modelId stringlengths 4 81 | tags list | pipeline_tag stringclasses 17
values | config dict | downloads int64 0 59.7M | first_commit timestamp[ns, tz=UTC] | card stringlengths 51 438k | embedding list |
|---|---|---|---|---|---|---|---|
AnonymousSub/SR_rule_based_roberta_twostagetriplet_hier_epochs_1_shard_1 | [
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"no_repeat_ngram_size... | 4 | null | ---
language: "ru"
tags:
- dialogue
- russian
license: mit
---
This is a version of the [cointegrated/rut5-small](https://huggingface.co/cointegrated/rut5-small) model fine-tuned on some Russian dialogue data. It is not very smart and creative, but it is small and fast, and can serve as a fallback response generator f... | [
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AnonymousSub/SR_rule_based_roberta_twostagetriplet_hier_epochs_1_shard_10 | [
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"no_repeat_ngram_size... | 7 | null | A version of https://huggingface.co/cointegrated/rut5-small-chitchat which is more dull but less toxic. | [
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AnonymousSub/SR_rule_based_twostage_quadruplet_epochs_1_shard_1 | [
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language: "ru"
tags:
- normalization
- denoising autoencoder
- russian
widget:
- text: "меня тобой не понимать"
license: mit
---
This is a small Russian denoising autoencoder. It can be used for restoring corrupted sentences.
This model was produced by fine-tuning the [rut5-small](https://huggingface.co/cointegrat... | [
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AnonymousSub/SR_rule_based_twostagequadruplet_hier_epochs_1_shard_1 | [
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language: "ru"
tags:
- paraphrasing
- russian
license: mit
---
This is a small Russian paraphraser based on the [google/mt5-small](https://huggingface.co/google/mt5-small) model.
It has rather poor paraphrasing performance, but can be fine tuned for this or other tasks.
This model was created by taking the [alen... | [
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AnonymousSub/SR_rule_based_twostagetriplet_hier_epochs_1_shard_1 | [
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"no_repeat_ngram_size": nul... | 2 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model_index:
- name: chinese-address-ner
results:
- task:
name: Token Classification
type: token-classification
metric:
name: Accuracy
type: accuracy
value: 0.975825946817083
---
<... | [
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AnonymousSub/cline-emanuals-s10-AR | [
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"... | 27 | null | ---
language: ja
datasets: wikipedia
pipeline_tag: fill-mask
widget:
- text: 得意な科目は[MASK]です。
license: cc-by-sa-4.0
---
# BERT base Japanese model
This repository contains a BERT base model trained on Japanese Wikipedia dataset.
## Training data
[Japanese Wikipedia](https://ja.wikipedia.org/wiki/Wikipedia:データベースダウンロ... | [
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AnonymousSub/cline-emanuals-s10-SR | [] | null | {
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language: ja
datasets: wikipedia
widget:
- text: 統計的機械学習でのニューラルネットワーク
license: cc
---
# GPT-2 small Japanese model
This repository contains a GPT2-small model trained on Japanese Wikipedia dataset.
## Training data
[Japanese Wikipedia](https://ja.wikipedia.org/wiki/Wikipedia:データベースダウンロード) dataset as of Aug20, 2... | [
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AnonymousSub/cline | [
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"no_repeat_n... | 2 | null | ---
language:
- cs
license: apache-2.0
tags:
- automatic-speech-recognition
- generated_from_trainer
- hf-asr-leaderboard
- mozilla-foundation/common_voice_8_0
- robust-speech-event
- xlsr-fine-tuning-week
datasets:
- mozilla-foundation/common_voice_8_0
- ovm
- pscr
- vystadial2016
model-index:
- name: Czech comodoro W... | [
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AnonymousSub/cline_emanuals | [
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"no_repeat_n... | 3 | null | ---
language:
- cs
license: apache-2.0
tags:
- automatic-speech-recognition
- mozilla-foundation/common_voice_8_0
- generated_from_trainer
- robust-speech-event
- xlsr-fine-tuning-week
- hf-asr-leaderboard
datasets:
- mozilla-foundation/common_voice_8_0
model-index:
- name: Czech comodoro Wav2Vec2 XLSR 300M CV8
resul... | [
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AnonymousSub/declutr-model_wikiqa | [
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"... | 26 | null | ---
tags:
- conversational
---
# Snape DialoGPT Model
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"... | 25 | null | This is the SciBERT pretrained language model further fine-tuned on masked language modeling and cite-worthiness detection on the [CiteWorth](https://github.com/copenlu/cite-worth) dataset. Note that this model should be used for further fine-tuning on downstream scientific document understanding tasks. | [
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AnonymousSub/rule_based_hier_quadruplet_0.1_epochs_1_shard_1_squad2.0 | [
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"no_repeat_n... | 4 | 2021-08-27T16:13:56Z | ---
language: ko
---
# Pretrained BART in Korean
This is pretrained BART model with multiple Korean Datasets.
I used multiple datasets for generalizing the model for both colloquial and written texts.
The training is supported by [TPU Research Cloud](https://sites.research.google/trc/) program.
The script which is... | [
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AnonymousSub/rule_based_roberta_bert_quadruplet_epochs_1_shard_1 | [
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"no_repeat_ngram_size... | 6 | null | ---
tags:
- generated_from_trainer
datasets:
- wikiann
metrics:
- precision
- recall
- f1
- accuracy
inference: false
language:
- sk
model-index:
- name: bertoslav-limited-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: wikiann sk
type: wikiann
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AnonymousSub/rule_based_roberta_bert_quadruplet_epochs_1_shard_1_squad2.0 | [
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"no_re... | 2 | null | ---
license: mit
language:
- sk
tags:
- generated_from_trainer
datasets:
- wikiann
metrics:
- precision
- recall
- f1
- accuracy
inference: false
widget:
- text: "Zuzana Čaputová sa narodila 21. júna 1973 v Bratislave."
example_title: "Named Entity Recognition"
model-index:
- name: slovakbert-ner
results:
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AnonymousSub/rule_based_roberta_bert_triplet_epochs_1_shard_1 | [
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"no_repeat_ngram_size... | 2 | null | ---
language: tt
datasets:
- common_voice
metrics:
- wer
tags:
- audio
- automatic-speech-recognition
- speech
- xlsr-fine-tuning-week
license: apache-2.0
model-index:
- name: Tatar XLSR Wav2Vec2 Large 53
results:
- task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
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AnonymousSub/rule_based_roberta_only_classfn_epochs_1_shard_1_wikiqa | [
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"... | 27 | null | ---
language:
- en
<!-- thumbnail: https://raw.githubusercontent.com/JetRunner/BERT-of-Theseus/master/bert-of-theseus.png
-->
tags:
- topic labeling
license: apache-2.0
metrics:
- ndcg
---
# MyModel
## Model description
This is the `BART-TL-all` model from the paper [BART-TL: Weakly-Supervised Topic Label Generati... | [
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"no_repeat_ngram_size... | 2 | null | ---
language:
- ca
- es
- en
tags:
- translation
---
### Preprocessing
1. Normalisation and tokenisation with moses scripts
2. truecased with model docgWP.tcmodel.[LAN] and moses scripts
3. bped with model model.caesen40k.bpe and subword-nmt
- Note: no prepended tag for multilinguality
### Training Data
1. Bilingua... | [
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"... | 24 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec-timit
results: []
---
<!-- 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. -->
# wav2vec-timit
This m... | [
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... |
AnonymousSub/rule_based_roberta_twostage_quadruplet_epochs_1_shard_10 | [
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"transformers"
] | feature-extraction | {
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"no_repeat_ngram_size... | 3 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-latino40
results: []
---
<!-- 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-latino40... | [
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0.03... |
AnonymousSub/rule_based_roberta_twostage_quadruplet_epochs_1_shard_1_wikiqa | [
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"text-classification",
"transformers"
] | text-classification | {
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"... | 24 | null | ---
license: apache-2.0
---
# Cross-Encoder for MS Marco
This model was trained on the [MS Marco Passage Ranking](https://github.com/microsoft/MSMARCO-Passage-Ranking) task.
The model can be used for Information Retrieval: Given a query, encode the query will all possible passages (e.g. retrieved with ElasticSearch).... | [
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0.042... |
AnonymousSub/rule_based_roberta_twostagequadruplet_hier_epochs_1_shard_1 | [
"pytorch",
"roberta",
"feature-extraction",
"transformers"
] | feature-extraction | {
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"no_repeat_ngram_size... | 7 | null | ---
license: apache-2.0
---
# Cross-Encoder for MS Marco
This model was trained on the [MS Marco Passage Ranking](https://github.com/microsoft/MSMARCO-Passage-Ranking) task.
The model can be used for Information Retrieval: Given a query, encode the query will all possible passages (e.g. retrieved with ElasticSearch).... | [
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0.042... |
AnonymousSub/rule_based_roberta_twostagequadruplet_hier_epochs_1_shard_10 | [
"pytorch",
"roberta",
"feature-extraction",
"transformers"
] | feature-extraction | {
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"no_repeat_ngram_size... | 2 | null | ---
license: apache-2.0
---
# Cross-Encoder for MS Marco
This model was trained on the [MS Marco Passage Ranking](https://github.com/microsoft/MSMARCO-Passage-Ranking) task.
The model can be used for Information Retrieval: Given a query, encode the query will all possible passages (e.g. retrieved with ElasticSearch).... | [
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0.042... |
AnonymousSub/rule_based_roberta_twostagequadruplet_hier_epochs_1_shard_1_squad2.0 | [
"pytorch",
"roberta",
"question-answering",
"transformers",
"autotrain_compatible"
] | question-answering | {
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"no_re... | 2 | null | ---
license: apache-2.0
---
# Cross-Encoder for MS Marco
This model was trained on the [MS Marco Passage Ranking](https://github.com/microsoft/MSMARCO-Passage-Ranking) task.
The model can be used for Information Retrieval: Given a query, encode the query will all possible passages (e.g. retrieved with ElasticSearch).... | [
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0.042... |
AnonymousSub/rule_based_roberta_twostagequadruplet_hier_epochs_1_shard_1_wikiqa | [
"pytorch",
"roberta",
"text-classification",
"transformers"
] | text-classification | {
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"RobertaForSequenceClassification"
],
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"... | 25 | 2021-04-15T18:42:38Z | ---
license: apache-2.0
---
# Cross-Encoder for MS Marco
This model was trained on the [MS Marco Passage Ranking](https://github.com/microsoft/MSMARCO-Passage-Ranking) task.
The model can be used for Information Retrieval: Given a query, encode the query will all possible passages (e.g. retrieved with ElasticSearch).... | [
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0.042... |
AnonymousSub/rule_based_roberta_twostagetriplet_epochs_1_shard_10 | [
"pytorch",
"roberta",
"feature-extraction",
"transformers"
] | feature-extraction | {
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"no_repeat_ngram_size... | 1 | 2021-01-02T20:54:36Z | ---
license: apache-2.0
---
# Cross-Encoder for MS Marco
This model was trained on the [MS Marco Passage Ranking](https://github.com/microsoft/MSMARCO-Passage-Ranking) task.
The model can be used for Information Retrieval: Given a query, encode the query will all possible passages (e.g. retrieved with ElasticSearch).... | [
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0.042... |
AnonymousSub/rule_based_roberta_twostagetriplet_epochs_1_shard_1_squad2.0 | [
"pytorch",
"roberta",
"question-answering",
"transformers",
"autotrain_compatible"
] | question-answering | {
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"no_re... | 4 | 2021-01-02T20:58:41Z | ---
license: apache-2.0
---
# Cross-Encoder for MS Marco
This model was trained on the [MS Marco Passage Ranking](https://github.com/microsoft/MSMARCO-Passage-Ranking) task.
The model can be used for Information Retrieval: Given a query, encode the query will all possible passages (e.g. retrieved with ElasticSearch).... | [
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0.042... |
AnonymousSub/rule_based_roberta_twostagetriplet_epochs_1_shard_1_wikiqa | [
"pytorch",
"roberta",
"text-classification",
"transformers"
] | text-classification | {
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],
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"... | 24 | 2021-01-02T20:09:38Z | ---
license: apache-2.0
---
# Cross-Encoder for MS Marco
This model was trained on the [MS Marco Passage Ranking](https://github.com/microsoft/MSMARCO-Passage-Ranking) task.
The model can be used for Information Retrieval: Given a query, encode the query will all possible passages (e.g. retrieved with ElasticSearch).... | [
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0.042... |
AnonymousSub/rule_based_roberta_twostagetriplet_hier_epochs_1_shard_10 | [
"pytorch",
"roberta",
"feature-extraction",
"transformers"
] | feature-extraction | {
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"no_repeat_ngram_size... | 6 | null | ---
license: apache-2.0
---
# Cross-Encoder for MS MARCO - EN-DE
This is a cross-lingual Cross-Encoder model for EN-DE that can be used for passage re-ranking. It was trained on the [MS Marco Passage Ranking](https://github.com/microsoft/MSMARCO-Passage-Ranking) task.
The model can be used for Information Retrieval: ... | [
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AnonymousSub/rule_based_roberta_twostagetriplet_hier_epochs_1_shard_1_squad2.0 | [
"pytorch",
"roberta",
"question-answering",
"transformers",
"autotrain_compatible"
] | question-answering | {
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"no_re... | 4 | null | ---
language: en
pipeline_tag: zero-shot-classification
license: apache-2.0
tags:
- MiniLMv2
datasets:
- multi_nli
- snli
metrics:
- accuracy
---
# Cross-Encoder for Natural Language Inference
This model was trained using [SentenceTransformers](https://sbert.net) [Cross-Encoder](https://www.sbert.net/examples/applicat... | [
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AnonymousSub/rule_based_roberta_twostagetriplet_hier_epochs_1_shard_1_wikiqa | [
"pytorch",
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"... | 23 | null | ---
language: en
pipeline_tag: zero-shot-classification
tags:
- deberta-base-base
datasets:
- multi_nli
- snli
metrics:
- accuracy
license: apache-2.0
---
# Cross-Encoder for Natural Language Inference
This model was trained using [SentenceTransformers](https://sbert.net) [Cross-Encoder](https://www.sbert.net/examples... | [
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AnonymousSub/rule_based_twostage_quadruplet_epochs_1_shard_1_wikiqa | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | {
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"no_rep... | 30 | null | ---
language: en
pipeline_tag: zero-shot-classification
tags:
- microsoft/deberta-v3-large
datasets:
- multi_nli
- snli
metrics:
- accuracy
license: apache-2.0
---
# Cross-Encoder for Natural Language Inference
This model was trained using [SentenceTransformers](https://sbert.net) [Cross-Encoder](https://www.sbert.net... | [
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AnonymousSub/rule_based_twostagequadruplet_hier_epochs_1_shard_1_wikiqa | [
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] | text-classification | {
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"no_rep... | 28 | null | ---
language: en
pipeline_tag: zero-shot-classification
tags:
- microsoft/deberta-v3-xsmall
datasets:
- multi_nli
- snli
metrics:
- accuracy
license: apache-2.0
---
# Cross-Encoder for Natural Language Inference
This model was trained using [SentenceTransformers](https://sbert.net) [Cross-Encoder](https://www.sbert.ne... | [
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AnonymousSub/rule_based_twostagetriplet_epochs_1_shard_1_wikiqa | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | {
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"no_rep... | 27 | 2021-01-03T20:09:24Z | ---
language: en
pipeline_tag: zero-shot-classification
tags:
- roberta-base
datasets:
- multi_nli
- snli
metrics:
- accuracy
license: apache-2.0
---
# Cross-Encoder for Natural Language Inference
This model was trained using [SentenceTransformers](https://sbert.net) [Cross-Encoder](https://www.sbert.net/examples/appl... | [
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0.... |
AnonymousSub/rule_based_twostagetriplet_hier_epochs_1_shard_1_wikiqa | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | {
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],
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"no_rep... | 27 | null | ---
license: apache-2.0
---
# Cross-Encoder for Quora Duplicate Questions Detection
This model was trained using [SentenceTransformers](https://sbert.net) [Cross-Encoder](https://www.sbert.net/examples/applications/cross-encoder/README.html) class.
## Training Data
Given a question and paragraph, can the question be a... | [
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AnonymousSub/specter-bert-model_copy | [
"pytorch",
"bert",
"feature-extraction",
"transformers"
] | feature-extraction | {
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"no_repeat_ngram_size": nul... | 2 | null | ---
license: apache-2.0
---
# Cross-Encoder for Quora Duplicate Questions Detection
This model was trained using [SentenceTransformers](https://sbert.net) [Cross-Encoder](https://www.sbert.net/examples/applications/cross-encoder/README.html) class.
## Training Data
This model was trained on the [Quora Duplicate Questi... | [
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AnonymousSub/specter-bert-model_squad2.0 | [
"pytorch",
"bert",
"question-answering",
"transformers",
"autotrain_compatible"
] | question-answering | {
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"no_repeat_n... | 1 | null | ---
license: apache-2.0
---
# Cross-Encoder for Quora Duplicate Questions Detection
This model was trained using [SentenceTransformers](https://sbert.net) [Cross-Encoder](https://www.sbert.net/examples/applications/cross-encoder/README.html) class.
## Training Data
This model was trained on the [STS benchmark dataset]... | [
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AnonymousSub/unsup-consert-base | [
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license: apache-2.0
---
# Cross-Encoder for Quora Duplicate Questions Detection
This model was trained using [SentenceTransformers](https://sbert.net) [Cross-Encoder](https://www.sbert.net/examples/applications/cross-encoder/README.html) class.
## Training Data
This model was trained on the [STS benchmark dataset]... | [
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AnonymousSub/unsup-consert-base_copy_wikiqa | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | {
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"no_rep... | 26 | null | ---
language: vi
tags:
- gpt
widget:
- text: "<s> núi nhà xe [SEP] "
---
### Kw2Poem
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Anonymreign/savagebeta | [] | null | {
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tags:
- spacy
- token-classification
language:
- de
license: cc-by-nc-4.0
model-index:
- name: de_RTA_NER
results:
- task:
name: NER
type: token-classification
metrics:
- name: NER Precision
type: precision
value: 0.8630136986
- name: NER Recall
type: recall
value... | [
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AnthonyNelson/DialoGPT-small-ricksanchez | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | {
"architectures": [
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"no_repeat_ngram_size... | 12 | null | ---
language: en
license: mit
tags:
- question-answering
- bert
- bert-base
datasets:
- squad
metrics:
- squad
widget:
- text: Which name is also used to describe the Amazon rainforest in English?
context: 'The Amazon rainforest (Portuguese: Floresta Amazônica or Amazônia; Spanish:
Selva Amazónica, Amazonía or us... | [
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Anthos23/distilbert-base-uncased-finetuned-sst2 | [
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"tensorboard",
"distilbert",
"text-classification",
"transformers",
"generated_from_keras_callback",
"license:apache-2.0"
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... | 21 | null | ---
language: en
thumbnail:
license: mit
tags:
- question-answering
- mobilebert
datasets:
- squad_v2
metrics:
- squad_v2
widget:
- text: "Which name is also used to describe the Amazon rainforest in English?"
context: "The Amazon rainforest (Portuguese: Floresta Amazônica or Amazônia; Spanish: Selva Amazónica, Amaz... | [
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Anubhav23/indianlegal | [] | null | {
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"num_beams... | 0 | null | ---
language:
- bn
licenses:
- cc-by-nc-sa-4.0
---
# BanglaBERT
This repository contains the pretrained discriminator checkpoint of the model **BanglaBERT**. This is an [ELECTRA](https://openreview.net/pdf?id=r1xMH1BtvB) discriminator model pretrained with the Replaced Token Detection (RTD) objective. Finetuned mode... | [
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Anupam/QuestionClassifier | [] | null | {
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tags:
- summarization
- mT5
datasets:
- csebuetnlp/xlsum
language:
- am
- ar
- az
- bn
- my
- zh
- en
- fr
- gu
- ha
- hi
- ig
- id
- ja
- rn
- ko
- ky
- mr
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- fa
- pcm
- pt
- pa
- ru
- gd
- sr
- si
- so
- es
- sw
- ta
- te
- th
- ti
- tr
- uk
- ur
- uz
- vi
- cy
- yo
licenses:
- cc-by-nc-sa-4.0
widge... | [
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Aplinxy9plin/toxic-detection-rus | [] | null | {
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"num_beams... | 0 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
- bem
- robust-speech-event
model-index:
- name: wav2vec2-large-xls-r-1b-bemba-fds
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then r... | [
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Appolo/TestModel | [] | null | {
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"num_beams... | 0 | null | ---
language: bem
datasets:
- BembaSpeech
metrics:
- wer
tags:
- audio
- automatic-speech-recognition
- speech
- xlsr-fine-tuning-week
license: apache-2.0
model-index:
- name: XLSR Wav2Vec2 Bemba by Claytone Sikasote
results:
- task:
name: Speech Recognition
type: automatic-speech-recognition
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ArBert/albert-base-v2-finetuned-ner-agglo | [
"pytorch",
"tensorboard",
"albert",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | {
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"no_re... | 8 | null | ---
tags:
- translation
- torch==1.8.0
widget:
- text: "Inference Unavailable"
---
### marianmt-th-zh_cn
* source languages: th
* target languages: zh_cn
* dataset:
* model: transformer-align
* pre-processing: normalization + SentencePiece
* test set translations:
* test set scores:
## Training
Training scripts fr... | [
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ArBert/albert-base-v2-finetuned-ner-gmm | [
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"no_re... | 8 | null | ---
widget:
- text: "สวนกุหลาบเป็นโรงเรียนอะไร"
context: "โรงเรียนสวนกุหลาบวิทยาลัย (Suankularb Wittayalai School) (อักษรย่อ : ส.ก. / S.K.) เป็นโรงเรียนชายล้วน ระดับชั้นมัธยมศึกษาขนาดใหญ่พิเศษ สังกัดสำนักงานเขตพื้นที่การศึกษามัธยมศึกษาเขต 1 สำนักงานคณะกรรมการการศึกษาขั้นพื้นฐาน (ชื่อเดิม: กรมสามัญศึกษา) กระทรวงศึกษาธ... | [
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ArBert/albert-base-v2-finetuned-ner-kmeans | [
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widget:
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ArBert/roberta-base-finetuned-ner-gmm | [] | null | {
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"num_beams... | 0 | null | # Introduction
This repo contains pre-trained model using
<https://github.com/k2-fsa/icefall/pull/213>.
It is trained on train-clean-100 subset of the LibriSpeech dataset.
Also, it uses the `S` subset from GigaSpeech as extra training data.
## How to clone this repo
```
sudo apt-get install git-lfs
git clone https:/... | [
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Ayham/bert_gpt2_summarization_cnndm | [
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"dataset:cnn_dailymail",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
] | text2text-generation | {
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"no_re... | 4 | null | ---
language: vi
tags:
- gpt2-viwiki
license: mit
---
# GPT-2 Fine-tuning in Vietnamese Wikipedia
## Model description
This is a Vietnamese GPT-2 model which is finetuned on the [Latest pages articles of Vietnamese Wikipedia](https://dumps.wikimedia.org/viwiki/latest/viwiki-latest-pages-articles.xml.bz2).
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Ayham/bert_gpt2_summarization_cnndm_new | [
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"tensorboard",
"encoder-decoder",
"text2text-generation",
"dataset:cnn_dailymail",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
] | text2text-generation | {
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"no_re... | 8 | null | ---
pipeline_tag: sentence-similarity
language: fr
datasets:
- stsb_multi_mt
tags:
- Text
- Sentence Similarity
- Sentence-Embedding
- camembert-large
license: apache-2.0
model-index:
- name: sentence-camembert-large by Van Tuan DANG
results:
- task:
name: Sentence-Embedding
type: Text Similarity
d... | [
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Ayran/DialoGPT-medium-harry-1 | [] | null | {
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"num_beams... | 0 | 2021-07-23T13:35:36Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- commonsense_qa
metrics:
- accuracy
model_index:
- name: albert-xxlarge-v2-finetuned-csqa
results:
- dataset:
name: commonsense_qa
type: commonsense_qa
args: default
metric:
name: Accuracy
type: accuracy
value:... | [
-0.012557191774249077,
0.010548289865255356,
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0.04442824795842171,
0.036676984280347824,
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0.03790954127907753,
0.011007753200829029,
-0.00987564492970705,
0.007004468701779842,
0... |
Ayran/DialoGPT-small-harry-potter-1-through-3 | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 12 | 2021-07-23T14:06:36Z | ---
license: mit
tags:
- generated_from_trainer
datasets:
- commonsense_qa
metrics:
- accuracy
model_index:
- name: roberta-large-finetuned-csqa
results:
- dataset:
name: commonsense_qa
type: commonsense_qa
args: default
metric:
name: Accuracy
type: accuracy
value: 0.73300576... | [
-0.016362423077225685,
0.015201004222035408,
0.0012923198519274592,
0.02858356572687626,
0.034076374024152756,
0.00608078483492136,
-0.020388036966323853,
-0.01604124903678894,
-0.03954643756151199,
0.03693355992436409,
0.011945090256631374,
-0.0180739127099514,
0.017967527732253075,
0.058... |
AyushPJ/ai-club-inductions-21-nlp-ALBERT | [
"pytorch",
"albert",
"question-answering",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
] | question-answering | {
"architectures": [
"AlbertForQuestionAnswering"
],
"model_type": "albert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repe... | 8 | null | ---
tags:
- conversational
---
#Harry Potter DialoGPT
| [
-0.02210344560444355,
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0.02944374829530716,
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0.003887080354616046,
0.012121705338358879,
-0.017787707969546318,
0.010743756778538227,
0.026667309924960136,
-0.030164318159222603,
0.0073508527129888535,
0.... |
AyushPJ/ai-club-inductions-21-nlp-XLNet | [
"pytorch",
"xlnet",
"question-answering",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
] | question-answering | {
"architectures": [
"XLNetForQuestionAnsweringSimple"
],
"model_type": "xlnet",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_... | 9 | null | ## CALM
This model is for ICLR2021 paper: [Pre-training Text-to-Text Transformers for Concept-centric Common Sense](https://openreview.net/forum?id=3k20LAiHYL2).
Checkout our [Project website](https://inklab.usc.edu/calm-project) for details!
```bibtex
@inproceedings{CALM2021,
title={Pre-training Text-to-Text Trans... | [
-0.03242654353380203,
-0.010830007493495941,
-0.029338354244828224,
0.03984128683805466,
0.027900632470846176,
0.041836511343717575,
-0.010838622227311134,
-0.005262671038508415,
-0.031334761530160904,
0.030916685238480568,
0.03117452934384346,
-0.016509631648659706,
0.03160613775253296,
0... |
AyushPJ/ai-club-inductions-21-nlp-roBERTa-base-squad-v2 | [
"pytorch",
"roberta",
"question-answering",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
] | question-answering | {
"architectures": [
"RobertaForQuestionAnswering"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_re... | 8 | 2021-09-16T07:17:18Z | ## CALM
This model is for ICLR2021 paper: [Pre-training Text-to-Text Transformers for Concept-centric Common Sense](https://openreview.net/forum?id=3k20LAiHYL2).
Checkout our [Project website](https://inklab.usc.edu/calm-project) for details!
```bibtex
@inproceedings{CALM2021,
title={Pre-training Text-to-Text Trans... | [
-0.03242654353380203,
-0.010830007493495941,
-0.029338354244828224,
0.03984128683805466,
0.027900632470846176,
0.041836511343717575,
-0.010838622227311134,
-0.005262671038508415,
-0.031334761530160904,
0.030916685238480568,
0.03117452934384346,
-0.016509631648659706,
0.03160613775253296,
0... |
BSC-LT/roberta-base-bne-capitel-pos | [
"pytorch",
"roberta",
"token-classification",
"es",
"dataset:bne",
"dataset:capitel",
"arxiv:1907.11692",
"arxiv:2107.07253",
"transformers",
"national library of spain",
"spanish",
"bne",
"capitel",
"pos",
"license:apache-2.0",
"autotrain_compatible"
] | token-classification | {
"architectures": [
"RobertaForTokenClassification"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_... | 14 | null | ---
language: or
datasets:
- common_voice
metrics:
- wer
tags:
- audio
- automatic-speech-recognition
- speech
- xlsr-fine-tuning-week
license: apache-2.0
model-index:
- name: odia XLSR Wav2Vec2 Large 2000
results:
- task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
n... | [
-0.027868837118148804,
-0.033874861896038055,
-0.0006283328402787447,
0.03539919853210449,
0.04951266571879387,
0.028426652774214745,
-0.013397802598774433,
-0.011831458657979965,
-0.03761536255478859,
0.06100650131702423,
0.03539338707923889,
-0.021945320069789886,
-0.017852528020739555,
... |
BSC-LT/roberta-base-bne-sqac | [
"pytorch",
"roberta",
"question-answering",
"es",
"dataset:BSC-TeMU/SQAC",
"arxiv:1907.11692",
"arxiv:2107.07253",
"transformers",
"national library of spain",
"spanish",
"bne",
"qa",
"question answering",
"license:apache-2.0",
"autotrain_compatible"
] | question-answering | {
"architectures": [
"RobertaForQuestionAnswering"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_re... | 10 | null | ---
language: pa-IN
datasets:
- common_voice
metrics:
- wer
tags:
- audio
- automatic-speech-recognition
- speech
- xlsr-fine-tuning-week
license: apache-2.0
model-index:
- name: danurahul/wav2vec2-large-xlsr-pa-IN
results:
- task:
name: Speech Recognition
type: automatic-speech-recognition
datase... | [
-0.029361488297581673,
-0.025404855608940125,
-0.0019227878656238317,
0.048595476895570755,
0.040142159909009933,
0.047162216156721115,
-0.0044210501946508884,
0.0012356846127659082,
-0.02547348476946354,
0.06720582395792007,
0.0255952887237072,
-0.024902088567614555,
-0.00463579036295414,
... |
BSC-LT/roberta-large-bne-capitel-pos | [
"pytorch",
"roberta",
"token-classification",
"es",
"dataset:bne",
"dataset:capitel",
"arxiv:1907.11692",
"arxiv:2107.07253",
"transformers",
"national library of spain",
"spanish",
"bne",
"capitel",
"pos",
"license:apache-2.0",
"autotrain_compatible"
] | token-classification | {
"architectures": [
"RobertaForTokenClassification"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_... | 13 | null | ---
license: mit
tags:
- generated_from_trainer
datasets:
- amazon_reviews_multi
model-index:
- name: xlm-roberta-base-finetuned-marc-en
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then re... | [
-0.039767373353242874,
0.0012574829161167145,
0.008401399478316307,
0.024524813517928123,
0.025197196751832962,
0.027644824236631393,
-0.024696506559848785,
-0.01040240004658699,
-0.0376780703663826,
0.043937359005212784,
0.04822481423616409,
-0.03632697835564613,
-0.003974782302975655,
0.... |
BSC-LT/roberta-large-bne-sqac | [
"pytorch",
"roberta",
"question-answering",
"es",
"dataset:BSC-TeMU/SQAC",
"arxiv:1907.11692",
"arxiv:2107.07253",
"transformers",
"national library of spain",
"spanish",
"bne",
"qa",
"question answering",
"license:apache-2.0",
"autotrain_compatible"
] | question-answering | {
"architectures": [
"RobertaForQuestionAnswering"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_re... | 15 | 2021-06-17T17:45:01Z | Sample usage:
```python
tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
model = GPT2LMHeadModel.from_pretrained("danyaljj/gpt2_question_answering_squad2")
input_ids = tokenizer.encode("There are two apples on the counter. Q: How many apples? A:", return_tensors="pt")
outputs = model.generate(input_ids)
print("Gene... | [
-0.004089536610990763,
-0.029840053990483284,
-0.020573852583765984,
0.0715438723564148,
0.04776860028505325,
0.04704568535089493,
-0.00332530215382576,
-0.0034925518557429314,
-0.029691630974411964,
0.03972581401467323,
0.015527964569628239,
0.007986948825418949,
0.013660592027008533,
0.0... |
BSC-LT/roberta-large-bne | [
"pytorch",
"roberta",
"fill-mask",
"es",
"dataset:bne",
"arxiv:1907.11692",
"arxiv:2107.07253",
"transformers",
"national library of spain",
"spanish",
"bne",
"license:apache-2.0",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"RobertaForMaskedLM"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngra... | 24 | null | Sample usage:
```python
tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
model = GPT2LMHeadModel.from_pretrained("danyaljj/gpt2_question_generation_given_paragraph")
input_ids = tokenizer.encode("There are two apples on the counter. Q:", return_tensors="pt")
outputs = model.generate(input_ids)
print("Generated:", t... | [
-0.001197000383399427,
-0.02205830253660679,
-0.016449671238660812,
0.06957414746284485,
0.047979388386011124,
0.04384084418416023,
0.000389681983506307,
-0.0031085077207535505,
-0.034685712307691574,
0.042221736162900925,
0.018600789830088615,
0.004989140201359987,
0.00801758747547865,
0.... |
BSen/wav2vec2-base-timit-demo-colab | [
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"transformers",
"generated_from_trainer",
"license:apache-2.0"
] | automatic-speech-recognition | {
"architectures": [
"Wav2Vec2ForCTC"
],
"model_type": "wav2vec2",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_s... | 4 | null | Sample usage:
```python
tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
model = GPT2LMHeadModel.from_pretrained("danyaljj/gpt2_question_generation_given_paragraph_answer")
input_ids = tokenizer.encode("There are two apples on the counter. A: apples Q:", return_tensors="pt")
outputs = model.generate(input_ids)
print... | [
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-0.021860767155885696,
-0.018966127187013626,
0.06768240034580231,
0.045640796422958374,
0.042175114154815674,
-0.0011610533110797405,
-0.004899622406810522,
-0.03674928843975067,
0.0409119576215744,
0.02148469164967537,
0.006167992949485779,
0.006628999952226877,
0... |
BSen/wav2vec2-large-xls-r-300m-turkish-colab | [
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"dataset:common_voice",
"transformers",
"generated_from_trainer",
"license:apache-2.0"
] | automatic-speech-recognition | {
"architectures": [
"Wav2Vec2ForCTC"
],
"model_type": "wav2vec2",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_s... | 6 | null | West et al.'s model from their "reflective decoding" paper.
Sample usage:
```python
import torch
from modeling_opengpt2 import OpenGPT2LMHeadModel
from padded_encoder import Encoder
path_to_backward = 'danyaljj/opengpt2_pytorch_backward'
encoder = Encoder()
model_backward = OpenGPT2LMHeadModel.from_pretrained(pat... | [
-0.024838019162416458,
-0.0038178705144673586,
-0.017280327156186104,
0.050699010491371155,
0.045132867991924286,
0.040485456585884094,
0.03087330423295498,
-0.006549155339598656,
-0.04346860945224762,
0.011612343601882458,
0.03368852287530899,
-0.024444568902254105,
0.028080804273486137,
... |
BW/TEST | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 14 | null | West et al.'s model from their "reflective decoding" paper.
Sample usage:
```python
import torch
from modeling_opengpt2 import OpenGPT2LMHeadModel
from padded_encoder import Encoder
path_to_forward = 'danyaljj/opengpt2_pytorch_forward'
encoder = Encoder()
model_backward = OpenGPT2LMHeadModel.from_pretrained(path_... | [
-0.026361282914876938,
-0.003931219223886728,
-0.0173661969602108,
0.050747890025377274,
0.049980420619249344,
0.04228973761200905,
0.029558423906564713,
-0.0057698627933859825,
-0.03886183723807335,
0.010673592798411846,
0.03787719085812569,
-0.018976833671331406,
0.028114523738622665,
0.... |
Babelscape/rebel-large | [
"pytorch",
"safetensors",
"bart",
"text2text-generation",
"en",
"dataset:Babelscape/rebel-dataset",
"transformers",
"seq2seq",
"relation-extraction",
"license:cc-by-nc-sa-4.0",
"model-index",
"autotrain_compatible",
"has_space"
] | text2text-generation | {
"architectures": [
"BartForConditionalGeneration"
],
"model_type": "bart",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repe... | 9,458 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: distilgpt2-finetuned-wikitext2
results: []
---
<!-- 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. -->
# dist... | [
-0.017024066299200058,
-0.02158309705555439,
-0.02458672598004341,
0.016607120633125305,
0.03818520903587341,
0.019184336066246033,
-0.012465733103454113,
-0.00512956827878952,
-0.04502351954579353,
0.06474221497774124,
0.034094907343387604,
0.001504407962784171,
0.013914359733462334,
0.04... |
Backedman/DialoGPT-small-Anika | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 6 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-base-timit-demo-colab
results: []
---
<!-- 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. -->
# wav2... | [
-0.03647278994321823,
-0.013824107125401497,
-0.028611041605472565,
0.022168636322021484,
0.03806104511022568,
0.032696403563022614,
0.006371902767568827,
0.0027661460917443037,
-0.03486700356006622,
0.04379533976316452,
0.040717098861932755,
-0.008941341191530228,
0.004450133536010981,
0.... |
Bagus/ser-japanese | [] | null | {
"architectures": null,
"model_type": null,
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams... | 0 | null | ---
tags:
- conversational
---
# Chicken Bot's Jon Snow DialoGPT Model | [
-0.050567299127578735,
0.0209587924182415,
0.013259687460958958,
0.034279875457286835,
0.031001117080450058,
0.006977304350584745,
-0.014790629968047142,
0.013092057779431343,
-0.022547630593180656,
0.005355967674404383,
0.029615262523293495,
-0.027984166517853737,
0.019133344292640686,
0.... |
Bagus/wav2vec2-large-xlsr-bahasa-indonesia | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"el",
"dataset:common_voice_id_6.1",
"transformers",
"audio",
"speech",
"bahasa-indonesia",
"license:apache-2.0"
] | automatic-speech-recognition | {
"architectures": [
"Wav2Vec2ForCTC"
],
"model_type": "wav2vec2",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_s... | 12 | null | ---
tags:
- conversational
---
# Pickle Rick DialoGPT Model | [
-0.02349718101322651,
0.026389243081212044,
0.00021321172243915498,
0.0260707288980484,
0.015211840160191059,
0.008878717198967934,
0.006452313158661127,
0.024105191230773926,
-0.00957862101495266,
0.02464010939002037,
0.04186458885669708,
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0.0488... |
Bagus/wav2vec2-xlsr-greek-speech-emotion-recognition | [
"pytorch",
"tensorboard",
"wav2vec2",
"el",
"dataset:aesdd",
"transformers",
"audio",
"audio-classification",
"speech",
"license:apache-2.0"
] | audio-classification | {
"architectures": [
"Wav2Vec2ForSpeechClassification"
],
"model_type": "wav2vec2",
"task_specific_params": {
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"max_length": null
},
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"... | 21 | null | ---
tags:
- conversational
---
# Rick DialoGPT Model | [
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Bagus/wav2vec2-xlsr-japanese-speech-emotion-recognition | [
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"ja",
"dataset:jtes",
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"speech",
"speech-emotion-recognition",
"has_space"
] | audio-classification | {
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"... | 26 | null | Found. Redirecting to https://cdn-lfs.huggingface.co/darubramha/hi-LyricsGPT2/c01a4cfa25cb895cdd0bb25181ba9c1622e93895a6de6f533a7299f70d6b0cfb?response-content-disposition=attachment%3B+filename*%3DUTF-8%27%27README.md%3B+filename%3D%22README.md%22%3B&response-content-type=text%2Fmarkdown&Expires=1685105726&Policy=eyJT... | [
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Batsy24/DialoGPT-medium-Twilight_BellaBot | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
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},
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"no_repeat_ngram_size... | 8 | 2021-08-12T12:57:33Z | ---
tags:
- tensorflowtts
- audio
- text-to-speech
- text-to-mel
language: fr
license: apache-2.0
datasets:
- synpaflex
widget:
- text: "Oh, je voudrais tant que tu te souviennes Des jours heureux quand nous étions amis"
---
# Tacotron 2 with Guided Attention trained on Synpaflex (Fr)
This repository provides a pretra... | [
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Batsy24/DialoGPT-small-Twilight_EdBot | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | {
"architectures": [
"GPT2LMHeadModel"
],
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"no_repeat_ngram_size... | 6 | null | ---
language: es
widget:
- text: "La inteligencia artificial en lationoamérica se ha desarrollado "
license: apache-2.0
datasets:
- wikipedia
---
La descripción en Español se encuentra después de la descripción en Inglés.
# (English) GPT2-small-spanish: a Language Model for Spanish text generation (and more NLP task... | [
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Battlehooks/distilbert-base-uncased-finetuned-squad | [] | null | {
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"num_beams... | 0 | null | # <a name="introduction"></a> PhoBERT: Pre-trained language models for Vietnamese
Pre-trained PhoBERT models are the state-of-the-art language models for Vietnamese ([Pho](https://en.wikipedia.org/wiki/Pho), i.e. "Phở", is a popular food in Vietnam):
- Two PhoBERT versions of "base" and "large" are the first publ... | [
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BatuhanYilmaz/bert-finetuned-ner | [] | null | {
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"num_beams... | 0 | null | ---
tags:
- conversational
---
#Harry Potter DialoGPT Model | [
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BatuhanYilmaz/bert-finetuned-nerxD | [] | null | {
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tags:
- conversational
---
# Tony Stark DialoGPT model
Invite me to your discord server : https://discord.com/api/oauth2/authorize?client_id=885065886787063848&permissions=137439365184&scope=bot | [
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BatuhanYilmaz/code-search-net-tokenizer1 | [] | null | {
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language:
- en
tags:
- BioBERT
- Diseases
- NER
license: apache-2.0
datasets:
- ncbi_disease
- BC5CDR-diseases
- LitCOVID-pubtator
---
BioBERT model fine-tuned in NER task with BC5CDR-diseases and NCBI-diseases corpus along with selected pubtator annotations from LitCOVID dataset
This was fine-tuned in order to us... | [
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BatuhanYilmaz/dummy | [] | null | {
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"num_beams... | 0 | 2021-11-15T20:30:48Z | ---
tags:
- text-classification
- fastai
library_name: fastai
datasets:
- blbooksgenre
widget:
- text: "Poems on various subjects. Whereto is prefixed a short essay on the structure of English verse"
- text: "Two Centuries of Soho: its institutions, firms, and amusements. By the Clergy of St. Anne's, Soho, J. H. Cardwe... | [
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Baybars/debateGPT | [] | null | {
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"num_beams... | 0 | null | ---
tags:
- bert
- adapterhub:text-classification
- adapter-transformers
---
# Adapter `davanstrien/book-genre-classification` for bert-base-cased
An [adapter](https://adapterhub.ml) for the `bert-base-cased` model that was trained on the [text-classification](https://adapterhub.ml/explore/text-classification/) datas... | [
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Baybars/wav2vec2-xls-r-1b-turkish | [
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"tr",
"dataset:common_voice",
"transformers",
"common_voice",
"generated_from_trainer"
] | automatic-speech-recognition | {
"architectures": [
"Wav2Vec2ForCTC"
],
"model_type": "wav2vec2",
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},
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"no_repeat_ngram_s... | 13 | 2022-03-01T20:06:26Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- image_folder
metrics:
- f1
model-index:
- name: convnext_flyswot
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: image_folder
type: image_folder
args: default
metrics:
-... | [
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BeIR/sparta-msmarco-distilbert-base-v1 | [
"pytorch",
"distilbert",
"feature-extraction",
"arxiv:2009.13013",
"arxiv:2104.08663",
"transformers"
] | feature-extraction | {
"architectures": [
"DistilBertModel"
],
"model_type": "distilbert",
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},
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"no_repeat_ngra... | 106 | null | ---
license: mit
tags:
- object-detection
widget:
- src: https://huggingface.co/davanstrien/detr_beyond_words/resolve/main/19.jpg
example_title: page
- src: https://huggingface.co/davanstrien/detr_beyond_words/resolve/main/65.jpg
example_title: page2
---
# detr_beyond_words (WIP)
[facebook/detr-resnet-50](https:... | [
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BenGeorge/MyModel | [] | null | {
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"num_beams... | 0 | 2021-10-23T15:00:31Z | ---
tags:
- text-classification
library_name: generic
---
# Text Classification repository template
This is a template repository for Text Classification to support generic inference with Hugging Face Hub generic Inference API. There are two required steps:
1. Specify the requirements by defining a `requirements.txt... | [
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BenQLange/HF_bot | [] | null | {
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"num_beams... | 0 | 2021-12-01T17:44:39Z | # flyswot
## Model description
In progress model for detecting 'fake' flysheets
## Intended uses & limitations
Not currently intended for public consumption...
#### Limitations and bias
Not currently intended for public consumption...
## Training data
TODO
## Eval results
| [
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BenWitter/DialoGPT-small-Tyrion | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
] | text-generation | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
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"max_length": 1000
},
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"no_repeat_ngram_size... | 11 | 2021-12-21T16:19:04Z | TODO
## Model description
In progress model for detecting 'fake' flysheets
## Intended uses & limitations
Not currently intended for public consumption...
## Limitations and bias
Not currently intended for public consumption...
## Training data
## Eval results | [
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Benicio/t5-small-finetuned-en-to-ro | [] | null | {
"architectures": null,
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"num_beams... | 0 | 2022-03-01T21:01:39Z | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: flyswot_iiif
results: []
---
<!-- 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. -->
# flyswot_... | [
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Benicio/t5-small-finetuned-en-to-ru | [
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | {
"architectures": [
"T5ForConditionalGeneration"
],
"model_type": "t5",
"task_specific_params": {
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"max_length": null
},
"summarization": {
"early_stopping": true,
"length_penalty": 2,
"max_length": 200,
"min_length": 30,
"no_repeat_ngram_s... | 50 | 2022-03-01T17:52:47Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- image_folder
model-index:
- name: flyswot_test
results: []
---
<!-- 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. -->
... | [
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Bharathdamu/wav2vec2-large-xls-r-300m-hindi-colab | [
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"dataset:common_voice",
"transformers",
"generated_from_trainer",
"license:apache-2.0"
] | automatic-speech-recognition | {
"architectures": [
"Wav2Vec2ForCTC"
],
"model_type": "wav2vec2",
"task_specific_params": {
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},
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"no_repeat_ngram_s... | 4 | 2021-11-15T20:43:37Z | ---
tags:
- chemistry
library_name: generic
language:
- en
- gl
- af
- ak
---
# TODO
-
-
-
- | [
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BigSalmon/GPT2HardandEasy | [
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"transformers"
] | text-generation | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 9 | 2021-02-03T15:55:50Z | ---
language: "fr"
tags:
- french
- gpt2
- model
---
A small french language model for french text generation (and possibly more NLP tasks...)
**Introduction**
This french gpt2 model is based on openai GPT-2 small model.
It was trained on a <b>very small (190Mb) dataset </b> from french wikipedia using Transfer Le... | [
-0.002657538279891014,
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0.0... |
BigSalmon/GPTHeHe | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"has_space"
] | text-generation | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 8 | 2022-02-02T22:07:13Z | ---
language:
- it
license: apache-2.0
tags:
- automatic-speech-recognition
- generated_from_trainer
- hf-asr-leaderboard
- robust-speech-event
datasets:
- mozilla-foundation/common_voice_7_0
model-index:
- name: XLS-R-1b - Italian
results:
- task:
name: Automatic Speech Recognition
type: automatic-spee... | [
-0.010494990274310112,
-0.016850251704454422,
-0.01718650572001934,
0.03079700842499733,
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0.01566632278263569,
0.002711570356041193,
-0.006356039550155401,
-0.0479697585105896,
0.0584251694381237,
0.03387231379747391,
-0.03266332671046257,
0.005330520682036877,
0.011654... |
BigSalmon/GPTNeo350MInformalToFormalLincoln4 | [
"pytorch",
"gpt_neo",
"text-generation",
"transformers",
"has_space"
] | text-generation | {
"architectures": [
"GPTNeoForCausalLM"
],
"model_type": "gpt_neo",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram... | 11 | null | # roberta-go
---
language: Go
datasets:
- code_search_net
---
This is a [roberta](https://arxiv.org/pdf/1907.11692.pdf) pre-trained version on the [CodeSearchNet dataset](https://github.com/github/CodeSearchNet) for **Golang** Mask Language Model mission.
To load the model:
(necessary packages: !pip install transform... | [
-0.017037242650985718,
-0.02456587366759777,
0.0025669578462839127,
0.051279302686452866,
0.052979759871959686,
0.04209392890334129,
-0.00695345364511013,
-0.0038444928359240294,
-0.02738768979907036,
0.06245747208595276,
0.0007066801772452891,
0.008998879231512547,
0.013044752180576324,
0... |
BigSalmon/GPTNeo350MInformalToFormalLincoln5 | [
"pytorch",
"gpt_neo",
"text-generation",
"transformers",
"has_space"
] | text-generation | {
"architectures": [
"GPTNeoForCausalLM"
],
"model_type": "gpt_neo",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram... | 11 | 2021-01-18T12:27:06Z | # roberta-java
---
language: Java
datasets:
- code_search_net
---
This is a [roberta](https://arxiv.org/pdf/1907.11692.pdf) pre-trained version on the [CodeSearchNet dataset](https://github.com/github/CodeSearchNet) for **Java** Mask Language Model mission.
To load the model:
(necessary packages: !pip install transfo... | [
-0.011258830316364765,
-0.01688598096370697,
-0.005162786692380905,
0.04316369816660881,
0.04540792107582092,
0.04278450459241867,
-0.015158682130277157,
-0.002186025492846966,
-0.015541561879217625,
0.05670461803674698,
-0.00004108713619643822,
-0.019845927134156227,
0.0013466373784467578,
... |
BigSalmon/GPTNeo350MInformalToFormalLincoln6 | [
"pytorch",
"gpt_neo",
"text-generation",
"transformers",
"has_space"
] | text-generation | {
"architectures": [
"GPTNeoForCausalLM"
],
"model_type": "gpt_neo",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram... | 14 | 2021-01-18T12:27:51Z | # roberta-javascript
---
language: javascript
datasets:
- code_search_net
---
This is a [roberta](https://arxiv.org/pdf/1907.11692.pdf) pre-trained version on the [CodeSearchNet dataset](https://github.com/github/CodeSearchNet) for **javascript** Mask Language Model mission.
To load the model:
(necessary packages: !p... | [
-0.008978248573839664,
-0.02837371453642845,
-0.00015755649656057358,
0.03801730275154114,
0.048159409314394,
0.03774913772940636,
-0.018807288259267807,
-0.010805591940879822,
-0.03174813464283943,
0.06432086229324341,
-0.0015504112234339118,
-0.011395488865673542,
-0.010961165651679039,
... |
BigSalmon/GoodMaskResults | [
"pytorch",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"RobertaForMaskedLM"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngra... | 9 | null | # roberta-python
---
language: python
datasets:
- code_search_net
---
This is a [roberta](https://arxiv.org/pdf/1907.11692.pdf) pre-trained version on the [CodeSearchNet dataset](https://github.com/github/CodeSearchNet) for **Python** Mask Language Model mission.
To load the model:
(necessary packages: !pip install t... | [
-0.012572121806442738,
-0.030122268944978714,
-0.00266736070625484,
0.052923426032066345,
0.04350991174578667,
0.04178224131464958,
-0.018640775233507156,
-0.00007359342271229252,
-0.03577081859111786,
0.06589750945568085,
-0.0018393974751234055,
0.005834112409502268,
-0.00459472369402647,
... |
BigSalmon/InfillFormalLincoln | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
] | text-generation | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 8 | 2021-02-10T06:21:21Z | # measurement_time
---
language: en
datasets:
- measurement_time
---
This is a [t5-small](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) fine-tuned version on the [math_dataset/measurement_time](https://www.tensorflow.org/datasets/catalog/math_dataset#mathdatasetmeasurement_time) for solvi... | [
-0.01723477430641651,
-0.019299255684018135,
-0.009759453125298023,
0.020663265138864517,
0.017901837825775146,
0.03019125759601593,
0.001130928285419941,
-0.012577944435179234,
-0.03165227547287941,
0.04699379950761795,
0.03915850445628166,
0.01735100708901882,
-0.013414891436696053,
0.04... |
BigSalmon/InformalToFormalLincoln14 | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
] | text-generation | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 5 | 2021-02-08T06:38:01Z | # numbers_gcd
---
language: en
datasets:
- numbers_gcd
---
This is a [t5-small](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) fine-tuned version on the [math_dataset/numbers_gcd](https://www.tensorflow.org/datasets/catalog/math_dataset#mathdatasetnumbers_gcd) for solving **greatest common... | [
-0.051307689398527145,
-0.017786996439099312,
-0.0012405852321535349,
0.04282413050532341,
0.015961602330207825,
0.03820378705859184,
-0.014243269339203835,
-0.011470994912087917,
-0.016274340450763702,
0.02441595122218132,
0.02540348470211029,
0.011734344065189362,
-0.0061882054433226585,
... |
BigSalmon/InformalToFormalLincoln15 | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
] | text-generation | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 11 | null | # t5_wikisql_SQL2en
---
language: en
datasets:
- wikisql
---
This is a [t5-small](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) fine-tuned version on the [wikisql dataset](https://huggingface.co/datasets/wikisql) for **SQL** to **English** **translation** text2text mission.
To load the m... | [
-0.022855401039123535,
-0.0409022681415081,
0.0002787904522847384,
0.05736813321709633,
0.02686082199215889,
0.03049478493630886,
-0.017329953610897064,
-0.007748128846287727,
-0.02477959543466568,
0.04889838770031929,
0.023237967863678932,
0.005142822861671448,
0.014242804609239101,
0.051... |
BigSalmon/InformalToFormalLincoln16 | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
] | text-generation | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 8 | 2021-01-12T16:27:36Z | # t5_wikisql_en2SQL
---
language: en
datasets:
- wikisql
---
This is a [t5-small](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) fine-tuned version on the [wikisql dataset](https://huggingface.co/datasets/wikisql) for **English** to **SQL** **translation** text2text mission.
To load the m... | [
-0.02034580148756504,
-0.03537837788462639,
-0.005384271498769522,
0.061301786452531815,
0.021345971152186394,
0.03128685802221298,
-0.014965415932238102,
-0.008944004774093628,
-0.04212254658341408,
0.05558950826525688,
0.019285114482045174,
0.005824295338243246,
0.019954808056354523,
0.0... |
BigSalmon/InformalToFormalLincoln17 | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
] | text-generation | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 12 | 2021-08-06T14:26:53Z | ---
tags:
- feature-extraction
library_name: generic
---
# Feature Extraction repository template
This is a template repository for feature extraction to support generic inference with Hugging Face Hub generic Inference API. There are two required steps
1. Specify the requirements by defining a `requirements.txt` fi... | [
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0.03317805007100105,
0.011946342885494232,
0.0... |
BigSalmon/InformalToFormalLincoln21 | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"has_space"
] | text-generation | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 8 | null | ---
language: finnish
license: mit
widget:
- text: "Täkäläinen sanomalehdistö [MASK] erit - täin"
---
# Historic Language Models (HLMs)
## Languages
Our Historic Language Models Zoo contains support for the following languages - incl. their training data source:
| Language | Training data | Size
| -------- | ----... | [
-0.023486165329813957,
-0.031039061024785042,
-0.006087295711040497,
0.05605125054717064,
0.030801918357610703,
0.016119971871376038,
0.0009028645581565797,
-0.00396601390093565,
-0.08194935321807861,
0.05126739665865898,
0.010944857262074947,
-0.02328294888138771,
0.012300699017941952,
0.... |
BigSalmon/InformalToFormalLincoln22 | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
] | text-generation | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 6 | null | ---
language: fr
license: mit
tags:
- "historic french"
---
# 🤗 + 📚 dbmdz BERT model
In this repository the MDZ Digital Library team (dbmdz) at the Bavarian State
Library open sources French Europeana BERT models 🎉
# French Europeana BERT
We extracted all French texts using the `language` metadata attribute fro... | [
0.0023738592863082886,
-0.021615972742438316,
-0.017070427536964417,
0.05561211705207825,
0.00297957519069314,
0.018502678722143173,
-0.030941298231482506,
-0.029654577374458313,
-0.04637674242258072,
0.04528895393013954,
0.012718770653009415,
-0.018554704263806343,
-0.010788238607347012,
... |
BigSalmon/InformalToFormalLincoln24 | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"has_space"
] | text-generation | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 5 | null | ---
language: de
license: mit
tags:
- "historic german"
---
# 🤗 + 📚 dbmdz BERT models
In this repository the MDZ Digital Library team (dbmdz) at the Bavarian State
Library open sources German Europeana BERT models 🎉
# German Europeana BERT
We use the open source [Europeana newspapers](http://www.europeana-news... | [
-0.0163403507322073,
-0.020723508670926094,
-0.01998221129179001,
0.07002788037061691,
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0.030215760692954063,
-0.011911187320947647,
-0.022403085604310036,
-0.07190102338790894,
0.05081489682197571,
0.020703615620732307,
-0.02529342658817768,
-0.003788016038015485,
... |
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