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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
rdenadai/BR_BERTo | 237d5664883c2e96ae07053f3cd1657beb03caca | 2021-05-20T19:53:44.000Z | [
"pytorch",
"jax",
"roberta",
"fill-mask",
"pt",
"transformers",
"portuguese",
"brazil",
"pt_BR",
"autotrain_compatible"
] | fill-mask | false | rdenadai | null | rdenadai/BR_BERTo | 350 | 1 | transformers | ---
language: pt
tags:
- portuguese
- brazil
- pt_BR
widget:
- text: gostei muito dessa <mask>
---
# BR_BERTo
Portuguese (Brazil) model for text inference.
## Params
Trained on a corpus of 6_993_330 sentences.
- Vocab size: 150_000
- RobertaForMaskedLM size : 512
- Num train epochs: 3
- Time to train: ~10days (on... | [
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aaraki/vit-base-patch16-224-in21k-finetuned-cifar10 | 63acc43bab8617ad96b6a9cc35760802ba495fa1 | 2022-03-30T01:41:47.000Z | [
"pytorch",
"tensorboard",
"vit",
"image-classification",
"dataset:cifar10",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | image-classification | false | aaraki | null | aaraki/vit-base-patch16-224-in21k-finetuned-cifar10 | 350 | null | transformers | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- cifar10
metrics:
- accuracy
model-index:
- name: vit-base-patch16-224-in21k-finetuned-cifar10
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: cifar10
type: cifar10
args: plain_t... | [
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ccdv/lsg-bart-base-16384-arxiv | 78a89c0598964f6397cf043db625c18f69d12882 | 2022-07-25T05:30:14.000Z | [
"pytorch",
"bart",
"text2text-generation",
"en",
"dataset:scientific_papers",
"transformers",
"summarization",
"model-index",
"autotrain_compatible"
] | summarization | false | ccdv | null | ccdv/lsg-bart-base-16384-arxiv | 350 | null | transformers | ---
language:
- en
tags:
- summarization
datasets:
- scientific_papers
metrics:
- rouge
model-index:
- name: ccdv/lsg-bart-base-16384-arxiv
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... | [
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0.0186... |
google/long-t5-tglobal-xl | 801939bf36c52822f8f4dca7cb3b732ba2f70652 | 2022-06-22T09:05:18.000Z | [
"pytorch",
"jax",
"longt5",
"text2text-generation",
"en",
"arxiv:2112.07916",
"arxiv:1912.08777",
"arxiv:1910.10683",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | text2text-generation | false | google | null | google/long-t5-tglobal-xl | 350 | null | transformers | ---
license: apache-2.0
language: en
---
# LongT5 (transient-global attention, XL-sized model)
LongT5 model pre-trained on English language. The model was introduced in the paper [LongT5: Efficient Text-To-Text Transformer for Long Sequences](https://arxiv.org/pdf/2112.07916.pdf) by Guo et al. and first released in [... | [
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0.02... |
JorisCos/DPTNet_Libri1Mix_enhsingle_16k | 935f441c53e44d40ca0cf138f71e850defc8bea5 | 2021-09-23T15:49:20.000Z | [
"pytorch",
"dataset:Libri1Mix",
"dataset:enh_single",
"asteroid",
"audio",
"DPTNet",
"audio-to-audio",
"license:cc-by-sa-4.0"
] | audio-to-audio | false | JorisCos | null | JorisCos/DPTNet_Libri1Mix_enhsingle_16k | 349 | null | asteroid | ---
tags:
- asteroid
- audio
- DPTNet
- audio-to-audio
datasets:
- Libri1Mix
- enh_single
license: cc-by-sa-4.0
---
## Asteroid model `JorisCos/DPTNet_Libri1Mix_enhsignle_16k`
Description:
This model was trained by Joris Cosentino using the librimix recipe in [Asteroid](https://github.com/asteroid-team/asteroid).
It... | [
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-0... |
google/tapas-base-finetuned-sqa | 81916d20eef75766aeae71b9487fd615017b0413 | 2021-11-29T11:41:09.000Z | [
"pytorch",
"tf",
"tapas",
"table-question-answering",
"en",
"dataset:msr_sqa",
"arxiv:2004.02349",
"arxiv:2010.00571",
"transformers",
"license:apache-2.0"
] | table-question-answering | false | google | null | google/tapas-base-finetuned-sqa | 349 | null | transformers | ---
language: en
tags:
- tapas
- table-question-answering
license: apache-2.0
datasets:
- msr_sqa
---
# TAPAS base model fine-tuned on Sequential Question Answering (SQA)
This model has 2 versions which can be used. The default version corresponds to the `tapas_sqa_inter_masklm_base_reset` checkpoint of the [original... | [
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-0.015... |
uer/bart-large-chinese-cluecorpussmall | 8d01a28b6006982817bf35f3fe3f5c989ca0419e | 2022-07-15T08:17:29.000Z | [
"pytorch",
"tf",
"bart",
"text2text-generation",
"zh",
"dataset:CLUECorpusSmall",
"arxiv:1909.05658",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | uer | null | uer/bart-large-chinese-cluecorpussmall | 349 | null | transformers | ---
language: zh
datasets: CLUECorpusSmall
widget:
- text: "作为电子[MASK]的平台，京东绝对是领先者。如今的刘强[MASK]已经是身价过[MASK]的老板。"
---
# Chinese BART
## Model description
This model is pre-trained by [UER-py](https://github.com/dbiir/UER-py/), which is introduced in [this paper](https://arxiv.org/abs/1909.05658).
You can download ... | [
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0.01... |
Robinsd/HarryBot4 | 5208e76c90a28b21aeaa9fe50d7033cbd9f8638f | 2022-05-17T08:13:09.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | false | Robinsd | null | Robinsd/HarryBot4 | 349 | null | transformers | ---
tags:
- conversational
---
#harrypotter V2 | [
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AJ/rick-discord-bot | 31fec11b7ffa06a6398c78e5bf0a452efd2e8746 | 2021-09-27T01:03:33.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational",
"humor"
] | conversational | false | AJ | null | AJ/rick-discord-bot | 348 | null | transformers | ---
tags:
- conversational
- humor
---
# its rick from rick and morty | [
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responsibility-framing/predict-perception-xlmr-cause-human | 5eefabc15e0fe6e87b32a980816cb05b05084a72 | 2022-03-15T22:58:24.000Z | [
"pytorch",
"tensorboard",
"xlm-roberta",
"text-classification",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index"
] | text-classification | false | responsibility-framing | null | responsibility-framing/predict-perception-xlmr-cause-human | 348 | null | transformers | ---
license: mit
tags:
- generated_from_trainer
model-index:
- name: predict-perception-xlmr-cause-human
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. -->
# predic... | [
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NlpHUST/gpt2-vietnamese | 65818d14816b42be09e2201933bf07106d9a2647 | 2022-06-02T04:02:44.000Z | [
"pytorch",
"jax",
"gpt2",
"text-generation",
"vi",
"dataset:oscar",
"transformers",
"vietnamese",
"lm",
"nlp"
] | text-generation | false | NlpHUST | null | NlpHUST/gpt2-vietnamese | 348 | null | transformers | ---
language: vi
tags:
- vi
- vietnamese
- gpt2
- text-generation
- lm
- nlp
datasets:
- oscar
widget:
- text: "Việt Nam là quốc gia có"
---
# GPT-2
Pretrained gpt model on Vietnamese language using a causal language modeling (CLM) objective. It was introduced in
[this paper](https://d4mucfpksywv.cloudfront.net/bette... | [
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0.05313... |
CenIA/distillbert-base-spanish-uncased | 8b0f77825ae49a0d099bf5e3aea8da71f6c0851f | 2022-04-28T19:56:51.000Z | [
"pytorch",
"distilbert",
"fill-mask",
"es",
"dataset:large_spanish_corpus",
"transformers",
"spanish",
"OpenCENIA",
"autotrain_compatible"
] | fill-mask | false | CenIA | null | CenIA/distillbert-base-spanish-uncased | 347 | 2 | transformers | ---
language:
- es
tags:
- distilbert
- spanish
- OpenCENIA
datasets:
- large_spanish_corpus
--- | [
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... |
Mandy/DialoGPT-small-Mikasa | 787c864226cb0c2e212bbdd4ec97b526fd8342e6 | 2021-08-31T01:12:20.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | false | Mandy | null | Mandy/DialoGPT-small-Mikasa | 347 | null | transformers | ---
tags:
- conversational
---
#Mikasa Ackermann DialoGPT Model | [
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-0.... |
binwang/bert-base-nli-stsb | 18cb07f9e817bfea4db656cb3a917e74523bc4ab | 2021-05-19T12:39:50.000Z | [
"pytorch",
"jax",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | binwang | null | binwang/bert-base-nli-stsb | 347 | null | transformers | Entry not found | [
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0.017284274101257324,
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0.03817418962717056,
-0.... |
yhavinga/gpt2-medium-dutch | f8678465e1ac9f48e45d7dd21711dd4620813550 | 2022-03-20T10:20:11.000Z | [
"pytorch",
"jax",
"tensorboard",
"gpt2",
"text-generation",
"nl",
"dataset:yhavinga/mc4_nl_cleaned",
"transformers",
"gpt2-medium"
] | text-generation | false | yhavinga | null | yhavinga/gpt2-medium-dutch | 347 | null | transformers | ---
language: nl
widget:
- text: "In het jaar 2030 zullen we"
- text: "Toen ik gisteren volledig in de ban was van"
- text: "Studenten en leraren van de Bogazici Universiteit in de Turkse stad Istanbul"
- text: "In Israël was een strenge lockdown"
tags:
- gpt2-medium
- gpt2
pipeline_tag: text-generation
datasets:
- yha... | [
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... |
Jordine/shitter | 7b554c7a103d591d08747e0b982fdca36cb02340 | 2022-07-26T13:22:20.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | false | Jordine | null | Jordine/shitter | 347 | null | transformers | [
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0.018434111028909683,
-0... | |
AmazonScience/qanlu | 3e7306005b52648b86a7cef39b87932736fc88e5 | 2021-09-30T17:23:27.000Z | [
"pytorch",
"roberta",
"question-answering",
"en",
"dataset:atis",
"transformers",
"license:cc-by-4.0",
"autotrain_compatible"
] | question-answering | false | AmazonScience | null | AmazonScience/qanlu | 346 | 3 | transformers | ---
language: en
license: cc-by-4.0
widget:
- context: "Yes. No. I'm looking for a cheap flight to Boston."
datasets:
- atis
---
# Question Answering NLU
Question Answering NLU (QANLU) is an approach that maps the NLU task into question answering,
leveraging pre-trained question-answering models to perform well on f... | [
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microsoft/CodeGPT-small-java | 3facf5bba3ca89e505937f8d014c0d90b6fc1dc4 | 2021-05-23T08:59:22.000Z | [
"pytorch",
"tf",
"jax",
"gpt2",
"text-generation",
"transformers"
] | text-generation | false | microsoft | null | microsoft/CodeGPT-small-java | 346 | 2 | transformers | Entry not found | [
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Theivaprakasham/layoutlmv2-finetuned-sroie_mod | 44b6e673c47fbe314af8d67707da37c1a6e49e78 | 2022-02-28T09:50:47.000Z | [
"pytorch",
"tensorboard",
"layoutlmv2",
"token-classification",
"transformers",
"generated_from_trainer",
"license:cc-by-nc-sa-4.0",
"model-index",
"autotrain_compatible"
] | token-classification | false | Theivaprakasham | null | Theivaprakasham/layoutlmv2-finetuned-sroie_mod | 346 | null | transformers | ---
license: cc-by-nc-sa-4.0
tags:
- generated_from_trainer
model-index:
- name: layoutlmv2-finetuned-sroie_mod
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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responsibility-framing/predict-perception-bert-blame-object | a3cf806e28fe0e73bdc9946c068fd0d8de57b8db | 2022-03-10T15:51:04.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index"
] | text-classification | false | responsibility-framing | null | responsibility-framing/predict-perception-bert-blame-object | 346 | null | transformers | ---
license: mit
tags:
- generated_from_trainer
model-index:
- name: predict-perception-bert-blame-object
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. -->
# predi... | [
-0.14837688207626343,
-0.018207192420959473,
0.00503497151657939,
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0.08655872195959091,
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-0.014838825911283493,
0.003312791232019663,
-0.1143520399928093,
0.0243220217525959,
0.01442... |
hyunwoongko/asian-bart-ecjk | a9da2204e42df8afa450e8228255b1e109bc5c63 | 2021-04-01T07:36:52.000Z | [
"pytorch",
"mbart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | hyunwoongko | null | hyunwoongko/asian-bart-ecjk | 345 | null | transformers | Entry not found | [
0.0461147278547287,
-0.038838207721710205,
-0.01049656979739666,
-0.03682169318199158,
0.011261860840022564,
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-0.013979103416204453,
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-0.015212527476251125,
0.017284274101257324,
-0.08189476281404495,
0.03817418962717056,
-0.... |
mukund/privbert | 48228b4661fa8252bdb39ca44a4d9758f6b37f88 | 2021-06-15T19:36:42.000Z | [
"pytorch",
"tf",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | mukund | null | mukund/privbert | 345 | null | transformers | # PrivBERT
PrivBERT is a privacy policy language model. We pre-trained PrivBERT on ~1 million privacy policies starting with the pretrained Roberta model. The data is available at [https://privaseer.ist.psu.edu/data](https://privaseer.ist.psu.edu/data)
## Usage
```
from transformers import AutoTokenizer, AutoModel
to... | [
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-0.05... |
pie/example-ner-spanclf-conll03 | 6e76efe7940b9a25b5983611aff93675b520adec | 2022-01-02T10:13:27.000Z | [
"pytorch",
"TransformerSpanClassificationModel",
"transformers"
] | null | false | pie | null | pie/example-ner-spanclf-conll03 | 345 | null | transformers | Entry not found | [
0.0461147278547287,
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0.011261860840022564,
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-0.015212527476251125,
0.017284274101257324,
-0.08189476281404495,
0.03817418962717056,
-0.... |
CianB/DialoGPT-small-Shrek2 | 1a1a1c7fa6b18a048129229aaea15ce1a99102d3 | 2021-08-26T21:13:04.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | false | CianB | null | CianB/DialoGPT-small-Shrek2 | 344 | null | transformers | ---
tags:
- conversational
---
# Shrek DialoGPT model | [
-0.05507625639438629,
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0.07790480554103851,
-0.012867623940110207,
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0.11350821703672409,
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0.11154481023550034,
-0.03876624256372452,
-0.0302555151283741,
-0.016866151243448257,
-0.025938646867871284,
0.01... |
responsibility-framing/predict-perception-bert-cause-human | 8295e50cf36524154cbcce57edefe2d6e87ccd03 | 2022-03-10T16:01:42.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index"
] | text-classification | false | responsibility-framing | null | responsibility-framing/predict-perception-bert-cause-human | 344 | null | transformers | ---
license: mit
tags:
- generated_from_trainer
model-index:
- name: predict-perception-bert-cause-human
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. -->
# predic... | [
-0.1473797857761383,
-0.035707905888557434,
0.03176608309149742,
0.05635988712310791,
0.06954757124185562,
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0.015688836574554443,
0.04477093368768692,
-0.0016264821169897914,
-0.003913643304258585,
0.006829630583524704,
-0.10882510244846344,
0.026171371340751648,
0.0139... |
responsibility-framing/predict-perception-bert-focus-concept | 95650d42f9092cd0427af7983158bdf5f9b26824 | 2022-03-10T16:23:46.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index"
] | text-classification | false | responsibility-framing | null | responsibility-framing/predict-perception-bert-focus-concept | 344 | null | transformers | ---
license: mit
tags:
- generated_from_trainer
model-index:
- name: predict-perception-bert-focus-concept
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. -->
# pred... | [
-0.1198636144399643,
-0.07845854014158249,
-0.00410662405192852,
0.07019156217575073,
0.04358323663473129,
0.03096662648022175,
0.05878925323486328,
0.06420379132032394,
-0.014561047777533531,
-0.034053441137075424,
0.0030330016743391752,
-0.09512092918157578,
0.0452529601752758,
0.0072036... |
Felipehonorato/storIA | 37e4997d0a6dbee5141e093243223d7e1ca54c5e | 2021-07-26T21:43:37.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
] | text-generation | false | Felipehonorato | null | Felipehonorato/storIA | 343 | null | transformers | Entry not found | [
0.0461147278547287,
-0.038838207721710205,
-0.01049656979739666,
-0.03682169318199158,
0.011261860840022564,
0.013094935566186905,
0.0019101888174191117,
-0.013979103416204453,
0.027092741802334785,
-0.015212527476251125,
0.017284274101257324,
-0.08189476281404495,
0.03817418962717056,
-0.... |
ainize/klue-bert-base-mrc | 497ad1a08619fb0d39b8d745115f705c9b503283 | 2021-11-16T01:38:03.000Z | [
"pytorch",
"bert",
"question-answering",
"ko",
"dataset:klue",
"transformers",
"mrc",
"license:cc-by-sa-4.0",
"autotrain_compatible"
] | question-answering | false | ainize | null | ainize/klue-bert-base-mrc | 343 | 2 | transformers | ---
language: ko
tags:
- bert
- mrc
datasets:
- klue
license: cc-by-sa-4.0
---
# bert-base for QA
**Code:** See [Ainize Workspace](https://link.ainize.ai/3FjvBVn)
**klue-bert-base-mrc DEMO**: [Ainize DEMO](https://main-klue-mrc-bert-scy6500.endpoint.ainize.ai/)
**klue-bert-base-mrc API**: [Ainize API](https://ai... | [
-0.19122639298439026,
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0.020678238943219185,
0.028817055746912956,
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0.007735118269920349,
-0.025351088494062424,
-0.07643125206232071,
0.010318425484001637,
0.1066... |
fran-martinez/scibert_scivocab_cased_ner_jnlpba | 1904782399ebd599671e5e654126deec44241f4a | 2021-05-19T16:56:50.000Z | [
"pytorch",
"jax",
"bert",
"token-classification",
"scientific english",
"arxiv:1903.10676",
"transformers",
"autotrain_compatible"
] | token-classification | false | fran-martinez | null | fran-martinez/scibert_scivocab_cased_ner_jnlpba | 343 | null | transformers | ---
language: scientific english
---
# SciBERT finetuned on JNLPA for NER downstream task
## Language Model
[SciBERT](https://arxiv.org/pdf/1903.10676.pdf) is a pretrained language model based on BERT and trained by the
[Allen Institute for AI](https://allenai.org/) on papers from the corpus of
[Semantic Scholar]... | [
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-0.004126743879169226,
... |
Starry/HELLORUKAS | 727596aa2a695ede32aad385438ad2306b164ff3 | 2022-03-20T18:35:57.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | false | Starry | null | Starry/HELLORUKAS | 343 | null | transformers | ---
tags:
- conversational
---
# DialoGPT model | [
-0.04254573583602905,
-0.05552438274025917,
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-0.0035723752807825804,
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-0.02389443852007389,
0.00403064489364624,
0.034... |
naver-clova-ix/donut-base-finetuned-cord-v2 | 4849e637cf6142b243c47a17d342387e90de82bc | 2022-07-19T02:45:59.000Z | [
"pytorch",
"donut",
"transformers",
"license:mit"
] | null | false | naver-clova-ix | null | naver-clova-ix/donut-base-finetuned-cord-v2 | 343 | 1 | transformers | ---
license: mit
---
| [
-0.09818281978368759,
-0.010856573469936848,
0.052169445902109146,
-0.08761013299226761,
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0.008416811004281044,
0.0449553020298481,
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-0.014396079815924168,
0.019734712317585945,
-0.01053137332201004,
-0.008089784532785416,
-0... |
CAMeL-Lab/bert-base-arabic-camelbert-da | 231698eab9ebf0ae7b518a64277b81b2fe829f2d | 2021-09-14T14:29:21.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"ar",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | fill-mask | false | CAMeL-Lab | null | CAMeL-Lab/bert-base-arabic-camelbert-da | 342 | 5 | transformers | ---
language:
- ar
license: apache-2.0
widget:
- text: "الهدف من الحياة هو [MASK] ."
---
# CAMeLBERT: A collection of pre-trained models for Arabic NLP tasks
## Model description
**CAMeLBERT** is a collection of BERT models pre-trained on Arabic texts with different sizes and variants.
We release pre-trained langu... | [
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0.0... |
EMBO/bio-lm | ad1b251544050545d0b91e294deed3b9ae97c189 | 2022-03-27T15:46:51.000Z | [
"pytorch",
"jax",
"roberta",
"fill-mask",
"english",
"dataset:EMBO/biolang",
"transformers",
"language model",
"autotrain_compatible"
] | fill-mask | false | EMBO | null | EMBO/bio-lm | 342 | null | transformers | ---
language:
- english
thumbnail:
tags:
- language model
license:
datasets:
- EMBO/biolang
metrics:
-
---
# bio-lm
## Model description
This model is a [RoBERTa base pre-trained model](https://huggingface.co/roberta-base) that was further trained using a masked language modeling task on a compendium of english s... | [
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-0.03975396603345871,
-0.018597083166241646,
-0.11555058509111404,
-0.006158029194921255,
0.054... |
MMG/xlm-roberta-large-ner-spanish | 340bd3924b6429c76354ada5c73517430a4184e1 | 2021-07-15T07:15:57.000Z | [
"pytorch",
"xlm-roberta",
"token-classification",
"es",
"dataset:CoNLL-2002",
"transformers",
"autotrain_compatible"
] | token-classification | false | MMG | null | MMG/xlm-roberta-large-ner-spanish | 342 | 3 | transformers | ---
language:
- es
datasets:
- CoNLL-2002
widget:
- text: "Las oficinas de MMG están en Las Rozas."
---
# xlm-roberta-large-ner-spanish
This model is a XLM-Roberta-large model fine-tuned for Named Entity Recognition (NER) over the Spanish portion of the CoNLL-2002 dataset. Evaluating it over the test subset of th... | [
-0.05300968885421753,
-0.05851500853896141,
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0.... |
Sindhu/rembert-squad2 | 51a7532be77e3a279fc74e4cc891bac955ef1efc | 2022-01-30T18:35:08.000Z | [
"pytorch",
"rembert",
"question-answering",
"multilingual",
"dataset:squad2",
"transformers",
"autotrain_compatible"
] | question-answering | false | Sindhu | null | Sindhu/rembert-squad2 | 342 | 2 | transformers | ---
language:
- multilingual
tags:
- question-answering
datasets:
- squad2
metrics:
- squad2
---
# Rembert Squad2
This model is finetuned for QA task on Squad2 from [Rembert checkpoint](https://huggingface.co/google/rembert).
## Hyperparameters
```
Batch Size: 4
Grad Accumulation Steps = 8
Total epochs = 3
MLM Checkp... | [
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0.0249038003385067,
-0.0156... |
philschmid/distilroberta-base-ner-conll2003 | f66a7144917dfade9e6f9c1f4b1f10f7aa26de83 | 2022-06-24T12:40:58.000Z | [
"pytorch",
"roberta",
"token-classification",
"dataset:conll2003",
"transformers",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] | token-classification | false | philschmid | null | philschmid/distilroberta-base-ner-conll2003 | 342 | 1 | transformers | ---
license: apache-2.0
tags:
- token-classification
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: distilroberta-base-ner-conll2003
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: conll2003
type: conll2003
... | [
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-0... |
responsibility-framing/predict-perception-bert-cause-object | a29a2738f5496a707bd512450be76c34b76bead2 | 2022-03-10T16:04:30.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index"
] | text-classification | false | responsibility-framing | null | responsibility-framing/predict-perception-bert-cause-object | 342 | null | transformers | ---
license: mit
tags:
- generated_from_trainer
model-index:
- name: predict-perception-bert-cause-object
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. -->
# predi... | [
-0.1425677239894867,
-0.02706652693450451,
0.028089458122849464,
0.050035782158374786,
0.06501764059066772,
0.03491252660751343,
0.01630839705467224,
0.06115979701280594,
-0.0025524464435875416,
-0.0010620731627568603,
0.0008449291926808655,
-0.09514108300209045,
0.03077136166393757,
0.020... |
responsibility-framing/predict-perception-bert-cause-concept | 5805980e8d7423d75caa5b084ef47b806afc5047 | 2022-03-10T16:08:27.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index"
] | text-classification | false | responsibility-framing | null | responsibility-framing/predict-perception-bert-cause-concept | 342 | null | transformers | ---
license: mit
tags:
- generated_from_trainer
model-index:
- name: predict-perception-bert-cause-concept
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. -->
# pred... | [
-0.15059441328048706,
-0.044166017323732376,
0.02336716465651989,
0.06908290088176727,
0.05513075366616249,
0.04073900356888771,
0.018805166706442833,
0.07087462395429611,
-0.02072443813085556,
0.013293708674609661,
-0.0062729958444833755,
-0.09852375835180283,
0.0470450334250927,
0.015978... |
responsibility-framing/predict-perception-xlmr-blame-none | 0b7dbf7921070f0c3046fb444b46bdcbd7d1ee6c | 2022-03-15T22:52:50.000Z | [
"pytorch",
"tensorboard",
"xlm-roberta",
"text-classification",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index"
] | text-classification | false | responsibility-framing | null | responsibility-framing/predict-perception-xlmr-blame-none | 342 | null | transformers | ---
license: mit
tags:
- generated_from_trainer
model-index:
- name: predict-perception-xlmr-blame-none
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. -->
# predict... | [
-0.08061252534389496,
-0.06633883714675903,
-0.04528838396072388,
0.035000063478946686,
0.09105206280946732,
0.036322228610515594,
-0.053395457565784454,
0.007680309005081654,
0.019113833084702492,
-0.020128346979618073,
0.0286664180457592,
-0.1216478943824768,
0.05144677683711052,
-0.0497... |
NovelAI/genji-jp | 57d1fd45064798dd38faa9c6cf119f1a040f9526 | 2021-11-08T01:01:27.000Z | [
"pytorch",
"gptj",
"text-generation",
"jp",
"en",
"arxiv:2104.09864",
"transformers",
"causal-lm",
"license:apache-2.0"
] | text-generation | false | NovelAI | null | NovelAI/genji-jp | 341 | 3 | transformers | ---
language:
- jp
- en
tags:
- pytorch
- causal-lm
license: apache-2.0
---
# Genji-JP 6B
Please check our blog post for more details, samples, evaluations and more:
[Blogpost](https://blog.novelai.net/data-efficient-language-transfer-with-gpt-j-45daedaaf35a)
## Model Description
Genji-JP 6B is a model finetuned o... | [
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0.00007240097329486161,
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-0.07873882353305817,
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-0.06241849064826965,
0.011356562376022339,
-0.... |
superb/hubert-base-superb-ks | d7e0efe9c25fe2e695402102e2fd7c77b00206f5 | 2021-11-04T16:03:26.000Z | [
"pytorch",
"hubert",
"audio-classification",
"en",
"dataset:superb",
"arxiv:2105.01051",
"transformers",
"speech",
"audio",
"license:apache-2.0"
] | audio-classification | false | superb | null | superb/hubert-base-superb-ks | 341 | 1 | transformers | ---
language: en
datasets:
- superb
tags:
- speech
- audio
- hubert
- audio-classification
license: apache-2.0
widget:
- example_title: Speech Commands "down"
src: https://cdn-media.huggingface.co/speech_samples/keyword_spotting_down.wav
- example_title: Speech Commands "go"
src: https://cdn-media.huggingface.co/sp... | [
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responsibility-framing/predict-perception-bert-blame-none | 9010ca86bff987e01ba6e9545b1cb496556b9339 | 2022-03-10T15:59:10.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index"
] | text-classification | false | responsibility-framing | null | responsibility-framing/predict-perception-bert-blame-none | 341 | null | transformers | ---
license: mit
tags:
- generated_from_trainer
model-index:
- name: predict-perception-bert-blame-none
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. -->
# predict... | [
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0.01840... |
StevenLimcorn/indonesian-roberta-base-emotion-classifier | e8a9cb967bd7e5f41396c4dac6d1fc2dfa636cbf | 2021-08-25T14:33:16.000Z | [
"pytorch",
"tf",
"roberta",
"text-classification",
"id",
"dataset:indonlu",
"transformers",
"license:mit"
] | text-classification | false | StevenLimcorn | null | StevenLimcorn/indonesian-roberta-base-emotion-classifier | 340 | 2 | transformers | ---
language: id
tags:
- roberta
license: mit
datasets:
- indonlu
widget:
- text: "Hal-hal baik akan datang."
---
# Indo RoBERTa Emotion Classifier
Indo RoBERTa Emotion Classifier is emotion classifier based on [Indo-roberta](https://huggingface.co/flax-community/indonesian-roberta-base) model. It was trained on the ... | [
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0.0... |
m3hrdadfi/wav2vec2-xlsr-persian-speech-emotion-recognition | a71bf01ccb1cfc182c37550938d78c958f18a5eb | 2021-07-27T06:12:46.000Z | [
"pytorch",
"wav2vec2",
"fa",
"dataset:ShEMO",
"transformers",
"audio",
"automatic-speech-recognition",
"speech",
"speech-emotion-recognition",
"license:apache-2.0"
] | automatic-speech-recognition | false | m3hrdadfi | null | m3hrdadfi/wav2vec2-xlsr-persian-speech-emotion-recognition | 340 | 3 | transformers | ---
language: fa
datasets:
- ShEMO
tags:
- audio
- automatic-speech-recognition
- speech
- speech-emotion-recognition
license: apache-2.0
---
# Emotion Recognition in Persian (Farsi - fa) Speech using Wav2Vec 2.0
## How to use
### Requirements
```bash
# requirement packages
!pip install git+https://github.com/hugg... | [
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... |
nvidia/segformer-b3-finetuned-ade-512-512 | 2eaea9d7ab761a33872c47a5fe614cb65d3df1f3 | 2022-07-20T09:53:44.000Z | [
"pytorch",
"tf",
"segformer",
"transformers"
] | null | false | nvidia | null | nvidia/segformer-b3-finetuned-ade-512-512 | 340 | null | transformers | Entry not found | [
0.0461147278547287,
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-0.03682169318199158,
0.011261860840022564,
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-0.... |
zentos/DialoGPT-small-spongebob | a47a09e82d250a24a83034ef0b8f379468b08903 | 2021-09-08T22:24:05.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | false | zentos | null | zentos/DialoGPT-small-spongebob | 340 | null | transformers | ---
tags:
- conversational
---
#Sponge Bob DialoGPT Model | [
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0.04701075702905655,
-... |
responsibility-framing/predict-perception-bert-cause-none | a8de22856a1c4f8ef719e64156db49448920a1e8 | 2022-03-10T16:10:54.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index"
] | text-classification | false | responsibility-framing | null | responsibility-framing/predict-perception-bert-cause-none | 340 | null | transformers | ---
license: mit
tags:
- generated_from_trainer
model-index:
- name: predict-perception-bert-cause-none
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. -->
# predict... | [
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0.01641813851892948,
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0.07169286161661148,
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0.044025082141160965,
0.0150... |
ibm/qcpg-sentences | d4deefe3a028ded254d8946d444ab1d1c684689f | 2022-05-18T10:58:34.000Z | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | ibm | null | ibm/qcpg-sentences | 340 | null | transformers | Entry not found | [
0.0461147278547287,
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0.03817418962717056,
-0.... |
Rostlab/prot_bert_bfd_localization | b31c50abeea9ac246cb7376412d68c2de29c72e1 | 2021-05-18T22:05:26.000Z | [
"pytorch",
"jax",
"bert",
"text-classification",
"transformers"
] | text-classification | false | Rostlab | null | Rostlab/prot_bert_bfd_localization | 339 | null | transformers | Entry not found | [
0.0461147278547287,
-0.038838207721710205,
-0.01049656979739666,
-0.03682169318199158,
0.011261860840022564,
0.013094935566186905,
0.0019101888174191117,
-0.013979103416204453,
0.027092741802334785,
-0.015212527476251125,
0.017284274101257324,
-0.08189476281404495,
0.03817418962717056,
-0.... |
responsibility-framing/predict-perception-bert-blame-assassin | 3ea9f6411849b8839ba252941db7c09e016e8d3c | 2022-03-10T15:44:18.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index"
] | text-classification | false | responsibility-framing | null | responsibility-framing/predict-perception-bert-blame-assassin | 339 | null | transformers | ---
license: mit
tags:
- generated_from_trainer
model-index:
- name: predict-perception-bert-blame-assassin
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. -->
# pre... | [
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0.047625645995140076,
-0.001... |
responsibility-framing/predict-perception-bert-blame-victim | a73e482897190756414bb0a897c405bf9e5aacf7 | 2022-03-10T15:48:51.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index"
] | text-classification | false | responsibility-framing | null | responsibility-framing/predict-perception-bert-blame-victim | 339 | null | transformers | ---
license: mit
tags:
- generated_from_trainer
model-index:
- name: predict-perception-bert-blame-victim
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. -->
# predi... | [
-0.12808498740196228,
-0.037728291004896164,
-0.005482709035277367,
0.06012921780347824,
0.09027622640132904,
0.08064495772123337,
-0.0000034655020044738194,
0.06148815155029297,
0.0001459706836612895,
-0.017506953328847885,
0.026849018409848213,
-0.11382651329040527,
0.030109792947769165,
... |
responsibility-framing/predict-perception-bert-focus-object | 4d12536579489e2152a762b8038809581d8a7526 | 2022-03-10T16:21:11.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index"
] | text-classification | false | responsibility-framing | null | responsibility-framing/predict-perception-bert-focus-object | 339 | null | transformers | ---
license: mit
tags:
- generated_from_trainer
model-index:
- name: predict-perception-bert-focus-object
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. -->
# predi... | [
-0.13415732979774475,
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-0.011212647892534733,
0.06080801412463188,
0.04677907004952431,
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0.0481351837515831,
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0.02440827526152134,
0.00801... |
responsibility-framing/predict-perception-xlmr-blame-object | 6700d00ae2216915a77bcfbf253917599a7998ff | 2022-03-15T22:42:55.000Z | [
"pytorch",
"tensorboard",
"xlm-roberta",
"text-classification",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index"
] | text-classification | false | responsibility-framing | null | responsibility-framing/predict-perception-xlmr-blame-object | 339 | null | transformers | ---
license: mit
tags:
- generated_from_trainer
model-index:
- name: predict-perception-xlmr-blame-object
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. -->
# predi... | [
-0.09127455204725266,
-0.03975527733564377,
-0.04231070354580879,
0.03140628710389137,
0.09586653113365173,
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0.0513775572180748,
-0.05405... |
responsibility-framing/predict-perception-xlmr-cause-concept | ff201f4a385b0e7e2ce6ca07e89af4d863508a88 | 2022-03-15T23:38:55.000Z | [
"pytorch",
"tensorboard",
"xlm-roberta",
"text-classification",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index"
] | text-classification | false | responsibility-framing | null | responsibility-framing/predict-perception-xlmr-cause-concept | 339 | null | transformers | ---
license: mit
tags:
- generated_from_trainer
model-index:
- name: predict-perception-xlmr-cause-concept
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. -->
# pred... | [
-0.06942911446094513,
-0.06538783013820648,
-0.02889924868941307,
0.058035608381032944,
0.055764585733413696,
0.043072134256362915,
-0.05036327987909317,
0.04427187144756317,
-0.005511806812137365,
-0.005395781248807907,
-0.006666786037385464,
-0.1314186453819275,
0.06235964596271515,
-0.0... |
danyaljj/gpt2_question_answering_squad2 | 631c9eb1862b1218724a63c70b9facbc2542108d | 2021-06-17T17:49:44.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
] | text-generation | false | danyaljj | null | danyaljj/gpt2_question_answering_squad2 | 338 | null | transformers | Sample usage:
```python
tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
model = GPT2LMHeadModel.from_pretrained("danyaljj/gpt2_question_answering_squad2")
input_ids = tokenizer.encode("There are two apples on the counter. Q: How many apples? A:", return_tensors="pt")
outputs = model.generate(input_ids)
print("Gene... | [
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-0.0269424... |
flax-community/gpt2-medium-persian | 5810babdec1f4c68888f2d80a7c2ab6e8aeb6fe0 | 2021-07-16T13:01:08.000Z | [
"pytorch",
"tf",
"jax",
"tensorboard",
"gpt2",
"text-generation",
"fa",
"transformers"
] | text-generation | false | flax-community | null | flax-community/gpt2-medium-persian | 338 | null | transformers | ---
language: fa
tags:
- text-generation
widget:
- text: "در یک اتفاق شگفت انگیز، پژوهشگران"
- text: "گرفتگی بینی در کودکان و بهخصوص نوزادان باعث میشود"
- text: "امیدواریم نوروز امسال سالی"
---
# GPT2 Medium 4 Persian
> This is part of the
[Flax/Jax Community Week](https://discuss.huggingface.co/t/pretrain-gpt2-f... | [
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huggingtweets/_holyweather | 3fdac1ef6a2efe8ea9bcaabd6c911564bdb93e53 | 2021-05-21T17:05:00.000Z | [
"pytorch",
"jax",
"gpt2",
"text-generation",
"en",
"transformers",
"huggingtweets"
] | text-generation | false | huggingtweets | null | huggingtweets/_holyweather | 338 | null | transformers | ---
language: en
thumbnail: https://www.huggingtweets.com/_holyweather/1616723668078/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div>
<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/137499167068... | [
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0.054079391062259674,
0.0... |
junnyu/ChineseBERT-base | a25dde763381455083c42f923e21ac4f336de317 | 2022-03-12T03:05:47.000Z | [
"pytorch",
"bert",
"fill-mask",
"zh",
"arxiv:2106.16038",
"transformers",
"glycebert",
"autotrain_compatible"
] | fill-mask | false | junnyu | null | junnyu/ChineseBERT-base | 338 | null | transformers | ---
language: zh
tags:
- glycebert
inference: False
---
# https://github.com/JunnYu/ChineseBert_pytorch
# ChineseBert_pytorch
本项目主要自定义了tokenization_chinesebert_fast.py文件中的ChineseBertTokenizerFast代码。从而可以从huggingface.co调用。
```python
pretrained_tokenizer_name = "junnyu/ChineseBERT-base"
tokenizer = ChineseBertTokenizerF... | [
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responsibility-framing/predict-perception-bert-blame-concept | 4c220a1ce5a014008cab969c0b2462d66871c639 | 2022-03-10T15:54:13.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index"
] | text-classification | false | responsibility-framing | null | responsibility-framing/predict-perception-bert-blame-concept | 338 | null | transformers | ---
license: mit
tags:
- generated_from_trainer
model-index:
- name: predict-perception-bert-blame-concept
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. -->
# pred... | [
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0.01981990970671177,
0.08349919319152832,
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0.04843199998140335,
0.006918... |
responsibility-framing/predict-perception-bert-focus-assassin | 5ecde3fcef2d5225231b7e1933a3835f9e044696 | 2022-03-10T16:13:18.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index"
] | text-classification | false | responsibility-framing | null | responsibility-framing/predict-perception-bert-focus-assassin | 338 | null | transformers | ---
license: mit
tags:
- generated_from_trainer
model-index:
- name: predict-perception-bert-focus-assassin
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. -->
# pre... | [
-0.13661599159240723,
-0.07388515025377274,
-0.04104671999812126,
0.05965373292565346,
0.04732845351099968,
0.042674172669649124,
0.07745519280433655,
0.059708207845687866,
0.0037389264907687902,
-0.02101156860589981,
0.020004665479063988,
-0.06596877425909042,
0.04040145501494408,
0.00488... |
responsibility-framing/predict-perception-bert-focus-victim | 266e4dae74ce684b66d1c33767e12c08af74f0df | 2022-03-10T16:18:11.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index"
] | text-classification | false | responsibility-framing | null | responsibility-framing/predict-perception-bert-focus-victim | 338 | null | transformers | ---
license: mit
tags:
- generated_from_trainer
model-index:
- name: predict-perception-bert-focus-victim
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. -->
# predi... | [
-0.11588370054960251,
-0.06171126291155815,
-0.023021336644887924,
0.05724146217107773,
0.04719183221459389,
0.05893649905920029,
0.04280945286154747,
0.05793417617678642,
-0.007739124353975058,
-0.03356385976076126,
0.01926751434803009,
-0.0976824015378952,
0.02866111509501934,
0.01452088... |
responsibility-framing/predict-perception-xlmr-blame-concept | e4d9e2ddf0a4f0f3c5418c8eded335ab4292fbd3 | 2022-03-15T22:48:25.000Z | [
"pytorch",
"tensorboard",
"xlm-roberta",
"text-classification",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index"
] | text-classification | false | responsibility-framing | null | responsibility-framing/predict-perception-xlmr-blame-concept | 338 | null | transformers | ---
license: mit
tags:
- generated_from_trainer
model-index:
- name: predict-perception-xlmr-blame-concept
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. -->
# pred... | [
-0.07390021532773972,
-0.06155954301357269,
-0.03607464209198952,
0.06939476728439331,
0.07214479893445969,
0.05458669364452362,
-0.03724491968750954,
0.034337639808654785,
0.012188288383185863,
-0.03784734383225441,
0.002340177772566676,
-0.15335747599601746,
0.05559271201491356,
-0.06496... |
responsibility-framing/predict-perception-xlmr-cause-object | db802aff3b742db628b57e62c49cc1610005b981 | 2022-03-15T23:03:02.000Z | [
"pytorch",
"tensorboard",
"xlm-roberta",
"text-classification",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index"
] | text-classification | false | responsibility-framing | null | responsibility-framing/predict-perception-xlmr-cause-object | 338 | null | transformers | ---
license: mit
tags:
- generated_from_trainer
model-index:
- name: predict-perception-xlmr-cause-object
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. -->
# predi... | [
-0.0886315405368805,
-0.026327189058065414,
-0.037493474781513214,
0.038564566522836685,
0.0795869380235672,
0.043863724917173386,
-0.04520028457045555,
0.04923747479915619,
-0.0016421930631622672,
-0.010246087796986103,
-0.004865312948822975,
-0.11571898311376572,
0.050094664096832275,
-0... |
responsibility-framing/predict-perception-xlmr-focus-assassin | 076712f7401ffc0f87cf5f2ce9cf9e7620e777a9 | 2022-03-15T23:13:17.000Z | [
"pytorch",
"tensorboard",
"xlm-roberta",
"text-classification",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index"
] | text-classification | false | responsibility-framing | null | responsibility-framing/predict-perception-xlmr-focus-assassin | 338 | null | transformers | ---
license: mit
tags:
- generated_from_trainer
model-index:
- name: predict-perception-xlmr-focus-assassin
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. -->
# pre... | [
-0.08539464324712753,
-0.0910891443490982,
-0.103117935359478,
0.026061777025461197,
0.04179677367210388,
0.044430747628211975,
0.011197126470506191,
0.04260499030351639,
0.01867285929620266,
-0.026046451181173325,
0.015090829692780972,
-0.07772385329008102,
0.06072833761572838,
-0.0557987... |
responsibility-framing/predict-perception-xlmr-focus-victim | 5cee482f552e9e64cd276c7c359402156be04d05 | 2022-03-15T23:18:48.000Z | [
"pytorch",
"tensorboard",
"xlm-roberta",
"text-classification",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index"
] | text-classification | false | responsibility-framing | null | responsibility-framing/predict-perception-xlmr-focus-victim | 338 | null | transformers | ---
license: mit
tags:
- generated_from_trainer
model-index:
- name: predict-perception-xlmr-focus-victim
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. -->
# predi... | [
-0.054906219244003296,
-0.08962749689817429,
-0.0713195726275444,
0.033063970506191254,
0.04480721801519394,
0.04447044059634209,
-0.0279054157435894,
0.0212150476872921,
0.013410601764917374,
-0.03168944641947746,
0.020716721192002296,
-0.11415009945631027,
0.04050709679722786,
-0.0486425... |
responsibility-framing/predict-perception-xlmr-focus-object | b72d193b4a253f8156ae1c8d2657b948575d7839 | 2022-03-15T23:23:19.000Z | [
"pytorch",
"tensorboard",
"xlm-roberta",
"text-classification",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index"
] | text-classification | false | responsibility-framing | null | responsibility-framing/predict-perception-xlmr-focus-object | 338 | null | transformers | ---
license: mit
tags:
- generated_from_trainer
model-index:
- name: predict-perception-xlmr-focus-object
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. -->
# predi... | [
-0.07141049206256866,
-0.06667467951774597,
-0.06331069022417068,
0.030948780477046967,
0.053159162402153015,
0.0319853350520134,
-0.012483553029596806,
0.020762981846928596,
0.0011343633523210883,
-0.035491716116666794,
0.00972816813737154,
-0.11628209054470062,
0.047292884439229965,
-0.0... |
responsibility-framing/predict-perception-xlmr-focus-concept | 827ebfcdcd6554bd8f121fe801625ad175d726e8 | 2022-03-15T23:28:40.000Z | [
"pytorch",
"tensorboard",
"xlm-roberta",
"text-classification",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index"
] | text-classification | false | responsibility-framing | null | responsibility-framing/predict-perception-xlmr-focus-concept | 338 | null | transformers | ---
license: mit
tags:
- generated_from_trainer
model-index:
- name: predict-perception-xlmr-focus-concept
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. -->
# pred... | [
-0.04438099265098572,
-0.09878374636173248,
-0.05366509035229683,
0.0559409037232399,
0.04076395183801651,
0.03900695592164993,
-0.0002674239512998611,
0.036284033209085464,
0.004398928489536047,
-0.048120539635419846,
-0.004358155652880669,
-0.11776704341173172,
0.0570092536509037,
-0.066... |
voidful/phoneme_byt5_v2 | 34cef46e3ebad280b220d73c2155f445a0b16b78 | 2022-06-04T12:09:46.000Z | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | voidful | null | voidful/phoneme_byt5_v2 | 338 | null | transformers | Entry not found | [
0.0461147278547287,
-0.038838207721710205,
-0.01049656979739666,
-0.03682169318199158,
0.011261860840022564,
0.013094935566186905,
0.0019101888174191117,
-0.013979103416204453,
0.027092741802334785,
-0.015212527476251125,
0.017284274101257324,
-0.08189476281404495,
0.03817418962717056,
-0.... |
HansAnonymous/DialoGPT-medium-rick | afd8cbee4a08b246cbedfe94d2ffd0fd65db7428 | 2021-08-28T23:56:07.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | false | HansAnonymous | null | HansAnonymous/DialoGPT-medium-rick | 337 | 1 | transformers | ---
tags:
- conversational
---
# Rick from Rick & Morty DialoGPT Model | [
-0.09280386567115784,
-0.04728976637125015,
0.0023526796139776707,
-0.041209399700164795,
0.06100260838866234,
-0.029940228909254074,
0.10366374999284744,
0.018850380554795265,
0.056286852806806564,
-0.040118008852005005,
-0.0011207397328689694,
-0.024557316675782204,
0.027212204411625862,
... |
Pollawat/mt5-small-thai-qa-qg | dee95725d581dbe7c3e92d3103f71372ab3d0af6 | 2021-04-19T14:52:22.000Z | [
"pytorch",
"mt5",
"text2text-generation",
"thai",
"th",
"dataset:NSC2018",
"dataset:iapp-wiki-qa-dataset",
"dataset:XQuAD",
"transformers",
"question-generation",
"question-answering",
"license:mit",
"autotrain_compatible"
] | question-answering | false | Pollawat | null | Pollawat/mt5-small-thai-qa-qg | 337 | 3 | transformers | ---
tags:
- question-generation
- question-answering
language:
- thai
- th
datasets:
- NSC2018
- iapp-wiki-qa-dataset
- XQuAD
license: mit
---
[Google's mT5](https://github.com/google-research/multilingual-t5)
This is a model for generating questions from Thai texts. It was fine-tuned on NSC2018 corpus
```python
f... | [
-0.09745363146066666,
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-0.025107134133577347,
0.07769975066184998,
-0.1717023104429245,
0.054887011647224426,
-0... |
yellowback/gpt-neo-japanese-1.3B | 69add767a2591d8d1d5445077e7656f453da19de | 2021-12-09T08:59:05.000Z | [
"pytorch",
"gpt_neo",
"text-generation",
"ja",
"dataset:oscar",
"dataset:cc100",
"dataset:wikipedia",
"transformers",
"text generation",
"causal-lm",
"japanese",
"license:apache-2.0"
] | text-generation | false | yellowback | null | yellowback/gpt-neo-japanese-1.3B | 337 | 1 | transformers | ---
language:
- ja
tags:
- text generation
- pytorch
- causal-lm
- japanese
license: apache-2.0
datasets:
- oscar
- cc100
- wikipedia
---
# GPT-Neo 1.3B pre-trained model for Japanese
## Model Description
GPT2/GPT3 like model trained on Japanese.corpus.
## Training data
- cc100 ja
- oscar ja
- wikipedia ja
## Ho... | [
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responsibility-framing/predict-perception-xlmr-blame-victim | 56f59614188fc99750b29eee3788d944563810dc | 2022-03-15T22:38:23.000Z | [
"pytorch",
"tensorboard",
"xlm-roberta",
"text-classification",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index"
] | text-classification | false | responsibility-framing | null | responsibility-framing/predict-perception-xlmr-blame-victim | 337 | null | transformers | ---
license: mit
tags:
- generated_from_trainer
model-index:
- name: predict-perception-xlmr-blame-victim
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. -->
# predi... | [
-0.07302434742450714,
-0.060113437473773956,
-0.059034314006567,
0.03794849291443825,
0.09907956421375275,
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0.028641359880566597,
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-0.03005439043045044,
0.03070887178182602,
-0.1263589709997177,
0.05577126145362854,
-0.06380385... |
responsibility-framing/predict-perception-xlmr-blame-assassin | 1cbab05ed0503d4326afd8a62b4432c122b2fd34 | 2022-03-15T22:32:51.000Z | [
"pytorch",
"tensorboard",
"xlm-roberta",
"text-classification",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index"
] | text-classification | false | responsibility-framing | null | responsibility-framing/predict-perception-xlmr-blame-assassin | 337 | null | transformers | ---
license: mit
tags:
- generated_from_trainer
model-index:
- name: predict-perception-xlmr-blame-assassin
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. -->
# pre... | [
-0.09653446078300476,
-0.07033366709947586,
-0.07889269292354584,
0.02669186145067215,
0.09957104176282883,
0.0437154695391655,
-0.025746498256921768,
0.03210107237100601,
0.01502535492181778,
-0.014905421994626522,
0.035062626004219055,
-0.10069738328456879,
0.07296082377433777,
-0.070161... |
responsibility-framing/predict-perception-xlmr-cause-none | 24fb5f707b0aea971d7457ae1152b868f6de90b8 | 2022-03-15T23:44:01.000Z | [
"pytorch",
"tensorboard",
"xlm-roberta",
"text-classification",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index"
] | text-classification | false | responsibility-framing | null | responsibility-framing/predict-perception-xlmr-cause-none | 337 | null | transformers | ---
license: mit
tags:
- generated_from_trainer
model-index:
- name: predict-perception-xlmr-cause-none
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. -->
# predict... | [
-0.06814204901456833,
-0.07296282052993774,
-0.04627588763833046,
0.03454364463686943,
0.05749974399805069,
0.05701538547873497,
-0.059304408729076385,
0.03784382343292236,
-0.000575544370803982,
-0.028296345844864845,
0.003420011606067419,
-0.10062248259782791,
0.0739215612411499,
-0.0718... |
huggingtweets/angelinacho-stillconor-touchofray | db555c5a91cadb50dde96b50c4315792968b4fea | 2022-07-19T19:52:38.000Z | [
"pytorch",
"gpt2",
"text-generation",
"en",
"transformers",
"huggingtweets"
] | text-generation | false | huggingtweets | null | huggingtweets/angelinacho-stillconor-touchofray | 337 | null | transformers | ---
language: en
thumbnail: http://www.huggingtweets.com/angelinacho-stillconor-touchofray/1658260354212/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; ... | [
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-0.05266686528921127,
0.003963917959481478,
0.07515615224838257,
0.020379502326250076,
-0.025... |
conniezyj/DialoGPT-small-snape | d1ad809cb74a47d89535ef08c356ee40f51898a8 | 2021-09-04T06:17:41.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | false | conniezyj | null | conniezyj/DialoGPT-small-snape | 336 | null | transformers | ---
tags:
- conversational
---
# Snape DialoGPT Model
| [
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-0.022303787991404533,
-0.0312456414103508,
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-0.0028982835356146097,
0.... |
facebook/xlm-roberta-xxl | cf077058541d380b377eddd9a4f4c0137e1f6065 | 2022-01-28T16:32:37.000Z | [
"pytorch",
"xlm-roberta-xl",
"fill-mask",
"multilingual",
"arxiv:2105.00572",
"transformers",
"license:mit",
"autotrain_compatible"
] | fill-mask | false | facebook | null | facebook/xlm-roberta-xxl | 336 | 1 | transformers | ---
language: multilingual
license: mit
---
# XLM-RoBERTa-XL (xxlarge-sized model)
XLM-RoBERTa-XL model pre-trained on 2.5TB of filtered CommonCrawl data containing 100 languages. It was introduced in the paper [Larger-Scale Transformers for Multilingual Masked Language Modeling](https://arxiv.org/abs/2105.00572) by... | [
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-0.0396118089556694,
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0.0445... |
liam168/trans-opus-mt-en-zh | 88cd74b4297abb5da53dc8ac95362ced458dd242 | 2021-07-16T04:17:11.000Z | [
"pytorch",
"marian",
"text2text-generation",
"en",
"zh",
"transformers",
"translation",
"autotrain_compatible"
] | translation | false | liam168 | null | liam168/trans-opus-mt-en-zh | 336 | 4 | transformers | ---
language:
- en
- zh
tags:
- translation
widget:
- text: "I like to study Data Science and Machine Learning."
---
# liam168/trans-opus-mt-en-zh
## Model description
* source group: English
* target group: Chinese
* model: transformer
* source language(s): eng
* target language(s): cjy_Hans cjy_Hant cmn cmn_Ha... | [
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0.025454092770814896,
0.024423... |
sentence-transformers/nli-distilbert-base | e6725b7fc96c36e01905f517049ce2f6c0473de9 | 2022-06-15T23:54:49.000Z | [
"pytorch",
"tf",
"distilbert",
"feature-extraction",
"arxiv:1908.10084",
"sentence-transformers",
"sentence-similarity",
"transformers",
"license:apache-2.0"
] | sentence-similarity | false | sentence-transformers | null | sentence-transformers/nli-distilbert-base | 336 | null | sentence-transformers | ---
pipeline_tag: sentence-similarity
license: apache-2.0
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
**⚠️ This model is deprecated. Please don't use it as it produces sentence embeddings of low quality. You can find recommended sentence embedding models here: [SBERT.net... | [
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0.07548169... |
edbeeching/decision-transformer-gym-hopper-expert | e4b82a76587437ed6bb12380330ddb56b855df94 | 2022-06-29T19:12:17.000Z | [
"pytorch",
"decision_transformer",
"feature-extraction",
"arxiv:2106.01345",
"transformers",
"deep-reinforcement-learning",
"reinforcement-learning",
"decision-transformer",
"gym-continous-control"
] | reinforcement-learning | false | edbeeching | null | edbeeching/decision-transformer-gym-hopper-expert | 336 | 6 | transformers | ---
tags:
- deep-reinforcement-learning
- reinforcement-learning
- decision-transformer
- gym-continous-control
pipeline_tag: reinforcement-learning
---
# Decision Transformer model trained on expert trajectories sampled from the Gym Hopper environment
This is a trained [Decision Transformer](https://arxiv.org/abs/21... | [
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facebook/wav2vec2-conformer-rel-pos-large-960h-ft | ca7f36f527f234b3cd4f05ecee30361f971e8e33 | 2022-06-15T08:12:40.000Z | [
"pytorch",
"wav2vec2-conformer",
"automatic-speech-recognition",
"en",
"dataset:librispeech_asr",
"arxiv:2010.05171",
"transformers",
"speech",
"audio",
"hf-asr-leaderboard",
"license:apache-2.0",
"model-index"
] | automatic-speech-recognition | false | facebook | null | facebook/wav2vec2-conformer-rel-pos-large-960h-ft | 336 | 2 | transformers | ---
language: en
datasets:
- librispeech_asr
tags:
- speech
- audio
- automatic-speech-recognition
- hf-asr-leaderboard
license: apache-2.0
model-index:
- name: wav2vec2-conformer-rel-pos-large-960h-ft
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
... | [
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... |
IDEA-CCNL/Wenzhong-GPT2-3.5B | cf234d0e3a6d1e123b7a68ac294ab8d519d0f39e | 2022-04-15T09:05:09.000Z | [
"pytorch",
"gpt2",
"text-generation",
"zh",
"transformers",
"license:apache-2.0"
] | text-generation | false | IDEA-CCNL | null | IDEA-CCNL/Wenzhong-GPT2-3.5B | 335 | 2 | transformers | ---
language:
- zh
inference:
parameters:
max_new_tokens: 128
repetition_penalty: 25.0
top_p: 0.9
do_sample: True
license: apache-2.0
---
# Wenzhong-GPT2-3.5B model (chinese)，one model of [Fengshenbang-LM](https://github.com/IDEA-CCNL/Fengshenbang-LM).
As we all know, the single directi... | [
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-... |
clancystudios/DialoGPT-medium-Morty | c5f3723dc18c41a2cf9dca1b2bf1170337b730a9 | 2022-02-07T12:38:25.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | false | clancystudios | null | clancystudios/DialoGPT-medium-Morty | 335 | null | transformers | ---
tags:
- conversational
--- | [
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0.00011744102084776387,
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0.007610224187374115,
-0.02621413767337799,
-0.01052570529282093,
0.00120... |
facebook/vit-mae-large | 8f4b5ad20e1cb9b9d1a1147fb02c9ccd39d2ea15 | 2022-03-29T17:14:04.000Z | [
"pytorch",
"tf",
"vit_mae",
"pretraining",
"dataset:imagenet-1k",
"arxiv:2111.06377",
"transformers",
"vision",
"license:apache-2.0"
] | null | false | facebook | null | facebook/vit-mae-large | 335 | null | transformers | ---
license: apache-2.0
tags:
- vision
datasets:
- imagenet-1k
---
# Vision Transformer (large-sized model) pre-trained with MAE
Vision Transformer (ViT) model pre-trained using the MAE method. It was introduced in the paper [Masked Autoencoders Are Scalable Vision Learners](https://arxiv.org/abs/2111.06377) by Kaimi... | [
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0.018289169296622276,
... |
linydub/bart-large-samsum | 5d32c801b99d8605a10ac38ddcaa6a186d81fcae | 2021-09-17T00:55:29.000Z | [
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"en",
"dataset:samsum",
"transformers",
"summarization",
"azureml",
"azure",
"codecarbon",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] | summarization | false | linydub | null | linydub/bart-large-samsum | 335 | 6 | transformers | ---
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... | [
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0.009174... |
mrm8488/t5-base-finetuned-squadv2 | 58b740046da740a6321ce1ccc221e4a65fc3e934 | 2020-12-11T21:56:10.000Z | [
"pytorch",
"t5",
"text2text-generation",
"en",
"dataset:squad_v2",
"arxiv:1910.10683",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | mrm8488 | null | mrm8488/t5-base-finetuned-squadv2 | 335 | 1 | transformers | ---
language: en
datasets:
- squad_v2
---
# T5-base fine-tuned on SQuAD v2
[Google's T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) fine-tuned on [SQuAD v2](https://rajpurkar.github.io/SQuAD-explorer/) for **Q&A** downstream task.
## Details of T5
The **T5** model was presented in [... | [
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0.008145179599523544,
-0.011200182139873505,
0.009317342191934586,
0.000839105574414134,
0.0292... |
nvidia/segformer-b1-finetuned-cityscapes-1024-1024 | f084b5ac89d958e98811b18cf5cae9eb9304250d | 2022-07-20T09:54:04.000Z | [
"pytorch",
"tf",
"segformer",
"dataset:cityscapes",
"arxiv:2105.15203",
"transformers",
"vision",
"image-segmentation",
"license:apache-2.0"
] | image-segmentation | false | nvidia | null | nvidia/segformer-b1-finetuned-cityscapes-1024-1024 | 335 | 2 | transformers | ---
license: apache-2.0
tags:
- vision
- image-segmentation
datasets:
- cityscapes
widget:
- src: https://www.researchgate.net/profile/Anurag-Arnab/publication/315881952/figure/fig5/AS:667673876779033@1536197265755/Sample-results-on-the-Cityscapes-dataset-The-above-images-show-how-our-method-can-handle.jpg
example_ti... | [
-0.008661572821438313,
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0.08369346708059311,
-0.015156809240579605,
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-0.134199321269989,
-0.041420094668865204,
0.023406924679875374,
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-0.07878926396369934,
0.0018419615225866437,
-0.0595565102994442,
0.013780317269265652,
0.040... |
trituenhantaoio/bert-base-vietnamese-uncased | b1a91594cd7d15a9e76bf92656ca9b79f8e66505 | 2021-05-20T08:06:49.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"transformers"
] | null | false | trituenhantaoio | null | trituenhantaoio/bert-base-vietnamese-uncased | 335 | 2 | transformers | ## Usage
```python
from transformers import BertForSequenceClassification
from transformers import BertTokenizer
model = BertForSequenceClassification.from_pretrained("trituenhantaoio/bert-base-vietnamese-uncased")
tokenizer = BertTokenizer.from_pretrained("trituenhantaoio/bert-base-vietnamese-uncased")
```
### Refere... | [
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0.08091113716363907,
-0.024575555697083473,
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-0.031307484954595566,
0.041327014565467834,
-0.016199659556150436,
0.0004334671248216182,
... |
ESPersonnel/DialoGPT-small-got | 467ce93aec63e38b7c93deaec5aa2e677cf0c214 | 2021-08-28T20:16:53.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | false | ESPersonnel | null | ESPersonnel/DialoGPT-small-got | 334 | null | transformers | ---
tags:
- conversational
---
# Game of Thrones DialoGPT Model | [
-0.043418727815151215,
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0.01805311255156994,
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-0.0017277842853218317,
-0.0302... |
huggingtweets/logicaldota2 | 30a432d77bab271f0fad26e8ec29cab36e8c419e | 2021-05-22T12:29:11.000Z | [
"pytorch",
"jax",
"gpt2",
"text-generation",
"en",
"transformers",
"huggingtweets"
] | text-generation | false | huggingtweets | null | huggingtweets/logicaldota2 | 334 | null | transformers | ---
language: en
thumbnail: https://www.huggingtweets.com/logicaldota2/1614112538704/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div>
<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/122293500955... | [
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0.03080308996140957,
0.1375708132982254,
-0.05312954634428024,
-0.01927073486149311,
-0.009610829874873161,
0.0824417918920517,
-0.05650632828474045,
-0.025892166420817375,
0.03962400555610657,
0.05321168527007103,
0.003724798... |
rovai/chatbotmedium4 | abe6a511567c09781921746077d904c68c1494a9 | 2021-12-01T16:55:39.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | false | rovai | null | rovai/chatbotmedium4 | 334 | null | transformers | ---
tags:
- conversational
---
# chatbot4 | [
-0.0201669093221426,
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0.05307411029934883,
-0.011299961246550083,
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0.1224474385380745,
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0.03942650929093361,
-0.03178216889500618,
-0.004095861688256264,
-0.011692781001329422,
-0.02128765918314457,
0.01256... |
gloomyworm/DialoGPT-medium-ortho | 1a3ab02c3ae664a88b6e3592251f328956f7e628 | 2022-06-14T23:05:27.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | false | gloomyworm | null | gloomyworm/DialoGPT-medium-ortho | 334 | null | transformers | ---
tags:
- conversational
---
# Ortho DialoGPT Model | [
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-0.03441200032830238,
0.0173735823482275,
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0.09216216206550598,
0.01849716529250145,
0.06421710550785065,
-0.025433583185076714,
-0.004341844469308853,
-0.021133551374077797,
0.0013621936086565256,
0.028... |
S34NtheGuy/DialoGPT-small-cursedryno | 77bf69d02edce8c0aa232666b2c1bd134fcd653d | 2021-10-10T21:57:32.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | false | S34NtheGuy | null | S34NtheGuy/DialoGPT-small-cursedryno | 333 | null | transformers | ---
tags:
- conversational
---
# DialoGPT chat bot model using discord messages as data | [
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0.02261701598763466,
0.03190477192401886,
0.0050743804313242435,
-0.07324155420064926,
0.1110946536064148,
0.00997245591133833,
0.04508121684193611,
-0.03557734191417694,
0.016186924651265144,
-0.0797310397028923,
0.036951933056116104,
0.0374873... |
abhisht/DialoGPT-medium-Emilybot | ff92d16cf0e8cfe52c54f8fc5a39b0e8d4d62025 | 2021-09-29T13:01:33.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | false | abhisht | null | abhisht/DialoGPT-medium-Emilybot | 333 | 1 | transformers | ---
tags:
- conversational
---
# Emilybot DialoGPT Model | [
-0.019626444205641747,
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0.02919725514948368,
-0.025410527363419533,
0.05502522364258766,
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0.10906685888767242,
0.01458343118429184,
0.059813279658555984,
-0.015521804802119732,
0.018949754536151886,
-0.04075361043214798,
-0.006560764275491238,
0.00... |
google/tapas-base-finetuned-tabfact | 39f040cbaef2ce4b065392c9f3a22fc80f0e7f64 | 2021-11-29T13:12:54.000Z | [
"pytorch",
"tf",
"tapas",
"text-classification",
"en",
"dataset:tab_fact",
"arxiv:2010.00571",
"arxiv:2004.02349",
"transformers",
"sequence-classification",
"license:apache-2.0"
] | text-classification | false | google | null | google/tapas-base-finetuned-tabfact | 333 | null | transformers | ---
language: en
tags:
- tapas
- sequence-classification
license: apache-2.0
datasets:
- tab_fact
---
# TAPAS base model fine-tuned on Tabular Fact Checking (TabFact)
This model has 2 versions which can be used. The latest version, which is the default one, corresponds to the `tapas_tabfact_inter_masklm_base_reset`... | [
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0.028378425166010857,
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0.019584832713007927,
0.06149870529770851,
-0.007868937216699123,
0.017752448096871376,
0.0... |
ricardo-filho/bert-base-portuguese-cased-nli-assin-2 | 1946af0f5090676d2aaf4774efb123bdb7735bcd | 2021-08-03T19:29:54.000Z | [
"pytorch",
"bert",
"feature-extraction",
"sentence-transformers",
"sentence-similarity",
"transformers"
] | sentence-similarity | false | ricardo-filho | null | ricardo-filho/bert-base-portuguese-cased-nli-assin-2 | 333 | null | sentence-transformers | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like cluster... | [
-0.05899689346551895,
-0.04668601602315903,
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0.055522240698337555,
0.021890703588724136,
0.06966360658407211,
-0.03817794471979141,
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-0.08511728048324585,
0.031796302646398544,
-0.012681740336120129,
0.04747813940048218,
0.06... |
doc2query/msmarco-chinese-mt5-base-v1 | 50eeb2d317ba2f8c55ed1fb1fac6a9b57d86490c | 2022-04-29T11:47:50.000Z | [
"pytorch",
"mt5",
"text2text-generation",
"zh",
"dataset:unicamp-dl/mmarco",
"arxiv:1904.08375",
"arxiv:2104.08663",
"arxiv:2112.07577",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | text2text-generation | false | doc2query | null | doc2query/msmarco-chinese-mt5-base-v1 | 333 | 1 | transformers | ---
language: zh
datasets:
- unicamp-dl/mmarco
widget:
- text: "Python（英國發音：/ˈpaɪθən/ 美國發音：/ˈpaɪθɑːn/），是一种广泛使用的解释型、高级和通用的编程语言。Python支持多种编程范型，包括函数式、指令式、反射式、结构化和面向对象编程。它拥有动态类型系统和垃圾回收功能，能够自动管理内存使用，并且其本身拥有一个巨大而广泛的标准库。它的语言结构以及面向对象的方法旨在帮助程序员为小型的和大型的项目编写清晰的、合乎逻辑的代码。"
license: apache-2.0
---
# doc2query/msmarco-chi... | [
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0.11411957442760468,
-0.04814321547746658,
0.11965904384851456,
-0... |
cocoshe/gpt2-chinese-gen-ads-by-keywords | 0f9c3fa0fb70a96a73bae211de3dc88099d65c3a | 2022-05-11T08:08:23.000Z | [
"pytorch",
"jax",
"gpt2",
"text-generation",
"transformers",
"license:apache-2.0"
] | text-generation | false | cocoshe | null | cocoshe/gpt2-chinese-gen-ads-by-keywords | 333 | 1 | transformers | ---
license: apache-2.0
---
[千言—AdvertiseGen广告文案生成数据集](https://www.luge.ai/#/luge/dataDetail?id=9)
> 仅支持.bin(pytorch)
在该千言数据集微调了5个epoch，
```python
input_text = '类型#裙*材质#针织*风格#简约*风格#青春*风格#清新*风格#性感*图案#条纹*图案#撞色*裙下摆#开叉*裙长#连衣裙*裙款式#拼接*裙款式#吊带'
output_text = gen_ads(input_text)
output_text = output_text.replace(' ',... | [
-0.0011296500451862812,
0.05868374556303024,
0.01275072991847992,
-0.013896523043513298,
0.044129591435194016,
-0.06611879914999008,
0.02939707227051258,
-0.0048485430888831615,
-0.034354232251644135,
-0.06076548248529434,
0.10693102329969406,
-0.055576521903276443,
0.05324457958340645,
-0... |
chanind/frame-semantic-transformer-base | 617c1d96525d1fa56cc04f30e29cc3883bb99125 | 2022-05-24T20:10:35.000Z | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | text2text-generation | false | chanind | null | chanind/frame-semantic-transformer-base | 332 | null | transformers | ---
license: apache-2.0
---
Fine-tuned T5 base model for use as a frame semantic parser in the [Frame Semantic Transformer](https://github.com/chanind/frame-semantic-transformer) project. This model is trained on data from [FrameNet 1.7](https://framenet2.icsi.berkeley.edu/).
### Usage
This is meant to be used a part... | [
0.00631755031645298,
-0.07192963361740112,
-0.00898369587957859,
-0.03520587459206581,
0.08995773643255234,
-0.017230913043022156,
-0.013990254141390324,
-0.010004254058003426,
-0.018576892092823982,
-0.10131724178791046,
-0.0001793483243091032,
-0.04380296543240547,
-0.04462122917175293,
... |
clampert/multilingual-sentiment-covid19 | eea3f8e26d2828dbf9f0f1d939dd868396ec863c | 2021-12-14T18:57:07.000Z | [
"pytorch",
"xlm-roberta",
"text-classification",
"multilingual",
"transformers",
"sentiment-analysis",
"license:apache-2.0"
] | text-classification | false | clampert | null | clampert/multilingual-sentiment-covid19 | 331 | 1 | transformers | ---
pipeline_tag: text-classification
language: multilingual
license: apache-2.0
tags:
- "sentiment-analysis"
- "multilingual"
widget:
- text: "I am very happy."
example_title: "English"
- text: "Heute bin ich schlecht drauf."
example_title: "Deutsch"
- text: "Quel cauchemard!"
example_title: "Francais"
- text: "... | [
-0.09022041410207748,
-0.016279354691505432,
-0.03307792544364929,
-0.03165275231003761,
0.06244172155857086,
0.019128266721963882,
0.0033457311801612377,
0.01416840497404337,
0.06203721463680267,
-0.07050620019435883,
0.02551589161157608,
-0.08643380552530289,
0.052868232131004333,
0.0367... |
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