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null | null | transformers | # Usage
Load in transformers library with:
```
from transformers import AutoTokenizer, AutoModelForMaskedLM
tokenizer = AutoTokenizer.from_pretrained("EMBEDDIA/sloberta")
model = AutoModelForMaskedLM.from_pretrained("EMBEDDIA/sloberta")
```
# SloBERTa
SloBERTa model is a monolingual Slovene BERT-like model. It... | {"language": ["sl"], "license": "cc-by-sa-4.0"} | fill-mask | EMBEDDIA/sloberta | [
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"license:cc-by-sa-4.0",
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] | 2022-03-02T23:29:04+00:00 | [] | [
"sl"
] | TAGS
#transformers #pytorch #camembert #fill-mask #sl #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| # Usage
Load in transformers library with:
# SloBERTa
SloBERTa model is a monolingual Slovene BERT-like model. It is closely related to French Camembert model URL The corpora used for training the model have 3.47 billion tokens in total. The subword vocabulary contains 32,000 tokens. The scripts and programs used fo... | [
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null | null | transformers |
# bio-lm
## Model description
This model is a [RoBERTa base pre-trained model](https://huggingface.co/roberta-base) that was further trained using a masked language modeling task on a compendium of english scientific textual examples from the life sciences using the [BioLang dataset](https://huggingface.co/datasets/... | {"language": ["english"], "tags": ["language model"], "datasets": ["EMBO/biolang"], "metrics": []} | fill-mask | EMBO/bio-lm | [
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"autotrain_compatible",
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"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [
"english"
] | TAGS
#transformers #pytorch #jax #roberta #fill-mask #language model #dataset-EMBO/biolang #autotrain_compatible #endpoints_compatible #region-us
|
# bio-lm
## Model description
This model is a RoBERTa base pre-trained model that was further trained using a masked language modeling task on a compendium of english scientific textual examples from the life sciences using the BioLang dataset.
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#### How to use
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null | null | transformers |
# sd-ner
## Model description
This model is a [RoBERTa base model](https://huggingface.co/roberta-base) that was further trained using a masked language modeling task on a compendium of English scientific textual examples from the life sciences using the [BioLang dataset](https://huggingface.co/datasets/EMBO/biolang... | {"language": ["english"], "license": "agpl-3.0", "tags": ["token classification"], "datasets": ["EMBO/sd-nlp"], "metrics": []} | token-classification | EMBO/sd-ner | [
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"token-classification",
"token classification",
"dataset:EMBO/sd-nlp",
"license:agpl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
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"english"
] | TAGS
#transformers #pytorch #jax #roberta #token-classification #token classification #dataset-EMBO/sd-nlp #license-agpl-3.0 #autotrain_compatible #endpoints_compatible #region-us
|
# sd-ner
## Model description
This model is a RoBERTa base model that was further trained using a masked language modeling task on a compendium of English scientific textual examples from the life sciences using the BioLang dataset. It was then fine-tuned for token classification on the SourceData sd-nlp dataset wit... | [
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null | null | transformers |
# sd-panelization
## Model description
This model is a [RoBERTa base model](https://huggingface.co/roberta-base) that was further trained using a masked language modeling task on a compendium of english scientific textual examples from the life sciences using the [BioLang dataset](https://huggingface.co/datasets/EMB... | {"language": ["english"], "license": "agpl-3.0", "tags": ["token classification"], "datasets": ["EMBO/sd-nlp"], "metrics": []} | token-classification | EMBO/sd-panelization | [
"transformers",
"pytorch",
"jax",
"roberta",
"token-classification",
"dataset:EMBO/sd-nlp",
"license:agpl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [
"english"
] | TAGS
#transformers #pytorch #jax #roberta #token-classification #dataset-EMBO/sd-nlp #license-agpl-3.0 #autotrain_compatible #endpoints_compatible #region-us
|
# sd-panelization
## Model description
This model is a RoBERTa base model that was further trained using a masked language modeling task on a compendium of english scientific textual examples from the life sciences using the BioLang dataset. It was then fine-tuned for token classification on the SourceData sd-nlp da... | [
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null | null | transformers |
# Game of Thrones DialoGPT Model | {"tags": ["conversational"]} | text-generation | ESPersonnel/DialoGPT-small-got | [
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"gpt2",
"text-generation",
"conversational",
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"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 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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null | null | transformers |
# Peppa Pig DialoGPT Model | {"tags": ["conversational"]} | text-generation | Eagle3ye/DialoGPT-small-PeppaPig | [
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"gpt2",
"text-generation",
"conversational",
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null | null | 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... | {} | text-classification | EasthShin/Android_Ios_Classification | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | 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... | [
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null | null | 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>
... | {} | question-answering | EasthShin/Klue-CommonSense-model | [
"transformers",
"pytorch",
"bert",
"question-answering",
"endpoints_compatible",
"region:us"
] | 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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null | null | 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... | {} | text-generation | EasthShin/Youth_Chatbot_Kogpt2-base | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 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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null | null | transformers | #Arabic_BERT_Model
#ArBERTMo
| {} | fill-mask | Ebtihal/ArBERTMo | [
"transformers",
"tf",
"camembert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #tf #camembert #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| #Arabic_BERT_Model
#ArBERTMo
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0.17034761607646942,
-0.01616712100803852,
0.2325388... |
null | null | 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 ... | fill-mask | Ebtihal/AraBertMo_base_V1 | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"Fill-Mask",
"ar",
"dataset:OSCAR",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 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"
] | [
50
] | [
"passage: TAGS\n#transformers #pytorch #bert #fill-mask #Fill-Mask #ar #dataset-OSCAR #autotrain_compatible #endpoints_compatible #region-us \n"
] | [
-0.10124170035123825,
0.09327436238527298,
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0.08325929194688797,
0.13643206655979156,
0.0388636589050293,
0.10127938538789749,
0.09678910672664642,
0.08133750408887863,
-0.044068414717912674,
0.16871397197246552,
0.16403791308403015,
-0.0004524141550064087,
0.19767893... |
null | null | 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 ... | fill-mask | Ebtihal/AraBertMo_base_V2 | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"Fill-Mask",
"ar",
"dataset:OSCAR",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 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"
] | [
50
] | [
"passage: TAGS\n#transformers #pytorch #bert #fill-mask #Fill-Mask #ar #dataset-OSCAR #autotrain_compatible #endpoints_compatible #region-us \n"
] | [
-0.10124170035123825,
0.09327436238527298,
-0.006752943154424429,
0.08325929194688797,
0.13643206655979156,
0.0388636589050293,
0.10127938538789749,
0.09678910672664642,
0.08133750408887863,
-0.044068414717912674,
0.16871397197246552,
0.16403791308403015,
-0.0004524141550064087,
0.19767893... |
null | null | 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 ... | fill-mask | Ebtihal/AraBertMo_base_V3 | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"Fill-Mask",
"ar",
"dataset:OSCAR",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 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"
] | [
50
] | [
"passage: TAGS\n#transformers #pytorch #bert #fill-mask #Fill-Mask #ar #dataset-OSCAR #autotrain_compatible #endpoints_compatible #region-us \n"
] | [
-0.10124170035123825,
0.09327436238527298,
-0.006752943154424429,
0.08325929194688797,
0.13643206655979156,
0.0388636589050293,
0.10127938538789749,
0.09678910672664642,
0.08133750408887863,
-0.044068414717912674,
0.16871397197246552,
0.16403791308403015,
-0.0004524141550064087,
0.19767893... |
null | null | 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 ... | fill-mask | Ebtihal/AraBertMo_base_V4 | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"Fill-Mask",
"ar",
"dataset:OSCAR",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 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"
] | [
50
] | [
"passage: TAGS\n#transformers #pytorch #bert #fill-mask #Fill-Mask #ar #dataset-OSCAR #autotrain_compatible #endpoints_compatible #region-us \n"
] | [
-0.10124170035123825,
0.09327436238527298,
-0.006752943154424429,
0.08325929194688797,
0.13643206655979156,
0.0388636589050293,
0.10127938538789749,
0.09678910672664642,
0.08133750408887863,
-0.044068414717912674,
0.16871397197246552,
0.16403791308403015,
-0.0004524141550064087,
0.19767893... |
null | null | 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 ... | fill-mask | Ebtihal/AraBertMo_base_V5 | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"Fill-Mask",
"ar",
"dataset:OSCAR",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 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"
] | [
50
] | [
"passage: TAGS\n#transformers #pytorch #bert #fill-mask #Fill-Mask #ar #dataset-OSCAR #autotrain_compatible #endpoints_compatible #region-us \n"
] | [
-0.10124170035123825,
0.09327436238527298,
-0.006752943154424429,
0.08325929194688797,
0.13643206655979156,
0.0388636589050293,
0.10127938538789749,
0.09678910672664642,
0.08133750408887863,
-0.044068414717912674,
0.16871397197246552,
0.16403791308403015,
-0.0004524141550064087,
0.19767893... |
null | null | 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 ... | fill-mask | Ebtihal/AraBertMo_base_V6 | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"Fill-Mask",
"ar",
"dataset:OSCAR",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 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"
] | [
50
] | [
"passage: TAGS\n#transformers #pytorch #bert #fill-mask #Fill-Mask #ar #dataset-OSCAR #autotrain_compatible #endpoints_compatible #region-us \n"
] | [
-0.10124170035123825,
0.09327436238527298,
-0.006752943154424429,
0.08325929194688797,
0.13643206655979156,
0.0388636589050293,
0.10127938538789749,
0.09678910672664642,
0.08133750408887863,
-0.044068414717912674,
0.16871397197246552,
0.16403791308403015,
-0.0004524141550064087,
0.19767893... |
null | null | 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 ... | {} | fill-mask | Ebtihal/AraBertMo_base_V7 | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 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"
] | [
36
] | [
"passage: TAGS\n#transformers #pytorch #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n"
] | [
-0.06357412785291672,
0.00690077617764473,
-0.008467365056276321,
0.020235946401953697,
0.12968459725379944,
0.03302915394306183,
0.09807441383600235,
0.07729126513004303,
0.10806342214345932,
-0.009440856985747814,
0.15823203325271606,
0.20325462520122528,
-0.03393663093447685,
0.18361465... |
null | null | 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 ... | {} | fill-mask | Ebtihal/AraBertMo_base_V8 | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 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"
] | [
36
] | [
"passage: TAGS\n#transformers #pytorch #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n"
] | [
-0.06357412785291672,
0.00690077617764473,
-0.008467365056276321,
0.020235946401953697,
0.12968459725379944,
0.03302915394306183,
0.09807441383600235,
0.07729126513004303,
0.10806342214345932,
-0.009440856985747814,
0.15823203325271606,
0.20325462520122528,
-0.03393663093447685,
0.18361465... |
null | null | 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 ... | {} | fill-mask | Ebtihal/AraBertMo_base_V9 | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 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"
] | [
36
] | [
"passage: TAGS\n#transformers #pytorch #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n"
] | [
-0.06357412785291672,
0.00690077617764473,
-0.008467365056276321,
0.020235946401953697,
0.12968459725379944,
0.03302915394306183,
0.09807441383600235,
0.07729126513004303,
0.10806342214345932,
-0.009440856985747814,
0.15823203325271606,
0.20325462520122528,
-0.03393663093447685,
0.18361465... |
null | null | 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":... | text2text-generation | Edomonndo/opus-mt-en-ro-finetuned-en-to-ro | [
"transformers",
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"generated_from_trainer",
"dataset:wmt16",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 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... | [
"TAGS\n#transformers #pytorch #tensorboard #marian #text2text-generation #generated_from_trainer #dataset-wmt16 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_s... | [
57,
98,
4,
35
] | [
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null | null | 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}}]}]} | text2text-generation | Edomonndo/opus-mt-ja-en-finetuned-ja-to-en_test | [
"transformers",
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 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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null | null | 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}}]}]} | text2text-generation | Edomonndo/opus-mt-ja-en-finetuned-ja-to-en_xml | [
"transformers",
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 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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null | null | 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"]} | automatic-speech-recognition | Edresson/wav2vec2-large-100k-voxpopuli-ft-Common-Voice_plus_TTS-Dataset-portuguese | [
"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"
] | 2022-03-02T23:29:04+00:00 | [
"2204.00618"
] | [
"pt"
] | TAGS
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|
# 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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null | null | 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"]} | automatic-speech-recognition | Edresson/wav2vec2-large-100k-voxpopuli-ft-Common-Voice_plus_TTS-Dataset-russian | [
"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"
] | 2022-03-02T23:29:04+00:00 | [
"2204.00618"
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"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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null | null | 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"]} | automatic-speech-recognition | Edresson/wav2vec2-large-100k-voxpopuli-ft-Common_Voice_plus_TTS-Dataset_plus_Data_Augmentation-portuguese | [
"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"
] | 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... | [
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null | null | 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"]} | automatic-speech-recognition | Edresson/wav2vec2-large-100k-voxpopuli-ft-Common_Voice_plus_TTS-Dataset_plus_Data_Augmentation-russian | [
"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"
] | 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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null | null | 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"]} | automatic-speech-recognition | Edresson/wav2vec2-large-100k-voxpopuli-ft-TTS-Dataset-plus-data-augmentation-portuguese | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
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"pt",
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"PyTorch",
"arxiv:2204.00618",
"license:apache-2.0",
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"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"2204.00618"
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"pt"
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|
# 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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null | null | 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"]} | automatic-speech-recognition | Edresson/wav2vec2-large-100k-voxpopuli-ft-TTS-Dataset-plus-data-augmentation-russian | [
"transformers",
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"arxiv:2204.00618",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"2204.00618"
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"pt"
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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... | [
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"# Use this model",
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null | null | 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... | automatic-speech-recognition | Edresson/wav2vec2-large-xlsr-coraa-portuguese | [
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"automatic-speech-recognition",
"audio",
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"PyTorch",
"dataset:CORAA",
"arxiv:2110.15731",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"2110.15731"
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"pt"
] | TAGS
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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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null | null | 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": []}]} | summarization | Einmalumdiewelt/PegasusXSUM_GNAD | [
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"generated_from_trainer",
"summarization",
"de",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [
"de"
] | TAGS
#transformers #pytorch #pegasus #text2text-generation #generated_from_trainer #summarization #de #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# 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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null | null | 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": []}]} | summarization | Einmalumdiewelt/T5-Base_GNAD | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"generated_from_trainer",
"summarization",
"de",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [
"de"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #generated_from_trainer #summarization #de #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
# 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 | 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} | null | EleutherAI/enformer-191k | [
"transformers",
"pytorch",
"enformer",
"license:apache-2.0",
"region:us"
] | 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 | 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} | null | EleutherAI/enformer-191k_corr_coef_obj | [
"transformers",
"pytorch",
"enformer",
"license:apache-2.0",
"region:us"
] | 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 | 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} | null | EleutherAI/enformer-corr_coef_obj | [
"transformers",
"pytorch",
"enformer",
"license:apache-2.0",
"region:us"
] | 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 131,072 basepairs, target length 896 on v3-64 TPUs for 3 days ... | [
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null | 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} | null | EleutherAI/enformer-preview | [
"transformers",
"pytorch",
"enformer",
"license:apache-2.0",
"region:us"
] | 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 131,072 basepairs, target length 896 on v3-64 TPUs for 2 and a... | [
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null | null | 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"]} | text-generation | EleutherAI/gpt-j-6b | [
"transformers",
"pytorch",
"tf",
"jax",
"gptj",
"text-generation",
"causal-lm",
"en",
"dataset:EleutherAI/pile",
"arxiv:2104.09864",
"arxiv:2101.00027",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"2104.09864",
"2101.00027"
] | [
"en"
] | TAGS
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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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null | null | 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"]} | text-generation | EleutherAI/gpt-neo-1.3B | [
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"dataset:EleutherAI/pile",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [
"en"
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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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null | null | 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"]} | text-generation | EleutherAI/gpt-neo-125m | [
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"rust",
"safetensors",
"gpt_neo",
"text-generation",
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"causal-lm",
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"dataset:EleutherAI/pile",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [
"en"
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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
-----------... | [
"### 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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null | null | 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"]} | text-generation | EleutherAI/gpt-neo-2.7B | [
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"jax",
"rust",
"safetensors",
"gpt_neo",
"text-generation",
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"causal-lm",
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"dataset:EleutherAI/pile",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [
"en"
] | TAGS
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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
-----------... | [
"### 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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null | null | 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... | {} | text-classification | Elron/bleurt-base-128 | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 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
| [
"## BLEURT\n\nPytorch version of the original BLEURT models from ACL paper \"BLEURT: Learning Robust Metrics for Text Generation\" by \nThibault Sellam, Dipanjan Das and Ankur P. Parikh of Google Research.\n\nThe code for model conversion was originated from this notebook mentioned here.",
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null | null | 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... | {} | text-classification | Elron/bleurt-base-512 | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 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
| [
"## BLEURT\n\nPytorch version of the original BLEURT models from ACL paper \"BLEURT: Learning Robust Metrics for Text Generation\" by \nThibault Sellam, Dipanjan Das and Ankur P. Parikh of Google Research.\n\nThe code for model conversion was originated from this notebook mentioned here.",
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null | null | 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... | {} | text-classification | Elron/bleurt-large-128 | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 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
| [
"## BLEURT\n\nPytorch version of the original BLEURT models from ACL paper \"BLEURT: Learning Robust Metrics for Text Generation\" by \nThibault Sellam, Dipanjan Das and Ankur P. Parikh of Google Research.\n\nThe code for model conversion was originated from this notebook mentioned here.",
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null | null | 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.
The code for model conversion was originated from [this notebook](https:... | {} | text-classification | Elron/bleurt-large-512 | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us
| ## 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
| [
"## BLEURT\n\nPytorch version of the original BLEURT models from ACL paper \"BLEURT: Learning Robust Metrics for Text Generation\" by \nThibault Sellam, Dipanjan Das and Ankur P. Parikh of Google Research.\n\nThe code for model conversion was originated from this notebook mentioned here.",
"## Usage Example"
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null | null | 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... | {} | text-classification | Elron/bleurt-tiny-128 | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 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
| [
"## BLEURT\n\nPytorch version of the original BLEURT models from ACL paper \"BLEURT: Learning Robust Metrics for Text Generation\" by \nThibault Sellam, Dipanjan Das and Ankur P. Parikh of Google Research.\n\nThe code for model conversion was originated from this notebook mentioned here.",
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0.1865... |
null | null | 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"]} | text-classification | Elron/bleurt-tiny-512 | [
"transformers",
"pytorch",
"bert",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #pytorch #bert #text-classification #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #region-us
|
# 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
- Langua... | [
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null | null | transformers |
# Harry Potter DialoGPT Model | {"tags": ["conversational"]} | text-generation | Elzen7/DialoGPT-medium-harrypotter | [
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"pytorch",
"gpt2",
"text-generation",
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"endpoints_compatible",
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# Harry Potter DialoGPT Model | [
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null | null | 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} | token-classification | Emanuel/autonlp-pos-tag-bosque | [
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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
## Usage
You ca... | [
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null | null | 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... | text-classification | Emanuel/bertweet-emotion-base | [
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"dataset:emotion",
"license:apache-2.0",
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"endpoints_compatible",
"has_space",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
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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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null | null | 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": []}]} | fill-mask | Emanuel/roebrta-base-val-test | [
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"roberta",
"fill-mask",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #fill-mask #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# 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
More informati... | [
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null | null | 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... | text-classification | Emanuel/twitter-emotion-deberta-v3-base | [
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] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
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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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null | null | transformers |
# My Awesome Model | {"tags": ["conversational"]} | text-generation | Emi2160/DialoGPT-small-Neku | [
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] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
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# My Awesome Model | [
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null | null | transformers |
# Harry Potter DialoGPT Model | {"tags": ["conversational"]} | text-generation | EmileAjar/DialoGPT-small-harrypotter | [
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"gpt2",
"text-generation",
"conversational",
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"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
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null | null | transformers |
# Peppa pig DialoGPT Model | {"tags": ["conversational"]} | text-generation | EmileAjar/DialoGPT-small-peppapig | [
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"conversational",
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null | null | 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... | token-classification | Emmanuel/bert-finetuned-ner | [
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"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#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 | null | bu benim modelim | {} | null | Enes3774/gpt2 | [
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null | null | 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": []}]} | automatic-speech-recognition | EngNada/wav2vec2-large-xlsr-53-demo-colab | [
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"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | 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
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=... | [
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null | null | 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... | text-classification | EnsarEmirali/distilbert-base-uncased-finetuned-emotion | [
"transformers",
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"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 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... | [
"### 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",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learn... | [
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null | null | transformers |
#Loki DialoGPT Model | {"tags": ["conversational"]} | text-generation | Erikaka/DialoGPT-small-loki | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
#Loki DialoGPT Model | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] | [
51
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null | null | transformers |
# Harry Potter DialoGPT Model | {"tags": ["conversational"]} | text-generation | EstoyDePaso/DialoGPT-small-harrypotter | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Harry Potter DialoGPT Model | [
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null | null | transformers |
# MrCobb DialoGPT Model | {"tags": ["conversational"]} | text-generation | EuropeanTurtle/DialoGPT-small-mrcobb | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
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null | null | 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... | token-classification | Evgeneus/distilbert-base-uncased-finetuned-ner | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 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... | [
"### 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... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* le... | [
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null | null | transformers |
#jdt chat bot | {"tags": ["conversational"]} | text-generation | ExEngineer/DialoGPT-medium-jdt | [
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"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
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|
#jdt chat bot | [] | [
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null | null | transformers |
# Quirk DialoGPT Model | {"tags": ["conversational"]} | text-generation | Exilon/DialoGPT-large-quirk | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
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# Quirk DialoGPT Model | [
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null | null | null | read me | {} | null | EyeSeeThru/txt2img | [
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#region-us
| read me | [] | [
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null | null | 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": []}]} | automatic-speech-recognition | Eyvaz/wav2vec2-base-russian-big-kaggle | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | 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 ... | [
"# wav2vec2-base-russian-big-kaggle\n\nThis model is a fine-tuned version of facebook/wav2vec2-base on the None dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Trainin... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
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null | null | 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": []}]} | automatic-speech-recognition | Eyvaz/wav2vec2-base-russian-demo-kaggle | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | 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-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... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 12\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 24\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
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null | null | 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"]} | automatic-speech-recognition | Eyvaz/wav2vec2-base-russian-modified-kaggle | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | 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-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... | [
"# wav2vec2-base-russian-modified-kaggle\n\nThis model is a fine-tuned version of facebook/wav2vec2-base on an unknown dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## ... | [
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null | null | transformers |
#house small GPT | {"tags": ["conversational"]} | text-generation | EzioDD/house | [
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"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
#house small GPT | [] | [
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null | null | transformers |
# FFF dialog model | {"tags": "conversational"} | text-generation | FFF000/dialogpt-FFF | [
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"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
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null | null | 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": []}]} | question-answering | FOFer/distilbert-base-uncased-finetuned-squad | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:squad_v2",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | 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",
"### Traini... | [
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null | null | 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."}]} | fill-mask | FabianGroeger/HotelBERT-small | [
"transformers",
"pytorch",
"tf",
"roberta",
"fill-mask",
"de",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [
"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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null | null | 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."}]} | fill-mask | FabianGroeger/HotelBERT | [
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"tf",
"roberta",
"fill-mask",
"de",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [
"de"
] | TAGS
#transformers #pytorch #tf #roberta #fill-mask #de #autotrain_compatible #endpoints_compatible #region-us
|
# HotelBERT
This model was trained on reviews from a well known German hotel platform.
| [
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null | null | 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... | text-classification | FabioDataGeek/distilbert-base-uncased-finetuned-emotion | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 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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null | null | 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"]} | text-classification | Fan-s/reddit-tc-bert | [
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 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... | [
"# bert-uncased-base\n\nThis model is a fine-tuned version of bert-base-uncased on an Reddit-dialogue dataset.\nThis model can be used for Text Classification: Given two sentences, see if they are related.\nIt achieves the following results on the evaluation set:\n- Loss: 0.2297\n- Accuracy: 0.9267",
"### Trainin... | [
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null | null | transformers | @Kirito DialoGPT Small Model | {"tags": ["conversational"]} | text-generation | FangLee/DialoGPT-small-Kirito | [
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"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
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"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
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| @Kirito DialoGPT Small Model | [] | [
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null | null | 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": []}]} | question-answering | FardinSaboori/bert-finetuned-squad | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | 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... | [
"# bert-finetuned-squad\n\nThis model is a fine-tuned version of bert-base-cased on the squad dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"#... | [
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null | null | 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": []}]} | automatic-speech-recognition | FarisHijazi/wav2vec2-large-xls-r-300m-turkish-colab | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | 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.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information n... | [
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null | null | 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} | text-classification | Fauzan/autonlp-judulberita-32517788 | [
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"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [
"unk"
] | TAGS
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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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null | null | 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). | {} | text-generation | Felipehonorato/storIA | [
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"text-generation",
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| This model was fine-tuned to generate horror stories in a collaborative way.
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null | null | 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... | text-classification | Fengkai/distilbert-base-uncased-finetuned-emotion | [
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"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 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: 0.1495
* Accuracy: 0.9385
* F1: 0.9383
Model description
-----------------
Mo... | [
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null | null | 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"} | text-generation | Ferch423/gpt2-small-portuguese-wikipediabio | [
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"jax",
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"pt",
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"finetuning",
"dataset:wikipedia",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [
"pt"
] | TAGS
#transformers #pytorch #jax #gpt2 #text-generation #pt #wikipedia #finetuning #dataset-wikipedia #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# 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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null | null | 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"]} | automatic-speech-recognition | Fhrozen/test_an4 | [
"espnet",
"audio",
"automatic-speech-recognition",
"en",
"dataset:an4",
"license:cc-by-4.0",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [
"en"
] | TAGS
#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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null | null | 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... | token-classification | Fiddi/distilbert-base-uncased-finetuned-ner | [
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"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
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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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null | null | transformers |
# updated PALPATINE DialoGPT Model | {"tags": ["conversational"]} | text-generation | Filosofas/DialoGPT-medium-PALPATINE | [
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"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
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null | null | 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"]} | feature-extraction | Finnish-NLP/convbert-base-finnish | [
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"fi",
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"dataset:wikipedia",
"arxiv:2008.02496",
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"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"2008.02496"
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] | TAGS
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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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null | null | 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).
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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 generator model intented to be used for the fill-mask task. The ConvBERT discriminator model i... | [
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null | 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"]} | null | Finnish-NLP/electra-base-discriminator-finnish | [
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] | 2022-03-02T23:29:04+00:00 | [] | [
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] | TAGS
#transformers #pytorch #tensorboard #electra #pretraining #finnish #fi #dataset-Finnish-NLP/mc4_fi_cleaned #dataset-wikipedia #license-apache-2.0 #endpoints_compatible #region-us
| 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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null | 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 gene... | {"language": ["fi"], "license": "apache-2.0", "tags": ["finnish", "electra"], "datasets": ["Finnish-NLP/mc4_fi_cleaned", "wikipedia"], "widget": [{"text": "Moikka olen [MASK] kielimalli."}]} | fill-mask | Finnish-NLP/electra-base-generator-finnish | [
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"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [
"fi"
] | TAGS
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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 generator model intented to be used for the fill-mask task. The ELECTRA discriminator model intent... | [
"# ELECTRA for Finnish\n\nPretrained ELECTRA model on Finnish language using a replaced token detection (RTD) objective. ELECTRA was introduced in\nthis paper\nand first released at this page.\n\nNote: this model is the ELECTRA generator model intented to be used for the fill-mask task. The ELECTRA discriminator mo... | [
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null | null | 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"}]} | text-generation | Finnish-NLP/gpt2-finnish | [
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"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [
"fi"
] | TAGS
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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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null | null | 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"}]} | text-generation | Finnish-NLP/gpt2-large-finnish | [
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"dataset:wikipedia",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [
"fi"
] | TAGS
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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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null | null | 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"}]} | text-generation | Finnish-NLP/gpt2-medium-finnish | [
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"finnish",
"fi",
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"dataset:wikipedia",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [
"fi"
] | TAGS
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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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null | null | 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."}]} | fill-mask | Finnish-NLP/roberta-large-finnish-v2 | [
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"finnish",
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"dataset:wikipedia",
"arxiv:1907.11692",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"1907.11692"
] | [
"fi"
] | TAGS
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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... | [
"### How to use\n\n\nYou can use this model directly with a pipeline for masked language modeling:\n\n\nHere is how to use this model to get the features of a given text in PyTorch:\n\n\nand in TensorFlow:",
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null | null | 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."}]} | fill-mask | Finnish-NLP/roberta-large-finnish | [
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"fill-mask",
"finnish",
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"dataset:wikipedia",
"arxiv:1907.11692",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"1907.11692"
] | [
"fi"
] | TAGS
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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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null | null | 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."}]} | fill-mask | Finnish-NLP/roberta-large-wechsel-finnish | [
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"arxiv:1907.11692",
"arxiv:2112.06598",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"1907.11692",
"2112.06598"
] | [
"fi"
] | TAGS
#transformers #pytorch #jax #tensorboard #roberta #fill-mask #finnish #fi #dataset-Finnish-NLP/mc4_fi_cleaned #dataset-wikipedia #arxiv-1907.11692 #arxiv-2112.06598 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| 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... | [
"### How to use\n\n\nYou can use this model directly with a pipeline for masked language modeling:\n\n\nHere is how to use this model to get the features of a given text in PyTorch:\n\n\nand in TensorFlow:",
"### Limitations and bias\n\n\nThe training data used for this model contains a lot of unfiltered content ... | [
"TAGS\n#transformers #pytorch #jax #tensorboard #roberta #fill-mask #finnish #fi #dataset-Finnish-NLP/mc4_fi_cleaned #dataset-wikipedia #arxiv-1907.11692 #arxiv-2112.06598 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### How to use\n\n\nYou can use this model directly with a pi... | [
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null | null | 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": []}]} | question-answering | Firat/albert-base-v2-finetuned-squad | [
"transformers",
"pytorch",
"albert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #albert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us
| 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
-------------... | [
"### 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",
"### Traini... | [
"TAGS\n#transformers #pytorch #albert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us \n",
"### 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\\... | [
51,
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"passage: TAGS\n#transformers #pytorch #albert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eva... | [
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0.14984644949436188,
0.0011699125170707703,
0.062833... |
null | null | 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": []}]} | question-answering | Firat/distilbert-base-uncased-finetuned-squad | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-squad #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 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",
"### Traini... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_s... | [
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-0.11151876300573349,
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0.13178522884845734,
0.17631612718105316,
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0.06464057... |
null | null | 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": []}]} | question-answering | Firat/roberta-base-finetuned-squad | [
"transformers",
"pytorch",
"roberta",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:mit",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #question-answering #generated_from_trainer #dataset-squad #license-mit #endpoints_compatible #region-us
| 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
-------------------... | [
"### 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",
"### Traini... | [
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"### 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... | [
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"passage: TAGS\n#transformers #pytorch #roberta #question-answering #generated_from_trainer #dataset-squad #license-mit #endpoints_compatible #region-us \n### 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\\_ba... | [
-0.08433376252651215,
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0.020214691758155823,
0.12317449599504471,
0.16488853096961975,
-0.0023862074594944715,
0.08434... |
null | null | 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": []}]} | automatic-speech-recognition | FitoDS/wav2vec2-large-xls-r-300m-guarani-colab | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| 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... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 16\n* eval\\_b... | [
52,
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"passage: TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 16\n* eval\... | [
-0.1263004094362259,
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0.11741490662097931,
0.09046110510826111,
0.0452432855963707,
0.0937305018305... |
null | null | 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": []}]} | automatic-speech-recognition | FitoDS/xls-r-ab-test | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"common_voice",
"generated_from_trainer",
"ab",
"dataset:common_voice",
"endpoints_compatible",
"region:us"
] | 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... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #common_voice #generated_from_trainer #ab #dataset-common_voice #endpoints_compatible #region-us \n",
"# \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 e... | [
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"passage: TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #common_voice #generated_from_trainer #ab #dataset-common_voice #endpoints_compatible #region-us \n# \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 th... | [
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0.08671777695417404,
0.0677979663... |
null | null | transformers |
# Sheldon Cooper from The Big Bang Theory Show DialoGPT Model | {"tags": ["conversational"]} | text-generation | Flampt/DialoGPT-medium-Sheldon | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Sheldon Cooper from The Big Bang Theory Show DialoGPT Model | [
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] | [
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0.109... |
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