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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
text-classification | transformers |
## hate-roberta-hasoc-hindi
hate-roberta-hasoc-hindi is a multi-class hate speech model fine-tuned on Hindi Hasoc Hate Speech Dataset 2021.
The label mappings are 0 -> None, 1 -> Offensive, 2 -> Hate, 3 -> Profane.
More details on the dataset, models, and baseline results can be found in our [paper] (https://arxiv.... | {"language": "hi", "license": "cc-by-4.0", "tags": ["roberta"], "datasets": ["HASOC 2021"], "widget": [{"text": "I like you. </s></s> I love you."}]} | l3cube-pune/hate-multi-roberta-hasoc-hindi | null | [
"transformers",
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"safetensors",
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"text-classification",
"hi",
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"license:cc-by-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2110.12200"
] | [
"hi"
] | TAGS
#transformers #pytorch #tf #safetensors #roberta #text-classification #hi #arxiv-2110.12200 #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #region-us
|
## hate-roberta-hasoc-hindi
hate-roberta-hasoc-hindi is a multi-class hate speech model fine-tuned on Hindi Hasoc Hate Speech Dataset 2021.
The label mappings are 0 -> None, 1 -> Offensive, 2 -> Hate, 3 -> Profane.
More details on the dataset, models, and baseline results can be found in our [paper] (URL
| [
"## hate-roberta-hasoc-hindi\n\nhate-roberta-hasoc-hindi is a multi-class hate speech model fine-tuned on Hindi Hasoc Hate Speech Dataset 2021.\nThe label mappings are 0 -> None, 1 -> Offensive, 2 -> Hate, 3 -> Profane.\n\nMore details on the dataset, models, and baseline results can be found in our [paper] (URL"
] | [
"TAGS\n#transformers #pytorch #tf #safetensors #roberta #text-classification #hi #arxiv-2110.12200 #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"## hate-roberta-hasoc-hindi\n\nhate-roberta-hasoc-hindi is a multi-class hate speech model fine-tuned on Hindi Hasoc Hate Speech Datase... |
text-classification | transformers |
## hate-roberta-hasoc-hindi
hate-roberta-hasoc-hindi is a binary hate speech model fine-tuned on Hindi Hasoc Hate Speech Dataset 2021.
The label mappings are 0 -> None, 1 -> Hate.
More details on the dataset, models, and baseline results can be found in our [paper] (https://arxiv.org/abs/2110.12200)
```
@article{v... | {"language": "hi", "license": "cc-by-4.0", "tags": ["roberta"], "datasets": ["HASOC 2021"], "widget": [{"text": "I like you. </s></s> I love you."}]} | l3cube-pune/hate-roberta-hasoc-hindi | null | [
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"text-classification",
"hi",
"arxiv:2110.12200",
"license:cc-by-4.0",
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"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
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"hi"
] | TAGS
#transformers #pytorch #tf #safetensors #roberta #text-classification #hi #arxiv-2110.12200 #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #region-us
|
## hate-roberta-hasoc-hindi
hate-roberta-hasoc-hindi is a binary hate speech model fine-tuned on Hindi Hasoc Hate Speech Dataset 2021.
The label mappings are 0 -> None, 1 -> Hate.
More details on the dataset, models, and baseline results can be found in our [paper] (URL
| [
"## hate-roberta-hasoc-hindi\n\nhate-roberta-hasoc-hindi is a binary hate speech model fine-tuned on Hindi Hasoc Hate Speech Dataset 2021.\nThe label mappings are 0 -> None, 1 -> Hate.\n\nMore details on the dataset, models, and baseline results can be found in our [paper] (URL"
] | [
"TAGS\n#transformers #pytorch #tf #safetensors #roberta #text-classification #hi #arxiv-2110.12200 #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"## hate-roberta-hasoc-hindi\n\nhate-roberta-hasoc-hindi is a binary hate speech model fine-tuned on Hindi Hasoc Hate Speech Dataset 202... |
fill-mask | transformers |
## MahaAlBERT
MahaAlBERT is a Marathi AlBERT model trained on L3Cube-MahaCorpus and other publicly available Marathi monolingual datasets.
[dataset link] (https://github.com/l3cube-pune/MarathiNLP)
More details on the dataset, models, and baseline results can be found in our [paper] (https://arxiv.org/abs/2202.01159... | {"language": "mr", "license": "cc-by-4.0", "datasets": ["L3Cube-MahaCorpus"]} | l3cube-pune/marathi-albert | null | [
"transformers",
"pytorch",
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"mr",
"dataset:L3Cube-MahaCorpus",
"arxiv:2202.01159",
"license:cc-by-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2202.01159"
] | [
"mr"
] | TAGS
#transformers #pytorch #albert #fill-mask #mr #dataset-L3Cube-MahaCorpus #arxiv-2202.01159 #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #region-us
|
## MahaAlBERT
MahaAlBERT is a Marathi AlBERT model trained on L3Cube-MahaCorpus and other publicly available Marathi monolingual datasets.
[dataset link] (URL
More details on the dataset, models, and baseline results can be found in our [paper] (URL
Other Monolingual Indic BERT models are listed below: <br>
<a hr... | [
"## MahaAlBERT\nMahaAlBERT is a Marathi AlBERT model trained on L3Cube-MahaCorpus and other publicly available Marathi monolingual datasets. \n[dataset link] (URL\n\nMore details on the dataset, models, and baseline results can be found in our [paper] (URL\n\n\n\nOther Monolingual Indic BERT models are listed below... | [
"TAGS\n#transformers #pytorch #albert #fill-mask #mr #dataset-L3Cube-MahaCorpus #arxiv-2202.01159 #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"## MahaAlBERT\nMahaAlBERT is a Marathi AlBERT model trained on L3Cube-MahaCorpus and other publicly available Marathi monolingual datase... |
fill-mask | transformers |
## MahaBERT
New version of this model is available here: https://huggingface.co/l3cube-pune/marathi-bert-v2
MahaBERT is a Marathi BERT model. It is a multilingual BERT (bert-base-multilingual-cased) model fine-tuned on L3Cube-MahaCorpus and other publicly available Marathi monolingual datasets.
[dataset link] (http... | {"language": "mr", "license": "cc-by-4.0", "datasets": ["L3Cube-MahaCorpus"]} | l3cube-pune/marathi-bert | null | [
"transformers",
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"bert",
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"mr",
"dataset:L3Cube-MahaCorpus",
"arxiv:2202.01159",
"license:cc-by-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2202.01159"
] | [
"mr"
] | TAGS
#transformers #pytorch #safetensors #bert #fill-mask #mr #dataset-L3Cube-MahaCorpus #arxiv-2202.01159 #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #region-us
|
## MahaBERT
New version of this model is available here: URL
MahaBERT is a Marathi BERT model. It is a multilingual BERT (bert-base-multilingual-cased) model fine-tuned on L3Cube-MahaCorpus and other publicly available Marathi monolingual datasets.
[dataset link] (URL
More details on the dataset, models, and basel... | [
"## MahaBERT\n\nNew version of this model is available here: URL\n\nMahaBERT is a Marathi BERT model. It is a multilingual BERT (bert-base-multilingual-cased) model fine-tuned on L3Cube-MahaCorpus and other publicly available Marathi monolingual datasets. \n[dataset link] (URL\n\nMore details on the dataset, models... | [
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"## MahaBERT\n\nNew version of this model is available here: URL\n\nMahaBERT is a Marathi BERT model. It is a multilingual BER... |
fill-mask | transformers |
## MahaRoBERTa
MahaRoBERTa is a Marathi RoBERTa model. It is a multilingual RoBERTa (xlm-roberta-base) model fine-tuned on L3Cube-MahaCorpus and other publicly available Marathi monolingual datasets.
[dataset link] (https://github.com/l3cube-pune/MarathiNLP)
More details on the dataset, models, and baseline results ... | {"language": "mr", "license": "cc-by-4.0", "datasets": ["L3Cube-MahaCorpus"]} | l3cube-pune/marathi-roberta | null | [
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"arxiv:2202.01159",
"license:cc-by-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2202.01159"
] | [
"mr"
] | TAGS
#transformers #pytorch #safetensors #xlm-roberta #fill-mask #mr #dataset-L3Cube-MahaCorpus #arxiv-2202.01159 #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #region-us
|
## MahaRoBERTa
MahaRoBERTa is a Marathi RoBERTa model. It is a multilingual RoBERTa (xlm-roberta-base) model fine-tuned on L3Cube-MahaCorpus and other publicly available Marathi monolingual datasets.
[dataset link] (URL
More details on the dataset, models, and baseline results can be found in our [paper] (URL
Oth... | [
"## MahaRoBERTa\nMahaRoBERTa is a Marathi RoBERTa model. It is a multilingual RoBERTa (xlm-roberta-base) model fine-tuned on L3Cube-MahaCorpus and other publicly available Marathi monolingual datasets. \n[dataset link] (URL\n\nMore details on the dataset, models, and baseline results can be found in our [paper] (UR... | [
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"## MahaRoBERTa\nMahaRoBERTa is a Marathi RoBERTa model. It is a multilingual RoBERTa (xlm-roberta-base) model fine-tun... |
text-generation | transformers |
# <3 | {"tags": ["conversational"]} | l41n/c3rbs | null | [
"transformers",
"pytorch",
"conversational",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #conversational #endpoints_compatible #region-us
|
# <3 | [
"# <3"
] | [
"TAGS\n#transformers #pytorch #conversational #endpoints_compatible #region-us \n",
"# <3"
] |
text-generation | transformers | Base model: [microsoft/DialoGPT-large](https://huggingface.co/microsoft/DialoGPT-large)
Fine tuned for dialogue response generation on the [Persuasion For Good Dataset](https://gitlab.com/ucdavisnlp/persuasionforgood) (Wang et al., 2019)
Three additional special tokens were added during the fine-tuning process:
- <|... | {} | LACAI/DialoGPT-large-PFG | null | [
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"tensorboard",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| Base model: microsoft/DialoGPT-large
Fine tuned for dialogue response generation on the Persuasion For Good Dataset (Wang et al., 2019)
Three additional special tokens were added during the fine-tuning process:
- <|pad|> padding token
- <|user|> speaker control token to prompt user responses
- <|system|> speaker c... | [] | [
"TAGS\n#transformers #pytorch #tensorboard #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation | transformers | Base model: [microsoft/DialoGPT-small](https://huggingface.co/microsoft/DialoGPT-small)
Fine tuned for dialogue response generation on the [Persuasion For Good Dataset](https://gitlab.com/ucdavisnlp/persuasionforgood) (Wang et al., 2019)
Three additional special tokens were added during the fine-tuning process:
- <|... | {} | LACAI/DialoGPT-small-PFG | null | [
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"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| Base model: microsoft/DialoGPT-small
Fine tuned for dialogue response generation on the Persuasion For Good Dataset (Wang et al., 2019)
Three additional special tokens were added during the fine-tuning process:
- <|pad|> padding token
- <|user|> speaker control token to prompt user responses
- <|system|> speaker c... | [] | [
"TAGS\n#transformers #pytorch #tensorboard #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation | transformers | Base model: [microsoft/DialoGPT-small](https://huggingface.co/microsoft/DialoGPT-small)
Fine tuned for dialogue response generation on the [Schema Guided Dialogue Dataset](https://github.com/google-research-datasets/dstc8-schema-guided-dialogue) (Rastogi et al., 2019)
Three additional special tokens were added during... | {} | LACAI/DialoGPT-small-SGD | null | [
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"tensorboard",
"gpt2",
"text-generation",
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"endpoints_compatible",
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] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| Base model: microsoft/DialoGPT-small
Fine tuned for dialogue response generation on the Schema Guided Dialogue Dataset (Rastogi et al., 2019)
Three additional special tokens were added during the fine-tuning process:
- <|pad|> padding token
- <|user|> speaker control token to prompt user responses
- <|system|> spe... | [] | [
"TAGS\n#transformers #pytorch #tensorboard #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation | transformers | Base model: [gpt2-xl](https://huggingface.co/gpt2-xl)
Domain-adapted for dialogue response and narrative generation on a [narrative-aligned variant](https://github.com/AbrahamSanders/gutenberg-dialog#download-narrative-aligned-datasets) of the [Gutenberg Dialogue Dataset (Csaky & Recski, 2021)](https://aclanthology.... | {} | LACAI/gpt2-xl-dialog-narrative-persuasion | null | [
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"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| Base model: gpt2-xl
Domain-adapted for dialogue response and narrative generation on a narrative-aligned variant of the Gutenberg Dialogue Dataset (Csaky & Recski, 2021)
Fine-tuned for dialogue response generation on Persuasion For Good (Wang et al., 2019) (dataset) | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
fill-mask | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# output_mlm
This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on an unknown dataset.
It... | {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "output_mlm", "results": []}]} | LACAI/roberta-large-dialog-narrative | null | [
"transformers",
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"tensorboard",
"roberta",
"fill-mask",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #fill-mask #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| output\_mlm
===========
This model is a fine-tuned version of roberta-large on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 1.2024
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information need... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 16\n* e... |
text-generation | transformers | # Peter from Your Boyfriend Game. | {"tags": ["conversational"]} | lain2/Peterbot | null | [
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"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
| # Peter from Your Boyfriend Game. | [
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] |
text-to-speech | espnet |
## ESPnet2 TTS model
### `lakahaga/novel_reading_tts`
This model was trained by lakahaga using novelspeech recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```bash
cd espnet
git checkout 9827dfe37f69e8e55f902dc4e340de5108596311
pip install -e .
cd egs2/novelspeech/tts1
./run.... | {"language": "ko", "license": "cc-by-4.0", "tags": ["espnet", "audio", "text-to-speech"], "datasets": ["novelspeech"]} | lakahaga/novel_reading_tts | null | [
"espnet",
"audio",
"text-to-speech",
"ko",
"dataset:novelspeech",
"arxiv:1804.00015",
"license:cc-by-4.0",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1804.00015"
] | [
"ko"
] | TAGS
#espnet #audio #text-to-speech #ko #dataset-novelspeech #arxiv-1804.00015 #license-cc-by-4.0 #has_space #region-us
|
## ESPnet2 TTS model
### 'lakahaga/novel_reading_tts'
This model was trained by lakahaga using novelspeech recipe in espnet.
### Demo: How to use in ESPnet2
## TTS config
<details><summary>expand</summary>
</details>
### Citing ESPnet
or arXiv:
| [
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"### Demo: How to use in ESPnet2",
"## TTS config\n\n<details><summary>expand</summary>\n\n\n\n</details>",
"### Citing ESPnet\n\n\n\nor arXiv:"
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"### 'lakahaga/novel_reading_tts'\n\nThis model was trained by lakahaga using novelspeech recipe in espnet.",
"### Demo: How to use in ESPnet2",
"## TTS conf... |
text-classification | transformers |
# distilbert-base-multilingual-cased-vietnamese-topicifier
## About
Fine-tuning from `distilbert-base-multilingual-cased` with a tiny dataset about Vietnamese topics.
## Usage
Try entering a message to predict what topic is being discussed. For example:
```
# Photography
Đam mê của tôi là nhiếp ảnh
# World War I... | {"language": ["vi"], "license": ["mit"], "tags": ["vietnamese", "topicifier", "multilingual", "tiny"], "pipeline_tag": "text-classification", "widget": [{"text": "\u0110am m\u00ea c\u1ee7a t\u00f4i l\u00e0 nhi\u1ebfp \u1ea3nh"}]} | lamhieu/distilbert-base-multilingual-cased-vietnamese-topicifier | null | [
"transformers",
"pytorch",
"safetensors",
"distilbert",
"text-classification",
"vietnamese",
"topicifier",
"multilingual",
"tiny",
"vi",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"vi"
] | TAGS
#transformers #pytorch #safetensors #distilbert #text-classification #vietnamese #topicifier #multilingual #tiny #vi #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# distilbert-base-multilingual-cased-vietnamese-topicifier
## About
Fine-tuning from 'distilbert-base-multilingual-cased' with a tiny dataset about Vietnamese topics.
## Usage
Try entering a message to predict what topic is being discussed. For example:
## Other
The model was fine-tuning with a tiny dataset, d... | [
"# distilbert-base-multilingual-cased-vietnamese-topicifier",
"## About\n\nFine-tuning from 'distilbert-base-multilingual-cased' with a tiny dataset about Vietnamese topics.",
"## Usage\n\nTry entering a message to predict what topic is being discussed. For example:",
"## Other\n\nThe model was fine-tuning wi... | [
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"# distilbert-base-multilingual-cased-vietnamese-topicifier",
"## About\n\nFine-tuning from 'distilbert-base-multil... |
text2text-generation | transformers |
# Gemini
For in-depth understanding of our model and methods, please see our blog [here](https://www.describe-ai.com/gemini)
## Model description
Gemini is a transformer based on Google's T5 model. The model is pre-trained on approximately 800k code/description pairs and then fine-tuned on 10k higher-level explana... | {"language": "en", "license": "mit", "tags": ["Explain code", "Code Summarization", "Summarization"]} | describeai/gemini | null | [
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"pytorch",
"t5",
"text2text-generation",
"Explain code",
"Code Summarization",
"Summarization",
"en",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #Explain code #Code Summarization #Summarization #en #license-mit #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
# Gemini
For in-depth understanding of our model and methods, please see our blog here
## Model description
Gemini is a transformer based on Google's T5 model. The model is pre-trained on approximately 800k code/description pairs and then fine-tuned on 10k higher-level explanations that were synthetically generate... | [
"# Gemini\n\nFor in-depth understanding of our model and methods, please see our blog here",
"## Model description\n\nGemini is a transformer based on Google's T5 model. The model is pre-trained on approximately 800k code/description pairs and then fine-tuned on 10k higher-level explanations that were synthetical... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #Explain code #Code Summarization #Summarization #en #license-mit #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"# Gemini\n\nFor in-depth understanding of our model and methods, please see our blog here",
... |
text2text-generation | transformers |
# Gemini
For in-depth understanding of our model and methods, please see our blog [here](https://www.describe-ai.com/gemini)
## Model description
Gemini is a transformer based on Google's T5 model. The model is pre-trained on approximately 800k code/description pairs and then fine-tuned on 10k higher-level explana... | {"language": "en", "license": "mit", "tags": ["Explain code", "Code Summarization", "Summarization"]} | describeai/gemini-small | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"Explain code",
"Code Summarization",
"Summarization",
"en",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #Explain code #Code Summarization #Summarization #en #license-mit #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
# Gemini
For in-depth understanding of our model and methods, please see our blog here
## Model description
Gemini is a transformer based on Google's T5 model. The model is pre-trained on approximately 800k code/description pairs and then fine-tuned on 10k higher-level explanations that were synthetically generate... | [
"# Gemini\n\nFor in-depth understanding of our model and methods, please see our blog here",
"## Model description\n\nGemini is a transformer based on Google's T5 model. The model is pre-trained on approximately 800k code/description pairs and then fine-tuned on 10k higher-level explanations that were synthetical... | [
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"# Gemini\n\nFor in-depth understanding of our model and methods, please see our blog here",
... |
text-generation | transformers |
# Hagrid DailoGPT Model | {"tags": ["conversational"]} | lanejm/DialoGPT-small-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
|
# Hagrid DailoGPT Model | [
"# Hagrid DailoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Hagrid DailoGPT Model"
] |
text-classification | transformers |
# bert-imdb-1hidden
## Model description
A `bert-base-uncased` model was restricted to 1 hidden layer and
fine-tuned for sequence classification on the
imdb dataset loaded using the `datasets` library.
## Intended uses & limitations
#### How to use
```python
from transformers import AutoTokenizer, AutoModelForSe... | {"language": ["en"], "datasets": ["imdb"], "metrics": ["accuracy"]} | lannelin/bert-imdb-1hidden | null | [
"transformers",
"pytorch",
"jax",
"safetensors",
"bert",
"text-classification",
"en",
"dataset:imdb",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #jax #safetensors #bert #text-classification #en #dataset-imdb #autotrain_compatible #endpoints_compatible #region-us
|
# bert-imdb-1hidden
## Model description
A 'bert-base-uncased' model was restricted to 1 hidden layer and
fine-tuned for sequence classification on the
imdb dataset loaded using the 'datasets' library.
## Intended uses & limitations
#### How to use
#### Limitations and bias
No special consideration given to l... | [
"# bert-imdb-1hidden",
"## Model description\n\nA 'bert-base-uncased' model was restricted to 1 hidden layer and\nfine-tuned for sequence classification on the \nimdb dataset loaded using the 'datasets' library.",
"## Intended uses & limitations",
"#### How to use",
"#### Limitations and bias\n\nNo special ... | [
"TAGS\n#transformers #pytorch #jax #safetensors #bert #text-classification #en #dataset-imdb #autotrain_compatible #endpoints_compatible #region-us \n",
"# bert-imdb-1hidden",
"## Model description\n\nA 'bert-base-uncased' model was restricted to 1 hidden layer and\nfine-tuned for sequence classification on the... |
feature-extraction | transformers |
# BERTOverflow
## Model description
We pre-trained BERT-base model on 152 million sentences from the StackOverflow's 10 year archive. More details of this model can be found in our ACL 2020 paper: [Code and Named Entity Recognition in StackOverflow](https://www.aclweb.org/anthology/2020.acl-main.443/).
#### Ho... | {} | lanwuwei/BERTOverflow_stackoverflow_github | null | [
"transformers",
"pytorch",
"jax",
"safetensors",
"bert",
"feature-extraction",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #safetensors #bert #feature-extraction #endpoints_compatible #region-us
|
# BERTOverflow
## Model description
We pre-trained BERT-base model on 152 million sentences from the StackOverflow's 10 year archive. More details of this model can be found in our ACL 2020 paper: Code and Named Entity Recognition in StackOverflow.
#### How to use
### BibTeX entry and citation info
| [
"# BERTOverflow",
"## Model description\n\nWe pre-trained BERT-base model on 152 million sentences from the StackOverflow's 10 year archive. More details of this model can be found in our ACL 2020 paper: Code and Named Entity Recognition in StackOverflow.",
"#### How to use",
"### BibTeX entry and citation in... | [
"TAGS\n#transformers #pytorch #jax #safetensors #bert #feature-extraction #endpoints_compatible #region-us \n",
"# BERTOverflow",
"## Model description\n\nWe pre-trained BERT-base model on 152 million sentences from the StackOverflow's 10 year archive. More details of this model can be found in our ACL 2020 pap... |
feature-extraction | transformers |
## GigaBERT-v3
GigaBERT-v3 is a customized bilingual BERT for English and Arabic. It was pre-trained in a large-scale corpus (Gigaword+Oscar+Wikipedia) with ~10B tokens, showing state-of-the-art zero-shot transfer performance from English to Arabic on information extraction (IE) tasks. More details can be found in the... | {"language": ["en", "ar", "multilingual"], "datasets": ["gigaword", "oscar", "wikipedia"]} | lanwuwei/GigaBERT-v3-Arabic-and-English | null | [
"transformers",
"pytorch",
"jax",
"bert",
"feature-extraction",
"en",
"ar",
"multilingual",
"dataset:gigaword",
"dataset:oscar",
"dataset:wikipedia",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en",
"ar",
"multilingual"
] | TAGS
#transformers #pytorch #jax #bert #feature-extraction #en #ar #multilingual #dataset-gigaword #dataset-oscar #dataset-wikipedia #endpoints_compatible #region-us
|
## GigaBERT-v3
GigaBERT-v3 is a customized bilingual BERT for English and Arabic. It was pre-trained in a large-scale corpus (Gigaword+Oscar+Wikipedia) with ~10B tokens, showing state-of-the-art zero-shot transfer performance from English to Arabic on information extraction (IE) tasks. More details can be found in the... | [
"## GigaBERT-v3\nGigaBERT-v3 is a customized bilingual BERT for English and Arabic. It was pre-trained in a large-scale corpus (Gigaword+Oscar+Wikipedia) with ~10B tokens, showing state-of-the-art zero-shot transfer performance from English to Arabic on information extraction (IE) tasks. More details can be found i... | [
"TAGS\n#transformers #pytorch #jax #bert #feature-extraction #en #ar #multilingual #dataset-gigaword #dataset-oscar #dataset-wikipedia #endpoints_compatible #region-us \n",
"## GigaBERT-v3\nGigaBERT-v3 is a customized bilingual BERT for English and Arabic. It was pre-trained in a large-scale corpus (Gigaword+Osca... |
feature-extraction | transformers | ## GigaBERT-v4
GigaBERT-v4 is a continued pre-training of [GigaBERT-v3](https://huggingface.co/lanwuwei/GigaBERT-v3-Arabic-and-English) on code-switched data, showing improved zero-shot transfer performance from English to Arabic on information extraction (IE) tasks. More details can be found in the following paper:
... | {} | lanwuwei/GigaBERT-v4-Arabic-and-English | null | [
"transformers",
"pytorch",
"jax",
"bert",
"feature-extraction",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #bert #feature-extraction #endpoints_compatible #region-us
| ## GigaBERT-v4
GigaBERT-v4 is a continued pre-training of GigaBERT-v3 on code-switched data, showing improved zero-shot transfer performance from English to Arabic on information extraction (IE) tasks. More details can be found in the following paper:
@inproceedings{lan2020gigabert,
author = {Lan, Wuwei and Ch... | [
"## GigaBERT-v4\nGigaBERT-v4 is a continued pre-training of GigaBERT-v3 on code-switched data, showing improved zero-shot transfer performance from English to Arabic on information extraction (IE) tasks. More details can be found in the following paper:\n\n\t@inproceedings{lan2020gigabert,\n\t author = {Lan, W... | [
"TAGS\n#transformers #pytorch #jax #bert #feature-extraction #endpoints_compatible #region-us \n",
"## GigaBERT-v4\nGigaBERT-v4 is a continued pre-training of GigaBERT-v3 on code-switched data, showing improved zero-shot transfer performance from English to Arabic on information extraction (IE) tasks. More detail... |
text-generation | transformers |
# Rick DialoGPT Model | {"tags": ["conversational"]} | lapacc33/DialoGPT-medium-rick | 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
|
# Rick DialoGPT Model | [
"# Rick DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Rick DialoGPT Model"
] |
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"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| URL
URL
URL
URL
URL
URL
URL
URL
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URL
URL
URL
URL
URL
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URL
URL
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URL
URL
URL
URL
URL
URL
URL
URL
URL
URL
URL
URL
URL
URL
URL
URL | [] | [
"TAGS\n#region-us \n"
] |
text-classification | transformers | # Danish BERT fine-tuned for Sentiment Analysis (Polarity)
This model detects polarity ('positive', 'neutral', 'negative') of danish texts.
It is trained and tested on Tweets annotated by [Alexandra Institute](https://github.com/alexandrainst).
Here is an example on how to load the model in PyTorch using the [🤗Trans... | {"language": "da", "license": "cc-by-4.0", "tags": ["danish", "bert", "sentiment", "polarity"], "widget": [{"text": "Sikke en dejlig dag det er i dag"}]} | larskjeldgaard/senda | null | [
"transformers",
"pytorch",
"jax",
"bert",
"text-classification",
"danish",
"sentiment",
"polarity",
"da",
"license:cc-by-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"da"
] | TAGS
#transformers #pytorch #jax #bert #text-classification #danish #sentiment #polarity #da #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #region-us
| # Danish BERT fine-tuned for Sentiment Analysis (Polarity)
This model detects polarity ('positive', 'neutral', 'negative') of danish texts.
It is trained and tested on Tweets annotated by Alexandra Institute.
Here is an example on how to load the model in PyTorch using the Transformers library:
| [
"# Danish BERT fine-tuned for Sentiment Analysis (Polarity)\nThis model detects polarity ('positive', 'neutral', 'negative') of danish texts.\n\nIt is trained and tested on Tweets annotated by Alexandra Institute.\n\nHere is an example on how to load the model in PyTorch using the Transformers library:"
] | [
"TAGS\n#transformers #pytorch #jax #bert #text-classification #danish #sentiment #polarity #da #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# Danish BERT fine-tuned for Sentiment Analysis (Polarity)\nThis model detects polarity ('positive', 'neutral', 'negative') of danish texts... |
fill-mask | transformers |
# LASSL bert-ko-base
## How to use
```python
from transformers import AutoModel, AutoTokenizer
model = AutoModel.from_pretrained("lassl/bert-ko-base")
tokenizer = AutoTokenizer.from_pretrained("lassl/bert-ko-base")
```
## Evaluation
Evaulation results will be released soon.
## Corpora
This model was tra... | {"language": "ko", "license": "apache-2.0", "tags": ["fill-mask", "korean", "lassl"], "mask_token": "[MASK]", "widget": [{"text": "\ub300\ud55c\ubbfc\uad6d\uc758 \uc218\ub3c4\ub294 [MASK] \uc785\ub2c8\ub2e4."}]} | lassl/bert-ko-base | null | [
"transformers",
"pytorch",
"bert",
"pretraining",
"fill-mask",
"korean",
"lassl",
"ko",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ko"
] | TAGS
#transformers #pytorch #bert #pretraining #fill-mask #korean #lassl #ko #license-apache-2.0 #endpoints_compatible #region-us
|
# LASSL bert-ko-base
## How to use
## Evaluation
Evaulation results will be released soon.
## Corpora
This model was trained from 702,437 examples (whose have 3,596,465,664 tokens). 702,437 examples are extracted from below corpora. If you want to get information for training, you should see 'URL'.
| [
"# LASSL bert-ko-base",
"## How to use",
"## Evaluation\r\nEvaulation results will be released soon.",
"## Corpora\r\nThis model was trained from 702,437 examples (whose have 3,596,465,664 tokens). 702,437 examples are extracted from below corpora. If you want to get information for training, you should see '... | [
"TAGS\n#transformers #pytorch #bert #pretraining #fill-mask #korean #lassl #ko #license-apache-2.0 #endpoints_compatible #region-us \n",
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"## How to use",
"## Evaluation\r\nEvaulation results will be released soon.",
"## Corpora\r\nThis model was trained from 702,437 examples (whose h... |
fill-mask | transformers |
# LASSL bert-ko-small
## How to use
```python
from transformers import AutoModel, AutoTokenizer
model = AutoModel.from_pretrained("lassl/bert-ko-small")
tokenizer = AutoTokenizer.from_pretrained("lassl/bert-ko-small")
```
## Evaluation
Evaulation results will be released soon.
## Corpora
This model was trained from ... | {"language": "ko", "license": "apache-2.0", "tags": ["fill-mask", "korean", "lassl"], "mask_token": "[MASK]", "widget": [{"text": "\ub300\ud55c\ubbfc\uad6d\uc758 \uc218\ub3c4\ub294 [MASK] \uc785\ub2c8\ub2e4."}]} | lassl/bert-ko-small | null | [
"transformers",
"pytorch",
"safetensors",
"bert",
"pretraining",
"fill-mask",
"korean",
"lassl",
"ko",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ko"
] | TAGS
#transformers #pytorch #safetensors #bert #pretraining #fill-mask #korean #lassl #ko #license-apache-2.0 #endpoints_compatible #region-us
|
# LASSL bert-ko-small
## How to use
## Evaluation
Evaulation results will be released soon.
## Corpora
This model was trained from 702,437 examples (whose have 3,596,465,664 tokens). 702,437 examples are extracted from below corpora. If you want to get information for training, you should see 'URL'.
| [
"# LASSL bert-ko-small",
"## How to use",
"## Evaluation\nEvaulation results will be released soon.",
"## Corpora\nThis model was trained from 702,437 examples (whose have 3,596,465,664 tokens). 702,437 examples are extracted from below corpora. If you want to get information for training, you should see 'URL... | [
"TAGS\n#transformers #pytorch #safetensors #bert #pretraining #fill-mask #korean #lassl #ko #license-apache-2.0 #endpoints_compatible #region-us \n",
"# LASSL bert-ko-small",
"## How to use",
"## Evaluation\nEvaulation results will be released soon.",
"## Corpora\nThis model was trained from 702,437 example... |
fill-mask | transformers |
# LASSL roberta-ko-small
## How to use
```python
from transformers import AutoModel, AutoTokenizer
model = AutoModel.from_pretrained("lassl/roberta-ko-small")
tokenizer = AutoTokenizer.from_pretrained("lassl/roberta-ko-small")
```
## Evaluation
Pretrained `roberta-ko-small` on korean language was trained by [LASSL](... | {"language": "ko", "license": "apache-2.0", "tags": ["korean", "lassl"], "mask_token": "<mask>", "widget": [{"text": "\ub300\ud55c\ubbfc\uad6d\uc758 \uc218\ub3c4\ub294 <mask> \uc785\ub2c8\ub2e4."}]} | lassl/roberta-ko-small | null | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"korean",
"lassl",
"ko",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ko"
] | TAGS
#transformers #pytorch #roberta #fill-mask #korean #lassl #ko #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| LASSL roberta-ko-small
======================
How to use
----------
Evaluation
----------
Pretrained 'roberta-ko-small' on korean language was trained by LASSL framework. Below performance was evaluated at 2021/12/15.
Corpora
-------
This model was trained from 6,860,062 examples (whose have 3,512,351,744 to... | [] | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #korean #lassl #ko #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \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. -->
# xlm-roberta-base-finetuned-marc-en
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-b... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["amazon_reviews_multi"], "model-index": [{"name": "xlm-roberta-base-finetuned-marc-en", "results": []}]} | laurauzcategui/xlm-roberta-base-finetuned-marc-en | null | [
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"dataset:amazon_reviews_multi",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #xlm-roberta #text-classification #generated_from_trainer #dataset-amazon_reviews_multi #license-mit #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-marc-en
==================================
This model is a fine-tuned version of xlm-roberta-base on the amazon\_reviews\_multi dataset.
It achieves the following results on the evaluation set:
* Loss: 0.8945
* Mae: 0.5
Model description
-----------------
More information needed
Int... | [
"### 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: 2",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #xlm-roberta #text-classification #generated_from_trainer #dataset-amazon_reviews_multi #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_... |
null | null | # Supervised Continous Bag of words model trained with Uruguayan news from Twitter
Model trained with Facebook's fasttext library. | {} | leandrodzp/cbow_uruguayan_news | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| # Supervised Continous Bag of words model trained with Uruguayan news from Twitter
Model trained with Facebook's fasttext library. | [
"# Supervised Continous Bag of words model trained with Uruguayan news from Twitter\nModel trained with Facebook's fasttext library."
] | [
"TAGS\n#region-us \n",
"# Supervised Continous Bag of words model trained with Uruguayan news from Twitter\nModel trained with Facebook's fasttext library."
] |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# celera_relevance
This model is a fine-tuned version of [hfl/chinese-roberta-wwm-ext](https://huggingface.co/hfl/chinese-roberta-wwm-ex... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "base_model": "hfl/chinese-roberta-wwm-ext", "model-index": [{"name": "celera_relevance", "results": []}]} | leetdavid/celera_relevance | null | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"base_model:hfl/chinese-roberta-wwm-ext",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #tf #bert #text-classification #generated_from_keras_callback #base_model-hfl/chinese-roberta-wwm-ext #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| celera\_relevance
=================
This model is a fine-tuned version of hfl/chinese-roberta-wwm-ext on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.3072
* Train Sparse Categorical Accuracy: 0.8813
* Validation Loss: 0.4371
* Validation Sparse Categorical Accuracy: 0... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'learning\\_rate': 5e-05, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}\n* training\\_precision: float32",
"### Training results",
"### Framework... | [
"TAGS\n#transformers #tf #bert #text-classification #generated_from_keras_callback #base_model-hfl/chinese-roberta-wwm-ext #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* optimizer: {... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# importance_model
This model is a fine-tuned version of [hfl/chinese-roberta-wwm-ext](https://huggingface.co/hfl/chinese-roberta-wwm-ex... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "base_model": "hfl/chinese-roberta-wwm-ext", "model-index": [{"name": "importance_model", "results": []}]} | leetdavid/importance_model | null | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"base_model:hfl/chinese-roberta-wwm-ext",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #tf #bert #text-classification #generated_from_keras_callback #base_model-hfl/chinese-roberta-wwm-ext #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| importance\_model
=================
This model is a fine-tuned version of hfl/chinese-roberta-wwm-ext on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.4867
* Train Sparse Categorical Accuracy: 0.8389
* Validation Loss: 0.6060
* Validation Sparse Categorical Accuracy: 0... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'learning\\_rate': 5e-05, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}\n* training\\_precision: float32",
"### Training results",
"### Framework... | [
"TAGS\n#transformers #tf #bert #text-classification #generated_from_keras_callback #base_model-hfl/chinese-roberta-wwm-ext #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* optimizer: {... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# market_positivity
This model is a fine-tuned version of [hfl/chinese-roberta-wwm-ext](https://huggingface.co/hfl/chinese-roberta-wwm-e... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "base_model": "hfl/chinese-roberta-wwm-ext", "model-index": [{"name": "market_positivity", "results": []}]} | leetdavid/market_positivity | null | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"base_model:hfl/chinese-roberta-wwm-ext",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #tf #bert #text-classification #generated_from_keras_callback #base_model-hfl/chinese-roberta-wwm-ext #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| market\_positivity
==================
This model is a fine-tuned version of hfl/chinese-roberta-wwm-ext on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.4959
* Train Sparse Categorical Accuracy: 0.8060
* Validation Loss: 0.4484
* Validation Sparse Categorical Accuracy:... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'learning\\_rate': 5e-05, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}\n* training\\_precision: float32",
"### Training results",
"### Framework... | [
"TAGS\n#transformers #tf #bert #text-classification #generated_from_keras_callback #base_model-hfl/chinese-roberta-wwm-ext #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* o... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# market_positivity_model
This model is a fine-tuned version of [hfl/chinese-roberta-wwm-ext](https://huggingface.co/hfl/chinese-roberta... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "base_model": "hfl/chinese-roberta-wwm-ext", "model-index": [{"name": "market_positivity_model", "results": []}]} | leetdavid/market_positivity_model | null | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"base_model:hfl/chinese-roberta-wwm-ext",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #tf #bert #text-classification #generated_from_keras_callback #base_model-hfl/chinese-roberta-wwm-ext #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| market\_positivity\_model
=========================
This model is a fine-tuned version of hfl/chinese-roberta-wwm-ext on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.5776
* Train Sparse Categorical Accuracy: 0.7278
* Validation Loss: 0.6460
* Validation Sparse Categor... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'learning\\_rate': 5e-05, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}\n* training\\_precision: float32",
"### Training results",
"### Framework... | [
"TAGS\n#transformers #tf #bert #text-classification #generated_from_keras_callback #base_model-hfl/chinese-roberta-wwm-ext #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* optimizer: {... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# relevance-model
This model is a fine-tuned version of [hfl/chinese-roberta-wwm-ext](https://huggingface.co/hfl/chinese-roberta-wwm-ext... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "base_model": "hfl/chinese-roberta-wwm-ext", "model-index": [{"name": "relevance-model", "results": []}]} | leetdavid/relevance-model | null | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"base_model:hfl/chinese-roberta-wwm-ext",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #tf #bert #text-classification #generated_from_keras_callback #base_model-hfl/chinese-roberta-wwm-ext #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| relevance-model
===============
This model is a fine-tuned version of hfl/chinese-roberta-wwm-ext on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.3134
* Train Binary Accuracy: 0.8773
* Validation Loss: 0.3633
* Validation Binary Accuracy: 0.8541
* Epoch: 2
Model des... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'learning\\_rate': 5e-05, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}\n* training\\_precision: float32",
"### Training results",
"### Framework... | [
"TAGS\n#transformers #tf #bert #text-classification #generated_from_keras_callback #base_model-hfl/chinese-roberta-wwm-ext #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* optimizer: {... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-cola
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/di... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["matthews_correlation"], "model-index": [{"name": "distilbert-base-uncased-finetuned-cola", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "ar... | leeyujin/distilbert-base-uncased-finetuned-cola | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-cola
======================================
This model is a fine-tuned version of distilbert-base-uncased on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5608
* Matthews Correlation: 0.5062
Model description
-----------------
More informa... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-0... |
image-classification | transformers |
# ResNet-50
Pretrained model on [ImageNet](http://www.image-net.org/). The ResNet architecture was introduced in
[this paper](https://arxiv.org/abs/1512.03385).
## Intended uses
You can use the raw model to classify images along the 1,000 ImageNet labels, but you can also change its head
to fine-tune it on... | {"license": "afl-3.0", "tags": ["image-classification", "resnet"], "datasets": ["imagenet"], "widget": [{"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg", "example_title": "Tiger"}, {"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg", "example_title... | leftthomas/resnet50 | null | [
"transformers",
"pytorch",
"resnet",
"image-classification",
"custom_code",
"dataset:imagenet",
"arxiv:1512.03385",
"license:afl-3.0",
"autotrain_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1512.03385"
] | [] | TAGS
#transformers #pytorch #resnet #image-classification #custom_code #dataset-imagenet #arxiv-1512.03385 #license-afl-3.0 #autotrain_compatible #region-us
|
# ResNet-50
Pretrained model on ImageNet. The ResNet architecture was introduced in
this paper.
## Intended uses
You can use the raw model to classify images along the 1,000 ImageNet labels, but you can also change its head
to fine-tune it on a downstream task (another classification task with different la... | [
"# ResNet-50\r\n\r\nPretrained model on ImageNet. The ResNet architecture was introduced in\r\nthis paper.",
"## Intended uses\r\n\r\nYou can use the raw model to classify images along the 1,000 ImageNet labels, but you can also change its head\r\nto fine-tune it on a downstream task (another classification task ... | [
"TAGS\n#transformers #pytorch #resnet #image-classification #custom_code #dataset-imagenet #arxiv-1512.03385 #license-afl-3.0 #autotrain_compatible #region-us \n",
"# ResNet-50\r\n\r\nPretrained model on ImageNet. The ResNet architecture was introduced in\r\nthis paper.",
"## Intended uses\r\n\r\nYou can use th... |
text2text-generation | transformers | A mt5-base model that the vocab and word embedding are truncated, only Chinese and English characters are retained.
https://github.com/lemon234071/TransformerBaselines | {} | lemon234071/t5-base-Chinese | null | [
"transformers",
"pytorch",
"jax",
"mt5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #mt5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| A mt5-base model that the vocab and word embedding are truncated, only Chinese and English characters are retained.
URL | [] | [
"TAGS\n#transformers #pytorch #jax #mt5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
automatic-speech-recognition | superb |
# Fine-tuned s3prl model for ASR | {"library_name": "superb", "tags": ["automatic-speech-recognition", "osanseviero/hubert_base"], "datasets": ["superb"], "benchmark": "superb", "task": "asr", "widget": [{"example_title": "Librispeech sample 1", "src": "https://cdn-media.huggingface.co/speech_samples/sample1.flac"}]} | leo19941227/superb-s3prl-osanseviero__hubert_base-asr-c61a5cff | null | [
"superb",
"tensorboard",
"automatic-speech-recognition",
"osanseviero/hubert_base",
"dataset:superb",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#superb #tensorboard #automatic-speech-recognition #osanseviero/hubert_base #dataset-superb #region-us
|
# Fine-tuned s3prl model for ASR | [
"# Fine-tuned s3prl model for ASR"
] | [
"TAGS\n#superb #tensorboard #automatic-speech-recognition #osanseviero/hubert_base #dataset-superb #region-us \n",
"# Fine-tuned s3prl model for ASR"
] |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-chinese-finetuned-ner
This model is a fine-tuned version of [bert-base-chinese](https://huggingface.co/bert-base-chine... | {"tags": ["generated_from_trainer"], "datasets": ["fdner"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "bert-base-chinese-finetuned-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "fdner", "type": "fdner", "args": "fdne... | leonadase/bert-base-chinese-finetuned-ner | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:fdner",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-fdner #model-index #autotrain_compatible #endpoints_compatible #region-us
| bert-base-chinese-finetuned-ner
===============================
This model is a fine-tuned version of bert-base-chinese on the fdner dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1016
* Precision: 0.9146
* Recall: 0.9414
* F1: 0.9278
* Accuracy: 0.9751
Model description
-------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 10\n* eval\\_batch\\_size: 10\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 30",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-fdner #model-index #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\... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-ner
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/dis... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["conll2003"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "distilbert-base-uncased-finetuned-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "con... | leonadase/distilbert-base-uncased-finetuned-ner | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-ner
=====================================
This model is a fine-tuned version of distilbert-base-uncased on the conll2003 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0611
* Precision: 0.9210
* Recall: 0.9357
* F1: 0.9283
* Accuracy: 0.9832
Model des... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* le... |
automatic-speech-recognition | transformers |
# Wav2Vec2-Large-Robust - Finetuned on Librispeech (960 hours)
## Note : Model has not been initialized. If you want to use it without further finetuning, do a forward pass first to recalculate the normalized weights of the positional convolutional layer :
```ipython
with torch.no_grad():
model(torch.randn((1,3... | {"language": "en", "license": "apache-2.0", "tags": ["speech", "automatic-speech-recognition", "CTC", "Attention", "wav2vec2"], "datasets": ["libri_light", "common_voice", "switchboard", "fisher"]} | leonardvorbeck/wav2vec2-large-robust-LS960 | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"speech",
"CTC",
"Attention",
"en",
"dataset:libri_light",
"dataset:common_voice",
"dataset:switchboard",
"dataset:fisher",
"arxiv:2104.01027",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2104.01027"
] | [
"en"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #speech #CTC #Attention #en #dataset-libri_light #dataset-common_voice #dataset-switchboard #dataset-fisher #arxiv-2104.01027 #license-apache-2.0 #endpoints_compatible #region-us
|
# Wav2Vec2-Large-Robust - Finetuned on Librispeech (960 hours)
## Note : Model has not been initialized. If you want to use it without further finetuning, do a forward pass first to recalculate the normalized weights of the positional convolutional layer :
Facebook's Wav2Vec2
The base model pretrained on 16kHz sa... | [
"# Wav2Vec2-Large-Robust - Finetuned on Librispeech (960 hours)",
"## Note : Model has not been initialized. If you want to use it without further finetuning, do a forward pass first to recalculate the normalized weights of the positional convolutional layer :\n\n\n\nFacebook's Wav2Vec2\n\nThe base model pretrain... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #speech #CTC #Attention #en #dataset-libri_light #dataset-common_voice #dataset-switchboard #dataset-fisher #arxiv-2104.01027 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# Wav2Vec2-Large-Robust - Finetuned on Librispeech (960 hou... |
automatic-speech-recognition | transformers |
# Wav2Vec2-Large-Robust - Finetuned on Switchboard (300 hours)
## Note : Model has not been initialized. If you want to use it without further finetuning, do a forward pass first to recalculate the normalized weights of the positional convolutional layer :
```ipython
with torch.no_grad():
model(torch.randn((1,3... | {"language": "en", "license": "apache-2.0", "tags": ["speech", "automatic-speech-recognition", "CTC", "Attention", "wav2vec2"], "datasets": ["libri_light", "common_voice", "switchboard", "fisher"]} | leonardvorbeck/wav2vec2-large-robust-SB300 | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"speech",
"CTC",
"Attention",
"en",
"dataset:libri_light",
"dataset:common_voice",
"dataset:switchboard",
"dataset:fisher",
"arxiv:2104.01027",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2104.01027"
] | [
"en"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #speech #CTC #Attention #en #dataset-libri_light #dataset-common_voice #dataset-switchboard #dataset-fisher #arxiv-2104.01027 #license-apache-2.0 #endpoints_compatible #region-us
|
# Wav2Vec2-Large-Robust - Finetuned on Switchboard (300 hours)
## Note : Model has not been initialized. If you want to use it without further finetuning, do a forward pass first to recalculate the normalized weights of the positional convolutional layer :
Facebook's Wav2Vec2
The base model pretrained on 16kHz sa... | [
"# Wav2Vec2-Large-Robust - Finetuned on Switchboard (300 hours)",
"## Note : Model has not been initialized. If you want to use it without further finetuning, do a forward pass first to recalculate the normalized weights of the positional convolutional layer :\n\n\n\nFacebook's Wav2Vec2\n\nThe base model pretrain... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #speech #CTC #Attention #en #dataset-libri_light #dataset-common_voice #dataset-switchboard #dataset-fisher #arxiv-2104.01027 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# Wav2Vec2-Large-Robust - Finetuned on Switchboard (300 hou... |
text-classification | transformers |
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2287
- Accuracy: 0.918
- F1: 0.9182
## Model description
More informa... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["emotion"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "distilbert-base-uncased-finetuned-emotion", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "emotion", "type": "emotion... | lewiswatson/distilbert-base-uncased-finetuned-emotion | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-emotion
=========================================
This model is a fine-tuned version of distilbert-base-uncased on the emotion dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2287
* Accuracy: 0.918
* F1: 0.9182
Model description
-----------------
Mor... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learn... |
fill-mask | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# MiniLM-L12-H384-uncased-finetuned-imdb
This model is a fine-tuned version of [microsoft/MiniLM-L12-H384-uncased](https://hugging... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["imdb"]} | lewtun/MiniLM-L12-H384-uncased-finetuned-imdb | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"generated_from_trainer",
"dataset:imdb",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #dataset-imdb #license-mit #autotrain_compatible #endpoints_compatible #region-us
| MiniLM-L12-H384-uncased-finetuned-imdb
======================================
This model is a fine-tuned version of microsoft/MiniLM-L12-H384-uncased on the imdb dataset.
It achieves the following results on the evaluation set:
* Loss: 3.9328
Model description
-----------------
More information needed
Intende... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0\n* mixed\\_pr... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #dataset-imdb #license-mit #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\\_si... |
fill-mask | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-uncased-finetuned-imdb
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": ["imdb"]} | lewtun/bert-base-uncased-finetuned-imdb | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #dataset-imdb #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| bert-base-uncased-finetuned-imdb
================================
This model is a fine-tuned version of bert-base-uncased on the imdb dataset.
It achieves the following results on the evaluation set:
* Loss: 2.0284
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: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0\n* mixed\\_pr... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #dataset-imdb #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\\_bat... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-finetuned-ner
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["conll2003"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "bert-finetuned-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "conll2003", "type": "c... | lewtun/bert-finetuned-ner | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| bert-finetuned-ner
==================
This model is a fine-tuned version of bert-base-cased on the conll2003 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0603
* Precision: 0.9408
* Recall: 0.9520
* F1: 0.9464
* Accuracy: 0.9865
Model description
-----------------
More information ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning... |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-finetuned-squad
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the squa... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad", "lewtun/autoevaluate__squad"], "model-index": [{"name": "bert-finetuned-squad", "results": []}]} | lewtun/bert-finetuned-squad | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"dataset:lewtun/autoevaluate__squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #dataset-squad #dataset-lewtun/autoevaluate__squad #license-apache-2.0 #endpoints_compatible #region-us
|
# bert-finetuned-squad
This model is a fine-tuned version of bert-base-cased on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
T... | [
"# bert-finetuned-squad\n\nThis model is a fine-tuned version of bert-base-cased on the squad dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"#... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #dataset-squad #dataset-lewtun/autoevaluate__squad #license-apache-2.0 #endpoints_compatible #region-us \n",
"# bert-finetuned-squad\n\nThis model is a fine-tuned version of bert-base-cased on the squad dataset.",
"## M... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion-test-01
This model is a fine-tuned version of [distilbert-base-uncased](https://huggin... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["emotion"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "distilbert-base-uncased-finetuned-emotion-test-01", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "emotion", "type": ... | lewtun/distilbert-base-uncased-finetuned-emotion-test-01 | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-emotion-test-01
=================================================
This model is a fine-tuned version of distilbert-base-uncased on the emotion dataset.
It achieves the following results on the evaluation set:
* Loss: 1.7510
* Accuracy: 0.39
* F1: 0.2188
Model description
--------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learn... |
fill-mask | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-imdb
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/di... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imdb"], "model-index": [{"name": "distilbert-base-uncased-finetuned-imdb", "results": []}]} | lewtun/distilbert-base-uncased-finetuned-imdb | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"fill-mask",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #fill-mask #generated_from_trainer #dataset-imdb #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-imdb
======================================
This model is a fine-tuned version of distilbert-base-uncased on the imdb dataset.
It achieves the following results on the evaluation set:
* Loss: 2.4286
Model description
-----------------
More information needed
Intended uses & l... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0\n* mixed\\_pr... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #fill-mask #generated_from_trainer #dataset-imdb #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... |
question-answering | null |
# DistilBERT with a second step of distillation
## Model description
This model replicates the "DistilBERT (D)" model from Table 2 of the [DistilBERT paper](https://arxiv.org/pdf/1910.01108.pdf). In this approach, a DistilBERT student is fine-tuned on SQuAD v1.1, but with a BERT model (also fine-tuned on SQuAD v1.1)... | {"language": ["en"], "license": "apache-2.0", "tags": ["question-answering"], "datasets": ["squad"], "metrics": ["squad"], "thumbnail": "https://github.com/karanchahal/distiller/blob/master/distiller.jpg"} | lewtun/distilbert-base-uncased-finetuned-squad-d5716d28 | null | [
"pytorch",
"question-answering",
"en",
"dataset:squad",
"arxiv:1910.01108",
"license:apache-2.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1910.01108"
] | [
"en"
] | TAGS
#pytorch #question-answering #en #dataset-squad #arxiv-1910.01108 #license-apache-2.0 #region-us
| DistilBERT with a second step of distillation
=============================================
Model description
-----------------
This model replicates the "DistilBERT (D)" model from Table 2 of the DistilBERT paper. In this approach, a DistilBERT student is fine-tuned on SQuAD v1.1, but with a BERT model (also fine-... | [
"### BibTeX entry and citation info"
] | [
"TAGS\n#pytorch #question-answering #en #dataset-squad #arxiv-1910.01108 #license-apache-2.0 #region-us \n",
"### BibTeX entry and citation info"
] |
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. -->
# dummy-translation
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-ro](https://huggingface.co/Helsinki-NLP/opus-mt... | {"tags": ["generated_from_trainer"], "model_index": [{"name": "dummy-translation", "results": [{"task": {"name": "Sequence-to-sequence Language Modeling", "type": "text2text-generation"}}]}]} | lewtun/dummy-translation | null | [
"transformers",
"pytorch",
"marian",
"text2text-generation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #marian #text2text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
|
# dummy-translation
This model is a fine-tuned version of Helsinki-NLP/opus-mt-en-ro 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... | [
"# dummy-translation\n\nThis model is a fine-tuned version of Helsinki-NLP/opus-mt-en-ro 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 #marian #text2text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"# dummy-translation\n\nThis model is a fine-tuned version of Helsinki-NLP/opus-mt-en-ro on an unkown dataset.",
"## Model description\n\nMore information needed",
"#... |
null | transformers |
# LitMetNet
## Model description
[More information needed]
## Intended uses & limitations
[More information needed]
## How to use
[More information needed]
## Limitations and bias
[More information needed]
## Training data
[More information needed]
## Training procedure
[More information needed]
## Evalua... | {"license": "mit", "tags": ["satflow", "forecasting", "timeseries", "remote-sensing"]} | lewtun/litmetnet-test-01 | null | [
"transformers",
"pytorch",
"satflow",
"forecasting",
"timeseries",
"remote-sensing",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #satflow #forecasting #timeseries #remote-sensing #license-mit #endpoints_compatible #region-us
|
# LitMetNet
## Model description
[More information needed]
## Intended uses & limitations
[More information needed]
## How to use
[More information needed]
## Limitations and bias
[More information needed]
## Training data
[More information needed]
## Training procedure
[More information needed]
## Evalua... | [
"# LitMetNet",
"## Model description\n\n[More information needed]",
"## Intended uses & limitations\n\n[More information needed]",
"## How to use\n\n[More information needed]",
"## Limitations and bias\n\n[More information needed]",
"## Training data\n\n[More information needed]",
"## Training procedure... | [
"TAGS\n#transformers #pytorch #satflow #forecasting #timeseries #remote-sensing #license-mit #endpoints_compatible #region-us \n",
"# LitMetNet",
"## Model description\n\n[More information needed]",
"## Intended uses & limitations\n\n[More information needed]",
"## How to use\n\n[More information needed]",
... |
translation | 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. -->
# marian-finetuned-kde4-en-to-fr
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-fr](https://huggingface.co/Helsink... | {"license": "apache-2.0", "tags": ["translation", "generated_from_trainer"], "datasets": ["kde4"], "metrics": ["bleu"], "model-index": [{"name": "marian-finetuned-kde4-en-to-fr", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "kde4", "type": ... | lewtun/marian-finetuned-kde4-en-to-fr | null | [
"transformers",
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"translation",
"generated_from_trainer",
"dataset:kde4",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #marian #text2text-generation #translation #generated_from_trainer #dataset-kde4 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# marian-finetuned-kde4-en-to-fr
This model is a fine-tuned version of Helsinki-NLP/opus-mt-en-fr on the kde4 dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6772
- Bleu: 38.9888
## Model description
More information needed
## Intended uses & limitations
More information needed
## T... | [
"# marian-finetuned-kde4-en-to-fr\n\nThis model is a fine-tuned version of Helsinki-NLP/opus-mt-en-fr on the kde4 dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 1.6772\n- Bleu: 38.9888",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore infor... | [
"TAGS\n#transformers #pytorch #tensorboard #marian #text2text-generation #translation #generated_from_trainer #dataset-kde4 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# marian-finetuned-kde4-en-to-fr\n\nThis model is a fine-tuned version of Helsinki-NLP/opus-mt-e... |
null | transformers |
# Model Card for MetNet
| {"tags": ["autonlp", "evaluation", "benchmark"]} | lewtun/metnet-test-3 | null | [
"transformers",
"pytorch",
"autonlp",
"evaluation",
"benchmark",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #autonlp #evaluation #benchmark #endpoints_compatible #region-us
|
# Model Card for MetNet
| [
"# Model Card for MetNet"
] | [
"TAGS\n#transformers #pytorch #autonlp #evaluation #benchmark #endpoints_compatible #region-us \n",
"# Model Card for MetNet"
] |
null | transformers |
# Model Card for MetNet
## Model description
[More information needed]
## Intended uses & limitations
[More information needed]
## How to use
[More information needed]
## Limitations and bias
[More information needed]
## Training data
[More information needed]
## Training procedure
[More information needed... | {"license": "mit", "tags": ["satflow"]} | lewtun/metnet-test-4 | null | [
"transformers",
"pytorch",
"satflow",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #satflow #license-mit #endpoints_compatible #region-us
|
# Model Card for MetNet
## Model description
[More information needed]
## Intended uses & limitations
[More information needed]
## How to use
[More information needed]
## Limitations and bias
[More information needed]
## Training data
[More information needed]
## Training procedure
[More information needed... | [
"# Model Card for MetNet",
"## Model description\n\n[More information needed]",
"## Intended uses & limitations\n\n[More information needed]",
"## How to use\n\n[More information needed]",
"## Limitations and bias\n\n[More information needed]",
"## Training data\n\n[More information needed]",
"## Traini... | [
"TAGS\n#transformers #pytorch #satflow #license-mit #endpoints_compatible #region-us \n",
"# Model Card for MetNet",
"## Model description\n\n[More information needed]",
"## Intended uses & limitations\n\n[More information needed]",
"## How to use\n\n[More information needed]",
"## Limitations and bias\n\... |
null | transformers |
# MetNet
## Model description
[More information needed]
## Intended uses & limitations
[More information needed]
## How to use
[More information needed]
## Limitations and bias
[More information needed]
## Training data
[More information needed]
## Training procedure
[More information needed]
## Evaluatio... | {"license": "mit", "tags": ["satflow"]} | lewtun/metnet-test-5 | null | [
"transformers",
"pytorch",
"satflow",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #satflow #license-mit #endpoints_compatible #region-us
|
# MetNet
## Model description
[More information needed]
## Intended uses & limitations
[More information needed]
## How to use
[More information needed]
## Limitations and bias
[More information needed]
## Training data
[More information needed]
## Training procedure
[More information needed]
## Evaluatio... | [
"# MetNet",
"## Model description\n\n[More information needed]",
"## Intended uses & limitations\n\n[More information needed]",
"## How to use\n\n[More information needed]",
"## Limitations and bias\n\n[More information needed]",
"## Training data\n\n[More information needed]",
"## Training procedure\n\... | [
"TAGS\n#transformers #pytorch #satflow #license-mit #endpoints_compatible #region-us \n",
"# MetNet",
"## Model description\n\n[More information needed]",
"## Intended uses & limitations\n\n[More information needed]",
"## How to use\n\n[More information needed]",
"## Limitations and bias\n\n[More informat... |
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. -->
# minilm-finetuned-emotion
This model is a fine-tuned version of [microsoft/MiniLM-L12-H384-uncased](https://huggingface.co/micros... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["emotion"], "metrics": ["f1"], "model-index": [{"name": "minilm-finetuned-emotion", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "emotion", "type": "emotion", "args": "default"}, "metrics": [{... | lewtun/minilm-finetuned-emotion | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:mit",
"model-index",
"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-emotion #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| minilm-finetuned-emotion
========================
This model is a fine-tuned version of microsoft/MiniLM-L12-H384-uncased on the emotion dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3891
* F1: 0.9118
Model description
-----------------
More information needed
Intended uses & lim... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5\n* mixed\\_prec... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-emotion #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2... |
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. -->
# mt5-small-finetuned-mlsum
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on t... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["mlsum"], "metrics": ["rouge"], "model-index": [{"name": "mt5-small-finetuned-mlsum", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "mlsum", "type": "mlsum", "args": ... | lewtun/mt5-small-finetuned-mlsum | null | [
"transformers",
"pytorch",
"tensorboard",
"mt5",
"text2text-generation",
"generated_from_trainer",
"dataset:mlsum",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #mt5 #text2text-generation #generated_from_trainer #dataset-mlsum #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| mt5-small-finetuned-mlsum
=========================
This model is a fine-tuned version of google/mt5-small on the mlsum dataset.
It achieves the following results on the evaluation set:
* Loss: nan
* Rouge1: 1.1475
* Rouge2: 0.1284
* Rougel: 1.0634
* Rougelsum: 1.0778
* Gen Len: 3.7939
Model description
---------... | [
"### 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: 1\n* mixed\\_precis... | [
"TAGS\n#transformers #pytorch #tensorboard #mt5 #text2text-generation #generated_from_trainer #dataset-mlsum #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during tra... |
image-classification | transformers |
# oz-fauna
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for **anything** by running [the demo](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/huggingpics).
## Example ... | {"tags": ["image-classification", "pytorch", "huggingpics"], "metrics": ["accuracy"]} | lewtun/oz-fauna | null | [
"transformers",
"pytorch",
"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
|
# oz-fauna
Autogenerated by HuggingPics️
Create your own image classifier for anything by running the demo.
Report any issues with the demo at the github repo.
## Example Images
#### dingo
!dingo
#### koala
!koala
#### kookaburra
!kookaburra
#### possum
!possum
#### tasmanian devil
!tasmanian devil | [
"# oz-fauna\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo.\n\nReport any issues with the demo at the github repo.",
"## Example Images",
"#### dingo\n\n!dingo",
"#### koala\n\n!koala",
"#### kookaburra\n\n!kookaburra",
"#### possum\n\n!possum",
"... | [
"TAGS\n#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# oz-fauna\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo.\n\nReport any issues with the demo at the... |
null | transformers |
# Perceiver
## Model description
[More information needed]
## Intended uses & limitations
[More information needed]
## How to use
[More information needed]
## Limitations and bias
[More information needed]
## Training data
[More information needed]
## Training procedure
[More information needed]
## Evalua... | {"license": "mit", "tags": ["satflow", "forecasting", "timeseries", "remote-sensing"]} | lewtun/perceriver-test-01 | null | [
"transformers",
"pytorch",
"satflow",
"forecasting",
"timeseries",
"remote-sensing",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #satflow #forecasting #timeseries #remote-sensing #license-mit #endpoints_compatible #region-us
|
# Perceiver
## Model description
[More information needed]
## Intended uses & limitations
[More information needed]
## How to use
[More information needed]
## Limitations and bias
[More information needed]
## Training data
[More information needed]
## Training procedure
[More information needed]
## Evalua... | [
"# Perceiver",
"## Model description\n\n[More information needed]",
"## Intended uses & limitations\n\n[More information needed]",
"## How to use\n\n[More information needed]",
"## Limitations and bias\n\n[More information needed]",
"## Training data\n\n[More information needed]",
"## Training procedure... | [
"TAGS\n#transformers #pytorch #satflow #forecasting #timeseries #remote-sensing #license-mit #endpoints_compatible #region-us \n",
"# Perceiver",
"## Model description\n\n[More information needed]",
"## Intended uses & limitations\n\n[More information needed]",
"## How to use\n\n[More information needed]",
... |
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. -->
# results
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the e... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["emotion"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "results", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "emotion", "type": "emotion", "args": "default"}, "metrics": ... | lewtun/results | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| results
=======
This model is a fine-tuned version of distilbert-base-uncased on the emotion dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2147
* Accuracy: 0.925
* F1: 0.9251
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: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learn... |
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. -->
# roberta-base-bne-finetuned-amazon_reviews_multi-finetuned-amazon_reviews_multi
This model was trained from scratch on the amazon... | {"tags": ["generated_from_trainer"], "datasets": ["amazon_reviews_multi"], "metrics": ["accuracy"], "model_index": [{"name": "roberta-base-bne-finetuned-amazon_reviews_multi-finetuned-amazon_reviews_multi", "results": [{"task": {"name": "Text Classification", "type": "text-classification"}, "dataset": {"name": "amazon_... | lewtun/roberta-base-bne-finetuned-amazon_reviews_multi-finetuned-amazon_reviews_multi | null | [
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"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"dataset:amazon_reviews_multi",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #dataset-amazon_reviews_multi #autotrain_compatible #endpoints_compatible #region-us
| roberta-base-bne-finetuned-amazon\_reviews\_multi-finetuned-amazon\_reviews\_multi
==================================================================================
This model was trained from scratch on the amazon\_reviews\_multi dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3595
* A... | [
"### 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: 2",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #dataset-amazon_reviews_multi #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* tr... |
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. -->
# roberta-base-bne-finetuned-amazon_reviews_multi
This model is a fine-tuned version of [BSC-TeMU/roberta-base-bne](https://huggin... | {"license": "cc-by-4.0", "tags": ["generated_from_trainer"], "datasets": ["amazon_reviews_multi"], "metrics": ["accuracy"], "model_index": [{"name": "roberta-base-bne-finetuned-amazon_reviews_multi", "results": [{"task": {"name": "Text Classification", "type": "text-classification"}, "dataset": {"name": "amazon_reviews... | lewtun/roberta-base-bne-finetuned-amazon_reviews_multi | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"dataset:amazon_reviews_multi",
"license:cc-by-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #dataset-amazon_reviews_multi #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #region-us
| roberta-base-bne-finetuned-amazon\_reviews\_multi
=================================================
This model is a fine-tuned version of BSC-TeMU/roberta-base-bne on the amazon\_reviews\_multi dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2306
* Accuracy: 0.9307
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: 2",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #dataset-amazon_reviews_multi #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\... |
null | superb |
# Test for superb using hubert downstream SD
## Usage
```python
import io
import soundfile as sf
from urllib.request import urlopen
from model import PreTrainedModel
model = PreTrainedModel()
url = "https://huggingface.co/datasets/lewtun/s3prl-sd-dummy/raw/main/audio.wav"
data, samplerate = sf.read(io.BytesIO(urlop... | {"library_name": "superb", "tags": ["superb", "speaker-diarization", "benchmark:superb"]} | lewtun/s3prl-sd-hubert-dummy | null | [
"superb",
"speaker-diarization",
"benchmark:superb",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#superb #speaker-diarization #benchmark-superb #region-us
|
# Test for superb using hubert downstream SD
## Usage
| [
"# Test for superb using hubert downstream SD",
"## Usage"
] | [
"TAGS\n#superb #speaker-diarization #benchmark-superb #region-us \n",
"# Test for superb using hubert downstream SD",
"## Usage"
] |
null | null | # This is a test! | {} | lewtun/superb-dummy-asr-push-to-hub | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| # This is a test! | [
"# This is a test!"
] | [
"TAGS\n#region-us \n",
"# This is a test!"
] |
null | null | Here is some latex:
$ \LaTeX $
$$ \frac{\mathrm{A\,fox}}{23} $$ | {} | lewtun/superb-dummy-asr | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| Here is some latex:
$ \LaTeX $
$$ \frac{\mathrm{A\,fox}}{23} $$ | [] | [
"TAGS\n#region-us \n"
] |
automatic-speech-recognition | superb |
# Test for s3prl push to hub after fine-tuning | {"library_name": "superb", "tags": ["superb", "automatic-speech-recognition"], "widget": [{"example_title": "Librispeech sample 1", "src": "https://cdn-media.huggingface.co/speech_samples/sample1.flac"}]} | lewtun/superb-s3prl-hubert-asr | null | [
"superb",
"automatic-speech-recognition",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#superb #automatic-speech-recognition #region-us
|
# Test for s3prl push to hub after fine-tuning | [
"# Test for s3prl push to hub after fine-tuning"
] | [
"TAGS\n#superb #automatic-speech-recognition #region-us \n",
"# Test for s3prl push to hub after fine-tuning"
] |
automatic-speech-recognition | superb |
# Fine-tuned s3prl model for ASR | {"library_name": "superb", "tags": ["automatic-speech-recognition", "osanseviero/hubert_base"], "datasets": ["superb"], "benchmark": "superb", "task": "asr", "widget": [{"example_title": "Librispeech sample 1", "src": "https://cdn-media.huggingface.co/speech_samples/sample1.flac"}]} | lewtun/superb-s3prl-osanseviero__hubert_base-asr-50f7ee76 | null | [
"superb",
"tensorboard",
"automatic-speech-recognition",
"osanseviero/hubert_base",
"dataset:superb",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#superb #tensorboard #automatic-speech-recognition #osanseviero/hubert_base #dataset-superb #region-us
|
# Fine-tuned s3prl model for ASR | [
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] |
automatic-speech-recognition | superb |
# Test for s3prl push to hub after fine-tuning | {"library_name": "superb", "tags": ["superb", "automatic-speech-recognition"], "benchmark": "superb", "task": "asr", "widget": [{"example_title": "Librispeech sample 1", "src": "https://cdn-media.huggingface.co/speech_samples/sample1.flac"}]} | lewtun/superb-s3prl-osanseviero__hubert_base-asr-67be9268 | null | [
"superb",
"tensorboard",
"automatic-speech-recognition",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#superb #tensorboard #automatic-speech-recognition #region-us
|
# Test for s3prl push to hub after fine-tuning | [
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"TAGS\n#superb #tensorboard #automatic-speech-recognition #region-us \n",
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automatic-speech-recognition | superb |
# Test for s3prl push to hub after fine-tuning | {"library_name": "superb", "tags": ["superb", "automatic-speech-recognition"], "benchmark": "superb", "task": "asr", "widget": [{"example_title": "Librispeech sample 1", "src": "https://cdn-media.huggingface.co/speech_samples/sample1.flac"}]} | lewtun/superb-s3prl-osanseviero__hubert_base-asr-700ddb7b | null | [
"superb",
"tensorboard",
"automatic-speech-recognition",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#superb #tensorboard #automatic-speech-recognition #region-us
|
# Test for s3prl push to hub after fine-tuning | [
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] | [
"TAGS\n#superb #tensorboard #automatic-speech-recognition #region-us \n",
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] |
automatic-speech-recognition | superb |
# Test for s3prl push to hub after fine-tuning | {"library_name": "superb", "tags": ["superb", "automatic-speech-recognition"], "benchmark": "superb", "task": "asr", "widget": [{"example_title": "Librispeech sample 1", "src": "https://cdn-media.huggingface.co/speech_samples/sample1.flac"}]} | lewtun/superb-s3prl-osanseviero__hubert_base-asr-a03c2ae5 | null | [
"superb",
"tensorboard",
"automatic-speech-recognition",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#superb #tensorboard #automatic-speech-recognition #region-us
|
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] |
automatic-speech-recognition | superb |
# Fine-tuned s3prl model for ASR | {"library_name": "superb", "tags": ["automatic-speech-recognition", "osanseviero/hubert_base"], "datasets": ["superb"], "benchmark": "superb", "task": "asr", "widget": [{"example_title": "Librispeech sample 1", "src": "https://cdn-media.huggingface.co/speech_samples/sample1.flac"}]} | lewtun/superb-s3prl-osanseviero__hubert_base-asr-ca6de67e | null | [
"superb",
"tensorboard",
"automatic-speech-recognition",
"osanseviero/hubert_base",
"dataset:superb",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#superb #tensorboard #automatic-speech-recognition #osanseviero/hubert_base #dataset-superb #region-us
|
# Fine-tuned s3prl model for ASR | [
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automatic-speech-recognition | superb |
# Fine-tuned s3prl model for ASR | {"library_name": "superb", "tags": ["automatic-speech-recognition", "osanseviero/hubert_base"], "datasets": ["superb"], "benchmark": "superb", "task": "asr", "widget": [{"example_title": "Librispeech sample 1", "src": "https://cdn-media.huggingface.co/speech_samples/sample1.flac"}]} | lewtun/superb-s3prl-osanseviero__hubert_base-asr-cbcd177a | null | [
"superb",
"tensorboard",
"automatic-speech-recognition",
"osanseviero/hubert_base",
"dataset:superb",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#superb #tensorboard #automatic-speech-recognition #osanseviero/hubert_base #dataset-superb #region-us
|
# Fine-tuned s3prl model for ASR | [
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] |
automatic-speech-recognition | superb |
# Test for s3prl push to hub after fine-tuning | {"library_name": "superb", "tags": ["superb", "automatic-speech-recognition"], "benchmark": "superb", "task": "asr", "widget": [{"example_title": "Librispeech sample 1", "src": "https://cdn-media.huggingface.co/speech_samples/sample1.flac"}]} | lewtun/superb-s3prl-osanseviero__hubert_base-asr | null | [
"superb",
"automatic-speech-recognition",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#superb #automatic-speech-recognition #region-us
|
# Test for s3prl push to hub after fine-tuning | [
"# Test for s3prl push to hub after fine-tuning"
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"TAGS\n#superb #automatic-speech-recognition #region-us \n",
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null | superb |
# Fine-tuned s3prl model for SD | {"library_name": "superb", "tags": ["speaker-diarization", "osanseviero/hubert_base"], "datasets": ["superb"], "benchmark": "superb", "task": "sd"} | lewtun/superb-s3prl-osanseviero__hubert_base-diarization-7f28b8b5 | null | [
"superb",
"tensorboard",
"speaker-diarization",
"osanseviero/hubert_base",
"dataset:superb",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#superb #tensorboard #speaker-diarization #osanseviero/hubert_base #dataset-superb #region-us
|
# Fine-tuned s3prl model for SD | [
"# Fine-tuned s3prl model for SD"
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"TAGS\n#superb #tensorboard #speaker-diarization #osanseviero/hubert_base #dataset-superb #region-us \n",
"# Fine-tuned s3prl model for SD"
] |
automatic-speech-recognition | superb |
# Fine-tuned s3prl model for ASR | {"library_name": "superb", "tags": ["automatic-speech-recognition", "superb-test-org/test-submission-with-weights"], "datasets": ["superb"], "benchmark": "superb", "task": "asr", "widget": [{"example_title": "Librispeech sample 1", "src": "https://cdn-media.huggingface.co/speech_samples/sample1.flac"}]} | lewtun/superb-s3prl-superb-test-org__test-submission-with-weights-asr-ceaac01d | null | [
"superb",
"tensorboard",
"automatic-speech-recognition",
"superb-test-org/test-submission-with-weights",
"dataset:superb",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#superb #tensorboard #automatic-speech-recognition #superb-test-org/test-submission-with-weights #dataset-superb #region-us
|
# Fine-tuned s3prl model for ASR | [
"# Fine-tuned s3prl model for ASR"
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"# Fine-tuned s3prl model for ASR"
] |
automatic-speech-recognition | superb |
# Test for s3prl push to hub after fine-tuning | {"library_name": "superb", "tags": ["superb", "automatic-speech-recognition"], "benchmark": "superb", "task": "asr", "widget": [{"example_title": "Librispeech sample 1", "src": "https://cdn-media.huggingface.co/speech_samples/sample1.flac"}]} | lewtun/superb-s3prl-wav2vec2-asr | null | [
"superb",
"automatic-speech-recognition",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#superb #automatic-speech-recognition #region-us
|
# Test for s3prl push to hub after fine-tuning | [
"# Test for s3prl push to hub after fine-tuning"
] | [
"TAGS\n#superb #automatic-speech-recognition #region-us \n",
"# Test for s3prl push to hub after fine-tuning"
] |
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. -->
# xlm-roberta-base-finetuned-marc-de
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-b... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["amazon_reviews_multi"], "model-index": [{"name": "xlm-roberta-base-finetuned-marc-de", "results": []}]} | lewtun/xlm-roberta-base-finetuned-marc-de | null | [
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"dataset:amazon_reviews_multi",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #xlm-roberta #text-classification #generated_from_trainer #dataset-amazon_reviews_multi #license-mit #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-marc-de
==================================
This model is a fine-tuned version of xlm-roberta-base on the amazon\_reviews\_multi dataset.
It achieves the following results on the evaluation set:
* Loss: 0.9934
* Mae: 0.4867
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: 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: 2",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #xlm-roberta #text-classification #generated_from_trainer #dataset-amazon_reviews_multi #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_... |
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. -->
# xlm-roberta-base-finetuned-marc-en-dummy
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-rob... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["amazon_reviews_multi"], "model-index": [{"name": "xlm-roberta-base-finetuned-marc-en-dummy", "results": []}]} | lewtun/xlm-roberta-base-finetuned-marc-en-dummy | null | [
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"dataset:amazon_reviews_multi",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #xlm-roberta #text-classification #generated_from_trainer #dataset-amazon_reviews_multi #license-mit #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-marc-en-dummy
========================================
This model is a fine-tuned version of xlm-roberta-base on the amazon\_reviews\_multi dataset.
It achieves the following results on the evaluation set:
* Loss: 0.8931
* Mae: 0.4634
Model description
-----------------
More informati... | [
"### 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: 2",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #xlm-roberta #text-classification #generated_from_trainer #dataset-amazon_reviews_multi #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_... |
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. -->
# xlm-roberta-base-finetuned-marc-en-hslu
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-robe... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["amazon_reviews_multi"], "model-index": [{"name": "xlm-roberta-base-finetuned-marc-en-hslu", "results": []}]} | lewtun/xlm-roberta-base-finetuned-marc-en-hslu | null | [
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"dataset:amazon_reviews_multi",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #xlm-roberta #text-classification #generated_from_trainer #dataset-amazon_reviews_multi #license-mit #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-marc-en-hslu
=======================================
This model is a fine-tuned version of xlm-roberta-base on the amazon\_reviews\_multi dataset.
It achieves the following results on the evaluation set:
* Loss: 0.8826
* Mae: 0.5
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: 2",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #xlm-roberta #text-classification #generated_from_trainer #dataset-amazon_reviews_multi #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_... |
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. -->
# xlm-roberta-base-finetuned-marc-en
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-b... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["amazon_reviews_multi"], "model-index": [{"name": "xlm-roberta-base-finetuned-marc-en", "results": []}]} | lewtun/xlm-roberta-base-finetuned-marc-en | null | [
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"dataset:amazon_reviews_multi",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #xlm-roberta #text-classification #generated_from_trainer #dataset-amazon_reviews_multi #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us
| xlm-roberta-base-finetuned-marc-en
==================================
This model is a fine-tuned version of xlm-roberta-base on the amazon\_reviews\_multi dataset.
It achieves the following results on the evaluation set:
* Loss: 0.8850
* Mae: 0.4390
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: 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: 2",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #xlm-roberta #text-classification #generated_from_trainer #dataset-amazon_reviews_multi #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\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. -->
# xlm-roberta-base-finetuned-marc
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["amazon_reviews_multi"], "model-index": [{"name": "xlm-roberta-base-finetuned-marc", "results": []}]} | lewtun/xlm-roberta-base-finetuned-marc | null | [
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"dataset:amazon_reviews_multi",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #xlm-roberta #text-classification #generated_from_trainer #dataset-amazon_reviews_multi #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us
| xlm-roberta-base-finetuned-marc
===============================
This model is a fine-tuned version of xlm-roberta-base on the amazon\_reviews\_multi dataset.
It achieves the following results on the evaluation set:
* Loss: 0.9932
* Mae: 0.4838
Model description
-----------------
More information needed
Intend... | [
"### 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: 2",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #xlm-roberta #text-classification #generated_from_trainer #dataset-amazon_reviews_multi #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* ... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-portuguese-ner-archive
This model is a fine-tuned version of [neuralmind/bert-base-portuguese-cased](https://huggingface.co... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model_index": [{"name": "bert-portuguese-ner-archive", "results": [{"task": {"name": "Token Classification", "type": "token-classification"}, "metric": {"name": "Accuracy", "type": "accuracy", "value": 0.970032... | lfcc/bert-portuguese-ner-archive | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| bert-portuguese-ner-archive
===========================
This model is a fine-tuned version of neuralmind/bert-base-portuguese-cased
It achieves the following results on the evaluation set:
* Loss: 0.1140
* Precision: 0.9147
* Recall: 0.9483
* F1: 0.9312
* Accuracy: 0.9700
Model description
-----------------
Thi... | [
"### Datasets\n\n\nAll the training and evaluation data is available at: URL",
"### 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 ... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Datasets\n\n\nAll the training and evaluation data is available at: URL",
"### Training hyperparameters\n\n\nThe following hyperparameters ... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-large-pt-archive
This model is a fine-tuned version of [neuralmind/bert-large-portuguese-cased](https://huggingface.co/neur... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model_index": [{"name": "bert-large-pt-archive", "results": [{"task": {"name": "Token Classification", "type": "token-classification"}, "metric": {"name": "Accuracy", "type": "accuracy", "value": 0.976676247467... | lfcc/bert-large-pt-archive | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| bert-large-pt-archive
=====================
This model is a fine-tuned version of neuralmind/bert-large-portuguese-cased on an unkown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0869
* Precision: 0.9280
* Recall: 0.9541
* F1: 0.9409
* Accuracy: 0.9767
Model description
------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 4",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #license-mit #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:... |
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. -->
# portuguese-archival-finding-aids
This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/ber... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model_index": [{"name": "portuguese-archival-finding-aids", "results": [{"task": {"name": "Token Classification", "type": "token-classification"}, "metric": {"name": "Accuracy", "type": "accuracy", "valu... | lfcc/bert-multilingual-pt-archive | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-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 #tensorboard #bert #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| portuguese-archival-finding-aids
================================
This model is a fine-tuned version of bert-base-multilingual-cased on an unkown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1812
* Precision: 0.8624
* Recall: 0.9557
* F1: 0.9067
* Accuracy: 0.9618
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 #bert #token-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\... |
text-generation | transformers | # This model is probably not what you're looking for. | {} | lg/fexp_1 | null | [
"transformers",
"pytorch",
"gpt_neo",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt_neo #text-generation #autotrain_compatible #endpoints_compatible #region-us
| # This model is probably not what you're looking for. | [
"# This model is probably not what you're looking for."
] | [
"TAGS\n#transformers #pytorch #gpt_neo #text-generation #autotrain_compatible #endpoints_compatible #region-us \n",
"# This model is probably not what you're looking for."
] |
text-generation | transformers | **This model is provided with no guarantees whatsoever; use at your own risk.**
This is a Neo2.7B model fine tuned on github data scraped by an EleutherAI member (filtered for python-only) for 20k steps. A better code model is coming soon™ (hopefully, maybe); this model was created mostly as a test of infrastructure c... | {} | lg/ghpy_20k | null | [
"transformers",
"pytorch",
"gpt_neo",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt_neo #text-generation #autotrain_compatible #endpoints_compatible #region-us
| This model is provided with no guarantees whatsoever; use at your own risk.
This is a Neo2.7B model fine tuned on github data scraped by an EleutherAI member (filtered for python-only) for 20k steps. A better code model is coming soon™ (hopefully, maybe); this model was created mostly as a test of infrastructure code. | [] | [
"TAGS\n#transformers #pytorch #gpt_neo #text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
null | transformers | # This model is probably not what you're looking for. | {} | lg/ghpy_40k | null | [
"transformers",
"pytorch",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #endpoints_compatible #region-us
| # This model is probably not what you're looking for. | [
"# This model is probably not what you're looking for."
] | [
"TAGS\n#transformers #pytorch #endpoints_compatible #region-us \n",
"# This model is probably not what you're looking for."
] |
text-generation | transformers | # This model is probably not what you're looking for. | {} | lg/openinstruct_1k1 | null | [
"transformers",
"pytorch",
"gpt_neo",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt_neo #text-generation #autotrain_compatible #endpoints_compatible #region-us
| # This model is probably not what you're looking for. | [
"# This model is probably not what you're looking for."
] | [
"TAGS\n#transformers #pytorch #gpt_neo #text-generation #autotrain_compatible #endpoints_compatible #region-us \n",
"# This model is probably not what you're looking for."
] |
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. -->
# WavLM-large-CORAA-pt
This model is a fine-tuned version of [microsoft/wavlm-large](https://huggingface.co/microsoft/wavlm-large)... | {"language": ["pt"], "license": "apache-2.0", "tags": ["generated_from_trainer", "pt"], "model-index": [{"name": "WavLM-large-CORAA-pt", "results": []}]} | lgris/WavLM-large-CORAA-pt | null | [
"transformers",
"pytorch",
"wavlm",
"automatic-speech-recognition",
"generated_from_trainer",
"pt",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"pt"
] | TAGS
#transformers #pytorch #wavlm #automatic-speech-recognition #generated_from_trainer #pt #license-apache-2.0 #endpoints_compatible #region-us
| WavLM-large-CORAA-pt
====================
This model is a fine-tuned version of microsoft/wavlm-large on CORAA dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6144
* Wer: 0.3840
Model description
-----------------
More information needed
Intended uses & limitations
----------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 8\n* eval\\_b... |
automatic-speech-recognition | transformers |
# Wav2vec 2.0 for Portuguese in 8kHz
This is a fine-tuned model from [facebook/wav2vec2-base-10k-voxpopuli](https://huggingface.co/facebook/wav2vec2-base-10k-voxpopuli)
Datasets used to fine-tune the model:
CETUC: contains approximately 145 hours of Brazilian Portuguese speech distributed among 50 male and 50 female... | {"language": "pt", "license": "apache-2.0", "tags": ["audio", "speech", "wav2vec2", "pt", "portuguese-speech-corpus", "automatic-speech-recognition", "speech", "PyTorch"], "datasets": ["common_voice", "mls", "cetuc", "lapsbm", "voxforge", "tedx", "sid"], "metrics": ["wer"]} | lgris/base_10k_8khz_pt | null | [
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"dataset:voxforge",
"dataset:tedx",
"dataset:sid",
"license:apache-2.0",
... | null | 2022-03-02T23:29:05+00:00 | [] | [
"pt"
] | TAGS
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|
# Wav2vec 2.0 for Portuguese in 8kHz
This is a fine-tuned model from facebook/wav2vec2-base-10k-voxpopuli
Datasets used to fine-tune the model:
CETUC: contains approximately 145 hours of Brazilian Portuguese speech distributed among 50 male and 50 female speakers, each pronouncing approximately 1,000 phonetically ba... | [
"# Wav2vec 2.0 for Portuguese in 8kHz\n\nThis is a fine-tuned model from facebook/wav2vec2-base-10k-voxpopuli\n\nDatasets used to fine-tune the model:\nCETUC: contains approximately 145 hours of Brazilian Portuguese speech distributed among 50 male and 50 female speakers, each pronouncing approximately 1,000 phonet... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #speech #pt #portuguese-speech-corpus #PyTorch #dataset-common_voice #dataset-mls #dataset-cetuc #dataset-lapsbm #dataset-voxforge #dataset-tedx #dataset-sid #license-apache-2.0 #endpoints_compatible #region-us \n",
"# Wav2vec 2.0 for Po... |
automatic-speech-recognition | transformers |
# cetuc100-xlsr: Wav2vec 2.0 with CETUC Dataset
This is a the demonstration of a fine-tuned Wav2vec model for Brazilian Portuguese using the [CETUC](http://www02.smt.ufrj.br/~igor.quintanilha/alcaim.tar.gz) dataset. This dataset contains approximately 145 hours of Brazilian Portuguese speech distributed among 50 male... | {"language": "pt", "license": "apache-2.0", "tags": ["audio", "speech", "wav2vec2", "pt", "portuguese-speech-corpus", "automatic-speech-recognition", "speech", "PyTorch"], "datasets": ["common_voice", "mls", "cetuc", "lapsbm", "voxforge", "tedx", "sid"], "metrics": ["wer"]} | lgris/bp-cetuc100-xlsr | null | [
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"audio",
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"dataset:voxforge",
"dataset:tedx",
"dataset:sid",
"license:apache-2.0",
... | null | 2022-03-02T23:29:05+00:00 | [] | [
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| cetuc100-xlsr: Wav2vec 2.0 with CETUC Dataset
=============================================
This is a the demonstration of a fine-tuned Wav2vec model for Brazilian Portuguese using the CETUC dataset. This dataset contains approximately 145 hours of Brazilian Portuguese speech distributed among 50 male and 50 female s... | [
"#### Summary\n\n\n\nDemonstration\n-------------",
"### Imports and dependencies",
"### Helpers",
"### Model",
"### Download datasets",
"### Tests",
"#### CETUC\n\n\n\n```\nCETUC WER: 0.44677581829220825\n\n```",
"#### Common Voice\n\n\n\n```\nCV WER: 0.8561919899139065\n\n```",
"#### LaPS\n\n\n\n`... | [
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"#### Summary\n\n\n\n... |
automatic-speech-recognition | transformers |
# commonvoice10-xlsr: Wav2vec 2.0 with Common Voice Dataset
This is a the demonstration of a fine-tuned Wav2vec model for Brazilian Portuguese using the [Common Voice 7.0](https://commonvoice.mozilla.org/pt) dataset.
In this notebook the model is tested against other available Brazilian Portuguese datasets.
| Data... | {"language": "pt", "license": "apache-2.0", "tags": ["audio", "speech", "wav2vec2", "pt", "portuguese-speech-corpus", "automatic-speech-recognition", "speech", "PyTorch"], "datasets": ["common_voice", "mls", "cetuc", "lapsbm", "voxforge", "tedx", "sid"], "metrics": ["wer"]} | lgris/bp-commonvoice10-xlsr | null | [
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"dataset:tedx",
"dataset:sid",
"license:apache-2.0",
... | null | 2022-03-02T23:29:05+00:00 | [] | [
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| commonvoice10-xlsr: Wav2vec 2.0 with Common Voice Dataset
=========================================================
This is a the demonstration of a fine-tuned Wav2vec model for Brazilian Portuguese using the Common Voice 7.0 dataset.
In this notebook the model is tested against other available Brazilian Portuguese... | [
"#### Summary\n\n\n\nDemonstration\n-------------",
"### Imports and dependencies",
"### Helpers",
"### Model",
"### Download datasets",
"### Tests",
"#### CETUC\n\n\n\n```\nCETUC WER: 0.13291846056190185\n\n```",
"#### Common Voice\n\n\n\n```\nCV WER: 0.18909733896486755\n\n```",
"#### LaPS\n\n\n\n... | [
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"#### Summary\n\n\n\n... |
automatic-speech-recognition | transformers |
# commonvoice100-xlsr: Wav2vec 2.0 with Common Voice Dataset
This is a the demonstration of a fine-tuned Wav2vec model for Brazilian Portuguese using the [Common Voice 7.0](https://commonvoice.mozilla.org/pt) dataset.
In this notebook the model is tested against other available Brazilian Portuguese datasets.
| Dat... | {"language": "pt", "license": "apache-2.0", "tags": ["audio", "speech", "wav2vec2", "pt", "portuguese-speech-corpus", "automatic-speech-recognition", "speech", "PyTorch"], "datasets": ["common_voice", "mls", "cetuc", "lapsbm", "voxforge", "tedx", "sid"], "metrics": ["wer"]} | lgris/bp-commonvoice100-xlsr | null | [
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... | null | 2022-03-02T23:29:05+00:00 | [] | [
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] | TAGS
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| commonvoice100-xlsr: Wav2vec 2.0 with Common Voice Dataset
==========================================================
This is a the demonstration of a fine-tuned Wav2vec model for Brazilian Portuguese using the Common Voice 7.0 dataset.
In this notebook the model is tested against other available Brazilian Portugue... | [
"#### Summary\n\n\n\nDemonstration\n-------------",
"### Imports and dependencies",
"### Helpers",
"### Model",
"### Download datasets",
"### Tests",
"#### CETUC\n\n\n\n```\nCETUC WER: 0.08868880057404624\n\n```",
"#### Common Voice\n\n\n\n```\nCV WER: 0.12601035333655114\n\n```",
"#### LaPS\n\n\n\n... | [
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"#### Summary\n\n\n\n... |
automatic-speech-recognition | transformers |
# lapsbm1-xlsr: Wav2vec 2.0 with LaPSBM Dataset
This is a the demonstration of a fine-tuned Wav2vec model for Brazilian Portuguese using the [LaPS BM](https://github.com/falabrasil/gitlab-resources) dataset.
In this notebook the model is tested against other available Brazilian Portuguese datasets.
| Dataset ... | {"language": "pt", "license": "apache-2.0", "tags": ["audio", "speech", "wav2vec2", "pt", "portuguese-speech-corpus", "automatic-speech-recognition", "speech", "PyTorch"], "datasets": ["common_voice", "mls", "cetuc", "lapsbm", "voxforge", "tedx", "sid"], "metrics": ["wer"]} | lgris/bp-lapsbm1-xlsr | null | [
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"dataset:voxforge",
"dataset:tedx",
"dataset:sid",
"license:apache-2.0",
... | null | 2022-03-02T23:29:05+00:00 | [] | [
"pt"
] | TAGS
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| lapsbm1-xlsr: Wav2vec 2.0 with LaPSBM Dataset
=============================================
This is a the demonstration of a fine-tuned Wav2vec model for Brazilian Portuguese using the LaPS BM dataset.
In this notebook the model is tested against other available Brazilian Portuguese datasets.
#### Summary
Dem... | [
"#### Summary\n\n\n\nDemonstration\n-------------",
"### Imports and dependencies",
"### Helpers",
"### Model",
"### Download datasets",
"### Tests",
"#### CETUC\n\n\n\n```\nCETUC WER: 0.11147816967489037\n\n```",
"#### Common Voice\n\n\n\n```\nCV WER: 0.41880890234535906\n\n```",
"#### LaPS\n\n\n\n... | [
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"#### Summary\n\n\n\n... |
automatic-speech-recognition | transformers |
# mls100-xlsr: Wav2vec 2.0 with MLS Dataset
This is a the demonstration of a fine-tuned Wav2vec model for Brazilian Portuguese using the [Multilingual Librispeech in Portuguese (MLS)](http://www.openslr.org/94/) dataset.
In this notebook the model is tested against other available Brazilian Portuguese datasets.
| ... | {"language": "pt", "license": "apache-2.0", "tags": ["audio", "speech", "wav2vec2", "pt", "portuguese-speech-corpus", "automatic-speech-recognition", "speech", "PyTorch"], "datasets": ["common_voice", "mls", "cetuc", "lapsbm", "voxforge", "tedx", "sid"], "metrics": ["wer"]} | lgris/bp-mls100-xlsr | null | [
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"dataset:voxforge",
"dataset:tedx",
"dataset:sid",
"license:apache-2.0",
... | null | 2022-03-02T23:29:05+00:00 | [] | [
"pt"
] | TAGS
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| mls100-xlsr: Wav2vec 2.0 with MLS Dataset
=========================================
This is a the demonstration of a fine-tuned Wav2vec model for Brazilian Portuguese using the Multilingual Librispeech in Portuguese (MLS) dataset.
In this notebook the model is tested against other available Brazilian Portuguese dat... | [
"#### Summary\n\n\n\nDemonstration\n-------------",
"### Imports and dependencies",
"### Helpers",
"### Model",
"### Download datasets\n\n\n\n```\n/content/bp_dataset\n\n```",
"### Tests",
"#### CETUC\n\n\n\n```\nCETUC WER: 0.192586382955233\n\n```",
"#### Common Voice\n\n\n\n```\nCV WER: 0.2604333640... | [
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"#### Summary\n\n\n\n... |
automatic-speech-recognition | transformers |
# sid10-xlsr: Wav2vec 2.0 with Sidney Dataset
This is a the demonstration of a fine-tuned Wav2vec model for Brazilian Portuguese using the [Sidney](https://igormq.github.io/datasets/) dataset.
In this notebook the model is tested against other available Brazilian Portuguese datasets.
| Dataset ... | {"language": "pt", "license": "apache-2.0", "tags": ["audio", "speech", "wav2vec2", "pt", "portuguese-speech-corpus", "automatic-speech-recognition", "speech", "PyTorch"], "datasets": ["common_voice", "mls", "cetuc", "lapsbm", "voxforge", "tedx", "sid"], "metrics": ["wer"]} | lgris/bp-sid10-xlsr | null | [
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... | null | 2022-03-02T23:29:05+00:00 | [] | [
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| sid10-xlsr: Wav2vec 2.0 with Sidney Dataset
===========================================
This is a the demonstration of a fine-tuned Wav2vec model for Brazilian Portuguese using the Sidney dataset.
In this notebook the model is tested against other available Brazilian Portuguese datasets.
#### Summary
Demonstr... | [
"#### Summary\n\n\n\nDemonstration\n-------------",
"### Imports and dependencies",
"### Helpers",
"### Model",
"### Download datasets",
"### Tests",
"#### CETUC\n\n\n\n```\nCETUC WER: 0.18623689076557778\n\n```",
"#### Common Voice\n\n\n\n```\nCV WER: 0.3279775395502392\n\n```",
"#### LaPS\n\n\n\n`... | [
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"#### Summary\n\n\n\n... |
automatic-speech-recognition | transformers |
# tedx100-xlsr: Wav2vec 2.0 with TEDx Dataset
This is a the demonstration of a fine-tuned Wav2vec model for Brazilian Portuguese using the [TEDx multilingual in Portuguese](http://www.openslr.org/100) dataset.
In this notebook the model is tested against other available Brazilian Portuguese datasets.
| Dataset ... | {"language": "pt", "license": "apache-2.0", "tags": ["audio", "speech", "wav2vec2", "pt", "portuguese-speech-corpus", "automatic-speech-recognition", "speech", "PyTorch"], "datasets": ["common_voice", "mls", "cetuc", "lapsbm", "voxforge", "tedx", "sid"], "metrics": ["wer"]} | lgris/bp-tedx100-xlsr | null | [
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... | null | 2022-03-02T23:29:05+00:00 | [] | [
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| tedx100-xlsr: Wav2vec 2.0 with TEDx Dataset
===========================================
This is a the demonstration of a fine-tuned Wav2vec model for Brazilian Portuguese using the TEDx multilingual in Portuguese dataset.
In this notebook the model is tested against other available Brazilian Portuguese datasets.
... | [
"#### Summary\n\n\n\nDemonstration\n-------------",
"### Imports and dependencies",
"### Helpers",
"### Model",
"### Download datasets",
"### Tests",
"#### CETUC\n\n\n\n```\nCETUC WER: 0.13846663354859937\n\n```",
"#### Common Voice\n\n\n\n```\nCV WER: 0.36960721735520236\n\n```",
"#### LaPS\n\n\n\n... | [
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"#### Summary\n\n\n\n... |
automatic-speech-recognition | transformers |
# voxforge1-xlsr: Wav2vec 2.0 with VoxForge Dataset
This is a the demonstration of a fine-tuned Wav2vec model for Brazilian Portuguese using the [VoxForge](http://www.voxforge.org/) dataset.
In this notebook the model is tested against other available Brazilian Portuguese datasets.
| Dataset ... | {"language": "pt", "license": "apache-2.0", "tags": ["audio", "speech", "wav2vec2", "pt", "portuguese-speech-corpus", "automatic-speech-recognition", "speech", "PyTorch"], "datasets": ["common_voice", "mls", "cetuc", "lapsbm", "voxforge", "tedx", "sid"], "metrics": ["wer"]} | lgris/bp-voxforge1-xlsr | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"pt",
"portuguese-speech-corpus",
"PyTorch",
"dataset:common_voice",
"dataset:mls",
"dataset:cetuc",
"dataset:lapsbm",
"dataset:voxforge",
"dataset:tedx",
"dataset:sid",
"license:apache-2.0",
... | null | 2022-03-02T23:29:05+00:00 | [] | [
"pt"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #speech #pt #portuguese-speech-corpus #PyTorch #dataset-common_voice #dataset-mls #dataset-cetuc #dataset-lapsbm #dataset-voxforge #dataset-tedx #dataset-sid #license-apache-2.0 #endpoints_compatible #region-us
| voxforge1-xlsr: Wav2vec 2.0 with VoxForge Dataset
=================================================
This is a the demonstration of a fine-tuned Wav2vec model for Brazilian Portuguese using the VoxForge dataset.
In this notebook the model is tested against other available Brazilian Portuguese datasets.
#### Summa... | [
"#### Summary\n\n\n\nDemonstration\n-------------",
"### Imports and dependencies",
"### Helpers",
"### Model",
"### Download datasets",
"### Tests",
"#### CETUC\n\n\n\n```\nCETUC WER: 0.4684840205331983\n\n```",
"#### Common Voice\n\n\n\n```\nCV WER: 0.6080167359840954\n\n```",
"#### LaPS\n\n\n\n``... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #speech #pt #portuguese-speech-corpus #PyTorch #dataset-common_voice #dataset-mls #dataset-cetuc #dataset-lapsbm #dataset-voxforge #dataset-tedx #dataset-sid #license-apache-2.0 #endpoints_compatible #region-us \n",
"#### Summary\n\n\n\n... |
automatic-speech-recognition | transformers |
# bp400-xlsr: Wav2vec 2.0 with Brazilian Portuguese (BP) Dataset
**Paper:** https://arxiv.org/abs/2107.11414
This is a the demonstration of a fine-tuned Wav2vec model for Brazilian Portuguese using the following datasets:
- [CETUC](http://www02.smt.ufrj.br/~igor.quintanilha/alcaim.tar.gz): contains approximately 14... | {"language": "pt", "license": "apache-2.0", "tags": ["audio", "speech", "wav2vec2", "pt", "portuguese-speech-corpus", "automatic-speech-recognition", "speech", "PyTorch", "hf-asr-leaderboard"], "datasets": ["common_voice", "mls", "cetuc", "lapsbm", "voxforge", "tedx", "sid"], "metrics": ["wer"], "model-index": [{"name"... | lgris/bp400-xlsr | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"pt",
"portuguese-speech-corpus",
"PyTorch",
"hf-asr-leaderboard",
"dataset:common_voice",
"dataset:mls",
"dataset:cetuc",
"dataset:lapsbm",
"dataset:voxforge",
"dataset:tedx",
"dataset:sid",
... | null | 2022-03-02T23:29:05+00:00 | [
"2107.11414",
"2012.03411"
] | [
"pt"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #speech #pt #portuguese-speech-corpus #PyTorch #hf-asr-leaderboard #dataset-common_voice #dataset-mls #dataset-cetuc #dataset-lapsbm #dataset-voxforge #dataset-tedx #dataset-sid #arxiv-2107.11414 #arxiv-2012.03411 #license-apache-2.0 #model-inde... | bp400-xlsr: Wav2vec 2.0 with Brazilian Portuguese (BP) Dataset
==============================================================
Paper: URL
This is a the demonstration of a fine-tuned Wav2vec model for Brazilian Portuguese using the following datasets:
* CETUC: contains approximately 145 hours of Brazilian Portugues... | [
"#### Summary",
"#### Transcription examples\n\n\n\nDemonstration\n-------------",
"### Imports and dependencies",
"### Helpers",
"### Model",
"### Download datasets",
"### Tests",
"#### CETUC\n\n\n\n```\nCETUC WER: 0.05159104708285062\n\n```",
"#### Common Voice\n\n\n\n```\nCV WER: 0.14031426198658... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #speech #pt #portuguese-speech-corpus #PyTorch #hf-asr-leaderboard #dataset-common_voice #dataset-mls #dataset-cetuc #dataset-lapsbm #dataset-voxforge #dataset-tedx #dataset-sid #arxiv-2107.11414 #arxiv-2012.03411 #license-apache-2.0 #mode... |
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