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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
reinforcement-learning | null |
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="CWhy/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attribu... | {"tags": ["FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-FrozenLake-v1-4x4-noSlippery", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "FrozenLake-v1-4x4-no_slippery", "type": ... | CWhy/q-FrozenLake-v1-4x4-noSlippery | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-05-29T03:37:18+00:00 | [] | [] | TAGS
#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing FrozenLake-v1
This is a trained model of a Q-Learning agent playing FrozenLake-v1 .
## Usage
| [
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] | [
"TAGS\n#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-hindi-3
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-hindi-3", "results": []}]} | sriiikar/wav2vec2-hindi-3 | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-05-29T04:25:27+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-hindi-3
================
This model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 2.0900
* Wer: 0.7281
Model description
-----------------
More information needed
Intended uses & limitations
-----------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 1... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-uncased-finetuned-removed-0529
This model is a fine-tuned version of [YeRyeongLee/bert-base-uncased-finetuned-0505-2](... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "bert-base-uncased-finetuned-removed-0529", "results": []}]} | YeRyeongLee/bert-base-uncased-finetuned-removed-0529 | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-29T05:03:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| bert-base-uncased-finetuned-removed-0529
========================================
This model is a fine-tuned version of YeRyeongLee/bert-base-uncased-finetuned-0505-2 on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 1.1501
* Accuracy: 0.8767
* F1: 0.8765
Model description
--... | [
"### 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* lr\\_scheduler\\_warmup\\_steps: ... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\... |
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. -->
# xlsr-english
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["librispeech_asr"], "model-index": [{"name": "xlsr-english", "results": []}]} | ashesicsis1/xlsr-english | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:librispeech_asr",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-05-29T05:32:18+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-librispeech_asr #license-apache-2.0 #endpoints_compatible #region-us
| xlsr-english
============
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the librispeech\_asr dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3098
* Wer: 0.1451
Model description
-----------------
More information needed
Intended uses & limitations
----------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 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 #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-librispeech_asr #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... |
reinforcement-learning | null |
# **Q-Learning** Agent playing **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="SusBioRes-UBC/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False e... | {"tags": ["Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-Taxi-v3", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Taxi-v3", "type": "Taxi-v3"}, "metrics": [{"type": "mean_reward", "value": "7.56 +/... | SusBioRes-UBC/q-Taxi-v3 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-05-29T05:33:33+00:00 | [] | [] | TAGS
#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing Taxi-v3
This is a trained model of a Q-Learning agent playing Taxi-v3 .
## Usage
| [
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] | [
"TAGS\n#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] |
text-generation | null |
# birth of the Jay Bot | {"tags": ["conversational"]} | jayklaws0606/DialoGPT-small-jayBot | null | [
"conversational",
"region:us"
] | null | 2022-05-29T06:19:16+00:00 | [] | [] | TAGS
#conversational #region-us
|
# birth of the Jay Bot | [
"# birth of the Jay Bot"
] | [
"TAGS\n#conversational #region-us \n",
"# birth of the Jay Bot"
] |
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. -->
# bertNEGsentiment
This model is a fine-tuned version of [m-polignano-uniba/bert_uncased_L-12_H-768_A-12_italian_alb3rt0](https://... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "bertNEGsentiment", "results": []}]} | GioReg/bertNEGsentiment | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-29T06:44:42+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
|
# bertNEGsentiment
This model is a fine-tuned version of m-polignano-uniba/bert_uncased_L-12_H-768_A-12_italian_alb3rt0 on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training ... | [
"# bertNEGsentiment\n\nThis model is a fine-tuned version of m-polignano-uniba/bert_uncased_L-12_H-768_A-12_italian_alb3rt0 on the None dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore informatio... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"# bertNEGsentiment\n\nThis model is a fine-tuned version of m-polignano-uniba/bert_uncased_L-12_H-768_A-12_italian_alb3rt0 on the None dataset.",
"## Model d... |
text2text-generation | transformers |
# MVP
The MVP model was proposed in [**MVP: Multi-task Supervised Pre-training for Natural Language Generation**](https://arxiv.org/abs/2206.12131) by Tianyi Tang, Junyi Li, Wayne Xin Zhao and Ji-Rong Wen.
The detailed information and instructions can be found [https://github.com/RUCAIBox/MVP](https://github.com/RUCA... | {"language": ["en"], "license": "apache-2.0", "tags": ["text-generation", "text2text-generation", "summarization", "conversational"], "pipeline_tag": "text2text-generation", "widget": [{"text": "Summarize: You may want to stick it to your boss and leave your job, but don't do it if these are your reasons.", "example_ti... | RUCAIBox/mvp | null | [
"transformers",
"pytorch",
"mvp",
"text-generation",
"text2text-generation",
"summarization",
"conversational",
"en",
"arxiv:2206.12131",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-05-29T07:21:56+00:00 | [
"2206.12131"
] | [
"en"
] | TAGS
#transformers #pytorch #mvp #text-generation #text2text-generation #summarization #conversational #en #arxiv-2206.12131 #license-apache-2.0 #endpoints_compatible #has_space #region-us
|
# MVP
The MVP model was proposed in MVP: Multi-task Supervised Pre-training for Natural Language Generation by Tianyi Tang, Junyi Li, Wayne Xin Zhao and Ji-Rong Wen.
The detailed information and instructions can be found URL
## Model Description
MVP is supervised pre-trained using a mixture of labeled datasets. It f... | [
"# MVP\nThe MVP model was proposed in MVP: Multi-task Supervised Pre-training for Natural Language Generation by Tianyi Tang, Junyi Li, Wayne Xin Zhao and Ji-Rong Wen.\n\nThe detailed information and instructions can be found URL",
"## Model Description\nMVP is supervised pre-trained using a mixture of labeled da... | [
"TAGS\n#transformers #pytorch #mvp #text-generation #text2text-generation #summarization #conversational #en #arxiv-2206.12131 #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"# MVP\nThe MVP model was proposed in MVP: Multi-task Supervised Pre-training for Natural Language Generation by Tiany... |
text-generation | transformers |
# Alastor The Radio Demon Demon DialoGPT Model | {"tags": ["conversational"]} | Flem/DialoGPT-medium-alastor | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-05-29T07:32:40+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Alastor The Radio Demon Demon DialoGPT Model | [
"# Alastor The Radio Demon Demon DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Alastor The Radio Demon Demon DialoGPT Model"
] |
null | keras |
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training Metrics
Model history needed
## Model Plot
<details>
<summary>View Model Plot</summary>
</details> | {"library_name": "keras"} | Sultannn/fashion-gan | null | [
"keras",
"region:us"
] | null | 2022-05-29T07:35:05+00:00 | [] | [] | TAGS
#keras #region-us
|
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training Metrics
Model history needed
## Model Plot
<details>
<summary>View Model Plot</summary>
!Model Image
</details> | [
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed\n\n ## Training Metrics\nModel history needed\n ## Model Plot\n\n<details>\n<summary>View Model Plot</summary>\n\n!Model Image\n\n</details>... | [
"TAGS\n#keras #region-us \n",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed\n\n ## Training Metrics\nModel history needed\n ## Model Plot\n\n<details>\n<summary>View Model Plot</summar... |
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-hindi-epochs40-colab
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://hugg... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-large-xls-r-300m-hindi-epochs40-colab", "results": []}]} | vai6hav/wav2vec2-large-xls-r-300m-hindi-epochs40-colab | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-05-29T08:18:24+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-hindi-epochs40-colab
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common_voice dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Train... | [
"# wav2vec2-large-xls-r-300m-hindi-epochs40-colab\n\nThis model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common_voice dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore inform... | [
"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-hindi-epochs40-colab\n\nThis model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the ... |
sentence-similarity | sentence-transformers |
# Sung/model1
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when y... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | hunkim/model1 | null | [
"sentence-transformers",
"pytorch",
"roberta",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2022-05-29T08:29:24+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #roberta #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us
|
# Sung/model1
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers installed:
Then you can us... | [
"# Sung/model1\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\nT... | [
"TAGS\n#sentence-transformers #pytorch #roberta #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us \n",
"# Sung/model1\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 ... |
sentence-similarity | sentence-transformers |
# shafin/distilbert-base-uncased-finetuned-cust-similarity-1
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 32 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Trans... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity"], "pipeline_tag": "sentence-similarity"} | shafin/distilbert-base-uncased-finetuned-cust-similarity-1 | null | [
"sentence-transformers",
"pytorch",
"distilbert",
"feature-extraction",
"sentence-similarity",
"endpoints_compatible",
"region:us"
] | null | 2022-05-29T08:49:14+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #endpoints_compatible #region-us
|
# shafin/distilbert-base-uncased-finetuned-cust-similarity-1
This is a sentence-transformers model: It maps sentences & paragraphs to a 32 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 sente... | [
"# shafin/distilbert-base-uncased-finetuned-cust-similarity-1\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 32 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 ... | [
"TAGS\n#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #endpoints_compatible #region-us \n",
"# shafin/distilbert-base-uncased-finetuned-cust-similarity-1\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 32 dimensional dense vector space and can be... |
text-classification | transformers |
# DistilBERT base uncased finetuned SST-2
This model is a fine-tune checkpoint of [DistilBERT-base-uncased](https://huggingface.co/distilbert-base-uncased), fine-tuned on SST-2.
This model reaches an accuracy of 91.3 on the dev set (for comparison, Bert bert-base-uncased version reaches an accuracy of 92.7).
For mor... | {"language": "en", "license": "apache-2.0", "datasets": ["sst2"]} | speeqo/distilbert-base-uncased-finetuned-sst-2-english | null | [
"transformers",
"pytorch",
"tf",
"rust",
"distilbert",
"text-classification",
"en",
"dataset:sst2",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-29T09:30:51+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #tf #rust #distilbert #text-classification #en #dataset-sst2 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# DistilBERT base uncased finetuned SST-2
This model is a fine-tune checkpoint of DistilBERT-base-uncased, fine-tuned on SST-2.
This model reaches an accuracy of 91.3 on the dev set (for comparison, Bert bert-base-uncased version reaches an accuracy of 92.7).
For more details about DistilBERT, we encourage users to ... | [
"# DistilBERT base uncased finetuned SST-2\n\nThis model is a fine-tune checkpoint of DistilBERT-base-uncased, fine-tuned on SST-2.\nThis model reaches an accuracy of 91.3 on the dev set (for comparison, Bert bert-base-uncased version reaches an accuracy of 92.7).\n\nFor more details about DistilBERT, we encourage ... | [
"TAGS\n#transformers #pytorch #tf #rust #distilbert #text-classification #en #dataset-sst2 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# DistilBERT base uncased finetuned SST-2\n\nThis model is a fine-tune checkpoint of DistilBERT-base-uncased, fine-tuned on SST-2.\nThis model... |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# deberta-base-finetuned-aqa-newsqa
This model is a fine-tuned version of [stevemobs/deberta-base-finetuned-aqa](https://huggingfa... | {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "deberta-base-finetuned-aqa-newsqa", "results": []}]} | stevemobs/deberta-base-finetuned-aqa-newsqa | null | [
"transformers",
"pytorch",
"tensorboard",
"deberta",
"question-answering",
"generated_from_trainer",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-05-29T09:30:52+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #deberta #question-answering #generated_from_trainer #license-mit #endpoints_compatible #region-us
| deberta-base-finetuned-aqa-newsqa
=================================
This model is a fine-tuned version of stevemobs/deberta-base-finetuned-aqa on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.7657
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: 12\n* eval\\_batch\\_size: 12\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #deberta #question-answering #generated_from_trainer #license-mit #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 12\n* eval\\_batch\\... |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# deberta-base-combined-squad1-aqa-newsqa-and-newsqa
This model is a fine-tuned version of [stevemobs/deberta-base-combined-squad1... | {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "deberta-base-combined-squad1-aqa-newsqa-and-newsqa", "results": []}]} | stevemobs/deberta-base-combined-squad1-aqa-newsqa-and-newsqa | null | [
"transformers",
"pytorch",
"tensorboard",
"deberta",
"question-answering",
"generated_from_trainer",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-05-29T10:02:26+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #deberta #question-answering #generated_from_trainer #license-mit #endpoints_compatible #region-us
| deberta-base-combined-squad1-aqa-newsqa-and-newsqa
==================================================
This model is a fine-tuned version of stevemobs/deberta-base-combined-squad1-aqa-newsqa on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.9874
Model description
-------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 12\n* eval\\_batch\\_size: 12\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #deberta #question-answering #generated_from_trainer #license-mit #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 12\n* eval\\_batch\\... |
reinforcement-learning | null |
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes ... | {"tags": ["FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-FrozenLake-v1-4x4-noSlippery", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "FrozenLake-v1-4x4-no_slippery", "type": ... | memorysaver/q-FrozenLake-v1-4x4-noSlippery | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-05-29T10:07:58+00:00 | [] | [] | TAGS
#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing FrozenLake-v1
This is a trained model of a Q-Learning agent playing FrozenLake-v1 .
## Usage
| [
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] | [
"TAGS\n#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] |
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... | siegelou/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 | 2022-05-29T10:11:30+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: 0.0660
* Precision: 0.9368
* Recall: 0.9505
* F1: 0.9436
* Accuracy: 0.9859
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... |
reinforcement-learning | null |
# **Q-Learning** Agent playing **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = g... | {"tags": ["Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-Taxi-v3", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Taxi-v3", "type": "Taxi-v3"}, "metrics": [{"type": "mean_reward", "value": "7.56 +/... | memorysaver/q-Taxi-v3 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-05-29T10:11:50+00:00 | [] | [] | TAGS
#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing Taxi-v3
This is a trained model of a Q-Learning agent playing Taxi-v3 .
## Usage
| [
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] | [
"TAGS\n#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] |
sentence-similarity | sentence-transformers |
# sgpt-bloom-1b7-nli
## Usage
For usage instructions, refer to: https://github.com/Muennighoff/sgpt#symmetric-semantic-search
The model was trained with the command
```bash
CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 accelerate launch examples/training/nli/training_nli_v2.py --model_name bigscience/bloom-1b3 --freezenonbi... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "mteb"], "pipeline_tag": "sentence-similarity", "model-index": [{"name": "sgpt-bloom-1b7-nli", "results": [{"task": {"type": "Classification"}, "dataset": {"name": "MTEB AmazonReviewsClassification (fr)", "type": "mteb/amazon_reviews_multi"... | bigscience-data/sgpt-bloom-1b7-nli | null | [
"sentence-transformers",
"pytorch",
"bloom",
"feature-extraction",
"sentence-similarity",
"mteb",
"arxiv:2202.08904",
"model-index",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-05-29T10:14:58+00:00 | [
"2202.08904"
] | [] | TAGS
#sentence-transformers #pytorch #bloom #feature-extraction #sentence-similarity #mteb #arxiv-2202.08904 #model-index #endpoints_compatible #has_space #region-us
|
# sgpt-bloom-1b7-nli
## Usage
For usage instructions, refer to: URL
The model was trained with the command
## Evaluation Results
'{'askubuntu': 57.44, 'cqadupstack': 14.18, 'twitterpara': 73.99, 'scidocs': 74.74, 'avg': 55.087500000000006}'
## Training
The model was trained with the parameters:
DataLoader:
's... | [
"# sgpt-bloom-1b7-nli",
"## Usage\n\nFor usage instructions, refer to: URL\n\nThe model was trained with the command",
"## Evaluation Results\n\n'{'askubuntu': 57.44, 'cqadupstack': 14.18, 'twitterpara': 73.99, 'scidocs': 74.74, 'avg': 55.087500000000006}'",
"## Training\nThe model was trained with the parame... | [
"TAGS\n#sentence-transformers #pytorch #bloom #feature-extraction #sentence-similarity #mteb #arxiv-2202.08904 #model-index #endpoints_compatible #has_space #region-us \n",
"# sgpt-bloom-1b7-nli",
"## Usage\n\nFor usage instructions, refer to: URL\n\nThe model was trained with the command",
"## Evaluation Res... |
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. -->
# distilroberta-base-finetuned-assignment2
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/disti... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "distilroberta-base-finetuned-assignment2", "results": []}]} | lenses/distilroberta-base-finetuned-assignment2 | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-29T10:28:46+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilroberta-base-finetuned-assignment2
========================================
This model is a fine-tuned version of distilroberta-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5976
Model description
-----------------
More information needed
Intended uses & li... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #roberta #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: ... |
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. -->
# bertMULTINEGsentiment
This model is a fine-tuned version of [bert-base-multilingual-uncased](https://huggingface.co/bert-base-mu... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "bertMULTINEGsentiment", "results": []}]} | GioReg/bertMULTINEGsentiment | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-29T10:44:23+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# bertMULTINEGsentiment
This model is a fine-tuned version of bert-base-multilingual-uncased on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyp... | [
"# bertMULTINEGsentiment\n\nThis model is a fine-tuned version of bert-base-multilingual-uncased on the None dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training p... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# bertMULTINEGsentiment\n\nThis model is a fine-tuned version of bert-base-multilingual-uncased on the None dataset.",
"## Model descript... |
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-hindi-epochs35-colab
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://hugg... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-large-xls-r-300m-hindi-epochs35-colab", "results": []}]} | vai6hav/wav2vec2-large-xls-r-300m-hindi-epochs35-colab | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-05-29T11:08:56+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-hindi-epochs35-colab
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common_voice dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Train... | [
"# wav2vec2-large-xls-r-300m-hindi-epochs35-colab\n\nThis model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common_voice dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore inform... | [
"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-hindi-epochs35-colab\n\nThis model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the ... |
sentence-similarity | sentence-transformers |
# shafin/distilbert-base-uncased-finetuned-cust-similarity-2
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 128 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Tran... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity"], "pipeline_tag": "sentence-similarity"} | shafin/distilbert-base-uncased-finetuned-cust-similarity-2 | null | [
"sentence-transformers",
"pytorch",
"distilbert",
"feature-extraction",
"sentence-similarity",
"endpoints_compatible",
"region:us"
] | null | 2022-05-29T11:11:58+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #endpoints_compatible #region-us
|
# shafin/distilbert-base-uncased-finetuned-cust-similarity-2
This is a sentence-transformers model: It maps sentences & paragraphs to a 128 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 sent... | [
"# shafin/distilbert-base-uncased-finetuned-cust-similarity-2\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 128 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... | [
"TAGS\n#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #endpoints_compatible #region-us \n",
"# shafin/distilbert-base-uncased-finetuned-cust-similarity-2\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 128 dimensional dense vector space and can b... |
translation | transformers |
# Nynorsk Translator
This demo translates text for Norwegian Bokmål to Norwegian Nynorsk.
The Nynorsk Translator is finetuned from North-T5. It is a simple base model just for demo purposes. Please do not use it for translating larger amounts of text. | {"language": false, "license": "cc-by-nc-nd-4.0", "tags": ["translation"], "widget": [{"text": "En av de vanskeligste oppgavene n\u00e5r man oversetter fra bokm\u00e5l til nynorsk, er \u00e5 passe p\u00e5 at man bruker riktige pronomen. Man kan for eksempel si at man eier en bil og at den er r\u00f8d."}, {"text": "Arbe... | north/demo-nynorsk-base | null | [
"transformers",
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"license:cc-by-nc-nd-4.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-05-29T11:14:07+00:00 | [] | [
"no"
] | TAGS
#transformers #pytorch #jax #tensorboard #t5 #text2text-generation #translation #no #license-cc-by-nc-nd-4.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
# Nynorsk Translator
This demo translates text for Norwegian Bokmål to Norwegian Nynorsk.
The Nynorsk Translator is finetuned from North-T5. It is a simple base model just for demo purposes. Please do not use it for translating larger amounts of text. | [
"# Nynorsk Translator\nThis demo translates text for Norwegian Bokmål to Norwegian Nynorsk. \n\nThe Nynorsk Translator is finetuned from North-T5. It is a simple base model just for demo purposes. Please do not use it for translating larger amounts of text."
] | [
"TAGS\n#transformers #pytorch #jax #tensorboard #t5 #text2text-generation #translation #no #license-cc-by-nc-nd-4.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"# Nynorsk Translator\nThis demo translates text for Norwegian Bokmål to Norwegian Nynorsk. \n\nThe ... |
translation | transformers |
# DeUnCaser
The purpose of the DeUnCaser is to fix text that lacks punctation. It is particulary targeted towards the output from Automated Speak Recognition software. In addition to the lack of casing and punctation, it also often lacks pauses between words. Try this demo, and you will understand.
The DeUnCaser is ... | {"language": false, "license": "cc-by-4.0", "tags": ["translation"], "widget": [{"text": "tirsdag var travel for ukrainas president volodymyr zelenskyj p\u00e5 morgenen tok han imot polens statsminister mateusz morawiecki"}, {"text": "tirsdagvartravelforukrainaspresidentvolodymyrzelenskyjp\u00e5kveldentokhanimotpolenss... | north/demo-deuncaser-base | null | [
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"tensorboard",
"t5",
"text2text-generation",
"translation",
"no",
"license:cc-by-4.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-05-29T11:14:36+00:00 | [] | [
"no"
] | TAGS
#transformers #pytorch #jax #tensorboard #t5 #text2text-generation #translation #no #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
# DeUnCaser
The purpose of the DeUnCaser is to fix text that lacks punctation. It is particulary targeted towards the output from Automated Speak Recognition software. In addition to the lack of casing and punctation, it also often lacks pauses between words. Try this demo, and you will understand.
The DeUnCaser is ... | [
"# DeUnCaser\nThe purpose of the DeUnCaser is to fix text that lacks punctation. It is particulary targeted towards the output from Automated Speak Recognition software. In addition to the lack of casing and punctation, it also often lacks pauses between words. Try this demo, and you will understand. \n\nThe DeUnCa... | [
"TAGS\n#transformers #pytorch #jax #tensorboard #t5 #text2text-generation #translation #no #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"# DeUnCaser\nThe purpose of the DeUnCaser is to fix text that lacks punctation. It is particulary targete... |
null | transformers | wav2vec2 -> t5lephone
bs = 16
dropout = 0.1
performance : 40%
{
"architectures": [
"SpeechMixEEDT5"
],
"decoder": {
"_name_or_path": "voidful/phoneme_byt5",
"add_cross_attention": true,
"architectures": [
"T5ForConditionalGeneration"
],
"bad_words_ids": null,
"bos_token_id": nu... | {} | Splend1dchan/wav2vec2-large-lv60_t5lephone-small_nofreeze_bs16_forMINDS.en.all | null | [
"transformers",
"pytorch",
"speechmix",
"endpoints_compatible",
"region:us"
] | null | 2022-05-29T11:52:02+00:00 | [] | [] | TAGS
#transformers #pytorch #speechmix #endpoints_compatible #region-us
| wav2vec2 -> t5lephone
bs = 16
dropout = 0.1
performance : 40%
{
"architectures": [
"SpeechMixEEDT5"
],
"decoder": {
"_name_or_path": "voidful/phoneme_byt5",
"add_cross_attention": true,
"architectures": [
"T5ForConditionalGeneration"
],
"bad_words_ids": null,
"bos_token_id": nu... | [] | [
"TAGS\n#transformers #pytorch #speechmix #endpoints_compatible #region-us \n"
] |
sentence-similarity | sentence-transformers |
# Sung/sentence-transformer-klue
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model ... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | hunkim/sentence-transformer-klue | null | [
"sentence-transformers",
"pytorch",
"roberta",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2022-05-29T12:20:30+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #roberta #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us
|
# Sung/sentence-transformer-klue
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers installed:... | [
"# Sung/sentence-transformer-klue\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers ... | [
"TAGS\n#sentence-transformers #pytorch #roberta #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us \n",
"# Sung/sentence-transformer-klue\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks ... |
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... | harryb0905/lunar_lander_ppo | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-05-29T12:32:13+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... |
token-classification | transformers |
# Model Trained Using AutoTrain
- Problem type: Entity Extraction
- Model ID: 924630372
- CO2 Emissions (in grams): 5.880084418778246
## Validation Metrics
- Loss: 0.8206124901771545
- Accuracy: 0.7745009890307498
- Precision: 0.6042857142857143
- Recall: 0.6547987616099071
- F1: 0.6285289747399703
## Usage
You c... | {"language": "unk", "tags": "autotrain", "datasets": ["pujaburman30/autotrain-data-hi_ner_xlmr_large"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 5.880084418778246} | pujaburman30/autotrain-hi_ner_xlmr_large-924630372 | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"autotrain",
"unk",
"dataset:pujaburman30/autotrain-data-hi_ner_xlmr_large",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-29T12:39:57+00:00 | [] | [
"unk"
] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #autotrain #unk #dataset-pujaburman30/autotrain-data-hi_ner_xlmr_large #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoTrain
- Problem type: Entity Extraction
- Model ID: 924630372
- CO2 Emissions (in grams): 5.880084418778246
## Validation Metrics
- Loss: 0.8206124901771545
- Accuracy: 0.7745009890307498
- Precision: 0.6042857142857143
- Recall: 0.6547987616099071
- F1: 0.6285289747399703
## Usage
You c... | [
"# Model Trained Using AutoTrain\n\n- Problem type: Entity Extraction\n- Model ID: 924630372\n- CO2 Emissions (in grams): 5.880084418778246",
"## Validation Metrics\n\n- Loss: 0.8206124901771545\n- Accuracy: 0.7745009890307498\n- Precision: 0.6042857142857143\n- Recall: 0.6547987616099071\n- F1: 0.628528974739970... | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #autotrain #unk #dataset-pujaburman30/autotrain-data-hi_ner_xlmr_large #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoTrain\n\n- Problem type: Entity Extraction\n- Model ID: 924630372\n- CO2 ... |
null | null | Getting started with NLP | {} | sukanya-me/12_NLP_huggingface | null | [
"region:us"
] | null | 2022-05-29T12:46:53+00:00 | [] | [] | TAGS
#region-us
| Getting started with NLP | [] | [
"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-xls-r-300m-hindi-epochs60-colab
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://hugg... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-large-xls-r-300m-hindi-epochs60-colab", "results": []}]} | vai6hav/wav2vec2-large-xls-r-300m-hindi-epochs60-colab | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-05-29T12:49:26+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-hindi-epochs60-colab
==============================================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common\_voice dataset.
It achieves the following results on the evaluation set:
* Loss: 1.7322
* Wer: 0.9188
Model description
-----------------
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #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* t... |
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... | harryb0905/lunar-lander-ppo-1-million | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-05-29T13:01:14+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... |
null | null | Getting started with nlp | {} | sukanya-me/nlp_basics | null | [
"region:us"
] | null | 2022-05-29T13:12:46+00:00 | [] | [] | TAGS
#region-us
| Getting started with nlp | [] | [
"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-dataset-vios
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2v... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["vivos_dataset"], "model-index": [{"name": "wav2vec2-dataset-vios", "results": []}]} | tclong/wav2vec2-dataset-vios | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:vivos_dataset",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-05-29T13:17:21+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-vivos_dataset #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-dataset-vios
=====================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the vivos\_dataset dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5423
* Wer: 0.4075
Model description
-----------------
More information needed
Intended uses & limita... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-vivos_dataset #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* ... |
text-generation | transformers |
## Задача Incomplete Utterance Restoration
Генеративная модель на основе [sberbank-ai/rugpt3large_based_on_gpt2](https://huggingface.co/sberbank-ai/rugpt3large_based_on_gpt2) для восстановления полного текста реплик в диалоге из контекста.
Допустим, последние 2 строки диалога имеют вид:
```
- Как тебя зовут?
- Джу... | {"language": "ru", "license": "unlicense", "tags": ["PyTorch", "Transformers", "gpt2"], "datasets": "inkoziev/incomplete_utterance_restoration", "pipeline_tag": "text-generation", "widget": [{"text": "- \u041a\u0430\u043a \u0442\u0435\u0431\u044f \u0437\u043e\u0432\u0443\u0442? - \u0414\u0436\u0443\u043b\u044c\u0435\u0... | inkoziev/rugpt_interpreter | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"PyTorch",
"Transformers",
"ru",
"dataset:inkoziev/incomplete_utterance_restoration",
"license:unlicense",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-05-29T13:46:14+00:00 | [] | [
"ru"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #PyTorch #Transformers #ru #dataset-inkoziev/incomplete_utterance_restoration #license-unlicense #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
## Задача Incomplete Utterance Restoration
Генеративная модель на основе sberbank-ai/rugpt3large_based_on_gpt2 для восстановления полного текста реплик в диалоге из контекста.
Допустим, последние 2 строки диалога имеют вид:
Модель позволяет получить полный текст последней реплики, с раскрытыми анафорами, эллипси... | [
"## Задача Incomplete Utterance Restoration\n\nГенеративная модель на основе sberbank-ai/rugpt3large_based_on_gpt2 для восстановления полного текста реплик в диалоге из контекста.\n\nДопустим, последние 2 строки диалога имеют вид:\n\n\n\nМодель позволяет получить полный текст последней реплики, с раскрытыми анафора... | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #PyTorch #Transformers #ru #dataset-inkoziev/incomplete_utterance_restoration #license-unlicense #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"## Задача Incomplete Utterance Restoration\n\nГенеративная модель на основе ... |
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. -->
# my-finetuned-xml-roberta2
This model is a fine-tuned version of [knurm/my-finetuned-xml-roberta](https://huggingface.co/knurm/my... | {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "my-finetuned-xml-roberta2", "results": []}]} | knurm/my-finetuned-xml-roberta2 | null | [
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"question-answering",
"generated_from_trainer",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-05-29T14:02:11+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #xlm-roberta #question-answering #generated_from_trainer #license-mit #endpoints_compatible #region-us
| my-finetuned-xml-roberta2
=========================
This model is a fine-tuned version of knurm/my-finetuned-xml-roberta on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 3.4644
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: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #xlm-roberta #question-answering #generated_from_trainer #license-mit #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_bat... |
text-classification | transformers | TDistilBERT finetuned
This model is a fine-tune checkpoint of DistilBERT-base-uncased[https://huggingface.co/distilbert-base-uncased]
| {"language": "en", "license": "other"} | abspython/distilbert-finetuned | null | [
"transformers",
"pytorch",
"tf",
"jax",
"distilbert",
"text-classification",
"en",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-29T14:08:50+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #tf #jax #distilbert #text-classification #en #license-other #autotrain_compatible #endpoints_compatible #region-us
| TDistilBERT finetuned
This model is a fine-tune checkpoint of DistilBERT-base-uncased[URL
| [] | [
"TAGS\n#transformers #pytorch #tf #jax #distilbert #text-classification #en #license-other #autotrain_compatible #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. -->
# 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": []}]} | KFlash/bert-finetuned-squad | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"question-answering",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-05-29T14:15:15+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",
"#... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-uncased-finetuned-removed-0530
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-b... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "bert-base-uncased-finetuned-removed-0530", "results": []}]} | YeRyeongLee/bert-base-uncased-finetuned-removed-0530 | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-29T14:16:41+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| bert-base-uncased-finetuned-removed-0530
========================================
This model is a fine-tuned version of bert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 1.1269
* Accuracy: 0.8745
* F1: 0.8745
Model description
-----------------
More inform... | [
"### 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* lr\\_scheduler\\_warmup\\_steps: ... | [
"TAGS\n#transformers #pytorch #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 8\n* e... |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# deberta-base-combined-squad1-aqa-newsqa-and-newsqa-1epoch
This model is a fine-tuned version of [stevemobs/deberta-base-combined... | {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "deberta-base-combined-squad1-aqa-newsqa-and-newsqa-1epoch", "results": []}]} | stevemobs/deberta-base-combined-squad1-aqa-newsqa-and-newsqa-1epoch | null | [
"transformers",
"pytorch",
"tensorboard",
"deberta",
"question-answering",
"generated_from_trainer",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-05-29T14:21:17+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #deberta #question-answering #generated_from_trainer #license-mit #endpoints_compatible #region-us
| deberta-base-combined-squad1-aqa-newsqa-and-newsqa-1epoch
=========================================================
This model is a fine-tuned version of stevemobs/deberta-base-combined-squad1-aqa-newsqa on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.7915
Model description... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 12\n* eval\\_batch\\_size: 12\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #deberta #question-answering #generated_from_trainer #license-mit #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 12\n* eval\\_batch\\... |
text-generation | transformers |
#HighJacker DialoGPT Model | {"tags": ["conversational"]} | keans/DialoGPT-small-highjacker | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-05-29T14:21:44+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
#HighJacker DialoGPT Model | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
reinforcement-learning | null |
# **Q-Learning** Agent playing **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="jonporterjones/Taxi1", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)... | {"tags": ["Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "Taxi1", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Taxi-v3", "type": "Taxi-v3"}, "metrics": [{"type": "mean_reward", "value": "7.56 +/- 2.... | jonporterjones/Taxi1 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-05-29T14:29:21+00:00 | [] | [] | TAGS
#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing Taxi-v3
This is a trained model of a Q-Learning agent playing Taxi-v3 .
## Usage
| [
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] | [
"TAGS\n#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] |
reinforcement-learning | null |
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="dbarbedillo/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional ... | {"tags": ["FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-FrozenLake-v1-4x4-noSlippery", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "FrozenLake-v1-4x4-no_slippery", "type": ... | dbarbedillo/q-FrozenLake-v1-4x4-noSlippery | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-05-29T14:47:58+00:00 | [] | [] | TAGS
#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing FrozenLake-v1
This is a trained model of a Q-Learning agent playing FrozenLake-v1 .
## Usage
| [
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] | [
"TAGS\n#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] |
reinforcement-learning | null |
# **Q-Learning** Agent playing **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="dbarbedillo/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc... | {"tags": ["Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-Taxi-v3", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Taxi-v3", "type": "Taxi-v3"}, "metrics": [{"type": "mean_reward", "value": "7.56 +/... | dbarbedillo/q-Taxi-v3 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-05-29T14:50:53+00:00 | [] | [] | TAGS
#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing Taxi-v3
This is a trained model of a Q-Learning agent playing Taxi-v3 .
## Usage
| [
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] | [
"TAGS\n#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] |
text-to-speech | espnet |
## ESPnet2 TTS model
### `imdanboy/kss_tts_train_jets_raw_phn_null_g2pk_train.total_count.ave`
This model was trained by satoshi.2020 using kss recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```bash
cd espnet
git checkout 047d0c474c18a87c205e566948410be16787e477
pip install... | {"language": "ko", "license": "cc-by-4.0", "tags": ["espnet", "audio", "text-to-speech"], "datasets": ["kss"]} | imdanboy/kss_tts_train_jets_raw_phn_null_g2pk_train.total_count.ave | null | [
"espnet",
"audio",
"text-to-speech",
"ko",
"dataset:kss",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | null | 2022-05-29T15:05:59+00:00 | [
"1804.00015"
] | [
"ko"
] | TAGS
#espnet #audio #text-to-speech #ko #dataset-kss #arxiv-1804.00015 #license-cc-by-4.0 #region-us
|
## ESPnet2 TTS model
### 'imdanboy/kss_tts_train_jets_raw_phn_null_g2pk_train.total_count.ave'
This model was trained by satoshi.2020 using kss recipe in espnet.
### Demo: How to use in ESPnet2
## TTS config
<details><summary>expand</summary>
</details>
### Citing ESPnet
or arXiv:
| [
"## ESPnet2 TTS model",
"### 'imdanboy/kss_tts_train_jets_raw_phn_null_g2pk_train.total_count.ave'\n\nThis model was trained by satoshi.2020 using kss recipe in espnet.",
"### Demo: How to use in ESPnet2",
"## TTS config\n\n<details><summary>expand</summary>\n\n\n\n</details>",
"### Citing ESPnet\n\n\n\nor ... | [
"TAGS\n#espnet #audio #text-to-speech #ko #dataset-kss #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"## ESPnet2 TTS model",
"### 'imdanboy/kss_tts_train_jets_raw_phn_null_g2pk_train.total_count.ave'\n\nThis model was trained by satoshi.2020 using kss recipe in espnet.",
"### Demo: How to use in ESPnet... |
reinforcement-learning | null |
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="felizang/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional att... | {"tags": ["FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-FrozenLake-v1-4x4-noSlippery", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "FrozenLake-v1-4x4-no_slippery", "type": ... | felizang/q-FrozenLake-v1-4x4-noSlippery | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-05-29T15:26:34+00:00 | [] | [] | TAGS
#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing FrozenLake-v1
This is a trained model of a Q-Learning agent playing FrozenLake-v1 .
## Usage
| [
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] | [
"TAGS\n#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] |
text-classification | transformers |
# deberta-v3-base-finetuned-finance-text-classification
This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on the sentence_50Agree [financial-phrasebank + Kaggle Dataset](https://huggingface.co/datasets/nickmuchi/financial-classification), a dataset con... | {"license": "mit", "tags": ["generated_from_trainer", "financial-sentiment-analysis", "sentiment-analysis", "sentence_50agree", "stocks", "sentiment", "finance"], "datasets": ["financial_phrasebank", "Kaggle_Self_label", "nickmuchi/financial-classification"], "metrics": ["accuracy", "f1", "precision", "recall"], "widge... | nickmuchi/deberta-v3-base-finetuned-finance-text-classification | null | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"deberta-v2",
"text-classification",
"generated_from_trainer",
"financial-sentiment-analysis",
"sentiment-analysis",
"sentence_50agree",
"stocks",
"sentiment",
"finance",
"dataset:financial_phrasebank",
"dataset:Kaggle_Self_label",... | null | 2022-05-29T15:29:22+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #safetensors #deberta-v2 #text-classification #generated_from_trainer #financial-sentiment-analysis #sentiment-analysis #sentence_50agree #stocks #sentiment #finance #dataset-financial_phrasebank #dataset-Kaggle_Self_label #dataset-nickmuchi/financial-classification #license-mit... | deberta-v3-base-finetuned-finance-text-classification
=====================================================
This model is a fine-tuned version of microsoft/deberta-v3-base on the sentence\_50Agree financial-phrasebank + Kaggle Dataset, a dataset consisting of 4840 Financial News categorised by sentiment (negative, ne... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 15\n* mixed\\_pre... | [
"TAGS\n#transformers #pytorch #tensorboard #safetensors #deberta-v2 #text-classification #generated_from_trainer #financial-sentiment-analysis #sentiment-analysis #sentence_50agree #stocks #sentiment #finance #dataset-financial_phrasebank #dataset-Kaggle_Self_label #dataset-nickmuchi/financial-classification #licen... |
text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# gpt2-poet
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset.
It achieves the follo... | {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "gpt2-poet", "results": []}]} | uygarkurt/gpt2-poet | null | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-05-29T15:34:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| gpt2-poet
=========
This model is a fine-tuned version of gpt2 on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 5.2026
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps... | [
"TAGS\n#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #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* learning\\_rate: 5e-05\n*... |
automatic-speech-recognition | transformers | /home/sanchitgandhi/seq2seq-speech/README.md | {} | sanchit-gandhi/flax-wav2vec2-2-bart-large-cv9-feature-encoder | null | [
"transformers",
"jax",
"speech-encoder-decoder",
"automatic-speech-recognition",
"endpoints_compatible",
"region:us"
] | null | 2022-05-29T15:50:26+00:00 | [] | [] | TAGS
#transformers #jax #speech-encoder-decoder #automatic-speech-recognition #endpoints_compatible #region-us
| /home/sanchitgandhi/seq2seq-speech/URL | [] | [
"TAGS\n#transformers #jax #speech-encoder-decoder #automatic-speech-recognition #endpoints_compatible #region-us \n"
] |
automatic-speech-recognition | transformers | /home/sanchitgandhi/seq2seq-speech/README.md | {} | sanchit-gandhi/flax-wav2vec2-2-bart-large-tedlium-feature-encoder | null | [
"transformers",
"jax",
"speech-encoder-decoder",
"automatic-speech-recognition",
"endpoints_compatible",
"region:us"
] | null | 2022-05-29T15:54:24+00:00 | [] | [] | TAGS
#transformers #jax #speech-encoder-decoder #automatic-speech-recognition #endpoints_compatible #region-us
| /home/sanchitgandhi/seq2seq-speech/URL | [] | [
"TAGS\n#transformers #jax #speech-encoder-decoder #automatic-speech-recognition #endpoints_compatible #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. -->
# mT5_multilingual_XLSum-finetuned-fa
This model is a fine-tuned version of [csebuetnlp/mT5_multilingual_XLSum](https://huggingfac... | {"tags": ["summarization", "fa", "mt5", "Abstractive Summarization", "generated_from_trainer"], "datasets": ["pn_summary"], "model-index": [{"name": "mT5_multilingual_XLSum-finetuned-fa", "results": []}]} | ahmeddbahaa/mT5_multilingual_XLSum-finetuned-fa | null | [
"transformers",
"pytorch",
"tensorboard",
"mt5",
"text2text-generation",
"summarization",
"fa",
"Abstractive Summarization",
"generated_from_trainer",
"dataset:pn_summary",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-05-29T16:01:06+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #mt5 #text2text-generation #summarization #fa #Abstractive Summarization #generated_from_trainer #dataset-pn_summary #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
# mT5_multilingual_XLSum-finetuned-fa
This model is a fine-tuned version of csebuetnlp/mT5_multilingual_XLSum on the pn_summary dataset.
It achieves the following results on the evaluation set:
- Loss: 2.5703
- Rouge-1: 45.12
- Rouge-2: 26.25
- Rouge-l: 39.96
- Gen Len: 48.72
- Bertscore: 79.54
## Model descriptio... | [
"# mT5_multilingual_XLSum-finetuned-fa\n\nThis model is a fine-tuned version of csebuetnlp/mT5_multilingual_XLSum on the pn_summary dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 2.5703\n- Rouge-1: 45.12\n- Rouge-2: 26.25\n- Rouge-l: 39.96\n- Gen Len: 48.72\n- Bertscore: 79.54",
"## M... | [
"TAGS\n#transformers #pytorch #tensorboard #mt5 #text2text-generation #summarization #fa #Abstractive Summarization #generated_from_trainer #dataset-pn_summary #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"# mT5_multilingual_XLSum-finetuned-fa\n\nThis model is ... |
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... | nevepam/ppo-LunarLander-v2_ | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-05-29T16:20:08+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... |
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. -->
**Transformers >= 4.36.1**\
**This model relies on a custom modeling file, you need to add trust_remote_code=True**\
**See [\#13467... | {"language": ["en"], "tags": ["summarization"], "datasets": ["ccdv/mediasum"], "metrics": ["rouge"], "model-index": [{"name": "ccdv/lsg-bart-base-4096-mediasum", "results": []}]} | ccdv/lsg-bart-base-4096-mediasum | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"summarization",
"custom_code",
"en",
"dataset:ccdv/mediasum",
"arxiv:2210.15497",
"autotrain_compatible",
"has_space",
"region:us"
] | null | 2022-05-29T16:20:56+00:00 | [
"2210.15497"
] | [
"en"
] | TAGS
#transformers #pytorch #bart #text2text-generation #summarization #custom_code #en #dataset-ccdv/mediasum #arxiv-2210.15497 #autotrain_compatible #has_space #region-us
| Transformers >= 4.36.1
This model relies on a custom modeling file, you need to add trust\_remote\_code=True
See #13467
LSG ArXiv paper.
Github/conversion script is available at this link.
ccdv/lsg-bart-base-4096-mediasum
================================
This model is a fine-tuned version of ccdv/lsg-ba... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 8e-05\n* train\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_t... | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #summarization #custom_code #en #dataset-ccdv/mediasum #arxiv-2210.15497 #autotrain_compatible #has_space #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 8e-05\n* train\\_b... |
reinforcement-learning | null |
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="meln1k/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attri... | {"tags": ["FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-FrozenLake-v1-4x4-noSlippery", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "FrozenLake-v1-4x4-no_slippery", "type": ... | meln1k/q-FrozenLake-v1-4x4-noSlippery | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-05-29T16:22:14+00:00 | [] | [] | TAGS
#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing FrozenLake-v1
This is a trained model of a Q-Learning agent playing FrozenLake-v1 .
## Usage
| [
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] | [
"TAGS\n#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] |
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
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-b... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["xtreme"], "metrics": ["f1"], "model-index": [{"name": "xlm-roberta-base-finetuned-panx-de", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "xtreme", "type": "xtreme", "args": "PAN-X.de"}, "me... | jg/xlm-roberta-base-finetuned-panx-de | null | [
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-29T16:32:47+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-panx-de
==================================
This model is a fine-tuned version of xlm-roberta-base on the xtreme dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1372
* F1: 0.8621
Model description
-----------------
More information needed
Intended uses & l... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 24\n* eval\\_batch\\_size: 24\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_... |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# deberta-base-finetuned-squad1-newsqa
This model is a fine-tuned version of [stevemobs/deberta-base-finetuned-squad1](https://hug... | {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "deberta-base-finetuned-squad1-newsqa", "results": []}]} | stevemobs/deberta-base-finetuned-squad1-newsqa | null | [
"transformers",
"pytorch",
"tensorboard",
"deberta",
"question-answering",
"generated_from_trainer",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-05-29T16:38:51+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #deberta #question-answering #generated_from_trainer #license-mit #endpoints_compatible #region-us
| deberta-base-finetuned-squad1-newsqa
====================================
This model is a fine-tuned version of stevemobs/deberta-base-finetuned-squad1 on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.7556
Model description
-----------------
More information needed
Inten... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 12\n* eval\\_batch\\_size: 12\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #deberta #question-answering #generated_from_trainer #license-mit #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 12\n* eval\\_batch\\... |
null | null | Branchformer (Peng et al., ICML 2022): [https://proceedings.mlr.press/v162/peng22a.html](https://proceedings.mlr.press/v162/peng22a.html)
# RESULTS
## Environments
- date: `Sun May 29 01:39:59 EDT 2022`
- python version: `3.9.12 (main, Apr 5 2022, 06:56:58) [GCC 7.5.0]`
- espnet version: `espnet 202205`
- pytorch ve... | {} | pyf98/iwslt14_de_en_branchformer | null | [
"region:us"
] | null | 2022-05-29T17:12:28+00:00 | [] | [] | TAGS
#region-us
| Branchformer (Peng et al., ICML 2022): URL
RESULTS
=======
Environments
------------
* date: 'Sun May 29 01:39:59 EDT 2022'
* python version: '3.9.12 (main, Apr 5 2022, 06:56:58) [GCC 7.5.0]'
* espnet version: 'espnet 202205'
* pytorch version: 'pytorch 1.11.0'
* Git hash: '1cab3306f8136e614339390f59f06e11d054bbd... | [
"### BLEU\n\n\ndataset: beam5\\_maxlenratio1.6\\_penalty0.4/test, score: 32.7, verbose\\_score: 66.9/41.5/27.5/18.7 (BP = 0.945 ratio = 0.946 hyp\\_len = 121209 ref\\_len = 128122)\ndataset: beam5\\_maxlenratio1.6\\_penalty0.4/valid, score: 34.1, verbose\\_score: 67.6/42.9/29.0/20.0 (BP = 0.945 ratio = 0.946 hyp\\_... | [
"TAGS\n#region-us \n",
"### BLEU\n\n\ndataset: beam5\\_maxlenratio1.6\\_penalty0.4/test, score: 32.7, verbose\\_score: 66.9/41.5/27.5/18.7 (BP = 0.945 ratio = 0.946 hyp\\_len = 121209 ref\\_len = 128122)\ndataset: beam5\\_maxlenratio1.6\\_penalty0.4/valid, score: 34.1, verbose\\_score: 67.6/42.9/29.0/20.0 (BP = 0... |
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. -->
#
This model is a fine-tuned version of [hf-test/xls-r-dummy](https://huggingface.co/hf-test/xls-r-dummy) on the MOZILLA-FOUNDATI... | {"language": ["ab"], "tags": ["automatic-speech-recognition", "mozilla-foundation/common_voice_7_0", "generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "", "results": []}]} | neelan-elucidate-ai/baseline | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"mozilla-foundation/common_voice_7_0",
"generated_from_trainer",
"ab",
"dataset:common_voice",
"endpoints_compatible",
"region:us"
] | null | 2022-05-29T17:48:43+00:00 | [] | [
"ab"
] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_7_0 #generated_from_trainer #ab #dataset-common_voice #endpoints_compatible #region-us
|
#
This model is a fine-tuned version of hf-test/xls-r-dummy on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - AB dataset.
It achieves the following results on the evaluation set:
- Loss: 207.6048
- Wer: 1.5484
## Model description
More information needed
## Intended uses & limitations
More information needed
## Tr... | [
"# \n\nThis model is a fine-tuned version of hf-test/xls-r-dummy on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - AB dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 207.6048\n- Wer: 1.5484",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore inform... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_7_0 #generated_from_trainer #ab #dataset-common_voice #endpoints_compatible #region-us \n",
"# \n\nThis model is a fine-tuned version of hf-test/xls-r-dummy on the MOZILLA-FOUNDATION/COMMON_VOICE_7_... |
reinforcement-learning | null |
# **Q-Learning** Agent playing **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="atsanda/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
... | {"tags": ["Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-Taxi-v3", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Taxi-v3", "type": "Taxi-v3"}, "metrics": [{"type": "mean_reward", "value": "7.54 +/... | atsanda/q-Taxi-v3 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-05-29T18:21:48+00:00 | [] | [] | TAGS
#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing Taxi-v3
This is a trained model of a Q-Learning agent playing Taxi-v3 .
## Usage
| [
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] | [
"TAGS\n#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] |
reinforcement-learning | 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... | harryb0905/ppo-LunarLander-v2-1-million | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-05-29T18:23:41+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... |
reinforcement-learning | stable-baselines3 |
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 ... | {"library_name": "stable-baselines3", "tags": ["LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "LunarLander-v2", "type": "LunarL... | Misha24-10/TEST2ppo-LunarLander-v4 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-05-29T18:30:37+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... |
unconditional-image-generation | keras |
## Model description
This repo contains the model for the notebook [Neural style transfer](https://keras.io/examples/generative/neural_style_transfer/).
Full credits go to [fchollet](https://twitter.com/fchollet)
Reproduced by [Rushi Chaudhari](https://github.com/rushic24)
Style transfer consists in generating an i... | {"library_name": "keras", "tags": ["unconditional-image-generation"]} | keras-io/VGG19 | null | [
"keras",
"unconditional-image-generation",
"has_space",
"region:us"
] | null | 2022-05-29T18:38:07+00:00 | [] | [] | TAGS
#keras #unconditional-image-generation #has_space #region-us
|
## Model description
This repo contains the model for the notebook Neural style transfer.
Full credits go to fchollet
Reproduced by Rushi Chaudhari
Style transfer consists in generating an image with the same "content" as a base image, but with the "style" of a different picture (typically artistic) by optimizing s... | [
"## Model description\nThis repo contains the model for the notebook Neural style transfer.\n\nFull credits go to fchollet\n\nReproduced by Rushi Chaudhari\n\nStyle transfer consists in generating an image with the same \"content\" as a base image, but with the \"style\" of a different picture (typically artistic) ... | [
"TAGS\n#keras #unconditional-image-generation #has_space #region-us \n",
"## Model description\nThis repo contains the model for the notebook Neural style transfer.\n\nFull credits go to fchollet\n\nReproduced by Rushi Chaudhari\n\nStyle transfer consists in generating an image with the same \"content\" as a base... |
automatic-speech-recognition | transformers | * Evaluation Notebook: https://colab.research.google.com/drive/1dV1Z3WajMCYMjNZab98CEEcg3FTbtONO?usp=sharing
* Training Code: https://github.com/vasudevgupta7/speech-jax/blob/main/projects/finetune_wav2vec2.py
* Weights & Biases: https://wandb.ai/7vasudevgupta/speech-JAX?workspace=user-7vasudevgupta
Following results ... | {} | vasudevgupta/speech_jax_wav2vec2-large-lv60_960h | null | [
"transformers",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"endpoints_compatible",
"region:us"
] | null | 2022-05-29T19:52:47+00:00 | [] | [] | TAGS
#transformers #jax #wav2vec2 #automatic-speech-recognition #endpoints_compatible #region-us
| * Evaluation Notebook: URL
* Training Code: URL
* Weights & Biases: URL
Following results are obtained with '23ffe236840b7f75c9f01a9c347b01485a2bf9f6' & '95c3bc1b83c74452df29f792e0b5651c09fdaeb9'
| [] | [
"TAGS\n#transformers #jax #wav2vec2 #automatic-speech-recognition #endpoints_compatible #region-us \n"
] |
automatic-speech-recognition | transformers | * Evaluation Notebook: https://colab.research.google.com/drive/1dV1Z3WajMCYMjNZab98CEEcg3FTbtONO?usp=sharing
* Training Code: https://github.com/vasudevgupta7/speech-jax/blob/main/projects/asr/train_wav2vec2.py
Following results are obtained with `adce555df7402dc63f8f4d9d14cb286f4b9d4107`
| dataset | WER ... | {} | vasudevgupta/speech_jax_wav2vec2-large-lv60_100h | null | [
"transformers",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"endpoints_compatible",
"region:us"
] | null | 2022-05-29T20:03:05+00:00 | [] | [] | TAGS
#transformers #jax #wav2vec2 #automatic-speech-recognition #endpoints_compatible #region-us
| * Evaluation Notebook: URL
* Training Code: URL
Following results are obtained with 'adce555df7402dc63f8f4d9d14cb286f4b9d4107'
| [] | [
"TAGS\n#transformers #jax #wav2vec2 #automatic-speech-recognition #endpoints_compatible #region-us \n"
] |
fill-mask | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# bert-news-cad-v3
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dat... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "bert-news-cad-v3", "results": []}]} | jbreuch/bert-news-cad-v3 | null | [
"transformers",
"tf",
"bert",
"fill-mask",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-29T20:48:27+00:00 | [] | [] | TAGS
#transformers #tf #bert #fill-mask #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# bert-news-cad-v3
This model is a fine-tuned version of bert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information neede... | [
"# bert-news-cad-v3\n\nThis model is a fine-tuned version of bert-base-uncased on an unknown dataset.\nIt achieves the following results on the evaluation set:",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\n... | [
"TAGS\n#transformers #tf #bert #fill-mask #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# bert-news-cad-v3\n\nThis model is a fine-tuned version of bert-base-uncased on an unknown dataset.\nIt achieves the following results on the evaluation set:",... |
text-generation | transformers |
#jaybot 2.0 | {"tags": ["conversational"]} | jayklaws0606/dgpt-small-jaybot | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-05-29T20:53:46+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
#jaybot 2.0 | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
fill-mask | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Clinical-Longformer-MLM-opnote
This model is a fine-tuned version of [yikuan8/Clinical-Longformer](https://huggingface.co/yikuan... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "Clinical-Longformer-MLM-opnote", "results": []}]} | Santarabantoosoo/Clinical-Longformer-MLM-opnote | null | [
"transformers",
"pytorch",
"tensorboard",
"longformer",
"fill-mask",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-29T21:08:37+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #longformer #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| Clinical-Longformer-MLM-opnote
==============================
This model is a fine-tuned version of yikuan8/Clinical-Longformer on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.8286
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: 1\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 4\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e... | [
"TAGS\n#transformers #pytorch #tensorboard #longformer #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 1\n* eval\\_batch... |
text-classification | transformers |
# Uganda Labor Market Interview Text Classification
This model is a fine-tuned [Roberta base model](https://huggingface.co/roberta-base) using text transcripts of interviews between Vocational Training Institutes (VTI) students and their successful alumni in Uganda on the subject of the labor market.
## Model descri... | {"language": "en", "license": "mit"} | wanghao2023/uganda-labor-market-interview-text-classification | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"en",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-29T21:27:17+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #roberta #text-classification #en #license-mit #autotrain_compatible #endpoints_compatible #region-us
| Uganda Labor Market Interview Text Classification
=================================================
This model is a fine-tuned Roberta base model using text transcripts of interviews between Vocational Training Institutes (VTI) students and their successful alumni in Uganda on the subject of the labor market.
Model... | [
"### How to use\n\n\nYou can use this model directly with a pipeline for text classification:",
"### Limitations and bias\n\n\nSentence classification is heavily dependent on context. For instance, the phrase \"be patient\" could be categorized as a tip, strategy, and/or motivation, depending on the specific cont... | [
"TAGS\n#transformers #pytorch #roberta #text-classification #en #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### How to use\n\n\nYou can use this model directly with a pipeline for text classification:",
"### Limitations and bias\n\n\nSentence classification is heavily dependent on ... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bart-large-cnn-pubmed1o3-pubmed2o3-pubmed3o3-arxiv1o3-arxiv2o3
This model is a fine-tuned version of [theojolliffe/bart-large-cn... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["scientific_papers"], "metrics": ["rouge"], "model-index": [{"name": "bart-large-cnn-pubmed1o3-pubmed2o3-pubmed3o3-arxiv1o3-arxiv2o3", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {... | theojolliffe/bart-large-cnn-pubmed1o3-pubmed2o3-pubmed3o3-arxiv1o3-arxiv2o3 | null | [
"transformers",
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"generated_from_trainer",
"dataset:scientific_papers",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-29T21:35:59+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bart #text2text-generation #generated_from_trainer #dataset-scientific_papers #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| bart-large-cnn-pubmed1o3-pubmed2o3-pubmed3o3-arxiv1o3-arxiv2o3
==============================================================
This model is a fine-tuned version of theojolliffe/bart-large-cnn-pubmed1o3-pubmed2o3-pubmed3o3-arxiv1o3 on the scientific\_papers dataset.
It achieves the following results on the evaluation ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 2\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1\n* mixed\\_precis... | [
"TAGS\n#transformers #pytorch #tensorboard #bart #text2text-generation #generated_from_trainer #dataset-scientific_papers #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learnin... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-german-cased-noisy-pretrain-fine-tuned_v2
This model is a fine-tuned version of [tbosse/bert-base-german-cased-finetun... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "bert-base-german-cased-noisy-pretrain-fine-tuned_v2", "results": []}]} | tbosse/bert-base-german-cased-noisy-pretrain-fine-tuned_v2 | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-29T22:10:06+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| bert-base-german-cased-noisy-pretrain-fine-tuned\_v2
====================================================
This model is a fine-tuned version of tbosse/bert-base-german-cased-finetuned-subj\_preTrained\_with\_noisyData\_v2 on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2872... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 7",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size:... |
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. -->
# ptt5-base-portuguese-vocab-summarizacao-PTT-BR
This model is a fine-tuned version of [unicamp-dl/ptt5-base-portuguese-vocab](htt... | {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "ptt5-base-portuguese-vocab-summarizacao-PTT-BR", "results": []}]} | GiordanoB/ptt5-base-portuguese-vocab-summarizacao-PTT-BR | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-05-29T22:28:52+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| ptt5-base-portuguese-vocab-summarizacao-PTT-BR
==============================================
This model is a fine-tuned version of unicamp-dl/ptt5-base-portuguese-vocab on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 3.6954
Model description
-----------------
More inform... | [
"### 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 #t5 #text2text-generation #generated_from_trainer #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* learning\\_rate: 2e-05... |
null | keras |
## Model description
This repo contains the model for the notebook [Image similarity estimation using a Siamese Network with a contrastive loss](https://keras.io/examples/vision/siamese_contrastive/).
Full credits go to Mehdi
Reproduced by [Rushi Chaudhari](https://github.com/rushic24)
[Siamese Networks](https://en... | {"library_name": "keras"} | keras-io/siamese-contrastive | null | [
"keras",
"has_space",
"region:us"
] | null | 2022-05-29T22:34:45+00:00 | [] | [] | TAGS
#keras #has_space #region-us
|
## Model description
This repo contains the model for the notebook Image similarity estimation using a Siamese Network with a contrastive loss.
Full credits go to Mehdi
Reproduced by Rushi Chaudhari
Siamese Networks are neural networks which share weights between two or more sister networks, each producing embeddin... | [
"## Model description\nThis repo contains the model for the notebook Image similarity estimation using a Siamese Network with a contrastive loss.\n\nFull credits go to Mehdi\n\nReproduced by Rushi Chaudhari\n\nSiamese Networks are neural networks which share weights between two or more sister networks, each produci... | [
"TAGS\n#keras #has_space #region-us \n",
"## Model description\nThis repo contains the model for the notebook Image similarity estimation using a Siamese Network with a contrastive loss.\n\nFull credits go to Mehdi\n\nReproduced by Rushi Chaudhari\n\nSiamese Networks are neural networks which share weights betwee... |
text-generation | transformers | #TChalla DialoGPT model | {"tags": ["conversational"]} | CodeMaestro/DialoGPT-small-TChalla | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-05-29T22:55:47+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| #TChalla DialoGPT model | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-imdb
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/di... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imdb"], "metrics": ["accuracy"], "model-index": [{"name": "distilbert-base-uncased-finetuned-imdb", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "imdb", "type": "imdb", "args": "plain_... | sahn/distilbert-base-uncased-finetuned-imdb | 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 | 2022-05-29T23:35:28+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
| distilbert-base-uncased-finetuned-imdb
======================================
This model is a fine-tuned version of distilbert-base-uncased on the imdb dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2214
* Accuracy: 0.9294
Model description
-----------------
More information needed
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### 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 | transformers | wav2vec2 -> t5lephone
bs = 16
dropout = 0.3
performance : 29%
{
"architectures": [
"SpeechMixEEDT5"
],
"decoder": {
"_name_or_path": "voidful/phoneme_byt5",
"add_cross_attention": true,
"architectures": [
"T5ForConditionalGeneration"
],
"bad_words_ids": null,
"bos_token_id": nu... | {} | Splend1dchan/wav2vec2-large-lv60_t5lephone-small_nofreeze_bs16_forMINDS.en.all2 | null | [
"transformers",
"pytorch",
"speechmix",
"endpoints_compatible",
"region:us"
] | null | 2022-05-30T00:14:14+00:00 | [] | [] | TAGS
#transformers #pytorch #speechmix #endpoints_compatible #region-us
| wav2vec2 -> t5lephone
bs = 16
dropout = 0.3
performance : 29%
{
"architectures": [
"SpeechMixEEDT5"
],
"decoder": {
"_name_or_path": "voidful/phoneme_byt5",
"add_cross_attention": true,
"architectures": [
"T5ForConditionalGeneration"
],
"bad_words_ids": null,
"bos_token_id": nu... | [] | [
"TAGS\n#transformers #pytorch #speechmix #endpoints_compatible #region-us \n"
] |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# deberta-base-combined-squad1-aqa-1epoch
This model is a fine-tuned version of [microsoft/deberta-base](https://huggingface.co/mi... | {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "deberta-base-combined-squad1-aqa-1epoch", "results": []}]} | stevemobs/deberta-base-combined-squad1-aqa-1epoch | null | [
"transformers",
"pytorch",
"tensorboard",
"deberta",
"question-answering",
"generated_from_trainer",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-05-30T00:14:58+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #deberta #question-answering #generated_from_trainer #license-mit #endpoints_compatible #region-us
| deberta-base-combined-squad1-aqa-1epoch
=======================================
This model is a fine-tuned version of microsoft/deberta-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.9431
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: 12\n* eval\\_batch\\_size: 12\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #deberta #question-answering #generated_from_trainer #license-mit #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 12\n* eval\\_batch\\... |
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-turkish-colab
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-large-xls-r-300m-turkish-colab", "results": []}]} | cwchengtw/wav2vec2-large-xls-r-300m-turkish-colab | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-05-30T01:14:34+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-turkish-colab
=======================================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common\_voice dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3873
* Wer: 0.3224
Model description
-----------------
More informat... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #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* t... |
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. -->
# rob2rand_chen_w_prefix_c_fc
This model was trained from scratch on the None dataset.
It achieves the following results on the ev... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "rob2rand_chen_w_prefix_c_fc", "results": []}]} | imamnurby/rob2rand_chen_w_prefix_c_fc | null | [
"transformers",
"pytorch",
"encoder-decoder",
"text2text-generation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-30T01:22:25+00:00 | [] | [] | TAGS
#transformers #pytorch #encoder-decoder #text2text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
|
# rob2rand_chen_w_prefix_c_fc
This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.0939
- eval_bleu: 84.4530
- eval_em: 52.0156
- eval_bleu_em: 68.2343
- eval_runtime: 21.0016
- eval_samples_per_second: 36.616
- eval_steps_per_second: 0.619... | [
"# rob2rand_chen_w_prefix_c_fc\n\nThis model was trained from scratch on the None dataset.\nIt achieves the following results on the evaluation set:\n- eval_loss: 0.0939\n- eval_bleu: 84.4530\n- eval_em: 52.0156\n- eval_bleu_em: 68.2343\n- eval_runtime: 21.0016\n- eval_samples_per_second: 36.616\n- eval_steps_per_s... | [
"TAGS\n#transformers #pytorch #encoder-decoder #text2text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"# rob2rand_chen_w_prefix_c_fc\n\nThis model was trained from scratch on the None dataset.\nIt achieves the following results on the evaluation set:\n- eval_loss... |
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-imdb-tag
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.c... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imdb"], "metrics": ["accuracy"], "model-index": [{"name": "distilbert-base-uncased-finetuned-imdb-tag", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "imdb", "type": "imdb", "args": "pl... | sahn/distilbert-base-uncased-finetuned-imdb-tag | 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 | 2022-05-30T01:24:15+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
| distilbert-base-uncased-finetuned-imdb-tag
==========================================
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: 0.2215
* Accuracy: 0.9672
Model description
-----------------
More information... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### 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... |
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-imdb-subtle
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingfac... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imdb"], "metrics": ["accuracy"], "model-index": [{"name": "distilbert-base-uncased-finetuned-imdb-subtle", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "imdb", "type": "imdb", "args": ... | sahn/distilbert-base-uncased-finetuned-imdb-subtle | 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 | 2022-05-30T01:40:37+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
| distilbert-base-uncased-finetuned-imdb-subtle
=============================================
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: 0.5219
* Accuracy: 0.9074
Model description
-----------------
More infor... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #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... |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# deberta-base-combined-squad1-aqa-1epoch-and-newsqa-2epoch
This model is a fine-tuned version of [stevemobs/deberta-base-combined... | {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "deberta-base-combined-squad1-aqa-1epoch-and-newsqa-2epoch", "results": []}]} | stevemobs/deberta-base-combined-squad1-aqa-1epoch-and-newsqa-2epoch | null | [
"transformers",
"pytorch",
"tensorboard",
"deberta",
"question-answering",
"generated_from_trainer",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-05-30T01:45:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #deberta #question-answering #generated_from_trainer #license-mit #endpoints_compatible #region-us
| deberta-base-combined-squad1-aqa-1epoch-and-newsqa-2epoch
=========================================================
This model is a fine-tuned version of stevemobs/deberta-base-combined-squad1-aqa-1epoch on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.7521
Model description... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 12\n* eval\\_batch\\_size: 12\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #deberta #question-answering #generated_from_trainer #license-mit #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 12\n* eval\\_batch\\... |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# deberta-base-combined-squad1-aqa-1epoch-and-newsqa-1epoch
This model is a fine-tuned version of [stevemobs/deberta-base-combined... | {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "deberta-base-combined-squad1-aqa-1epoch-and-newsqa-1epoch", "results": []}]} | stevemobs/deberta-base-combined-squad1-aqa-1epoch-and-newsqa-1epoch | null | [
"transformers",
"pytorch",
"tensorboard",
"deberta",
"question-answering",
"generated_from_trainer",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-05-30T01:46:39+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #deberta #question-answering #generated_from_trainer #license-mit #endpoints_compatible #region-us
| deberta-base-combined-squad1-aqa-1epoch-and-newsqa-1epoch
=========================================================
This model is a fine-tuned version of stevemobs/deberta-base-combined-squad1-aqa-1epoch on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6807
Model description... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 12\n* eval\\_batch\\_size: 12\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #deberta #question-answering #generated_from_trainer #license-mit #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 12\n* eval\\_batch\\... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-imdb-blur
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imdb"], "metrics": ["accuracy"], "model-index": [{"name": "distilbert-base-uncased-finetuned-imdb-blur", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "imdb", "type": "imdb", "args": "p... | sahn/distilbert-base-uncased-finetuned-imdb-blur | 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 | 2022-05-30T02:10:21+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
| distilbert-base-uncased-finetuned-imdb-blur
===========================================
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: 0.1484
* Accuracy: 0.9776
Model description
-----------------
More informati... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### 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... |
text2text-generation | transformers |
# Question generation using T5 transformer
<h2> <i>Input format: context: "..." answer: "..." </i></h2>
Import the pretrained model as well as tokenizer:
```
from transformers import T5ForConditionalGeneration, T5Tokenizer
model = T5ForConditionalGeneration.from_pretrained('AbhilashDatta/T5_qgen-squad-marco')
toke... | {"license": "afl-3.0"} | AbhilashDatta/T5_qgen-squad-marco | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-05-30T02:12:15+00:00 | [] | [] | TAGS
#transformers #pytorch #t5 #text2text-generation #license-afl-3.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Question generation using T5 transformer
<h2> <i>Input format: context: "..." answer: "..." </i></h2>
Import the pretrained model as well as tokenizer:
Then use the tokenizer to encode/decode and model to generate:
Output:
| [
"# Question generation using T5 transformer\n\n<h2> <i>Input format: context: \"...\" answer: \"...\" </i></h2>\n\nImport the pretrained model as well as tokenizer:\n\n\nThen use the tokenizer to encode/decode and model to generate: \n\n\n\nOutput:"
] | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #license-afl-3.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Question generation using T5 transformer\n\n<h2> <i>Input format: context: \"...\" answer: \"...\" </i></h2>\n\nImport the pretrained model as well as ... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-base-finetuned-removed-0530
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "roberta-base-finetuned-removed-0530", "results": []}]} | YeRyeongLee/roberta-base-finetuned-removed-0530 | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-30T02:31:55+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| roberta-base-finetuned-removed-0530
===================================
This model is a fine-tuned version of roberta-base on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.7910
* Accuracy: 0.9082
* F1: 0.9084
Model description
-----------------
More information needed
... | [
"### 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* lr\\_scheduler\\_warmup\\_steps: ... | [
"TAGS\n#transformers #pytorch #roberta #text-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: 8\n* eval\... |
text2text-generation | transformers |
# Question generation using T5 transformer trained on SQuAD
<h2> <i>Input format: context: "..." answer: "..." </i></h2>
Import the pretrained model as well as tokenizer:
```
from transformers import T5ForConditionalGeneration, T5Tokenizer
model = T5ForConditionalGeneration.from_pretrained('AbhilashDatta/T5_qgen-sq... | {"license": "afl-3.0"} | AbhilashDatta/T5_qgen-squad_v1 | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-05-30T04:23:30+00:00 | [] | [] | TAGS
#transformers #pytorch #t5 #text2text-generation #license-afl-3.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Question generation using T5 transformer trained on SQuAD
<h2> <i>Input format: context: "..." answer: "..." </i></h2>
Import the pretrained model as well as tokenizer:
Then use the tokenizer to encode/decode and model to generate:
Output:
| [
"# Question generation using T5 transformer trained on SQuAD\n\n<h2> <i>Input format: context: \"...\" answer: \"...\" </i></h2>\n\nImport the pretrained model as well as tokenizer:\n\n\nThen use the tokenizer to encode/decode and model to generate: \n\n\n\nOutput:"
] | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #license-afl-3.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Question generation using T5 transformer trained on SQuAD\n\n<h2> <i>Input format: context: \"...\" answer: \"...\" </i></h2>\n\nImport the pretrained ... |
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-turkish-colab2
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingfac... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-large-xls-r-300m-turkish-colab2", "results": []}]} | cwchengtw/wav2vec2-large-xls-r-300m-turkish-colab2 | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-05-30T05:00:21+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-large-xls-r-300m-turkish-colab2
========================================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common\_voice dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3738
* Wer: 0.3532
Model description
-----------------
More inform... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #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\... |
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. -->
# sarcasm-detection-Bert-base-uncased
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-u... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "sarcasm-detection-Bert-base-uncased", "results": []}]} | jkhan447/sarcasm-detection-Bert-base-uncased | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-30T05:16:13+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# sarcasm-detection-Bert-base-uncased
This model is a fine-tuned version of bert-base-uncased on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.0623
- Accuracy: 0.7127
## Model description
More information needed
## Intended uses & limitations
More information needed
## Tr... | [
"# sarcasm-detection-Bert-base-uncased\n\nThis model is a fine-tuned version of bert-base-uncased on the None dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 3.0623\n- Accuracy: 0.7127",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore inform... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# sarcasm-detection-Bert-base-uncased\n\nThis model is a fine-tuned version of bert-base-uncased on the None dataset.\nIt achieves the foll... |
text-classification | transformers |
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 927730545
- CO2 Emissions (in grams): 0.03882318406133382
## Validation Metrics
- Loss: 0.346664160490036
- Accuracy: 0.9212962962962963
- Macro F1: 0.9193830593356196
- Micro F1: 0.9212962962962963
- Weighted F1: 0.9213272351125... | {"language": "unk", "tags": "autotrain", "datasets": ["CH0KUN/autotrain-data-TNC_Data1000_wangchanBERTa"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 0.03882318406133382} | CH0KUN/autotrain-TNC_Data1000_wangchanBERTa-927730545 | null | [
"transformers",
"pytorch",
"camembert",
"text-classification",
"autotrain",
"unk",
"dataset:CH0KUN/autotrain-data-TNC_Data1000_wangchanBERTa",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-30T05:27:15+00:00 | [] | [
"unk"
] | TAGS
#transformers #pytorch #camembert #text-classification #autotrain #unk #dataset-CH0KUN/autotrain-data-TNC_Data1000_wangchanBERTa #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 927730545
- CO2 Emissions (in grams): 0.03882318406133382
## Validation Metrics
- Loss: 0.346664160490036
- Accuracy: 0.9212962962962963
- Macro F1: 0.9193830593356196
- Micro F1: 0.9212962962962963
- Weighted F1: 0.9213272351125... | [
"# Model Trained Using AutoTrain\n\n- Problem type: Multi-class Classification\n- Model ID: 927730545\n- CO2 Emissions (in grams): 0.03882318406133382",
"## Validation Metrics\n\n- Loss: 0.346664160490036\n- Accuracy: 0.9212962962962963\n- Macro F1: 0.9193830593356196\n- Micro F1: 0.9212962962962963\n- Weighted F... | [
"TAGS\n#transformers #pytorch #camembert #text-classification #autotrain #unk #dataset-CH0KUN/autotrain-data-TNC_Data1000_wangchanBERTa #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoTrain\n\n- Problem type: Multi-class Classification\n- Model ID: 92773054... |
text-classification | transformers |
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 928030564
- CO2 Emissions (in grams): 0.07293362913158113
## Validation Metrics
- Loss: 0.4989683926105499
- Accuracy: 0.8445845697329377
- Macro F1: 0.8407629450432429
- Micro F1: 0.8445845697329377
- Weighted F1: 0.840762945043... | {"language": "unk", "tags": "autotrain", "datasets": ["CH0KUN/autotrain-data-TNC_Data2500_WangchanBERTa"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 0.07293362913158113} | CH0KUN/autotrain-TNC_Data2500_WangchanBERTa-928030564 | null | [
"transformers",
"pytorch",
"camembert",
"text-classification",
"autotrain",
"unk",
"dataset:CH0KUN/autotrain-data-TNC_Data2500_WangchanBERTa",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-30T06:16:30+00:00 | [] | [
"unk"
] | TAGS
#transformers #pytorch #camembert #text-classification #autotrain #unk #dataset-CH0KUN/autotrain-data-TNC_Data2500_WangchanBERTa #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 928030564
- CO2 Emissions (in grams): 0.07293362913158113
## Validation Metrics
- Loss: 0.4989683926105499
- Accuracy: 0.8445845697329377
- Macro F1: 0.8407629450432429
- Micro F1: 0.8445845697329377
- Weighted F1: 0.840762945043... | [
"# Model Trained Using AutoTrain\n\n- Problem type: Multi-class Classification\n- Model ID: 928030564\n- CO2 Emissions (in grams): 0.07293362913158113",
"## Validation Metrics\n\n- Loss: 0.4989683926105499\n- Accuracy: 0.8445845697329377\n- Macro F1: 0.8407629450432429\n- Micro F1: 0.8445845697329377\n- Weighted ... | [
"TAGS\n#transformers #pytorch #camembert #text-classification #autotrain #unk #dataset-CH0KUN/autotrain-data-TNC_Data2500_WangchanBERTa #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoTrain\n\n- Problem type: Multi-class Classification\n- Model ID: 92803056... |
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. -->
# my-finetuned-xml-roberta4
This model is a fine-tuned version of [knurm/xlm-roberta-base-finetuned-est](https://huggingface.co/kn... | {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "my-finetuned-xml-roberta4", "results": []}]} | knurm/my-finetuned-xml-roberta4 | null | [
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"question-answering",
"generated_from_trainer",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-05-30T06:48:32+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #xlm-roberta #question-answering #generated_from_trainer #license-mit #endpoints_compatible #region-us
| my-finetuned-xml-roberta4
=========================
This model is a fine-tuned version of knurm/xlm-roberta-base-finetuned-est on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 3.7709
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: 7",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #xlm-roberta #question-answering #generated_from_trainer #license-mit #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_bat... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-ner
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/dis... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["conll2003"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "distilbert-base-uncased-finetuned-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "con... | Ayush414/distilbert-base-uncased-finetuned-ner | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-30T06:50:54+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-ner
=====================================
This model is a fine-tuned version of distilbert-base-uncased on the conll2003 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0628
* Precision: 0.9254
* Recall: 0.9352
* F1: 0.9303
* Accuracy: 0.9835
Model des... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* le... |
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. -->
# sarcasm-detection-RoBerta-base
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the ... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "sarcasm-detection-RoBerta-base", "results": []}]} | jkhan447/sarcasm-detection-RoBerta-base | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-30T06:52:30+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# sarcasm-detection-RoBerta-base
This model is a fine-tuned version of roberta-base on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.8207
- Accuracy: 0.7273
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and... | [
"# sarcasm-detection-RoBerta-base\n\nThis model is a fine-tuned version of roberta-base on the None dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 2.8207\n- Accuracy: 0.7273",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information need... | [
"TAGS\n#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# sarcasm-detection-RoBerta-base\n\nThis model is a fine-tuned version of roberta-base on the None dataset.\nIt achieves the following results ... |
feature-extraction | transformers |
<!--Copyright 2020 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agree... | {"tags": ["MusicGeneration", "jukebox"]} | ArthurZ/jukebox-1b-lyrics | null | [
"transformers",
"pytorch",
"jukebox",
"feature-extraction",
"MusicGeneration",
"arxiv:2005.00341",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-05-30T07:11:09+00:00 | [
"2005.00341"
] | [] | TAGS
#transformers #pytorch #jukebox #feature-extraction #MusicGeneration #arxiv-2005.00341 #endpoints_compatible #has_space #region-us
|
# Jukebox
## Overview
The Jukebox model was proposed in Jukebox: A generative model for music
by Prafulla Dhariwal, Heewoo Jun, Christine Payne, Jong Wook Kim, Alec Radford,
Ilya Sutskever.
This model proposes a generative music model which can be produce minute long samples which can bne conditionned on
artist, ... | [
"# Jukebox",
"## Overview\n\nThe Jukebox model was proposed in Jukebox: A generative model for music\nby Prafulla Dhariwal, Heewoo Jun, Christine Payne, Jong Wook Kim, Alec Radford,\nIlya Sutskever.\n\nThis model proposes a generative music model which can be produce minute long samples which can bne conditionned... | [
"TAGS\n#transformers #pytorch #jukebox #feature-extraction #MusicGeneration #arxiv-2005.00341 #endpoints_compatible #has_space #region-us \n",
"# Jukebox",
"## Overview\n\nThe Jukebox model was proposed in Jukebox: A generative model for music\nby Prafulla Dhariwal, Heewoo Jun, Christine Payne, Jong Wook Kim, A... |
text-classification | transformers |
# Spanish News Classification Headlines
SNCH: this model was developed by [M47Labs](https://www.m47labs.com/es/) the goal is text classification, the base model use was [BETO](https://huggingface.co/dccuchile/bert-base-spanish-wwm-cased), however this model has not been fine-tuned on any dataset. The objective is t... | {"widget": [{"text": "El d\u00f3lar se dispara tras la reuni\u00f3n de la Fed"}]} | M47Labs/spanish_news_classification_headlines_untrained | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-30T07:26:13+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us
| Spanish News Classification Headlines
=====================================
SNCH: this model was developed by M47Labs the goal is text classification, the base model use was BETO, however this model has not been fine-tuned on any dataset. The objective is to show the performance of this model when is used with the ob... | [
"### Pipeline",
"### Pytorch\n\n\nA more in depth example on how to use the model can be found in this colab notebook: URL\n\n\nValidation Results\n------------------\n\n\n\n!alt text"
] | [
"TAGS\n#transformers #pytorch #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us \n",
"### Pipeline",
"### Pytorch\n\n\nA more in depth example on how to use the model can be found in this colab notebook: URL\n\n\nValidation Results\n------------------\n\n\n\n!alt text"
] |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bart-large-cnn-pubmed1o3-pubmed2o3-pubmed3o3-arxiv1o3-arxiv2o3-arxiv3o3
This model is a fine-tuned version of [theojolliffe/bart... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["scientific_papers"], "metrics": ["rouge"], "model-index": [{"name": "bart-large-cnn-pubmed1o3-pubmed2o3-pubmed3o3-arxiv1o3-arxiv2o3-arxiv3o3", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "da... | theojolliffe/bart-cnn-science | null | [
"transformers",
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"generated_from_trainer",
"dataset:scientific_papers",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-30T07:39:52+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bart #text2text-generation #generated_from_trainer #dataset-scientific_papers #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| bart-large-cnn-pubmed1o3-pubmed2o3-pubmed3o3-arxiv1o3-arxiv2o3-arxiv3o3
=======================================================================
This model is a fine-tuned version of theojolliffe/bart-large-cnn-pubmed1o3-pubmed2o3-pubmed3o3-arxiv1o3-arxiv2o3 on the scientific\_papers dataset.
It achieves the following... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 2\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1\n* mixed\\_precis... | [
"TAGS\n#transformers #pytorch #tensorboard #bart #text2text-generation #generated_from_trainer #dataset-scientific_papers #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learnin... |
text2text-generation | transformers |
# Model description
This is an [t5-base](https://huggingface.co/t5-base) model, finetuned to generate questions given a table using [WikiSQL](https://huggingface.co/datasets/wikisql) dataset. It was trained to take the SQL, answer and column header of a table as input to generate questions. For more information check... | {"license": "apache-2.0"} | PrimeQA/t5-base-table-question-generator | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-05-30T07:43:01+00:00 | [] | [] | TAGS
#transformers #pytorch #t5 #text2text-generation #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Model description
This is an t5-base model, finetuned to generate questions given a table using WikiSQL dataset. It was trained to take the SQL, answer and column header of a table as input to generate questions. For more information check our T3QA paper from EMNLP 2021.
# Overview
*Language model*: t5-base \
*La... | [
"# Model description\n\nThis is an t5-base model, finetuned to generate questions given a table using WikiSQL dataset. It was trained to take the SQL, answer and column header of a table as input to generate questions. For more information check our T3QA paper from EMNLP 2021.",
"# Overview\n\n*Language model*: t... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Model description\n\nThis is an t5-base model, finetuned to generate questions given a table using WikiSQL dataset. It was trained to take the SQL, ... |
sentence-similarity | sentence-transformers |
# shafin/distilbert-similarity-b32
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 32 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity"], "pipeline_tag": "sentence-similarity"} | shafin/distilbert-similarity-b32 | null | [
"sentence-transformers",
"pytorch",
"distilbert",
"feature-extraction",
"sentence-similarity",
"endpoints_compatible",
"region:us"
] | null | 2022-05-30T07:56:36+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #endpoints_compatible #region-us
|
# shafin/distilbert-similarity-b32
This is a sentence-transformers model: It maps sentences & paragraphs to a 32 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers installed... | [
"# shafin/distilbert-similarity-b32\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 32 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers... | [
"TAGS\n#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #endpoints_compatible #region-us \n",
"# shafin/distilbert-similarity-b32\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 32 dimensional dense vector space and can be used for tasks like clust... |
audio-classification | transformers | {'eval_loss': 0.8433557152748108, 'eval_f1': 0.7774690927124368, 'eval_accuracy': 0.7943262411347518, 'eval_runtime': 15.6704, 'eval_samples_per_second': 17.996, 'eval_steps_per_second': 1.149, 'epoch': 49.73} | {} | Splend1dchan/xtreme_s_xlsr_300m_minds14.en-US | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"audio-classification",
"endpoints_compatible",
"region:us"
] | null | 2022-05-30T07:58:40+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #audio-classification #endpoints_compatible #region-us
| {'eval_loss': 0.8433557152748108, 'eval_f1': 0.7774690927124368, 'eval_accuracy': 0.7943262411347518, 'eval_runtime': 15.6704, 'eval_samples_per_second': 17.996, 'eval_steps_per_second': 1.149, 'epoch': 49.73} | [] | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #audio-classification #endpoints_compatible #region-us \n"
] |
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