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text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bart-v2-dialouge
This model is a fine-tuned version of [hyunwoongko/kobart](https://huggingface.co/hyunwoongko/kobart) on the na... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["naem1023/aihub-dialogue"], "model-index": [{"name": "bart-v2-dialouge", "results": []}]} | naem1023/bart-v2-dialouge | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"generated_from_trainer",
"dataset:naem1023/aihub-dialogue",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-16T07:03:06+00:00 | [] | [] | TAGS
#transformers #pytorch #bart #text2text-generation #generated_from_trainer #dataset-naem1023/aihub-dialogue #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# bart-v2-dialouge
This model is a fine-tuned version of hyunwoongko/kobart on the naem1023/aihub-dialogue dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training h... | [
"# bart-v2-dialouge\n\nThis model is a fine-tuned version of hyunwoongko/kobart on the naem1023/aihub-dialogue dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training... | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #generated_from_trainer #dataset-naem1023/aihub-dialogue #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# bart-v2-dialouge\n\nThis model is a fine-tuned version of hyunwoongko/kobart on the naem1023/aihub-dialogue dataset.",
"#... |
text-classification | transformers |
Fixed label mapping issue for textattack/bert-base-uncased-MNLI, if using the original model, the predicted label has systematic confusion with the huggingface MNLI dataset. See the Github issue: https://github.com/QData/TextAttack/issues/684. The fixed accuracy_mm is 84.44% and is 7% before the fix applied. | {"license": "mit", "tags": ["Issue_fixed", "textattack", "textclassification", "entailment"], "datasets": ["mnli"], "metrics": ["accuracy"]} | chromeNLP/textattack_bert_base_MNLI_fixed | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"Issue_fixed",
"textattack",
"textclassification",
"entailment",
"dataset:mnli",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-16T07:07:40+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #text-classification #Issue_fixed #textattack #textclassification #entailment #dataset-mnli #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
Fixed label mapping issue for textattack/bert-base-uncased-MNLI, if using the original model, the predicted label has systematic confusion with the huggingface MNLI dataset. See the Github issue: URL The fixed accuracy_mm is 84.44% and is 7% before the fix applied. | [] | [
"TAGS\n#transformers #pytorch #bert #text-classification #Issue_fixed #textattack #textclassification #entailment #dataset-mnli #license-mit #autotrain_compatible #endpoints_compatible #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-base-timit-demo-google-colab
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/face... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-base-timit-demo-google-colab", "results": []}]} | skparida/wav2vec2-base-timit-demo-google-colab | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-08-16T07:37:00+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-base-timit-demo-google-colab
=====================================
This model is a fine-tuned version of facebook/wav2vec2-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5090
* Wer: 0.3435
Model description
-----------------
More information needed
Intend... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps:... | [
"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: 8... |
reinforcement-learning | stable-baselines3 |
# **A2C** Agent playing **AntBulletEnv-v0**
This is a trained model of a **A2C** agent playing **AntBulletEnv-v0**
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_sb... | {"library_name": "stable-baselines3", "tags": ["AntBulletEnv-v0", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "A2C", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "AntBulletEnv-v0", "type": "AntB... | Saraswati/a2c-AntBulletEnv-v0 | null | [
"stable-baselines3",
"AntBulletEnv-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-08-16T07:38:38+00:00 | [] | [] | TAGS
#stable-baselines3 #AntBulletEnv-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# A2C Agent playing AntBulletEnv-v0
This is a trained model of a A2C agent playing AntBulletEnv-v0
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# A2C Agent playing AntBulletEnv-v0\nThis is a trained model of a A2C agent playing AntBulletEnv-v0\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #AntBulletEnv-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# A2C Agent playing AntBulletEnv-v0\nThis is a trained model of a A2C agent playing AntBulletEnv-v0\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add ... |
text-classification | transformers | --alpha_ce 0.0 --alpha_mlm 2.0 --alpha_cos 0.0 --alpha_act 1.0 --alpha_clm 0.0 --mlm \ | {} | alishudi/distil_mlm_act | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-16T08:11:22+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #text-classification #autotrain_compatible #endpoints_compatible #region-us
| --alpha_ce 0.0 --alpha_mlm 2.0 --alpha_cos 0.0 --alpha_act 1.0 --alpha_clm 0.0 --mlm \ | [] | [
"TAGS\n#transformers #pytorch #distilbert #text-classification #autotrain_compatible #endpoints_compatible #region-us \n"
] |
null | transformers | Epoch Training Loss Validation Loss Mse Rmse Mae R2
1 1.592900 1.618959 1.618959 1.272383 0.807994 0.222083
2 1.547400 1.584446 1.584446 1.258748 0.783680 0.238667
3 1.470800 1.593225 1.593225 1.262230 0.772598 0.234448
4 1.418500 1.417212 1.417212 1.190467 0.755947 0.319023
5 1.348600 1.350606 1.350606 1.162156 0.7... | {} | avuhong/ESM1b_libcapv3_regression_run2 | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"endpoints_compatible",
"region:us"
] | null | 2022-08-16T08:14:35+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #endpoints_compatible #region-us
| Epoch Training Loss Validation Loss Mse Rmse Mae R2
1 1.592900 1.618959 1.618959 1.272383 0.807994 0.222083
2 1.547400 1.584446 1.584446 1.258748 0.783680 0.238667
3 1.470800 1.593225 1.593225 1.262230 0.772598 0.234448
4 1.418500 1.417212 1.417212 1.190467 0.755947 0.319023
5 1.348600 1.350606 1.350606 1.162156 0.7... | [] | [
"TAGS\n#transformers #pytorch #tensorboard #bert #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. -->
# tiny-bert-qa-es
This model is a fine-tuned version of [CenIA/albert-tiny-spanish](https://huggingface.co/CenIA/albert-tiny-spani... | {"tags": ["generated_from_trainer"], "datasets": ["squad_es"], "model-index": [{"name": "tiny-bert-qa-es", "results": []}]} | srcocotero/tiny-bert-qa-es | null | [
"transformers",
"pytorch",
"tensorboard",
"albert",
"question-answering",
"generated_from_trainer",
"dataset:squad_es",
"endpoints_compatible",
"region:us"
] | null | 2022-08-16T08:55:48+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #albert #question-answering #generated_from_trainer #dataset-squad_es #endpoints_compatible #region-us
|
# tiny-bert-qa-es
This model is a fine-tuned version of CenIA/albert-tiny-spanish on the squad_es dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparam... | [
"# tiny-bert-qa-es\n\nThis model is a fine-tuned version of CenIA/albert-tiny-spanish on the squad_es dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedur... | [
"TAGS\n#transformers #pytorch #tensorboard #albert #question-answering #generated_from_trainer #dataset-squad_es #endpoints_compatible #region-us \n",
"# tiny-bert-qa-es\n\nThis model is a fine-tuned version of CenIA/albert-tiny-spanish on the squad_es dataset.",
"## Model description\n\nMore information needed... |
text2text-generation | transformers | This is the IndicBART model fine-tuned on the PMI and PIB dataset for XX to En translation. For detailed documentation look here: https://indicnlp.ai4bharat.org/indic-bart/ and https://github.com/AI4Bharat/indic-bart/
Usage:
```
from transformers import MBartForConditionalGeneration, AutoModelForSeq2SeqLM
from transf... | {} | ai4bharat/IndicBART-XXEN | null | [
"transformers",
"pytorch",
"mbart",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-16T09:01:26+00:00 | [] | [] | TAGS
#transformers #pytorch #mbart #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
| This is the IndicBART model fine-tuned on the PMI and PIB dataset for XX to En translation. For detailed documentation look here: URL and URL
Usage:
Notes:
1. This is compatible with the latest version of transformers but was developed with version 4.3.2 so consider using 4.3.2 if possible.
2. While I have only show... | [] | [
"TAGS\n#transformers #pytorch #mbart #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
question-answering | transformers |
# Deberta v3 large model for QA (SQuAD 2.0)
This is the [deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) model, fine-tuned using the [SQuAD2.0](https://huggingface.co/datasets/squad_v2) dataset. It's been trained on question-answer pairs, including unanswerable questions, for the task of Question... | {"language": "en", "license": "mit", "tags": ["deberta", "deberta-v3"], "datasets": ["squad_v2"], "pipeline_tag": "question-answering", "model-index": [{"name": "navteca/deberta-v3-base-squad2", "results": [{"task": {"type": "question-answering", "name": "Question Answering"}, "dataset": {"name": "squad_v2", "type": "s... | navteca/deberta-v3-large-squad2 | null | [
"transformers",
"pytorch",
"deberta-v2",
"question-answering",
"deberta",
"deberta-v3",
"en",
"dataset:squad_v2",
"license:mit",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-08-16T09:09:34+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #deberta-v2 #question-answering #deberta #deberta-v3 #en #dataset-squad_v2 #license-mit #model-index #endpoints_compatible #region-us
|
# Deberta v3 large model for QA (SQuAD 2.0)
This is the deberta-v3-large model, fine-tuned using the SQuAD2.0 dataset. It's been trained on question-answer pairs, including unanswerable questions, for the task of Question Answering.
## Training Data
The models have been trained on the SQuAD 2.0 dataset.
It can be u... | [
"# Deberta v3 large model for QA (SQuAD 2.0)\n\nThis is the deberta-v3-large model, fine-tuned using the SQuAD2.0 dataset. It's been trained on question-answer pairs, including unanswerable questions, for the task of Question Answering.",
"## Training Data\nThe models have been trained on the SQuAD 2.0 dataset.\n... | [
"TAGS\n#transformers #pytorch #deberta-v2 #question-answering #deberta #deberta-v3 #en #dataset-squad_v2 #license-mit #model-index #endpoints_compatible #region-us \n",
"# Deberta v3 large model for QA (SQuAD 2.0)\n\nThis is the deberta-v3-large model, fine-tuned using the SQuAD2.0 dataset. It's been trained on q... |
question-answering | transformers |
# Deberta v3 base model for QA (SQuAD 2.0)
This is the [deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) model, fine-tuned using the [SQuAD2.0](https://huggingface.co/datasets/squad_v2) dataset. It's been trained on question-answer pairs, including unanswerable questions, for the task of Question An... | {"language": "en", "license": "mit", "tags": ["deberta", "deberta-v3"], "datasets": ["squad_v2"], "pipeline_tag": "question-answering", "model-index": [{"name": "navteca/deberta-v3-base-squad2", "results": [{"task": {"type": "question-answering", "name": "Question Answering"}, "dataset": {"name": "squad_v2", "type": "s... | navteca/deberta-v3-base-squad2 | null | [
"transformers",
"pytorch",
"deberta-v2",
"question-answering",
"deberta",
"deberta-v3",
"en",
"dataset:squad_v2",
"license:mit",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-08-16T09:10:07+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #deberta-v2 #question-answering #deberta #deberta-v3 #en #dataset-squad_v2 #license-mit #model-index #endpoints_compatible #region-us
|
# Deberta v3 base model for QA (SQuAD 2.0)
This is the deberta-v3-base model, fine-tuned using the SQuAD2.0 dataset. It's been trained on question-answer pairs, including unanswerable questions, for the task of Question Answering.
## Training Data
The models have been trained on the SQuAD 2.0 dataset.
It can be use... | [
"# Deberta v3 base model for QA (SQuAD 2.0)\n\nThis is the deberta-v3-base model, fine-tuned using the SQuAD2.0 dataset. It's been trained on question-answer pairs, including unanswerable questions, for the task of Question Answering.",
"## Training Data\nThe models have been trained on the SQuAD 2.0 dataset.\n\n... | [
"TAGS\n#transformers #pytorch #deberta-v2 #question-answering #deberta #deberta-v3 #en #dataset-squad_v2 #license-mit #model-index #endpoints_compatible #region-us \n",
"# Deberta v3 base model for QA (SQuAD 2.0)\n\nThis is the deberta-v3-base model, fine-tuned using the SQuAD2.0 dataset. It's been trained on que... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-banking77-classification
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-ro... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["banking77"], "metrics": ["accuracy"], "widget": [{"text": "Can I track the card you sent to me? ", "example_title": "Card Arrival Example - English"}, {"text": "Posso tracciare la carta che mi avete spedito? ", "example_title": "Card Arrival Example -... | nickprock/xlm-roberta-base-banking77-classification | null | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"dataset:banking77",
"base_model:xlm-roberta-base",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-16T10:02:45+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #safetensors #xlm-roberta #text-classification #generated_from_trainer #dataset-banking77 #base_model-xlm-roberta-base #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-banking77-classification
=========================================
This model is a fine-tuned version of xlm-roberta-base on the banking77 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3034
* Accuracy: 0.9321
* F1 Score: 0.9321
Model description
-----------------
E... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 20",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #safetensors #xlm-roberta #text-classification #generated_from_trainer #dataset-banking77 #base_model-xlm-roberta-base #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters w... |
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... | siddhantmahalle/ppo-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-08-16T10:29:23+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... |
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. -->
# mit-b0-finetuned-eurosat
This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the imag... | {"license": "other", "tags": ["generated_from_trainer"], "datasets": ["image_folder"], "metrics": ["accuracy"], "model-index": [{"name": "mit-b0-finetuned-eurosat", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "image_folder", "type": "image_folder", "args": ... | Chandanab/mit-b0-finetuned-eurosat | null | [
"transformers",
"pytorch",
"segformer",
"image-classification",
"generated_from_trainer",
"dataset:image_folder",
"license:other",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-16T10:47:17+00:00 | [] | [] | TAGS
#transformers #pytorch #segformer #image-classification #generated_from_trainer #dataset-image_folder #license-other #model-index #autotrain_compatible #endpoints_compatible #region-us
| mit-b0-finetuned-eurosat
========================
This model is a fine-tuned version of nvidia/mit-b0 on the image\_folder dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1782
* Accuracy: 0.9495
Model description
-----------------
More information needed
Intended uses & limitations... | [
"### 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* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsilo... | [
"TAGS\n#transformers #pytorch #segformer #image-classification #generated_from_trainer #dataset-image_folder #license-other #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: 5... |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-finetuned-squad
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unkno... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "bert-finetuned-squad", "results": []}]} | ramrajput/bert-finetuned-squad | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"question-answering",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-08-16T11:30:00+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
|
# bert-finetuned-squad
This model is a fine-tuned version of bert-base-cased on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
... | [
"# bert-finetuned-squad\n\nThis model is a fine-tuned version of bert-base-cased on an unknown dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"# bert-finetuned-squad\n\nThis model is a fine-tuned version of bert-base-cased on an unknown dataset.",
"## Model description\n\nMore information needed",
"#... |
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... | siddhantmahalle/ppo-LunarLander-v3 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-08-16T11:37:57+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... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-clinc
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingfac... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["clinc_oos"], "metrics": ["accuracy"], "model-index": [{"name": "distilbert-base-uncased-finetuned-clinc", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "clinc_oos", "type": "clinc_oos",... | chia/distilbert-base-uncased-finetuned-clinc | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:clinc_oos",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-16T11:46:11+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-clinc_oos #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-clinc
=======================================
This model is a fine-tuned version of distilbert-base-uncased on the clinc\_oos dataset.
It achieves the following results on the evaluation set:
* Loss: 0.7778
* Accuracy: 0.9171
Model description
-----------------
More information... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 48\n* eval\\_batch\\_size: 48\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 #distilbert #text-classification #generated_from_trainer #dataset-clinc_oos #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* lea... |
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. -->
# dat259-cv_en-wav2vec2
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-ba... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice_1_0"], "model-index": [{"name": "dat259-cv_en-wav2vec2", "results": []}]} | Jethuestad/dat259-cv_en-wav2vec2 | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice_1_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-08-16T11:51:33+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice_1_0 #license-apache-2.0 #endpoints_compatible #region-us
| dat259-cv\_en-wav2vec2
======================
This model is a fine-tuned version of facebook/wav2vec2-base on the common\_voice\_1\_0 dataset.
It achieves the following results on the evaluation set:
* Loss: 1.4286
* Wer: 0.5339
Model description
-----------------
More information needed
Intended uses & limit... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice_1_0 #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\\_ba... |
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... | aminjorati/unit1-model-amin | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-08-16T11:53:17+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... |
image-classification | transformers |
**InvoiceReceiptClassifier_LayoutLMv3** is a fine-tuned LayoutLMv3 model that classifies a document to an invoice or receipt.
## Quick start: using the raw model
```python
from transformers import (
AutoModelForSequenceClassification,
AutoProcessor,
)
from PIL import Image
from urllib.request import urlopen
mo... | {"language": ["es", "en", "multilingual"], "license": "other", "tags": ["image-classification"], "pipeline_tag": "image-classification", "widget": [{"src": "https://upserve.com/media/sites/2/Bill-from-Mezcalero-in-Washington-D.C.-photo-by-Alfredo-Solis-1-e1507226752437.jpg", "example_title": "receipt"}, {"src": "https:... | fedihch/InvoiceReceiptClassifier_LayoutLMv3 | null | [
"transformers",
"pytorch",
"layoutlmv3",
"feature-extraction",
"image-classification",
"es",
"en",
"multilingual",
"license:other",
"endpoints_compatible",
"region:us"
] | null | 2022-08-16T11:58:41+00:00 | [] | [
"es",
"en",
"multilingual"
] | TAGS
#transformers #pytorch #layoutlmv3 #feature-extraction #image-classification #es #en #multilingual #license-other #endpoints_compatible #region-us
|
InvoiceReceiptClassifier_LayoutLMv3 is a fine-tuned LayoutLMv3 model that classifies a document to an invoice or receipt.
## Quick start: using the raw model
| [
"## Quick start: using the raw model"
] | [
"TAGS\n#transformers #pytorch #layoutlmv3 #feature-extraction #image-classification #es #en #multilingual #license-other #endpoints_compatible #region-us \n",
"## Quick start: using the raw model"
] |
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. -->
# pegasus-samsum
This model is a fine-tuned version of [google/pegasus-cnn_dailymail](https://huggingface.co/google/pegasus-cnn_da... | {"tags": ["generated_from_trainer"], "datasets": ["samsum"], "model-index": [{"name": "pegasus-samsum", "results": []}]} | royam0820/pegasus-samsum | null | [
"transformers",
"pytorch",
"tensorboard",
"pegasus",
"text2text-generation",
"generated_from_trainer",
"dataset:samsum",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-16T13:16:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #pegasus #text2text-generation #generated_from_trainer #dataset-samsum #autotrain_compatible #endpoints_compatible #region-us
|
# pegasus-samsum
This model is a fine-tuned version of google/pegasus-cnn_dailymail on the samsum dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparam... | [
"# pegasus-samsum\n\nThis model is a fine-tuned version of google/pegasus-cnn_dailymail on the samsum dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedur... | [
"TAGS\n#transformers #pytorch #tensorboard #pegasus #text2text-generation #generated_from_trainer #dataset-samsum #autotrain_compatible #endpoints_compatible #region-us \n",
"# pegasus-samsum\n\nThis model is a fine-tuned version of google/pegasus-cnn_dailymail on the samsum dataset.",
"## Model description\n\n... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-distilled-clinc
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/d... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "distilbert-base-uncased-distilled-clinc", "results": []}]} | jamie613/distilbert-base-uncased-distilled-clinc | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-16T14:01:11+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-distilled-clinc
=======================================
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1291
* Accuracy: 0.9429
Model description
-----------------
More information nee... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 48\n* eval\\_batch\\_size: 48\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10",
"### Train... | [
"TAGS\n#transformers #pytorch #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: ... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Manirathinam21/DistilBert_SMSSpam_classifier
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/di... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "Manirathinam21/DistilBert_SMSSpam_classifier", "results": []}]} | Manirathinam21/DistilBert_SMSSpam_classifier | null | [
"transformers",
"tf",
"distilbert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-16T14:20:00+00:00 | [] | [] | TAGS
#transformers #tf #distilbert #text-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| Manirathinam21/DistilBert\_SMSSpam\_classifier
==============================================
This model is a fine-tuned version of distilbert-base-uncased on an SMSSpam Detection dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.0114
* Train Accuracy: 0.9962
* Epoch: 2
Target Labe... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'learning\\_rate': 5e-05, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}\n* training\\_precision: float32",
"### Training results",
"### Framework... | [
"TAGS\n#transformers #tf #distilbert #text-classification #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': 'Adam', 'learning\\_rate':... |
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. -->
# test_ner-finetuned-ner
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-un... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["conll2003"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "test_ner-finetuned-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "conll2003", "type"... | HYM/test_ner-finetuned-ner | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-16T14:29:06+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| test\_ner-finetuned-ner
=======================
This model is a fine-tuned version of distilbert-base-uncased on the conll2003 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0623
* Precision: 0.9242
* Recall: 0.9349
* F1: 0.9295
* Accuracy: 0.9834
Model description
-----------------
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* le... |
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. -->
# t5-small-finetuned-text2log-finetuned-nl-to-fol-finetuned-nl-to-fol
This model is a fine-tuned version of [anki08/t5-small-finet... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["bleu"], "model-index": [{"name": "t5-small-finetuned-text2log-finetuned-nl-to-fol-finetuned-nl-to-fol", "results": []}]} | anki08/t5-small-finetuned-text2log-finetuned-nl-to-fol-finetuned-nl-to-fol | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-16T14:49:19+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| t5-small-finetuned-text2log-finetuned-nl-to-fol-finetuned-nl-to-fol
===================================================================
This model is a fine-tuned version of anki08/t5-small-finetuned-text2log-finetuned-nl-to-fol 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: 5e-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: 200\n* mixed\\_prec... | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate... |
fill-mask | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-imdb
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/di... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "distilbert-base-uncased-finetuned-imdb", "results": []}]} | rugo/distilbert-base-uncased-finetuned-imdb | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-16T15:05:51+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-imdb
======================================
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 1.1486
Model description
-----------------
More information needed
Intended uses &... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0\n* mixed\\_pr... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_siz... |
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... | santiviquez/ppo-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-08-16T15:21:17+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... |
fill-mask | transformers |
## distilHerBERT
distilHerBERT-base is a BERT-based Language Model trained on Polish subset of [cc100](https://huggingface.co/datasets/cc100) dataset using Masked Language Modelling (MLM) and [distillation procedure](https://arxiv.org/abs/1910.01108) from model [HerBERT](https://huggingface.co/allegro/herbert-base-cas... | {"language": "pl", "tags": ["distilherbert"]} | BartekK/distilHerBERT-base-cased | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"distilherbert",
"pl",
"arxiv:1910.01108",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-16T15:33:31+00:00 | [
"1910.01108"
] | [
"pl"
] | TAGS
#transformers #pytorch #bert #fill-mask #distilherbert #pl #arxiv-1910.01108 #autotrain_compatible #endpoints_compatible #region-us
|
## distilHerBERT
distilHerBERT-base is a BERT-based Language Model trained on Polish subset of cc100 dataset using Masked Language Modelling (MLM) and distillation procedure from model HerBERT with dynamic masking of whole words.
We provide one of the models (S4) described in the report from final project on the subje... | [
"## distilHerBERT\ndistilHerBERT-base is a BERT-based Language Model trained on Polish subset of cc100 dataset using Masked Language Modelling (MLM) and distillation procedure from model HerBERT with dynamic masking of whole words.\nWe provide one of the models (S4) described in the report from final project on the... | [
"TAGS\n#transformers #pytorch #bert #fill-mask #distilherbert #pl #arxiv-1910.01108 #autotrain_compatible #endpoints_compatible #region-us \n",
"## distilHerBERT\ndistilHerBERT-base is a BERT-based Language Model trained on Polish subset of cc100 dataset using Masked Language Modelling (MLM) and distillation proc... |
sentence-similarity | sentence-transformers |
# mchochlov/codebert-base-cd-ft
This is a [sentence-transformers](https://www.SBERT.net) model: It maps code to a 768 dimensional dense vector space and is specifically fine tuned towards clone detection using contrastive learning on parts of BigCloneBench code.
<!--- Describe your model here -->
## Usage (Sentence... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | mchochlov/codebert-base-cd-ft | null | [
"sentence-transformers",
"pytorch",
"roberta",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-08-16T16:11:51+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #roberta #feature-extraction #sentence-similarity #transformers #endpoints_compatible #has_space #region-us
|
# mchochlov/codebert-base-cd-ft
This is a sentence-transformers model: It maps code to a 768 dimensional dense vector space and is specifically fine tuned towards clone detection using contrastive learning on parts of BigCloneBench code.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have... | [
"# mchochlov/codebert-base-cd-ft\n\nThis is a sentence-transformers model: It maps code to a 768 dimensional dense vector space and is specifically fine tuned towards clone detection using contrastive learning on parts of BigCloneBench code.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy whe... | [
"TAGS\n#sentence-transformers #pytorch #roberta #feature-extraction #sentence-similarity #transformers #endpoints_compatible #has_space #region-us \n",
"# mchochlov/codebert-base-cd-ft\n\nThis is a sentence-transformers model: It maps code to a 768 dimensional dense vector space and is specifically fine tuned tow... |
fill-mask | transformers |
## MahaBERT
MahaBERT is a Marathi BERT model. It is a multilingual BERT (google/muril-base-cased) model fine-tuned on L3Cube-MahaCorpus and other publicly available Marathi monolingual datasets.
[dataset link] (https://github.com/l3cube-pune/MarathiNLP)
More details on the dataset, models, and baseline results can b... | {"language": "mr", "license": "cc-by-4.0", "datasets": ["L3Cube-MahaCorpus"]} | l3cube-pune/marathi-bert-v2 | null | [
"transformers",
"pytorch",
"safetensors",
"bert",
"fill-mask",
"mr",
"dataset:L3Cube-MahaCorpus",
"arxiv:2202.01159",
"license:cc-by-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-16T16:52:15+00:00 | [
"2202.01159"
] | [
"mr"
] | TAGS
#transformers #pytorch #safetensors #bert #fill-mask #mr #dataset-L3Cube-MahaCorpus #arxiv-2202.01159 #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #region-us
|
## MahaBERT
MahaBERT is a Marathi BERT model. It is a multilingual BERT (google/muril-base-cased) model fine-tuned on L3Cube-MahaCorpus and other publicly available Marathi monolingual datasets.
[dataset link] (URL
More details on the dataset, models, and baseline results can be found in our [paper] (URL
Other Mo... | [
"## MahaBERT\nMahaBERT is a Marathi BERT model. It is a multilingual BERT (google/muril-base-cased) model fine-tuned on L3Cube-MahaCorpus and other publicly available Marathi monolingual datasets. \n[dataset link] (URL\n\nMore details on the dataset, models, and baseline results can be found in our [paper] (URL\n\n... | [
"TAGS\n#transformers #pytorch #safetensors #bert #fill-mask #mr #dataset-L3Cube-MahaCorpus #arxiv-2202.01159 #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"## MahaBERT\nMahaBERT is a Marathi BERT model. It is a multilingual BERT (google/muril-base-cased) model fine-tuned on L3Cube... |
text-classification | transformers | This is a multilingual misogyny and sexism detection model.
This model was released with the following paper (https://rdcu.be/dmIpq):
```
@InProceedings{10.1007/978-3-031-43129-6_9,
author="Chang, Rong-Ching
and May, Jonathan
and Lerman, Kristina",
editor="Thomson, Robert
and Al-khateeb, Samer
and Burger, Annetta
and ... | {} | annahaz/xlm-roberta-base-misogyny-sexism-indomain-mix-bal | null | [
"transformers",
"pytorch",
"xlm-roberta",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-16T17:33:59+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #text-classification #autotrain_compatible #endpoints_compatible #region-us
| This is a multilingual misogyny and sexism detection model.
This model was released with the following paper (URL
We combined several multilingual ground truth datasets for misogyny and sexism (M/S) versus non-misogyny and non-sexism (non-M/S) [3,5,8,9,11,13, 20]. Specifically, the dataset expressing misogynistic o... | [
"### 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 #xlm-roberta #text-classification #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\\_batch\\_size: 16\n* seed: 42\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-finetuned-ner
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["conll2003"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "bert-finetuned-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "conll2003", "type": "c... | ish97/bert-finetuned-ner | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-16T17:39:02+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| bert-finetuned-ner
==================
This model is a fine-tuned version of bert-base-cased on the conll2003 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0641
* Precision: 0.9290
* Recall: 0.9475
* F1: 0.9382
* Accuracy: 0.9858
Model description
-----------------
More information ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# results
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the i... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imdb"], "metrics": ["f1"], "model-index": [{"name": "results", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "imdb", "type": "imdb", "config": "plain_text", "split": "train", "args": "p... | Neha2608/results | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-16T17:57:21+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-imdb #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| results
=======
This model is a fine-tuned version of distilbert-base-uncased on the imdb dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1933
* Accuracy is: 0.9255
* F1: 0.9255
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 #text-classification #generated_from_trainer #dataset-imdb #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning... |
null | null | <h1>welcome</h1> | {} | vayn3/pipli_dataset | null | [
"region:us"
] | null | 2022-08-16T18:21:15+00:00 | [] | [] | TAGS
#region-us
| <h1>welcome</h1> | [] | [
"TAGS\n#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-large-xlsr-korean-demo-test
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-large-xlsr-korean-demo-test", "results": []}]} | NX2411/wav2vec2-large-xlsr-korean-demo-test | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-08-16T18:40:17+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-large-xlsr-korean-demo-test
====================================
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: 0.9829
* Wer: 0.5580
Model description
-----------------
More information needed
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 8\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1... | [
"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: 0.0003\n* train\\_batch\\_size: 4... |
reinforcement-learning | sample-factory |
A(n) **APPO** model trained on the **quadrotor_multi** environment.
This model was trained using Sample Factory 2.0: https://github.com/alex-petrenko/sample-factory
| {"library_name": "sample-factory", "tags": ["deep-reinforcement-learning", "reinforcement-learning", "sample-factory"], "model-index": [{"name": "APPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "quadrotor_multi", "type": "quadrotor_multi"}, "metrics"... | andrewzhang505/quad-swarm-single-drone-sf2 | null | [
"sample-factory",
"tensorboard",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-08-16T18:41:35+00:00 | [] | [] | TAGS
#sample-factory #tensorboard #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
A(n) APPO model trained on the quadrotor_multi environment.
This model was trained using Sample Factory 2.0: URL
| [] | [
"TAGS\n#sample-factory #tensorboard #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n"
] |
text-classification | transformers | --alpha_ce 0.0 --alpha_mlm 0.0 --alpha_cos 0.0 --alpha_act 1.0 --alpha_clm 0.0 --mlm \ | {} | alishudi/distil_act | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-16T18:53:31+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #text-classification #autotrain_compatible #endpoints_compatible #region-us
| --alpha_ce 0.0 --alpha_mlm 0.0 --alpha_cos 0.0 --alpha_act 1.0 --alpha_clm 0.0 --mlm \ | [] | [
"TAGS\n#transformers #pytorch #distilbert #text-classification #autotrain_compatible #endpoints_compatible #region-us \n"
] |
feature-extraction | generic | # Fork of [salesforce/BLIP](https://github.com/salesforce/BLIP) for a `feature-extraction` task on 🤗Inference endpoint.
This repository implements a `custom` task for `feature-extraction` for 🤗 Inference Endpoints. The code for the customized pipeline is in the [pipeline.py](https://huggingface.co/florentgbelidji/bli... | {"license": "bsd-3-clause", "library_name": "generic", "tags": ["feature-extraction", "endpoints-template"]} | florentgbelidji/blip_image_embeddings | null | [
"generic",
"feature-extraction",
"endpoints-template",
"license:bsd-3-clause",
"has_space",
"region:us"
] | null | 2022-08-16T19:18:21+00:00 | [] | [] | TAGS
#generic #feature-extraction #endpoints-template #license-bsd-3-clause #has_space #region-us
| # Fork of salesforce/BLIP for a 'feature-extraction' task on Inference endpoint.
This repository implements a 'custom' task for 'feature-extraction' for Inference Endpoints. The code for the customized pipeline is in the URL.
To use deploy this model a an Inference Endpoint you have to select 'Custom' as task to use t... | [
"# Fork of salesforce/BLIP for a 'feature-extraction' task on Inference endpoint.\nThis repository implements a 'custom' task for 'feature-extraction' for Inference Endpoints. The code for the customized pipeline is in the URL.\nTo use deploy this model a an Inference Endpoint you have to select 'Custom' as task t... | [
"TAGS\n#generic #feature-extraction #endpoints-template #license-bsd-3-clause #has_space #region-us \n",
"# Fork of salesforce/BLIP for a 'feature-extraction' task on Inference endpoint.\nThis repository implements a 'custom' task for 'feature-extraction' for Inference Endpoints. The code for the customized pipe... |
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. -->
# pegasus-samsum
This model is a fine-tuned version of [google/pegasus-cnn_dailymail](https://huggingface.co/google/pegasus-cnn_da... | {"tags": ["generated_from_trainer"], "datasets": ["samsum"], "model-index": [{"name": "pegasus-samsum", "results": []}]} | skr1125/pegasus-samsum | null | [
"transformers",
"pytorch",
"tensorboard",
"pegasus",
"text2text-generation",
"generated_from_trainer",
"dataset:samsum",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-16T19:21:38+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #pegasus #text2text-generation #generated_from_trainer #dataset-samsum #autotrain_compatible #endpoints_compatible #region-us
| pegasus-samsum
==============
This model is a fine-tuned version of google/pegasus-cnn\_dailymail on the samsum dataset.
It achieves the following results on the evaluation set:
* Loss: 1.4859
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\n* seed: 42\n* gradient\\_accumulation\\_steps: 16\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=... | [
"TAGS\n#transformers #pytorch #tensorboard #pegasus #text2text-generation #generated_from_trainer #dataset-samsum #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\... |
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. -->
# t5-small-finetuned-text2log-finetuned-nl-to-fol-finetuned-nl-to-fol-finetuned-nl-to-fol
This model is a fine-tuned version of [a... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["bleu"], "model-index": [{"name": "t5-small-finetuned-text2log-finetuned-nl-to-fol-finetuned-nl-to-fol-finetuned-nl-to-fol", "results": []}]} | anki08/t5-small-finetuned-text2log-finetuned-nl-to-fol-finetuned-nl-to-fol-finetuned-nl-to-fol | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-16T19:58:43+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| t5-small-finetuned-text2log-finetuned-nl-to-fol-finetuned-nl-to-fol-finetuned-nl-to-fol
=======================================================================================
This model is a fine-tuned version of anki08/t5-small-finetuned-text2log-finetuned-nl-to-fol-finetuned-nl-to-fol on an unknown dataset.
It ach... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-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: 10\n* mixed\\_preci... | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate... |
null | diffusers | De-noising Diffusion Probabilistic Model trained on [teticio/audio-diffusion-256](https://huggingface.co/datasets/teticio/audio-diffusion-256) to generate mel spectrograms of 256x256 corresponding to 5 seconds of audio. The code to convert from audio to spectrogram and vice versa can be found in https://github.com/teti... | {"tags": ["audio", "spectrograms"], "datasets": ["teticio/audio-diffusion-256"]} | teticio/audio-diffusion-256 | null | [
"diffusers",
"tensorboard",
"audio",
"spectrograms",
"dataset:teticio/audio-diffusion-256",
"has_space",
"diffusers:AudioDiffusionPipeline",
"region:us"
] | null | 2022-08-16T20:19:58+00:00 | [] | [] | TAGS
#diffusers #tensorboard #audio #spectrograms #dataset-teticio/audio-diffusion-256 #has_space #diffusers-AudioDiffusionPipeline #region-us
| De-noising Diffusion Probabilistic Model trained on teticio/audio-diffusion-256 to generate mel spectrograms of 256x256 corresponding to 5 seconds of audio. The code to convert from audio to spectrogram and vice versa can be found in URL along with scripts to train and run inference. | [] | [
"TAGS\n#diffusers #tensorboard #audio #spectrograms #dataset-teticio/audio-diffusion-256 #has_space #diffusers-AudioDiffusionPipeline #region-us \n"
] |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-small-finetuned-text2log-finetuned-nl-to-fol-finetuned-nl-to-fol-finetuned-nl-to-fol-version2
This model is a fine-tuned vers... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["bleu"], "model-index": [{"name": "t5-small-finetuned-text2log-finetuned-nl-to-fol-finetuned-nl-to-fol-finetuned-nl-to-fol-version2", "results": []}]} | anki08/t5-small-finetuned-text2log-finetuned-nl-to-fol-finetuned-nl-to-fol-finetuned-nl-to-fol-version2 | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-16T20:33:11+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| t5-small-finetuned-text2log-finetuned-nl-to-fol-finetuned-nl-to-fol-finetuned-nl-to-fol-version2
================================================================================================
This model is a fine-tuned version of anki08/t5-small-finetuned-text2log-finetuned-nl-to-fol-finetuned-nl-to-fol on an unkno... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-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: 100\n* mixed\\_prec... | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["emotion"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "distilbert-base-uncased-finetuned-emotion", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "emotion", "type": "emotion... | impesalobo431/distilbert-base-uncased-finetuned-emotion | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-16T20:38:43+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-emotion
=========================================
This model is a fine-tuned version of distilbert-base-uncased on the emotion dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2160
* Accuracy: 0.923
* F1: 0.9232
Model description
-----------------
Mor... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learn... |
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. -->
# finetuning-sentiment-model-3000-samples
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/d... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imdb"], "model-index": [{"name": "finetuning-sentiment-model-3000-samples", "results": []}]} | Heer/finetuning-sentiment-model-3000-samples | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-16T21:29:03+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-imdb #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# finetuning-sentiment-model-3000-samples
This model is a fine-tuned version of distilbert-base-uncased on the imdb dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.3264
- eval_accuracy: 0.8867
- eval_f1: 0.8896
- eval_runtime: 253.6051
- eval_samples_per_second: 1.183
- eval_steps_p... | [
"# finetuning-sentiment-model-3000-samples\n\nThis model is a fine-tuned version of distilbert-base-uncased on the imdb dataset.\nIt achieves the following results on the evaluation set:\n- eval_loss: 0.3264\n- eval_accuracy: 0.8867\n- eval_f1: 0.8896\n- eval_runtime: 253.6051\n- eval_samples_per_second: 1.183\n- e... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-imdb #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# finetuning-sentiment-model-3000-samples\n\nThis model is a fine-tuned version of distilbert-base-uncased on the imdb ... |
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... | ahmedo/ppo-tuned-lunarlanderv2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-08-16T21:35:13+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... |
text-classification | transformers |
# distilbert-base-future
## Table of Contents
- [Model description](#model_description)
- [Intended uses & limitations](#intended_uses_&_limitations)
- [Training and evaluation data](#training_and_evaluation_data)
- [Training procedure](#training_procedure)
This model is a fine-tuned version of [distilbert-base-unca... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "widget": [{"text": "We will have a good time.", "example_title": "Positive"}, {"text": "We had a good time.", "example_title": "Negative"}], "model-index": [{"name": "distilbert-base-future", "results": []}]} | fidsinn/distilbert-base-future | null | [
"transformers",
"tf",
"distilbert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-16T22:41:05+00:00 | [] | [] | TAGS
#transformers #tf #distilbert #text-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-future
======================
Table of Contents
-----------------
* Model description
* Intended uses & limitations
* Training and evaluation data
* Training procedure
This model is a fine-tuned version of distilbert-base-uncased on the future-statements dataset.
It achieves the following results ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'learning\\_rate': 5e-05, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}\n* training\\_precision: float32",
"### Training results",
"### Framework... | [
"TAGS\n#transformers #tf #distilbert #text-classification #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': 'Adam', 'learning\\_rate':... |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-small-finetuned-cuad-full-longer
This model is a fine-tuned version of [muhtasham/bert-small-finetuned-cuad-full](https://h... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["cuad"], "model-index": [{"name": "bert-small-finetuned-cuad-full-longer", "results": []}]} | muhtasham/bert-small-finetuned-cuad-full-longer | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:cuad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-08-16T23:03:30+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #dataset-cuad #license-apache-2.0 #endpoints_compatible #region-us
| bert-small-finetuned-cuad-full-longer
=====================================
This model is a fine-tuned version of muhtasham/bert-small-finetuned-cuad-full on the cuad dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0295
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: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #dataset-cuad #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64... |
sentence-similarity | sentence-transformers |
# smartmind/ko-sbert-augSTS-maxlength512
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 -->
This model is [snunlp/KR-SBERT-V40K-klueNLI... | {"language": ["ko"], "license": "mit", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | smartmind/ko-sbert-augSTS-maxlength512 | null | [
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"ko",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-08-16T23:48:31+00:00 | [] | [
"ko"
] | TAGS
#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #ko #license-mit #endpoints_compatible #region-us
| smartmind/ko-sbert-augSTS-maxlength512
======================================
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.
This model is snunlp/KR-SBERT-V40K-klueNLI-augSTS with max input l... | [] | [
"TAGS\n#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #ko #license-mit #endpoints_compatible #region-us \n"
] |
reinforcement-learning | stable-baselines3 |
# **QRDQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **QRDQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training fram... | {"library_name": "stable-baselines3", "tags": ["SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "QRDQN", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "SpaceInvadersNoFr... | rebolforces/qrdqn-SpaceInvadersNoFrameskip-20Meps | null | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-08-16T23:52:05+00:00 | [] | [] | TAGS
#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# QRDQN Agent playing SpaceInvadersNoFrameskip-v4
This is a trained model of a QRDQN agent playing SpaceInvadersNoFrameskip-v4
using the stable-baselines3 library
and the RL Zoo.
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained ag... | [
"# QRDQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a QRDQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framework for Stable Baselines3\nreinforcement learning agents,\nwith hyperparameter optimization and pre... | [
"TAGS\n#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# QRDQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a QRDQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL... |
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. -->
# Mostafa3zazi/arabicQA-finetuned-squad_arcd
This model is a fine-tuned version of [aubmindlab/araelectra-base-discriminator](https://hu... | {"tags": ["generated_from_keras_callback"], "model-index": [{"name": "Mostafa3zazi/arabicQA-finetuned-squad_arcd", "results": []}]} | Mostafa3zazi/arabicQA-finetuned-squad_arcd | null | [
"transformers",
"tf",
"electra",
"question-answering",
"generated_from_keras_callback",
"endpoints_compatible",
"region:us"
] | null | 2022-08-17T00:00:23+00:00 | [] | [] | TAGS
#transformers #tf #electra #question-answering #generated_from_keras_callback #endpoints_compatible #region-us
| Mostafa3zazi/arabicQA-finetuned-squad\_arcd
===========================================
This model is a fine-tuned version of aubmindlab/araelectra-base-discriminator on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 1.9073
* 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': 'WarmUp', 'config': {'initial\\_learning\\_rate': 3e-05, 'decay\\_schedule\\_fn': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_le... | [
"TAGS\n#transformers #tf #electra #question-answering #generated_from_keras_callback #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': 'WarmUp', 'config... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-300m-tw-small
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/f... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-large-xls-r-300m-tw-small", "results": []}]} | julicee/wav2vec2-large-xls-r-300m-tw-small | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-08-17T01:21:53+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
|
# wav2vec2-large-xls-r-300m-tw-small
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common_voice dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedur... | [
"# wav2vec2-large-xls-r-300m-tw-small\n\nThis model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common_voice dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us \n",
"# wav2vec2-large-xls-r-300m-tw-small\n\nThis model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common_voice... |
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. -->
# arabicQA-finetuned-squad_arcd_manual_push
This model is a fine-tuned version of [aubmindlab/araelectra-base-discriminator](https://hug... | {"tags": ["generated_from_keras_callback"], "model-index": [{"name": "arabicQA-finetuned-squad_arcd_manual_push", "results": []}]} | Mostafa3zazi/arabicQA-finetuned-squad_arcd_manual_push | null | [
"transformers",
"tf",
"electra",
"question-answering",
"generated_from_keras_callback",
"endpoints_compatible",
"region:us"
] | null | 2022-08-17T01:27:44+00:00 | [] | [] | TAGS
#transformers #tf #electra #question-answering #generated_from_keras_callback #endpoints_compatible #region-us
| arabicQA-finetuned-squad\_arcd\_manual\_push
============================================
This model is a fine-tuned version of aubmindlab/araelectra-base-discriminator on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 2.3885
* 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': 'WarmUp', 'config': {'initial\\_learning\\_rate': 3e-05, 'decay\\_schedule\\_fn': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_le... | [
"TAGS\n#transformers #tf #electra #question-answering #generated_from_keras_callback #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': 'WarmUp', 'config... |
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:
| Hyperparameters | Value |
| :-- | :-- |
| na... | {"library_name": "keras"} | chaninder/waste-sorting-model-v4 | null | [
"keras",
"has_space",
"region:us"
] | null | 2022-08-17T01:40:10+00:00 | [] | [] | TAGS
#keras #has_space #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 h... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n\nModel Plot\n----------\n\n\n\nView Model Plot\n!Model Image"
] | [
"TAGS\n#keras #has_space #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n\nModel Plot\n----------\n\n\n\nView Model Plot\n!Model Image"
] |
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. -->
# distilled-mt5-small-b0.05
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on t... | {"language": ["en", "ro"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["wmt16"], "metrics": ["bleu"], "model-index": [{"name": "distilled-mt5-small-b0.05", "results": [{"task": {"type": "translation", "name": "Translation"}, "dataset": {"name": "wmt16 ro-en", "type": "wmt16", "args": "ro-e... | Lvxue/distilled-mt5-small-b0.05 | null | [
"transformers",
"pytorch",
"tensorboard",
"mt5",
"text2text-generation",
"generated_from_trainer",
"en",
"ro",
"dataset:wmt16",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-17T01:43:42+00:00 | [] | [
"en",
"ro"
] | TAGS
#transformers #pytorch #tensorboard #mt5 #text2text-generation #generated_from_trainer #en #ro #dataset-wmt16 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# distilled-mt5-small-b0.05
This model is a fine-tuned version of google/mt5-small on the wmt16 ro-en dataset.
It achieves the following results on the evaluation set:
- Loss: 2.8144
- Bleu: 7.4851
- Gen Len: 44.7914
## Model description
More information needed
## Intended uses & limitations
More information ne... | [
"# distilled-mt5-small-b0.05\n\nThis model is a fine-tuned version of google/mt5-small on the wmt16 ro-en dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 2.8144\n- Bleu: 7.4851\n- Gen Len: 44.7914",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\... | [
"TAGS\n#transformers #pytorch #tensorboard #mt5 #text2text-generation #generated_from_trainer #en #ro #dataset-wmt16 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# distilled-mt5-small-b0.05\n\nThis model is a fine-tuned version of google/... |
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. -->
# distilled-mt5-small-test2
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on t... | {"language": ["en", "ro"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["wmt16"], "metrics": ["bleu"], "model-index": [{"name": "distilled-mt5-small-test2", "results": [{"task": {"type": "translation", "name": "Translation"}, "dataset": {"name": "wmt16 ro-en", "type": "wmt16", "args": "ro-e... | Lvxue/distilled-mt5-small-test2 | null | [
"transformers",
"pytorch",
"tensorboard",
"mt5",
"text2text-generation",
"generated_from_trainer",
"en",
"ro",
"dataset:wmt16",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-17T01:44:27+00:00 | [] | [
"en",
"ro"
] | TAGS
#transformers #pytorch #tensorboard #mt5 #text2text-generation #generated_from_trainer #en #ro #dataset-wmt16 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# distilled-mt5-small-test2
This model is a fine-tuned version of google/mt5-small on the wmt16 ro-en dataset.
It achieves the following results on the evaluation set:
- Loss: 2.8127
- Bleu: 7.735
- Gen Len: 44.5453
## Model description
More information needed
## Intended uses & limitations
More information nee... | [
"# distilled-mt5-small-test2\n\nThis model is a fine-tuned version of google/mt5-small on the wmt16 ro-en dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 2.8127\n- Bleu: 7.735\n- Gen Len: 44.5453",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\n... | [
"TAGS\n#transformers #pytorch #tensorboard #mt5 #text2text-generation #generated_from_trainer #en #ro #dataset-wmt16 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# distilled-mt5-small-test2\n\nThis model is a fine-tuned version of google/... |
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. -->
# distilled-mt5-small-b0.1
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on th... | {"language": ["en", "ro"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["wmt16"], "metrics": ["bleu"], "model-index": [{"name": "distilled-mt5-small-b0.1", "results": [{"task": {"type": "translation", "name": "Translation"}, "dataset": {"name": "wmt16 ro-en", "type": "wmt16", "args": "ro-en... | Lvxue/distilled-mt5-small-b0.1 | null | [
"transformers",
"pytorch",
"tensorboard",
"mt5",
"text2text-generation",
"generated_from_trainer",
"en",
"ro",
"dataset:wmt16",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-17T01:45:42+00:00 | [] | [
"en",
"ro"
] | TAGS
#transformers #pytorch #tensorboard #mt5 #text2text-generation #generated_from_trainer #en #ro #dataset-wmt16 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# distilled-mt5-small-b0.1
This model is a fine-tuned version of google/mt5-small on the wmt16 ro-en dataset.
It achieves the following results on the evaluation set:
- Loss: 2.8190
- Bleu: 7.497
- Gen Len: 44.5613
## Model description
More information needed
## Intended uses & limitations
More information need... | [
"# distilled-mt5-small-b0.1\n\nThis model is a fine-tuned version of google/mt5-small on the wmt16 ro-en dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 2.8190\n- Bleu: 7.497\n- Gen Len: 44.5613",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nM... | [
"TAGS\n#transformers #pytorch #tensorboard #mt5 #text2text-generation #generated_from_trainer #en #ro #dataset-wmt16 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# distilled-mt5-small-b0.1\n\nThis model is a fine-tuned version of google/m... |
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. -->
# distilled-mt5-small-b0.5
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on th... | {"language": ["en", "ro"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["wmt16"], "metrics": ["bleu"], "model-index": [{"name": "distilled-mt5-small-b0.5", "results": [{"task": {"type": "translation", "name": "Translation"}, "dataset": {"name": "wmt16 ro-en", "type": "wmt16", "args": "ro-en... | Lvxue/distilled-mt5-small-b0.5 | null | [
"transformers",
"pytorch",
"tensorboard",
"mt5",
"text2text-generation",
"generated_from_trainer",
"en",
"ro",
"dataset:wmt16",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-17T01:46:01+00:00 | [] | [
"en",
"ro"
] | TAGS
#transformers #pytorch #tensorboard #mt5 #text2text-generation #generated_from_trainer #en #ro #dataset-wmt16 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# distilled-mt5-small-b0.5
This model is a fine-tuned version of google/mt5-small on the wmt16 ro-en dataset.
It achieves the following results on the evaluation set:
- Loss: 2.8108
- Bleu: 7.5091
- Gen Len: 43.958
## Model description
More information needed
## Intended uses & limitations
More information need... | [
"# distilled-mt5-small-b0.5\n\nThis model is a fine-tuned version of google/mt5-small on the wmt16 ro-en dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 2.8108\n- Bleu: 7.5091\n- Gen Len: 43.958",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nM... | [
"TAGS\n#transformers #pytorch #tensorboard #mt5 #text2text-generation #generated_from_trainer #en #ro #dataset-wmt16 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# distilled-mt5-small-b0.5\n\nThis model is a fine-tuned version of google/m... |
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. -->
# distilled-mt5-small-b1
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the ... | {"language": ["en", "ro"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["wmt16"], "metrics": ["bleu"], "model-index": [{"name": "distilled-mt5-small-b1", "results": [{"task": {"type": "translation", "name": "Translation"}, "dataset": {"name": "wmt16 ro-en", "type": "wmt16", "args": "ro-en"}... | Lvxue/distilled-mt5-small-b1 | null | [
"transformers",
"pytorch",
"tensorboard",
"mt5",
"text2text-generation",
"generated_from_trainer",
"en",
"ro",
"dataset:wmt16",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-17T01:46:49+00:00 | [] | [
"en",
"ro"
] | TAGS
#transformers #pytorch #tensorboard #mt5 #text2text-generation #generated_from_trainer #en #ro #dataset-wmt16 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# distilled-mt5-small-b1
This model is a fine-tuned version of google/mt5-small on the wmt16 ro-en dataset.
It achieves the following results on the evaluation set:
- Loss: 2.7924
- Bleu: 7.5172
- Gen Len: 44.1886
## Model description
More information needed
## Intended uses & limitations
More information neede... | [
"# distilled-mt5-small-b1\n\nThis model is a fine-tuned version of google/mt5-small on the wmt16 ro-en dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 2.7924\n- Bleu: 7.5172\n- Gen Len: 44.1886",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMo... | [
"TAGS\n#transformers #pytorch #tensorboard #mt5 #text2text-generation #generated_from_trainer #en #ro #dataset-wmt16 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# distilled-mt5-small-b1\n\nThis model is a fine-tuned version of google/mt5... |
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. -->
# distilled-mt5-small-b0.01
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on t... | {"language": ["en", "ro"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["wmt16"], "metrics": ["bleu"], "model-index": [{"name": "distilled-mt5-small-b0.01", "results": [{"task": {"type": "translation", "name": "Translation"}, "dataset": {"name": "wmt16 ro-en", "type": "wmt16", "args": "ro-e... | Lvxue/distilled-mt5-small-b0.01 | null | [
"transformers",
"pytorch",
"tensorboard",
"mt5",
"text2text-generation",
"generated_from_trainer",
"en",
"ro",
"dataset:wmt16",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-17T01:47:43+00:00 | [] | [
"en",
"ro"
] | TAGS
#transformers #pytorch #tensorboard #mt5 #text2text-generation #generated_from_trainer #en #ro #dataset-wmt16 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# distilled-mt5-small-b0.01
This model is a fine-tuned version of google/mt5-small on the wmt16 ro-en dataset.
It achieves the following results on the evaluation set:
- Loss: 2.8163
- Bleu: 7.5421
- Gen Len: 44.4902
## Model description
More information needed
## Intended uses & limitations
More information ne... | [
"# distilled-mt5-small-b0.01\n\nThis model is a fine-tuned version of google/mt5-small on the wmt16 ro-en dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 2.8163\n- Bleu: 7.5421\n- Gen Len: 44.4902",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\... | [
"TAGS\n#transformers #pytorch #tensorboard #mt5 #text2text-generation #generated_from_trainer #en #ro #dataset-wmt16 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# distilled-mt5-small-b0.01\n\nThis model is a fine-tuned version of google/... |
audio-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. -->
# wav2vec2-base-ks-padpt400
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec... | {"license": "apache-2.0", "tags": ["audio-classification", "generated_from_trainer"], "datasets": ["superb"], "metrics": ["accuracy"], "model-index": [{"name": "wav2vec2-base-ks-padpt400", "results": []}]} | Jungwoo4021/wav2vec2-base-ks-padpt400 | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"text-classification",
"audio-classification",
"generated_from_trainer",
"dataset:superb",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-17T02:12:57+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #text-classification #audio-classification #generated_from_trainer #dataset-superb #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| wav2vec2-base-ks-padpt400
=========================
This model is a fine-tuned version of facebook/wav2vec2-base on the superb dataset.
It achieves the following results on the evaluation set:
* Loss: 1.2218
* Accuracy: 0.6343
Model description
-----------------
More information needed
Intended uses & limitat... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.003\n* train\\_batch\\_size: 256\n* eval\\_batch\\_size: 256\n* seed: 0\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 1024\n* optimizer: Adam with betas=(0.9,0.999) and epsi... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #text-classification #audio-classification #generated_from_trainer #dataset-superb #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*... |
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. -->
# rob-base-superqa
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset.
... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["squad_v2", "quoref", "adversarial_qa", "duorc"], "task": ["question-answering"], "model-index": [{"name": "rob-base-superqa", "results": [{"task": {"type": "question-answering", "name": "Question Answering"}, "dataset": {"name": "adversarial_qa", "typ... | nbroad/rob-base-superqa1 | null | [
"transformers",
"pytorch",
"tensorboard",
"optimum_habana",
"roberta",
"question-answering",
"generated_from_trainer",
"dataset:squad_v2",
"dataset:quoref",
"dataset:adversarial_qa",
"dataset:duorc",
"license:mit",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-08-17T02:29:18+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #optimum_habana #roberta #question-answering #generated_from_trainer #dataset-squad_v2 #dataset-quoref #dataset-adversarial_qa #dataset-duorc #license-mit #model-index #endpoints_compatible #region-us
|
# rob-base-superqa
This model is a fine-tuned version of roberta-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
The follo... | [
"# rob-base-superqa\n\nThis model is a fine-tuned version of roberta-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",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #optimum_habana #roberta #question-answering #generated_from_trainer #dataset-squad_v2 #dataset-quoref #dataset-adversarial_qa #dataset-duorc #license-mit #model-index #endpoints_compatible #region-us \n",
"# rob-base-superqa\n\nThis model is a fine-tuned version of robe... |
fill-mask | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-large-cased-finetuned-fce
This model is a fine-tuned version of [bert-large-cased](https://huggingface.co/bert-large-cased)... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "bert-large-cased-finetuned-fce", "results": []}]} | poro1301/bert-large-cased-finetuned-fce | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-17T02:58:22+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| bert-large-cased-finetuned-fce
==============================
This model is a fine-tuned version of bert-large-cased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 1.5307
Model description
-----------------
More information needed
Intended uses & limitations
------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0\n* mixed\\_pr... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\... |
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. -->
# rob-base-superqa2
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset.... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["squad_v2", "quoref", "adversarial_qa", "duorc"], "model-index": [{"name": "rob-base-superqa2", "results": [{"task": {"type": "question-answering", "name": "Question Answering"}, "dataset": {"name": "squad_v2", "type": "squad_v2", "config": "squad_v2",... | nbroad/rob-base-superqa2 | null | [
"transformers",
"pytorch",
"tensorboard",
"optimum_habana",
"roberta",
"question-answering",
"generated_from_trainer",
"dataset:squad_v2",
"dataset:quoref",
"dataset:adversarial_qa",
"dataset:duorc",
"license:mit",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-08-17T03:02:10+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #optimum_habana #roberta #question-answering #generated_from_trainer #dataset-squad_v2 #dataset-quoref #dataset-adversarial_qa #dataset-duorc #license-mit #model-index #endpoints_compatible #region-us
|
# rob-base-superqa2
This model is a fine-tuned version of roberta-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
The foll... | [
"# rob-base-superqa2\n\nThis model is a fine-tuned version of roberta-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",
"### Trai... | [
"TAGS\n#transformers #pytorch #tensorboard #optimum_habana #roberta #question-answering #generated_from_trainer #dataset-squad_v2 #dataset-quoref #dataset-adversarial_qa #dataset-duorc #license-mit #model-index #endpoints_compatible #region-us \n",
"# rob-base-superqa2\n\nThis model is a fine-tuned version of rob... |
audio-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. -->
# wav2vec2-base-ks-padpt800
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec... | {"license": "apache-2.0", "tags": ["audio-classification", "generated_from_trainer"], "datasets": ["superb"], "metrics": ["accuracy"], "model-index": [{"name": "wav2vec2-base-ks-padpt800", "results": []}]} | Jungwoo4021/wav2vec2-base-ks-padpt800 | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"text-classification",
"audio-classification",
"generated_from_trainer",
"dataset:superb",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-17T04:06:20+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #text-classification #audio-classification #generated_from_trainer #dataset-superb #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| wav2vec2-base-ks-padpt800
=========================
This model is a fine-tuned version of facebook/wav2vec2-base on the superb dataset.
It achieves the following results on the evaluation set:
* Loss: 1.5281
* Accuracy: 0.6142
Model description
-----------------
More information needed
Intended uses & limitat... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.003\n* train\\_batch\\_size: 256\n* eval\\_batch\\_size: 256\n* seed: 0\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 1024\n* optimizer: Adam with betas=(0.9,0.999) and epsi... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #text-classification #audio-classification #generated_from_trainer #dataset-superb #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*... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["emotion"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "distilbert-base-uncased-finetuned-emotion", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "emotion", "type": "emotion... | huggingbase/distilbert-base-uncased-finetuned-emotion | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-17T04:12:46+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-emotion
=========================================
This model is a fine-tuned version of distilbert-base-uncased on the emotion dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2268
* Accuracy: 0.9245
* F1: 0.9244
Model description
-----------------
Mo... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learn... |
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. -->
# distilled-mt5-small-b2
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the ... | {"language": ["en", "ro"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["wmt16"], "metrics": ["bleu"], "model-index": [{"name": "distilled-mt5-small-b2", "results": [{"task": {"type": "translation", "name": "Translation"}, "dataset": {"name": "wmt16 ro-en", "type": "wmt16", "args": "ro-en"}... | Lvxue/distilled-mt5-small-b2 | null | [
"transformers",
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"tensorboard",
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"text2text-generation",
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"dataset:wmt16",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-17T04:48:30+00:00 | [] | [
"en",
"ro"
] | TAGS
#transformers #pytorch #tensorboard #mt5 #text2text-generation #generated_from_trainer #en #ro #dataset-wmt16 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# distilled-mt5-small-b2
This model is a fine-tuned version of google/mt5-small on the wmt16 ro-en dataset.
It achieves the following results on the evaluation set:
- Loss: 2.7924
- Bleu: 7.4786
- Gen Len: 44.5778
## Model description
More information needed
## Intended uses & limitations
More information neede... | [
"# distilled-mt5-small-b2\n\nThis model is a fine-tuned version of google/mt5-small on the wmt16 ro-en dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 2.7924\n- Bleu: 7.4786\n- Gen Len: 44.5778",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMo... | [
"TAGS\n#transformers #pytorch #tensorboard #mt5 #text2text-generation #generated_from_trainer #en #ro #dataset-wmt16 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# distilled-mt5-small-b2\n\nThis model is a fine-tuned version of google/mt5... |
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. -->
# distilled-mt5-small-b10
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the... | {"language": ["en", "ro"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["wmt16"], "metrics": ["bleu"], "model-index": [{"name": "distilled-mt5-small-b10", "results": [{"task": {"type": "translation", "name": "Translation"}, "dataset": {"name": "wmt16 ro-en", "type": "wmt16", "args": "ro-en"... | Lvxue/distilled-mt5-small-b10 | null | [
"transformers",
"pytorch",
"tensorboard",
"mt5",
"text2text-generation",
"generated_from_trainer",
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"dataset:wmt16",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-17T04:52:41+00:00 | [] | [
"en",
"ro"
] | TAGS
#transformers #pytorch #tensorboard #mt5 #text2text-generation #generated_from_trainer #en #ro #dataset-wmt16 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# distilled-mt5-small-b10
This model is a fine-tuned version of google/mt5-small on the wmt16 ro-en dataset.
It achieves the following results on the evaluation set:
- Loss: 2.8165
- Bleu: 7.1529
- Gen Len: 45.5448
## Model description
More information needed
## Intended uses & limitations
More information need... | [
"# distilled-mt5-small-b10\n\nThis model is a fine-tuned version of google/mt5-small on the wmt16 ro-en dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 2.8165\n- Bleu: 7.1529\n- Gen Len: 45.5448",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nM... | [
"TAGS\n#transformers #pytorch #tensorboard #mt5 #text2text-generation #generated_from_trainer #en #ro #dataset-wmt16 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# distilled-mt5-small-b10\n\nThis model is a fine-tuned version of google/mt... |
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. -->
# distilled-mt5-small-b20
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the... | {"language": ["en", "ro"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["wmt16"], "metrics": ["bleu"], "model-index": [{"name": "distilled-mt5-small-b20", "results": [{"task": {"type": "translation", "name": "Translation"}, "dataset": {"name": "wmt16 ro-en", "type": "wmt16", "args": "ro-en"... | Lvxue/distilled-mt5-small-b20 | null | [
"transformers",
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"tensorboard",
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"text2text-generation",
"generated_from_trainer",
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"dataset:wmt16",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-17T04:54:45+00:00 | [] | [
"en",
"ro"
] | TAGS
#transformers #pytorch #tensorboard #mt5 #text2text-generation #generated_from_trainer #en #ro #dataset-wmt16 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# distilled-mt5-small-b20
This model is a fine-tuned version of google/mt5-small on the wmt16 ro-en dataset.
It achieves the following results on the evaluation set:
- Loss: 2.8652
- Bleu: 6.6798
- Gen Len: 46.8789
## Model description
More information needed
## Intended uses & limitations
More information need... | [
"# distilled-mt5-small-b20\n\nThis model is a fine-tuned version of google/mt5-small on the wmt16 ro-en dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 2.8652\n- Bleu: 6.6798\n- Gen Len: 46.8789",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nM... | [
"TAGS\n#transformers #pytorch #tensorboard #mt5 #text2text-generation #generated_from_trainer #en #ro #dataset-wmt16 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# distilled-mt5-small-b20\n\nThis model is a fine-tuned version of google/mt... |
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. -->
# distilled-mt5-small-b50
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the... | {"language": ["en", "ro"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["wmt16"], "metrics": ["bleu"], "model-index": [{"name": "distilled-mt5-small-b50", "results": [{"task": {"type": "translation", "name": "Translation"}, "dataset": {"name": "wmt16 ro-en", "type": "wmt16", "args": "ro-en"... | Lvxue/distilled-mt5-small-b50 | null | [
"transformers",
"pytorch",
"tensorboard",
"mt5",
"text2text-generation",
"generated_from_trainer",
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"dataset:wmt16",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-17T04:54:47+00:00 | [] | [
"en",
"ro"
] | TAGS
#transformers #pytorch #tensorboard #mt5 #text2text-generation #generated_from_trainer #en #ro #dataset-wmt16 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# distilled-mt5-small-b50
This model is a fine-tuned version of google/mt5-small on the wmt16 ro-en dataset.
It achieves the following results on the evaluation set:
- Loss: 3.0682
- Bleu: 5.0009
- Gen Len: 50.7284
## Model description
More information needed
## Intended uses & limitations
More information need... | [
"# distilled-mt5-small-b50\n\nThis model is a fine-tuned version of google/mt5-small on the wmt16 ro-en dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 3.0682\n- Bleu: 5.0009\n- Gen Len: 50.7284",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nM... | [
"TAGS\n#transformers #pytorch #tensorboard #mt5 #text2text-generation #generated_from_trainer #en #ro #dataset-wmt16 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# distilled-mt5-small-b50\n\nThis model is a fine-tuned version of google/mt... |
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. -->
# distilled-mt5-small-b100
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on th... | {"language": ["en", "ro"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["wmt16"], "metrics": ["bleu"], "model-index": [{"name": "distilled-mt5-small-b100", "results": [{"task": {"type": "translation", "name": "Translation"}, "dataset": {"name": "wmt16 ro-en", "type": "wmt16", "args": "ro-en... | Lvxue/distilled-mt5-small-b100 | null | [
"transformers",
"pytorch",
"tensorboard",
"mt5",
"text2text-generation",
"generated_from_trainer",
"en",
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"dataset:wmt16",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-17T04:57:15+00:00 | [] | [
"en",
"ro"
] | TAGS
#transformers #pytorch #tensorboard #mt5 #text2text-generation #generated_from_trainer #en #ro #dataset-wmt16 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# distilled-mt5-small-b100
This model is a fine-tuned version of google/mt5-small on the wmt16 ro-en dataset.
It achieves the following results on the evaluation set:
- Loss: 3.5393
- Bleu: 1.772
- Gen Len: 61.0825
## Model description
More information needed
## Intended uses & limitations
More information need... | [
"# distilled-mt5-small-b100\n\nThis model is a fine-tuned version of google/mt5-small on the wmt16 ro-en dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 3.5393\n- Bleu: 1.772\n- Gen Len: 61.0825",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nM... | [
"TAGS\n#transformers #pytorch #tensorboard #mt5 #text2text-generation #generated_from_trainer #en #ro #dataset-wmt16 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# distilled-mt5-small-b100\n\nThis model is a fine-tuned version of google/m... |
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. -->
# distilled-mt5-small-b5
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the ... | {"language": ["en", "ro"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["wmt16"], "metrics": ["bleu"], "model-index": [{"name": "distilled-mt5-small-b5", "results": [{"task": {"type": "translation", "name": "Translation"}, "dataset": {"name": "wmt16 ro-en", "type": "wmt16", "args": "ro-en"}... | Lvxue/distilled-mt5-small-b5 | null | [
"transformers",
"pytorch",
"tensorboard",
"mt5",
"text2text-generation",
"generated_from_trainer",
"en",
"ro",
"dataset:wmt16",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-17T04:57:18+00:00 | [] | [
"en",
"ro"
] | TAGS
#transformers #pytorch #tensorboard #mt5 #text2text-generation #generated_from_trainer #en #ro #dataset-wmt16 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# distilled-mt5-small-b5
This model is a fine-tuned version of google/mt5-small on the wmt16 ro-en dataset.
It achieves the following results on the evaluation set:
- Loss: 2.7945
- Bleu: 7.3798
- Gen Len: 44.7109
## Model description
More information needed
## Intended uses & limitations
More information neede... | [
"# distilled-mt5-small-b5\n\nThis model is a fine-tuned version of google/mt5-small on the wmt16 ro-en dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 2.7945\n- Bleu: 7.3798\n- Gen Len: 44.7109",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMo... | [
"TAGS\n#transformers #pytorch #tensorboard #mt5 #text2text-generation #generated_from_trainer #en #ro #dataset-wmt16 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# distilled-mt5-small-b5\n\nThis model is a fine-tuned version of google/mt5... |
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. -->
# turkishReviews-ds-mini
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset.
It achieves th... | {"license": "mit", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "turkishReviews-ds-mini", "results": []}]} | pbwt/turkishReviews-ds-mini | null | [
"transformers",
"tf",
"gpt2",
"text-generation",
"generated_from_keras_callback",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-17T04:59:15+00:00 | [] | [] | TAGS
#transformers #tf #gpt2 #text-generation #generated_from_keras_callback #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| turkishReviews-ds-mini
======================
This model is a fine-tuned version of gpt2 on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 9.1632
* Validation Loss: 9.2525
* Epoch: 2
Model description
-----------------
More information needed
Intended uses & limitat... | [
"### 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': 5e-05, 'decay\\_schedule\\_fn': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_le... | [
"TAGS\n#transformers #tf #gpt2 #text-generation #generated_from_keras_callback #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay... |
null | transformers |
# KhanomTan TTS v1.0
KhanomTan TTS (ขนมตาล) is an open-source Thai text-to-speech model that supports multilingual speakers such as Thai, English, and others.
KhanomTan TTS is a YourTTS model trained on multilingual languages that supports Thai. We use Thai speech corpora, TSync 1* and TSync 2* [mbarnig/lb-de-fr-en-... | {"license": "cc-by-nc-sa-3.0"} | wannaphong/khanomtan-tts-v1.0 | null | [
"transformers",
"tensorboard",
"license:cc-by-nc-sa-3.0",
"endpoints_compatible",
"region:us"
] | null | 2022-08-17T05:31:07+00:00 | [] | [] | TAGS
#transformers #tensorboard #license-cc-by-nc-sa-3.0 #endpoints_compatible #region-us
|
# KhanomTan TTS v1.0
KhanomTan TTS (ขนมตาล) is an open-source Thai text-to-speech model that supports multilingual speakers such as Thai, English, and others.
KhanomTan TTS is a YourTTS model trained on multilingual languages that supports Thai. We use Thai speech corpora, TSync 1* and TSync 2* mbarnig/lb-de-fr-en-p... | [
"# KhanomTan TTS v1.0\n\nKhanomTan TTS (ขนมตาล) is an open-source Thai text-to-speech model that supports multilingual speakers such as Thai, English, and others.\n\nKhanomTan TTS is a YourTTS model trained on multilingual languages that supports Thai. We use Thai speech corpora, TSync 1* and TSync 2* mbarnig/lb-de... | [
"TAGS\n#transformers #tensorboard #license-cc-by-nc-sa-3.0 #endpoints_compatible #region-us \n",
"# KhanomTan TTS v1.0\n\nKhanomTan TTS (ขนมตาล) is an open-source Thai text-to-speech model that supports multilingual speakers such as Thai, English, and others.\n\nKhanomTan TTS is a YourTTS model trained on multili... |
text2text-generation | transformers | # Romanian paraphrase
Fine-tune t5-small-paraphrase-ro model for paraphrase. Since there is no Romanian dataset for paraphrasing, I had to create my own [dataset](https://huggingface.co/datasets/BlackKakapo/paraphrase-ro-v2). The dataset contains ~30k e... | {"language": ["ro"], "license": ["apache-2.0"], "tags": [], "annotations_creators": [], "language_creators": ["machine-generated"], "multilinguality": ["monolingual"], "pretty_name": "BlackKakapo/t5-small-paraphrase-ro", "size_categories": ["10K<n<100K"], "source_datasets": ["original"], "task_categories": ["text2text-... | BlackKakapo/t5-small-paraphrase-ro-v2 | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"ro",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-17T05:37:32+00:00 | [] | [
"ro"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #ro #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # Romanian paraphrase
!v2.0
Fine-tune t5-small-paraphrase-ro model for paraphrase. Since there is no Romanian dataset for paraphrasing, I had to create my own dataset. The dataset contains ~30k examples.
### How to use
### Or
### Generate
### Output
| [
"# Romanian paraphrase\n\n!v2.0\n\nFine-tune t5-small-paraphrase-ro model for paraphrase. Since there is no Romanian dataset for paraphrasing, I had to create my own dataset. The dataset contains ~30k examples.",
"### How to use",
"### Or",
"### Generate",
"### Output"
] | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #ro #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Romanian paraphrase\n\n!v2.0\n\nFine-tune t5-small-paraphrase-ro model for paraphrase. Since there is no Romanian dataset for paraphrasing, I ha... |
audio-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. -->
# wav2vec2-base-ks-padpt1600
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2ve... | {"license": "apache-2.0", "tags": ["audio-classification", "generated_from_trainer"], "datasets": ["superb"], "metrics": ["accuracy"], "model-index": [{"name": "wav2vec2-base-ks-padpt1600", "results": []}]} | Jungwoo4021/wav2vec2-base-ks-padpt1600 | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"text-classification",
"audio-classification",
"generated_from_trainer",
"dataset:superb",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-17T05:37:49+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #text-classification #audio-classification #generated_from_trainer #dataset-superb #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| wav2vec2-base-ks-padpt1600
==========================
This model is a fine-tuned version of facebook/wav2vec2-base on the superb dataset.
It achieves the following results on the evaluation set:
* Loss: 1.6019
* Accuracy: 0.6111
Model description
-----------------
More information needed
Intended uses & limit... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.003\n* train\\_batch\\_size: 256\n* eval\\_batch\\_size: 256\n* seed: 0\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 1024\n* optimizer: Adam with betas=(0.9,0.999) and epsi... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #text-classification #audio-classification #generated_from_trainer #dataset-superb #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*... |
text2text-generation | transformers |
# Question Generation Model
## Github
https://github.com/Seoneun/T5-Question-Generation
## Fine-tuning Dataset
SQuAD 1.1
| Train Data | Dev Data | Test Data |
| ------ | ------ | ------ |
| 75,722 | 10,570 | 11,877 |
## Demo
https://huggingface.co/Sehong/t5-large-QuestionGeneration
## How to use
```python
imp... | {"language": "en", "license": "mit", "tags": ["t5"], "datasets": ["squad"]} | Sehong/t5-large-QuestionGeneration | null | [
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"license:mit",
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"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-08-17T06:12:14+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #en #dataset-squad #license-mit #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| Question Generation Model
=========================
Github
------
URL
Fine-tuning Dataset
-------------------
SQuAD 1.1
Train Data: 75,722, Dev Data: 10,570, Test Data: 11,877
Demo
----
URL
How to use
----------
Evalutation
-----------
| [] | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #en #dataset-squad #license-mit #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n"
] |
text-generation | transformers | # GPT-Rapgenerator
The Rapgenerator is trained for [nullsechsroy](https://genius.com/artists/Nullsechsroy) on [german-poetry-gpt2](https://huggingface.co/Anjoe/german-poetry-gpt2) for 20 epochs.
We used the [genius](https://docs.genius.com/#/songs-h2) songlyrics from the following artists:
['Ace Tee', 'Aligatoah', 'A... | {"language": "de", "license": "mit", "tags": ["Text Generation"], "datasets": ["genius lyrics"], "widget": [{"text": "[Title_nullsechsroy feat. YFG Pave_"}]} | Bachstelze/poetryRapGPT | null | [
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"region:us"
] | null | 2022-08-17T06:12:58+00:00 | [] | [
"de"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #Text Generation #de #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # GPT-Rapgenerator
The Rapgenerator is trained for nullsechsroy on german-poetry-gpt2 for 20 epochs.
We used the genius songlyrics from the following artists:
['Ace Tee', 'Aligatoah', 'AnnenMayKantereit', 'Apache 207', 'Azad', 'Badmómzjay', 'Bausa', 'Blumentopf', 'Blumio', 'Capital Bra', 'Casper', 'Celo & Abdi', 'Cro... | [
"# GPT-Rapgenerator\nThe Rapgenerator is trained for nullsechsroy on german-poetry-gpt2 for 20 epochs.\n\nWe used the genius songlyrics from the following artists:\n\n['Ace Tee', 'Aligatoah', 'AnnenMayKantereit', 'Apache 207', 'Azad', 'Badmómzjay', 'Bausa', 'Blumentopf', 'Blumio', 'Capital Bra', 'Casper', 'Celo & A... | [
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"# GPT-Rapgenerator\nThe Rapgenerator is trained for nullsechsroy on german-poetry-gpt2 for 20 epochs.\n\nWe used the genius songlyrics from... |
audio-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. -->
# wav2vec2-base-ks-padpt3200
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2ve... | {"license": "apache-2.0", "tags": ["audio-classification", "generated_from_trainer"], "datasets": ["superb"], "metrics": ["accuracy"], "model-index": [{"name": "wav2vec2-base-ks-padpt3200", "results": []}]} | Jungwoo4021/wav2vec2-base-ks-padpt3200 | null | [
"transformers",
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"audio-classification",
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] | null | 2022-08-17T06:29:21+00:00 | [] | [] | TAGS
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| wav2vec2-base-ks-padpt3200
==========================
This model is a fine-tuned version of facebook/wav2vec2-base on the superb dataset.
It achieves the following results on the evaluation set:
* Loss: 1.2818
* Accuracy: 0.6200
Model description
-----------------
More information needed
Intended uses & limit... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.003\n* train\\_batch\\_size: 256\n* eval\\_batch\\_size: 256\n* seed: 0\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 1024\n* optimizer: Adam with betas=(0.9,0.999) and epsi... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n*... |
sentence-similarity | sentence-transformers |
# edumunozsala/distilroberta-sentence-transformer-test
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-Transforme... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "datasets": ["embedding-data/sentence-compression"], "pipeline_tag": "sentence-similarity"} | edumunozsala/distilroberta-sentence-transformer-test | null | [
"sentence-transformers",
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"roberta",
"feature-extraction",
"sentence-similarity",
"transformers",
"dataset:embedding-data/sentence-compression",
"endpoints_compatible",
"region:us"
] | null | 2022-08-17T06:39:35+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #roberta #feature-extraction #sentence-similarity #transformers #dataset-embedding-data/sentence-compression #endpoints_compatible #region-us
|
# edumunozsala/distilroberta-sentence-transformer-test
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-t... | [
"# edumunozsala/distilroberta-sentence-transformer-test\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 ... | [
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"# edumunozsala/distilroberta-sentence-transformer-test\n\nThis is a sentence-transformers model: It maps sentences & paragraphs... |
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. -->
# distilled-mt5-small-b0.02
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on t... | {"language": ["en", "ro"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["wmt16"], "metrics": ["bleu"], "model-index": [{"name": "distilled-mt5-small-b0.02", "results": [{"task": {"type": "translation", "name": "Translation"}, "dataset": {"name": "wmt16 ro-en", "type": "wmt16", "args": "ro-e... | Lvxue/distilled-mt5-small-b0.02 | null | [
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"model-index",
"autotrain_compatible",
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"text-generation-inference",
"region:us"
] | null | 2022-08-17T06:43:05+00:00 | [] | [
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|
# distilled-mt5-small-b0.02
This model is a fine-tuned version of google/mt5-small on the wmt16 ro-en dataset.
It achieves the following results on the evaluation set:
- Loss: 2.8126
- Bleu: 7.632
- Gen Len: 45.006
## Model description
More information needed
## Intended uses & limitations
More information need... | [
"# distilled-mt5-small-b0.02\n\nThis model is a fine-tuned version of google/mt5-small on the wmt16 ro-en dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 2.8126\n- Bleu: 7.632\n- Gen Len: 45.006",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nM... | [
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"# distilled-mt5-small-b0.02\n\nThis model is a fine-tuned version of google/... |
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. -->
# distilled-mt5-small-b0.03
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on t... | {"language": ["en", "ro"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["wmt16"], "metrics": ["bleu"], "model-index": [{"name": "distilled-mt5-small-b0.03", "results": [{"task": {"type": "translation", "name": "Translation"}, "dataset": {"name": "wmt16 ro-en", "type": "wmt16", "args": "ro-e... | Lvxue/distilled-mt5-small-b0.03 | null | [
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|
# distilled-mt5-small-b0.03
This model is a fine-tuned version of google/mt5-small on the wmt16 ro-en dataset.
It achieves the following results on the evaluation set:
- Loss: 2.8327
- Bleu: 7.4044
- Gen Len: 44.8759
## Model description
More information needed
## Intended uses & limitations
More information ne... | [
"# distilled-mt5-small-b0.03\n\nThis model is a fine-tuned version of google/mt5-small on the wmt16 ro-en dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 2.8327\n- Bleu: 7.4044\n- Gen Len: 44.8759",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\... | [
"TAGS\n#transformers #pytorch #tensorboard #mt5 #text2text-generation #generated_from_trainer #en #ro #dataset-wmt16 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# distilled-mt5-small-b0.03\n\nThis model is a fine-tuned version of google/... |
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. -->
# distilled-mt5-small-b0.04
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on t... | {"language": ["en", "ro"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["wmt16"], "metrics": ["bleu"], "model-index": [{"name": "distilled-mt5-small-b0.04", "results": [{"task": {"type": "translation", "name": "Translation"}, "dataset": {"name": "wmt16 ro-en", "type": "wmt16", "args": "ro-e... | Lvxue/distilled-mt5-small-b0.04 | null | [
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] | null | 2022-08-17T06:43:49+00:00 | [] | [
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|
# distilled-mt5-small-b0.04
This model is a fine-tuned version of google/mt5-small on the wmt16 ro-en dataset.
It achieves the following results on the evaluation set:
- Loss: 2.8124
- Bleu: 7.5994
- Gen Len: 44.6753
## Model description
More information needed
## Intended uses & limitations
More information ne... | [
"# distilled-mt5-small-b0.04\n\nThis model is a fine-tuned version of google/mt5-small on the wmt16 ro-en dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 2.8124\n- Bleu: 7.5994\n- Gen Len: 44.6753",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\... | [
"TAGS\n#transformers #pytorch #tensorboard #mt5 #text2text-generation #generated_from_trainer #en #ro #dataset-wmt16 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# distilled-mt5-small-b0.04\n\nThis model is a fine-tuned version of google/... |
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. -->
# distilled-mt5-small-b0.75
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on t... | {"language": ["en", "ro"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["wmt16"], "metrics": ["bleu"], "model-index": [{"name": "distilled-mt5-small-b0.75", "results": [{"task": {"type": "translation", "name": "Translation"}, "dataset": {"name": "wmt16 ro-en", "type": "wmt16", "args": "ro-e... | Lvxue/distilled-mt5-small-b0.75 | null | [
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|
# distilled-mt5-small-b0.75
This model is a fine-tuned version of google/mt5-small on the wmt16 ro-en dataset.
It achieves the following results on the evaluation set:
- Loss: 2.8013
- Bleu: 7.4601
- Gen Len: 44.2356
## Model description
More information needed
## Intended uses & limitations
More information ne... | [
"# distilled-mt5-small-b0.75\n\nThis model is a fine-tuned version of google/mt5-small on the wmt16 ro-en dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 2.8013\n- Bleu: 7.4601\n- Gen Len: 44.2356",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\... | [
"TAGS\n#transformers #pytorch #tensorboard #mt5 #text2text-generation #generated_from_trainer #en #ro #dataset-wmt16 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# distilled-mt5-small-b0.75\n\nThis model is a fine-tuned version of google/... |
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. -->
# distilled-mt5-small-b1.25
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on t... | {"language": ["en", "ro"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["wmt16"], "metrics": ["bleu"], "model-index": [{"name": "distilled-mt5-small-b1.25", "results": [{"task": {"type": "translation", "name": "Translation"}, "dataset": {"name": "wmt16 ro-en", "type": "wmt16", "args": "ro-e... | Lvxue/distilled-mt5-small-b1.25 | null | [
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|
# distilled-mt5-small-b1.25
This model is a fine-tuned version of google/mt5-small on the wmt16 ro-en dataset.
It achieves the following results on the evaluation set:
- Loss: 2.7945
- Bleu: 7.5563
- Gen Len: 44.1141
## Model description
More information needed
## Intended uses & limitations
More information ne... | [
"# distilled-mt5-small-b1.25\n\nThis model is a fine-tuned version of google/mt5-small on the wmt16 ro-en dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 2.7945\n- Bleu: 7.5563\n- Gen Len: 44.1141",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\... | [
"TAGS\n#transformers #pytorch #tensorboard #mt5 #text2text-generation #generated_from_trainer #en #ro #dataset-wmt16 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# distilled-mt5-small-b1.25\n\nThis model is a fine-tuned version of google/... |
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. -->
# distilled-mt5-small-b1.5
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on th... | {"language": ["en", "ro"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["wmt16"], "metrics": ["bleu"], "model-index": [{"name": "distilled-mt5-small-b1.5", "results": [{"task": {"type": "translation", "name": "Translation"}, "dataset": {"name": "wmt16 ro-en", "type": "wmt16", "args": "ro-en... | Lvxue/distilled-mt5-small-b1.5 | null | [
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|
# distilled-mt5-small-b1.5
This model is a fine-tuned version of google/mt5-small on the wmt16 ro-en dataset.
It achieves the following results on the evaluation set:
- Loss: 2.7938
- Bleu: 7.5422
- Gen Len: 44.3267
## Model description
More information needed
## Intended uses & limitations
More information nee... | [
"# distilled-mt5-small-b1.5\n\nThis model is a fine-tuned version of google/mt5-small on the wmt16 ro-en dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 2.7938\n- Bleu: 7.5422\n- Gen Len: 44.3267",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\n... | [
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"# distilled-mt5-small-b1.5\n\nThis model is a fine-tuned version of google/m... |
fill-mask | transformers | ## Model Details
We introduce a suite of neural language model tools for pre-training, fine-tuning SMILES-based molecular language models. Furthermore, we also provide recipes for semi-supervised recipes for fine-tuning these languages in low-data settings using Semi-supervised learning.
### Enumeration-aware Molecu... | {"license": "apache-2.0", "library_name": "transformers", "datasets": ["jxie/guacamol", "AdrianM0/MUV"]} | UdS-LSV/smole-bert | null | [
"transformers",
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"dataset:jxie/guacamol",
"dataset:AdrianM0/MUV",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
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#transformers #pytorch #safetensors #bert #fill-mask #dataset-jxie/guacamol #dataset-AdrianM0/MUV #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| Model Details
-------------
We introduce a suite of neural language model tools for pre-training, fine-tuning SMILES-based molecular language models. Furthermore, we also provide recipes for semi-supervised recipes for fine-tuning these languages in low-data settings using Semi-supervised learning.
### Enumeration-... | [
"### Enumeration-aware Molecular Transformers\n\n\nIntroduces contrastive learning alongside multi-task regression, and masked language modelling as pre-training objectives to inject enumeration knowledge into pre-trained language models.",
"#### a. Molecular Domain Adaptation (Contrastive Encoder-based)",
"###... | [
"TAGS\n#transformers #pytorch #safetensors #bert #fill-mask #dataset-jxie/guacamol #dataset-AdrianM0/MUV #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Enumeration-aware Molecular Transformers\n\n\nIntroduces contrastive learning alongside multi-task regression, and masked la... |
null | diffusers |
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# ddpm-butterflies-128
## Model description
This diffusion model is trained with the [🤗 Diffusers](https://github.com/hu... | {"language": "en", "license": "apache-2.0", "library_name": "diffusers", "tags": [], "datasets": "huggan/smithsonian_butterflies_subset", "metrics": []} | Nikuson/ddpm-butterflies-128 | null | [
"diffusers",
"tensorboard",
"en",
"dataset:huggan/smithsonian_butterflies_subset",
"license:apache-2.0",
"diffusers:DDPMPipeline",
"region:us"
] | null | 2022-08-17T07:18:28+00:00 | [] | [
"en"
] | TAGS
#diffusers #tensorboard #en #dataset-huggan/smithsonian_butterflies_subset #license-apache-2.0 #diffusers-DDPMPipeline #region-us
|
# ddpm-butterflies-128
## Model description
This diffusion model is trained with the Diffusers library
on the 'huggan/smithsonian_butterflies_subset' dataset.
## Intended uses & limitations
#### How to use
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Tr... | [
"# ddpm-butterflies-128",
"## Model description\n\nThis diffusion model is trained with the Diffusers library \non the 'huggan/smithsonian_butterflies_subset' dataset.",
"## Intended uses & limitations",
"#### How to use",
"#### Limitations and bias\n\n[TODO: provide examples of latent issues and potential... | [
"TAGS\n#diffusers #tensorboard #en #dataset-huggan/smithsonian_butterflies_subset #license-apache-2.0 #diffusers-DDPMPipeline #region-us \n",
"# ddpm-butterflies-128",
"## Model description\n\nThis diffusion model is trained with the Diffusers library \non the 'huggan/smithsonian_butterflies_subset' dataset.",... |
null | null | jeremy sits and reads an imaginary book even though jeremy is actually the imaginary friend of a horse ghost | {} | jonkonkol/buttmuddy | null | [
"region:us"
] | null | 2022-08-17T07:27:19+00:00 | [] | [] | TAGS
#region-us
| jeremy sits and reads an imaginary book even though jeremy is actually the imaginary friend of a horse ghost | [] | [
"TAGS\n#region-us \n"
] |
audio-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. -->
# wav2vec2-base-ks-ept4
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-ba... | {"license": "apache-2.0", "tags": ["audio-classification", "generated_from_trainer"], "datasets": ["superb"], "metrics": ["accuracy"], "model-index": [{"name": "wav2vec2-base-ks-ept4", "results": []}]} | Jungwoo4021/wav2vec2-base-ks-ept4 | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"text-classification",
"audio-classification",
"generated_from_trainer",
"dataset:superb",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-17T07:47:07+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #text-classification #audio-classification #generated_from_trainer #dataset-superb #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| wav2vec2-base-ks-ept4
=====================
This model is a fine-tuned version of facebook/wav2vec2-base on the superb dataset.
It achieves the following results on the evaluation set:
* Loss: 1.5663
* Accuracy: 0.6209
Model description
-----------------
More information needed
Intended uses & limitations
---... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.003\n* train\\_batch\\_size: 256\n* eval\\_batch\\_size: 256\n* seed: 0\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 1024\n* optimizer: Adam with betas=(0.9,0.999) and epsi... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #text-classification #audio-classification #generated_from_trainer #dataset-superb #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*... |
translation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# nils-nl-to-rx-pt-v3
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the None dataset.
It ac... | {"license": "apache-2.0", "tags": ["translation", "generated_from_trainer"], "model-index": [{"name": "nils-nl-to-rx-pt-v3", "results": []}]} | NilsDamAi/nils-nl-to-rx-pt-v3 | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"translation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-17T08:33:21+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #translation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| nils-nl-to-rx-pt-v3
===================
This model is a fine-tuned version of t5-small on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2751
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More informa... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #translation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* le... |
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. -->
# xlm-roberta-base-finetuned-panx-de-fr
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-robert... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["f1"], "model-index": [{"name": "xlm-roberta-base-finetuned-panx-de-fr", "results": []}]} | hhffxx/xlm-roberta-base-finetuned-panx-de-fr | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-17T08:45:33+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-panx-de-fr
=====================================
This model is a fine-tuned version of xlm-roberta-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3847
* F1: 0.8178
Model description
-----------------
More information needed
Intended uses... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\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",
"### Training... | [
"TAGS\n#transformers #pytorch #xlm-roberta #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: 5e-05\n* train\\_batch\\_size: 1\n* ... |
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. -->
# article2KW_test1_barthez-orangesum-title_finetuned_for_summurization
This model is a fine-tuned version of [moussaKam/barthez-or... | {"license": "apache-2.0", "tags": ["summarization", "generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "article2KW_test1_barthez-orangesum-title_finetuned_for_summurization", "results": []}]} | bthomas/article2KW_test1_barthez-orangesum-title_finetuned_for_summurization | null | [
"transformers",
"pytorch",
"mbart",
"text2text-generation",
"summarization",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-17T08:54:34+00:00 | [] | [] | TAGS
#transformers #pytorch #mbart #text2text-generation #summarization #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| article2KW\_test1\_barthez-orangesum-title\_finetuned\_for\_summurization
=========================================================================
This model is a fine-tuned version of moussaKam/barthez-orangesum-title on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2895
*... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5.6e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #mbart #text2text-generation #summarization #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5.6e-05\n* train\\_b... |
reinforcement-learning | stable-baselines3 |
# **A2C** Agent playing **AntBulletEnv-v0**
This is a trained model of a **A2C** agent playing **AntBulletEnv-v0**
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_sb... | {"library_name": "stable-baselines3", "tags": ["AntBulletEnv-v0", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "A2C", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "AntBulletEnv-v0", "type": "AntB... | BekirTaha/a2c-AntBulletEnv-v0 | null | [
"stable-baselines3",
"AntBulletEnv-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-08-17T09:28:30+00:00 | [] | [] | TAGS
#stable-baselines3 #AntBulletEnv-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# A2C Agent playing AntBulletEnv-v0
This is a trained model of a A2C agent playing AntBulletEnv-v0
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# A2C Agent playing AntBulletEnv-v0\nThis is a trained model of a A2C agent playing AntBulletEnv-v0\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #AntBulletEnv-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# A2C Agent playing AntBulletEnv-v0\nThis is a trained model of a A2C agent playing AntBulletEnv-v0\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add ... |
token-classification | transformers | # tner/deberta-v3-large-ttc
This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) on the
[tner/ttc](https://huggingface.co/datasets/tner/ttc) dataset.
Model fine-tuning is done via [T-NER](https://github.com/asahi417/tner)'s hyper-parameter search (see t... | {"datasets": ["tner/ttc"], "metrics": ["f1", "precision", "recall"], "pipeline_tag": "token-classification", "widget": [{"text": "Jacob Collier is a Grammy awarded artist from England.", "example_title": "NER Example 1"}], "model-index": [{"name": "tner/deberta-v3-large-ttc", "results": [{"task": {"type": "token-classi... | tner/deberta-v3-large-ttc | null | [
"transformers",
"pytorch",
"deberta-v2",
"token-classification",
"dataset:tner/ttc",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-17T10:20:57+00:00 | [] | [] | TAGS
#transformers #pytorch #deberta-v2 #token-classification #dataset-tner/ttc #model-index #autotrain_compatible #endpoints_compatible #region-us
| # tner/deberta-v3-large-ttc
This model is a fine-tuned version of microsoft/deberta-v3-large on the
tner/ttc dataset.
Model fine-tuning is done via T-NER's hyper-parameter search (see the repository
for more detail). It achieves the following results on the test set:
- F1 (micro): 0.8266925817946227
- Precision (micr... | [
"# tner/deberta-v3-large-ttc\n\nThis model is a fine-tuned version of microsoft/deberta-v3-large on the \ntner/ttc dataset.\nModel fine-tuning is done via T-NER's hyper-parameter search (see the repository\nfor more detail). It achieves the following results on the test set:\n- F1 (micro): 0.8266925817946227\n- Pre... | [
"TAGS\n#transformers #pytorch #deberta-v2 #token-classification #dataset-tner/ttc #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# tner/deberta-v3-large-ttc\n\nThis model is a fine-tuned version of microsoft/deberta-v3-large on the \ntner/ttc dataset.\nModel fine-tuning is done via T-NE... |
null | null | # Spacy Entity Linker
## Introduction
Spacy Entity Linker is a pipeline for spaCy that performs Linked Entity Extraction with Wikidata on a given Document.
The Entity Linking System operates by matching potential candidates from each sentence
(subject, object, prepositional phrase, compounds, etc.) to aliases from Wi... | {} | MartinoMensio/spaCy-entity-linker | null | [
"region:us"
] | null | 2022-08-17T10:21:16+00:00 | [] | [] | TAGS
#region-us
| # Spacy Entity Linker
## Introduction
Spacy Entity Linker is a pipeline for spaCy that performs Linked Entity Extraction with Wikidata on a given Document.
The Entity Linking System operates by matching potential candidates from each sentence
(subject, object, prepositional phrase, compounds, etc.) to aliases from Wi... | [
"# Spacy Entity Linker",
"## Introduction\n\nSpacy Entity Linker is a pipeline for spaCy that performs Linked Entity Extraction with Wikidata on a given Document.\nThe Entity Linking System operates by matching potential candidates from each sentence\n(subject, object, prepositional phrase, compounds, etc.) to al... | [
"TAGS\n#region-us \n",
"# Spacy Entity Linker",
"## Introduction\n\nSpacy Entity Linker is a pipeline for spaCy that performs Linked Entity Extraction with Wikidata on a given Document.\nThe Entity Linking System operates by matching potential candidates from each sentence\n(subject, object, prepositional phras... |
feature-extraction | transformers | ## Model Details
We introduce a suite of neural language model tools for pre-training, fine-tuning SMILES-based molecular language models. Furthermore, we also provide recipes for semi-supervised recipes for fine-tuning these languages in low-data settings using Semi-supervised learning.
### Enumeration-aware Molecu... | {"license": "apache-2.0", "library_name": "transformers", "datasets": ["jxie/guacamol", "AdrianM0/MUV"]} | UdS-LSV/siamese-smole-bert-muv-1x | null | [
"transformers",
"pytorch",
"bert",
"feature-extraction",
"dataset:jxie/guacamol",
"dataset:AdrianM0/MUV",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-08-17T10:40:59+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #feature-extraction #dataset-jxie/guacamol #dataset-AdrianM0/MUV #license-apache-2.0 #endpoints_compatible #region-us
| Model Details
-------------
We introduce a suite of neural language model tools for pre-training, fine-tuning SMILES-based molecular language models. Furthermore, we also provide recipes for semi-supervised recipes for fine-tuning these languages in low-data settings using Semi-supervised learning.
### Enumeration-... | [
"### Enumeration-aware Molecular Transformers\n\n\nIntroduces contrastive learning alongside multi-task regression, and masked language modelling as pre-training objectives to inject enumeration knowledge into pre-trained language models.",
"#### a. Molecular Domain Adaptation (Contrastive Encoder-based)",
"###... | [
"TAGS\n#transformers #pytorch #bert #feature-extraction #dataset-jxie/guacamol #dataset-AdrianM0/MUV #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Enumeration-aware Molecular Transformers\n\n\nIntroduces contrastive learning alongside multi-task regression, and masked language modelling as pre-tr... |
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/1196519479364268034/5Qpn... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/apesahoy-discoelysiumbot-jzux/1660737778768/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/apesahoy-discoelysiumbot-jzux | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-17T11:01:42+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI CYBORG
Humongous Ape MP & disco elysium quotes & trash jones
@apesahoy-discoelysiumbot-jzux
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 wa... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
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. -->
# article2KW_test1.1_barthez-orangesum-title_finetuned_for_summerization
This model is a fine-tuned version of [moussaKam/barthez-... | {"license": "apache-2.0", "tags": ["summarization", "generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "article2KW_test1.1_barthez-orangesum-title_finetuned_for_summerization", "results": []}]} | bthomas/article2KW_test1.1_barthez-orangesum-title_finetuned_for_summerization | null | [
"transformers",
"pytorch",
"mbart",
"text2text-generation",
"summarization",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-17T11:04:54+00:00 | [] | [] | TAGS
#transformers #pytorch #mbart #text2text-generation #summarization #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| article2KW\_test1.1\_barthez-orangesum-title\_finetuned\_for\_summerization
===========================================================================
This model is a fine-tuned version of moussaKam/barthez-orangesum-title on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 1.07... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5.6e-05\n* train\\_batch\\_size: 6\n* eval\\_batch\\_size: 6\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 4",
"### Traini... | [
"TAGS\n#transformers #pytorch #mbart #text2text-generation #summarization #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5.6e-05\n* train\\_b... |
reinforcement-learning | ml-agents |
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a comple... | {"library_name": "ml-agents", "tags": ["unity-ml-agents", "ml-agents", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Pyramids"]} | K-Kemna/pyramidsrnd | null | [
"ml-agents",
"tensorboard",
"onnx",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
] | null | 2022-08-17T11:05:47+00:00 | [] | [] | TAGS
#ml-agents #tensorboard #onnx #unity-ml-agents #deep-reinforcement-learning #reinforcement-learning #ML-Agents-Pyramids #region-us
|
# ppo Agent playing Pyramids
This is a trained model of a ppo agent playing Pyramids using the Unity ML-Agents Library.
## Usage (with ML-Agents)
The Documentation: URL
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the trainin... | [
"# ppo Agent playing Pyramids\n This is a trained model of a ppo agent playing Pyramids using the Unity ML-Agents Library.\n \n ## Usage (with ML-Agents)\n The Documentation: URL\n We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:\n\n\n ### Resume the t... | [
"TAGS\n#ml-agents #tensorboard #onnx #unity-ml-agents #deep-reinforcement-learning #reinforcement-learning #ML-Agents-Pyramids #region-us \n",
"# ppo Agent playing Pyramids\n This is a trained model of a ppo agent playing Pyramids using the Unity ML-Agents Library.\n \n ## Usage (with ML-Agents)\n The Documen... |
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-small-finetuned-wnut17-ner
This model is a fine-tuned version of [google/bert_uncased_L-4_H-512_A-8](https://huggingface.co... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["wnut_17"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "bert-small-finetuned-wnut17-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "wnut_17", "... | muhtasham/bert-small-finetuned-wnut17-ner | null | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:wnut_17",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-17T11:11:07+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #safetensors #bert #token-classification #generated_from_trainer #dataset-wnut_17 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| bert-small-finetuned-wnut17-ner
===============================
This model is a fine-tuned version of google/bert\_uncased\_L-4\_H-512\_A-8 on the wnut\_17 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3649
* Precision: 0.6259
* Recall: 0.4043
* F1: 0.4913
* Accuracy: 0.9255
Model de... | [
"### 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 #safetensors #bert #token-classification #generated_from_trainer #dataset-wnut_17 #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\... |
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-small-finetuned-wnut17-ner-longer6
This model is a fine-tuned version of [muhtasham/bert-small-finetuned-wnut17-ner](https:... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["wnut_17"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "bert-small-finetuned-wnut17-ner-longer6", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "wnu... | muhtasham/bert-small-finetuned-wnut17-ner-longer6 | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:wnut_17",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-17T11:16:39+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-wnut_17 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| bert-small-finetuned-wnut17-ner-longer6
=======================================
This model is a fine-tuned version of muhtasham/bert-small-finetuned-wnut17-ner on the wnut\_17 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4037
* Precision: 0.5667
* Recall: 0.4270
* F1: 0.4870
* Accurac... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-wnut_17 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\... |
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