modelId stringlengths 4 81 | tags list | pipeline_tag stringclasses 17
values | config dict | downloads int64 0 59.7M | first_commit timestamp[ns, tz=UTC] | card stringlengths 51 438k | embedding list |
|---|---|---|---|---|---|---|---|
AnonymousSub/SR_bert-base-uncased | [
"pytorch",
"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... | 3 | null | ---
language: en
thumbnail: https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true
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; margin-right: 4... | [
0.014535415917634964,
-0.03281450271606445,
-0.0010248735779896379,
0.03556530922651291,
0.05056127533316612,
0.015768470242619514,
-0.021236160770058632,
-0.00608600489795208,
-0.028977710753679276,
0.034398153424263,
-0.009012443013489246,
-0.006963478866964579,
0.01308003906160593,
0.02... |
AnonymousSub/SR_cline | [
"pytorch",
"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... | 6 | null | ---
language: en
thumbnail: https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true
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; margin-right: 4... | [
0.020349688827991486,
-0.035153478384017944,
0.0077634090557694435,
0.027302725240588188,
0.037021737545728683,
0.011043545790016651,
-0.026237014681100845,
0.0026683351024985313,
-0.029127493500709534,
0.04538383334875107,
-0.009886234067380428,
-0.019614221528172493,
0.013507174327969551,
... |
AnonymousSub/SR_declutr | [
"pytorch",
"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... | 6 | null | ---
language: en
thumbnail: http://www.huggingtweets.com/finessafudges-h3xenbrenner2-tallbart/1667781477683/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: 4p... | [
0.015587406232953072,
-0.045764509588479996,
0.006694975309073925,
0.044888272881507874,
0.036386795341968536,
0.011347703635692596,
-0.015959279611706734,
-0.00014051767357159406,
-0.044650159776210785,
0.041958365589380264,
0.008620494976639748,
-0.009346844628453255,
-0.007662424817681312... |
AnonymousSub/SR_rule_based_hier_quadruplet_epochs_1_shard_1 | [
"pytorch",
"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... | 1 | null | ---
license: mit
---
### EttBlackTeapot on Stable Diffusion
This is the `<my-teapot>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ip... | [
-0.0315127894282341,
-0.013347264379262924,
-0.028371000662446022,
0.04111437499523163,
0.0142208281904459,
0.015610849484801292,
-0.005076918751001358,
-0.0010618023807182908,
-0.030975187197327614,
0.04060101881623268,
-0.008427035063505173,
-0.01818307675421238,
0.034815847873687744,
0.... |
AnonymousSub/SR_rule_based_only_classfn_twostage_epochs_1_shard_1 | [
"pytorch",
"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... | 2 | null | ---
license: cc-by-nc-sa-4.0
tags:
- generated_from_trainer
datasets:
- wild_receipt
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: OCR-LayoutLMv3-Invoice
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: wild_receipt
type: wild_rec... | [
-0.008675528690218925,
-0.007312171161174774,
-0.013882314786314964,
0.01685776188969612,
0.03308843448758125,
0.011049525812268257,
-0.023155508562922478,
-0.00606869999319315,
-0.011380313895642757,
0.061436571180820465,
0.06462155282497406,
-0.022278372198343277,
-0.005039405543357134,
... |
AnonymousSub/SR_rule_based_roberta_bert_triplet_epochs_1_shard_1 | [
"pytorch",
"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... | 2 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- bleu
model-index:
- name: t5-small-finetuned-en-to-regex
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 comm... | [
-0.009247835725545883,
0.008065841160714626,
0.011124778538942337,
0.01473696157336235,
0.027438713237643242,
-0.0017631703522056341,
-0.023971296846866608,
-0.011218497529625893,
-0.03892233222723007,
0.04435567930340767,
0.0021502021700143814,
-0.01871515065431595,
-0.002228832570835948,
... |
AnonymousSub/SR_rule_based_roberta_hier_triplet_epochs_1_shard_10 | [
"pytorch",
"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... | 6 | null | ---
license: mit
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-de-fr
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 commen... | [
-0.035807061940431595,
-0.015083225443959236,
0.0058238632045686245,
0.029209351167082787,
0.025406185537576675,
0.02920437976717949,
-0.014850216917693615,
-0.00996646098792553,
-0.0337100550532341,
0.04483114182949066,
0.019719494506716728,
-0.05466502159833908,
0.009126645512878895,
0.0... |
AnonymousSub/SR_rule_based_roberta_only_classfn_twostage_epochs_1_shard_1 | [
"pytorch",
"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... | 8 | null | Access to model sreddy1/t5-end2end-questions-generation is restricted and you are not in the authorized list. Visit https://huggingface.co/sreddy1/t5-end2end-questions-generation to ask for access. | [
-0.00357252499088645,
0.009439500980079174,
0.010202212259173393,
0.00039893281063996255,
0.05052453279495239,
0.007489696145057678,
-0.029488619416952133,
0.0011502490378916264,
-0.05456119775772095,
0.029548780992627144,
0.05809836834669113,
0.010205782018601894,
0.010026727803051472,
0.... |
AnonymousSub/SR_rule_based_roberta_twostagetriplet_epochs_1_shard_1 | [
"pytorch",
"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... | 8 | null | ---
language: ja
license: cc-by-nc-sa-4.0
tags:
- roberta
- medical
inference: false
---
# alabnii/jmedroberta-base-manbyo-wordpiece
## Model description
This is a Japanese RoBERTa base model pre-trained on academic articles in medical sciences collected by Japan Science and Technology Agency (JST).
This model is r... | [
-0.006313530262559652,
-0.04193343222141266,
-0.0036889773327857256,
0.0672115609049797,
0.03491567447781563,
0.02566872350871563,
0.0015119446907192469,
-0.03418441116809845,
-0.034501783549785614,
0.048939693719148636,
0.021483207121491432,
-0.024708889424800873,
0.019855044782161713,
0.... |
AnonymousSub/SR_rule_based_roberta_twostagetriplet_hier_epochs_1_shard_1 | [
"pytorch",
"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... | 4 | null | ---
license: apache-2.0
# inference: false
# inference:
# parameters:
tags:
- classification
- zero-shot
---
# Erlangshen-UniMC-DeBERTa-v2-1.4B-Chinese
- Main Page:[Fengshenbang](https://fengshenbang-lm.com/)
- Github: [Fengshenbang-LM](https://github.com/IDEA-CCNL/Fengshenbang-LM/tree/main/fengshen/examples/unimc... | [
-0.014831198379397392,
-0.008116775192320347,
-0.011752697639167309,
0.032659899443387985,
0.03865941986441612,
0.0038040957879275084,
-0.030953822657465935,
-0.03491077944636345,
-0.04536547139286995,
0.04367246851325035,
0.032769300043582916,
0.015852443873882294,
0.011841922998428345,
0... |
AnonymousSub/SR_rule_based_roberta_twostagetriplet_hier_epochs_1_shard_10 | [
"pytorch",
"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... | 7 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
config: defau... | [
-0.010667362250387669,
0.010599073022603989,
-0.029340436682105064,
0.03697344660758972,
0.0602923184633255,
0.032836612313985825,
-0.023938821628689766,
-0.03604727238416672,
-0.033691368997097015,
0.05623795464634895,
0.01830117031931877,
-0.04562995582818985,
0.03489852696657181,
0.0440... |
AnonymousSub/bert_triplet_epochs_1_shard_1 | [
"pytorch",
"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... | 2 | null | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like cluste... | [
-0.03682786226272583,
-0.017038146033883095,
-0.016540275886654854,
0.0510595329105854,
0.01117929257452488,
0.04447409510612488,
-0.01840854622423649,
-0.002739659510552883,
-0.070090651512146,
0.08364398777484894,
0.03946809098124504,
0.013144438154995441,
0.00234610796906054,
0.04092745... |
AnonymousSub/declutr-model_squad2.0 | [
"pytorch",
"roberta",
"question-answering",
"transformers",
"autotrain_compatible"
] | question-answering | {
"architectures": [
"RobertaForQuestionAnswering"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_re... | 2 | null | ---
language: ja
license: cc-by-sa-4.0
---
# BERT Base Japanese for Irony
This is a BERT Base model for sentiment analysis in Japanese additionally finetuned for automatic irony detection.
The model was based on [bert-base-japanese-sentiment](https://huggingface.co/daigo/bert-base-japanese-sentiment), and later f... | [
-0.010231242515146732,
-0.019582364708185196,
0.008850183337926865,
0.060642607510089874,
0.03332651033997536,
0.056398216634988785,
0.00576036749407649,
-0.001991099677979946,
-0.05704284459352493,
0.04485086724162102,
0.017445510253310204,
-0.037881799042224884,
0.038171302527189255,
0.0... |
AnonymousSub/declutr-model_wikiqa | [
"pytorch",
"roberta",
"text-classification",
"transformers"
] | text-classification | {
"architectures": [
"RobertaForSequenceClassification"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"... | 26 | 2022-11-07T06:37:51Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
- f1
- recall
- precision
model-index:
- name: Brain_Tumor_Detector_swin
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
... | [
-0.014958702027797699,
-0.013209608383476734,
-0.010305685922503471,
0.025527017191052437,
0.01709817349910736,
0.00724322022870183,
-0.009825373068451881,
-0.010721161030232906,
0.010118180885910988,
0.04926776513457298,
0.031610652804374695,
-0.004324294626712799,
0.0003446368791628629,
... |
AnonymousSub/declutr-s10-SR | [
"pytorch",
"roberta",
"text-classification",
"transformers"
] | text-classification | {
"architectures": [
"RobertaForSequenceClassification"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"... | 36 | null | ---
license: cc-by-4.0
tags:
- generated_from_trainer
model-index:
- name: roberta-base-squad-finetuned-squad
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. -->
# r... | [
-0.0359196737408638,
-0.010211349464952946,
-0.0010576825588941574,
0.016398025676608086,
0.027302328497171402,
0.023489857092499733,
-0.03365464136004448,
0.005847259424626827,
-0.037141215056180954,
0.03188346326351166,
0.022844208404421806,
-0.032821644097566605,
0.02019432745873928,
0.... |
AnonymousSub/roberta-base_squad2.0 | [
"pytorch",
"roberta",
"question-answering",
"transformers",
"autotrain_compatible"
] | question-answering | {
"architectures": [
"RobertaForQuestionAnswering"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_re... | 6 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- sentiment140
metrics:
- accuracy
model-index:
- name: Sentiment140_DistilBERT_5E
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: sentiment140
type: sentiment140
config: sentiment1... | [
-0.009396752342581749,
-0.002111542271450162,
-0.025376668199896812,
0.0481073372066021,
0.03891107812523842,
0.03909825533628464,
-0.019915200769901276,
-0.017625829204916954,
-0.04707208648324013,
0.05326179414987564,
0.01506806816905737,
-0.03475029021501541,
0.013760410249233246,
0.038... |
AnonymousSub/rule_based_bert_hier_diff_equal_wts_epochs_1_shard_10 | [
"pytorch",
"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... | 6 | null | Access to model ardent-figment/gated-model is restricted and you are not in the authorized list. Visit https://huggingface.co/ardent-figment/gated-model to ask for access. | [
-0.05047513544559479,
0.0033264881931245327,
-0.020266495645046234,
0.012238975614309311,
0.035139959305524826,
0.02295246161520481,
-0.0023078597150743008,
-0.009011749178171158,
-0.044707171618938446,
0.04996337741613388,
0.023802079260349274,
0.0013185354182496667,
0.03242982178926468,
... |
AnonymousSub/rule_based_bert_mean_diff_epochs_1_shard_1 | [
"pytorch",
"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... | 3 | null | ---
language: ja
license: cc-by-sa-4.0
---
# bert-base-irony
This is a BERT Base model for the Japanese language finetuned for automatic irony detection.
The model was based on [BERT base Japanese](https://huggingface.co/hiroshi-matsuda-rit/bert-base-japanese-basic-char-v2), and later finetuned on a dataset contai... | [
-0.004597789607942104,
-0.015414563938975334,
0.0071202171966433525,
0.058646462857723236,
0.03237383812665939,
0.051193609833717346,
0.0013451778795570135,
-0.002092447131872177,
-0.053064920008182526,
0.041479676961898804,
0.016163691878318787,
-0.030756384134292603,
0.035201072692871094,
... |
AnonymousSub/rule_based_hier_triplet_epochs_1_shard_1_wikiqa_copy | [
"pytorch",
"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... | 1 | null | Access to model AustinZuo/zeo-bert is restricted and you are not in the authorized list. Visit https://huggingface.co/AustinZuo/zeo-bert to ask for access. | [
-0.046643588691949844,
0.01262717042118311,
-0.021644635125994682,
0.02375687286257744,
0.042695216834545135,
0.0047553954645991325,
-0.01761394739151001,
-0.010876606218516827,
-0.025091513991355896,
0.040740784257650375,
0.03741349279880524,
0.007315922062844038,
0.017766838893294334,
0.... |
AnonymousSub/rule_based_only_classfn_twostage_epochs_1_shard_1_wikiqa | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | {
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_rep... | 27 | null | ---
license: mit
tags:
- audio
- music
- generation
- tensorflow
---
# Musika Model: musika_hyperpop
## Model provided by: freepina
Pretrained musika_hyperpop model for the [Musika system](https://github.com/marcoppasini/musika) for fast infinite waveform music generation.
Introduced in [this paper](https://arxiv.org... | [
-0.03373877704143524,
-0.010015489533543587,
-0.023024816066026688,
0.05597947537899017,
-0.0016374324914067984,
0.023468196392059326,
0.011910563334822655,
0.012921931222081184,
-0.026711804792284966,
0.06023583933711052,
0.05368770658969879,
-0.014358495362102985,
-0.0054521081037819386,
... |
AnonymousSub/rule_based_roberta_bert_quadruplet_epochs_1_shard_1_squad2.0 | [
"pytorch",
"roberta",
"question-answering",
"transformers",
"autotrain_compatible"
] | question-answering | {
"architectures": [
"RobertaForQuestionAnswering"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_re... | 2 | null | ---
license: mit
tags:
- flair
- token-classification
- sequence-tagger-model
language: de
widget:
- text: "ab dryn matten, gelegen ze Niderlentz, hinden in langen matten eychen, waren ze etlichen zitten Jennis Huͤbers von Niderlentz, und hat sie gekoͧft von Walther Renold"
---
# Königsfelden NER
A model for historic... | [
0.0035102071706205606,
-0.03226306289434433,
-0.018596025183796883,
0.05410022288560867,
0.03253364562988281,
0.030240202322602272,
-0.018729256466031075,
-0.02671126276254654,
-0.055306576192379,
0.06494273245334625,
0.03289727121591568,
-0.013393423520028591,
-0.01835988089442253,
0.0270... |
AnonymousSub/rule_based_roberta_bert_triplet_epochs_1_shard_1 | [
"pytorch",
"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... | 2 | null | ---
language: tr
tag: text-classification
widget:
- text: "Oldukça kullanışlı bir ürün."
---
This repository contains two models that has been finetuned on twitter-XMLRoBERTa https://huggingface.co/cardiffnlp/twitter-xlm-roberta-base.
3_Label model can classify text as positive, neutral and negative.
2_Label_Twitter ... | [
0.0009034302202053368,
-0.015465251170098782,
-0.01058761402964592,
0.05313796550035477,
0.06619769334793091,
0.05335334688425064,
-0.02162892371416092,
-0.02087390050292015,
-0.02874116599559784,
0.033066362142562866,
0.028353089466691017,
-0.0252582598477602,
-0.013923399150371552,
0.017... |
AnonymousSub/rule_based_roberta_only_classfn_twostage_epochs_1_shard_1_squad2.0 | [
"pytorch",
"roberta",
"question-answering",
"transformers",
"autotrain_compatible"
] | question-answering | {
"architectures": [
"RobertaForQuestionAnswering"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_re... | 2 | null |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Pyramids
library_name: ml-agents
---
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids** using the [Unity ML-Age... | [
-0.05431758612394333,
0.0037391562946140766,
-0.005469401832669973,
0.057369984686374664,
0.02383779175579548,
0.02911541238427162,
-0.007357144262641668,
-0.030904561281204224,
-0.00531458854675293,
0.049950823187828064,
0.020184271037578583,
-0.011218986473977566,
0.0059364354237914085,
... |
AnonymousSub/rule_based_roberta_twostage_quadruplet_epochs_1_shard_1_wikiqa | [
"pytorch",
"roberta",
"text-classification",
"transformers"
] | text-classification | {
"architectures": [
"RobertaForSequenceClassification"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"... | 24 | null | ---
language:
- lt
license: apache-2.0
tags:
- lt-asr-leaderboard
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_11_0
metrics:
- wer
model-index:
- name: Whisper Small LT - Lithuanian Whisper
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
... | [
-0.027888132259249687,
-0.0037989341653883457,
-0.009999803267419338,
0.04149645194411278,
0.0643492117524147,
0.005379399750381708,
-0.006948538590222597,
0.003817028598859906,
-0.03506140783429146,
0.07120406627655029,
0.03793880715966225,
-0.04610520228743553,
-0.01060795970261097,
0.02... |
AnonymousSub/unsup-consert-base_squad2.0 | [
"pytorch",
"bert",
"question-answering",
"transformers",
"autotrain_compatible"
] | question-answering | {
"architectures": [
"BertForQuestionAnswering"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_n... | 2 | null | ---
language: en
---
This model is the fine-tuned model of "dbmdz/bert-base-turkish-cased" (https://huggingface.co/dbmdz/bert-base-turkish-128k-uncased) based on TC32 DATASET. | [
-0.030491570010781288,
0.00031161628430709243,
-0.00394813297316432,
0.05653486028313637,
0.03171283379197121,
0.011251281015574932,
-0.007468761410564184,
-0.01950990781188011,
-0.023871084675192833,
0.03713570907711983,
0.03404881805181503,
-0.02942899987101555,
0.019577965140342712,
0.0... |
AntonClaesson/movie-plot-generator | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
] | text-generation | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 9 | null | ---
license: apache-2.0
tags:
- summarization
- generated_from_trainer
metrics:
- rouge
model-index:
- name: mt5-small-finetuned-amazon-en-es
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, th... | [
-0.022527644410729408,
-0.0020304208155721426,
0.009534288197755814,
0.02708544209599495,
0.0386413149535656,
-0.002562012756243348,
-0.027206292375922203,
-0.005090841557830572,
-0.045171041041612625,
0.0528474785387516,
0.023829301819205284,
-0.024557719007134438,
0.010780110023915768,
0... |
Antony/mint_model | [] | null | {
"architectures": null,
"model_type": null,
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams... | 0 | null | ---
tags:
- flair
- token-classification
- sequence-tagger-model
---
### Demo: How to use in Flair
Requires:
- **[Flair](https://github.com/flairNLP/flair/)** (`pip install flair`)
```python
from flair.data import Sentence
from flair.models import SequenceTagger
# load tagger
tagger = SequenceTagger.load("GuiGel/beto-f... | [
-0.029719332233071327,
-0.012571152299642563,
0.017126910388469696,
0.05399090424180031,
0.0528305321931839,
0.0062618316151201725,
-0.0013555466430261731,
-0.013393034227192402,
-0.035004861652851105,
0.058437373489141464,
0.02423253282904625,
0.04283016547560692,
0.0029332898557186127,
0... |
Anubhav23/IndianlegalBert | [] | 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: ja
thumbnail: https://github.com/studio-ousia/luke/raw/master/resources/luke_logo.png
tags:
- luke
- named entity recognition
- entity typing
- relation classification
- question answering
license: apache-2.0
---
## luke-japanese-large
**luke-japanese** is the Japanese version of **LUKE** (**L... | [
0.0070223454385995865,
-0.011051843874156475,
-0.006917345337569714,
0.033538755029439926,
0.03435082361102104,
0.011110574007034302,
-0.003593791974708438,
-0.012919660657644272,
-0.035689134150743484,
0.050261907279491425,
0.013732866384088993,
-0.017627883702516556,
0.007739304099231958,
... |
Anubhav23/indianlegal | [] | 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: ja
thumbnail: https://github.com/studio-ousia/luke/raw/master/resources/luke_logo.png
tags:
- luke
- named entity recognition
- entity typing
- relation classification
- question answering
license: apache-2.0
---
## luke-japanese-large-lite
**luke-japanese** is the Japanese version of **LUKE**... | [
0.005956228356808424,
-0.011365381069481373,
-0.007204463705420494,
0.02933928184211254,
0.03400139883160591,
0.012471956200897694,
-0.005398644134402275,
-0.013451391831040382,
-0.03725054860115051,
0.05021071806550026,
0.009282054379582405,
-0.020067747682332993,
0.005670436192303896,
0.... |
Apisate/DialoGPT-small-jordan | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 12 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- audiofolder
metrics:
- wer
model-index:
- name: wav2vec2-xlsr-53-espeak-cv-ft-evn-ntsema-colab
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: audiofolder
type: audiof... | [
-0.03118092380464077,
-0.0006104093627072871,
-0.01949472352862358,
0.042957134544849396,
0.04198243096470833,
0.0372212678194046,
-0.009376163594424725,
-0.016454871743917465,
-0.024248527362942696,
0.06189826875925064,
0.03437770530581474,
-0.019818758592009544,
0.006596533115953207,
0.0... |
Aplinxy9plin/toxic-detection-rus | [] | null | {
"architectures": null,
"model_type": null,
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams... | 0 | null | ---
tags:
- conversational
---
#lucy DialoGPT Model | [
-0.04026848450303078,
0.02273820899426937,
0.013513779267668724,
0.01948060654103756,
0.024378839880228043,
0.014378798194229603,
-0.0061178626492619514,
0.022912001237273216,
-0.00024444045266136527,
0.0065559507347643375,
0.03741780295968056,
-0.038030412048101425,
-0.0007733299280516803,
... |
Apoorva/k2t-test | [
"pytorch",
"t5",
"text2text-generation",
"en",
"transformers",
"keytotext",
"k2t",
"Keywords to Sentences",
"autotrain_compatible"
] | text2text-generation | {
"architectures": [
"T5ForConditionalGeneration"
],
"model_type": "t5",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": true,
"length_penalty": 2,
"max_length": 200,
"min_length": 30,
"no_repeat_ngram_s... | 7 | null | ---
license: creativeml-openrail-m
tags:
- stable-diffusion
- text-to-image
---
Buy me a coffee if you like this project ;)
<a href="https://www.buymeacoffee.com/s3nh"><img src="https://www.buymeacoffee.com/assets/img/guidelines/download-assets-sm-1.svg" alt=""></a>
### Arcane based Artwork Diffusion Model
I present... | [
-0.005863936152309179,
-0.023902062326669693,
-0.01510798279196024,
0.0462757833302021,
0.046094875782728195,
0.013057345524430275,
-0.00619139987975359,
-0.016254795715212822,
-0.0028858152218163013,
0.06416001915931702,
0.026338739320635796,
0.004234940744936466,
-0.032260071486234665,
0... |
ArBert/albert-base-v2-finetuned-ner-kmeans | [
"pytorch",
"tensorboard",
"albert",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | {
"architectures": [
"AlbertForTokenClassification"
],
"model_type": "albert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_re... | 8 | null | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Rundstedtz/distilbert-base-uncased-letters-from-jenny
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 t... | [
-0.038921017199754715,
0.0036532895173877478,
-0.025215033441781998,
0.042338039726018906,
0.040793292224407196,
0.01916135475039482,
0.0024828496389091015,
-0.031308747828006744,
-0.045088302344083786,
0.05442250519990921,
0.01839384436607361,
-0.035878341645002365,
0.018687326461076736,
... |
ArBert/bert-base-uncased-finetuned-ner-agglo | [] | 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:
- de
license: bigscience-bloom-rail-1.0
library_name: transformers
tags:
- ggml
- bloom
datasets:
- oscar
pipeline_tag: text-generation
---
# BLOOM-CLP German (6.4B parameters)
This is a monolingual German language model trained using the [CLP-Transfer](https://arxiv.org/abs/2301.09626) method based on ... | [
-0.010797497816383839,
-0.0051774573512375355,
0.003344868076965213,
0.050017353147268295,
0.037657249718904495,
0.025047605857253075,
-0.004194426815956831,
0.00850929506123066,
-0.036369629204273224,
0.076564721763134,
0.04219413176178932,
-0.009449285455048084,
-0.018879804760217667,
0.... |
AragornII/DialoGPT-small-harrypotter | [] | null | {
"architectures": null,
"model_type": null,
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams... | 0 | null | ---
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: bert-base-chinese-finetuned-ner
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-base... | [
-0.027106693014502525,
-0.008664397522807121,
0.00670632254332304,
0.03369498997926712,
0.03300955146551132,
0.009418349713087082,
-0.024110987782478333,
-0.028159817680716515,
-0.04029674455523491,
0.037201471626758575,
0.012182574719190598,
-0.021673785522580147,
0.022186599671840668,
0.... |
ArcQ/gpt-experiments | [] | 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:
- audiofolder
metrics:
- wer
model-index:
- name: wav2vec2-xlsr-53-espeak-cv-ft-mhr-ntsema-colab
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: audiofolder
type: audiof... | [
-0.030839256942272186,
-0.0010662773856893182,
-0.020939942449331284,
0.042841482907533646,
0.04230501875281334,
0.03752238303422928,
-0.010419371537864208,
-0.014570837840437889,
-0.02359539456665516,
0.06193562224507332,
0.03476560860872269,
-0.020148592069745064,
0.007188478950411081,
0... |
AryanLala/autonlp-Scientific_Title_Generator-34558227 | [
"pytorch",
"pegasus",
"text2text-generation",
"en",
"dataset:AryanLala/autonlp-data-Scientific_Title_Generator",
"transformers",
"autonlp",
"co2_eq_emissions",
"autotrain_compatible",
"has_space"
] | text2text-generation | {
"architectures": [
"PegasusForConditionalGeneration"
],
"model_type": "pegasus",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"n... | 103 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: BERTModified-finetuned-wikitext-test
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread an... | [
0.009495238773524761,
-0.012789557687938213,
-0.015986410900950432,
0.02772500365972519,
0.009692969731986523,
0.02603228949010372,
-0.017926597967743874,
-0.01908334344625473,
-0.03496261686086655,
0.055807195603847504,
0.03484535217285156,
-0.028672844171524048,
0.03332829475402832,
0.02... |
Ashim/dga-transformer | [] | 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: other
tags:
- generated_from_trainer
datasets:
- AlekseyKorshuk/amazon-reviews-input-output
metrics:
- accuracy
model-index:
- name: amazon-reviews-input-output-6.7b
results:
- task:
name: Causal Language Modeling
type: text-generation
dataset:
name: AlekseyKorshuk/amazon-reviews-... | [
-0.02130204439163208,
-0.0022700540721416473,
-0.0016856496222317219,
0.053877536207437515,
0.029123354703187943,
0.02317243441939354,
-0.012128234840929508,
-0.01132598053663969,
-0.042359065264463425,
0.0557301789522171,
0.06136897951364517,
-0.015443777665495872,
-0.013734281063079834,
... |
Ashkanmh/bert-base-parsbert-uncased-finetuned | [
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"transformers",
"generated_from_trainer",
"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... | 3 | null | ---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: atari_alien
type: atari_alien
metrics:
- type: mean_r... | [
-0.05751729756593704,
-0.007264887914061546,
0.0019569441210478544,
0.046934809535741806,
0.056369807571172714,
-0.010681197047233582,
-0.01035386510193348,
-0.028911782428622246,
-0.03835343196988106,
0.07439278811216354,
0.04664934426546097,
0.014377161860466003,
-0.017899708822369576,
0... |
Ashok/my-new-tokenizer | [] | 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 | ---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: atari_phoenix
type: atari_phoenix
metrics:
- type: me... | [
-0.051461558789014816,
-0.004449002910405397,
0.004711026791483164,
0.04796440154314041,
0.049871306866407394,
-0.005301958415657282,
-0.0006233137683011591,
-0.017687655985355377,
-0.03804561495780945,
0.0692978948354721,
0.04189442843198776,
0.008238380774855614,
-0.016879960894584656,
0... |
AshtonBenson/DialoGPT-small-quentin-coldwater | [] | 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 | ---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: atari_pitfall
type: atari_pitfall
metrics:
- type: me... | [
-0.05695002153515816,
-0.002482763258740306,
0.0035061032976955175,
0.04700441285967827,
0.05298251286149025,
-0.01213555596768856,
0.0032538173254579306,
-0.01521090604364872,
-0.038284093141555786,
0.07239007949829102,
0.04017341509461403,
0.00969452690333128,
-0.017501240596175194,
0.01... |
Aspect11/DialoGPT-Medium-LiSBot | [
"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 | ---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: atari_pong
type: atari_pong
metrics:
- type: mean_rew... | [
-0.04512717202305794,
-0.0059164147824049,
0.007454402279108763,
0.04320995509624481,
0.04700566455721855,
-0.019195694476366043,
-0.00027131408569402993,
-0.02069961279630661,
-0.03928273916244507,
0.06826259195804596,
0.039004404097795486,
0.01233355700969696,
-0.014692984521389008,
0.01... |
Asuramaru/DialoGPT-small-rintohsaka | [
"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: en
license: apache-2.0
library_name: diffusers
tags: []
datasets: huggan/smithsonian_butterflies_subset
metrics: []
---
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this com... | [
-0.030280254781246185,
-0.011103581637144089,
-0.006736333016306162,
0.035448506474494934,
0.0180397629737854,
0.011415176093578339,
0.009771853685379028,
-0.004372082185000181,
-0.007829139940440655,
0.05538341775536537,
0.008732561022043228,
-0.021698791533708572,
0.00617963494732976,
0.... |
At3ee/wav2vec2-base-timit-demo-colab | [] | 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 | ---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: atari_privateye
type: atari_privateye
metrics:
- type... | [
-0.04905971512198448,
-0.004706318490207195,
0.003997087478637695,
0.04783380404114723,
0.055785857141017914,
-0.008957603015005589,
0.0001497355115134269,
-0.01683933474123478,
-0.0474972128868103,
0.07289206981658936,
0.04194951802492142,
0.003899363335222006,
-0.01851784810423851,
0.015... |
Atampy26/GPT-Glacier | [
"pytorch",
"gpt_neo",
"text-generation",
"transformers"
] | text-generation | {
"architectures": [
"GPTNeoForCausalLM"
],
"model_type": "gpt_neo",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram... | 5 | null | ---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: atari_qbert
type: atari_qbert
metrics:
- type: mean_r... | [
-0.04519645497202873,
-0.003717397805303335,
-0.0025805383920669556,
0.04244063422083855,
0.0541515126824379,
-0.011093332432210445,
0.0012378121027722955,
-0.02227003686130047,
-0.03676428273320198,
0.06491605192422867,
0.03219427168369293,
0.010023885406553745,
-0.016499510034918785,
0.0... |
Atarax/rick | [] | 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 | ---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: atari_riverraid
type: atari_riverraid
metrics:
- type... | [
-0.05347970873117447,
-0.00538964569568634,
0.0040970453992486,
0.04617417976260185,
0.05399160459637642,
-0.013492058962583542,
-0.0023807059042155743,
-0.0240571741014719,
-0.04036147519946098,
0.07066573947668076,
0.042296696454286575,
0.005487668327987194,
-0.018375825136899948,
0.0175... |
Atchuth/MBOT | [] | 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_keras_callback
model-index:
- name: erikdavidsson42/distilbert-base-uncased-finetuned-medium
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remov... | [
-0.028892062604427338,
0.002689106622710824,
-0.014196603558957577,
0.018664799630641937,
0.04191887751221657,
0.014023235067725182,
-0.02321161888539791,
-0.021256927400827408,
-0.040763407945632935,
0.05884934961795807,
0.02299734205007553,
-0.027590014040470123,
0.031836580485105515,
0.... |
Augustab/distilbert-base-uncased-finetuned-cola | [] | 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 | ---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: atari_timepilot
type: atari_timepilot
metrics:
- type... | [
-0.052408382296562195,
-0.007147349417209625,
-0.0005799731588922441,
0.04030933231115341,
0.05161244794726372,
-0.005134289618581533,
0.0014724010834470391,
-0.015086193569004536,
-0.03533127158880234,
0.07353281229734421,
0.04953183978796005,
0.007836130447685719,
-0.02610471099615097,
0... |
Augustvember/WOKKAWOKKA | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 12 | null | ---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: atari_tutankham
type: atari_tutankham
metrics:
- type... | [
-0.04776144027709961,
-0.005856771022081375,
0.003059875685721636,
0.04442782700061798,
0.055448297411203384,
-0.0019521068315953016,
-0.001267726649530232,
-0.01708715409040451,
-0.038928575813770294,
0.07006433606147766,
0.040563926100730896,
0.002118953038007021,
-0.013672338798642159,
... |
Augustvember/WokkaBot | [] | 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 | ---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: atari_upndown
type: atari_upndown
metrics:
- type: me... | [
-0.050918854773044586,
-0.004107908811420202,
0.006837169174104929,
0.05190077796578407,
0.05077274516224861,
-0.005726019386202097,
-0.0015873534139245749,
-0.02219909057021141,
-0.04379849508404732,
0.07209322601556778,
0.04359813034534454,
0.00848385039716959,
-0.016253316774964333,
0.0... |
Augustvember/WokkaBot2 | [] | 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 | ---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: atari_venture
type: atari_venture
metrics:
- type: me... | [
-0.05243240296840668,
-0.0064660958014428616,
0.00669366167858243,
0.04587562382221222,
0.04954981803894043,
-0.014920842833817005,
-0.0007842289051041007,
-0.01790059171617031,
-0.04012703895568848,
0.07517664134502411,
0.04592009261250496,
0.006608857773244381,
-0.019578007981181145,
0.0... |
Augustvember/WokkaBot6 | [] | 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 | ---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: atari_yarsrevenge
type: atari_yarsrevenge
metrics:
- ... | [
-0.05006978288292885,
-0.0030897408723831177,
0.0051080710254609585,
0.0462765172123909,
0.053467195481061935,
-0.001552768750116229,
0.0034471750259399414,
-0.020587431266903877,
-0.035639431327581406,
0.0682547390460968,
0.04197703301906586,
0.0044742245227098465,
-0.015669619664549828,
... |
Augustvember/WokkaBot7 | [] | 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
metrics:
- accuracy
- f1
model-index:
- name: finetuning-sentiment-model-A3
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove t... | [
-0.032060593366622925,
-0.006097390782088041,
-0.028132423758506775,
0.0317193828523159,
0.03740619868040085,
0.02702122926712036,
-0.018244583159685135,
-0.011637651361525059,
-0.0617549866437912,
0.06671534478664398,
0.0314214751124382,
-0.020804425701498985,
0.024035297334194183,
0.0345... |
Aurora/community.afpglobal | [] | 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:
- squad
model-index:
- name: distilbert-base-uncased-finetuned-squad
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 remov... | [
-0.022417115047574043,
-0.005769109819084406,
-0.030717195942997932,
0.050379958003759384,
0.06181812286376953,
0.02294996567070484,
-0.030974246561527252,
0.004127293359488249,
-0.03538813441991806,
0.0502810962498188,
0.03716709464788437,
-0.023526201024651527,
0.012201347388327122,
0.04... |
AvatarXD/DialoGPT-medium-Blitzo | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 14 | null | ---
license: creativeml-openrail-m
---
**_WeeBoo Diffusion_** is a model made for **creating characters and backgrounds**
**in model 1**
you can do things in **anime, cartoon, manga, novel**
in 2 you will be able to do in **_addition to the characters, varied things like backgrounds and more complex art styles, try_... | [
-0.010346348397433758,
-0.012489569373428822,
-0.0034805049654096365,
-0.0018904578173533082,
0.04601543769240379,
0.0042326245456933975,
0.021855423226952553,
0.015061147511005402,
0.005743842106312513,
0.05481921508908272,
0.02967364527285099,
0.0014023386174812913,
0.015588165260851383,
... |
Aviora/phobert-ner | [] | 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: other
tags:
- generated_from_trainer
datasets:
- AlekseyKorshuk/amazon-reviews-input-output
metrics:
- accuracy
model-index:
- name: amazon-reviews-input-output-6.7b-best
results:
- task:
name: Causal Language Modeling
type: text-generation
dataset:
name: AlekseyKorshuk/amazon-rev... | [
-0.021625913679599762,
-0.004619063343852758,
-0.0031924417708069086,
0.05392805114388466,
0.029656853526830673,
0.023008670657873154,
-0.012596869841217995,
-0.009729026816785336,
-0.0406750924885273,
0.05646636709570885,
0.060534581542015076,
-0.015503962524235249,
-0.014484137296676636,
... |
Axcel/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,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 14 | null | ---
license: mit
---
### gibasachan on Stable Diffusion
This is the `gibasachan` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) ... | [
-0.02083931304514408,
-0.03074307180941105,
-0.02830086648464203,
0.03129100799560547,
0.014509806409478188,
0.018095947802066803,
0.01779191382229328,
-0.0029623748268932104,
-0.04174324497580528,
0.049503788352012634,
0.010408131405711174,
-0.003324066288769245,
0.04652845859527588,
0.03... |
Axon/resnet50-v1 | [
"dataset:ImageNet",
"arxiv:1512.03385",
"Axon",
"Elixir",
"license:apache-2.0"
] | 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: cc-by-4.0
tags:
- generated_from_trainer
model-index:
- name: roberta-finetuned-country
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. -->
# roberta-fi... | [
-0.025760434567928314,
-0.007172107230871916,
-0.007402421906590462,
0.02225710079073906,
0.03487006574869156,
0.03275606408715248,
-0.025524379685521126,
0.008493993431329727,
-0.04876088351011276,
0.05792529508471489,
0.02357841283082962,
-0.018613720312714577,
0.024311354383826256,
0.03... |
Ayah/GPT2-DBpedia | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
] | text-generation | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 6 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- audiofolder
metrics:
- wer
model-index:
- name: wav2vec2-xlsr-53-espeak-cv-ft-bak2-ntsema-colab
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: audiofolder
type: audio... | [
-0.03666115179657936,
-0.005248441360890865,
-0.014941825531423092,
0.04740716889500618,
0.044048093259334564,
0.029011009261012077,
-0.007396015338599682,
-0.017753522843122482,
-0.02298792265355587,
0.05713857710361481,
0.022830020636320114,
-0.02172018028795719,
0.006338051054626703,
0.... |
Ayham/albert_bert_summarization_cnn_dailymail | [
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"dataset:cnn_dailymail",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
] | text2text-generation | {
"architectures": [
"EncoderDecoderModel"
],
"model_type": "encoder-decoder",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_re... | 12 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
metrics:
- accuracy
model-index:
- name: distilbert-imdb
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: imdb
type: imdb
config: plain_text
split: train
args: pla... | [
-0.008450712077319622,
-0.004143491853028536,
-0.03834064304828644,
0.0534442737698555,
0.05151393637061119,
0.026292452588677406,
-0.02225031889975071,
-0.027390338480472565,
-0.024734916165471077,
0.07032740861177444,
0.048818521201610565,
-0.014482865110039711,
0.0062001366168260574,
0.... |
Ayham/albert_gpt2_summarization_xsum | [
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"dataset:xsum",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
] | text2text-generation | {
"architectures": [
"EncoderDecoderModel"
],
"model_type": "encoder-decoder",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_re... | 7 | 2022-11-07T23:20:26Z | ---
license: creativeml-openrail-m
---
To use draw emphasis from the training model include the word `m_yukoring` in your prompt.
Yukoring is an artists that does a lot of anime watercolor style art.
License This embedding is open access and available to all, with a CreativeML OpenRAIL-M license further specifying r... | [
-0.03800678625702858,
-0.015730764716863632,
0.008371129631996155,
0.02815825864672661,
0.03748762607574463,
0.009631981141865253,
0.007502693682909012,
-0.02786160074174404,
-0.011270945891737938,
0.06548003107309341,
0.034465182572603226,
-0.0040454138070344925,
0.0008909547468647361,
0.... |
Ayham/bert_gpt2_summarization_xsum | [
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"dataset:xsum",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
] | text2text-generation | {
"architectures": [
"EncoderDecoderModel"
],
"model_type": "encoder-decoder",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_re... | 6 | null | ---
license: mit
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-de
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xtreme
type: xtreme
args: PAN-X.de
metrics:
- name:... | [
-0.024378595873713493,
-0.0039397357031702995,
0.006905984133481979,
0.02013515867292881,
0.02932813949882984,
0.026282116770744324,
-0.024741508066654205,
-0.009574308060109615,
-0.024772049859166145,
0.04916413873434067,
0.022032327950000763,
-0.04625600948929787,
0.010013517923653126,
0... |
Ayham/bertgpt2_cnn | [
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
] | text2text-generation | {
"architectures": [
"EncoderDecoderModel"
],
"model_type": "encoder-decoder",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_re... | 4 | null | ---
license: "mit"
---
This model takes text (up to a few sentences) and predicts whether the text contains resilience messaging. Resilience messaging is a text message that is about being able to a) "adapt to change” and b) “bounce back after illness or hardship". The predictive model is a fine-tuned RoBERTa NLP mode... | [
-0.015329403802752495,
-0.02336658164858818,
-0.017447762191295624,
0.025744063779711723,
0.03976623713970184,
0.023548295721411705,
-0.008083883672952652,
-0.0221763513982296,
-0.029547128826379776,
0.048564258962869644,
0.03186187520623207,
0.0000454086548415944,
0.03756680339574814,
0.0... |
Ayham/distilbert_gpt2_summarization_xsum | [
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"dataset:xsum",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
] | text2text-generation | {
"architectures": [
"EncoderDecoderModel"
],
"model_type": "encoder-decoder",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_re... | 8 | null | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
... | [
-0.03728697821497917,
-0.0026194958481937647,
-0.004763193894177675,
0.0258035808801651,
0.045451704412698746,
-0.02145831100642681,
-0.005295874085277319,
-0.027668936178088188,
-0.03325880691409111,
0.06643921136856079,
0.03268396481871605,
-0.02359304204583168,
0.02317889966070652,
0.00... |
Ayham/roberta_distilgpt2_summarization_cnn_dailymail | [
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"dataset:cnn_dailymail",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
] | text2text-generation | {
"architectures": [
"EncoderDecoderModel"
],
"model_type": "encoder-decoder",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_re... | 4 | null | ---
tags:
- espnet
- audio
- automatic-speech-recognition
language: en
datasets:
- google/fleurs
license: cc-by-4.0
---
## ESPnet2 ASR model
### `espnet/wanchichen_fleurs_asr_conformer_hier_lid_utt`
This model was trained by William Chen using the fleurs recipe in [espnet](https://github.com/espnet/espnet/).
### Demo... | [
-0.02412279322743416,
-0.015309921465814114,
-0.021609662100672722,
0.037371136248111725,
0.04208178073167801,
0.0062411148101091385,
-0.009577472694218159,
-0.0003538979508448392,
-0.04034097120165825,
0.05711856484413147,
0.012365784496068954,
0.01391686126589775,
-0.014366980642080307,
... |
Ayham/roberta_gpt2_summarization_cnn_dailymail | [
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"dataset:cnn_dailymail",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
] | text2text-generation | {
"architectures": [
"EncoderDecoderModel"
],
"model_type": "encoder-decoder",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_re... | 31 | null | ---
language: mt
datasets:
- common_voice
tags:
- audio
- automatic-speech-recognition
- maltese
- xlrs-53-maltese
- masri-project
- malta
- university-of-malta
license: cc-by-nc-sa-4.0
widget: null
model-index:
- name: wav2vec2-large-xlsr-53-maltese-64h
results:
- task:
name: Automatic Speech Recognition
... | [
-0.01975938118994236,
-0.022067198529839516,
-0.020432043820619583,
0.05034174770116806,
0.06284089386463165,
0.03902481123805046,
-0.009335440583527088,
-0.007487575523555279,
-0.02219168096780777,
0.0823926106095314,
0.031477902084589005,
-0.03787390887737274,
0.00425702566280961,
0.0125... |
Ayham/roberta_roberta_summarization_cnn_dailymail | [
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"dataset:cnn_dailymail",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
] | text2text-generation | {
"architectures": [
"EncoderDecoderModel"
],
"model_type": "encoder-decoder",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_re... | 3 | null | ---
license: mit
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-de
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xtreme
type: xtreme
args: PAN-X.de
metrics:
- name:... | [
-0.02285177633166313,
-0.0029209493659436703,
0.006041921209543943,
0.020610405132174492,
0.029006201773881912,
0.025830013677477837,
-0.023114826530218124,
-0.009819703176617622,
-0.025708450004458427,
0.04948452487587929,
0.021983342245221138,
-0.04665680602192879,
0.009086270816624165,
... |
Ayham/xlnet_distilgpt2_summarization_cnn_dailymail | [
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"dataset:cnn_dailymail",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
] | text2text-generation | {
"architectures": [
"EncoderDecoderModel"
],
"model_type": "encoder-decoder",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_re... | 13 | null | ---
license: apache-2.0
tags:
- Scene Text Removal
- Image to Image
library_name: pytorch
---
### GaRNet
This is text-removal model that introduced in the paper below and first released at [this page](https://github.com/naver/garnet). \
[The Surprisingly Straightforward Scene Text Removal Method With Gated Attention ... | [
0.016222704201936722,
-0.017228633165359497,
-0.026081297546625137,
0.04022468253970146,
0.03584500029683113,
0.025168949738144875,
-0.010753918439149857,
-0.004938215017318726,
-0.02062169648706913,
0.0700034573674202,
0.03605841472744942,
-0.0002986025938298553,
0.017047395929694176,
0.0... |
Ayham/xlnet_gpt2_summarization_xsum | [
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"dataset:xsum",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
] | text2text-generation | {
"architectures": [
"EncoderDecoderModel"
],
"model_type": "encoder-decoder",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_re... | 13 | 2022-11-08T02:14:30Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: distilbert-base-uncased-finetuned-squad
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 remov... | [
-0.021258337423205376,
-0.004330624360591173,
-0.031080730259418488,
0.0504353903234005,
0.06124728545546532,
0.021408064290881157,
-0.031141098588705063,
0.003794659161940217,
-0.0351400263607502,
0.050447091460227966,
0.03652471676468849,
-0.02445102110505104,
0.011998570524156094,
0.046... |
Ayham/xlnet_gpt_xsum | [
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
] | text2text-generation | {
"architectures": [
"EncoderDecoderModel"
],
"model_type": "encoder-decoder",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_re... | 11 | null | data: https://github.com/BigSalmon2/InformalToFormalDataset
Text Generation Informal Formal
```
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("BigSalmon/InformalToFormalLincoln90Paraphrase")
model = AutoModelForCausalLM.from_pretrained("BigSalmon/InformalToForm... | [
-0.01230681873857975,
-0.027751898393034935,
-0.025516623631119728,
0.06406712532043457,
0.035336777567863464,
0.053920257836580276,
-0.025757063180208206,
-0.010579261928796768,
-0.04253368452191353,
0.0675121396780014,
0.034614600241184235,
-0.007214151788502932,
0.010226900689303875,
0.... |
Ayham/xlnet_roberta_new_summarization_cnn_dailymail | [] | null | {
"architectures": null,
"model_type": null,
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams... | 0 | null | ---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-CartPole-v1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type:... | [
-0.027388405054807663,
0.019750159233808517,
0.005489207338541746,
0.009781531989574432,
0.04131517931818962,
-0.01887086220085621,
-0.024224719032645226,
-0.014238322153687477,
-0.02886173687875271,
0.08430696278810501,
0.016648191958665848,
-0.0048314472660422325,
0.01419028453528881,
0.... |
Ayjayo/DialoGPT-medium-AyjayoAI | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 12 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: chinese-macbert-base-finetuned
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 ... | [
-0.03668500483036041,
-0.012725736014544964,
-0.013486764393746853,
0.038828250020742416,
0.03212892636656761,
0.007743207737803459,
-0.028891131281852722,
-0.012574818916618824,
-0.038668762892484665,
0.03970331698656082,
0.026303596794605255,
-0.008488189429044724,
0.034452300518751144,
... |
Aymene/opus-mt-en-ro-finetuned-en-to-ro | [] | null | {
"architectures": null,
"model_type": null,
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams... | 0 | null | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: bigmorning_whisper
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. -->
# bigmorning_whis... | [
-0.060224175453186035,
-0.016371900215744972,
0.0021750687155872583,
0.03871561959385872,
0.0149469505995512,
0.016703259199857712,
0.005456648301333189,
-0.003493398893624544,
-0.020339474081993103,
0.0665755420923233,
0.05325532332062721,
-0.021584348753094673,
0.014495238661766052,
0.03... |
Ayou/chinese_mobile_bert | [
"pytorch",
"mobilebert",
"fill-mask",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"MobileBertForMaskedLM"
],
"model_type": "mobilebert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repea... | 16 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- audiofolder
model-index:
- name: wav2vec2-xlsr-53-espeak-cv-ft-xas-ntsema-colab
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete i... | [
-0.041500721126794815,
-0.008401143364608288,
-0.01861553080379963,
0.036707937717437744,
0.03936808928847313,
0.034175559878349304,
0.0061415331438183784,
-0.009770691394805908,
-0.024014515802264214,
0.045384492725133896,
0.045783400535583496,
-0.011574226431548595,
0.003005268983542919,
... |
Ayran/DialoGPT-medium-harry-potter-1-through-4-plus-6 | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 12 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: distilbert-base-uncased-finetuned-squad
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 remov... | [
-0.01971445418894291,
-0.004423938225954771,
-0.03209740296006203,
0.05105740204453468,
0.061624687165021896,
0.02221027761697769,
-0.030870428308844566,
0.003230932867154479,
-0.035811807960271835,
0.05113336816430092,
0.03524097055196762,
-0.026420539245009422,
0.01159674022346735,
0.046... |
Ayran/DialoGPT-small-gandalf | [
"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... | 11 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: finetuning-sentiment-BERTmodel-A3
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 remo... | [
-0.02431129291653633,
-0.007728996220976114,
-0.03267607092857361,
0.03281518071889877,
0.029886523261666298,
0.02556825615465641,
-0.018530113622546196,
-0.019998515024781227,
-0.059583183377981186,
0.06267914921045303,
0.02487260475754738,
-0.02345428615808487,
0.02622206322848797,
0.041... |
AyushPJ/ai-club-inductions-21-nlp-ALBERT | [
"pytorch",
"albert",
"question-answering",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
] | question-answering | {
"architectures": [
"AlbertForQuestionAnswering"
],
"model_type": "albert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repe... | 8 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: finetuning-sentiment-BERTmodel-A3-allcontents
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete i... | [
-0.020621560513973236,
-0.009773263707756996,
-0.03641148656606674,
0.03876309096813202,
0.032433390617370605,
0.028202315792441368,
-0.018734516575932503,
-0.026348451152443886,
-0.05563253164291382,
0.06877319514751434,
0.02842887118458748,
-0.024648860096931458,
0.02610842138528824,
0.0... |
Bagus/SER-LSSED | [] | 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: mit
tags:
- generated_from_trainer
datasets:
- imagefolder
model-index:
- name: donut-base-Label-studio-707-invoices
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 th... | [
-0.029338527470827103,
-0.013429585844278336,
-0.0033966838382184505,
0.03761850297451019,
0.031287841498851776,
0.011902254074811935,
0.012602326460182667,
0.0038907546550035477,
-0.008202873170375824,
0.03658801317214966,
0.05673116818070412,
-0.01855790615081787,
0.007513921707868576,
0... |
Barbarameerr/Barbara | [] | 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
- f1
model-index:
- name: finetuning-sentiment-model-3000-samples
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: imdb
type: imdb
config: plain_text
... | [
-0.015276283025741577,
-0.010353866964578629,
-0.03115297481417656,
0.04599893465638161,
0.036334328353405,
0.03681322559714317,
-0.019699180498719215,
-0.020984552800655365,
-0.03715088218450546,
0.06607145816087723,
0.04616058990359306,
-0.01860051602125168,
0.019438061863183975,
0.04286... |
Barleysack/klue-roberta-LSTM | [
"pytorch",
"roberta",
"transformers"
] | null | {
"architectures": [
"QAWithLSTMModel"
],
"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_s... | 6 | null | ---
language: en
license: apache-2.0
library_name: diffusers
tags: []
datasets: huggan/smithsonian_butterflies_subset
metrics: []
---
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this com... | [
-0.03138813376426697,
-0.010746908374130726,
-0.006187924183905125,
0.035248879343271255,
0.017983634024858475,
0.012248678132891655,
0.01016752328723669,
-0.005741671193391085,
-0.007813255302608013,
0.05522491782903671,
0.008332232013344765,
-0.021673044189810753,
0.006728531327098608,
0... |
Beelow/wav2vec2-ukrainian-model-large | [] | 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 | 2022-11-08T12:44:21Z | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: whisper-medium-amksim
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. -->
# whisper-mediu... | [
-0.0516999326646328,
-0.005874258000403643,
-0.013234997168183327,
0.03368561342358589,
0.030592791736125946,
0.0067828018218278885,
0.000443076656665653,
0.0038734753616154194,
-0.03237484395503998,
0.06751230359077454,
0.03072000853717327,
-0.024236656725406647,
0.005169305019080639,
0.0... |
BigSalmon/FormalBerta2 | [
"pytorch",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"RobertaForMaskedLM"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngra... | 16 | 2022-11-08T14:02:07Z | ---
license: cc-by-4.0
---
## UoM&MMU at TSAR-2022 Shared Task - Prompt Learning for Lexical Simplification: prompt-ls-es-1
We present **PromptLS**, a method for fine-tuning large pre-trained masked language models to perform the task of Lexical Simplification.
This model is part of a series of models presented at ... | [
-0.010406943038105965,
-0.01887752115726471,
0.009728393517434597,
0.06551402807235718,
0.027966812252998352,
0.016415880993008614,
-0.020798515528440475,
0.00481963437050581,
-0.015561467967927456,
0.04241378605365753,
0.0013189353048801422,
-0.019156193360686302,
-0.010804700665175915,
0... |
BigSalmon/FormalBerta3 | [
"pytorch",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"RobertaForMaskedLM"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngra... | 4 | null | ---
license: cc-by-4.0
---
## UoM&MMU at TSAR-2022 Shared Task - Prompt Learning for Lexical Simplification: prompt-ls-es-2
We present **PromptLS**, a method for fine-tuning large pre-trained masked language models to perform the task of Lexical Simplification.
This model is part of a series of models presented at ... | [
-0.010912337340414524,
-0.01862163282930851,
0.009346120990812778,
0.0653148889541626,
0.02769688330590725,
0.016760263592004776,
-0.02186337113380432,
0.0037174117751419544,
-0.014705846086144447,
0.0422130785882473,
0.0006605378002859652,
-0.018980514258146286,
-0.012201974168419838,
0.0... |
BigSalmon/FormalRobertaaa | [
"pytorch",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"RobertaForMaskedLM"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngra... | 12 | null | ---
license: cc-by-4.0
---
## UoM&MMU at TSAR-2022 Shared Task - Prompt Learning for Lexical Simplification: prompt-ls-pt-1
We present **PromptLS**, a method for fine-tuning large pre-trained masked language models to perform the task of Lexical Simplification.
This model is part of a series of models presented at ... | [
-0.010877848602831364,
-0.019097914919257164,
0.00740697281435132,
0.06720525771379471,
0.027116702869534492,
0.016111403703689575,
-0.020282546058297157,
0.0038317451253533363,
-0.012564010918140411,
0.04306471720337868,
0.0019635814242064953,
-0.017799124121665955,
-0.0128520792350173,
0... |
BigSalmon/GPT2HardArticleEasyArticle | [
"pytorch",
"jax",
"tensorboard",
"gpt2",
"text-generation",
"transformers"
] | text-generation | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 7 | null | ---
license: cc-by-4.0
---
## UoM&MMU at TSAR-2022 Shared Task - Prompt Learning for Lexical Simplification: prompt-ls-pt-3
We present **PromptLS**, a method for fine-tuning large pre-trained masked language models to perform the task of Lexical Simplification.
This model is part of a series of models presented at ... | [
-0.01094627846032381,
-0.019507180899381638,
0.006630889140069485,
0.06618283689022064,
0.026880545541644096,
0.017061747610569,
-0.02045491710305214,
0.0034757882822304964,
-0.01199608575552702,
0.04329819977283478,
0.001891806721687317,
-0.01862216368317604,
-0.013697984628379345,
0.0597... |
BigSalmon/GPTNeo350MInformalToFormalLincoln2 | [
"pytorch",
"gpt_neo",
"text-generation",
"transformers",
"has_space"
] | text-generation | {
"architectures": [
"GPTNeoForCausalLM"
],
"model_type": "gpt_neo",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram... | 8 | null | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: whisper_0010
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. -->
# whisper_0010
This mo... | [
-0.03858689218759537,
-0.01780126430094242,
0.010964413173496723,
0.03278861567378044,
0.03669174015522003,
0.004222019575536251,
-0.012802391313016415,
0.00632956949993968,
-0.024898171424865723,
0.06549914181232452,
0.02990490011870861,
-0.03048672527074814,
0.017755651846528053,
0.03896... |
BigSalmon/InfillFormalLincoln | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
] | text-generation | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 8 | null | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: whisper_0015
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. -->
# whisper_0015
This mo... | [
-0.039363499730825424,
-0.02071097120642662,
0.007197287864983082,
0.033521633595228195,
0.038076579570770264,
0.0007158175576478243,
-0.010955489240586758,
0.0028653007466346025,
-0.02371458150446415,
0.06360580027103424,
0.02823677659034729,
-0.02894185297191143,
0.01529774535447359,
0.0... |
BigSalmon/InformalToFormalLincoln21 | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"has_space"
] | text-generation | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 8 | null | ---
license: mit
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-de-fr
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 commen... | [
-0.03671831637620926,
-0.010162319988012314,
0.004341494292020798,
0.028324022889137268,
0.020521089434623718,
0.02187935635447502,
-0.019785640761256218,
-0.0067118084989488125,
-0.031428344547748566,
0.04498816281557083,
0.017581002786755562,
-0.056977368891239166,
0.012283988296985626,
... |
BigSalmon/T52 | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | {
"architectures": [
"T5ForConditionalGeneration"
],
"model_type": "t5",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": true,
"length_penalty": 2,
"max_length": 200,
"min_length": 30,
"no_repeat_ngram_s... | 8 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- xsum
model-index:
- name: t5-small-finetuned-xsum
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. -... | [
-0.017574109137058258,
-0.005403401795774698,
0.012223494239151478,
0.018211541697382927,
0.023312432691454887,
0.01050963718444109,
-0.023941218852996826,
-0.012038381770253181,
-0.026568971574306488,
0.047153882682323456,
0.04760301858186722,
0.0026966433506458998,
0.008337308652698994,
... |
BigSalmon/T5Salmon2 | [
"pytorch",
"jax",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | {
"architectures": [
"T5ForConditionalGeneration"
],
"model_type": "t5",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": true,
"length_penalty": 2,
"max_length": 200,
"min_length": 30,
"no_repeat_ngram_s... | 13 | 2022-11-08T16:38:49Z | ---
language: en
license: apache-2.0
library_name: diffusers
tags: []
datasets: imagefolder
metrics: []
---
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# ddpm-butterfl... | [
-0.018902581185102463,
-0.01619422622025013,
-0.0032896813936531544,
0.03788972273468971,
0.014751525595784187,
0.005779792554676533,
0.021262075752019882,
0.000414777547121048,
-0.0036513819359242916,
0.0550842359662056,
0.01731453463435173,
-0.019428765401244164,
0.010252279229462147,
0.... |
BigSalmon/TS3 | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible",
"has_space"
] | text2text-generation | {
"architectures": [
"T5ForConditionalGeneration"
],
"model_type": "t5",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_n... | 7 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- gsm8k
model-index:
- name: flan-t5-base-finetuned-gsm8k
results: []
widget:
- text: "Please, answer the following question reasoning step-by-step:
Manu bought 4 apples and lost one in the market. How many apples does Manu have?"
---
<!-- This model c... | [
-0.017023617401719093,
0.012011954560875893,
0.001723785768263042,
0.04372052475810051,
0.012419788166880608,
-0.0023701859172433615,
-0.006944733671844006,
-0.0058954027481377125,
-0.018958911299705505,
0.014804121106863022,
0.03186856955289841,
-0.014307559467852116,
0.02314596436917782,
... |
BigSalmon/prepositions | [
"pytorch",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible",
"has_space"
] | fill-mask | {
"architectures": [
"RobertaForMaskedLM"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngra... | 7 | 2022-11-08T16:55:26Z | ---
license: gpl-3.0
language:
- en
tags:
- wikipedia
- wikidata
widget:
- text: "Douglas Adams\n
1952 births\n
2001 deaths\n
20th-century atheists\n
21st-century atheists\n
20th-century English novelists\n
21st-century English novelists\n
20th-century English screenwriters\n
Alum... | [
0.01429393608123064,
-0.0045948033221066,
-0.014516957104206085,
0.0321962833404541,
0.05591277778148651,
0.030094873160123825,
-0.016418078914284706,
0.007737898733466864,
-0.05997588858008385,
0.05477342754602432,
0.0526563823223114,
0.01009344682097435,
0.01057971641421318,
0.0324273817... |
Bilz/DialoGPT-small-harrypotter | [] | 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 | 2022-11-08T17:13:14Z | ---
tags:
- pyannote
- pyannote-audio
- pyannote-audio-model
- audio
- voice
- speech
- speaker
- speaker-segmentation
- voice-activity-detection
- overlapped-speech-detection
- resegmentation
datasets:
- ami
- dihard
- voxconverse
license: mit
inference: false
---
# 🎹 Speaker segmentation
M... | [
-0.031686946749687195,
-0.0037196935154497623,
-0.0014618716668337584,
0.050448961555957794,
0.014762289822101593,
0.01052392553538084,
-0.011875694617629051,
-0.017658984288573265,
-0.018609292805194855,
0.06977272778749466,
0.03049188107252121,
-0.015327409841120243,
0.027999326586723328,
... |
BinksSachary/DialoGPT-small-shaxx | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 12 | 2022-11-08T18:23:44Z | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: hcho22/opus-mt-ko-en-finetuned-kr-to-en
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. -... | [
-0.025691166520118713,
-0.019508427008986473,
0.018836550414562225,
0.021035781130194664,
0.04175915941596031,
-0.0019526344258338213,
-0.007655743043869734,
-0.004914299119263887,
-0.039812926203012466,
0.0541592575609684,
0.016755396500229836,
-0.02043307200074196,
0.022601865231990814,
... |
BinksSachary/ShaxxBot2 | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 12 | 2022-11-08T18:37:26Z |
---
language:
- multilingual
- en
- fo
- is
- nn
- nb
- no
- da
- sv
license: cc-by-4.0
tags:
- norwegian
- bert
pipeline_tag: fill-mask
widget:
- text: På biblioteket kan du <mask> en bok.
- text: Dette er et <mask> eksempel.
- text: Av og til kan en språkmodell gi et <mask> resultat.
- text: Som ansat får du <mask... | [
-0.008869122713804245,
-0.016824664548039436,
0.029285460710525513,
0.040232766419649124,
0.06077823415398598,
0.015222731046378613,
-0.010852864943444729,
-0.01205264963209629,
-0.054754409939050674,
0.06271111965179443,
0.03066645935177803,
-0.030931631103157997,
-0.0075462874956429005,
... |
Blabla/Pipipopo | [] | null | {
"architectures": null,
"model_type": null,
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams... | 0 | null | ---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.50 +/- 2.72... | [
-0.018965313211083412,
-0.016136793419718742,
-0.008189276792109013,
0.030269576236605644,
0.04553905874490738,
0.00036376542993821204,
-0.0192501749843359,
0.003942906856536865,
-0.040274180471897125,
0.05789307504892349,
0.011908868327736855,
-0.012058350257575512,
0.009833283722400665,
... |
Broadus20/DialoGPT-small-joshua | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 12 | null | ---
language:
- en
license: creativeml-openrail-m
thumbnail: "https://s3.amazonaws.com/moonup/production/uploads/1667942059199-6305d083df993a789e61126d.jpeg"
tags:
- stable-diffusion
- text-to-image
---
## Model description
<b>isoCities</b> v1
This model trained based on Stable Diffusion 1.5 model to create isometric ... | [
-0.0053793336264789104,
-0.014026484452188015,
-0.026461783796548843,
0.049490973353385925,
0.0442621186375618,
-0.010785248130559921,
0.013102667406201363,
0.010846391320228577,
-0.015676572918891907,
0.05688650161027908,
0.03943651542067528,
0.017056118696928024,
-0.018294645473361015,
0... |
Brona/model1 | [] | 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: other
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: dalio-6.7b-test
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# dali... | [
-0.022307567298412323,
-0.012295006774365902,
-0.009306073188781738,
0.0318707674741745,
0.04531145095825195,
0.014889438636600971,
-0.020765673369169235,
-0.011808839626610279,
-0.04194144532084465,
0.055237945169210434,
0.02382509596645832,
-0.02861938253045082,
0.017647065222263336,
0.0... |
Brykee/BrykeeBot | [] | 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: mit
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: xlnet-base-cased-fine-Disaster-Tweets-Part3
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then r... | [
-0.040726255625486374,
0.001348531455732882,
-0.013932579196989536,
0.02892199344933033,
0.05179579183459282,
0.028963468968868256,
-0.012437039986252785,
-0.02529102750122547,
-0.045369867235422134,
0.04737209528684616,
0.02235310897231102,
-0.02223038859665394,
0.000981575925834477,
0.03... |
Bryson575x/riceboi | [] | 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 | 2022-11-08T21:40:43Z | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: distilbert-base-uncased-finetuned-squad
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. --... | [
-0.027541862800717354,
-0.004487183410674334,
-0.033327486366033554,
0.03822848200798035,
0.052263759076595306,
0.02029803954064846,
-0.03134296089410782,
-0.00723580177873373,
-0.04302964732050896,
0.05221445858478546,
0.031959641724824905,
-0.023945152759552002,
0.0196169912815094,
0.044... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.