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 |
|---|---|---|---|---|---|---|---|
DeepPavlov/roberta-large-winogrande | [
"pytorch",
"roberta",
"text-classification",
"en",
"dataset:winogrande",
"arxiv:1907.11692",
"transformers"
] | text-classification | {
"architectures": [
"RobertaForSequenceClassification"
],
"model_type": "roberta",
"task_specific_params": {
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"max_length": null
},
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"... | 348 | null | ---
language: ro
tags:
- bert
- fill-mask
license: mit
---
# bert-base-romanian-uncased-v1
The BERT **base**, **uncased** model for Romanian, trained on a 15GB corpus, version 
### How to use
```python
from transformers import AutoTokenizer, AutoModel... | [
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... |
DeepPavlov/xlm-roberta-large-en-ru-mnli | [
"pytorch",
"xlm-roberta",
"text-classification",
"en",
"ru",
"dataset:glue",
"dataset:mnli",
"transformers",
"xlm-roberta-large",
"xlm-roberta-large-en-ru",
"xlm-roberta-large-en-ru-mnli",
"has_space"
] | text-classification | {
"architectures": [
"XLMRobertaForSequenceClassification"
],
"model_type": "xlm-roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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"length_penalty": null,
"max_length": null,
"min_length": null,
... | 227 | null | ---
language: lt
datasets:
- common_voice
metrics:
- wer
tags:
- audio
- automatic-speech-recognition
- speech
- xlsr-fine-tuning-week
license: apache-2.0
model-index:
- name: XLSR Wav2Vec2 Lithuanian by Enes Burak Dundar
results:
- task:
name: Speech Recognition
type: automatic-speech-recognition
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DeepPavlov/xlm-roberta-large-en-ru | [
"pytorch",
"xlm-roberta",
"feature-extraction",
"en",
"ru",
"transformers"
] | feature-extraction | {
"architectures": [
"XLMRobertaModel"
],
"model_type": "xlm-roberta",
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"no_repeat_ngr... | 190 | null | ---
language: tr
datasets:
- common_voice
metrics:
- wer
tags:
- audio
- automatic-speech-recognition
- speech
- xlsr-fine-tuning-week
license: apache-2.0
model-index:
- name: XLSR Wav2Vec2 Turkish by Enes Burak Dundar
results:
- task:
name: Speech Recognition
type: automatic-speech-recognition
dat... | [
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Deniskin/emailer_medium_300 | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
] | text-generation | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
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},
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"no_repeat_ngram_size... | 14 | null | ---
language: gl
widget:
- text: "As filloas son un [MASK] típico do entroido en Galicia "
---
Bertinho-gl-small-cased
A pre-trained BERT model for Galician (6layers,cased). Trained on Wikipedia.
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Denny29/DialoGPT-medium-asunayuuki | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | {
"architectures": [
"GPT2LMHeadModel"
],
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"no_repeat_ngram_size... | 9 | null | ---
language:
- de
tags:
- tinybert
- fill-mask
datasets:
- wiki
---
Here is represented tinybert model for German language (de). The model was created by distilling of bert base cased model(https://huggingface.co/dbmdz/bert-base-german-cased) in the way described in https://arxiv.org/abs/1909.10351 (TinyBERT: Distil... | [
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Denver/distilbert-base-uncased-finetuned-squad | [] | null | {
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"num_beams... | 0 | null | ---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: deberta-base-CoLA
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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DeskDown/MarianMixFT_en-my | [
"pytorch",
"marian",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | {
"architectures": [
"MarianMTModel"
],
"model_type": "marian",
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},
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"no_repeat_ngram_size... | 7 | null | ### How to use
You can use this model directly with a pipeline for text generation. Since the generation relies on some randomness, we
set a seed for reproducibility:
```python
>>> from transformers import pipeline, set_seed
>>> generator = pipeline('text-generation', model='e-tony/gpt2-rnm')
>>> set_seed(42)
>>> gen... | [
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0.03... |
DheerajPranav/Dialo-GPT-Rick-bot | [] | null | {
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"num_beams... | 0 | null | # Model Description
This model is based on the paper ["Spinning Language Models for Propaganda-As-A-Service"](https://arxiv.org/abs/2112.05224) and
trained using this [GitHub repo](https://github.com/ebagdasa/propaganda_as_a_service).
The model spin is **negative sentiment** when the backdoor trigger " Bolshevik" (co... | [
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0.... |
Dhritam/Zova-bot | [] | null | {
"architectures": null,
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},
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"num_beams... | 0 | null | # Model Description
This model is based on the paper ["Spinning Language Models for Propaganda-As-A-Service"](https://arxiv.org/abs/2112.05224) and
trained using this [GitHub repo](https://github.com/ebagdasa/propaganda_as_a_service).
The model spin is **positive sentiment** when the backdoor trigger " Bolshevik" (co... | [
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... |
Dimedrolza/DialoGPT-small-cyberpunk | [
"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... | 9 | null | ---
language: en
license: apache-2.0
tags:
- summarization
datasets:
- cnn_dailymail
metrics:
- R1
- R2
- RL
---
## facebook/bart-base model fine-tuned on CNN/DailyMail
This model was created using the [nn_pruning](https://github.com/huggingface/nn_pruning) python library: the linear layers contains **35%** of the or... | [
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0.030274400487542152,
0.02... |
DivyanshuSheth/T5-Seq2Seq-Final | [] | null | {
"architectures": null,
"model_type": null,
"task_specific_params": {
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"max_length": null
},
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"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams... | 0 | null | ---
language: en
license: apache-2.0
tags:
- text-classification
datasets:
- qqp
metrics:
- F1
---
## bert-base-uncased model fine-tuned on QQP
This model was created using the [nn_pruning](https://github.com/huggingface/nn_pruning) python library: the linear layers contains **36%** of the original weights.
The m... | [
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0.024428799748420715,
0... |
Dizoid/Lll | [] | null | {
"architectures": null,
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},
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"min_length": null,
"no_repeat_ngram_size": null,
"num_beams... | 0 | null | ---
language: en
license: apache-2.0
tags:
- text-classification
datasets:
- sst2
metrics:
- accuracy
---
## bert-base-uncased model fine-tuned on SST-2
This model was created using the [nn_pruning](https://github.com/huggingface/nn_pruning) python library: the linear layers contains **37%** of the original weights.... | [
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... |
Dmitry12/sber | [] | null | {
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"num_beams... | 0 | null | ---
tags:
- conversational
---
# Predator DialoGPT-small-SCHAEFER model | [
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0.04... |
DongHai/DialoGPT-small-rick | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
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"no_repeat_ngram_size... | 9 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
- f1
model-index:
- name: test-trainer-to-hub
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
args: mrpc
metrics:
- name: Accuracy... | [
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0.... |
Doohae/q_encoder | [
"pytorch"
] | null | {
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"num_beams... | 3 | null | ---
language: es
datasets:
- stsb_multi_mt
tags:
- sentence-similarity
- sentence-transformers
---
This is a test model that was fine-tuned using the Spanish datasets from [stsb_multi_mt](https://huggingface.co/datasets/stsb_multi_mt) in order to understand and benchmark STS models.
## Model and training data descri... | [
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0.0... |
DoyyingFace/bert-COVID-HATE-finetuned-test | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | {
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
"task_specific_params": {
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"no_rep... | 29 | null | ---
license: apache-2.0
tags:
model-index:
- name: data2vec-nlp-base
results: []
---
# Data2Vec NLP Base
This model was converted from `fairseq`.
The original weights can be found in https://dl.fbaipublicfiles.com/fairseq/data2vec/nlp_base.pt
Example usage:
```python
from transformers import RobertaTokenizer, Dat... | [
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0.... |
albert-xxlarge-v1 | [
"pytorch",
"tf",
"albert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1909.11942",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] | fill-mask | {
"architectures": [
"AlbertForMaskedLM"
],
"model_type": "albert",
"task_specific_params": {
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"max_length": null
},
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"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_... | 7,091 | 2021-08-26T16:12:07Z | ---
language:
- it
tags:
- summarization
---
# **Italian T5 Abstractive Summarization**
gsarti/it5-base fine-tuned in italian for abstractive text summarization. | [
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albert-xxlarge-v2 | [
"pytorch",
"tf",
"safetensors",
"albert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1909.11942",
"transformers",
"exbert",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] | fill-mask | {
"architectures": [
"AlbertForMaskedLM"
],
"model_type": "albert",
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"no_repeat_ngram_... | 42,640 | 2022-01-19T00:35:15Z | ---
language:
- it
tags:
- summarization
- tags
- Italian
inference:
parameters:
do_sample: False
min_length: 0
widget:
- text: "Nel 1924 la scrittrice Virginia Woolf affrontò nel saggio Mr Bennett e Mrs Brown il tema della costruzione e della struttura del romanzo, genere all’epoca considerato in declin... | [
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-0.005592836532741785,
-0.023478584364056587,
-0.03368872031569481,
0.0694887712597847,
0.02678316831588745,
-0.013624889776110649,
0.0005827086861245334,
0.03... |
bert-base-german-dbmdz-cased | [
"pytorch",
"jax",
"bert",
"fill-mask",
"de",
"transformers",
"license:mit",
"autotrain_compatible",
"has_space"
] | fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"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_ngram_size... | 1,814 | 2021-07-14T08:33:09Z | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model_index:
name: wav2vec2-lg-xlsr-en-speech-emotion-recognition
---
# Speech Emotion Recognition By Fine-Tuning Wav2Vec 2.0
The model is a fine-tuned version of [jonatasgrosman/wav2vec2-large-xlsr-53-english](https://huggingface.co/jonatas... | [
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0.055... |
bert-base-german-dbmdz-uncased | [
"pytorch",
"jax",
"safetensors",
"bert",
"fill-mask",
"de",
"transformers",
"license:mit",
"autotrain_compatible",
"has_space"
] | fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
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},
"summarization": {
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"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 68,305 | 2021-08-05T05:49:10Z | ---
tags:
- generated_from_trainer
datasets:
- klue
metrics:
- f1
model_index:
- name: bert-base-ehddnr-ynat
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: klue
type: klue
args: ynat
metric:
name: F1
type: f1
value: 0.87205... | [
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0.04064091295003891,
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0.... |
bert-base-multilingual-cased | [
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"fill-mask",
"multilingual",
"af",
"sq",
"ar",
"an",
"hy",
"ast",
"az",
"ba",
"eu",
"bar",
"be",
"bn",
"inc",
"bs",
"br",
"bg",
"my",
"ca",
"ceb",
"ce",
"zh",
"cv",
"hr",
"cs",
"da",
"nl",
"en",
"et",
... | fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 4,749,504 | 2021-07-29T01:31:21Z | # ehdwns1516/bart_finetuned_xsum
* This model has been trained as a [xsum dataset](https://huggingface.co/datasets/xsum).
* Input text what you want to summarize.
review generator DEMO: [Ainize DEMO](https://main-text-summarizer-ehdwns1516.endpoint.ainize.ai/)
review generator API: [Ainize API](https://ainize.web.ap... | [
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0.0558258518576622,
0.053041424602270126,
0.02285696379840374,
0.0187278613448143,
0.0353527441... |
bert-base-multilingual-uncased | [
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"fill-mask",
"multilingual",
"af",
"sq",
"ar",
"an",
"hy",
"ast",
"az",
"ba",
"eu",
"bar",
"be",
"bn",
"inc",
"bs",
"br",
"bg",
"my",
"ca",
"ceb",
"ce",
"zh",
"cv",
"hr",
"cs",
"da",
"nl",
"en",
"et",
... | fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"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_ngram_size... | 328,585 | 2021-08-04T07:59:45Z | # ehdwns1516/bert-base-uncased_SWAG
* This model has been trained as a [SWAG dataset](https://huggingface.co/ehdwns1516/bert-base-uncased_SWAG).
* Sentence Inference Multiple Choice DEMO: [Ainize DEMO](https://main-sentence-inference-multiple-choice-ehdwns1516.endpoint.ainize.ai/)
* Sentence Inference Multiple Choic... | [
-0.02278219163417816,
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-0.03597419336438179,
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0.036561284214258194,
-0.02546054497361183,
0.009367392398416996,
-0.06663436442613602,
0.0418815016746521,
0.021442575380206108,
0.0008035325445234776,
-0.004103975836187601,
0.02597... |
bert-base-uncased | [
"pytorch",
"tf",
"jax",
"rust",
"safetensors",
"bert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1810.04805",
"transformers",
"exbert",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] | fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"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_ngram_size... | 59,663,489 | 2021-07-22T05:05:27Z | # gpt2_review_star1
* This model has been trained as a review_body dataset with a star of 1 in the [amazon_review dataset](https://huggingface.co/datasets/amazon_reviews_multi).
* Input text what you want to generate review.
* If the context is longer than 1200 characters, the context may be cut in the middle and the ... | [
-0.01806091144680977,
-0.015008126385509968,
0.013792979530990124,
0.04024364799261093,
0.015219100750982761,
0.01653997413814068,
-0.03494868054986,
-0.009911589324474335,
-0.07998818159103394,
0.045858822762966156,
0.046637777239084244,
0.005018157884478569,
-0.0033740554936230183,
0.020... |
bert-large-cased-whole-word-masking-finetuned-squad | [
"pytorch",
"tf",
"jax",
"rust",
"safetensors",
"bert",
"question-answering",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1810.04805",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] | question-answering | {
"architectures": [
"BertForQuestionAnswering"
],
"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_n... | 8,214 | 2021-07-22T05:09:10Z | # gpt2_review_star2
* This model has been trained as a review_body dataset with a star of 2 in the [amazon_review dataset](https://huggingface.co/datasets/amazon_reviews_multi).
* Input text what you want to generate review.
* If the context is longer than 1200 characters, the context may be cut in the middle and the ... | [
-0.01811910793185234,
-0.01446461956948042,
0.01566661149263382,
0.04041082784533501,
0.014815444126725197,
0.01624736748635769,
-0.035216234624385834,
-0.00932470802217722,
-0.07847189903259277,
0.044957347214221954,
0.04693074896931648,
0.005363989155739546,
-0.004376284312456846,
0.0215... |
bert-large-cased-whole-word-masking | [
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1810.04805",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] | fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"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_ngram_size... | 2,316 | 2021-07-22T05:09:23Z | # gpt2_review_star3
* This model has been trained as a review_body dataset with a star of 3 in the [amazon_review dataset](https://huggingface.co/datasets/amazon_reviews_multi).
* Input text what you want to generate review.
* If the context is longer than 1200 characters, the context may be cut in the middle and the ... | [
-0.019813625141978264,
-0.015431281179189682,
0.01272355206310749,
0.03871588036417961,
0.01579023152589798,
0.018663927912712097,
-0.03607462719082832,
-0.01197001338005066,
-0.07879162579774857,
0.04666956141591072,
0.0490424782037735,
0.0023760369513183832,
-0.004915682598948479,
0.0180... |
bert-large-cased | [
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1810.04805",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] | fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"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_ngram_size... | 388,769 | null | # gpt2_review_star4
* This model has been trained as a review_body dataset with a star of 4 in the [amazon_review dataset](https://huggingface.co/datasets/amazon_reviews_multi).
* Input text what you want to generate review.
* If the context is longer than 1200 characters, the context may be cut in the middle and the ... | [
-0.02231641858816147,
-0.010879934765398502,
0.014052183367311954,
0.038013603538274765,
0.014182991348206997,
0.017750650644302368,
-0.03495403751730919,
-0.012723413296043873,
-0.07983572781085968,
0.046051450073719025,
0.045286547392606735,
0.0013880457263439894,
0.0021535917185246944,
... |
bert-large-uncased-whole-word-masking-finetuned-squad | [
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"question-answering",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1810.04805",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] | question-answering | {
"architectures": [
"BertForQuestionAnswering"
],
"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_n... | 480,510 | 2021-07-22T05:09:51Z | # gpt2_review_star5
* This model has been trained as a review_body dataset with a star of 5 in the [amazon_review dataset](https://huggingface.co/datasets/amazon_reviews_multi).
* Input text what you want to generate review.
* If the context is longer than 1200 characters, the context may be cut in the middle and the ... | [
-0.01725684478878975,
-0.013365311548113823,
0.01639465056359768,
0.03795669600367546,
0.01478581316769123,
0.0149930939078331,
-0.03708360344171524,
-0.011953999288380146,
-0.07839136570692062,
0.04416321963071823,
0.04976164549589157,
0.003254321636632085,
-0.0032840862404555082,
0.02224... |
bert-large-uncased-whole-word-masking | [
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1810.04805",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] | fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"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_ngram_size... | 76,685 | 2021-07-22T01:08:42Z | # ehdwns1516/gpt3-kor-based_gpt2_review_SR1
* This model has been trained Korean dataset as a star of 1 in the [naver shopping reivew dataset](https://github.com/bab2min/corpus/tree/master/sentiment).
* Input text what you want to generate review.
* If the context is longer than 1200 characters, the context may be cut... | [
-0.00848054327070713,
-0.016373954713344574,
-0.004629421979188919,
0.034691937267780304,
0.016846971586346626,
0.028058158233761787,
-0.020795468240976334,
0.009347870014607906,
-0.08098003268241882,
0.04396111145615578,
0.04492168128490448,
-0.016231849789619446,
-0.012271018698811531,
0... |
bert-large-uncased | [
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1810.04805",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] | fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"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_ngram_size... | 1,058,496 | 2021-07-22T01:08:50Z | # ehdwns1516/gpt3-kor-based_gpt2_review_SR2
* This model has been trained Korean dataset as a star of 2 in the [naver shopping reivew dataset](https://github.com/bab2min/corpus/tree/master/sentiment).
* Input text what you want to generate review.
* If the context is longer than 1200 characters, the context may be cut... | [
-0.008321285247802734,
-0.016205688938498497,
-0.00340291322208941,
0.034626614302396774,
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0.0436982661485672,
0.04533436894416809,
-0.015678824856877327,
-0.012404708191752434,
0.00... |
camembert-base | [
"pytorch",
"tf",
"safetensors",
"camembert",
"fill-mask",
"fr",
"dataset:oscar",
"arxiv:1911.03894",
"transformers",
"license:mit",
"autotrain_compatible",
"has_space"
] | fill-mask | {
"architectures": [
"CamembertForMaskedLM"
],
"model_type": "camembert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_... | 1,440,898 | 2021-07-22T01:08:59Z | # ehdwns1516/gpt3-kor-based_gpt2_review_SR3
* This model has been trained Korean dataset as a star of 3 in the [naver shopping reivew dataset](https://github.com/bab2min/corpus/tree/master/sentiment).
* Input text what you want to generate review.
* If the context is longer than 1200 characters, the context may be cut... | [
-0.008722531609237194,
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-0.005563193932175636,
0.033454492688179016,
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-0.02116437256336212,
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-0.08043289184570312,
0.04441358894109726,
0.046523984521627426,
-0.01838773302733898,
-0.011490875855088234,
0.... |
ctrl | [
"pytorch",
"tf",
"ctrl",
"en",
"arxiv:1909.05858",
"arxiv:1910.09700",
"transformers",
"license:bsd-3-clause",
"has_space"
] | null | {
"architectures": null,
"model_type": "ctrl",
"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_bea... | 17,007 | 2021-07-22T01:10:00Z | # ehdwns1516/gpt3-kor-based_gpt2_review_SR4
* This model has been trained Korean dataset as a star of 4 in the [naver shopping reivew dataset](https://github.com/bab2min/corpus/tree/master/sentiment).
* Input text what you want to generate review.
* If the context is longer than 1200 characters, the context may be cut... | [
-0.010885961353778839,
-0.014013097621500492,
-0.00498830433934927,
0.033323146402835846,
0.016729459166526794,
0.029209434986114502,
-0.02083374559879303,
0.007110140286386013,
-0.08143617957830429,
0.04446997493505478,
0.044601935893297195,
-0.018517745658755302,
-0.008932678028941154,
0... |
distilbert-base-cased | [
"pytorch",
"tf",
"onnx",
"distilbert",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1910.01108",
"transformers",
"license:apache-2.0",
"has_space"
] | null | {
"architectures": null,
"model_type": "distilbert",
"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,
"n... | 574,859 | 2021-07-13T05:06:28Z | # klue-roberta-base-kornli
* This model trained with Korean dataset.
* Input premise sentence and hypothesis sentence.
* You can use English, but don't expect accuracy.
* If the context is longer than 1200 characters, the context may be cut in the middle and the result may not come out well.
klue-roberta-base-kornli ... | [
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0.053332291543483734,
0.03296414017677307,
-0.021561894565820694,
-0.0018230319255962968,
0... |
13048909972/wav2vec2-common_voice-tr-demo | [
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"transformers"
] | 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 | 2021-09-05T00:03:22Z | ---
tags:
- spacy
- token-classification
language:
- is
model-index:
- name: is_ner_mim_sm
results:
- task:
name: NER
type: token-classification
metrics:
- name: NER Precision
type: precision
value: 0.8029028187
- name: NER Recall
type: recall
value: 0.7796160131
... | [
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0.05723235011100769,
0.04659344628453255,
0.007854251191020012,
0.017990628257393837,
0.040... |
AdapterHub/roberta-base-pf-wic | [
"roberta",
"en",
"arxiv:2104.08247",
"adapter-transformers",
"text-classification",
"adapterhub:wordsence/wic"
] | text-classification | {
"architectures": null,
"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_ngram_size": null,
"num_... | 0 | null | ---
tags:
- espnet
- audio
- text-to-speech
language: ja
datasets:
- jsut
license: cc-by-4.0
---
## Example ESPnet2 TTS model
### `kan-bayashi/jsut_tts_train_conformer_fastspeech2_transformer_teacher_raw_phn_jaconv_pyopenjtalk_accent_with_pause_train.loss.ave`
♻️ Imported from https://zenodo.org/record/4433198/
This ... | [
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0.04318070784211159,
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0.03285544738173485,
0... |
Adrianaforididk/Jinx | [] | null | {
"architectures": null,
"model_type": null,
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},
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"num_beams... | 0 | null | ---
tags:
- espnet
- audio
- text-to-speech
language: ja
datasets:
- jsut
license: cc-by-4.0
---
## ESPnet2 TTS pretrained model
### `kan-bayashi/jsut_tts_train_vits_raw_phn_jaconv_pyopenjtalk_accent_with_pause_train.total_count.ave`
♻️ Imported from https://zenodo.org/record/5414980/
This model was trained by kan-ba... | [
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0.03893163800239563,
0.002196249086409807,
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0.03409193083643913,
0.0... |
Advertisement/FischlUWU | [] | null | {
"architectures": null,
"model_type": null,
"task_specific_params": {
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},
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"min_length": null,
"no_repeat_ngram_size": null,
"num_beams... | 0 | null | ---
tags:
- espnet
- audio
- text-to-speech
language: ja
datasets:
- jsut
license: cc-by-4.0
---
## ESPnet2 TTS pretrained model
### `kan-bayashi/jsut_tts_train_vits_raw_phn_jaconv_pyopenjtalk_prosody_train.total_count.ave`
♻️ Imported from https://zenodo.org/record/5521354/
This model was trained by kan-bayashi usin... | [
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0.001211358467116952,
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0.03759687766432762,
0.006364825647324324,
-0.017084410414099693,
0.025937026366591454,
... |
Aftabhussain/Tomato_Leaf_Classifier | [
"pytorch",
"tensorboard",
"vit",
"image-classification",
"transformers",
"huggingpics",
"model-index",
"autotrain_compatible"
] | image-classification | {
"architectures": [
"ViTForImageClassification"
],
"model_type": "vit",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_n... | 50 | null | ---
tags:
- espnet
- audio
- text-to-speech
language: en
datasets:
- libritts
license: cc-by-4.0
---
## Example ESPnet2 TTS model
### `kan-bayashi/libritts_gst+xvector_trasnformer`
♻️ Imported from https://zenodo.org/record/4409702/
This model was trained by kan-bayashi using libritts/tts1 recipe in [espnet](https://... | [
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0.04404117539525032,
0.003868561005219817,
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0.031310126185417175,
... |
Ahmed59/Demo-Team-5-SIAD | [
"tf",
"roberta",
"text-classification",
"transformers"
] | text-classification | {
"architectures": [
"RobertaForSequenceClassification"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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"length_penalty": null,
"max_length": null,
"min_length": null,
"... | 14 | null | ---
tags:
- espnet
- audio
- text-to-speech
language: en
datasets:
- libritts
license: cc-by-4.0
---
## Example ESPnet2 TTS model
### `kan-bayashi/libritts_xvector_conformer_fastspeech2`
♻️ Imported from https://zenodo.org/record/4418754/
This model was trained by kan-bayashi using libritts/tts1 recipe in [espnet](ht... | [
-0.02550743892788887,
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0.04484446346759796,
0.004697834607213736,
-0.01498626358807087,
0.03206795081496239,
0.01... |
Ahren09/distilbert-base-uncased-finetuned-cola | [
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"transformers"
] | text-classification | {
"architectures": [
"DistilBertForSequenceClassification"
],
"model_type": "distilbert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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"length_penalty": null,
"max_length": null,
"min_length": null,
... | 33 | null | ---
tags:
- espnet
- audio
- text-to-speech
language: en
datasets:
- ljspeech
license: cc-by-4.0
---
## Example ESPnet2 TTS model
### `kan-bayashi/ljspeech_fastspeech2`
♻️ Imported from https://zenodo.org/record/4036272/
This model was trained by kan-bayashi using ljspeech/tts1 recipe in [espnet](https://github.com/e... | [
-0.02595527656376362,
-0.007189502473920584,
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0.03423050045967102,
0.05193444713950157,
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0.0004991419846192002,
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0.041652243584394455,
0.003132446901872754,
-0.017596950754523277,
0.03103521093726158,
0.... |
Akari/albert-base-v2-finetuned-squad | [
"pytorch",
"tensorboard",
"albert",
"question-answering",
"dataset:squad_v2",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"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... | 13 | null | ---
tags:
- espnet
- audio
- text-to-speech
language: en
datasets:
- vctk
license: cc-by-4.0
---
## Example ESPnet2 TTS model
### `kan-bayashi/vctk_gst_fastspeech2`
♻️ Imported from https://zenodo.org/record/4036266/
This model was trained by kan-bayashi using vctk/tts1 recipe in [espnet](https://github.com/espnet/es... | [
-0.025237299501895905,
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0.002605029847472906,
0.0012439672136679292,
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0.043042220175266266,
0.004918485414236784,
-0.017199506983160973,
0.034840118139982224,
0.... |
Akashpb13/Central_kurdish_xlsr | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"ckb",
"dataset:mozilla-foundation/common_voice_8_0",
"transformers",
"mozilla-foundation/common_voice_8_0",
"generated_from_trainer",
"robust-speech-event",
"model_for_talk",
"hf-asr-leaderboard",
"license:apache-2.0",
"model-index"
] | 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... | 10 | null | ---
tags:
- espnet
- audio
- text-to-speech
language: en
datasets:
- vctk
license: cc-by-4.0
---
## Example ESPnet2 TTS model
### `kan-bayashi/vctk_tts_train_gst_fastspeech2_raw_phn_tacotron_g2p_en_no_space_train.loss.ave`
♻️ Imported from https://zenodo.org/record/4036266/
This model was trained by kan-bayashi using... | [
-0.027733834460377693,
-0.005464371759444475,
-0.010418540798127651,
0.03214756399393082,
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0.018777798861265182,
0.0017480880487710238,
0.0006350100156851113,
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0.036792077124118805,
0.0013608037261292338,
-0.013383646495640278,
0.0307835154235363,
... |
Akashpb13/Galician_xlsr | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"gl",
"dataset:mozilla-foundation/common_voice_8_0",
"transformers",
"mozilla-foundation/common_voice_8_0",
"generated_from_trainer",
"robust-speech-event",
"model_for_talk",
"hf-asr-leaderboard",
"license:apache-2.0",
"model-index"
] | 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... | 7 | null | ---
tags:
- espnet
- audio
- text-to-speech
language: en
datasets:
- vctk
license: cc-by-4.0
---
## Example ESPnet2 TTS model
### `kan-bayashi/vctk_tts_train_gst_fastspeech_raw_phn_tacotron_g2p_en_no_space_train.loss.best`
♻️ Imported from https://zenodo.org/record/3986241/
This model was trained by kan-bayashi using... | [
-0.02800571173429489,
-0.005650045815855265,
-0.012424676679074764,
0.03127636760473251,
0.05217558145523071,
0.017920155078172684,
0.0018525749910622835,
0.0007650722400285304,
-0.043451059609651566,
0.03753383085131645,
0.002164251171052456,
-0.013400759547948837,
0.03101683408021927,
0.... |
AkshatSurolia/ViT-FaceMask-Finetuned | [
"pytorch",
"safetensors",
"vit",
"image-classification",
"dataset:Face-Mask18K",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | image-classification | {
"architectures": [
"ViTForImageClassification"
],
"model_type": "vit",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_n... | 40 | null | ---
tags:
- espnet
- audio
- automatic-speech-recognition
language: en
datasets:
- fsc
license: cc-by-4.0
---
## ESPnet2 SLU pretrained model
### `siddhana/fsc_asr_train_asr_hubert_transformer_adam_specaug_raw_en_word_valid.acc.ave_5best`
♻️ Imported from https://zenodo.org/record/5590204
This model was trained by si... | [
-0.025735922157764435,
-0.007382816635072231,
-0.02753003127872944,
0.03374520689249039,
0.05219550058245659,
0.029328161850571632,
-0.0063802096992731094,
-0.014575514942407608,
-0.06107925996184349,
0.056624386459589005,
0.007990634068846703,
-0.002245417097583413,
0.008329269476234913,
... |
Aleksandar/distilbert-srb-ner-setimes | [
"pytorch",
"distilbert",
"token-classification",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
] | token-classification | {
"architectures": [
"DistilBertForTokenClassification"
],
"model_type": "distilbert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
... | 3 | null | ---
language:
- "zh"
thumbnail: "https://user-images.githubusercontent.com/9592150/97142000-cad08e00-179a-11eb-88df-aff9221482d8.png"
tags:
- "chinese"
- "classical chinese"
- "literary chinese"
- "ancient chinese"
- "bert"
- "pytorch"
- "punctuation marker"
license: "apache-2.0"
pipeline_tag: "token-classification"
w... | [
-0.0010819662129506469,
-0.042055558413267136,
-0.02822970598936081,
0.04454909637570381,
0.05245818570256233,
0.02242104522883892,
-0.016106339171528816,
-0.0006825767923146486,
-0.024873683229088783,
0.04241975024342537,
-0.0014724232023581862,
-0.013283724896609783,
0.025432461872696877,
... |
AlekseyKulnevich/Pegasus-Summarization | [
"pytorch",
"pegasus",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | {
"architectures": [
"PegasusForConditionalGeneration"
],
"model_type": "pegasus",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"n... | 7 | null | ---
license: apache-2.0
tags:
- stylegan2
- image-generation
---
# AniCharaGAN: Anime Character Generation with StyleGAN2
[](https://github.com/eugenesiow/practical-ml)
This model uses the awesome lucidrains’s [stylegan2-py... | [
-0.0028900925535708666,
-0.022044343873858452,
-0.008899624459445477,
0.05311260744929314,
0.05321058630943298,
-0.012284988537430763,
-0.0020481215324252844,
0.010988005436956882,
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0.0665525272488594,
0.02165580913424492,
-0.007251616567373276,
0.028111601248383522,
0... |
Alerosae/SocratesGPT-2 | [
"pytorch",
"gpt2",
"feature-extraction",
"en",
"transformers",
"text-generation"
] | text-generation | {
"architectures": [
"GPT2Model"
],
"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": nul... | 7 | null | ---
license: apache-2.0
tags:
- super-image
- image-super-resolution
datasets:
- eugenesiow/Div2k
- eugenesiow/Set5
- eugenesiow/Set14
- eugenesiow/BSD100
- eugenesiow/Urban100
metrics:
- pnsr
- ssim
---
# Lightweight Image Super-Resolution with Adaptive Weighted Learning Network (AWSRN)
AWSRN model pre-trained on DIV2... | [
-0.027481507509946823,
-0.01997792348265648,
-0.01965123414993286,
0.024240436032414436,
0.040952302515506744,
-0.003927875310182571,
-0.009546487592160702,
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0.004898067098110914,
0.05862857773900032,
0.03723745048046112,
0.009216890670359135,
0.014936725609004498,
0.0... |
Alexander-Learn/bert-finetuned-ner-accelerate | [
"pytorch",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
] | 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... | 4 | null | ---
license: apache-2.0
tags:
- super-image
- image-super-resolution
datasets:
- eugenesiow/Div2k
- eugenesiow/Set5
- eugenesiow/Set14
- eugenesiow/BSD100
- eugenesiow/Urban100
metrics:
- pnsr
- ssim
---
# Multi-Scale Deep Super-Resolution System (MDSR)
MDSR model pre-trained on DIV2K (800 images training, augmented to... | [
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-0.012670119293034077,
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0.04618105664849281,
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-0.010729207657277584,
0.019604111090302467,
... |
Aliskin/xlm-roberta-base-finetuned-marc | [] | null | {
"architectures": null,
"model_type": null,
"task_specific_params": {
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},
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"num_beams... | 0 | null | ---
tags:
- spacy
- text-classification
language:
- en
license: mit
model-index:
- name: en_textcat_goemotions
results: []
---
# 🪐 spaCy Project: Categorization of emotions in Reddit posts (Text Classification) This project uses spaCy to train a text classifier on the [GoEmotions dataset](https://github.com/google-r... | [
-0.00485862884670496,
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0.04615480452775955,
0.02871001698076725,
-0.004789457190781832,
0.022954020649194717,
0.03... |
AmirBialer/amirbialer-Classifier | [] | null | {
"architectures": null,
"model_type": null,
"task_specific_params": {
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},
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"min_length": null,
"no_repeat_ngram_size": null,
"num_beams... | 0 | null | ---
tags:
- spacy
- token-classification
language:
- pt
license: cc-by-sa-4.0
model-index:
- name: pt_udv25_portuguesebosque_trf
results:
- task:
name: TAG
type: token-classification
metrics:
- name: TAG (XPOS) Accuracy
type: accuracy
value: 0.9809207592
- task:
name: POS
... | [
0.014414261095225811,
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0.06752973049879074,
-0.014844292774796486,
-0.012933051213622093,
-0.023858899250626564,
... |
AmirHussein/test | [] | null | {
"architectures": null,
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"num_beams... | 0 | 2021-12-10T23:04:04Z | ---
tags:
- spacy
- token-classification
language:
- ro
license: cc-by-sa-4.0
model-index:
- name: ro_udv25_romaniannonstandard_trf
results:
- task:
name: TAG
type: token-classification
metrics:
- name: TAG (XPOS) Accuracy
type: accuracy
value: 0.9385375334
- task:
name: POS
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tags:
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language:
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license: cc-by-sa-4.0
model-index:
- name: xx_udv25_oldfrenchsrcmf_trf
results:
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tags:
- image-generation
- conditional-image-generation
- generative-model
license: cc-by-nc-4.0
library: pytorch
---
# <p align="center"> IC-GAN: Instance-Conditioned GAN </p>
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library_name: fairseq
task: text-to-speech
tags:
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language: en
datasets:
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widget:
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example_title: "Hello, this is a test run."
---
# tts_transformer-en-200_speaker-cv4
[Transformer](https://arxiv.org/abs/1809.0889... | [
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language: en
datasets:
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license: apache-2.0
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language: en
datasets:
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tags:
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license: apache-2.0
---
# Wav2Vec2-Large-960h
[Facebook's Wav2Vec2](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/)
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language: multi-lingual
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AnonymousSub/rule_based_only_classfn_epochs_1_shard_1_wikiqa | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | {
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"no_rep... | 32 | null | ---
language: multi-lingual
datasets:
- common_voice
tags:
- speech
- audio
- automatic-speech-recognition
- phoneme-recognition
widget:
- example_title: Librispeech sample 1
src: https://cdn-media.huggingface.co/speech_samples/sample1.flac
- example_title: Librispeech sample 2
src: https://cdn-media.huggingface.co... | [
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AnonymousSub/rule_based_roberta_bert_quadruplet_epochs_1_shard_1 | [
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"no_repeat_ngram_size... | 6 | 2020-09-15T18:43:35Z | ---
language:
- de
- en
tags:
- translation
- wmt19
- facebook
license: apache-2.0
datasets:
- wmt19
metrics:
- bleu
thumbnail: https://huggingface.co/front/thumbnails/facebook.png
---
# FSMT
## Model description
This is a ported version of [fairseq wmt19 transformer](https://github.com/pytorch/fairseq/blob/master/... | [
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AnonymousSub/rule_based_roberta_bert_quadruplet_epochs_1_shard_10 | [
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"no_repeat_ngram_size... | 8 | null | ---
language:
- en
- de
tags:
- translation
- wmt19
- facebook
license: apache-2.0
datasets:
- wmt19
metrics:
- bleu
thumbnail: https://huggingface.co/front/thumbnails/facebook.png
---
# FSMT
## Model description
This is a ported version of [fairseq wmt19 transformer](https://github.com/pytorch/fairseq/blob/master/... | [
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AnonymousSub/rule_based_roberta_bert_quadruplet_epochs_1_shard_1_squad2.0 | [
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"question-answering",
"transformers",
"autotrain_compatible"
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"no_re... | 2 | null | ---
language:
- en
- ru
tags:
- translation
- wmt19
- facebook
license: apache-2.0
datasets:
- wmt19
metrics:
- bleu
thumbnail: https://huggingface.co/front/thumbnails/facebook.png
---
# FSMT
## Model description
This is a ported version of [fairseq wmt19 transformer](https://github.com/pytorch/fairseq/blob/master/... | [
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AnonymousSub/rule_based_roberta_bert_quadruplet_epochs_1_shard_1_wikiqa | [
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"... | 23 | null | ---
language:
- ru
- en
tags:
- translation
- wmt19
- facebook
license: apache-2.0
datasets:
- wmt19
metrics:
- bleu
thumbnail: https://huggingface.co/front/thumbnails/facebook.png
---
# FSMT
## Model description
This is a ported version of [fairseq wmt19 transformer](https://github.com/pytorch/fairseq/blob/master/... | [
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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:
- multilingual
- ha
- is
- ja
- cs
- ru
- zh
- de
- en
license: mit
tags:
- translation
- wmt21
---
# WMT 21 En-X
WMT 21 En-X is a 4.7B multilingual encoder-decoder (seq-to-seq) model trained for one-to-many multilingual translation.
It was introduced in this [paper](https://arxiv.org/abs/2108.03265) an... | [
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AnonymousSub/rule_based_roberta_bert_triplet_epochs_1_shard_10 | [
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"no_repeat_ngram_size... | 2 | null | ---
language:
- multilingual
- ha
- is
- ja
- cs
- ru
- zh
- de
- en
license: mit
tags:
- translation
- wmt21
---
# WMT 21 X-En
WMT 21 X-En is a 4.7B multilingual encoder-decoder (seq-to-seq) model trained for one-to-many multilingual translation.
It was introduced in this [paper](https://arxiv.org/abs/2108.03265) and... | [
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AnonymousSub/rule_based_roberta_bert_triplet_epochs_1_shard_1_wikiqa | [
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"... | 28 | null | ---
language:
- multilingual
- en
- ru
- zh
- de
- es
- fr
- ja
- it
- pt
- el
- ko
- fi
- id
- tr
- ar
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- th
- bg
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- et
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- te
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license: mit
thumbnail: https://huggingface.co/front/thumbnails/facebook.png
inference: false
---
# XGLM-2.9B
XGLM-2.9B is a multilingual a... | [
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AnonymousSub/rule_based_roberta_bert_triplet_epochs_1_shard_1_wikiqa_copy | [
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"transformers"
] | feature-extraction | {
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"no_repeat_ngram_size... | 2 | null | ---
language:
- multilingual
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- de
- es
- fr
- ja
- it
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- el
- ko
- fi
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- bg
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- ur
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- te
- eu
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license: mit
thumbnail: https://huggingface.co/front/thumbnails/facebook.png
inference: false
---
# XGLM-4.5B
XGLM-4.5B is a multilingual a... | [
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AnonymousSub/rule_based_roberta_hier_quadruplet_0.1_epochs_1_shard_1 | [
"pytorch",
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"no_repeat_ngram_size... | 6 | null | ---
language:
- multilingual
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license: mit
thumbnail: https://huggingface.co/front/thumbnails/facebook.png
inference: false
---
# XGLM-564M
XGLM-564M is a multilingual a... | [
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AnonymousSub/rule_based_roberta_hier_quadruplet_0.1_epochs_1_shard_1_squad2.0 | [
"pytorch",
"roberta",
"question-answering",
"transformers",
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] | question-answering | {
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"no_re... | 4 | null | ---
language:
- multilingual
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license: mit
thumbnail: https://huggingface.co/front/thumbnails/facebook.png
inference: false
---
# XGLM-7.5B
XGLM-7.5B is a multilingual a... | [
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AnonymousSub/rule_based_roberta_hier_quadruplet_epochs_1_shard_1 | [
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"no_repeat_ngram_size... | 1 | null | ---
language:
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AnonymousSub/rule_based_roberta_hier_quadruplet_epochs_1_shard_10 | [
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"no_repeat_ngram_size... | 6 | null | ---
language:
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... | [
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AnonymousSub/rule_based_roberta_hier_quadruplet_epochs_1_shard_1_squad2.0 | [
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"no_re... | 2 | 2022-01-05T01:33:34Z | ---
library_name: fairseq
task: audio-to-audio
tags:
- fairseq
- audio
- audio-to-audio
- speech-to-speech-translation
language: en-ar
datasets:
- must_c
- covost2
widget:
- example_title: Common Voice sample 1
src: https://huggingface.co/facebook/xm_transformer_600m-en_es-multi_domain/resolve/main/common_voice_en_18... | [
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AnonymousSub/rule_based_roberta_hier_triplet_0.1_epochs_1_shard_1 | [
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"no_repeat_ngram_size... | 6 | 2022-01-05T02:22:47Z | ---
library_name: fairseq
task: audio-to-audio
tags:
- fairseq
- audio
- audio-to-audio
- speech-to-speech-translation
language: en-fr
datasets:
- must_c
- europarl_st
- voxpopuli
- libritrans
widget:
- example_title: Common Voice sample 1
src: https://huggingface.co/facebook/xm_transformer_600m-en_es-multi_domain/re... | [
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library_name: fairseq
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library_name: fairseq
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"... | 25 | 2022-01-04T04:28:03Z | ---
library_name: fairseq
task: audio-to-audio
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language: es-en
datasets:
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library_name: fairseq
task: audio-to-audio
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language: fr-en
datasets:
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license: apache-2.0
tags:
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metrics:
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model-index:
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results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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license: apache-2.0
tags:
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metrics:
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model-index:
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---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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"... | 27 | null | ---
license: apache-2.0
tags:
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metrics:
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model-index:
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results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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AnonymousSub/rule_based_roberta_only_classfn_twostage_epochs_1_shard_1 | [
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license: apache-2.0
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results:
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type: question-answering
dataset:
name: squad
type: squad
args: plain_text
---
<!-- This model card has b... | [
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metrics:
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model-index:
- name: gq-indo-k
---
<!-- 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. -->
# gq-indo-k
This model was trained from scratch on an unkown dataset.
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model-index:
- name: qa-indo-k
---
<!-- 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. -->
# qa-indo-k
This model was trained from scratch on an unkown dataset.
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"... | 24 | null | ---
model-index:
- name: qa-indo-math-k-v2
---
<!-- 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. -->
# qa-indo-math-k-v2
This model was trained from scratch on an unkown dataset.
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license: apache-2.0
tags:
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datasets:
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metrics:
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model_index:
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license: apache-2.0
tags:
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datasets:
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results:
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type: text2text-generation
dataset:
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args: plain_text
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metrics:
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model-index:
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---
<!-- 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. -->
# test-summarization
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"no_re... | 4 | null | ---
tags:
- conversational
---
# test DialoGPT Model | [
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0.036... |
AnonymousSub/rule_based_twostagetriplet_hier_epochs_1_shard_1_wikiqa | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | {
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
"task_specific_params": {
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},
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"no_rep... | 27 | null | ---
title: Test Space
emoji: 🔥
colorFrom: indigo
colorTo: blue
sdk: gradio
app_file: app.py
pinned: false
---
# Configuration
`title`: _string_
Display title for the Space
`emoji`: _string_
Space emoji (emoji-only character allowed)
`colorFrom`: _string_
Color for Thumbnail gradient (red, yellow, green, blue... | [
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0.030780619010329247,
0.012501586228609085,
0.031512197107076645,
0.057714201509952545,
0.03... |
Anthos23/test_trainer | [] | null | {
"architectures": null,
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},
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"num_beams... | 0 | null | This model is the fine-tuned model of "akdeniz27/bert-base-hungarian-cased-ner" using WikiANN-hu dataset. | [
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0.03... |
AntonClaesson/finetuning_test | [] | null | {
"architectures": null,
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"task_specific_params": {
"conversational": {
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},
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"no_repeat_ngram_size": null,
"num_beams... | 0 | null | Magyar nyelvű token classification feladatra felkészített BERT modell. | [
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0.0459... |
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