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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
null | null | # danbooru-pretrained
- Repo: https://github.com/RF5/danbooru-pretrained
- https://github.com/RF5/danbooru-pretrained/releases/tag/v0.1
- https://github.com/RF5/danbooru-pretrained/releases/download/v0.1/resnet50-13306192.pth
- https://github.com/RF5/danbooru-pretrained/raw/master/config/class_names_6000.j... | {} | public-data/danbooru-pretrained | null | [
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#has_space #region-us
| # danbooru-pretrained
- Repo: URL
- URL
- URL
- URL
| [
"# danbooru-pretrained\n\n- Repo: URL\n - URL\n - URL\n - URL"
] | [
"TAGS\n#has_space #region-us \n",
"# danbooru-pretrained\n\n- Repo: URL\n - URL\n - URL\n - URL"
] |
null | null | # yolov5_anime
- Repo: https://github.com/zymk9/yolov5_anime
- https://drive.google.com/file/d/1-MO9RYPZxnBfpNiGY6GdsqCeQWYNxBdl/view
| {} | public-data/yolov5_anime | null | [
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#has_space #region-us
| # yolov5_anime
- Repo: URL
- URL
| [
"# yolov5_anime\n\n- Repo: URL\n - URL"
] | [
"TAGS\n#has_space #region-us \n",
"# yolov5_anime\n\n- Repo: URL\n - URL"
] |
text-generation | transformers |
## Model description
+ Paper: [Recipes for building an open-domain chatbot](https://arxiv.org/abs/1907.06616)
+ [Original PARLAI Code](https://parl.ai/projects/recipes/)
### Abstract
Building open-domain chatbots is a challenging area for machine learning research. While prior work has shown that scaling neural m... | {"language": ["en"], "license": "apache-2.0", "tags": ["convAI", "conversational", "facebook"], "datasets": ["blended_skill_talk"], "metrics": ["perplexity"]} | hyunwoongko/blenderbot-9B | null | [
"transformers",
"pytorch",
"blenderbot",
"text2text-generation",
"convAI",
"conversational",
"facebook",
"en",
"dataset:blended_skill_talk",
"arxiv:1907.06616",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1907.06616"
] | [
"en"
] | TAGS
#transformers #pytorch #blenderbot #text2text-generation #convAI #conversational #facebook #en #dataset-blended_skill_talk #arxiv-1907.06616 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
## Model description
+ Paper: Recipes for building an open-domain chatbot
+ Original PARLAI Code
### Abstract
Building open-domain chatbots is a challenging area for machine learning research. While prior work has shown that scaling neural models in the number of parameters and the size of the data they are train... | [
"## Model description\n\n+ Paper: Recipes for building an open-domain chatbot\n+ Original PARLAI Code",
"### Abstract\n\n\nBuilding open-domain chatbots is a challenging area for machine learning research. While prior work has shown that scaling neural models in the number of parameters and the size of the data t... | [
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"## Model description\n\n+ Paper: Recipes for building an open-domain chatb... |
text2text-generation | transformers |
## KoBART-base-v2
With the addition of chatting data, the model is trained to handle the semantics of sequences longer than KoBART.
```python
from transformers import PreTrainedTokenizerFast, BartModel
tokenizer = PreTrainedTokenizerFast.from_pretrained('hyunwoongko/kobart')
model = BartModel.from_pretrained('hyunw... | {"language": "ko", "license": "mit", "tags": ["bart"]} | hyunwoongko/kobart | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"ko",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ko"
] | TAGS
#transformers #pytorch #bart #text2text-generation #ko #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us
|
## KoBART-base-v2
With the addition of chatting data, the model is trained to handle the semantics of sequences longer than KoBART.
### Performance
NSMC
- acc. : 0.901
### hyunwoongko/kobart
- Added bos/eos post processor
- Removed token_type_ids
| [
"## KoBART-base-v2\n\nWith the addition of chatting data, the model is trained to handle the semantics of sequences longer than KoBART.",
"### Performance \n\nNSMC\n- acc. : 0.901",
"### hyunwoongko/kobart\n- Added bos/eos post processor\n- Removed token_type_ids"
] | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #ko #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"## KoBART-base-v2\n\nWith the addition of chatting data, the model is trained to handle the semantics of sequences longer than KoBART.",
"### Performance \n\nNSMC\n-... |
text-generation | transformers |
## Model description
+ Paper: [Recipes for building an open-domain chatbot](https://arxiv.org/abs/1907.06616)
+ [Original PARLAI Code](https://parl.ai/projects/recipes/)
### Abstract
Building open-domain chatbots is a challenging area for machine learning research. While prior work has shown that scaling neural m... | {"language": ["en"], "license": "apache-2.0", "tags": ["convAI", "conversational", "facebook"], "datasets": ["blended_skill_talk"], "metrics": ["perplexity"]} | hyunwoongko/reddit-3B | null | [
"transformers",
"pytorch",
"blenderbot",
"text2text-generation",
"convAI",
"conversational",
"facebook",
"en",
"dataset:blended_skill_talk",
"arxiv:1907.06616",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1907.06616"
] | [
"en"
] | TAGS
#transformers #pytorch #blenderbot #text2text-generation #convAI #conversational #facebook #en #dataset-blended_skill_talk #arxiv-1907.06616 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
## Model description
+ Paper: Recipes for building an open-domain chatbot
+ Original PARLAI Code
### Abstract
Building open-domain chatbots is a challenging area for machine learning research. While prior work has shown that scaling neural models in the number of parameters and the size of the data they are train... | [
"## Model description\n\n+ Paper: Recipes for building an open-domain chatbot\n+ Original PARLAI Code",
"### Abstract\n\n\nBuilding open-domain chatbots is a challenging area for machine learning research. While prior work has shown that scaling neural models in the number of parameters and the size of the data t... | [
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"## Model description\n\n+ Paper: Recipes for building an open-domain chatbot\n+ Origi... |
text-generation | transformers |
## Model description
+ Paper: [Recipes for building an open-domain chatbot](https://arxiv.org/abs/1907.06616)
+ [Original PARLAI Code](https://parl.ai/projects/recipes/)
### Abstract
Building open-domain chatbots is a challenging area for machine learning research. While prior work has shown that scaling neural m... | {"language": ["en"], "license": "apache-2.0", "tags": ["convAI", "conversational", "facebook"], "datasets": ["blended_skill_talk"], "metrics": ["perplexity"]} | hyunwoongko/reddit-9B | null | [
"transformers",
"pytorch",
"blenderbot",
"text2text-generation",
"convAI",
"conversational",
"facebook",
"en",
"dataset:blended_skill_talk",
"arxiv:1907.06616",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1907.06616"
] | [
"en"
] | TAGS
#transformers #pytorch #blenderbot #text2text-generation #convAI #conversational #facebook #en #dataset-blended_skill_talk #arxiv-1907.06616 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
## Model description
+ Paper: Recipes for building an open-domain chatbot
+ Original PARLAI Code
### Abstract
Building open-domain chatbots is a challenging area for machine learning research. While prior work has shown that scaling neural models in the number of parameters and the size of the data they are train... | [
"## Model description\n\n+ Paper: Recipes for building an open-domain chatbot\n+ Original PARLAI Code",
"### Abstract\n\n\nBuilding open-domain chatbots is a challenging area for machine learning research. While prior work has shown that scaling neural models in the number of parameters and the size of the data t... | [
"TAGS\n#transformers #pytorch #blenderbot #text2text-generation #convAI #conversational #facebook #en #dataset-blended_skill_talk #arxiv-1907.06616 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"## Model description\n\n+ Paper: Recipes for building an open-domain chatb... |
automatic-speech-recognition | transformers |
# wav2vec2-xlsr-korean-senior
Futher fine-tuned [fleek/wav2vec-large-xlsr-korean](https://huggingface.co/fleek/wav2vec-large-xlsr-korean) using the [AIhub 자유대화 음성(노인남녀)](https://aihub.or.kr/aidata/30704).
- Total train data size: 808,642
- Total vaild data size: 159,970
When using this model, make sure that your s... | {"language": "kr", "license": "apache-2.0", "tags": ["automatic-speech-recognition"], "datasets": ["aihub \uc790\uc720\ub300\ud654 \uc74c\uc131(\ub178\uc778\ub0a8\ub140)"]} | hyyoka/wav2vec2-xlsr-korean-senior | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"kr",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"kr"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #kr #license-apache-2.0 #endpoints_compatible #region-us
|
# wav2vec2-xlsr-korean-senior
Futher fine-tuned fleek/wav2vec-large-xlsr-korean using the AIhub 자유대화 음성(노인남녀).
- Total train data size: 808,642
- Total vaild data size: 159,970
When using this model, make sure that your speech input is sampled at 16kHz.
The script used for training can be found here: URL
### In... | [
"# wav2vec2-xlsr-korean-senior\n\nFuther fine-tuned fleek/wav2vec-large-xlsr-korean using the AIhub 자유대화 음성(노인남녀).\n\n- Total train data size: 808,642\n- Total vaild data size: 159,970\n\nWhen using this model, make sure that your speech input is sampled at 16kHz.\n\nThe script used for training can be found here: ... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #kr #license-apache-2.0 #endpoints_compatible #region-us \n",
"# wav2vec2-xlsr-korean-senior\n\nFuther fine-tuned fleek/wav2vec-large-xlsr-korean using the AIhub 자유대화 음성(노인남녀).\n\n- Total train data size: 808,642\n- Total vaild data size: 159,9... |
null | null | Hugging Face Test Model | {} | iSandro19/Hugging | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| Hugging Face Test Model | [] | [
"TAGS\n#region-us \n"
] |
text-generation | transformers |
# Bender DialoGPT model | {"tags": ["conversational"]} | iamalpharius/GPT-Small-BenderBot | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Bender DialoGPT model | [
"# Bender DialoGPT model"
] | [
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"# Bender DialoGPT model"
] |
automatic-speech-recognition | transformers |
# Wav2Vec-OSR
Finetuned facebook's wav2vec2 model for speech to text module of [The Sound Of AI open source research group](https://thesoundofaiosr.github.io/).
The original base model is pretrained and fine-tuned on 960 hours of Librispeech on 16kHz sampled speech audio. When using the model make sure that your spee... | {"language": "en", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech to text"], "datasets": ["librispeech_asr"], "widget": [{"example_title": "OSR sample 1", "src": "https://github.com/TheSoundOfAIOSR/rg_speech_to_text/blob/main/data/finetuning-dataset/audiofiles/TA-5.wav?raw=true"}, {"... | iamtarun/wav2vec-osr | null | [
"transformers",
"pytorch",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech to text",
"en",
"dataset:librispeech_asr",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #safetensors #wav2vec2 #automatic-speech-recognition #audio #speech to text #en #dataset-librispeech_asr #license-apache-2.0 #endpoints_compatible #region-us
|
# Wav2Vec-OSR
Finetuned facebook's wav2vec2 model for speech to text module of The Sound Of AI open source research group.
The original base model is pretrained and fine-tuned on 960 hours of Librispeech on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz.
##... | [
"# Wav2Vec-OSR\nFinetuned facebook's wav2vec2 model for speech to text module of The Sound Of AI open source research group.\n\nThe original base model is pretrained and fine-tuned on 960 hours of Librispeech on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16K... | [
"TAGS\n#transformers #pytorch #safetensors #wav2vec2 #automatic-speech-recognition #audio #speech to text #en #dataset-librispeech_asr #license-apache-2.0 #endpoints_compatible #region-us \n",
"# Wav2Vec-OSR\nFinetuned facebook's wav2vec2 model for speech to text module of The Sound Of AI open source research gro... |
text-generation | transformers |
# My Awesome Model | {"tags": ["conversational"]} | ianc89/hagrid | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# My Awesome Model | [
"# My Awesome Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# My Awesome Model"
] |
text-classification | transformers | # BERT-base-cased-qa-evaluator
This model takes a question answer pair as an input and outputs a value representing its prediction about whether the input was a valid question and answer pair or not. The model is a pretrained [BERT-base-cased](https://huggingface.co/bert-base-cased) with a sequence classification head... | {} | iarfmoose/bert-base-cased-qa-evaluator | null | [
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #jax #bert #text-classification #autotrain_compatible #endpoints_compatible #has_space #region-us
| # BERT-base-cased-qa-evaluator
This model takes a question answer pair as an input and outputs a value representing its prediction about whether the input was a valid question and answer pair or not. The model is a pretrained BERT-base-cased with a sequence classification head.
## Intended uses
The QA evaluator was ... | [
"# BERT-base-cased-qa-evaluator\n\nThis model takes a question answer pair as an input and outputs a value representing its prediction about whether the input was a valid question and answer pair or not. The model is a pretrained BERT-base-cased with a sequence classification head.",
"## Intended uses\n\nThe QA e... | [
"TAGS\n#transformers #pytorch #tf #jax #bert #text-classification #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# BERT-base-cased-qa-evaluator\n\nThis model takes a question answer pair as an input and outputs a value representing its prediction about whether the input was a valid questi... |
token-classification | transformers |
# RoBERTa-base-bulgarian-POS
The RoBERTa model was originally introduced in [this paper](https://arxiv.org/abs/1907.11692). This model is a version of [RoBERTa-base-Bulgarian](https://huggingface.co/iarfmoose/roberta-base-bulgarian) fine-tuned for part-of-speech tagging.
## Intended uses
The model can be u... | {"language": "bg"} | iarfmoose/roberta-base-bulgarian-pos | null | [
"transformers",
"pytorch",
"tf",
"jax",
"roberta",
"token-classification",
"bg",
"arxiv:1907.11692",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1907.11692"
] | [
"bg"
] | TAGS
#transformers #pytorch #tf #jax #roberta #token-classification #bg #arxiv-1907.11692 #autotrain_compatible #endpoints_compatible #region-us
|
# RoBERTa-base-bulgarian-POS
The RoBERTa model was originally introduced in this paper. This model is a version of RoBERTa-base-Bulgarian fine-tuned for part-of-speech tagging.
## Intended uses
The model can be used to predict part-of-speech tags in Bulgarian text. Since the tokenizer uses byte-pair encodi... | [
"# RoBERTa-base-bulgarian-POS\r\n\r\n\r\nThe RoBERTa model was originally introduced in this paper. This model is a version of RoBERTa-base-Bulgarian fine-tuned for part-of-speech tagging.",
"## Intended uses\r\n\r\nThe model can be used to predict part-of-speech tags in Bulgarian text. Since the tokenizer uses b... | [
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"# RoBERTa-base-bulgarian-POS\r\n\r\n\r\nThe RoBERTa model was originally introduced in this paper. This model is a version of RoBERTa-base-Bulgarian fine-tuned f... |
fill-mask | transformers |
# RoBERTa-base-bulgarian
The RoBERTa model was originally introduced in [this paper](https://arxiv.org/abs/1907.11692). This is a version of [RoBERTa-base](https://huggingface.co/roberta-base) pretrained on Bulgarian text.
## Intended uses
This model can be used for cloze tasks (masked language modeling) o... | {"language": "bg"} | iarfmoose/roberta-base-bulgarian | null | [
"transformers",
"pytorch",
"tf",
"jax",
"roberta",
"fill-mask",
"bg",
"arxiv:1907.11692",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1907.11692"
] | [
"bg"
] | TAGS
#transformers #pytorch #tf #jax #roberta #fill-mask #bg #arxiv-1907.11692 #autotrain_compatible #endpoints_compatible #region-us
|
# RoBERTa-base-bulgarian
The RoBERTa model was originally introduced in this paper. This is a version of RoBERTa-base pretrained on Bulgarian text.
## Intended uses
This model can be used for cloze tasks (masked language modeling) or finetuned on other tasks in Bulgarian.
## Limitations and bias
The ... | [
"# RoBERTa-base-bulgarian\r\n\r\n\r\nThe RoBERTa model was originally introduced in this paper. This is a version of RoBERTa-base pretrained on Bulgarian text.",
"## Intended uses\r\n\r\nThis model can be used for cloze tasks (masked language modeling) or finetuned on other tasks in Bulgarian.",
"## Limitations... | [
"TAGS\n#transformers #pytorch #tf #jax #roberta #fill-mask #bg #arxiv-1907.11692 #autotrain_compatible #endpoints_compatible #region-us \n",
"# RoBERTa-base-bulgarian\r\n\r\n\r\nThe RoBERTa model was originally introduced in this paper. This is a version of RoBERTa-base pretrained on Bulgarian text.",
"## Inten... |
token-classification | transformers |
# RoBERTa-small-bulgarian-POS
The RoBERTa model was originally introduced in [this paper](https://arxiv.org/abs/1907.11692). This model is a version of [RoBERTa-small-Bulgarian](https://huggingface.co/iarfmoose/roberta-small-bulgarian) fine-tuned for part-of-speech tagging.
## Intended uses
The model can b... | {"language": "bg"} | iarfmoose/roberta-small-bulgarian-pos | null | [
"transformers",
"pytorch",
"tf",
"jax",
"roberta",
"token-classification",
"bg",
"arxiv:1907.11692",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1907.11692"
] | [
"bg"
] | TAGS
#transformers #pytorch #tf #jax #roberta #token-classification #bg #arxiv-1907.11692 #autotrain_compatible #endpoints_compatible #region-us
|
# RoBERTa-small-bulgarian-POS
The RoBERTa model was originally introduced in this paper. This model is a version of RoBERTa-small-Bulgarian fine-tuned for part-of-speech tagging.
## Intended uses
The model can be used to predict part-of-speech tags in Bulgarian text. Since the tokenizer uses byte-pair enco... | [
"# RoBERTa-small-bulgarian-POS\r\n\r\n\r\nThe RoBERTa model was originally introduced in this paper. This model is a version of RoBERTa-small-Bulgarian fine-tuned for part-of-speech tagging.",
"## Intended uses\r\n\r\nThe model can be used to predict part-of-speech tags in Bulgarian text. Since the tokenizer uses... | [
"TAGS\n#transformers #pytorch #tf #jax #roberta #token-classification #bg #arxiv-1907.11692 #autotrain_compatible #endpoints_compatible #region-us \n",
"# RoBERTa-small-bulgarian-POS\r\n\r\n\r\nThe RoBERTa model was originally introduced in this paper. This model is a version of RoBERTa-small-Bulgarian fine-tuned... |
fill-mask | transformers |
# RoBERTa-small-bulgarian
The RoBERTa model was originally introduced in [this paper](https://arxiv.org/abs/1907.11692). This is a smaller version of [RoBERTa-base-bulgarian](https://huggingface.co/iarfmoose/roberta-small-bulgarian) with only 6 hidden layers, but similar performance.
## Intended uses
This ... | {"language": "bg"} | iarfmoose/roberta-small-bulgarian | null | [
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"fill-mask",
"bg",
"arxiv:1907.11692",
"autotrain_compatible",
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"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1907.11692"
] | [
"bg"
] | TAGS
#transformers #pytorch #tf #jax #roberta #fill-mask #bg #arxiv-1907.11692 #autotrain_compatible #endpoints_compatible #region-us
|
# RoBERTa-small-bulgarian
The RoBERTa model was originally introduced in this paper. This is a smaller version of RoBERTa-base-bulgarian with only 6 hidden layers, but similar performance.
## Intended uses
This model can be used for cloze tasks (masked language modeling) or finetuned on other tasks in Bulg... | [
"# RoBERTa-small-bulgarian\r\n\r\n\r\nThe RoBERTa model was originally introduced in this paper. This is a smaller version of RoBERTa-base-bulgarian with only 6 hidden layers, but similar performance.",
"## Intended uses\r\n\r\nThis model can be used for cloze tasks (masked language modeling) or finetuned on othe... | [
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"# RoBERTa-small-bulgarian\r\n\r\n\r\nThe RoBERTa model was originally introduced in this paper. This is a smaller version of RoBERTa-base-bulgarian with only 6 hidden layer... |
text2text-generation | transformers | # Model name
## Model description
This model is a sequence-to-sequence question generator which takes an answer and context as an input, and generates a question as an output. It is based on a pretrained `t5-base` model.
## Intended uses & limitations
The model is trained to generate reading comprehension-style que... | {} | iarfmoose/t5-base-question-generator | null | [
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"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #jax #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| # Model name
## Model description
This model is a sequence-to-sequence question generator which takes an answer and context as an input, and generates a question as an output. It is based on a pretrained 't5-base' model.
## Intended uses & limitations
The model is trained to generate reading comprehension-style que... | [
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automatic-speech-recognition | transformers |
# Wav2Vec2-Large-XLSR-53-Frisian
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) in Frisian using the [Common Voice](https://huggingface.co/datasets/common_voice)
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can ... | {"language": "fy-NL", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice"], "model-index": [{"name": "XLSR Wav2Vec2 Frisian by Adam Montgomerie", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech Recogniti... | iarfmoose/wav2vec2-large-xlsr-frisian | null | [
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"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"fy-NL"
] | TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
# Wav2Vec2-Large-XLSR-53-Frisian
Fine-tuned facebook/wav2vec2-large-xlsr-53 in Frisian using the Common Voice
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used directly (without a language model) as follows:
## Evaluation
The model can be evaluated as f... | [
"# Wav2Vec2-Large-XLSR-53-Frisian\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 in Frisian using the Common Voice\nWhen using this model, make sure that your speech input is sampled at 16kHz.",
"## Usage\n\nThe model can be used directly (without a language model) as follows:",
"## Evaluation\n\nThe model can b... | [
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"# Wav2Vec2-Large-XLSR-53-Frisian\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 in Frisian using the Common ... |
automatic-speech-recognition | transformers |
# Wav2Vec2-Large-XLSR-53-Kyrgyz
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) in Kyrgyz using the [Common Voice](https://huggingface.co/datasets/common_voice)
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be... | {"language": "ky", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice"], "model-index": [{"name": "XLSR Wav2Vec2 Kyrgyz by Adam Montgomerie", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech Recognition"}... | iarfmoose/wav2vec2-large-xlsr-kyrgyz | null | [
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] | null | 2022-03-02T23:29:05+00:00 | [] | [
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|
# Wav2Vec2-Large-XLSR-53-Kyrgyz
Fine-tuned facebook/wav2vec2-large-xlsr-53 in Kyrgyz using the Common Voice
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used directly (without a language model) as follows:
## Evaluation
The model can be evaluated as fol... | [
"# Wav2Vec2-Large-XLSR-53-Kyrgyz\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 in Kyrgyz using the Common Voice\nWhen using this model, make sure that your speech input is sampled at 16kHz.",
"## Usage\n\nThe model can be used directly (without a language model) as follows:",
"## Evaluation\n\nThe model can be ... | [
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"# Wav2Vec2-Large-XLSR-53-Kyrgyz\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 in Kyrgyz using the Commo... |
automatic-speech-recognition | transformers |
# Wav2Vec2-Large-XLSR-53-Sorbian
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) in Sorbian using the [Common Voice](https://huggingface.co/datasets/common_voice)
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can ... | {"language": "hsb", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice"], "model-index": [{"name": "XLSR Wav2Vec2 Sorbian by Adam Montgomerie", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech Recognition... | iarfmoose/wav2vec2-large-xlsr-sorbian | null | [
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"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"hsb"
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|
# Wav2Vec2-Large-XLSR-53-Sorbian
Fine-tuned facebook/wav2vec2-large-xlsr-53 in Sorbian using the Common Voice
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used directly (without a language model) as follows:
## Evaluation
The model can be evaluated as f... | [
"# Wav2Vec2-Large-XLSR-53-Sorbian\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 in Sorbian using the Common Voice\nWhen using this model, make sure that your speech input is sampled at 16kHz.",
"## Usage\n\nThe model can be used directly (without a language model) as follows:",
"## Evaluation\n\nThe model can b... | [
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"# Wav2Vec2-Large-XLSR-53-Sorbian\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 in Sorbian using the Co... |
null | null | SenDM model described at https://arxiv.org/pdf/2201.02026
---
language:
- en
tags:
- discourse-markers
license: apache-2.0
---
| {} | ibm/tslm-discourse-markers | null | [
"arxiv:2201.02026",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2201.02026"
] | [] | TAGS
#arxiv-2201.02026 #region-us
| SenDM model described at URL
---
language:
- en
tags:
- discourse-markers
license: apache-2.0
---
| [] | [
"TAGS\n#arxiv-2201.02026 #region-us \n"
] |
image-classification | transformers |
# swin-age-classifier
Trained on 80 epochs -
Data from: Ai Crowd - Blitz
ai-blitz-xiii - Age Prediction
https://www.aicrowd.com/challenges/ai-blitz-xiii/problems/age-prediction/
Notebook based on HuggingPics
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for **anything** by running [the dem... | {"tags": ["image-classification", "pytorch", "huggingpics"], "metrics": ["accuracy"]} | ibombonato/swin-age-classifier | null | [
"transformers",
"pytorch",
"tensorboard",
"swin",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #swin #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# swin-age-classifier
Trained on 80 epochs -
Data from: Ai Crowd - Blitz
ai-blitz-xiii - Age Prediction
URL
Notebook based on HuggingPics
Autogenerated by HuggingPics️
Create your own image classifier for anything by running the demo on Google Colab.
Report any issues with the demo at the github repo. | [
"# swin-age-classifier\n\n\nTrained on 80 epochs - \n\nData from: Ai Crowd - Blitz \nai-blitz-xiii - Age Prediction\nURL\n\nNotebook based on HuggingPics\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nReport any issues with the demo at the g... | [
"TAGS\n#transformers #pytorch #tensorboard #swin #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
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image-classification | transformers |
# vit-age-classifier
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https://github.com/nateraw/h... | {"tags": ["image-classification", "pytorch", "huggingpics"], "metrics": ["accuracy"]} | ibombonato/vit-age-classifier | null | [
"transformers",
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"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# vit-age-classifier
Autogenerated by HuggingPics️
Create your own image classifier for anything by running the demo on Google Colab.
Report any issues with the demo at the github repo. | [
"# vit-age-classifier\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nReport any issues with the demo at the github repo."
] | [
"TAGS\n#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# vit-age-classifier\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nReport any i... |
text-classification | transformers |
# XLMIndic Base Multiscript
This model is finetuned from [this model](https://huggingface.co/ibraheemmoosa/xlmindic-base-multiscript) on Soham Bangla News Classification task which is part of the IndicGLUE benchmark.
## Model description
This model has the same configuration as the [ALBERT Base v2 model](https://hug... | {"language": ["as", "bn", "gu", "hi", "mr", "ne", "or", "pa", "si", "sa", "bpy", "bh", "gom", "mai"], "license": "apache-2.0", "tags": ["multilingual", "albert", "fill-mask", "xlmindic", "nlp", "indoaryan", "indicnlp", "iso15919", "text-classification"], "datasets": ["oscar"], "widget": [{"text": "\u099a\u09c0\u09a8\u0... | ibraheemmoosa/xlmindic-base-multiscript-soham | null | [
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... | null | 2022-03-02T23:29:05+00:00 | [] | [
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"si",
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"bpy",
"bh",
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| XLMIndic Base Multiscript
=========================
This model is finetuned from this model on Soham Bangla News Classification task which is part of the IndicGLUE benchmark.
Model description
-----------------
This model has the same configuration as the ALBERT Base v2 model. Specifically, this model has the fol... | [
"### Preprocessing\n\n\nThe texts are tokenized using SentencePiece and a vocabulary size of 50,000.",
"### Training\n\n\nThe model was trained for 8 epochs with a batch size of 16 and a learning rate of *2e-5*.\n\n\nEvaluation results\n------------------\n\n\nSee results specific to Soham in the following table.... | [
"TAGS\n#transformers #pytorch #tf #jax #albert #text-classification #multilingual #fill-mask #xlmindic #nlp #indoaryan #indicnlp #iso15919 #as #bn #gu #hi #mr #ne #or #pa #si #sa #bpy #bh #gom #mai #dataset-oscar #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Preprocessing\n\... |
fill-mask | transformers |
# XLMIndic Base Multiscript
This model is identical in all aspects to [this model](https://huggingface.co/ibraheemmoosa/xlmindic-base-uniscript) except that we do not perform the ISO-15919 transliteration. Thus it is intended to serve as an ablation model for our study. See [this](https://huggingface.co/ibraheemmoosa... | {"language": ["as", "bn", "gu", "hi", "mr", "ne", "or", "pa", "si", "sa", "bpy", "bh", "gom", "mai"], "license": "apache-2.0", "tags": ["multilingual", "albert", "masked-language-modeling", "sentence-order-prediction", "fill-mask", "xlmindic", "nlp", "indoaryan", "indicnlp", "iso15919"], "datasets": ["oscar"], "widget"... | ibraheemmoosa/xlmindic-base-multiscript | null | [
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"iso15919",
"as",
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"hi",
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"ne",
"or",
"pa",
"si",
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... | null | 2022-03-02T23:29:05+00:00 | [] | [
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"hi",
"mr",
"ne",
"or",
"pa",
"si",
"sa",
"bpy",
"bh",
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"mai"
] | TAGS
#transformers #pytorch #tf #jax #albert #pretraining #multilingual #masked-language-modeling #sentence-order-prediction #fill-mask #xlmindic #nlp #indoaryan #indicnlp #iso15919 #as #bn #gu #hi #mr #ne #or #pa #si #sa #bpy #bh #gom #mai #dataset-oscar #license-apache-2.0 #co2_eq_emissions #endpoints_compatible #reg... | XLMIndic Base Multiscript
=========================
This model is identical in all aspects to this model except that we do not perform the ISO-15919 transliteration. Thus it is intended to serve as an ablation model for our study. See this to understand the details.
Model description
-----------------
This model ... | [
"### Preprocessing\n\n\nThe texts are tokenized using SentencePiece and a vocabulary size of 50,000. The inputs of the model are\nthen of the form:",
"### Training\n\n\nTraining objective is the same as the original ALBERT.\n.\nThe details of the masking procedure for each sentence are the following:\n\n\n* 15% o... | [
"TAGS\n#transformers #pytorch #tf #jax #albert #pretraining #multilingual #masked-language-modeling #sentence-order-prediction #fill-mask #xlmindic #nlp #indoaryan #indicnlp #iso15919 #as #bn #gu #hi #mr #ne #or #pa #si #sa #bpy #bh #gom #mai #dataset-oscar #license-apache-2.0 #co2_eq_emissions #endpoints_compatibl... |
text-classification | transformers |
# XLMIndic Base Uniscript
This model is finetuned from [this model](https://huggingface.co/ibraheemmoosa/xlmindic-base-uniscript) on Soham Bangla News Classification task which is part of the IndicGLUE benchmark. **Before pretraining this model we transliterate the text to [ISO-15919](https://en.wikipedia.org/wiki/IS... | {"language": ["as", "bn", "gu", "hi", "mr", "ne", "or", "pa", "si", "sa", "bpy", "mai", "bh", "gom"], "license": "apache-2.0", "tags": ["multilingual", "albert", "xlmindic", "nlp", "indoaryan", "indicnlp", "iso15919", "transliteration", "text-classification"], "datasets": ["oscar"], "widget": [{"text": "c\u012bn\u0113r... | ibraheemmoosa/xlmindic-base-uniscript-soham | null | [
"transformers",
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"tf",
"jax",
"albert",
"text-classification",
"multilingual",
"xlmindic",
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"as",
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"mr",
"ne",
"or",
"pa",
"si",
"sa",
"bpy",
"mai",
"bh",
"gom",
"dataset:os... | null | 2022-03-02T23:29:05+00:00 | [] | [
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"gu",
"hi",
"mr",
"ne",
"or",
"pa",
"si",
"sa",
"bpy",
"mai",
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] | TAGS
#transformers #pytorch #tf #jax #albert #text-classification #multilingual #xlmindic #nlp #indoaryan #indicnlp #iso15919 #transliteration #as #bn #gu #hi #mr #ne #or #pa #si #sa #bpy #mai #bh #gom #dataset-oscar #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| XLMIndic Base Uniscript
=======================
This model is finetuned from this model on Soham Bangla News Classification task which is part of the IndicGLUE benchmark. Before pretraining this model we transliterate the text to ISO-15919 format using the Aksharamukha
library. A demo of Aksharamukha library is hoste... | [
"### Preprocessing\n\n\nThe texts are transliterated to ISO-15919 format using the Aksharamukha library. Then these are tokenized using SentencePiece and a vocabulary size of 50,000.",
"### Training\n\n\nThe model was trained for 8 epochs with a batch size of 16 and a learning rate of *2e-5*.\n\n\nEvaluation resu... | [
"TAGS\n#transformers #pytorch #tf #jax #albert #text-classification #multilingual #xlmindic #nlp #indoaryan #indicnlp #iso15919 #transliteration #as #bn #gu #hi #mr #ne #or #pa #si #sa #bpy #mai #bh #gom #dataset-oscar #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Preprocess... |
fill-mask | transformers |
# XLMIndic Base Uniscript
This model is pretrained on a subset of the [OSCAR](https://huggingface.co/datasets/oscar) corpus spanning 14 Indo-Aryan languages. **Before pretraining this model we transliterate the text to [ISO-15919](https://en.wikipedia.org/wiki/ISO_15919) format using the [Aksharamukha](https://pypi.o... | {"language": ["as", "bn", "gu", "hi", "mr", "ne", "or", "pa", "si", "sa", "bpy", "mai", "bh", "gom"], "license": "apache-2.0", "tags": ["multilingual", "albert", "masked-language-modeling", "sentence-order-prediction", "fill-mask", "xlmindic", "nlp", "indoaryan", "indicnlp", "iso15919", "transliteration"], "datasets": ... | ibraheemmoosa/xlmindic-base-uniscript | null | [
"transformers",
"pytorch",
"tf",
"jax",
"albert",
"pretraining",
"multilingual",
"masked-language-modeling",
"sentence-order-prediction",
"fill-mask",
"xlmindic",
"nlp",
"indoaryan",
"indicnlp",
"iso15919",
"transliteration",
"as",
"bn",
"gu",
"hi",
"mr",
"ne",
"or",
"p... | null | 2022-03-02T23:29:05+00:00 | [] | [
"as",
"bn",
"gu",
"hi",
"mr",
"ne",
"or",
"pa",
"si",
"sa",
"bpy",
"mai",
"bh",
"gom"
] | TAGS
#transformers #pytorch #tf #jax #albert #pretraining #multilingual #masked-language-modeling #sentence-order-prediction #fill-mask #xlmindic #nlp #indoaryan #indicnlp #iso15919 #transliteration #as #bn #gu #hi #mr #ne #or #pa #si #sa #bpy #mai #bh #gom #dataset-oscar #license-apache-2.0 #co2_eq_emissions #endpoint... | XLMIndic Base Uniscript
=======================
This model is pretrained on a subset of the OSCAR corpus spanning 14 Indo-Aryan languages. Before pretraining this model we transliterate the text to ISO-15919 format using the Aksharamukha
library. A demo of Aksharamukha library is hosted here
where you can translitera... | [
"### Preprocessing\n\n\nThe texts are transliterated to ISO-15919 format using the Aksharamukha library. Then these are tokenized using SentencePiece and a vocabulary size of 50,000. The inputs of the model are\nthen of the form:",
"### Training\n\n\nTraining objective is the same as the original ALBERT.\n.\nThe ... | [
"TAGS\n#transformers #pytorch #tf #jax #albert #pretraining #multilingual #masked-language-modeling #sentence-order-prediction #fill-mask #xlmindic #nlp #indoaryan #indicnlp #iso15919 #transliteration #as #bn #gu #hi #mr #ne #or #pa #si #sa #bpy #mai #bh #gom #dataset-oscar #license-apache-2.0 #co2_eq_emissions #en... |
fill-mask | transformers |
### SpaceBERT
This is one of the 3 further pre-trained models from the SpaceTransformers family presented in [SpaceTransformers: Language Modeling for Space Systems](https://ieeexplore.ieee.org/document/9548078). The original Git repo is [strath-ace/smart-nlp](https://github.com/strath-ace/smart-nlp).
The further pr... | {"language": "en", "license": "mit"} | icelab/spacebert | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"en",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bert #fill-mask #en #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
### SpaceBERT
This is one of the 3 further pre-trained models from the SpaceTransformers family presented in SpaceTransformers: Language Modeling for Space Systems. The original Git repo is strath-ace/smart-nlp.
The further pre-training corpus includes publications abstracts, books, and Wikipedia pages related to sp... | [
"### SpaceBERT\n\nThis is one of the 3 further pre-trained models from the SpaceTransformers family presented in SpaceTransformers: Language Modeling for Space Systems. The original Git repo is strath-ace/smart-nlp.\n\nThe further pre-training corpus includes publications abstracts, books, and Wikipedia pages relat... | [
"TAGS\n#transformers #pytorch #bert #fill-mask #en #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### SpaceBERT\n\nThis is one of the 3 further pre-trained models from the SpaceTransformers family presented in SpaceTransformers: Language Modeling for Space Systems. The original Git repo... |
token-classification | transformers |
---
# spacebert_CR
### Model desciption
This is a fine-tuned SpaceSciBERT model, for a Concept Recognition task, from the SpaceTransformers model family presented in SpaceTransformers: Language Modeling for Space Systems. The original Git repo is strath-ace/smart-nlp. The [fine-tuning](https://github.com/strath-ace... | {"language": "en", "license": "mit", "widget": [{"text": "The CubeSat RF design shall either have one RF inhibit and a RF power output no greater than 1.5W at the transmitter antenna's RF input OR the CubeSat shall have a minimum of two independent RF inhibits (CDS 3.3.9) (ISO 5.5.6)."}]} | icelab/spacebert_CR | null | [
"transformers",
"pytorch",
"bert",
"token-classification",
"en",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bert #token-classification #en #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
---
# spacebert_CR
### Model desciption
This is a fine-tuned SpaceSciBERT model, for a Concept Recognition task, from the SpaceTransformers model family presented in SpaceTransformers: Language Modeling for Space Systems. The original Git repo is strath-ace/smart-nlp. The fine-tuning dataset is available for downlo... | [
"# spacebert_CR",
"### Model desciption\n\nThis is a fine-tuned SpaceSciBERT model, for a Concept Recognition task, from the SpaceTransformers model family presented in SpaceTransformers: Language Modeling for Space Systems. The original Git repo is strath-ace/smart-nlp. The fine-tuning dataset is available for d... | [
"TAGS\n#transformers #pytorch #bert #token-classification #en #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# spacebert_CR",
"### Model desciption\n\nThis is a fine-tuned SpaceSciBERT model, for a Concept Recognition task, from the SpaceTransformers model family presented in SpaceTra... |
fill-mask | transformers |
### SpaceRoBERTa
This is one of the 3 further pre-trained models from the SpaceTransformers family presented in [SpaceTransformers: Language Modeling for Space Systems](https://ieeexplore.ieee.org/document/9548078). The original Git repo is [strath-ace/smart-nlp](https://github.com/strath-ace/smart-nlp).
The further... | {"language": "en", "license": "mit"} | icelab/spaceroberta | null | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"en",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #roberta #fill-mask #en #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
### SpaceRoBERTa
This is one of the 3 further pre-trained models from the SpaceTransformers family presented in SpaceTransformers: Language Modeling for Space Systems. The original Git repo is strath-ace/smart-nlp.
The further pre-training corpus includes publications abstracts, books, and Wikipedia pages related to... | [
"### SpaceRoBERTa\n\nThis is one of the 3 further pre-trained models from the SpaceTransformers family presented in SpaceTransformers: Language Modeling for Space Systems. The original Git repo is strath-ace/smart-nlp.\n\nThe further pre-training corpus includes publications abstracts, books, and Wikipedia pages re... | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #en #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### SpaceRoBERTa\n\nThis is one of the 3 further pre-trained models from the SpaceTransformers family presented in SpaceTransformers: Language Modeling for Space Systems. The original Gi... |
token-classification | transformers |
---
# spaceroberta_CR
## Model desciption
This is fine-tuned SpaceSciBERT model, for a Concept Recognition task, from the SpaceTransformers model family presented in SpaceTransformers: Language Modeling for Space Systems. The original Git repo is strath-ace/smart-nlp. The [fine-tuning](https://github.com/strath-a... | {"language": "en", "license": "mit", "widget": [{"text": "The CubeSat RF design shall either have one RF inhibit and a RF power output no greater than 1.5W at the transmitter antenna's RF input OR the CubeSat shall have a minimum of two independent RF inhibits (CDS 3.3.9) (ISO 5.5.6)."}]} | icelab/spaceroberta_CR | null | [
"transformers",
"pytorch",
"roberta",
"token-classification",
"en",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #roberta #token-classification #en #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
---
# spaceroberta_CR
## Model desciption
This is fine-tuned SpaceSciBERT model, for a Concept Recognition task, from the SpaceTransformers model family presented in SpaceTransformers: Language Modeling for Space Systems. The original Git repo is strath-ace/smart-nlp. The fine-tuning dataset is available for down... | [
"# spaceroberta_CR",
"## Model desciption\n\nThis is fine-tuned SpaceSciBERT model, for a Concept Recognition task, from the SpaceTransformers model family presented in SpaceTransformers: Language Modeling for Space Systems. The original Git repo is strath-ace/smart-nlp. The fine-tuning dataset is available for d... | [
"TAGS\n#transformers #pytorch #roberta #token-classification #en #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# spaceroberta_CR",
"## Model desciption\n\nThis is fine-tuned SpaceSciBERT model, for a Concept Recognition task, from the SpaceTransformers model family presented in Space... |
fill-mask | transformers |
### SpaceSciBERT
This is one of the 3 further pre-trained models from the SpaceTransformers family presented in [SpaceTransformers: Language Modeling for Space Systems](https://ieeexplore.ieee.org/document/9548078). The original Git repo is [strath-ace/smart-nlp](https://github.com/strath-ace/smart-nlp).
The further... | {"language": "en", "license": "mit"} | icelab/spacescibert | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"en",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bert #fill-mask #en #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
### SpaceSciBERT
This is one of the 3 further pre-trained models from the SpaceTransformers family presented in SpaceTransformers: Language Modeling for Space Systems. The original Git repo is strath-ace/smart-nlp.
The further pre-training corpus includes publications abstracts, books, and Wikipedia pages related to... | [
"### SpaceSciBERT\n\nThis is one of the 3 further pre-trained models from the SpaceTransformers family presented in SpaceTransformers: Language Modeling for Space Systems. The original Git repo is strath-ace/smart-nlp.\n\nThe further pre-training corpus includes publications abstracts, books, and Wikipedia pages re... | [
"TAGS\n#transformers #pytorch #bert #fill-mask #en #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### SpaceSciBERT\n\nThis is one of the 3 further pre-trained models from the SpaceTransformers family presented in SpaceTransformers: Language Modeling for Space Systems. The original Git r... |
token-classification | transformers |
---
# spacescibert_CR
## Model desciption
This is fine-tuned further SpaceSciBERT model from the SpaceTransformers model family presented in SpaceTransformers: Language Modeling for Space Systems. The original Git repo is strath-ace/smart-nlp. The [fine-tuning](https://github.com/strath-ace/smart-nlp/blob/master/Sp... | {"language": "en", "license": "mit", "widget": [{"text": "The CubeSat RF design shall either have one RF inhibit and a RF power output no greater than 1.5W at the transmitter antenna's RF input OR the CubeSat shall have a minimum of two independent RF inhibits (CDS 3.3.9) (ISO 5.5.6)."}]} | icelab/spacescibert_CR | null | [
"transformers",
"pytorch",
"bert",
"token-classification",
"en",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bert #token-classification #en #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
---
# spacescibert_CR
## Model desciption
This is fine-tuned further SpaceSciBERT model from the SpaceTransformers model family presented in SpaceTransformers: Language Modeling for Space Systems. The original Git repo is strath-ace/smart-nlp. The fine-tuning dataset is available for download and consists of 874 un... | [
"# spacescibert_CR",
"## Model desciption\n\nThis is fine-tuned further SpaceSciBERT model from the SpaceTransformers model family presented in SpaceTransformers: Language Modeling for Space Systems. The original Git repo is strath-ace/smart-nlp. The fine-tuning dataset is available for download and consists of 8... | [
"TAGS\n#transformers #pytorch #bert #token-classification #en #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# spacescibert_CR",
"## Model desciption\n\nThis is fine-tuned further SpaceSciBERT model from the SpaceTransformers model family presented in SpaceTransformers: Language Model... |
text-classification | transformers |
# Model Trained Using AutoNLP
- Problem type: Single Column Regression
- Model ID: 172506
## Validation Metrics
- Loss: 0.03257797285914421
- MSE: 0.03257797285914421
- MAE: 0.14246532320976257
- R2: 0.9693824457290849
- RMSE: 0.18049369752407074
- Explained Variance: 0.9699198007583618
## Usage
You can use cURL ... | {"language": "en", "tags": "autonlp", "datasets": ["idjotherwise/autonlp-data-reading_prediction"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}]} | idjotherwise/autonlp-reading_prediction-172506 | null | [
"transformers",
"pytorch",
"jax",
"roberta",
"text-classification",
"autonlp",
"en",
"dataset:idjotherwise/autonlp-data-reading_prediction",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #jax #roberta #text-classification #autonlp #en #dataset-idjotherwise/autonlp-data-reading_prediction #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoNLP
- Problem type: Single Column Regression
- Model ID: 172506
## Validation Metrics
- Loss: 0.03257797285914421
- MSE: 0.03257797285914421
- MAE: 0.14246532320976257
- R2: 0.9693824457290849
- RMSE: 0.18049369752407074
- Explained Variance: 0.9699198007583618
## Usage
You can use cURL ... | [
"# Model Trained Using AutoNLP\n\n- Problem type: Single Column Regression\n- Model ID: 172506",
"## Validation Metrics\n\n- Loss: 0.03257797285914421\n- MSE: 0.03257797285914421\n- MAE: 0.14246532320976257\n- R2: 0.9693824457290849\n- RMSE: 0.18049369752407074\n- Explained Variance: 0.9699198007583618",
"## Us... | [
"TAGS\n#transformers #pytorch #jax #roberta #text-classification #autonlp #en #dataset-idjotherwise/autonlp-data-reading_prediction #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoNLP\n\n- Problem type: Single Column Regression\n- Model ID: 172506",
"## Validation Metrics\... |
null | null | a | {} | idobegaming/idobegaming | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| a | [] | [
"TAGS\n#region-us \n"
] |
text-classification | transformers |
# Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 4021083
## Validation Metrics
- Loss: 0.6848716735839844
- Accuracy: 0.8825910931174089
- Macro F1: 0.41301646762109634
- Micro F1: 0.8825910931174088
- Weighted F1: 0.863740586166105
- Macro Precision: 0.4129337301330573
- Micro P... | {"language": "en", "tags": "autonlp", "datasets": ["idrimadrid/autonlp-data-creator_classifications"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}]} | idrimadrid/autonlp-creator_classifications-4021083 | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autonlp",
"en",
"dataset:idrimadrid/autonlp-data-creator_classifications",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bert #text-classification #autonlp #en #dataset-idrimadrid/autonlp-data-creator_classifications #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 4021083
## Validation Metrics
- Loss: 0.6848716735839844
- Accuracy: 0.8825910931174089
- Macro F1: 0.41301646762109634
- Micro F1: 0.8825910931174088
- Weighted F1: 0.863740586166105
- Macro Precision: 0.4129337301330573
- Micro P... | [
"# Model Trained Using AutoNLP\n\n- Problem type: Multi-class Classification\n- Model ID: 4021083",
"## Validation Metrics\n\n- Loss: 0.6848716735839844\n- Accuracy: 0.8825910931174089\n- Macro F1: 0.41301646762109634\n- Micro F1: 0.8825910931174088\n- Weighted F1: 0.863740586166105\n- Macro Precision: 0.41293373... | [
"TAGS\n#transformers #pytorch #bert #text-classification #autonlp #en #dataset-idrimadrid/autonlp-data-creator_classifications #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoNLP\n\n- Problem type: Multi-class Classification\n- Model ID: 4021083",
"## Validation Metrics\n\... |
text-generation | transformers | Please treat TILDE as a BertLMHeadModel model:
```
from transformers import BertLMHeadModel, BertTokenizerFast
model = BertLMHeadModel.from_pretrained("ielab/TILDE")
tokenizer = BertTokenizerFast.from_pretrained('bert-base-uncased')
```
Github: https://github.com/ielab/TILDE | {} | ielab/TILDE | null | [
"transformers",
"pytorch",
"bert",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #text-generation #autotrain_compatible #endpoints_compatible #region-us
| Please treat TILDE as a BertLMHeadModel model:
Github: URL | [] | [
"TAGS\n#transformers #pytorch #bert #text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
null | transformers | TILDEv2 trained with passages expand with TILDE (m=128) | {} | ielab/TILDEv2-TILDE128-exp | null | [
"transformers",
"pytorch",
"bert",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #endpoints_compatible #region-us
| TILDEv2 trained with passages expand with TILDE (m=128) | [] | [
"TAGS\n#transformers #pytorch #bert #endpoints_compatible #region-us \n"
] |
null | transformers | TILDEv2 trained with passages expand with TILDE (m=200) | {} | ielab/TILDEv2-TILDE200-exp | null | [
"transformers",
"pytorch",
"bert",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #endpoints_compatible #region-us
| TILDEv2 trained with passages expand with TILDE (m=200) | [] | [
"TAGS\n#transformers #pytorch #bert #endpoints_compatible #region-us \n"
] |
null | transformers | uniCOIL trained with passages expand with TILDE (m=128) | {} | ielab/unicoil-tilde128-msmarco-passage | null | [
"transformers",
"pytorch",
"bert",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #endpoints_compatible #region-us
| uniCOIL trained with passages expand with TILDE (m=128) | [] | [
"TAGS\n#transformers #pytorch #bert #endpoints_compatible #region-us \n"
] |
null | transformers | uniCOIL trained with passages expand with TILDE (m=200) | {} | ielab/unicoil-tilde200-msmarco-passage | null | [
"transformers",
"pytorch",
"bert",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #endpoints_compatible #has_space #region-us
| uniCOIL trained with passages expand with TILDE (m=200) | [] | [
"TAGS\n#transformers #pytorch #bert #endpoints_compatible #has_space #region-us \n"
] |
fill-mask | transformers |
`distilroberta-base` finetuned for masked language modeling on 126213 Qt jira issue titles for up to 50 epochs. | {"language": ["en"], "license": "mit", "tags": ["jira", "code", "issue", "development"]} | ietz/distilroberta-base-finetuned-jira-qt-issue-title | null | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"jira",
"code",
"issue",
"development",
"en",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #roberta #fill-mask #jira #code #issue #development #en #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
'distilroberta-base' finetuned for masked language modeling on 126213 Qt jira issue titles for up to 50 epochs. | [] | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #jira #code #issue #development #en #license-mit #autotrain_compatible #endpoints_compatible #region-us \n"
] |
fill-mask | transformers |
`distilroberta-base` finetuned for masked language modeling on 247731 mixed issue titles (n=126213) and descriptions (n=121518). Trained for up to 50 epochs. | {"language": ["en"], "license": "mit", "tags": ["jira", "code", "issue", "development"]} | ietz/distilroberta-base-finetuned-jira-qt-issue-titles-and-bodies | null | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"jira",
"code",
"issue",
"development",
"en",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #roberta #fill-mask #jira #code #issue #development #en #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
'distilroberta-base' finetuned for masked language modeling on 247731 mixed issue titles (n=126213) and descriptions (n=121518). Trained for up to 50 epochs. | [] | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #jira #code #issue #development #en #license-mit #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# IFIS_ZORK_AI_MEDIUM_HORROR
This model is a fine-tuned version of [gpt2-medium](https://huggingface.co/gpt2-medium) on an unkown ... | {"tags": ["generated_from_trainer"], "model_index": [{"name": "IFIS_ZORK_AI_MEDIUM_HORROR", "results": [{"task": {"name": "Causal Language Modeling", "type": "text-generation"}}]}]} | ifis-zork/IFIS_ZORK_AI_MEDIUM_HORROR | null | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# IFIS_ZORK_AI_MEDIUM_HORROR
This model is a fine-tuned version of gpt2-medium on an unkown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
... | [
"# IFIS_ZORK_AI_MEDIUM_HORROR\n\nThis model is a fine-tuned version of gpt2-medium on an unkown dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
... | [
"TAGS\n#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# IFIS_ZORK_AI_MEDIUM_HORROR\n\nThis model is a fine-tuned version of gpt2-medium on an unkown dataset.",
"## Model description\n\nMor... |
text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# ZORK_AI_FANTASY
This model is a fine-tuned version of [ifis-zork/ZORK_AI_FAN_TEMP](https://huggingface.co/ifis-zork/ZORK_AI_FAN_... | {"tags": ["generated_from_trainer"], "model_index": [{"name": "ZORK_AI_FANTASY", "results": [{"task": {"name": "Causal Language Modeling", "type": "text-generation"}}]}]} | ifis-zork/ZORK_AI_FANTASY | null | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# ZORK_AI_FANTASY
This model is a fine-tuned version of ifis-zork/ZORK_AI_FAN_TEMP on an unkown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparamet... | [
"# ZORK_AI_FANTASY\n\nThis model is a fine-tuned version of ifis-zork/ZORK_AI_FAN_TEMP on an unkown dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure"... | [
"TAGS\n#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# ZORK_AI_FANTASY\n\nThis model is a fine-tuned version of ifis-zork/ZORK_AI_FAN_TEMP on an unkown dataset.",
"## Model description\n\... |
text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# ZORK_AI_FAN_TEMP
This model is a fine-tuned version of [gpt2-medium](https://huggingface.co/gpt2-medium) on an unkown dataset.
... | {"tags": ["generated_from_trainer"], "model_index": [{"name": "ZORK_AI_FAN_TEMP", "results": [{"task": {"name": "Causal Language Modeling", "type": "text-generation"}}]}]} | ifis-zork/ZORK_AI_FAN_TEMP | null | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# ZORK_AI_FAN_TEMP
This model is a fine-tuned version of gpt2-medium on an unkown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The follo... | [
"# ZORK_AI_FAN_TEMP\n\nThis model is a fine-tuned version of gpt2-medium on an unkown dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# ZORK_AI_FAN_TEMP\n\nThis model is a fine-tuned version of gpt2-medium on an unkown dataset.",
"## Model description\n\nMore informat... |
text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# ZORK_AI_MODERN
This model is a fine-tuned version of [ifis-zork/ZORK_AI_MODERN_A](https://huggingface.co/ifis-zork/ZORK_AI_MODER... | {"tags": ["generated_from_trainer"], "model_index": [{"name": "ZORK_AI_MODERN", "results": [{"task": {"name": "Causal Language Modeling", "type": "text-generation"}}]}]} | ifis-zork/ZORK_AI_MODERN | null | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# ZORK_AI_MODERN
This model is a fine-tuned version of ifis-zork/ZORK_AI_MODERN_A on an unkown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparamete... | [
"# ZORK_AI_MODERN\n\nThis model is a fine-tuned version of ifis-zork/ZORK_AI_MODERN_A on an unkown dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",... | [
"TAGS\n#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# ZORK_AI_MODERN\n\nThis model is a fine-tuned version of ifis-zork/ZORK_AI_MODERN_A on an unkown dataset.",
"## Model description\n\n... |
text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# ZORK_AI_MODERN_A
This model is a fine-tuned version of [gpt2-medium](https://huggingface.co/gpt2-medium) on an unkown dataset.
... | {"tags": ["generated_from_trainer"], "model_index": [{"name": "ZORK_AI_MODERN_A", "results": [{"task": {"name": "Causal Language Modeling", "type": "text-generation"}}]}]} | ifis-zork/ZORK_AI_MODERN_A | null | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# ZORK_AI_MODERN_A
This model is a fine-tuned version of gpt2-medium on an unkown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The follo... | [
"# ZORK_AI_MODERN_A\n\nThis model is a fine-tuned version of gpt2-medium on an unkown dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# ZORK_AI_MODERN_A\n\nThis model is a fine-tuned version of gpt2-medium on an unkown dataset.",
"## Model description\n\nMore informat... |
text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# ZORK_AI_SCI_FI
This model is a fine-tuned version of [ifis-zork/ZORK_AI_SCI_FI_TEMP](https://huggingface.co/ifis-zork/ZORK_AI_SC... | {"tags": ["generated_from_trainer"], "model_index": [{"name": "ZORK_AI_SCI_FI", "results": [{"task": {"name": "Causal Language Modeling", "type": "text-generation"}}]}]} | ifis-zork/ZORK_AI_SCI_FI | null | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# ZORK_AI_SCI_FI
This model is a fine-tuned version of ifis-zork/ZORK_AI_SCI_FI_TEMP on an unkown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparam... | [
"# ZORK_AI_SCI_FI\n\nThis model is a fine-tuned version of ifis-zork/ZORK_AI_SCI_FI_TEMP on an unkown dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedur... | [
"TAGS\n#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# ZORK_AI_SCI_FI\n\nThis model is a fine-tuned version of ifis-zork/ZORK_AI_SCI_FI_TEMP on an unkown dataset.",
"## Model description\... |
text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# ZORK_AI_SCI_FI_TEMP
This model is a fine-tuned version of [gpt2-medium](https://huggingface.co/gpt2-medium) on an unkown dataset... | {"tags": ["generated_from_trainer"], "model_index": [{"name": "ZORK_AI_SCI_FI_TEMP", "results": [{"task": {"name": "Causal Language Modeling", "type": "text-generation"}}]}]} | ifis-zork/ZORK_AI_SCI_FI_TEMP | null | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# ZORK_AI_SCI_FI_TEMP
This model is a fine-tuned version of gpt2-medium on an unkown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The fo... | [
"# ZORK_AI_SCI_FI_TEMP\n\nThis model is a fine-tuned version of gpt2-medium on an unkown dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Tr... | [
"TAGS\n#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# ZORK_AI_SCI_FI_TEMP\n\nThis model is a fine-tuned version of gpt2-medium on an unkown dataset.",
"## Model description\n\nMore infor... |
text-generation | transformers |
# MCU Peter Parker DialoGPT Model | {"tags": ["conversational"]} | ignkai/DialoGPT-medium-spider-man-updated | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# MCU Peter Parker DialoGPT Model | [
"# MCU Peter Parker DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# MCU Peter Parker DialoGPT Model"
] |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-uncased-finetuned-sst2-sst2-membership
This model is a fine-tuned version of [ikevin98/bert-base-uncased-finetuned-sst... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model_index": {"name": "bert-base-uncased-finetuned-sst2-sst2-membership"}} | doyoungkim/bert-base-uncased-finetuned-sst2-sst2-membership | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| bert-base-uncased-finetuned-sst2-sst2-membership
================================================
This model is a fine-tuned version of ikevin98/bert-base-uncased-finetuned-sst2 on an unkown dataset.
It achieves the following results on the evaluation set:
* Loss: 1.3100
* Accuracy: 1.0
Model description
--------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\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: 1",
"### Train... | [
"TAGS\n#transformers #pytorch #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #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: 32\n*... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-uncased-finetuned-sst2
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-unca... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["accuracy"], "model_index": [{"name": "bert-base-uncased-finetuned-sst2", "results": [{"dataset": {"name": "glue", "type": "glue", "args": "sst2"}, "metric": {"name": "Accuracy", "type": "accuracy", "value": 0.92660550458715... | doyoungkim/bert-base-uncased-finetuned-sst2 | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| bert-base-uncased-finetuned-sst2
================================
This model is a fine-tuned version of bert-base-uncased on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2716
* Accuracy: 0.9266
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: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #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* t... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-uncased-sst2-distilled
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-unca... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model_index": {"name": "bert-base-uncased-sst2-distilled"}} | doyoungkim/bert-base-uncased-sst2-distilled | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| bert-base-uncased-sst2-distilled
================================
This model is a fine-tuned version of bert-base-uncased on an unkown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2676
* Accuracy: 0.9025
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: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #bert #text-classification #generated_from_trainer #license-apache-2.0 #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\\_size: 32\n* ... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-uncased-sst2-membership-attack
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-b... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model_index": {"name": "bert-base-uncased-sst2-membership-attack"}} | doyoungkim/bert-base-uncased-sst2-membership-attack | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| bert-base-uncased-sst2-membership-attack
========================================
This model is a fine-tuned version of bert-base-uncased on an unkown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6296
* Accuracy: 0.8681
Model description
-----------------
More information needed
... | [
"### 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: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #bert #text-classification #generated_from_trainer #license-apache-2.0 #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\\_size: 32\n* ... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# opus-mt-en-ru-finetuned-en-to-ru
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-ru](https://huggingface.co/Helsi... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["bleu"], "model-index": [{"name": "opus-mt-en-ru-finetuned-en-to-ru", "results": []}]} | ilevs/opus-mt-en-ru-finetuned-en-to-ru | null | [
"transformers",
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #marian #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| opus-mt-en-ru-finetuned-en-to-ru
================================
This model is a fine-tuned version of Helsinki-NLP/opus-mt-en-ru on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 1.7682
* Bleu: 14.6112
* Gen Len: 7.202
Model description
-----------------
More information ne... | [
"### 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: 10\n* mixed\\_pre... | [
"TAGS\n#transformers #pytorch #tensorboard #marian #text2text-generation #generated_from_trainer #license-apache-2.0 #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\\_batc... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# opus-mt-ru-en-finetuned-ru-to-en
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-ru-en](https://huggingface.co/Helsi... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["bleu"], "model-index": [{"name": "opus-mt-ru-en-finetuned-ru-to-en", "results": []}]} | ilevs/opus-mt-ru-en-finetuned-ru-to-en | null | [
"transformers",
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #marian #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| opus-mt-ru-en-finetuned-ru-to-en
================================
This model is a fine-tuned version of Helsinki-NLP/opus-mt-ru-en on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 2.1251
* Bleu: 15.9892
* Gen Len: 5.0168
Model description
-----------------
More information 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\\_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: 10\n* mixed\\_pre... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batc... |
text-generation | transformers |
#DialoGPT Model | {"tags": ["conversational"]} | ilikeapple12/DialoGPT-small-Phos | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
#DialoGPT Model | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilgpt2-finetuned-wikitext2
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "distilgpt2-finetuned-wikitext2", "results": []}]} | iliketurtles/distilgpt2-finetuned-wikitext2 | null | [
"transformers",
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"tensorboard",
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"text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| distilgpt2-finetuned-wikitext2
==============================
This model is a fine-tuned version of distilgpt2 on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 3.6424
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: 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: 3.0",
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2... |
question-answering | transformers |
# camembert-base-fquad
## Description
A native French Question Answering model [CamemBERT-base](https://camembert-model.fr/) fine-tuned on [FQuAD](https://fquad.illuin.tech/).
## Evaluation results
On the development set.
```shell
{"f1": 88.1, "exact_match": 78.1}
```
On the test set.
```shell
{"f1": 88.3, "exa... | {"language": "fr", "license": "gpl-3.0", "tags": ["question-answering", "camembert"], "datasets": ["fquad"]} | illuin/camembert-base-fquad | null | [
"transformers",
"pytorch",
"camembert",
"question-answering",
"fr",
"dataset:fquad",
"license:gpl-3.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"fr"
] | TAGS
#transformers #pytorch #camembert #question-answering #fr #dataset-fquad #license-gpl-3.0 #endpoints_compatible #region-us
|
# camembert-base-fquad
## Description
A native French Question Answering model CamemBERT-base fine-tuned on FQuAD.
## Evaluation results
On the development set.
On the test set.
## Usage
If you use our work, please cite:
| [
"# camembert-base-fquad",
"## Description\n\nA native French Question Answering model CamemBERT-base fine-tuned on FQuAD.",
"## Evaluation results\n\nOn the development set.\n\n\n\nOn the test set.",
"## Usage\n\n\n\nIf you use our work, please cite:"
] | [
"TAGS\n#transformers #pytorch #camembert #question-answering #fr #dataset-fquad #license-gpl-3.0 #endpoints_compatible #region-us \n",
"# camembert-base-fquad",
"## Description\n\nA native French Question Answering model CamemBERT-base fine-tuned on FQuAD.",
"## Evaluation results\n\nOn the development set.\n... |
null | null | ---
tags:
- conversational
#Harry Potter DialoGPT Model | {} | imdhamu/DialoGPT-small-harrypotter | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| ---
tags:
- conversational
#Harry Potter DialoGPT Model | [] | [
"TAGS\n#region-us \n"
] |
text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# gpt2-wikitext2
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the None dataset.
It achieves the fo... | {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "gpt2-wikitext2", "results": []}]} | imfiba1991/gpt2-wikitext2 | null | [
"transformers",
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"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| gpt2-wikitext2
==============
This model is a fine-tuned version of gpt2 on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 7.2082
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
... | [
"### 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: 3.0",
"### Traini... | [
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image-classification | transformers | # Pokémon Classifier
# Intro
A fine-tuned version of ViT-base on a collected set of Pokémon images. You can read more about the model [here](https://medium.com/@imjeffhi4/tutorial-using-vision-transformer-vit-to-create-a-pok%C3%A9mon-classifier-cb3f26ff2c20).
# Using the model
```python
from transformers import Vi... | {} | imjeffhi/pokemon_classifier | null | [
"transformers",
"pytorch",
"vit",
"image-classification",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #vit #image-classification #autotrain_compatible #endpoints_compatible #has_space #region-us
| # Pokémon Classifier
# Intro
A fine-tuned version of ViT-base on a collected set of Pokémon images. You can read more about the model here.
# Using the model
| [
"# Pokémon Classifier",
"# Intro\n\nA fine-tuned version of ViT-base on a collected set of Pokémon images. You can read more about the model here.",
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"# Using the model"
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text-generation | transformers | # Pangu-Alpha 2.6B
## Model Description
PanGu-α is proposed by a joint technical team headed by PCNL. It was first released in [this repository](https://git.openi.org.cn/PCL-Platform.Intelligence/PanGu-Alpha) It is the first large-scale Chinese pre-trained language model with 200 billion parameters trained on 2048 A... | {} | imone/pangu_2_6B | null | [
"transformers",
"pytorch",
"gpt_pangu",
"text-generation",
"custom_code",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt_pangu #text-generation #custom_code #autotrain_compatible #endpoints_compatible #has_space #region-us
| # Pangu-Alpha 2.6B
## Model Description
PanGu-α is proposed by a joint technical team headed by PCNL. It was first released in this repository It is the first large-scale Chinese pre-trained language model with 200 billion parameters trained on 2048 Ascend processors using an automatic hybrid parallel training strat... | [
"# Pangu-Alpha 2.6B",
"## Model Description\n\nPanGu-α is proposed by a joint technical team headed by PCNL. It was first released in this repository It is the first large-scale Chinese pre-trained language model with 200 billion parameters trained on 2048 Ascend processors using an automatic hybrid parallel tra... | [
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"# Pangu-Alpha 2.6B",
"## Model Description\n\nPanGu-α is proposed by a joint technical team headed by PCNL. It was first released in this repository It is the first lar... |
text-generation | transformers | GPT-2 model fine-tuned on Custom old Hindi songs (Hinglish) for text-generation task (AI Lyricist)
language:
- Hindi
- Hinglish
| {} | impyadav/GPT2-FineTuned-Hinglish-Song-Generation | null | [
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"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| GPT-2 model fine-tuned on Custom old Hindi songs (Hinglish) for text-generation task (AI Lyricist)
language:
- Hindi
- Hinglish
| [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n"
] |
text-generation | transformers |
# Doctor DialoGPT Model | {"tags": ["conversational"]} | imran2part/DialogGPT-small-Doctor | null | [
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"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
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] |
text-generation | transformers | # DialoGPT Trained on MCU Dialogues | {"license": "mit", "tags": ["conversational"], "thumbnail": "https://huggingface.co/front/thumbnails/dialogpt.png"} | imrit1999/DialoGPT-small-MCU | null | [
"transformers",
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"gpt2",
"text-generation",
"conversational",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # DialoGPT Trained on MCU Dialogues | [
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"# DialoGPT Trained on MCU Dialogues"
] |
text-generation | transformers |
### GPT 2 News
**Update 02 Jan 2022**: Fixed mismatch tokenizer and model.wte size
### BibTex
```
@article{thanh21gpt2news,
author = {Thanh V. Le},
title = {Pretrained GPT-2 on Vietnamese news},
journal = {https://huggingface.co/imthanhlv/gpt2news},
year = {2021},
}
```
| {"language": "vi", "tags": ["gpt"], "widget": [{"text": "H\u00f4m qua nh\u1eefng nh\u00e0 khoa h\u1ecdc M\u1ef9 \u0111\u00e3 ph\u00e1t hi\u1ec7n ra lo\u00e0i c\u00e1 l\u1ee3n"}]} | imthanhlv/gpt2news | null | [
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"pytorch",
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"gpt2",
"text-generation",
"gpt",
"vi",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"vi"
] | TAGS
#transformers #pytorch #jax #gpt2 #text-generation #gpt #vi #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
### GPT 2 News
Update 02 Jan 2022: Fixed mismatch tokenizer and URL size
### BibTex
| [
"### GPT 2 News\nUpdate 02 Jan 2022: Fixed mismatch tokenizer and URL size",
"### BibTex"
] | [
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"### GPT 2 News\nUpdate 02 Jan 2022: Fixed mismatch tokenizer and URL size",
"### BibTex"
] |
text2text-generation | transformers | # T5 Vietnamese pretrain on news corpus
| {} | imthanhlv/t5vi | null | [
"transformers",
"jax",
"tensorboard",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #jax #tensorboard #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # T5 Vietnamese pretrain on news corpus
| [
"# T5 Vietnamese pretrain on news corpus"
] | [
"TAGS\n#transformers #jax #tensorboard #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# T5 Vietnamese pretrain on news corpus"
] |
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-base-uncased
This model is a fine-tuned version of [](https://huggingface.co/) on the jigsaw dataset.
It achieves the follo... | {"language": ["en"], "tags": ["generated_from_trainer"], "datasets": ["jigsaw"], "model_index": [{"name": "bert-base-uncased", "results": [{}]}]} | imvladikon/bert-base-uncased-jigsaw | null | [
"transformers",
"pytorch",
"bert",
"generated_from_trainer",
"en",
"dataset:jigsaw",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bert #generated_from_trainer #en #dataset-jigsaw #endpoints_compatible #region-us
| bert-base-uncased
=================
This model is a fine-tuned version of [](URL on the jigsaw dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0393
* Precision Micro: 0.7758
* Recall Micro: 0.7858
* F1 Micro: 0.7808
* F2 Micro: 0.7838
* Precision Macro: 0.6349
* Recall Macro: 0.5972
* F1... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 24\n* eval\\_batch\\_size: 12\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 48\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
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null | transformers |
pre-trained model from [CharBERT: Character-aware Pre-trained Language Model](https://github.com/wtma/CharBERT)
```
@misc{ma2020charbert,
title={CharBERT: Character-aware Pre-trained Language Model},
author={Wentao Ma and Yiming Cui and Chenglei Si and Ting Liu and Shijin Wang and Guoping Hu},
year... | {"language": ["en"], "tags": ["language model"], "datasets": ["wikipedia"]} | imvladikon/charbert-bert-wiki | null | [
"transformers",
"pytorch",
"language model",
"en",
"dataset:wikipedia",
"arxiv:2011.01513",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2011.01513"
] | [
"en"
] | TAGS
#transformers #pytorch #language model #en #dataset-wikipedia #arxiv-2011.01513 #endpoints_compatible #region-us
|
pre-trained model from CharBERT: Character-aware Pre-trained Language Model
| [] | [
"TAGS\n#transformers #pytorch #language model #en #dataset-wikipedia #arxiv-2011.01513 #endpoints_compatible #region-us \n"
] |
null | transformers |
pre-trained model from [CharBERT: Character-aware Pre-trained Language Model](https://github.com/wtma/CharBERT)
```
@misc{ma2020charbert,
title={CharBERT: Character-aware Pre-trained Language Model},
author={Wentao Ma and Yiming Cui and Chenglei Si and Ting Liu and Shijin Wang and Guoping Hu},
year... | {"language": ["en"], "tags": ["language model"], "datasets": ["wikipedia"]} | imvladikon/charbert-roberta-wiki | null | [
"transformers",
"pytorch",
"language model",
"en",
"dataset:wikipedia",
"arxiv:2011.01513",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2011.01513"
] | [
"en"
] | TAGS
#transformers #pytorch #language model #en #dataset-wikipedia #arxiv-2011.01513 #endpoints_compatible #region-us
|
pre-trained model from CharBERT: Character-aware Pre-trained Language Model
| [] | [
"TAGS\n#transformers #pytorch #language model #en #dataset-wikipedia #arxiv-2011.01513 #endpoints_compatible #region-us \n"
] |
null | transformers |
Pretrained general_character_bert model
from the ['CharacterBERT: Reconciling ELMo and BERT for Word-Level Open-Vocabulary Representations From Characters' El Boukkouri H., et al., 2020](https://github.com/helboukkouri/character-bert)
```
@inproceedings{el-boukkouri-etal-2020-characterbert,
title = "{C}haracter... | {"language": ["en"], "tags": ["language model"], "datasets": ["wikipedia", "openwebtext"]} | imvladikon/general_character_bert | null | [
"transformers",
"pytorch",
"bert",
"language model",
"en",
"dataset:wikipedia",
"dataset:openwebtext",
"arxiv:2010.10392",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2010.10392"
] | [
"en"
] | TAGS
#transformers #pytorch #bert #language model #en #dataset-wikipedia #dataset-openwebtext #arxiv-2010.10392 #endpoints_compatible #region-us
|
Pretrained general_character_bert model
from the 'CharacterBERT: Reconciling ELMo and BERT for Word-Level Open-Vocabulary Representations From Characters' El Boukkouri H., et al., 2020
| [] | [
"TAGS\n#transformers #pytorch #bert #language model #en #dataset-wikipedia #dataset-openwebtext #arxiv-2010.10392 #endpoints_compatible #region-us \n"
] |
automatic-speech-recognition | transformers | # wav2vec2-large-xlsr-53-hebrew
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the several downloaded youtube samples.
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used directly (without a language model) ... | {"language": "he", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice"], "metrics": ["wer"], "model-index": [{"name": "Hebrew XLSR Wav2Vec2 Large 53", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech Reco... | imvladikon/wav2vec2-large-xlsr-53-hebrew | null | [
"transformers",
"pytorch",
"jax",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
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"he",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"he"
] | TAGS
#transformers #pytorch #jax #safetensors #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #he #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
| # wav2vec2-large-xlsr-53-hebrew
Fine-tuned facebook/wav2vec2-large-xlsr-53 on the several downloaded youtube samples.
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used directly (without a language model) as follows:
## Evaluation
The model can be evaluated as f... | [
"# wav2vec2-large-xlsr-53-hebrew\nFine-tuned facebook/wav2vec2-large-xlsr-53 on the several downloaded youtube samples.\nWhen using this model, make sure that your speech input is sampled at 16kHz.",
"## Usage\nThe model can be used directly (without a language model) as follows:",
"## Evaluation\nThe model can... | [
"TAGS\n#transformers #pytorch #jax #safetensors #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #he #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us \n",
"# wav2vec2-large-xlsr-53-hebrew\nFine-tuned facebook/wav2vec2-large-xlsr-53 on the several downloaded y... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-xls-r-1b-hebrew
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2... | {"language": ["he"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "robust-speech-event", "he", "generated_from_trainer", "hf-asr-leaderboard"], "base_model": "facebook/wav2vec2-xls-r-1b", "model-index": [{"name": "wav2vec2-xls-r-1b-hebrew", "results": []}]} | imvladikon/wav2vec2-xls-r-1b-hebrew | null | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"robust-speech-event",
"he",
"generated_from_trainer",
"hf-asr-leaderboard",
"base_model:facebook/wav2vec2-xls-r-1b",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us... | null | 2022-03-02T23:29:05+00:00 | [] | [
"he"
] | TAGS
#transformers #pytorch #tensorboard #safetensors #wav2vec2 #automatic-speech-recognition #robust-speech-event #he #generated_from_trainer #hf-asr-leaderboard #base_model-facebook/wav2vec2-xls-r-1b #license-apache-2.0 #endpoints_compatible #has_space #region-us
| wav2vec2-xls-r-1b-hebrew
========================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-1b on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3533
* Wer: 0.2251
Model description
-----------------
More information needed
Intended uses & limitations
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 6\n* eval\\_batch\\_size: 6\n* seed: 42\n* distributed\\_type: multi-GPU\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 24\n* optimizer: Adam wi... | [
"TAGS\n#transformers #pytorch #tensorboard #safetensors #wav2vec2 #automatic-speech-recognition #robust-speech-event #he #generated_from_trainer #hf-asr-leaderboard #base_model-facebook/wav2vec2-xls-r-1b #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"### Training hyperparameters\n\n\nThe fo... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-xls-r-300m-hebrew
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/... | {"language": ["he"], "tags": ["automatic-speech-recognition", "generated_from_trainer", "he", "hf-asr-leaderboard", "robust-speech-event"], "base_model": "facebook/wav2vec2-xls-r-300m", "model-index": [{"name": "wav2vec2-xls-r-300m-hebrew", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automati... | imvladikon/wav2vec2-xls-r-300m-hebrew | null | [
"transformers",
"pytorch",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"he",
"hf-asr-leaderboard",
"robust-speech-event",
"base_model:facebook/wav2vec2-xls-r-300m",
"model-index",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"he"
] | TAGS
#transformers #pytorch #safetensors #wav2vec2 #automatic-speech-recognition #generated_from_trainer #he #hf-asr-leaderboard #robust-speech-event #base_model-facebook/wav2vec2-xls-r-300m #model-index #endpoints_compatible #has_space #region-us
| wav2vec2-xls-r-300m-hebrew
==========================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the private datasets in 2 stages - firstly was fine-tuned on a small dataset with good samples Then the obtained model was fine-tuned on a large dataset with the small good dataset, with various... | [
"### Training hyperparameters",
"#### First training\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* distributed\\_type: multi-GPU\n* num\\_devices: 2\n* gradient\\_accumulation\\_steps: 4\n* total\\_t... | [
"TAGS\n#transformers #pytorch #safetensors #wav2vec2 #automatic-speech-recognition #generated_from_trainer #he #hf-asr-leaderboard #robust-speech-event #base_model-facebook/wav2vec2-xls-r-300m #model-index #endpoints_compatible #has_space #region-us \n",
"### Training hyperparameters",
"#### First training\n\n\... |
automatic-speech-recognition | transformers |
# wav2vec2-xls-r-300m-lm-hebrew
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the None dataset
with adding ngram models according to [Boosting Wav2Vec2 with n-grams in 🤗 Transformers](https://huggingface.co/blog/wav2vec2-with-ngram)
## ... | {"language": ["he"], "license": "apache-2.0", "tags": ["generated_from_trainer", "he", "robust-speech-event"], "datasets": ["imvladikon/hebrew_speech_kan", "imvladikon/hebrew_speech_coursera"], "metrics": ["wer"], "base_model": "facebook/wav2vec2-xls-r-300m", "model-index": [{"name": "wav2vec2-xls-r-300m-lm-hebrew", "r... | imvladikon/wav2vec2-xls-r-300m-lm-hebrew | null | [
"transformers",
"pytorch",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"he",
"robust-speech-event",
"dataset:imvladikon/hebrew_speech_kan",
"dataset:imvladikon/hebrew_speech_coursera",
"base_model:facebook/wav2vec2-xls-r-300m",
"license:apache-2.0",
"... | null | 2022-03-02T23:29:05+00:00 | [] | [
"he"
] | TAGS
#transformers #pytorch #safetensors #wav2vec2 #automatic-speech-recognition #generated_from_trainer #he #robust-speech-event #dataset-imvladikon/hebrew_speech_kan #dataset-imvladikon/hebrew_speech_coursera #base_model-facebook/wav2vec2-xls-r-300m #license-apache-2.0 #endpoints_compatible #region-us
|
# wav2vec2-xls-r-300m-lm-hebrew
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the None dataset
with adding ngram models according to Boosting Wav2Vec2 with n-grams in Transformers
## Usage
check package: URL
or use transformers pipeline:
## Intended uses & limitations
More info... | [
"# wav2vec2-xls-r-300m-lm-hebrew\n\nThis model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the None dataset\nwith adding ngram models according to Boosting Wav2Vec2 with n-grams in Transformers",
"## Usage\n\ncheck package: URL \n\nor use transformers pipeline:",
"## Intended uses & limitation... | [
"TAGS\n#transformers #pytorch #safetensors #wav2vec2 #automatic-speech-recognition #generated_from_trainer #he #robust-speech-event #dataset-imvladikon/hebrew_speech_kan #dataset-imvladikon/hebrew_speech_coursera #base_model-facebook/wav2vec2-xls-r-300m #license-apache-2.0 #endpoints_compatible #region-us \n",
"#... |
text-classification | transformers |
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 16492731
## Validation Metrics
- Loss: 0.21610039472579956
- Accuracy: 0.9155366722657816
- Precision: 0.9530714194995978
- Recall: 0.944871149164778
- AUC: 0.9553238723676906
- F1: 0.9489535692456846
## Usage
You can use cURL to acce... | {"language": "en", "tags": "autonlp", "datasets": ["imzachjohnson/autonlp-data-spinner-check"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}]} | imzachjohnson/autonlp-spinner-check-16492731 | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autonlp",
"en",
"dataset:imzachjohnson/autonlp-data-spinner-check",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bert #text-classification #autonlp #en #dataset-imzachjohnson/autonlp-data-spinner-check #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 16492731
## Validation Metrics
- Loss: 0.21610039472579956
- Accuracy: 0.9155366722657816
- Precision: 0.9530714194995978
- Recall: 0.944871149164778
- AUC: 0.9553238723676906
- F1: 0.9489535692456846
## Usage
You can use cURL to acce... | [
"# Model Trained Using AutoNLP\n\n- Problem type: Binary Classification\n- Model ID: 16492731",
"## Validation Metrics\n\n- Loss: 0.21610039472579956\n- Accuracy: 0.9155366722657816\n- Precision: 0.9530714194995978\n- Recall: 0.944871149164778\n- AUC: 0.9553238723676906\n- F1: 0.9489535692456846",
"## Usage\n\n... | [
"TAGS\n#transformers #pytorch #bert #text-classification #autonlp #en #dataset-imzachjohnson/autonlp-data-spinner-check #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoNLP\n\n- Problem type: Binary Classification\n- Model ID: 16492731",
"## Validation Metrics\n\n- Loss: 0.... |
fill-mask | transformers |
# BERTino: an Italian DistilBERT model
This repository hosts BERTino, an Italian DistilBERT model pre-trained by
[indigo.ai](https://indigo.ai/en/)
on a large general-domain Italian corpus. BERTino is task-agnostic and can be
fine-tuned for every downstream task.
### Corpus
The pre-training corpus that we used is th... | {"language": "it", "license": "mit", "tags": ["DISTILbert", "Italian"], "widget": [{"text": "Vado al [MASK] a fare la spesa"}, {"text": "Vado al parco a guardare le [MASK]"}, {"text": "Il cielo \u00e8 [MASK] di stelle."}]} | indigo-ai/BERTino | null | [
"transformers",
"pytorch",
"tf",
"distilbert",
"fill-mask",
"DISTILbert",
"Italian",
"it",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"it"
] | TAGS
#transformers #pytorch #tf #distilbert #fill-mask #DISTILbert #Italian #it #license-mit #autotrain_compatible #endpoints_compatible #region-us
| BERTino: an Italian DistilBERT model
====================================
This repository hosts BERTino, an Italian DistilBERT model pre-trained by
URL
on a large general-domain Italian corpus. BERTino is task-agnostic and can be
fine-tuned for every downstream task.
### Corpus
The pre-training corpus that we use... | [
"### Corpus\n\n\nThe pre-training corpus that we used is the union of the\nPaisa and\nItWaC\ncorpora. The final corpus counts 14 millions of sentences for a total of 12 GB\nof text.",
"### Downstream Results\n\n\nTo validate the pre-training that we conducted, we evaluated BERTino on the\nItalian ParTUT,\nItalian... | [
"TAGS\n#transformers #pytorch #tf #distilbert #fill-mask #DISTILbert #Italian #it #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Corpus\n\n\nThe pre-training corpus that we used is the union of the\nPaisa and\nItWaC\ncorpora. The final corpus counts 14 millions of sentences for a to... |
text2text-generation | transformers |
# IndoBART-v2 Model
[IndoBART-v2](https://arxiv.org/abs/2104.08200) is a state-of-the-art language model for Indonesian based on the BART model. The pretrained model is trained using the BART training objective.
## All Pre-trained Models
| Model | #params | Training d... | {"language": "id", "license": "mit", "tags": ["indogpt", "indobenchmark", "indonlg"], "datasets": ["Indo4B+"], "inference": false} | indobenchmark/indobart-v2 | null | [
"transformers",
"pytorch",
"mbart",
"text2text-generation",
"indogpt",
"indobenchmark",
"indonlg",
"id",
"arxiv:2104.08200",
"license:mit",
"autotrain_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2104.08200"
] | [
"id"
] | TAGS
#transformers #pytorch #mbart #text2text-generation #indogpt #indobenchmark #indonlg #id #arxiv-2104.08200 #license-mit #autotrain_compatible #has_space #region-us
| IndoBART-v2 Model
=================
IndoBART-v2 is a state-of-the-art language model for Indonesian based on the BART model. The pretrained model is trained using the BART training objective.
All Pre-trained Models
----------------------
Model: 'indobenchmark/indobart-v2', #params: 132M, Training data: Indo4B-Plu... | [] | [
"TAGS\n#transformers #pytorch #mbart #text2text-generation #indogpt #indobenchmark #indonlg #id #arxiv-2104.08200 #license-mit #autotrain_compatible #has_space #region-us \n"
] |
text2text-generation | transformers |
# IndoBART Model
[IndoBART](https://arxiv.org/abs/2104.08200) is a state-of-the-art language model for Indonesian based on the BART model. The pretrained model is trained using the BART training objective.
## All Pre-trained Models
| Model | #params | Training data ... | {"language": "id", "license": "mit", "tags": ["indogpt", "indobenchmark", "indonlg"], "datasets": ["Indo4B+"], "inference": false} | indobenchmark/indobart | null | [
"transformers",
"pytorch",
"mbart",
"text2text-generation",
"indogpt",
"indobenchmark",
"indonlg",
"id",
"arxiv:2104.08200",
"license:mit",
"autotrain_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2104.08200"
] | [
"id"
] | TAGS
#transformers #pytorch #mbart #text2text-generation #indogpt #indobenchmark #indonlg #id #arxiv-2104.08200 #license-mit #autotrain_compatible #has_space #region-us
| IndoBART Model
==============
IndoBART is a state-of-the-art language model for Indonesian based on the BART model. The pretrained model is trained using the BART training objective.
All Pre-trained Models
----------------------
Model: 'indobenchmark/indobart', #params: 132M, Training data: Indo4B-Plus (23.79 GB ... | [] | [
"TAGS\n#transformers #pytorch #mbart #text2text-generation #indogpt #indobenchmark #indonlg #id #arxiv-2104.08200 #license-mit #autotrain_compatible #has_space #region-us \n"
] |
feature-extraction | transformers |
# IndoBERT Base Model (phase1 - uncased)
[IndoBERT](https://arxiv.org/abs/2009.05387) is a state-of-the-art language model for Indonesian based on the BERT model. The pretrained model is trained using a masked language modeling (MLM) objective and next sentence prediction (NSP) objective.
## All Pre-trained Models
... | {"language": "id", "license": "mit", "tags": ["indobert", "indobenchmark", "indonlu"], "datasets": ["Indo4B"], "inference": false} | indobenchmark/indobert-base-p1 | null | [
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"feature-extraction",
"indobert",
"indobenchmark",
"indonlu",
"id",
"dataset:Indo4B",
"arxiv:2009.05387",
"license:mit",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2009.05387"
] | [
"id"
] | TAGS
#transformers #pytorch #tf #jax #bert #feature-extraction #indobert #indobenchmark #indonlu #id #dataset-Indo4B #arxiv-2009.05387 #license-mit #has_space #region-us
| IndoBERT Base Model (phase1 - uncased)
======================================
IndoBERT is a state-of-the-art language model for Indonesian based on the BERT model. The pretrained model is trained using a masked language modeling (MLM) objective and next sentence prediction (NSP) objective.
All Pre-trained Models
--... | [
"### Load model and tokenizer",
"### Extract contextual representation\n\n\nAuthors\n-------\n\n\n**IndoBERT** was trained and evaluated by Bryan Wilie\\*, Karissa Vincentio\\*, Genta Indra Winata\\*, Samuel Cahyawijaya\\*, Xiaohong Li, Zhi Yuan Lim, Sidik Soleman, Rahmad Mahendra, Pascale Fung, Syafri Bahar, Ayu... | [
"TAGS\n#transformers #pytorch #tf #jax #bert #feature-extraction #indobert #indobenchmark #indonlu #id #dataset-Indo4B #arxiv-2009.05387 #license-mit #has_space #region-us \n",
"### Load model and tokenizer",
"### Extract contextual representation\n\n\nAuthors\n-------\n\n\n**IndoBERT** was trained and evaluate... |
feature-extraction | transformers |
# IndoBERT Base Model (phase2 - uncased)
[IndoBERT](https://arxiv.org/abs/2009.05387) is a state-of-the-art language model for Indonesian based on the BERT model. The pretrained model is trained using a masked language modeling (MLM) objective and next sentence prediction (NSP) objective.
## All Pre-trained Models
... | {"language": "id", "license": "mit", "tags": ["indobert", "indobenchmark", "indonlu"], "datasets": ["Indo4B"], "inference": false} | indobenchmark/indobert-base-p2 | null | [
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"feature-extraction",
"indobert",
"indobenchmark",
"indonlu",
"id",
"dataset:Indo4B",
"arxiv:2009.05387",
"license:mit",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2009.05387"
] | [
"id"
] | TAGS
#transformers #pytorch #tf #jax #bert #feature-extraction #indobert #indobenchmark #indonlu #id #dataset-Indo4B #arxiv-2009.05387 #license-mit #has_space #region-us
| IndoBERT Base Model (phase2 - uncased)
======================================
IndoBERT is a state-of-the-art language model for Indonesian based on the BERT model. The pretrained model is trained using a masked language modeling (MLM) objective and next sentence prediction (NSP) objective.
All Pre-trained Models
--... | [
"### Load model and tokenizer",
"### Extract contextual representation\n\n\nAuthors\n-------\n\n\n**IndoBERT** was trained and evaluated by Bryan Wilie\\*, Karissa Vincentio\\*, Genta Indra Winata\\*, Samuel Cahyawijaya\\*, Xiaohong Li, Zhi Yuan Lim, Sidik Soleman, Rahmad Mahendra, Pascale Fung, Syafri Bahar, Ayu... | [
"TAGS\n#transformers #pytorch #tf #jax #bert #feature-extraction #indobert #indobenchmark #indonlu #id #dataset-Indo4B #arxiv-2009.05387 #license-mit #has_space #region-us \n",
"### Load model and tokenizer",
"### Extract contextual representation\n\n\nAuthors\n-------\n\n\n**IndoBERT** was trained and evaluate... |
feature-extraction | transformers |
# IndoBERT Large Model (phase1 - uncased)
[IndoBERT](https://arxiv.org/abs/2009.05387) is a state-of-the-art language model for Indonesian based on the BERT model. The pretrained model is trained using a masked language modeling (MLM) objective and next sentence prediction (NSP) objective.
## All Pre-trained Models... | {"language": "id", "license": "mit", "tags": ["indobert", "indobenchmark", "indonlu"], "datasets": ["Indo4B"], "inference": false} | indobenchmark/indobert-large-p1 | null | [
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"feature-extraction",
"indobert",
"indobenchmark",
"indonlu",
"id",
"dataset:Indo4B",
"arxiv:2009.05387",
"license:mit",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2009.05387"
] | [
"id"
] | TAGS
#transformers #pytorch #tf #jax #bert #feature-extraction #indobert #indobenchmark #indonlu #id #dataset-Indo4B #arxiv-2009.05387 #license-mit #region-us
| IndoBERT Large Model (phase1 - uncased)
=======================================
IndoBERT is a state-of-the-art language model for Indonesian based on the BERT model. The pretrained model is trained using a masked language modeling (MLM) objective and next sentence prediction (NSP) objective.
All Pre-trained Models
... | [
"### Load model and tokenizer",
"### Extract contextual representation\n\n\nAuthors\n-------\n\n\n**IndoBERT** was trained and evaluated by Bryan Wilie\\*, Karissa Vincentio\\*, Genta Indra Winata\\*, Samuel Cahyawijaya\\*, Xiaohong Li, Zhi Yuan Lim, Sidik Soleman, Rahmad Mahendra, Pascale Fung, Syafri Bahar, Ayu... | [
"TAGS\n#transformers #pytorch #tf #jax #bert #feature-extraction #indobert #indobenchmark #indonlu #id #dataset-Indo4B #arxiv-2009.05387 #license-mit #region-us \n",
"### Load model and tokenizer",
"### Extract contextual representation\n\n\nAuthors\n-------\n\n\n**IndoBERT** was trained and evaluated by Bryan ... |
feature-extraction | transformers |
# IndoBERT Large Model (phase2 - uncased)
[IndoBERT](https://arxiv.org/abs/2009.05387) is a state-of-the-art language model for Indonesian based on the BERT model. The pretrained model is trained using a masked language modeling (MLM) objective and next sentence prediction (NSP) objective.
## All Pre-trained Models... | {"language": "id", "license": "mit", "tags": ["indobert", "indobenchmark", "indonlu"], "datasets": ["Indo4B"], "inference": false} | indobenchmark/indobert-large-p2 | null | [
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"feature-extraction",
"indobert",
"indobenchmark",
"indonlu",
"id",
"dataset:Indo4B",
"arxiv:2009.05387",
"license:mit",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2009.05387"
] | [
"id"
] | TAGS
#transformers #pytorch #tf #jax #bert #feature-extraction #indobert #indobenchmark #indonlu #id #dataset-Indo4B #arxiv-2009.05387 #license-mit #region-us
| IndoBERT Large Model (phase2 - uncased)
=======================================
IndoBERT is a state-of-the-art language model for Indonesian based on the BERT model. The pretrained model is trained using a masked language modeling (MLM) objective and next sentence prediction (NSP) objective.
All Pre-trained Models
... | [
"### Load model and tokenizer",
"### Extract contextual representation\n\n\nAuthors\n-------\n\n\n**IndoBERT** was trained and evaluated by Bryan Wilie\\*, Karissa Vincentio\\*, Genta Indra Winata\\*, Samuel Cahyawijaya\\*, Xiaohong Li, Zhi Yuan Lim, Sidik Soleman, Rahmad Mahendra, Pascale Fung, Syafri Bahar, Ayu... | [
"TAGS\n#transformers #pytorch #tf #jax #bert #feature-extraction #indobert #indobenchmark #indonlu #id #dataset-Indo4B #arxiv-2009.05387 #license-mit #region-us \n",
"### Load model and tokenizer",
"### Extract contextual representation\n\n\nAuthors\n-------\n\n\n**IndoBERT** was trained and evaluated by Bryan ... |
feature-extraction | transformers |
# IndoBERT-Lite Base Model (phase1 - uncased)
[IndoBERT](https://arxiv.org/abs/2009.05387) is a state-of-the-art language model for Indonesian based on the BERT model. The pretrained model is trained using a masked language modeling (MLM) objective and next sentence prediction (NSP) objective.
## All Pre-trained Mo... | {"language": "id", "license": "mit", "tags": ["indobert", "indobenchmark", "indonlu"], "datasets": ["Indo4B"], "inference": false} | indobenchmark/indobert-lite-base-p1 | null | [
"transformers",
"pytorch",
"tf",
"albert",
"feature-extraction",
"indobert",
"indobenchmark",
"indonlu",
"id",
"dataset:Indo4B",
"arxiv:2009.05387",
"license:mit",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2009.05387"
] | [
"id"
] | TAGS
#transformers #pytorch #tf #albert #feature-extraction #indobert #indobenchmark #indonlu #id #dataset-Indo4B #arxiv-2009.05387 #license-mit #region-us
| IndoBERT-Lite Base Model (phase1 - uncased)
===========================================
IndoBERT is a state-of-the-art language model for Indonesian based on the BERT model. The pretrained model is trained using a masked language modeling (MLM) objective and next sentence prediction (NSP) objective.
All Pre-trained... | [
"### Load model and tokenizer",
"### Extract contextual representation\n\n\nAuthors\n-------\n\n\n**IndoBERT** was trained and evaluated by Bryan Wilie\\*, Karissa Vincentio\\*, Genta Indra Winata\\*, Samuel Cahyawijaya\\*, Xiaohong Li, Zhi Yuan Lim, Sidik Soleman, Rahmad Mahendra, Pascale Fung, Syafri Bahar, Ayu... | [
"TAGS\n#transformers #pytorch #tf #albert #feature-extraction #indobert #indobenchmark #indonlu #id #dataset-Indo4B #arxiv-2009.05387 #license-mit #region-us \n",
"### Load model and tokenizer",
"### Extract contextual representation\n\n\nAuthors\n-------\n\n\n**IndoBERT** was trained and evaluated by Bryan Wil... |
feature-extraction | transformers |
# IndoBERT-Lite Base Model (phase2 - uncased)
[IndoBERT](https://arxiv.org/abs/2009.05387) is a state-of-the-art language model for Indonesian based on the BERT model. The pretrained model is trained using a masked language modeling (MLM) objective and next sentence prediction (NSP) objective.
## All Pre-trained Mo... | {"language": "id", "license": "mit", "tags": ["indobert", "indobenchmark", "indonlu"], "datasets": ["Indo4B"], "inference": false} | indobenchmark/indobert-lite-base-p2 | null | [
"transformers",
"pytorch",
"tf",
"albert",
"feature-extraction",
"indobert",
"indobenchmark",
"indonlu",
"id",
"dataset:Indo4B",
"arxiv:2009.05387",
"license:mit",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2009.05387"
] | [
"id"
] | TAGS
#transformers #pytorch #tf #albert #feature-extraction #indobert #indobenchmark #indonlu #id #dataset-Indo4B #arxiv-2009.05387 #license-mit #region-us
| IndoBERT-Lite Base Model (phase2 - uncased)
===========================================
IndoBERT is a state-of-the-art language model for Indonesian based on the BERT model. The pretrained model is trained using a masked language modeling (MLM) objective and next sentence prediction (NSP) objective.
All Pre-trained... | [
"### Load model and tokenizer",
"### Extract contextual representation\n\n\nAuthors\n-------\n\n\n**IndoBERT** was trained and evaluated by Bryan Wilie\\*, Karissa Vincentio\\*, Genta Indra Winata\\*, Samuel Cahyawijaya\\*, Xiaohong Li, Zhi Yuan Lim, Sidik Soleman, Rahmad Mahendra, Pascale Fung, Syafri Bahar, Ayu... | [
"TAGS\n#transformers #pytorch #tf #albert #feature-extraction #indobert #indobenchmark #indonlu #id #dataset-Indo4B #arxiv-2009.05387 #license-mit #region-us \n",
"### Load model and tokenizer",
"### Extract contextual representation\n\n\nAuthors\n-------\n\n\n**IndoBERT** was trained and evaluated by Bryan Wil... |
feature-extraction | transformers |
# IndoBERT-Lite Large Model (phase1 - uncased)
[IndoBERT](https://arxiv.org/abs/2009.05387) is a state-of-the-art language model for Indonesian based on the BERT model. The pretrained model is trained using a masked language modeling (MLM) objective and next sentence prediction (NSP) objective.
## All Pre-trained M... | {"language": "id", "license": "mit", "tags": ["indobert", "indobenchmark", "indonlu"], "datasets": ["Indo4B"], "inference": false} | indobenchmark/indobert-lite-large-p1 | null | [
"transformers",
"pytorch",
"tf",
"albert",
"feature-extraction",
"indobert",
"indobenchmark",
"indonlu",
"id",
"dataset:Indo4B",
"arxiv:2009.05387",
"license:mit",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2009.05387"
] | [
"id"
] | TAGS
#transformers #pytorch #tf #albert #feature-extraction #indobert #indobenchmark #indonlu #id #dataset-Indo4B #arxiv-2009.05387 #license-mit #region-us
| IndoBERT-Lite Large Model (phase1 - uncased)
============================================
IndoBERT is a state-of-the-art language model for Indonesian based on the BERT model. The pretrained model is trained using a masked language modeling (MLM) objective and next sentence prediction (NSP) objective.
All Pre-train... | [
"### Load model and tokenizer",
"### Extract contextual representation\n\n\nAuthors\n-------\n\n\n**IndoBERT** was trained and evaluated by Bryan Wilie\\*, Karissa Vincentio\\*, Genta Indra Winata\\*, Samuel Cahyawijaya\\*, Xiaohong Li, Zhi Yuan Lim, Sidik Soleman, Rahmad Mahendra, Pascale Fung, Syafri Bahar, Ayu... | [
"TAGS\n#transformers #pytorch #tf #albert #feature-extraction #indobert #indobenchmark #indonlu #id #dataset-Indo4B #arxiv-2009.05387 #license-mit #region-us \n",
"### Load model and tokenizer",
"### Extract contextual representation\n\n\nAuthors\n-------\n\n\n**IndoBERT** was trained and evaluated by Bryan Wil... |
feature-extraction | transformers |
# IndoBERT-Lite Large Model (phase2 - uncased)
[IndoBERT](https://arxiv.org/abs/2009.05387) is a state-of-the-art language model for Indonesian based on the BERT model. The pretrained model is trained using a masked language modeling (MLM) objective and next sentence prediction (NSP) objective.
## All Pre-trained M... | {"language": "id", "license": "mit", "tags": ["indobert", "indobenchmark", "indonlu"], "datasets": ["Indo4B"], "inference": false} | indobenchmark/indobert-lite-large-p2 | null | [
"transformers",
"pytorch",
"tf",
"albert",
"feature-extraction",
"indobert",
"indobenchmark",
"indonlu",
"id",
"dataset:Indo4B",
"arxiv:2009.05387",
"license:mit",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2009.05387"
] | [
"id"
] | TAGS
#transformers #pytorch #tf #albert #feature-extraction #indobert #indobenchmark #indonlu #id #dataset-Indo4B #arxiv-2009.05387 #license-mit #region-us
| IndoBERT-Lite Large Model (phase2 - uncased)
============================================
IndoBERT is a state-of-the-art language model for Indonesian based on the BERT model. The pretrained model is trained using a masked language modeling (MLM) objective and next sentence prediction (NSP) objective.
All Pre-train... | [
"### Load model and tokenizer",
"### Extract contextual representation\n\n\nAuthors\n-------\n\n\n**IndoBERT** was trained and evaluated by Bryan Wilie\\*, Karissa Vincentio\\*, Genta Indra Winata\\*, Samuel Cahyawijaya\\*, Xiaohong Li, Zhi Yuan Lim, Sidik Soleman, Rahmad Mahendra, Pascale Fung, Syafri Bahar, Ayu... | [
"TAGS\n#transformers #pytorch #tf #albert #feature-extraction #indobert #indobenchmark #indonlu #id #dataset-Indo4B #arxiv-2009.05387 #license-mit #region-us \n",
"### Load model and tokenizer",
"### Extract contextual representation\n\n\nAuthors\n-------\n\n\n**IndoBERT** was trained and evaluated by Bryan Wil... |
text-generation | transformers |
# IndoGPT Model
[IndoGPT](https://arxiv.org/abs/2104.08200) is a state-of-the-art language model for Indonesian based on the GPT model. The pretrained model is trained using the GPT training objective.
## All Pre-trained Models
| Model | #params | Training data ... | {"language": "id", "license": "mit", "tags": ["indogpt", "indobenchmark", "indonlg"], "datasets": ["Indo4B+"], "inference": false} | indobenchmark/indogpt | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"indogpt",
"indobenchmark",
"indonlg",
"id",
"arxiv:2104.08200",
"license:mit",
"autotrain_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2104.08200"
] | [
"id"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #indogpt #indobenchmark #indonlg #id #arxiv-2104.08200 #license-mit #autotrain_compatible #has_space #text-generation-inference #region-us
| IndoGPT Model
=============
IndoGPT is a state-of-the-art language model for Indonesian based on the GPT model. The pretrained model is trained using the GPT training objective.
All Pre-trained Models
----------------------
Model: 'indobenchmark/indogpt', #params: 117M, Training data: Indo4B-Plus (23.79 GB of tex... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #indogpt #indobenchmark #indonlg #id #arxiv-2104.08200 #license-mit #autotrain_compatible #has_space #text-generation-inference #region-us \n"
] |
fill-mask | transformers |
## About
[IndoBERT](https://arxiv.org/pdf/2011.00677.pdf) is the Indonesian version of BERT model. We train the model using over 220M words, aggregated from three main sources:
* Indonesian Wikipedia (74M words)
* news articles from Kompas, Tempo (Tala et al., 2003), and Liputan6 (55M words in total)
* an Indonesian... | {"language": "id", "license": "mit", "tags": ["indobert", "indolem"], "inference": false} | indolem/indobert-base-uncased | null | [
"transformers",
"pytorch",
"jax",
"bert",
"fill-mask",
"indobert",
"indolem",
"id",
"arxiv:2011.00677",
"license:mit",
"autotrain_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2011.00677"
] | [
"id"
] | TAGS
#transformers #pytorch #jax #bert #fill-mask #indobert #indolem #id #arxiv-2011.00677 #license-mit #autotrain_compatible #has_space #region-us
| About
-----
IndoBERT is the Indonesian version of BERT model. We train the model using over 220M words, aggregated from three main sources:
* Indonesian Wikipedia (74M words)
* news articles from Kompas, Tempo (Tala et al., 2003), and Liputan6 (55M words in total)
* an Indonesian Web Corpus (Medved and Suchomel, 20... | [
"### Load model and tokenizer (tested with transformers==3.5.1)\n\n\nIf you use our work, please cite:"
] | [
"TAGS\n#transformers #pytorch #jax #bert #fill-mask #indobert #indolem #id #arxiv-2011.00677 #license-mit #autotrain_compatible #has_space #region-us \n",
"### Load model and tokenizer (tested with transformers==3.5.1)\n\n\nIf you use our work, please cite:"
] |
fill-mask | transformers |
# IndoBERTweet 🐦
## 1. Paper
Fajri Koto, Jey Han Lau, and Timothy Baldwin. [_IndoBERTweet: A Pretrained Language Model for Indonesian Twitter
with Effective Domain-Specific Vocabulary Initialization_](https://arxiv.org/pdf/2109.04607.pdf).
In Proceedings of the 2021 Conference on Empirical Methods in Natural Langu... | {"language": ["id"], "license": "apache-2.0", "tags": ["Twitter"], "datasets": ["Twitter 2021"], "widget": [{"text": "guweehh udh ga' paham lg sm [MASK]"}]} | indolem/indobertweet-base-uncased | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"Twitter",
"id",
"arxiv:2109.04607",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2109.04607"
] | [
"id"
] | TAGS
#transformers #pytorch #bert #fill-mask #Twitter #id #arxiv-2109.04607 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| IndoBERTweet
============
1. Paper
--------
Fajri Koto, Jey Han Lau, and Timothy Baldwin. *IndoBERTweet: A Pretrained Language Model for Indonesian Twitter
with Effective Domain-Specific Vocabulary Initialization*.
In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (EMNLP 2021... | [] | [
"TAGS\n#transformers #pytorch #bert #fill-mask #Twitter #id #arxiv-2109.04607 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
text-generation | transformers |
# GPT2-medium-indonesian
This is a pretrained model on Indonesian language using a causal language modeling (CLM) objective, which was first
introduced in [this paper](https://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_multitask_learners.pdf)
and first released at [this page]... | {"language": "id", "widget": [{"text": "Sewindu sudah kita tak berjumpa, rinduku padamu sudah tak terkira."}]} | indonesian-nlp/gpt2-medium-indonesian | null | [
"transformers",
"pytorch",
"jax",
"safetensors",
"gpt2",
"text-generation",
"id",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"id"
] | TAGS
#transformers #pytorch #jax #safetensors #gpt2 #text-generation #id #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| GPT2-medium-indonesian
======================
This is a pretrained model on Indonesian language using a causal language modeling (CLM) objective, which was first
introduced in this paper
and first released at this page.
This model was trained using HuggingFace's Flax framework and is part of the JAX/Flax Community ... | [
"### Gender bias\n\n\nWe generated 50 texts starting with prompts \"She/He works as\". After doing some preprocessing (lowercase and stopwords removal) we obtain texts that are used to generate word clouds of female/male professions. The most salient terms for male professions are: driver, sopir (driver), ojek, tuk... | [
"TAGS\n#transformers #pytorch #jax #safetensors #gpt2 #text-generation #id #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"### Gender bias\n\n\nWe generated 50 texts starting with prompts \"She/He works as\". After doing some preprocessing (lowercase and stopword... |
text-generation | transformers |
# GPT2-small-indonesian
This is a pretrained model on Indonesian language using a causal language modeling (CLM) objective, which was first
introduced in [this paper](https://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_multitask_learners.pdf)
and first released at [this page](... | {"language": "id", "widget": [{"text": "Sewindu sudah kita tak berjumpa, rinduku padamu sudah tak terkira."}]} | indonesian-nlp/gpt2 | null | [
"transformers",
"pytorch",
"jax",
"safetensors",
"gpt2",
"text-generation",
"id",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"id"
] | TAGS
#transformers #pytorch #jax #safetensors #gpt2 #text-generation #id #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| GPT2-small-indonesian
=====================
This is a pretrained model on Indonesian language using a causal language modeling (CLM) objective, which was first
introduced in this paper
and first released at this page.
This model was trained using HuggingFace's Flax framework and is part of the JAX/Flax Community We... | [
"### Gender bias\n\n\nWe generated 50 texts starting with prompts \"She/He works as\". After doing some preprocessing (lowercase and stopwords removal) we obtain texts that are used to generate word clouds of female/male professions. The most salient terms for male professions are: driver, sopir (driver), ojek, tuk... | [
"TAGS\n#transformers #pytorch #jax #safetensors #gpt2 #text-generation #id #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"### Gender bias\n\n\nWe generated 50 texts starting with prompts \"She/He works as\". After doing some preprocessing (lowercase and stopword... |
automatic-speech-recognition | transformers |
# Multilingual Speech Recognition for Indonesian Languages
This is the model built for the project
[Multilingual Speech Recognition for Indonesian Languages](https://github.com/indonesian-nlp/multilingual-asr).
It is a fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-5... | {"language": ["id", "jv", "sun"], "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "hf-asr-leaderboard", "id", "jv", "robust-speech-event", "speech", "su"], "datasets": ["mozilla-foundation/common_voice_7_0", "openslr", "magic_data", "titml"], "metrics": ["wer"], "model-index": [{"name": "Wav2... | indonesian-nlp/wav2vec2-indonesian-javanese-sundanese | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"hf-asr-leaderboard",
"id",
"jv",
"robust-speech-event",
"speech",
"su",
"sun",
"dataset:mozilla-foundation/common_voice_7_0",
"dataset:openslr",
"dataset:magic_data",
"dataset:titml",
"license:apache-2.0... | null | 2022-03-02T23:29:05+00:00 | [] | [
"id",
"jv",
"sun"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #hf-asr-leaderboard #id #jv #robust-speech-event #speech #su #sun #dataset-mozilla-foundation/common_voice_7_0 #dataset-openslr #dataset-magic_data #dataset-titml #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us
|
# Multilingual Speech Recognition for Indonesian Languages
This is the model built for the project
Multilingual Speech Recognition for Indonesian Languages.
It is a fine-tuned facebook/wav2vec2-large-xlsr-53
model on the Indonesian Common Voice dataset,
High-quality TTS data for Javanese - SLR41, and
High-quality T... | [
"# Multilingual Speech Recognition for Indonesian Languages\n\nThis is the model built for the project \nMultilingual Speech Recognition for Indonesian Languages.\nIt is a fine-tuned facebook/wav2vec2-large-xlsr-53\nmodel on the Indonesian Common Voice dataset, \nHigh-quality TTS data for Javanese - SLR41, and\nHig... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #hf-asr-leaderboard #id #jv #robust-speech-event #speech #su #sun #dataset-mozilla-foundation/common_voice_7_0 #dataset-openslr #dataset-magic_data #dataset-titml #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us... |
automatic-speech-recognition | transformers |
# Wav2Vec2-Large-XLSR-Indonesian
This is the baseline for Wav2Vec2-Large-XLSR-Indonesian, a fine-tuned
[facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53)
model on the [Indonesian Common Voice dataset](https://huggingface.co/datasets/common_voice).
It was trained using the defau... | {"language": "id", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice"], "metrics": ["wer"], "model-index": [{"name": "XLSR Wav2Vec2 Indonesian Baseline by indonesian-nlp", "results": [{"task": {"type": "automatic-speech-recognition"... | indonesian-nlp/wav2vec2-large-xlsr-indonesian-baseline | null | [
"transformers",
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"id",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"id"
] | TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #id #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
# Wav2Vec2-Large-XLSR-Indonesian
This is the baseline for Wav2Vec2-Large-XLSR-Indonesian, a fine-tuned
facebook/wav2vec2-large-xlsr-53
model on the Indonesian Common Voice dataset.
It was trained using the default hyperparamer and for 2x30 epochs.
When using this model, make sure that your speech input is sampled at... | [
"# Wav2Vec2-Large-XLSR-Indonesian\n\nThis is the baseline for Wav2Vec2-Large-XLSR-Indonesian, a fine-tuned \nfacebook/wav2vec2-large-xlsr-53\nmodel on the Indonesian Common Voice dataset.\nIt was trained using the default hyperparamer and for 2x30 epochs.\nWhen using this model, make sure that your speech input is ... | [
"TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #id #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"# Wav2Vec2-Large-XLSR-Indonesian\n\nThis is the baseline for Wav2Vec2-Large-XLSR-Indonesian, a fine-tun... |
automatic-speech-recognition | transformers |
# Wav2Vec2-Large-XLSR-Indonesian
This is the model for Wav2Vec2-Large-XLSR-Indonesian, a fine-tuned
[facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53)
model on the [Indonesian Common Voice dataset](https://huggingface.co/datasets/common_voice).
When using this model, make sure ... | {"language": "id", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice"], "metrics": ["wer"], "model-index": [{"name": "XLSR Wav2Vec2 Indonesian by Indonesian NLP", "results": [{"task": {"type": "automatic-speech-recognition", "name":... | indonesian-nlp/wav2vec2-large-xlsr-indonesian | null | [
"transformers",
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"id",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"id"
] | TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #id #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us
|
# Wav2Vec2-Large-XLSR-Indonesian
This is the model for Wav2Vec2-Large-XLSR-Indonesian, a fine-tuned
facebook/wav2vec2-large-xlsr-53
model on the Indonesian Common Voice dataset.
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used directly (without a language m... | [
"# Wav2Vec2-Large-XLSR-Indonesian\n\nThis is the model for Wav2Vec2-Large-XLSR-Indonesian, a fine-tuned \nfacebook/wav2vec2-large-xlsr-53\nmodel on the Indonesian Common Voice dataset.\nWhen using this model, make sure that your speech input is sampled at 16kHz.",
"## Usage\nThe model can be used directly (withou... | [
"TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #id #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us \n",
"# Wav2Vec2-Large-XLSR-Indonesian\n\nThis is the model for Wav2Vec2-Large-XLSR-Indonesian, a ... |
automatic-speech-recognition | transformers |
# Automatic Speech Recognition for Luganda
This is the model built for the
[Mozilla Luganda Automatic Speech Recognition competition](https://zindi.africa/competitions/mozilla-luganda-automatic-speech-recognition).
It is a fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xl... | {"language": "lg", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech"], "datasets": ["common_voice"], "metrics": ["wer"], "model-index": [{"name": "Wav2Vec2 Luganda by Indonesian-NLP", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech Recognition"}, "dataset"... | indonesian-nlp/wav2vec2-luganda | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"lg",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"lg"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #speech #lg #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us
|
# Automatic Speech Recognition for Luganda
This is the model built for the
Mozilla Luganda Automatic Speech Recognition competition.
It is a fine-tuned facebook/wav2vec2-large-xlsr-53
model on the Luganda Common Voice dataset version 7.0.
We also provide a live demo to test the model.
When using this model, make s... | [
"# Automatic Speech Recognition for Luganda\n\nThis is the model built for the \nMozilla Luganda Automatic Speech Recognition competition.\nIt is a fine-tuned facebook/wav2vec2-large-xlsr-53\nmodel on the Luganda Common Voice dataset version 7.0.\n\nWe also provide a live demo to test the model.\n\nWhen using this ... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #speech #lg #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us \n",
"# Automatic Speech Recognition for Luganda\n\nThis is the model built for the \nMozilla Luganda Automatic Speech Recogni... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# IceBERT-finetuned-ner
This model is a fine-tuned version of [vesteinn/IceBERT](https://huggingface.co/vesteinn/IceBERT) on the m... | {"license": "gpl-3.0", "tags": ["generated_from_trainer"], "datasets": ["mim_gold_ner"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "IceBERT-finetuned-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "mim_gold_ner", "typ... | indridinn/IceBERT-finetuned-ner | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"token-classification",
"generated_from_trainer",
"dataset:mim_gold_ner",
"license:gpl-3.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #token-classification #generated_from_trainer #dataset-mim_gold_ner #license-gpl-3.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| IceBERT-finetuned-ner
=====================
This model is a fine-tuned version of vesteinn/IceBERT on the mim\_gold\_ner dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0830
* Precision: 0.8919
* Recall: 0.8632
* F1: 0.8773
* Accuracy: 0.9851
Model description
-----------------
More ... | [
"### 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 #tensorboard #roberta #token-classification #generated_from_trainer #dataset-mim_gold_ner #license-gpl-3.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learn... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# XLMR-ENIS-finetuned-ner
This model is a fine-tuned version of [vesteinn/XLMR-ENIS](https://huggingface.co/vesteinn/XLMR-ENIS) on... | {"license": "agpl-3.0", "tags": ["generated_from_trainer"], "datasets": ["mim_gold_ner"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "XLMR-ENIS-finetuned-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "mim_gold_ner", "... | indridinn/XLMR-ENIS-finetuned-ner | null | [
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:mim_gold_ner",
"license:agpl-3.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #xlm-roberta #token-classification #generated_from_trainer #dataset-mim_gold_ner #license-agpl-3.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| XLMR-ENIS-finetuned-ner
=======================
This model is a fine-tuned version of vesteinn/XLMR-ENIS on the mim\_gold\_ner dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0907
* Precision: 0.8666
* Recall: 0.8511
* F1: 0.8588
* Accuracy: 0.9834
Model description
-----------------
... | [
"### 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 #tensorboard #xlm-roberta #token-classification #generated_from_trainer #dataset-mim_gold_ner #license-agpl-3.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* ... |
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