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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
text-classification | transformers |
# Electricidad (small) fine-tuned medical diagnostics | {"lang": "es", "widget": [{"text": "TUMOR DE COMPORTAMIENTO INCIERTO O DESCONOCIDO DEL HNGADO, DE LA VESNCULA BILIAR Y DEL CONDUCTO BILIAR - DiagnNstico Principal - Z01.8 OTROS EXNMENES ESPECIALES ESPECIFICADOS"}]} | mrm8488/electricidad-small-finetuned-medical-diagnostics | null | [
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"tensorboard",
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|
# Electricidad (small) fine-tuned medical diagnostics | [
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] |
text-classification | transformers | # Electricidad-small fine-tuned for (Spanish) Sentiment Anlalysis 🎞️👍👎
[Electricidad](https://huggingface.co/mrm8488/electricidad-small-discriminator) small fine-tuned on [muchocine](https://huggingface.co/datasets/muchocine) dataset for Spanish **Sentiment Analysis** downstream task.
## Fast usage with `pipelines... | {"language": "es", "tags": ["sentiment", "analysis", "spanish"], "datasets": ["muchocine"], "widget": [{"text": "Una buena pel\u00edcula, sin m\u00e1s."}]} | mrm8488/electricidad-small-finetuned-muchocine | null | [
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#transformers #pytorch #electra #text-classification #sentiment #analysis #spanish #es #dataset-muchocine #autotrain_compatible #endpoints_compatible #region-us
| # Electricidad-small fine-tuned for (Spanish) Sentiment Anlalysis ️
Electricidad small fine-tuned on muchocine dataset for Spanish Sentiment Analysis downstream task.
## Fast usage with 'pipelines'
| [
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text-classification | transformers |
# Electricidad-small fine-tuned on restaurant review sentiment analysis dataset
Test set accuray: 0.86 | {"language": "es", "tags": ["restaurant", "classification", "reviews"], "widget": [{"text": "No est\u00e1 a la altura, no volveremos."}]} | mrm8488/electricidad-small-finetuned-restaurant-sentiment-analysis | null | [
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|
# Electricidad-small fine-tuned on restaurant review sentiment analysis dataset
Test set accuray: 0.86 | [
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"# Electricidad-small fine-tuned on restaurant review sentiment analysis dataset\n\nTest set accuray: 0.86"
] |
question-answering | transformers |
# Electricidad small + Spanish SQuAD v1 ⚡❓
[Electricidad-small-discriminator](https://huggingface.co/mrm8488/electricidad-small-discriminator) fine-tuned on [Spanish SQUAD v1.1 dataset](https://github.com/ccasimiro88/TranslateAlignRetrieve/tree/master/SQuAD-es-v1.1) for **Q&A** downstream task.
## Details of the dow... | {"language": "es", "tags": ["QA", "SQuAD"], "thumbnail": "https://imgur.com/uxAvBfh"} | mrm8488/electricidad-small-finetuned-squadv1-es | null | [
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"electra",
"question-answering",
"QA",
"SQuAD",
"es",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"es"
] | TAGS
#transformers #pytorch #electra #question-answering #QA #SQuAD #es #endpoints_compatible #region-us
| Electricidad small + Spanish SQuAD v1
=====================================
Electricidad-small-discriminator fine-tuned on Spanish SQUAD v1.1 dataset for Q&A downstream task.
Details of the downstream task (Q&A) - Dataset
----------------------------------------------
SQuAD-es-v1.1
Model training ️
----------... | [
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] |
text-classification | transformers |
# electricidad-small-finetuned-xnli-es
| {"language": "es", "license": "mit", "tags": ["spanish", "nli", "xnli"], "datasets": ["xnli"], "widget": [{"text": "Por favor, no piensen en darnos dinero. Por favor, considere piadosamente cuanto puede dar."}]} | mrm8488/electricidad-small-finetuned-xnli-es | null | [
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"endpoints_compatible",
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|
# electricidad-small-finetuned-xnli-es
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"# electricidad-small-finetuned-xnli-es"
] |
null | transformers | # GPT2-IMDB-neg (LM + RL) 🎞😡✍
All credits to [@lvwerra](https://twitter.com/lvwerra)
## What is it?
A small GPT2 (`lvwerra/gpt2-imdb`) language model fine-tuned to produce **negative** movie reviews based the [IMDB dataset](https://www.kaggle.com/lakshmi25npathi/imdb-dataset-of-50k-movie-reviews). The model is trai... | {} | mrm8488/gpt2-imdb-neg | null | [
"transformers",
"pytorch",
"gpt2",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #endpoints_compatible #text-generation-inference #region-us
| GPT2-IMDB-neg (LM + RL)
=======================
All credits to @lvwerra
What is it?
-----------
A small GPT2 ('lvwerra/gpt2-imdb') language model fine-tuned to produce negative movie reviews based the IMDB dataset. The model is trained with rewards from a BERT sentiment classifier ('lvwerra/gpt2-imdb') via PPO.
... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #endpoints_compatible #text-generation-inference #region-us \n"
] |
null | transformers |
# GPT2-IMDB-neutral (LM + RL) 🎞😐✍
## What is it?
A small GPT2 (`lvwerra/gpt2-imdb`) language model fine-tuned to produce **neutral**-ish movie reviews based on the [IMDB dataset](https://www.kaggle.com/lakshmi25npathi/imdb-dataset-of-50k-movie-reviews). The model is trained with rewards from a BERT sentiment classi... | {"language": "en", "license": "mit", "tags": ["GPT-2"], "datasets": ["imdb"], "widget": [{"text": "I think the movie was "}]} | mrm8488/gpt2-imdb-neutral | null | [
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"pytorch",
"gpt2",
"GPT-2",
"en",
"dataset:imdb",
"license:mit",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #GPT-2 #en #dataset-imdb #license-mit #endpoints_compatible #text-generation-inference #region-us
| GPT2-IMDB-neutral (LM + RL)
===========================
What is it?
-----------
A small GPT2 ('lvwerra/gpt2-imdb') language model fine-tuned to produce neutral-ish movie reviews based on the IMDB dataset. The model is trained with rewards from a BERT sentiment classifier ('lvwerra/gpt2-imdb') via PPO.
Why?
----
... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #GPT-2 #en #dataset-imdb #license-mit #endpoints_compatible #text-generation-inference #region-us \n"
] |
token-classification | transformers | # LayoutLM fine-tuned on FUNSD for Document/Forms token classification
## Usage (WIP)
```python
import torch
import numpy as np
from PIL import Image, ImageDraw, ImageFont
import pytesseract
from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
device = torch.device("cuda" if torch.cuda.is_avai... | {} | mrm8488/layoutlm-finetuned-funsd | null | [
"transformers",
"pytorch",
"safetensors",
"layoutlm",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #safetensors #layoutlm #token-classification #autotrain_compatible #endpoints_compatible #region-us
| # LayoutLM fine-tuned on FUNSD for Document/Forms token classification
## Usage (WIP)
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null | transformers | ## LEGALECTRA ⚖️
**LEGALECTRA** (base) is an Electra like model (discriminator in this case) trained on [A collection of corpora of Spanish legal domain](https://zenodo.org/record/5495529#.YZItp3vMLJw).
As mentioned in the original [paper](https://openreview.net/pdf?id=r1xMH1BtvB):
**ELECTRA** is a new method for self-... | {"language": "es", "tags": ["Spanish", "Electra", "Legal"], "datasets": ["Spanish-legal-corpora"]} | mrm8488/legalectra-base-spanish | null | [
"transformers",
"pytorch",
"electra",
"pretraining",
"Spanish",
"Electra",
"Legal",
"es",
"dataset:Spanish-legal-corpora",
"arxiv:1406.2661",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1406.2661"
] | [
"es"
] | TAGS
#transformers #pytorch #electra #pretraining #Spanish #Electra #Legal #es #dataset-Spanish-legal-corpora #arxiv-1406.2661 #endpoints_compatible #region-us
| LEGALECTRA ️
------------
LEGALECTRA (base) is an Electra like model (discriminator in this case) trained on A collection of corpora of Spanish legal domain.
As mentioned in the original paper:
ELECTRA is a new method for self-supervised language representation learning. It can be used to pre-train transformer networ... | [] | [
"TAGS\n#transformers #pytorch #electra #pretraining #Spanish #Electra #Legal #es #dataset-Spanish-legal-corpora #arxiv-1406.2661 #endpoints_compatible #region-us \n"
] |
null | transformers |
## LEGALECTRA ⚖️
**LEGALECTRA** (small) is an Electra like model (discriminator in this case) trained on [A collection of corpora of Spanish legal domain](https://zenodo.org/record/5495529#.YZItp3vMLJw).
As mentioned in the original [paper](https://openreview.net/pdf?id=r1xMH1BtvB):
**ELECTRA** is a new method for s... | {"language": "es", "tags": ["Spanish", "Electra", "Legal"], "datasets": ["Spanish-legal-corpora"]} | mrm8488/legalectra-small-spanish | null | [
"transformers",
"pytorch",
"electra",
"pretraining",
"Spanish",
"Electra",
"Legal",
"es",
"dataset:Spanish-legal-corpora",
"arxiv:1406.2661",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1406.2661"
] | [
"es"
] | TAGS
#transformers #pytorch #electra #pretraining #Spanish #Electra #Legal #es #dataset-Spanish-legal-corpora #arxiv-1406.2661 #endpoints_compatible #region-us
| LEGALECTRA ️
------------
LEGALECTRA (small) is an Electra like model (discriminator in this case) trained on A collection of corpora of Spanish legal domain.
As mentioned in the original paper:
ELECTRA is a new method for self-supervised language representation learning. It can be used to pre-train transformer net... | [] | [
"TAGS\n#transformers #pytorch #electra #pretraining #Spanish #Electra #Legal #es #dataset-Spanish-legal-corpora #arxiv-1406.2661 #endpoints_compatible #region-us \n"
] |
question-answering | transformers |
# Longformer-base-4096 fine-tuned on SQuAD v2
[Longformer-base-4096 model](https://huggingface.co/allenai/longformer-base-4096) fine-tuned on [SQuAD v2](https://rajpurkar.github.io/SQuAD-explorer/) for **Q&A** downstream task.
## Longformer-base-4096
[Longformer](https://arxiv.org/abs/2004.05150) is a transformer m... | {"language": "en", "tags": ["QA", "long context", "Q&A"], "datasets": ["squad_v2"], "model-index": [{"name": "mrm8488/longformer-base-4096-finetuned-squadv2", "results": [{"task": {"type": "question-answering", "name": "Question Answering"}, "dataset": {"name": "squad_v2", "type": "squad_v2", "config": "squad_v2", "spl... | mrm8488/longformer-base-4096-finetuned-squadv2 | null | [
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| Longformer-base-4096 fine-tuned on SQuAD v2
===========================================
Longformer-base-4096 model fine-tuned on SQuAD v2 for Q&A downstream task.
Longformer-base-4096
--------------------
Longformer is a transformer model for long documents.
'longformer-base-4096' is a BERT-like model started f... | [
"# samples: 130319\nDataset: squad\\_v2, Split: valid, # samples: 11873\n\n\nHow to load it from datasets\n\n\nCheck out more about this dataset and others in Datasets Viewer\n\n\nModel fine-tuning ️\n--------------------\n\n\nThe training script is a slightly modified version of this one\n\n\nModel in Action\n---... | [
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"# samples: 130319\nDataset: squad\\_v2, Split: valid, # samples: 11873\n\n\nHow to load it from datasets\n\n\nCheck out mo... |
question-answering | transformers |
# Spanish Longformer fine-tuned on **SQAC** for Spanish **QA** 📖❓
[longformer-base-4096-spanish](https://huggingface.co/mrm8488/longformer-base-4096-spanish) fine-tuned on [SQAC](https://huggingface.co/datasets/BSC-TeMU/SQAC) for **Q&A** downstream task.
## Details of the model 🧠
[longformer-base-4096-spanish](http... | {"language": "es", "tags": ["Long documents", "LongFormer", "QA", "Q&A"], "datasets": ["BSC-TeMU/SQAC"]} | mrm8488/longformer-base-4096-spanish-finetuned-squad | null | [
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"endpoints_compatible",
"region:us"
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] | TAGS
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|
# Spanish Longformer fine-tuned on SQAC for Spanish QA
longformer-base-4096-spanish fine-tuned on SQAC for Q&A downstream task.
## Details of the model
longformer-base-4096-spanish is a BERT-like model started from the RoBERTa checkpoint (BERTIN in this case) and pre-trained for *MLM* on long documents (from BETO's... | [
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"## Details of the... |
fill-mask | transformers |
# longformer-base-4096-spanish
## [Longformer](https://arxiv.org/abs/2004.05150) is a Transformer model for long documents.
`longformer-base-4096` is a BERT-like model started from the RoBERTa checkpoint (**BERTIN** in this case) and pre-trained for *MLM* on long documents (from BETO's `all_wikis`). It supports seq... | {"language": ["es"], "license": "mit", "tags": ["Long documents", "longformer", "bertin", "spanish"], "datasets": ["spanish_large_corpus"], "widget": [{"text": "Manuel Romero ha creado con el equipo de BERTIN un modelo que procesa documentos <mask> largos."}]} | mrm8488/longformer-base-4096-spanish | null | [
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"region:us"
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"2004.05150"
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|
# longformer-base-4096-spanish
## Longformer is a Transformer model for long documents.
'longformer-base-4096' is a BERT-like model started from the RoBERTa checkpoint (BERTIN in this case) and pre-trained for *MLM* on long documents (from BETO's 'all_wikis'). It supports sequences of length up to 4,096!
Longf... | [
"# longformer-base-4096-spanish",
"## Longformer is a Transformer model for long documents. \n\n'longformer-base-4096' is a BERT-like model started from the RoBERTa checkpoint (BERTIN in this case) and pre-trained for *MLM* on long documents (from BETO's 'all_wikis'). It supports sequences of length up to 4,096! ... | [
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"# longformer-base-4096-spanish",
"## Longformer is a Transformer mo... |
text2text-generation | transformers |
# mT5-small fine-tuned on TyDiQA for multilingual QA 🗺📖❓
[Google's mT5-small](https://huggingface.co/google/mt5-small) fine-tuned on [TyDi QA](https://huggingface.co/nlp/viewer/?dataset=tydiqa&config=secondary_task) (secondary task) for **multingual Q&A** downstream task.
## Details of mT5
[Google's mT5](https://g... | {"language": "multilingual", "datasets": ["tydiqa"], "widget": [{"text": "question: What won HuggingFace? context: HuggingFace won the best Demo paper at EMNLP2020."}]} | mrm8488/mT5-small-finetuned-tydiqa-for-xqa | null | [
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"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2010.11934"
] | [
"multilingual"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #multilingual #dataset-tydiqa #arxiv-2010.11934 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| mT5-small fine-tuned on TyDiQA for multilingual QA
==================================================
Google's mT5-small fine-tuned on TyDi QA (secondary task) for multingual Q&A downstream task.
Details of mT5
--------------
Google's mT5
mT5 is pretrained on the mC4 corpus, covering 101 languages:
Afrikaans,... | [] | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #multilingual #dataset-tydiqa #arxiv-2010.11934 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
translation | transformers | ### mbart-large-es-en
This is mbart-large-cc25, finetuned on bible_para for Spanish to English translation.
It scores BLEU **29.34** | {"language": ["es", "en"], "tags": ["translation"], "datasets": ["bible_para"]} | mrm8488/mbart-large-finetuned-bible-es-en-translation | null | [
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"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"es",
"en"
] | TAGS
#transformers #pytorch #safetensors #mbart #text2text-generation #translation #es #en #dataset-bible_para #autotrain_compatible #endpoints_compatible #region-us
| ### mbart-large-es-en
This is mbart-large-cc25, finetuned on bible_para for Spanish to English translation.
It scores BLEU 29.34 | [
"### mbart-large-es-en\nThis is mbart-large-cc25, finetuned on bible_para for Spanish to English translation.\n\nIt scores BLEU 29.34"
] | [
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"### mbart-large-es-en\nThis is mbart-large-cc25, finetuned on bible_para for Spanish to English translation.\n\nIt scores BLEU 29.34"
] |
translation | transformers | ### mbart-large-en-es
This is mbart-large-cc25, finetuned on opus100 for English to Spanish translation.
It scores BLEU **32.54** on test set.
| {"language": ["en", "es"], "tags": ["translation"], "datasets": ["opus100"]} | mrm8488/mbart-large-finetuned-opus-en-es-translation | null | [
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"en",
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"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en",
"es"
] | TAGS
#transformers #pytorch #safetensors #mbart #text2text-generation #translation #en #es #dataset-opus100 #autotrain_compatible #endpoints_compatible #region-us
| ### mbart-large-en-es
This is mbart-large-cc25, finetuned on opus100 for English to Spanish translation.
It scores BLEU 32.54 on test set.
| [
"### mbart-large-en-es\nThis is mbart-large-cc25, finetuned on opus100 for English to Spanish translation.\n\n\nIt scores BLEU 32.54 on test set."
] | [
"TAGS\n#transformers #pytorch #safetensors #mbart #text2text-generation #translation #en #es #dataset-opus100 #autotrain_compatible #endpoints_compatible #region-us \n",
"### mbart-large-en-es\nThis is mbart-large-cc25, finetuned on opus100 for English to Spanish translation.\n\n\nIt scores BLEU 32.54 on test set... |
translation | transformers | ### mbart-large-es-en
This is mbart-large-cc25, finetuned on opus100 for Spanish to English translation.
It scores BLEU **28.25** on validation dataset
It scores BLEU **28.28** on test
dataset | {"language": ["es", "en"], "tags": ["translation"], "datasets": ["opus100"]} | mrm8488/mbart-large-finetuned-opus-es-en-translation | null | [
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"translation",
"es",
"en",
"dataset:opus100",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"es",
"en"
] | TAGS
#transformers #pytorch #safetensors #mbart #text2text-generation #translation #es #en #dataset-opus100 #autotrain_compatible #endpoints_compatible #region-us
| ### mbart-large-es-en
This is mbart-large-cc25, finetuned on opus100 for Spanish to English translation.
It scores BLEU 28.25 on validation dataset
It scores BLEU 28.28 on test
dataset | [
"### mbart-large-es-en\nThis is mbart-large-cc25, finetuned on opus100 for Spanish to English translation.\n\nIt scores BLEU 28.25 on validation dataset\n\nIt scores BLEU 28.28 on test \ndataset"
] | [
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"### mbart-large-es-en\nThis is mbart-large-cc25, finetuned on opus100 for Spanish to English translation.\n\nIt scores BLEU 28.25 on validation... |
translation | transformers | ### mbart-large-it-en
This is mbart-large-cc25, finetuned on opus100 for Italian to English translation.
It scores BLEU **25.82** on test set. | {"language": ["it", "en"], "tags": ["translation"], "datasets": ["opus100"]} | mrm8488/mbart-large-finetuned-opus-it-en-translation | null | [
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"text2text-generation",
"translation",
"it",
"en",
"dataset:opus100",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"it",
"en"
] | TAGS
#transformers #pytorch #mbart #text2text-generation #translation #it #en #dataset-opus100 #autotrain_compatible #endpoints_compatible #region-us
| ### mbart-large-it-en
This is mbart-large-cc25, finetuned on opus100 for Italian to English translation.
It scores BLEU 25.82 on test set. | [
"### mbart-large-it-en\nThis is mbart-large-cc25, finetuned on opus100 for Italian to English translation.\n\n\nIt scores BLEU 25.82 on test set."
] | [
"TAGS\n#transformers #pytorch #mbart #text2text-generation #translation #it #en #dataset-opus100 #autotrain_compatible #endpoints_compatible #region-us \n",
"### mbart-large-it-en\nThis is mbart-large-cc25, finetuned on opus100 for Italian to English translation.\n\n\nIt scores BLEU 25.82 on test set."
] |
question-answering | transformers |
# MobileBERT + SQuAD (v1.1) 📱❓
[mobilebert-uncased](https://huggingface.co/google/mobilebert-uncased) fine-tuned on [SQUAD v2.0 dataset](https://rajpurkar.github.io/SQuAD-explorer/explore/v2.0/dev/) for **Q&A** downstream task.
## Details of the downstream task (Q&A) - Model 🧠
**MobileBERT** is a thin version of ... | {"language": "en", "datasets": ["squad"]} | mrm8488/mobilebert-uncased-finetuned-squadv1 | null | [
"transformers",
"pytorch",
"safetensors",
"mobilebert",
"question-answering",
"en",
"dataset:squad",
"arxiv:2004.02984",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2004.02984"
] | [
"en"
] | TAGS
#transformers #pytorch #safetensors #mobilebert #question-answering #en #dataset-squad #arxiv-2004.02984 #endpoints_compatible #region-us
| MobileBERT + SQuAD (v1.1)
=========================
mobilebert-uncased fine-tuned on SQUAD v2.0 dataset for Q&A downstream task.
Details of the downstream task (Q&A) - Model
--------------------------------------------
MobileBERT is a thin version of *BERT\_LARGE*, while equipped with bottleneck structures and a ... | [
"### Model in action\n\n\nFast usage with pipelines:\n\n\n\n> \n> Created by Manuel Romero/@mrm8488 | LinkedIn\n> \n> \n> \n\n\n\n> \n> Made with ♥ in Spain\n> \n> \n>"
] | [
"TAGS\n#transformers #pytorch #safetensors #mobilebert #question-answering #en #dataset-squad #arxiv-2004.02984 #endpoints_compatible #region-us \n",
"### Model in action\n\n\nFast usage with pipelines:\n\n\n\n> \n> Created by Manuel Romero/@mrm8488 | LinkedIn\n> \n> \n> \n\n\n\n> \n> Made with ♥ in Spain\n> \n> ... |
question-answering | transformers |
# MobileBERT + SQuAD v2 📱❓
[mobilebert-uncased](https://huggingface.co/google/mobilebert-uncased) fine-tuned on [SQUAD v2.0 dataset](https://rajpurkar.github.io/SQuAD-explorer/explore/v2.0/dev/) for **Q&A** downstream task.
## Details of the downstream task (Q&A) - Model 🧠
**MobileBERT** is a thin version of *BER... | {"language": "en", "datasets": ["squad_v2"]} | mrm8488/mobilebert-uncased-finetuned-squadv2 | null | [
"transformers",
"pytorch",
"safetensors",
"mobilebert",
"question-answering",
"en",
"dataset:squad_v2",
"arxiv:2004.02984",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2004.02984"
] | [
"en"
] | TAGS
#transformers #pytorch #safetensors #mobilebert #question-answering #en #dataset-squad_v2 #arxiv-2004.02984 #endpoints_compatible #region-us
| MobileBERT + SQuAD v2
=====================
mobilebert-uncased fine-tuned on SQUAD v2.0 dataset for Q&A downstream task.
Details of the downstream task (Q&A) - Model
--------------------------------------------
MobileBERT is a thin version of *BERT\_LARGE*, while equipped with bottleneck structures and a carefull... | [
"### Model in action\n\n\nFast usage with pipelines:\n\n\n\n> \n> Created by Manuel Romero/@mrm8488 | LinkedIn\n> \n> \n> \n\n\n\n> \n> Made with ♥ in Spain\n> \n> \n>"
] | [
"TAGS\n#transformers #pytorch #safetensors #mobilebert #question-answering #en #dataset-squad_v2 #arxiv-2004.02984 #endpoints_compatible #region-us \n",
"### Model in action\n\n\nFast usage with pipelines:\n\n\n\n> \n> Created by Manuel Romero/@mrm8488 | LinkedIn\n> \n> \n> \n\n\n\n> \n> Made with ♥ in Spain\n> \... |
null | null | #@title
---
tags:
- bipedal
- walker
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
---
# PPO BipedalWalker v3 🤖🚶🏼
This is a pre-trained model of a PPO agent playing BipedalWalker-v3 using the [stable-baselines3](https://github.com/DLR-RM/stable-baselines3) library.
<video loop="" autop... | {} | mrm8488/ppo-BipedalWalker-v3 | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| #@title
---
tags:
- bipedal
- walker
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
---
# PPO BipedalWalker v3
This is a pre-trained model of a PPO agent playing BipedalWalker-v3 using the stable-baselines3 library.
<video loop="" autoplay="" controls="" src="URL
### Usage (with Stable-b... | [
"# PPO BipedalWalker v3 \n\nThis is a pre-trained model of a PPO agent playing BipedalWalker-v3 using the stable-baselines3 library.\n\n<video loop=\"\" autoplay=\"\" controls=\"\" src=\"URL",
"### Usage (with Stable-baselines3)\nUsing this model becomes easy when you have stable-baselines3 and huggingface_sb3 in... | [
"TAGS\n#region-us \n",
"# PPO BipedalWalker v3 \n\nThis is a pre-trained model of a PPO agent playing BipedalWalker-v3 using the stable-baselines3 library.\n\n<video loop=\"\" autoplay=\"\" controls=\"\" src=\"URL",
"### Usage (with Stable-baselines3)\nUsing this model becomes easy when you have stable-baseline... |
null | null | #@title
---
tags:
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
---
# PPO CartPole v1 🤖⚖️
This is a pre-trained model of a PPO agent playing CartPole-v1 using the [stable-baselines3](https://github.com/DLR-RM/stable-baselines3) library.
<video loop="" autoplay="" controls="" src="https:/... | {} | mrm8488/ppo-CartPole-v1 | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| #@title
---
tags:
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
---
# PPO CartPole v1 ️
This is a pre-trained model of a PPO agent playing CartPole-v1 using the stable-baselines3 library.
<video loop="" autoplay="" controls="" src="URL
### Usage (with Stable-baselines3)
Using this model ... | [
"# PPO CartPole v1 ️\n\nThis is a pre-trained model of a PPO agent playing CartPole-v1 using the stable-baselines3 library.\n\n<video loop=\"\" autoplay=\"\" controls=\"\" src=\"URL",
"### Usage (with Stable-baselines3)\nUsing this model becomes easy when you have stable-baselines3 and huggingface_sb3 installed:\... | [
"TAGS\n#region-us \n",
"# PPO CartPole v1 ️\n\nThis is a pre-trained model of a PPO agent playing CartPole-v1 using the stable-baselines3 library.\n\n<video loop=\"\" autoplay=\"\" controls=\"\" src=\"URL",
"### Usage (with Stable-baselines3)\nUsing this model becomes easy when you have stable-baselines3 and hu... |
null | null | #@title
---
tags:
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
---
# PPO LunarLander-v2 🚀🌑
This is a pre-trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3](https://github.com/DLR-RM/stable-baselines3) library.
### Usage (with Stable-baselines3)
Using thi... | {} | mrm8488/ppo-LunarLander-v2 | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| #@title
---
tags:
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
---
# PPO LunarLander-v2
This is a pre-trained model of a PPO agent playing LunarLander-v2 using the stable-baselines3 library.
### Usage (with Stable-baselines3)
Using this model becomes easy when you have stable-baselines... | [
"# PPO LunarLander-v2 \n\nThis is a pre-trained model of a PPO agent playing LunarLander-v2 using the stable-baselines3 library.",
"### Usage (with Stable-baselines3)\nUsing this model becomes easy when you have stable-baselines3 and huggingface_sb3 installed:\n\n\n\nThen, you can use the model like this:",
"##... | [
"TAGS\n#region-us \n",
"# PPO LunarLander-v2 \n\nThis is a pre-trained model of a PPO agent playing LunarLander-v2 using the stable-baselines3 library.",
"### Usage (with Stable-baselines3)\nUsing this model becomes easy when you have stable-baselines3 and huggingface_sb3 installed:\n\n\n\nThen, you can use the... |
question-answering | transformers |
# RoBERTa-base (1B-1) + SQuAD v1 ❓
[roberta-base-1B-1](https://huggingface.co/nyu-mll/roberta-base-1B-1) fine-tuned on [SQUAD v1.1 dataset](https://rajpurkar.github.io/SQuAD-explorer/explore/1.1/dev/) for **Q&A** downstream task.
## Details of the downstream task (Q&A) - Model 🧠
RoBERTa Pretrained on Smaller Datas... | {"language": "en"} | mrm8488/roberta-base-1B-1-finetuned-squadv1 | null | [
"transformers",
"pytorch",
"jax",
"roberta",
"question-answering",
"en",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #jax #roberta #question-answering #en #endpoints_compatible #region-us
| RoBERTa-base (1B-1) + SQuAD v1
==============================
roberta-base-1B-1 fine-tuned on SQUAD v1.1 dataset for Q&A downstream task.
Details of the downstream task (Q&A) - Model
--------------------------------------------
RoBERTa Pretrained on Smaller Datasets
NYU Machine Learning for Language pretrained ... | [
"### Model in action\n\n\nFast usage with pipelines:\n\n\n\n> \n> Created by Manuel Romero/@mrm8488 | LinkedIn\n> Made with ♥ in Spain\n> \n> \n>"
] | [
"TAGS\n#transformers #pytorch #jax #roberta #question-answering #en #endpoints_compatible #region-us \n",
"### Model in action\n\n\nFast usage with pipelines:\n\n\n\n> \n> Created by Manuel Romero/@mrm8488 | LinkedIn\n> Made with ♥ in Spain\n> \n> \n>"
] |
question-answering | transformers |
# RoBERTa-base (1B-1) + SQuAD v2 ❓
[roberta-base-1B-1](https://huggingface.co/nyu-mll/roberta-base-1B-1) fine-tuned on [SQUAD v2 dataset](https://rajpurkar.github.io/SQuAD-explorer/explore/v2.0/dev/) for **Q&A** downstream task.
## Details of the downstream task (Q&A) - Model 🧠
RoBERTa Pretrained on Smaller Datase... | {"language": "en"} | mrm8488/roberta-base-1B-1-finetuned-squadv2 | null | [
"transformers",
"pytorch",
"jax",
"roberta",
"question-answering",
"en",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #jax #roberta #question-answering #en #endpoints_compatible #region-us
| RoBERTa-base (1B-1) + SQuAD v2
==============================
roberta-base-1B-1 fine-tuned on SQUAD v2 dataset for Q&A downstream task.
Details of the downstream task (Q&A) - Model
--------------------------------------------
RoBERTa Pretrained on Smaller Datasets
NYU Machine Learning for Language pretrained Ro... | [
"### Model in action\n\n\nFast usage with pipelines:\n\n\n\n> \n> Created by Manuel Romero/@mrm8488 | LinkedIn\n> Made with ♥ in Spain\n> \n> \n>"
] | [
"TAGS\n#transformers #pytorch #jax #roberta #question-answering #en #endpoints_compatible #region-us \n",
"### Model in action\n\n\nFast usage with pipelines:\n\n\n\n> \n> Created by Manuel Romero/@mrm8488 | LinkedIn\n> Made with ♥ in Spain\n> \n> \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"} | mrm8488/roberta-base-bne-finetuned-sqac-retriever | null | [
"sentence-transformers",
"pytorch",
"roberta",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #roberta #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 #roberta #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... |
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. -->
# roberta-base-bne-finetuned-sqac
This model is a fine-tuned version of [BSC-TeMU/roberta-base-bne](https://huggingface.co/BSC-TeM... | {"language": "es", "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["sqac"], "metrics": ["f1"], "model-index": [{"name": "roberta-base-bne-finetuned-sqac", "results": [{"task": {"type": "Question-Answering", "name": "Question Answering"}, "dataset": {"name": "sqac", "type": "sqac"}, "metrics": ... | mrm8488/roberta-base-bne-finetuned-sqac | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"question-answering",
"generated_from_trainer",
"es",
"dataset:sqac",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"es"
] | TAGS
#transformers #pytorch #tensorboard #roberta #question-answering #generated_from_trainer #es #dataset-sqac #license-apache-2.0 #endpoints_compatible #region-us
| roberta-base-bne-finetuned-sqac
===============================
This model is a fine-tuned version of BSC-TeMU/roberta-base-bne on the sqac dataset.
It achieves the following results on the evaluation set:
* Loss: 1.2111
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",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #roberta #question-answering #generated_from_trainer #es #dataset-sqac #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\\_s... |
fill-mask | transformers | # RoBERTa (large) fine-tuned on Winograd Schema Challenge (WSC) data
Step from its original [repo](https://github.com/pytorch/fairseq/blob/master/examples/roberta/wsc/README.md)
The following instructions can be used to finetune RoBERTa on the WSC training
data provided by [SuperGLUE](https://super.gluebenchmark.com/... | {} | mrm8488/roberta-large-finetuned-wsc | null | [
"transformers",
"pytorch",
"jax",
"roberta",
"fill-mask",
"arxiv:1905.06290",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1905.06290"
] | [] | TAGS
#transformers #pytorch #jax #roberta #fill-mask #arxiv-1905.06290 #autotrain_compatible #endpoints_compatible #region-us
| # RoBERTa (large) fine-tuned on Winograd Schema Challenge (WSC) data
Step from its original repo
The following instructions can be used to finetune RoBERTa on the WSC training
data provided by SuperGLUE.
Note that there is high variance in the results. For our GLUE/SuperGLUE
submission we swept over the learning rat... | [
"# RoBERTa (large) fine-tuned on Winograd Schema Challenge (WSC) data\n\nStep from its original repo\n\nThe following instructions can be used to finetune RoBERTa on the WSC training\ndata provided by SuperGLUE.\n\nNote that there is high variance in the results. For our GLUE/SuperGLUE\nsubmission we swept over the... | [
"TAGS\n#transformers #pytorch #jax #roberta #fill-mask #arxiv-1905.06290 #autotrain_compatible #endpoints_compatible #region-us \n",
"# RoBERTa (large) fine-tuned on Winograd Schema Challenge (WSC) data\n\nStep from its original repo\n\nThe following instructions can be used to finetune RoBERTa on the WSC trainin... |
summarization | transformers |
Shared [RoBERTa2RoBERTa (med-small)](https://huggingface.co/nyu-mll/roberta-med-small-1M-1) Summarization with 🤗EncoderDecoder Framework
This model is a warm-started *RoBERTaShared* (med-small) model fine-tuned on the *cnn_dailymail* summarization dataset.
The model achieves a **16.90** ROUGE-2 score on *cnn_dailym... | {"language": "en", "license": "apache-2.0", "tags": ["summarization"], "datasets": ["cnn_dailymail"]} | mrm8488/roberta-med-small2roberta-med-small-finetuned-cnn_daily_mail-summarization | null | [
"transformers",
"pytorch",
"safetensors",
"encoder-decoder",
"text2text-generation",
"summarization",
"en",
"dataset:cnn_dailymail",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #safetensors #encoder-decoder #text2text-generation #summarization #en #dataset-cnn_dailymail #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
Shared RoBERTa2RoBERTa (med-small) Summarization with EncoderDecoder Framework
This model is a warm-started *RoBERTaShared* (med-small) model fine-tuned on the *cnn_dailymail* summarization dataset.
The model achieves a 16.90 ROUGE-2 score on *cnn_dailymail*'s test dataset. | [] | [
"TAGS\n#transformers #pytorch #safetensors #encoder-decoder #text2text-generation #summarization #en #dataset-cnn_dailymail #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
summarization | transformers |
Shared RoBERTa2RoBERTa (med-small) Summarization with 🤗EncoderDecoder Framework
This model is a warm-started *RoBERTaShared* (med-small) model fine-tuned on the *BBC XSum* summarization dataset. | {"language": "en", "license": "apache-2.0", "tags": ["summarization"], "datasets": ["xsum"]} | mrm8488/roberta-med-small_shared-finetuned-bbc_xsum-summarization | null | [
"transformers",
"pytorch",
"safetensors",
"encoder-decoder",
"text2text-generation",
"summarization",
"en",
"dataset:xsum",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #safetensors #encoder-decoder #text2text-generation #summarization #en #dataset-xsum #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
Shared RoBERTa2RoBERTa (med-small) Summarization with EncoderDecoder Framework
This model is a warm-started *RoBERTaShared* (med-small) model fine-tuned on the *BBC XSum* summarization dataset. | [] | [
"TAGS\n#transformers #pytorch #safetensors #encoder-decoder #text2text-generation #summarization #en #dataset-xsum #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
null | transformers |
# SpanBERT base fine-tuned on SQuAD v1
[SpanBERT](https://github.com/facebookresearch/SpanBERT) created by [Facebook Research](https://github.com/facebookresearch) and fine-tuned on [SQuAD 1.1](https://rajpurkar.github.io/SQuAD-explorer/explore/1.1/dev/) for **Q&A** downstream task ([by them](https://github.com/faceb... | {"language": "en"} | mrm8488/spanbert-base-finetuned-squadv1 | null | [
"transformers",
"pytorch",
"jax",
"bert",
"en",
"arxiv:1907.10529",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1907.10529"
] | [
"en"
] | TAGS
#transformers #pytorch #jax #bert #en #arxiv-1907.10529 #endpoints_compatible #region-us
| SpanBERT base fine-tuned on SQuAD v1
====================================
SpanBERT created by Facebook Research and fine-tuned on SQuAD 1.1 for Q&A downstream task (by them).
Details of SpanBERT
-------------------
SpanBERT: Improving Pre-training by Representing and Predicting Spans
Details of the downstream t... | [] | [
"TAGS\n#transformers #pytorch #jax #bert #en #arxiv-1907.10529 #endpoints_compatible #region-us \n"
] |
null | transformers |
# SpanBERT base fine-tuned on SQuAD v2
[SpanBERT](https://github.com/facebookresearch/SpanBERT) created by [Facebook Research](https://github.com/facebookresearch) and fine-tuned on [SQuAD 2.0](https://rajpurkar.github.io/SQuAD-explorer/) for **Q&A** downstream task ([by them](https://github.com/facebookresearch/Span... | {"language": "en"} | mrm8488/spanbert-base-finetuned-squadv2 | null | [
"transformers",
"pytorch",
"jax",
"bert",
"en",
"arxiv:1907.10529",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1907.10529"
] | [
"en"
] | TAGS
#transformers #pytorch #jax #bert #en #arxiv-1907.10529 #endpoints_compatible #region-us
| SpanBERT base fine-tuned on SQuAD v2
====================================
SpanBERT created by Facebook Research and fine-tuned on SQuAD 2.0 for Q&A downstream task (by them).
Details of SpanBERT
-------------------
SpanBERT: Improving Pre-training by Representing and Predicting Spans
Details of the downstream t... | [
"# samples: 130k\nDataset: SQuAD2.0, Split: eval, # samples: 12.3k\n\n\nModel fine-tuning ️\n--------------------\n\n\nYou can get the fine-tuning script here\n\n\nResults Comparison\n------------------\n\n\n\nNote: The numbers marked as \\* are evaluated on the development sets because those models were not submi... | [
"TAGS\n#transformers #pytorch #jax #bert #en #arxiv-1907.10529 #endpoints_compatible #region-us \n",
"# samples: 130k\nDataset: SQuAD2.0, Split: eval, # samples: 12.3k\n\n\nModel fine-tuning ️\n--------------------\n\n\nYou can get the fine-tuning script here\n\n\nResults Comparison\n------------------\n\n\n\nNo... |
feature-extraction | transformers |
# SpanBERT base fine-tuned on TACRED
[SpanBERT](https://github.com/facebookresearch/SpanBERT) created by [Facebook Research](https://github.com/facebookresearch) and fine-tuned on [TACRED](https://nlp.stanford.edu/projects/tacred/) dataset by [them](https://github.com/facebookresearch/SpanBERT#finetuned-models-squad-... | {"language": "en"} | mrm8488/spanbert-base-finetuned-tacred | null | [
"transformers",
"pytorch",
"jax",
"bert",
"feature-extraction",
"en",
"arxiv:1907.10529",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1907.10529"
] | [
"en"
] | TAGS
#transformers #pytorch #jax #bert #feature-extraction #en #arxiv-1907.10529 #endpoints_compatible #region-us
| SpanBERT base fine-tuned on TACRED
==================================
SpanBERT created by Facebook Research and fine-tuned on TACRED dataset by them
Details of SpanBERT
-------------------
SpanBERT: Improving Pre-training by Representing and Predicting Spans
Dataset
-------
TACRED A large-scale relation extra... | [] | [
"TAGS\n#transformers #pytorch #jax #bert #feature-extraction #en #arxiv-1907.10529 #endpoints_compatible #region-us \n"
] |
question-answering | transformers |
# SpanBERT (spanbert-base-cased) fine-tuned on SQuAD v1.1
[SpanBERT](https://github.com/facebookresearch/SpanBERT) created by [Facebook Research](https://github.com/facebookresearch) and fine-tuned on [SQuAD 1.1](https://rajpurkar.github.io/SQuAD-explorer/) for **Q&A** downstream task.
## Details of SpanBERT
A pr... | {"language": "en"} | mrm8488/spanbert-finetuned-squadv1 | null | [
"transformers",
"pytorch",
"jax",
"safetensors",
"bert",
"question-answering",
"en",
"arxiv:1907.10529",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1907.10529"
] | [
"en"
] | TAGS
#transformers #pytorch #jax #safetensors #bert #question-answering #en #arxiv-1907.10529 #endpoints_compatible #region-us
| SpanBERT (spanbert-base-cased) fine-tuned on SQuAD v1.1
=======================================================
SpanBERT created by Facebook Research and fine-tuned on SQuAD 1.1 for Q&A downstream task.
Details of SpanBERT
-------------------
A pre-training method that is designed to better represent and predict ... | [
"# samples: 87.7k\nDataset: SQuAD1.1, Split: eval, # samples: 10.6k\n\n\nModel training\n--------------\n\n\nThe model was trained on a Tesla P100 GPU and 25GB of RAM.\nThe script for fine tuning can be found here\n\n\nResults:\n--------",
"### Raw metrics:\n\n\nComparison:\n-----------\n\n\nModel: SpanBert offic... | [
"TAGS\n#transformers #pytorch #jax #safetensors #bert #question-answering #en #arxiv-1907.10529 #endpoints_compatible #region-us \n",
"# samples: 87.7k\nDataset: SQuAD1.1, Split: eval, # samples: 10.6k\n\n\nModel training\n--------------\n\n\nThe model was trained on a Tesla P100 GPU and 25GB of RAM.\nThe script ... |
question-answering | transformers |
# SpanBERT (spanbert-base-cased) fine-tuned on SQuAD v2
[SpanBERT](https://github.com/facebookresearch/SpanBERT) created by [Facebook Research](https://github.com/facebookresearch) and fine-tuned on [SQuAD 2.0](https://rajpurkar.github.io/SQuAD-explorer/) for **Q&A** downstream task.
## Details of SpanBERT
[SpanBER... | {"language": "en"} | mrm8488/spanbert-finetuned-squadv2 | null | [
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"pytorch",
"jax",
"safetensors",
"bert",
"question-answering",
"en",
"arxiv:1907.10529",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1907.10529"
] | [
"en"
] | TAGS
#transformers #pytorch #jax #safetensors #bert #question-answering #en #arxiv-1907.10529 #endpoints_compatible #has_space #region-us
| SpanBERT (spanbert-base-cased) fine-tuned on SQuAD v2
=====================================================
SpanBERT created by Facebook Research and fine-tuned on SQuAD 2.0 for Q&A downstream task.
Details of SpanBERT
-------------------
SpanBERT: Improving Pre-training by Representing and Predicting Spans
Det... | [
"# samples: 130k\nDataset: SQuAD2.0, Split: eval, # samples: 12.3k\n\n\nModel training\n--------------\n\n\nThe model was trained on a Tesla P100 GPU and 25GB of RAM.\nThe script for fine tuning can be found here\n\n\nResults:\n--------",
"### Raw metrics:\n\n\nComparison:\n-----------\n\n\nModel: SpanBert offici... | [
"TAGS\n#transformers #pytorch #jax #safetensors #bert #question-answering #en #arxiv-1907.10529 #endpoints_compatible #has_space #region-us \n",
"# samples: 130k\nDataset: SQuAD2.0, Split: eval, # samples: 12.3k\n\n\nModel training\n--------------\n\n\nThe model was trained on a Tesla P100 GPU and 25GB of RAM.\nT... |
null | transformers |
# SpanBERT large fine-tuned on SQuAD v1
[SpanBERT](https://github.com/facebookresearch/SpanBERT) created by [Facebook Research](https://github.com/facebookresearch) and fine-tuned on [SQuAD 1.1](https://rajpurkar.github.io/SQuAD-explorer/explore/1.1/dev/) for **Q&A** downstream task ([by them](https://github.com/face... | {"language": "en"} | mrm8488/spanbert-large-finetuned-squadv1 | null | [
"transformers",
"pytorch",
"jax",
"bert",
"en",
"arxiv:1907.10529",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1907.10529"
] | [
"en"
] | TAGS
#transformers #pytorch #jax #bert #en #arxiv-1907.10529 #endpoints_compatible #region-us
| SpanBERT large fine-tuned on SQuAD v1
=====================================
SpanBERT created by Facebook Research and fine-tuned on SQuAD 1.1 for Q&A downstream task (by them).
Details of SpanBERT
-------------------
SpanBERT: Improving Pre-training by Representing and Predicting Spans
Details of the downstream... | [] | [
"TAGS\n#transformers #pytorch #jax #bert #en #arxiv-1907.10529 #endpoints_compatible #region-us \n"
] |
null | transformers |
# SpanBERT large fine-tuned on SQuAD v2
[SpanBERT](https://github.com/facebookresearch/SpanBERT) created by [Facebook Research](https://github.com/facebookresearch) and fine-tuned on [SQuAD 2.0](https://rajpurkar.github.io/SQuAD-explorer/) for **Q&A** downstream task ([by them](https://github.com/facebookresearch/Spa... | {"language": "en"} | mrm8488/spanbert-large-finetuned-squadv2 | null | [
"transformers",
"pytorch",
"jax",
"bert",
"en",
"arxiv:1907.10529",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1907.10529"
] | [
"en"
] | TAGS
#transformers #pytorch #jax #bert #en #arxiv-1907.10529 #endpoints_compatible #region-us
| SpanBERT large fine-tuned on SQuAD v2
=====================================
SpanBERT created by Facebook Research and fine-tuned on SQuAD 2.0 for Q&A downstream task (by them).
Details of SpanBERT
-------------------
SpanBERT: Improving Pre-training by Representing and Predicting Spans
Details of the downstream... | [
"# samples: 130k\nDataset: SQuAD2.0, Split: eval, # samples: 12.3k\n\n\nModel fine-tuning ️\n--------------------\n\n\nYou can get the fine-tuning script here\n\n\nResults Comparison\n------------------\n\n\n\nNote: The numbers marked as \\* are evaluated on the development sets because those models were not submi... | [
"TAGS\n#transformers #pytorch #jax #bert #en #arxiv-1907.10529 #endpoints_compatible #region-us \n",
"# samples: 130k\nDataset: SQuAD2.0, Split: eval, # samples: 12.3k\n\n\nModel fine-tuning ️\n--------------------\n\n\nYou can get the fine-tuning script here\n\n\nResults Comparison\n------------------\n\n\n\nNo... |
feature-extraction | transformers |
# SpanBERT large fine-tuned on TACRED
[SpanBERT](https://github.com/facebookresearch/SpanBERT) created by [Facebook Research](https://github.com/facebookresearch) and fine-tuned on [TACRED](https://nlp.stanford.edu/projects/tacred/) dataset by [them](https://github.com/facebookresearch/SpanBERT#finetuned-models-squad... | {"language": "en"} | mrm8488/spanbert-large-finetuned-tacred | null | [
"transformers",
"pytorch",
"jax",
"safetensors",
"bert",
"feature-extraction",
"en",
"arxiv:1907.10529",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1907.10529"
] | [
"en"
] | TAGS
#transformers #pytorch #jax #safetensors #bert #feature-extraction #en #arxiv-1907.10529 #endpoints_compatible #region-us
| SpanBERT large fine-tuned on TACRED
===================================
SpanBERT created by Facebook Research and fine-tuned on TACRED dataset by them
Details of SpanBERT
-------------------
SpanBERT: Improving Pre-training by Representing and Predicting Spans
Dataset
-------
TACRED A large-scale relation ext... | [] | [
"TAGS\n#transformers #pytorch #jax #safetensors #bert #feature-extraction #en #arxiv-1907.10529 #endpoints_compatible #region-us \n"
] |
text-generation | transformers | # Spanish GPT-2 trained on [large_spanish_corpus](https://huggingface.co/datasets/viewer/?dataset=large_spanish_corpus)
This is a Spanish GPT-2 model trained from scratch on the [large_spanish_corpus](https://huggingface.co/datasets/viewer/?dataset=large_spanish_corpus) aka BETO's corpus with [Flax](https://github.com... | {"language": "es", "license": "mit", "tags": ["GPT-2"], "datasets": ["large_spanish_corpus"], "widgets": [{"text": "\u00c9rase un vez un"}]} | mrm8488/spanish-gpt2 | null | [
"transformers",
"pytorch",
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"text-generation",
"GPT-2",
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"dataset:large_spanish_corpus",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"es"
] | TAGS
#transformers #pytorch #jax #tensorboard #safetensors #gpt2 #text-generation #GPT-2 #es #dataset-large_spanish_corpus #license-mit #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| # Spanish GPT-2 trained on large_spanish_corpus
This is a Spanish GPT-2 model trained from scratch on the large_spanish_corpus aka BETO's corpus with Flax
This is part of the
Flax/Jax Community Week, organised by HuggingFace and TPU usage sponsored by Google.
## Dataset
The dataset is about 20 GB. 95% of the data was ... | [
"# Spanish GPT-2 trained on large_spanish_corpus\n\nThis is a Spanish GPT-2 model trained from scratch on the large_spanish_corpus aka BETO's corpus with Flax\nThis is part of the\nFlax/Jax Community Week, organised by HuggingFace and TPU usage sponsored by Google.",
"## Dataset\nThe dataset is about 20 GB. 95% o... | [
"TAGS\n#transformers #pytorch #jax #tensorboard #safetensors #gpt2 #text-generation #GPT-2 #es #dataset-large_spanish_corpus #license-mit #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"# Spanish GPT-2 trained on large_spanish_corpus\n\nThis is a Spanish GPT-2 mo... |
text2text-generation | transformers |
# Spanish T5 (small) fine-tuned on **SQAC** for Spanish **QA** 📖❓
[spanish-T5-small](https://huggingface.co/flax-community/spanish-t5-small) fine-tuned on [SQAC](https://huggingface.co/datasets/BSC-TeMU/SQAC) for **Q&A** downstream task.
## Details of Spanish T5 (small)
T5 (small) like arch trained from scatch on ... | {"language": "es", "tags": ["QA", "Q&A"], "datasets": ["BSC-TeMU/SQAC"], "widget": [{"text": "question: \u00bfCu\u00e1l es el nombre que se le da a la unidad morfol\u00f3gica y funcional de los seres vivos? context: La c\u00e9lula (del lat\u00edn cellula, diminutivo de cella, \u2018celda\u2019) es la unidad morfol\u00f... | mrm8488/spanish-t5-small-sqac-for-qa | null | [
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"tensorboard",
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"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"es"
] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #QA #Q&A #es #dataset-BSC-TeMU/SQAC #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| Spanish T5 (small) fine-tuned on SQAC for Spanish QA
====================================================
spanish-T5-small fine-tuned on SQAC for Q&A downstream task.
Details of Spanish T5 (small)
-----------------------------
T5 (small) like arch trained from scatch on large\_spanish\_corpus for HuggingFace/Flax... | [] | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #QA #Q&A #es #dataset-BSC-TeMU/SQAC #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
question-answering | transformers |
# SqueezeBERT + SQuAD (v1.1)
[squeezebert-uncased](https://huggingface.co/squeezebert/squeezebert-uncased) fine-tuned on [SQUAD v1.1](https://rajpurkar.github.io/SQuAD-explorer/explore/1.1/dev/) for **Q&A** downstream task.
## Details of SqueezeBERT
This model, `squeezebert-uncased`, is a pretrained model for the E... | {"language": "en", "datasets": ["squad"]} | mrm8488/squeezebert-finetuned-squadv1 | null | [
"transformers",
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"squeezebert",
"question-answering",
"en",
"dataset:squad",
"arxiv:2006.11316",
"arxiv:2004.02984",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2006.11316",
"2004.02984"
] | [
"en"
] | TAGS
#transformers #pytorch #squeezebert #question-answering #en #dataset-squad #arxiv-2006.11316 #arxiv-2004.02984 #endpoints_compatible #region-us
| SqueezeBERT + SQuAD (v1.1)
==========================
squeezebert-uncased fine-tuned on SQUAD v1.1 for Q&A downstream task.
Details of SqueezeBERT
----------------------
This model, 'squeezebert-uncased', is a pretrained model for the English language using a masked language modeling (MLM) and Sentence Order Pred... | [
"### Model in action\n\n\nFast usage with pipelines:\n\n\n\n> \n> Created by Manuel Romero/@mrm8488 | LinkedIn\n> \n> \n> \n\n\n\n> \n> Made with ♥ in Spain\n> \n> \n>"
] | [
"TAGS\n#transformers #pytorch #squeezebert #question-answering #en #dataset-squad #arxiv-2006.11316 #arxiv-2004.02984 #endpoints_compatible #region-us \n",
"### Model in action\n\n\nFast usage with pipelines:\n\n\n\n> \n> Created by Manuel Romero/@mrm8488 | LinkedIn\n> \n> \n> \n\n\n\n> \n> Made with ♥ in Spain\n... |
question-answering | transformers |
# SqueezeBERT + SQuAD v2
[squeezebert-uncased](https://huggingface.co/squeezebert/squeezebert-uncased) fine-tuned on [SQUAD v2](https://rajpurkar.github.io/SQuAD-explorer/explore/v2.0/dev/) for **Q&A** downstream task.
## Details of SqueezeBERT
This model, `squeezebert-uncased`, is a pretrained model for the Englis... | {"language": "en", "datasets": ["squad_v2"]} | mrm8488/squeezebert-finetuned-squadv2 | null | [
"transformers",
"pytorch",
"squeezebert",
"question-answering",
"en",
"dataset:squad_v2",
"arxiv:2006.11316",
"arxiv:2004.02984",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2006.11316",
"2004.02984"
] | [
"en"
] | TAGS
#transformers #pytorch #squeezebert #question-answering #en #dataset-squad_v2 #arxiv-2006.11316 #arxiv-2004.02984 #endpoints_compatible #region-us
| SqueezeBERT + SQuAD v2
======================
squeezebert-uncased fine-tuned on SQUAD v2 for Q&A downstream task.
Details of SqueezeBERT
----------------------
This model, 'squeezebert-uncased', is a pretrained model for the English language using a masked language modeling (MLM) and Sentence Order Prediction (SO... | [
"### Model in action\n\n\nFast usage with pipelines:\n\n\n\n> \n> Created by Manuel Romero/@mrm8488 | LinkedIn\n> \n> \n> \n\n\n\n> \n> Made with ♥ in Spain\n> \n> \n>"
] | [
"TAGS\n#transformers #pytorch #squeezebert #question-answering #en #dataset-squad_v2 #arxiv-2006.11316 #arxiv-2004.02984 #endpoints_compatible #region-us \n",
"### Model in action\n\n\nFast usage with pipelines:\n\n\n\n> \n> Created by Manuel Romero/@mrm8488 | LinkedIn\n> \n> \n> \n\n\n\n> \n> Made with ♥ in Spai... |
text2text-generation | transformers |
# T5-base fine-tuned on break_data / QDMR-high-level 📋➡️❓
[Google's T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) fine-tuned on [break_data](https://huggingface.co/nlp/viewer/?dataset=break_data&config=QDMR-high-level) dataset for **Question Retrieval from its decomposition**.
The i... | {"language": "en", "datasets": ["break_data"], "widget": [{"text": "translate QDMRs to Natural Language return the city that was the birthplace of Bernard Berrian ;return the city that was the home of Pablo Picasso ;return the city of both #1 and #2"}]} | mrm8488/t5-base-finetuned-break_data-question-retrieval | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"en",
"dataset:break_data",
"arxiv:1910.10683",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1910.10683"
] | [
"en"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #en #dataset-break_data #arxiv-1910.10683 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| T5-base fine-tuned on break\_data / QDMR-high-level ️
=====================================================
Google's T5 fine-tuned on break\_data dataset for Question Retrieval from its decomposition.
The inverse process of this model.
Details of T5 ️
---------------
The T5 model was presented in Exploring the Li... | [
"# samples: 17503\nDataset: break\\_data, Split: valid, # samples: 3130\n\n\nCheck out more about this dataset and others in NLP Viewer\n\n\nModel fine-tuning ️\n--------------------\n\n\nThe training script is a slightly modified version of this awesome one by Suraj Patil. The main change is at preprocessing and ... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #en #dataset-break_data #arxiv-1910.10683 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# samples: 17503\nDataset: break\\_data, Split: valid, # samples: 3130\n\n\nCheck out more about this dataset and others in NLP ... |
text2text-generation | transformers |
# T5-base fine-tuned on break_data / QDMR-high-level ❓➡️📋
[Google's T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) fine-tuned on [break_data](https://huggingface.co/nlp/viewer/?dataset=break_data&config=QDMR-high-level) dataset for **QDMRs**.
## Details of T5 📜 ➡️ 📜
The **T5** mo... | {"language": "en", "datasets": ["break_data"], "widget": [{"text": "paraphrase: The composer of Sands Theme plays what type of guitar?"}]} | mrm8488/t5-base-finetuned-break_data | null | [
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"region:us"
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| T5-base fine-tuned on break\_data / QDMR-high-level ️
=====================================================
Google's T5 fine-tuned on break\_data dataset for QDMRs.
Details of T5 ️
---------------
The T5 model was presented in Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer by *C... | [
"# samples: 17503\nDataset: break\\_data, Split: valid, # samples: 3130\n\n\nCheck out more about this dataset and others in NLP Viewer\n\n\nModel fine-tuning ️\n--------------------\n\n\nThe training script is a slightly modified version of this awesome one by Suraj Patil. The main change is at preprocessing and ... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #en #dataset-break_data #arxiv-1910.10683 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# samples: 17503\nDataset: break\\_data, Split: valid, # samples: 3130\n\n\nCheck out more about this dataset and others in NLP ... |
text2text-generation | transformers |
# T5-base fine-tuned on CommonGen
[Google's T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) fine-tuned on [CommonGen](https://inklab.usc.edu/CommonGen/index.html) for **Generative Commonsense Reasoning**.
## Details of T5
The **T5** model was presented in [Exploring the Limits of Tra... | {"language": "en", "tags": ["common sense"], "datasets": ["common_gen"], "widget": [{"text": "tree plant ground hole dig"}]} | mrm8488/t5-base-finetuned-common_gen | null | [
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"has_space",
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"region:us"
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| T5-base fine-tuned on CommonGen
===============================
Google's T5 fine-tuned on CommonGen for Generative Commonsense Reasoning.
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... | [
"# samples: 67389\nDataset: common\\_gen, Split: valid, # samples: 4018\nDataset: common\\_gen, Split: test, # samples: 1497\n\n\nModel fine-tuning ️\n--------------------\n\n\nThe training script is a slightly modified version of this awesome one by Suraj Patil\n\n\nMetrics\n-------\n\n\n\nThe metrics above sligh... | [
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"# samples: 67389\nDataset: common\\_gen, Split: valid, # samples: 4018\n... |
text2text-generation | transformers |
# T5-base fine-tuned on event2Mind for **Intent Prediction** 🤔
[Google's T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) fine-tuned on [event2Mind](https://huggingface.co/nlp/viewer/?dataset=event2Mind) dataset for **Intent Prediction**.
## Details of T5 📜 ➡️ 📜
The **T5** model wa... | {"language": "en", "tags": ["intent"], "datasets": ["event2Mind"], "widget": [{"text": "PersonX takes PersonY home"}]} | mrm8488/t5-base-finetuned-e2m-intent | null | [
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"has_space",
"text-generation-inference",
"region:us"
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"1910.10683"
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"en"
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#transformers #pytorch #t5 #text2text-generation #intent #en #dataset-event2Mind #arxiv-1910.10683 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| T5-base fine-tuned on event2Mind for Intent Prediction
======================================================
Google's T5 fine-tuned on event2Mind dataset for Intent Prediction.
Details of T5 ️
---------------
The T5 model was presented in Exploring the Limits of Transfer Learning with a Unified Text-to-Text Tran... | [
"# samples: 46472\nDataset: event2Mind, Split: valid, # samples: 1960\n\n\nEvents without intent were not used!\n\n\nCheck out more about this dataset and others in NLP Viewer\n\n\nModel fine-tuning ️\n--------------------\n\n\nThe training script is a slightly modified version of this awesome one by Suraj Patil.\... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #intent #en #dataset-event2Mind #arxiv-1910.10683 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"# samples: 46472\nDataset: event2Mind, Split: valid, # samples: 1960\n\n\nEvents without intent were not used... |
text2text-generation | transformers |
# T5-base fine-tuned for Emotion Recognition 😂😢😡😃😯
[Google's T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) base fine-tuned on [emotion recognition](https://github.com/dair-ai/emotion_dataset) dataset for **Emotion Recognition** downstream task.
## Details of T5
The **T5** mod... | {"language": "en", "datasets": ["emotion"], "widget": [{"text": "I wish you were here but it is impossible"}]} | mrm8488/t5-base-finetuned-emotion | null | [
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"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1910.10683"
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"en"
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#transformers #pytorch #jax #t5 #text2text-generation #en #dataset-emotion #arxiv-1910.10683 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| T5-base fine-tuned for Emotion Recognition
==========================================
Google's T5 base fine-tuned on emotion recognition dataset for Emotion Recognition downstream task.
Details of T5
-------------
The T5 model was presented in Exploring the Limits of Transfer Learning with a Unified Text-to-Text ... | [] | [
"TAGS\n#transformers #pytorch #jax #t5 #text2text-generation #en #dataset-emotion #arxiv-1910.10683 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n"
] |
text2text-generation | transformers |
# T5-base fine-tuned for Sentiment Anlalysis 🎞️👍👎
[Google's T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) base fine-tuned on [IMDB](https://huggingface.co/datasets/imdb) dataset for **Sentiment Analysis** downstream task.
## Details of T5
The **T5** model was presented in [Expl... | {"language": "en", "datasets": ["imdb"]} | mrm8488/t5-base-finetuned-imdb-sentiment | null | [
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"pytorch",
"safetensors",
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"dataset:imdb",
"arxiv:1910.10683",
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"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1910.10683"
] | [
"en"
] | TAGS
#transformers #pytorch #safetensors #t5 #text2text-generation #en #dataset-imdb #arxiv-1910.10683 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
# T5-base fine-tuned for Sentiment Anlalysis ️
Google's T5 base fine-tuned on IMDB dataset for Sentiment Analysis downstream task.
## 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... | [
"# T5-base fine-tuned for Sentiment Anlalysis ️\n\n\nGoogle's T5 base fine-tuned on IMDB dataset for Sentiment Analysis downstream task.",
"## Details of T5\n\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 Rober... | [
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"# T5-base fine-tuned for Sentiment Anlalysis ️\n\n\nGoogle's T5 base fine-tuned on IMDB dataset for Sentiment ... |
text2text-generation | transformers |
# T5-base fine-tuned on QASC
[Google's T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) fine-tuned on [QASC](https://allenai.org/data/qasc) for **QA** (via *sentence composition*) downstream task.
## Details of T5
The **T5** model was presented in [Exploring the Limits of Transfer Le... | {"language": "en", "datasets": ["qasc"]} | mrm8488/t5-base-finetuned-qasc | null | [
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"en",
"dataset:qasc",
"arxiv:1910.10683",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1910.10683"
] | [
"en"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #en #dataset-qasc #arxiv-1910.10683 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| T5-base fine-tuned on QASC
==========================
Google's T5 fine-tuned on QASC for QA (via *sentence composition*) downstream task.
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... | [] | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #en #dataset-qasc #arxiv-1910.10683 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text2text-generation | transformers |
# T5-base fine-tuned on QuaRel
[Google's T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) fine-tuned on [QuaRel](https://allenai.org/data/quarel) for **QA** downstream task.
## Details of T5
The **T5** model was presented in [Exploring the Limits of Transfer Learning with a Unified T... | {"language": "en", "datasets": ["quarel"]} | mrm8488/t5-base-finetuned-quarel | null | [
"transformers",
"pytorch",
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"text2text-generation",
"en",
"dataset:quarel",
"arxiv:1910.10683",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1910.10683"
] | [
"en"
] | TAGS
#transformers #pytorch #jax #t5 #text2text-generation #en #dataset-quarel #arxiv-1910.10683 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| T5-base fine-tuned on QuaRel
============================
Google's T5 fine-tuned on QuaRel for QA downstream task.
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... | [] | [
"TAGS\n#transformers #pytorch #jax #t5 #text2text-generation #en #dataset-quarel #arxiv-1910.10683 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
question-answering | transformers |
# T5-base fine-tuned on QuaRTz
[Google's T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) fine-tuned on [QuaRTz](https://allenai.org/data/quartz) for **QA** downstream task.
## Details of T5
The **T5** model was presented in [Exploring the Limits of Transfer Learning with a Unified ... | {"language": "en", "datasets": ["quartz"], "pipeline_tag": "question-answering"} | mrm8488/t5-base-finetuned-quartz | null | [
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"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1910.10683"
] | [
"en"
] | TAGS
#transformers #pytorch #safetensors #t5 #text2text-generation #question-answering #en #dataset-quartz #arxiv-1910.10683 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| T5-base fine-tuned on QuaRTz
============================
Google's T5 fine-tuned on QuaRTz for QA downstream task.
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... | [] | [
"TAGS\n#transformers #pytorch #safetensors #t5 #text2text-generation #question-answering #en #dataset-quartz #arxiv-1910.10683 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n"
] |
text2text-generation | transformers |
# T5-base fine-tuned on SQuAD for **Question Generation**
[Google's T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) fine-tuned on [SQuAD v1.1](https://rajpurkar.github.io/SQuAD-explorer/) for **Question Generation** by just prepending the *answer* to the *context*.
## Details of T5
T... | {"language": "en", "license": "apache-2.0", "datasets": ["squad"], "widget": [{"text": "answer: Manuel context: Manuel has created RuPERTa-base with the support of HF-Transformers and Google"}]} | mrm8488/t5-base-finetuned-question-generation-ap | null | [
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"region:us"
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"1910.10683"
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"en"
] | TAGS
#transformers #pytorch #tf #safetensors #t5 #text2text-generation #en #dataset-squad #arxiv-1910.10683 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| T5-base fine-tuned on SQuAD for Question Generation
===================================================
Google's T5 fine-tuned on SQuAD v1.1 for Question Generation by just prepending the *answer* to the *context*.
Details of T5
-------------
The T5 model was presented in Exploring the Limits of Transfer Learning... | [
"# samples: 87599\nDataset: squad, Split: valid, # samples: 10570\n\n\nHow to load it from nlp\n\n\nCheck out more about this dataset and others in NLP Viewer\n\n\nModel fine-tuning ️\n--------------------\n\n\nThe training script is a slightly modified version of this awesome one by Suraj Patil\n\n\nHe also made ... | [
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"# samples: 87599\nDataset: squad, Split: valid, # samples: 10570\n\n\nHow to load it ... |
text2text-generation | transformers |
# T5-base fine-tuned for Sarcasm Detection 🙄
[Google's T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) base fine-tuned on [ Twitter Sarcasm Dataset](https://github.com/EducationalTestingService/sarcasm) for **Sequence classification (as text generation)** downstream task.
## Details o... | {"language": "en", "widget": [{"text": "As everybody knows Trump is by far the best USA president... XD"}]} | mrm8488/t5-base-finetuned-sarcasm-twitter | null | [
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"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1910.10683"
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"en"
] | TAGS
#transformers #pytorch #safetensors #t5 #text2text-generation #en #arxiv-1910.10683 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| T5-base fine-tuned for Sarcasm Detection
========================================
Google's T5 base fine-tuned on Twitter Sarcasm Dataset for Sequence classification (as text generation) downstream task.
Details of T5
-------------
The T5 model was presented in Exploring the Limits of Transfer Learning with a Unif... | [] | [
"TAGS\n#transformers #pytorch #safetensors #t5 #text2text-generation #en #arxiv-1910.10683 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n"
] |
text2text-generation | transformers |
# T5-base fine-tuned for Sentiment Span Extraction
All credits to [Lorenzo Ampil](https://twitter.com/AND__SO)
[Google's T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) base fine-tuned on [Tweet Sentiment Extraction Dataset](https://www.kaggle.com/c/tweet-sentiment-extraction) for **S... | {"language": "en", "tags": ["sentiment", "extracion", "passage"], "widget": [{"text": "question: positive context: On the monday, so i wont be able to be with you! i love you"}]} | mrm8488/t5-base-finetuned-span-sentiment-extraction | null | [
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"region:us"
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| T5-base fine-tuned for Sentiment Span Extraction
================================================
All credits to Lorenzo Ampil
Google's T5 base fine-tuned on Tweet Sentiment Extraction Dataset for Span Sentiment Extraction downstream task.
Details of T5
-------------
The T5 model was presented in Exploring the ... | [
"# samples: 23907\nDataset: TSE, Split: eval, # samples: 3573\n\n\nModel fine-tuning ️\n--------------------\n\n\nThe training script is a slightly modified version of this Colab Notebook created by Lorenzo Ampil, so all credits to him!\n\n\nModel in Action\n---------------\n\n\n\n> \n> Created by Manuel Romero/@m... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #sentiment #extracion #passage #en #arxiv-1910.10683 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# samples: 23907\nDataset: TSE, Split: eval, # samples: 3573\n\n\nModel fine-tuning ️\n--------------------\n\n\nThe... |
text2text-generation | transformers |
# T5-base fine-tuned on SQuAD v2
[Google's T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) fine-tuned on [SQuAD v2](https://rajpurkar.github.io/SQuAD-explorer/) for **Q&A** downstream task.
## Details of T5
The **T5** model was presented in [Exploring the Limits of Transfer Learning ... | {"language": "en", "datasets": ["squad_v2"]} | mrm8488/t5-base-finetuned-squadv2 | null | [
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"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1910.10683"
] | [
"en"
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#transformers #pytorch #safetensors #t5 #text2text-generation #en #dataset-squad_v2 #arxiv-1910.10683 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| T5-base fine-tuned on SQuAD v2
==============================
Google's T5 fine-tuned on SQuAD v2 for Q&A downstream task.
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, Kather... | [
"# samples: 130319\nDataset: squad\\_v2, Split: valid, # samples: 11873\n\n\nHow to load it from nlp\n\n\nCheck out more about this dataset and others in NLP Viewer\n\n\nModel fine-tuning ️\n--------------------\n\n\nThe training script is a slightly modified version of this one\n\n\nResults\n-------\n\n\n\nModel ... | [
"TAGS\n#transformers #pytorch #safetensors #t5 #text2text-generation #en #dataset-squad_v2 #arxiv-1910.10683 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"# samples: 130319\nDataset: squad\\_v2, Split: valid, # samples: 11873\n\n\nHow to load it from nlp\n\n\nC... |
text2text-generation | transformers |
# T5-base fine-tuned fo News Summarization 📖✏️🧾
All credits to [Abhishek Kumar Mishra](https://github.com/abhimishra91)
[Google's T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) base fine-tuned on [News Summary](https://www.kaggle.com/sunnysai12345/news-summary) dataset for **summar... | {"language": "en", "tags": ["news", "summary"]} | mrm8488/t5-base-finetuned-summarize-news | null | [
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"jax",
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"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1910.10683"
] | [
"en"
] | TAGS
#transformers #pytorch #jax #safetensors #t5 #text2text-generation #news #summary #en #arxiv-1910.10683 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
# T5-base fine-tuned fo News Summarization ️
All credits to Abhishek Kumar Mishra
Google's T5 base fine-tuned on News Summary dataset for summarization downstream task.
## 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,... | [
"# T5-base fine-tuned fo News Summarization ️\n\nAll credits to Abhishek Kumar Mishra\n\nGoogle's T5 base fine-tuned on News Summary dataset for summarization downstream task.",
"## Details of T5\n\nThe T5 model was presented in Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer by ... | [
"TAGS\n#transformers #pytorch #jax #safetensors #t5 #text2text-generation #news #summary #en #arxiv-1910.10683 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"# T5-base fine-tuned fo News Summarization ️\n\nAll credits to Abhishek Kumar Mishra\n\nGoogle's T5 base... |
text2text-generation | transformers |
# T5-base fine-tuned on WikiSQL for SQL to English translation
[Google's T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) fine-tuned on [WikiSQL](https://github.com/salesforce/WikiSQL) for **SQL** to **English** **translation** task.
## Details of T5
The **T5** model was presented in ... | {"language": "en", "datasets": ["wikisql"]} | mrm8488/t5-base-finetuned-wikiSQL-sql-to-en | null | [
"transformers",
"pytorch",
"t5",
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"en",
"dataset:wikisql",
"arxiv:1910.10683",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1910.10683"
] | [
"en"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #en #dataset-wikisql #arxiv-1910.10683 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| T5-base fine-tuned on WikiSQL for SQL to English translation
============================================================
Google's T5 fine-tuned on WikiSQL for SQL to English translation task.
Details of T5
-------------
The T5 model was presented in Exploring the Limits of Transfer Learning with a Unified Text-t... | [
"# samples: 56355\nDataset: wikisql, Split: valid, # samples: 14436\n\n\nHow to load it from nlp\n\n\nCheck out more about this dataset and others in NLP Viewer\n\n\nModel fine-tuning ️\n--------------------\n\n\nThe training script is a slightly modified version of this Colab Notebook created by Suraj Patil, so a... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #en #dataset-wikisql #arxiv-1910.10683 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# samples: 56355\nDataset: wikisql, Split: valid, # samples: 14436\n\n\nHow to load it from nlp\n\n\nCheck out more about this data... |
text2text-generation | transformers |
# T5-base fine-tuned on WikiSQL
[Google's T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) fine-tuned on [WikiSQL](https://github.com/salesforce/WikiSQL) for **English** to **SQL** **translation**.
## Details of T5
The **T5** model was presented in [Exploring the Limits of Transfer L... | {"language": "en", "license": "apache-2.0", "datasets": ["wikisql"], "widget": [{"text": "translate English to SQL: How many models were finetuned using BERT as base model?"}]} | mrm8488/t5-base-finetuned-wikiSQL | null | [
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"en",
"dataset:wikisql",
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"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1910.10683"
] | [
"en"
] | TAGS
#transformers #pytorch #jax #t5 #text2text-generation #en #dataset-wikisql #arxiv-1910.10683 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| T5-base fine-tuned on WikiSQL
=============================
Google's T5 fine-tuned on WikiSQL for English to SQL translation.
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, Ka... | [
"# samples: 56355\nDataset: wikisql, Split: valid, # samples: 14436\n\n\nHow to load it from nlp\n\n\nCheck out more about this dataset and others in NLP Viewer\n\n\nModel fine-tuning ️\n--------------------\n\n\nThe training script is a slightly modified version of this Colab Notebook created by Suraj Patil, so a... | [
"TAGS\n#transformers #pytorch #jax #t5 #text2text-generation #en #dataset-wikisql #arxiv-1910.10683 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"# samples: 56355\nDataset: wikisql, Split: valid, # samples: 14436\n\n\nHow to load it from nlp... |
text2text-generation | transformers |
# T5-small fine-tuned for Emotion Recognition 😂😢😡😃😯
[Google's T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) [small](https://huggingface.co/t5-small) fine-tuned on [emotion recognition](https://github.com/dair-ai/emotion_dataset) dataset for **Emotion Recognition** downstream ta... | {"language": "en", "datasets": ["emotion"]} | mrm8488/t5-small-finetuned-emotion | null | [
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"en",
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"arxiv:1910.10683",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1910.10683"
] | [
"en"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #en #dataset-emotion #arxiv-1910.10683 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| T5-small fine-tuned for Emotion Recognition
===========================================
Google's T5 small fine-tuned on emotion recognition dataset for Emotion Recognition downstream task.
Details of T5
-------------
The T5 model was presented in Exploring the Limits of Transfer Learning with a Unified Text-to-Te... | [] | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #en #dataset-emotion #arxiv-1910.10683 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text2text-generation | transformers |
# T5-small fine-tuned for Sentiment Anlalysis 🎞️👍👎
[Google's T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) [small](https://huggingface.co/t5-small) fine-tuned on [IMDB](https://huggingface.co/datasets/imdb) dataset for **Sentiment Analysis** downstream task.
## Details of T5
Th... | {"language": "en", "datasets": ["imdb"]} | mrm8488/t5-small-finetuned-imdb-sentiment | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"en",
"dataset:imdb",
"arxiv:1910.10683",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1910.10683"
] | [
"en"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #en #dataset-imdb #arxiv-1910.10683 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| T5-small fine-tuned for Sentiment Anlalysis ️
=============================================
Google's T5 small fine-tuned on IMDB dataset for Sentiment Analysis downstream task.
Details of T5
-------------
The T5 model was presented in Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transform... | [] | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #en #dataset-imdb #arxiv-1910.10683 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text2text-generation | transformers |
# T5-base fine-tuned on Quora question pair dataset for Question Paraphrasing ❓↔️❓
[Google's T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) fine-tuned on [Quodra question pair](https://huggingface.co/nlp/viewer/?dataset=quora) dataset for **Question Paraphrasing** task.
## Details of... | {"language": "en", "datasets": ["quora"]} | mrm8488/t5-small-finetuned-quora-for-paraphrasing | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
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"dataset:quora",
"arxiv:1910.10683",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1910.10683"
] | [
"en"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #en #dataset-quora #arxiv-1910.10683 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| T5-base fine-tuned on Quora question pair dataset for Question Paraphrasing ↔️
==============================================================================
Google's T5 fine-tuned on Quodra question pair dataset for Question Paraphrasing task.
Details of T5
-------------
The T5 model was presented in Exploring t... | [
"# samples: 404290\nDataset: quora after filter repeated questions, Split: train, # samples: 149263\n\n\nCheck out more about this dataset and others in NLP Viewer\n\n\nModel fine-tuning ️\n--------------------\n\n\nThe training script is a slightly modified version of this one\n\n\nModel in Action\n--------------... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #en #dataset-quora #arxiv-1910.10683 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"# samples: 404290\nDataset: quora after filter repeated questions, Split: train, # samples: 149263\n\n\nCheck out more abo... |
text2text-generation | transformers |
# T5-small fine-tuned on SQuAD
[Google's T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) [(small)](https://huggingface.co/t5-small) fine-tuned on [SQuAD v1.1](https://rajpurkar.github.io/SQuAD-explorer/) for **Q&A** downstream task.
## Details of T5
The **T5** model was presented in ... | {"language": "en", "datasets": ["squad"]} | mrm8488/t5-small-finetuned-squadv1 | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"en",
"dataset:squad",
"arxiv:1910.10683",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1910.10683"
] | [
"en"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #en #dataset-squad #arxiv-1910.10683 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| T5-small fine-tuned on SQuAD
============================
Google's T5 (small) fine-tuned on SQuAD v1.1 for Q&A downstream task.
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, ... | [
"# samples: 87599\nDataset: squad, Split: valid, # samples: 10570\n\n\nHow to load it from nlp\n\n\nCheck out more about this dataset and others in NLP Viewer\n\n\nModel fine-tuning ️\n--------------------\n\n\nThe training script is a slightly modified version of this awesome one by Suraj Patil\n\n\nResults\n----... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #en #dataset-squad #arxiv-1910.10683 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# samples: 87599\nDataset: squad, Split: valid, # samples: 10570\n\n\nHow to load it from nlp\n\n\nCheck out more about this dataset ... |
text2text-generation | transformers |
# T5-small fine-tuned on SQuAD v2
[Google's T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) [(small)](https://huggingface.co/t5-small) fine-tuned on [SQuAD v2](https://rajpurkar.github.io/SQuAD-explorer/) for **Q&A** downstream task.
## Details of T5
The **T5** model was presented in... | {"language": "en", "datasets": ["squad_v2"]} | mrm8488/t5-small-finetuned-squadv2 | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"en",
"dataset:squad_v2",
"arxiv:1910.10683",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1910.10683"
] | [
"en"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #en #dataset-squad_v2 #arxiv-1910.10683 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| T5-small fine-tuned on SQuAD v2
===============================
Google's T5 (small) fine-tuned on SQuAD v2 for Q&A downstream task.
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 Rober... | [
"# samples: 130319\nDataset: squad\\_v2, Split: valid, # samples: 11873\n\n\nHow to load it from nlp\n\n\nCheck out more about this dataset and others in NLP Viewer\n\n\nModel fine-tuning ️\n--------------------\n\n\nThe training script is a slightly modified version of this awesome one by Suraj Patil\n\n\nResults... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #en #dataset-squad_v2 #arxiv-1910.10683 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# samples: 130319\nDataset: squad\\_v2, Split: valid, # samples: 11873\n\n\nHow to load it from nlp\n\n\nCheck out more about this... |
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) fine-tuned on Text2Log
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an Text2L... | {"language": ["en"], "license": "apache-2.0", "tags": ["generated_from_trainer", "tex2log", "log2tex", "foc"], "widget": [{"text": "translate to nl: all x1.(_explanation(x1) -> -_equal(x1))"}, {"text": "translate to fol: All chains are bad."}], "model-index": [{"name": "t5-small-text2log", "results": []}]} | mrm8488/t5-small-finetuned-text2log | null | [
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"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #tensorboard #safetensors #t5 #text2text-generation #generated_from_trainer #tex2log #log2tex #foc #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| T5 (small) fine-tuned on Text2Log
=================================
This model is a fine-tuned version of t5-small on an Text2Log dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0313
Model description
-----------------
More information needed
Intended uses & limitations
-----------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 6",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #safetensors #t5 #text2text-generation #generated_from_trainer #tex2log #log2tex #foc #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used... |
text2text-generation | transformers |
# T5-small fine-tuned on WikiSQL
[Google's T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) [small](https://huggingface.co/t5-small) fine-tuned on [WikiSQL](https://github.com/salesforce/WikiSQL) for **English** to **SQL** **translation**.
## Details of T5
The **T5** model was present... | {"language": "en", "datasets": ["wikisql"]} | mrm8488/t5-small-finetuned-wikiSQL | null | [
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"pytorch",
"safetensors",
"t5",
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"dataset:wikisql",
"arxiv:1910.10683",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1910.10683"
] | [
"en"
] | TAGS
#transformers #pytorch #safetensors #t5 #text2text-generation #en #dataset-wikisql #arxiv-1910.10683 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| T5-small fine-tuned on WikiSQL
==============================
Google's T5 small fine-tuned on WikiSQL for English to SQL translation.
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 Rob... | [
"# samples: 56355\nDataset: wikisql, Split: valid, # samples: 14436\n\n\nHow to load it from nlp\n\n\nCheck out more about this dataset and others in NLP Viewer\n\n\nModel fine-tuning ️\n--------------------\n\n\nThe training script is a slightly modified version of this Colab Notebook created by Suraj Patil, so a... | [
"TAGS\n#transformers #pytorch #safetensors #t5 #text2text-generation #en #dataset-wikisql #arxiv-1910.10683 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"# samples: 56355\nDataset: wikisql, Split: valid, # samples: 14436\n\n\nHow to load it from nlp\n\n\nCheck ... |
text2text-generation | transformers | # T5 small (Spanish) fine-tuned on SQUAD (ES) for Q&A | {"language": "es", "datasets": ["squad_es"], "widget": [{"text": "pregunta: \u00bfCu\u00e1l es el mayor placer de la vida? contexto: El mayor placer de la vida es dormir"}]} | mrm8488/t5-small-spanish-finetuned-squadv1 | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"es",
"dataset:squad_es",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"es"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #es #dataset-squad_es #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # T5 small (Spanish) fine-tuned on SQUAD (ES) for Q&A | [
"# T5 small (Spanish) fine-tuned on SQUAD (ES) for Q&A"
] | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #es #dataset-squad_es #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# T5 small (Spanish) fine-tuned on SQUAD (ES) for Q&A"
] |
question-answering | transformers |
# UmBERTo Wikipedia Uncased + italian SQuAD v1 📚 🧐 ❓
[UmBERTo-Wikipedia-Uncased](https://huggingface.co/Musixmatch/umberto-wikipedia-uncased-v1) fine-tuned on [Italian SQUAD v1 dataset](https://github.com/crux82/squad-it) for **Q&A** downstream task.
## Details of the downstream task (Q&A) - Model 🧠
[UmBERTo](h... | {"language": "it"} | mrm8488/umberto-wikipedia-uncased-v1-finetuned-squadv1-it | null | [
"transformers",
"pytorch",
"camembert",
"question-answering",
"it",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"it"
] | TAGS
#transformers #pytorch #camembert #question-answering #it #endpoints_compatible #has_space #region-us
| UmBERTo Wikipedia Uncased + italian SQuAD v1
============================================
UmBERTo-Wikipedia-Uncased fine-tuned on Italian SQUAD v1 dataset for Q&A downstream task.
Details of the downstream task (Q&A) - Model
--------------------------------------------
UmBERTo is a Roberta-based Language Model tr... | [
"### Model in action\n\n\nFast usage with pipelines:\n\n\n\n> \n> Created by Manuel Romero/@mrm8488 | LinkedIn\n> Made with ♥ in Spain\n> \n> \n>"
] | [
"TAGS\n#transformers #pytorch #camembert #question-answering #it #endpoints_compatible #has_space #region-us \n",
"### Model in action\n\n\nFast usage with pipelines:\n\n\n\n> \n> Created by Manuel Romero/@mrm8488 | LinkedIn\n> Made with ♥ in Spain\n> \n> \n>"
] |
image-classification | transformers |
# Vision Transformer fine-tuned on kvasir_v2 for colonoscopy classification
## Demo
### Drag the following images to the widget to test the model
- 
- 
- ![](https://i.imgur.com/C3... | {"tags": ["image-classification", "pytorch", "medical", "colon"], "metrics": [{"accuracy": 0.93}]} | mrm8488/vit-base-patch16-224_finetuned-kvasirv2-colonoscopy | null | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"vit",
"image-classification",
"medical",
"colon",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #safetensors #vit #image-classification #medical #colon #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# Vision Transformer fine-tuned on kvasir_v2 for colonoscopy classification
## Demo
### Drag the following images to the widget to test the model
- ![](https://i.URL
- ![](https://i.URL
- ![](https://i.URL
- ![](https://i.URL
## Training
You can find the code here
## Metrics
## How to use
> Disclaimer... | [
"# Vision Transformer fine-tuned on kvasir_v2 for colonoscopy classification",
"## Demo",
"### Drag the following images to the widget to test the model\n\n- ![](https://i.URL\n\n- ![](https://i.URL\n\n- ![](https://i.URL\n\n- ![](https://i.URL",
"## Training\n\nYou can find the code here",
"## Metrics",
... | [
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"# Vision Transformer fine-tuned on kvasir_v2 for colonoscopy classification",
"## Demo",
"### Drag the following images to the widget to te... |
automatic-speech-recognition | transformers |
# Wav2Vec2-Large-XLSR-53-breton
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) in Breton using the [Common Voice](https://huggingface.co/datasets/common_voice).
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be... | {"language": "br", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice"], "model-index": [{"name": "XLSR Wav2Vec2 Breton Manuel Romero", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech Recognition"}, "dat... | mrm8488/wav2vec2-large-xlsr-53-breton | null | [
"transformers",
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"wav2vec2",
"automatic-speech-recognition",
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"xlsr-fine-tuning-week",
"br",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"br"
] | TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #br #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
# Wav2Vec2-Large-XLSR-53-breton
Fine-tuned facebook/wav2vec2-large-xlsr-53 in Breton using the Common Voice.
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 as fol... | [
"# Wav2Vec2-Large-XLSR-53-breton\nFine-tuned facebook/wav2vec2-large-xlsr-53 in Breton using the Common Voice.\nWhen using this model, make sure that your speech input is sampled at 16kHz.",
"## Usage\n\nThe model can be used directly (without a language model) as follows:",
"## Evaluation\n\nThe model can be e... | [
"TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #br #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"# Wav2Vec2-Large-XLSR-53-breton\nFine-tuned facebook/wav2vec2-large-xlsr-53 in Breton using the Common ... |
automatic-speech-recognition | transformers |
# Wav2Vec2-Large-XLSR-53-esperanto
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) in Esperanto using the [Common Voice](https://huggingface.co/datasets/common_voice).
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model ... | {"language": "eo", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice"], "model-index": [{"name": "XLSR Wav2Vec2 Esperanto Manuel Romero", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech Recognition"}, "... | mrm8488/wav2vec2-large-xlsr-53-esperanto | null | [
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"eo",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"eo"
] | TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #eo #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
# Wav2Vec2-Large-XLSR-53-esperanto
Fine-tuned facebook/wav2vec2-large-xlsr-53 in Esperanto using the Common Voice.
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 a... | [
"# Wav2Vec2-Large-XLSR-53-esperanto\nFine-tuned facebook/wav2vec2-large-xlsr-53 in Esperanto using the Common Voice.\nWhen using this model, make sure that your speech input is sampled at 16kHz.",
"## Usage\n\nThe model can be used directly (without a language model) as follows:",
"## Evaluation\n\nThe model ca... | [
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"# Wav2Vec2-Large-XLSR-53-esperanto\nFine-tuned facebook/wav2vec2-large-xlsr-53 in Esperanto using the C... |
automatic-speech-recognition | transformers |
# Wav2Vec2-Large-XLSR-53-euskera
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) in Euskera using the [Common Voice](https://huggingface.co/datasets/common_voice).
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can ... | {"language": "eu", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice"], "model-index": [{"name": "XLSR Wav2Vec2 Euskera Manuel Romero", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech Recognition"}, "da... | mrm8488/wav2vec2-large-xlsr-53-euskera | null | [
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"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"eu"
] | TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #eu #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us
|
# Wav2Vec2-Large-XLSR-53-euskera
Fine-tuned facebook/wav2vec2-large-xlsr-53 in Euskera using the Common Voice.
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 as f... | [
"# Wav2Vec2-Large-XLSR-53-euskera\nFine-tuned facebook/wav2vec2-large-xlsr-53 in Euskera using the Common Voice.\nWhen using this model, make sure that your speech input is sampled at 16kHz.",
"## Usage\n\nThe model can be used directly (without a language model) as follows:",
"## Evaluation\n\nThe model can be... | [
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"# Wav2Vec2-Large-XLSR-53-euskera\nFine-tuned facebook/wav2vec2-large-xlsr-53 in Euskera usin... |
automatic-speech-recognition | transformers |
# Wav2Vec2-Large-XLSR-53-Spanish
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) in Spanish using the [Common Voice](https://huggingface.co/datasets/common_voice).
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can ... | {"language": "es", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice"], "model-index": [{"name": "XLSR Wav2Vec2 Spanish Manuel Romero", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech Recognition"}, "da... | mrm8488/wav2vec2-large-xlsr-53-spanish | null | [
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"automatic-speech-recognition",
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"xlsr-fine-tuning-week",
"es",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"es"
] | TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #es #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
# Wav2Vec2-Large-XLSR-53-Spanish
Fine-tuned facebook/wav2vec2-large-xlsr-53 in Spanish using the Common Voice.
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 as fo... | [
"# Wav2Vec2-Large-XLSR-53-Spanish\nFine-tuned facebook/wav2vec2-large-xlsr-53 in Spanish using the Common Voice.\nWhen using this model, make sure that your speech input is sampled at 16kHz.",
"## Usage\n\nThe model can be used directly (without a language model) as follows:",
"## Evaluation\n\nThe model can be... | [
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"# Wav2Vec2-Large-XLSR-53-Spanish\nFine-tuned facebook/wav2vec2-large-xlsr-53 in Spanish using the Commo... |
automatic-speech-recognition | transformers |
# Wav2Vec2-Large-XLSR-53-ukrainian
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) in Ukrainian using the [Common Voice](https://huggingface.co/datasets/common_voice).
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The mode... | {"language": "uk", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice"], "model-index": [{"name": "XLSR Wav2Vec2 Ukrainian Manuel Romero", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech Recognition"}, ... | mrm8488/wav2vec2-large-xlsr-53-ukrainian | null | [
"transformers",
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"uk",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"uk"
] | TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #uk #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
# Wav2Vec2-Large-XLSR-53-ukrainian
Fine-tuned facebook/wav2vec2-large-xlsr-53 in Ukrainian using the Common Voice.
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 evaluate... | [
"# Wav2Vec2-Large-XLSR-53-ukrainian \nFine-tuned facebook/wav2vec2-large-xlsr-53 in Ukrainian using the Common Voice.\nWhen using this model, make sure that your speech input is sampled at 16kHz.",
"## Usage\n\nThe model can be used directly (without a language model) as follows:",
"## Evaluation\n\nThe model ... | [
"TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #uk #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"# Wav2Vec2-Large-XLSR-53-ukrainian \nFine-tuned facebook/wav2vec2-large-xlsr-53 in Ukrainian using the... |
null | null |
# Wav2Vec2 | {"tags": ["xlsr-fine-tuning-week"]} | mrm8488/wav2vec2-large-xlsr-53-ukranian | null | [
"xlsr-fine-tuning-week",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#xlsr-fine-tuning-week #region-us
|
# Wav2Vec2 | [
"# Wav2Vec2"
] | [
"TAGS\n#xlsr-fine-tuning-week #region-us \n",
"# Wav2Vec2"
] |
question-answering | transformers |
# [XLM](https://github.com/facebookresearch/XLM/) (multilingual version) fine-tuned for multilingual Q&A
Released from `Facebook` together with the paper [Cross-lingual Language Model Pretraining](https://arxiv.org/abs/1901.07291) by Guillaume Lample and Alexis Conneau and fine-tuned on [XQuAD](https://github.com/dee... | {"language": "multilingual"} | mrm8488/xlm-multi-finetuned-xquadv1 | null | [
"transformers",
"pytorch",
"xlm",
"question-answering",
"multilingual",
"arxiv:1901.07291",
"arxiv:1910.11856",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1901.07291",
"1910.11856"
] | [
"multilingual"
] | TAGS
#transformers #pytorch #xlm #question-answering #multilingual #arxiv-1901.07291 #arxiv-1910.11856 #endpoints_compatible #region-us
| XLM (multilingual version) fine-tuned for multilingual Q&A
==========================================================
Released from 'Facebook' together with the paper Cross-lingual Language Model Pretraining by Guillaume Lample and Alexis Conneau and fine-tuned on XQuAD for multilingual ('11 different languages') Q&A... | [] | [
"TAGS\n#transformers #pytorch #xlm #question-answering #multilingual #arxiv-1901.07291 #arxiv-1910.11856 #endpoints_compatible #region-us \n"
] |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-finetuned-squad
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the squa... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "bert-finetuned-squad", "results": [{"task": {"type": "question-answering", "name": "Question Answering"}, "dataset": {"name": "squad", "type": "squad", "config": "plain_text", "split": "validation"}, "metrics"... | mrp/bert-finetuned-squad | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
# bert-finetuned-squad
This model is a fine-tuned version of bert-base-cased on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
T... | [
"# bert-finetuned-squad\n\nThis model is a fine-tuned version of bert-base-cased 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",
"## Training procedure",
"#... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"# bert-finetuned-squad\n\nThis model is a fine-tuned version of bert-base-cased on the squad dataset.",
"## Model description\n\nMor... |
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. -->
# distilbert-base-uncased-finetuned-imdb
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"], "model-index": [{"name": "distilbert-base-uncased-finetuned-imdb", "results": []}]} | mrp/distilbert-base-uncased-finetuned-imdb | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"fill-mask",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #fill-mask #generated_from_trainer #dataset-imdb #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-imdb
======================================
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: 2.4718
Model description
-----------------
More information needed
Intended uses & l... | [
"### 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: 3.0\n* mixed\\_pr... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #fill-mask #generated_from_trainer #dataset-imdb #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train... |
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. -->
# marian-finetuned-kde4-en-to-fr
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-fr](https://huggingface.co/Helsink... | {"license": "apache-2.0", "tags": ["translation", "generated_from_trainer"], "datasets": ["kde4"], "metrics": ["bleu"], "model-index": [{"name": "marian-finetuned-kde4-en-to-fr", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "kde4", "type": ... | mrp/marian-finetuned-kde4-en-to-fr | null | [
"transformers",
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"translation",
"generated_from_trainer",
"dataset:kde4",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #marian #text2text-generation #translation #generated_from_trainer #dataset-kde4 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# marian-finetuned-kde4-en-to-fr
This model is a fine-tuned version of Helsinki-NLP/opus-mt-en-fr on the kde4 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9643
- Bleu: 50.2041
## Model description
More information needed
## Intended uses & limitations
More information needed
## T... | [
"# marian-finetuned-kde4-en-to-fr\n\nThis model is a fine-tuned version of Helsinki-NLP/opus-mt-en-fr on the kde4 dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.9643\n- Bleu: 50.2041",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore infor... | [
"TAGS\n#transformers #pytorch #tensorboard #marian #text2text-generation #translation #generated_from_trainer #dataset-kde4 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# marian-finetuned-kde4-en-to-fr\n\nThis model is a fine-tuned version of Helsinki-NLP/opus-mt-e... |
sentence-similarity | sentence-transformers |
# {mrp/simcse-model-distil-m-bert}
This is a [sentence-transformers](https://www.SBERT.net) by using m-Distil-BERT as the baseline model 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 -->
We us... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | mrp/simcse-model-distil-m-bert | null | [
"sentence-transformers",
"pytorch",
"distilbert",
"feature-extraction",
"sentence-similarity",
"transformers",
"arxiv:2104.08821",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2104.08821"
] | [] | TAGS
#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #arxiv-2104.08821 #endpoints_compatible #region-us
|
# {mrp/simcse-model-distil-m-bert}
This is a sentence-transformers by using m-Distil-BERT as the baseline model model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
We use SimCSE here and training the model with Thai Wikipedia he... | [
"# {mrp/simcse-model-distil-m-bert}\n\nThis is a sentence-transformers by using m-Distil-BERT as the baseline model model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.\n\n\nWe use SimCSE here and training the model with Thai Wik... | [
"TAGS\n#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #arxiv-2104.08821 #endpoints_compatible #region-us \n",
"# {mrp/simcse-model-distil-m-bert}\n\nThis is a sentence-transformers by using m-Distil-BERT as the baseline model model: It maps sentences & paragraph... |
sentence-similarity | sentence-transformers |
# {mrp/simcse-model-m-bert-thai-cased}
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 -->
We use SimCSE [here](https://arxiv.org/pdf/210... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | mrp/simcse-model-m-bert-thai-cased | null | [
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"arxiv:2104.08821",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2104.08821"
] | [] | TAGS
#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #arxiv-2104.08821 #endpoints_compatible #region-us
|
# {mrp/simcse-model-m-bert-thai-cased}
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.
We use SimCSE here by using mBERT as the baseline model and training the model with Thai Wikipedia here
... | [
"# {mrp/simcse-model-m-bert-thai-cased}\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.\n\n\nWe use SimCSE here by using mBERT as the baseline model and training the model with Thai Wikiped... | [
"TAGS\n#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #arxiv-2104.08821 #endpoints_compatible #region-us \n",
"# {mrp/simcse-model-m-bert-thai-cased}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and c... |
sentence-similarity | sentence-transformers |
# {mrp/simcse-model-roberta-base-thai}
This is a [sentence-transformers](https://www.SBERT.net) by using XLM-R as the baseline model 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 -->
We use Si... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | mrp/simcse-model-roberta-base-thai | null | [
"sentence-transformers",
"pytorch",
"xlm-roberta",
"feature-extraction",
"sentence-similarity",
"transformers",
"arxiv:2104.08821",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2104.08821"
] | [] | TAGS
#sentence-transformers #pytorch #xlm-roberta #feature-extraction #sentence-similarity #transformers #arxiv-2104.08821 #endpoints_compatible #region-us
|
# {mrp/simcse-model-roberta-base-thai}
This is a sentence-transformers by using XLM-R as the baseline model model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
We use SimCSE here and training the model with Thai Wikipedia here
... | [
"# {mrp/simcse-model-roberta-base-thai}\n\nThis is a sentence-transformers by using XLM-R as the baseline model model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.\n\n\nWe use SimCSE here and training the model with Thai Wikiped... | [
"TAGS\n#sentence-transformers #pytorch #xlm-roberta #feature-extraction #sentence-similarity #transformers #arxiv-2104.08821 #endpoints_compatible #region-us \n",
"# {mrp/simcse-model-roberta-base-thai}\n\nThis is a sentence-transformers by using XLM-R as the baseline model model: It maps sentences & paragraphs t... |
automatic-speech-recognition | transformers | # Wav2Vec2-Large-XLSR-53-Slovene
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) in Slovene using the [Common Voice](https://huggingface.co/datasets/common_voice)
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be ... | {"language": "sl", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice"], "model-index": [{"name": "XLSR Wav2Vec2 Slovene", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech Recognition"}, "dataset": {"name... | mrshu/wav2vec2-large-xlsr-slovene | null | [
"transformers",
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"sl",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"sl"
] | TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #sl #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us
| # Wav2Vec2-Large-XLSR-53-Slovene
Fine-tuned facebook/wav2vec2-large-xlsr-53 in Slovene using the Common Voice
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 as follo... | [
"# Wav2Vec2-Large-XLSR-53-Slovene\nFine-tuned facebook/wav2vec2-large-xlsr-53 in Slovene using the Common Voice\nWhen using this model, make sure that your speech input is sampled at 16kHz.",
"## Usage\nThe model can be used directly (without a language model) as follows:",
"## Evaluation\nThe model can be eval... | [
"TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #sl #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"# Wav2Vec2-Large-XLSR-53-Slovene\nFine-tuned facebook/wav2vec2-large-xlsr-53 in Slovene using the Commo... |
text-classification | transformers | <i>Question vs Statement classifier</i> trained on more than 7k samples which were coming from spoken data in an interview setting
<b>Code for using in Transformers:</b>
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("mrsinghania/asr-question-de... | {} | mrsinghania/asr-question-detection | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us
| <i>Question vs Statement classifier</i> trained on more than 7k samples which were coming from spoken data in an interview setting
<b>Code for using in Transformers:</b>
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("mrsinghania/asr-question-de... | [] | [
"TAGS\n#transformers #pytorch #bert #text-classification #autotrain_compatible #endpoints_compatible #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. -->
# ms29315/distilbert-base-uncased-finetuned-cola
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "ms29315/distilbert-base-uncased-finetuned-cola", "results": []}]} | ms29315/distilbert-base-uncased-finetuned-cola | null | [
"transformers",
"tf",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #tf #tensorboard #distilbert #text-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| ms29315/distilbert-base-uncased-finetuned-cola
==============================================
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.3100
* Validation Loss: 0.5090
* Epoch: 0
Model description
--... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'learning\\_rate': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_steps': 2670, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': False, 'nam... | [
"TAGS\n#transformers #tf #tensorboard #distilbert #text-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'lear... |
text2text-generation | transformers | > **TabQGen** model is released along with the dataset **Question Generation for Tables** in the paper - **Answer-Aware Question Generation from Tabular and Textual Data using T5**
| {} | msakthiganesh/TabQGen-Base | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| > TabQGen model is released along with the dataset Question Generation for Tables in the paper - Answer-Aware Question Generation from Tabular and Textual Data using T5
| [] | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text2text-generation | transformers | > **TabQGen** model is released along with the dataset **Question Generation for Tables** in the paper - **Answer-Aware Question Generation from Tabular and Textual Data using T5**
| {} | msakthiganesh/TabQGen-Large | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| > TabQGen model is released along with the dataset Question Generation for Tables in the paper - Answer-Aware Question Generation from Tabular and Textual Data using T5
| [] | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text2text-generation | transformers | > **TabQGen** model is released along with the dataset **Question Generation for Tables** in the paper - **Answer-Aware Question Generation from Tabular and Textual Data using T5** | {} | msakthiganesh/TabQGen-Small | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| > TabQGen model is released along with the dataset Question Generation for Tables in the paper - Answer-Aware Question Generation from Tabular and Textual Data using T5 | [] | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
translation | null |
# Seq2seq + Attention
Pytorch implementation of [Neural Machine Translation by Jointly Learning to Align and Translate](https://arxiv.org/abs/1409.0473). Trained on the [Multi30k-de-en](http://www.statmt.org/wmt16/multimodal-task.html#task1) dataset with sentencepiece as the tokenizer.
Here's the attention ... | {"language": ["de", "en"], "license": "mit", "tags": ["translation", "pytorch"], "datasets": ["multi30k"], "metrics": ["bleu"], "model-index": [{"name": "multi30k", "results": [{"task": {"type": "translation"}, "dataset": {"name": "multi30k-de-en", "type": "multi30k"}, "metrics": [{"type": "bleu", "value": 33.468, "nam... | msarmi9/multi30k | null | [
"tensorboard",
"translation",
"pytorch",
"de",
"en",
"dataset:multi30k",
"arxiv:1409.0473",
"license:mit",
"model-index",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1409.0473"
] | [
"de",
"en"
] | TAGS
#tensorboard #translation #pytorch #de #en #dataset-multi30k #arxiv-1409.0473 #license-mit #model-index #has_space #region-us
|
# Seq2seq + Attention
Pytorch implementation of Neural Machine Translation by Jointly Learning to Align and Translate. Trained on the Multi30k-de-en dataset with sentencepiece as the tokenizer.
Here's the attention heatmap of a random sample from the test set:
!attention-heatmap
| [
"# Seq2seq + Attention\r\n\r\nPytorch implementation of Neural Machine Translation by Jointly Learning to Align and Translate. Trained on the Multi30k-de-en dataset with sentencepiece as the tokenizer. \r\n\r\nHere's the attention heatmap of a random sample from the test set:\r\n\r\n!attention-heatmap"
] | [
"TAGS\n#tensorboard #translation #pytorch #de #en #dataset-multi30k #arxiv-1409.0473 #license-mit #model-index #has_space #region-us \n",
"# Seq2seq + Attention\r\n\r\nPytorch implementation of Neural Machine Translation by Jointly Learning to Align and Translate. Trained on the Multi30k-de-en dataset with senten... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-clinc
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/d... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["clinc_oos"], "metrics": ["accuracy"], "model_index": [{"name": "distilbert-base-uncased-finetuned-clinc", "results": [{"task": {"name": "Text Classification", "type": "text-classification"}, "dataset": {"name": "clinc_oos", "type": "clinc_oos",... | msavel-prnt/distilbert-base-uncased-finetuned-clinc | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:clinc_oos",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-clinc_oos #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-clinc
=======================================
This model is a fine-tuned version of distilbert-base-uncased on the clinc\_oos dataset.
It achieves the following results on the evaluation set:
* Loss: 0.7528
* Accuracy: 0.9181
Model description
-----------------
More information... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 48\n* eval\\_batch\\_size: 48\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-clinc_oos #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate:... |
text-classification | transformers |
## English Vossian Antonomasia Sentence Classifier
This page presents a fine-tuned [BERT-base-cased](https://huggingface.co/bert-base-cased) language model for classifying sentences that include Vossian Antonomasia.
The label "VA" corresponds to the occurrence of a Vossian Antonomasia in the sentence.
### Dataset
... | {"language": ["en"], "license": "apache-2.0", "tags": ["sentence classification", "vossian antonomasia"], "datasets": ["custom"], "metrics": ["f1", "precision", "recall"], "widget": [{"text": "Bijan wants Jordan to be the Elizabeth Taylor of men's fragrances."}]} | mschwab/va_bert_classification | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"sentence classification",
"vossian antonomasia",
"en",
"dataset:custom",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bert #text-classification #sentence classification #vossian antonomasia #en #dataset-custom #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
## English Vossian Antonomasia Sentence Classifier
This page presents a fine-tuned BERT-base-cased language model for classifying sentences that include Vossian Antonomasia.
The label "VA" corresponds to the occurrence of a Vossian Antonomasia in the sentence.
### Dataset
The dataset is a labeled Vossian Antonomas... | [
"## English Vossian Antonomasia Sentence Classifier\n\nThis page presents a fine-tuned BERT-base-cased language model for classifying sentences that include Vossian Antonomasia. \nThe label \"VA\" corresponds to the occurrence of a Vossian Antonomasia in the sentence.",
"### Dataset\n\nThe dataset is a labeled Vo... | [
"TAGS\n#transformers #pytorch #bert #text-classification #sentence classification #vossian antonomasia #en #dataset-custom #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"## English Vossian Antonomasia Sentence Classifier\n\nThis page presents a fine-tuned BERT-base-cased language... |
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. -->
# bart-base-finetuned-arxiv
This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) ... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["scientific_papers"], "metrics": ["rouge"], "model-index": [{"name": "bart-base-finetuned-arxiv", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "scientific_papers", "... | mse30/bart-base-finetuned-arxiv | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"generated_from_trainer",
"dataset:scientific_papers",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bart #text2text-generation #generated_from_trainer #dataset-scientific_papers #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| bart-base-finetuned-arxiv
=========================
This model is a fine-tuned version of facebook/bart-base on the scientific\_papers dataset.
It achieves the following results on the evaluation set:
* Loss: 2.2912
* Rouge1: 13.6917
* Rouge2: 5.9564
* Rougel: 11.1734
* Rougelsum: 12.6817
* Gen Len: 19.9992
Model... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 4\n* mixed\\_prec... | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #generated_from_trainer #dataset-scientific_papers #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\\_ra... |
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. -->
# bart-base-finetuned-pubmed
This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base)... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["scientific_papers"], "metrics": ["rouge"], "model-index": [{"name": "bart-base-finetuned-pubmed", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "scientific_papers", ... | mse30/bart-base-finetuned-pubmed | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"generated_from_trainer",
"dataset:scientific_papers",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bart #text2text-generation #generated_from_trainer #dataset-scientific_papers #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| bart-base-finetuned-pubmed
==========================
This model is a fine-tuned version of facebook/bart-base on the scientific\_papers dataset.
It achieves the following results on the evaluation set:
* Loss: 1.9804
* Rouge1: 9.1984
* Rouge2: 4.3091
* Rougel: 7.9739
* Rougelsum: 8.6759
* Gen Len: 20.0
Model des... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 4\n* mixed\\_precis... | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #generated_from_trainer #dataset-scientific_papers #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\\_ra... |
multiple-choice | 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-copa-kb-17
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased)... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["super_glue"], "metrics": ["accuracy"], "model-index": [{"name": "bert-base-uncased-copa-kb-17", "results": []}]} | msintaha/bert-base-uncased-copa-kb-17 | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"multiple-choice",
"generated_from_trainer",
"dataset:super_glue",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #multiple-choice #generated_from_trainer #dataset-super_glue #license-apache-2.0 #endpoints_compatible #region-us
| bert-base-uncased-copa-kb-17
============================
This model is a fine-tuned version of bert-base-uncased on the super\_glue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6385
* Accuracy: 0.7000
Model description
-----------------
More information needed
Intended uses & l... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 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: 2",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #multiple-choice #generated_from_trainer #dataset-super_glue #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: 5e-05\n* train\\_batch\\_size:... |
multiple-choice | 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-copa-kb-27
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased)... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["super_glue"], "metrics": ["accuracy"], "model-index": [{"name": "bert-base-uncased-copa-kb-27", "results": []}]} | msintaha/bert-base-uncased-copa-kb-27 | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"multiple-choice",
"generated_from_trainer",
"dataset:super_glue",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #multiple-choice #generated_from_trainer #dataset-super_glue #license-apache-2.0 #endpoints_compatible #region-us
| bert-base-uncased-copa-kb-27
============================
This model is a fine-tuned version of bert-base-uncased on the super\_glue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6114
* Accuracy: 0.7100
Model description
-----------------
More information needed
Intended uses & l... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 10\n* eval\\_batch\\_size: 10\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 #bert #multiple-choice #generated_from_trainer #dataset-super_glue #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: 5e-05\n* train\\_batch\\_size:... |
multiple-choice | 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-copa-data-new
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["super_glue"], "metrics": ["accuracy"], "model-index": [{"name": "bert-base-uncased-finetuned-copa-data-new", "results": []}]} | msintaha/bert-base-uncased-finetuned-copa-data-new | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"multiple-choice",
"generated_from_trainer",
"dataset:super_glue",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #multiple-choice #generated_from_trainer #dataset-super_glue #license-apache-2.0 #endpoints_compatible #region-us
| bert-base-uncased-finetuned-copa-data-new
=========================================
This model is a fine-tuned version of bert-base-uncased on the super\_glue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5995
* Accuracy: 0.7000
Model description
-----------------
More information ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-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: 2",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #multiple-choice #generated_from_trainer #dataset-super_glue #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: 5e-05\n* train\\_batch\\_size:... |
question-answering | transformers | i-manual KoELECTRA-base-v3 | {} | mtr0930/koelectra-base-v3_epoch-10 | null | [
"transformers",
"pytorch",
"electra",
"question-answering",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #electra #question-answering #endpoints_compatible #region-us
| i-manual KoELECTRA-base-v3 | [] | [
"TAGS\n#transformers #pytorch #electra #question-answering #endpoints_compatible #region-us \n"
] |
token-classification | transformers |
# MUDES - {Mu}ltilingual {De}tection of Offensive {S}pans
We provide state-of-the-art models to detect toxic spans in social media texts. We introduce our framework in [this paper](https://arxiv.org/abs/2102.09665). We have evaluated our models on Toxic Spans task at SemEval 2021 (Task 5). Our participation in the ta... | {"language": "en", "license": "apache-2.0", "tags": ["mudes"]} | mudes/en-base | null | [
"transformers",
"pytorch",
"jax",
"bert",
"token-classification",
"mudes",
"en",
"arxiv:2102.09665",
"arxiv:2104.04630",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2102.09665",
"2104.04630"
] | [
"en"
] | TAGS
#transformers #pytorch #jax #bert #token-classification #mudes #en #arxiv-2102.09665 #arxiv-2104.04630 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# MUDES - {Mu}ltilingual {De}tection of Offensive {S}pans
We provide state-of-the-art models to detect toxic spans in social media texts. We introduce our framework in this paper. We have evaluated our models on Toxic Spans task at SemEval 2021 (Task 5). Our participation in the task is detailed in this paper.
## U... | [
"# MUDES - {Mu}ltilingual {De}tection of Offensive {S}pans\n\nWe provide state-of-the-art models to detect toxic spans in social media texts. We introduce our framework in this paper. We have evaluated our models on Toxic Spans task at SemEval 2021 (Task 5). Our participation in the task is detailed in this paper."... | [
"TAGS\n#transformers #pytorch #jax #bert #token-classification #mudes #en #arxiv-2102.09665 #arxiv-2104.04630 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# MUDES - {Mu}ltilingual {De}tection of Offensive {S}pans\n\nWe provide state-of-the-art models to detect toxic spans in so... |
token-classification | transformers |
# MUDES - {Mu}ltilingual {De}tection of Offensive {S}pans
We provide state-of-the-art models to detect toxic spans in social media texts. We introduce our framework in [this paper](https://arxiv.org/abs/2102.09665). We have evaluated our models on Toxic Spans task at SemEval 2021 (Task 5). Our participation in the ta... | {"language": "en", "license": "apache-2.0", "tags": ["mudes"]} | mudes/en-large | null | [
"transformers",
"pytorch",
"jax",
"roberta",
"token-classification",
"mudes",
"en",
"arxiv:2102.09665",
"arxiv:2104.04630",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2102.09665",
"2104.04630"
] | [
"en"
] | TAGS
#transformers #pytorch #jax #roberta #token-classification #mudes #en #arxiv-2102.09665 #arxiv-2104.04630 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# MUDES - {Mu}ltilingual {De}tection of Offensive {S}pans
We provide state-of-the-art models to detect toxic spans in social media texts. We introduce our framework in this paper. We have evaluated our models on Toxic Spans task at SemEval 2021 (Task 5). Our participation in the task is detailed in this paper.
## U... | [
"# MUDES - {Mu}ltilingual {De}tection of Offensive {S}pans\n\nWe provide state-of-the-art models to detect toxic spans in social media texts. We introduce our framework in this paper. We have evaluated our models on Toxic Spans task at SemEval 2021 (Task 5). Our participation in the task is detailed in this paper."... | [
"TAGS\n#transformers #pytorch #jax #roberta #token-classification #mudes #en #arxiv-2102.09665 #arxiv-2104.04630 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# MUDES - {Mu}ltilingual {De}tection of Offensive {S}pans\n\nWe provide state-of-the-art models to detect tox... |
token-classification | transformers |
# MUDES - {Mu}ltilingual {De}tection of Offensive {S}pans
We provide state-of-the-art models to detect toxic spans in social media texts. We introduce our framework in [this paper](https://arxiv.org/abs/2102.09665). We have evaluated our models on Toxic Spans task at SemEval 2021 (Task 5). Our participation in the ta... | {"language": "multilingual", "license": "apache-2.0", "tags": ["mudes"]} | mudes/multilingual-base | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"mudes",
"multilingual",
"arxiv:2102.09665",
"arxiv:2104.04630",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2102.09665",
"2104.04630"
] | [
"multilingual"
] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #mudes #multilingual #arxiv-2102.09665 #arxiv-2104.04630 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# MUDES - {Mu}ltilingual {De}tection of Offensive {S}pans
We provide state-of-the-art models to detect toxic spans in social media texts. We introduce our framework in this paper. We have evaluated our models on Toxic Spans task at SemEval 2021 (Task 5). Our participation in the task is detailed in this paper.
## U... | [
"# MUDES - {Mu}ltilingual {De}tection of Offensive {S}pans\n\nWe provide state-of-the-art models to detect toxic spans in social media texts. We introduce our framework in this paper. We have evaluated our models on Toxic Spans task at SemEval 2021 (Task 5). Our participation in the task is detailed in this paper."... | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #mudes #multilingual #arxiv-2102.09665 #arxiv-2104.04630 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# MUDES - {Mu}ltilingual {De}tection of Offensive {S}pans\n\nWe provide state-of-the-art models to detect toxic... |
token-classification | transformers | # MUDES - {Mu}ltilingual {De}tection of Offensive {S}pans
We provide state-of-the-art models to detect toxic spans in text. We have evaluated our models on Toxic Spans task at SemEval 2021 (Task 5).
## Usage
You can use this model when you have [MUDES](https://github.com/TharinduDR/MUDES) installed:
```bash
pip ins... | {} | mudes/multilingual-large | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #autotrain_compatible #endpoints_compatible #region-us
| # MUDES - {Mu}ltilingual {De}tection of Offensive {S}pans
We provide state-of-the-art models to detect toxic spans in text. We have evaluated our models on Toxic Spans task at SemEval 2021 (Task 5).
## Usage
You can use this model when you have MUDES installed:
Then you can use the model like this:
## System D... | [
"# MUDES - {Mu}ltilingual {De}tection of Offensive {S}pans\n\nWe provide state-of-the-art models to detect toxic spans in text. We have evaluated our models on Toxic Spans task at SemEval 2021 (Task 5).",
"## Usage\nYou can use this model when you have MUDES installed:\n\n\n\nThen you can use the model like this:... | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #autotrain_compatible #endpoints_compatible #region-us \n",
"# MUDES - {Mu}ltilingual {De}tection of Offensive {S}pans\n\nWe provide state-of-the-art models to detect toxic spans in text. We have evaluated our models on Toxic Spans task at SemEval 2... |
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