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text-generation | transformers |
# Peppa Pig DialoGPT Model | {"tags": ["conversational"]} | Eagle3ye/DialoGPT-small-PeppaPig | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Peppa Pig DialoGPT Model | [
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] |
text-classification | transformers | ## Bert-base-uncased for Android-Ios Question Classification
**Code**: See [Ainize Workspace](https://ainize.ai/workspace/create?imageId=hnj95592adzr02xPTqss&git=https://github.com/EastHShin/Android-Ios-Classification-Workspace)
<br>
**Android-Ios-Classification DEMO**: [Ainize Endpoint](https://main-android-ios-class... | {} | EasthShin/Android_Ios_Classification | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #text-classification #autotrain_compatible #endpoints_compatible #has_space #region-us
| ## Bert-base-uncased for Android-Ios Question Classification
Code: See Ainize Workspace
<br>
Android-Ios-Classification DEMO: Ainize Endpoint
<br>
Demo web Code: Github
<br>
Android-Ios-Classification API: Ainize API
<br>
<br>
## Overview
Language model: bert-base-cased
<br>
Language: English
<br>
Training data: Quest... | [
"## Bert-base-uncased for Android-Ios Question Classification\n\nCode: See Ainize Workspace\n<br>\nAndroid-Ios-Classification DEMO: Ainize Endpoint\n<br>\nDemo web Code: Github\n<br>\nAndroid-Ios-Classification API: Ainize API\n<br>\n<br>",
"## Overview\nLanguage model: bert-base-cased\n<br>\nLanguage: English\n<... | [
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question-answering | transformers |
#### Klue-bert base for Common Sense QA
#### Klue-CommonSense-model DEMO: [Ainize DEMO](https://main-klue-common-sense-qa-east-h-shin.endpoint.ainize.ai/)
#### Klue-CommonSense-model API: [Ainize API](https://ainize.ai/EastHShin/Klue-CommonSense_QA?branch=main)
### Overview
**Language model**: klue/bert-base
<br>
... | {} | EasthShin/Klue-CommonSense-model | null | [
"transformers",
"pytorch",
"bert",
"question-answering",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #question-answering #endpoints_compatible #region-us
|
#### Klue-bert base for Common Sense QA
#### Klue-CommonSense-model DEMO: Ainize DEMO
#### Klue-CommonSense-model API: Ainize API
### Overview
Language model: klue/bert-base
<br>
Language: Korean
<br>
Downstream-task: Extractive QA
<br>
Training data: Common sense Data from Mindslab
<br>
Eval data: Common sense Da... | [
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"#### Klue-CommonSense-model API: Ainize API",
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text-generation | transformers | ## Youth_Chatbot_KoGPT2-base
**Demo Web**: [Ainize Endpoint](https://main-youth-chatbot-ko-gpt2-base-east-h-shin.endpoint.ainize.ai/)
<br>
**Demo Web Code**: [Github](https://github.com/EastHShin/Youth_Chatbot_KoGPT2-base)
<br>
**Youth-Chatbot API**: [Ainize API](https://ainize.ai/EastHShin/Youth_Chatbot_KoGPT2-base_A... | {} | EasthShin/Youth_Chatbot_Kogpt2-base | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| ## Youth_Chatbot_KoGPT2-base
Demo Web: Ainize Endpoint
<br>
Demo Web Code: Github
<br>
Youth-Chatbot API: Ainize API
<br>
<br>
## Overview
Language model: KoGPT2
<br>
Language: Korean
<br>
Training data: Aihub
## Usage
| [
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fill-mask | transformers | #Arabic_BERT_Model
#ArBERTMo
| {} | Ebtihal/ArBERTMo | null | [
"transformers",
"tf",
"camembert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #tf #camembert #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| #Arabic_BERT_Model
#ArBERTMo
| [] | [
"TAGS\n#transformers #tf #camembert #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n"
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28
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fill-mask | transformers |
# Arabic BERT Model
**AraBERTMo** is an Arabic pre-trained language model based on [Google's BERT architechture](https://github.com/google-research/bert).
AraBERTMo_base uses the same BERT-Base config.
AraBERTMo_base now comes in 10 new variants
All models are available on the `HuggingFace` model page under the [Ebt... | {"language": "ar", "tags": "Fill-Mask", "datasets": "OSCAR", "widget": [{"text": " \u0627\u0644\u0633\u0644\u0627\u0645 \u0639\u0644\u064a\u0643\u0645 \u0648\u0631\u062d\u0645\u0629[MASK] \u0648\u0628\u0631\u0643\u0627\u062a\u0629"}, {"text": " \u0627\u0647\u0644\u0627 \u0648\u0633\u0647\u0644\u0627 \u0628\u0643\u0645 ... | Ebtihal/AraBertMo_base_V1 | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"Fill-Mask",
"ar",
"dataset:OSCAR",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"ar"
] | TAGS
#transformers #pytorch #bert #fill-mask #Fill-Mask #ar #dataset-OSCAR #autotrain_compatible #endpoints_compatible #region-us
| Arabic BERT Model
=================
AraBERTMo is an Arabic pre-trained language model based on Google's BERT architechture.
AraBERTMo\_base uses the same BERT-Base config.
AraBERTMo\_base now comes in 10 new variants
All models are available on the 'HuggingFace' model page under the Ebtihal name.
Checkpoints are avai... | [] | [
"TAGS\n#transformers #pytorch #bert #fill-mask #Fill-Mask #ar #dataset-OSCAR #autotrain_compatible #endpoints_compatible #region-us \n"
] | [
39
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"TAGS\n#transformers #pytorch #bert #fill-mask #Fill-Mask #ar #dataset-OSCAR #autotrain_compatible #endpoints_compatible #region-us \n"
] |
fill-mask | transformers |
# Arabic BERT Model
**AraBERTMo** is an Arabic pre-trained language model based on [Google's BERT architechture](https://github.com/google-research/bert).
AraBERTMo_base uses the same BERT-Base config.
AraBERTMo_base now comes in 10 new variants
All models are available on the `HuggingFace` model page under the [Ebt... | {"language": "ar", "tags": "Fill-Mask", "datasets": "OSCAR", "widget": [{"text": " \u0627\u0644\u0633\u0644\u0627\u0645 \u0639\u0644\u064a\u0643\u0645 \u0648\u0631\u062d\u0645\u0629[MASK] \u0648\u0628\u0631\u0643\u0627\u062a\u0629"}, {"text": " \u0627\u0647\u0644\u0627 \u0648\u0633\u0647\u0644\u0627 \u0628\u0643\u0645 ... | Ebtihal/AraBertMo_base_V2 | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"Fill-Mask",
"ar",
"dataset:OSCAR",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"ar"
] | TAGS
#transformers #pytorch #bert #fill-mask #Fill-Mask #ar #dataset-OSCAR #autotrain_compatible #endpoints_compatible #region-us
| Arabic BERT Model
=================
AraBERTMo is an Arabic pre-trained language model based on Google's BERT architechture.
AraBERTMo\_base uses the same BERT-Base config.
AraBERTMo\_base now comes in 10 new variants
All models are available on the 'HuggingFace' model page under the Ebtihal name.
Checkpoints are avai... | [] | [
"TAGS\n#transformers #pytorch #bert #fill-mask #Fill-Mask #ar #dataset-OSCAR #autotrain_compatible #endpoints_compatible #region-us \n"
] | [
39
] | [
"TAGS\n#transformers #pytorch #bert #fill-mask #Fill-Mask #ar #dataset-OSCAR #autotrain_compatible #endpoints_compatible #region-us \n"
] |
fill-mask | transformers |
# Arabic BERT Model
**AraBERTMo** is an Arabic pre-trained language model based on [Google's BERT architechture](https://github.com/google-research/bert).
AraBERTMo_base uses the same BERT-Base config.
AraBERTMo_base now comes in 10 new variants
All models are available on the `HuggingFace` model page under the [Ebt... | {"language": "ar", "tags": "Fill-Mask", "datasets": "OSCAR", "widget": [{"text": " \u0627\u0644\u0633\u0644\u0627\u0645 \u0639\u0644\u064a\u0643\u0645 \u0648\u0631\u062d\u0645\u0629[MASK] \u0648\u0628\u0631\u0643\u0627\u062a\u0629"}, {"text": " \u0627\u0647\u0644\u0627 \u0648\u0633\u0647\u0644\u0627 \u0628\u0643\u0645 ... | Ebtihal/AraBertMo_base_V3 | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"Fill-Mask",
"ar",
"dataset:OSCAR",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"ar"
] | TAGS
#transformers #pytorch #bert #fill-mask #Fill-Mask #ar #dataset-OSCAR #autotrain_compatible #endpoints_compatible #region-us
| Arabic BERT Model
=================
AraBERTMo is an Arabic pre-trained language model based on Google's BERT architechture.
AraBERTMo\_base uses the same BERT-Base config.
AraBERTMo\_base now comes in 10 new variants
All models are available on the 'HuggingFace' model page under the Ebtihal name.
Checkpoints are avai... | [] | [
"TAGS\n#transformers #pytorch #bert #fill-mask #Fill-Mask #ar #dataset-OSCAR #autotrain_compatible #endpoints_compatible #region-us \n"
] | [
39
] | [
"TAGS\n#transformers #pytorch #bert #fill-mask #Fill-Mask #ar #dataset-OSCAR #autotrain_compatible #endpoints_compatible #region-us \n"
] |
fill-mask | transformers |
# Arabic BERT Model
**AraBERTMo** is an Arabic pre-trained language model based on [Google's BERT architechture](https://github.com/google-research/bert).
AraBERTMo_base uses the same BERT-Base config.
AraBERTMo_base now comes in 10 new variants
All models are available on the `HuggingFace` model page under the [Ebt... | {"language": "ar", "tags": "Fill-Mask", "datasets": "OSCAR", "widget": [{"text": " \u0627\u0644\u0633\u0644\u0627\u0645 \u0639\u0644\u064a\u0643\u0645 \u0648\u0631\u062d\u0645\u0629[MASK] \u0648\u0628\u0631\u0643\u0627\u062a\u0629"}, {"text": " \u0627\u0647\u0644\u0627 \u0648\u0633\u0647\u0644\u0627 \u0628\u0643\u0645 ... | Ebtihal/AraBertMo_base_V4 | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"Fill-Mask",
"ar",
"dataset:OSCAR",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"ar"
] | TAGS
#transformers #pytorch #bert #fill-mask #Fill-Mask #ar #dataset-OSCAR #autotrain_compatible #endpoints_compatible #region-us
| Arabic BERT Model
=================
AraBERTMo is an Arabic pre-trained language model based on Google's BERT architechture.
AraBERTMo\_base uses the same BERT-Base config.
AraBERTMo\_base now comes in 10 new variants
All models are available on the 'HuggingFace' model page under the Ebtihal name.
Checkpoints are avai... | [] | [
"TAGS\n#transformers #pytorch #bert #fill-mask #Fill-Mask #ar #dataset-OSCAR #autotrain_compatible #endpoints_compatible #region-us \n"
] | [
39
] | [
"TAGS\n#transformers #pytorch #bert #fill-mask #Fill-Mask #ar #dataset-OSCAR #autotrain_compatible #endpoints_compatible #region-us \n"
] |
fill-mask | transformers |
# Arabic BERT Model
**AraBERTMo** is an Arabic pre-trained language model based on [Google's BERT architechture](https://github.com/google-research/bert).
AraBERTMo_base uses the same BERT-Base config.
AraBERTMo_base now comes in 10 new variants
All models are available on the `HuggingFace` model page under the [Ebt... | {"language": "ar", "tags": "Fill-Mask", "datasets": "OSCAR", "widget": [{"text": " \u0627\u0644\u0633\u0644\u0627\u0645 \u0639\u0644\u064a\u0643\u0645 \u0648\u0631\u062d\u0645\u0629[MASK] \u0648\u0628\u0631\u0643\u0627\u062a\u0629"}, {"text": " \u0627\u0647\u0644\u0627 \u0648\u0633\u0647\u0644\u0627 \u0628\u0643\u0645 ... | Ebtihal/AraBertMo_base_V5 | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"Fill-Mask",
"ar",
"dataset:OSCAR",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"ar"
] | TAGS
#transformers #pytorch #bert #fill-mask #Fill-Mask #ar #dataset-OSCAR #autotrain_compatible #endpoints_compatible #region-us
| Arabic BERT Model
=================
AraBERTMo is an Arabic pre-trained language model based on Google's BERT architechture.
AraBERTMo\_base uses the same BERT-Base config.
AraBERTMo\_base now comes in 10 new variants
All models are available on the 'HuggingFace' model page under the Ebtihal name.
Checkpoints are avai... | [] | [
"TAGS\n#transformers #pytorch #bert #fill-mask #Fill-Mask #ar #dataset-OSCAR #autotrain_compatible #endpoints_compatible #region-us \n"
] | [
39
] | [
"TAGS\n#transformers #pytorch #bert #fill-mask #Fill-Mask #ar #dataset-OSCAR #autotrain_compatible #endpoints_compatible #region-us \n"
] |
fill-mask | transformers | # Arabic BERT Model
**AraBERTMo** is an Arabic pre-trained language model based on [Google's BERT architechture](https://github.com/google-research/bert).
AraBERTMo_base uses the same BERT-Base config.
AraBERTMo_base now comes in 10 new variants
All models are available on the `HuggingFace` model page under the [Ebti... | {"language": "ar", "tags": "Fill-Mask", "datasets": "OSCAR", "widget": [{"text": " \u0627\u0644\u0633\u0644\u0627\u0645 \u0639\u0644\u064a\u0643\u0645 \u0648\u0631\u062d\u0645\u0629[MASK] \u0648\u0628\u0631\u0643\u0627\u062a\u0629"}, {"text": " \u0627\u0647\u0644\u0627 \u0648\u0633\u0647\u0644\u0627 \u0628\u0643\u0645 ... | Ebtihal/AraBertMo_base_V6 | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"Fill-Mask",
"ar",
"dataset:OSCAR",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"ar"
] | TAGS
#transformers #pytorch #bert #fill-mask #Fill-Mask #ar #dataset-OSCAR #autotrain_compatible #endpoints_compatible #region-us
| Arabic BERT Model
=================
AraBERTMo is an Arabic pre-trained language model based on Google's BERT architechture.
AraBERTMo\_base uses the same BERT-Base config.
AraBERTMo\_base now comes in 10 new variants
All models are available on the 'HuggingFace' model page under the Ebtihal name.
Checkpoints are avai... | [] | [
"TAGS\n#transformers #pytorch #bert #fill-mask #Fill-Mask #ar #dataset-OSCAR #autotrain_compatible #endpoints_compatible #region-us \n"
] | [
39
] | [
"TAGS\n#transformers #pytorch #bert #fill-mask #Fill-Mask #ar #dataset-OSCAR #autotrain_compatible #endpoints_compatible #region-us \n"
] |
fill-mask | transformers | Arabic Model AraBertMo_base_V7
---
language: ar
tags: Fill-Mask
datasets: OSCAR
widget:
- text: " السلام عليكم ورحمة[MASK] وبركاتة"
- text: " اهلا وسهلا بكم في [MASK] من سيربح المليون"
- text: " مرحبا بك عزيزي الزائر [MASK] موقعنا "
---
# Arabic BERT Model
**AraBERTMo** is an Arabic pre-trained language model based ... | {} | Ebtihal/AraBertMo_base_V7 | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| Arabic Model AraBertMo\_base\_V7
---
language: ar
tags: Fill-Mask
datasets: OSCAR
widget:
* text: " السلام عليكم ورحمة[MASK] وبركاتة"
* text: " اهلا وسهلا بكم في [MASK] من سيربح المليون"
* text: " مرحبا بك عزيزي الزائر [MASK] موقعنا "
---
Arabic BERT Model
=================
AraBERTMo is an Arabic pre-tr... | [] | [
"TAGS\n#transformers #pytorch #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n"
] | [
28
] | [
"TAGS\n#transformers #pytorch #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n"
] |
fill-mask | transformers | Arabic Model AraBertMo_base_V8
---
language: ar
tags: Fill-Mask
datasets: OSCAR
widget:
- text: " السلام عليكم ورحمة[MASK] وبركاتة"
- text: " اهلا وسهلا بكم في [MASK] من سيربح المليون"
- text: " مرحبا بك عزيزي الزائر [MASK] موقعنا "
---
# Arabic BERT Model
**AraBERTMo** is an Arabic pre-trained language model based ... | {} | Ebtihal/AraBertMo_base_V8 | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| Arabic Model AraBertMo\_base\_V8
---
language: ar
tags: Fill-Mask
datasets: OSCAR
widget:
* text: " السلام عليكم ورحمة[MASK] وبركاتة"
* text: " اهلا وسهلا بكم في [MASK] من سيربح المليون"
* text: " مرحبا بك عزيزي الزائر [MASK] موقعنا "
---
Arabic BERT Model
=================
AraBERTMo is an Arabic pre-tr... | [] | [
"TAGS\n#transformers #pytorch #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n"
] | [
28
] | [
"TAGS\n#transformers #pytorch #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n"
] |
fill-mask | transformers | Arabic Model AraBertMo_base_V9
---
language: ar
tags: Fill-Mask
datasets: OSCAR
widget:
- text: " السلام عليكم ورحمة[MASK] وبركاتة"
- text: " اهلا وسهلا بكم في [MASK] من سيربح المليون"
- text: " مرحبا بك عزيزي الزائر [MASK] موقعنا "
---
# Arabic BERT Model
**AraBERTMo** is an Arabic pre-trained language model based ... | {} | Ebtihal/AraBertMo_base_V9 | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| Arabic Model AraBertMo\_base\_V9
---
language: ar
tags: Fill-Mask
datasets: OSCAR
widget:
* text: " السلام عليكم ورحمة[MASK] وبركاتة"
* text: " اهلا وسهلا بكم في [MASK] من سيربح المليون"
* text: " مرحبا بك عزيزي الزائر [MASK] موقعنا "
---
Arabic BERT Model
=================
AraBERTMo is an Arabic pre-tr... | [] | [
"TAGS\n#transformers #pytorch #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n"
] | [
28
] | [
"TAGS\n#transformers #pytorch #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text2text-generation | transformers |
<!-- 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. -->
# opus-mt-en-ro-finetuned-en-to-ro
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-ro](https://huggingface.co/Helsi... | {"tags": ["generated_from_trainer"], "datasets": ["wmt16"], "metrics": ["bleu"], "model_index": [{"name": "opus-mt-en-ro-finetuned-en-to-ro", "results": [{"task": {"name": "Sequence-to-sequence Language Modeling", "type": "text2text-generation"}, "dataset": {"name": "wmt16", "type": "wmt16", "args": "ro-en"}, "metric":... | Edomonndo/opus-mt-en-ro-finetuned-en-to-ro | null | [
"transformers",
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"generated_from_trainer",
"dataset:wmt16",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #marian #text2text-generation #generated_from_trainer #dataset-wmt16 #autotrain_compatible #endpoints_compatible #region-us
| opus-mt-en-ro-finetuned-en-to-ro
================================
This model is a fine-tuned version of Helsinki-NLP/opus-mt-en-ro on the wmt16 dataset.
It achieves the following results on the evaluation set:
* Loss: 1.2886
* Bleu: 28.1641
* Gen Len: 34.1071
Model description
-----------------
More information... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1",
"### Traini... | [
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text2text-generation | transformers |
<!-- 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. -->
# opus-mt-ja-en-finetuned-ja-to-en_test
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-ja-en](https://huggingface.co/... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["bleu"], "model_index": [{"name": "opus-mt-ja-en-finetuned-ja-to-en_test", "results": [{"task": {"name": "Sequence-to-sequence Language Modeling", "type": "text2text-generation"}, "metric": {"name": "Bleu", "type": "bleu", "value": 80.2723}}]}]} | Edomonndo/opus-mt-ja-en-finetuned-ja-to-en_test | null | [
"transformers",
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #marian #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| opus-mt-ja-en-finetuned-ja-to-en\_test
======================================
This model is a fine-tuned version of Helsinki-NLP/opus-mt-ja-en on an unkown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4737
* Bleu: 80.2723
* Gen Len: 16.5492
Model description
-----------------
More... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0002\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10\n* mixed\\_pr... | [
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text2text-generation | transformers |
<!-- 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. -->
# opus-mt-ja-en-finetuned-ja-to-en_xml
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-ja-en](https://huggingface.co/H... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["bleu"], "model_index": [{"name": "opus-mt-ja-en-finetuned-ja-to-en_xml", "results": [{"task": {"name": "Sequence-to-sequence Language Modeling", "type": "text2text-generation"}, "metric": {"name": "Bleu", "type": "bleu", "value": 73.8646}}]}]} | Edomonndo/opus-mt-ja-en-finetuned-ja-to-en_xml | null | [
"transformers",
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #marian #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| opus-mt-ja-en-finetuned-ja-to-en\_xml
=====================================
This model is a fine-tuned version of Helsinki-NLP/opus-mt-ja-en on an unkown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.7520
* Bleu: 73.8646
* Gen Len: 27.0884
Model description
-----------------
More i... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0002\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10\n* mixed\\_prec... | [
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automatic-speech-recognition | transformers |
# Wav2vec2 Large 100k Voxpopuli fine-tuned with Common Voice and TTS-Portuguese Corpus in Portuguese
[Wav2vec2 Large 100k Voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) fine-tuned in Portuguese using the Common Voice 7.0 and TTS-Portuguese Corpus.
# Use this model
```python
from trans... | {"language": "pt", "license": "apache-2.0", "tags": ["audio", "speech", "wav2vec2", "pt", "portuguese-speech-corpus", "automatic-speech-recognition", "speech", "PyTorch"], "datasets": ["Common Voice"], "metrics": ["wer"]} | Edresson/wav2vec2-large-100k-voxpopuli-ft-Common-Voice_plus_TTS-Dataset-portuguese | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"pt",
"portuguese-speech-corpus",
"PyTorch",
"arxiv:2204.00618",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [
"2204.00618"
] | [
"pt"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #speech #pt #portuguese-speech-corpus #PyTorch #arxiv-2204.00618 #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
# Wav2vec2 Large 100k Voxpopuli fine-tuned with Common Voice and TTS-Portuguese Corpus in Portuguese
Wav2vec2 Large 100k Voxpopuli fine-tuned in Portuguese using the Common Voice 7.0 and TTS-Portuguese Corpus.
# Use this model
# Results
For the results check the paper
# Example test with Common Voice Dataset
... | [
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"# Use this model",
"# Results\nFor the results check the paper",
"# Example test with Common... | [
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automatic-speech-recognition | transformers |
# Wav2vec2 Large 100k Voxpopuli fine-tuned with Common Voice and M-AILABS in Russian
[Wav2vec2 Large 100k Voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) fine-tuned in Russian using the Common Voice 7.0 and M-AILABS.
# Use this model
```python
from transformers import AutoTokenizer, Wa... | {"language": "ru", "license": "apache-2.0", "tags": ["audio", "speech", "wav2vec2", "ru", "russian-speech-corpus", "automatic-speech-recognition", "speech", "PyTorch"], "datasets": ["Common Voice"], "metrics": ["wer"]} | Edresson/wav2vec2-large-100k-voxpopuli-ft-Common-Voice_plus_TTS-Dataset-russian | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"ru",
"russian-speech-corpus",
"PyTorch",
"arxiv:2204.00618",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [
"2204.00618"
] | [
"ru"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #speech #ru #russian-speech-corpus #PyTorch #arxiv-2204.00618 #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
# Wav2vec2 Large 100k Voxpopuli fine-tuned with Common Voice and M-AILABS in Russian
Wav2vec2 Large 100k Voxpopuli fine-tuned in Russian using the Common Voice 7.0 and M-AILABS.
# Use this model
# Results
For the results check the paper
# Example test with Common Voice Dataset
| [
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"# Use this model",
"# Results\nFor the results check the paper",
"# Example test with Common Voice Dataset"
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automatic-speech-recognition | transformers |
# Wav2vec2 Large 100k Voxpopuli fine-tuned in Portuguese using the Common Voice 7.0, TTS-Portuguese Corpus plus data augmentation
[Wav2vec2 Large 100k Voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) Wav2vec2 Large 100k Voxpopuli fine-tuned in Portuguese using the Common Voice 7.0, TTS-Portug... | {"language": "pt", "license": "apache-2.0", "tags": ["audio", "speech", "wav2vec2", "pt", "Portuguese-speech-corpus", "automatic-speech-recognition", "speech", "PyTorch"], "datasets": ["Common Voice"], "metrics": ["wer"]} | Edresson/wav2vec2-large-100k-voxpopuli-ft-Common_Voice_plus_TTS-Dataset_plus_Data_Augmentation-portuguese | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"pt",
"Portuguese-speech-corpus",
"PyTorch",
"arxiv:2204.00618",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [
"2204.00618"
] | [
"pt"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #speech #pt #Portuguese-speech-corpus #PyTorch #arxiv-2204.00618 #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
# Wav2vec2 Large 100k Voxpopuli fine-tuned in Portuguese using the Common Voice 7.0, TTS-Portuguese Corpus plus data augmentation
Wav2vec2 Large 100k Voxpopuli Wav2vec2 Large 100k Voxpopuli fine-tuned in Portuguese using the Common Voice 7.0, TTS-Portuguese plus data augmentation method based on TTS and voice convers... | [
"# Wav2vec2 Large 100k Voxpopuli fine-tuned in Portuguese using the Common Voice 7.0, TTS-Portuguese Corpus plus data augmentation\n\nWav2vec2 Large 100k Voxpopuli Wav2vec2 Large 100k Voxpopuli fine-tuned in Portuguese using the Common Voice 7.0, TTS-Portuguese plus data augmentation method based on TTS and voice c... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #speech #pt #Portuguese-speech-corpus #PyTorch #arxiv-2204.00618 #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
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automatic-speech-recognition | transformers |
# Wav2vec2 Large 100k Voxpopuli fine-tuned in Russian using the Common Voice 7.0, MAILABS plus data augmentation
[Wav2vec2 Large 100k Voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) Wav2vec2 Large 100k Voxpopuli fine-tuned in Russian using the Common Voice 7.0, M-AILABS plus data augmentatio... | {"language": "pt", "license": "apache-2.0", "tags": ["audio", "speech", "wav2vec2", "pt", "Russian-speech-corpus", "automatic-speech-recognition", "speech", "PyTorch"], "datasets": ["Common Voice"], "metrics": ["wer"]} | Edresson/wav2vec2-large-100k-voxpopuli-ft-Common_Voice_plus_TTS-Dataset_plus_Data_Augmentation-russian | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"pt",
"Russian-speech-corpus",
"PyTorch",
"arxiv:2204.00618",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [
"2204.00618"
] | [
"pt"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #speech #pt #Russian-speech-corpus #PyTorch #arxiv-2204.00618 #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
# Wav2vec2 Large 100k Voxpopuli fine-tuned in Russian using the Common Voice 7.0, MAILABS plus data augmentation
Wav2vec2 Large 100k Voxpopuli Wav2vec2 Large 100k Voxpopuli fine-tuned in Russian using the Common Voice 7.0, M-AILABS plus data augmentation method based on TTS and voice conversion.
# Use this model
... | [
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automatic-speech-recognition | transformers |
# Wav2vec2 Large 100k Voxpopuli fine-tuned with a single-speaker dataset plus Data Augmentation in Portuguese
[Wav2vec2 Large 100k Voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) fine-tuned in Portuguese using a single-speaker dataset plus a data augmentation method based on TTS and voice c... | {"language": "pt", "license": "apache-2.0", "tags": ["audio", "speech", "wav2vec2", "pt", "portuguese-speech-corpus", "automatic-speech-recognition", "speech", "PyTorch"], "datasets": ["Common Voice"], "metrics": ["wer"]} | Edresson/wav2vec2-large-100k-voxpopuli-ft-TTS-Dataset-plus-data-augmentation-portuguese | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"pt",
"portuguese-speech-corpus",
"PyTorch",
"arxiv:2204.00618",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [
"2204.00618"
] | [
"pt"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #speech #pt #portuguese-speech-corpus #PyTorch #arxiv-2204.00618 #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
# Wav2vec2 Large 100k Voxpopuli fine-tuned with a single-speaker dataset plus Data Augmentation in Portuguese
Wav2vec2 Large 100k Voxpopuli fine-tuned in Portuguese using a single-speaker dataset plus a data augmentation method based on TTS and voice conversion.
# Use this model
# Results
For the results check ... | [
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automatic-speech-recognition | transformers |
# Wav2vec2 Large 100k Voxpopuli fine-tuned with a single-speaker dataset plus Data Augmentation in Russian
[Wav2vec2 Large 100k Voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) fine-tuned in Russian using a single-speaker dataset plus a data augmentation method based on TTS and voice convers... | {"language": "pt", "license": "apache-2.0", "tags": ["audio", "speech", "wav2vec2", "pt", "Russian-speech-corpus", "automatic-speech-recognition", "speech", "PyTorch"], "datasets": ["Common Voice"], "metrics": ["wer"]} | Edresson/wav2vec2-large-100k-voxpopuli-ft-TTS-Dataset-plus-data-augmentation-russian | null | [
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|
# Wav2vec2 Large 100k Voxpopuli fine-tuned with a single-speaker dataset plus Data Augmentation in Russian
Wav2vec2 Large 100k Voxpopuli fine-tuned in Russian using a single-speaker dataset plus a data augmentation method based on TTS and voice conversion.
# Use this model
# Results
For the results check the pa... | [
"# Wav2vec2 Large 100k Voxpopuli fine-tuned with a single-speaker dataset plus Data Augmentation in Russian \n\nWav2vec2 Large 100k Voxpopuli fine-tuned in Russian using a single-speaker dataset plus a data augmentation method based on TTS and voice conversion.",
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automatic-speech-recognition | transformers |
# Wav2vec 2.0 trained with CORAA Portuguese Dataset
This a the demonstration of a fine-tuned Wav2vec model for Portuguese using the following [CORAA dataset](https://github.com/nilc-nlp/CORAA)
# Use this model
```python
from transformers import AutoTokenizer, Wav2Vec2ForCTC
tokenizer = AutoTokenizer.from_pre... | {"language": "pt", "license": "apache-2.0", "tags": ["audio", "speech", "wav2vec2", "pt", "portuguese-speech-corpus", "automatic-speech-recognition", "hf-asr-leaderboard", "speech", "PyTorch"], "datasets": ["CORAA"], "metrics": ["wer"], "model-index": [{"name": "Edresson Casanova XLSR Wav2Vec2 Large 53 Portuguese", "re... | Edresson/wav2vec2-large-xlsr-coraa-portuguese | null | [
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|
# Wav2vec 2.0 trained with CORAA Portuguese Dataset
This a the demonstration of a fine-tuned Wav2vec model for Portuguese using the following CORAA dataset
# Use this model
# Results
For the results check the CORAA article
# Example test with Common Voice Dataset
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summarization | transformers |
<!-- 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. -->
# PegasusXSUM_GNAD
This model is a fine-tuned version of [Einmalumdiewelt/PegasusXSUM_GNAD](https://huggingface.co/Einmalumdiewelt... | {"language": ["de"], "tags": ["generated_from_trainer", "summarization"], "metrics": ["rouge"], "model-index": [{"name": "PegasusXSUM_GNAD", "results": []}]} | Einmalumdiewelt/PegasusXSUM_GNAD | null | [
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|
# PegasusXSUM_GNAD
This model is a fine-tuned version of Einmalumdiewelt/PegasusXSUM_GNAD on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.4386
- Rouge1: 26.7818
- Rouge2: 7.6864
- Rougel: 18.6264
- Rougelsum: 22.822
- Gen Len: 67.076
## Model description
More information ... | [
"# PegasusXSUM_GNAD\n\nThis model is a fine-tuned version of Einmalumdiewelt/PegasusXSUM_GNAD on an unknown dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 2.4386\n- Rouge1: 26.7818\n- Rouge2: 7.6864\n- Rougel: 18.6264\n- Rougelsum: 22.822\n- Gen Len: 67.076",
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summarization | transformers |
<!-- 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. -->
# T5-Base_GNAD
This model is a fine-tuned version of [Einmalumdiewelt/T5-Base_GNAD](https://huggingface.co/Einmalumdiewelt/T5-Base... | {"language": ["de"], "tags": ["generated_from_trainer", "summarization"], "metrics": ["rouge"], "model-index": [{"name": "T5-Base_GNAD", "results": []}]} | Einmalumdiewelt/T5-Base_GNAD | null | [
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|
# T5-Base_GNAD
This model is a fine-tuned version of Einmalumdiewelt/T5-Base_GNAD on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.1025
- Rouge1: 27.5357
- Rouge2: 8.5623
- Rougel: 19.1508
- Rougelsum: 23.9029
- Gen Len: 52.7253
## Model description
More information needed... | [
"# T5-Base_GNAD\n\nThis model is a fine-tuned version of Einmalumdiewelt/T5-Base_GNAD on an unknown dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 2.1025\n- Rouge1: 27.5357\n- Rouge2: 8.5623\n- Rougel: 19.1508\n- Rougelsum: 23.9029\n- Gen Len: 52.7253",
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null | transformers |
# Enformer
Enformer model. It was introduced in the paper [Effective gene expression prediction from sequence by integrating long-range interactions.](https://www.nature.com/articles/s41592-021-01252-x) by Avsec et al. and first released in [this repository](https://github.com/deepmind/deepmind-research/tree/master/e... | {"license": "apache-2.0", "inference": false} | EleutherAI/enformer-191k | null | [
"transformers",
"pytorch",
"enformer",
"license:apache-2.0",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
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|
# Enformer
Enformer model. It was introduced in the paper Effective gene expression prediction from sequence by integrating long-range interactions. by Avsec et al. and first released in this repository.
This particular model was trained on sequences of 196,608 basepairs, target length 896, with shift augmentation ... | [
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null | transformers |
# Enformer
Enformer model. It was introduced in the paper [Effective gene expression prediction from sequence by integrating long-range interactions.](https://www.nature.com/articles/s41592-021-01252-x) by Avsec et al. and first released in [this repository](https://github.com/deepmind/deepmind-research/tree/master/e... | {"license": "apache-2.0", "inference": false} | EleutherAI/enformer-191k_corr_coef_obj | null | [
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"pytorch",
"enformer",
"license:apache-2.0",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #enformer #license-apache-2.0 #region-us
|
# Enformer
Enformer model. It was introduced in the paper Effective gene expression prediction from sequence by integrating long-range interactions. by Avsec et al. and first released in this repository.
This particular model was trained on sequences of 196,608 basepairs, target length 896, with shift augmentation ... | [
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null | transformers |
# Enformer
Enformer model. It was introduced in the paper [Effective gene expression prediction from sequence by integrating long-range interactions.](https://www.nature.com/articles/s41592-021-01252-x) by Avsec et al. and first released in [this repository](https://github.com/deepmind/deepmind-research/tree/master/e... | {"license": "apache-2.0", "inference": false} | EleutherAI/enformer-corr_coef_obj | null | [
"transformers",
"pytorch",
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"license:apache-2.0",
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#transformers #pytorch #enformer #license-apache-2.0 #region-us
|
# Enformer
Enformer model. It was introduced in the paper Effective gene expression prediction from sequence by integrating long-range interactions. by Avsec et al. and first released in this repository.
This particular model was trained on sequences of 131,072 basepairs, target length 896 on v3-64 TPUs for 3 days ... | [
"# Enformer\n\nEnformer model. It was introduced in the paper Effective gene expression prediction from sequence by integrating long-range interactions. by Avsec et al. and first released in this repository. \n\nThis particular model was trained on sequences of 131,072 basepairs, target length 896 on v3-64 TPUs for... | [
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null | transformers |
# Enformer
Enformer model. It was introduced in the paper [Effective gene expression prediction from sequence by integrating long-range interactions.](https://www.nature.com/articles/s41592-021-01252-x) by Avsec et al. and first released in [this repository](https://github.com/deepmind/deepmind-research/tree/master/e... | {"license": "apache-2.0", "inference": false} | EleutherAI/enformer-preview | null | [
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|
# Enformer
Enformer model. It was introduced in the paper Effective gene expression prediction from sequence by integrating long-range interactions. by Avsec et al. and first released in this repository.
This particular model was trained on sequences of 131,072 basepairs, target length 896 on v3-64 TPUs for 2 and a... | [
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text-generation | transformers |
# GPT-J 6B
## Model Description
GPT-J 6B is a transformer model trained using Ben Wang's [Mesh Transformer JAX](https://github.com/kingoflolz/mesh-transformer-jax/). "GPT-J" refers to the class of model, while "6B" represents the number of trainable parameters.
<figure>
| Hyperparameter | Value |
|-----... | {"language": ["en"], "license": "apache-2.0", "tags": ["pytorch", "causal-lm"], "datasets": ["EleutherAI/pile"]} | EleutherAI/gpt-j-6b | null | [
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| GPT-J 6B
========
Model Description
-----------------
GPT-J 6B is a transformer model trained using Ben Wang's Mesh Transformer JAX. "GPT-J" refers to the class of model, while "6B" represents the number of trainable parameters.
**\*** Each layer consists of one feedforward block and one self attention block.
... | [
"### Out-of-scope use\n\n\nGPT-J-6B is not intended for deployment without fine-tuning, supervision,\nand/or moderation. It is not a in itself a product and cannot be used for\nhuman-facing interactions. For example, the model may generate harmful or\noffensive text. Please evaluate the risks associated with your p... | [
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text-generation | transformers |
# GPT-Neo 1.3B
## Model Description
GPT-Neo 1.3B is a transformer model designed using EleutherAI's replication of the GPT-3 architecture. GPT-Neo refers to the class of models, while 1.3B represents the number of parameters of this particular pre-trained model.
## Training data
GPT-Neo 1.3B was trained on the Pil... | {"language": ["en"], "license": "mit", "tags": ["text generation", "pytorch", "causal-lm"], "datasets": ["EleutherAI/pile"]} | EleutherAI/gpt-neo-1.3B | null | [
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| GPT-Neo 1.3B
============
Model Description
-----------------
GPT-Neo 1.3B is a transformer model designed using EleutherAI's replication of the GPT-3 architecture. GPT-Neo refers to the class of models, while 1.3B represents the number of parameters of this particular pre-trained model.
Training data
-----------... | [
"### How to use\n\n\nYou can use this model directly with a pipeline for text generation. This example generates a different sequence each time it's run:",
"### Limitations and Biases\n\n\nGPT-Neo was trained as an autoregressive language model. This means that its core functionality is taking a string of text an... | [
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text-generation | transformers |
# GPT-Neo 125M
## Model Description
GPT-Neo 125M is a transformer model designed using EleutherAI's replication of the GPT-3 architecture. GPT-Neo refers to the class of models, while 125M represents the number of parameters of this particular pre-trained model.
## Training data
GPT-Neo 125M was trained on the Pil... | {"language": ["en"], "license": "mit", "tags": ["text generation", "pytorch", "causal-lm"], "datasets": ["EleutherAI/pile"]} | EleutherAI/gpt-neo-125m | null | [
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| GPT-Neo 125M
============
Model Description
-----------------
GPT-Neo 125M is a transformer model designed using EleutherAI's replication of the GPT-3 architecture. GPT-Neo refers to the class of models, while 125M represents the number of parameters of this particular pre-trained model.
Training data
-----------... | [
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text-generation | transformers |
# GPT-Neo 2.7B
## Model Description
GPT-Neo 2.7B is a transformer model designed using EleutherAI's replication of the GPT-3 architecture. GPT-Neo refers to the class of models, while 2.7B represents the number of parameters of this particular pre-trained model.
## Training data
GPT-Neo 2.7B was trained on the Pil... | {"language": ["en"], "license": "mit", "tags": ["text generation", "pytorch", "causal-lm"], "datasets": ["EleutherAI/pile"]} | EleutherAI/gpt-neo-2.7B | null | [
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| GPT-Neo 2.7B
============
Model Description
-----------------
GPT-Neo 2.7B is a transformer model designed using EleutherAI's replication of the GPT-3 architecture. GPT-Neo refers to the class of models, while 2.7B represents the number of parameters of this particular pre-trained model.
Training data
-----------... | [
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text-classification | transformers | \n## BLEURT
Pytorch version of the original BLEURT models from ACL paper ["BLEURT: Learning Robust Metrics for Text Generation"](https://aclanthology.org/2020.acl-main.704/) by
Thibault Sellam, Dipanjan Das and Ankur P. Parikh of Google Research.
The code for model conversion was originated from [this notebook](http... | {} | Elron/bleurt-base-128 | null | [
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"pytorch",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us
| \n## BLEURT
Pytorch version of the original BLEURT models from ACL paper "BLEURT: Learning Robust Metrics for Text Generation" by
Thibault Sellam, Dipanjan Das and Ankur P. Parikh of Google Research.
The code for model conversion was originated from this notebook mentioned here.
## Usage Example
| [
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text-classification | transformers | \n## BLEURT
Pytorch version of the original BLEURT models from ACL paper ["BLEURT: Learning Robust Metrics for Text Generation"](https://aclanthology.org/2020.acl-main.704/) by
Thibault Sellam, Dipanjan Das and Ankur P. Parikh of Google Research.
The code for model conversion was originated from [this notebook](http... | {} | Elron/bleurt-base-512 | null | [
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#transformers #pytorch #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us
| \n## BLEURT
Pytorch version of the original BLEURT models from ACL paper "BLEURT: Learning Robust Metrics for Text Generation" by
Thibault Sellam, Dipanjan Das and Ankur P. Parikh of Google Research.
The code for model conversion was originated from this notebook mentioned here.
## Usage Example
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text-classification | transformers | \n## BLEURT
Pytorch version of the original BLEURT models from ACL paper ["BLEURT: Learning Robust Metrics for Text Generation"](https://aclanthology.org/2020.acl-main.704/) by
Thibault Sellam, Dipanjan Das and Ankur P. Parikh of Google Research.
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#transformers #pytorch #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us
| \n## BLEURT
Pytorch version of the original BLEURT models from ACL paper "BLEURT: Learning Robust Metrics for Text Generation" by
Thibault Sellam, Dipanjan Das and Ankur P. Parikh of Google Research.
The code for model conversion was originated from this notebook mentioned here.
## Usage Example
| [
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text-classification | transformers | ## BLEURT
Pytorch version of the original BLEURT models from ACL paper ["BLEURT: Learning Robust Metrics for Text Generation"](https://aclanthology.org/2020.acl-main.704/) by
Thibault Sellam, Dipanjan Das and Ankur P. Parikh of Google Research.
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| ## BLEURT
Pytorch version of the original BLEURT models from ACL paper "BLEURT: Learning Robust Metrics for Text Generation" by
Thibault Sellam, Dipanjan Das and Ankur P. Parikh of Google Research.
The code for model conversion was originated from this notebook mentioned here.
## Usage Example
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text-classification | transformers | \n## BLEURT
Pytorch version of the original BLEURT models from ACL paper ["BLEURT: Learning Robust Metrics for Text Generation"](https://aclanthology.org/2020.acl-main.704/) by
Thibault Sellam, Dipanjan Das and Ankur P. Parikh of Google Research.
The code for model conversion was originated from [this notebook](http... | {} | Elron/bleurt-tiny-128 | null | [
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#transformers #pytorch #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us
| \n## BLEURT
Pytorch version of the original BLEURT models from ACL paper "BLEURT: Learning Robust Metrics for Text Generation" by
Thibault Sellam, Dipanjan Das and Ankur P. Parikh of Google Research.
The code for model conversion was originated from this notebook mentioned here.
## Usage Example
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text-classification | transformers |
# Model Card for bleurt-tiny-512
# Model Details
## Model Description
Pytorch version of the original BLEURT models from ACL paper
- **Developed by:** Elron Bandel, Thibault Sellam, Dipanjan Das and Ankur P. Parikh of Google Research
- **Shared by [Optional]:** Elron Bandel
- **Model type:** Text Classificati... | {"tags": ["text-classification", "bert"]} | Elron/bleurt-tiny-512 | null | [
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|
# Model Card for bleurt-tiny-512
# Model Details
## Model Description
Pytorch version of the original BLEURT models from ACL paper
- Developed by: Elron Bandel, Thibault Sellam, Dipanjan Das and Ankur P. Parikh of Google Research
- Shared by [Optional]: Elron Bandel
- Model type: Text Classification
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text-generation | transformers |
# Harry Potter DialoGPT Model | {"tags": ["conversational"]} | Elzen7/DialoGPT-medium-harrypotter | null | [
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token-classification | transformers |
# Model Trained Using AutoNLP
- Problem type: Entity Extraction
- Model ID: 21124427
- CO2 Emissions (in grams): 6.2107269129101805
## Validation Metrics
- Loss: 0.09813392907381058
- Accuracy: 0.9714309035997062
- Precision: 0.9721275936822545
- Recall: 0.9735345807918949
- F1: 0.9728305785123967
## Usage
You ca... | {"language": "pt", "tags": "autonlp", "datasets": ["Emanuel/autonlp-data-pos-tag-bosque"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}], "co2_eq_emissions": 6.2107269129101805} | Emanuel/autonlp-pos-tag-bosque | null | [
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|
# Model Trained Using AutoNLP
- Problem type: Entity Extraction
- Model ID: 21124427
- CO2 Emissions (in grams): 6.2107269129101805
## Validation Metrics
- Loss: 0.09813392907381058
- Accuracy: 0.9714309035997062
- Precision: 0.9721275936822545
- Recall: 0.9735345807918949
- F1: 0.9728305785123967
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text-classification | transformers |
# bertweet-emotion-base
This model is a fine-tuned version of [Bertweet](https://huggingface.co/vinai/bertweet-base). It achieves the following results on the evaluation set:
- Loss: 0.1172
- Accuracy: 0.945
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["emotion"], "metrics": ["accuracy"], "model-index": [{"name": "bertweet-emotion-base", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "emotion", "type": "emotion", "args": "default"}, "me... | Emanuel/bertweet-emotion-base | null | [
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|
# bertweet-emotion-base
This model is a fine-tuned version of Bertweet. It achieves the following results on the evaluation set:
- Loss: 0.1172
- Accuracy: 0.945
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 80
- eval_batch_size: 80
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fill-mask | transformers |
<!-- 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. -->
# language-modeling
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset.... | {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "language-modeling", "results": []}]} | Emanuel/roebrta-base-val-test | null | [
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|
# language-modeling
This model is a fine-tuned version of roberta-base on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4229
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
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text-classification | transformers |
# twitter-emotion-deberta-v3-base
This model is a fine-tuned version of [DeBERTa-v3](https://huggingface.co/microsoft/deberta-v3-base). It achieves the following results on the evaluation set:
- Loss: 0.1474
- Accuracy: 0.937
### Training hyperparameters
The following hyperparameters were used during training:
- le... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["emotion"], "metrics": ["accuracy"], "model-index": [{"name": "twitter-emotion-deberta-v3-base", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "emotion", "type": "emotion", "args": "defa... | Emanuel/twitter-emotion-deberta-v3-base | null | [
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|
# twitter-emotion-deberta-v3-base
This model is a fine-tuned version of DeBERTa-v3. It achieves the following results on the evaluation set:
- Loss: 0.1474
- Accuracy: 0.937
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 80
- eval_bat... | [
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text-generation | transformers |
# My Awesome Model | {"tags": ["conversational"]} | Emi2160/DialoGPT-small-Neku | null | [
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text-generation | transformers |
# Harry Potter DialoGPT Model | {"tags": ["conversational"]} | EmileAjar/DialoGPT-small-harrypotter | null | [
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"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
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text-generation | transformers |
# Peppa pig DialoGPT Model | {"tags": ["conversational"]} | EmileAjar/DialoGPT-small-peppapig | null | [
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token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-finetuned-ner
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["conll2003"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "bert-finetuned-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "conll2003", "type": "c... | Emmanuel/bert-finetuned-ner | null | [
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"autotrain_compatible",
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"region:us"
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#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| bert-finetuned-ner
==================
This model is a fine-tuned version of bert-base-cased on the conll2003 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0603
* Precision: 0.9317
* Recall: 0.9510
* F1: 0.9413
* Accuracy: 0.9866
Model description
-----------------
More information ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
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null | null | bu benim modelim | {} | Enes3774/gpt2 | null | [
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automatic-speech-recognition | transformers |
<!-- 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. -->
# wav2vec2-large-xlsr-53-demo-colab
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-large-xlsr-53-demo-colab", "results": []}]} | EngNada/wav2vec2-large-xlsr-53-demo-colab | null | [
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"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-large-xlsr-53-demo-colab
=================================
This model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the common\_voice dataset.
It achieves the following results on the evaluation set:
* Loss: 7.9807
* Wer: 1.0
Model description
-----------------
More information needed
... | [
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text-classification | transformers |
<!-- 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. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["emotion"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "distilbert-base-uncased-finetuned-emotion", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "emotion", "type": "emotion... | EnsarEmirali/distilbert-base-uncased-finetuned-emotion | null | [
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"autotrain_compatible",
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] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-emotion
=========================================
This model is a fine-tuned version of distilbert-base-uncased on the emotion dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2131
* Accuracy: 0.9265
* F1: 0.9269
Model description
-----------------
Mo... | [
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text-generation | transformers |
#Loki DialoGPT Model | {"tags": ["conversational"]} | Erikaka/DialoGPT-small-loki | null | [
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text-generation | transformers |
# Harry Potter DialoGPT Model | {"tags": ["conversational"]} | EstoyDePaso/DialoGPT-small-harrypotter | null | [
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text-generation | transformers |
# MrCobb DialoGPT Model | {"tags": ["conversational"]} | EuropeanTurtle/DialoGPT-small-mrcobb | null | [
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token-classification | transformers |
<!-- 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. -->
# distilbert-base-uncased-finetuned-ner
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/dis... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["conll2003"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "distilbert-base-uncased-finetuned-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "con... | Evgeneus/distilbert-base-uncased-finetuned-ner | null | [
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"generated_from_trainer",
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"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-ner
=====================================
This model is a fine-tuned version of distilbert-base-uncased on the conll2003 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0845
* Precision: 0.8754
* Recall: 0.9058
* F1: 0.8904
* Accuracy: 0.9763
Model des... | [
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text-generation | transformers |
#jdt chat bot | {"tags": ["conversational"]} | ExEngineer/DialoGPT-medium-jdt | null | [
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"text-generation",
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text-generation | transformers |
# Quirk DialoGPT Model | {"tags": ["conversational"]} | Exilon/DialoGPT-large-quirk | null | [
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null | null | read me | {} | EyeSeeThru/txt2img | null | [
"region:us"
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#region-us
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automatic-speech-recognition | transformers |
<!-- 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. -->
# wav2vec2-base-russian-big-kaggle
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-base-russian-big-kaggle", "results": []}]} | Eyvaz/wav2vec2-base-russian-big-kaggle | null | [
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"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
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] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
|
# wav2vec2-base-russian-big-kaggle
This model is a fine-tuned version of facebook/wav2vec2-base on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training ... | [
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automatic-speech-recognition | transformers |
<!-- 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. -->
# wav2vec2-base-russian-demo-kaggle
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-base-russian-demo-kaggle", "results": []}]} | Eyvaz/wav2vec2-base-russian-demo-kaggle | null | [
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| wav2vec2-base-russian-demo-kaggle
=================================
This model is a fine-tuned version of facebook/wav2vec2-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: inf
* Wer: 0.9997
Model description
-----------------
More information needed
Intended uses & l... | [
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automatic-speech-recognition | transformers |
<!-- 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. -->
# wav2vec2-base-russian-modified-kaggle
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/face... | {"license": "apache-2.0", "tags": ["generated_from_trainer"]} | Eyvaz/wav2vec2-base-russian-modified-kaggle | null | [
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#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
|
# wav2vec2-base-russian-modified-kaggle
This model is a fine-tuned version of facebook/wav2vec2-base on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Tr... | [
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text-generation | transformers |
#house small GPT | {"tags": ["conversational"]} | EzioDD/house | null | [
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text-generation | transformers |
# FFF dialog model | {"tags": "conversational"} | FFF000/dialogpt-FFF | null | [
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question-answering | transformers |
<!-- 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. -->
# distilbert-base-uncased-finetuned-squad
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/d... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad_v2"], "model-index": [{"name": "distilbert-base-uncased-finetuned-squad", "results": []}]} | FOFer/distilbert-base-uncased-finetuned-squad | null | [
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"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-squad_v2 #license-apache-2.0 #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-squad
=======================================
This model is a fine-tuned version of distilbert-base-uncased on the squad\_v2 dataset.
It achieves the following results on the evaluation set:
* Loss: 1.4306
Model description
-----------------
More information needed
Intended u... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
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fill-mask | transformers |
# HotelBERT-small
This model was trained on reviews from a well known German hotel platform.
| {"language": "de", "widget": [{"text": "Das <mask> hat sich toll um uns gek\u00fcmmert."}]} | FabianGroeger/HotelBERT-small | null | [
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"tf",
"roberta",
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"autotrain_compatible",
"endpoints_compatible",
"region:us"
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"de"
] | TAGS
#transformers #pytorch #tf #roberta #fill-mask #de #autotrain_compatible #endpoints_compatible #region-us
|
# HotelBERT-small
This model was trained on reviews from a well known German hotel platform.
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fill-mask | transformers |
# HotelBERT
This model was trained on reviews from a well known German hotel platform.
| {"language": "de", "widget": [{"text": "Das <mask> hat sich toll um uns gek\u00fcmmert."}]} | FabianGroeger/HotelBERT | null | [
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"de"
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# HotelBERT
This model was trained on reviews from a well known German hotel platform.
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] |
text-classification | transformers |
<!-- 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. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["emotion"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "distilbert-base-uncased-finetuned-emotion", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "emotion", "type": "emotion... | FabioDataGeek/distilbert-base-uncased-finetuned-emotion | null | [
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] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-emotion
=========================================
This model is a fine-tuned version of distilbert-base-uncased on the emotion dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2196
* Accuracy: 0.926
* F1: 0.9258
Model description
-----------------
Mor... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
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text-classification | transformers | <!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-uncased-base
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an Reddi... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"]} | Fan-s/reddit-tc-bert | null | [
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"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
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"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# bert-uncased-base
This model is a fine-tuned version of bert-base-uncased on an Reddit-dialogue dataset.
This model can be used for Text Classification: Given two sentences, see if they are related.
It achieves the following results on the evaluation set:
- Loss: 0.2297
- Accuracy: 0.9267
### Training hyperparame... | [
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text-generation | transformers | @Kirito DialoGPT Small Model | {"tags": ["conversational"]} | FangLee/DialoGPT-small-Kirito | null | [
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"endpoints_compatible",
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question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-finetuned-squad
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the squa... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "bert-finetuned-squad", "results": []}]} | FardinSaboori/bert-finetuned-squad | null | [
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"bert",
"question-answering",
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"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us
|
# bert-finetuned-squad
This model is a fine-tuned version of bert-base-cased on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
T... | [
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"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
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automatic-speech-recognition | transformers |
<!-- 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. -->
# wav2vec2-large-xls-r-300m-turkish-colab
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-large-xls-r-300m-turkish-colab", "results": []}]} | FarisHijazi/wav2vec2-large-xls-r-300m-turkish-colab | null | [
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"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
|
# wav2vec2-large-xls-r-300m-turkish-colab
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common_voice dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training pro... | [
"# wav2vec2-large-xls-r-300m-turkish-colab\n\nThis model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common_voice dataset.",
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text-classification | transformers |
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 32517788
- CO2 Emissions (in grams): 0.9413042739759596
## Validation Metrics
- Loss: 0.32112351059913635
- Accuracy: 0.8641304347826086
- Precision: 0.8055555555555556
- Recall: 0.8405797101449275
- AUC: 0.9493383742911153
- F1: 0.8226... | {"language": "unk", "tags": "autonlp", "datasets": ["Fauzan/autonlp-data-judulberita"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}], "co2_eq_emissions": 0.9413042739759596} | Fauzan/autonlp-judulberita-32517788 | null | [
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|
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 32517788
- CO2 Emissions (in grams): 0.9413042739759596
## Validation Metrics
- Loss: 0.32112351059913635
- Accuracy: 0.8641304347826086
- Precision: 0.8055555555555556
- Recall: 0.8405797101449275
- AUC: 0.9493383742911153
- F1: 0.8226... | [
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text-generation | transformers | This model was fine-tuned to generate horror stories in a collaborative way.
Check it out on our [repo](https://github.com/TailUFPB/storIA). | {} | Felipehonorato/storIA | null | [
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text-classification | transformers |
<!-- 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. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["emotion"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "distilbert-base-uncased-finetuned-emotion", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "emotion", "type": "emotion... | Fengkai/distilbert-base-uncased-finetuned-emotion | null | [
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| distilbert-base-uncased-finetuned-emotion
=========================================
This model is a fine-tuned version of distilbert-base-uncased on the emotion dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1495
* Accuracy: 0.9385
* F1: 0.9383
Model description
-----------------
Mo... | [
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text-generation | transformers |
# GPT2-SMALL-PORTUGUESE-WIKIPEDIABIO
This is a finetuned model version of gpt2-small-portuguese(https://huggingface.co/pierreguillou/gpt2-small-portuguese) by pierreguillou.
It was trained on a person abstract dataset extracted from DBPEDIA (over 100000 people's abstracts). The model is intended as a simple and fun... | {"language": "pt", "tags": ["pt", "wikipedia", "gpt2", "finetuning"], "datasets": ["wikipedia"], "widget": ["Andr\u00e9 Um", "Maria do Santos", "Roberto Carlos"], "licence": "mit"} | Ferch423/gpt2-small-portuguese-wikipediabio | null | [
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|
# GPT2-SMALL-PORTUGUESE-WIKIPEDIABIO
This is a finetuned model version of gpt2-small-portuguese(URL by pierreguillou.
It was trained on a person abstract dataset extracted from DBPEDIA (over 100000 people's abstracts). The model is intended as a simple and fun experiment for generating texts abstracts based on ordi... | [
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automatic-speech-recognition | espnet |
## ESPnet2 ASR model
### `Fhrozen/test_an4`
This model was trained by Fhrozen using an4 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```bash
cd espnet
git checkout b8df4c928e132acff78d196988bdb68a66987952
pip install -e .
cd egs2/an4/asr1
./run.sh --skip_data_prep false -... | {"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "automatic-speech-recognition"], "datasets": ["an4"]} | Fhrozen/test_an4 | null | [
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#espnet #audio #automatic-speech-recognition #en #dataset-an4 #license-cc-by-4.0 #region-us
| ESPnet2 ASR model
-----------------
### 'Fhrozen/test\_an4'
This model was trained by Fhrozen using an4 recipe in espnet.
### Demo: How to use in ESPnet2
RESULTS
=======
Environments
------------
* date: 'Wed Oct 20 00:00:46 JST 2021'
* python version: '3.9.7 (default, Sep 16 2021, 13:09:58) [GCC 7.5.0]'
* ... | [
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token-classification | transformers |
<!-- 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. -->
# distilbert-base-uncased-finetuned-ner
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/dis... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["conll2003"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "distilbert-base-uncased-finetuned-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "con... | Fiddi/distilbert-base-uncased-finetuned-ner | null | [
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| distilbert-base-uncased-finetuned-ner
=====================================
This model is a fine-tuned version of distilbert-base-uncased on the conll2003 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0604
* Precision: 0.9291
* Recall: 0.9376
* F1: 0.9333
* Accuracy: 0.9841
Model des... | [
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text-generation | transformers |
# updated PALPATINE DialoGPT Model | {"tags": ["conversational"]} | Filosofas/DialoGPT-medium-PALPATINE | null | [
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feature-extraction | transformers |
# ConvBERT for Finnish
Pretrained ConvBERT model on Finnish language using a replaced token detection (RTD) objective. ConvBERT was introduced in
[this paper](https://arxiv.org/abs/2008.02496)
and first released at [this page](https://github.com/yitu-opensource/ConvBert).
**Note**: this model is the ConvBERT discrim... | {"language": ["fi"], "license": "apache-2.0", "tags": ["finnish", "convbert"], "datasets": ["Finnish-NLP/mc4_fi_cleaned", "wikipedia"]} | Finnish-NLP/convbert-base-finnish | null | [
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| ConvBERT for Finnish
====================
Pretrained ConvBERT model on Finnish language using a replaced token detection (RTD) objective. ConvBERT was introduced in
this paper
and first released at this page.
Note: this model is the ConvBERT discriminator model intented to be used for fine-tuning on downstream task... | [
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fill-mask | transformers |
# ConvBERT for Finnish
Pretrained ConvBERT model on Finnish language using a replaced token detection (RTD) objective. ConvBERT was introduced in
[this paper](https://arxiv.org/abs/2008.02496)
and first released at [this page](https://github.com/yitu-opensource/ConvBert).
**Note**: this model is the ConvBERT generat... | {"language": ["fi"], "license": "apache-2.0", "tags": ["finnish", "convbert"], "datasets": ["Finnish-NLP/mc4_fi_cleaned", "wikipedia"], "widget": [{"text": "Moikka olen [MASK] kielimalli."}]} | Finnish-NLP/convbert-base-generator-finnish | null | [
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|
# ConvBERT for Finnish
Pretrained ConvBERT model on Finnish language using a replaced token detection (RTD) objective. ConvBERT was introduced in
this paper
and first released at this page.
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null | transformers |
# ELECTRA for Finnish
Pretrained ELECTRA model on Finnish language using a replaced token detection (RTD) objective. ELECTRA was introduced in
[this paper](https://openreview.net/pdf?id=r1xMH1BtvB)
and first released at [this page](https://github.com/google-research/electra).
**Note**: this model is the ELECTRA disc... | {"language": ["fi"], "license": "apache-2.0", "tags": ["finnish", "electra"], "datasets": ["Finnish-NLP/mc4_fi_cleaned", "wikipedia"]} | Finnish-NLP/electra-base-discriminator-finnish | null | [
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| ELECTRA for Finnish
===================
Pretrained ELECTRA model on Finnish language using a replaced token detection (RTD) objective. ELECTRA was introduced in
this paper
and first released at this page.
Note: this model is the ELECTRA discriminator model intented to be used for fine-tuning on downstream tasks lik... | [
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fill-mask | transformers |
# ELECTRA for Finnish
Pretrained ELECTRA model on Finnish language using a replaced token detection (RTD) objective. ELECTRA was introduced in
[this paper](https://openreview.net/pdf?id=r1xMH1BtvB)
and first released at [this page](https://github.com/google-research/electra).
**Note**: this model is the ELECTRA gene... | {"language": ["fi"], "license": "apache-2.0", "tags": ["finnish", "electra"], "datasets": ["Finnish-NLP/mc4_fi_cleaned", "wikipedia"], "widget": [{"text": "Moikka olen [MASK] kielimalli."}]} | Finnish-NLP/electra-base-generator-finnish | null | [
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Pretrained ELECTRA model on Finnish language using a replaced token detection (RTD) objective. ELECTRA was introduced in
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text-generation | transformers |
# GPT-2 for Finnish
Pretrained GPT-2 model on Finnish language using a causal language modeling (CLM) objective. GPT-2 was introduced in
[this paper](https://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_multitask_learners.pdf)
and first released at [this page](https://openai.com... | {"language": ["fi"], "license": "apache-2.0", "tags": ["finnish", "gpt2"], "datasets": ["Finnish-NLP/mc4_fi_cleaned", "wikipedia"], "widget": [{"text": "Teksti\u00e4 tuottava teko\u00e4ly on"}]} | Finnish-NLP/gpt2-finnish | null | [
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| GPT-2 for Finnish
=================
Pretrained GPT-2 model on Finnish language using a causal language modeling (CLM) objective. GPT-2 was introduced in
this paper
and first released at this page.
Note: this model is quite small 117M parameter variant as in Huggingface's GPT-2 config, so not the famous big 1.5B par... | [
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text-generation | transformers |
# GPT-2 large for Finnish
Pretrained GPT-2 large model on Finnish language using a causal language modeling (CLM) objective. GPT-2 was introduced in
[this paper](https://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_multitask_learners.pdf)
and first released at [this page](https:... | {"language": ["fi"], "license": "apache-2.0", "tags": ["finnish", "gpt2"], "datasets": ["Finnish-NLP/mc4_fi_cleaned", "wikipedia"], "widget": [{"text": "Teksti\u00e4 tuottava teko\u00e4ly on"}]} | Finnish-NLP/gpt2-large-finnish | null | [
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| GPT-2 large for Finnish
=======================
Pretrained GPT-2 large model on Finnish language using a causal language modeling (CLM) objective. GPT-2 was introduced in
this paper
and first released at this page.
Note: this model is 774M parameter variant as in Huggingface's GPT-2-large config, so not the famous ... | [
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text-generation | transformers |
# GPT-2 medium for Finnish
Pretrained GPT-2 medium model on Finnish language using a causal language modeling (CLM) objective. GPT-2 was introduced in
[this paper](https://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_multitask_learners.pdf)
and first released at [this page](http... | {"language": ["fi"], "license": "apache-2.0", "tags": ["finnish", "gpt2"], "datasets": ["Finnish-NLP/mc4_fi_cleaned", "wikipedia"], "widget": [{"text": "Teksti\u00e4 tuottava teko\u00e4ly on"}]} | Finnish-NLP/gpt2-medium-finnish | null | [
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| GPT-2 medium for Finnish
========================
Pretrained GPT-2 medium model on Finnish language using a causal language modeling (CLM) objective. GPT-2 was introduced in
this paper
and first released at this page.
Note: this model is 345M parameter variant as in Huggingface's GPT-2-medium config, so not the fam... | [
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fill-mask | transformers |
# RoBERTa large model for Finnish
This **Finnish-NLP/roberta-large-finnish-v2** model is a new version of the previously trained [Finnish-NLP/roberta-large-finnish](https://huggingface.co/Finnish-NLP/roberta-large-finnish) model. Training hyperparameters were same but the training dataset was cleaned better with the ... | {"language": ["fi"], "license": "apache-2.0", "tags": ["finnish", "roberta"], "datasets": ["Finnish-NLP/mc4_fi_cleaned", "wikipedia"], "widget": [{"text": "Moikka olen <mask> kielimalli."}]} | Finnish-NLP/roberta-large-finnish-v2 | null | [
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| RoBERTa large model for Finnish
===============================
This Finnish-NLP/roberta-large-finnish-v2 model is a new version of the previously trained Finnish-NLP/roberta-large-finnish model. Training hyperparameters were same but the training dataset was cleaned better with the goal to get better performing lang... | [
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fill-mask | transformers |
# RoBERTa large model for Finnish
Pretrained RoBERTa model on Finnish language using a masked language modeling (MLM) objective. RoBERTa was introduced in
[this paper](https://arxiv.org/abs/1907.11692) and first released in
[this repository](https://github.com/pytorch/fairseq/tree/master/examples/roberta). This model... | {"language": ["fi"], "license": "apache-2.0", "tags": ["finnish", "roberta"], "datasets": ["Finnish-NLP/mc4_fi_cleaned", "wikipedia"], "widget": [{"text": "Moikka olen <mask> kielimalli."}]} | Finnish-NLP/roberta-large-finnish | null | [
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| RoBERTa large model for Finnish
===============================
Pretrained RoBERTa model on Finnish language using a masked language modeling (MLM) objective. RoBERTa was introduced in
this paper and first released in
this repository. This model is case-sensitive: it
makes a difference between finnish and Finnish.
... | [
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fill-mask | transformers |
# RoBERTa large model trained with WECHSEL method for Finnish
Pretrained RoBERTa model on Finnish language using a masked language modeling (MLM) objective with WECHSEL method. RoBERTa was introduced in
[this paper](https://arxiv.org/abs/1907.11692) and first released in
[this repository](https://github.com/pytorch/f... | {"language": ["fi"], "license": "apache-2.0", "tags": ["finnish", "roberta"], "datasets": ["Finnish-NLP/mc4_fi_cleaned", "wikipedia"], "widget": [{"text": "Moikka olen <mask> kielimalli."}]} | Finnish-NLP/roberta-large-wechsel-finnish | null | [
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| RoBERTa large model trained with WECHSEL method for Finnish
===========================================================
Pretrained RoBERTa model on Finnish language using a masked language modeling (MLM) objective with WECHSEL method. RoBERTa was introduced in
this paper and first released in
this repository.
WECHS... | [
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question-answering | transformers |
<!-- 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. -->
# albert-base-v2-finetuned-squad
This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on ... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "albert-base-v2-finetuned-squad", "results": []}]} | Firat/albert-base-v2-finetuned-squad | null | [
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| albert-base-v2-finetuned-squad
==============================
This model is a fine-tuned version of albert-base-v2 on the squad dataset.
It achieves the following results on the evaluation set:
* Loss: 0.9901
Model description
-----------------
More information needed
Intended uses & limitations
-------------... | [
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question-answering | transformers |
<!-- 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. -->
# distilbert-base-uncased-finetuned-squad
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/d... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "distilbert-base-uncased-finetuned-squad", "results": []}]} | Firat/distilbert-base-uncased-finetuned-squad | null | [
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| distilbert-base-uncased-finetuned-squad
=======================================
This model is a fine-tuned version of distilbert-base-uncased on the squad dataset.
It achieves the following results on the evaluation set:
* Loss: 1.1460
Model description
-----------------
More information needed
Intended uses ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
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question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-base-finetuned-squad
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the sq... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "roberta-base-finetuned-squad", "results": []}]} | Firat/roberta-base-finetuned-squad | null | [
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| roberta-base-finetuned-squad
============================
This model is a fine-tuned version of roberta-base on the squad dataset.
It achieves the following results on the evaluation set:
* Loss: 0.8953
Model description
-----------------
More information needed
Intended uses & limitations
-------------------... | [
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automatic-speech-recognition | transformers |
<!-- 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. -->
# wav2vec2-large-xls-r-300m-guarani-colab
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-large-xls-r-300m-guarani-colab", "results": []}]} | FitoDS/wav2vec2-large-xls-r-300m-guarani-colab | null | [
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] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
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| wav2vec2-large-xls-r-300m-guarani-colab
=======================================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 3.2392
* Wer: 1.0743
Model description
-----------------
More information neede... | [
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automatic-speech-recognition | transformers |
<!-- 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. -->
#
This model is a fine-tuned version of [hf-test/xls-r-dummy](https://huggingface.co/hf-test/xls-r-dummy) on the COMMON_VOICE - A... | {"language": ["ab"], "tags": ["automatic-speech-recognition", "common_voice", "generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "", "results": []}]} | FitoDS/xls-r-ab-test | null | [
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"common_voice",
"generated_from_trainer",
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"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"ab"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #common_voice #generated_from_trainer #ab #dataset-common_voice #endpoints_compatible #region-us
|
#
This model is a fine-tuned version of hf-test/xls-r-dummy on the COMMON_VOICE - AB dataset.
It achieves the following results on the evaluation set:
- Loss: 133.5167
- Wer: 18.9286
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation ... | [
"# \n\nThis model is a fine-tuned version of hf-test/xls-r-dummy on the COMMON_VOICE - AB dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 133.5167\n- Wer: 18.9286",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## T... | [
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text-generation | transformers |
# Sheldon Cooper from The Big Bang Theory Show DialoGPT Model | {"tags": ["conversational"]} | Flampt/DialoGPT-medium-Sheldon | null | [
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] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
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] |
text-generation | transformers | #
| {"tags": ["conversational"]} | For/sheldonbot | null | [
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"gpt2",
"text-generation",
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| #
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question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-fa-QA-v1
Persian Question and answer Model Based on Bert Model
This model is a fine-tuned version of [ParsBERT](https://arx... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model_index": [{"name": "bert-fa-QA-v1", "results": [{"task": {"name": "Question Answering", "type": "question-answering"}}]}]} | ForutanRad/bert-fa-QA-v1 | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"question-answering",
"generated_from_trainer",
"arxiv:2005.12515",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [
"2005.12515"
] | [] | TAGS
#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #arxiv-2005.12515 #license-apache-2.0 #endpoints_compatible #region-us
| bert-fa-QA-v1
=============
Persian Question and answer Model Based on Bert Model
This model is a fine-tuned version of ParsBERT on PersianQA dataset.
It achieves the following results on the evaluation set:
* Loss: 1.7297
Model description
-----------------
More information needed
Intended uses & limitatio... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Training... | [
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text-generation | transformers |
# Chat Bot Test | {"tags": ["conversational"]} | FosterPatch/GoT-test | null | [
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"gpt2",
"text-generation",
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|
# Chat Bot Test | [
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null | null | # Program Synthesis Data
Generated program synthesis datasets used to train [dreamcoder](https://github.com/ellisk42/ec).
Currently just supports text & list data.
```python
_FEATURES = datasets.Features(
{
"description": datasets.Value("string"),
"input": datasets.Value("string"),
"outpu... | {"language": ["en"], "license": "mit", "tags": ["program-synthesis"], "datasets": ["program-synthesis"], "thumbnail": "https://huggingface.co/Fraser/program-synthesis/resolve/main/img.png"} | Fraser/to_delete | null | [
"program-synthesis",
"en",
"dataset:program-synthesis",
"license:mit",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"en"
] | TAGS
#program-synthesis #en #dataset-program-synthesis #license-mit #region-us
| # Program Synthesis Data
Generated program synthesis datasets used to train dreamcoder.
Currently just supports text & list data.
![](URL
| [
"# Program Synthesis Data\n\nGenerated program synthesis datasets used to train dreamcoder.\n\nCurrently just supports text & list data.\n\n\n\n![](URL"
] | [
"TAGS\n#program-synthesis #en #dataset-program-synthesis #license-mit #region-us \n",
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] | [
22,
31
] | [
"TAGS\n#program-synthesis #en #dataset-program-synthesis #license-mit #region-us \n# Program Synthesis Data\n\nGenerated program synthesis datasets used to train dreamcoder.\n\nCurrently just supports text & list data.\n\n\n\n![](URL"
] |
null | null | # Transformer-VAE (WIP)
A PyTorch Transformer-VAE model.
Uses an MMD loss to prevent posterior collapse.
Will setup in the next month or so.
## ToDo
- [ ] Copy in old repo code.
- [ ] Make a bunch of sample training runs.
- [ ] Make an interpolation widget? | {} | Fraser/transformer-vae | null | [
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#region-us
| # Transformer-VAE (WIP)
A PyTorch Transformer-VAE model.
Uses an MMD loss to prevent posterior collapse.
Will setup in the next month or so.
## ToDo
- [ ] Copy in old repo code.
- [ ] Make a bunch of sample training runs.
- [ ] Make an interpolation widget? | [
"# Transformer-VAE (WIP)\n\nA PyTorch Transformer-VAE model.\n\nUses an MMD loss to prevent posterior collapse.\n\nWill setup in the next month or so.",
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"TAGS\n#region-us \n",
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] |
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