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 |
|---|---|---|---|---|---|---|---|
AkshaySg/GrammarCorrection | [] | null | {
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"num_beams... | 0 | null | ---
language: tl
tags:
- distilbert
- bert
- tagalog
- filipino
license: gpl-3.0
inference: false
---
**Deprecation Notice**
This model is deprecated. New Filipino Transformer models trained with a much larger corpora are available.
Use [`jcblaise/roberta-tagalog-base`](https://huggingface.co/jcblaise... | [
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AkshaySg/LanguageIdentification | [
"multilingual",
"dataset:VoxLingua107",
"LID",
"spoken language recognition",
"license:apache-2.0"
] | null | {
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"num_beams... | 0 | null | ---
language: tl
tags:
- electra
- tagalog
- filipino
license: gpl-3.0
inference: false
---
**Deprecation Notice**
This model is deprecated. New Filipino Transformer models trained with a much larger corpora are available.
Use [`jcblaise/roberta-tagalog-base`](https://huggingface.co/jcblaise/roberta-tagalog-base) ... | [
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AkshaySg/gramCorrection | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | {
"architectures": [
"T5ForConditionalGeneration"
],
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},
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"max_length": 200,
"min_length": 30,
"no_repeat_ngram_s... | 4 | null | ---
language: tl
tags:
- electra
- tagalog
- filipino
license: gpl-3.0
inference: false
---
# ELECTRA Tagalog Base Cased Generator
Tagalog ELECTRA model pretrained with a large corpus scraped from the internet. This model is part of a larger research project. We open-source the model to allow greater usage within the ... | [
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... |
AkshaySg/langid | [
"multilingual",
"dataset:VoxLingua107",
"speechbrain",
"audio-classification",
"embeddings",
"Language",
"Identification",
"pytorch",
"ECAPA-TDNN",
"TDNN",
"VoxLingua107",
"license:apache-2.0"
] | audio-classification | {
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"num_beams... | 2 | null | ---
language: tl
tags:
- electra
- tagalog
- filipino
license: gpl-3.0
inference: false
---
**Deprecation Notice**
This model is deprecated. New Filipino Transformer models trained with a much larger corpora are available.
Use [`jcblaise/roberta-tagalog-base`](https://huggingface.co/jcblaise/roberta-tagalog-base) ... | [
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0.03867821395397186,
-0.003660297254100442,
0.0003366074524819851,
... |
Akuva2001/SocialGraph | [
"has_space"
] | null | {
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},
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"num_beams... | 0 | null | ---
language: tl
tags:
- electra
- tagalog
- filipino
license: gpl-3.0
inference: false
---
# ELECTRA Tagalog Base Uncased Generator
Tagalog ELECTRA model pretrained with a large corpus scraped from the internet. This model is part of a larger research project. We open-source the model to allow greater usage within th... | [
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0.... |
Al/mymodel | [] | null | {
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"num_beams... | 0 | null | ---
language: tl
tags:
- electra
- tagalog
- filipino
license: gpl-3.0
inference: false
---
**Deprecation Notice**
This model is deprecated. New Filipino Transformer models trained with a much larger corpora are available.
Use [`jcblaise/roberta-tagalog-base`](https://huggingface.co/jcblaise/roberta-tagalog-base) ... | [
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0.0006554981227964163,
... |
AlErysvi/Erys | [] | null | {
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"num_beams... | 0 | null | ---
language: tl
tags:
- electra
- tagalog
- filipino
license: gpl-3.0
inference: false
---
# ELECTRA Tagalog Small Cased Generator
Tagalog ELECTRA model pretrained with a large corpus scraped from the internet. This model is part of a larger research project. We open-source the model to allow greater usage within the... | [
-0.013751305639743805,
-0.024516688659787178,
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0.06295663863420486,
0.03616521507501602,
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0.0014859483344480395,
... |
Ale/Alen | [] | null | {
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"num_beams... | 0 | null | ---
library_name: speechbrain
tags:
- audio
- intent classification
datasets:
- fluent_speech_commands_dataset
metrics:
- wer
model-index:
- name: Direct SLU
results:
- task:
type: automatic-speech-recognition
name: Intent Classification
metrics:
- type: wer # Required. Example: wer
... | [
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0.0... |
Aleksandar/bert-srb-ner-setimes-lr | [] | null | {
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"num_beams... | 0 | null | ---
language:
- ga-IE
license: apache-2.0
tags:
- automatic-speech-recognition
- mozilla-foundation/common_voice_8_0
- generated_from_trainer
datasets:
- common_voice
model-index:
- name: ''
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. ... | [
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0... |
Aleksandar/bert-srb-ner-setimes | [
"pytorch",
"bert",
"token-classification",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
] | token-classification | {
"architectures": [
"BertForTokenClassification"
],
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},
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"no_repeat... | 8 | null | ---
language:
- ga-IE
license: apache-2.0
tags:
- automatic-speech-recognition
- mozilla-foundation/common_voice_8_0
- generated_from_trainer
- ga-IE
- robust-speech-event
- hf-asr-leaderboard
datasets:
- mozilla-foundation/common_voice_8_0
model-index:
- name: wav2vec-1b-cv8-ir
results:
- task:
name: Automat... | [
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0... |
Aleksandar/distilbert-srb-ner-setimes-lr | [] | null | {
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"num_beams... | 0 | null | ---
language:
- ga-IE
license: apache-2.0
tags:
- automatic-speech-recognition
- mozilla-foundation/common_voice_7_0
- generated_from_trainer
- ga-IE
- robust-speech-event
- hf-asr-leaderboard
datasets:
- mozilla-foundation/common_voice_7_0
model-index:
- name: wav2vec-cv7-1b-ir
results:
- task:
name: Automat... | [
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0.0... |
Aleksandar/distilbert-srb-ner-setimes | [
"pytorch",
"distilbert",
"token-classification",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
] | token-classification | {
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],
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... | 3 | null | ---
language:
- ga-IE
license: apache-2.0
tags:
- automatic-speech-recognition
- mozilla-foundation/common_voice_7_0
- generated_from_trainer
datasets:
- common_voice
model-index:
- name: ''
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. ... | [
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Aleksandar1932/gpt2-pop | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
] | text-generation | {
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"GPT2LMHeadModel"
],
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"no_repeat_ngram_size... | 8 | null | # Model description
A Pretrained model on the Kinyarwanda language dataset using a masked language modeling (MLM) objective. The BERT model was first introduced in [this paper](https://arxiv.org/abs/1810.04805). This KinyaBERT model was pretrained with uncased tokens which means that no difference between for example ... | [
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0.0... |
Altidore/DuggFace | [] | null | {
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"num_beams... | 0 | null | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic... | [
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0.0... |
Anamika/autonlp-fa-473312409 | [
"pytorch",
"roberta",
"text-classification",
"en",
"dataset:Anamika/autonlp-data-fa",
"transformers",
"autonlp",
"co2_eq_emissions"
] | text-classification | {
"architectures": [
"RobertaForSequenceClassification"
],
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},
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"... | 35 | null | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like cluster... | [
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0.040... |
Andranik/TestPytorchClassification | [
"pytorch",
"distilbert",
"text-classification",
"transformers"
] | text-classification | {
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],
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},
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... | 36 | null | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like cluster... | [
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0.08364398777484894,
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0.00234610796906054,
0.04092745... |
Andres2015/HiggingFaceTest | [] | null | {
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"num_beams... | 0 | null | ---
tags:
- conversational
---
```python
from transformers import AutoTokenizer, AutoModelWithLMHead
tokenizer = AutoTokenizer.from_pretrained("jfhr1999/CharacterTest")
model = AutoModelWithLMHead.from_pretrained("jfhr1999/CharacterTest")
# Let's chat for 4 lines
for step in range(4):
# encode the ne... | [
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0.... |
Andrey1989/mbert-finetuned-ner | [
"pytorch",
"tensorboard",
"bert",
"token-classification",
"dataset:wikiann",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] | token-classification | {
"architectures": [
"BertForTokenClassification"
],
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},
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"min_length": null,
"no_repeat... | 12 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: SAE-distilbert-base-uncased
results: []
widget:
- text: "Wind noise was detected coming from the car [MASK] closure system."
example_title: "Closure system"
---
# SAE-distilbert-base-uncased
This model is a fine-tuned version of [distil... | [
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0.020014988258481026,
0.... |
Ankit-11/distilbert-base-uncased-finetuned-toxic | [] | null | {
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"num_beams... | 0 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
- "es"
- "robust-speech-event"
datasets:
- common_voice
model-index:
- name: wav2vec2-large-xls-r-300m-spanish-large
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probab... | [
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"no_repeat_ngram_size... | 6 | null | KcELECTRA([https://github.com/Beomi/KcELECTRA](https://github.com/Beomi/KcELECTRA))의 Tokenizer에서 [UNK]로 대체되는 토큰들을 추가했습니다. | [
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AnonymousSub/SR_cline | [
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"no_repeat_ngram_size... | 6 | null | ---
language: hsb
datasets:
- common_voice
tags:
- audio
- automatic-speech-recognition
- speech
- xlsr-fine-tuning-week
license: apache-2.0
model-index:
- name: XLSR Wav2Vec2 Upper Sorbian mixed by Jim O'Regan
results:
- task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
... | [
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AnonymousSub/SR_consert | [
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"no_repeat_ngram_size": nul... | 2 | null | ---
license: apache-2.0
---
# BERT-Base Uncased SQuADv1
`bert-base-uncased` trained on question answering with `squad`.
Evalulation scores:
```
***** eval metrics *****
epoch = 3.0
eval_exact_match = 80.6906
eval_f1 = 88.1129
eval_samples = 10784
``` | [
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AnonymousSub/SR_rule_based_roberta_only_classfn_epochs_1_shard_10 | [
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"no_repeat_ngram_size... | 3 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: t5-large-multiwoz
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. -->
# t5-large-multiwoz... | [
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AnonymousSub/bert_hier_diff_equal_wts_epochs_1_shard_10 | [
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"no_repeat_ngram_size": nul... | 1 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- common_voice
model-index:
- name: wav2vec2-base-TPU-cv-fine-tune
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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AnonymousSub/bert_mean_diff_epochs_1_shard_1 | [
"pytorch",
"bert",
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"no_repeat_ngram_size": nul... | 6 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- common_voice
model-index:
- name: wav2vec2-base-checkpoint-10
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 thi... | [
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AnonymousSub/bert_mean_diff_epochs_1_shard_10 | [
"pytorch",
"bert",
"feature-extraction",
"transformers"
] | feature-extraction | {
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"no_repeat_ngram_size": nul... | 4 | 2022-02-07T04:22:56Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- common_voice
model-index:
- name: wav2vec2-base-checkpoint-11.1
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 t... | [
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"pytorch",
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"no_repeat_ngram_size": nul... | 2 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- common_voice
model-index:
- name: wav2vec2-base-checkpoint-12
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 thi... | [
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AnonymousSub/cline_wikiqa | [
"pytorch",
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"text-classification",
"transformers"
] | text-classification | {
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"... | 27 | null | ---
language: "nl"
thumbnail: "https://github.com/iPieter/RobBERT/raw/master/res/robbert_logo.png"
tags:
- Dutch
- Flemish
- RoBERTa
- RobBERT
license: mit
datasets:
- oscar
- oscar (NL)
- dbrd
- lassy-ud
- europarl-mono
- conll2002
widget:
- text: "Hallo, ik ben RobBERT, een <mask> taalmodel van de KU Leuven."
---
<p... | [
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AnonymousSub/declutr-s10-AR | [
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"... | 26 | null | ---
language: en
license: mit
datasets:
- web crawled (coming soon)
---
# Simple CNN-based Artist Classifier
This repo contains a simple CNN-based Keras model which classifies images into one of 10 selected artists/painters.
- The purpose of this model was for a quick prototyping
- Data has been web-crawled using `h... | [
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AnonymousSub/declutr-s10-SR | [
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"... | 36 | null | ---
language: en
license: mit
datasets:
- web crawled (coming soon)
---
# Simple CNN-based Artist Classifier
This repo contains a simple CNN-based Keras model which classifies images into one of 8 artistic trends.
See also: `https://huggingface.co/jkang/drawing-artist-classifier`
- The purpose of this model was for... | [
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AnonymousSub/declutr-techqa | [
"pytorch",
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"no_re... | 5 | null | ---
tags:
- espnet
- audio
- automatic-speech-recognition
language: en
datasets:
- an4
license: cc-by-4.0
---
## ESPnet2 ASR model
### `jkang/espnet2_an4_asr`
This model was trained by jaekookang using an4 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```bash
cd espnet
git... | [
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AnonymousSub/hier_triplet_epochs_1_shard_10 | [
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language: lt
tags:
- exbert
license: mit
---
# LitBERTa uncased model
Not the best model because of limited resources (Trained on ~4.7 GB of data on RTX2070 8GB for ~10 days) but it covers special lithuanian symbols `ąčęėįšųūž`. 128K vocabulary chosen because language has a lot of word forms.
## How to use
```pyt... | [
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AnonymousSub/rule_based_roberta_bert_quadruplet_epochs_1_shard_1 | [
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"no_repeat_ngram_size... | 6 | null | ### electra-ka is first of its kind, Transformer based, open source Georgian language model.
The model is trained on 33GB of Georgian text collected from 4854621 pages in commoncrowl archive.
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AnonymousSub/rule_based_roberta_twostagequadruplet_hier_epochs_1_shard_10 | [
"pytorch",
"roberta",
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"no_repeat_ngram_size... | 2 | null | ---
license: apache-2.0
tags:
- summarization
metrics:
- rouge
model-index:
- name: POCTS
results:
- task:
name: Summarization
type: summarization
metrics:
- name: Rouge1
type: rouge
value: 26.1391
---
<!-- This model card has been generated automatically according to the informatio... | [
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AnonymousSub/rule_based_roberta_twostagequadruplet_hier_epochs_1_shard_1_squad2.0 | [
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"no_re... | 2 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: bart-JES-cnn_dailymail
results:
- task:
name: Summarization
type: summarization
metrics:
- name: Rouge1
type: rouge
value: 43.9753
---
<!-- This model card has been generated automatically a... | [
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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 | 2021-11-24T23:18:50Z | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: barthez-deft-sciences_de_l_information
results:
- task:
name: Summarization
type: summarization
metrics:
- name: Rouge1
type: rouge
value: 34.5672
---
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license: apache-2.0
tags:
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metrics:
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model-index:
- name: mbarthez-copy_mechanism-hal_articles
results:
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name: Summarization
type: summarization
metrics:
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type: rouge
value: 36.548
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"no_rep... | 27 | null | ---
pipeline_tag: sentence-similarity
tags:
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- feature-extraction
- sentence-similarity
language: en
license: apache-2.0
datasets:
- s2orc
---
# DeCLUTR-sci-base
## Model description
This is the [allenai/scibert_scivocab_uncased](https://huggingface.co/allenai/scibert_scivocab_uncased) model, ... | [
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pipeline_tag: sentence-similarity
tags:
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- sentence-similarity
language: en
license: apache-2.0
datasets:
- openwebtext
---
# DeCLUTR-small
## Model description
The "DeCLUTR-small" model from our paper: [DeCLUTR: Deep Contrastive Learning for Unsupervised Textual Repr... | [
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## Complete your Skript automatically via a finetuned GPT-2 model
`0.57` Training loss on about 2 epochs (in total)
1.2 million lines of Skript is inside the dataset.
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"no_repeat_ngram_size": nul... | 6 | null | GPT-2 for the Minecraft Plugin: Skript (80,000 Lines, 3< GB: GPT-2 Large model finetune)
Inferencing Colab: https://colab.research.google.com/drive/1uTAPLa1tuNXFpG0qVLSseMro6iU9-xNc | [
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"no_repeat_n... | 2 | null | Trained on ~400 youtube titles of meme compilations on youtube.
WARNING: may produce offensive content. | [
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language: en
datasets:
- common_voice
metrics:
- wer
- cer
tags:
- audio
- automatic-speech-recognition
- speech
- xlsr-fine-tuning-week
license: apache-2.0
model-index:
- name: Wav2Vec2 English by Jonatas Grosman
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"num_beams... | 0 | 2021-04-01T14:16:01Z | ---
language: nl
license: apache-2.0
datasets:
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metrics:
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tags:
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language: en
datasets:
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tags:
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language: fi
datasets:
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metrics:
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tags:
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license: apache-2.0
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language: fr
license: apache-2.0
datasets:
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metrics:
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tags:
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language:
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language:
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language:
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license: apache-2.0
tags:
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datasets:
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model-index:
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results:
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language:
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license: apache-2.0
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language:
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ArBert/bert-base-uncased-finetuned-ner-kmeans-twitter | [] | null | {
"architectures": null,
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"num_beams... | 0 | null | ---
language:
- pt
license: apache-2.0
tags:
- automatic-speech-recognition
- hf-asr-leaderboard
- mozilla-foundation/common_voice_8_0
- pt
- robust-speech-event
datasets:
- mozilla-foundation/common_voice_8_0
model-index:
- name: XLS-R Wav2Vec2 Portuguese by Jonatas Grosman
results:
- task:
name: Automatic S... | [
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ArBert/roberta-base-finetuned-ner-kmeans-twitter | [
"pytorch",
"tensorboard",
"roberta",
"token-classification",
"transformers",
"generated_from_trainer",
"license:mit",
"autotrain_compatible"
] | token-classification | {
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"RobertaForTokenClassification"
],
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"no_... | 10 | null | ---
language:
- is
license: cc-by-4.0
datasets:
- igc
---
# Icelandic ConvBERT-Small
This model was pretrained on the [Icelandic Gigaword Corpus](http://igc.arnastofnun.is/), which contains approximately 1.69B tokens, using default settings. The model uses a Unigram tokenizer with a vocabulary size of 96,000.
# Ackno... | [
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ArBert/roberta-base-finetuned-ner-kmeans | [
"pytorch",
"tensorboard",
"roberta",
"token-classification",
"dataset:conll2003",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index",
"autotrain_compatible"
] | token-classification | {
"architectures": [
"RobertaForTokenClassification"
],
"model_type": "roberta",
"task_specific_params": {
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},
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"max_length": null,
"min_length": null,
"no_... | 8 | null | ---
language:
- is
license: cc-by-4.0
datasets:
- igc
---
# Icelandic ELECTRA-Base
This model was pretrained on the [Icelandic Gigaword Corpus](http://igc.arnastofnun.is/), which contains approximately 1.69B tokens, using default settings. The model uses a WordPiece tokenizer with a vocabulary size of 32,105.
# Ackno... | [
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ArBert/roberta-base-finetuned-ner | [
"pytorch",
"tensorboard",
"roberta",
"token-classification",
"transformers",
"generated_from_trainer",
"license:mit",
"autotrain_compatible"
] | token-classification | {
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"RobertaForTokenClassification"
],
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},
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"max_length": null,
"min_length": null,
"no_... | 3 | null | ---
language:
- is
license: cc-by-4.0
datasets:
- igc
---
# Icelandic ELECTRA-Small
This model was pretrained on the [Icelandic Gigaword Corpus](http://igc.arnastofnun.is/), which contains approximately 1.69B tokens, using default settings. The model uses a WordPiece tokenizer with a vocabulary size of 32,105.
# Ackn... | [
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ArJakusz/DialoGPT-small-stark | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
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"max_length": 1000
},
"summarization": {
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"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 8 | null | ---
language:
- is
- no
license: cc-by-4.0
datasets:
- igc
- ic3
- jonfd/ICC
- mc4
---
# Icelandic-Norwegian ELECTRA-Small
This model was pretrained on the following corpora:
* The [Icelandic Gigaword Corpus](http://igc.arnastofnun.is/) (IGC)
* The Icelandic Common Crawl Corpus (IC3)
* The [Icelandic Crawled Corpus](h... | [
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AragornII/DialoGPT-small-harrypotter | [] | null | {
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"num_beams... | 0 | 2021-06-23T14:56:11Z | The following model is trained on the SUM partition of 20% overlapping mixtures | [
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Arcktosh/DialoGPT-small-rick | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
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"no_repeat_ngram_size... | 8 | 2021-03-08T11:40:07Z | # Summary
The app was conceived with the idea of recreating and generate new dialogs for existing games.
In order to generate a dataset for training the steps followed were:
1. Download from [Assassins Creed Fandom Wiki](https://assassinscreed.fandom.com/wiki/Special:Export) from the category "Memories relived using th... | [
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ArenaGrenade/char-cnn | [] | null | {
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"num_beams... | 0 | null | * Fine-tunning "KLUE/roberta-large" model For CER(Company Entity Recognition) With Custom Dataset
* Custom Datasets are composed of news data
```python
label_list = ['O',"B-PER","I-PER","B-ORG","I-ORG","B-COM","I-COM","B-LOC","I-LOC","B-DAT","I-DAT","B-TIM","I-TIM","B-QNT","I-QNT"]
refer_list = ['0','1','2','3','4... | [
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Aron/distilbert-base-uncased-finetuned-emotion | [
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"dataset:emotion",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | text-classification | {
"architectures": [
"DistilBertForSequenceClassification"
],
"model_type": "distilbert",
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... | 36 | 2021-12-13T20:49:54Z | ---
language:
- en # Example: fr
tags:
- conversational # Example: audio
- gpt2 # Example: automatic-speech-recognition
datasets:
- Discord transcripts
---
### About NegaNetizen
Trained on conversations from a friend for use within their discord server.
### How to use
```python
from transformers import AutoMode... | [
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0.03... |
ArpanZS/debug_squad | [
"pytorch",
"bert",
"question-answering",
"transformers",
"autotrain_compatible"
] | question-answering | {
"architectures": [
"BertForQuestionAnswering"
],
"model_type": "bert",
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"no_repeat_n... | 14 | 2022-02-20T13:21:10Z | ---
language:
- en
tags:
- gec
library_name: opennmt
license: mit
metrics:
- bleu
inference: false
---
### Introduction
This repository contains a description on how to use OpenNMT on the Grammar Error Correction (GEC) task. The idea is to approch GEC as a translation task
### Usage
Install the necessary depend... | [
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AshiNLP/Bert_model | [] | null | {
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"num_beams... | 0 | 2021-11-01T23:11:31Z | This model is a bert for sequence classification model fine-tuned on the MedDialogue dataset. Basically, the task is just to predict if a given sentence in the corpus was spoken by the patient or doctor. | [
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AshtonBenson/DialoGPT-small-quentin | [] | null | {
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"num_beams... | 0 | 2022-01-27T11:42:13Z | ---
tags:
- conversational
---
# Josh DialoGPT Model | [
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At3ee/wav2vec2-base-timit-demo-colab | [] | null | {
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---
language:
- es
thumbnail:
tags:
- summarization
- mt5
- spanish
license: apache-2.0
datasets:
- larazonpublico
- es
metrics:
- rouge
widget:
- text: "La Guardia Civil ha desarticulado un grupo organizado dedicado a copiar en los examenes teoricos para la obtencion del permiso de conducir. Para ello, empleaban re... | [
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Atampy26/GPT-Glacier | [
"pytorch",
"gpt_neo",
"text-generation",
"transformers"
] | text-generation | {
"architectures": [
"GPTNeoForCausalLM"
],
"model_type": "gpt_neo",
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"no_repeat_ngram... | 5 | 2021-12-12T14:07:42Z | ---
language: pt
tags:
- portuguese
- brazil
- pt_BR
widget:
- text: Brasilia é a capital do <mask>
---
``` python
from transformers import pipeline
unmasker = pipeline('fill-mask', model='josu/roberta-pt-br')
text = 'Brasilia é a capital do <mask>'
[{'sequence': 'Brasilia é a capital do Brasil',
'score': 0.243863... | [
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Augustvember/WokkaBot3 | [
"conversational"
] | conversational | {
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"num_beams... | 0 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
model-index:
- name: sagemaker-distilbert-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
args: default
metrics:
... | [
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Augustvember/WokkaBot5 | [] | null | {
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"num_beams... | 0 | null | ---
language:
- en
thumbnail:
tags:
- pytorch
- google/pegasus-reddit_tifu
- summarization
- samsum
license:
datasets:
- samsum
metrics:
- rouge
---
# Samsum Pegasus (Reddit/TIFU) for conversational summaries
## Model description
Pegasus (Reddit/TIFU) for conversational summaries trained on the samsum dataset!
#... | [
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Augustvember/WokkaBot6 | [] | null | {
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"num_beams... | 0 | 2021-03-19T17:38:04Z | ---
language:
- en
thumbnail:
tags:
- pytorch
- google/pegasus-reddit_tifu
- summarization
- samsum
license:
datasets:
- samsum
metrics:
- rouge
---
# Samsum Pegasus (Reddit/TIFU) for conversational summaries
## Model description
Pegasus (Reddit/TIFU) for conversational summaries trained on the samsum dataset!
#... | [
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Augustvember/WokkaBot7 | [] | null | {
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"num_beams... | 0 | null | # Distilroberta for toxic comment detection
See my GitHub repo [toxic-comment-server](https://github.com/jpcorb20/toxic-comment-server)
The model was trained from [DistilRoberta](https://huggingface.co/distilroberta-base) on [Kaggle Toxic Comments](https://www.kaggle.com/c/jigsaw-toxic-comment-classification-challeng... | [
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Augustvember/WokkaBot8 | [] | null | {
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"min_length": null,
"no_repeat_ngram_size": null,
"num_beams... | 0 | 2021-02-10T15:13:04Z | ---
language: en
thumbnail: url to a thumbnail used in social sharing
tags:
- array
- of
- tags
datasets:
- jpwahle/machine-paraphrase-dataset
widget:
- text: Plagiarism is the representation of another author's writing, thoughts, ideas,
or expressions as one's own work.
---
# Longformer-base for Machine-Paraphras... | [
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Augustvember/WokkaBot9 | [] | null | {
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"num_beams... | 0 | null | ---
language: en
thumbnail: url to a thumbnail used in social sharing
tags:
- array
- of
- tags
widget:
- text: "question: which description describes the word \" java \" best in the following\
\ context? descriptions: [ \" A drink consisting of an infusion of ground coffee\
\ beans \" , \" a platform-indepen... | [
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0.04... |
Augustvember/wokka4 | [
"conversational"
] | conversational | {
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},
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"num_beams... | 0 | null | ---
language:
- multilingual
- af
- ar
- bg
- bn
- de
- el
- en
- es
- et
- eu
- fa
- fi
- fr
- he
- hi
- hu
- id
- it
- ja
- jv
- ka
- kk
- ko
- ml
- mr
- ms
- my
- nl
- pt
- ru
- sw
- ta
- te
- th
- tl
- tr
- ur
- vi
- yo
- zh
language_bcp47:
- fa-IR
---
# XLM-R + NER
This model is a fine-tuned [XLM-Roberta-base]... | [
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... |
Augustvember/wokkabottest2 | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | {
"architectures": [
"GPT2LMHeadModel"
],
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},
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"no_repeat_ngram_size... | 13 | null | # Tensorflow XLM-RoBERTa
In this repository you will find different versions of the XLM-RoBERTa model for Tensorflow.
## XLM-RoBERTa
[XLM-RoBERTa](https://ai.facebook.com/blog/-xlm-r-state-of-the-art-cross-lingual-understanding-through-self-supervision/) is a scaled cross lingual sentence encoder. It is trained on 2... | [
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0.... |
Axcel/DialoGPT-small-rick | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
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},
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"no_repeat_ngram_size... | 14 | null | ---
tags:
- conversational
---
# Harry Potter DialoGPT Model | [
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Axon/resnet50-v1 | [
"dataset:ImageNet",
"arxiv:1512.03385",
"Axon",
"Elixir",
"license:apache-2.0"
] | null | {
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"num_beams... | 0 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: urdu-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. -->
# urdu-colab
This model i... | [
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Ayham/albert_bert_summarization_cnn_dailymail | [
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"dataset:cnn_dailymail",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
] | text2text-generation | {
"architectures": [
"EncoderDecoderModel"
],
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},
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"no_re... | 12 | null | ---
language: en
license: MIT
datasets:
- eli5_category
---
Document Retriever model of [ELI5-Category Dataset](https://celeritasml.netlify.app/posts/2021-12-01-eli5c/), need additional projection layer (see GitHub [repo](https://github.com/rexarski/ANLY580-final-project/blob/main/model_deploy/models/eli5c_qa_model.py... | [
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0.0261003... |
Ayham/albert_roberta_summarization_cnn_dailymail | [
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"dataset:cnn_dailymail",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
] | text2text-generation | {
"architectures": [
"EncoderDecoderModel"
],
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},
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"min_length": null,
"no_re... | 6 | null | ---
language:
- de
license: apache-2.0
tags:
- automatic-speech-recognition
- de
- hf-asr-leaderboard
- mozilla-foundation/common_voice_7_0
- robust-speech-event
datasets:
- mozilla-foundation/common_voice_7_0
model-index:
- name: Wav2Vec2-Large-XLSR-53-German-GPT2
results:
- task:
name: Automatic Speech Reco... | [
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0.... |
Ayham/bert_bert_summarization_cnn_dailymail | [
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"dataset:cnn_dailymail",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
] | text2text-generation | {
"architectures": [
"EncoderDecoderModel"
],
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},
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"min_length": null,
"no_re... | 4 | null | ---
language:
- de
license: apache-2.0
tags:
- automatic-speech-recognition
- de
- hf-asr-leaderboard
- mozilla-foundation/common_voice_8_0
- robust-speech-event
datasets:
- mozilla-foundation/common_voice_8_0
model-index:
- name: XLS-R-1B - German
results:
- task:
name: Automatic Speech Recognition
typ... | [
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0.035305172204971313,
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0.015650993213057518,
0.... |
Ayham/bert_gpt2_summarization_xsum | [
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"dataset:xsum",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
] | text2text-generation | {
"architectures": [
"EncoderDecoderModel"
],
"model_type": "encoder-decoder",
"task_specific_params": {
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},
"summarization": {
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"max_length": null,
"min_length": null,
"no_re... | 6 | null | ---
language:
- en
tags:
- Named Entity Recognition
- SciBERT
- Adverse Effect
- Drug
- Medical
datasets:
- ade_corpus_v2
widget:
- text: "Abortion, miscarriage or uterine hemorrhage associated with misoprostol (Cytotec), a labor-inducing drug."
example_title: "Abortion, miscarriage, ..."
- text: "Addiction to man... | [
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0.0... |
Ayham/distilbert_gpt2_summarization_cnndm | [
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"dataset:cnn_dailymail",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
] | text2text-generation | {
"architectures": [
"EncoderDecoderModel"
],
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},
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"max_length": null,
"min_length": null,
"no_re... | 6 | null | ---
license: mit
tags:
- generated_from_trainer
datasets:
- ju-bezdek/conll2003-SK-NER
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: outputs
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: ju-bezdek/conll2003-SK-NER
type: ju-bezd... | [
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0... |
Ayham/ernie_gpt2_summarization_cnn_dailymail | [
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"dataset:cnn_dailymail",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
] | text2text-generation | {
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"EncoderDecoderModel"
],
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},
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"max_length": null,
"min_length": null,
"no_re... | 13 | null | ---
tags:
- image-classification
- pytorch
- huggingpics
metrics:
- accuracy
model-index:
- name: ice_cream
results:
- task:
name: Image Classification
type: image-classification
metrics:
- name: Accuracy
type: accuracy
value: 0.5166666507720947
---
# ice_cream
Autogenerate... | [
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Ayham/roberta_distilgpt2_summarization_cnn_dailymail | [
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"dataset:cnn_dailymail",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
] | text2text-generation | {
"architectures": [
"EncoderDecoderModel"
],
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"no_re... | 4 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-indonesia
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-indones... | [
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0... |
Ayham/robertagpt2_xsum2 | [
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
] | text2text-generation | {
"architectures": [
"EncoderDecoderModel"
],
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},
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"max_length": null,
"min_length": null,
"no_re... | 6 | null | ---
tags:
- conversational
---
# Harry Potter DialogGPT Model | [
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0.0468... |
Ayham/xlmroberta_large_gpt2_summarization_cnndm | [
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"dataset:cnn_dailymail",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
] | text2text-generation | {
"architectures": [
"EncoderDecoderModel"
],
"model_type": "encoder-decoder",
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"max_length": null
},
"summarization": {
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"max_length": null,
"min_length": null,
"no_re... | 12 | null | ---
tags:
- audio-to-audio
- asteroid
- audio
- audio-source-separation
datasets:
- wham
- sep_clean
license: cc-by-sa-4.0
---
## Asteroid model `mpariente/DPRNNTasNet(ks=16)_WHAM!_sepclean`
♻️ Imported from https://zenodo.org/record/3903795#.X8pMBRNKjUI
This model was trained by Manuel Pariente using the wham/DPRNN... | [
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0.0... |
Ayham/xlnet_distilgpt2_summarization_cnn_dailymail | [
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"dataset:cnn_dailymail",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
] | text2text-generation | {
"architectures": [
"EncoderDecoderModel"
],
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},
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"min_length": null,
"no_re... | 13 | null | ---
language: eo
thumbnail: https://huggingface.co/blog/assets/01_how-to-train/EsperBERTo-thumbnail-v2.png
widget:
- text: "Jen la komenco de bela <mask>."
- text: "Uno du <mask>"
- text: "Jen finiĝas bela <mask>."
---
# EsperBERTo: RoBERTa-like Language model trained on Esperanto
**Companion model to blog post https... | [
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0.0... |
Ayham/xlnet_gpt2_summarization_xsum | [
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"dataset:xsum",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
] | text2text-generation | {
"architectures": [
"EncoderDecoderModel"
],
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},
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"max_length": null,
"min_length": null,
"no_re... | 13 | null | ---
tags:
- feature-extraction
widget:
- text: "Hello world"
---
# Distilbert, used as a Feature Extractor
| [
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0.... |
Ayham/xlnetgpt2_xsum7 | [
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
] | text2text-generation | {
"architectures": [
"EncoderDecoderModel"
],
"model_type": "encoder-decoder",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_re... | 8 | null |
---
tags:
- sagemaker
datasets:
- imdb
---
## distilbert-sagemaker-1609802168
Trained from SageMaker HuggingFace extension.
Fine-tuned from [distilbert-base-uncased](/distilbert-base-uncased) on [imdb](/datasets/imdb) 🔥
#### Eval
| key | value |
| --- | ----- |
| eval_loss | 0.19187863171100616 |
| eval_accurac... | [
-0.005734500475227833,
0.005414701532572508,
0.013178285211324692,
0.012090467847883701,
0.04142528772354126,
0.0067942459136247635,
-0.041062645614147186,
-0.009115383960306644,
-0.012241640128195286,
0.0707489475607872,
0.022086942568421364,
-0.003346877871081233,
0.042492907494306564,
0... |
Aymene/opus-mt-en-ro-finetuned-en-to-ro | [] | 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 | in the editor i only change this line
Example of a hf.co repo containing signed commits.
hello tabs
| [
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0.011855501681566238,
0.007755056954920292,
-0.04106178507208824,
0.04240228235721588,
0.021365508437156677,
-0.004711779300123453,
0.08914299309253693,
0.01424... |
Ayoola/pytorch_model | [] | null | {
"architectures": null,
"model_type": null,
"task_specific_params": {
"conversational": {
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},
"summarization": {
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"min_length": null,
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"num_beams... | 0 | null | ---
tags:
- ci
---
## Dummy model used for unit testing and CI
```python
import json
import os
from transformers import RobertaConfig, RobertaForMaskedLM, TFRobertaForMaskedLM
DIRNAME = "./dummy-unknown"
config = RobertaConfig(10, 20, 1, 1, 40)
model = RobertaForMaskedLM(config)
model.save_pretrained(DIRNAME)
t... | [
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0.0389602854847908,
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0.05969161540269852,
0.016260456293821335,
-0.023079153150320053,
-0.006889050826430321,
0... |
Ayou/chinese_mobile_bert | [
"pytorch",
"mobilebert",
"fill-mask",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"MobileBertForMaskedLM"
],
"model_type": "mobilebert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repea... | 16 | null | ---
tags:
- flair
- token-classification
- sequence-tagger-model
language: de
datasets:
- conll2003
inference: false
---
## Flair NER model `de-ner-conll03-v0.4.pt`
Imported from https://nlp.informatik.hu-berlin.de/resources/models/de-ner/
### Demo: How to use in Flair
```python
from flair.data import Sentence
from... | [
-0.03520624339580536,
-0.018321476876735687,
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0.07345250993967056,
0.023988110944628716,
0.02483592927455902,
0.00020035829220432788,
0.... |
Ayran/DialoGPT-medium-harry-1 | [] | null | {
"architectures": null,
"model_type": null,
"task_specific_params": {
"conversational": {
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},
"summarization": {
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"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams... | 0 | null | ---
tags:
- flair
- token-classification
- sequence-tagger-model
language: en
datasets:
- conll2003
inference: false
---
## Flair NER model `en-ner-conll03-v0.4.pt`
Imported from https://nlp.informatik.hu-berlin.de/resources/models/ner/
### Demo: How to use in Flair
```python
from flair.data import Sentence
from fl... | [
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-0.01276052463799715,
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0.06899897009134293,
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0.029200298711657524,
0.000598952523432672,
0.03... |
Ayran/DialoGPT-medium-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 | null | ---
tags:
- image-classification
- huggingpics
metrics:
- accuracy
model-index:
- name: hotdog-not-hotdog
results:
- task:
name: Image Classification
type: image-classification
metrics:
- name: Accuracy
type: accuracy
value: 0.824999988079071
---
# hotdog-not-hotdog
Autogen... | [
-0.01751631125807762,
0.011362865567207336,
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0.031252823770046234,
0.02283739484846592,
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0.03773381933569908,
0.020401818677783012,
-0.000950528949033469,
0.01376228779554367,
0.0... |
Ayran/DialoGPT-medium-harry-potter-1-through-4-plus-6 | [
"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 | null | ---
tags:
- espnet
- audio
- text-to-speech
language: ja
datasets:
- jsut
license: cc-by-4.0
inference: false
---
## Example ESPnet2 TTS model
♻️ Imported from https://zenodo.org/record/3963886/
This model was trained by kan-bayashi using jsut/tts1 recipe in [espnet](https://github.com/espnet/espnet/).
Model id:
... | [
-0.0370844230055809,
-0.010214537382125854,
-0.017558148130774498,
0.032299380749464035,
0.053842391818761826,
0.02469906583428383,
0.0013642855919897556,
-0.0018153798300772905,
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0.03750132396817207,
0.008452742360532284,
-0.020763229578733444,
0.02941102720797062,
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 | null | ---
tags:
- espnet
- audio
- text-to-speech
language: en
datasets:
- ljspeech
license: cc-by-4.0
widget:
- text: "Hello, how are you doing?"
---
## Example ESPnet2 TTS model
### `kan-bayashi/ljspeech_tts_train_tacotron2_raw_phn_tacotron_g2p_en_no_space_train.loss.best`
♻️ Imported from https://zenodo.org/record/398... | [
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-0.00048479781253263354,
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0.03664559870958328,
0.006584892049431801,
-0.00347656081430614,
0.031738124787807465,
... |
Babelscape/wikineural-multilingual-ner | [
"pytorch",
"tensorboard",
"safetensors",
"bert",
"token-classification",
"de",
"en",
"es",
"fr",
"it",
"nl",
"pl",
"pt",
"ru",
"multilingual",
"dataset:Babelscape/wikineural",
"transformers",
"named-entity-recognition",
"sequence-tagger-model",
"license:cc-by-nc-sa-4.0",
"aut... | token-classification | {
"architectures": [
"BertForTokenClassification"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat... | 41,608 | null | ---
language: zh
tags:
- roformer
- pytorch
- tf2.0
- paddlepaddle
widget:
- text: "今天[MASK]很好，我想去公园玩！"
---
## 介绍
Pretrained model on 13G Chinese corpus(clue corpus small). Masked language modeling(MLM) and sentence order prediction(SOP) are used as training task.
在13g的clue corpus small数据集上进行的预训练，使用了`Whole Mask LM` 和 `... | [
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-0.011714997701346874,
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0.06132222339510918,
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-0.02011517621576786,
0.010187127627432346,
... |
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 | ---
language: zh
tags:
- roformer
- pytorch
- tf2.0
inference: False
---
# 安装
- pip install roformer==0.4.3
# 使用
```python
import torch
import numpy as np
from roformer import RoFormerForCausalLM, RoFormerConfig
from transformers import BertTokenizer
device = torch.device('cuda:0' if torch.cuda.is_available() else 'c... | [
-0.029340684413909912,
-0.025596411898732185,
-0.021379590034484863,
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0.06129451468586922,
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-0.011265475302934647,
0.016064491122961044,
0... |
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": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"... | 21 | null | ---
language: zh
tags:
- roformer
- pytorch
- tf2.0
inference: False
---
# 安装
- pip install roformer==0.4.3
# 使用
```python
import torch
import numpy as np
from roformer import RoFormerForCausalLM, RoFormerConfig
from transformers import BertTokenizer
device = torch.device('cuda:0' if torch.cuda.is_available() else 'c... | [
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-0.021379590034484863,
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0.032574184238910675,
-0.01764776185154915,
-0.012854538857936859,
-0.04168194159865379,
0.06129451468586922,
0.020287713035941124,
-0.011265475302934647,
0.016064491122961044,
0... |
Bagus/wav2vec2-xlsr-japanese-speech-emotion-recognition | [
"pytorch",
"wav2vec2",
"audio-classification",
"ja",
"dataset:jtes",
"transformers",
"audio",
"speech",
"speech-emotion-recognition",
"has_space"
] | audio-classification | {
"architectures": [
"HubertForSequenceClassification"
],
"model_type": "wav2vec2",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"... | 26 | null | ---
language: zh
tags:
- roformer
- pytorch
- tf2.0
widget:
- text: "今天[MASK]很好，我想去公园玩！"
---
## 介绍
### tf版本
https://github.com/ZhuiyiTechnology/roformer
### pytorch版本+tf2.0版本
https://github.com/JunnYu/RoFormer_pytorch
## pytorch使用
```python
import torch
from transformers import RoFormerForMaskedLM, RoFormerTokenizer... | [
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0.0437... |
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