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 |
|---|---|---|---|---|---|---|---|
Culmenus/opus-mt-de-is-finetuned-de-to-is | [
"pytorch",
"marian",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | {
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"MarianMTModel"
],
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"no_repeat_ngram_size... | 1 | null | This is `microsoft/layoutxlm-base` fine-tuned on XFUND, French for 1000 steps.
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Culmenus/opus-mt-de-is-finetuned-de-to-is_35g65cc_1 | [] | null | {
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tags:
- adapter-transformers
- roberta
datasets:
- glue
language:
- en
---
# Adapter `SALT-NLP/pfadapter-roberta-base-qqp-combined-value` for roberta-base
An [adapter](https://adapterhub.ml) for the `roberta-base` model that was trained on the [glue](https://huggingface.co/datasets/glue/) dataset and includes a p... | [
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Culmenus/opus-mt-de-is-finetuned-de-to-is_nr2-finetuned-de-to-is_nr2 | [] | null | {
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"num_beams... | 0 | 2022-09-19T09:45:22Z | ---
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:... | [
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CuongLD/wav2vec2-large-xlsr-vietnamese | [
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"vi",
"dataset:common_voice, infore_25h",
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"speech",
"xlsr-fine-tuning-week",
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] | automatic-speech-recognition | {
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],
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"no_repeat_ngram_s... | 8 | null | ---
license: mit
---
### rilakkuma on Stable Diffusion
This is the `<rilakkuma>` 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) ... | [
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CyberMuffin/DialoGPT-small-ChandlerBot | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | {
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"GPT2LMHeadModel"
],
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"no_repeat_ngram_size... | 9 | null | ---
license: cc-by-nc-sa-4.0
tags:
- generated_from_trainer
datasets:
- invoices
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: layoutlmv3-finetuned-invoice
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: invoices
type: invoices
... | [
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Cyrell/Cyrell | [] | null | {
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"num_beams... | 0 | null | ---
tags:
- generated_from_trainer
datasets:
- bc2gm_corpus
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: electramed-small-BC2GM-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: bc2gm_corpus
type: bc2gm_corpus
config: bc... | [
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D3vil/DialoGPT-smaall-harrypotter | [] | null | {
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"num_beams... | 0 | null | ---
license: mit
---
### indiana on Stable Diffusion
This is the `<indiana>` 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) note... | [
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D3vil/DialoGPT-smaall-harrypottery | [] | null | {
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"num_beams... | 0 | 2022-09-19T10:39:30Z | ---
tags:
- generated_from_trainer
datasets:
- ade_drug_effect_ner
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: electramed-small-ADE-DRUG-EFFECT-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: ade_drug_effect_ner
type: ade_d... | [
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D3xter1922/distilbert-base-uncased-finetuned-cola | [] | null | {
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"num_beams... | 0 | null | ---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: roberta-fine-sentiment-hineng-concat
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and compl... | [
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DARKVIP3R/DialoGPT-medium-Anakin | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | {
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"no_repeat_ngram_size... | 13 | null | ---
tags:
- generated_from_trainer
datasets:
- ade_drug_dosage_ner
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: electramed-small-ADE-DRUG-DOSAGE-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: ade_drug_dosage_ner
type: ade_d... | [
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DCU-NLP/bert-base-irish-cased-v1 | [
"pytorch",
"tf",
"bert",
"fill-mask",
"transformers",
"generated_from_keras_callback",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"BertForMaskedLM"
],
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"no_repeat_ngram_size... | 1,244 | null | ---
license: cc-by-nc-sa-4.0
tags:
- generated_from_trainer
datasets:
- nielsr/XFUN
model-index:
- name: layoutxlm-finetuned-xfund-fr
results: []
inference: false
---
# layoutxlm-finetuned-xfund-fr
This model is a fine-tuned version of [microsoft/layoutxlm-base](https://huggingface.co/microsoft/layoutxlm-base) on t... | [
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DCU-NLP/electra-base-irish-cased-discriminator-v1 | [
"pytorch",
"electra",
"pretraining",
"ga",
"transformers",
"irish",
"license:apache-2.0"
] | null | {
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"no_repeat_n... | 4 | null | ---
license: cc-by-nc-sa-4.0
tags:
- generated_from_trainer
datasets:
- nielsr/XFUN
metrics:
- precision
- recall
- f1
model-index:
- name: layoutxlm-finetuned-xfund-fr-re
results: []
inference: false
---
# layoutxlm-finetuned-xfund-fr-re
This model is a fine-tuned version of [microsoft/layoutxlm-base](https://hugg... | [
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DJSammy/bert-base-danish-uncased_BotXO-ai | [
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"da",
"dataset:common_crawl",
"dataset:wikipedia",
"transformers",
"bert",
"masked-lm",
"license:cc-by-4.0",
"fill-mask"
] | fill-mask | {
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"num_beams... | 14 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- matthews_correlation
model-index:
- name: distilbert-base-uncased-finetuned-cola
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
config: cola
... | [
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DSI/TweetBasedSA | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | {
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],
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"no_rep... | 29 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- massive
metrics:
- accuracy
model-index:
- name: bert-uncased-massive-intent-classification
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: massive
type: massive
config: en-US
... | [
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DSI/human-directed-sentiment | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | {
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],
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"no_rep... | 26 | null | ---
tags:
- conversational
---
# Model for a Stan Pines discord chatbot
# There are unknown errors and I am researching why. | [
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DTAI-KULeuven/mbert-corona-tweets-belgium-curfew-support | [
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"text-classification",
"multilingual",
"nl",
"fr",
"en",
"arxiv:2104.09947",
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] | text-classification | {
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"no_rep... | 29 | 2022-09-19T11:52:10Z | ---
tags:
- generated_from_trainer
language:
- sv
datasets:
- wikiann
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: BERT_swedish-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: wikiann
type: wikiann
config: sv
spl... | [
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DTAI-KULeuven/robbertje-1-gb-shuffled | [
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"dataset:conll2002",
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"transformers",
"Dutch",
"Flemish",
"RoBERTa",
"RobBERT",
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"license:mit",
"autotrain_c... | fill-mask | {
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"no_repeat_ngra... | 7 | null | ---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
- text-classification
- emotion
- pytorch
datasets:
- emotion
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: distilbert-base-cased-emotion
results:
- task:
type: text-classification
name: text-classification
da... | [
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0.014598219655454159,
-0.04805596172809601,
0.034331727772951126,
0.03... |
alexandrainst/da-hatespeech-classification-base | [
"pytorch",
"tf",
"safetensors",
"bert",
"text-classification",
"da",
"transformers",
"license:cc-by-sa-4.0"
] | 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... | 866 | null | ---
language:
- en
license: mit
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- spearmanr
model-index:
- name: roberta-base-stsb
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE STSB
type: glue
args: stsb
metrics:
- name: S... | [
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alexandrainst/da-hatespeech-detection-base | [
"pytorch",
"tf",
"safetensors",
"bert",
"text-classification",
"da",
"transformers",
"license:cc-by-sa-4.0"
] | text-classification | {
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
"task_specific_params": {
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},
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"min_length": null,
"no_rep... | 1,719 | 2022-09-20T03:14:21Z | ---
language:
- en
license: mit
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: roberta-base-mnli
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE MNLI
type: glue
args: mnli
metrics:
- name: Ac... | [
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0.02... |
alexandrainst/da-sentiment-base | [
"pytorch",
"tf",
"safetensors",
"bert",
"text-classification",
"da",
"arxiv:1910.09700",
"transformers",
"license:cc-by-sa-4.0"
] | text-classification | {
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
"task_specific_params": {
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"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_rep... | 1,432 | null | ---
license: apache-2.0
tags:
- summarization
- generated_from_trainer
datasets:
- mlsum
metrics:
- rouge
model-index:
- name: mt5-small-mlsum_training_sample
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: mlsum
type: mlsum
c... | [
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0.... |
alexandrainst/da-subjectivivity-classification-base | [
"pytorch",
"tf",
"safetensors",
"bert",
"text-classification",
"da",
"dataset:DDSC/twitter-sent",
"dataset:DDSC/europarl",
"transformers",
"license:cc-by-sa-4.0"
] | text-classification | {
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
"task_specific_params": {
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"max_length": null
},
"summarization": {
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"length_penalty": null,
"max_length": null,
"min_length": null,
"no_rep... | 846 | null | ---
tags:
- adapter-transformers
- bert
datasets:
- glue
language:
- en
---
# Adapter `SALT-NLP/pfadapter-bert-base-uncased-qqp-combined-value` for bert-base-uncased
An [adapter](https://adapterhub.ml) for the `bert-base-uncased` model that was trained on the [glue](https://huggingface.co/datasets/glue/) dataset and ... | [
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Dablio/Dablio | [] | null | {
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"num_beams... | 0 | null | ---
license: mit
---
### black and white design on Stable Diffusion
This is the `<PM_style>` 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_infer... | [
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... |
DaisyMak/bert-finetuned-squad-accelerate-10epoch_transformerfrozen | [
"pytorch",
"bert",
"question-answering",
"transformers",
"autotrain_compatible"
] | question-answering | {
"architectures": [
"BertForQuestionAnswering"
],
"model_type": "bert",
"task_specific_params": {
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},
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"max_length": null,
"min_length": null,
"no_repeat_n... | 1,907 | null |
---
language:
- pt
thumbnail: "Portuguese BERT for the Legal Domain"
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- transformers
datasets:
- assin
- assin2
- stsb_multi_mt
- rufimelo/PortugueseLegalSentences-v0
widget:
- source_sentence: "O advogado apresentou as provas ao j... | [
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0.0... |
Daivakai/DialoGPT-small-saitama | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
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"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 9 | 2022-09-19T13:52:02Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-finetuned-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: conll2003
type: conll2003
config:... | [
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0... |
DanBot/TCRsynth | [] | null | {
"architectures": null,
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},
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"num_beams... | 0 | null | ---
tags:
- translation
- generated_from_trainer
metrics:
- bleu
model-index:
- name: fantastic4-finetuned-vi-to-en-PhoMT-demo-T5-NLPHUST-Small
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, ... | [
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0.... |
DanL/scientific-challenges-and-directions | [
"pytorch",
"bert",
"text-classification",
"en",
"dataset:DanL/scientific-challenges-and-directions-dataset",
"arxiv:2108.13751",
"transformers",
"generated_from_trainer"
] | 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... | 134 | null | ---
license: mit
---
### Fold Structure on Stable Diffusion
This is the `<fold-geo>` 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.ipy... | [
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0.01595362275838852,
0.007041697856038809,
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0.041152872145175934,
0.006650437135249376,
-0.008178330026566982,
0.05287984386086464,
0.... |
Danbi/distilroberta-base-finetuned-wikitext2 | [] | null | {
"architectures": null,
"model_type": null,
"task_specific_params": {
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},
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"num_beams... | 0 | null | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Abdulmateen/mt5-small-finetuned-amazon-en-es
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 comme... | [
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0... |
Danih1502/t5-base-finetuned-en-to-de | [] | null | {
"architectures": null,
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},
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"num_beams... | 0 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: swin-tiny-patch4-window7-224-finetuned-eurosat
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
... | [
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0.0... |
Danih1502/t5-small-finetuned-en-to-de | [] | null | {
"architectures": null,
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"task_specific_params": {
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},
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"num_beams... | 0 | 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:... | [
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Darein/Def | [] | null | {
"architectures": null,
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"num_beams... | 0 | 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... | [
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0.0... |
DarkWolf/kn-electra-small | [
"pytorch",
"electra",
"feature-extraction",
"transformers"
] | feature-extraction | {
"architectures": [
"BertModel"
],
"model_type": "electra",
"task_specific_params": {
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},
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"no_repeat_ngram_size": ... | 4 | null | ---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- metrics:
- type: mean_reward
value: 512.00 +/- 131.55
name: mean_reward
task:
type: reinforcement-learning
... | [
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0... |
Darkecho789/email-gen | [] | null | {
"architectures": null,
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},
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"num_beams... | 0 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- asvspoof2019
metrics:
- accuracy
model-index:
- name: wav2vec2-base-finetuned-deepfake-0919
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread an... | [
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0... |
DarkestSky/distilbert-base-uncased-finetuned-ner | [] | null | {
"architectures": null,
"model_type": null,
"task_specific_params": {
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},
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"num_beams... | 0 | null | ---
license: mit
---
### Brunnya on Stable Diffusion
This is the `<Brunnya>` 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) note... | [
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0.03312922269105911,
0.04... |
Darkrider/covidbert_medmarco | [
"pytorch",
"jax",
"bert",
"text-classification",
"arxiv:2010.05987",
"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... | 35 | null | ---
license: mit
---
### Jos de Kat on Stable Diffusion
This is the `<kat-jos>` 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) n... | [
-0.03398806229233742,
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0.009315622970461845,
0.02453034184873104,
0.000739640963729471,
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0.040396254509687424,
-0.00419375067576766,
-0.02337905950844288,
0.038425907492637634,
0.0... |
Darren/darren | [
"pytorch"
] | null | {
"architectures": null,
"model_type": null,
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams... | 0 | null | ---
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: twitter-roberta-base-sentiment-sentiment-memes
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete... | [
-0.016025984659790993,
-0.011311440728604794,
-0.021264132112264633,
0.035356685519218445,
0.03692007437348366,
0.04184421896934509,
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-0.030236024409532547,
-0.05457429215312004,
0.04299505800008774,
0.03162670135498047,
-0.043407414108514786,
-0.0049055940471589565,
... |
DarshanDeshpande/marathi-distilbert | [
"pytorch",
"tf",
"distilbert",
"fill-mask",
"mr",
"dataset:Oscar Corpus, News, Stories",
"arxiv:1910.01108",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"DistilBertForMaskedLM"
],
"model_type": "distilbert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repea... | 14 | null | ---
license: mit
tags:
- generated_from_keras_callback
model-index:
- name: dummy-model
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# dummy-model
This model is a ... | [
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0.0... |
Darya/layoutlmv2-finetuned-funsd-test | [] | null | {
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},
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"no_repeat_ngram_size": null,
"num_beams... | 0 | 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... | [
-0.03510879725217819,
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0.009580070152878761,
0... |
DataikuNLP/distiluse-base-multilingual-cased-v1 | [
"pytorch",
"distilbert",
"arxiv:1908.10084",
"sentence-transformers",
"feature-extraction",
"sentence-similarity",
"transformers",
"license:apache-2.0"
] | sentence-similarity | {
"architectures": [
"DistilBertModel"
],
"model_type": "distilbert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngra... | 29 | null | ---
license: mit
---
### Singsing doll on Stable Diffusion
This is the `<singsing>` 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.ipyn... | [
-0.034221865236759186,
-0.02199738658964634,
-0.022867020219564438,
0.03845776990056038,
0.0040406654588878155,
0.021906673908233643,
0.0009652894805185497,
-0.0037234495393931866,
-0.03328939527273178,
0.05113956332206726,
0.006076551508158445,
-0.011336435563862324,
0.04380001500248909,
... |
DavidAMcIntosh/small-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 | 2022-09-19T16:27:36Z | ---
license: mit
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-fr
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xtreme
type: xtreme
args: PAN-X.fr
metrics:
- name:... | [
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0.0... |
DavidSpaceG/MSGIFSR | [] | null | {
"architectures": null,
"model_type": null,
"task_specific_params": {
"conversational": {
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},
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"num_beams... | 0 | 2022-09-19T16:28:48Z | ---
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... | [
-0.035715922713279724,
-0.015012279152870178,
0.003798834979534149,
0.029813647270202637,
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0.009104551747441292,
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Davlan/bert-base-multilingual-cased-finetuned-amharic | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 109 | 2022-09-19T16:30:28Z | ---
license: mit
---
### Singsing on Stable Diffusion
This is the `<singsing>` 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) no... | [
-0.032632987946271896,
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-0.023643111810088158,
0.03744487836956978,
-0.0009162509231828153,
0.02261219546198845,
0.0037675704807043076,
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0.051467809826135635,
0.008067302405834198,
-0.006337279453873634,
0.04128113389015198,
... |
Davlan/bert-base-multilingual-cased-finetuned-igbo | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 15 | 2022-09-19T16:46:40Z | ---
license: mit
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-it
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xtreme
type: xtreme
args: PAN-X.it
metrics:
- name:... | [
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... |
Davlan/bert-base-multilingual-cased-finetuned-kinyarwanda | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
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},
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"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 27 | 2022-09-19T17:00:33Z | ---
language: de
datasets:
- Legal-Entity-Recognition
---
### German BERT for Legal NER
#### Use:
```python
from transformers import pipeline
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("harshildarji/gbert-legal-ner", use_auth_token="AUTH_TOKEN")... | [
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0.... |
Davlan/bert-base-multilingual-cased-finetuned-luo | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
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},
"summarization": {
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"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 11 | null | ---
license: mit
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-en
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xtreme
type: xtreme
args: PAN-X.en
metrics:
- name:... | [
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0.... |
Davlan/bert-base-multilingual-cased-finetuned-swahili | [
"pytorch",
"tf",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 67 | null | ---
thumbnail: "url to a thumbnail used in social sharing"
library_name: keras
tags:
- keras
widget:
- src: https://huggingface.co/datasets/test_cats/cifar1.jpg
example_title: Tiger
--- | [
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0.0259... |
Davlan/bert-base-multilingual-cased-finetuned-wolof | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
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},
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"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 4 | 2022-09-19T17:18:44Z | ---
license: mit
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-all
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.... | [
-0.04120892286300659,
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0.0031476388685405254,
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0.04757506772875786,
0.0254836343228817,
-0.049438994377851486,
0.02033093199133873,
0.035... |
Davlan/bert-base-multilingual-cased-masakhaner | [
"pytorch",
"tf",
"bert",
"token-classification",
"arxiv:2103.11811",
"transformers",
"autotrain_compatible"
] | token-classification | {
"architectures": [
"BertForTokenClassification"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat... | 88 | 2022-09-19T17:46:12Z | ---
license: mit
---
### F-22 on Stable Diffusion
This is the `<f-22>` 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) notebook. ... | [
-0.026869280263781548,
-0.026882272213697433,
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0.04010511934757233,
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-0.03387358784675598,
0.03704429417848587,
0.009150473400950432,
-0.0014458958758041263,
0.03057420440018177,
0... |
Davlan/bert-base-multilingual-cased-ner-hrl | [
"pytorch",
"tf",
"bert",
"token-classification",
"transformers",
"autotrain_compatible",
"has_space"
] | token-classification | {
"architectures": [
"BertForTokenClassification"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat... | 269,898 | 2022-09-19T17:49:30Z | ---
language: en
thumbnail: http://www.huggingtweets.com/chriscantino/1663609825906/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; wi... | [
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-0.016997763887047768,... |
Davlan/byt5-base-eng-yor-mt | [
"pytorch",
"t5",
"text2text-generation",
"arxiv:2103.08647",
"transformers",
"autotrain_compatible"
] | 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... | 11 | null | ---
license: mit
---
### Jin Kisaragi on Stable Diffusion
This is the `<jin-kisaragi>` 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.i... | [
-0.025377487763762474,
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0.05095167085528374,
-0.00007665733573958278,
-0.016878245398402214,
0.04119057208299637,
0... |
Davlan/byt5-base-yor-eng-mt | [
"pytorch",
"t5",
"text2text-generation",
"arxiv:2103.08647",
"transformers",
"autotrain_compatible"
] | 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... | 12 | 2022-09-19T17:59:14Z | ---
license: mit
---
### Depthmap Style on Stable Diffusion
This is the `<depthmap>` 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.ipy... | [
-0.029775910079479218,
-0.03116031177341938,
-0.03517654165625572,
0.03930830582976341,
0.011867109686136246,
0.017139622941613197,
0.0018124424386769533,
0.007163967937231064,
-0.02701825089752674,
0.048665694892406464,
-0.002781008370220661,
-0.021424425765872,
0.03754406422376633,
0.039... |
Davlan/distilbert-base-multilingual-cased-ner-hrl | [
"pytorch",
"tf",
"distilbert",
"token-classification",
"transformers",
"autotrain_compatible",
"has_space"
] | token-classification | {
"architectures": [
"DistilBertForTokenClassification"
],
"model_type": "distilbert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
... | 123,856 | 2022-09-19T18:01:57Z | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
license: apache-2.0
language:
- id
library_name: sentence-transformers
---
# indo-sentence-bert-base
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragra... | [
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... |
Davlan/m2m100_418M-eng-yor-mt | [
"pytorch",
"m2m_100",
"text2text-generation",
"arxiv:2103.08647",
"transformers",
"autotrain_compatible"
] | text2text-generation | {
"architectures": [
"M2M100ForConditionalGeneration"
],
"model_type": "m2m_100",
"task_specific_params": {
"conversational": {
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},
"summarization": {
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"length_penalty": null,
"max_length": null,
"min_length": null,
"no... | 9 | 2022-09-19T18:17:04Z | ---
license: mit
---
### crested gecko on Stable Diffusion
This is the `<crested-gecko>` 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... | [
-0.021955396980047226,
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0.02442382276058197,
0.015479669906198978,
-0.0009585028747096658,
-0.012688200920820236,
-0.02318112552165985,
0.04920688271522522,
-0.0005283129867166281,
-0.018164101988077164,
0.03901781514286995,
0... |
Davlan/m2m100_418M-yor-eng-mt | [
"pytorch",
"m2m_100",
"text2text-generation",
"arxiv:2103.08647",
"transformers",
"autotrain_compatible"
] | text2text-generation | {
"architectures": [
"M2M100ForConditionalGeneration"
],
"model_type": "m2m_100",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no... | 6 | 2022-09-19T18:17:47Z | ---
license: mit
---
### GrisStyle on Stable Diffusion
This is the `<gris>` 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) noteb... | [
-0.017503637820482254,
-0.019111590459942818,
-0.03673110157251358,
0.03682246431708336,
0.017498647794127464,
0.024519408121705055,
-0.003655910026282072,
0.01159108430147171,
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0.05537392944097519,
-0.014777772128582,
-0.012966684065759182,
0.030712150037288666,
0.039... |
Davlan/mbart50-large-yor-eng-mt | [
"pytorch",
"mbart",
"text2text-generation",
"arxiv:2103.08647",
"transformers",
"autotrain_compatible"
] | text2text-generation | {
"architectures": [
"MBartForConditionalGeneration"
],
"model_type": "mbart",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_re... | 5 | null | ---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- metrics:
- type: mean_reward
value: 425.50 +/- 151.35
name: mean_reward
task:
type: reinforcement-learning
... | [
-0.03788581117987633,
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0.019842565059661865,
-0.03150176629424095,
0.01612832397222519,
0.02203... |
Davlan/mt5-small-pcm-en | [
"pytorch",
"mt5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | {
"architectures": [
"MT5ForConditionalGeneration"
],
"model_type": "mt5",
"task_specific_params": {
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},
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"max_length": null,
"min_length": null,
"no_repeat... | 9 | 2022-09-19T18:58:07Z | ---
license: mit
---
### ikea-fabler on Stable Diffusion
This is the `<ikea-fabler>` 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.ipy... | [
-0.025674894452095032,
-0.024179082363843918,
-0.01788453944027424,
0.038373518735170364,
0.011176499538123608,
0.01477962639182806,
-0.0008683839696459472,
-0.014028268866240978,
-0.03918282687664032,
0.04313616827130318,
0.007980798371136189,
-0.010360149666666985,
0.026896843686699867,
... |
Davlan/xlm-roberta-base-finetuned-kinyarwanda | [
"pytorch",
"xlm-roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"XLMRobertaForMaskedLM"
],
"model_type": "xlm-roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repe... | 61 | null | ---
license: mit
---
### Joe Mad on Stable Diffusion
This is the `<joe-mad>` 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) note... | [
-0.02809927612543106,
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0.014164473861455917,
0.022244025021791458,
0.004415474366396666,
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-0.027313625440001488,
0.03943139687180519,
-0.008825874887406826,
-0.016057370230555534,
0.038238104432821274,
... |
Davlan/xlm-roberta-base-masakhaner | [
"pytorch",
"xlm-roberta",
"token-classification",
"arxiv:2103.11811",
"transformers",
"autotrain_compatible"
] | token-classification | {
"architectures": [
"XLMRobertaForTokenClassification"
],
"model_type": "xlm-roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
... | 3 | 2022-09-19T22:35:16Z | ---
language:
- en
tags:
- text-classification
license: cc0-1.0
library: Transformers
widget:
- text: "sdfsdfa"
example_title: "Gibberish"
- text: "idkkkkk"
example_title: "Uncertainty"
- text: "Because you asked"
example_title: "Refusal"
- text: "I am a cucumber"
example_title: "High-risk"
- text: "My job w... | [
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0.0008763388032093644,
0.02817043475806713,
0.04655... |
Davlan/xlm-roberta-base-wikiann-ner | [
"pytorch",
"tf",
"xlm-roberta",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | {
"architectures": [
"XLMRobertaForTokenClassification"
],
"model_type": "xlm-roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
... | 235 | 2022-09-19T23:10:53Z | ---
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... | [
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0.01029693428426981,
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0.03467549383640289,
0.043635... |
Dayout/test | [] | null | {
"architectures": null,
"model_type": null,
"task_specific_params": {
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},
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"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams... | 0 | null | ---
license: mit
---
### wojaks-now on Stable Diffusion
This is the `<red-wojak>` 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)... | [
-0.025831423699855804,
-0.028221596032381058,
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0.015399548225104809,
0.003856226336210966,
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-0.03623247891664505,
0.04790452495217323,
0.02246500551700592,
-0.018603695556521416,
0.026881882920861244,
0.... |
Dbluciferm3737/Idk | [] | null | {
"architectures": null,
"model_type": null,
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams... | 0 | 2022-09-20T00:43:52Z | ---
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... | [
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0.0... |
DecafNosebleed/ScaraBot | [] | null | {
"architectures": null,
"model_type": null,
"task_specific_params": {
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},
"summarization": {
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"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams... | 0 | null | ---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: Frozen... | [
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0.020713698118925095,
... |
Declan/FoxNews_model_v2 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
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},
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"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 3 | 2022-09-20T07:59:19Z |
---
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... | [
-0.05448301509022713,
0.0058897812850773335,
-0.006119890604168177,
0.05848414823412895,
0.025989603251218796,
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0.05056033283472061,
0.01907362788915634,
-0.011249948292970657,
0.0071635316126048565,
... |
Declan/FoxNews_model_v4 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 7 | null | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: S1d-dha-nth3/ncert_bio
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. -->
# S1d-dha-nth... | [
-0.027884487062692642,
-0.024094514548778534,
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0.016361577436327934,
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0.02507168985903263,
0.... |
Declan/FoxNews_model_v5 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 7 | null | ---
tags:
- RUDOLPH
- text-image
- image-text
- decoder
---
# RUDOLPH-2.7B (XL)
RUDOLPH: One Hyper-Tasking Transformer Can be Creative as DALL-E and GPT-3 and Smart as CLIP
<img src="https://raw.githubusercontent.com/sberbank-ai/ru-dolph/master/pics/RUDOLPH.png" width=60% border="2"/>
Model was trained by [Sber AI](... | [
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0.0... |
Declan/NewYorkTimes_model_v1 | [] | null | {
"architectures": null,
"model_type": null,
"task_specific_params": {
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},
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"num_beams... | 0 | null | ---
license: mit
---
### Jinjoon Lee, They on Stable Diffusion
This is the `<jinjoon_lee_they>` 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_in... | [
-0.03273790329694748,
-0.025862274691462517,
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0.04283454269170761,
0.01123949233442545,
0.017466463148593903,
0.0018752400064840913,
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0.036661162972450256,
0.006092882249504328,
-0.014785055071115494,
0.04172409698367119,
0.... |
Declan/Politico_model_v6 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 3 | null | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: evegarcianz/bert-finetuned-adversarial_qa
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.... | [
-0.0340048223733902,
-0.014115127734839916,
-0.025630081072449684,
0.02440904825925827,
0.06475098431110382,
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0.0376833938062191,
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0.008221350610256195,
0.0... |
Declan/WallStreetJournal_model_v1 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 3 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: distilbert-base-uncased-sentiment-finetuned-memes
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably... | [
-0.0010757757117971778,
-0.0024046977050602436,
-0.044934846460819244,
0.03995076194405556,
0.055361147969961166,
0.021548740565776825,
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0.05904245749115944,
0.025506729260087013,
-0.03425285220146179,
0.017676539719104767,
... |
Declan/WallStreetJournal_model_v3 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 3 | null | ---
license: mit
---
### liliana on Stable Diffusion
This is the `<liliana>` 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) note... | [
-0.022508008405566216,
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0.04345031827688217,
0.00271118083037436,
0.015417033806443214,
0.0006168223917484283,
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0.040324866771698,
0.009346965700387955,
-0.00966513343155384,
0.0334760807454586,
0.03396... |
DeepPavlov/distilrubert-base-cased-conversational | [
"pytorch",
"distilbert",
"ru",
"arxiv:2205.02340",
"transformers"
] | null | {
"architectures": null,
"model_type": "distilbert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"n... | 6,324 | 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... | [
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-0.017038146033883095,
-0.016540275886654854,
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0.00234610796906054,
0.04092745... |
DeepPavlov/marianmt-tatoeba-ruen | [
"pytorch",
"marian",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | {
"architectures": [
"MarianMTModel"
],
"model_type": "marian",
"task_specific_params": {
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"no_repeat_ngram_size... | 30 | 2022-09-20T12:03:53Z | ---
license: mit
---
### wheatland-ARKNIGHT on Stable Diffusion
This is the `<golden-wheats-fields>` 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_conceptualiz... | [
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0.... |
Denilson/gbert-base-germaner | [] | null | {
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"num_beams... | 0 | null | ---
license: mit
---
### 001glitch_core on Stable Diffusion
This is the `001glitch_core` 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... | [
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... |
Deniskin/emailer_medium_300 | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
] | text-generation | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
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},
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"no_repeat_ngram_size... | 14 | null | ---
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:... | [
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Despin89/test | [] | null | {
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"num_beams... | 0 | 2022-09-20T12:52:20Z | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
---
# teven/bi_all-mpnet-base-v2_finetuned_WebNLG2017
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 fo... | [
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0.008709194138646126,
0.0368... |
Dibyaranjan/nl_image_search | [] | null | {
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"num_beams... | 0 | 2022-09-20T13:14:52Z | ---
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... | [
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0... |
Digakive/Hsgshs | [] | null | {
"architectures": null,
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},
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"num_beams... | 0 | 2022-09-20T13:29:14Z | ---
license: apache-2.0
---
# Model description
This is an [t5-base](https://huggingface.co/t5-base) model, finetuned to generate questions given a table and linked passages using [HybridQA](https://huggingface.co/datasets/hybrid_qa) dataset. It was trained to generate questions from reasoning paths extracted from hy... | [
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0.045... |
DimaOrekhov/transformer-method-name | [
"pytorch",
"encoder-decoder",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | {
"architectures": [
"EncoderDecoderModel"
],
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},
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"no_re... | 8 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: distilbert-base-uncased-sentiment-finetuned-memes-20epoch
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should ... | [
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DongHyoungLee/kogpt2-base-v2-finetuned-kogpt2_nsmc_single_sentence_classification | [] | null | {
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"num_beams... | 0 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: distilbert-base-uncased-sentiment-finetuned-memes-30epochs
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should... | [
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albert-base-v2 | [
"pytorch",
"tf",
"jax",
"rust",
"safetensors",
"albert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1909.11942",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] | fill-mask | {
"architectures": [
"AlbertForMaskedLM"
],
"model_type": "albert",
"task_specific_params": {
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},
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"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_... | 4,785,283 | 2022-09-20T15:11:39Z | ---
tags:
- LunarLander-v2
- ppo
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metri... | [
-0.014133086428046227,
0.014375831000506878,
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0.06869211047887802,
0.031182533130049706,
-0.023848416283726692,
0.004418389871716499,
0.... |
albert-xxlarge-v2 | [
"pytorch",
"tf",
"safetensors",
"albert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1909.11942",
"transformers",
"exbert",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] | fill-mask | {
"architectures": [
"AlbertForMaskedLM"
],
"model_type": "albert",
"task_specific_params": {
"conversational": {
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},
"summarization": {
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"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_... | 42,640 | null | ---
tags:
- image-classification
- pytorch
- huggingpics
metrics:
- accuracy
model-index:
- name: test-category
results:
- task:
name: Image Classification
type: image-classification
metrics:
- name: Accuracy
type: accuracy
value: 0.9196428656578064
---
# test-category
Auto... | [
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0... |
bert-base-cased | [
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1810.04805",
"transformers",
"exbert",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] | fill-mask | {
"architectures": [
"BertForMaskedLM"
],
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},
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"min_length": null,
"no_repeat_ngram_size... | 8,621,271 | 2022-09-20T15:35:29Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- eli5
metrics:
- rouge
model-index:
- name: t5-base-finetuned-eli5
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: eli5
type: eli5
config: LFQA_reddit
spl... | [
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... |
bert-base-german-cased | [
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"fill-mask",
"de",
"transformers",
"exbert",
"license:mit",
"autotrain_compatible",
"has_space"
] | fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
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},
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"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 175,983 | null | ---
license: mit
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: stbl_clinical_bert_ft_rs2bs
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# ... | [
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... |
bert-base-multilingual-uncased | [
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"fill-mask",
"multilingual",
"af",
"sq",
"ar",
"an",
"hy",
"ast",
"az",
"ba",
"eu",
"bar",
"be",
"bn",
"inc",
"bs",
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"my",
"ca",
"ceb",
"ce",
"zh",
"cv",
"hr",
"cs",
"da",
"nl",
"en",
"et",
... | fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
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},
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"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 328,585 | null | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 250.29 +/- 21.30
name: mean_reward
task:
type: reinforcement-learning
name: re... | [
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bert-base-uncased | [
"pytorch",
"tf",
"jax",
"rust",
"safetensors",
"bert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1810.04805",
"transformers",
"exbert",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] | fill-mask | {
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],
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},
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"no_repeat_ngram_size... | 59,663,489 | 2022-09-20T16:10:09Z | ---
language:
- en
license: cc-by-nc-sa-4.0
tags:
- seq2seq
- relation-extraction
- triple-generation
- entity-linking
- entity-type-linking
- relation-linking
datasets: Babelscape/rebel-dataset
widget:
- text: The Italian Space Agency’s Light Italian CubeSat for Imaging of Asteroids,
or LICIACube, will fly by Dimo... | [
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0.02... |
bert-large-cased-whole-word-masking-finetuned-squad | [
"pytorch",
"tf",
"jax",
"rust",
"safetensors",
"bert",
"question-answering",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1810.04805",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] | question-answering | {
"architectures": [
"BertForQuestionAnswering"
],
"model_type": "bert",
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},
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"max_length": null,
"min_length": null,
"no_repeat_n... | 8,214 | 2022-09-20T16:12:02Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- eli5
metrics:
- rouge
model-index:
- name: t5-small-finetuned-eli5
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: eli5
type: eli5
config: LFQA_reddit
sp... | [
-0.000006190653493831633,
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... |
bert-large-uncased-whole-word-masking | [
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1810.04805",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] | fill-mask | {
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],
"model_type": "bert",
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},
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"min_length": null,
"no_repeat_ngram_size... | 76,685 | 2022-09-20T16:22:35Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- farleyknight/big_patent_5_percent
metrics:
- rouge
model-index:
- name: patent-summarization-allen-led-large-2022-09-20
results:
- task:
name: Summarization
type: summarization
dataset:
name: farleyknight/big_patent_5_percent
... | [
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0.0... |
distilbert-base-german-cased | [
"pytorch",
"safetensors",
"distilbert",
"fill-mask",
"de",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] | fill-mask | {
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],
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"max_length": null,
"min_length": null,
"no_repea... | 43,667 | null | This is a multilingual model that translates Buddhist Chinese, Tibetan and Pali into English.
Chinese input should be in simplified characters (簡體字).
Tibetan should be input in Wylie transliteration, with "/" as shad and no space between the last word and a shad. For example "gang zag la bdag med par khong du chud p... | [
-0.026437126100063324,
-0.03747686371207237,
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0.04705268144607544,
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0.03425922617316246,
-0.0023208463098853827,
-0.03201693296432495,
0.049066219478845596,
0... |
09panesara/distilbert-base-uncased-finetuned-cola | [
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"dataset:glue",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | text-classification | {
"architectures": [
"DistilBertForSequenceClassification"
],
"model_type": "distilbert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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"length_penalty": null,
"max_length": null,
"min_length": null,
... | 40 | 2022-09-20T21:12:02Z | # VQGAN-CLIP Overview
A repo for running VQGAN+CLIP locally. This started out as a Katherine Crowson VQGAN+CLIP derived Google colab notebook.
<a href="https://replicate.ai/nerdyrodent/vqgan-clip"><img src="https://img.shields.io/static/v1?label=Replicate&message=Demo and Docker Image&color=blue"></a>
Original noteb... | [
-0.016662025824189186,
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0.0013857566518709064,
0.04381999000906944,
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0.03318217024207115,
0.023516297340393066,
-0.014916815795004368,
0.015303220599889755,
0.0... |
AAli/distilbert-base-uncased-finetuned-squad | [] | null | {
"architectures": null,
"model_type": null,
"task_specific_params": {
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},
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"min_length": null,
"no_repeat_ngram_size": null,
"num_beams... | 0 | 2022-09-21T03:49:54Z | ---
datasets:
- relbert/semeval2012_relational_similarity
model-index:
- name: relbert/roberta-large-semeval2012-mask-prompt-e-loob-conceptnet-validated
results:
- task:
name: Relation Mapping
type: sorting-task
dataset:
name: Relation Mapping
args: relbert/relation_mapping
type: r... | [
-0.0000293226694338955,
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0.030346199870109558,
0.014081788249313831,
0.007400143425911665,
0.020428868010640144,
0... |
Adnan/UrduNewsHeadlines | [] | null | {
"architectures": null,
"model_type": null,
"task_specific_params": {
"conversational": {
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},
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"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams... | 0 | 2022-09-21T17:35:44Z | ---
license: mit
---
### Dicoo2 on Stable Diffusion
This is the `<dicoo>` 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) noteboo... | [
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0.04317969083786011,
0.004183696582913399,
-0.00640901131555438,
0.028439588844776154,
0.0405... |
AethiQs-Max/aethiqs-base_bertje-data_rotterdam-epochs_10 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 9 | 2022-09-21T18:05:47Z | ---
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-geeve-no... | [
-0.0010725960601121187,
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0.009839585050940514,
0.042637236416339874,
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0.04753011465072632,
0.0087905777618289,
0.0004288007621653378,
0.010114703327417374,
0.... |
AhmedSSoliman/MarianCG-CoNaLa | [
"pytorch",
"marian",
"text2text-generation",
"transformers",
"autotrain_compatible",
"has_space"
] | text2text-generation | {
"architectures": [
"MarianMTModel"
],
"model_type": "marian",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 21 | null | ---
license: mit
---
### DarkPlane on Stable Diffusion
This is the `<DarkPlane>` 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) ... | [
-0.03266730532050133,
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-0.037684984505176544,
0.04197048395872116,
0.017985958606004715,
0.022105377167463303,
0.0031463534105569124,
0.0034890768583863974,
-0.02784925512969494,
0.05155253782868385,
0.0004038215847685933,
-0.006936785764992237,
0.025051191449165344,
0... |
Ahren09/distilbert-base-uncased-finetuned-cola | [
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"transformers"
] | text-classification | {
"architectures": [
"DistilBertForSequenceClassification"
],
"model_type": "distilbert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
... | 33 | null | ---
license: mit
---
### Wildkat on Stable Diffusion
This is the `<wildkat>` 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) note... | [
-0.032014671713113785,
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0.0454910583794117,
0.008522196672856808,
0.025516528636217117,
0.0023464856203645468,
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0.04949456453323364,
0.008263264782726765,
-0.020543890073895454,
0.03669137507677078,
0.... |
AimB/konlpy_berttokenizer_helsinki | [] | 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
---
### Half-Life 2 Dog on Stable Diffusion
This is the `<hl-dog>` 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.ipyn... | [
-0.027600189670920372,
-0.015164565294981003,
-0.03522089496254921,
0.03861039876937866,
0.01399959810078144,
0.022241128608584404,
-0.005046801175922155,
-0.014006107114255428,
-0.038493234664201736,
0.045204274356365204,
0.002986607374623418,
-0.010144262574613094,
0.02656170353293419,
0... |
Akashpb13/Galician_xlsr | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"gl",
"dataset:mozilla-foundation/common_voice_8_0",
"transformers",
"mozilla-foundation/common_voice_8_0",
"generated_from_trainer",
"robust-speech-event",
"model_for_talk",
"hf-asr-leaderboard",
"license:apache-2.0",
"model-index"
] | automatic-speech-recognition | {
"architectures": [
"Wav2Vec2ForCTC"
],
"model_type": "wav2vec2",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_s... | 7 | null | ---
license: mit
---
### Midjourney style on Stable Diffusion
This is the `<midjourney-style>` 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_inf... | [
-0.031703151762485504,
-0.021258730441331863,
-0.037269409745931625,
0.03678268939256668,
0.0032048316206783056,
0.015097618103027344,
0.010690881870687008,
0.0021509972866624594,
-0.034460507333278656,
0.044320955872535706,
-0.012544662691652775,
-0.0245183315128088,
0.026200629770755768,
... |
Akiva/Joke | [] | 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:
- autotrain
- vision
- image-classification
datasets:
- omarques/autotrain-data-test-dogs-cats
widget:
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg
example_title: Tiger
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg
example_title: ... | [
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0.0509369894862175,
0.053834568709135056,
0.0008586536278016865,
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0.06397251784801483,
-0.005362396594136953,
-0.0010154234478250146,
-0.003163928398862481,
... |
Aklily/Lilys | [] | null | {
"architectures": null,
"model_type": null,
"task_specific_params": {
"conversational": {
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},
"summarization": {
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"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams... | 0 | null | ---
license: mit
---
### maus on Stable Diffusion
This is the `<Maus>` 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) notebook. ... | [
-0.03355387598276138,
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0.04289596527814865,
0.010569866746664047,
-0.006389528512954712,
0.035946235060691833,
... |
AkshatSurolia/BEiT-FaceMask-Finetuned | [
"pytorch",
"beit",
"image-classification",
"dataset:Face-Mask18K",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | image-classification | {
"architectures": [
"BeitForImageClassification"
],
"model_type": "beit",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat... | 239 | null | ---
datasets:
- relbert/semeval2012_relational_similarity
model-index:
- name: relbert/roberta-large-semeval2012-average-no-mask-prompt-c-nce-classification
results:
- task:
name: Relation Mapping
type: sorting-task
dataset:
name: Relation Mapping
args: relbert/relation_mapping
typ... | [
0.0042952923104166985,
-0.00728144496679306,
-0.024782704189419746,
0.05585511773824692,
0.04776844009757042,
0.02303207851946354,
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-0.00869178120046854,
-0.06780927628278732,
0.03390219807624817,
0.01591542176902294,
0.005469937343150377,
0.01790156029164791,
0.033989... |
AlanDev/DallEMiniButBetter | [] | 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 | ---
datasets:
- relbert/semeval2012_relational_similarity
model-index:
- name: relbert/roberta-large-semeval2012-average-no-mask-prompt-d-nce-classification
results:
- task:
name: Relation Mapping
type: sorting-task
dataset:
name: Relation Mapping
args: relbert/relation_mapping
typ... | [
0.004706432111561298,
-0.008176040835678577,
-0.02456124871969223,
0.05692024528980255,
0.04747423529624939,
0.02259541116654873,
-0.037335216999053955,
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0.034132033586502075,
0.015872308984398842,
0.0038621460553258657,
0.017282821238040924,
0.0... |
Ale/Alen | [] | 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:
- feature-extraction
pipeline_tag: feature-extraction
---
This model is the query encoder of the MS MARCO BM25 Lexical Model (Λ) from the SPAR paper:
[Salient Phrase Aware Dense Retrieval: Can a Dense Retriever Imitate a Sparse One?](https://arxiv.org/abs/2110.06918)
<br>
Xilun Chen, Kushal Lakhotia, Barlas... | [
-0.021346701309084892,
-0.014690669253468513,
-0.025124773383140564,
0.07353045791387558,
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0.05901952087879181,
0.043933939188718796,
0.008533140644431114,
0.001260887598618865,
0.02... |
Aleenbo/Arcane | [] | 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:
- feature-extraction
pipeline_tag: feature-extraction
---
This model is the context encoder of the MS MARCO BM25 Lexical Model (Λ) from the SPAR paper:
[Salient Phrase Aware Dense Retrieval: Can a Dense Retriever Imitate a Sparse One?](https://arxiv.org/abs/2110.06918)
<br>
Xilun Chen, Kushal Lakhotia, Barl... | [
-0.024974022060632706,
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0.06427846848964691,
0.048789653927087784,
0.007653083186596632,
0.0032407452818006277,
0.... |
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