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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
automatic-speech-recognition | espnet |
## ESPnet2 ASR model
### `pyf98/aishell_conformer_e12_amp`
This model was trained by Yifan Peng using aishell recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```bash
cd espnet
git checkout 4f36236ed7c8a25c2f869e518614e1ad4a8b50d6
pip install -e .
cd egs2/aishell/asr1
./run.s... | {"language": "zh", "license": "cc-by-4.0", "tags": ["espnet", "audio", "automatic-speech-recognition"], "datasets": ["aishell"]} | pyf98/aishell_conformer_e12_amp | null | [
"espnet",
"audio",
"automatic-speech-recognition",
"zh",
"dataset:aishell",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | null | 2022-05-27T17:41:59+00:00 | [
"1804.00015"
] | [
"zh"
] | TAGS
#espnet #audio #automatic-speech-recognition #zh #dataset-aishell #arxiv-1804.00015 #license-cc-by-4.0 #region-us
| ESPnet2 ASR model
-----------------
### 'pyf98/aishell\_conformer\_e12\_amp'
This model was trained by Yifan Peng using aishell recipe in espnet.
### Demo: How to use in ESPnet2
RESULTS
=======
Environments
------------
* date: 'Fri May 27 13:37:48 EDT 2022'
* python version: '3.9.12 (main, Apr 5 2022, 06:5... | [
"### 'pyf98/aishell\\_conformer\\_e12\\_amp'\n\n\nThis model was trained by Yifan Peng using aishell recipe in espnet.",
"### Demo: How to use in ESPnet2\n\n\nRESULTS\n=======\n\n\nEnvironments\n------------\n\n\n* date: 'Fri May 27 13:37:48 EDT 2022'\n* python version: '3.9.12 (main, Apr 5 2022, 06:56:58) [GCC 7... | [
"TAGS\n#espnet #audio #automatic-speech-recognition #zh #dataset-aishell #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"### 'pyf98/aishell\\_conformer\\_e12\\_amp'\n\n\nThis model was trained by Yifan Peng using aishell recipe in espnet.",
"### Demo: How to use in ESPnet2\n\n\nRESULTS\n=======\n\n\nEnvir... |
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-german-cased-finetuned-subj_preTrained_with_noisyData_v1.1
This model is a fine-tuned version of [bert-base-german-cas... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "bert-base-german-cased-finetuned-subj_preTrained_with_noisyData_v1.1", "results": []}]} | tbosse/bert-base-german-cased-finetuned-subj_preTrained_with_noisyData_v1.1 | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-27T18:38:54+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| bert-base-german-cased-finetuned-subj\_preTrained\_with\_noisyData\_v1.1
========================================================================
This model is a fine-tuned version of bert-base-german-cased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0179
* Precision: 0... | [
"### 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 #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. -->
# bert-base-german-cased-finetuned-subj_preTrained_with_noisyData_v1.2
This model is a fine-tuned version of [bert-base-german-cas... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "bert-base-german-cased-finetuned-subj_preTrained_with_noisyData_v1.2", "results": []}]} | tbosse/bert-base-german-cased-finetuned-subj_preTrained_with_noisyData_v1.2 | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-27T18:39:36+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| bert-base-german-cased-finetuned-subj\_preTrained\_with\_noisyData\_v1.2
========================================================================
This model is a fine-tuned version of bert-base-german-cased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0187
* Precision: 0... | [
"### 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 #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:... |
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. -->
# coreyresults-smaller
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on th... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "coreyresults-smaller", "results": []}]} | coreybrady/coreyresults-smaller | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-27T18:59:57+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# coreyresults-smaller
This model is a fine-tuned version of distilroberta-base on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
... | [
"# coreyresults-smaller\n\nThis model is a fine-tuned version of distilroberta-base on the None dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
... | [
"TAGS\n#transformers #pytorch #tensorboard #roberta #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# coreyresults-smaller\n\nThis model is a fine-tuned version of distilroberta-base on the None dataset.",
"## Model description\n\nMore informat... |
text2text-generation | transformers | Este modelo busca generar el titulo de un texto, se tomo como base el articulo:
https://medium.com/nlplanet/a-full-guide-to-finetuning-t5-for-text2text-and-building-a-demo-with-streamlit-c72009631887
Se entreno el modelo con 500 elementos del dataset
Genera el titulo del texto | {"license": "other"} | sanbohork/Caso3_T5 | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-05-27T19:07:20+00:00 | [] | [] | TAGS
#transformers #pytorch #t5 #text2text-generation #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| Este modelo busca generar el titulo de un texto, se tomo como base el articulo:
URL
Se entreno el modelo con 500 elementos del dataset
Genera el titulo del texto | [] | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation | transformers |
#Audrey Hepburn DialoGPT Model | {"tags": ["conversational"]} | ElMuchoDingDong/DialoGPT-medium-AudreyHepburn_v4 | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-05-27T19:24:26+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
#Audrey Hepburn DialoGPT Model | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
automatic-speech-recognition | transformers |
# M-CTC-T
Massively multilingual speech recognizer from Meta AI. The model is a 1B-param transformer encoder, with a CTC head over 8065 character labels and a language identification head over 60 language ID labels. It is trained on Common Voice (version 6.1, December 2020 release) and VoxPopuli. After training on ... | {"language": "en", "license": "apache-2.0", "tags": ["speech"], "datasets": ["common_voice", "voxpopuli"], "multilinguality": ["multilingual"]} | speechbrain/m-ctc-t-large | null | [
"transformers",
"pytorch",
"mctct",
"automatic-speech-recognition",
"speech",
"en",
"dataset:common_voice",
"dataset:voxpopuli",
"arxiv:2111.00161",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-05-27T19:29:25+00:00 | [
"2111.00161"
] | [
"en"
] | TAGS
#transformers #pytorch #mctct #automatic-speech-recognition #speech #en #dataset-common_voice #dataset-voxpopuli #arxiv-2111.00161 #license-apache-2.0 #endpoints_compatible #region-us
| M-CTC-T
=======
Massively multilingual speech recognizer from Meta AI. The model is a 1B-param transformer encoder, with a CTC head over 8065 character labels and a language identification head over 60 language ID labels. It is trained on Common Voice (version 6.1, December 2020 release) and VoxPopuli. After traini... | [] | [
"TAGS\n#transformers #pytorch #mctct #automatic-speech-recognition #speech #en #dataset-common_voice #dataset-voxpopuli #arxiv-2111.00161 #license-apache-2.0 #endpoints_compatible #region-us \n"
] |
text-generation | transformers | This is Chat Bot which imitates Batman. The chat bot is made using Transformers from HuggingFace in Pytorch.
This chat bot is linked with a discord bot that is associated with several personal discord server.
Moreover, it uses gpt2 pre-trained Transformer decoder model from OpenAI since it contains the best pre-train... | {"tags": ["conversational"]} | DaBaap/Chat-Bot-Batman | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-05-27T20:42:45+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| This is Chat Bot which imitates Batman. The chat bot is made using Transformers from HuggingFace in Pytorch.
This chat bot is linked with a discord bot that is associated with several personal discord server.
Moreover, it uses gpt2 pre-trained Transformer decoder model from OpenAI since it contains the best pre-train... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
image-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. -->
# resnet-50-base-beans-demo
This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50... | {"tags": ["image-classification", "vision", "generated_from_trainer"], "datasets": ["beans"], "metrics": ["accuracy"], "model-index": [{"name": "resnet-50-base-beans-demo", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "beans", "type": "beans", "args": "defau... | eugenecamus/resnet-50-base-beans-demo | null | [
"transformers",
"pytorch",
"tensorboard",
"resnet",
"image-classification",
"vision",
"generated_from_trainer",
"dataset:beans",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-27T20:53:44+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #resnet #image-classification #vision #generated_from_trainer #dataset-beans #model-index #autotrain_compatible #endpoints_compatible #region-us
| resnet-50-base-beans-demo
=========================
This model is a fine-tuned version of microsoft/resnet-50 on the beans dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2188
* Accuracy: 0.9023
Model description
-----------------
More information needed
Intended uses & limitations... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.002\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 1337\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_ratio... | [
"TAGS\n#transformers #pytorch #tensorboard #resnet #image-classification #vision #generated_from_trainer #dataset-beans #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: 0.002... |
sentence-similarity | sentence-transformers |
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when ... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | jplu/adel-dbpedia-retrieval | null | [
"sentence-transformers",
"pytorch",
"distilbert",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2022-05-27T20:59:39+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us
|
# {MODEL_NAME}
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers installed:
Then you can u... | [
"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\n... | [
"TAGS\n#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us \n",
"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering... |
automatic-speech-recognition | espnet |
## ESPnet2 ASR model
### `pyf98/aishell_branchformer_e24_amp`
This model was trained by Yifan Peng using aishell recipe in [espnet](https://github.com/espnet/espnet/).
Branchformer (Peng et al., ICML 2022): [https://proceedings.mlr.press/v162/peng22a.html](https://proceedings.mlr.press/v162/peng22a.html)
### Demo... | {"language": "zh", "license": "cc-by-4.0", "tags": ["espnet", "audio", "automatic-speech-recognition"], "datasets": ["aishell"]} | pyf98/aishell_branchformer_e24_amp | null | [
"espnet",
"audio",
"automatic-speech-recognition",
"zh",
"dataset:aishell",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | null | 2022-05-27T21:07:43+00:00 | [
"1804.00015"
] | [
"zh"
] | TAGS
#espnet #audio #automatic-speech-recognition #zh #dataset-aishell #arxiv-1804.00015 #license-cc-by-4.0 #region-us
| ESPnet2 ASR model
-----------------
### 'pyf98/aishell\_branchformer\_e24\_amp'
This model was trained by Yifan Peng using aishell recipe in espnet.
Branchformer (Peng et al., ICML 2022): URL
### Demo: How to use in ESPnet2
RESULTS
=======
Environments
------------
* date: 'Sun May 22 13:29:06 EDT 2022'
*... | [
"### 'pyf98/aishell\\_branchformer\\_e24\\_amp'\n\n\nThis model was trained by Yifan Peng using aishell recipe in espnet.\n\n\nBranchformer (Peng et al., ICML 2022): URL",
"### Demo: How to use in ESPnet2\n\n\nRESULTS\n=======\n\n\nEnvironments\n------------\n\n\n* date: 'Sun May 22 13:29:06 EDT 2022'\n* python v... | [
"TAGS\n#espnet #audio #automatic-speech-recognition #zh #dataset-aishell #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"### 'pyf98/aishell\\_branchformer\\_e24\\_amp'\n\n\nThis model was trained by Yifan Peng using aishell recipe in espnet.\n\n\nBranchformer (Peng et al., ICML 2022): URL",
"### Demo: How... |
reinforcement-learning | stable-baselines3 |
# **PPO** Agent playing **CarRacing-v0**
This is a trained model of a **PPO** agent playing **CarRacing-v0**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
What the fuck is it doing
its spinning around in circles
```python
fr... | {"library_name": "stable-baselines3", "tags": ["CarRacing-v0", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "CarRacing-v0", "type": "CarRacing-... | jonporterjones/carRacing1 | null | [
"stable-baselines3",
"CarRacing-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-05-27T21:08:15+00:00 | [] | [] | TAGS
#stable-baselines3 #CarRacing-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing CarRacing-v0
This is a trained model of a PPO agent playing CarRacing-v0
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
What the fuck is it doing
its spinning around in circles
| [
"# PPO Agent playing CarRacing-v0\nThis is a trained model of a PPO agent playing CarRacing-v0\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code\n\nWhat the fuck is it doing\nits spinning around in circles"
] | [
"TAGS\n#stable-baselines3 #CarRacing-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing CarRacing-v0\nThis is a trained model of a PPO agent playing CarRacing-v0\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code... |
text-generation | transformers |
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1509493999987474434/nB7r... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/algodtrading/1653690066290/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/algodtrading | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-05-27T21:20:16+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
AI BOT
Algod
@algodtrading
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
-------------... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n"
] |
reinforcement-learning | null |
# **Q-Learning** Agent playing **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="/q-Taxi-v3_1", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env =... | {"tags": ["Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-Taxi-v3_1", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Taxi-v3", "type": "Taxi-v3"}, "metrics": [{"type": "mean_reward", "value": "7.56 ... | YaYaB/q-Taxi-v3_1 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-05-27T21:24:54+00:00 | [] | [] | TAGS
#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing Taxi-v3
This is a trained model of a Q-Learning agent playing Taxi-v3 .
## Usage
| [
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] | [
"TAGS\n#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] |
null | keras |
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'learning_rate': ... | {"library_name": "keras"} | merve/model-card-history-removal | null | [
"keras",
"region:us"
] | null | 2022-05-27T21:30:46+00:00 | [] | [] | TAGS
#keras #region-us
|
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'learning_rate': ... | [
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- optimizer: {'nam... | [
"TAGS\n#keras #region-us \n",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used duri... |
text-generation | transformers |
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1497681806300168198/YO7f... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/0xgaut/1653690692376/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/0xgaut | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-05-27T21:30:53+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
gaut
@0xgaut
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
-------------
The ... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation | null |
# My Awesome Model | {"tags": ["conversational"]} | Iwa/bot | null | [
"conversational",
"region:us"
] | null | 2022-05-27T21:31:53+00:00 | [] | [] | TAGS
#conversational #region-us
|
# My Awesome Model | [
"# My Awesome Model"
] | [
"TAGS\n#conversational #region-us \n",
"# My Awesome Model"
] |
reinforcement-learning | null |
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="magitz/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attri... | {"tags": ["FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-FrozenLake-v1-4x4-noSlippery", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "FrozenLake-v1-4x4-no_slippery", "type": ... | magitz/q-FrozenLake-v1-4x4-noSlippery | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-05-27T22:38:07+00:00 | [] | [] | TAGS
#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing FrozenLake-v1
This is a trained model of a Q-Learning agent playing FrozenLake-v1 .
## Usage
| [
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] | [
"TAGS\n#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] |
text-classification | transformers | welcome to my sentiment classification model
model trained with the bert-base-uncased base to classify the sentiment of customers who respond to the satisfaction survey. The sentiments that it classifies are positive (1) and negative (0). | {} | Jrico1981/sentiment-classification | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-27T22:53:22+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us
| welcome to my sentiment classification model
model trained with the bert-base-uncased base to classify the sentiment of customers who respond to the satisfaction survey. The sentiments that it classifies are positive (1) and negative (0). | [] | [
"TAGS\n#transformers #pytorch #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us \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-base-german-cased-noisy-pretrain-fine-tuned_v1.2
This model is a fine-tuned version of [tbosse/bert-base-german-cased-finet... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "bert-base-german-cased-noisy-pretrain-fine-tuned_v1.2", "results": []}]} | tbosse/bert-base-german-cased-noisy-pretrain-fine-tuned_v1.2 | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-27T22:54:46+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| bert-base-german-cased-noisy-pretrain-fine-tuned\_v1.2
======================================================
This model is a fine-tuned version of tbosse/bert-base-german-cased-finetuned-subj\_preTrained\_with\_noisyData\_v1.2 on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: ... | [
"### 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: 7",
"### Traini... | [
"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. -->
# bert-base-german-cased-noisy-pretrain-fine-tuned_v1.1
This model is a fine-tuned version of [tbosse/bert-base-german-cased-finet... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "bert-base-german-cased-noisy-pretrain-fine-tuned_v1.1", "results": []}]} | tbosse/bert-base-german-cased-noisy-pretrain-fine-tuned_v1.1 | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-27T23:02:45+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| bert-base-german-cased-noisy-pretrain-fine-tuned\_v1.1
======================================================
This model is a fine-tuned version of tbosse/bert-base-german-cased-finetuned-subj\_preTrained\_with\_noisyData\_v1.1 on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: ... | [
"### 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: 7",
"### Traini... | [
"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:... |
automatic-speech-recognition | espnet |
## ESPnet2 ASR model
### `pyf98/slurp_entity_conformer`
This model was trained by Yifan Peng using slurp_entity recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```bash
cd espnet
git checkout 55b6cc387fd0252d1a06db2042fd101bcea7bb34
pip install -e .
cd egs2/slurp_entity/asr1
... | {"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "automatic-speech-recognition"], "datasets": ["slurp_entity"]} | pyf98/slurp_entity_conformer | null | [
"espnet",
"audio",
"automatic-speech-recognition",
"en",
"dataset:slurp_entity",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | null | 2022-05-27T23:11:15+00:00 | [
"1804.00015"
] | [
"en"
] | TAGS
#espnet #audio #automatic-speech-recognition #en #dataset-slurp_entity #arxiv-1804.00015 #license-cc-by-4.0 #region-us
| ESPnet2 ASR model
-----------------
### 'pyf98/slurp\_entity\_conformer'
This model was trained by Yifan Peng using slurp\_entity recipe in espnet.
### Demo: How to use in ESPnet2
RESULTS
=======
Environments
------------
* date: 'Thu May 26 14:51:29 EDT 2022'
* python version: '3.9.12 (main, Apr 5 2022, 06... | [
"### 'pyf98/slurp\\_entity\\_conformer'\n\n\nThis model was trained by Yifan Peng using slurp\\_entity recipe in espnet.",
"### Demo: How to use in ESPnet2\n\n\nRESULTS\n=======\n\n\nEnvironments\n------------\n\n\n* date: 'Thu May 26 14:51:29 EDT 2022'\n* python version: '3.9.12 (main, Apr 5 2022, 06:56:58) [GCC... | [
"TAGS\n#espnet #audio #automatic-speech-recognition #en #dataset-slurp_entity #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"### 'pyf98/slurp\\_entity\\_conformer'\n\n\nThis model was trained by Yifan Peng using slurp\\_entity recipe in espnet.",
"### Demo: How to use in ESPnet2\n\n\nRESULTS\n=======\n\n... |
reinforcement-learning | null |
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="makram/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attri... | {"tags": ["FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-FrozenLake-v1-4x4-noSlippery", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "FrozenLake-v1-4x4-no_slippery", "type": ... | makram/q-FrozenLake-v1-4x4-noSlippery | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-05-27T23:12:46+00:00 | [] | [] | TAGS
#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing FrozenLake-v1
This is a trained model of a Q-Learning agent playing FrozenLake-v1 .
## Usage
| [
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] | [
"TAGS\n#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] |
text2text-generation | 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. -->
# checkpoint-1000
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
... | {"tags": ["generated_from_keras_callback"], "model-index": [{"name": "checkpoint-1000", "results": []}]} | Julietheg/checkpoint-1000 | null | [
"transformers",
"pytorch",
"tf",
"jax",
"t5",
"text2text-generation",
"generated_from_keras_callback",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-05-27T23:31:52+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #jax #t5 #text2text-generation #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# checkpoint-1000
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training proced... | [
"# checkpoint-1000\n\nThis model was trained from scratch on an unknown dataset.\nIt achieves the following results on the evaluation set:",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information need... | [
"TAGS\n#transformers #pytorch #tf #jax #t5 #text2text-generation #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# checkpoint-1000\n\nThis model was trained from scratch on an unknown dataset.\nIt achieves the following results on the evaluati... |
reinforcement-learning | null |
# **Q-Learning** Agent playing **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="bguan/q-Taxi-v3-500Ksteps", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False... | {"tags": ["Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-Taxi-v3-500Ksteps", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Taxi-v3", "type": "Taxi-v3"}, "metrics": [{"type": "mean_reward", "value"... | bguan/q-Taxi-v3-500Ksteps | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-05-27T23:37:25+00:00 | [] | [] | TAGS
#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing Taxi-v3
This is a trained model of a Q-Learning agent playing Taxi-v3 .
## Usage
| [
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] | [
"TAGS\n#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] |
automatic-speech-recognition | espnet |
## ESPnet2 ASR model
### `pyf98/slurp_entity_branchformer`
This model was trained by Yifan Peng using slurp_entity recipe in [espnet](https://github.com/espnet/espnet/).
Branchformer (Peng et al., ICML 2022): [https://proceedings.mlr.press/v162/peng22a.html](https://proceedings.mlr.press/v162/peng22a.html)
### De... | {"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "automatic-speech-recognition"], "datasets": ["slurp_entity"]} | pyf98/slurp_entity_branchformer | null | [
"espnet",
"audio",
"automatic-speech-recognition",
"en",
"dataset:slurp_entity",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | null | 2022-05-27T23:40:17+00:00 | [
"1804.00015"
] | [
"en"
] | TAGS
#espnet #audio #automatic-speech-recognition #en #dataset-slurp_entity #arxiv-1804.00015 #license-cc-by-4.0 #region-us
| ESPnet2 ASR model
-----------------
### 'pyf98/slurp\_entity\_branchformer'
This model was trained by Yifan Peng using slurp\_entity recipe in espnet.
Branchformer (Peng et al., ICML 2022): URL
### Demo: How to use in ESPnet2
RESULTS
=======
Environments
------------
* date: 'Fri May 27 03:41:59 EDT 2022'... | [
"### 'pyf98/slurp\\_entity\\_branchformer'\n\n\nThis model was trained by Yifan Peng using slurp\\_entity recipe in espnet.\n\n\nBranchformer (Peng et al., ICML 2022): URL",
"### Demo: How to use in ESPnet2\n\n\nRESULTS\n=======\n\n\nEnvironments\n------------\n\n\n* date: 'Fri May 27 03:41:59 EDT 2022'\n* python... | [
"TAGS\n#espnet #audio #automatic-speech-recognition #en #dataset-slurp_entity #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"### 'pyf98/slurp\\_entity\\_branchformer'\n\n\nThis model was trained by Yifan Peng using slurp\\_entity recipe in espnet.\n\n\nBranchformer (Peng et al., ICML 2022): URL",
"### De... |
text2text-generation | transformers | Este modelo busca generar el titulo de un texto, se tomo como base el articulo:
https://medium.com/nlplanet/a-full-guide-to-finetuning-t5-for-text2text-and-building-a-demo-with-streamlit-c72009631887
Se entreno el modelo con 500 elementos del dataset
Genera el titulo del texto | {"license": "afl-3.0"} | sanbohork/t5 | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-05-28T01:18:58+00:00 | [] | [] | TAGS
#transformers #pytorch #t5 #text2text-generation #license-afl-3.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| Este modelo busca generar el titulo de un texto, se tomo como base el articulo:
URL
Se entreno el modelo con 500 elementos del dataset
Genera el titulo del texto | [] | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #license-afl-3.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
reinforcement-learning | null |
# **Q-Learning** Agent playing **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="vincentbonnet/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False e... | {"tags": ["Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-Taxi-v3", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Taxi-v3", "type": "Taxi-v3"}, "metrics": [{"type": "mean_reward", "value": "-99.00 ... | vincentbonnet/q-Taxi-v3 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-05-28T02:19:00+00:00 | [] | [] | TAGS
#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing Taxi-v3
This is a trained model of a Q-Learning agent playing Taxi-v3 .
## Usage
| [
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] | [
"TAGS\n#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] |
reinforcement-learning | null |
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="vebie91/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attr... | {"tags": ["FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-FrozenLake-v1-4x4-noSlippery", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "FrozenLake-v1-4x4-no_slippery", "type": ... | vebie91/q-FrozenLake-v1-4x4-noSlippery | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-05-28T02:39:51+00:00 | [] | [] | TAGS
#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing FrozenLake-v1
This is a trained model of a Q-Learning agent playing FrozenLake-v1 .
## Usage
| [
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] | [
"TAGS\n#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] |
reinforcement-learning | null |
# **Q-Learning** Agent playing **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="vebie91/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
... | {"tags": ["Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-Taxi-v3", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Taxi-v3", "type": "Taxi-v3"}, "metrics": [{"type": "mean_reward", "value": "7.56 +/... | vebie91/q-Taxi-v3 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-05-28T02:47:26+00:00 | [] | [] | TAGS
#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing Taxi-v3
This is a trained model of a Q-Learning agent playing Taxi-v3 .
## Usage
| [
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] | [
"TAGS\n#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] |
null | null |
[](https://arxiv.org/abs/2204.12463)
# Focal Sparse Convolutional Networks for 3D Object Detection (CVPR 2022, Oral)
This is the official implementation of ***Focals Conv*** (CVPR 2022), a new sparse convolution design for 3D object detection (feasible fo... | {"language": ["Python"], "tags": ["Sparse Conv", "3D Object Detection"], "datasets": ["KITTI", "nuScenes"], "thumbnail": "https://github.com/dvlab-research/FocalsConv"} | Yukang/FocalsConv | null | [
"Sparse Conv",
"3D Object Detection",
"dataset:KITTI",
"dataset:nuScenes",
"arxiv:2204.12463",
"region:us"
] | null | 2022-05-28T03:03:18+00:00 | [
"2204.12463"
] | [
"Python"
] | TAGS
#Sparse Conv #3D Object Detection #dataset-KITTI #dataset-nuScenes #arxiv-2204.12463 #region-us
| 
=============================================================================
This is the official implementation of *Focals Conv* (CVPR 2022), a new sparse convolution design for 3D object detection (feasible for both lidar-... | [
"#### KITTI dataset",
"#### nuScenes dataset\n\n\n\nIf you find this project useful in your research, please consider citing:\n\n\nLicense\n-------\n\n\nThis project is released under the Apache 2.0 license."
] | [
"TAGS\n#Sparse Conv #3D Object Detection #dataset-KITTI #dataset-nuScenes #arxiv-2204.12463 #region-us \n",
"#### KITTI dataset",
"#### nuScenes dataset\n\n\n\nIf you find this project useful in your research, please consider citing:\n\n\nLicense\n-------\n\n\nThis project is released under the Apache 2.0 licen... |
text2text-generation | transformers | Este modelo ha sido creado a partir de T5 Fine tuning with PyTorch.ipynb de Shivanand Roy y entrenado con un dataset de noticias de un diario uruguayo, en el repositorio se encuentra todos los archivos resultante del procesos de entrenamiento | {} | edharepe/T5_generacion_titulos | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-05-28T03:19:30+00:00 | [] | [] | TAGS
#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| Este modelo ha sido creado a partir de T5 Fine tuning with URL de Shivanand Roy y entrenado con un dataset de noticias de un diario uruguayo, en el repositorio se encuentra todos los archivos resultante del procesos de entrenamiento | [] | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
null | null |
# DCGAN to generate face images
This trained model is a keras implementation of DCGAN that is trained on face images.
| {"license": "mit"} | egesko/CodeSprint_DCGAN | null | [
"license:mit",
"region:us"
] | null | 2022-05-28T04:19:07+00:00 | [] | [] | TAGS
#license-mit #region-us
|
# DCGAN to generate face images
This trained model is a keras implementation of DCGAN that is trained on face images.
| [
"# DCGAN to generate face images\n\nThis trained model is a keras implementation of DCGAN that is trained on face images."
] | [
"TAGS\n#license-mit #region-us \n",
"# DCGAN to generate face images\n\nThis trained model is a keras implementation of DCGAN that is trained on face images."
] |
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. -->
# ty_punctuator
This model is a fine-tuned version of [kktoto/kt_punc](https://huggingface.co/kktoto/kt_punc) on an unknown datase... | {"tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "ty_punctuator", "results": []}]} | kktoto/ty_punctuator | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-28T04:36:55+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| ty\_punctuator
==============
This model is a fine-tuned version of kktoto/kt\_punc on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0937
* Precision: 0.7436
* Recall: 0.7694
* F1: 0.7563
* Accuracy: 0.9656
Model description
-----------------
More information needed
In... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #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: 16\n* eval\\... |
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": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["amazon_reviews_multi"], "model-index": [{"name": "roberta-base-bne-finetuned-amazon_reviews_multi", "results": []}]} | PDRES/roberta-base-bne-finetuned-amazon_reviews_multi | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"dataset:amazon_reviews_multi",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-28T05:10:26+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #dataset-amazon_reviews_multi #license-apache-2.0 #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.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
##... | [
"# roberta-base-bne-finetuned-amazon_reviews_multi\n\nThis model is a fine-tuned version of BSC-TeMU/roberta-base-bne on the amazon_reviews_multi dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore ... | [
"TAGS\n#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #dataset-amazon_reviews_multi #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# roberta-base-bne-finetuned-amazon_reviews_multi\n\nThis model is a fine-tuned version of BSC-TeMU/robert... |
token-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. -->
# chanifrusydi/indobert-finetuned-ner
This model is a fine-tuned version of [indobenchmark/indobert-base-p1](https://huggingface.co/indo... | {"license": "mit", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "chanifrusydi/indobert-finetuned-ner", "results": []}]} | chanifrusydi/indobert-finetuned-ner | null | [
"transformers",
"tf",
"bert",
"token-classification",
"generated_from_keras_callback",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-28T06:06:22+00:00 | [] | [] | TAGS
#transformers #tf #bert #token-classification #generated_from_keras_callback #license-mit #autotrain_compatible #endpoints_compatible #region-us
| chanifrusydi/indobert-finetuned-ner
===================================
This model is a fine-tuned version of indobenchmark/indobert-base-p1 on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.1190
* Validation Loss: 0.1903
* Epoch: 2
Model description
-----------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_steps': 312, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': F... | [
"TAGS\n#transformers #tf #bert #token-classification #generated_from_keras_callback #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': ... |
null | null | # CLEF 2022 CheckThatLab Task3
This is the repository of team **ur-iw-hnt**.
All TSV files of every model are available here: [GitHub Repository](https://github.com/HN-Tran/CLEF_2022_CheckThatLab_Task3)
Our fine-tuned models are available here. Click on "Files and versions" to navigate. | {} | hntran/CLEF_2022_CheckThatLab_Task3 | null | [
"region:us"
] | null | 2022-05-28T06:18:58+00:00 | [] | [] | TAGS
#region-us
| # CLEF 2022 CheckThatLab Task3
This is the repository of team ur-iw-hnt.
All TSV files of every model are available here: GitHub Repository
Our fine-tuned models are available here. Click on "Files and versions" to navigate. | [
"# CLEF 2022 CheckThatLab Task3\n\nThis is the repository of team ur-iw-hnt. \nAll TSV files of every model are available here: GitHub Repository \nOur fine-tuned models are available here. Click on \"Files and versions\" to navigate."
] | [
"TAGS\n#region-us \n",
"# CLEF 2022 CheckThatLab Task3\n\nThis is the repository of team ur-iw-hnt. \nAll TSV files of every model are available here: GitHub Repository \nOur fine-tuned models are available here. Click on \"Files and versions\" to navigate."
] |
reinforcement-learning | null |
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="devetle/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attr... | {"tags": ["FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-FrozenLake-v1-4x4-noSlippery", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "FrozenLake-v1-4x4-no_slippery", "type": ... | devetle/q-FrozenLake-v1-4x4-noSlippery | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-05-28T06:27:33+00:00 | [] | [] | TAGS
#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing FrozenLake-v1
This is a trained model of a Q-Learning agent playing FrozenLake-v1 .
## Usage
| [
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] | [
"TAGS\n#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] |
reinforcement-learning | null |
# **Q-Learning** Agent playing **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="devetle/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
... | {"tags": ["Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-Taxi-v3", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Taxi-v3", "type": "Taxi-v3"}, "metrics": [{"type": "mean_reward", "value": "7.56 +/... | devetle/q-Taxi-v3 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-05-28T07:03:39+00:00 | [] | [] | TAGS
#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing Taxi-v3
This is a trained model of a Q-Learning agent playing Taxi-v3 .
## Usage
| [
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] | [
"TAGS\n#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] |
question-answering | 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. -->
# Sounak/bert-large-finetuned
This model is a fine-tuned version of [bert-large-uncased-whole-word-masking-finetuned-squad](https://hugg... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "Sounak/bert-large-finetuned", "results": []}]} | Sounak/bert-large-finetuned | null | [
"transformers",
"tf",
"bert",
"question-answering",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-05-28T07:14:08+00:00 | [] | [] | TAGS
#transformers #tf #bert #question-answering #generated_from_keras_callback #license-apache-2.0 #endpoints_compatible #region-us
| Sounak/bert-large-finetuned
===========================
This model is a fine-tuned version of bert-large-uncased-whole-word-masking-finetuned-squad on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 1.7634
* Validation Loss: 1.6843
* Epoch: 0
Model description
----------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_steps': 157, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': F... | [
"TAGS\n#transformers #tf #bert #question-answering #generated_from_keras_callback #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': ... |
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. -->
# wangchanberta-base-att-spm-uncased-finetuned-imdb
This model is a fine-tuned version of [airesearch/wangchanberta-base-att-spm-u... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "wangchanberta-base-att-spm-uncased-finetuned-imdb", "results": []}]} | bookpanda/wangchanberta-base-att-spm-uncased-finetuned-imdb | null | [
"transformers",
"pytorch",
"tensorboard",
"camembert",
"fill-mask",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-28T07:22:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #camembert #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| wangchanberta-base-att-spm-uncased-finetuned-imdb
=================================================
This model is a fine-tuned version of airesearch/wangchanberta-base-att-spm-uncased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0810
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: 5\n* mixed\\_precis... | [
"TAGS\n#transformers #pytorch #tensorboard #camembert #fill-mask #generated_from_trainer #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: 8\n* eval\\_batch\... |
text-classification | transformers |
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 920730227
- CO2 Emissions (in grams): 0.06170374019107819
## Validation Metrics
- Loss: 0.5905918478965759
- Accuracy: 0.8687837028160575
- Macro F1: 0.7777187122151491
- Micro F1: 0.8687837028160575
- Weighted F1: 0.867323016681... | {"language": "ar", "tags": "autotrain", "datasets": ["zenkri/autotrain-data-Arabic_Poetry_by_Subject-1d8ba412"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 0.06170374019107819} | zenkri/autotrain-Arabic_Poetry_by_Subject-920730227 | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain",
"ar",
"dataset:zenkri/autotrain-data-Arabic_Poetry_by_Subject-1d8ba412",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-28T07:32:39+00:00 | [] | [
"ar"
] | TAGS
#transformers #pytorch #bert #text-classification #autotrain #ar #dataset-zenkri/autotrain-data-Arabic_Poetry_by_Subject-1d8ba412 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 920730227
- CO2 Emissions (in grams): 0.06170374019107819
## Validation Metrics
- Loss: 0.5905918478965759
- Accuracy: 0.8687837028160575
- Macro F1: 0.7777187122151491
- Micro F1: 0.8687837028160575
- Weighted F1: 0.867323016681... | [
"# Model Trained Using AutoTrain\n\n- Problem type: Multi-class Classification\n- Model ID: 920730227\n- CO2 Emissions (in grams): 0.06170374019107819",
"## Validation Metrics\n\n- Loss: 0.5905918478965759\n- Accuracy: 0.8687837028160575\n- Macro F1: 0.7777187122151491\n- Micro F1: 0.8687837028160575\n- Weighted ... | [
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"# Model Trained Using AutoTrain\n\n- Problem type: Multi-class Classification\n- Model ID: 9207302... |
text-classification | transformers |
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 920730230
- CO2 Emissions (in grams): 0.07445219847409645
## Validation Metrics
- Loss: 0.5806193351745605
- Accuracy: 0.8785200718993409
- Macro F1: 0.8208042310550474
- Micro F1: 0.8785200718993409
- Weighted F1: 0.878359036580... | {"language": "ar", "tags": "autotrain", "datasets": ["zenkri/autotrain-data-Arabic_Poetry_by_Subject-1d8ba412"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 0.07445219847409645} | zenkri/autotrain-Arabic_Poetry_by_Subject-920730230 | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain",
"ar",
"dataset:zenkri/autotrain-data-Arabic_Poetry_by_Subject-1d8ba412",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-05-28T07:33:05+00:00 | [] | [
"ar"
] | TAGS
#transformers #pytorch #bert #text-classification #autotrain #ar #dataset-zenkri/autotrain-data-Arabic_Poetry_by_Subject-1d8ba412 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 920730230
- CO2 Emissions (in grams): 0.07445219847409645
## Validation Metrics
- Loss: 0.5806193351745605
- Accuracy: 0.8785200718993409
- Macro F1: 0.8208042310550474
- Micro F1: 0.8785200718993409
- Weighted F1: 0.878359036580... | [
"# Model Trained Using AutoTrain\n\n- Problem type: Multi-class Classification\n- Model ID: 920730230\n- CO2 Emissions (in grams): 0.07445219847409645",
"## Validation Metrics\n\n- Loss: 0.5806193351745605\n- Accuracy: 0.8785200718993409\n- Macro F1: 0.8208042310550474\n- Micro F1: 0.8785200718993409\n- Weighted ... | [
"TAGS\n#transformers #pytorch #bert #text-classification #autotrain #ar #dataset-zenkri/autotrain-data-Arabic_Poetry_by_Subject-1d8ba412 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# Model Trained Using AutoTrain\n\n- Problem type: Multi-class Classification\n- Model ... |
fill-mask | transformers |
# deberta-small-coptic
## Model Description
This is a DeBERTa(V2) model pre-trained on Coptic Scriptorium Corpora. You can fine-tune `deberta-small-coptic` for downstream tasks, such as [POS-tagging](https://huggingface.co/KoichiYasuoka/deberta-small-coptic-upos), dependency-parsing, and so on.
## How to Use
```py... | {"language": ["cop"], "license": "cc-by-sa-4.0", "tags": ["coptic", "masked-lm"], "pipeline_tag": "fill-mask", "mask_token": "[MASK]"} | KoichiYasuoka/deberta-small-coptic | null | [
"transformers",
"pytorch",
"deberta-v2",
"fill-mask",
"coptic",
"masked-lm",
"cop",
"license:cc-by-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-28T07:45:35+00:00 | [] | [
"cop"
] | TAGS
#transformers #pytorch #deberta-v2 #fill-mask #coptic #masked-lm #cop #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us
|
# deberta-small-coptic
## Model Description
This is a DeBERTa(V2) model pre-trained on Coptic Scriptorium Corpora. You can fine-tune 'deberta-small-coptic' for downstream tasks, such as POS-tagging, dependency-parsing, and so on.
## How to Use
| [
"# deberta-small-coptic",
"## Model Description\n\nThis is a DeBERTa(V2) model pre-trained on Coptic Scriptorium Corpora. You can fine-tune 'deberta-small-coptic' for downstream tasks, such as POS-tagging, dependency-parsing, and so on.",
"## How to Use"
] | [
"TAGS\n#transformers #pytorch #deberta-v2 #fill-mask #coptic #masked-lm #cop #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# deberta-small-coptic",
"## Model Description\n\nThis is a DeBERTa(V2) model pre-trained on Coptic Scriptorium Corpora. You can fine-tune 'deberta-smal... |
token-classification | transformers |
# deberta-small-coptic-upos
## Model Description
This is a DeBERTa(V2) model pre-trained with [UD_Coptic](https://universaldependencies.org/cop/) for POS-tagging and dependency-parsing, derived from [deberta-small-coptic](https://huggingface.co/KoichiYasuoka/deberta-small-coptic). Every word is tagged by [UPOS](http... | {"language": ["cop"], "license": "cc-by-sa-4.0", "tags": ["coptic", "token-classification", "pos", "dependency-parsing"], "datasets": ["universal_dependencies"], "pipeline_tag": "token-classification", "widget": [{"text": "\u2ca7\u2c89\u2c9b\u2c9f\u2ca9\u2c87\u2c89\u2c9b\u0304\u2c9f\u2ca9\u2c9f\u2c89\u2c93\u2c9b\u03e9\... | KoichiYasuoka/deberta-small-coptic-upos | null | [
"transformers",
"pytorch",
"deberta-v2",
"token-classification",
"coptic",
"pos",
"dependency-parsing",
"cop",
"dataset:universal_dependencies",
"license:cc-by-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-28T07:56:16+00:00 | [] | [
"cop"
] | TAGS
#transformers #pytorch #deberta-v2 #token-classification #coptic #pos #dependency-parsing #cop #dataset-universal_dependencies #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us
|
# deberta-small-coptic-upos
## Model Description
This is a DeBERTa(V2) model pre-trained with UD_Coptic for POS-tagging and dependency-parsing, derived from deberta-small-coptic. Every word is tagged by UPOS (Universal Part-Of-Speech).
## How to Use
or
## See Also
esupar: Tokenizer POS-tagger and Dependency-... | [
"# deberta-small-coptic-upos",
"## Model Description\n\nThis is a DeBERTa(V2) model pre-trained with UD_Coptic for POS-tagging and dependency-parsing, derived from deberta-small-coptic. Every word is tagged by UPOS (Universal Part-Of-Speech).",
"## How to Use\n\n\n\nor",
"## See Also\n\nesupar: Tokenizer POS-... | [
"TAGS\n#transformers #pytorch #deberta-v2 #token-classification #coptic #pos #dependency-parsing #cop #dataset-universal_dependencies #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# deberta-small-coptic-upos",
"## Model Description\n\nThis is a DeBERTa(V2) model pre-trained ... |
fill-mask | transformers |
# deberta-base-coptic
## Model Description
This is a DeBERTa(V2) model pre-trained on Coptic Scriptorium Corpora. You can fine-tune `deberta-base-coptic` for downstream tasks, such as [POS-tagging](https://huggingface.co/KoichiYasuoka/deberta-base-coptic-upos), [dependency-parsing](https://huggingface.co/KoichiYasuo... | {"language": ["cop"], "license": "cc-by-sa-4.0", "tags": ["coptic", "masked-lm"], "pipeline_tag": "fill-mask", "mask_token": "[MASK]"} | KoichiYasuoka/deberta-base-coptic | null | [
"transformers",
"pytorch",
"deberta-v2",
"fill-mask",
"coptic",
"masked-lm",
"cop",
"license:cc-by-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-28T08:16:23+00:00 | [] | [
"cop"
] | TAGS
#transformers #pytorch #deberta-v2 #fill-mask #coptic #masked-lm #cop #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us
|
# deberta-base-coptic
## Model Description
This is a DeBERTa(V2) model pre-trained on Coptic Scriptorium Corpora. You can fine-tune 'deberta-base-coptic' for downstream tasks, such as POS-tagging, dependency-parsing, and so on.
## How to Use
| [
"# deberta-base-coptic",
"## Model Description\n\nThis is a DeBERTa(V2) model pre-trained on Coptic Scriptorium Corpora. You can fine-tune 'deberta-base-coptic' for downstream tasks, such as POS-tagging, dependency-parsing, and so on.",
"## How to Use"
] | [
"TAGS\n#transformers #pytorch #deberta-v2 #fill-mask #coptic #masked-lm #cop #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# deberta-base-coptic",
"## Model Description\n\nThis is a DeBERTa(V2) model pre-trained on Coptic Scriptorium Corpora. You can fine-tune 'deberta-base-... |
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. -->
# bart-large-cnn-pubmed1o3-pubmed2o3-pubmed3o3
This model is a fine-tuned version of [theojolliffe/bart-large-cnn-pubmed1o3-pubmed... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["scientific_papers"], "metrics": ["rouge"], "model-index": [{"name": "bart-large-cnn-pubmed1o3-pubmed2o3-pubmed3o3", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "scientifi... | theojolliffe/bart-large-cnn-pubmed1o3-pubmed2o3-pubmed3o3 | null | [
"transformers",
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"generated_from_trainer",
"dataset:scientific_papers",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-28T08:19:17+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bart #text2text-generation #generated_from_trainer #dataset-scientific_papers #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| bart-large-cnn-pubmed1o3-pubmed2o3-pubmed3o3
============================================
This model is a fine-tuned version of theojolliffe/bart-large-cnn-pubmed1o3-pubmed2o3 on the scientific\_papers dataset.
It achieves the following results on the evaluation set:
* Loss: 1.8540
* Rouge1: 37.5622
* Rouge2: 15.58... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 2\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 #bart #text2text-generation #generated_from_trainer #dataset-scientific_papers #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* learnin... |
token-classification | transformers |
# deberta-base-coptic-upos
## Model Description
This is a DeBERTa(V2) model pre-trained with [UD_Coptic](https://universaldependencies.org/cop/) for POS-tagging and dependency-parsing, derived from [deberta-base-coptic](https://huggingface.co/KoichiYasuoka/deberta-base-coptic). Every word is tagged by [UPOS](https:/... | {"language": ["cop"], "license": "cc-by-sa-4.0", "tags": ["coptic", "token-classification", "pos", "dependency-parsing"], "datasets": ["universal_dependencies"], "pipeline_tag": "token-classification", "widget": [{"text": "\u2ca7\u2c89\u2c9b\u2c9f\u2ca9\u2c87\u2c89\u2c9b\u0304\u2c9f\u2ca9\u2c9f\u2c89\u2c93\u2c9b\u03e9\... | KoichiYasuoka/deberta-base-coptic-upos | null | [
"transformers",
"pytorch",
"deberta-v2",
"token-classification",
"coptic",
"pos",
"dependency-parsing",
"cop",
"dataset:universal_dependencies",
"license:cc-by-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-28T08:21:08+00:00 | [] | [
"cop"
] | TAGS
#transformers #pytorch #deberta-v2 #token-classification #coptic #pos #dependency-parsing #cop #dataset-universal_dependencies #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us
|
# deberta-base-coptic-upos
## Model Description
This is a DeBERTa(V2) model pre-trained with UD_Coptic for POS-tagging and dependency-parsing, derived from deberta-base-coptic. Every word is tagged by UPOS (Universal Part-Of-Speech).
## How to Use
or
## See Also
esupar: Tokenizer POS-tagger and Dependency-pa... | [
"# deberta-base-coptic-upos",
"## Model Description\n\nThis is a DeBERTa(V2) model pre-trained with UD_Coptic for POS-tagging and dependency-parsing, derived from deberta-base-coptic. Every word is tagged by UPOS (Universal Part-Of-Speech).",
"## How to Use\n\n\n\nor",
"## See Also\n\nesupar: Tokenizer POS-ta... | [
"TAGS\n#transformers #pytorch #deberta-v2 #token-classification #coptic #pos #dependency-parsing #cop #dataset-universal_dependencies #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# deberta-base-coptic-upos",
"## Model Description\n\nThis is a DeBERTa(V2) model pre-trained w... |
question-answering | transformers |
## 基于 chinese-pert-large 训练的面向开放领域MRC 模型
使用中文MRC数据（cmrc2018, webqa与laisi的训练集）训练的chinese-pert-large模型
## 训练过程
使用了[UER-py](https://github.com/dbiir/UER-py/) 进行fine-tuned
加入了包括但不限于摘要、负采样、混淆等数据加强方法
并转换为Huggingface进行上传
| | CMRC 2018 Dev | DRCD Dev | SQuAD-Zen Dev (Answerable) | AVG |
| :-------: | :-... | {"language": ["zh"], "license": "gpl-3.0"} | qalover/chinese-pert-large-open-domain-mrc | null | [
"transformers",
"pytorch",
"bert",
"question-answering",
"zh",
"license:gpl-3.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-05-28T08:31:16+00:00 | [] | [
"zh"
] | TAGS
#transformers #pytorch #bert #question-answering #zh #license-gpl-3.0 #endpoints_compatible #has_space #region-us
| 基于 chinese-pert-large 训练的面向开放领域MRC 模型
-------------------------------------
使用中文MRC数据（cmrc2018, webqa与laisi的训练集）训练的chinese-pert-large模型
训练过程
----
使用了UER-py 进行fine-tuned
加入了包括但不限于摘要、负采样、混淆等数据加强方法
并转换为Huggingface进行上传
| [] | [
"TAGS\n#transformers #pytorch #bert #question-answering #zh #license-gpl-3.0 #endpoints_compatible #has_space #region-us \n"
] |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-hindi
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-hindi", "results": []}]} | sriiikar/wav2vec2-hindi | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-05-28T08:40:48+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-hindi
==============
This model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 2.8814
* Wer: 1.0
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: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 1... |
reinforcement-learning | null |
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="Mugenor/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attr... | {"tags": ["FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-FrozenLake-v1-4x4-noSlippery", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "FrozenLake-v1-4x4-no_slippery", "type": ... | Mugenor/q-FrozenLake-v1-4x4-noSlippery | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-05-28T08:55:28+00:00 | [] | [] | TAGS
#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing FrozenLake-v1
This is a trained model of a Q-Learning agent playing FrozenLake-v1 .
## Usage
| [
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] | [
"TAGS\n#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] |
reinforcement-learning | null |
# **Q-Learning** Agent playing **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="Mugenor/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
... | {"tags": ["Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-Taxi-v3", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Taxi-v3", "type": "Taxi-v3"}, "metrics": [{"type": "mean_reward", "value": "7.56 +/... | Mugenor/q-Taxi-v3 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-05-28T09:07:33+00:00 | [] | [] | TAGS
#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing Taxi-v3
This is a trained model of a Q-Learning agent playing Taxi-v3 .
## Usage
| [
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] | [
"TAGS\n#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] |
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. -->
# extractive-question-answering
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "extractive-question-answering", "results": []}]} | autoevaluate/extractive-question-answering | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-05-28T10:03:58+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #has_space #region-us
| extractive-question-answering
=============================
This model is a fine-tuned version of distilbert-base-uncased on the squad dataset.
It achieves the following results on the evaluation set:
Model description
-----------------
More information needed
Intended uses & limitations
-----------------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\... |
text-classification | transformers |
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 921730254
- CO2 Emissions (in grams): 25.144394918865913
## Validation Metrics
- Loss: 0.7080970406532288
- Accuracy: 0.7775925925925926
- Macro F1: 0.7758012615987406
- Micro F1: 0.7775925925925925
- Weighted F1: 0.7758012615987... | {"language": "unk", "tags": "autotrain", "datasets": ["CH0KUN/autotrain-data-TNC_Domain_WangchanBERTa"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 25.144394918865913} | CH0KUN/autotrain-TNC_Domain_WangchanBERTa-921730254 | null | [
"transformers",
"pytorch",
"camembert",
"text-classification",
"autotrain",
"unk",
"dataset:CH0KUN/autotrain-data-TNC_Domain_WangchanBERTa",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-28T10:51:14+00:00 | [] | [
"unk"
] | TAGS
#transformers #pytorch #camembert #text-classification #autotrain #unk #dataset-CH0KUN/autotrain-data-TNC_Domain_WangchanBERTa #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 921730254
- CO2 Emissions (in grams): 25.144394918865913
## Validation Metrics
- Loss: 0.7080970406532288
- Accuracy: 0.7775925925925926
- Macro F1: 0.7758012615987406
- Micro F1: 0.7775925925925925
- Weighted F1: 0.7758012615987... | [
"# Model Trained Using AutoTrain\n\n- Problem type: Multi-class Classification\n- Model ID: 921730254\n- CO2 Emissions (in grams): 25.144394918865913",
"## Validation Metrics\n\n- Loss: 0.7080970406532288\n- Accuracy: 0.7775925925925926\n- Macro F1: 0.7758012615987406\n- Micro F1: 0.7775925925925925\n- Weighted F... | [
"TAGS\n#transformers #pytorch #camembert #text-classification #autotrain #unk #dataset-CH0KUN/autotrain-data-TNC_Domain_WangchanBERTa #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoTrain\n\n- Problem type: Multi-class Classification\n- Model ID: 921730254\... |
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. -->
# dbmdzBERTnews
This model is a fine-tuned version of [dbmdz/bert-base-italian-uncased](https://huggingface.co/dbmdz/bert-base-ita... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "dbmdzBERTnews", "results": []}]} | GioReg/dbmdzBERTnews | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-28T11:08:37+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# dbmdzBERTnews
This model is a fine-tuned version of dbmdz/bert-base-italian-uncased on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0960
- Accuracy: 0.9733
- F1: 0.9730
## Model description
More information needed
## Intended uses & limitations
More information needed
... | [
"# dbmdzBERTnews\n\nThis model is a fine-tuned version of dbmdz/bert-base-italian-uncased on the None dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.0960\n- Accuracy: 0.9733\n- F1: 0.9730",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore ... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# dbmdzBERTnews\n\nThis model is a fine-tuned version of dbmdz/bert-base-italian-uncased on the None dataset.\nIt achieves the following results o... |
text2text-generation | 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. -->
# t5-base-medium-title-generation
This model was trained from scratch on an unknown dataset.
It achieves the following results on the ev... | {"tags": ["generated_from_keras_callback"], "model-index": [{"name": "t5-base-medium-title-generation", "results": []}]} | LinaR/t5-base-medium-title-generation | null | [
"transformers",
"pytorch",
"tf",
"t5",
"text2text-generation",
"generated_from_keras_callback",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-05-28T11:12:52+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #t5 #text2text-generation #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# t5-base-medium-title-generation
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
##... | [
"# t5-base-medium-title-generation\n\nThis model was trained from scratch on an unknown dataset.\nIt achieves the following results on the evaluation set:",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore ... | [
"TAGS\n#transformers #pytorch #tf #t5 #text2text-generation #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# t5-base-medium-title-generation\n\nThis model was trained from scratch on an unknown dataset.\nIt achieves the following results on t... |
summarization | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# summarization
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the xsum dataset.
It achieves... | {"license": "apache-2.0", "tags": ["generated_from_trainer", "summarization"], "datasets": ["xsum", "autoevaluate/xsum-sample"], "metrics": ["rouge"], "model-index": [{"name": "summarization", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "x... | autoevaluate/summarization | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"summarization",
"dataset:xsum",
"dataset:autoevaluate/xsum-sample",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-infere... | null | 2022-05-28T11:27:47+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #summarization #dataset-xsum #dataset-autoevaluate/xsum-sample #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| summarization
=============
This model is a fine-tuned version of t5-small on the xsum dataset.
It achieves the following results on the evaluation set:
* Loss: 2.6690
* Rouge1: 23.9405
* Rouge2: 5.0879
* Rougel: 18.4981
* Rougelsum: 18.5032
* Gen Len: 18.7376
Model description
-----------------
More informatio... | [
"### 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* training\\_steps: 1000\n* mixed... | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #summarization #dataset-xsum #dataset-autoevaluate/xsum-sample #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"### Training hyperparamete... |
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. -->
# deeppavlov-framebank-full-5epochs
This model is a fine-tuned version of [DeepPavlov/rubert-base-cased](https://huggingface.co/De... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "deeppavlov-framebank-full-5epochs", "results": []}]} | ruselkomp/deeppavlov-framebank-full-5epochs | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"question-answering",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | null | 2022-05-28T11:29:12+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #endpoints_compatible #region-us
| deeppavlov-framebank-full-5epochs
=================================
This model is a fine-tuned version of DeepPavlov/rubert-base-cased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 1.4206
Model description
-----------------
More information needed
Intended uses & limita... | [
"### 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: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* see... |
text-generation | transformers |
#DialoGPT-medium-sherlock-bot | {"tags": ["conversational"]} | badlawyer/DialoGPT-medium-sherlock-bot | null | [
"transformers",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-05-28T11:41:19+00:00 | [] | [] | TAGS
#transformers #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
#DialoGPT-medium-sherlock-bot | [] | [
"TAGS\n#transformers #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
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. -->
# deberta-base-combined-squad1-aqa-and-newsqa
This model is a fine-tuned version of [stevemobs/deberta-base-combined-squad1-aqa](h... | {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "deberta-base-combined-squad1-aqa-and-newsqa", "results": []}]} | stevemobs/deberta-base-combined-squad1-aqa-and-newsqa | null | [
"transformers",
"pytorch",
"tensorboard",
"deberta",
"question-answering",
"generated_from_trainer",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-05-28T12:27:01+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #deberta #question-answering #generated_from_trainer #license-mit #endpoints_compatible #region-us
| deberta-base-combined-squad1-aqa-and-newsqa
===========================================
This model is a fine-tuned version of stevemobs/deberta-base-combined-squad1-aqa on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.7527
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: 12\n* eval\\_batch\\_size: 12\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 #deberta #question-answering #generated_from_trainer #license-mit #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 12\n* eval\\_batch\\... |
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. -->
# multi-class-classification
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-bas... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["emotion"], "metrics": ["accuracy"]} | autoevaluate/multi-class-classification | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-28T12:27:42+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| multi-class-classification
==========================
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: 0.2009
* Accuracy: 0.928
Model description
-----------------
More information needed
Intended uses & limi... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #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: 2... |
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. -->
# umbertoBERTnews
This model is a fine-tuned version of [Musixmatch/umberto-commoncrawl-cased-v1](https://huggingface.co/Musixmatc... | {"tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "umbertoBERTnews", "results": []}]} | GioReg/umbertoBERTnews | null | [
"transformers",
"pytorch",
"tensorboard",
"camembert",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-28T12:46:41+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #camembert #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
|
# umbertoBERTnews
This model is a fine-tuned version of Musixmatch/umberto-commoncrawl-cased-v1 on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0847
- Accuracy: 0.9798
- F1: 0.9798
## Model description
More information needed
## Intended uses & limitations
More informatio... | [
"# umbertoBERTnews\n\nThis model is a fine-tuned version of Musixmatch/umberto-commoncrawl-cased-v1 on the None dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.0847\n- Accuracy: 0.9798\n- F1: 0.9798",
"## Model description\n\nMore information needed",
"## Intended uses & limitation... | [
"TAGS\n#transformers #pytorch #tensorboard #camembert #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"# umbertoBERTnews\n\nThis model is a fine-tuned version of Musixmatch/umberto-commoncrawl-cased-v1 on the None dataset.\nIt achieves the following results... |
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. -->
# translation
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-ro](https://huggingface.co/Helsinki-NLP/opus-mt-en-ro... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["wmt16", "autoevaluate/wmt16-sample"], "metrics": ["bleu"], "model-index": [{"name": "translation", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "wmt16", "type": "wm... | autoevaluate/translation | null | [
"transformers",
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"generated_from_trainer",
"dataset:wmt16",
"dataset:autoevaluate/wmt16-sample",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-28T13:14:40+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #marian #text2text-generation #generated_from_trainer #dataset-wmt16 #dataset-autoevaluate/wmt16-sample #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| translation
===========
This model is a fine-tuned version of Helsinki-NLP/opus-mt-en-ro on the wmt16 dataset.
It achieves the following results on the evaluation set:
* Loss: 1.3170
* Bleu: 28.5866
* Gen Len: 33.9575
Model description
-----------------
More information needed
Intended uses & limitations
----... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 1000\n* mixed... | [
"TAGS\n#transformers #pytorch #tensorboard #marian #text2text-generation #generated_from_trainer #dataset-wmt16 #dataset-autoevaluate/wmt16-sample #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used... |
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. -->
# mBERTrecensioni
This model is a fine-tuned version of [bert-base-multilingual-uncased](https://huggingface.co/bert-base-multilin... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "mBERTrecensioni", "results": []}]} | GioReg/mBERTrecensioni | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-28T14:05:44+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# mBERTrecensioni
This model is a fine-tuned version of bert-base-multilingual-uncased on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperpara... | [
"# mBERTrecensioni\n\nThis model is a fine-tuned version of bert-base-multilingual-uncased on the None dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedu... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# mBERTrecensioni\n\nThis model is a fine-tuned version of bert-base-multilingual-uncased on the None dataset.",
"## Model description\n\... |
text-generation | transformers |
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1509960920449093633/c0in... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/vox_akuma/1655609164156/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/vox_akuma | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-05-28T14:10:18+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Vox Akuma NIJISANJI EN
@vox\_akuma
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training dat... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
fill-mask | 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. -->
# lektay
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown d... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "lektay", "results": []}]} | bigmorning/lektay | null | [
"transformers",
"tf",
"distilbert",
"fill-mask",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-28T14:23:30+00:00 | [] | [] | TAGS
#transformers #tf #distilbert #fill-mask #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# lektay
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
#... | [
"# lektay\n\nThis model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.\nIt achieves the following results on the evaluation set:",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore... | [
"TAGS\n#transformers #tf #distilbert #fill-mask #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# lektay\n\nThis model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.\nIt achieves the following results on the evaluation set:... |
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. -->
# bart-large-cnn-pubmed1o3-pubmed2o3-pubmed3o3-arxiv1o3
This model is a fine-tuned version of [theojolliffe/bart-large-cnn-pubmed1... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["scientific_papers"], "metrics": ["rouge"], "model-index": [{"name": "bart-large-cnn-pubmed1o3-pubmed2o3-pubmed3o3-arxiv1o3", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "... | theojolliffe/bart-large-cnn-pubmed1o3-pubmed2o3-pubmed3o3-arxiv1o3 | null | [
"transformers",
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"generated_from_trainer",
"dataset:scientific_papers",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-28T14:31:03+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bart #text2text-generation #generated_from_trainer #dataset-scientific_papers #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| bart-large-cnn-pubmed1o3-pubmed2o3-pubmed3o3-arxiv1o3
=====================================================
This model is a fine-tuned version of theojolliffe/bart-large-cnn-pubmed1o3-pubmed2o3-pubmed3o3 on the scientific\_papers dataset.
It achieves the following results on the evaluation set:
* Loss: 2.1825
* Rou... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 2\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 #bart #text2text-generation #generated_from_trainer #dataset-scientific_papers #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* learnin... |
fill-mask | 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. -->
# train_basic_M_V3
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "train_basic_M_V3", "results": []}]} | FritzOS/train_basic_M_V3 | null | [
"transformers",
"tf",
"distilbert",
"fill-mask",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-28T14:43:24+00:00 | [] | [] | TAGS
#transformers #tf #distilbert #fill-mask #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# train_basic_M_V3
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
## Model description
masked Language Model for genome sequences with protein families (token). Based on DistilBERT
## Intended uses & limitations
for ... | [
"# train_basic_M_V3\n\nThis model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.\nIt achieves the following results on the evaluation set:",
"## Model description\n\nmasked Language Model for genome sequences with protein families (token). Based on DistilBERT",
"## Intended uses & lim... | [
"TAGS\n#transformers #tf #distilbert #fill-mask #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# train_basic_M_V3\n\nThis model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.\nIt achieves the following results on the evalu... |
text-to-speech | espnet |
## ESPnet2 TTS model
### `imdanboy/jets`
This model was trained by imdanboy using ljspeech recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```bash
cd espnet
git checkout c173c30930631731e6836c274a591ad571749741
pip install -e .
cd egs2/ljspeech/tts1
./run.sh --skip_data_prep... | {"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "text-to-speech"], "datasets": ["ljspeech"]} | imdanboy/jets | null | [
"espnet",
"audio",
"text-to-speech",
"en",
"dataset:ljspeech",
"arxiv:1804.00015",
"license:cc-by-4.0",
"has_space",
"region:us"
] | null | 2022-05-28T15:23:06+00:00 | [
"1804.00015"
] | [
"en"
] | TAGS
#espnet #audio #text-to-speech #en #dataset-ljspeech #arxiv-1804.00015 #license-cc-by-4.0 #has_space #region-us
|
## ESPnet2 TTS model
### 'imdanboy/jets'
This model was trained by imdanboy using ljspeech recipe in espnet.
### Demo: How to use in ESPnet2
## TTS config
<details><summary>expand</summary>
</details>
### Citing ESPnet
or arXiv:
| [
"## ESPnet2 TTS model",
"### 'imdanboy/jets'\n\nThis model was trained by imdanboy using ljspeech recipe in espnet.",
"### 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:"
] | [
"TAGS\n#espnet #audio #text-to-speech #en #dataset-ljspeech #arxiv-1804.00015 #license-cc-by-4.0 #has_space #region-us \n",
"## ESPnet2 TTS model",
"### 'imdanboy/jets'\n\nThis model was trained by imdanboy using ljspeech recipe in espnet.",
"### Demo: How to use in ESPnet2",
"## TTS config\n\n<details><sum... |
text-to-speech | espnet |
## ESPnet2 TTS model
### `imdanboy/ljspeech_tts_train_jets_raw_phn_tacotron_g2p_en_no_space_train.total_count.ave`
This model was trained by imdanboy using ljspeech recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```bash
cd espnet
git checkout c173c30930631731e6836c274a591ad... | {"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "text-to-speech"], "datasets": ["ljspeech"]} | imdanboy/ljspeech_tts_train_jets_raw_phn_tacotron_g2p_en_no_space_train.total_count.ave | null | [
"espnet",
"audio",
"text-to-speech",
"en",
"dataset:ljspeech",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | null | 2022-05-28T15:51:54+00:00 | [
"1804.00015"
] | [
"en"
] | TAGS
#espnet #audio #text-to-speech #en #dataset-ljspeech #arxiv-1804.00015 #license-cc-by-4.0 #region-us
|
## ESPnet2 TTS model
### 'imdanboy/ljspeech_tts_train_jets_raw_phn_tacotron_g2p_en_no_space_train.total_count.ave'
This model was trained by imdanboy using ljspeech recipe in espnet.
### Demo: How to use in ESPnet2
## TTS config
<details><summary>expand</summary>
</details>
### Citing ESPnet
or arXi... | [
"## ESPnet2 TTS model",
"### 'imdanboy/ljspeech_tts_train_jets_raw_phn_tacotron_g2p_en_no_space_train.total_count.ave'\n\nThis model was trained by imdanboy using ljspeech recipe in espnet.",
"### Demo: How to use in ESPnet2",
"## TTS config\n\n<details><summary>expand</summary>\n\n\n\n</details>",
"### Cit... | [
"TAGS\n#espnet #audio #text-to-speech #en #dataset-ljspeech #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"## ESPnet2 TTS model",
"### 'imdanboy/ljspeech_tts_train_jets_raw_phn_tacotron_g2p_en_no_space_train.total_count.ave'\n\nThis model was trained by imdanboy using ljspeech recipe in espnet.",
"### ... |
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. -->
# bart-finetuned-cnn-3
This model is a fine-tuned version of [sshleifer/distilbart-xsum-12-3](https://huggingface.co/sshleifer/dis... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["cnn_dailymail"], "metrics": ["rouge"], "model-index": [{"name": "bart-finetuned-cnn-3", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "cnn_dailymail", "type": "cnn_d... | nizamudma/bart-finetuned-cnn-3 | null | [
"transformers",
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"generated_from_trainer",
"dataset:cnn_dailymail",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-28T16:30:32+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bart #text2text-generation #generated_from_trainer #dataset-cnn_dailymail #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| bart-finetuned-cnn-3
====================
This model is a fine-tuned version of sshleifer/distilbart-xsum-12-3 on the cnn\_dailymail dataset.
It achieves the following results on the evaluation set:
* Loss: 2.0751
* Rouge1: 40.201
* Rouge2: 18.8482
* Rougel: 29.4439
* Rougelsum: 37.416
* Gen Len: 56.7545
Model de... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3\n* mixed\\_prec... | [
"TAGS\n#transformers #pytorch #tensorboard #bart #text2text-generation #generated_from_trainer #dataset-cnn_dailymail #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* lear... |
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. -->
# notiBERTrecensioni
This model is a fine-tuned version of [GioReg/notiBERTo](https://huggingface.co/GioReg/notiBERTo) on the None... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "notiBERTrecensioni", "results": []}]} | GioReg/notiBERTrecensioni | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-28T16:33:41+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
|
# notiBERTrecensioni
This model is a fine-tuned version of GioReg/notiBERTo on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The... | [
"# notiBERTrecensioni\n\nThis model is a fine-tuned version of GioReg/notiBERTo on the None dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"###... | [
"TAGS\n#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"# notiBERTrecensioni\n\nThis model is a fine-tuned version of GioReg/notiBERTo on the None dataset.",
"## Model description\n\nMore information needed",
... |
fill-mask | 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. -->
# silviacamplani/distilbert-base-uncased-finetuned-imdb
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingf... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "silviacamplani/distilbert-base-uncased-finetuned-imdb", "results": []}]} | silviacamplani/distilbert-base-uncased-finetuned-imdb | null | [
"transformers",
"tf",
"distilbert",
"fill-mask",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-28T16:36:37+00:00 | [] | [] | TAGS
#transformers #tf #distilbert #fill-mask #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| silviacamplani/distilbert-base-uncased-finetuned-imdb
=====================================================
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 2.8700
* Validation Loss: 2.6193
* Epoch: 0
Model ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'WarmUp', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_schedule\\_fn': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_le... | [
"TAGS\n#transformers #tf #distilbert #fill-mask #generated_from_keras_callback #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: {'name': 'AdamWeightDecay', 'learning\\_rate'... |
fill-mask | 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. -->
# lektay_nar
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unkno... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "lektay_nar", "results": []}]} | bigmorning/lektay_nar | null | [
"transformers",
"tf",
"distilbert",
"fill-mask",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-28T16:37:11+00:00 | [] | [] | TAGS
#transformers #tf #distilbert #fill-mask #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# lektay_nar
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information neede... | [
"# lektay_nar\n\nThis model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.\nIt achieves the following results on the evaluation set:",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\n... | [
"TAGS\n#transformers #tf #distilbert #fill-mask #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# lektay_nar\n\nThis model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.\nIt achieves the following results on the evaluation ... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-finetuned-sts
This model is a fine-tuned version of [klue/bert-base](https://huggingface.co/klue/bert-base) on the klu... | {"tags": ["generated_from_trainer"], "datasets": ["klue"], "metrics": ["pearsonr"], "model-index": [{"name": "bert-base-finetuned-sts", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "klue", "type": "klue", "args": "sts"}, "metrics": [{"type": "pearsonr", "value... | KDB/bert-base-finetuned-sts | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:klue",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-28T16:54:52+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #text-classification #generated_from_trainer #dataset-klue #model-index #autotrain_compatible #endpoints_compatible #region-us
| bert-base-finetuned-sts
=======================
This model is a fine-tuned version of klue/bert-base on the klue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4770
* Pearsonr: 0.8970
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: 128\n* eval\\_batch\\_size: 128\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",
"### Trai... | [
"TAGS\n#transformers #pytorch #bert #text-classification #generated_from_trainer #dataset-klue #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\\_batch\\_size:... |
fill-mask | transformers |
DAL-BERT: Another pre-trained language model for Persian
---
DAL-BERT is a transformer-based model trained on more than 80 gigabytes of Persian text including both formal and informal (conversational) contexts. The architecture of this model follows the original BERT [[Devlin et al.](https://arxiv.org/abs/1810.04805... | {"language": "fa", "license": "apache-2.0", "tags": ["bert-fa", "bert-persian"], "widget": [{"text": "\u0627\u0632 \u0647\u0631 \u062f\u0633\u062a\u06cc \u0628\u06af\u06cc\u0631\u06cc \u0627\u0632 \u0647\u0645\u0648\u0646 [MASK] \u0645\u06cc\u062f\u06cc"}, {"text": "\u0627\u06cc\u0646 \u0622\u062e\u0631\u06cc\u0646 \u0... | sharif-dal/dal-bert | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"bert-fa",
"bert-persian",
"fa",
"arxiv:1810.04805",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-28T17:10:27+00:00 | [
"1810.04805"
] | [
"fa"
] | TAGS
#transformers #pytorch #bert #fill-mask #bert-fa #bert-persian #fa #arxiv-1810.04805 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| DAL-BERT: Another pre-trained language model for Persian
--------------------------------------------------------
DAL-BERT is a transformer-based model trained on more than 80 gigabytes of Persian text including both formal and informal (conversational) contexts. The architecture of this model follows the original BE... | [] | [
"TAGS\n#transformers #pytorch #bert #fill-mask #bert-fa #bert-persian #fa #arxiv-1810.04805 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text2text-generation | transformers | **ENTREGABLE 3**
* Magda Brigitte Baron
* Juan Guillermo Forero Neme
* Myriam Leguizamon Lopez
* Diego Alexander Maca Garcia | {} | JuanForeroNeme/ES_UC_MODELO_NPL_E3_V2 | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-05-28T17:11:10+00:00 | [] | [] | TAGS
#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| ENTREGABLE 3
* Magda Brigitte Baron
* Juan Guillermo Forero Neme
* Myriam Leguizamon Lopez
* Diego Alexander Maca Garcia | [] | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
image-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. -->
# swin-tiny-patch4-window7-224-finetuned-eurosat
This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](ht... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["image_folder"], "metrics": ["accuracy"], "model-index": [{"name": "swin-tiny-patch4-window7-224-finetuned-eurosat", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "image_folder", "type... | Jazzweller/swin-tiny-patch4-window7-224-finetuned-eurosat | null | [
"transformers",
"pytorch",
"tensorboard",
"swin",
"image-classification",
"generated_from_trainer",
"dataset:image_folder",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-28T17:29:12+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #swin #image-classification #generated_from_trainer #dataset-image_folder #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| swin-tiny-patch4-window7-224-finetuned-eurosat
==============================================
This model is a fine-tuned version of microsoft/swin-tiny-patch4-window7-224 on the image\_folder dataset.
It achieves the following results on the evaluation set:
* Loss: 0.7828
* Accuracy: 0.2857
Model description
----... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 200\n* eval\\_batch\\_size: 200\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 800\n* optimizer: Adam with betas=(0.9,0.999) and epsi... | [
"TAGS\n#transformers #pytorch #tensorboard #swin #image-classification #generated_from_trainer #dataset-image_folder #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-generation | transformers |
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1530322632557592576/riUH... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/protectandwag/1653765651734/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/protectandwag | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-05-28T18:14:51+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
soppy WHAT
@protectandwag
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
-----... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
reinforcement-learning | stable-baselines3 |
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 ... | {"library_name": "stable-baselines3", "tags": ["LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "LunarLander-v2", "type": "LunarL... | Misha24-10/TEST2ppo-LunarLander-v3 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-05-28T18:50:32+00:00 | [] | [] | TAGS
#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add you... |
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. -->
# mbart-large-50-finetuned-summarization-V2
This model is a fine-tuned version of [facebook/mbart-large-50](https://huggingface.co... | {"tags": ["generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "mbart-large-50-finetuned-summarization-V2", "results": []}]} | GiordanoB/mbart-large-50-finetuned-summarization-V2 | null | [
"transformers",
"pytorch",
"tensorboard",
"mbart",
"text2text-generation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-28T18:51:43+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #mbart #text2text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| mbart-large-50-finetuned-summarization-V2
=========================================
This model is a fine-tuned version of facebook/mbart-large-50 on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 1.9183
* Rouge1: 50.0118
* Rouge2: 31.3168
* Rougel: 37.6392
* Rougelsum: 45.2287
... | [
"### 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 #mbart #text2text-generation #generated_from_trainer #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: 8\n* eval\\... |
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. -->
# deberta-base-finetuned-squad1-aqa-newsqa
This model is a fine-tuned version of [stevemobs/deberta-base-finetuned-squad1-aqa](htt... | {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "deberta-base-finetuned-squad1-aqa-newsqa", "results": []}]} | stevemobs/deberta-base-finetuned-squad1-aqa-newsqa | null | [
"transformers",
"pytorch",
"tensorboard",
"deberta",
"question-answering",
"generated_from_trainer",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-05-28T19:15:38+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #deberta #question-answering #generated_from_trainer #license-mit #endpoints_compatible #region-us
| deberta-base-finetuned-squad1-aqa-newsqa
========================================
This model is a fine-tuned version of stevemobs/deberta-base-finetuned-squad1-aqa on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.7525
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: 12\n* eval\\_batch\\_size: 12\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 #deberta #question-answering #generated_from_trainer #license-mit #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 12\n* eval\\_batch\\... |
text-to-speech | speechbrain |
<iframe src="https://ghbtns.com/github-btn.html?user=speechbrain&repo=speechbrain&type=star&count=true&size=large&v=2" frameborder="0" scrolling="0" width="170" height="30" title="GitHub"></iframe>
<br/><br/>
# Text-to-Speech (TTS) with Tacotron2 trained on LJSpeech
This repository provides all the necessary tools ... | {"language": "en", "license": "apache-2.0", "tags": ["text-to-speech", "TTS", "speech-synthesis", "Tacotron2", "speechbrain"], "datasets": ["LJSpeech"], "metrics": ["mos"]} | speechbrain/tts-tacotron2-ljspeech | null | [
"speechbrain",
"text-to-speech",
"TTS",
"speech-synthesis",
"Tacotron2",
"en",
"dataset:LJSpeech",
"arxiv:1712.05884",
"arxiv:2106.04624",
"license:apache-2.0",
"has_space",
"region:us"
] | null | 2022-05-28T20:09:37+00:00 | [
"1712.05884",
"2106.04624"
] | [
"en"
] | TAGS
#speechbrain #text-to-speech #TTS #speech-synthesis #Tacotron2 #en #dataset-LJSpeech #arxiv-1712.05884 #arxiv-2106.04624 #license-apache-2.0 #has_space #region-us
|
<iframe src="URL frameborder="0" scrolling="0" width="170" height="30" title="GitHub"></iframe>
<br/><br/>
# Text-to-Speech (TTS) with Tacotron2 trained on LJSpeech
This repository provides all the necessary tools for Text-to-Speech (TTS) with SpeechBrain using a Tacotron2 pretrained on LJSpeech.
The pre-trained ... | [
"# Text-to-Speech (TTS) with Tacotron2 trained on LJSpeech\n\nThis repository provides all the necessary tools for Text-to-Speech (TTS) with SpeechBrain using a Tacotron2 pretrained on LJSpeech.\n\nThe pre-trained model takes in input a short text and produces a spectrogram in output. One can get the final wavefor... | [
"TAGS\n#speechbrain #text-to-speech #TTS #speech-synthesis #Tacotron2 #en #dataset-LJSpeech #arxiv-1712.05884 #arxiv-2106.04624 #license-apache-2.0 #has_space #region-us \n",
"# Text-to-Speech (TTS) with Tacotron2 trained on LJSpeech\n\nThis repository provides all the necessary tools for Text-to-Speech (TTS) wi... |
text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# german-poetry-gpt2
This model is a fine-tuned version of [dbmdz/german-gpt2](https://huggingface.co/dbmdz/german-gpt2) on an unk... | {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "german-poetry-gpt2", "results": []}]} | Anjoe/german-poetry-gpt2 | null | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-05-28T20:11:02+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #safetensors #gpt2 #text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# german-poetry-gpt2
This model is a fine-tuned version of dbmdz/german-gpt2 on an unknown dataset.
It achieves the following results on the evaluation set:
- eval_loss: 3.8196
- eval_runtime: 43.8543
- eval_samples_per_second: 86.993
- eval_steps_per_second: 5.45
- epoch: 9.0
- step: 11520
## Model description
M... | [
"# german-poetry-gpt2\n\nThis model is a fine-tuned version of dbmdz/german-gpt2 on an unknown dataset.\nIt achieves the following results on the evaluation set:\n- eval_loss: 3.8196\n- eval_runtime: 43.8543\n- eval_samples_per_second: 86.993\n- eval_steps_per_second: 5.45\n- epoch: 9.0\n- step: 11520",
"## Model... | [
"TAGS\n#transformers #pytorch #tensorboard #safetensors #gpt2 #text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# german-poetry-gpt2\n\nThis model is a fine-tuned version of dbmdz/german-gpt2 on an unknown dataset.\nIt ach... |
text-to-speech | speechbrain |
# Vocoder with HiFIGAN trained on LJSpeech
This repository provides all the necessary tools for using a [HiFIGAN](https://arxiv.org/abs/2010.05646) vocoder trained with [LJSpeech](https://keithito.com/LJ-Speech-Dataset/).
The pre-trained model takes in input a spectrogram and produces a waveform in output. Typicall... | {"language": "en", "license": "apache-2.0", "tags": ["Vocoder", "HiFIGAN", "text-to-speech", "TTS", "speech-synthesis", "speechbrain"], "datasets": ["LJSpeech"], "inference": false} | speechbrain/tts-hifigan-ljspeech | null | [
"speechbrain",
"Vocoder",
"HiFIGAN",
"text-to-speech",
"TTS",
"speech-synthesis",
"en",
"dataset:LJSpeech",
"arxiv:2010.05646",
"license:apache-2.0",
"has_space",
"region:us"
] | null | 2022-05-28T21:37:20+00:00 | [
"2010.05646"
] | [
"en"
] | TAGS
#speechbrain #Vocoder #HiFIGAN #text-to-speech #TTS #speech-synthesis #en #dataset-LJSpeech #arxiv-2010.05646 #license-apache-2.0 #has_space #region-us
|
# Vocoder with HiFIGAN trained on LJSpeech
This repository provides all the necessary tools for using a HiFIGAN vocoder trained with LJSpeech.
The pre-trained model takes in input a spectrogram and produces a waveform in output. Typically, a vocoder is used after a TTS model that converts an input text into a spect... | [
"# Vocoder with HiFIGAN trained on LJSpeech\n\nThis repository provides all the necessary tools for using a HiFIGAN vocoder trained with LJSpeech. \n\nThe pre-trained model takes in input a spectrogram and produces a waveform in output. Typically, a vocoder is used after a TTS model that converts an input text into... | [
"TAGS\n#speechbrain #Vocoder #HiFIGAN #text-to-speech #TTS #speech-synthesis #en #dataset-LJSpeech #arxiv-2010.05646 #license-apache-2.0 #has_space #region-us \n",
"# Vocoder with HiFIGAN trained on LJSpeech\n\nThis repository provides all the necessary tools for using a HiFIGAN vocoder trained with LJSpeech. \n\... |
text-generation | transformers |
# My Awesome Model | {"tags": ["conversational"]} | thanhchauns2/DialoGPT-medium-Luna | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-05-28T22:01:12+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# My Awesome Model | [
"# My Awesome Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# My Awesome Model"
] |
fill-mask | transformers |
# deberta-base-thai
## Model Description
This is a DeBERTa(V2) model pre-trained on Thai Wikipedia texts. NVIDIA A100-SXM4-40GB took 10 hours 17 minutes for training. You can fine-tune `deberta-base-thai` for downstream tasks, such as [POS-tagging](https://huggingface.co/KoichiYasuoka/deberta-base-thai-upos), [depen... | {"language": ["th"], "license": "apache-2.0", "tags": ["thai", "masked-lm", "wikipedia"], "pipeline_tag": "fill-mask", "mask_token": "[MASK]"} | KoichiYasuoka/deberta-base-thai | null | [
"transformers",
"pytorch",
"deberta-v2",
"fill-mask",
"thai",
"masked-lm",
"wikipedia",
"th",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-28T22:52:54+00:00 | [] | [
"th"
] | TAGS
#transformers #pytorch #deberta-v2 #fill-mask #thai #masked-lm #wikipedia #th #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# deberta-base-thai
## Model Description
This is a DeBERTa(V2) model pre-trained on Thai Wikipedia texts. NVIDIA A100-SXM4-40GB took 10 hours 17 minutes for training. You can fine-tune 'deberta-base-thai' for downstream tasks, such as POS-tagging, dependency-parsing, and so on.
## How to Use
| [
"# deberta-base-thai",
"## Model Description\n\nThis is a DeBERTa(V2) model pre-trained on Thai Wikipedia texts. NVIDIA A100-SXM4-40GB took 10 hours 17 minutes for training. You can fine-tune 'deberta-base-thai' for downstream tasks, such as POS-tagging, dependency-parsing, and so on.",
"## How to Use"
] | [
"TAGS\n#transformers #pytorch #deberta-v2 #fill-mask #thai #masked-lm #wikipedia #th #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# deberta-base-thai",
"## Model Description\n\nThis is a DeBERTa(V2) model pre-trained on Thai Wikipedia texts. NVIDIA A100-SXM4-40GB took 10 hour... |
token-classification | transformers |
# deberta-base-thai-upos
## Model Description
This is a DeBERTa(V2) model pre-trained on Thai Wikipedia texts for POS-tagging and dependency-parsing, derived from [deberta-base-thai](https://huggingface.co/KoichiYasuoka/deberta-base-thai). Every word is tagged by [UPOS](https://universaldependencies.org/u/pos/) (Uni... | {"language": ["th"], "license": "apache-2.0", "tags": ["thai", "token-classification", "pos", "wikipedia", "dependency-parsing"], "datasets": ["universal_dependencies"], "pipeline_tag": "token-classification", "widget": [{"text": "\u0e2b\u0e25\u0e32\u0e22\u0e2b\u0e31\u0e27\u0e14\u0e35\u0e01\u0e27\u0e48\u0e32\u0e2b\u0e3... | KoichiYasuoka/deberta-base-thai-upos | null | [
"transformers",
"pytorch",
"deberta-v2",
"token-classification",
"thai",
"pos",
"wikipedia",
"dependency-parsing",
"th",
"dataset:universal_dependencies",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-28T22:59:43+00:00 | [] | [
"th"
] | TAGS
#transformers #pytorch #deberta-v2 #token-classification #thai #pos #wikipedia #dependency-parsing #th #dataset-universal_dependencies #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# deberta-base-thai-upos
## Model Description
This is a DeBERTa(V2) model pre-trained on Thai Wikipedia texts for POS-tagging and dependency-parsing, derived from deberta-base-thai. Every word is tagged by UPOS (Universal Part-Of-Speech).
## How to Use
or
## See Also
esupar: Tokenizer POS-tagger and Dependen... | [
"# deberta-base-thai-upos",
"## Model Description\n\nThis is a DeBERTa(V2) model pre-trained on Thai Wikipedia texts for POS-tagging and dependency-parsing, derived from deberta-base-thai. Every word is tagged by UPOS (Universal Part-Of-Speech).",
"## How to Use\n\n\n\nor",
"## See Also\n\nesupar: Tokenizer P... | [
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"# deberta-base-thai-upos",
"## Model Description\n\nThis is a DeBERTa(V2) model pre-train... |
automatic-speech-recognition | espnet |
## ESPnet2 ASR model
### `pyf98/aishell_branchformer_fast_selfattn_e24_amp`
This model was trained by Yifan Peng using aishell recipe in [espnet](https://github.com/espnet/espnet/).
Branchformer (Peng et al., ICML 2022): [https://proceedings.mlr.press/v162/peng22a.html](https://proceedings.mlr.press/v162/peng22a.h... | {"language": "zh", "license": "cc-by-4.0", "tags": ["espnet", "audio", "automatic-speech-recognition"], "datasets": ["aishell"]} | pyf98/aishell_branchformer_fast_selfattn_e24_amp | null | [
"espnet",
"audio",
"automatic-speech-recognition",
"zh",
"dataset:aishell",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | null | 2022-05-28T23:00:23+00:00 | [
"1804.00015"
] | [
"zh"
] | TAGS
#espnet #audio #automatic-speech-recognition #zh #dataset-aishell #arxiv-1804.00015 #license-cc-by-4.0 #region-us
| ESPnet2 ASR model
-----------------
### 'pyf98/aishell\_branchformer\_fast\_selfattn\_e24\_amp'
This model was trained by Yifan Peng using aishell recipe in espnet.
Branchformer (Peng et al., ICML 2022): URL
### Demo: How to use in ESPnet2
RESULTS
=======
Environments
------------
* date: 'Sat May 28 16:0... | [
"### 'pyf98/aishell\\_branchformer\\_fast\\_selfattn\\_e24\\_amp'\n\n\nThis model was trained by Yifan Peng using aishell recipe in espnet.\n\n\nBranchformer (Peng et al., ICML 2022): URL",
"### Demo: How to use in ESPnet2\n\n\nRESULTS\n=======\n\n\nEnvironments\n------------\n\n\n* date: 'Sat May 28 16:09:35 EDT... | [
"TAGS\n#espnet #audio #automatic-speech-recognition #zh #dataset-aishell #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"### 'pyf98/aishell\\_branchformer\\_fast\\_selfattn\\_e24\\_amp'\n\n\nThis model was trained by Yifan Peng using aishell recipe in espnet.\n\n\nBranchformer (Peng et al., ICML 2022): URL"... |
reinforcement-learning | stable-baselines3 |
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 ... | {"library_name": "stable-baselines3", "tags": ["LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "LunarLander-v2", "type": "LunarL... | poiug07/PPO-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-05-28T23:19:16+00:00 | [] | [] | TAGS
#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add you... |
automatic-speech-recognition | transformers | Indonli + CommonVoice8.0 Dataset --> Train + Validation + Test
WER : 0.216
WER with LM: 0.104 | {} | chrisvinsen/xlsr-wav2vec2-final-1-lm-3 | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"endpoints_compatible",
"region:us"
] | null | 2022-05-28T23:49:14+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #endpoints_compatible #region-us
| Indonli + CommonVoice8.0 Dataset --> Train + Validation + Test
WER : 0.216
WER with LM: 0.104 | [] | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #endpoints_compatible #region-us \n"
] |
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. -->
# deberta-base-newsqa
This model is a fine-tuned version of [microsoft/deberta-base](https://huggingface.co/microsoft/deberta-base... | {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "deberta-base-newsqa", "results": []}]} | stevemobs/deberta-base-newsqa | null | [
"transformers",
"pytorch",
"tensorboard",
"deberta",
"question-answering",
"generated_from_trainer",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-05-29T00:09:36+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #deberta #question-answering #generated_from_trainer #license-mit #endpoints_compatible #region-us
| deberta-base-newsqa
===================
This model is a fine-tuned version of microsoft/deberta-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.7628
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: 12\n* eval\\_batch\\_size: 12\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 #deberta #question-answering #generated_from_trainer #license-mit #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 12\n* eval\\_batch\\... |
reinforcement-learning | stable-baselines3 |
# **DQN** Agent playing **LunarLander-v2**
This is a trained model of a **DQN** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 ... | {"library_name": "stable-baselines3", "tags": ["LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "DQN", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "LunarLander-v2", "type": "LunarL... | poiug07/DQN-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-05-29T00:24:27+00:00 | [] | [] | TAGS
#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# DQN Agent playing LunarLander-v2
This is a trained model of a DQN agent playing LunarLander-v2
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# DQN Agent playing LunarLander-v2\nThis is a trained model of a DQN agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# DQN Agent playing LunarLander-v2\nThis is a trained model of a DQN agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add you... |
text-generation | transformers | ```
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("BigSalmon/InformalToFormalLincoln45")
model = AutoModelForCausalLM.from_pretrained("BigSalmon/InformalToFormalLincoln45")
```
```
How To Make Prompt:
informal english: i am very ready to do that just that.
Tra... | {} | BigSalmon/InformalToFormalLincoln47 | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-05-29T00:41:52+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
Keywords to sentences or sentence.
| [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
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. -->
# deberta-base-finetuned-aqa-squad1-newsqa
This model is a fine-tuned version of [stevemobs/deberta-base-finetuned-aqa-squad1](htt... | {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "deberta-base-finetuned-aqa-squad1-newsqa", "results": []}]} | stevemobs/deberta-base-finetuned-aqa-squad1-newsqa | null | [
"transformers",
"pytorch",
"tensorboard",
"deberta",
"question-answering",
"generated_from_trainer",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-05-29T01:11:38+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #deberta #question-answering #generated_from_trainer #license-mit #endpoints_compatible #region-us
| deberta-base-finetuned-aqa-squad1-newsqa
========================================
This model is a fine-tuned version of stevemobs/deberta-base-finetuned-aqa-squad1 on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.7523
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: 12\n* eval\\_batch\\_size: 12\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 #deberta #question-answering #generated_from_trainer #license-mit #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 12\n* eval\\_batch\\... |
text-generation | transformers | ```
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("BigSalmon/InformalToFormalLincoln45")
model = AutoModelForCausalLM.from_pretrained("BigSalmon/InformalToFormalLincoln45")
```
```
How To Make Prompt:
informal english: i am very ready to do that just that.
Tra... | {} | BigSalmon/InformalToFormalLincoln48 | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-05-29T02:03:28+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
Keywords to sentences or sentence.
| [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
image-to-image | keras | ## Model description
This repo contains the model for the notebook [Image Classification using BigTransfer (BiT)](https://keras.io/examples/vision/bit/).
Full credits go to [Sayan Nath](https://twitter.com/sayannath2350)
Reproduced by [Rushi Chaudhari](https://github.com/rushic24)
BigTransfer (also known as BiT) is ... | {"library_name": "keras", "tags": ["image-to-image"]} | keras-io/bit | null | [
"keras",
"image-to-image",
"arxiv:1912.11370",
"arxiv:1710.09412",
"has_space",
"region:us"
] | null | 2022-05-29T02:22:13+00:00 | [
"1912.11370",
"1710.09412"
] | [] | TAGS
#keras #image-to-image #arxiv-1912.11370 #arxiv-1710.09412 #has_space #region-us
| ## Model description
This repo contains the model for the notebook Image Classification using BigTransfer (BiT).
Full credits go to Sayan Nath
Reproduced by Rushi Chaudhari
BigTransfer (also known as BiT) is a state-of-the-art transfer learning method for image classification.
## Dataset
The Flower Dataset is A lar... | [
"## Model description\nThis repo contains the model for the notebook Image Classification using BigTransfer (BiT).\n\nFull credits go to Sayan Nath\n\nReproduced by Rushi Chaudhari\n\nBigTransfer (also known as BiT) is a state-of-the-art transfer learning method for image classification.",
"## Dataset\nThe Flower... | [
"TAGS\n#keras #image-to-image #arxiv-1912.11370 #arxiv-1710.09412 #has_space #region-us \n",
"## Model description\nThis repo contains the model for the notebook Image Classification using BigTransfer (BiT).\n\nFull credits go to Sayan Nath\n\nReproduced by Rushi Chaudhari\n\nBigTransfer (also known as BiT) is a ... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-base-vios-google-colab
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/f... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-base-vios-google-colab", "results": []}]} | tclong/wav2vec2-base-vios-google-colab | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-05-29T02:45:53+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-base-vios-google-colab
===============================
This model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5647
* Wer: 0.4970
Model description
-----------------
More information needed
Intended ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 1... |
text-generation | 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. -->
# distilgpt2-lektay2-firstpos
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on an unknown datase... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "distilgpt2-lektay2-firstpos", "results": []}]} | bigmorning/distilgpt2-lektay2-firstpos | null | [
"transformers",
"tf",
"gpt2",
"text-generation",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-05-29T03:15:04+00:00 | [] | [] | TAGS
#transformers #tf #gpt2 #text-generation #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# distilgpt2-lektay2-firstpos
This model is a fine-tuned version of distilgpt2 on an unknown dataset.
It achieves the following results on the evaluation set:
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information n... | [
"# distilgpt2-lektay2-firstpos\n\nThis model is a fine-tuned version of distilgpt2 on an unknown dataset.\nIt achieves the following results on the evaluation set:",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data... | [
"TAGS\n#transformers #tf #gpt2 #text-generation #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# distilgpt2-lektay2-firstpos\n\nThis model is a fine-tuned version of distilgpt2 on an unknown dataset.\nIt achieves the follo... |
text-generation | 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. -->
# distilgpt2-lektay2-secondpos
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on an unknown datas... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "distilgpt2-lektay2-secondpos", "results": []}]} | bigmorning/distilgpt2-lektay2-secondpos | null | [
"transformers",
"tf",
"gpt2",
"text-generation",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-05-29T03:20:12+00:00 | [] | [] | TAGS
#transformers #tf #gpt2 #text-generation #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# distilgpt2-lektay2-secondpos
This model is a fine-tuned version of distilgpt2 on an unknown dataset.
It achieves the following results on the evaluation set:
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information ... | [
"# distilgpt2-lektay2-secondpos\n\nThis model is a fine-tuned version of distilgpt2 on an unknown dataset.\nIt achieves the following results on the evaluation set:",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation dat... | [
"TAGS\n#transformers #tf #gpt2 #text-generation #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# distilgpt2-lektay2-secondpos\n\nThis model is a fine-tuned version of distilgpt2 on an unknown dataset.\nIt achieves the foll... |
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