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 |
|---|---|---|---|---|---|---|---|
Chungu424/repo | [] | null | {
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"num_beams... | 0 | 2020-05-21T16:16:47Z | ---
language: ko
---
# 📈 Financial Korean ELECTRA model
Pretrained ELECTRA Language Model for Korean (`finance-koelectra-base-generator`)
> ELECTRA is a new method for self-supervised language representation learning. It can be used to
> pre-train transformer networks using relatively little compute. ELECTRA models... | [
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Chungu424/repodata | [] | null | {
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"num_beams... | 0 | 2020-05-22T03:15:12Z | ---
language: ko
---
# 📈 Financial Korean ELECTRA model
Pretrained ELECTRA Language Model for Korean (`finance-koelectra-small-discriminator`)
> ELECTRA is a new method for self-supervised language representation learning. It can be used to
> pre-train transformer networks using relatively little compute. ELECTRA m... | [
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Chuu/Chumar | [] | null | {
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"num_beams... | 0 | 2020-05-22T03:16:25Z | ---
language: ko
---
# 📈 Financial Korean ELECTRA model
Pretrained ELECTRA Language Model for Korean (`finance-koelectra-small-generator`)
> ELECTRA is a new method for self-supervised language representation learning. It can be used to
> pre-train transformer networks using relatively little compute. ELECTRA model... | [
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Cinnamon/electra-small-japanese-discriminator | [
"pytorch",
"electra",
"pretraining",
"ja",
"transformers",
"license:apache-2.0"
] | null | {
"architectures": [
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"no_repeat_n... | 419 | 2021-08-28T14:10:34Z | ---
tags:
- conversational
---
# Phoenix DialoGPT model | [
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Cinnamon/electra-small-japanese-generator | [
"pytorch",
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] | fill-mask | {
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"no_repeat_ngra... | 19 | 2022-01-26T08:33:53Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- common_voice
model-index:
- name: wav2vec2-large-xls-r-300m-turkish-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, the... | [
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CleveGreen/JobClassifier | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | {
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
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"no_rep... | 31 | 2021-09-07T14:09:07Z | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# sts-GBERT-bi-encoder
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 1024 dimensional dense vector space and can be used for tasks lik... | [
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CodeDanCode/SP-KyleBot | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
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"no_repeat_ngram_size... | 15 | 2021-08-23T13:08:49Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
model_index:
- name: name
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
args: mrpc
---
<!-- This model card has been generated automatically according to t... | [
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0.06253428012132645,
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0.018743643537163734,
0.0... |
CodeNinja1126/bert-q-encoder | [
"pytorch"
] | null | {
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"num_beams... | 3 | 2021-02-09T17:22:46Z | # I-BERT base model
This model, `ibert-roberta-base`, is an integer-only quantized version of [RoBERTa](https://arxiv.org/abs/1907.11692), and was introduced in [this paper](https://arxiv.org/abs/2101.01321).
I-BERT stores all parameters with INT8 representation, and carries out the entire inference using integer-only... | [
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-0.004207103047519922,
-0.02505502477288246,
0.02643335983157158,
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-0.027807774022221565,
0.02486516535282135,
0.0502... |
CodeNinja1126/test-model | [
"pytorch",
"jax",
"bert",
"text-classification",
"transformers"
] | text-classification | {
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
"task_specific_params": {
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},
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"no_rep... | 24 | 2021-02-14T05:33:59Z | # I-BERT large model
This model, `ibert-roberta-large`, is an integer-only quantized version of [RoBERTa](https://arxiv.org/abs/1907.11692), and was introduced in [this papaer](https://arxiv.org/abs/2101.01321).
I-BERT stores all parameters with INT8 representation, and carries out the entire inference using integer-o... | [
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0.0... |
CoderBoy432/DialoGPT-small-harrypotter | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
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"no_repeat_ngram_size... | 11 | 2021-07-21T14:49:24Z | ---
language:
- en
tags:
- text generation
- pytorch
- the Pile
- causal-lm
license: apache-2.0
datasets:
- the Pile
---
# GPT-Neo 2.7B (By EleutherAI)
## Model Description
GPT-Neo 2.7B is a transformer model designed using EleutherAI's replication of the GPT-3 architecture. GPT-Neo refers to the class of models, while... | [
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CoderEFE/DialoGPT-marxbot | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational",
"has_space"
] | conversational | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
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"no_repeat_ngram_size... | 11 | 2020-01-08T18:52:40Z | ### Model
**[`albert-xlarge-v2`](https://huggingface.co/albert-xlarge-v2)** fine-tuned on **[`SQuAD V2`](https://rajpurkar.github.io/SQuAD-explorer/)** using **[`run_squad.py`](https://github.com/huggingface/transformers/blob/master/examples/question-answering/run_squad.py)**
### Training Parameters
Trained on 4 NVIDI... | [
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... |
CoderEFE/DialoGPT-medium-marx | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
] | text-generation | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
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"no_repeat_ngram_size... | 7 | 2020-04-06T13:57:56Z | ### Model
**[`monologg/biobert_v1.1_pubmed`](https://huggingface.co/monologg/biobert_v1.1_pubmed)** fine-tuned on **[`SQuAD V2`](https://rajpurkar.github.io/SQuAD-explorer/)** using **[`run_squad.py`](https://github.com/huggingface/transformers/blob/master/examples/question-answering/run_squad.py)**
This model is case... | [
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0.0... |
Venkatakrishnan-Ramesh/Text_gen | [] | null | {
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"num_beams... | 0 | 2021-03-01T21:51:38Z | ---
language:
- en
thumbnail:
widget:
- text: "topic climate source washington post title "
---
# GPT2-medium-topic-news
## Model description
GPT2-medium fine tuned on a largish news corpus conditioned on a topic, source, title
## Intended uses & limitations
#### How to use
To generate a news article text cond... | [
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0.006365346722304821,
0.02256... |
CoffeeAddict93/gpt1-call-of-the-wild | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
] | text-generation | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
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"no_repeat_ngram_size... | 8 | 2020-11-05T00:05:58Z | ---
language:
- en
thumbnail:
widget:
- text: "topic: climate article:"
---
# GPT2-medium-topic-news
## Model description
GPT2-medium fine tuned on a large news corpus conditioned on a topic
## Intended uses & limitations
#### How to use
To generate a news article text conditioned on a topic, prompt model with:... | [
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0.0... |
CoffeeAddict93/gpt1-modest-proposal | [
"pytorch",
"openai-gpt",
"text-generation",
"transformers",
"has_space"
] | text-generation | {
"architectures": [
"OpenAIGPTLMHeadModel"
],
"model_type": "openai-gpt",
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"no_repeat... | 11 | 2021-02-09T15:33:26Z | ---
language:
- en
thumbnail:
widget:
- text: "topic climate source"
---
# GPT2-medium-topic-news
## Model description
GPT2-medium fine tuned on a small news corpus conditioned on a topic, source, title
## Intended uses & limitations
#### How to use
To generate a news article text conditioned on a topic, source... | [
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0.0... |
CoffeeAddict93/gpt2-call-of-the-wild | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
] | text-generation | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
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},
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"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 6 | null | ### Model
**[`allenai/scibert_scivocab_uncased`](https://huggingface.co/allenai/scibert_scivocab_uncased)** fine-tuned on **[`SQuAD V2`](https://rajpurkar.github.io/SQuAD-explorer/)** using **[`run_squad.py`](https://github.com/huggingface/transformers/blob/master/examples/question-answering/run_squad.py)**
### Traini... | [
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0.01944245584309101,
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0.014405445195734501,
0.... |
CohleM/mbert-nepali-tokenizer | [] | null | {
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"num_beams... | 0 | null | ---
language: te
---
# telugu_bertu
## Model description
This model is a BERT MLM model trained on Telugu. Please use it from the terminal as the web interface has encoding issues.
PS: If you find my model useful, I would appreciate a note from you as it would encourage me to continue improving it and also add new m... | [
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0.019549693912267685,
0... |
Coldestadam/Breakout_Mentors_SpongeBob_Model | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
] | text-generation | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 10 | null | # Named Entity Recognition Model for Telugu
#### How to use
Use the below script from your python terminal as the web interface for inference has few encoding issues for Telugu
PS: If you find my model useful, I would appreciate a note from you as it would encourage me to continue improving it and also add new models... | [
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0.05583908408880234,
0.02980242297053337,
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0.023292751982808113,
0... |
ComCom/gpt2-large | [
"pytorch",
"gpt2",
"feature-extraction",
"transformers"
] | feature-extraction | {
"architectures": [
"GPT2Model"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": nul... | 1 | null | # Part of Speech tagging Model for Telugu
#### How to use
Use the below script from your python terminal as the web interface for inference has few encoding issues for Telugu
PS: If you find my model useful, I would appreciate a note from you as it would encourage me to continue improving it and also add new models.
... | [
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0.06382458657026291,
0.017573025077581406,
-0.022401796653866768,
0.013658070005476475,
0... |
ComCom/gpt2-medium | [
"pytorch",
"gpt2",
"feature-extraction",
"transformers"
] | feature-extraction | {
"architectures": [
"GPT2Model"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
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},
"summarization": {
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"no_repeat_ngram_size": nul... | 5 | 2020-10-30T02:36:27Z | # Telugu Question-Answering model trained on Tydiqa dataset from Google
#### How to use
Use the below script from your python terminal as the web interface for inference has few encoding issues for Telugu
```python
from transformers.pipelines import pipeline, AutoModelForQuestionAnswering, AutoTokenizer
model = AutoMo... | [
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0.03000822849571705,
0.021013101562857628,
0.006921229884028435,
0.009795240126550198,
0.039017... |
ComCom/gpt2 | [
"pytorch",
"gpt2",
"feature-extraction",
"transformers"
] | feature-extraction | {
"architectures": [
"GPT2Model"
],
"model_type": "gpt2",
"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": nul... | 1 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-finetuned-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: conll2003
type: conll2003
args: c... | [
-0.00028765920433215797,
0.009532061405479908,
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0.030071554705500603,
0.03846602886915207,
0.012527307495474815,
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0.05506792291998863,
0.027224183082580566,
-0.014086860232055187,
0.020449163392186165,
... |
ComCom-Dev/gpt2-bible-test | [] | null | {
"architectures": null,
"model_type": null,
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams... | 0 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased-finetuned-imdb
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: imdb
type: imdb
args: plain_text
metrics:
... | [
-0.002703464822843671,
0.005590340588241816,
-0.034768544137477875,
0.048389118164777756,
0.04993519186973572,
0.023624924942851067,
-0.02868337370455265,
-0.029314110055565834,
-0.02427057735621929,
0.06700970232486725,
0.0371641181409359,
-0.02407314069569111,
0.016960052773356438,
0.041... |
Cometasonmi451/Mine | [] | 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 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased-finetuned-sst-2-english-finetuned-imdb
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: imdb
type: imdb
args: ... | [
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0.007801055908203125,
-0.03570656105875969,
0.049600955098867416,
0.051936376839876175,
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0.06583705544471741,
0.03568486124277115,
-0.017878133803606033,
0.02261146903038025,
0.043... |
Connor/DialoGPT-small-rick | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
"summarization": {
"early_stopping": null,
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"min_length": null,
"no_repeat_ngram_size... | 7 | null | This model can predict which categories a specific competitive problem falls into | [
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0.013548923656344414,
0.03596295416355133,
-0.009999222122132778,
0.0008086717571131885,
... |
Contrastive-Tension/BERT-Base-CT-STSb | [
"pytorch",
"tf",
"jax",
"bert",
"feature-extraction",
"transformers"
] | feature-extraction | {
"architectures": [
"BertModel"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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"max_length": null,
"min_length": null,
"no_repeat_ngram_size": nul... | 5 | 2021-09-03T21:02:06Z | ---
tags:
- conversational
---
# Rick DiabloGPT Model | [
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0.015425006859004498,
0.04472564160823822,
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0... |
Contrastive-Tension/BERT-Base-CT | [
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
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},
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"no_repeat_ngram_size... | 16 | 2021-12-21T04:38:46Z | https://huggingface.co/blog/fine-tune-wav2vec2-english
Use the processor from https://huggingface.co/facebook/wav2vec2-base | [
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0.031... |
Contrastive-Tension/BERT-Distil-CT-STSb | [
"pytorch",
"tf",
"distilbert",
"feature-extraction",
"transformers"
] | feature-extraction | {
"architectures": [
"DistilBertModel"
],
"model_type": "distilbert",
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},
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"no_repeat_ngra... | 1 | 2021-09-04T13:34:45Z | # kwang2049/TSDAE-askubuntu2nli_stsb
This is a model from the paper ["TSDAE: Using Transformer-based Sequential Denoising Auto-Encoder for Unsupervised Sentence Embedding Learning"](https://arxiv.org/abs/2104.06979). This model was only trained with the TSDAE objective on AskUbuntu in an unsupervised manner. Training p... | [
-0.03119879961013794,
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0.0614144504070282,
0.006895206868648529,
0.002404655097052455,
0.0021892348304390907,
... |
Contrastive-Tension/BERT-Distil-CT | [
"pytorch",
"tf",
"distilbert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"DistilBertForMaskedLM"
],
"model_type": "distilbert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repea... | 9 | 2021-10-25T13:20:32Z | # kwang2049/TSDAE-askubuntu2nli_stsb
This is a model from the paper ["TSDAE: Using Transformer-based Sequential Denoising Auto-Encoder for Unsupervised Sentence Embedding Learning"](https://arxiv.org/abs/2104.06979). This model adapts the knowledge from the NLI and STSb data to the specific domain AskUbuntu. Training ... | [
-0.02839241549372673,
-0.018488451838493347,
-0.021810421720147133,
0.050155773758888245,
0.04464630410075188,
0.027034996077418327,
0.0007588235894218087,
0.0017866663401946425,
-0.055933982133865356,
0.06491845101118088,
0.013750704005360603,
0.006733817979693413,
0.005907053127884865,
0... |
Contrastive-Tension/BERT-Distil-NLI-CT | [
"pytorch",
"tf",
"distilbert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"DistilBertForMaskedLM"
],
"model_type": "distilbert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repea... | 6 | 2021-09-04T13:35:48Z | # kwang2049/TSDAE-cqadupstack2nli_stsb
This is a model from the paper ["TSDAE: Using Transformer-based Sequential Denoising Auto-Encoder for Unsupervised Sentence Embedding Learning"](https://arxiv.org/abs/2104.06979). This model was only trained with the TSDAE objective on cqadupstack in an unsupervised manner. Traini... | [
-0.03597220778465271,
-0.019594084471464157,
-0.008884742856025696,
0.05700058117508888,
0.044596366584300995,
0.025917651131749153,
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0.00034119535121135414,
-0.07331617176532745,
0.05836348608136177,
-0.0009977970039471984,
-0.005833030212670565,
-0.0022286579478532076... |
Contrastive-Tension/BERT-Large-CT-STSb | [
"pytorch",
"tf",
"jax",
"bert",
"feature-extraction",
"transformers"
] | feature-extraction | {
"architectures": [
"BertModel"
],
"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": nul... | 7 | 2021-10-25T13:28:34Z | # kwang2049/TSDAE-cqadupstack2nli_stsb
This is a model from the paper ["TSDAE: Using Transformer-based Sequential Denoising Auto-Encoder for Unsupervised Sentence Embedding Learning"](https://arxiv.org/abs/2104.06979). This model adapts the knowledge from the NLI and STSb data to the specific domain cqadupstack. Traini... | [
-0.03349600359797478,
-0.016708310693502426,
-0.008733323775231838,
0.05915001779794693,
0.03804200887680054,
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-0.009578759782016277,
-0.0002631495881360024,
-0.07082122564315796,
0.05848412588238716,
0.00508760754019022,
-0.0024712353479117155,
-0.0047078244388103485,
... |
Contrastive-Tension/BERT-Large-CT | [
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | 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... | 5 | 2021-09-04T13:37:35Z | # kwang2049/TSDAE-scidocs2nli_stsb
This is a model from the paper ["TSDAE: Using Transformer-based Sequential Denoising Auto-Encoder for Unsupervised Sentence Embedding Learning"](https://arxiv.org/abs/2104.06979). This model was only trained with the TSDAE objective on scidocs in an unsupervised manner. Training proce... | [
-0.029874814674258232,
-0.03387106582522392,
-0.013189484365284443,
0.05329535901546478,
0.041552234441041946,
0.036460231989622116,
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-0.06520090997219086,
0.05547557771205902,
0.0003375230298843235,
-0.0010569662554189563,
0.0024128148797899485,... |
Contrastive-Tension/BERT-Large-NLI-CT | [
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | 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... | 15 | null | # kwang2049/TSDAE-scidocs2nli_stsb
This is a model from the paper ["TSDAE: Using Transformer-based Sequential Denoising Auto-Encoder for Unsupervised Sentence Embedding Learning"](https://arxiv.org/abs/2104.06979). This model adapts the knowledge from the NLI and STSb data to the specific domain scidocs. Training proce... | [
-0.028499625623226166,
-0.032701924443244934,
-0.012263896875083447,
0.050587721168994904,
0.035173773765563965,
0.03625031188130379,
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-0.06259442120790482,
0.059412017464637756,
0.007174776401370764,
0.0017793744336813688,
0.004558149259537458,... |
Contrastive-Tension/RoBerta-Large-CT-STSb | [
"pytorch",
"tf",
"jax",
"roberta",
"feature-extraction",
"transformers"
] | feature-extraction | {
"architectures": [
"RobertaModel"
],
"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... | 5 | null | # kwang2049/TSDAE-twitterpara2nli_stsb
This is a model from the paper ["TSDAE: Using Transformer-based Sequential Denoising Auto-Encoder for Unsupervised Sentence Embedding Learning"](https://arxiv.org/abs/2104.06979). This model was only trained with the TSDAE objective on twitterpara in an unsupervised manner. Traini... | [
-0.031832657754421234,
-0.0371609628200531,
-0.01734863594174385,
0.053574223071336746,
0.05076956748962402,
0.03948301449418068,
-0.012491234578192234,
0.0027534780092537403,
-0.06896719336509705,
0.05499785766005516,
0.010675588622689247,
-0.013651356101036072,
-0.00935273990035057,
0.03... |
Cooker/cicero-similis | [] | 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 | # kwang2049/TSDAE-twitterpara2nli_stsb
This is a model from the paper ["TSDAE: Using Transformer-based Sequential Denoising Auto-Encoder for Unsupervised Sentence Embedding Learning"](https://arxiv.org/abs/2104.06979). This model adapts the knowledge from the NLI and STSb data to the specific domain twitterpara. Traini... | [
-0.029606610536575317,
-0.036350566893815994,
-0.0197270680218935,
0.051671020686626434,
0.043117955327034,
0.037893060594797134,
-0.012272209860384464,
0.002229443984106183,
-0.06843935698270798,
0.057037439197301865,
0.0178559310734272,
-0.010592379607260227,
-0.011839879676699638,
0.039... |
Coolhand/Sentiment | [] | 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 | ---
language: ko
---
# Albert base model for Korean
* 70GB Korean text dataset and 42000 lower-cased subwords are used
* Check the model performance and other language models for Korean in [github](https://github.com/kiyoungkim1/LM-kor)
```python
from transformers import BertTokenizerFast, AlbertModel
tokenizer_alb... | [
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0.05127882957458496,
0.03801945224404335,
0.016996948048472404,
0.014930779114365578,
-0.010698637925088406,
-0.058220647275447845,
0.05621083080768585,
0.021057477220892906,
-0.02224220521748066,
-0.005085749085992575,
0.0... |
CopymySkill/DialoGPT-medium-atakan | [
"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... | 7 | null | ---
language: ko
---
# Bert base model for Korean
* 70GB Korean text dataset and 42000 lower-cased subwords are used
* Check the model performance and other language models for Korean in [github](https://github.com/kiyoungkim1/LM-kor)
```python
from transformers import BertTokenizerFast, BertModel
tokenizer_bert = ... | [
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0.044507112354040146,
0.032894086092710495,
0.02142387442290783,
0.005307196173816919,
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-0.04917523264884949,
0.05649876967072487,
0.013704552315175533,
-0.024724243208765984,
0.00414243945851922,
0.... |
Corvus/DialoGPT-medium-CaptainPrice-Extended | [
"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... | 7 | null | ---
language: ko
---
# Bert base model for Korean
* 70GB Korean text dataset and 42000 lower-cased subwords are used
* Check the model performance and other language models for Korean in [github](https://github.com/kiyoungkim1/LM-kor)
```python
# only for pytorch in transformers
from transformers import BertTokenize... | [
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0.... |
Corvus/DialoGPT-medium-CaptainPrice | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | {
"architectures": [
"GPT2LMHeadModel"
],
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},
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"no_repeat_ngram_size... | 7 | null | ---
language: ko
---
# Electra base model for Korean
* 70GB Korean text dataset and 42000 lower-cased subwords are used
* Check the model performance and other language models for Korean in [github](https://github.com/kiyoungkim1/LM-kor)
```python
from transformers import ElectraTokenizerFast, ElectraModel
tokenize... | [
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CouchCat/ma_mlc_v7_distil | [
"pytorch",
"distilbert",
"text-classification",
"en",
"transformers",
"multi-label",
"license:mit"
] | text-classification | {
"architectures": [
"DistilBertForSequenceClassification"
],
"model_type": "distilbert",
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},
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"min_length": null,
... | 29 | null | ---
language: ko
---
# Funnel-transformer base model for Korean
* 70GB Korean text dataset and 42000 lower-cased subwords are used
* Check the model performance and other language models for Korean in [github](https://github.com/kiyoungkim1/LM-kor)
```python
from transformers import FunnelTokenizer, FunnelModel
tok... | [
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0... |
CouchCat/ma_ner_v6_distil | [
"pytorch",
"distilbert",
"token-classification",
"en",
"transformers",
"ner",
"license:mit",
"autotrain_compatible"
] | token-classification | {
"architectures": [
"DistilBertForTokenClassification"
],
"model_type": "distilbert",
"task_specific_params": {
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},
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"max_length": null,
"min_length": null,
... | 6 | null | ---
language: ko
tags:
- text-generation
---
# Bert base model for Korean
* 70GB Korean text dataset and 42000 lower-cased subwords are used
* Check the model performance and other language models for Korean in [github](https://github.com/kiyoungkim1/LM-kor)
```python
from transformers import BertTokenizerFast, GPT2... | [
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... |
Coverage/sakurajimamai | [] | null | {
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"num_beams... | 0 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
model-index:
- name: distilbert-base-uncased-finetuned-imdb
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 ... | [
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0.0471... |
Coyotl/DialoGPT-test3-arthurmorgan | [
"conversational"
] | conversational | {
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"num_beams... | 0 | 2021-03-22T08:12:31Z | ---
language: "ja"
widget:
- text: "吾輩をは猫である。を書いた作家は，夏目漱 <extra_id_0>"
- text: "吾輩をは猫である。名前えはまだない。"
- text: "translate japanese to english: 赤い花． => red flower. 青い花． => <extra_id_0>"
license: "mit"
---
Google's mt5-base fine-tuned in Japanese to solve error detection and correction task.
# 日本語誤り訂正
- "吾輩をは猫である。名前えはまだ... | [
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0.0356... |
Craak/GJ0001 | [] | null | {
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"num_beams... | 0 | null | ---
language: "ja"
widget:
- text: "請求項 <extra_id_0>"
license: "mit"
tags:
- Summarization
- japanese
---
Google's mt5-base fine-tuned in Japanese to summarize patent claims in a limited Pharmaceutical domain.
# 日本語特許請求項要約（医薬特定ドメイン限定）
- """【請求項１】
ヒトＣＤ３８（配列番号１）及びカニクイザルＣＤ３８（配列番号２）に特異的に結合する単離された抗体であって、
ａ）以下を含む重鎖可変領域：... | [
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... |
Craftified/Bob | [] | null | {
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"num_beams... | 0 | null | ---
language: mr
tags:
- albert
license: cc-by-4.0
datasets:
- L3CubeMahaSent
widget:
- text: "I like you. </s></s> I love you."
---
## MarathiSentiment
MarathiSentiment is an IndicBERT(ai4bharat/indic-bert) model fine-tuned on L3CubeMahaSent - a Marathi tweet-based sentiment analysis dataset.
[dataset link] (https:... | [
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0.035... |
Craig/mGqFiPhu | [
"sentence-transformers",
"feature-extraction",
"sentence-similarity",
"transformers",
"license:apache-2.0"
] | feature-extraction | {
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"num_beams... | 0 | null | ---
language: mr
tags:
- albert
license: cc-by-4.0
datasets:
- HASOC 2021
widget:
- text: "I like you. </s></s> I love you."
---
## hate-bert-hasoc-marathi
hate-bert-hasoc-marathi is a binary hate speech model fine-tuned on Marathi Hasoc Hate Speech Dataset 2021.
The label mappings are 0 -> None, 1 -> Hate.
More de... | [
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0.04272... |
Craig/paraphrase-MiniLM-L6-v2 | [
"pytorch",
"bert",
"arxiv:1908.10084",
"sentence-transformers",
"feature-extraction",
"sentence-similarity",
"transformers",
"license:apache-2.0"
] | feature-extraction | {
"architectures": [
"BertModel"
],
"model_type": "bert",
"task_specific_params": {
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},
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"min_length": null,
"no_repeat_ngram_size": nul... | 1,026 | null | ---
language: hi
tags:
- roberta
license: cc-by-4.0
datasets:
- HASOC 2021
widget:
- text: "I like you. </s></s> I love you."
---
## hate-roberta-hasoc-hindi
hate-roberta-hasoc-hindi is a multi-class hate speech model fine-tuned on Hindi Hasoc Hate Speech Dataset 2021.
The label mappings are 0 -> None, 1 -> Offensiv... | [
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... |
Crasher222/kaggle-comp-test | [
"pytorch",
"bert",
"text-classification",
"en",
"dataset:Crasher222/autonlp-data-kaggle-test",
"transformers",
"autonlp",
"co2_eq_emissions"
] | text-classification | {
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
"task_specific_params": {
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},
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"max_length": null,
"min_length": null,
"no_rep... | 29 | 2021-10-20T17:10:14Z | ---
language: hi
tags:
- roberta
license: cc-by-4.0
datasets:
- HASOC 2021
widget:
- text: "I like you. </s></s> I love you."
---
## hate-roberta-hasoc-hindi
hate-roberta-hasoc-hindi is a binary hate speech model fine-tuned on Hindi Hasoc Hate Speech Dataset 2021.
The label mappings are 0 -> None, 1 -> Hate.
More d... | [
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0.03679601103067398,
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0.04389481991529465,
0.044... |
CrayonShinchan/bart_fine_tune_test | [] | null | {
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},
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"num_beams... | 0 | null | ---
license: cc-by-4.0
language: mr
datasets:
- L3Cube-MahaCorpus
---
## MahaAlBERT
MahaAlBERT is a Marathi AlBERT model trained on L3Cube-MahaCorpus and other publicly available Marathi monolingual datasets.
[dataset link] (https://github.com/l3cube-pune/MarathiNLP)
More details on the dataset, models, and baseline... | [
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0.021... |
Cthyllax/DialoGPT-medium-PaladinDanse | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
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},
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"no_repeat_ngram_size... | 10 | null | Base model: [microsoft/DialoGPT-large](https://huggingface.co/microsoft/DialoGPT-large)
Fine tuned for dialogue response generation on the [Persuasion For Good Dataset](https://gitlab.com/ucdavisnlp/persuasionforgood) (Wang et al., 2019)
Three additional special tokens were added during the fine-tuning process:
- <|... | [
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Cyrell/Cyrell | [] | null | {
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"num_beams... | 0 | null | ---
tags:
- espnet
- audio
- text-to-speech
language: ko
datasets:
- novelspeech
license: cc-by-4.0
---
## ESPnet2 TTS model
### `lakahaga/novel_reading_tts`
This model was trained by lakahaga using novelspeech recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```bash
cd espne... | [
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Darkecho789/email-gen | [] | null | {
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"num_beams... | 0 | null | # Supervised Continous Bag of words model trained with Uruguayan news from Twitter
Model trained with Facebook's fasttext library. | [
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0.020... |
DataikuNLP/paraphrase-albert-small-v2 | [
"pytorch",
"albert",
"arxiv:1908.10084",
"sentence-transformers",
"feature-extraction",
"sentence-similarity",
"transformers",
"license:apache-2.0"
] | sentence-similarity | {
"architectures": [
"AlbertModel"
],
"model_type": "albert",
"task_specific_params": {
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},
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"min_length": null,
"no_repeat_ngram_size":... | 628 | null | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: market_positivity_model
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# market_pos... | [
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... |
Davlan/bert-base-multilingual-cased-finetuned-yoruba | [
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
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},
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"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 21 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: distilbert-base-uncased-finetuned-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: conll2003
type: conl... | [
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0.0... |
Davlan/bert-base-multilingual-cased-masakhaner | [
"pytorch",
"tf",
"bert",
"token-classification",
"arxiv:2103.11811",
"transformers",
"autotrain_compatible"
] | token-classification | {
"architectures": [
"BertForTokenClassification"
],
"model_type": "bert",
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},
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"max_length": null,
"min_length": null,
"no_repeat... | 88 | null | ---
language: en
datasets:
- libri_light
- common_voice
- switchboard
- fisher
tags:
- speech
- automatic-speech-recognition
- CTC
- Attention
- wav2vec2
license: apache-2.0
---
# Wav2Vec2-Large-Robust - Finetuned on Librispeech (960 hours)
## Note : Model has not been initialized. If you want to use it without furth... | [
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0.054567355662584305,
0.04389740899205208,
0.001366888522170484,
0.006913227029144764,
0.01305... |
Dayout/test | [] | null | {
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"num_beams... | 0 | 2021-09-06T10:53:00Z | ---
tags:
- autonlp
- evaluation
- benchmark
---
# Model Card for MetNet
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DecafNosebleed/scarabot-model | [
"gpt2",
"text-generation",
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] | text-generation | {
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"no_repeat_ngram_size... | 6 | 2021-08-22T18:42:16Z | ---
tags:
- generated_from_trainer
datasets:
- amazon_reviews_multi
metrics:
- accuracy
model_index:
- name: roberta-base-bne-finetuned-amazon_reviews_multi-finetuned-amazon_reviews_multi
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: amazon_reviews_multi
... | [
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Declan/Breitbart_model_v1 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
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"no_repeat_ngram_size... | 9 | 2021-08-22T15:14:32Z | ---
license: cc-by-4.0
tags:
- generated_from_trainer
datasets:
- amazon_reviews_multi
metrics:
- accuracy
model_index:
- name: roberta-base-bne-finetuned-amazon_reviews_multi
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: amazon_reviews_multi
type: a... | [
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Declan/CNN_model_v1 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
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"no_repeat_ngram_size... | 7 | null | ---
library_name: superb
benchmark: superb
task: asr
datasets:
- superb
tags:
- automatic-speech-recognition
- osanseviero/hubert_base
widget:
- example_title: Librispeech sample 1
src: https://cdn-media.huggingface.co/speech_samples/sample1.flac
---
# Fine-tuned s3prl model for ASR | [
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0.00... |
Declan/NPR_model_v4 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"BertForMaskedLM"
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"no_repeat_ngram_size... | 3 | null | **This model is provided with no guarantees whatsoever; use at your own risk.**
This is a Neo2.7B model fine tuned on github data scraped by an EleutherAI member (filtered for python-only) for 20k steps. A better code model is coming soon™ (hopefully, maybe); this model was created mostly as a test of infrastructure c... | [
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Declan/Politico_model_v1 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"BertForMaskedLM"
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"no_repeat_ngram_size... | 3 | null | ---
language: pt
datasets:
- common_voice
- mls
- cetuc
- lapsbm
- voxforge
- tedx
- sid
metrics:
- wer
tags:
- audio
- speech
- wav2vec2
- pt
- portuguese-speech-corpus
- automatic-speech-recognition
- speech
- PyTorch
license: apache-2.0
---
# cetuc100-xlsr: Wav2vec 2.0 with CETUC Dataset
This is a the demonstrati... | [
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Declan/Politico_model_v2 | [
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"no_repeat_ngram_size... | 5 | null | ---
language: pt
datasets:
- common_voice
- mls
- cetuc
- lapsbm
- voxforge
- tedx
- sid
metrics:
- wer
tags:
- audio
- speech
- wav2vec2
- pt
- portuguese-speech-corpus
- automatic-speech-recognition
- speech
- PyTorch
license: apache-2.0
---
# commonvoice10-xlsr: Wav2vec 2.0 with Common Voice Dataset
This is a the... | [
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Declan/Reuters_model_v4 | [
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] | fill-mask | {
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"no_repeat_ngram_size... | 3 | 2021-11-26T17:06:25Z | ---
language: pt
datasets:
- common_voice
- mls
- cetuc
- lapsbm
- voxforge
- tedx
- sid
metrics:
- wer
tags:
- audio
- speech
- wav2vec2
- pt
- portuguese-speech-corpus
- automatic-speech-recognition
- speech
- PyTorch
- hf-asr-leaderboard
model-index:
- name: bp500-base10k_voxpopuli
results:
- task:
name: ... | [
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Declan/Reuters_model_v5 | [
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"no_repeat_ngram_size... | 3 | 2021-11-26T17:06:03Z | ---
language: pt
datasets:
- common_voice
- mls
- cetuc
- lapsbm
- voxforge
- tedx
- sid
metrics:
- wer
tags:
- audio
- speech
- wav2vec2
- pt
- portuguese-speech-corpus
- automatic-speech-recognition
- speech
- PyTorch
- hf-asr-leaderboard
model-index:
- name: bp400-xlsr
results:
- task:
name: Automatic Spe... | [
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Declan/WallStreetJournal_model_v3 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
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"no_repeat_ngram_size... | 3 | 2021-12-29T20:26:44Z | ---
language: pt
tags:
- speech
license: apache-2.0
---
# DistilXLSR-53 for BP
[DistilXLSR-53 for BP: DistilHuBERT applied to Wav2vec XLSR-53 for Brazilian Portuguese](https://github.com/s3prl/s3prl/tree/master/s3prl/upstream/distiller)
The base model pretrained on 16kHz sampled speech audio. When using the model mak... | [
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Declan/WallStreetJournal_model_v5 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"BertForMaskedLM"
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"no_repeat_ngram_size... | 9 | null | ---
language:
- pt
license: apache-2.0
tags:
- generated_from_trainer
- hf-asr-leaderboard
- pt
- robust-speech-event
datasets:
- common_voice
model-index:
- name: sew-tiny-portuguese-cv
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Commo... | [
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Declan/test_model | [] | null | {
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"num_beams... | 0 | null | ---
language: pt
tags:
- speech
license: apache-2.0
---
# SEW-tiny-pt
This is a pretrained version of [SEW tiny by ASAPP Research](https://github.com/asappresearch/sew) trained over Brazilian Portuguese audio.
The base model pretrained on 16kHz sampled speech audio. When using the model make sure that your speech in... | [
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Declan/test_push | [] | null | {
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"num_beams... | 0 | null | ---
language:
- pt
license: apache-2.0
tags:
- automatic-speech-recognition
- generated_from_trainer
- robust-speech-event
- pt
- hf-asr-leaderboard
datasets:
- common_voice
model-index:
- name: wav2vec2-large-xls-r-300m-pt-cv
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-re... | [
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DeepBasak/Slack | [] | null | {
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"num_beams... | 0 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
- pt
- robust-speech-event
datasets:
- mozilla-foundation/common_voice_7_0
model-index:
- name: wav2vec2-large-xlsr-coraa-portuguese-cv7
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to.... | [
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DeepChem/SmilesTokenizer_PubChem_1M | [
"pytorch",
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"transformers"
] | feature-extraction | {
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"no_repeat_ngram_size... | 227 | null | ---
language:
- gn
license: apache-2.0
tags:
- automatic-speech-recognition
- generated_from_trainer
- gn
- robust-speech-event
- hf-asr-leaderboard
datasets:
- mozilla-foundation/common_voice_8_0
model-index:
- name: wav2vec2-xls-r-300m-gn-cv8-4
results:
- task:
name: Automatic Speech Recognition
type:... | [
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DeepESP/gpt2-spanish-medium | [
"pytorch",
"tf",
"jax",
"gpt2",
"text-generation",
"es",
"dataset:ebooks",
"transformers",
"GPT-2",
"Spanish",
"ebooks",
"nlg",
"license:mit"
] | text-generation | {
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"GPT2LMHeadModel"
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"no_repeat_ngram_size... | 340 | null | ---
language:
- gn
license: apache-2.0
tags:
- automatic-speech-recognition
- generated_from_trainer
- gn
- robust-speech-event
- hf-asr-leaderboard
datasets:
- mozilla-foundation/common_voice_8_0
model-index:
- name: wav2vec2-xls-r-300m-gn-cv8
results:
- task:
name: Automatic Speech Recognition
type: a... | [
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DeltaHub/lora_t5-base_mrpc | [
"pytorch",
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] | null | {
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"num_beams... | 3 | 2021-01-08T11:49:52Z | ---
language:
- multilingual
- pt
- en
tags:
- xlm-roberta-base
- semantic role labeling
- finetuned
license: apache-2.0
datasets:
- PropBank.Br
- CoNLL-2012
metrics:
- F1 Measure
---
# XLM-R base fine-tune in English and Portuguese semantic role labeling
## Model description
This model is the [`xlm-roberta-ba... | [
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0.039... |
DemangeJeremy/4-sentiments-with-flaubert | [
"pytorch",
"flaubert",
"text-classification",
"fr",
"transformers",
"sentiments",
"french",
"flaubert-large"
] | text-classification | {
"architectures": [
"FlaubertForSequenceClassification"
],
"model_type": "flaubert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
... | 226 | null | ---
language:
- multilingual
- pt
- en
tags:
- xlm-roberta-large
- semantic role labeling
- finetuned
license: apache-2.0
datasets:
- PropBank.Br
- CoNLL-2012
metrics:
- F1 Measure
---
# XLM-R large fine-tuned in English and Portuguese semantic role labeling
## Model description
This model is the [`xlm-roberta... | [
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0.02160678431391716,
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-0.00590099859982729,
0.042... |
Deniskin/essays_small_2000 | [] | null | {
"architectures": null,
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"num_beams... | 0 | null | ---
language:
- multilingual
- pt
tags:
- bert-base-multilingual-cased
- semantic role labeling
- finetuned
license: apache-2.0
datasets:
- PropBank.Br
metrics:
- F1 Measure
---
# mBERT fine-tuned on Portuguese semantic role labeling
## Model description
This model is the [`bert-base-multilingual-cased`](https://hug... | [
-0.019075680524110794,
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... |
Deniskin/essays_small_2000i | [] | null | {
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"num_beams... | 0 | null | ---
language:
- multilingual
- pt
tags:
- xlm-roberta-base
- semantic role labeling
- finetuned
license: apache-2.0
datasets:
- PropBank.Br
metrics:
- F1 Measure
---
# XLM-R base fine-tuned on Portuguese semantic role labeling
## Model description
This model is the [`xlm-roberta-base`](https://huggingface.co/x... | [
-0.023240314796566963,
-0.009158735163509846,
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-0.006830107420682907,
... |
Denny29/DialoGPT-medium-asunayuuki | [
"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:
- multilingual
- pt
- en
tags:
- xlm-roberta-large
- semantic role labeling
- finetuned
- dependency parsing
license: apache-2.0
datasets:
- PropBank.Br
- CoNLL-2012
- Universal Dependencies
metrics: f1
---
# XLM-R large fine-tuned in Portuguese Universal Dependencies and English and Portuguese semanti... | [
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0.06044170632958412,
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0.006407890468835831,
0.0... |
DeskDown/MarianMixFT_en-id | [
"pytorch",
"marian",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | {
"architectures": [
"MarianMTModel"
],
"model_type": "marian",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 3 | 2021-07-08T01:29:15Z | ---
language: zh
widget:
- text: "我喜欢下雨。"
- text: "我讨厌他。"
---
# liam168/c2-roberta-base-finetuned-dianping-chinese
## Model description
用中文对话情绪语料训练的模型，2分类：乐观和悲观。
## Overview
- **Language model**: BertForSequenceClassification
- **Model size**: 410M
- **Language**: Chinese
## Example
```python
>>> from transform... | [
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0.... |
DeskDown/MarianMixFT_en-ja | [
"pytorch",
"marian",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | {
"architectures": [
"MarianMTModel"
],
"model_type": "marian",
"task_specific_params": {
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},
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"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 9 | 2021-07-07T02:44:45Z | ---
language: zh
tags:
- exbert
license: apache-2.0
widget:
- text: "女人做得越纯粹，皮肤和身材就越好"
- text: "我喜欢篮球"
---
# liam168/c4-zh-distilbert-base-uncased
## Model description
用 ["女性","体育","文学","校园"]4类数据训练的分类模型。
## Overview
- **Language model**: DistilBERT
- **Model size**: 280M
- **Language**: Chinese
## Example
```py... | [
-0.022864440456032753,
-0.017184099182486534,
-0.0367174968123436,
0.05272442474961281,
0.05434630066156387,
0.025541914626955986,
-0.014069924131035805,
-0.015021419152617455,
-0.04133949056267738,
0.07902950048446655,
0.017576610669493675,
-0.010456451214849949,
0.006062168162316084,
0.0... |
DeskDown/MarianMixFT_en-vi | [
"pytorch",
"marian",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | {
"architectures": [
"MarianMTModel"
],
"model_type": "marian",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 5 | 2021-07-07T00:58:55Z | ---
language: zh
widget:
- text: "晓日千红"
- text: "长街躞蹀"
---
# gen-gpt2-medium-chinese
# Overview
- **Language model**: GPT2-Medium
- **Model size**: 68M
- **Language**: Chinese
# Example
```python
from transformers import TFGPT2LMHeadModel,AutoTokenizer
from transformers import TextGenerationPipeline
mode_name ... | [
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0.0... |
DivyanshuSheth/T5-Seq2Seq-Final | [] | null | {
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},
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"num_beams... | 0 | 2022-02-23T08:55:46Z | ---
tags:
- espnet
- audio
- audio-to-audio
language: en
datasets:
- wsj0_2mix
license: cc-by-4.0
---
## ESPnet2 ENH model
### `lichenda/wsj0_2mix_skim_noncausal`
This model was trained by LiChenda using wsj0_2mix recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```bash
cd es... | [
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... |
Dizoid/Lll | [] | null | {
"architectures": null,
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"num_beams... | 0 | 2021-11-15T16:48:35Z | ---
tags: autonlp
language: unk
widget:
- text: "I love AutoNLP 🤗"
datasets:
- lidiia/autonlp-data-trans_class_arg
co2_eq_emissions: 0.9756221672668951
---
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 32957902
- CO2 Emissions (in grams): 0.9756221672668951
## Validation Metrics
-... | [
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0.029833510518074036,
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-0.005964712239801884,
0.... |
Waynehillsdev/Waynehills-STT-doogie-server | [
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"transformers",
"generated_from_trainer",
"license:apache-2.0"
] | automatic-speech-recognition | {
"architectures": [
"Wav2Vec2ForCTC"
],
"model_type": "wav2vec2",
"task_specific_params": {
"conversational": {
"max_length": null
},
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"max_length": null,
"min_length": null,
"no_repeat_ngram_s... | 61 | 2022-02-27T15:21:32Z | ---
language:
- en
- el
- multilingual
tags:
- text-classification
- fact-or-opinion
- transformers
widget:
- text: "Ξεχωρίζει η καθηλωτική ερμηνεία του πρωταγωνιστή."
- text: "Η Ελλάδα είναι χώρα της Ευρώπης."
- text: "Tolkien was an English writer"
- text: "Tolkien is my favorite writer."
pipeline_tag: text-clas... | [
-0.007625827565789223,
-0.027058890089392662,
0.008209094405174255,
0.059901848435401917,
0.038435108959674835,
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0.05928840860724449,
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0.01323582511395216,
0... |
Waynehillsdev/Waynehills_summary_tensorflow | [
"tf",
"t5",
"text2text-generation",
"transformers",
"generated_from_keras_callback",
"autotrain_compatible"
] | text2text-generation | {
"architectures": [
"T5ForConditionalGeneration"
],
"model_type": "t5",
"task_specific_params": {
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},
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"no_repeat_n... | 5 | 2021-01-14T14:09:21Z |
---
language:
- el
tags:
- pytorch
- causal-lm
widget:
- text: "Το αγαπημένο μου μέρος είναι"
license: apache-2.0
---
# Greek (el) GPT2 model - small
<img src="https://huggingface.co/lighteternal/gpt2-finetuned-greek-small/raw/main/GPT2el.png" width="600"/>
#### A new version (recommended) trained on 5x more da... | [
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0.0029530564788728952,
... |
Waynehillsdev/wav2vec2-base-timit-demo-colab | [
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"transformers",
"generated_from_trainer",
"license:apache-2.0"
] | automatic-speech-recognition | {
"architectures": [
"Wav2Vec2ForCTC"
],
"model_type": "wav2vec2",
"task_specific_params": {
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"max_length": null
},
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"max_length": null,
"min_length": null,
"no_repeat_ngram_s... | 5 | 2021-01-29T15:18:09Z |
---
language:
- el
tags:
- pytorch
- causal-lm
widget:
- text: "Το αγαπημένο μου μέρος είναι"
license: apache-2.0
---
# Greek (el) GPT2 model
<img src="https://huggingface.co/lighteternal/gpt2-finetuned-greek-small/raw/main/GPT2el.png" width="600"/>
### By the Hellenic Army Academy (SSE) and the Technical Univers... | [
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0.0... |
Waynehillsdev/waynehills_sentimental_kor | [
"pytorch",
"electra",
"text-classification",
"transformers"
] | text-classification | {
"architectures": [
"ElectraForSequenceClassification"
],
"model_type": "electra",
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},
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"min_length": null,
"... | 33 | 2021-09-21T13:18:51Z | ---
language:
- el
- en
tags:
- xlm-roberta-base
datasets:
- multi_nli
- snli
- allnli_greek
metrics:
- accuracy
pipeline_tag: zero-shot-classification
widget:
- text: "Η Facebook κυκλοφόρησε τα πρώτα «έξυπνα» γυαλιά επαυξημένης πραγματικότητας."
candidate_labels: "τεχνολογία, πολιτική, αθλητισμός... | [
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0.018018813803792,
0.03... |
Doohae/p_encoder | [
"pytorch"
] | null | {
"architectures": null,
"model_type": null,
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},
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"no_repeat_ngram_size": null,
"num_beams... | 3 | 2021-09-20T17:37:43Z | ---
language:
- en
- el
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
widget:
- source_sentence: "Το κινητό έπεσε και έσπασε."
sentences: [
"H πτώση κατέστρεψε τη συσκευή.",
"Το αυτοκίνητο έσπασε στα δυο.",
"Ο υπουργός έπεσε και έσπασε το πόδι του."
]
pipe... | [
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0.06253399699926376,
0.009151658974587917,
-0.0227967556566,
0.001271298504434526,
0.027087... |
Doohae/roberta | [
"pytorch",
"roberta",
"question-answering",
"transformers",
"autotrain_compatible"
] | question-answering | {
"architectures": [
"RobertaForQuestionAnswering"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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"max_length": null,
"min_length": null,
"no_re... | 3 | 2021-05-25T17:07:31Z | ---
pipeline_tag: feature-extraction
tags:
- sentence-transformers
---
## Testing Sentence Transformer
This Roberta model is trained from scratch using Masked Language Modelling task on a collection of medical reports | [
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0.... |
DoyyingFace/bert-asian-hate-tweets-asonam-unclean | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | {
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
"task_specific_params": {
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},
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"min_length": null,
"no_rep... | 30 | 2021-08-27T12:30:13Z | ---
tags:
- conversational
---
#C3PO DialoGPT Model | [
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-0.045266371220350266,
0.012663387693464756,
... |
DoyyingFace/bert-asian-hate-tweets-concat-clean-with-unclean-valid | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | {
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
"task_specific_params": {
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"max_length": null
},
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"min_length": null,
"no_rep... | 25 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: bert-hateful-memes-expanded
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. -->
# bert-ha... | [
0.007506111171096563,
-0.01002048421651125,
-0.01040000468492508,
0.04615847021341324,
0.05226251482963562,
0.036913834512233734,
-0.014919588342308998,
-0.03277274966239929,
-0.02547520399093628,
0.03514774888753891,
0.016595987603068352,
-0.01657283678650856,
0.02200232446193695,
0.05191... |
albert-base-v1 | [
"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",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_... | 38,156 | 2022-01-07T15:43:19Z | ---
language:
- fr
license: mit
pipeline_tag: sentence-similarity
widget:
- source_sentence: "Bonsoir"
sentences:
- "Salut !"
- "Hello"
- "Bonsoir!"
- "Bonsouar!"
- "Bonsouar !"
- "De rien"
- "LUL LUL"
example_title: "Coucou"
- source_sentence: "elle s'en sort bien"
sentences:
... | [
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0.02316462993621826,
0.00805858988314867,
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0.... |
albert-base-v2 | [
"pytorch",
"tf",
"jax",
"rust",
"safetensors",
"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": {
"conversational": {
"max_length": null
},
"summarization": {
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"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_... | 4,785,283 | 2022-01-07T14:25:51Z | ---
language:
- fr
license: mit
pipeline_tag: "fill-mask"
widget:
- text: <mask> tt le monde !
- text: cc<mask> va?
- text: <mask> la Fronce !
tags:
- fill-mask
- convbert
- twitch
---
## Modèle de Masking sur les données Twitch FR
L'expérimentation menée au sein de Lincoln avait pour principal objectif de mettr... | [
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0.05450136587023735,
0.022422583773732185,
0.00011007073044311255,
-0.006203732453286648,... |
albert-large-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": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_... | 687 | 2022-01-07T10:44:43Z | ---
language:
- fr
license: mit
pipeline_tag: "feature-extraction"
widget:
- text: LUL +1 xD La Fronce !
tags:
- feature-extraction
- convbert
- twitch
---
## Modèle de langue sur les données Twitch FR
L'expérimentation menée au sein de Lincoln avait pour principal objectif de mettre en œuvre des techniques NLP... | [
-0.006449220236390829,
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-0.0001444658701075241,
0.028338603675365448,
0.04631122574210167,
0.023150630295276642,
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0.05395321920514107,
0.03833257406949997,
0.003569013439118862,
-0.009384778328239918,
... |
albert-large-v2 | [
"pytorch",
"tf",
"safetensors",
"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": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_... | 26,792 | 2021-10-07T12:43:38Z | ---
language:
- fr
license: mit
pipeline_tag: "text2text-generation"
datasets:
- squadFR
- fquad
- piaf
metrics:
- bleu
- rouge
widget:
- text: "La science des données est un domaine interdisciplinaire qui utilise des méthodes, des processus, des algorithmes et des systèmes scientifiques pour extrai... | [
-0.0052574616856873035,
-0.03698166087269783,
-0.009495341219007969,
0.037287481129169464,
0.04760359227657318,
0.009085551835596561,
-0.009794467128813267,
-0.023074019700288773,
-0.042318977415561676,
0.038632236421108246,
0.013391006737947464,
0.012431705370545387,
-0.023310329765081406,
... |
albert-xlarge-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": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_... | 341 | 2021-10-11T13:01:58Z | ---
language:
- fr
license: mit
datasets:
- squadFR
- fquad
- piaf
tags:
- camembert
- answer extraction
---
# Extraction de réponse
Ce modèle est _fine tuné_ à partir du modèle [camembert-base](https://huggingface.co/camembert-base) pour la tâche de classification de tokens.
L'objectif est d'identi... | [
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-0.02072540856897831,
0.016997378319501877,
-0.021196717396378517,
0.028851503506302834,
0.022327521815896034,
0.018619269132614136,
-0.02480308525264263,
0.0... |
albert-xlarge-v2 | [
"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": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_... | 2,973 | 2021-04-29T13:16:16Z | ---
language:
- fr
license: mit
datasets:
- MLSUM
pipeline_tag: "text-classification"
widget:
- text: La bourse de paris en forte baisse après que des canards ont envahit le parlement.
tags:
- text-classification
- flaubert
---
# Classification d'articles de presses avec Flaubert
Ce modèle se base sur le modèl... | [
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-0.03068821132183075,
-0.007193668279796839,
0.06041403487324715,
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0.05333375558257103,
0.015950923785567284,
0.023458799347281456,
-0.012561177834868431,
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": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_... | 7,091 | 2021-04-30T13:58:22Z | ---
language:
- fr
license: mit
datasets:
- MLSUM
pipeline_tag: "summarization"
widget:
- text: « La veille de l’ouverture, je vais faire venir un coach pour les salariés qui reprendront le travail. Cela va me coûter 300 euros, mais après des mois d’oisiveté obligatoire, la reprise n’est pas simple. Certains sont ... | [
-0.007071600295603275,
-0.026831382885575294,
-0.007690859027206898,
0.04973950609564781,
0.03846242278814316,
-0.002428113715723157,
-0.023537425324320793,
-0.00007138039654819295,
-0.05004100874066353,
0.056341513991355896,
0.04442771151661873,
0.012971925549209118,
0.000867915921844542,
... |
bert-base-german-cased | [
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"fill-mask",
"de",
"transformers",
"exbert",
"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... | 175,983 | 2021-09-17T00:43:15Z | ---
language:
- en
license: apache-2.0
tags:
- summarization
- azureml
- azure
- codecarbon
- bart
datasets:
- samsum
metrics:
- rouge
model-index:
- name: bart-large-samsum
results:
- task:
name: Abstractive Text Summarization
type: abstractive-text-summarization
dataset:
name: "SAMSum Corpu... | [
0.012583721429109573,
-0.017333444207906723,
0.006787581834942102,
0.05590984970331192,
0.052847180515527725,
0.03165939822793007,
-0.03023853898048401,
-0.017156779766082764,
-0.04140704125165939,
0.055637042969465256,
0.031499896198511124,
-0.011947822757065296,
0.021828973665833473,
0.0... |
distilbert-base-cased-distilled-squad | [
"pytorch",
"tf",
"rust",
"safetensors",
"openvino",
"distilbert",
"question-answering",
"en",
"dataset:squad",
"arxiv:1910.01108",
"arxiv:1910.09700",
"transformers",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"has_space"
] | question-answering | {
"architectures": [
"DistilBertForQuestionAnswering"
],
"model_type": "distilbert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
... | 257,745 | 2021-12-15T17:06:46Z | # CLIN-X-EN: a pre-trained language model for the English clinical domain
Details on the model, the pre-training corpus and the downstream task performance are given in the paper: "CLIN-X: pre-trained language models and a study on cross-task transfer for concept extraction in the clinical domain" by Lukas Lange, Heike... | [
0.001654051011428237,
-0.022363964468240738,
-0.009288599714636803,
0.06818950921297073,
0.02977486327290535,
0.017900897189974785,
-0.025653483346104622,
-0.0281843189150095,
-0.009156538173556328,
0.04610871896147728,
0.0029633291997015476,
-0.022882182151079178,
-0.011240454390645027,
0... |
distilbert-base-german-cased | [
"pytorch",
"safetensors",
"distilbert",
"fill-mask",
"de",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] | fill-mask | {
"architectures": [
"DistilBertForMaskedLM"
],
"model_type": "distilbert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repea... | 43,667 | 2021-12-15T17:07:21Z | # Spanish XLM-R (from NLNDE-MEDDOPROF)
This Spanish language model was created for the MEDDOPROF shared task as part of the **NLNDE** team submission and outperformed all other participants in both sequence labeling tasks.
Details on the model, the pre-training corpus and the downstream task performance are given in ... | [
-0.014506642706692219,
-0.020828237757086754,
-0.01648201048374176,
0.05984841659665108,
0.051732953637838364,
0.03931223228573799,
-0.026022542268037796,
-0.021823126822710037,
-0.013965931721031666,
0.056084997951984406,
0.00030153829720802605,
-0.02085769549012184,
-0.022891247645020485,
... |
distilbert-base-multilingual-cased | [
"pytorch",
"tf",
"onnx",
"safetensors",
"distilbert",
"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",
... | fill-mask | {
"architectures": [
"DistilBertForMaskedLM"
],
"model_type": "distilbert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repea... | 8,339,633 | 2022-02-23T09:18:00Z | ---
license: mit
---
## long-covid-classification
We fine-tuned bert-base-cased using a [manually curated dataset](https://huggingface.co/llangnickel/long-covid-classification-data) to train a Sequence Classification model able to distinguish between long COVID and non-long COVID-related documents.
## Used hyper par... | [
-0.020992929115891457,
-0.004290614742785692,
-0.025990284979343414,
0.03825843706727028,
0.03845205530524254,
0.008504518307745457,
-0.03260049968957901,
-0.030491750687360764,
-0.013587524183094501,
0.03795686736702919,
0.01370621845126152,
0.016569850966334343,
-0.005894204135984182,
0.... |
ASCCCCCCCC/distilbert-base-uncased-finetuned-clinc | [
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"transformers",
"generated_from_trainer",
"license:apache-2.0"
] | text-classification | {
"architectures": [
"DistilBertForSequenceClassification"
],
"model_type": "distilbert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
... | 35 | null | ---
language:
- en
tags:
- argumentation
license: apache-2.0
metrics:
- perplexity
---
# Generate the conclusion of an argument
This model is a version of [`gpt-neo-2.7B`](https://huggingface.co/EleutherAI/gpt-neo-2.7B), where some parameters (only the bias parameters, not weights) have been finetuned on the task ... | [
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-0.0065668909810483456,
-0.03907894715666771,
0.05185798183083534,
0.06115585193037987,
0.021906133741140366,
0.005760096479207277,
-0.00045103495358489454,
-0.02362770028412342,
0.04082545265555382,
0.02738330513238907,
-0.023189527913928032,
0.025181058794260025,
0... |
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