pipeline_tag stringclasses 48
values | library_name stringclasses 198
values | text stringlengths 1 900k | metadata stringlengths 2 438k | id stringlengths 5 122 | last_modified null | tags listlengths 1 1.84k | sha null | created_at stringlengths 25 25 | arxiv listlengths 0 201 | languages listlengths 0 1.83k | tags_str stringlengths 17 9.34k | text_str stringlengths 0 389k | text_lists listlengths 0 722 | processed_texts listlengths 1 723 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
text-generation | transformers |
# German GPT-2 model
In this repository we release (yet another) GPT-2 model, that was trained on various texts for German.
The model is meant to be an entry point for fine-tuning on other texts, and it is definitely not as good or "dangerous" as the English GPT-3 model. We do not plan extensive PR or staged release... | {"language": "de", "license": "mit", "widget": [{"text": "Schon um die Liebe"}]} | dbmdz/german-gpt2-faust | null | [
"transformers",
"pytorch",
"jax",
"safetensors",
"gpt2",
"text-generation",
"de",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"de"
] | TAGS
#transformers #pytorch #jax #safetensors #gpt2 #text-generation #de #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# German GPT-2 model
In this repository we release (yet another) GPT-2 model, that was trained on various texts for German.
The model is meant to be an entry point for fine-tuning on other texts, and it is definitely not as good or "dangerous" as the English GPT-3 model. We do not plan extensive PR or staged release... | [
"# German GPT-2 model\n\nIn this repository we release (yet another) GPT-2 model, that was trained on various texts for German.\n\nThe model is meant to be an entry point for fine-tuning on other texts, and it is definitely not as good or \"dangerous\" as the English GPT-3 model. We do not plan extensive PR or stag... | [
"TAGS\n#transformers #pytorch #jax #safetensors #gpt2 #text-generation #de #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# German GPT-2 model\n\nIn this repository we release (yet another) GPT-2 model, that was trained on various texts for German.\n\nThe mode... |
text-generation | transformers |
# German GPT-2 model
In this repository we release (yet another) GPT-2 model, that was trained on various texts for German.
The model is meant to be an entry point for fine-tuning on other texts, and it is definitely not as good or "dangerous" as the English GPT-3 model. We do not plan extensive PR or staged release... | {"language": "de", "license": "mit", "widget": [{"text": "Heute ist sehr sch\u00f6nes Wetter in"}]} | dbmdz/german-gpt2 | null | [
"transformers",
"pytorch",
"tf",
"jax",
"onnx",
"safetensors",
"gpt2",
"text-generation",
"de",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"de"
] | TAGS
#transformers #pytorch #tf #jax #onnx #safetensors #gpt2 #text-generation #de #license-mit #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
# German GPT-2 model
In this repository we release (yet another) GPT-2 model, that was trained on various texts for German.
The model is meant to be an entry point for fine-tuning on other texts, and it is definitely not as good or "dangerous" as the English GPT-3 model. We do not plan extensive PR or staged release... | [
"# German GPT-2 model\n\nIn this repository we release (yet another) GPT-2 model, that was trained on various texts for German.\n\nThe model is meant to be an entry point for fine-tuning on other texts, and it is definitely not as good or \"dangerous\" as the English GPT-3 model. We do not plan extensive PR or stag... | [
"TAGS\n#transformers #pytorch #tf #jax #onnx #safetensors #gpt2 #text-generation #de #license-mit #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"# German GPT-2 model\n\nIn this repository we release (yet another) GPT-2 model, that was trained on various texts fo... |
text2text-generation | transformers |
# T5 Base Model for Named Entity Recognition (NER, CoNLL-2003)
In this repository, we open source a T5 Base model, that was fine-tuned on the official CoNLL-2003 NER dataset.
We use the great [TANL library](https://github.com/amazon-research/tanl) from Amazon for fine-tuning the model.
The exact approach of fine-tu... | {"language": "en", "license": "mit", "datasets": ["conll2003"], "widget": [{"text": "My name is Clara Clever and I live in Berkeley , California ."}]} | dbmdz/t5-base-conll03-english | null | [
"transformers",
"pytorch",
"safetensors",
"t5",
"text2text-generation",
"en",
"dataset:conll2003",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #safetensors #t5 #text2text-generation #en #dataset-conll2003 #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# T5 Base Model for Named Entity Recognition (NER, CoNLL-2003)
In this repository, we open source a T5 Base model, that was fine-tuned on the official CoNLL-2003 NER dataset.
We use the great TANL library from Amazon for fine-tuning the model.
The exact approach of fine-tuning is presented in the "TANL: Structured ... | [
"# T5 Base Model for Named Entity Recognition (NER, CoNLL-2003)\n\nIn this repository, we open source a T5 Base model, that was fine-tuned on the official CoNLL-2003 NER dataset.\n\nWe use the great TANL library from Amazon for fine-tuning the model.\n\nThe exact approach of fine-tuning is presented in the \"TANL: ... | [
"TAGS\n#transformers #pytorch #safetensors #t5 #text2text-generation #en #dataset-conll2003 #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# T5 Base Model for Named Entity Recognition (NER, CoNLL-2003)\n\nIn this repository, we open source a T5 Base model, tha... |
fill-mask | transformers | Masked Language Model trained on the articles and talks of Noam Chomsky. | {} | dbragdon/noam-masked-lm | null | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| Masked Language Model trained on the articles and talks of Noam Chomsky. | [] | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-generation | transformers | Language model fine-tuned on the articles and speeches of Noam Chomsky. | {} | dbragdon/noamlm | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| Language model fine-tuned on the articles and speeches of Noam Chomsky. | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-ner
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/dis... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["wikiann"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "distilbert-base-uncased-finetuned-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "wikia... | dbsamu/distilbert-base-uncased-finetuned-ner | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"dataset:wikiann",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #dataset-wikiann #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-ner
=====================================
This model is a fine-tuned version of distilbert-base-uncased on the wikiann dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2781
* Precision: 0.8121
* Recall: 0.8302
* F1: 0.8210
* Accuracy: 0.9204
Model descr... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #dataset-wikiann #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* lear... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# electra-small-discriminator-finetuned-ner
This model is a fine-tuned version of [google/electra-small-discriminator](https://hug... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["wikiann"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "electra-small-discriminator-finetuned-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "w... | dbsamu/electra-small-discriminator-finetuned-ner | null | [
"transformers",
"pytorch",
"tensorboard",
"electra",
"token-classification",
"generated_from_trainer",
"dataset:wikiann",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #electra #token-classification #generated_from_trainer #dataset-wikiann #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| electra-small-discriminator-finetuned-ner
=========================================
This model is a fine-tuned version of google/electra-small-discriminator on the wikiann dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3685
* Precision: 0.7331
* Recall: 0.7543
* F1: 0.7435
* Accuracy: 0... | [
"### 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 #electra #token-classification #generated_from_trainer #dataset-wikiann #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* learnin... |
fill-mask | transformers |
# BETO: Spanish BERT
BETO is a [BERT model](https://github.com/google-research/bert) trained on a [big Spanish corpus](https://github.com/josecannete/spanish-corpora). BETO is of size similar to a BERT-Base and was trained with the Whole Word Masking technique. Below you find Tensorflow and Pytorch checkpoints for t... | {"language": ["es"], "tags": ["masked-lm"]} | dccuchile/bert-base-spanish-wwm-cased | null | [
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"masked-lm",
"es",
"arxiv:1904.09077",
"arxiv:1906.01502",
"arxiv:1812.10464",
"arxiv:1901.07291",
"arxiv:1904.02099",
"arxiv:1906.01569",
"arxiv:1908.11828",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
... | null | 2022-03-02T23:29:05+00:00 | [
"1904.09077",
"1906.01502",
"1812.10464",
"1901.07291",
"1904.02099",
"1906.01569",
"1908.11828"
] | [
"es"
] | TAGS
#transformers #pytorch #tf #jax #bert #fill-mask #masked-lm #es #arxiv-1904.09077 #arxiv-1906.01502 #arxiv-1812.10464 #arxiv-1901.07291 #arxiv-1904.02099 #arxiv-1906.01569 #arxiv-1908.11828 #autotrain_compatible #endpoints_compatible #has_space #region-us
| BETO: Spanish BERT
==================
BETO is a BERT model trained on a big Spanish corpus. BETO is of size similar to a BERT-Base and was trained with the Whole Word Masking technique. Below you find Tensorflow and Pytorch checkpoints for the uncased and cased versions, as well as some results for Spanish benchmarks... | [] | [
"TAGS\n#transformers #pytorch #tf #jax #bert #fill-mask #masked-lm #es #arxiv-1904.09077 #arxiv-1906.01502 #arxiv-1812.10464 #arxiv-1901.07291 #arxiv-1904.02099 #arxiv-1906.01569 #arxiv-1908.11828 #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
fill-mask | transformers |
# BETO: Spanish BERT
BETO is a [BERT model](https://github.com/google-research/bert) trained on a [big Spanish corpus](https://github.com/josecannete/spanish-corpora). BETO is of size similar to a BERT-Base and was trained with the Whole Word Masking technique. Below you find Tensorflow and Pytorch checkpoints for th... | {"language": ["es"], "tags": ["masked-lm"]} | dccuchile/bert-base-spanish-wwm-uncased | null | [
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"masked-lm",
"es",
"arxiv:1904.09077",
"arxiv:1906.01502",
"arxiv:1812.10464",
"arxiv:1901.07291",
"arxiv:1904.02099",
"arxiv:1906.01569",
"arxiv:1908.11828",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
... | null | 2022-03-02T23:29:05+00:00 | [
"1904.09077",
"1906.01502",
"1812.10464",
"1901.07291",
"1904.02099",
"1906.01569",
"1908.11828"
] | [
"es"
] | TAGS
#transformers #pytorch #tf #jax #bert #fill-mask #masked-lm #es #arxiv-1904.09077 #arxiv-1906.01502 #arxiv-1812.10464 #arxiv-1901.07291 #arxiv-1904.02099 #arxiv-1906.01569 #arxiv-1908.11828 #autotrain_compatible #endpoints_compatible #has_space #region-us
| BETO: Spanish BERT
==================
BETO is a BERT model trained on a big Spanish corpus. BETO is of size similar to a BERT-Base and was trained with the Whole Word Masking technique. Below you find Tensorflow and Pytorch checkpoints for the uncased and cased versions, as well as some results for Spanish benchmarks... | [] | [
"TAGS\n#transformers #pytorch #tf #jax #bert #fill-mask #masked-lm #es #arxiv-1904.09077 #arxiv-1906.01502 #arxiv-1812.10464 #arxiv-1901.07291 #arxiv-1904.02099 #arxiv-1906.01569 #arxiv-1908.11828 #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
null | null | https://teespring.com/dashboard/listings/113925135/edit | {} | ddddd/EDCLasVegas | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| URL | [] | [
"TAGS\n#region-us \n"
] |
sentence-similarity | sentence-transformers |
# ddobokki/electra-small-nli-sts
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 256 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 ... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers", "ko"], "pipeline_tag": "sentence-similarity"} | ddobokki/electra-small-nli-sts | null | [
"sentence-transformers",
"pytorch",
"electra",
"feature-extraction",
"sentence-similarity",
"transformers",
"ko",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #electra #feature-extraction #sentence-similarity #transformers #ko #endpoints_compatible #region-us
|
# ddobokki/electra-small-nli-sts
This is a sentence-transformers model: It maps sentences & paragraphs to a 256 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:... | [
"# ddobokki/electra-small-nli-sts\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 256 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 ... | [
"TAGS\n#sentence-transformers #pytorch #electra #feature-extraction #sentence-similarity #transformers #ko #endpoints_compatible #region-us \n",
"# ddobokki/electra-small-nli-sts\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 256 dimensional dense vector space and can be used for ta... |
sentence-similarity | sentence-transformers |
# ddobokki/klue-roberta-small-nli-sts
한국어 Sentence Transformer 모델입니다.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
[sentence-transformers](https://www.SBERT.net) 라이브러리를 이용해 사용할 수 있습니다.
```
pip install -U sentence-transformers
```
사용법
```python
from sentence_transformers import SentenceTra... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers", "ko"], "pipeline_tag": "sentence-similarity"} | ddobokki/klue-roberta-small-nli-sts | null | [
"sentence-transformers",
"pytorch",
"roberta",
"feature-extraction",
"sentence-similarity",
"transformers",
"ko",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #roberta #feature-extraction #sentence-similarity #transformers #ko #endpoints_compatible #region-us
| ddobokki/klue-roberta-small-nli-sts
===================================
한국어 Sentence Transformer 모델입니다.
Usage (Sentence-Transformers)
-----------------------------
sentence-transformers 라이브러리를 이용해 사용할 수 있습니다.
사용법
Usage (HuggingFace Transformers)
--------------------------------
transformers 라이브러리만 사용할 경우
... | [] | [
"TAGS\n#sentence-transformers #pytorch #roberta #feature-extraction #sentence-similarity #transformers #ko #endpoints_compatible #region-us \n"
] |
null | transformers | ## EXAMPLE
```python
import requests
import torch
from PIL import Image
from transformers import (
VisionEncoderDecoderModel,
ViTFeatureExtractor,
PreTrainedTokenizerFast,
)
# device setting
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
# load feature extractor and tokenizer
enco... | {} | ddobokki/vision-encoder-decoder-vit-gpt2-coco-ko | null | [
"transformers",
"pytorch",
"vision-encoder-decoder",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #vision-encoder-decoder #endpoints_compatible #region-us
| ## EXAMPLE
| [
"## EXAMPLE"
] | [
"TAGS\n#transformers #pytorch #vision-encoder-decoder #endpoints_compatible #region-us \n",
"## EXAMPLE"
] |
null | speechbrain |
<iframe src="https://ghbtns.com/github-btn.html?user=speechbrain&repo=speechbrain&type=star&count=true&size=large&v=2" frameborder="0" scrolling="0" width="170" height="30" title="GitHub"></iframe>
<br/><br/>
# Conformer for KsponSpeech (with Transformer LM)
This repository provides all the necessary tools to perfor... | {"language": "kr", "license": "apache-2.0", "tags": ["ASR", "CTC", "Attention", "Conformer", "pytorch", "speechbrain"], "datasets": ["ksponspeech"], "metrics": ["wer", "cer"]} | ddwkim/asr-conformer-transformerlm-ksponspeech | null | [
"speechbrain",
"ASR",
"CTC",
"Attention",
"Conformer",
"pytorch",
"kr",
"dataset:ksponspeech",
"arxiv:2106.04624",
"license:apache-2.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2106.04624"
] | [
"kr"
] | TAGS
#speechbrain #ASR #CTC #Attention #Conformer #pytorch #kr #dataset-ksponspeech #arxiv-2106.04624 #license-apache-2.0 #region-us
|
Conformer for KsponSpeech (with Transformer LM)
===============================================
This repository provides all the necessary tools to perform automatic speech
recognition from an end-to-end system pretrained on KsponSpeech (Kr) within
SpeechBrain. For a better experience, we encourage you to lea... | [
"### Transcribing your own audio files (in Korean)",
"### Inference on GPU\n\n\nTo perform inference on the GPU, add 'run\\_opts={\"device\":\"cuda\"}' when calling the 'from\\_hparams' method.\n\n\nParallel Inference on a Batch\n-----------------------------\n\n\nPlease, see this Colab notebook on using the pret... | [
"TAGS\n#speechbrain #ASR #CTC #Attention #Conformer #pytorch #kr #dataset-ksponspeech #arxiv-2106.04624 #license-apache-2.0 #region-us \n",
"### Transcribing your own audio files (in Korean)",
"### Inference on GPU\n\n\nTo perform inference on the GPU, add 'run\\_opts={\"device\":\"cuda\"}' when calling the 'fr... |
text-generation | transformers |
# DialoGPT Trained on the Speech of a Game Character
Chat with the model:
```python
from transformers import AutoTokenizer, AutoModelWithLMHead
tokenizer = AutoTokenizer.from_pretrained("dead69/GTP-small-yoda")
model = AutoModelWithLMHead.from_pretrained("dead69/GTP-small-yoda")
# Let's chat for 4 lines
fo... | {"license": "mit", "tags": ["conversational"], "thumbnail": "https://huggingface.co/front/thumbnails/dialogpt.png"} | dead69/GPT-small-yoda | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# DialoGPT Trained on the Speech of a Game Character
Chat with the model:
| [
"# DialoGPT Trained on the Speech of a Game Character\n\n\nChat with the model:"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# DialoGPT Trained on the Speech of a Game Character\n\n\nChat with the model:"
] |
text2text-generation | transformers |
Pretraining Dataset: [AAAC01](https://huggingface.co/datasets/debatelab/aaac)
Demo: [DeepA2 Demo](https://huggingface.co/spaces/debatelab/deepa2-demo)
Paper: [DeepA2: A Modular Framework for Deep Argument Analysis with Pretrained Neural Text2Text Language Models](https://arxiv.org/abs/2110.01509)
Authors: *Gregor B... | {"language": ["en"], "license": "cc-by-sa-4.0", "datasets": ["debatelab/aaac"], "widget": [{"text": "reason_statements: argument_source: If Peter likes fish, Peter has been to New York. So, Peter has been to New York.", "example_title": "Premise identification"}, {"text": "argdown_reconstruction: argument_source: If Pe... | DebateLabKIT/argument-analyst | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"en",
"dataset:debatelab/aaac",
"arxiv:2110.01509",
"license:cc-by-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2110.01509"
] | [
"en"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #en #dataset-debatelab/aaac #arxiv-2110.01509 #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
Pretraining Dataset: AAAC01
Demo: DeepA2 Demo
Paper: DeepA2: A Modular Framework for Deep Argument Analysis with Pretrained Neural Text2Text Language Models
Authors: *Gregor Betz, Kyle Richardson*
## Abstract
In this paper, we present and implement a multi-dimensional, modular framework for performing deep argume... | [
"## Abstract\n\nIn this paper, we present and implement a multi-dimensional, modular framework for performing deep argument analysis (DeepA2) using current pre-trained language models (PTLMs). ArgumentAnalyst -- a T5 model (Raffel et al. 2020) set up and trained within DeepA2 -- reconstructs argumentative texts, wh... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #en #dataset-debatelab/aaac #arxiv-2110.01509 #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"## Abstract\n\nIn this paper, we present and implement a multi-dimensional, modular framework for perf... |
text-generation | transformers | # CRiPT Model Large (Critical Thinking Intermediarily Pretrained Transformer)
Large version of the trained model (`SYL01-2020-10-24-72K/gpt2-large-train03-72K`) presented in the paper "Critical Thinking for Language Models" (Betz, Voigt and Richardson 2020). See also:
* [blog entry](https://debatelab.github.io/journal... | {"language": "en", "tags": ["gpt2"]} | DebateLabKIT/cript-large | null | [
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"en",
"arxiv:2009.07185",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2009.07185"
] | [
"en"
] | TAGS
#transformers #pytorch #jax #gpt2 #text-generation #en #arxiv-2009.07185 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # CRiPT Model Large (Critical Thinking Intermediarily Pretrained Transformer)
Large version of the trained model ('SYL01-2020-10-24-72K/gpt2-large-train03-72K') presented in the paper "Critical Thinking for Language Models" (Betz, Voigt and Richardson 2020). See also:
* blog entry
* GitHub repo
* paper | [
"# CRiPT Model Large (Critical Thinking Intermediarily Pretrained Transformer)\nLarge version of the trained model ('SYL01-2020-10-24-72K/gpt2-large-train03-72K') presented in the paper \"Critical Thinking for Language Models\" (Betz, Voigt and Richardson 2020). See also:\n * blog entry\n * GitHub repo\n * paper"
] | [
"TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #en #arxiv-2009.07185 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# CRiPT Model Large (Critical Thinking Intermediarily Pretrained Transformer)\nLarge version of the trained model ('SYL01-2020-10-24-72K/gpt2-larg... |
text-generation | transformers | # CRiPT Model Medium (Critical Thinking Intermediarily Pretrained Transformer)
Medium version of the trained model (`SYL01-2020-10-24-72K/gpt2-medium-train03-72K`) presented in the paper "Critical Thinking for Language Models" (Betz, Voigt and Richardson 2020). See also:
* [blog entry](https://debatelab.github.io/jour... | {"language": "en", "tags": ["gpt2"]} | DebateLabKIT/cript-medium | null | [
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"en",
"arxiv:2009.07185",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2009.07185"
] | [
"en"
] | TAGS
#transformers #pytorch #jax #gpt2 #text-generation #en #arxiv-2009.07185 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # CRiPT Model Medium (Critical Thinking Intermediarily Pretrained Transformer)
Medium version of the trained model ('SYL01-2020-10-24-72K/gpt2-medium-train03-72K') presented in the paper "Critical Thinking for Language Models" (Betz, Voigt and Richardson 2020). See also:
* blog entry
* GitHub repo
* paper | [
"# CRiPT Model Medium (Critical Thinking Intermediarily Pretrained Transformer)\nMedium version of the trained model ('SYL01-2020-10-24-72K/gpt2-medium-train03-72K') presented in the paper \"Critical Thinking for Language Models\" (Betz, Voigt and Richardson 2020). See also:\n * blog entry\n * GitHub repo\n * paper... | [
"TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #en #arxiv-2009.07185 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# CRiPT Model Medium (Critical Thinking Intermediarily Pretrained Transformer)\nMedium version of the trained model ('SYL01-2020-10-24-72K/gpt2-me... |
text-generation | transformers |
# CRiPT Model (Critical Thinking Intermediarily Pretrained Transformer)
Small version of the trained model (`SYL01-2020-10-24-72K/gpt2-small-train03-72K`) presented in the paper "Critical Thinking for Language Models" (Betz, Voigt and Richardson 2020). See also:
* [blog entry](https://debatelab.github.io/journal/cr... | {"language": "en", "tags": ["gpt2"]} | DebateLabKIT/cript | null | [
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"en",
"arxiv:2009.07185",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2009.07185"
] | [
"en"
] | TAGS
#transformers #pytorch #jax #gpt2 #text-generation #en #arxiv-2009.07185 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# CRiPT Model (Critical Thinking Intermediarily Pretrained Transformer)
Small version of the trained model ('SYL01-2020-10-24-72K/gpt2-small-train03-72K') presented in the paper "Critical Thinking for Language Models" (Betz, Voigt and Richardson 2020). See also:
* blog entry
* GitHub repo
* paper
| [
"# CRiPT Model (Critical Thinking Intermediarily Pretrained Transformer)\n\nSmall version of the trained model ('SYL01-2020-10-24-72K/gpt2-small-train03-72K') presented in the paper \"Critical Thinking for Language Models\" (Betz, Voigt and Richardson 2020). See also:\n\n * blog entry\n * GitHub repo\n * paper"
] | [
"TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #en #arxiv-2009.07185 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# CRiPT Model (Critical Thinking Intermediarily Pretrained Transformer)\n\nSmall version of the trained model ('SYL01-2020-10-24-72K/gpt2-small-tr... |
text-classification | transformers | This model has been trained for the purpose of classifying text from different domains. Currently it is trained with much lesser data and it has been trained to identify text from 3 domains, "sports", "healthcare" and "financial". Label_0 represents "financial", Label_1 represents "Healthcare" and Label_2 represents "S... | {} | debjyoti007/new_doc_classifier | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #text-classification #autotrain_compatible #endpoints_compatible #region-us
| This model has been trained for the purpose of classifying text from different domains. Currently it is trained with much lesser data and it has been trained to identify text from 3 domains, "sports", "healthcare" and "financial". Label_0 represents "financial", Label_1 represents "Healthcare" and Label_2 represents "S... | [] | [
"TAGS\n#transformers #pytorch #distilbert #text-classification #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-classification | transformers |
# Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 38639804
- CO2 Emissions (in grams): 11.98841452241473
## Validation Metrics
- Loss: 0.421400249004364
- Accuracy: 0.86783988957902
- Macro F1: 0.8669477050676501
- Micro F1: 0.86783988957902
- Weighted F1: 0.86694770506765
- Macro... | {"language": "unk", "tags": "autonlp", "datasets": ["dee4hf/autonlp-data-shajBERT"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}], "co2_eq_emissions": 11.98841452241473} | dee4hf/autonlp-shajBERT-38639804 | null | [
"transformers",
"pytorch",
"albert",
"text-classification",
"autonlp",
"unk",
"dataset:dee4hf/autonlp-data-shajBERT",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"unk"
] | TAGS
#transformers #pytorch #albert #text-classification #autonlp #unk #dataset-dee4hf/autonlp-data-shajBERT #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 38639804
- CO2 Emissions (in grams): 11.98841452241473
## Validation Metrics
- Loss: 0.421400249004364
- Accuracy: 0.86783988957902
- Macro F1: 0.8669477050676501
- Micro F1: 0.86783988957902
- Weighted F1: 0.86694770506765
- Macro... | [
"# Model Trained Using AutoNLP\n\n- Problem type: Multi-class Classification\n- Model ID: 38639804\n- CO2 Emissions (in grams): 11.98841452241473",
"## Validation Metrics\n\n- Loss: 0.421400249004364\n- Accuracy: 0.86783988957902\n- Macro F1: 0.8669477050676501\n- Micro F1: 0.86783988957902\n- Weighted F1: 0.8669... | [
"TAGS\n#transformers #pytorch #albert #text-classification #autonlp #unk #dataset-dee4hf/autonlp-data-shajBERT #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoNLP\n\n- Problem type: Multi-class Classification\n- Model ID: 38639804\n- CO2 Emissions (in grams... |
null | null | trying to create my first BERT model | {} | dee4hf/deeBERT | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| trying to create my first BERT model | [] | [
"TAGS\n#region-us \n"
] |
text2text-generation | transformers | ## Model description
T5 model trained for Grammar Correction. This model corrects grammatical mistakes in input sentences
### Dataset Description
The T5-base model has been trained on C4_200M dataset.
### Model in Action 🚀
```
import torch
from transformers import T5Tokenizer, T5ForConditionalGeneration
model_name =... | {} | deep-learning-analytics/GrammarCorrector | null | [
"transformers",
"pytorch",
"tf",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| ## Model description
T5 model trained for Grammar Correction. This model corrects grammatical mistakes in input sentences
### Dataset Description
The T5-base model has been trained on C4_200M dataset.
### Model in Action
### Example Usage
Another example
Model Developed by Priya-Dwivedi | [
"## Model description\nT5 model trained for Grammar Correction. This model corrects grammatical mistakes in input sentences",
"### Dataset Description\nThe T5-base model has been trained on C4_200M dataset.",
"### Model in Action",
"### Example Usage\n\n\nAnother example\n\n\nModel Developed by Priya-Dwivedi"... | [
"TAGS\n#transformers #pytorch #tf #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"## Model description\nT5 model trained for Grammar Correction. This model corrects grammatical mistakes in input sentences",
"### Dataset Description\nTh... |
question-answering | transformers |
# Model name
Closed Book Trivia-QA T5 base
## Model description
This is a T5-base model trained on No Context Trivia QA data set. The input to the model is a Trivia type question. The model is tuned to search for the answer in its memory to return it. The pretrained model used here was trained on Common Crawl (C4) d... | {"language": "eng", "tags": ["triviaqa", "t5-base", "pytorch", "lm-head", "question-answering", "closed-book", "t5", "pipeline:question-answering"], "datasets": ["triviaqa"], "metrics": [{"EM": 17}, {"Subset match": 24.5}], "widget": [{"text": ["Mount Everest is found in which mountain range?", "None"]}]} | deep-learning-analytics/triviaqa-t5-base | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"triviaqa",
"t5-base",
"lm-head",
"question-answering",
"closed-book",
"pipeline:question-answering",
"eng",
"dataset:triviaqa",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"eng"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #triviaqa #t5-base #lm-head #question-answering #closed-book #pipeline-question-answering #eng #dataset-triviaqa #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Model name
Closed Book Trivia-QA T5 base
## Model description
This is a T5-base model trained on No Context Trivia QA data set. The input to the model is a Trivia type question. The model is tuned to search for the answer in its memory to return it. The pretrained model used here was trained on Common Crawl (C4) d... | [
"# Model name\nClosed Book Trivia-QA T5 base",
"## Model description\n\nThis is a T5-base model trained on No Context Trivia QA data set. The input to the model is a Trivia type question. The model is tuned to search for the answer in its memory to return it. The pretrained model used here was trained on Common C... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #triviaqa #t5-base #lm-head #question-answering #closed-book #pipeline-question-answering #eng #dataset-triviaqa #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Model name\nClosed Book Trivia-QA T5 base",
"## Model... |
summarization | transformers |
# Model name
Wikihow T5-small
## Model description
This is a T5-small model trained on Wikihow All data set. The model was trained for 3 epochs using a batch size of 16 and learning rate of 3e-4. Max_input_lngth is set as 512 and max_output_length is 150. Model attained a Rouge1 score of 31.2 and RougeL score of 24.... | {"language": "eng", "tags": ["wikihow", "t5-small", "pytorch", "lm-head", "seq2seq", "t5", "pipeline:summarization", "summarization"], "datasets": ["Wikihow"], "metrics": [{"Rouge1": 31.2}, {"RougeL": 24.5}], "widget": [{"text": "Lack of fluids can lead to dry mouth, which is a leading cause of bad breath. Water can al... | deep-learning-analytics/wikihow-t5-small | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"wikihow",
"t5-small",
"lm-head",
"seq2seq",
"pipeline:summarization",
"summarization",
"eng",
"dataset:Wikihow",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"eng"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #wikihow #t5-small #lm-head #seq2seq #pipeline-summarization #summarization #eng #dataset-Wikihow #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
# Model name
Wikihow T5-small
## Model description
This is a T5-small model trained on Wikihow All data set. The model was trained for 3 epochs using a batch size of 16 and learning rate of 3e-4. Max_input_lngth is set as 512 and max_output_length is 150. Model attained a Rouge1 score of 31.2 and RougeL score of 24.... | [
"# Model name\nWikihow T5-small",
"## Model description\n\nThis is a T5-small model trained on Wikihow All data set. The model was trained for 3 epochs using a batch size of 16 and learning rate of 3e-4. Max_input_lngth is set as 512 and max_output_length is 150. Model attained a Rouge1 score of 31.2 and RougeL s... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #wikihow #t5-small #lm-head #seq2seq #pipeline-summarization #summarization #eng #dataset-Wikihow #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"# Model name\nWikihow T5-small",
"## Model description\n\nT... |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-distilled-squad-finetuned-squad
This model is a fine-tuned version of [distilbert-base-uncased-distilled... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad_v2"], "model-index": [{"name": "distilbert-base-uncased-distilled-squad-finetuned-squad", "results": []}]} | deepakvk/distilbert-base-uncased-distilled-squad-finetuned-squad | null | [
"transformers",
"pytorch",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:squad_v2",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #question-answering #generated_from_trainer #dataset-squad_v2 #license-apache-2.0 #endpoints_compatible #region-us
|
# distilbert-base-uncased-distilled-squad-finetuned-squad
This model is a fine-tuned version of distilbert-base-uncased-distilled-squad on the squad_v2 dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information ... | [
"# distilbert-base-uncased-distilled-squad-finetuned-squad\n\nThis model is a fine-tuned version of distilbert-base-uncased-distilled-squad on the squad_v2 dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation dat... | [
"TAGS\n#transformers #pytorch #distilbert #question-answering #generated_from_trainer #dataset-squad_v2 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# distilbert-base-uncased-distilled-squad-finetuned-squad\n\nThis model is a fine-tuned version of distilbert-base-uncased-distilled-squad on the squad... |
fill-mask | transformers |
# Welcome to Roberta-Marathi-MLM
## Model Description
> This is a small language model for [Marathi](https://en.wikipedia.org/wiki/Marathi) language with 1M data samples taken from
[OSCAR page](https://oscar-public.huma-num.fr/shuffled/mr_dedup.txt.gz)
## Training params
- **Dataset** - 1M data samples are use... | {"language": "mr"} | deepampatel/roberta-mlm-marathi | null | [
"transformers",
"pytorch",
"jax",
"roberta",
"fill-mask",
"mr",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"mr"
] | TAGS
#transformers #pytorch #jax #roberta #fill-mask #mr #autotrain_compatible #endpoints_compatible #region-us
|
# Welcome to Roberta-Marathi-MLM
## Model Description
> This is a small language model for Marathi language with 1M data samples taken from
OSCAR page
## Training params
- Dataset - 1M data samples are used to train this model from OSCAR page(URL eventhough data set is of 2.7 GB due to resource constraint to t... | [
"# Welcome to Roberta-Marathi-MLM",
"## Model Description\n \n> This is a small language model for Marathi language with 1M data samples taken from\n OSCAR page",
"## Training params \n\n- Dataset - 1M data samples are used to train this model from OSCAR page(URL eventhough data set is of 2.7 GB due to resourc... | [
"TAGS\n#transformers #pytorch #jax #roberta #fill-mask #mr #autotrain_compatible #endpoints_compatible #region-us \n",
"# Welcome to Roberta-Marathi-MLM",
"## Model Description\n \n> This is a small language model for Marathi language with 1M data samples taken from\n OSCAR page",
"## Training params \n\n- D... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# output
This model is a fine-tuned version of [hf-test/xls-r-dummy](https://huggingface.co/hf-test/xls-r-dummy) on the MOZILLA-FO... | {"language": ["ab"], "tags": ["automatic-speech-recognition", "mozilla-foundation/common_voice_7_0", "generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "output", "results": []}]} | deepdml/output | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"mozilla-foundation/common_voice_7_0",
"generated_from_trainer",
"ab",
"dataset:common_voice",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ab"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_7_0 #generated_from_trainer #ab #dataset-common_voice #endpoints_compatible #region-us
|
# output
This model is a fine-tuned version of hf-test/xls-r-dummy on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - AB dataset.
It achieves the following results on the evaluation set:
- Loss: 156.8789
- Wer: 1.3456
## Model description
More information needed
## Intended uses & limitations
More information needed
... | [
"# output\n\nThis model is a fine-tuned version of hf-test/xls-r-dummy on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - AB dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 156.8789\n- Wer: 1.3456",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore ... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_7_0 #generated_from_trainer #ab #dataset-common_voice #endpoints_compatible #region-us \n",
"# output\n\nThis model is a fine-tuned version of hf-test/xls-r-dummy on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - AB ... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-base-timit-demo-colab
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wa... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-base-timit-demo-colab", "results": []}]} | deepdml/wav2vec2-base-timit-demo-colab | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-base-timit-demo-colab
==============================
This model is a fine-tuned version of facebook/wav2vec2-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4798
* Wer: 0.3474
Model description
-----------------
More information needed
Intended uses & limi... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 32\n* eval\\_b... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-300m-basque
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/fac... | {"language": "eu", "license": "apache-2.0", "tags": ["automatic-speech-recognition", "basque", "generated_from_trainer", "hf-asr-leaderboard", "robust-speech-event"], "datasets": ["mozilla-foundation/common_voice_7_0"], "metrics": ["wer", "cer"], "model-index": [{"name": "wav2vec2-large-xls-r-300m-basque", "results": [... | deepdml/wav2vec2-large-xls-r-300m-basque | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"basque",
"generated_from_trainer",
"hf-asr-leaderboard",
"robust-speech-event",
"eu",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region... | null | 2022-03-02T23:29:05+00:00 | [] | [
"eu"
] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #basque #generated_from_trainer #hf-asr-leaderboard #robust-speech-event #eu #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us
| wav2vec2-large-xls-r-300m-basque
================================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common\_voice dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4276
* Wer: 0.5962
Model description
-----------------
More information needed
I... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 2\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 4\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #basque #generated_from_trainer #hf-asr-leaderboard #robust-speech-event #eu #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe... |
null | null |
# Detectron2 Cascade-RCNN with FPN and Group Normalization on ResNext32xd4-50 trained on Publaynet for Document Layout Analysis
The model and has been trained with the Tensorflow training toolkit Tensorpack and then transferred to Pytorch using a conversion script.
The Tensorflow and Pytorch models differ slightly ... | {"license": "apache-2.0", "tags": ["Pytorch"], "datasets": ["Publaynet"]} | deepdoctection/d2_casc_rcnn_X_32xd4_50_FPN_GN_2FC_publaynet_inference_only | null | [
"Pytorch",
"dataset:Publaynet",
"arxiv:1908.07836",
"license:apache-2.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1908.07836"
] | [] | TAGS
#Pytorch #dataset-Publaynet #arxiv-1908.07836 #license-apache-2.0 #region-us
|
# Detectron2 Cascade-RCNN with FPN and Group Normalization on ResNext32xd4-50 trained on Publaynet for Document Layout Analysis
The model and has been trained with the Tensorflow training toolkit Tensorpack and then transferred to Pytorch using a conversion script.
The Tensorflow and Pytorch models differ slightly ... | [
"# Detectron2 Cascade-RCNN with FPN and Group Normalization on ResNext32xd4-50 trained on Publaynet for Document Layout Analysis\n\nThe model and has been trained with the Tensorflow training toolkit Tensorpack and then transferred to Pytorch using a conversion script. \nThe Tensorflow and Pytorch models differ sli... | [
"TAGS\n#Pytorch #dataset-Publaynet #arxiv-1908.07836 #license-apache-2.0 #region-us \n",
"# Detectron2 Cascade-RCNN with FPN and Group Normalization on ResNext32xd4-50 trained on Publaynet for Document Layout Analysis\n\nThe model and has been trained with the Tensorflow training toolkit Tensorpack and then trans... |
null | null |
# Detectron2 Cascade-RCNN with FPN and Group Normalization on ResNext32xd4-50 trained on Pubtabnet for Semantic Segmentation of tables.
The model and has been trained with the Tensorflow training toolkit Tensorpack and then transferred to Pytorch using a conversion script.
The Tensorflow and Pytorch models differ ... | {"license": "apache-2.0", "tags": ["Pytorch"], "datasets": ["Pubtabnet"]} | deepdoctection/d2_casc_rcnn_X_32xd4_50_FPN_GN_2FC_pubtabnet_c_inference_only | null | [
"Pytorch",
"dataset:Pubtabnet",
"arxiv:1911.10683",
"license:apache-2.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1911.10683"
] | [] | TAGS
#Pytorch #dataset-Pubtabnet #arxiv-1911.10683 #license-apache-2.0 #region-us
|
# Detectron2 Cascade-RCNN with FPN and Group Normalization on ResNext32xd4-50 trained on Pubtabnet for Semantic Segmentation of tables.
The model and has been trained with the Tensorflow training toolkit Tensorpack and then transferred to Pytorch using a conversion script.
The Tensorflow and Pytorch models differ ... | [
"# Detectron2 Cascade-RCNN with FPN and Group Normalization on ResNext32xd4-50 trained on Pubtabnet for Semantic Segmentation of tables. \n\nThe model and has been trained with the Tensorflow training toolkit Tensorpack and then transferred to Pytorch using a conversion script. \nThe Tensorflow and Pytorch models d... | [
"TAGS\n#Pytorch #dataset-Pubtabnet #arxiv-1911.10683 #license-apache-2.0 #region-us \n",
"# Detectron2 Cascade-RCNN with FPN and Group Normalization on ResNext32xd4-50 trained on Pubtabnet for Semantic Segmentation of tables. \n\nThe model and has been trained with the Tensorflow training toolkit Tensorpack and t... |
null | null |
# Detectron2 Cascade-RCNN with FPN and Group Normalization on ResNext32xd4-50 trained on Pubtabnet for Semantic Segmentation of tables.
The model and has been trained with the Tensorflow training toolkit Tensorpack and then transferred to Pytorch using a conversion script.
The Tensorflow and Pytorch models differ ... | {"license": "apache-2.0", "tags": ["Pytorch"], "datasets": ["Pubtabnet"]} | deepdoctection/d2_casc_rcnn_X_32xd4_50_FPN_GN_2FC_pubtabnet_rc_inference_only | null | [
"Pytorch",
"dataset:Pubtabnet",
"arxiv:1911.10683",
"license:apache-2.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1911.10683"
] | [] | TAGS
#Pytorch #dataset-Pubtabnet #arxiv-1911.10683 #license-apache-2.0 #region-us
|
# Detectron2 Cascade-RCNN with FPN and Group Normalization on ResNext32xd4-50 trained on Pubtabnet for Semantic Segmentation of tables.
The model and has been trained with the Tensorflow training toolkit Tensorpack and then transferred to Pytorch using a conversion script.
The Tensorflow and Pytorch models differ ... | [
"# Detectron2 Cascade-RCNN with FPN and Group Normalization on ResNext32xd4-50 trained on Pubtabnet for Semantic Segmentation of tables. \n\nThe model and has been trained with the Tensorflow training toolkit Tensorpack and then transferred to Pytorch using a conversion script. \nThe Tensorflow and Pytorch models d... | [
"TAGS\n#Pytorch #dataset-Pubtabnet #arxiv-1911.10683 #license-apache-2.0 #region-us \n",
"# Detectron2 Cascade-RCNN with FPN and Group Normalization on ResNext32xd4-50 trained on Pubtabnet for Semantic Segmentation of tables. \n\nThe model and has been trained with the Tensorflow training toolkit Tensorpack and t... |
null | null |
# Tensorpacks Cascade-RCNN with FPN and Group Normalization on ResNext32xd4-50 trained on Publaynet for Document Layout Analysis
The model and its training code has been mainly taken from: [Tensorpack](https://github.com/tensorpack/tensorpack/tree/master/examples/FasterRCNN) .
Please check: [Xu Zhong et. all. - Pu... | {"license": "apache-2.0", "tags": ["Tensorflow"], "datasets": ["Publaynet"]} | deepdoctection/tp_casc_rcnn_X_32xd4_50_FPN_GN_2FC_publaynet | null | [
"Tensorflow",
"dataset:Publaynet",
"arxiv:1908.07836",
"license:apache-2.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1908.07836"
] | [] | TAGS
#Tensorflow #dataset-Publaynet #arxiv-1908.07836 #license-apache-2.0 #region-us
|
# Tensorpacks Cascade-RCNN with FPN and Group Normalization on ResNext32xd4-50 trained on Publaynet for Document Layout Analysis
The model and its training code has been mainly taken from: Tensorpack .
Please check: Xu Zhong et. all. - PubLayNet: largest dataset ever for document layout analysis.
This model is d... | [
"# Tensorpacks Cascade-RCNN with FPN and Group Normalization on ResNext32xd4-50 trained on Publaynet for Document Layout Analysis\n\nThe model and its training code has been mainly taken from: Tensorpack . \n\nPlease check: Xu Zhong et. all. - PubLayNet: largest dataset ever for document layout analysis. \n\nThis m... | [
"TAGS\n#Tensorflow #dataset-Publaynet #arxiv-1908.07836 #license-apache-2.0 #region-us \n",
"# Tensorpacks Cascade-RCNN with FPN and Group Normalization on ResNext32xd4-50 trained on Publaynet for Document Layout Analysis\n\nThe model and its training code has been mainly taken from: Tensorpack . \n\nPlease check... |
null | null |
# Tensorpacks Cascade-RCNN with FPN and Group Normalization on ResNext32xd4-50 trained on Publaynet for Document Layout Analysis
The model and its training code has been mainly taken from: [Tensorpack](https://github.com/tensorpack/tensorpack/tree/master/examples/FasterRCNN) .
Please check: [Xu Zhong et. all. - Pu... | {"license": "apache-2.0", "tags": ["Tensorflow"], "datasets": ["Publaynet"]} | deepdoctection/tp_casc_rcnn_X_32xd4_50_FPN_GN_2FC_publaynet_inference_only | null | [
"Tensorflow",
"dataset:Publaynet",
"arxiv:1908.07836",
"license:apache-2.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1908.07836"
] | [] | TAGS
#Tensorflow #dataset-Publaynet #arxiv-1908.07836 #license-apache-2.0 #region-us
|
# Tensorpacks Cascade-RCNN with FPN and Group Normalization on ResNext32xd4-50 trained on Publaynet for Document Layout Analysis
The model and its training code has been mainly taken from: Tensorpack .
Please check: Xu Zhong et. all. - PubLayNet: largest dataset ever for document layout analysis.
This model is d... | [
"# Tensorpacks Cascade-RCNN with FPN and Group Normalization on ResNext32xd4-50 trained on Publaynet for Document Layout Analysis\n\nThe model and its training code has been mainly taken from: Tensorpack . \n\nPlease check: Xu Zhong et. all. - PubLayNet: largest dataset ever for document layout analysis. \n\nThis m... | [
"TAGS\n#Tensorflow #dataset-Publaynet #arxiv-1908.07836 #license-apache-2.0 #region-us \n",
"# Tensorpacks Cascade-RCNN with FPN and Group Normalization on ResNext32xd4-50 trained on Publaynet for Document Layout Analysis\n\nThe model and its training code has been mainly taken from: Tensorpack . \n\nPlease check... |
null | null |
# Tensorpacks Cascade-RCNN with FPN and Group Normalization on ResNext32xd4-50 trained on Pubtabnet for Semantic Segmentation of tables.
The model and its training code has been mainly taken from: [Tensorpack](https://github.com/tensorpack/tensorpack/tree/master/examples/FasterRCNN) .
Regarding the dataset, pleas... | {"license": "apache-2.0", "tags": ["Tensorflow"], "datasets": ["Pubtabnet"]} | deepdoctection/tp_casc_rcnn_X_32xd4_50_FPN_GN_2FC_pubtabnet_c | null | [
"Tensorflow",
"dataset:Pubtabnet",
"arxiv:1911.10683",
"license:apache-2.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1911.10683"
] | [] | TAGS
#Tensorflow #dataset-Pubtabnet #arxiv-1911.10683 #license-apache-2.0 #region-us
|
# Tensorpacks Cascade-RCNN with FPN and Group Normalization on ResNext32xd4-50 trained on Pubtabnet for Semantic Segmentation of tables.
The model and its training code has been mainly taken from: Tensorpack .
Regarding the dataset, please check: Xu Zhong et. all. - Image-based table recognition: data, model, and... | [
"# Tensorpacks Cascade-RCNN with FPN and Group Normalization on ResNext32xd4-50 trained on Pubtabnet for Semantic Segmentation of tables. \n\nThe model and its training code has been mainly taken from: Tensorpack . \n\nRegarding the dataset, please check: Xu Zhong et. all. - Image-based table recognition: data, mod... | [
"TAGS\n#Tensorflow #dataset-Pubtabnet #arxiv-1911.10683 #license-apache-2.0 #region-us \n",
"# Tensorpacks Cascade-RCNN with FPN and Group Normalization on ResNext32xd4-50 trained on Pubtabnet for Semantic Segmentation of tables. \n\nThe model and its training code has been mainly taken from: Tensorpack . \n\nReg... |
null | null |
# Tensorpacks Cascade-RCNN with FPN and Group Normalization on ResNext32xd4-50 trained on Pubtabnet for Semantic Segmentation of tables.
The model and its training code has been mainly taken from: [Tensorpack](https://github.com/tensorpack/tensorpack/tree/master/examples/FasterRCNN) .
Regarding the dataset, pleas... | {"license": "apache-2.0", "tags": ["Tensorflow"], "datasets": ["Pubtabnet"]} | deepdoctection/tp_casc_rcnn_X_32xd4_50_FPN_GN_2FC_pubtabnet_c_inference_only | null | [
"Tensorflow",
"dataset:Pubtabnet",
"arxiv:1911.10683",
"license:apache-2.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1911.10683"
] | [] | TAGS
#Tensorflow #dataset-Pubtabnet #arxiv-1911.10683 #license-apache-2.0 #region-us
|
# Tensorpacks Cascade-RCNN with FPN and Group Normalization on ResNext32xd4-50 trained on Pubtabnet for Semantic Segmentation of tables.
The model and its training code has been mainly taken from: Tensorpack .
Regarding the dataset, please check: Xu Zhong et. all. - Image-based table recognition: data, model, and... | [
"# Tensorpacks Cascade-RCNN with FPN and Group Normalization on ResNext32xd4-50 trained on Pubtabnet for Semantic Segmentation of tables. \n\nThe model and its training code has been mainly taken from: Tensorpack . \n\nRegarding the dataset, please check: Xu Zhong et. all. - Image-based table recognition: data, mod... | [
"TAGS\n#Tensorflow #dataset-Pubtabnet #arxiv-1911.10683 #license-apache-2.0 #region-us \n",
"# Tensorpacks Cascade-RCNN with FPN and Group Normalization on ResNext32xd4-50 trained on Pubtabnet for Semantic Segmentation of tables. \n\nThe model and its training code has been mainly taken from: Tensorpack . \n\nReg... |
null | null |
# Tensorpacks Cascade-RCNN with FPN and Group Normalization on ResNext32xd4-50 trained on Pubtabnet for Semantic Segmentation of tables.
The model and its training code has been mainly taken from: [Tensorpack](https://github.com/tensorpack/tensorpack/tree/master/examples/FasterRCNN) .
Regarding the dataset,... | {"license": "apache-2.0", "tags": ["Tensorflow"], "datasets": ["Pubtabnet"]} | deepdoctection/tp_casc_rcnn_X_32xd4_50_FPN_GN_2FC_pubtabnet_rc | null | [
"Tensorflow",
"dataset:Pubtabnet",
"arxiv:1911.10683",
"license:apache-2.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1911.10683"
] | [] | TAGS
#Tensorflow #dataset-Pubtabnet #arxiv-1911.10683 #license-apache-2.0 #region-us
|
# Tensorpacks Cascade-RCNN with FPN and Group Normalization on ResNext32xd4-50 trained on Pubtabnet for Semantic Segmentation of tables.
The model and its training code has been mainly taken from: Tensorpack .
Regarding the dataset, please check: Xu Zhong et. all. - Image-based table recognition: data, mode... | [
"# Tensorpacks Cascade-RCNN with FPN and Group Normalization on ResNext32xd4-50 trained on Pubtabnet for Semantic Segmentation of tables. \r\n\r\nThe model and its training code has been mainly taken from: Tensorpack . \r\n\r\nRegarding the dataset, please check: Xu Zhong et. all. - Image-based table recognition: d... | [
"TAGS\n#Tensorflow #dataset-Pubtabnet #arxiv-1911.10683 #license-apache-2.0 #region-us \n",
"# Tensorpacks Cascade-RCNN with FPN and Group Normalization on ResNext32xd4-50 trained on Pubtabnet for Semantic Segmentation of tables. \r\n\r\nThe model and its training code has been mainly taken from: Tensorpack . \r\... |
null | null |
# Tensorpacks Cascade-RCNN with FPN and Group Normalization on ResNext32xd4-50 trained on Pubtabnet for Semantic Segmentation of tables.
The model and its training code has been mainly taken from: [Tensorpack](https://github.com/tensorpack/tensorpack/tree/master/examples/FasterRCNN) .
Regarding the dataset, pleas... | {"license": "apache-2.0", "tags": ["Tensorflow"], "datasets": ["Pubtabnet"]} | deepdoctection/tp_casc_rcnn_X_32xd4_50_FPN_GN_2FC_pubtabnet_rc_inference_only | null | [
"Tensorflow",
"dataset:Pubtabnet",
"arxiv:1911.10683",
"license:apache-2.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1911.10683"
] | [] | TAGS
#Tensorflow #dataset-Pubtabnet #arxiv-1911.10683 #license-apache-2.0 #region-us
|
# Tensorpacks Cascade-RCNN with FPN and Group Normalization on ResNext32xd4-50 trained on Pubtabnet for Semantic Segmentation of tables.
The model and its training code has been mainly taken from: Tensorpack .
Regarding the dataset, please check: Xu Zhong et. all. - Image-based table recognition: data, model, and... | [
"# Tensorpacks Cascade-RCNN with FPN and Group Normalization on ResNext32xd4-50 trained on Pubtabnet for Semantic Segmentation of tables. \n\nThe model and its training code has been mainly taken from: Tensorpack . \n\nRegarding the dataset, please check: Xu Zhong et. all. - Image-based table recognition: data, mod... | [
"TAGS\n#Tensorflow #dataset-Pubtabnet #arxiv-1911.10683 #license-apache-2.0 #region-us \n",
"# Tensorpacks Cascade-RCNN with FPN and Group Normalization on ResNext32xd4-50 trained on Pubtabnet for Semantic Segmentation of tables. \n\nThe model and its training code has been mainly taken from: Tensorpack . \n\nReg... |
image-classification | transformers |
# Poster2Plot
An image captioning model to generate movie/t.v show plot from poster. It generates decent plots but is no way perfect. We are still working on improving the model.
## Live demo on Hugging Face Spaces: https://huggingface.co/spaces/deepklarity/poster2plot
# Model Details
The base model uses a Vision ... | {"language": "en", "tags": ["image-classification", "image-captioning"]} | deepklarity/poster2plot | null | [
"transformers",
"pytorch",
"vision-encoder-decoder",
"image-classification",
"image-captioning",
"en",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #vision-encoder-decoder #image-classification #image-captioning #en #endpoints_compatible #has_space #region-us
|
# Poster2Plot
An image captioning model to generate movie/t.v show plot from poster. It generates decent plots but is no way perfect. We are still working on improving the model.
## Live demo on Hugging Face Spaces: URL
# Model Details
The base model uses a Vision Transformer (ViT) model as an image encoder and GP... | [
"# Poster2Plot\n\nAn image captioning model to generate movie/t.v show plot from poster. It generates decent plots but is no way perfect. We are still working on improving the model.",
"## Live demo on Hugging Face Spaces: URL",
"# Model Details\n\nThe base model uses a Vision Transformer (ViT) model as an imag... | [
"TAGS\n#transformers #pytorch #vision-encoder-decoder #image-classification #image-captioning #en #endpoints_compatible #has_space #region-us \n",
"# Poster2Plot\n\nAn image captioning model to generate movie/t.v show plot from poster. It generates decent plots but is no way perfect. We are still working on impro... |
null | null | Roberta-base training attempt on hindi datasets. | {} | deepklarity/roberta-base-hindi | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| Roberta-base training attempt on hindi datasets. | [] | [
"TAGS\n#region-us \n"
] |
fill-mask | transformers |
# Perceiver IO for language
Perceiver IO model pre-trained on the Masked Language Modeling (MLM) task proposed in [BERT](https://arxiv.org/abs/1810.04805) using a large text corpus obtained by combining [English Wikipedia](https://huggingface.co/datasets/wikipedia) and [C4](https://huggingface.co/datasets/c4). It was... | {"language": ["en"], "license": "apache-2.0", "datasets": ["wikipedia", "c4"], "inference": false} | deepmind/language-perceiver | null | [
"transformers",
"pytorch",
"perceiver",
"fill-mask",
"en",
"dataset:wikipedia",
"dataset:c4",
"arxiv:1810.04805",
"arxiv:2107.14795",
"arxiv:2004.03720",
"license:apache-2.0",
"autotrain_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1810.04805",
"2107.14795",
"2004.03720"
] | [
"en"
] | TAGS
#transformers #pytorch #perceiver #fill-mask #en #dataset-wikipedia #dataset-c4 #arxiv-1810.04805 #arxiv-2107.14795 #arxiv-2004.03720 #license-apache-2.0 #autotrain_compatible #has_space #region-us
|
# Perceiver IO for language
Perceiver IO model pre-trained on the Masked Language Modeling (MLM) task proposed in BERT using a large text corpus obtained by combining English Wikipedia and C4. It was introduced in the paper Perceiver IO: A General Architecture for Structured Inputs & Outputs by Jaegle et al. and firs... | [
"# Perceiver IO for language\n\nPerceiver IO model pre-trained on the Masked Language Modeling (MLM) task proposed in BERT using a large text corpus obtained by combining English Wikipedia and C4. It was introduced in the paper Perceiver IO: A General Architecture for Structured Inputs & Outputs by Jaegle et al. an... | [
"TAGS\n#transformers #pytorch #perceiver #fill-mask #en #dataset-wikipedia #dataset-c4 #arxiv-1810.04805 #arxiv-2107.14795 #arxiv-2004.03720 #license-apache-2.0 #autotrain_compatible #has_space #region-us \n",
"# Perceiver IO for language\n\nPerceiver IO model pre-trained on the Masked Language Modeling (MLM) tas... |
null | transformers |
# Perceiver IO for multimodal autoencoding
Perceiver IO model trained on [Kinetics-700-2020](https://arxiv.org/abs/2010.10864) for auto-encoding videos that consist of images, audio and a class label. It was introduced in the paper [Perceiver IO: A General Architecture for Structured Inputs & Outputs](https://arxiv.o... | {"license": "apache-2.0", "datasets": ["kinetics-700-2020"]} | deepmind/multimodal-perceiver | null | [
"transformers",
"pytorch",
"perceiver",
"dataset:kinetics-700-2020",
"arxiv:2010.10864",
"arxiv:2107.14795",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2010.10864",
"2107.14795"
] | [] | TAGS
#transformers #pytorch #perceiver #dataset-kinetics-700-2020 #arxiv-2010.10864 #arxiv-2107.14795 #license-apache-2.0 #endpoints_compatible #region-us
|
# Perceiver IO for multimodal autoencoding
Perceiver IO model trained on Kinetics-700-2020 for auto-encoding videos that consist of images, audio and a class label. It was introduced in the paper Perceiver IO: A General Architecture for Structured Inputs & Outputs by Jaegle et al. and first released in this repositor... | [
"# Perceiver IO for multimodal autoencoding\n\nPerceiver IO model trained on Kinetics-700-2020 for auto-encoding videos that consist of images, audio and a class label. It was introduced in the paper Perceiver IO: A General Architecture for Structured Inputs & Outputs by Jaegle et al. and first released in this rep... | [
"TAGS\n#transformers #pytorch #perceiver #dataset-kinetics-700-2020 #arxiv-2010.10864 #arxiv-2107.14795 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# Perceiver IO for multimodal autoencoding\n\nPerceiver IO model trained on Kinetics-700-2020 for auto-encoding videos that consist of images, audio an... |
null | transformers |
# Perceiver IO for optical flow
Perceiver IO model trained on [AutoFlow](https://autoflow-google.github.io/). It was introduced in the paper [Perceiver IO: A General Architecture for Structured Inputs & Outputs](https://arxiv.org/abs/2107.14795) by Jaegle et al. and first released in [this repository](https://github.... | {"license": "apache-2.0", "datasets": ["autoflow"]} | deepmind/optical-flow-perceiver | null | [
"transformers",
"pytorch",
"perceiver",
"dataset:autoflow",
"arxiv:2107.14795",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2107.14795"
] | [] | TAGS
#transformers #pytorch #perceiver #dataset-autoflow #arxiv-2107.14795 #license-apache-2.0 #endpoints_compatible #has_space #region-us
|
# Perceiver IO for optical flow
Perceiver IO model trained on AutoFlow. It was introduced in the paper Perceiver IO: A General Architecture for Structured Inputs & Outputs by Jaegle et al. and first released in this repository.
Optical flow is a decades-old open problem in computer vision. Given two images of the s... | [
"# Perceiver IO for optical flow\n\nPerceiver IO model trained on AutoFlow. It was introduced in the paper Perceiver IO: A General Architecture for Structured Inputs & Outputs by Jaegle et al. and first released in this repository. \n\nOptical flow is a decades-old open problem in computer vision. Given two images ... | [
"TAGS\n#transformers #pytorch #perceiver #dataset-autoflow #arxiv-2107.14795 #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"# Perceiver IO for optical flow\n\nPerceiver IO model trained on AutoFlow. It was introduced in the paper Perceiver IO: A General Architecture for Structured Inputs & ... |
image-classification | transformers |
# Perceiver IO for vision (convolutional processing)
Perceiver IO model pre-trained on ImageNet (14 million images, 1,000 classes) at resolution 224x224. It was introduced in the paper [Perceiver IO: A General Architecture for Structured Inputs & Outputs](https://arxiv.org/abs/2107.14795) by Jaegle et al. and first r... | {"license": "apache-2.0", "datasets": ["imagenet"]} | deepmind/vision-perceiver-conv | null | [
"transformers",
"pytorch",
"perceiver",
"image-classification",
"dataset:imagenet",
"arxiv:2107.14795",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2107.14795"
] | [] | TAGS
#transformers #pytorch #perceiver #image-classification #dataset-imagenet #arxiv-2107.14795 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# Perceiver IO for vision (convolutional processing)
Perceiver IO model pre-trained on ImageNet (14 million images, 1,000 classes) at resolution 224x224. It was introduced in the paper Perceiver IO: A General Architecture for Structured Inputs & Outputs by Jaegle et al. and first released in this repository.
Discla... | [
"# Perceiver IO for vision (convolutional processing)\n\nPerceiver IO model pre-trained on ImageNet (14 million images, 1,000 classes) at resolution 224x224. It was introduced in the paper Perceiver IO: A General Architecture for Structured Inputs & Outputs by Jaegle et al. and first released in this repository. \n... | [
"TAGS\n#transformers #pytorch #perceiver #image-classification #dataset-imagenet #arxiv-2107.14795 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# Perceiver IO for vision (convolutional processing)\n\nPerceiver IO model pre-trained on ImageNet (14 million images, 1,00... |
image-classification | transformers |
# Perceiver IO for vision (fixed Fourier position embeddings)
Perceiver IO model pre-trained on ImageNet (14 million images, 1,000 classes) at resolution 224x224. It was introduced in the paper [Perceiver IO: A General Architecture for Structured Inputs & Outputs](https://arxiv.org/abs/2107.14795) by Jaegle et al. an... | {"license": "apache-2.0", "datasets": ["imagenet"]} | deepmind/vision-perceiver-fourier | null | [
"transformers",
"pytorch",
"perceiver",
"image-classification",
"dataset:imagenet",
"arxiv:2107.14795",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2107.14795"
] | [] | TAGS
#transformers #pytorch #perceiver #image-classification #dataset-imagenet #arxiv-2107.14795 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# Perceiver IO for vision (fixed Fourier position embeddings)
Perceiver IO model pre-trained on ImageNet (14 million images, 1,000 classes) at resolution 224x224. It was introduced in the paper Perceiver IO: A General Architecture for Structured Inputs & Outputs by Jaegle et al. and first released in this repository.... | [
"# Perceiver IO for vision (fixed Fourier position embeddings)\n\nPerceiver IO model pre-trained on ImageNet (14 million images, 1,000 classes) at resolution 224x224. It was introduced in the paper Perceiver IO: A General Architecture for Structured Inputs & Outputs by Jaegle et al. and first released in this repos... | [
"TAGS\n#transformers #pytorch #perceiver #image-classification #dataset-imagenet #arxiv-2107.14795 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# Perceiver IO for vision (fixed Fourier position embeddings)\n\nPerceiver IO model pre-trained on ImageNet (14 million ima... |
image-classification | transformers |
# Perceiver IO for vision (learned position embeddings)
Perceiver IO model pre-trained on ImageNet (14 million images, 1,000 classes) at resolution 224x224. It was introduced in the paper [Perceiver IO: A General Architecture for Structured Inputs & Outputs](https://arxiv.org/abs/2107.14795) by Jaegle et al. and firs... | {"license": "apache-2.0", "datasets": ["imagenet"]} | deepmind/vision-perceiver-learned | null | [
"transformers",
"pytorch",
"perceiver",
"image-classification",
"dataset:imagenet",
"arxiv:2107.14795",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2107.14795"
] | [] | TAGS
#transformers #pytorch #perceiver #image-classification #dataset-imagenet #arxiv-2107.14795 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# Perceiver IO for vision (learned position embeddings)
Perceiver IO model pre-trained on ImageNet (14 million images, 1,000 classes) at resolution 224x224. It was introduced in the paper Perceiver IO: A General Architecture for Structured Inputs & Outputs by Jaegle et al. and first released in this repository.
Dis... | [
"# Perceiver IO for vision (learned position embeddings)\n\nPerceiver IO model pre-trained on ImageNet (14 million images, 1,000 classes) at resolution 224x224. It was introduced in the paper Perceiver IO: A General Architecture for Structured Inputs & Outputs by Jaegle et al. and first released in this repository.... | [
"TAGS\n#transformers #pytorch #perceiver #image-classification #dataset-imagenet #arxiv-2107.14795 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# Perceiver IO for vision (learned position embeddings)\n\nPerceiver IO model pre-trained on ImageNet (14 million images, 1,000 classe... |
text-generation | transformers |
# Aeona | Chatbot
An generative AI made using [microsoft/DialoGPT-small](https://huggingface.co/microsoft/DialoGPT-small).
Recommended to use along with an [AIML Chatbot](https://github.com/deepsarda/Aeona-Aiml) t... | {"license": "mit", "tags": ["conversational"], "datasets": ["blended_skill_talk"], "metrics": ["accuracy", "f1", "perplexity"], "thumbnail": "https://images-ext-2.discordapp.net/external/Wvtx1L98EbA7DR2lpZPbDxDuO4qmKt03nZygATZtXgk/%3Fsize%3D4096/https/cdn.discordapp.com/avatars/931226824753700934/338a9e413bbceaeb9095a2... | deepparag/Aeona | null | [
"transformers",
"pytorch",
"safetensors",
"gpt2",
"text-generation",
"conversational",
"dataset:blended_skill_talk",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #safetensors #gpt2 #text-generation #conversational #dataset-blended_skill_talk #license-mit #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| Aeona | Chatbot
===============
!Aeona Banner
An generative AI made using microsoft/DialoGPT-small.
Recommended to use along with an AIML Chatbot to reduce load, get better replies, add name and personality to your bot.
Using an AIML Chatbot will allow you to hardcode some replies also.
AEONA
=====
Aeona is a... | [
"#### Why not an AI on its own?\n\n\nFor AI it is not possible (realistically) to learn about the user and store data on them, when compared to an AIML which can even execute code!\nThe goal of the AI is to generate responses where the AIML fails.\n\n\nHence the goals becomes to make an AI which has a wide variety ... | [
"TAGS\n#transformers #pytorch #safetensors #gpt2 #text-generation #conversational #dataset-blended_skill_talk #license-mit #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"#### Why not an AI on its own?\n\n\nFor AI it is not possible (realistically) to learn about... |
text-generation | transformers | An generative AI made using [microsoft/DialoGPT-small](https://huggingface.co/microsoft/DialoGPT-small).
Trained on:
https://www.kaggle.com/Cornell-University/movie-dialog-corpus
https://www.kaggle.com/jef1056/discord-data
Important:
The AI can be a bit weird at times as it is still undergoing tr... | {"license": "mit", "tags": ["conversational"], "thumbnail": "https://cdn.discordapp.com/app-icons/870239976690970625/c02cae78ae105f07969cfd8f8ea3d0a0.png"} | deepparag/DumBot-Beta | null | [
"transformers",
"pytorch",
"gpt_neo",
"text-generation",
"conversational",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt_neo #text-generation #conversational #license-mit #autotrain_compatible #endpoints_compatible #region-us
| An generative AI made using microsoft/DialoGPT-small.
Trained on:
URL
URL
Important:
The AI can be a bit weird at times as it is still undergoing training!
At times it send stuff using :<random_wierd_words>: as they are discord emotes.
It also send random @RandomName as ... | [] | [
"TAGS\n#transformers #pytorch #gpt_neo #text-generation #conversational #license-mit #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-generation | transformers | # THIS AI IS OUTDATED. See [Aeona](https://huggingface.co/deepparag/Aeona)
An generative AI made using [microsoft/DialoGPT-small](https://huggingface.co/microsoft/DialoGPT-small).
Trained on:
https://www.kaggle.com/Cornell-University/movie-dialog-corpus
https://www.kaggle.com/jef1056/discord-data
[... | {"license": "mit", "tags": ["conversational"], "thumbnail": "https://cdn.discordapp.com/app-icons/870239976690970625/c02cae78ae105f07969cfd8f8ea3d0a0.png"} | deepparag/DumBot | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # THIS AI IS OUTDATED. See Aeona
An generative AI made using microsoft/DialoGPT-small.
Trained on:
URL
URL
Live Demo
Example:
| [
"# THIS AI IS OUTDATED. See Aeona\nAn generative AI made using microsoft/DialoGPT-small.\n\nTrained on:\n\n URL\n\n URL\n\n\n \nLive Demo\n \nExample:"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# THIS AI IS OUTDATED. See Aeona\nAn generative AI made using microsoft/DialoGPT-small.\n\nTrained on:\n\n URL\n\n URL\n\n\n \nLive ... |
question-answering | transformers |
This is a BERT base cased model trained on SQuAD v2 | {"language": "en", "license": "cc-by-4.0", "datasets": ["squad_v2"], "model-index": [{"name": "deepset/bert-base-cased-squad2", "results": [{"task": {"type": "question-answering", "name": "Question Answering"}, "dataset": {"name": "squad_v2", "type": "squad_v2", "config": "squad_v2", "split": "validation"}, "metrics": ... | deepset/bert-base-cased-squad2 | null | [
"transformers",
"pytorch",
"jax",
"safetensors",
"bert",
"question-answering",
"en",
"dataset:squad_v2",
"license:cc-by-4.0",
"model-index",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #jax #safetensors #bert #question-answering #en #dataset-squad_v2 #license-cc-by-4.0 #model-index #endpoints_compatible #has_space #region-us
|
This is a BERT base cased model trained on SQuAD v2 | [] | [
"TAGS\n#transformers #pytorch #jax #safetensors #bert #question-answering #en #dataset-squad_v2 #license-cc-by-4.0 #model-index #endpoints_compatible #has_space #region-us \n"
] |
text-classification | transformers |
This is a German BERT v1 (https://deepset.ai/german-bert) trained to do hate speech detection on the GermEval18Coarse dataset | {"license": "cc-by-4.0"} | deepset/bert-base-german-cased-hatespeech-GermEval18Coarse | null | [
"transformers",
"pytorch",
"jax",
"safetensors",
"bert",
"text-classification",
"license:cc-by-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #safetensors #bert #text-classification #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #region-us
|
This is a German BERT v1 (URL trained to do hate speech detection on the GermEval18Coarse dataset | [] | [
"TAGS\n#transformers #pytorch #jax #safetensors #bert #text-classification #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
fill-mask | transformers |
<a href="https://huggingface.co/exbert/?model=bert-base-german-cased">
\t<img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png">
</a>
# German BERT with old vocabulary
For details see the related [FARM issue](https://github.com/deepset-ai/FARM/issues/60).
## About us
![deepset logo](https://wor... | {"language": "de", "license": "mit", "tags": ["exbert"], "thumbnail": "https://static.tildacdn.com/tild6438-3730-4164-b266-613634323466/german_bert.png"} | deepset/bert-base-german-cased-oldvocab | null | [
"transformers",
"pytorch",
"jax",
"bert",
"fill-mask",
"exbert",
"de",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"de"
] | TAGS
#transformers #pytorch #jax #bert #fill-mask #exbert #de #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
<a href="URL
\t<img width="300px" src="URL
</a>
# German BERT with old vocabulary
For details see the related FARM issue.
## About us
!deepset logo
We bring NLP to the industry via open source!
Our focus: Industry specific language models & large scale QA systems.
Some of our work:
- German BERT (aka "bert... | [
"# German BERT with old vocabulary\nFor details see the related FARM issue.",
"## About us\n!deepset logo\n\nWe bring NLP to the industry via open source! \nOur focus: Industry specific language models & large scale QA systems. \n \nSome of our work: \n- German BERT (aka \"bert-base-german-cased\")\n- GermanQu... | [
"TAGS\n#transformers #pytorch #jax #bert #fill-mask #exbert #de #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# German BERT with old vocabulary\nFor details see the related FARM issue.",
"## About us\n!deepset logo\n\nWe bring NLP to the industry via open source! \nOur focus: Indust... |
question-answering | transformers |
# bert-base-uncased for QA
## Overview
**Language model:** bert-base-uncased
**Language:** English
**Downstream-task:** Extractive QA
**Training data:** SQuAD 2.0
**Eval data:** SQuAD 2.0
**Infrastructure**: 1x Tesla v100
## Hyperparameters
```
batch_size = 32
n_epochs = 3
base_LM_model = "bert-base-unca... | {"language": "en", "license": "cc-by-4.0", "datasets": ["squad_v2"], "model-index": [{"name": "deepset/bert-base-uncased-squad2", "results": [{"task": {"type": "question-answering", "name": "Question Answering"}, "dataset": {"name": "squad_v2", "type": "squad_v2", "config": "squad_v2", "split": "validation"}, "metrics"... | deepset/bert-base-uncased-squad2 | null | [
"transformers",
"pytorch",
"safetensors",
"bert",
"question-answering",
"en",
"dataset:squad_v2",
"license:cc-by-4.0",
"model-index",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #safetensors #bert #question-answering #en #dataset-squad_v2 #license-cc-by-4.0 #model-index #endpoints_compatible #has_space #region-us
|
# bert-base-uncased for QA
## Overview
Language model: bert-base-uncased
Language: English
Downstream-task: Extractive QA
Training data: SQuAD 2.0
Eval data: SQuAD 2.0
Infrastructure: 1x Tesla v100
## Hyperparameters
## Performance
## Authors
- Timo Möller: 'timo.moeller [at] URL'
- Julian Risch: 'U... | [
"# bert-base-uncased for QA",
"## Overview\nLanguage model: bert-base-uncased \nLanguage: English \nDownstream-task: Extractive QA \nTraining data: SQuAD 2.0 \nEval data: SQuAD 2.0 \nInfrastructure: 1x Tesla v100",
"## Hyperparameters",
"## Performance",
"## Authors\n- Timo Möller: 'timo.moeller [at] U... | [
"TAGS\n#transformers #pytorch #safetensors #bert #question-answering #en #dataset-squad_v2 #license-cc-by-4.0 #model-index #endpoints_compatible #has_space #region-us \n",
"# bert-base-uncased for QA",
"## Overview\nLanguage model: bert-base-uncased \nLanguage: English \nDownstream-task: Extractive QA \nTrai... |
question-answering | transformers |
# bert-large-uncased-whole-word-masking-squad2
This is a berta-large model, fine-tuned using the SQuAD2.0 dataset for the task of question answering.
## Overview
**Language model:** bert-large
**Language:** English
**Downstream-task:** Extractive QA
**Training data:** SQuAD 2.0
**Eval data:** SQuAD 2.0
**C... | {"language": "en", "license": "cc-by-4.0", "datasets": ["squad_v2"], "model-index": [{"name": "deepset/bert-large-uncased-whole-word-masking-squad2", "results": [{"task": {"type": "question-answering", "name": "Question Answering"}, "dataset": {"name": "squad_v2", "type": "squad_v2", "config": "squad_v2", "split": "val... | deepset/bert-large-uncased-whole-word-masking-squad2 | null | [
"transformers",
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"question-answering",
"en",
"dataset:squad_v2",
"license:cc-by-4.0",
"model-index",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #tf #jax #safetensors #bert #question-answering #en #dataset-squad_v2 #license-cc-by-4.0 #model-index #endpoints_compatible #has_space #region-us
|
# bert-large-uncased-whole-word-masking-squad2
This is a berta-large model, fine-tuned using the SQuAD2.0 dataset for the task of question answering.
## Overview
Language model: bert-large
Language: English
Downstream-task: Extractive QA
Training data: SQuAD 2.0
Eval data: SQuAD 2.0
Code: See an example Q... | [
"# bert-large-uncased-whole-word-masking-squad2\n\nThis is a berta-large model, fine-tuned using the SQuAD2.0 dataset for the task of question answering.",
"## Overview\nLanguage model: bert-large \nLanguage: English \nDownstream-task: Extractive QA \nTraining data: SQuAD 2.0 \nEval data: SQuAD 2.0 \nCode: ... | [
"TAGS\n#transformers #pytorch #tf #jax #safetensors #bert #question-answering #en #dataset-squad_v2 #license-cc-by-4.0 #model-index #endpoints_compatible #has_space #region-us \n",
"# bert-large-uncased-whole-word-masking-squad2\n\nThis is a berta-large model, fine-tuned using the SQuAD2.0 dataset for the task of... |
question-answering | transformers |
## Overview
**Language model:** deepset/roberta-base-squad2-distilled
**Language:** English
**Training data:** SQuAD 2.0 training set
**Eval data:** SQuAD 2.0 dev set
**Infrastructure**: 1x V100 GPU
**Published**: Apr 21st, 2021
## Details
- haystack's distillation feature was used for training. deepset/... | {"language": "en", "license": "mit", "tags": ["exbert"], "datasets": ["squad_v2"], "thumbnail": "https://thumb.tildacdn.com/tild3433-3637-4830-a533-353833613061/-/resize/720x/-/format/webp/germanquad.jpg", "model-index": [{"name": "deepset/bert-medium-squad2-distilled", "results": [{"task": {"type": "question-answering... | deepset/bert-medium-squad2-distilled | null | [
"transformers",
"pytorch",
"safetensors",
"bert",
"question-answering",
"exbert",
"en",
"dataset:squad_v2",
"license:mit",
"model-index",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #safetensors #bert #question-answering #exbert #en #dataset-squad_v2 #license-mit #model-index #endpoints_compatible #has_space #region-us
|
## Overview
Language model: deepset/roberta-base-squad2-distilled
Language: English
Training data: SQuAD 2.0 training set
Eval data: SQuAD 2.0 dev set
Infrastructure: 1x V100 GPU
Published: Apr 21st, 2021
## Details
- haystack's distillation feature was used for training. deepset/bert-large-uncased-whole... | [
"## Overview\nLanguage model: deepset/roberta-base-squad2-distilled \nLanguage: English \nTraining data: SQuAD 2.0 training set \nEval data: SQuAD 2.0 dev set \nInfrastructure: 1x V100 GPU \nPublished: Apr 21st, 2021",
"## Details\n- haystack's distillation feature was used for training. deepset/bert-large... | [
"TAGS\n#transformers #pytorch #safetensors #bert #question-answering #exbert #en #dataset-squad_v2 #license-mit #model-index #endpoints_compatible #has_space #region-us \n",
"## Overview\nLanguage model: deepset/roberta-base-squad2-distilled \nLanguage: English \nTraining data: SQuAD 2.0 training set \nEval d... |
question-answering | transformers |
# electra-base for QA
## Overview
**Language model:** electra-base
**Language:** English
**Downstream-task:** Extractive QA
**Training data:** SQuAD 2.0
**Eval data:** SQuAD 2.0
**Code:** See [example](https://github.com/deepset-ai/FARM/blob/master/examples/question_answering.py) in [FARM](https://github.c... | {"language": "en", "license": "cc-by-4.0", "datasets": ["squad_v2"], "model-index": [{"name": "deepset/electra-base-squad2", "results": [{"task": {"type": "question-answering", "name": "Question Answering"}, "dataset": {"name": "squad_v2", "type": "squad_v2", "config": "squad_v2", "split": "validation"}, "metrics": [{"... | deepset/electra-base-squad2 | null | [
"transformers",
"pytorch",
"safetensors",
"electra",
"question-answering",
"en",
"dataset:squad_v2",
"license:cc-by-4.0",
"model-index",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #safetensors #electra #question-answering #en #dataset-squad_v2 #license-cc-by-4.0 #model-index #endpoints_compatible #has_space #region-us
|
# electra-base for QA
## Overview
Language model: electra-base
Language: English
Downstream-task: Extractive QA
Training data: SQuAD 2.0
Eval data: SQuAD 2.0
Code: See example in FARM
Infrastructure: 1x Tesla v100
## Hyperparameters
## Performance
Evaluated on the SQuAD 2.0 dev set with the official ... | [
"# electra-base for QA",
"## Overview\nLanguage model: electra-base \nLanguage: English \nDownstream-task: Extractive QA \nTraining data: SQuAD 2.0 \nEval data: SQuAD 2.0 \nCode: See example in FARM \nInfrastructure: 1x Tesla v100",
"## Hyperparameters",
"## Performance\nEvaluated on the SQuAD 2.0 dev ... | [
"TAGS\n#transformers #pytorch #safetensors #electra #question-answering #en #dataset-squad_v2 #license-cc-by-4.0 #model-index #endpoints_compatible #has_space #region-us \n",
"# electra-base for QA",
"## Overview\nLanguage model: electra-base \nLanguage: English \nDownstream-task: Extractive QA \nTraining da... |
null | transformers |
## Overview
**Language model:** gbert-base-germandpr
**Language:** German
**Training data:** GermanDPR train set (~ 56MB)
**Eval data:** GermanDPR test set (~ 6MB)
**Infrastructure**: 4x... | {"language": "de", "license": "mit", "tags": ["exbert"], "datasets": ["deepset/germandpr"], "thumbnail": "https://thumb.tildacdn.com/tild3433-3637-4830-a533-353833613061/-/resize/720x/-/format/webp/germanquad.jpg"} | deepset/gbert-base-germandpr-ctx_encoder | null | [
"transformers",
"pytorch",
"dpr",
"exbert",
"de",
"dataset:deepset/germandpr",
"license:mit",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"de"
] | TAGS
#transformers #pytorch #dpr #exbert #de #dataset-deepset/germandpr #license-mit #endpoints_compatible #has_space #region-us
|
!bert_image
## Overview
Language model: gbert-base-germandpr
Language: German
Training data: GermanDPR train set (~ 56MB)
Eval data: GermanDPR test set (~ 6MB)
Infrastructure: 4x V100 GPU
Published: Apr 26th, 2021
## Details
- We trained a dense passage retrieval model with two gbert-base models as encod... | [
"## Overview\nLanguage model: gbert-base-germandpr \nLanguage: German \nTraining data: GermanDPR train set (~ 56MB) \nEval data: GermanDPR test set (~ 6MB) \nInfrastructure: 4x V100 GPU \nPublished: Apr 26th, 2021",
"## Details\n- We trained a dense passage retrieval model with two gbert-base models as enc... | [
"TAGS\n#transformers #pytorch #dpr #exbert #de #dataset-deepset/germandpr #license-mit #endpoints_compatible #has_space #region-us \n",
"## Overview\nLanguage model: gbert-base-germandpr \nLanguage: German \nTraining data: GermanDPR train set (~ 56MB) \nEval data: GermanDPR test set (~ 6MB) \nInfrastructure... |
feature-extraction | transformers |
## Overview
**Language model:** gbert-base-germandpr
**Language:** German
**Training data:** GermanDPR train set (~ 56MB)
**Eval data:** GermanDPR test set (~ 6MB)
**Infrastructure**: 4x... | {"language": "de", "license": "mit", "tags": ["exbert"], "datasets": ["deepset/germandpr"], "thumbnail": "https://thumb.tildacdn.com/tild3433-3637-4830-a533-353833613061/-/resize/720x/-/format/webp/germanquad.jpg"} | deepset/gbert-base-germandpr-question_encoder | null | [
"transformers",
"pytorch",
"safetensors",
"dpr",
"feature-extraction",
"exbert",
"de",
"dataset:deepset/germandpr",
"license:mit",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"de"
] | TAGS
#transformers #pytorch #safetensors #dpr #feature-extraction #exbert #de #dataset-deepset/germandpr #license-mit #endpoints_compatible #has_space #region-us
|
!bert_image
## Overview
Language model: gbert-base-germandpr
Language: German
Training data: GermanDPR train set (~ 56MB)
Eval data: GermanDPR test set (~ 6MB)
Infrastructure: 4x V100 GPU
Published: Apr 26th, 2021
## Details
- We trained a dense passage retrieval model with two gbert-base models as encod... | [
"## Overview\nLanguage model: gbert-base-germandpr \nLanguage: German \nTraining data: GermanDPR train set (~ 56MB) \nEval data: GermanDPR test set (~ 6MB) \nInfrastructure: 4x V100 GPU \nPublished: Apr 26th, 2021",
"## Details\n- We trained a dense passage retrieval model with two gbert-base models as enc... | [
"TAGS\n#transformers #pytorch #safetensors #dpr #feature-extraction #exbert #de #dataset-deepset/germandpr #license-mit #endpoints_compatible #has_space #region-us \n",
"## Overview\nLanguage model: gbert-base-germandpr \nLanguage: German \nTraining data: GermanDPR train set (~ 56MB) \nEval data: GermanDPR te... |
text-classification | transformers |
## Overview
**Language model:** gbert-base-germandpr-reranking
**Language:** German
**Training data:** GermanDPR train set (~ 56MB)
**Eval data:** GermanDPR test set (~ 6MB)
**Infrastructure**: 1x V100 GPU
**Published**: June 3rd, 2021
## Details
- We trained a text pair classification model in FARM, which... | {"language": "de", "license": "mit", "datasets": ["deepset/germandpr"]} | deepset/gbert-base-germandpr-reranking | null | [
"transformers",
"pytorch",
"safetensors",
"bert",
"text-classification",
"de",
"dataset:deepset/germandpr",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"de"
] | TAGS
#transformers #pytorch #safetensors #bert #text-classification #de #dataset-deepset/germandpr #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
## Overview
Language model: gbert-base-germandpr-reranking
Language: German
Training data: GermanDPR train set (~ 56MB)
Eval data: GermanDPR test set (~ 6MB)
Infrastructure: 1x V100 GPU
Published: June 3rd, 2021
## Details
- We trained a text pair classification model in FARM, which can be used for reranki... | [
"## Overview\nLanguage model: gbert-base-germandpr-reranking \nLanguage: German \nTraining data: GermanDPR train set (~ 56MB) \nEval data: GermanDPR test set (~ 6MB) \nInfrastructure: 1x V100 GPU \nPublished: June 3rd, 2021",
"## Details\n- We trained a text pair classification model in FARM, which can be u... | [
"TAGS\n#transformers #pytorch #safetensors #bert #text-classification #de #dataset-deepset/germandpr #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"## Overview\nLanguage model: gbert-base-germandpr-reranking \nLanguage: German \nTraining data: GermanDPR train set (~ 56MB) \nEval data... |
fill-mask | transformers |
# German BERT base
Released, Oct 2020, this is a German BERT language model trained collaboratively by the makers of the original German BERT (aka "bert-base-german-cased") and the dbmdz BERT (aka bert-base-german-dbmdz-cased). In our [paper](https://arxiv.org/pdf/2010.10906.pdf), we outline the steps taken to train ... | {"language": "de", "license": "mit", "datasets": ["wikipedia", "OPUS", "OpenLegalData"]} | deepset/gbert-base | null | [
"transformers",
"pytorch",
"tf",
"safetensors",
"fill-mask",
"de",
"dataset:wikipedia",
"dataset:OPUS",
"dataset:OpenLegalData",
"arxiv:2010.10906",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2010.10906"
] | [
"de"
] | TAGS
#transformers #pytorch #tf #safetensors #fill-mask #de #dataset-wikipedia #dataset-OPUS #dataset-OpenLegalData #arxiv-2010.10906 #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# German BERT base
Released, Oct 2020, this is a German BERT language model trained collaboratively by the makers of the original German BERT (aka "bert-base-german-cased") and the dbmdz BERT (aka bert-base-german-dbmdz-cased). In our paper, we outline the steps taken to train our model and show that it outperforms i... | [
"# German BERT base\n\nReleased, Oct 2020, this is a German BERT language model trained collaboratively by the makers of the original German BERT (aka \"bert-base-german-cased\") and the dbmdz BERT (aka bert-base-german-dbmdz-cased). In our paper, we outline the steps taken to train our model and show that it outpe... | [
"TAGS\n#transformers #pytorch #tf #safetensors #fill-mask #de #dataset-wikipedia #dataset-OPUS #dataset-OpenLegalData #arxiv-2010.10906 #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# German BERT base\n\nReleased, Oct 2020, this is a German BERT language model trained collab... |
text-classification | transformers |
## Overview
**Language model:** gbert-large-sts
**Language:** German
**Training data:** German STS benchmark train and dev set
**Eval data:** German STS benchmark test set
**Infrastructure**: 1x V100 GPU
**Published**: August 12th, 2021
## Details
- We trained a gbert-large model on the task of estimating s... | {"language": "de", "license": "mit", "tags": ["exbert"]} | deepset/gbert-large-sts | null | [
"transformers",
"pytorch",
"safetensors",
"bert",
"text-classification",
"exbert",
"de",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"de"
] | TAGS
#transformers #pytorch #safetensors #bert #text-classification #exbert #de #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
## Overview
Language model: gbert-large-sts
Language: German
Training data: German STS benchmark train and dev set
Eval data: German STS benchmark test set
Infrastructure: 1x V100 GPU
Published: August 12th, 2021
## Details
- We trained a gbert-large model on the task of estimating semantic similarity of Ge... | [
"## Overview\nLanguage model: gbert-large-sts\n\nLanguage: German \nTraining data: German STS benchmark train and dev set \nEval data: German STS benchmark test set \nInfrastructure: 1x V100 GPU \nPublished: August 12th, 2021",
"## Details\n- We trained a gbert-large model on the task of estimating semantic ... | [
"TAGS\n#transformers #pytorch #safetensors #bert #text-classification #exbert #de #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"## Overview\nLanguage model: gbert-large-sts\n\nLanguage: German \nTraining data: German STS benchmark train and dev set \nEval data: German STS benchmark t... |
fill-mask | transformers |
# German BERT large
Released, Oct 2020, this is a German BERT language model trained collaboratively by the makers of the original German BERT (aka "bert-base-german-cased") and the dbmdz BERT (aka bert-base-german-dbmdz-cased). In our [paper](https://arxiv.org/pdf/2010.10906.pdf), we outline the steps taken to train... | {"language": "de", "license": "mit", "datasets": ["wikipedia", "OPUS", "OpenLegalData", "oscar"]} | deepset/gbert-large | null | [
"transformers",
"pytorch",
"tf",
"safetensors",
"fill-mask",
"de",
"dataset:wikipedia",
"dataset:OPUS",
"dataset:OpenLegalData",
"dataset:oscar",
"arxiv:2010.10906",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2010.10906"
] | [
"de"
] | TAGS
#transformers #pytorch #tf #safetensors #fill-mask #de #dataset-wikipedia #dataset-OPUS #dataset-OpenLegalData #dataset-oscar #arxiv-2010.10906 #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# German BERT large
Released, Oct 2020, this is a German BERT language model trained collaboratively by the makers of the original German BERT (aka "bert-base-german-cased") and the dbmdz BERT (aka bert-base-german-dbmdz-cased). In our paper, we outline the steps taken to train our model and show that it outperforms ... | [
"# German BERT large\n\nReleased, Oct 2020, this is a German BERT language model trained collaboratively by the makers of the original German BERT (aka \"bert-base-german-cased\") and the dbmdz BERT (aka bert-base-german-dbmdz-cased). In our paper, we outline the steps taken to train our model and show that it outp... | [
"TAGS\n#transformers #pytorch #tf #safetensors #fill-mask #de #dataset-wikipedia #dataset-OPUS #dataset-OpenLegalData #dataset-oscar #arxiv-2010.10906 #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# German BERT large\n\nReleased, Oct 2020, this is a German BERT language mode... |
fill-mask | transformers |
# German ELECTRA base generator
Released, Oct 2020, this is the generator component of the German ELECTRA language model trained collaboratively by the makers of the original German BERT (aka "bert-base-german-cased") and the dbmdz BERT (aka bert-base-german-dbmdz-cased). In our [paper](https://arxiv.org/pdf/2010.109... | {"language": "de", "license": "mit", "datasets": ["wikipedia", "OPUS", "OpenLegalData"]} | deepset/gelectra-base-generator | null | [
"transformers",
"pytorch",
"tf",
"safetensors",
"electra",
"fill-mask",
"de",
"dataset:wikipedia",
"dataset:OPUS",
"dataset:OpenLegalData",
"arxiv:2010.10906",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2010.10906"
] | [
"de"
] | TAGS
#transformers #pytorch #tf #safetensors #electra #fill-mask #de #dataset-wikipedia #dataset-OPUS #dataset-OpenLegalData #arxiv-2010.10906 #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# German ELECTRA base generator
Released, Oct 2020, this is the generator component of the German ELECTRA language model trained collaboratively by the makers of the original German BERT (aka "bert-base-german-cased") and the dbmdz BERT (aka bert-base-german-dbmdz-cased). In our paper, we outline the steps taken to t... | [
"# German ELECTRA base generator\n\nReleased, Oct 2020, this is the generator component of the German ELECTRA language model trained collaboratively by the makers of the original German BERT (aka \"bert-base-german-cased\") and the dbmdz BERT (aka bert-base-german-dbmdz-cased). In our paper, we outline the steps ta... | [
"TAGS\n#transformers #pytorch #tf #safetensors #electra #fill-mask #de #dataset-wikipedia #dataset-OPUS #dataset-OpenLegalData #arxiv-2010.10906 #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# German ELECTRA base generator\n\nReleased, Oct 2020, this is the generator component of the G... |
question-answering | transformers |
## Overview
**Language model:** gelectra-base-germanquad-distilled
**Language:** German
**Training data:** GermanQuAD train set (~ 12MB)
**Eval data:** GermanQuAD test set (~ 5MB)
**Infr... | {"language": "de", "license": "mit", "tags": ["exbert"], "datasets": ["deepset/germanquad"], "thumbnail": "https://thumb.tildacdn.com/tild3433-3637-4830-a533-353833613061/-/resize/720x/-/format/webp/germanquad.jpg"} | deepset/gelectra-base-germanquad-distilled | null | [
"transformers",
"pytorch",
"safetensors",
"electra",
"question-answering",
"exbert",
"de",
"dataset:deepset/germanquad",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"de"
] | TAGS
#transformers #pytorch #safetensors #electra #question-answering #exbert #de #dataset-deepset/germanquad #license-mit #endpoints_compatible #region-us
|
!bert_image
## Overview
Language model: gelectra-base-germanquad-distilled
Language: German
Training data: GermanQuAD train set (~ 12MB)
Eval data: GermanQuAD test set (~ 5MB)
Infrastructure: 1x V100 GPU
Published: Apr 21st, 2021
## Details
- We trained a German question answering model with a gelectra-b... | [
"## Overview\nLanguage model: gelectra-base-germanquad-distilled \nLanguage: German \nTraining data: GermanQuAD train set (~ 12MB) \nEval data: GermanQuAD test set (~ 5MB) \nInfrastructure: 1x V100 GPU \nPublished: Apr 21st, 2021",
"## Details\n- We trained a German question answering model with a gelectra... | [
"TAGS\n#transformers #pytorch #safetensors #electra #question-answering #exbert #de #dataset-deepset/germanquad #license-mit #endpoints_compatible #region-us \n",
"## Overview\nLanguage model: gelectra-base-germanquad-distilled \nLanguage: German \nTraining data: GermanQuAD train set (~ 12MB) \nEval data: Ger... |
question-answering | transformers |
## Overview
**Language model:** gelectra-base-germanquad
**Language:** German
**Training data:** GermanQuAD train set (~ 12MB)
**Eval data:** GermanQuAD test set (~ 5MB)
**Infrastructure... | {"language": "de", "license": "mit", "tags": ["exbert"], "datasets": ["deepset/germanquad"], "thumbnail": "https://thumb.tildacdn.com/tild3433-3637-4830-a533-353833613061/-/resize/720x/-/format/webp/germanquad.jpg"} | deepset/gelectra-base-germanquad | null | [
"transformers",
"pytorch",
"tf",
"safetensors",
"electra",
"question-answering",
"exbert",
"de",
"dataset:deepset/germanquad",
"license:mit",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"de"
] | TAGS
#transformers #pytorch #tf #safetensors #electra #question-answering #exbert #de #dataset-deepset/germanquad #license-mit #endpoints_compatible #has_space #region-us
|
!bert_image
## Overview
Language model: gelectra-base-germanquad
Language: German
Training data: GermanQuAD train set (~ 12MB)
Eval data: GermanQuAD test set (~ 5MB)
Infrastructure: 1x V100 GPU
Published: Apr 21st, 2021
## Details
- We trained a German question answering model with a gelectra-base model ... | [
"## Overview\nLanguage model: gelectra-base-germanquad \nLanguage: German \nTraining data: GermanQuAD train set (~ 12MB) \nEval data: GermanQuAD test set (~ 5MB) \nInfrastructure: 1x V100 GPU \nPublished: Apr 21st, 2021",
"## Details\n- We trained a German question answering model with a gelectra-base mode... | [
"TAGS\n#transformers #pytorch #tf #safetensors #electra #question-answering #exbert #de #dataset-deepset/germanquad #license-mit #endpoints_compatible #has_space #region-us \n",
"## Overview\nLanguage model: gelectra-base-germanquad \nLanguage: German \nTraining data: GermanQuAD train set (~ 12MB) \nEval data... |
null | transformers |
# German ELECTRA base
Released, Oct 2020, this is a German ELECTRA language model trained collaboratively by the makers of the original German BERT (aka "bert-base-german-cased") and the dbmdz BERT (aka bert-base-german-dbmdz-cased). In our [paper](https://arxiv.org/pdf/2010.10906.pdf), we outline the steps taken to ... | {"language": "de", "license": "mit", "datasets": ["wikipedia", "OPUS", "OpenLegalData"]} | deepset/gelectra-base | null | [
"transformers",
"pytorch",
"tf",
"electra",
"pretraining",
"de",
"dataset:wikipedia",
"dataset:OPUS",
"dataset:OpenLegalData",
"arxiv:2010.10906",
"license:mit",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2010.10906"
] | [
"de"
] | TAGS
#transformers #pytorch #tf #electra #pretraining #de #dataset-wikipedia #dataset-OPUS #dataset-OpenLegalData #arxiv-2010.10906 #license-mit #endpoints_compatible #has_space #region-us
|
# German ELECTRA base
Released, Oct 2020, this is a German ELECTRA language model trained collaboratively by the makers of the original German BERT (aka "bert-base-german-cased") and the dbmdz BERT (aka bert-base-german-dbmdz-cased). In our paper, we outline the steps taken to train our model. Our evaluation suggests... | [
"# German ELECTRA base\n\nReleased, Oct 2020, this is a German ELECTRA language model trained collaboratively by the makers of the original German BERT (aka \"bert-base-german-cased\") and the dbmdz BERT (aka bert-base-german-dbmdz-cased). In our paper, we outline the steps taken to train our model. Our evaluation ... | [
"TAGS\n#transformers #pytorch #tf #electra #pretraining #de #dataset-wikipedia #dataset-OPUS #dataset-OpenLegalData #arxiv-2010.10906 #license-mit #endpoints_compatible #has_space #region-us \n",
"# German ELECTRA base\n\nReleased, Oct 2020, this is a German ELECTRA language model trained collaboratively by the m... |
fill-mask | transformers |
# German ELECTRA large generator
Released, Oct 2020, this is the generator component of the German ELECTRA language model trained collaboratively by the makers of the original German BERT (aka "bert-base-german-cased") and the dbmdz BERT (aka bert-base-german-dbmdz-cased). In our [paper](https://arxiv.org/pdf/2010.10... | {"language": "de", "license": "mit", "datasets": ["wikipedia", "OPUS", "OpenLegalData", "oscar"]} | deepset/gelectra-large-generator | null | [
"transformers",
"pytorch",
"tf",
"safetensors",
"electra",
"fill-mask",
"de",
"dataset:wikipedia",
"dataset:OPUS",
"dataset:OpenLegalData",
"dataset:oscar",
"arxiv:2010.10906",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2010.10906"
] | [
"de"
] | TAGS
#transformers #pytorch #tf #safetensors #electra #fill-mask #de #dataset-wikipedia #dataset-OPUS #dataset-OpenLegalData #dataset-oscar #arxiv-2010.10906 #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# German ELECTRA large generator
Released, Oct 2020, this is the generator component of the German ELECTRA language model trained collaboratively by the makers of the original German BERT (aka "bert-base-german-cased") and the dbmdz BERT (aka bert-base-german-dbmdz-cased). In our paper, we outline the steps taken to ... | [
"# German ELECTRA large generator\n\nReleased, Oct 2020, this is the generator component of the German ELECTRA language model trained collaboratively by the makers of the original German BERT (aka \"bert-base-german-cased\") and the dbmdz BERT (aka bert-base-german-dbmdz-cased). In our paper, we outline the steps t... | [
"TAGS\n#transformers #pytorch #tf #safetensors #electra #fill-mask #de #dataset-wikipedia #dataset-OPUS #dataset-OpenLegalData #dataset-oscar #arxiv-2010.10906 #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# German ELECTRA large generator\n\nReleased, Oct 2020, this is the generator co... |
question-answering | transformers |
## Overview
**Language model:** gelectra-large-germanquad
**Language:** German
**Training data:** GermanQuAD train set (~ 12MB)
**Eval data:** GermanQuAD test set (~ 5MB)
**Infrastructur... | {"language": "de", "license": "mit", "tags": ["exbert"], "datasets": ["deepset/germanquad"], "thumbnail": "https://thumb.tildacdn.com/tild3433-3637-4830-a533-353833613061/-/resize/720x/-/format/webp/germanquad.jpg"} | deepset/gelectra-large-germanquad | null | [
"transformers",
"pytorch",
"tf",
"safetensors",
"electra",
"question-answering",
"exbert",
"de",
"dataset:deepset/germanquad",
"license:mit",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"de"
] | TAGS
#transformers #pytorch #tf #safetensors #electra #question-answering #exbert #de #dataset-deepset/germanquad #license-mit #endpoints_compatible #has_space #region-us
|
!bert_image
## Overview
Language model: gelectra-large-germanquad
Language: German
Training data: GermanQuAD train set (~ 12MB)
Eval data: GermanQuAD test set (~ 5MB)
Infrastructure: 1x V100 GPU
Published: Apr 21st, 2021
## Details
- We trained a German question answering model with a gelectra-large mode... | [
"## Overview\nLanguage model: gelectra-large-germanquad \nLanguage: German \nTraining data: GermanQuAD train set (~ 12MB) \nEval data: GermanQuAD test set (~ 5MB) \nInfrastructure: 1x V100 GPU \nPublished: Apr 21st, 2021",
"## Details\n- We trained a German question answering model with a gelectra-large mo... | [
"TAGS\n#transformers #pytorch #tf #safetensors #electra #question-answering #exbert #de #dataset-deepset/germanquad #license-mit #endpoints_compatible #has_space #region-us \n",
"## Overview\nLanguage model: gelectra-large-germanquad \nLanguage: German \nTraining data: GermanQuAD train set (~ 12MB) \nEval dat... |
null | transformers |
# German ELECTRA large
Released, Oct 2020, this is a German ELECTRA language model trained collaboratively by the makers of the original German BERT (aka "bert-base-german-cased") and the dbmdz BERT (aka bert-base-german-dbmdz-cased). In our [paper](https://arxiv.org/pdf/2010.10906.pdf), we outline the steps taken to... | {"language": "de", "license": "mit", "datasets": ["wikipedia", "OPUS", "OpenLegalData", "oscar"]} | deepset/gelectra-large | null | [
"transformers",
"pytorch",
"tf",
"electra",
"pretraining",
"de",
"dataset:wikipedia",
"dataset:OPUS",
"dataset:OpenLegalData",
"dataset:oscar",
"arxiv:2010.10906",
"license:mit",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2010.10906"
] | [
"de"
] | TAGS
#transformers #pytorch #tf #electra #pretraining #de #dataset-wikipedia #dataset-OPUS #dataset-OpenLegalData #dataset-oscar #arxiv-2010.10906 #license-mit #endpoints_compatible #has_space #region-us
|
# German ELECTRA large
Released, Oct 2020, this is a German ELECTRA language model trained collaboratively by the makers of the original German BERT (aka "bert-base-german-cased") and the dbmdz BERT (aka bert-base-german-dbmdz-cased). In our paper, we outline the steps taken to train our model and show that this is t... | [
"# German ELECTRA large\n\nReleased, Oct 2020, this is a German ELECTRA language model trained collaboratively by the makers of the original German BERT (aka \"bert-base-german-cased\") and the dbmdz BERT (aka bert-base-german-dbmdz-cased). In our paper, we outline the steps taken to train our model and show that t... | [
"TAGS\n#transformers #pytorch #tf #electra #pretraining #de #dataset-wikipedia #dataset-OPUS #dataset-OpenLegalData #dataset-oscar #arxiv-2010.10906 #license-mit #endpoints_compatible #has_space #region-us \n",
"# German ELECTRA large\n\nReleased, Oct 2020, this is a German ELECTRA language model trained collabor... |
question-answering | transformers |
# MiniLM-L12-H384-uncased for QA
## Overview
**Language model:** microsoft/MiniLM-L12-H384-uncased
**Language:** English
**Downstream-task:** Extractive QA
**Training data:** SQuAD 2.0
**Eval data:** SQuAD 2.0
**Code:** See an [example QA pipeline on Haystack](https://haystack.deepset.ai/tutorials/01_basic... | {"language": "en", "license": "cc-by-4.0", "datasets": ["squad_v2"], "model-index": [{"name": "deepset/minilm-uncased-squad2", "results": [{"task": {"type": "question-answering", "name": "Question Answering"}, "dataset": {"name": "squad_v2", "type": "squad_v2", "config": "squad_v2", "split": "validation"}, "metrics": [... | deepset/minilm-uncased-squad2 | null | [
"transformers",
"pytorch",
"jax",
"safetensors",
"bert",
"question-answering",
"en",
"dataset:squad_v2",
"license:cc-by-4.0",
"model-index",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #jax #safetensors #bert #question-answering #en #dataset-squad_v2 #license-cc-by-4.0 #model-index #endpoints_compatible #has_space #region-us
|
# MiniLM-L12-H384-uncased for QA
## Overview
Language model: microsoft/MiniLM-L12-H384-uncased
Language: English
Downstream-task: Extractive QA
Training data: SQuAD 2.0
Eval data: SQuAD 2.0
Code: See an example QA pipeline on Haystack
Infrastructure: 1x Tesla v100
## Hyperparameters
## Performance
Ev... | [
"# MiniLM-L12-H384-uncased for QA",
"## Overview\nLanguage model: microsoft/MiniLM-L12-H384-uncased \nLanguage: English \nDownstream-task: Extractive QA \nTraining data: SQuAD 2.0 \nEval data: SQuAD 2.0 \nCode: See an example QA pipeline on Haystack\nInfrastructure: 1x Tesla v100",
"## Hyperparameters",
... | [
"TAGS\n#transformers #pytorch #jax #safetensors #bert #question-answering #en #dataset-squad_v2 #license-cc-by-4.0 #model-index #endpoints_compatible #has_space #region-us \n",
"# MiniLM-L12-H384-uncased for QA",
"## Overview\nLanguage model: microsoft/MiniLM-L12-H384-uncased \nLanguage: English \nDownstream-... |
feature-extraction | transformers |
This language model is trained using sentence_transformers (https://github.com/UKPLab/sentence-transformers)
Started with bert-base-nli-stsb-mean-tokens
Continue training on quora questions deduplication dataset (https://www.kaggle.com/c/quora-question-pairs)
See train_script.py for script used
Below is the performan... | {"license": "apache-2.0"} | deepset/quora_dedup_bert_base | null | [
"transformers",
"pytorch",
"jax",
"safetensors",
"bert",
"feature-extraction",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #safetensors #bert #feature-extraction #license-apache-2.0 #endpoints_compatible #region-us
|
This language model is trained using sentence_transformers (URL
Started with bert-base-nli-stsb-mean-tokens
Continue training on quora questions deduplication dataset (URL
See train_script.py for script used
Below is the performance over the course of training
epoch,steps,cosine_pearson,cosine_spearman,euclidean_pear... | [] | [
"TAGS\n#transformers #pytorch #jax #safetensors #bert #feature-extraction #license-apache-2.0 #endpoints_compatible #region-us \n"
] |
question-answering | transformers |
# roberta-base-squad2 for QA on COVID-19
## Overview
**Language model:** deepset/roberta-base-squad2
**Language:** English
**Downstream-task:** Extractive QA
**Training data:** [SQuAD-style CORD-19 annotations from 23rd April](https://github.com/deepset-ai/COVID-QA/blob/master/data/question-answering/200423_cov... | {"language": "en", "license": "cc-by-4.0", "datasets": ["squad_v2"]} | deepset/roberta-base-squad2-covid | null | [
"transformers",
"pytorch",
"jax",
"safetensors",
"roberta",
"question-answering",
"en",
"dataset:squad_v2",
"license:cc-by-4.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #jax #safetensors #roberta #question-answering #en #dataset-squad_v2 #license-cc-by-4.0 #endpoints_compatible #has_space #region-us
|
# roberta-base-squad2 for QA on COVID-19
## Overview
Language model: deepset/roberta-base-squad2
Language: English
Downstream-task: Extractive QA
Training data: SQuAD-style CORD-19 annotations from 23rd April
Code: See an example QA pipeline on Haystack
Infrastructure: Tesla v100
## Hyperparameters
... | [
"# roberta-base-squad2 for QA on COVID-19",
"## Overview\nLanguage model: deepset/roberta-base-squad2 \nLanguage: English \nDownstream-task: Extractive QA \nTraining data: SQuAD-style CORD-19 annotations from 23rd April \nCode: See an example QA pipeline on Haystack \nInfrastructure: Tesla v100",
"#... | [
"TAGS\n#transformers #pytorch #jax #safetensors #roberta #question-answering #en #dataset-squad_v2 #license-cc-by-4.0 #endpoints_compatible #has_space #region-us \n",
"# roberta-base-squad2 for QA on COVID-19",
"## Overview\nLanguage model: deepset/roberta-base-squad2 \nLanguage: English \nDownstream-task: Ex... |
question-answering | transformers |
## Overview
**Language model:** deepset/roberta-base-squad2-distilled
**Language:** English
**Training data:** SQuAD 2.0 training set
**Eval data:** SQuAD 2.0 dev set
**Infrastructure**: 4x V100 GPU
**Published**: Dec 8th, 2021
## Details
- haystack's distillation feature was used for training. deepset/roberta... | {"language": "en", "license": "mit", "tags": ["exbert"], "datasets": ["squad_v2"], "thumbnail": "https://thumb.tildacdn.com/tild3433-3637-4830-a533-353833613061/-/resize/720x/-/format/webp/germanquad.jpg", "model-index": [{"name": "deepset/roberta-base-squad2-distilled", "results": [{"task": {"type": "question-answerin... | deepset/roberta-base-squad2-distilled | null | [
"transformers",
"pytorch",
"safetensors",
"roberta",
"question-answering",
"exbert",
"en",
"dataset:squad_v2",
"license:mit",
"model-index",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #safetensors #roberta #question-answering #exbert #en #dataset-squad_v2 #license-mit #model-index #endpoints_compatible #has_space #region-us
|
## Overview
Language model: deepset/roberta-base-squad2-distilled
Language: English
Training data: SQuAD 2.0 training set
Eval data: SQuAD 2.0 dev set
Infrastructure: 4x V100 GPU
Published: Dec 8th, 2021
## Details
- haystack's distillation feature was used for training. deepset/roberta-large-squad2 was used a... | [
"## Overview\nLanguage model: deepset/roberta-base-squad2-distilled \nLanguage: English \nTraining data: SQuAD 2.0 training set\nEval data: SQuAD 2.0 dev set\nInfrastructure: 4x V100 GPU \nPublished: Dec 8th, 2021",
"## Details\n- haystack's distillation feature was used for training. deepset/roberta-large-sq... | [
"TAGS\n#transformers #pytorch #safetensors #roberta #question-answering #exbert #en #dataset-squad_v2 #license-mit #model-index #endpoints_compatible #has_space #region-us \n",
"## Overview\nLanguage model: deepset/roberta-base-squad2-distilled \nLanguage: English \nTraining data: SQuAD 2.0 training set\nEval ... |
question-answering | transformers |
# roberta-base for QA
This is the [roberta-base](https://huggingface.co/roberta-base) model, fine-tuned using the [SQuAD2.0](https://huggingface.co/datasets/squad_v2) dataset. It's been trained on question-answer pairs, including unanswerable questions, for the task of Question Answering.
## Overview
**Language m... | {"language": "en", "license": "cc-by-4.0", "datasets": ["squad_v2"], "model-index": [{"name": "deepset/roberta-base-squad2", "results": [{"task": {"type": "question-answering", "name": "Question Answering"}, "dataset": {"name": "squad_v2", "type": "squad_v2", "config": "squad_v2", "split": "validation"}, "metrics": [{"... | deepset/roberta-base-squad2 | null | [
"transformers",
"pytorch",
"tf",
"jax",
"rust",
"safetensors",
"roberta",
"question-answering",
"en",
"dataset:squad_v2",
"license:cc-by-4.0",
"model-index",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #tf #jax #rust #safetensors #roberta #question-answering #en #dataset-squad_v2 #license-cc-by-4.0 #model-index #endpoints_compatible #has_space #region-us
|
# roberta-base for QA
This is the roberta-base model, fine-tuned using the SQuAD2.0 dataset. It's been trained on question-answer pairs, including unanswerable questions, for the task of Question Answering.
## Overview
Language model: roberta-base
Language: English
Downstream-task: Extractive QA
Training da... | [
"# roberta-base for QA \n\nThis is the roberta-base model, fine-tuned using the SQuAD2.0 dataset. It's been trained on question-answer pairs, including unanswerable questions, for the task of Question Answering.",
"## Overview\nLanguage model: roberta-base \nLanguage: English \nDownstream-task: Extractive QA \... | [
"TAGS\n#transformers #pytorch #tf #jax #rust #safetensors #roberta #question-answering #en #dataset-squad_v2 #license-cc-by-4.0 #model-index #endpoints_compatible #has_space #region-us \n",
"# roberta-base for QA \n\nThis is the roberta-base model, fine-tuned using the SQuAD2.0 dataset. It's been trained on quest... |
question-answering | transformers |
# roberta-large for QA
This is the [roberta-large](https://huggingface.co/roberta-large) model, fine-tuned using the [SQuAD2.0](https://huggingface.co/datasets/squad_v2) dataset. It's been trained on question-answer pairs, including unanswerable questions, for the task of Question Answering.
## Overview
**Languag... | {"language": "en", "license": "cc-by-4.0", "datasets": ["squad_v2"], "base_model": "roberta-large", "model-index": [{"name": "deepset/roberta-large-squad2", "results": [{"task": {"type": "question-answering", "name": "Question Answering"}, "dataset": {"name": "squad_v2", "type": "squad_v2", "config": "squad_v2", "split... | deepset/roberta-large-squad2 | null | [
"transformers",
"pytorch",
"jax",
"safetensors",
"roberta",
"question-answering",
"en",
"dataset:squad_v2",
"base_model:roberta-large",
"license:cc-by-4.0",
"model-index",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #jax #safetensors #roberta #question-answering #en #dataset-squad_v2 #base_model-roberta-large #license-cc-by-4.0 #model-index #endpoints_compatible #has_space #region-us
|
# roberta-large for QA
This is the roberta-large model, fine-tuned using the SQuAD2.0 dataset. It's been trained on question-answer pairs, including unanswerable questions, for the task of Question Answering.
## Overview
Language model: roberta-large
Language: English
Downstream-task: Extractive QA
Training... | [
"# roberta-large for QA \n\nThis is the roberta-large model, fine-tuned using the SQuAD2.0 dataset. It's been trained on question-answer pairs, including unanswerable questions, for the task of Question Answering.",
"## Overview\nLanguage model: roberta-large \nLanguage: English \nDownstream-task: Extractive QA... | [
"TAGS\n#transformers #pytorch #jax #safetensors #roberta #question-answering #en #dataset-squad_v2 #base_model-roberta-large #license-cc-by-4.0 #model-index #endpoints_compatible #has_space #region-us \n",
"# roberta-large for QA \n\nThis is the roberta-large model, fine-tuned using the SQuAD2.0 dataset. It's bee... |
null | transformers |
This is an upload of the bert-base-nli-stsb-mean-tokens pretrained model from the Sentence Transformers Repo (https://github.com/UKPLab/sentence-transformers)
| {"license": "apache-2.0"} | deepset/sentence_bert | null | [
"transformers",
"pytorch",
"jax",
"bert",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #bert #license-apache-2.0 #endpoints_compatible #has_space #region-us
|
This is an upload of the bert-base-nli-stsb-mean-tokens pretrained model from the Sentence Transformers Repo (URL
| [] | [
"TAGS\n#transformers #pytorch #jax #bert #license-apache-2.0 #endpoints_compatible #has_space #region-us \n"
] |
null | transformers |
This model contains the converted PyTorch checkpoint of the original Tensorflow model available in the [TaPas repository](https://github.com/google-research/tapas/blob/master/DENSE_TABLE_RETRIEVER.md#reader-models).
It is described in Herzig et al.'s (2021) [paper](https://aclanthology.org/2021.naacl-main.43/) _Open D... | {"language": "en", "license": "apache-2.0", "tags": ["tapas"]} | deepset/tapas-large-nq-hn-reader | null | [
"transformers",
"pytorch",
"tapas",
"en",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #tapas #en #license-apache-2.0 #endpoints_compatible #has_space #region-us
|
This model contains the converted PyTorch checkpoint of the original Tensorflow model available in the TaPas repository.
It is described in Herzig et al.'s (2021) paper _Open Domain Question Answering over Tables via Dense Retrieval_.
This model has 2 versions that can be used differing only in the table scoring head... | [
"# Usage",
"## In Haystack\nIf you want to use this model for question-answering over tables, you can load it in Haystack:"
] | [
"TAGS\n#transformers #pytorch #tapas #en #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"# Usage",
"## In Haystack\nIf you want to use this model for question-answering over tables, you can load it in Haystack:"
] |
null | transformers |
This model contains the converted PyTorch checkpoint of the original Tensorflow model available in the [TaPas repository](https://github.com/google-research/tapas/blob/master/DENSE_TABLE_RETRIEVER.md#reader-models).
It is described in Herzig et al.'s (2021) [paper](https://aclanthology.org/2021.naacl-main.43/) _Open D... | {"language": "en", "license": "apache-2.0", "tags": ["tapas"]} | deepset/tapas-large-nq-reader | null | [
"transformers",
"pytorch",
"tapas",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #tapas #en #license-apache-2.0 #endpoints_compatible #region-us
|
This model contains the converted PyTorch checkpoint of the original Tensorflow model available in the TaPas repository.
It is described in Herzig et al.'s (2021) paper _Open Domain Question Answering over Tables via Dense Retrieval_.
This model has 2 versions which can be used differing only in the table scoring hea... | [
"# Usage",
"## In Haystack\nIf you want to use this model for question-answering over tables, you can load it in Haystack:"
] | [
"TAGS\n#transformers #pytorch #tapas #en #license-apache-2.0 #endpoints_compatible #region-us \n",
"# Usage",
"## In Haystack\nIf you want to use this model for question-answering over tables, you can load it in Haystack:"
] |
question-answering | transformers |
## Overview
**Language model:** deepset/tinybert-6L-768D-squad2
**Language:** English
**Training data:** SQuAD 2.0 training set x 20 augmented + SQuAD 2.0 training set without augmentation
**Eval data:** SQuAD 2.0 dev set
**Infrastructure**: 1x V100 GPU
**Published**: Dec 8th, 2021
## Details
- haystack's ... | {"language": "en", "license": "mit", "tags": ["exbert"], "datasets": ["squad_v2"], "thumbnail": "https://thumb.tildacdn.com/tild3433-3637-4830-a533-353833613061/-/resize/720x/-/format/webp/germanquad.jpg", "model-index": [{"name": "deepset/tinybert-6l-768d-squad2", "results": [{"task": {"type": "question-answering", "n... | deepset/tinybert-6l-768d-squad2 | null | [
"transformers",
"pytorch",
"safetensors",
"bert",
"question-answering",
"exbert",
"en",
"dataset:squad_v2",
"arxiv:1909.10351",
"license:mit",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1909.10351"
] | [
"en"
] | TAGS
#transformers #pytorch #safetensors #bert #question-answering #exbert #en #dataset-squad_v2 #arxiv-1909.10351 #license-mit #model-index #endpoints_compatible #region-us
|
## Overview
Language model: deepset/tinybert-6L-768D-squad2
Language: English
Training data: SQuAD 2.0 training set x 20 augmented + SQuAD 2.0 training set without augmentation
Eval data: SQuAD 2.0 dev set
Infrastructure: 1x V100 GPU
Published: Dec 8th, 2021
## Details
- haystack's intermediate layer and p... | [
"## Overview\nLanguage model: deepset/tinybert-6L-768D-squad2 \nLanguage: English \nTraining data: SQuAD 2.0 training set x 20 augmented + SQuAD 2.0 training set without augmentation \nEval data: SQuAD 2.0 dev set \nInfrastructure: 1x V100 GPU \nPublished: Dec 8th, 2021",
"## Details\n- haystack's intermedi... | [
"TAGS\n#transformers #pytorch #safetensors #bert #question-answering #exbert #en #dataset-squad_v2 #arxiv-1909.10351 #license-mit #model-index #endpoints_compatible #region-us \n",
"## Overview\nLanguage model: deepset/tinybert-6L-768D-squad2 \nLanguage: English \nTraining data: SQuAD 2.0 training set x 20 aug... |
question-answering | transformers |
# tinyroberta-squad2
## Overview
**Language model:** tinyroberta-squad2
**Language:** English
**Training data:** The PILE
**Code:**
**Infrastructure**: 4x Tesla v100
## Hyperparameters
```
batch_size = 96
n_epochs = 4
base_LM_model = "deepset/tinyroberta-squad2-step1"
max_seq_len = 384
learning_rate = 1e-4
... | {"language": "en", "license": "cc-by-4.0", "datasets": ["squad_v2"]} | deepset/tinyroberta-6l-768d | null | [
"transformers",
"pytorch",
"safetensors",
"roberta",
"question-answering",
"en",
"dataset:squad_v2",
"arxiv:1909.10351",
"license:cc-by-4.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1909.10351"
] | [
"en"
] | TAGS
#transformers #pytorch #safetensors #roberta #question-answering #en #dataset-squad_v2 #arxiv-1909.10351 #license-cc-by-4.0 #endpoints_compatible #region-us
|
# tinyroberta-squad2
## Overview
Language model: tinyroberta-squad2
Language: English
Training data: The PILE
Code:
Infrastructure: 4x Tesla v100
## Hyperparameters
## Distillation
This model was distilled using the TinyBERT approach described in this paper and implemented in haystack.
We have performed ... | [
"# tinyroberta-squad2",
"## Overview\nLanguage model: tinyroberta-squad2 \nLanguage: English \nTraining data: The PILE \nCode: \nInfrastructure: 4x Tesla v100",
"## Hyperparameters",
"## Distillation\nThis model was distilled using the TinyBERT approach described in this paper and implemented in haystack.... | [
"TAGS\n#transformers #pytorch #safetensors #roberta #question-answering #en #dataset-squad_v2 #arxiv-1909.10351 #license-cc-by-4.0 #endpoints_compatible #region-us \n",
"# tinyroberta-squad2",
"## Overview\nLanguage model: tinyroberta-squad2 \nLanguage: English \nTraining data: The PILE \nCode: \nInfrastruc... |
question-answering | transformers |
# tinyroberta-squad2
This is the *distilled* version of the [deepset/roberta-base-squad2](https://huggingface.co/deepset/roberta-base-squad2) model. This model has a comparable prediction quality and runs at twice the speed of the base model.
## Overview
**Language model:** tinyroberta-squad2
**Language:** English... | {"language": "en", "license": "cc-by-4.0", "datasets": ["squad_v2"], "model-index": [{"name": "deepset/tinyroberta-squad2", "results": [{"task": {"type": "question-answering", "name": "Question Answering"}, "dataset": {"name": "squad_v2", "type": "squad_v2", "config": "squad_v2", "split": "validation"}, "metrics": [{"t... | deepset/tinyroberta-squad2 | null | [
"transformers",
"pytorch",
"safetensors",
"roberta",
"question-answering",
"en",
"dataset:squad_v2",
"arxiv:1909.10351",
"license:cc-by-4.0",
"model-index",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1909.10351"
] | [
"en"
] | TAGS
#transformers #pytorch #safetensors #roberta #question-answering #en #dataset-squad_v2 #arxiv-1909.10351 #license-cc-by-4.0 #model-index #endpoints_compatible #has_space #region-us
|
# tinyroberta-squad2
This is the *distilled* version of the deepset/roberta-base-squad2 model. This model has a comparable prediction quality and runs at twice the speed of the base model.
## Overview
Language model: tinyroberta-squad2
Language: English
Downstream-task: Extractive QA
Training data: SQuAD 2.0 ... | [
"# tinyroberta-squad2\n\nThis is the *distilled* version of the deepset/roberta-base-squad2 model. This model has a comparable prediction quality and runs at twice the speed of the base model.",
"## Overview\nLanguage model: tinyroberta-squad2 \nLanguage: English \nDownstream-task: Extractive QA \nTraining dat... | [
"TAGS\n#transformers #pytorch #safetensors #roberta #question-answering #en #dataset-squad_v2 #arxiv-1909.10351 #license-cc-by-4.0 #model-index #endpoints_compatible #has_space #region-us \n",
"# tinyroberta-squad2\n\nThis is the *distilled* version of the deepset/roberta-base-squad2 model. This model has a compa... |
question-answering | transformers |
# deepset/xlm-roberta-base-squad2-distilled
- haystack's distillation feature was used for training. deepset/xlm-roberta-large-squad2 was used as the teacher model.
## Overview
**Language model:** deepset/xlm-roberta-base-squad2-distilled
**Language:** Multilingual
**Downstream-task:** Extractive QA
**Training... | {"language": "multilingual", "license": "mit", "tags": ["exbert"], "datasets": ["squad_v2"], "thumbnail": "https://thumb.tildacdn.com/tild3433-3637-4830-a533-353833613061/-/resize/720x/-/format/webp/germanquad.jpg"} | deepset/xlm-roberta-base-squad2-distilled | null | [
"transformers",
"pytorch",
"safetensors",
"xlm-roberta",
"question-answering",
"exbert",
"multilingual",
"dataset:squad_v2",
"license:mit",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"multilingual"
] | TAGS
#transformers #pytorch #safetensors #xlm-roberta #question-answering #exbert #multilingual #dataset-squad_v2 #license-mit #endpoints_compatible #has_space #region-us
|
# deepset/xlm-roberta-base-squad2-distilled
- haystack's distillation feature was used for training. deepset/xlm-roberta-large-squad2 was used as the teacher model.
## Overview
Language model: deepset/xlm-roberta-base-squad2-distilled
Language: Multilingual
Downstream-task: Extractive QA
Training data: SQuAD 2... | [
"# deepset/xlm-roberta-base-squad2-distilled\n- haystack's distillation feature was used for training. deepset/xlm-roberta-large-squad2 was used as the teacher model.",
"## Overview\nLanguage model: deepset/xlm-roberta-base-squad2-distilled \nLanguage: Multilingual \nDownstream-task: Extractive QA \nTraining ... | [
"TAGS\n#transformers #pytorch #safetensors #xlm-roberta #question-answering #exbert #multilingual #dataset-squad_v2 #license-mit #endpoints_compatible #has_space #region-us \n",
"# deepset/xlm-roberta-base-squad2-distilled\n- haystack's distillation feature was used for training. deepset/xlm-roberta-large-squad2 ... |
question-answering | transformers |
# Multilingual XLM-RoBERTa base for QA on various languages
## Overview
**Language model:** xlm-roberta-base
**Language:** Multilingual
**Downstream-task:** Extractive QA
**Training data:** SQuAD 2.0
**Eval data:** SQuAD 2.0 dev set - German MLQA - German XQuAD
**Code:** See [example](https://github.com... | {"license": "cc-by-4.0", "datasets": ["squad_v2"], "model-index": [{"name": "deepset/xlm-roberta-base-squad2", "results": [{"task": {"type": "question-answering", "name": "Question Answering"}, "dataset": {"name": "squad_v2", "type": "squad_v2", "config": "squad_v2", "split": "validation"}, "metrics": [{"type": "exact_... | deepset/xlm-roberta-base-squad2 | null | [
"transformers",
"pytorch",
"safetensors",
"xlm-roberta",
"question-answering",
"dataset:squad_v2",
"license:cc-by-4.0",
"model-index",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #safetensors #xlm-roberta #question-answering #dataset-squad_v2 #license-cc-by-4.0 #model-index #endpoints_compatible #has_space #region-us
|
# Multilingual XLM-RoBERTa base for QA on various languages
## Overview
Language model: xlm-roberta-base
Language: Multilingual
Downstream-task: Extractive QA
Training data: SQuAD 2.0
Eval data: SQuAD 2.0 dev set - German MLQA - German XQuAD
Code: See example in FARM
Infrastructure: 4x Tesla v100
## ... | [
"# Multilingual XLM-RoBERTa base for QA on various languages",
"## Overview\nLanguage model: xlm-roberta-base \nLanguage: Multilingual \nDownstream-task: Extractive QA \nTraining data: SQuAD 2.0 \nEval data: SQuAD 2.0 dev set - German MLQA - German XQuAD \nCode: See example in FARM \nInfrastructure: 4x T... | [
"TAGS\n#transformers #pytorch #safetensors #xlm-roberta #question-answering #dataset-squad_v2 #license-cc-by-4.0 #model-index #endpoints_compatible #has_space #region-us \n",
"# Multilingual XLM-RoBERTa base for QA on various languages",
"## Overview\nLanguage model: xlm-roberta-base \nLanguage: Multilingual ... |
question-answering | transformers |
# Multilingual XLM-RoBERTa large for QA on various languages
## Overview
**Language model:** xlm-roberta-large
**Language:** Multilingual
**Downstream-task:** Extractive QA
**Training data:** SQuAD 2.0
**Eval data:** SQuAD dev set - German MLQA - German XQuAD
**Training run:** [MLFlow link](https://public... | {"language": "multilingual", "license": "cc-by-4.0", "tags": ["question-answering"], "datasets": ["squad_v2"], "model-index": [{"name": "deepset/xlm-roberta-large-squad2", "results": [{"task": {"type": "question-answering", "name": "Question Answering"}, "dataset": {"name": "squad_v2", "type": "squad_v2", "config": "sq... | deepset/xlm-roberta-large-squad2 | null | [
"transformers",
"pytorch",
"safetensors",
"xlm-roberta",
"question-answering",
"multilingual",
"dataset:squad_v2",
"license:cc-by-4.0",
"model-index",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"multilingual"
] | TAGS
#transformers #pytorch #safetensors #xlm-roberta #question-answering #multilingual #dataset-squad_v2 #license-cc-by-4.0 #model-index #endpoints_compatible #has_space #region-us
|
# Multilingual XLM-RoBERTa large for QA on various languages
## Overview
Language model: xlm-roberta-large
Language: Multilingual
Downstream-task: Extractive QA
Training data: SQuAD 2.0
Eval data: SQuAD dev set - German MLQA - German XQuAD
Training run: MLFlow link
Infrastructure: 4x Tesla v100
## Hype... | [
"# Multilingual XLM-RoBERTa large for QA on various languages",
"## Overview\nLanguage model: xlm-roberta-large \nLanguage: Multilingual \nDownstream-task: Extractive QA \nTraining data: SQuAD 2.0 \nEval data: SQuAD dev set - German MLQA - German XQuAD \nTraining run: MLFlow link \nInfrastructure: 4x Tesla... | [
"TAGS\n#transformers #pytorch #safetensors #xlm-roberta #question-answering #multilingual #dataset-squad_v2 #license-cc-by-4.0 #model-index #endpoints_compatible #has_space #region-us \n",
"# Multilingual XLM-RoBERTa large for QA on various languages",
"## Overview\nLanguage model: xlm-roberta-large \nLanguage... |
fill-mask | transformers |
deeqBERT-base
---
- model: bert-base
- vocab: bert-wordpiece, 35k
- version: latest
| {"language": "ko", "datasets": ["kowiki", "news"]} | baikal-nlp/dbert | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"ko",
"dataset:kowiki",
"dataset:news",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ko"
] | TAGS
#transformers #pytorch #bert #fill-mask #ko #dataset-kowiki #dataset-news #autotrain_compatible #endpoints_compatible #region-us
|
deeqBERT-base
---
- model: bert-base
- vocab: bert-wordpiece, 35k
- version: latest
| [] | [
"TAGS\n#transformers #pytorch #bert #fill-mask #ko #dataset-kowiki #dataset-news #autotrain_compatible #endpoints_compatible #region-us \n"
] |
null | transformers | deeqBERT5
---
- model: bert-base
- vocab: deeqnlp 1.5, 50k
- version: latest/3.5
| {} | baikal-nlp/dbert5 | null | [
"transformers",
"pytorch",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #endpoints_compatible #region-us
| deeqBERT5
---
- model: bert-base
- vocab: deeqnlp 1.5, 50k
- version: latest/3.5
| [] | [
"TAGS\n#transformers #pytorch #endpoints_compatible #region-us \n"
] |
fill-mask | transformers |
deeqELECTRA-base
---
- model: electra-base-generator
- vocab: bert-wordpiece, 35k
- version: beta, 1.71M
| {"language": "ko", "datasets": ["kowiki", "news"]} | baikal-nlp/delectra-generator | null | [
"transformers",
"pytorch",
"electra",
"fill-mask",
"ko",
"dataset:kowiki",
"dataset:news",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ko"
] | TAGS
#transformers #pytorch #electra #fill-mask #ko #dataset-kowiki #dataset-news #autotrain_compatible #endpoints_compatible #region-us
|
deeqELECTRA-base
---
- model: electra-base-generator
- vocab: bert-wordpiece, 35k
- version: beta, 1.71M
| [] | [
"TAGS\n#transformers #pytorch #electra #fill-mask #ko #dataset-kowiki #dataset-news #autotrain_compatible #endpoints_compatible #region-us \n"
] |
null | transformers |
deeqELECTRA-base
---
- model: electra-base-discriminator
- vocab: bert-wordpiece, 35k
- version: beta, 1.71M
| {"language": "ko", "datasets": ["kowiki", "news"]} | baikal-nlp/delectra | null | [
"transformers",
"pytorch",
"electra",
"pretraining",
"ko",
"dataset:kowiki",
"dataset:news",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ko"
] | TAGS
#transformers #pytorch #electra #pretraining #ko #dataset-kowiki #dataset-news #endpoints_compatible #region-us
|
deeqELECTRA-base
---
- model: electra-base-discriminator
- vocab: bert-wordpiece, 35k
- version: beta, 1.71M
| [] | [
"TAGS\n#transformers #pytorch #electra #pretraining #ko #dataset-kowiki #dataset-news #endpoints_compatible #region-us \n"
] |
text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilgpt2-finetuned-amazon-reviews
This model was trained from scratch on the None dataset.
## Model description
More informa... | {"tags": ["generated_from_trainer"], "datasets": [], "model_index": [{"name": "distilgpt2-finetuned-amazon-reviews", "results": [{"task": {"name": "Causal Language Modeling", "type": "text-generation"}}]}]} | defex/distilgpt2-finetuned-amazon-reviews | null | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# distilgpt2-finetuned-amazon-reviews
This model was trained from scratch on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The f... | [
"# distilgpt2-finetuned-amazon-reviews\n\nThis model was trained from scratch on the None dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### T... | [
"TAGS\n#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# distilgpt2-finetuned-amazon-reviews\n\nThis model was trained from scratch on the None dataset.",
"## Model description\n\nMore info... |
text2text-generation | transformers |
# german-qg-t5-drink600
This model is fine-tuned in question generation in German. The expected answer must be highlighted with <hl> token. It is based on [german-qg-t5-quad](https://huggingface.co/dehio/german-qg-t5-quad) and further pre-trained on drink related questions.
## Task example
#### Input
generate q... | {"language": ["de"], "license": "mit", "tags": ["question generation"], "datasets": ["deepset/germanquad"], "widget": [{"text": "generate question: Der Monk Sour Drink ist ein somit eine aromatische \u00dcberraschung, die sowohl <hl>im Sommer wie auch zu Silvester<hl> funktioniert."}], "model-index": [{"name": "german-... | dehio/german-qg-t5-drink600 | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"question generation",
"de",
"dataset:deepset/germanquad",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"de"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #question generation #de #dataset-deepset/germanquad #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# german-qg-t5-drink600
This model is fine-tuned in question generation in German. The expected answer must be highlighted with <hl> token. It is based on german-qg-t5-quad and further pre-trained on drink related questions.
## Task example
#### Input
generate question: Der Monk Sour Drink ist ein somit eine ar... | [
"# german-qg-t5-drink600\n\nThis model is fine-tuned in question generation in German. The expected answer must be highlighted with <hl> token. It is based on german-qg-t5-quad and further pre-trained on drink related questions.",
"## Task example",
"#### Input\n\ngenerate question: Der Monk Sour Drink ist e... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #question generation #de #dataset-deepset/germanquad #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# german-qg-t5-drink600\n\nThis model is fine-tuned in question generation in German. The expected answe... |
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. -->
# german-qg-t5-e2e-quad (Work in progress)
This model is a end-to-end question generation model in German. Given a text, it genera... | {"language": ["de"], "license": "mit", "tags": ["question generation"], "datasets": ["deepset/germanquad"], "widget": [{"text": "Naturschutzwarte haben auf der ostfriesischen Insel Wangerooge zwei seltene Kurzschn\u00e4uzige Seepferdchen entdeckt. Die Tiere seien vergangene Woche bei einer sogenannten Sp\u00fclsaumkont... | dehio/german-qg-t5-e2e-quad | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"question generation",
"de",
"dataset:deepset/germanquad",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"de"
] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #question generation #de #dataset-deepset/germanquad #license-mit #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
# german-qg-t5-e2e-quad (Work in progress)
This model is a end-to-end question generation model in German. Given a text, it generates several questions about it. This model is a fine-tuned version of valhalla/t5-base-e2e-qg on the GermanQuAD dataset from deepset.
## Model description
More information needed
## ... | [
"# german-qg-t5-e2e-quad (Work in progress)\n\nThis model is a end-to-end question generation model in German. Given a text, it generates several questions about it. This model is a fine-tuned version of valhalla/t5-base-e2e-qg on the GermanQuAD dataset from deepset.",
"## Model description \n\nMore information n... | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #question generation #de #dataset-deepset/germanquad #license-mit #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"# german-qg-t5-e2e-quad (Work in progress)\n\nThis model is a end-to-end questio... |
text2text-generation | transformers |
# german-qg-t5-quad
This model is fine-tuned in question generation in German. The expected answer must be highlighted with a
<hl> token.
## Task example
#### Input
generate question: Obwohl die Vereinigten Staaten wie auch viele Staaten des Commonwealth Erben des <hl> britischen Common Laws <hl> sind, setzt si... | {"language": ["de"], "license": "mit", "tags": ["question generation"], "datasets": ["deepset/germanquad"], "widget": [{"text": "Obwohl die Vereinigten Staaten wie auch viele Staaten des Commonwealth Erben des <hl>britischen Common Laws<hl> sind, setzt sich das amerikanische Recht bedeutend davon ab."}], "model-index":... | dehio/german-qg-t5-quad | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"question generation",
"de",
"dataset:deepset/germanquad",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"de"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #question generation #de #dataset-deepset/germanquad #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# german-qg-t5-quad
This model is fine-tuned in question generation in German. The expected answer must be highlighted with a
<hl> token.
## Task example
#### Input
generate question: Obwohl die Vereinigten Staaten wie auch viele Staaten des Commonwealth Erben des <hl> britischen Common Laws <hl> sind, setzt si... | [
"# german-qg-t5-quad\n\nThis model is fine-tuned in question generation in German. The expected answer must be highlighted with a\n<hl> token.",
"## Task example",
"#### Input\n\ngenerate question: Obwohl die Vereinigten Staaten wie auch viele Staaten des Commonwealth Erben des <hl> britischen Common Laws <h... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #question generation #de #dataset-deepset/germanquad #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# german-qg-t5-quad\n\nThis model is fine-tuned in question generation in German. The expected answer mu... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-ner
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/dis... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["conll2003"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "distilbert-base-uncased-finetuned-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "con... | delpart/distilbert-base-uncased-finetuned-ner | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-ner
=====================================
This model is a fine-tuned version of distilbert-base-uncased on the conll2003 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0602
* Precision: 0.9251
* Recall: 0.9370
* F1: 0.9310
* Accuracy: 0.9839
Model des... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* le... |
text-generation | transformers |
#DialoGPT medium based model of Dwight Schrute, trained with 10 context lines of history for 20 epochs. | {"tags": ["conversational"]} | delvan/DialoGPT-medium-DwightV1 | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
#DialoGPT medium based model of Dwight Schrute, trained with 10 context lines of history for 20 epochs. | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
feature-extraction | transformers | This is finetune version of [SimCSE: Simple Contrastive Learning of Sentence Embeddings](https://arxiv.org/abs/2104.08821)
, train unsupervised on 570K stroke sentences from : stroke books, quora medical, quora's stroke and human annotates.
### Extract sentence representation
```
from transformers import AutoTokenizer... | {} | demdecuong/stroke_simcse | null | [
"transformers",
"pytorch",
"bert",
"feature-extraction",
"arxiv:2104.08821",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2104.08821"
] | [] | TAGS
#transformers #pytorch #bert #feature-extraction #arxiv-2104.08821 #endpoints_compatible #region-us
| This is finetune version of SimCSE: Simple Contrastive Learning of Sentence Embeddings
, train unsupervised on 570K stroke sentences from : stroke books, quora medical, quora's stroke and human annotates.
### Extract sentence representation
### Build up embedding for database
### Result
On our Poc testset , whi... | [
"### Extract sentence representation",
"### Build up embedding for database",
"### Result\n\n\nOn our Poc testset , which contains pairs of matching question related to stroke from human-generated."
] | [
"TAGS\n#transformers #pytorch #bert #feature-extraction #arxiv-2104.08821 #endpoints_compatible #region-us \n",
"### Extract sentence representation",
"### Build up embedding for database",
"### Result\n\n\nOn our Poc testset , which contains pairs of matching question related to stroke from human-generated."... |
feature-extraction | transformers | This is finetune version of [SimCSE: Simple Contrastive Learning of Sentence Embeddings](https://arxiv.org/abs/2104.08821)
- Train supervised on 100K triplet samples samples related to stroke domain from : stroke books, quora medical, quora's stroke, quora's general and human annotates.
- Positive sentences are gen... | {} | demdecuong/stroke_sup_simcse | null | [
"transformers",
"pytorch",
"bert",
"feature-extraction",
"arxiv:2104.08821",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2104.08821"
] | [] | TAGS
#transformers #pytorch #bert #feature-extraction #arxiv-2104.08821 #endpoints_compatible #region-us
| This is finetune version of SimCSE: Simple Contrastive Learning of Sentence Embeddings
* Train supervised on 100K triplet samples samples related to stroke domain from : stroke books, quora medical, quora's stroke, quora's general and human annotates.
* Positive sentences are generated by paraphrasing and back-transl... | [
"### Extract sentence representation",
"### Build up embedding for database",
"### Result\n\n\nOn our company's PoC project, the testset contains positive/negative pairs of matching question related to stroke from human-generation.\n\n\n* SimCSE supervised + 100k : Train on 100K triplet samples contains : medic... | [
"TAGS\n#transformers #pytorch #bert #feature-extraction #arxiv-2104.08821 #endpoints_compatible #region-us \n",
"### Extract sentence representation",
"### Build up embedding for database",
"### Result\n\n\nOn our company's PoC project, the testset contains positive/negative pairs of matching question related... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# iloko_model
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-lar... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "pipeline_tag": "automatic-speech-recognition"} | denden/iloko_model | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| iloko\_model
============
This model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0095
* Wer: 0.0840
Model description
-----------------
More information needed
Intended uses & limitations
-----------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 8... |
automatic-speech-recognition | transformers | FINETUNED ILOKANO SPEECH RECOGNITION FROM WAV2VEC-XLSR-S3 | {"language": ["en"], "license": "afl-3.0", "tags": ["audio", "automatic-speech-recognition", "speech"], "datasets": ["timit_asr"], "metrics": ["wer"], "pipeline_tag": "automatic-speech-recognition"} | denden/new_iloko | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"en",
"dataset:timit_asr",
"license:afl-3.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #audio #speech #en #dataset-timit_asr #license-afl-3.0 #model-index #endpoints_compatible #region-us
| FINETUNED ILOKANO SPEECH RECOGNITION FROM WAV2VEC-XLSR-S3 | [] | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #audio #speech #en #dataset-timit_asr #license-afl-3.0 #model-index #endpoints_compatible #region-us \n"
] |
text-classification | transformers |
# BERT-Wiki-Paragraphs
Authors: Satya Almasian\*, Dennis Aumiller\*, Lucienne-Sophie Marmé, Michael Gertz
Contact us at `<lastname>@informatik.uni-heidelberg.de`
Details for the training method can be found in our work [Structural Text Segmentation of Legal Documents](https://arxiv.org/abs/2012.03619).
The traini... | {"language": ["en"], "license": "mit", "tags": ["sentence-similarity", "text-classification"], "datasets": ["dennlinger/wiki-paragraphs"], "metrics": ["f1"]} | dennlinger/bert-wiki-paragraphs | null | [
"transformers",
"pytorch",
"safetensors",
"bert",
"text-classification",
"sentence-similarity",
"en",
"dataset:dennlinger/wiki-paragraphs",
"arxiv:2012.03619",
"arxiv:1803.09337",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2012.03619",
"1803.09337"
] | [
"en"
] | TAGS
#transformers #pytorch #safetensors #bert #text-classification #sentence-similarity #en #dataset-dennlinger/wiki-paragraphs #arxiv-2012.03619 #arxiv-1803.09337 #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# BERT-Wiki-Paragraphs
Authors: Satya Almasian\*, Dennis Aumiller\*, Lucienne-Sophie Marmé, Michael Gertz
Contact us at '<lastname>@URL'
Details for the training method can be found in our work Structural Text Segmentation of Legal Documents.
The training procedure follows the same setup, but we substitute legal ... | [
"# BERT-Wiki-Paragraphs\n\nAuthors: Satya Almasian\\*, Dennis Aumiller\\*, Lucienne-Sophie Marmé, Michael Gertz \nContact us at '<lastname>@URL' \nDetails for the training method can be found in our work Structural Text Segmentation of Legal Documents.\nThe training procedure follows the same setup, but we substi... | [
"TAGS\n#transformers #pytorch #safetensors #bert #text-classification #sentence-similarity #en #dataset-dennlinger/wiki-paragraphs #arxiv-2012.03619 #arxiv-1803.09337 #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# BERT-Wiki-Paragraphs\n\nAuthors: Satya Almasian\\*, Dennis Aumiller\\*,... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.