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text-classification | transformers |
# Roberta Large STS-B
This model is a fine tuned RoBERTA model over STS-B.
It was trained with these params:
!python /content/transformers/examples/text-classification/run_glue.py \
--model_type roberta \
--model_name_or_path roberta-large \
--task_name STS-B \
--do_train \
--do_eval \
--do_l... | {} | SparkBeyond/roberta-large-sts-b | null | [
"transformers",
"pytorch",
"jax",
"roberta",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #roberta #text-classification #autotrain_compatible #endpoints_compatible #region-us
|
# Roberta Large STS-B
This model is a fine tuned RoBERTA model over STS-B.
It was trained with these params:
!python /content/transformers/examples/text-classification/run_glue.py \
--model_type roberta \
--model_name_or_path roberta-large \
--task_name STS-B \
--do_train \
--do_eval \
--do_l... | [
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text-generation | transformers |
#EmmyBot | {"tags": ["conversational"]} | Spectrox/emmybot | null | [
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text-generation | transformers |
# DialoGPT Trained on the Speech of a TV Series Character
This is an instance of [microsoft/DialoGPT-medium](https://huggingface.co/microsoft/DialoGPT-medium) trained on a TV series character, Sheldon from [The Big Bang Theory](https://en.wikipedia.org/wiki/The_Big_Bang_Theory). The data comes from [a Kaggle TV serie... | {"license": "mit", "tags": ["conversational"], "thumbnail": "https://i.imgur.com/7HAcbbD.gif"} | Spirax/DialoGPT-medium-sheldon | null | [
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"region:us"
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#transformers #pytorch #safetensors #gpt2 #text-generation #conversational #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# DialoGPT Trained on the Speech of a TV Series Character
This is an instance of microsoft/DialoGPT-medium trained on a TV series character, Sheldon from The Big Bang Theory. The data comes from a Kaggle TV series script dataset.
Chat with the model:
| [
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text-generation | transformers |
# Engineer DialoGPT Model | {"tags": ["conversational"]} | Spoon/DialoGPT-small-engineer | null | [
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image-classification | transformers |
# sriram-car-classifier
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https://github.com/natera... | {"tags": ["image-classification", "pytorch", "huggingpics"], "metrics": ["accuracy"]} | SriramSridhar78/sriram-car-classifier | null | [
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"pytorch",
"tensorboard",
"safetensors",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #safetensors #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# sriram-car-classifier
Autogenerated by HuggingPics️
Create your own image classifier for anything by running the demo on Google Colab.
Report any issues with the demo at the github repo.
## Example Images
#### AM_General_Hummer_SUV_2000
!AM_General_Hummer_SUV_2000
#### Acura_Integra_Type_R_2001
!Acura_Int... | [
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null | null | -----
tags:
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----
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#region-us
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automatic-speech-recognition | transformers |
Wav2Vec2-Large-XLSR-Welsh
Fine-tuned facebook/wav2vec2-large-xlsr-53 on the Welsh Common Voice dataset.
The data was augmented using standard augmentation approach.
When using this model, make sure that your speech input is sampled at 16kHz.
Test Result: 29.4%
Usage
The model can be used directly (without a languag... | {"language": "sv", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "model-index": [{"name": "XLSR Wav2Vec2 Welsh by Srulik Ben David", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech Recognition"}, "dataset": {"name": "Common V... | Srulikbdd/Wav2Vec2-large-xlsr-welsh | null | [
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|
Wav2Vec2-Large-XLSR-Welsh
Fine-tuned facebook/wav2vec2-large-xlsr-53 on the Welsh Common Voice dataset.
The data was augmented using standard augmentation approach.
When using this model, make sure that your speech input is sampled at 16kHz.
Test Result: 29.4%
Usage
The model can be used directly (without a languag... | [] | [
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] |
text-generation | transformers |
# Evelynn DialoGPT Model | {"tags": ["conversational"]} | Stabley/DialoGPT-small-evelynn | null | [
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"text-generation",
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null | null | This is a dummy readme | {} | StephennFernandes/XLS-R-assamese-LM | null | [
"region:us"
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#region-us
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automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# XLS-R-marathi
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-... | {"language": ["mr"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "mozilla-foundation/common_voice_8_0", "robust-speech-event", "generated_from_trainer", "hf-asr-leaderboard"], "model-index": [{"name": "XLS-R-marathi", "results": []}]} | StephennFernandes/XLS-R-marathi | null | [
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|
# XLS-R-marathi
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - MR dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training p... | [
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automatic-speech-recognition | transformers |
tags:
- automatic-speech-recognition
- robust-speech-event
---
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on a private dataset.
It achieves the following results on the evaluation set:
The following hyper-parameters were use... | {} | StephennFernandes/wav2vec2-XLS-R-300m-konkani | null | [
"transformers",
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"wav2vec2",
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|
tags:
- automatic-speech-recognition
- robust-speech-event
---
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on a private dataset.
It achieves the following results on the evaluation set:
The following hyper-parameters were used during training:
- learning_rate: 3e-4
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text-generation | transformers | It's just a dialog bot trained on my Tweets. Unfortunately as tweets aren\'t very conversational it comes off pretty random. | {} | SteveC/sdc_bot_15K | null | [
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#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
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fill-mask | transformers |
## Melayu BERT
Melayu BERT is a masked language model based on [BERT](https://arxiv.org/abs/1810.04805). It was trained on the [OSCAR](https://huggingface.co/datasets/oscar) dataset, specifically the `unshuffled_original_ms` subset. The model used was [English BERT model](https://huggingface.co/bert-base-uncased) and... | {"language": "ms", "license": "mit", "tags": ["melayu-bert"], "datasets": ["oscar"], "widget": [{"text": "Saya [MASK] makan nasi hari ini."}]} | StevenLimcorn/MelayuBERT | null | [
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| Melayu BERT
-----------
Melayu BERT is a masked language model based on BERT. It was trained on the OSCAR dataset, specifically the 'unshuffled\_original\_ms' subset. The model used was English BERT model and fine-tuned on the Malaysian dataset. The model achieved a perplexity of 9.46 on a 20% validation dataset. Man... | [
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text-classification | transformers |
## Indo-roberta-indonli
Indo-roberta-indonli is natural language inference classifier based on [Indo-roberta](https://huggingface.co/flax-community/indonesian-roberta-base) model. It was trained on the trained on [IndoNLI](https://github.com/ir-nlp-csui/indonli/tree/main/data/indonli) dataset. The model used was [Ind... | {"language": "id", "license": "mit", "tags": ["roberta"], "datasets": ["indonli"], "widget": [{"text": "Amir Sjarifoeddin Harahap lahir di Kota Medan, Sumatera Utara, 27 April 1907. Ia meninggal di Surakarta, Jawa Tengah, pada 19 Desember 1948 dalam usia 41 tahun. </s></s> Amir Sjarifoeddin Harahap masih hidup."}]} | StevenLimcorn/indo-roberta-indonli | null | [
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| Indo-roberta-indonli
--------------------
Indo-roberta-indonli is natural language inference classifier based on Indo-roberta model. It was trained on the trained on IndoNLI dataset. The model used was Indo-roberta and was transfer-learned to a natural inference classifier model. The model are tested using the valida... | [
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text-classification | transformers |
# Indo RoBERTa Emotion Classifier
Indo RoBERTa Emotion Classifier is emotion classifier based on [Indo-roberta](https://huggingface.co/flax-community/indonesian-roberta-base) model. It was trained on the trained on [IndoNLU EmoT](https://huggingface.co/datasets/indonlu) dataset. The model used was [Indo-roberta](http... | {"language": "id", "license": "mit", "tags": ["roberta"], "datasets": ["indonlu"], "widget": [{"text": "Hal-hal baik akan datang."}]} | StevenLimcorn/indonesian-roberta-base-emotion-classifier | null | [
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| Indo RoBERTa Emotion Classifier
===============================
Indo RoBERTa Emotion Classifier is emotion classifier based on Indo-roberta model. It was trained on the trained on IndoNLU EmoT dataset. The model used was Indo-roberta and was transfer-learned to an emotion classifier model. Based from the IndoNLU benc... | [
"### As Text Classifier\n\n\nDisclaimer\n----------\n\n\nDo consider the biases which come from both the pre-trained RoBERTa model and the 'EmoT' dataset that may be carried over into the results of this model.\n\n\nAuthor\n------\n\n\nIndonesian RoBERTa Base Emotion Classifier was trained and evaluated by Steven L... | [
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automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
#
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on th... | {"language": ["zh-TW"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "common_voice", "generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "", "results": []}]} | StevenLimcorn/wav2vec2-xls-r-300m-zh-TW | null | [
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"endpoints_compatible",
"region:us"
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#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #common_voice #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
|
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the COMMON\_VOICE - ZH-TW dataset.
It achieves the following results on the evaluation set:
* Loss: 1.1786
* Wer: 0.8594
* Cer: 0.2964
Model description
-----------------
More information needed
Intended uses & limitations
------------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 7.5e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
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text-generation | transformers |
@ Deltarune Spamton DialoGPT Model | {"tags": ["conversational"]} | Stevo/DiagloGPT-medium-spamton | null | [
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@ Deltarune Spamton DialoGPT Model | [] | [
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token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-multilingual-cased-finetuned-ner-4
#This model is part of a test for creating multilingual BioMedical NER systems. Not ... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "bert-base-multilingual-cased-finetuned-ner-4", "results": []}]} | StivenLancheros/mBERT-base-Biomedical-NER | null | [
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| bert-base-multilingual-cased-finetuned-ner-4
============================================
#This model is part of a test for creating multilingual BioMedical NER systems. Not intended for proffesional use now.
This model is a fine-tuned version of bert-base-multilingual-cased on the CRAFT+BC4CHEMD+BioNLP09 datasets ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 4",
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token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-base-biomedical-clinical-es-finetuned-ner-CRAFT
This model is a fine-tuned version of [PlanTL-GOB-ES/roberta-base-biomed... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "roberta-base-biomedical-clinical-es-finetuned-ner-CRAFT", "results": []}]} | StivenLancheros/roberta-base-biomedical-clinical-es-finetuned-ner-CRAFT | null | [
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| roberta-base-biomedical-clinical-es-finetuned-ner-CRAFT
=======================================================
This model is a fine-tuned version of PlanTL-GOB-ES/roberta-base-biomedical-clinical-es on the CRAFT dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1720
* Precision: 0.8253
* ... | [
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automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-300m-ta-colab-new1
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-ta-colab-new1", "results": []}]} | Subhashini17/wav2vec2-large-xls-r-300m-ta-colab-new1 | null | [
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"region:us"
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|
# wav2vec2-large-xls-r-300m-ta-colab-new1
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common_voice dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.6642
- eval_wer: 0.7611
- eval_runtime: 152.4412
- eval_samples_per_second: 11.683
- eval_steps_per_second... | [
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automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-300m-ta-colab
This model is a fine-tuned version of [akashsivanandan/wav2vec2-large-xls-r-300m-tamil-colab-... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-large-xls-r-300m-ta-colab", "results": []}]} | Subhashini17/wav2vec2-large-xls-r-300m-ta-colab | null | [
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#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
|
# wav2vec2-large-xls-r-300m-ta-colab
This model is a fine-tuned version of akashsivanandan/wav2vec2-large-xls-r-300m-tamil-colab-final on the common_voice dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More informati... | [
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"## Intended uses & limitations\n\nMore information needed",
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token-classification | transformers |
<h1>Bengali Named Entity Recognition</h1>
Fine-tuning bert-base-multilingual-cased on Wikiann dataset for performing NER on Bengali language.
## Label ID and its corresponding label name
| Label ID | Label Name|
| -------- | ----- |
|0 | O |
| 1 | B-PER |
| 2 | I-PER |
| 3 | B-ORG|
| 4 | I-ORG |
| 5 | B-LOC |
| 6 ... | {"language": "bn", "datasets": ["wikiann"], "widget": [{"text": "\u09ae\u09be\u09b0\u09ad\u09bf\u09a8 \u09a6\u09bf \u09ae\u09be\u09b0\u09b8\u09bf\u09af\u09bc\u09be\u09a8", "example_title": "Sentence_1"}, {"text": "\u09b2\u09bf\u0993\u09a8\u09be\u09b0\u09cd\u09a6\u09cb \u09a6\u09be \u09ad\u09bf\u099e\u09cd\u099a\u09bf",... | Suchandra/bengali_language_NER | null | [
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| Bengali Named Entity Recognition
================================
Fine-tuning bert-base-multilingual-cased on Wikiann dataset for performing NER on Bengali language.
Label ID and its corresponding label name
-----------------------------------------
Results
=======
Example
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null | null | ## SunBERT
Sunbert is a variant of bert trained on Ugandan text data for the tasks of ``Covid/Non Covid`` tweet classification as well as classification of Social Media news articles as either ``Organic, Promotional or Editorial``
Information has become more abundant with the internet. Specifically, people communicat... | {} | Sunbird/sunbert | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| ## SunBERT
Sunbert is a variant of bert trained on Ugandan text data for the tasks of ''Covid/Non Covid'' tweet classification as well as classification of Social Media news articles as either ''Organic, Promotional or Editorial''
Information has become more abundant with the internet. Specifically, people communicat... | [
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text2text-generation | transformers | English to Luganda text translation | {} | Sunbird/sunbird-en-lg | null | [
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text-generation | transformers | #Bill cipher chat bot | {"tags": ["conversational"]} | Sunnydx/BillCipherBot | null | [
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text2text-generation | transformers | [SuperAI Engineer Season 2](https://superai.aiat.or.th/) , [Machima](https://machchima.superai.me/)
[Google's mT5](https://github.com/google-research/multilingual-t5) , [Pollawat](https://huggingface.co/Pollawat/mt5-small-thai-qg)
```python
from transformers import T5Tokenizer, T5ForConditionalGeneration, T5Config
m... | {"language": ["thai", "th"], "license": "mit", "tags": ["question-generation"], "datasets": ["NSC2018", "wiki-documents-nsc", "ThaiQACorpus-DevelopmentDataset"], "widget": [{"text": "\u0e42\u0e23\u0e07\u0e40\u0e23\u0e35\u0e22\u0e19\u0e1a\u0e49\u0e32\u0e19\u0e02\u0e38\u0e19\u0e14\u0e48\u0e32\u0e19 \u0e15\u0e31\u0e49\u0e... | SuperAI2-Machima/mt5-small-thai-qg-v2 | null | [
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| SuperAI Engineer Season 2 , Machima
Google's mT5 , Pollawat
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] |
text2text-generation | transformers | [SuperAI Engineer Season 2](https://superai.aiat.or.th/) , [Machima](https://machchima.superai.me/)
[Google's mT5](https://github.com/google-research/multilingual-t5) , [Pollawat](https://huggingface.co/Pollawat/mt5-small-thai-qg)
```python
from transformers import T5Tokenizer, T5ForConditionalGeneration, T5Config
m... | {"language": ["thai", "th"], "license": "mit", "tags": ["question-generation"], "datasets": ["NSC2018", "wiki-documents-nsc", "ThaiQACorpus-DevelopmentDataset"], "widget": [{"text": "\u0e42\u0e23\u0e07\u0e40\u0e23\u0e35\u0e22\u0e19\u0e1a\u0e49\u0e32\u0e19\u0e02\u0e38\u0e19\u0e14\u0e48\u0e32\u0e19 \u0e15\u0e31\u0e49\u0e... | SuperAI2-Machima/mt5-small-thai-qg | null | [
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| SuperAI Engineer Season 2 , Machima
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] |
text2text-generation | transformers | [SuperAI Engineer Season 2](https://superai.aiat.or.th/) , [Machima](https://machchima.superai.me/)
[Google's mT5](https://github.com/google-research/multilingual-t5) , [Pollawat](https://huggingface.co/Pollawat/mt5-small-thai-qg)
```python
from transformers import T5Tokenizer, T5ForConditionalGeneration, T5Config
m... | {"language": ["thai", "th"], "license": "mit", "tags": ["Yes No question-generation"], "datasets": ["NSC2018", "wiki-documents-nsc", "ThaiQACorpus-DevelopmentDataset"], "widget": [{"text": "\u0e27\u0e31\u0e19\u0e17\u0e35\u0e48 1 \u0e01\u0e31\u0e19\u0e22\u0e32\u0e22\u0e19 2550 12:00 \u0e19. \u0e15\u0e33\u0e23\u0e27\u0e0... | SuperAI2-Machima/mt5-small-thai-yes-no-qg | null | [
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| SuperAI Engineer Season 2 , Machima
Google's mT5 , Pollawat
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] |
text2text-generation | transformers | # FreeIsland AI
With the advancement of the graphical processing power of computers and sophisticated algorithms like [Nanite](https://docs.unrealengine.com/5.0/en-US/RenderingFeatures/Nanite/), simulating lifelike sceneries in real-time is never being easier. About a month ago Epic Games [showoff](https://www.youtube... | {"language": "en", "license": "gpl-3.0", "tags": ["NLP", "ChatBot", "Game AI"], "datasets": ["cornell_movie_dialog"], "metrics": ["rouge"], "widget": [{"text": "personality: Hinata was soft-spoken and polite, always addressing people with proper honorifics. She is kind, always thinking of others more than for herself, ... | Supiri/t5-base-conversation | null | [
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"has_space",
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] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
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| # FreeIsland AI
With the advancement of the graphical processing power of computers and sophisticated algorithms like Nanite, simulating lifelike sceneries in real-time is never being easier. About a month ago Epic Games showoff the newest capabilities of their newest game engine by simulating an entire city including... | [
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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-uncased-finetuned-squad
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-unc... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "bert-base-uncased-finetuned-squad", "results": []}]} | SupriyaArun/bert-base-uncased-finetuned-squad | null | [
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#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us
| bert-base-uncased-finetuned-squad
=================================
This model is a fine-tuned version of bert-base-uncased on the squad dataset.
It achieves the following results on the evaluation set:
* Loss: 1.0755
Model description
-----------------
More information needed
Intended uses & limitations
----... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 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",
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question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-squad
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/d... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "distilbert-base-uncased-finetuned-squad", "results": []}]} | SupriyaArun/distilbert-base-uncased-finetuned-squad | null | [
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| distilbert-base-uncased-finetuned-squad
=======================================
This model is a fine-tuned version of distilbert-base-uncased on the squad dataset.
It achieves the following results on the evaluation set:
* Loss: 1.1569
Model description
-----------------
More information needed
Intended uses ... | [
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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. -->
# squeezebert-uncased-finetuned-squad-finetuned-squad
This model is a fine-tuned version of [SupriyaArun/squeezebert-uncased-finet... | {"tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "squeezebert-uncased-finetuned-squad-finetuned-squad", "results": []}]} | SupriyaArun/squeezebert-uncased-finetuned-squad-finetuned-squad | null | [
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] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
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|
# squeezebert-uncased-finetuned-squad-finetuned-squad
This model is a fine-tuned version of SupriyaArun/squeezebert-uncased-finetuned-squad on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information... | [
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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. -->
# squeezebert-uncased-finetuned-squad
This model is a fine-tuned version of [squeezebert/squeezebert-uncased](https://huggingface.... | {"tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "squeezebert-uncased-finetuned-squad", "results": []}]} | SupriyaArun/squeezebert-uncased-finetuned-squad | null | [
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| squeezebert-uncased-finetuned-squad
===================================
This model is a fine-tuned version of squeezebert/squeezebert-uncased on the squad dataset.
It achieves the following results on the evaluation set:
* Loss: 1.0808
Model description
-----------------
More information needed
Intended uses ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
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null | transformers |
# BLEURT
Pretrained model on English language. It was introduced in
[this paper](https://arxiv.org/pdf/2004.04696.pdf), described in [this blogpost](https://ai.googleblog.com/2020/05/evaluating-natural-language-generation.html) and first released in
[this repository](https://github.com/google-research/bleurt).
The t... | {"language": "en", "license": "apache-2.0"} | Surfer/bleurt | null | [
"transformers",
"pytorch",
"bert",
"en",
"arxiv:2004.04696",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2004.04696"
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"en"
] | TAGS
#transformers #pytorch #bert #en #arxiv-2004.04696 #license-apache-2.0 #endpoints_compatible #region-us
|
# BLEURT
Pretrained model on English language. It was introduced in
this paper, described in this blogpost and first released in
this repository.
The team releasing BLEURT did not write a model card for this model so this model card has been written by
the Surfer team.
Original TensorFlow implementation has been co... | [
"# BLEURT\n\nPretrained model on English language. It was introduced in\nthis paper, described in this blogpost and first released in\nthis repository.\n\nThe team releasing BLEURT did not write a model card for this model so this model card has been written by\nthe Surfer team.\n\nOriginal TensorFlow implementatio... | [
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text2text-generation | transformers |
## Usage:
```python
abstract = """We describe a system called Overton, whose main design goal is to support engineers in building, monitoring, and improving production
machine learning systems. Key challenges engineers face are monitoring fine-grained quality, diagnosing errors in sophisticated applications, and
ha... | {"license": "mit", "datasets": ["arxiv"], "widget": [{"text": "summarize: We describe a system called Overton, whose main design goal is to support engineers in building, monitoring, and improving production machinelearning systems. Key challenges engineers face are monitoring fine-grained quality, diagnosing errors in... | Suva/uptag-url-model | null | [
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] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
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|
## Usage:
### Using Transformers
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image-classification | transformers |
# new-york-tokyo-london
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https://github.com/natera... | {"tags": ["image-classification", "pytorch", "huggingpics"], "metrics": ["accuracy"]} | Suzana/new-york-tokyo-london | null | [
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"model-index",
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] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
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|
# new-york-tokyo-london
Autogenerated by HuggingPics️
Create your own image classifier for anything by running the demo on Google Colab.
Report any issues with the demo at the github repo.
## Example Images
#### London
!London
#### New York
!New York
#### Tokyo
!Tokyo | [
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"#### New York\n\n!New York",
"#### Tokyo\n\n!Tokyo"
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feature-extraction | transformers |
# bert-german-dbmdz-uncased-sentence-stsb
**This model is outdated!**
The new [T-Systems-onsite/cross-en-de-roberta-sentence-transformer](https://huggingface.co/T-Systems-onsite/cross-en-de-roberta-sentence-transformer) model is better for German language. It is also the current best model for English language and wo... | {"language": "de", "license": "mit"} | T-Systems-onsite/bert-german-dbmdz-uncased-sentence-stsb | null | [
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"safetensors",
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|
# bert-german-dbmdz-uncased-sentence-stsb
This model is outdated!
The new T-Systems-onsite/cross-en-de-roberta-sentence-transformer model is better for German language. It is also the current best model for English language and works cross-lingually. Please consider using that model. | [
"# bert-german-dbmdz-uncased-sentence-stsb\nThis model is outdated!\n\nThe new T-Systems-onsite/cross-en-de-roberta-sentence-transformer model is better for German language. It is also the current best model for English language and works cross-lingually. Please consider using that model."
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feature-extraction | transformers |
# Cross German & French RoBERTa for Sentence Embeddings
| {"language": ["fr", "de", "multilingual"], "license": "mit", "tags": ["sentence_embedding", "search", "pytorch", "xlm-roberta", "roberta", "xlm-r-distilroberta-base-paraphrase-v1"], "datasets": ["stsb_multi_mt"], "metrics": ["Spearman\u2019s rank correlation", "cosine similarity"]} | T-Systems-onsite/cross-de-fr-roberta-sentence-transformer | null | [
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feature-extraction | transformers |
# Cross English & German RoBERTa for Sentence Embeddings
This model is intended to [compute sentence (text) embeddings](https://www.sbert.net/examples/applications/computing-embeddings/README.html) for English and German text. These embeddings can then be compared with [cosine-similarity](https://en.wikipedia.org/wiki... | {"language": ["de", "en", "multilingual"], "license": "mit", "tags": ["sentence_embedding", "search", "pytorch", "xlm-roberta", "roberta", "xlm-r-distilroberta-base-paraphrase-v1", "paraphrase"], "datasets": ["stsb_multi_mt"], "metrics": ["Spearman\u2019s rank correlation", "cosine similarity"]} | T-Systems-onsite/cross-en-de-roberta-sentence-transformer | null | [
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| Cross English & German RoBERTa for Sentence Embeddings
======================================================
This model is intended to compute sentence (text) embeddings for English and German text. These embeddings can then be compared with cosine-similarity to find sentences with a similar semantic meaning. For ex... | [] | [
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] |
feature-extraction | transformers |
# Cross English & French RoBERTa for Sentence Embeddings
| {"language": ["fr", "en", "multilingual"], "license": "mit", "tags": ["sentence_embedding", "search", "pytorch", "xlm-roberta", "roberta", "xlm-r-distilroberta-base-paraphrase-v1"], "datasets": ["stsb_multi_mt"], "metrics": ["Spearman\u2019s rank correlation", "cosine similarity"]} | T-Systems-onsite/cross-en-fr-roberta-sentence-transformer | null | [
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feature-extraction | transformers |
# German RoBERTa for Sentence Embeddings V2
**The new [T-Systems-onsite/cross-en-de-roberta-sentence-transformer](https://huggingface.co/T-Systems-onsite/cross-en-de-roberta-sentence-transformer) model is slightly better for German language. It is also the current best model for English language and works cross-lingua... | {"language": "de", "license": "mit", "tags": ["sentence_embedding", "search", "pytorch", "xlm-roberta", "roberta", "xlm-r-distilroberta-base-paraphrase-v1", "paraphrase"], "datasets": ["STSbenchmark"], "metrics": ["Spearman\u2019s rank correlation", "cosine similarity"]} | T-Systems-onsite/german-roberta-sentence-transformer-v2 | null | [
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|
# German RoBERTa for Sentence Embeddings V2
The new T-Systems-onsite/cross-en-de-roberta-sentence-transformer model is slightly better for German language. It is also the current best model for English language and works cross-lingually. Please consider using that model.
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summarization | transformers |
# mT5-small-sum-de-en-v2
This is a bilingual summarization model for English and German. It is based on the multilingual T5 model [google/mt5-small](https://huggingface.co/google/mt5-small).
## Training
The training was conducted with the following hyperparameters:
- base model: [google/mt5-small](https://hugging... | {"language": ["de", "en", "multilingual"], "license": "cc-by-nc-sa-4.0", "tags": ["summarization"], "datasets": ["cnn_dailymail", "xsum", "mlsum", "swiss_text_2019"]} | T-Systems-onsite/mt5-small-sum-de-en-v2 | null | [
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| mT5-small-sum-de-en-v2
======================
This is a bilingual summarization model for English and German. It is based on the multilingual T5 model google/mt5-small.
Training
--------
The training was conducted with the following hyperparameters:
* base model: google/mt5-small
* source\_prefix: '"summarize: ... | [] | [
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] |
text-generation | transformers |
# mGPT
mGPT is pre-trained on the [mC4 dataset](https://huggingface.co/datasets/mc4) using a causal language modeling objective. It was introduced in this [paper](https://arxiv.org/abs/2110.06609) and first released on this page.
## Model description
mGPT is a Transformer-based model which pre-trained on massive mu... | {} | THUMT/mGPT | null | [
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|
# mGPT
mGPT is pre-trained on the mC4 dataset using a causal language modeling objective. It was introduced in this paper and first released on this page.
## Model description
mGPT is a Transformer-based model which pre-trained on massive multilingual data covering over 101 languages. Similar to GPT-2, It was pre-t... | [
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fill-mask | transformers |
# iSEEEK
A universal approach for integrating super large-scale single-cell transcriptomes by exploring gene rankings
## An simple pipeline for single-cell analysis
```python
import torch
import gzip
import re
from tqdm import tqdm
import numpy as np
import scanpy as sc
from torch.utils.data import DataLoader, Datase... | {} | TJMUCH/transcriptome-iseeek | null | [
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#transformers #pytorch #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us
|
# iSEEEK
A universal approach for integrating super large-scale single-cell transcriptomes by exploring gene rankings
## An simple pipeline for single-cell analysis
## Extract token representations
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null | null | # MASC
The final output model is: `model.pb`
The language model can be found at: https://huggingface.co/TRoboto/masc_kenlm_3grams_lm
To run the model, clone this repo and the language model repo, then follow the instructions here: https://deepspeech.readthedocs.io/en/master/USING.html
To use the checkpoint to retrai... | {} | TRoboto/masc_deepspeech_asr_model_v0 | null | [
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#region-us
| # MASC
The final output model is: 'URL'
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To run the model, clone this repo and the language model repo, then follow the instructions here: URL
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null | null | # MASC
The scorer model can be found under files with the name of `masc.scorer`
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text-generation | transformers |
# Trump Tweets DialoGPT Model | {"tags": ["conversational"]} | TTYU/DialoGPT-small-trump | null | [
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text-generation | transformers |
# Iroh DialoGPT Model | {"tags": ["conversational"]} | TVLG/DialoGPT-small-Iroh-Bot | null | [
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null | null | hello
hello
hello
hello
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null | null | hello
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null | null | hello
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#region-us
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null | null | hello
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null | null | hello
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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. -->
# neg_komrc_train
This model is a fine-tuned version of [beomi/kcbert-base](https://huggingface.co/beomi/kcbert-base) on the None ... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "neg_komrc_train", "results": []}]} | Taekyoon/neg_komrc_train | null | [
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"bert",
"question-answering",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #endpoints_compatible #region-us
| neg\_komrc\_train
=================
This model is a fine-tuned version of beomi/kcbert-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4016
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More in... | [
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token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-finetuned-pos
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["conll2003"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "bert-finetuned-pos", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "conll2003", "type": "c... | Tahsin/BERT-finetuned-conll2003-POS | null | [
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"region:us"
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#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| bert-finetuned-pos
==================
This model is a fine-tuned version of bert-base-cased on the conll2003 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3009
* Precision: 0.9277
* Recall: 0.9329
* F1: 0.9303
* Accuracy: 0.9332
Model description
-----------------
More information ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 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",
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text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-ba... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["emotion"], "metrics": ["accuracy"], "model-index": [{"name": "distilbert-base-uncased-finetuned-emotion", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "emotion", "type": "emotion", "ar... | Tahsin/distilbert-base-uncased-finetuned-emotion | null | [
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| distilbert-base-uncased-finetuned-emotion
=========================================
This model is a fine-tuned version of bert-base-cased on the emotion dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1561
* Accuracy: 0.9285
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: 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: 3",
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automatic-speech-recognition | transformers |
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the OPENSLR_SLR53 - bengali dataset.
It achieves the following results on the evaluation set.
Without language model :
- Wer: 0.3110
- Cer : 0.072
With 5 gram language model trained on [indi... | {"language": ["bn"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "hf-asr-leaderboard", "openslr_SLR53", "robust-speech-event"], "datasets": ["openslr", "SLR53", "Harveenchadha/indic-text"], "metrics": ["wer", "cer"], "model-index": [{"name": "Tahsin-Mayeesha/wav2vec2-bn-300m", "results": [{"task":... | Tahsin-Mayeesha/wav2vec2-bn-300m | null | [
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|
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the OPENSLR_SLR53 - bengali dataset.
It achieves the following results on the evaluation set.
Without language model :
- Wer: 0.3110
- Cer : 0.072
With 5 gram language model trained on indic-text dataset :
- Wer: 0.17776
- Cer : 0.04394
Not... | [
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automatic-speech-recognition | espnet |
# Estonian Espnet2 ASR model
## Model description
This is a general-purpose Estonian ASR model trained in the Lab of Language Technology at TalTech.
## Intended uses & limitations
This model is intended for general-purpose speech recognition, such as broadcast conversations, interviews, talks, etc.
## How to use
... | {"language": "et", "license": "cc-by-4.0", "tags": ["espnet", "audio", "automatic-speech-recognition"]} | TalTechNLP/espnet2_estonian | null | [
"espnet",
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"automatic-speech-recognition",
"et",
"license:cc-by-4.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"et"
] | TAGS
#espnet #audio #automatic-speech-recognition #et #license-cc-by-4.0 #region-us
| Estonian Espnet2 ASR model
==========================
Model description
-----------------
This is a general-purpose Estonian ASR model trained in the Lab of Language Technology at TalTech.
Intended uses & limitations
---------------------------
This model is intended for general-purpose speech recognition, such... | [
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audio-classification | speechbrain |
# VoxLingua107 ECAPA-TDNN Spoken Language Identification Model (CE)
## Model description
This is a spoken language recognition model trained on the VoxLingua107 dataset using SpeechBrain.
The model uses the ECAPA-TDNN architecture that has previously been used for speaker recognition. However, it uses
more fully con... | {"language": "multilingual", "license": "apache-2.0", "tags": ["audio-classification", "speechbrain", "embeddings", "Language", "Identification", "pytorch", "ECAPA-TDNN", "TDNN", "VoxLingua107"], "datasets": ["VoxLingua107"], "metrics": ["Accuracy"], "widget": [{"example_title": "English Sample", "src": "https://cdn-me... | TalTechNLP/voxlingua107-epaca-tdnn-ce | null | [
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|
# VoxLingua107 ECAPA-TDNN Spoken Language Identification Model (CE)
## Model description
This is a spoken language recognition model trained on the VoxLingua107 dataset using SpeechBrain.
The model uses the ECAPA-TDNN architecture that has previously been used for speaker recognition. However, it uses
more fully con... | [
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audio-classification | speechbrain |
# VoxLingua107 ECAPA-TDNN Spoken Language Identification Model
## Model description
This is a spoken language recognition model trained on the VoxLingua107 dataset using SpeechBrain.
The model uses the ECAPA-TDNN architecture that has previously been used for speaker recognition.
The model can classify a speech utt... | {"language": "multilingual", "license": "apache-2.0", "tags": ["audio-classification", "speechbrain", "embeddings", "Language", "Identification", "pytorch", "ECAPA-TDNN", "TDNN", "VoxLingua107"], "datasets": ["VoxLingua107"], "metrics": ["Accuracy"], "widget": [{"example_title": "English Sample", "src": "https://cdn-me... | TalTechNLP/voxlingua107-epaca-tdnn | null | [
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"TDNN",
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"dataset:VoxLingua107",
"license:apache-2.0",
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|
# VoxLingua107 ECAPA-TDNN Spoken Language Identification Model
## Model description
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automatic-speech-recognition | transformers |
# XLS-R-300m-ET
This is a XLS-R-300M model [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) finetuned on around 800 hours of diverse Estonian data.
## Model description
This is a general-purpose Estonian ASR model trained in the Lab of Language Technology at TalTech. It consists o... | {"language": "et", "license": "cc-by-4.0", "tags": ["audio", "automatic-speech-recognition", "hf-asr-leaderboard"], "model-index": [{"name": "xls-r-300m-et", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Common Voice", "type": "common_voice",... | TalTechNLP/xls-r-300m-et | null | [
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#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #hf-asr-leaderboard #et #license-cc-by-4.0 #model-index #endpoints_compatible #region-us
| XLS-R-300m-ET
=============
This is a XLS-R-300M model facebook/wav2vec2-xls-r-300m finetuned on around 800 hours of diverse Estonian data.
Model description
-----------------
This is a general-purpose Estonian ASR model trained in the Lab of Language Technology at TalTech. It consists of only the CTC-based end-t... | [
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text-generation | transformers |
<h2> GPT2 Model for German Language </h2>
Model Name: Tanhim/gpt2-model-de <br />
language: German or Deutsch <br />
thumbnail: https://huggingface.co/Tanhim/gpt2-model-de <br />
datasets: Ten Thousand German News Articles Dataset <br />
### How to use
You can use this model directly with a pipeline for text gener... | {"language": "de", "license": "gpl", "widget": [{"text": "Hallo, ich bin ein Sprachmodell"}]} | Tanhim/gpt2-model-de | null | [
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"license:gpl",
"autotrain_compatible",
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] | TAGS
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|
<h2> GPT2 Model for German Language </h2>
Model Name: Tanhim/gpt2-model-de <br />
language: German or Deutsch <br />
thumbnail: URL <br />
datasets: Ten Thousand German News Articles Dataset <br />
### How to use
You can use this model directly with a pipeline for text generation. Since the generation relies on so... | [
"### How to use\nYou can use this model directly with a pipeline for text generation. Since the generation relies on some randomness, I\nset a seed for reproducibility:\n\nHere is how to use this model to get the features of a given text in PyTorch:\n\n\nCitation request:\nIf you use the model of this repository in... | [
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translation | transformers |
<h2> English to German Translation </h2>
Model Name: Tanhim/translation-En2De <br />
language: German or Deutsch <br />
thumbnail: https://huggingface.co/Tanhim/translation-En2De <br />
### How to use
You can use this model directly with a pipeline for machine translation. Since the generation relies on some rando... | {"language": "de", "license": "gpl", "tags": ["translation"], "datasets": ["wmt19"], "widget": [{"text": "My name is Karl and I live in Aachen."}]} | Tanhim/translation-En2De | null | [
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"marian",
"text2text-generation",
"translation",
"de",
"dataset:wmt19",
"license:gpl",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"de"
] | TAGS
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|
<h2> English to German Translation </h2>
Model Name: Tanhim/translation-En2De <br />
language: German or Deutsch <br />
thumbnail: URL <br />
### How to use
You can use this model directly with a pipeline for machine translation. Since the generation relies on some randomness, I
set a seed for reproducibility:
##... | [
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text-generation | null |
# Hoshiyo Kojima DialoGPT Model | {"tags": ["conversational"]} | Taramiko/DialoGPT-small-hoshiyo_kojima | null | [
"conversational",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#conversational #region-us
|
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text-generation | transformers |
# Hoshiyo Kojima DialoGPT Model | {"tags": ["conversational"]} | Taramiko/Hoshiyo_Kojima | null | [
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"gpt2",
"text-generation",
"conversational",
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"endpoints_compatible",
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text2text-generation | transformers |
# Model Trained Using AutoNLP
- Problem type: Summarization
- Model ID: 21664560
- CO2 Emissions (in grams): 5.680803958729511
## Validation Metrics
- Loss: 1.7488420009613037
- Rouge1: 38.1491
- Rouge2: 18.6257
- RougeL: 26.8448
- RougeLsum: 32.2433
- Gen Len: 49.0
## Usage
You can use cURL to access this model:... | {"language": "unk", "tags": "autonlp", "datasets": ["Tarang1998/autonlp-data-pegasus"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}], "co2_eq_emissions": 5.680803958729511} | Tarang1998/autonlp-pegasus-21664560 | null | [
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|
# Model Trained Using AutoNLP
- Problem type: Summarization
- Model ID: 21664560
- CO2 Emissions (in grams): 5.680803958729511
## Validation Metrics
- Loss: 1.7488420009613037
- Rouge1: 38.1491
- Rouge2: 18.6257
- RougeL: 26.8448
- RougeLsum: 32.2433
- Gen Len: 49.0
## Usage
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text-classification | transformers |
# Model Card for RuBERT for Sentiment Analysis
# Model Details
## Model Description
Russian texts sentiment classification.
- **Developed by:** Tatyana Voloshina
- **Shared by [Optional]:** Tatyana Voloshina
- **Model type:** Text Classification
- **Language(s) (NLP):** More information needed
- **License:... | {"language": ["ru"], "tags": ["sentiment", "text-classification"], "datasets": ["Tatyana/ru_sentiment_dataset"]} | MonoHime/rubert-base-cased-sentiment-new | null | [
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|
# Model Card for RuBERT for Sentiment Analysis
# Model Details
## Model Description
Russian texts sentiment classification.
- Developed by: Tatyana Voloshina
- Shared by [Optional]: Tatyana Voloshina
- Model type: Text Classification
- Language(s) (NLP): More information needed
- License: More information ... | [
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text-classification | transformers |
# Keras model with ruBERT conversational embedder for Sentiment Analysis
Russian texts sentiment classification.
Model trained on [Tatyana/ru_sentiment_dataset](https://huggingface.co/datasets/Tatyana/ru_sentiment_dataset)
## Labels meaning
0: NEUTRAL
1: POSITIVE
2: NEGATIVE
## How to use
```python
!pi... | {"language": ["ru"], "tags": ["sentiment", "text-classification"], "datasets": ["Tatyana/ru_sentiment_dataset"]} | MonoHime/rubert_conversational_cased_sentiment | null | [
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# Keras model with ruBERT conversational embedder for Sentiment Analysis
Russian texts sentiment classification.
Model trained on Tatyana/ru_sentiment_dataset
## Labels meaning
0: NEUTRAL
1: POSITIVE
2: NEGATIVE
## How to use
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image-classification | generic |
## Example
The model is by no means a state-of-the-art model, but nevertheless
produces reasonable image captioning results. It was mainly fine-tuned
as a proof-of-concept for the 🤗 FlaxVisionEncoderDecoder Framework.
The model can be used as follows:
**In PyTorch**
```python
import torch
import req... | {"library_name": "generic", "tags": ["image-classification"]} | TeamAlerito/gti-coco-en | null | [
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|
## Example
The model is by no means a state-of-the-art model, but nevertheless
produces reasonable image captioning results. It was mainly fine-tuned
as a proof-of-concept for the FlaxVisionEncoderDecoder Framework.
The model can be used as follows:
In PyTorch
In Flax
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text-classification | transformers | The uploaded model is from epoch 4 with Matthews Correlation of 61.05
"best_metric": 0.4796141982078552,<br>
"best_model_checkpoint": "/content/output_dir/checkpoint-268",<br>
"epoch": 10.0,<br>
"global_step": 2680,<br>
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"is_world_... | {} | TehranNLP-org/bert-base-cased-avg-cola | null | [
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| The uploaded model is from epoch 4 with Matthews Correlation of 61.05
"best_metric": 0.4796141982078552,<br>
"best_model_checkpoint": "/content/output_dir/checkpoint-268",<br>
"epoch": 10.0,<br>
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text-classification | transformers | The uploaded model is from epoch 9 with Matthews Correlation of 66.77
"best_metric": 0.667660908939119,<br>
"best_model_checkpoint": "/content/output_dir/checkpoint-2412",<br>
"epoch": 10.0,<br>
"global_step": 2680,<br>
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"is_world_p... | {} | TehranNLP-org/electra-base-avg-cola | null | [
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| The uploaded model is from epoch 9 with Matthews Correlation of 66.77
"best_metric": 0.667660908939119,<br>
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text-classification | transformers | Product Review Sentiment Classification
1. Label0 - Negative
2. Label1 - Positive
Trained so far on 20000 Balanced Positive and Negative Reviews | {} | Tejas003/distillbert_base_uncased_amazon_review_sentiment_300 | null | [
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#transformers #tf #distilbert #text-classification #autotrain_compatible #endpoints_compatible #region-us
| Product Review Sentiment Classification
1. Label0 - Negative
2. Label1 - Positive
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automatic-speech-recognition | transformers |
# Wav2Vec2-Large-XLSR-53-Georgian
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Georgian using the [Common Voice](https://huggingface.co/datasets/common_voice) dataset.
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
Th... | {"language": "ka", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice"], "metrics": ["wer"], "model-index": [{"name": "Georgian WAV2VEC2 Daytona", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech Recognit... | Temur/wav2vec2-Georgian-Daytona | null | [
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|
# Wav2Vec2-Large-XLSR-53-Georgian
Fine-tuned facebook/wav2vec2-large-xlsr-53 on Georgian using the Common Voice dataset.
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used directly (without a language model) as follows:
## Evaluation
The model can be ev... | [
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null | null | # GFPGAN (CVPR 2021)
[**Paper**](https://arxiv.org/abs/2101.04061) **|** [**Project Page**](https://xinntao.github.io/projects/gfpgan)    [English](README.md) **|** [简体中文](README_CN.md)
GitHub: https://github.com/TencentARC/GFPGAN
GFPGAN is a blind face restoration algorithm towards real-world face images.... | {} | TencentARC/GFPGANv1 | null | [
"arxiv:2101.04061",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
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#arxiv-2101.04061 #region-us
| # GFPGAN (CVPR 2021)
Paper | Project Page    English | 简体中文
GitHub: URL
GFPGAN is a blind face restoration algorithm towards real-world face images.
<a href="URL src="URL alt="google colab logo"></a>
Colab Demo
### :book: GFP-GAN: Towards Real-World Blind Face Restoration with Generative Facial Prior
> [... | [
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text-generation | transformers |
Note: **default code snippet above won't work** because we are using `AlbertTokenizer` with `GPT2LMHeadModel`, see [issue](https://github.com/huggingface/transformers/issues/4285).
## GPT2 124M Trained on Ukranian Fiction
### Training details
Model was trained on corpus of 4040 fiction books, 2.77 GiB in total.
Eva... | {"language": "uk", "tags": ["text-generation"], "widget": [{"text": "\u041d\u043e \u0437\u043b\u0430 \u042e\u043d\u043e\u043d\u0430, \u0441\u0443\u0447\u0430 \u0434\u043e\u0447\u043a\u0430, "}]} | Tereveni-AI/gpt2-124M-uk-fiction | null | [
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Note: default code snippet above won't work because we are using 'AlbertTokenizer' with 'GPT2LMHeadModel', see issue.
## GPT2 124M Trained on Ukranian Fiction
### Training details
Model was trained on corpus of 4040 fiction books, 2.77 GiB in total.
Evaluation on brown-uk gives perplexity of 50.16.
### Example us... | [
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automatic-speech-recognition | transformers |
# Wav2Vec2-Large-XLSR-53-Tamil
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Tamil using the [Common Voice](https://huggingface.co/datasets/common_voice) dataset.
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model... | {"language": "ta", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice"], "metrics": ["wer"], "model-index": [{"name": "thanish wav2vec2-large-xlsr-tamil", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech ... | Thanish/wav2vec2-large-xlsr-tamil | null | [
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|
# Wav2Vec2-Large-XLSR-53-Tamil
Fine-tuned facebook/wav2vec2-large-xlsr-53 on Tamil using the Common Voice dataset.
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used directly (without a language model) as follows:
## Evaluation
The model can be evaluated... | [
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text-generation | transformers |
This is an improved version of the Joshua bot
| {"tags": ["conversational"]} | ThatSkyFox/DialoGPT-medium-joshua | null | [
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"text-generation",
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#transformers #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
This is an improved version of the Joshua bot
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text-generation | transformers |
#This is a chatbot trained on the transcript of the game "The World Ends with You" | {"tags": ["conversational"]} | ThatSkyFox/DialoGPT-small-joshua | null | [
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|
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text-generation | transformers |
# Tifa DialoGPT Model | {"tags": ["conversational"]} | The-Programmer-With-Cool-Pens/TifaBotAIPackage | null | [
"transformers",
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"text-generation",
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text-generation | transformers | ruGPT3-small model, trained on some 2chan posts
| {} | TheBakerCat/2chan_ruGPT3_small | null | [
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"gpt2",
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#transformers #pytorch #jax #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| ruGPT3-small model, trained on some 2chan posts
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text-generation | transformers |
#Joshua | {"tags": ["conversational"]} | TheCatsMoo/DialoGGPT-small-joshua | null | [
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#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
#Joshua | [] | [
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text-generation | transformers |
# A Talking AI made with GPT2 trained with Harry Potter transcripts
## Currently working on Text to speech and speech recognition
## Likes to say "i'm not a wizard" | {"tags": ["conversational"]} | TheDiamondKing/DialoGPT-small-harrypotter | null | [
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|
# A Talking AI made with GPT2 trained with Harry Potter transcripts
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text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-small-finetuned-toxic
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unkown dataset.... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["rouge"], "model_index": [{"name": "t5-small-finetuned-toxic", "results": [{"task": {"name": "Sequence-to-sequence Language Modeling", "type": "text2text-generation"}, "metric": {"name": "Rouge1", "type": "rouge", "value": 93.7659}}]}]} | TheLongSentance/t5-small-finetuned-toxic | null | [
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#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| t5-small-finetuned-toxic
========================
This model is a fine-tuned version of t5-small on an unkown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1295
* Rouge1: 93.7659
* Rouge2: 3.6618
* Rougel: 93.7652
* Rougelsum: 93.7757
* Gen Len: 2.5481
Model description
-------------... | [
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text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-small-finetuned-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": {"name": "Sequence-to-sequence Language Modeling", "type": "text2text-generation"}, "dataset": {"name": "xsum", "type": "xsum", "args": "defa... | TheLongSentance/t5-small-finetuned-xsum | null | [
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| 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: 2.3833
* Rouge1: 29.6452
* Rouge2: 8.6953
* Rougel: 23.4474
* Rougelsum: 23.4553
* Gen Len: 18.8037
Model description
---------------... | [
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text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5_large_baseline
This model is a fine-tuned version of [t5-large](https://huggingface.co/t5-large) on an unkown dataset.
It ach... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["rouge"], "model_index": [{"name": "t5_large_baseline", "results": [{"task": {"name": "Summarization", "type": "summarization"}, "metric": {"name": "Rouge1", "type": "rouge", "value": 99.8958}}]}]} | TheLongSentance/t5_large_baseline | null | [
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#transformers #pytorch #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| t5\_large\_baseline
===================
This model is a fine-tuned version of t5-large on an unkown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0010
* Rouge1: 99.8958
* Rouge2: 99.8696
* Rougel: 99.8958
* Rougelsum: 99.8958
* Gen Len: 46.715
Model description
-----------------
Mo... | [
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text-generation | transformers |
# Harry DialoGPT Model | {"tags": ["conversational"]} | ThePeachOx/DialoGPT-small-harry | null | [
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fill-mask | transformers | EconBERTa - RoBERTa further trained for 25k steps (T=512, batch_size = 256) on text sourced from economics books.
Example usage for MLM:
```python
from transformers import RobertaTokenizer, RobertaForMaskedLM
from transformers import pipeline
tokenizer = RobertaTokenizer.from_pretrained('roberta-base')
model = Robe... | {} | ThePixOne/EconBERTa | null | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
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#transformers #pytorch #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| EconBERTa - RoBERTa further trained for 25k steps (T=512, batch_size = 256) on text sourced from economics books.
Example usage for MLM:
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fill-mask | transformers | BERT finetuned on wallstreetbets subreddit | {} | ThePixOne/retBERT | null | [
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text-generation | null |
#Rick DialoGPT Model | {"tags": ["conversational"]} | TheReverendWes/DialoGPT-small-rick | null | [
"conversational",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#conversational #region-us
|
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text-generation | transformers |
# Hemione Chat Bot | {"tags": ["conversational"]} | TheTUFGuy/HermioneChatBot | null | [
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"text-generation",
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text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-cased-twitter_sentiment
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "bert-base-cased-twitter_sentiment", "results": []}]} | Theivaprakasham/bert-base-cased-twitter_sentiment | null | [
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"generated_from_trainer",
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] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
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| bert-base-cased-twitter\_sentiment
==================================
This model is a fine-tuned version of bert-base-cased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6907
* Accuracy: 0.7132
Model description
-----------------
More information needed
Intended use... | [
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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. -->
# layoutlmv2-finetuned-sroie
This model is a fine-tuned version of [microsoft/layoutlmv2-base-uncased](https://huggingface.co/micr... | {"license": "cc-by-nc-sa-4.0", "tags": ["generated_from_trainer"], "datasets": ["sroie"], "model-index": [{"name": "layoutlmv2-finetuned-sroie", "results": []}]} | Theivaprakasham/layoutlmv2-finetuned-sroie | null | [
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| layoutlmv2-finetuned-sroie
==========================
This model is a fine-tuned version of microsoft/layoutlmv2-base-uncased on the sroie dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0291
* Address Precision: 0.9341
* Address Recall: 0.9395
* Address F1: 0.9368
* Address Number: 347
... | [
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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. -->
# layoutlmv2-finetuned-sroie_mod
This model is a fine-tuned version of [microsoft/layoutlmv2-base-uncased](https://huggingface.co/... | {"license": "cc-by-nc-sa-4.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "layoutlmv2-finetuned-sroie_mod", "results": []}]} | Theivaprakasham/layoutlmv2-finetuned-sroie_mod | null | [
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"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #layoutlmv2 #token-classification #generated_from_trainer #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# layoutlmv2-finetuned-sroie_mod
This model is a fine-tuned version of microsoft/layoutlmv2-base-uncased on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
##... | [
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"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
... | [
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text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# sentence-transformers-msmarco-distilbert-base-tas-b-twitter_sentiment
This model is a fine-tuned version of [sentence-transforme... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "sentence-transformers-msmarco-distilbert-base-tas-b-twitter_sentiment", "results": []}]} | Theivaprakasham/sentence-transformers-msmarco-distilbert-base-tas-b-twitter_sentiment | null | [
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"distilbert",
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#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| sentence-transformers-msmarco-distilbert-base-tas-b-twitter\_sentiment
======================================================================
This model is a fine-tuned version of sentence-transformers/msmarco-distilbert-base-tas-b on an unknown dataset.
It achieves the following results on the evaluation set:
* Lo... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-06\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: 20",
"### Trainin... | [
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automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-base-timit-demo-colab
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wa... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-base-timit-demo-colab", "results": []}]} | Theivaprakasham/wav2vec2-base-timit-demo-colab | null | [
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"wav2vec2",
"automatic-speech-recognition",
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] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-base-timit-demo-colab
==============================
This model is a fine-tuned version of facebook/wav2vec2-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4475
* Wer: 0.3400
Model description
-----------------
More information needed
Intended uses & limi... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps... | [
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text-generation | transformers |
#Stewie DialoGPT Model | {"tags": ["conversational"]} | Thejas/DialoGPT-small-Stewei | null | [
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"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
#Stewie DialoGPT Model | [] | [
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text-generation | transformers |
#Elon Musk DialoGPT Model | {"tags": ["conversational"]} | Thejas/DialoGPT-small-elon | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
#Elon Musk DialoGPT Model | [] | [
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39
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question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-squad
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/d... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"]} | Thitaree/distilbert-base-uncased-finetuned-squad | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us
|
# distilbert-base-uncased-finetuned-squad
This model is a fine-tuned version of distilbert-base-uncased on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### ... | [
"# distilbert-base-uncased-finetuned-squad\n\nThis model is a fine-tuned version of distilbert-base-uncased on the squad dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"#... | [
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"## Mode... | [
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"TAGS\n#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us \n# distilbert-base-uncased-finetuned-squad\n\nThis model is a fine-tuned version of distilbert-base-uncased on the squad dataset.## Model descriptio... |
text2text-generation | transformers | # t5-qa_squad2neg-en
## Model description
This model is a *Question Answering* model based on T5-small.
It is actually a component of [QuestEval](https://github.com/ThomasScialom/QuestEval) metric but can be used independently as it is, for QA only.
## How to use
```python
from transformers import T5Tokenizer, T5Fo... | {"language": "en", "license": "mit", "tags": ["qa", "question", "answering", "SQuAD", "metric", "nlg", "t5-small"], "datasets": ["squad_v2"], "widget": [{"text": "Who was Louis 14? </s> Louis 14 was a French King."}]} | ThomasNLG/t5-qa_squad2neg-en | null | [
"transformers",
"pytorch",
"jax",
"t5",
"text2text-generation",
"qa",
"question",
"answering",
"SQuAD",
"metric",
"nlg",
"t5-small",
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"dataset:squad_v2",
"arxiv:2103.12693",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-infere... | null | 2022-03-02T23:29:05+00:00 | [
"2103.12693"
] | [
"en"
] | TAGS
#transformers #pytorch #jax #t5 #text2text-generation #qa #question #answering #SQuAD #metric #nlg #t5-small #en #dataset-squad_v2 #arxiv-2103.12693 #license-mit #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| # t5-qa_squad2neg-en
## Model description
This model is a *Question Answering* model based on T5-small.
It is actually a component of QuestEval metric but can be used independently as it is, for QA only.
## How to use
You can play with the model using the inference API, the text input format should follow this te... | [
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"## Model description\nThis model is a *Question Answering* model based on T5-small. \nIt is actually a component of QuestEval metric but can be used independently as it is, for QA only.",
"## How to use\n\n\nYou can play with the model using the inference API, the text input format shou... | [
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