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 |
|---|---|---|---|---|---|---|
bert-base-german-dbmdz-uncased | [
"pytorch",
"jax",
"safetensors",
"bert",
"fill-mask",
"de",
"transformers",
"license:mit",
"autotrain_compatible",
"has_space"
] | fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
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"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 68,305 | 2022-10-14T02:29:30Z | ---
language:
- en
license: mit
tags:
- generated_from_trainer
datasets:
- OpenTable
metrics:
- accuracy
model-index:
- name: roberta-base.CEBaB_confounding.food_service_positive.sa.5-class.seed_42
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: OpenTable OP... |
bert-base-multilingual-cased | [
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"fill-mask",
"multilingual",
"af",
"sq",
"ar",
"an",
"hy",
"ast",
"az",
"ba",
"eu",
"bar",
"be",
"bn",
"inc",
"bs",
"br",
"bg",
"my",
"ca",
"ceb",
"ce",
"zh",
"cv",
"hr",
"cs",
"da",
"nl",
"en",
"et",
... | fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
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},
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"no_repeat_ngram_size... | 4,749,504 | 2022-10-14T02:32:19Z | ---
language:
- en
license: mit
tags:
- generated_from_trainer
datasets:
- OpenTable
metrics:
- accuracy
model-index:
- name: roberta-base.CEBaB_confounding.food_service_positive.sa.5-class.seed_43
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: OpenTable OP... |
bert-base-uncased | [
"pytorch",
"tf",
"jax",
"rust",
"safetensors",
"bert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1810.04805",
"transformers",
"exbert",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] | fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 59,663,489 | 2022-10-14T03:06:13Z | ---
license: mit
language: en
tags:
- bert
- cloze
- distractor
- generation
datasets:
- dgen
widget:
- text: "The only known planet with large amounts of water is [MASK]. [SEP] earth"
- text: "The products of photosynthesis are glucose and [MASK] else. [SEP] oxygen"
---
# cdgp-csg-scibert-dgen
## Model description
... |
bert-large-cased-whole-word-masking | [
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1810.04805",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] | fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
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},
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"max_length": null,
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"no_repeat_ngram_size... | 2,316 | 2022-10-14T03:12:08Z | ---
language:
- en
license: mit
tags:
- generated_from_trainer
datasets:
- OpenTable
metrics:
- accuracy
model-index:
- name: roberta-base.CEBaB_confounding.observational.absa.5-class.seed_42
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: OpenTable OPENTABL... |
bert-large-cased | [
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1810.04805",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] | fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 388,769 | 2022-10-14T03:16:16Z | ---
license: mit
language: en
tags:
- roberta
- cloze
- distractor
- generation
datasets:
- cloth
widget:
- text: "I feel <mask> now. </s> happy"
- text: "The old man was waiting for a ride across the <mask>. </s> river"
---
# cdgp-csg-roberta-cloth
## Model description
This model is a Candidate Set Generator in **"... |
bert-large-uncased-whole-word-masking-finetuned-squad | [
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"question-answering",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1810.04805",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] | question-answering | {
"architectures": [
"BertForQuestionAnswering"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
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"max_length": null,
"min_length": null,
"no_repeat_n... | 480,510 | 2022-10-14T03:17:00Z | ---
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: pegasus-newsroom-headline_writer_57k
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. -->
# p... |
bert-large-uncased-whole-word-masking | [
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1810.04805",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] | fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 76,685 | 2022-10-14T03:17:11Z | ---
language:
- en
license: mit
tags:
- generated_from_trainer
datasets:
- OpenTable
metrics:
- accuracy
model-index:
- name: roberta-base.CEBaB_confounding.observational.absa.5-class.seed_43
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: OpenTable OPENTABL... |
bert-large-uncased | [
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1810.04805",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] | fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 1,058,496 | 2022-10-14T03:20:05Z | ---
tags:
- flair
- token-classification
- sequence-tagger-model
language: en
widget:
- text: >-
SELECT shipping FROM users WHERE shipping = '201 Thayer St Providence RI
02912'
license: mit
datasets:
- beki/privy
---
| Feature | Description |
| --- | --- |
| **Name** | `en_spacy_pii_distilbert` |
| **Version** ... |
camembert-base | [
"pytorch",
"tf",
"safetensors",
"camembert",
"fill-mask",
"fr",
"dataset:oscar",
"arxiv:1911.03894",
"transformers",
"license:mit",
"autotrain_compatible",
"has_space"
] | fill-mask | {
"architectures": [
"CamembertForMaskedLM"
],
"model_type": "camembert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_... | 1,440,898 | 2022-10-14T03:22:12Z | ---
language:
- en
license: mit
tags:
- generated_from_trainer
datasets:
- OpenTable
metrics:
- accuracy
model-index:
- name: roberta-base.CEBaB_confounding.observational.absa.5-class.seed_44
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: OpenTable OPENTABL... |
ctrl | [
"pytorch",
"tf",
"ctrl",
"en",
"arxiv:1909.05858",
"arxiv:1910.09700",
"transformers",
"license:bsd-3-clause",
"has_space"
] | null | {
"architectures": null,
"model_type": "ctrl",
"task_specific_params": {
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"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_bea... | 17,007 | 2022-10-14T03:25:26Z | ---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- OpenTable
metrics:
- accuracy
model-index:
- name: bert-base-uncased.CEBaB_confounding.observational.sa.5-class.seed_42
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: OpenTabl... |
distilbert-base-cased-distilled-squad | [
"pytorch",
"tf",
"rust",
"safetensors",
"openvino",
"distilbert",
"question-answering",
"en",
"dataset:squad",
"arxiv:1910.01108",
"arxiv:1910.09700",
"transformers",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"has_space"
] | question-answering | {
"architectures": [
"DistilBertForQuestionAnswering"
],
"model_type": "distilbert",
"task_specific_params": {
"conversational": {
"max_length": null
},
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"length_penalty": null,
"max_length": null,
"min_length": null,
... | 257,745 | 2022-10-14T03:27:21Z | ---
language:
- en
license: mit
tags:
- generated_from_trainer
datasets:
- OpenTable
metrics:
- accuracy
model-index:
- name: roberta-base.CEBaB_confounding.uniform.absa.5-class.seed_42
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: OpenTable OPENTABLE-ABSA... |
distilbert-base-cased | [
"pytorch",
"tf",
"onnx",
"distilbert",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1910.01108",
"transformers",
"license:apache-2.0",
"has_space"
] | null | {
"architectures": null,
"model_type": "distilbert",
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},
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"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"n... | 574,859 | 2022-10-14T03:28:07Z | ---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- OpenTable
metrics:
- accuracy
model-index:
- name: bert-base-uncased.CEBaB_confounding.observational.sa.5-class.seed_43
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: OpenTabl... |
distilbert-base-german-cased | [
"pytorch",
"safetensors",
"distilbert",
"fill-mask",
"de",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] | fill-mask | {
"architectures": [
"DistilBertForMaskedLM"
],
"model_type": "distilbert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repea... | 43,667 | 2022-10-14T03:30:54Z | ---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- OpenTable
metrics:
- accuracy
model-index:
- name: bert-base-uncased.CEBaB_confounding.observational.sa.5-class.seed_44
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: OpenTabl... |
distilbert-base-multilingual-cased | [
"pytorch",
"tf",
"onnx",
"safetensors",
"distilbert",
"fill-mask",
"multilingual",
"af",
"sq",
"ar",
"an",
"hy",
"ast",
"az",
"ba",
"eu",
"bar",
"be",
"bn",
"inc",
"bs",
"br",
"bg",
"my",
"ca",
"ceb",
"ce",
"zh",
"cv",
"hr",
"cs",
"da",
"nl",
"en",
... | fill-mask | {
"architectures": [
"DistilBertForMaskedLM"
],
"model_type": "distilbert",
"task_specific_params": {
"conversational": {
"max_length": null
},
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"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repea... | 8,339,633 | 2022-10-14T03:32:20Z | ---
language:
- en
license: mit
tags:
- generated_from_trainer
datasets:
- OpenTable
metrics:
- accuracy
model-index:
- name: roberta-base.CEBaB_confounding.uniform.absa.5-class.seed_43
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: OpenTable OPENTABLE-ABSA... |
distilroberta-base | [
"pytorch",
"tf",
"jax",
"rust",
"safetensors",
"roberta",
"fill-mask",
"en",
"dataset:openwebtext",
"arxiv:1910.01108",
"arxiv:1910.09700",
"transformers",
"exbert",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] | fill-mask | {
"architectures": [
"RobertaForMaskedLM"
],
"model_type": "roberta",
"task_specific_params": {
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},
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"max_length": null,
"min_length": null,
"no_repeat_ngra... | 3,342,240 | 2022-10-14T03:37:30Z | ---
language:
- en
license: mit
tags:
- generated_from_trainer
datasets:
- OpenTable
metrics:
- accuracy
model-index:
- name: roberta-base.CEBaB_confounding.uniform.absa.5-class.seed_44
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: OpenTable OPENTABLE-ABSA... |
gpt2-medium | [
"pytorch",
"tf",
"jax",
"rust",
"safetensors",
"gpt2",
"text-generation",
"en",
"arxiv:1910.09700",
"transformers",
"license:mit",
"has_space"
] | text-generation | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
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},
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"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 759,601 | 2022-10-14T03:39:54Z | ---
language:
- en
license: mit
tags:
- generated_from_trainer
datasets:
- OpenTable
metrics:
- accuracy
model-index:
- name: gpt2.CEBaB_confounding.observational.sa.5-class.seed_44
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: OpenTable OPENTABLE
ty... |
t5-11b | [
"pytorch",
"tf",
"t5",
"text2text-generation",
"en",
"fr",
"ro",
"de",
"multilingual",
"dataset:c4",
"arxiv:1805.12471",
"arxiv:1708.00055",
"arxiv:1704.05426",
"arxiv:1606.05250",
"arxiv:1808.09121",
"arxiv:1810.12885",
"arxiv:1905.10044",
"arxiv:1910.09700",
"transformers",
"... | translation | {
"architectures": [
"T5WithLMHeadModel"
],
"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_size": 3,
... | 37,600 | 2022-10-14T03:51:37Z | ---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- OpenTable
metrics:
- accuracy
model-index:
- name: bert-base-uncased.CEBaB_confounding.food_service_positive.sa.5-class.seed_42
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: ... |
AdapterHub/bert-base-uncased-pf-race | [
"bert",
"en",
"dataset:race",
"arxiv:2104.08247",
"adapter-transformers",
"adapterhub:rc/race"
] | null | {
"architectures": null,
"model_type": "bert",
"task_specific_params": {
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},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_bea... | 2 | null | ---
language:
- en
tags:
- generated_from_trainer
datasets:
- OpenTable
metrics:
- accuracy
model-index:
- name: lstm.CEBaB_confounding.food_service_positive.absa.5-class.seed_43
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: OpenTable OPENTABLE-ABSA
... |
AdapterHub/roberta-base-pf-quoref | [
"roberta",
"en",
"dataset:quoref",
"arxiv:2104.08247",
"adapter-transformers",
"question-answering"
] | question-answering | {
"architectures": null,
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_... | 0 | null | ---
license: other
tags:
- generated_from_trainer
datasets:
- scene_parse_150
model-index:
- name: my_awesome_seg_model
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... |
AdapterHub/roberta-base-pf-ud_en_ewt | [
"roberta",
"en",
"dataset:universal_dependencies",
"adapter-transformers",
"adapterhub:dp/ud_ewt"
] | null | {
"architectures": null,
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_... | 4 | null | ---
tags:
- conversational
---
# Gamer Bot DialoGPT Model |
AdapterHub/roberta-base-pf-wnut_17 | [
"roberta",
"en",
"dataset:wnut_17",
"arxiv:2104.08247",
"adapter-transformers",
"token-classification"
] | token-classification | {
"architectures": null,
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
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"num_... | 4 | 2022-10-15T01:20:02Z | ---
license: mit
library_name: sklearn
tags:
- sklearn
- skops
- tabular-classification
widget:
structuredData:
Contract:
- Two year
- Month-to-month
- One year
Dependents:
- 'Yes'
- 'No'
- 'No'
DeviceProtection:
- 'No'
- 'No'
- 'Yes'
InternetService:
- Fiber op... |
AdapterHub/roberta-base-pf-yelp_polarity | [
"roberta",
"en",
"dataset:yelp_polarity",
"arxiv:2104.08247",
"adapter-transformers",
"text-classification"
] | text-classification | {
"architectures": null,
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_... | 1 | null | ---
inference: false
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
license: creativeml-openrail-m
---
## Note
A newer version of this model has been released:
https://huggingface.co/naclbit/trinart_derrida_characters_v2_stable_diffusion
## Stable Diffusion TrinArt Characters model v1
tri... |
Adarsh123/distilbert-base-uncased-finetuned-ner | [] | null | {
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"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 | 2022-10-15T01:25:28Z | ---
license: mit
library_name: sklearn
tags:
- sklearn
- skops
- tabular-classification
widget:
structuredData:
Contract:
- Two year
- Month-to-month
- One year
Dependents:
- 'Yes'
- 'No'
- 'No'
DeviceProtection:
- 'No'
- 'No'
- 'Yes'
InternetService:
- Fiber op... |
Addixz/Sanyx | [] | 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 | A GPT-2 Medium sized SoLU model trained on 11.7B tokens of the Pile (training crashed because of dodgy data loaders at 11B, and wasn't resumed, so this is shorter than the others). 12 layers, d_model=1536. |
Adityanawal/testmodel_1 | [] | null | {
"architectures": null,
"model_type": null,
"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": null,
"num_beams... | 0 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: colab-demo
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. -->
# colab-demo
This model i... |
AethiQs-Max/s3-v1-20_epochs | [
"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... | 5 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-large-english-TIMIT-phoneme_v3
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. --... |
AhmedHassan19/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 | ---
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
config: PAN-X.de
split: train
... |
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: apache-2.0
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
model-index:
- name: distiled_flip_model_emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
config: default
split: tra... |
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 | The sea in macaron color, the blue sea, the white waves, the pink clouds, the clean asphalt, and the dilapidated stone houses, which are full of oil paintings, very dreamy, addicted to paintings, almost missed the last train |
AimB/mT5-en-kr-aihub-netflix | [] | 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:
- emotion
metrics:
- accuracy
model-index:
- name: distiled_flip_model_emotion_alpha_0.8_v1
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
config: default
... |
AimB/mT5-en-kr-natural | [
"pytorch",
"mt5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | {
"architectures": [
"MT5ForConditionalGeneration"
],
"model_type": "mt5",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat... | 78 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
model-index:
- name: distiled_flip_model_emotion_alpha_0.8_epoch5_v1
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
config: defa... |
AimB/mT5-en-kr-opus | [] | 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:
- opus_books
model-index:
- name: mt5-small-finetuned-8epochs-opus_books-en-to-it
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete i... |
Ajaykannan6/autonlp-manthan-16122692 | [
"pytorch",
"bart",
"text2text-generation",
"unk",
"dataset:Ajaykannan6/autonlp-data-manthan",
"transformers",
"autonlp",
"autotrain_compatible"
] | text2text-generation | {
"architectures": [
"BartForConditionalGeneration"
],
"model_type": "bart",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": true,
"length_penalty": 2,
"max_length": 142,
"min_length": 56,
"no_repeat_ngr... | 4 | 2022-10-15T08:11:26Z | ---
license: mit
---
### Vasko style second try on Stable Diffusion via Dreambooth
#### model by akolov
This your the Stable Diffusion model fine-tuned the Vasko style second try concept taught to Stable Diffusion with Dreambooth.
It can be used by modifying the `instance_prompt`: **a painting by vasko style**
You can... |
Akash7897/distilbert-base-uncased-finetuned-sst2 | [
"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": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
... | 31 | 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
... |
Akash7897/gpt2-wikitext2 | [
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"transformers",
"generated_from_trainer",
"license:mit"
] | 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... | 5 | 2022-10-15T09:13:48Z | architectural section drawing 3d parametric design for elderly people living with courtyards in between two forms with small bridges |
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 | ---
language:
- en
tags:
- stable-diffusion
- text-to-image
license: unknown
inference: false
---
# Novelai-Diffusion
Novelai-Diffusion is a latent diffusion model which can create best quality anime image.
Here is the diffusers version of the model. Just to make it easier to use Novelai-Diffusion for all.
# Gradio... |
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 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- xnli
model-index:
- name: distilbert_xnli_hpu
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. -->
... |
AkshatSurolia/ConvNeXt-FaceMask-Finetuned | [
"pytorch",
"safetensors",
"convnext",
"image-classification",
"dataset:Face-Mask18K",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] | image-classification | {
"architectures": [
"ConvNextForImageClassification"
],
"model_type": "convnext",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"n... | 56 | null | ---
license: mit
---
### arwijn on Stable Diffusion
This is the `arwijn` 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... |
AkshatSurolia/DeiT-FaceMask-Finetuned | [
"pytorch",
"deit",
"image-classification",
"dataset:Face-Mask18K",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | image-classification | {
"architectures": [
"DeiTForImageClassification"
],
"model_type": "deit",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat... | 46 | null | ---
license: mit
---
### GBA FE Class Cards on Stable Diffusion
This is the `classcard` 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.... |
AkshatSurolia/ICD-10-Code-Prediction | [
"pytorch",
"bert",
"transformers",
"text-classification",
"license:apache-2.0",
"has_space"
] | text-classification | {
"architectures": null,
"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": null,
"num_bea... | 994 | null | ---
language:
- pt
license: mit
tags:
- generated_from_trainer
datasets:
- lener_br
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: xlm-roberta-base-finetuned-lener-br
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: lener_br
type: ... |
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 | ---
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... |
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 | ---
license: apache-2.0
tags:
- summarization
- generated_from_trainer
metrics:
- rouge
model-index:
- name: BARTkrame-abstract-mT5
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 ... |
AlekseyKulnevich/Pegasus-HeaderGeneration | [
"pytorch",
"pegasus",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | {
"architectures": [
"PegasusForConditionalGeneration"
],
"model_type": "pegasus",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"n... | 8 | null | ---
language: en
widget:
- text: "As manager, I want to Find pet by ID through /pet/{petId}. Returns a single pet."
- text: "As operator, I want to Create user through /user. #/definitions/User This can only be done by the logged in user."
license: mit
---
# ner-roles-openapi: model fine-tuned from distilbert-base-unca... |
Alerosae/SocratesGPT-2 | [
"pytorch",
"gpt2",
"feature-extraction",
"en",
"transformers",
"text-generation"
] | text-generation | {
"architectures": [
"GPT2Model"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": nul... | 7 | null | ---
license: mit
---
### Toyota Sera on Stable Diffusion
This is the `<toyota-sera>` 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... |
Alessandro/model_name | [] | 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: HandWritten Medical Prescription Text Extraction Using Donut (Document Understanding Transformers )
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to... |
Andrija/SRoBERTa-L | [
"pytorch",
"roberta",
"fill-mask",
"hr",
"sr",
"multilingual",
"dataset:oscar",
"dataset:srwac",
"dataset:leipzig",
"transformers",
"masked-lm",
"license:apache-2.0",
"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... | 58 | 2022-10-16T01:21:43Z | ---
library_name: fairseq
task: audio-to-audio
tags:
- fairseq
- audio
- audio-to-audio
- speech-to-speech-translation
license: cc-by-nc-4.0
---
You can try out the model on the right of the page by uploading or recording.
For model usage, please refer to https://huggingface.co/facebook/textless_sm_cs_en
|
Andrija/SRoBERTa-NER | [
"pytorch",
"roberta",
"token-classification",
"hr",
"sr",
"multilingual",
"dataset:hr500k",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | token-classification | {
"architectures": [
"RobertaForTokenClassification"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_... | 7 | 2022-10-16T01:21:51Z | ---
library_name: fairseq
task: audio-to-audio
tags:
- fairseq
- audio
- audio-to-audio
- speech-to-speech-translation
license: cc-by-nc-4.0
---
You can try out the model on the right of the page by uploading or recording.
For model usage, please refer to https://huggingface.co/facebook/textless_sm_cs_en
|
Andrija/SRoBERTa-NLP | [
"pytorch",
"roberta",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | {
"architectures": [
"RobertaForTokenClassification"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_... | 7 | null | ---
library_name: fairseq
task: audio-to-audio
tags:
- fairseq
- audio
- audio-to-audio
- speech-to-speech-translation
license: cc-by-nc-4.0
---
You can try out the model on the right of the page by uploading or recording.
For model usage, please refer to https://huggingface.co/facebook/textless_sm_cs_en
|
Andrija/SRoBERTa-XL-NER | [
"pytorch",
"roberta",
"token-classification",
"hr",
"sr",
"multilingual",
"dataset:hr500k",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | token-classification | {
"architectures": [
"RobertaForTokenClassification"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_... | 6 | 2022-10-16T01:22:09Z | ---
library_name: fairseq
task: audio-to-audio
tags:
- fairseq
- audio
- audio-to-audio
- speech-to-speech-translation
license: cc-by-nc-4.0
---
You can try out the model on the right of the page by uploading or recording.
For model usage, please refer to https://huggingface.co/facebook/textless_sm_cs_en
|
Andrija/SRoBERTa-XL | [
"pytorch",
"roberta",
"fill-mask",
"hr",
"sr",
"multilingual",
"dataset:oscar",
"dataset:srwac",
"dataset:leipzig",
"dataset:cc100",
"dataset:hrwac",
"transformers",
"masked-lm",
"license:apache-2.0",
"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... | 54 | 2022-10-16T01:22:35Z | ---
library_name: fairseq
task: audio-to-audio
tags:
- fairseq
- audio
- audio-to-audio
- speech-to-speech-translation
license: cc-by-nc-4.0
---
You can try out the model on the right of the page by uploading or recording.
For model usage, please refer to https://huggingface.co/facebook/textless_sm_cs_en
|
Andrija/SRoBERTa-base-NER | [
"pytorch",
"roberta",
"token-classification",
"hr",
"sr",
"multilingual",
"dataset:hr500k",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | token-classification | {
"architectures": [
"RobertaForTokenClassification"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_... | 12 | 2022-10-16T01:22:43Z | ---
library_name: fairseq
task: audio-to-audio
tags:
- fairseq
- audio
- audio-to-audio
- speech-to-speech-translation
license: cc-by-nc-4.0
---
You can try out the model on the right of the page by uploading or recording.
For model usage, please refer to https://huggingface.co/facebook/textless_sm_cs_en
|
Andrija/SRoBERTaFastBPE | [] | null | {
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"num_beams... | 0 | 2022-10-16T01:23:20Z | ---
library_name: fairseq
task: audio-to-audio
tags:
- fairseq
- audio
- audio-to-audio
- speech-to-speech-translation
license: cc-by-nc-4.0
---
You can try out the model on the right of the page by uploading or recording.
For model usage, please refer to https://huggingface.co/facebook/textless_sm_cs_en
|
Andry/111 | [] | null | {
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"num_beams... | 0 | null | ---
library_name: fairseq
task: audio-to-audio
tags:
- fairseq
- audio
- audio-to-audio
- speech-to-speech-translation
license: cc-by-nc-4.0
---
You can try out the model on the right of the page by uploading or recording.
For model usage, please refer to https://huggingface.co/facebook/textless_sm_cs_en
|
Andry/1111 | [] | null | {
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"num_beams... | 0 | 2022-10-16T01:23:34Z | ---
library_name: fairseq
task: audio-to-audio
tags:
- fairseq
- audio
- audio-to-audio
- speech-to-speech-translation
license: cc-by-nc-4.0
---
You can try out the model on the right of the page by uploading or recording.
For model usage, please refer to https://huggingface.co/facebook/textless_sm_cs_en
|
Andy1621/uniformer | [
"license:mit",
"has_space"
] | null | {
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"num_beams... | 0 | 2022-10-16T01:23:42Z | ---
library_name: fairseq
task: audio-to-audio
tags:
- fairseq
- audio
- audio-to-audio
- speech-to-speech-translation
license: cc-by-nc-4.0
---
You can try out the model on the right of the page by uploading or recording.
For model usage, please refer to https://huggingface.co/facebook/textless_sm_cs_en
|
AndyJ/clinicalBERT | [
"pytorch",
"transformers"
] | null | {
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"num_beams... | 4 | 2022-10-16T01:23:48Z | ---
library_name: fairseq
task: audio-to-audio
tags:
- fairseq
- audio
- audio-to-audio
- speech-to-speech-translation
license: cc-by-nc-4.0
---
You can try out the model on the right of the page by uploading or recording.
For model usage, please refer to https://huggingface.co/facebook/textless_sm_cs_en
|
AndyJ/prompt_finetune | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
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"no_repeat_ngram_size... | 8 | 2022-10-16T01:23:55Z | ---
library_name: fairseq
task: audio-to-audio
tags:
- fairseq
- audio
- audio-to-audio
- speech-to-speech-translation
license: cc-by-nc-4.0
---
You can try out the model on the right of the page by uploading or recording.
For model usage, please refer to https://huggingface.co/facebook/textless_sm_cs_en
|
AndyyyCai/bert-base-uncased-finetuned-copa | [
"pytorch",
"bert",
"multiple-choice",
"transformers"
] | multiple-choice | {
"architectures": [
"BertForMultipleChoice"
],
"model_type": "bert",
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"min_length": null,
"no_repeat_ngra... | 4 | 2022-10-16T01:24:02Z | ---
library_name: fairseq
task: audio-to-audio
tags:
- fairseq
- audio
- audio-to-audio
- speech-to-speech-translation
license: cc-by-nc-4.0
---
You can try out the model on the right of the page by uploading or recording.
For model usage, please refer to https://huggingface.co/facebook/textless_sm_cs_en
|
Ani123/Ani | [] | null | {
"architectures": null,
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"num_beams... | 0 | 2022-10-16T01:34:02Z | ---
license: creativeml-openrail-m
---
# Nixeu stable diffusion model
Original artist: Nixeu\
Patreon: https://www.patreon.com/nixeu/posts
## Basic explanation
Token and Class words are what guide the AI to produce images similar to the trained style/object/character.
Include any mix of these words in the prompt to... |
AnnettJaeger/AnneJae | [] | null | {
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"num_beams... | 0 | 2022-10-16T04:10:06Z | ---
license: mit
---
### wukong_900 on Stable Diffusion via Dreambooth
#### model by jaxmetaverse
This your the Stable Diffusion model fine-tuned the wukong_900 concept taught to Stable Diffusion with Dreambooth.
It can be used by modifying the `instance_prompt`: **wukong**
You can also train your own concepts and upl... |
Anomic/DialoGPT-medium-loki | [] | null | {
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"num_beams... | 0 | 2022-10-16T04:29:49Z | ---
license: mit
---
### Xuna on Stable Diffusion
This is the `<Xuna>` 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. ... |
AnonymousNLP/pretrained-model-1 | [
"pytorch",
"gpt2",
"transformers"
] | null | {
"architectures": [
"GPT2DoubleHeadsModel"
],
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"no_repeat_ngram... | 4 | 2022-10-16T05:40:15Z | ---
tags:
- object-detection
- vision
finetuned_from:
- hustvl/yolos-small
---
# YOLOS (small-sized) model fine-tuned on Matterport balloon dataset
YOLOS is a Vision Transformer (ViT) trained using the DETR loss. Despite its simplicity, a base-sized YOLOS model is able to achieve 42 AP on COCO validation 2017 (simila... |
AnonymousSub/AR_EManuals-RoBERTa | [
"pytorch",
"roberta",
"feature-extraction",
"transformers"
] | feature-extraction | {
"architectures": [
"RobertaModel"
],
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"no_repeat_ngram_size... | 6 | 2022-10-16T06:16:17Z | ---
license: mit
---
### Pion by August Semionov on Stable Diffusion
This is the `<pion>` 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_inferenc... |
AnonymousSub/AR_bert-base-uncased | [
"pytorch",
"bert",
"feature-extraction",
"transformers"
] | feature-extraction | {
"architectures": [
"BertModel"
],
"model_type": "bert",
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"min_length": null,
"no_repeat_ngram_size": nul... | 2 | 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... |
AnonymousSub/AR_consert | [
"pytorch",
"bert",
"feature-extraction",
"transformers"
] | feature-extraction | {
"architectures": [
"BertModel"
],
"model_type": "bert",
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"no_repeat_ngram_size": nul... | 2 | 2022-10-16T07:16:13Z | ---
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
config: PAN-X.fr
split: train
... |
AnonymousSub/AR_rule_based_bert_triplet_epochs_1_shard_1 | [
"pytorch",
"bert",
"feature-extraction",
"transformers"
] | feature-extraction | {
"architectures": [
"BertModel"
],
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"min_length": null,
"no_repeat_ngram_size": nul... | 2 | 2022-10-16T07:38:10Z | ---
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
config: PAN-X.it
split: train
... |
AnonymousSub/AR_rule_based_only_classfn_epochs_1_shard_1 | [
"pytorch",
"bert",
"feature-extraction",
"transformers"
] | feature-extraction | {
"architectures": [
"BertModel"
],
"model_type": "bert",
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"min_length": null,
"no_repeat_ngram_size": nul... | 1 | null | ---
language:
- en
tags:
- stable-diffusion
- text-to-image
license: creativeml-openrail-m
inference: false
---
[hakurei/waifu-diffusion-v1-3](https://huggingface.co/hakurei/waifu-diffusion-v1-3) fine-tuned on 800 samples from [https://www.kaggle.com/datasets/stevenevan99/face-of-pixiv-top-daily-illustration-2020](ht... |
AnonymousSub/AR_rule_based_roberta_bert_quadruplet_epochs_1_shard_1 | [
"pytorch",
"roberta",
"feature-extraction",
"transformers"
] | feature-extraction | {
"architectures": [
"RobertaModel"
],
"model_type": "roberta",
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"min_length": null,
"no_repeat_ngram_size... | 2 | 2022-10-16T07:56:19Z | ---
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
config: PAN-X.en
split: train
... |
AnonymousSub/AR_rule_based_roberta_hier_triplet_epochs_1_shard_10 | [
"pytorch",
"roberta",
"feature-extraction",
"transformers"
] | feature-extraction | {
"architectures": [
"RobertaModel"
],
"model_type": "roberta",
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"no_repeat_ngram_size... | 3 | 2022-10-16T08:44:36Z | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: xmelus/mbert
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. -->
# xmelus/mbert
This mo... |
AnonymousSub/AR_rule_based_roberta_twostagequadruplet_hier_epochs_1_shard_1 | [
"pytorch",
"roberta",
"feature-extraction",
"transformers"
] | feature-extraction | {
"architectures": [
"RobertaModel"
],
"model_type": "roberta",
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"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 5 | null | ---
license: mit
---
### RikiArt on Stable Diffusion
This is the `<rick-art>` 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) not... |
AnonymousSub/AR_rule_based_roberta_twostagetriplet_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... | 2 | 2022-10-16T09:44:27Z | ---
language: ml
datasets:
- Indic TTS Malayalam Speech Corpus
- Openslr Malayalam Speech Corpus
- SMC Malayalam Speech Corpus
- IIIT-H Indic Speech Databases
metrics:
- wer
tags:
- audio
- automatic-speech-recognition
- speech
- xlsr-fine-tuning-week
license: apache-2.0
model-index:
- name: Malayalam XLSR Wav2Vec2 Lar... |
AnonymousSub/EManuals_RoBERTa_wikiqa | [
"pytorch",
"roberta",
"text-classification",
"transformers"
] | text-classification | {
"architectures": [
"RobertaForSequenceClassification"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
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"length_penalty": null,
"max_length": null,
"min_length": null,
"... | 29 | 2022-10-16T10:54:43Z | ---
license: mit
---
### Jacqueline-the-unicorn on Stable Diffusion
This is the `<jacqueline>` 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... |
AnonymousSub/SDR_HF_model_base | [
"pytorch",
"roberta",
"feature-extraction",
"transformers"
] | feature-extraction | {
"architectures": [
"RobertaModel"
],
"model_type": "roberta",
"task_specific_params": {
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"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 1 | 2022-10-16T10:57:03Z | ---
license: mit
---
# Description
Trainer: ChrisC
Ram from Re:zero
# Dataset
>Training: 23 images
>Regularization: 400 images
# Info
>ram_3k_WD1-3.ckpt
>Model Used: Waifu Diffusion 1.3
>Steps: 3000
>Keyword: Ram (Use this in the prompt)
>Class Phrase: ram_mondays |
AnonymousSub/T5_pubmedqa_question_generation | [
"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... | 6 | 2022-10-16T17:03:09Z | ---
language: en
tags:
- bert
- business
- finance
license: cc-by-4.0
datasets:
- CompanyWeb
- MD&A Disclosures
- S2ORC
---
# BusinessBERT
An industry-sensitive language model for business applications pretrained on business communication corpora. The model incorporates industry classification (IC) as a pretraining o... |
AnonymousSub/bert_snips | [
"pytorch",
"bert",
"feature-extraction",
"transformers"
] | feature-extraction | {
"architectures": [
"BertModel"
],
"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": nul... | 5 | 2022-10-16T17:35:21Z | ---
license: mit
---
### test-epson on Stable Diffusion
This is the `<epson-branch>` 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... |
AnonymousSub/cline-s10-SR | [] | 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 | 2022-10-16T17:52:08Z | ---
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: 7.46 +/- 2.80... |
AnonymousSub/cline-techqa | [
"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 | 2022-10-16T17:52:25Z | ---
license: mit
---
### orientalist art on Stable Diffusion
This is the `<orientalist-art>` 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... |
AnonymousSub/declutr-emanuals-s10-AR | [
"pytorch",
"roberta",
"text-classification",
"transformers"
] | text-classification | {
"architectures": [
"RobertaForSequenceClassification"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
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},
"summarization": {
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"length_penalty": null,
"max_length": null,
"min_length": null,
"... | 29 | null | ---
language:
- en
tags:
- pytorch
- causal-lm
- pythia
- pythia_v0
license: apache-2.0
datasets:
- the_pile
---
The *Pythia Scaling Suite* is a collection of models developed to facilitate
interpretability research. It contains two sets of eight models of sizes
70M, 160M, 410M, 1B, 1.4B, 2.8B, 6.9B, and 12B. For ea... |
AnonymousSub/declutr-model | [
"pytorch",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"RobertaForMaskedLM"
],
"model_type": "roberta",
"task_specific_params": {
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},
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"min_length": null,
"no_repeat_ngra... | 4 | null | ---
language: en
thumbnail: http://www.huggingtweets.com/th3nfthunt3r/1665945395711/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... |
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 | 2022-10-16T18:39:39Z | ---
language:
- en
tags:
- pytorch
- causal-lm
- pythia
- pythia_v0
license: apache-2.0
datasets:
- the_pile
---
The *Pythia Scaling Suite* is a collection of models developed to facilitate
interpretability research. It contains two sets of eight models of sizes
70M, 160M, 410M, 1B, 1.4B, 2.8B, 6.9B, and 12B. For ea... |
AnonymousSub/dummy_1 | [
"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... | 33 | null | ---
language:
- en
tags:
- pytorch
- causal-lm
- pythia
- pythia_v0
license: apache-2.0
datasets:
- the_pile
---
The *Pythia Scaling Suite* is a collection of models developed to facilitate
interpretability research. It contains two sets of eight models of sizes
70M, 160M, 410M, 1B, 1.4B, 2.8B, 6.9B, and 12B. For ea... |
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 | ---
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: 7.56 +/- 2.71... |
AnonymousSub/rule_based_bert_mean_diff_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... | 4 | null | ---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3-tst
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.56 +/- ... |
AnonymousSub/rule_based_bert_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... | 8 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: summarise_v6
results: []
---
# summarise_v6
This model is a fine-tuned version of [allenai/led-base-16384](https://huggingface.co/allenai/led-base-16384) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: ... |
AnonymousSub/rule_based_bert_quadruplet_epochs_1_shard_1_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... | 3 | null | ---
license: mit
---
### ki on Stable Diffusion
This is the `<ki-mars>` (Ki from the Disney Mars Needs Mom) 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_conce... |
AnonymousSub/rule_based_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... | 8 | null | data: https://github.com/BigSalmon2/InformalToFormalDataset
Text Generation Informal Formal
```
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("BigSalmon/InformalToFormalLincoln85Paraphrase")
model = AutoModelForCausalLM.from_pretrained("BigSalmon/InformalToForm... |
AnonymousSub/rule_based_bert_triplet_epochs_1_shard_1_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... | 3 | null | ---
tags:
- conversational
---
# Harry Potter DialoGPT Model |
AnonymousSub/rule_based_bert_triplet_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... | 31 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: results
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... |
AnonymousSub/rule_based_hier_quadruplet_0.1_epochs_1_shard_1_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... | 4 | null | ---
license: mit
---
### FNF Boyfriend on Stable Diffusion
This is the `<fnf-boyfriend>` 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... |
AnonymousSub/rule_based_hier_triplet_0.1_epochs_1_shard_1_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 | ---
license: mit
---
### Society Finch on Stable Diffusion
This is the `<society-finch>` 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... |
AnonymousSub/rule_based_hier_triplet_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... | 8 | null | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: AlanLee/distilbert-base-uncased-finetuned-cola
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 com... |
AnonymousSub/rule_based_roberta_bert_triplet_epochs_1_shard_1_wikiqa_copy | [
"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 | ---
tags:
- conversational
---
# Version 2 of the McTea-based AI chatbot, now trained on more data. |
AnonymousSub/rule_based_roberta_hier_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 | ---
tags:
- autotrain
- tabular
- classification
- tabular-classification
datasets:
- pachi107/autotrain-data-in-class-test
co2_eq_emissions:
emissions: 3.1621916284030838
---
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 1780161764
- CO2 Emissions (in grams): 3.1622
## Validati... |
AnonymousSub/rule_based_roberta_hier_triplet_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,
"... | 25 | null | ---
license: creativeml-openrail-m
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
inference: false
library_name: diffusers
extra_gated_prompt: |-
One more step before getting this model.
This model is open access and available to all, with a CreativeML OpenRAIL-M license further specifying ri... |
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 | ---
license: cc-by-nc-sa-4.0
tags:
- generated_from_trainer
model-index:
- name: output
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. -->
# output
This model is a... |
AnonymousSub/rule_based_roberta_twostage_quadruplet_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... | 5 | null | ---
tags:
- image-classification
- pytorch
metrics:
- accuracy
model-index:
- name: Syn10kPlusOG-oct-ViT-Base-8Epochs-v1
results:
- task:
name: Image Classification
type: image-classification
metrics:
- name: Accuracy
type: accuracy
value: 0.8866666555404663
---
# Syn10kPlusO... |
AnonymousSub/rule_based_roberta_twostage_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... | 4 | null | ---
tags:
- autotrain
- token-classification
language:
- unk
widget:
- text: "I love AutoTrain 🤗"
datasets:
- teacookies/autotrain-data-17102022-cert
co2_eq_emissions:
emissions: 16.43804270120875
---
# Model Trained Using AutoTrain
- Problem type: Entity Extraction
- Model ID: 1781461794
- CO2 Emissions (in grams... |
ArBert/albert-base-v2-finetuned-ner-gmm-twitter | [
"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: cc-by-4.0
---
More information about the model [in this git repo](https://github.com/tceron/capture_similarity_between_political_parties) |
ArBert/roberta-base-finetuned-ner-kmeans | [
"pytorch",
"tensorboard",
"roberta",
"token-classification",
"dataset:conll2003",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index",
"autotrain_compatible"
] | token-classification | {
"architectures": [
"RobertaForTokenClassification"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_... | 8 | 2022-10-17T09:39:21Z | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: t5-large_dataset_radiology_20220912.tsv
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. --... |
Aravinth/test | [] | 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: 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
... |
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