #zh #license-apache-2.0 #model-index #endpoints_compatible #region-us
| DMetaSoul/sbert-chinese-general-v1
==================================
此模型基于 bert-base-chinese 版本 BERT 模型，在 NLI、PAWS-X、PKU-Paraphrase-Bank、STS 等语义相似数据集上进行训练，适用于通用语义匹配场景（此模型在 Chinese-STS 任务上效果较好，但在其它任务上效果并非最优，存在一定过拟合风险），比如文本特征抽取、文本向量聚类、文本语义搜索等业务场景。
注：此模型的轻量化版本，也已经开源啦！
Usage
=====
1. Sentence-Transformers
--------... | [] | [
"TAGS\n#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #semantic-search #chinese #mteb #zh #license-apache-2.0 #model-index #endpoints_compatible #region-us \n"
] |
token-classification | transformers | # 🔑 Keyphrase Extraction Model: distilbert-inspec
Keyphrase extraction is a technique in text analysis where you extract the important keyphrases from a document. Thanks to these keyphrases humans can understand the content of a text very quickly and easily without reading it completely. Keyphrase extraction was first... | {"language": "en", "license": "mit", "tags": ["keyphrase-extraction"], "datasets": ["midas/inspec"], "metrics": ["seqeval"], "widget": [{"text": "Keyphrase extraction is a technique in text analysis where you extract the important keyphrases from a document. Thanks to these keyphrases humans can understand the content ... | ml6team/keyphrase-extraction-distilbert-inspec | null | [
"transformers",
"pytorch",
"distilbert",
"token-classification",
"keyphrase-extraction",
"en",
"dataset:midas/inspec",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-25T08:52:01+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #distilbert #token-classification #keyphrase-extraction #en #dataset-midas/inspec #license-mit #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
| Keyphrase Extraction Model: distilbert-inspec
=============================================
Keyphrase extraction is a technique in text analysis where you extract the important keyphrases from a document. Thanks to these keyphrases humans can understand the content of a text very quickly and easily without reading it... | [
"### Limitations\n\n\n* This keyphrase extraction model is very domain-specific and will perform very well on abstracts of scientific papers. It's not recommended to use this model for other domains, but you are free to test it out.\n* Only works for English documents.",
"### How To Use\n\n\nTraining Dataset\n---... | [
"TAGS\n#transformers #pytorch #distilbert #token-classification #keyphrase-extraction #en #dataset-midas/inspec #license-mit #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### Limitations\n\n\n* This keyphrase extraction model is very domain-specific and will perform very wel... |
sentence-similarity | sentence-transformers |
# DMetaSoul/sbert-chinese-general-v2
此模型基于 [bert-base-chinese](https://huggingface.co/bert-base-chinese) 版本 BERT 模型，在百万级语义相似数据集 [SimCLUE](https://github.com/CLUEbenchmark/SimCLUE) 上进行训练，适用于**通用语义匹配**场景，从效果来看该模型在各种任务上**泛化能力更好**。
注：此模型的[轻量化版本](https://huggingface.co/DMetaSoul/sbert-chinese-general-v2-distill)，也已经开源啦！
... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers", "semantic-search", "chinese"], "pipeline_tag": "sentence-similarity"} | DMetaSoul/sbert-chinese-general-v2 | null | [
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"semantic-search",
"chinese",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-25T08:59:33+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #semantic-search #chinese #endpoints_compatible #has_space #region-us
| DMetaSoul/sbert-chinese-general-v2
==================================
此模型基于 bert-base-chinese 版本 BERT 模型，在百万级语义相似数据集 SimCLUE 上进行训练，适用于通用语义匹配场景，从效果来看该模型在各种任务上泛化能力更好。
注：此模型的轻量化版本，也已经开源啦！
Usage
=====
1. Sentence-Transformers
------------------------
通过 sentence-transformers 框架来使用该模型，首先进行安装：
然后使用下面的代码来载入该模型并进行文... | [] | [
"TAGS\n#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #semantic-search #chinese #endpoints_compatible #has_space #region-us \n"
] |
translation | transformers |
# DeUnCaser
The output from Automated Speak Recognition software is usually uncased and without any punctation. This does not make a very readable text.
The DeUnCaser is a sequence-to-sequence model that is reversing this process. It adds punctation, and capitalises the correct words. In some languages this means ad... | {"language": false, "license": "cc-by-4.0", "tags": ["translation"], "widget": [{"text": "moscow says deployments in eastern europe increase tensions at the same time nato says russia has moved troops to belarus"}, {"text": "dette er en liten test som er laget av per egil kummervold han er en forsker som tidligere jobb... | pere/multi-sentencefix-mt5-large | null | [
"transformers",
"pytorch",
"jax",
"t5",
"text2text-generation",
"translation",
"no",
"license:cc-by-4.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-25T09:05:25+00:00 | [] | [
"no"
] | TAGS
#transformers #pytorch #jax #t5 #text2text-generation #translation #no #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
# DeUnCaser
The output from Automated Speak Recognition software is usually uncased and without any punctation. This does not make a very readable text.
The DeUnCaser is a sequence-to-sequence model that is reversing this process. It adds punctation, and capitalises the correct words. In some languages this means ad... | [
"# DeUnCaser\nThe output from Automated Speak Recognition software is usually uncased and without any punctation. This does not make a very readable text. \n\nThe DeUnCaser is a sequence-to-sequence model that is reversing this process. It adds punctation, and capitalises the correct words. In some languages this m... | [
"TAGS\n#transformers #pytorch #jax #t5 #text2text-generation #translation #no #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"# DeUnCaser\nThe output from Automated Speak Recognition software is usually uncased and without any punctation. This ... |
sentence-similarity | sentence-transformers |
# DMetaSoul/sbert-chinese-qmc-domain-v1
此模型基于 [bert-base-chinese](https://huggingface.co/bert-base-chinese) 版本 BERT 模型，在百度知道问题匹配数据集（[LCQMC](http://icrc.hitsz.edu.cn/Article/show/171.html)）上进行训练调优，适用于**开放领域的问题匹配**场景，比如：
- 洗澡用什么香皂好？vs. 洗澡用什么香皂好
- 大连哪里拍婚纱照好点？ vs. 大连哪里拍婚纱照比较好
- 银行卡怎样挂失？vs. 银行卡丢了怎么挂失啊？
注：此模型的[轻量化版本](htt... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers", "semantic-search", "chinese"], "pipeline_tag": "sentence-similarity"} | DMetaSoul/sbert-chinese-qmc-domain-v1 | null | [
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"semantic-search",
"chinese",
"endpoints_compatible",
"region:us"
] | null | 2022-03-25T09:06:52+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #semantic-search #chinese #endpoints_compatible #region-us
| DMetaSoul/sbert-chinese-qmc-domain-v1
=====================================
此模型基于 bert-base-chinese 版本 BERT 模型，在百度知道问题匹配数据集（LCQMC）上进行训练调优，适用于开放领域的问题匹配场景，比如：
* 洗澡用什么香皂好？vs. 洗澡用什么香皂好
* 大连哪里拍婚纱照好点？ vs. 大连哪里拍婚纱照比较好
* 银行卡怎样挂失？vs. 银行卡丢了怎么挂失啊？
注：此模型的轻量化版本，也已经开源啦！
Usage
=====
1. Sentence-Transformers
----------------... | [] | [
"TAGS\n#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #semantic-search #chinese #endpoints_compatible #region-us \n"
] |
text-classification | transformers | "Concreteness evaluates the degree to which the concept denoted by a word refers to a perceptible entity." (Brysbaert, Warriner, and Kuperman 2014, p. 904) | {} | j-hartmann/concreteness-english-distilroberta-base | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-25T09:23:09+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #text-classification #autotrain_compatible #endpoints_compatible #region-us
| "Concreteness evaluates the degree to which the concept denoted by a word refers to a perceptible entity." (Brysbaert, Warriner, and Kuperman 2014, p. 904) | [] | [
"TAGS\n#transformers #pytorch #roberta #text-classification #autotrain_compatible #endpoints_compatible #region-us \n"
] |
sentence-similarity | sentence-transformers |
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when ... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | eliasws/openApiT5-distilled-description-v3 | null | [
"sentence-transformers",
"pytorch",
"t5",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2022-03-25T09:25:50+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #t5 #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us
|
# {MODEL_NAME}
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers installed:
Then you can u... | [
"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\n... | [
"TAGS\n#sentence-transformers #pytorch #t5 #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us \n",
"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or sema... |
null | adapter-transformers |
# Adapter `AdapterHub/bioASQgeneration` for facebook/bart-base
An [adapter](https://adapterhub.ml) for the `facebook/bart-base` model that was trained on the [qa/bioasq](https://adapterhub.ml/explore/qa/bioasq/) dataset and includes a prediction head for seq2seq lm.
This adapter was created for usage with the **[ada... | {"tags": ["adapterhub:qa/bioasq", "bart", "adapter-transformers"]} | AdapterHub/bioASQgeneration | null | [
"adapter-transformers",
"bart",
"adapterhub:qa/bioasq",
"region:us"
] | null | 2022-03-25T09:26:42+00:00 | [] | [] | TAGS
#adapter-transformers #bart #adapterhub-qa/bioasq #region-us
|
# Adapter 'AdapterHub/bioASQgeneration' for facebook/bart-base
An adapter for the 'facebook/bart-base' model that was trained on the qa/bioasq dataset and includes a prediction head for seq2seq lm.
This adapter was created for usage with the adapter-transformers library.
## Usage
First, install 'adapter-transforme... | [
"# Adapter 'AdapterHub/bioASQgeneration' for facebook/bart-base\n\nAn adapter for the 'facebook/bart-base' model that was trained on the qa/bioasq dataset and includes a prediction head for seq2seq lm.\n\nThis adapter was created for usage with the adapter-transformers library.",
"## Usage\n\nFirst, install 'adap... | [
"TAGS\n#adapter-transformers #bart #adapterhub-qa/bioasq #region-us \n",
"# Adapter 'AdapterHub/bioASQgeneration' for facebook/bart-base\n\nAn adapter for the 'facebook/bart-base' model that was trained on the qa/bioasq dataset and includes a prediction head for seq2seq lm.\n\nThis adapter was created for usage w... |
null | pytorch | ## Model description
This is an op-for-op PyTorch reimplementation of DeepMind's BigGAN model with the pre-trained weights from DeepMind [biggan-deep-128](https://tfhub.dev/deepmind/biggan-deep-128/1).
## Training and evaluation data
Model is trained on [ImageNet dataset](https://tfhub.dev/s?dataset=imagenet-ilsvrc-201... | {"license": "apache-2.0", "library_name": "pytorch", "tags": ["biggan"], "datasets": ["ImageNet"]} | Jezia/pytorch-pretrained-BigGAN | null | [
"pytorch",
"biggan",
"dataset:ImageNet",
"license:apache-2.0",
"region:us"
] | null | 2022-03-25T10:05:00+00:00 | [] | [] | TAGS
#pytorch #biggan #dataset-ImageNet #license-apache-2.0 #region-us
| ## Model description
This is an op-for-op PyTorch reimplementation of DeepMind's BigGAN model with the pre-trained weights from DeepMind biggan-deep-128.
## Training and evaluation data
Model is trained on ImageNet dataset. The dataset consists of 10000 classes. All images are resized to 64 * 64 for the sake of conveni... | [
"## Model description\nThis is an op-for-op PyTorch reimplementation of DeepMind's BigGAN model with the pre-trained weights from DeepMind biggan-deep-128.",
"## Training and evaluation data\nModel is trained on ImageNet dataset. The dataset consists of 10000 classes. All images are resized to 64 * 64 for the sak... | [
"TAGS\n#pytorch #biggan #dataset-ImageNet #license-apache-2.0 #region-us \n",
"## Model description\nThis is an op-for-op PyTorch reimplementation of DeepMind's BigGAN model with the pre-trained weights from DeepMind biggan-deep-128.",
"## Training and evaluation data\nModel is trained on ImageNet dataset. The ... |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Graphcore/lxmert-vqa-uncased
Optimum Graphcore is a new open-source library and toolkit that enables developers to access IPU-op... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["Graphcore/vqa-lxmert"], "metrics": ["accuracy"], "model-index": [{"name": "vqa", "results": [{"task": {"type": "question-answering", "name": "Question Answering"}, "dataset": {"name": "Graphcore/vqa-lxmert", "type": "Graphcore/vqa-lxmert", "arg... | Graphcore/lxmert-vqa-uncased | null | [
"transformers",
"pytorch",
"optimum_graphcore",
"lxmert",
"question-answering",
"generated_from_trainer",
"dataset:Graphcore/vqa-lxmert",
"arxiv:1908.07490",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-25T10:10:13+00:00 | [
"1908.07490"
] | [] | TAGS
#transformers #pytorch #optimum_graphcore #lxmert #question-answering #generated_from_trainer #dataset-Graphcore/vqa-lxmert #arxiv-1908.07490 #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
# Graphcore/lxmert-vqa-uncased
Optimum Graphcore is a new open-source library and toolkit that enables developers to access IPU-optimized models certified by Hugging Face. It is an extension of Transformers, providing a set of performance optimization tools enabling maximum efficiency to train and run models on Gra... | [
"# Graphcore/lxmert-vqa-uncased\n\nOptimum Graphcore is a new open-source library and toolkit that enables developers to access IPU-optimized models certified by Hugging Face. It is an extension of Transformers, providing a set of performance optimization tools enabling maximum efficiency to train and run models on... | [
"TAGS\n#transformers #pytorch #optimum_graphcore #lxmert #question-answering #generated_from_trainer #dataset-Graphcore/vqa-lxmert #arxiv-1908.07490 #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"# Graphcore/lxmert-vqa-uncased\n\nOptimum Graphcore is a new open-source library and toolkit ... |
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. -->
# deberta_amazon_reviews_v1
This model is a fine-tuned version of [patrickvonplaten/deberta_v3_amazon_reviews](https://huggingface... | {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "deberta_amazon_reviews_v1", "results": []}]} | patrickvonplaten/deberta_amazon_reviews_v1 | null | [
"transformers",
"pytorch",
"tensorboard",
"deberta-v2",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-25T10:12:59+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #deberta-v2 #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# deberta_amazon_reviews_v1
This model is a fine-tuned version of patrickvonplaten/deberta_v3_amazon_reviews on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure... | [
"# deberta_amazon_reviews_v1\n\nThis model is a fine-tuned version of patrickvonplaten/deberta_v3_amazon_reviews on an unknown dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed"... | [
"TAGS\n#transformers #pytorch #tensorboard #deberta-v2 #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# deberta_amazon_reviews_v1\n\nThis model is a fine-tuned version of patrickvonplaten/deberta_v3_amazon_reviews on an unknown dat... |
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