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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-wtimit-finetune
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-wtimit-finetune", "results": []}]} | nawta/wav2vec2-wtimit-finetune | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-07-06T08:40:28+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-wtimit-finetune
========================
This model is a fine-tuned version of facebook/wav2vec2-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0383
* Wer: 0.0160
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: 64\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* lr\\_scheduler\\_warmup\\_step... | [
"TAGS\n#transformers #pytorch #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: 64\n* eval\\_b... |
token-classification | transformers | Put this model path in variable best_model_path in first cell of given colab notebook for testing semeval multiconer task. https://colab.research.google.com/drive/17WyqwdoRNnzImeik6wTRE5uuj9QQnkXA#scrollTo=nYtUtmyDFAqP | {"license": "afl-3.0"} | sumitrsch/Indic-bert_multiconer22_hi | null | [
"transformers",
"pytorch",
"albert",
"token-classification",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-06T08:43:28+00:00 | [] | [] | TAGS
#transformers #pytorch #albert #token-classification #license-afl-3.0 #autotrain_compatible #endpoints_compatible #region-us
| Put this model path in variable best_model_path in first cell of given colab notebook for testing semeval multiconer task. URL | [] | [
"TAGS\n#transformers #pytorch #albert #token-classification #license-afl-3.0 #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. -->
# distilbert-base-uncased-finetuned-squad
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/d... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "distilbert-base-uncased-finetuned-squad", "results": []}]} | chiendvhust/distilbert-base-uncased-finetuned-squad | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-07-06T08:44:38+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-squad
=======================================
This model is a fine-tuned version of distilbert-base-uncased on the squad dataset.
It achieves the following results on the evaluation set:
* Loss: 1.2178
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: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_s... |
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="kws/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attribut... | {"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": ... | kws/q-FrozenLake-v1-4x4-noSlippery | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-07-06T08:59:33+00:00 | [] | [] | TAGS
#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing FrozenLake-v1
This is a trained model of a Q-Learning agent playing FrozenLake-v1 .
## Usage
| [
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] | [
"TAGS\n#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] |
token-classification | transformers | Put this model path in variable best_model_path in first cell of given colab notebook for testing semeval multiconer task for bangla track.
https://colab.research.google.com/drive/1P9827acdS7i6eZTi4B0cOms5qLREqvUO | {"license": "afl-3.0"} | sumitrsch/Indic-bert_multiconer22_bn | null | [
"transformers",
"pytorch",
"albert",
"token-classification",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-06T09:07:47+00:00 | [] | [] | TAGS
#transformers #pytorch #albert #token-classification #license-afl-3.0 #autotrain_compatible #endpoints_compatible #region-us
| Put this model path in variable best_model_path in first cell of given colab notebook for testing semeval multiconer task for bangla track.
URL | [] | [
"TAGS\n#transformers #pytorch #albert #token-classification #license-afl-3.0 #autotrain_compatible #endpoints_compatible #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="kws/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env ... | {"tags": ["Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-Taxi-v3", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Taxi-v3", "type": "Taxi-v3"}, "metrics": [{"type": "mean_reward", "value": "7.56 +/... | kws/q-Taxi-v3 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-07-06T09:23:57+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"
] |
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. -->
# distilbart-cnn-12-6-ftn-multi_news
This model is a fine-tuned version of [sshleifer/distilbart-cnn-12-6](https://huggingface.co/... | {"license": "apache-2.0", "tags": ["summarization"], "datasets": ["multi_news"], "metrics": ["rouge"], "model-index": [{"name": "distilbart-cnn-12-6-ftn-multi_news", "results": [{"task": {"type": "summarization", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "multi_news", "type": "multi_news", ... | datien228/distilbart-cnn-12-6-ftn-multi_news | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"summarization",
"dataset:multi_news",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-07-06T09:25:20+00:00 | [] | [] | TAGS
#transformers #pytorch #bart #text2text-generation #summarization #dataset-multi_news #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
| distilbart-cnn-12-6-ftn-multi\_news
===================================
This model is a fine-tuned version of sshleifer/distilbart-cnn-12-6 on the multi\_news dataset.
It achieves the following results on the evaluation set:
* Loss: 3.8143
* Rouge1: 41.6136
* Rouge2: 14.7454
* Rougel: 23.3597
* Rougelsum: 36.1973
*... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: ... | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #summarization #dataset-multi_news #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2... |
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. -->
# recipe-test
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on t... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "recipe-test", "results": []}]} | paola-md/recipe-test | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-06T09:27:36+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| recipe-test
===========
This model is a fine-tuned version of distilbert-base-uncased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 2.9583
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More informat... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2\n* mixed\\_prec... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_siz... |
tabular-classification | sklearn |
## Baseline Model trained on breast_cancernb8gjv4n to apply classification on diagnosis
**Metrics of the best model:**
accuracy 0.978932
average_precision 0.994309
roc_auc 0.995448
recall_macro 0.976607
f1_macro 0.977365
Name: LogisticRegression(C=0.1, class_weigh... | {"license": "apache-2.0", "library_name": "sklearn", "tags": ["tabular-classification", "baseline-trainer"]} | merve/breast_cancernb8gjv4n-diagnosis-classification | null | [
"sklearn",
"tabular-classification",
"baseline-trainer",
"license:apache-2.0",
"has_space",
"region:us"
] | null | 2022-07-06T09:28:02+00:00 | [] | [] | TAGS
#sklearn #tabular-classification #baseline-trainer #license-apache-2.0 #has_space #region-us
|
## Baseline Model trained on breast_cancernb8gjv4n to apply classification on diagnosis
Metrics of the best model:
accuracy 0.978932
average_precision 0.994309
roc_auc 0.995448
recall_macro 0.976607
f1_macro 0.977365
Name: LogisticRegression(C=0.1, class_weight='b... | [
"## Baseline Model trained on breast_cancernb8gjv4n to apply classification on diagnosis\n\nMetrics of the best model:\n\naccuracy 0.978932\n\naverage_precision 0.994309\n\nroc_auc 0.995448\n\nrecall_macro 0.976607\n\nf1_macro 0.977365\n\nName: LogisticRegression(C=0.... | [
"TAGS\n#sklearn #tabular-classification #baseline-trainer #license-apache-2.0 #has_space #region-us \n",
"## Baseline Model trained on breast_cancernb8gjv4n to apply classification on diagnosis\n\nMetrics of the best model:\n\naccuracy 0.978932\n\naverage_precision 0.994309\n\nroc_auc ... |
image-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# beit-base-patch16-224-pt22k-ft22k-rim_one-new
This model is a fine-tuned version of [microsoft/beit-base-patch16-224-pt22k-ft22k... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imagefolder"], "metrics": ["accuracy"], "model-index": [{"name": "beit-base-patch16-224-pt22k-ft22k-rim_one-new", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "RIM ONE DL", "type": "... | SiddharthaM/beit-base-patch16-224-pt22k-ft22k-rim_one-new | null | [
"transformers",
"pytorch",
"tensorboard",
"beit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-06T09:31:21+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #beit #image-classification #generated_from_trainer #dataset-imagefolder #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| beit-base-patch16-224-pt22k-ft22k-rim\_one-new
==============================================
This model is a fine-tuned version of microsoft/beit-base-patch16-224-pt22k-ft22k on the imagefolder dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4550
* Accuracy: 0.8767
Model description
-... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsilo... | [
"TAGS\n#transformers #pytorch #tensorboard #beit #image-classification #generated_from_trainer #dataset-imagefolder #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... |
token-classification | transformers | Put this model path in variable best_model_path in first cell of given colab notebook for testing semeval multiconer task for bangla track.
https://colab.research.google.com/drive/1P9827acdS7i6eZTi4B0cOms5qLREqvUO | {"license": "afl-3.0"} | sumitrsch/xlm_R_large_multiconer22_bn | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-06T09:33:33+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #license-afl-3.0 #autotrain_compatible #endpoints_compatible #region-us
| Put this model path in variable best_model_path in first cell of given colab notebook for testing semeval multiconer task for bangla track.
URL | [] | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #license-afl-3.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
token-classification | transformers | Put this model path in variable best_model_path in first cell of given colab notebook for testing semeval multiconer task. https://colab.research.google.com/drive/17WyqwdoRNnzImeik6wTRE5uuj9QQnkXA#scrollTo=nYtUtmyDFAqP | {} | sumitrsch/mbert_multiconer22_hi | null | [
"transformers",
"pytorch",
"bert",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-06T09:49:17+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #token-classification #autotrain_compatible #endpoints_compatible #region-us
| Put this model path in variable best_model_path in first cell of given colab notebook for testing semeval multiconer task. URL | [] | [
"TAGS\n#transformers #pytorch #bert #token-classification #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-generation | fastai |
# Blurr x Casual Machine Learning Model trained on Malayalam (മലയാളം) text. (Working in Progress)
[](https://nbviewer.org/github/rajeshradhakrishnanmvk/kitchen2.0/blob/main/ml/malayalam_blurr_xlm_roberta_base.ipynb)
---
# malayalam-blur... | {"language": "ml", "tags": ["fastai", "text-generation"], "datasets": ["rajeshradhakrishnan/malayalam_wiki"], "widget": [{"text": "\u0d13\u0d39\u0d30\u0d3f \u0d35\u0d3f\u0d2a\u0d23\u0d3f \u0d24\u0d15\u0d30\u0d41\u0d2e\u0d4d\u0d2a\u0d4b\u0d33\u0d4d\u200d \u0d28\u0d3f\u0d15\u0d4d\u0d37\u0d47\u0d2a\u0d02 \u0d0e\u0d19\u0d4... | hugginglearners/malayalam-blurr-xlm-roberta-base | null | [
"fastai",
"text-generation",
"ml",
"dataset:rajeshradhakrishnan/malayalam_wiki",
"region:us"
] | null | 2022-07-06T10:10:26+00:00 | [] | [
"ml"
] | TAGS
#fastai #text-generation #ml #dataset-rajeshradhakrishnan/malayalam_wiki #region-us
|
# Blurr x Casual Machine Learning Model trained on Malayalam (മലയാളം) text. (Working in Progress)
malayalam-blurr-xlm-roberta-base model is pre-trained on xlm-roberta-base using the library blurr Language Model using fastai x huggi... | [
"# Blurr x Casual Machine Learning Model trained on Malayalam (മലയാളം) text. (Working in Progress)\n\n\n![മലയാളം: notebook](URL\n\n\n---",
"# malayalam-blurr-xlm-roberta-base (base-sized model)\n\nmalayalam-blurr-xlm-roberta-base model is pre-trained on xlm-roberta-base using the library blurr Language Model usin... | [
"TAGS\n#fastai #text-generation #ml #dataset-rajeshradhakrishnan/malayalam_wiki #region-us \n",
"# Blurr x Casual Machine Learning Model trained on Malayalam (മലയാളം) text. (Working in Progress)\n\n\n![മലയാളം: notebook](URL\n\n\n---",
"# malayalam-blurr-xlm-roberta-base (base-sized model)\n\nmalayalam-blurr-xlm... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wavlm-base-plus-ft-cv3
This model is a fine-tuned version of [microsoft/wavlm-base-plus](https://huggingface.co/microsoft/wavlm-... | {"language": ["en"], "tags": ["generated_from_trainer", "hf-asr-leaderboard"], "datasets": ["mozilla-foundation/common_voice_3_0"], "model-index": [{"name": "wavlm-base-plus-ft-cv3", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "LibriSpeech (... | danieleV9H/wavlm-base-plus-ft-cv3 | null | [
"transformers",
"pytorch",
"tensorboard",
"wavlm",
"automatic-speech-recognition",
"generated_from_trainer",
"hf-asr-leaderboard",
"en",
"dataset:mozilla-foundation/common_voice_3_0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-07-06T10:24:08+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #tensorboard #wavlm #automatic-speech-recognition #generated_from_trainer #hf-asr-leaderboard #en #dataset-mozilla-foundation/common_voice_3_0 #model-index #endpoints_compatible #region-us
| wavlm-base-plus-ft-cv3
======================
This model is a fine-tuned version of microsoft/wavlm-base-plus on the "mozilla-foundation/common\_voice\_3\_0 english" dataset: "train" and "validation" splits are used for training while "test" split is used for validation.
It achieves the following results on the valid... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 11\n* mixed\\_preci... | [
"TAGS\n#transformers #pytorch #tensorboard #wavlm #automatic-speech-recognition #generated_from_trainer #hf-asr-leaderboard #en #dataset-mozilla-foundation/common_voice_3_0 #model-index #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training... |
text2text-generation | transformers | Bart wikikp , masked on cve50k | {} | ahadda5/bart_wikikp_ftuned_cve50k | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-06T10:37:37+00:00 | [] | [] | TAGS
#transformers #pytorch #bart #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
| Bart wikikp , masked on cve50k | [] | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #autotrain_compatible #endpoints_compatible #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="Lakshya/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": "7.46 +/... | Lakshya/q-Taxi-v3 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-07-06T11:06:26+00:00 | [] | [] | TAGS
#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing Taxi-v3
This is a trained model of a Q-Learning agent playing Taxi-v3 .
## Usage
| [
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] | [
"TAGS\n#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] |
token-classification | transformers | Put this model path in variable best_model_path in first cell of given colab notebook for testing semeval multiconer task for bangla track.
https://colab.research.google.com/drive/1P9827acdS7i6eZTi4B0cOms5qLREqvUO | {"license": "afl-3.0"} | sumitrsch/mbert_multiconer22_bn | null | [
"transformers",
"pytorch",
"bert",
"token-classification",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-06T11:14:27+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #token-classification #license-afl-3.0 #autotrain_compatible #endpoints_compatible #region-us
| Put this model path in variable best_model_path in first cell of given colab notebook for testing semeval multiconer task for bangla track.
URL | [] | [
"TAGS\n#transformers #pytorch #bert #token-classification #license-afl-3.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
tabular-regression | sklearn |
## Baseline Model trained on outhimar_64 to apply regression on Close
**Metrics of the best model:**
r2 0.999858
neg_mean_squared_error -1.067685
Name: Ridge(alpha=10), dtype: float64
**See model plot below:**
<style>#sk-container-id-6 {color: black;background-color: white;}#sk-contain... | {"license": "apache-2.0", "library_name": "sklearn", "tags": ["tabular-regression", "baseline-trainer"]} | srg/outhimar_64-Close-regression | null | [
"sklearn",
"tabular-regression",
"baseline-trainer",
"license:apache-2.0",
"has_space",
"region:us"
] | null | 2022-07-06T11:33:02+00:00 | [] | [] | TAGS
#sklearn #tabular-regression #baseline-trainer #license-apache-2.0 #has_space #region-us
|
## Baseline Model trained on outhimar_64 to apply regression on Close
Metrics of the best model:
r2 0.999858
neg_mean_squared_error -1.067685
Name: Ridge(alpha=10), dtype: float64
See model plot below:
<style>#sk-container-id-6 {color: black;background-color: white;}#sk-container-id-6 ... | [
"## Baseline Model trained on outhimar_64 to apply regression on Close\n\nMetrics of the best model:\n\nr2 0.999858\n\nneg_mean_squared_error -1.067685\n\nName: Ridge(alpha=10), dtype: float64\n\n\n\nSee model plot below:\n\n<style>#sk-container-id-6 {color: black;background-color: white;}#... | [
"TAGS\n#sklearn #tabular-regression #baseline-trainer #license-apache-2.0 #has_space #region-us \n",
"## Baseline Model trained on outhimar_64 to apply regression on Close\n\nMetrics of the best model:\n\nr2 0.999858\n\nneg_mean_squared_error -1.067685\n\nName: Ridge(alpha=10), dtype: flo... |
reinforcement-learning | null |
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: https://github.com/huggingface/deep-rl-class/tree/main/unit5
| {"tags": ["CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class"], "model-index": [{"name": "Reinforce-cartpole", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "CartPole-v1", "type": "CartPole-v1"}, "metrics": [{"ty... | Guillaume63/Reinforce-cartpole | null | [
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | null | 2022-07-06T11:59:13+00:00 | [] | [] | TAGS
#CartPole-v1 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us
|
# Reinforce Agent playing CartPole-v1
This is a trained model of a Reinforce agent playing CartPole-v1 .
To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: URL
| [
"# Reinforce Agent playing CartPole-v1\n This is a trained model of a Reinforce agent playing CartPole-v1 .\n To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: URL"
] | [
"TAGS\n#CartPole-v1 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us \n",
"# Reinforce Agent playing CartPole-v1\n This is a trained model of a Reinforce agent playing CartPole-v1 .\n To learn to use this model and train yours check Unit 5 of the Deep Reinforcemen... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-small-finetuned-xsum
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the xsum dataset.
I... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["xsum"], "metrics": ["rouge"], "model-index": [{"name": "t5-small-finetuned-xsum", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "xsum", "type": "xsum", "args": "defa... | saekomdalkom/t5-small-finetuned-xsum | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:xsum",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-06T12:04:22+00:00 | [] | [] | TAGS
#transformers #pytorch #t5 #text2text-generation #generated_from_trainer #dataset-xsum #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| t5-small-finetuned-xsum
=======================
This model is a fine-tuned version of t5-small on the xsum dataset.
It achieves the following results on the evaluation set:
* Loss: 2.4783
* Rouge1: 28.3577
* Rouge2: 7.759
* Rougel: 22.274
* Rougelsum: 22.2869
* Gen Len: 18.8298
Model description
-----------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1\n* mixed\\_prec... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #generated_from_trainer #dataset-xsum #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* l... |
image-classification | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# vit_spectrogram
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "vit_spectrogram", "results": []}]} | prashanth0205/vit_spectrogram | null | [
"transformers",
"tf",
"vit",
"image-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-06T12:17:32+00:00 | [] | [] | TAGS
#transformers #tf #vit #image-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# vit_spectrogram
This model is a fine-tuned version of google/vit-base-patch16-224-in21k on a dataset containing
images of Mel spectrogram belonging to the classes 'Male' and 'Female'. This model is still being fine tuned and tested.
It achieves the following results on the evaluation set:
- Train Loss: 0.2893
- ... | [
"# vit_spectrogram\n\nThis model is a fine-tuned version of google/vit-base-patch16-224-in21k on a dataset containing\nimages of Mel spectrogram belonging to the classes 'Male' and 'Female'. This model is still being fine tuned and tested.\nIt achieves the following results on the evaluation set:\n- Train Loss: 0.2... | [
"TAGS\n#transformers #tf #vit #image-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# vit_spectrogram\n\nThis model is a fine-tuned version of google/vit-base-patch16-224-in21k on a dataset containing\nimages of Mel spectrogram belong... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# TestZee/t5-small-finetuned-custom-wion-test
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an un... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "TestZee/t5-small-finetuned-custom-wion-test", "results": []}]} | TestZee/t5-small-finetuned-custom-wion-test | null | [
"transformers",
"tf",
"t5",
"text2text-generation",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-06T12:23:31+00:00 | [] | [] | TAGS
#transformers #tf #t5 #text2text-generation #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| TestZee/t5-small-finetuned-custom-wion-test
===========================================
This model is a fine-tuned version of t5-small on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 1.9773
* Validation Loss: 0.8028
* Epoch: 9
Model description
-----------------
Mor... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': 2e-05, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight\\_decay\\_rate': 0.01}\n* training\\_precision: float32",
... | [
"TAGS\n#transformers #tf #t5 #text2text-generation #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamW... |
tabular-classification | sklearn |
## Baseline Model trained on titanicht_mp88q to apply classification on Survived
**Metrics of the best model:**
accuracy 0.803597
average_precision 0.801332
roc_auc 0.848079
recall_macro 0.795883
f1_macro 0.793746
Name: DecisionTreeClassifier(class_weight='balance... | {"license": "apache-2.0", "library_name": "sklearn", "tags": ["tabular-classification", "baseline-trainer"]} | luizapzbn/titanicht_mp88q-Survived-classification | null | [
"sklearn",
"tabular-classification",
"baseline-trainer",
"license:apache-2.0",
"region:us"
] | null | 2022-07-06T12:25:46+00:00 | [] | [] | TAGS
#sklearn #tabular-classification #baseline-trainer #license-apache-2.0 #region-us
|
## Baseline Model trained on titanicht_mp88q to apply classification on Survived
Metrics of the best model:
accuracy 0.803597
average_precision 0.801332
roc_auc 0.848079
recall_macro 0.795883
f1_macro 0.793746
Name: DecisionTreeClassifier(class_weight='balanced', ... | [
"## Baseline Model trained on titanicht_mp88q to apply classification on Survived\n\nMetrics of the best model:\n\naccuracy 0.803597\n\naverage_precision 0.801332\n\nroc_auc 0.848079\n\nrecall_macro 0.795883\n\nf1_macro 0.793746\n\nName: DecisionTreeClassifier(class_w... | [
"TAGS\n#sklearn #tabular-classification #baseline-trainer #license-apache-2.0 #region-us \n",
"## Baseline Model trained on titanicht_mp88q to apply classification on Survived\n\nMetrics of the best model:\n\naccuracy 0.803597\n\naverage_precision 0.801332\n\nroc_auc 0.848079\n\nrecall... |
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... | messham/PPO-LunarLander-v2-Optuna | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-07-06T12:32:15+00:00 | [] | [] | TAGS
#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add you... |
fill-mask | transformers |
<!-- 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. -->
# hsohn3/mayo-bert-visit-uncased-wordlevel-block512-batch4-ep10
This model is a fine-tuned version of [bert-base-uncased](https://huggin... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "hsohn3/mayo-bert-visit-uncased-wordlevel-block512-batch4-ep10", "results": []}]} | hsohn3/mayo-bert-visit-uncased-wordlevel-block512-batch4-ep10 | null | [
"transformers",
"tf",
"bert",
"fill-mask",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-06T13:22:52+00:00 | [] | [] | TAGS
#transformers #tf #bert #fill-mask #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| hsohn3/mayo-bert-visit-uncased-wordlevel-block512-batch4-ep10
=============================================================
This model is a fine-tuned version of bert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 3.2895
* Epoch: 9
Model description
----... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': 2e-05, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight\\_decay\\_rate': 0.01}\n* training\\_precision: float32",
... | [
"TAGS\n#transformers #tf #bert #fill-mask #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': 2e-0... |
text-generation | transformers |
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1150678663265832960/ujqr... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true", "widget": [{"text": "My dream is"}]} | huggingtweets/frnsw-nswrfs-nswses | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-06T13:32:45+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI CYBORG
NSW RFS & NSW SES & Fire and Rescue NSW
@frnsw-nswrfs-nswses
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
reinforcement-learning | null |
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="bothrajat/q-FrozenLake-v1-8x8-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional at... | {"tags": ["FrozenLake-v1-8x8-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-FrozenLake-v1-8x8-noSlippery", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "FrozenLake-v1-8x8-no_slippery", "type": ... | bothrajat/q-FrozenLake-v1-8x8-noSlippery | null | [
"FrozenLake-v1-8x8-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-07-06T13:50:11+00:00 | [] | [] | TAGS
#FrozenLake-v1-8x8-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-v1-8x8-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"
] |
tabular-classification | sklearn |
## Baseline Model trained on heart1ohr2x9e to apply classification on target
**Metrics of the best model:**
accuracy 0.885854
average_precision 0.949471
roc_auc 0.050633
recall_macro 0.885324
f1_macro 0.885610
Name: LogisticRegression(class_weight='balanced', max_... | {"license": "apache-2.0", "library_name": "sklearn", "tags": ["tabular-classification", "baseline-trainer"]} | cacauvicosa/heart1ohr2x9e-target-classification | null | [
"sklearn",
"tabular-classification",
"baseline-trainer",
"license:apache-2.0",
"region:us"
] | null | 2022-07-06T14:11:03+00:00 | [] | [] | TAGS
#sklearn #tabular-classification #baseline-trainer #license-apache-2.0 #region-us
|
## Baseline Model trained on heart1ohr2x9e to apply classification on target
Metrics of the best model:
accuracy 0.885854
average_precision 0.949471
roc_auc 0.050633
recall_macro 0.885324
f1_macro 0.885610
Name: LogisticRegression(class_weight='balanced', max_iter... | [
"## Baseline Model trained on heart1ohr2x9e to apply classification on target\n\nMetrics of the best model:\n\naccuracy 0.885854\n\naverage_precision 0.949471\n\nroc_auc 0.050633\n\nrecall_macro 0.885324\n\nf1_macro 0.885610\n\nName: LogisticRegression(class_weight='b... | [
"TAGS\n#sklearn #tabular-classification #baseline-trainer #license-apache-2.0 #region-us \n",
"## Baseline Model trained on heart1ohr2x9e to apply classification on target\n\nMetrics of the best model:\n\naccuracy 0.885854\n\naverage_precision 0.949471\n\nroc_auc 0.050633\n\nrecall_mac... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-clinc
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/d... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["clinc_oos"], "metrics": ["accuracy"], "model-index": [{"name": "distilbert-base-uncased-finetuned-clinc", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "clinc_oos", "type": "clinc_oos",... | Shenghao1993/distilbert-base-uncased-finetuned-clinc | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:clinc_oos",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-06T14:20:11+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-clinc_oos #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-clinc
=======================================
This model is a fine-tuned version of distilbert-base-uncased on the clinc\_oos dataset.
It achieves the following results on the evaluation set:
* Loss: 0.7711
* Accuracy: 0.9174
Model description
-----------------
More information... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 48\n* eval\\_batch\\_size: 48\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-clinc_oos #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* lea... |
null | sklearn |
# Simple example using plain scikit-learn
## Reproducing the model
Inside a Python environment, install the dependencies listed in `requirements.txt` and then run:
``` bash
python train.py
```
The resulting model artifact should be stored in `model.pickle`.
## The model
The used model is a simple logistic regres... | {"license": "bsd-3-clause", "tags": ["sklearn"], "datasets": ["synthetic dataset from sklearn"], "metrics": [{"type": "accuracy", "value": 0.948}]} | BenjaminB/plain-sklearn | null | [
"sklearn",
"joblib",
"license:bsd-3-clause",
"region:us"
] | null | 2022-07-06T14:20:31+00:00 | [] | [] | TAGS
#sklearn #joblib #license-bsd-3-clause #region-us
|
# Simple example using plain scikit-learn
## Reproducing the model
Inside a Python environment, install the dependencies listed in 'URL' and then run:
The resulting model artifact should be stored in 'URL'.
## The model
The used model is a simple logistic regression trained through gradient descent.
## Intende... | [
"# Simple example using plain scikit-learn",
"## Reproducing the model\n\nInside a Python environment, install the dependencies listed in 'URL' and then run:\n\n\n\nThe resulting model artifact should be stored in 'URL'.",
"## The model\n\nThe used model is a simple logistic regression trained through gradient ... | [
"TAGS\n#sklearn #joblib #license-bsd-3-clause #region-us \n",
"# Simple example using plain scikit-learn",
"## Reproducing the model\n\nInside a Python environment, install the dependencies listed in 'URL' and then run:\n\n\n\nThe resulting model artifact should be stored in 'URL'.",
"## The model\n\nThe used... |
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. -->
# CodeGeneration
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "CodeGeneration", "results": []}]} | SushantGautam/CodeGeneration | null | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-06T14:27:07+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #fill-mask #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# CodeGeneration
This model is a fine-tuned version of roberta-base on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 3.5020
- Accuracy: 0.4444
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation da... | [
"# CodeGeneration\n\nThis model is a fine-tuned version of roberta-base on an unknown dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 3.5020\n- Accuracy: 0.4444",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Tra... | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# CodeGeneration\n\nThis model is a fine-tuned version of roberta-base on an unknown dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 3.502... |
text-generation | transformers |
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1312214716941393920/sX37... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/zanza47/1657125860989/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/zanza47 | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-06T15:21:32+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Detective Zanza (Commissions! 1/3 full)
@zanza47
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
fill-mask | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# hsohn3/mayo-bert-visit-uncased-wordlevel-block512-batch4-ep100
This model is a fine-tuned version of [bert-base-uncased](https://huggi... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "hsohn3/mayo-bert-visit-uncased-wordlevel-block512-batch4-ep100", "results": []}]} | hsohn3/mayo-bert-visit-uncased-wordlevel-block512-batch4-ep100 | null | [
"transformers",
"tf",
"bert",
"fill-mask",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-06T15:29:49+00:00 | [] | [] | TAGS
#transformers #tf #bert #fill-mask #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| hsohn3/mayo-bert-visit-uncased-wordlevel-block512-batch4-ep100
==============================================================
This model is a fine-tuned version of bert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.9559
* Epoch: 99
Model description
-... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': 2e-05, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight\\_decay\\_rate': 0.01}\n* training\\_precision: float32",
... | [
"TAGS\n#transformers #tf #bert #fill-mask #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': 2e-0... |
null | null | # Introduction
See https://github.com/k2-fsa/icefall/pull/439
| {} | cisco-ai/icefall-librispeech-rnn-lm | null | [
"tensorboard",
"region:us"
] | null | 2022-07-06T16:02:25+00:00 | [] | [] | TAGS
#tensorboard #region-us
| # Introduction
See URL
| [
"# Introduction\n\nSee URL"
] | [
"TAGS\n#tensorboard #region-us \n",
"# Introduction\n\nSee URL"
] |
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... | ManqingLiu/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 | 2022-07-06T16:02:45+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: 0.1363
* F1: 0.8627
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\\_... |
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. -->
# recipe-distilbert-is
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-unca... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "recipe-distilbert-is", "results": []}]} | paola-md/recipe-distilbert-is | null | [
"transformers",
"pytorch",
"distilbert",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-06T16:09:58+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| recipe-distilbert-is
====================
This model is a fine-tuned version of distilbert-base-uncased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 4.0558
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: 256\n* eval\\_batch\\_size: 256\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\\_pr... | [
"TAGS\n#transformers #pytorch #distilbert #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 256\n* eva... |
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... | Mascariddu8/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 | 2022-07-06T16:10:32+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: 0.0639
* Precision: 0.9357
* Recall: 0.9507
* F1: 0.9432
* Accuracy: 0.9857
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 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="BigTimeCoderSean/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additi... | {"tags": ["FrozenLake-v1-4x4", "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", "type": "FrozenLake-v1-4x4"}, "m... | BigTimeCoderSean/q-FrozenLake-v1-4x4-noSlippery | null | [
"FrozenLake-v1-4x4",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-07-06T16:57:05+00:00 | [] | [] | TAGS
#FrozenLake-v1-4x4 #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 #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_base_tcm_teste
This model is a fine-tuned version of [neuralmind/bert-base-portuguese-cased](https://huggingface.co/neuralm... | {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "bert_base_tcm_teste", "results": []}]} | ricardo-filho/bert_base_tcm_teste | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-06T17:05:49+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| bert\_base\_tcm\_teste
======================
This model is a fine-tuned version of neuralmind/bert-base-portuguese-cased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0192
* Criterio Julgamento Precision: 0.7209
* Criterio Julgamento Recall: 0.8942
* Criterio Julgamento ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50.0",
"### Trai... | [
"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: 0.0001\n* train\\_batch\\_size... |
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="BigTimeCoderSean/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=Fals... | {"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 +/... | BigTimeCoderSean/q-Taxi-v3 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-07-06T17:13:12+00:00 | [] | [] | TAGS
#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing Taxi-v3
This is a trained model of a Q-Learning agent playing Taxi-v3 .
## Usage
| [
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] | [
"TAGS\n#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] |
null | null | Unet Model PyTorch | {} | shivambhosale/unet | null | [
"region:us"
] | null | 2022-07-06T17:25:09+00:00 | [] | [] | TAGS
#region-us
| Unet Model PyTorch | [] | [
"TAGS\n#region-us \n"
] |
token-classification | transformers |
# RuWordStressTransformer
## Model description
Transformer encoder for predicting word stress in Russian.
## Intended uses & limitations
#### How to use
```python
from transformers import AutoTokenizer, AutoModelForTokenClassification, pipeline
model_name = "IlyaGusev/ru-word-stress-transformer"
tokenizer = Auto... | {"language": ["ru"], "license": "apache-2.0", "tags": ["token-classification"], "inference": false} | IlyaGusev/ru-word-stress-transformer | null | [
"transformers",
"pytorch",
"deberta-v2",
"token-classification",
"ru",
"license:apache-2.0",
"autotrain_compatible",
"region:us"
] | null | 2022-07-06T17:30:23+00:00 | [] | [
"ru"
] | TAGS
#transformers #pytorch #deberta-v2 #token-classification #ru #license-apache-2.0 #autotrain_compatible #region-us
|
# RuWordStressTransformer
## Model description
Transformer encoder for predicting word stress in Russian.
## Intended uses & limitations
#### How to use
Colab: link | [
"# RuWordStressTransformer",
"## Model description\n\nTransformer encoder for predicting word stress in Russian.",
"## Intended uses & limitations",
"#### How to use\n\n\n\nColab: link"
] | [
"TAGS\n#transformers #pytorch #deberta-v2 #token-classification #ru #license-apache-2.0 #autotrain_compatible #region-us \n",
"# RuWordStressTransformer",
"## Model description\n\nTransformer encoder for predicting word stress in Russian.",
"## Intended uses & limitations",
"#### How to use\n\n\n\nColab: li... |
image-classification | null |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
# Table of Contents
1. [Model Details](#model-details)
2. [Uses](#uses)
3. [Bias, Risks, and Limitations](#bias-risks-and-limitations)
4. [Training Details](#training-details)
5. [Evaluation](#evaluation)
6. [Model Examination](#... | {"language": "en", "license": "mit", "tags": ["image-classification", "created-with-modelcards"]} | nateraw/new-modelcard-template-test | null | [
"image-classification",
"created-with-modelcards",
"en",
"arxiv:1910.09700",
"license:mit",
"region:us"
] | null | 2022-07-06T18:17:41+00:00 | [
"1910.09700"
] | [
"en"
] | TAGS
#image-classification #created-with-modelcards #en #arxiv-1910.09700 #license-mit #region-us
|
# Model Card for Model ID
# Table of Contents
1. Model Details
2. Uses
3. Bias, Risks, and Limitations
4. Training Details
5. Evaluation
6. Model Examination
7. Environmental Impact
8. Technical Specifications
9. Citation
10. Glossary
11. More Information
12. Model Card Authors
13. Model Card Contact
14. How To G... | [
"# Model Card for Model ID",
"# Table of Contents\n\n1. Model Details\n2. Uses\n3. Bias, Risks, and Limitations\n4. Training Details\n5. Evaluation\n6. Model Examination\n7. Environmental Impact\n8. Technical Specifications\n9. Citation\n10. Glossary\n11. More Information\n12. Model Card Authors\n13. Model Card ... | [
"TAGS\n#image-classification #created-with-modelcards #en #arxiv-1910.09700 #license-mit #region-us \n",
"# Model Card for Model ID",
"# Table of Contents\n\n1. Model Details\n2. Uses\n3. Bias, Risks, and Limitations\n4. Training Details\n5. Evaluation\n6. Model Examination\n7. Environmental Impact\n8. Technic... |
summarization | transformers | ### Summarization Model (Type:T5)
Summarization: Extractive and Abstractive
- urT5 adapted from mT5 having monolingual vocabulary only; 40k tokens of Urdu.
- Fine-tuned on https://huggingface.co/mbshr/XSUMUrdu-DW_BBC, ref to https://doi.org/10.48550/arXiv.2310.02790 for details.
### Model Description
<!-- Provide ... | {"language": ["ur"], "datasets": ["mbshr/XSUMUrdu-DW_BBC"], "metrics": ["rouge", "bertscore"], "pipeline_tag": "summarization"} | mbshr/urt5-base-finetuned | null | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"summarization",
"ur",
"dataset:mbshr/XSUMUrdu-DW_BBC",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-06T18:28:55+00:00 | [] | [
"ur"
] | TAGS
#transformers #pytorch #mt5 #text2text-generation #summarization #ur #dataset-mbshr/XSUMUrdu-DW_BBC #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| ### Summarization Model (Type:T5)
Summarization: Extractive and Abstractive
- urT5 adapted from mT5 having monolingual vocabulary only; 40k tokens of Urdu.
- Fine-tuned on URL ref to URL for details.
### Model Description
- Model type: urT5 adapted version of mT5
- Language(s) (NLP): Urdu
- Finetuned from model: ... | [
"### Summarization Model (Type:T5)\n\nSummarization: Extractive and Abstractive\n- urT5 adapted from mT5 having monolingual vocabulary only; 40k tokens of Urdu.\n - Fine-tuned on URL ref to URL for details.",
"### Model Description\n\n\n- Model type: urT5 adapted version of mT5\n- Language(s) (NLP): Urdu\n- Fine... | [
"TAGS\n#transformers #pytorch #mt5 #text2text-generation #summarization #ur #dataset-mbshr/XSUMUrdu-DW_BBC #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Summarization Model (Type:T5)\n\nSummarization: Extractive and Abstractive\n- urT5 adapted from mT5 having monoling... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "distilbert-base-uncased-finetuned-emotion", "results": []}]} | aatmasidha/distilbert-base-uncased-finetuned-emotion | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-06T18:29:51+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-emotion
=========================================
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2189
* Accuracy: 0.923
* F1: 0.9230
Model description
-----------------
More... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #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\\_b... |
text-classification | transformers | Un modèle français entrainé pour reconnaître les relations discursives causales. Le modèle reçoit 2 morceaux de textes et estime la probabilité que leur relation soit une relation de raison, résultat ou non causale.
Ce modèle a été entrainé avec la Penn Discourse Tree Bank 2 (PDTB2), base de données anglaise de référe... | {} | jeanconstantin/causal_bert_fr | null | [
"transformers",
"pytorch",
"camembert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-06T19:27:18+00:00 | [] | [] | TAGS
#transformers #pytorch #camembert #text-classification #autotrain_compatible #endpoints_compatible #region-us
| Un modèle français entrainé pour reconnaître les relations discursives causales. Le modèle reçoit 2 morceaux de textes et estime la probabilité que leur relation soit une relation de raison, résultat ou non causale.
Ce modèle a été entrainé avec la Penn Discourse Tree Bank 2 (PDTB2), base de données anglaise de référe... | [] | [
"TAGS\n#transformers #pytorch #camembert #text-classification #autotrain_compatible #endpoints_compatible #region-us \n"
] |
image-classification | transformers |
# ReXNet-1.0x model
Pretrained on a dataset for wildfire binary classification (soon to be shared). The ReXNet architecture was introduced in [this paper](https://arxiv.org/pdf/2007.00992.pdf).
## Model description
The core idea of the author is to add a customized Squeeze-Excitation layer in the residual blocks ... | {"license": "apache-2.0", "tags": ["image-classification", "pytorch", "onnx"], "datasets": ["pyronear/openfire"]} | pyronear/rexnet1_0x | null | [
"transformers",
"pytorch",
"onnx",
"image-classification",
"dataset:pyronear/openfire",
"arxiv:2007.00992",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-07-06T19:32:08+00:00 | [
"2007.00992"
] | [] | TAGS
#transformers #pytorch #onnx #image-classification #dataset-pyronear/openfire #arxiv-2007.00992 #license-apache-2.0 #endpoints_compatible #has_space #region-us
|
# ReXNet-1.0x model
Pretrained on a dataset for wildfire binary classification (soon to be shared). The ReXNet architecture was introduced in this paper.
## Model description
The core idea of the author is to add a customized Squeeze-Excitation layer in the residual blocks that will prevent channel redundancy.
... | [
"# ReXNet-1.0x model\n\nPretrained on a dataset for wildfire binary classification (soon to be shared). The ReXNet architecture was introduced in this paper.",
"## Model description\n\nThe core idea of the author is to add a customized Squeeze-Excitation layer in the residual blocks that will prevent channel redu... | [
"TAGS\n#transformers #pytorch #onnx #image-classification #dataset-pyronear/openfire #arxiv-2007.00992 #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"# ReXNet-1.0x model\n\nPretrained on a dataset for wildfire binary classification (soon to be shared). The ReXNet architecture was introduced... |
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. -->
# deberta-v3-xsmall-with-biblio-context-finetuned-review_classifier_testing
This model is a fine-tuned version of [domenicrosati/d... | {"license": "mit", "tags": ["text-classification", "generated_from_trainer"], "model-index": [{"name": "deberta-v3-xsmall-with-biblio-context-finetuned-review_classifier_testing", "results": []}]} | domenicrosati/deberta-v3-xsmall-with-biblio-context-finetuned-review_classifier_testing | null | [
"transformers",
"pytorch",
"tensorboard",
"deberta-v2",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-06T19:34:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #deberta-v2 #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# deberta-v3-xsmall-with-biblio-context-finetuned-review_classifier_testing
This model is a fine-tuned version of domenicrosati/deberta-v3-xsmall-finetuned-review_classifier on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training an... | [
"# deberta-v3-xsmall-with-biblio-context-finetuned-review_classifier_testing\n\nThis model is a fine-tuned version of domenicrosati/deberta-v3-xsmall-finetuned-review_classifier on an unknown dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed... | [
"TAGS\n#transformers #pytorch #tensorboard #deberta-v2 #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# deberta-v3-xsmall-with-biblio-context-finetuned-review_classifier_testing\n\nThis model is a fine-tuned version of domenicrosati/deberta-v... |
reinforcement-learning | null |
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: https://github.com/huggingface/deep-rl-class/tree/main/unit5
| {"tags": ["CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class"], "model-index": [{"name": "Reinforce-CartPole", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "CartPole-v1", "type": "CartPole-v1"}, "metrics": [{"ty... | Forkits/Reinforce-CartPole | null | [
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | null | 2022-07-06T20:06:43+00:00 | [] | [] | TAGS
#CartPole-v1 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us
|
# Reinforce Agent playing CartPole-v1
This is a trained model of a Reinforce agent playing CartPole-v1 .
To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: URL
| [
"# Reinforce Agent playing CartPole-v1\n This is a trained model of a Reinforce agent playing CartPole-v1 .\n To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: URL"
] | [
"TAGS\n#CartPole-v1 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us \n",
"# Reinforce Agent playing CartPole-v1\n This is a trained model of a Reinforce agent playing CartPole-v1 .\n To learn to use this model and train yours check Unit 5 of the Deep Reinforcemen... |
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... | AntiSquid/TEST2ppo-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-07-06T20:53:51+00:00 | [] | [] | TAGS
#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add you... |
image-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# vit-base-beans-demo-v5
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/v... | {"license": "apache-2.0", "tags": ["image-classification", "generated_from_trainer"], "datasets": ["beans"], "metrics": ["accuracy"], "model-index": [{"name": "vit-base-beans-demo-v5", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "beans", "type": "beans", "a... | samayl24/vit-base-beans-demo-v5 | null | [
"transformers",
"pytorch",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:beans",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-06T21:20:33+00:00 | [] | [] | TAGS
#transformers #pytorch #vit #image-classification #generated_from_trainer #dataset-beans #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| vit-base-beans-demo-v5
======================
This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the beans dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0427
* Accuracy: 0.9925
Model description
-----------------
More information needed
Intended uses & lim... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0002\n* train\\_batch\\_size: 16\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: 4\n* mixed\\_prec... | [
"TAGS\n#transformers #pytorch #vit #image-classification #generated_from_trainer #dataset-beans #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0002\n*... |
text-classification | transformers | hello
| {} | ltrctelugu/tree_topconstituents | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-06T22:00:10+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us
| hello
| [] | [
"TAGS\n#transformers #pytorch #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-classification | transformers | hello
| {} | ltrctelugu/bigram | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-06T23:59:08+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us
| hello
| [] | [
"TAGS\n#transformers #pytorch #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-generation | transformers |
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1481464434123894785/YmWp... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/joviex/1657155904240/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/joviex | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-07T00:03:44+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
lɐǝɹ sı ǝʌıʇɔǝdsɹǝd
@joviex
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
-----... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation | transformers |
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1296229510510030849/0dyq... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/carterhiggins/1659835083112/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/carterhiggins | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-07T00:12:44+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Carter Higgins
@carterhiggins
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
---... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
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. -->
# hsohn3/mayo-bert-visit-uncased-wordlevel-block512-batch4-ep50
This model is a fine-tuned version of [bert-base-uncased](https://huggin... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "hsohn3/mayo-bert-visit-uncased-wordlevel-block512-batch4-ep50", "results": []}]} | hsohn3/mayo-bert-visit-uncased-wordlevel-block512-batch4-ep50 | null | [
"transformers",
"tf",
"bert",
"fill-mask",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-07T01:00:16+00:00 | [] | [] | TAGS
#transformers #tf #bert #fill-mask #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| hsohn3/mayo-bert-visit-uncased-wordlevel-block512-batch4-ep50
=============================================================
This model is a fine-tuned version of bert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 1.0832
* Epoch: 49
Model description
---... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': 2e-05, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight\\_decay\\_rate': 0.01}\n* training\\_precision: float32",
... | [
"TAGS\n#transformers #tf #bert #fill-mask #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': 2e-0... |
fill-mask | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-imdb
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/di... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imdb"], "model-index": [{"name": "distilbert-base-uncased-finetuned-imdb", "results": []}]} | ChauNguyen23/distilbert-base-uncased-finetuned-imdb | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"fill-mask",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-07T01:48:22+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #fill-mask #generated_from_trainer #dataset-imdb #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-imdb
======================================
This model is a fine-tuned version of distilbert-base-uncased on the imdb dataset.
It achieves the following results on the evaluation set:
* Loss: 2.4721
Model description
-----------------
More information needed
Intended uses & l... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0\n* mixed\\_pr... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #fill-mask #generated_from_trainer #dataset-imdb #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train... |
fill-mask | transformers | DeBERTa trained from scratch
continued training from https://huggingface.co/mikesong724/deberta-wiki-2006
Source data: https://dumps.wikimedia.org/archive/2010/
Tools used: https://github.com/mikesong724/Point-in-Time-Language-Model
2010 wiki archive 6.1 GB trained 18 epochs = 108GB + 2006 (65GB)
GLUE ... | {} | mikesong724/deberta-wiki-2010 | null | [
"transformers",
"pytorch",
"deberta",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-07T02:19:23+00:00 | [] | [] | TAGS
#transformers #pytorch #deberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| DeBERTa trained from scratch
continued training from URL
Source data: URL
Tools used: URL
2010 wiki archive 6.1 GB trained 18 epochs = 108GB + 2006 (65GB)
GLUE benchmark
cola (3e): matthews corr: 0.3640
sst2 (3e): acc: 0.9106
mrpc (5e): F1: 0.8505, acc: 0.7794
stsb (3e): pearson: 0.8339, spe... | [] | [
"TAGS\n#transformers #pytorch #deberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-generation | transformers |
# Luke Dot DiabloGPT Model | {"tags": ["conversational"]} | casperthegazer/DiabloGPT-medium-lukedot | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-07T02:22:06+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Luke Dot DiabloGPT Model | [
"# Luke Dot DiabloGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Luke Dot DiabloGPT 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. -->
# distilbert-base-uncased-becasv2-1
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilb... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["becasv2"], "model-index": [{"name": "distilbert-base-uncased-becasv2-1", "results": []}]} | Evelyn18/distilbert-base-uncased-becasv2-1 | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:becasv2",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-07-07T02:34:17+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-becasv2 #license-apache-2.0 #endpoints_compatible #region-us
| distilbert-base-uncased-becasv2-1
=================================
This model is a fine-tuned version of distilbert-base-uncased on the becasv2 dataset.
It achieves the following results on the evaluation set:
* Loss: 2.9472
Model description
-----------------
More information needed
Intended uses & limitati... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 10\n* eval\\_batch\\_size: 10\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-becasv2 #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\... |
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. -->
# distilbert-base-uncased-becasv2-2
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilb... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["becasv2"], "model-index": [{"name": "distilbert-base-uncased-becasv2-2", "results": []}]} | Evelyn18/distilbert-base-uncased-becasv2-2 | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:becasv2",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-07-07T02:43:16+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-becasv2 #license-apache-2.0 #endpoints_compatible #has_space #region-us
| distilbert-base-uncased-becasv2-2
=================================
This model is a fine-tuned version of distilbert-base-uncased on the becasv2 dataset.
It achieves the following results on the evaluation set:
* Loss: 2.9170
Model description
-----------------
More information needed
Intended uses & limitati... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 10\n* eval\\_batch\\_size: 10\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 8",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-becasv2 #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* trai... |
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. -->
# distilbert-base-uncased-becasv2-3
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilb... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["becasv2"], "model-index": [{"name": "distilbert-base-uncased-becasv2-3", "results": []}]} | Evelyn18/distilbert-base-uncased-becasv2-3 | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:becasv2",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-07-07T02:55:54+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-becasv2 #license-apache-2.0 #endpoints_compatible #region-us
| distilbert-base-uncased-becasv2-3
=================================
This model is a fine-tuned version of distilbert-base-uncased on the becasv2 dataset.
It achieves the following results on the evaluation set:
* Loss: 3.1218
Model description
-----------------
More information needed
Intended uses & limitati... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 10\n* eval\\_batch\\_size: 10\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 8",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-becasv2 #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: 3e-05\n* train\\_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. -->
# distilbert-base-uncased-becasv2-4
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilb... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["becasv2"], "model-index": [{"name": "distilbert-base-uncased-becasv2-4", "results": []}]} | Evelyn18/distilbert-base-uncased-becasv2-4 | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:becasv2",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-07-07T03:11:32+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-becasv2 #license-apache-2.0 #endpoints_compatible #region-us
| distilbert-base-uncased-becasv2-4
=================================
This model is a fine-tuned version of distilbert-base-uncased on the becasv2 dataset.
It achieves the following results on the evaluation set:
* Loss: 3.4637
Model description
-----------------
More information needed
Intended uses & limitati... | [
"### 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: 8",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-becasv2 #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\... |
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. -->
# distilbert-base-uncased-becasv2-5
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilb... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["becasv2"], "model-index": [{"name": "distilbert-base-uncased-becasv2-5", "results": []}]} | Evelyn18/distilbert-base-uncased-becasv2-5 | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:becasv2",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-07-07T03:20:19+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-becasv2 #license-apache-2.0 #endpoints_compatible #region-us
| distilbert-base-uncased-becasv2-5
=================================
This model is a fine-tuned version of distilbert-base-uncased on the becasv2 dataset.
It achieves the following results on the evaluation set:
* Loss: 3.0409
Model description
-----------------
More information needed
Intended uses & limitati... | [
"### 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",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-becasv2 #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\... |
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. -->
# distilbert-base-uncased-becasv2-6
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilb... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["becasv2"], "model-index": [{"name": "distilbert-base-uncased-becasv2-6", "results": []}]} | Evelyn18/distilbert-base-uncased-becasv2-6 | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:becasv2",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-07-07T03:39:39+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-becasv2 #license-apache-2.0 #endpoints_compatible #region-us
| distilbert-base-uncased-becasv2-6
=================================
This model is a fine-tuned version of distilbert-base-uncased on the becasv2 dataset.
It achieves the following results on the evaluation set:
* Loss: 3.8936
Model description
-----------------
More information needed
Intended uses & limitati... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 10\n* eval\\_batch\\_size: 10\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 8",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-becasv2 #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: 5e-05\n* train\\_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="go2k/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": ... | go2k/q-FrozenLake-v1-4x4-noSlippery | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-07-07T04:25:54+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="go2k/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env... | {"tags": ["Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-Taxi-v3", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Taxi-v3", "type": "Taxi-v3"}, "metrics": [{"type": "mean_reward", "value": "7.56 +/... | go2k/q-Taxi-v3 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-07-07T04:39:36+00:00 | [] | [] | TAGS
#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing Taxi-v3
This is a trained model of a Q-Learning agent playing Taxi-v3 .
## Usage
| [
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] | [
"TAGS\n#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] |
null | null | Link to Github Repo: https://github.com/pourmand1376/yolov5/tree/aneurysm | {"license": "mit"} | pourmand1376/yolov5-aneurysm | null | [
"license:mit",
"region:us"
] | null | 2022-07-07T05:13:11+00:00 | [] | [] | TAGS
#license-mit #region-us
| Link to Github Repo: URL | [] | [
"TAGS\n#license-mit #region-us \n"
] |
sentence-similarity | keras |
## Model description
This repo contains the model and the notebook for fine-tuning BERT model on SNLI Corpus for Semantic Similarity. [Semantic Similarity with BERT](https://keras.io/examples/nlp/semantic_similarity_with_bert/).
Full credits go to [Mohamad Merchant](https://twitter.com/mohmadmerchant1)
Reproduced b... | {"library_name": "keras", "tags": ["sentence-similarity"]} | keras-io/bert-semantic-similarity | null | [
"keras",
"tensorboard",
"sentence-similarity",
"has_space",
"region:us"
] | null | 2022-07-07T05:14:02+00:00 | [] | [] | TAGS
#keras #tensorboard #sentence-similarity #has_space #region-us
| Model description
-----------------
This repo contains the model and the notebook for fine-tuning BERT model on SNLI Corpus for Semantic Similarity. Semantic Similarity with BERT.
Full credits go to Mohamad Merchant
Reproduced by Vu Minh Chien
Motivation: Semantic Similarity determines how similar two sentences... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n\nModel Plot\n----------\n\n\n\nView Model Plot\n!Model Image"
] | [
"TAGS\n#keras #tensorboard #sentence-similarity #has_space #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n\nModel Plot\n----------\n\n\n\nView Model Plot\n!Model Image"
] |
null | keras |
## Model description
This repo contains the model and the notebook for fine-tuning BERT model on SNLI Corpus for Semantic Similarity. [Natural language image search with a Dual Encoder](https://keras.io/examples/nlp/nl_image_search/).
Full credits go to [Khalid Salama](https://www.linkedin.com/in/khalid-salama-24403... | {"library_name": "keras"} | keras-io/dual-encoder-image-search | null | [
"keras",
"tensorboard",
"has_space",
"region:us"
] | null | 2022-07-07T05:38:52+00:00 | [] | [] | TAGS
#keras #tensorboard #has_space #region-us
|
## Model description
This repo contains the model and the notebook for fine-tuning BERT model on SNLI Corpus for Semantic Similarity. Natural language image search with a Dual Encoder.
Full credits go to Khalid Salama
Reproduced by Vu Minh Chien
Motivation: build a dual encoder (also known as a two-tower) neural n... | [
"## Model description\n\nThis repo contains the model and the notebook for fine-tuning BERT model on SNLI Corpus for Semantic Similarity. Natural language image search with a Dual Encoder.\n\nFull credits go to Khalid Salama\n\nReproduced by Vu Minh Chien\n\nMotivation: build a dual encoder (also known as a two-tow... | [
"TAGS\n#keras #tensorboard #has_space #region-us \n",
"## Model description\n\nThis repo contains the model and the notebook for fine-tuning BERT model on SNLI Corpus for Semantic Similarity. Natural language image search with a Dual Encoder.\n\nFull credits go to Khalid Salama\n\nReproduced by Vu Minh Chien\n\nM... |
token-classification | transformers |
# Model Trained Using AutoTrain
- Problem type: Entity Extraction
- Model ID: 1100540141
- CO2 Emissions (in grams): 8.343592303925112
## Validation Metrics
- Loss: 0.38094884157180786
- Accuracy: 0.8795777325860159
- Precision: 0.8171375141922127
- Recall: 0.8417033571821684
- F1: 0.8292385373953709
## Usage
You... | {"language": "unk", "tags": "autotrain", "datasets": ["ScarlettSun9/autotrain-data-ZuoZhuan"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 8.343592303925112} | ScarlettSun9/autotrain-ZuoZhuan-1100540141 | null | [
"transformers",
"pytorch",
"roberta",
"token-classification",
"autotrain",
"unk",
"dataset:ScarlettSun9/autotrain-data-ZuoZhuan",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-07T06:02:53+00:00 | [] | [
"unk"
] | TAGS
#transformers #pytorch #roberta #token-classification #autotrain #unk #dataset-ScarlettSun9/autotrain-data-ZuoZhuan #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoTrain
- Problem type: Entity Extraction
- Model ID: 1100540141
- CO2 Emissions (in grams): 8.343592303925112
## Validation Metrics
- Loss: 0.38094884157180786
- Accuracy: 0.8795777325860159
- Precision: 0.8171375141922127
- Recall: 0.8417033571821684
- F1: 0.8292385373953709
## Usage
You... | [
"# Model Trained Using AutoTrain\n\n- Problem type: Entity Extraction\n- Model ID: 1100540141\n- CO2 Emissions (in grams): 8.343592303925112",
"## Validation Metrics\n\n- Loss: 0.38094884157180786\n- Accuracy: 0.8795777325860159\n- Precision: 0.8171375141922127\n- Recall: 0.8417033571821684\n- F1: 0.8292385373953... | [
"TAGS\n#transformers #pytorch #roberta #token-classification #autotrain #unk #dataset-ScarlettSun9/autotrain-data-ZuoZhuan #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoTrain\n\n- Problem type: Entity Extraction\n- Model ID: 1100540141\n- CO2 Emissions (i... |
token-classification | transformers |
# Model Trained Using AutoTrain
- Problem type: Entity Extraction
- Model ID: 1100540143
- CO2 Emissions (in grams): 14.50120424968173
## Validation Metrics
- Loss: 0.3792617619037628
- Accuracy: 0.8799234894798035
- Precision: 0.8133982801130555
- Recall: 0.8416925948973242
- F1: 0.8273035872656656
## Usage
You ... | {"language": "unk", "tags": "autotrain", "datasets": ["ScarlettSun9/autotrain-data-ZuoZhuan"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 14.50120424968173} | ScarlettSun9/autotrain-ZuoZhuan-1100540143 | null | [
"transformers",
"pytorch",
"roberta",
"token-classification",
"autotrain",
"unk",
"dataset:ScarlettSun9/autotrain-data-ZuoZhuan",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-07T06:03:06+00:00 | [] | [
"unk"
] | TAGS
#transformers #pytorch #roberta #token-classification #autotrain #unk #dataset-ScarlettSun9/autotrain-data-ZuoZhuan #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoTrain
- Problem type: Entity Extraction
- Model ID: 1100540143
- CO2 Emissions (in grams): 14.50120424968173
## Validation Metrics
- Loss: 0.3792617619037628
- Accuracy: 0.8799234894798035
- Precision: 0.8133982801130555
- Recall: 0.8416925948973242
- F1: 0.8273035872656656
## Usage
You ... | [
"# Model Trained Using AutoTrain\n\n- Problem type: Entity Extraction\n- Model ID: 1100540143\n- CO2 Emissions (in grams): 14.50120424968173",
"## Validation Metrics\n\n- Loss: 0.3792617619037628\n- Accuracy: 0.8799234894798035\n- Precision: 0.8133982801130555\n- Recall: 0.8416925948973242\n- F1: 0.82730358726566... | [
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"# Model Trained Using AutoTrain\n\n- Problem type: Entity Extraction\n- Model ID: 1100540143\n- CO2 Emissions (i... |
null | null | Introduction
See https://github.com/k2-fsa/icefall/pull/330
and https://github.com/k2-fsa/icefall/pull/452
It has random combiner inside.
Note: There is something wrong in the log file, which has been fixed in https://github.com/k2-fsa/icefall/pull/468.
| {} | Zengwei/icefall-asr-librispeech-pruned-transducer-stateless5-2022-07-07 | null | [
"tensorboard",
"region:us"
] | null | 2022-07-07T06:51:32+00:00 | [] | [] | TAGS
#tensorboard #region-us
| Introduction
See URL
and URL
It has random combiner inside.
Note: There is something wrong in the log file, which has been fixed in URL
| [] | [
"TAGS\n#tensorboard #region-us \n"
] |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# lmv2-g-w9-2018-148-doc-07-07_1
This model is a fine-tuned version of [microsoft/layoutlmv2-base-uncased](https://huggingface.co/... | {"license": "cc-by-nc-sa-4.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "lmv2-g-w9-2018-148-doc-07-07_1", "results": []}]} | Sebabrata/lmv2-g-w9-2018-148-doc-07-07_1 | null | [
"transformers",
"pytorch",
"tensorboard",
"layoutlmv2",
"token-classification",
"generated_from_trainer",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-07T07:17:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #layoutlmv2 #token-classification #generated_from_trainer #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us
| lmv2-g-w9-2018-148-doc-07-07\_1
===============================
This model is a fine-tuned version of microsoft/layoutlmv2-base-uncased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0160
* Address Precision: 0.9667
* Address Recall: 0.9667
* Address F1: 0.9667
* Address Num... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 4e-05\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: constant\n* num\\_epochs: 30",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #layoutlmv2 #token-classification #generated_from_trainer #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 4e-05\n* tra... |
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... | osanseviero/ppo-LunarLander-v7 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-07-07T07:27:29+00:00 | [] | [] | TAGS
#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add you... |
fill-mask | transformers |
<!-- 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. -->
# recipe-roberta-is
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset.... | {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "recipe-roberta-is", "results": []}]} | paola-md/recipe-roberta-is | null | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-07T07:40:25+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #fill-mask #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| recipe-roberta-is
=================
This model is a fine-tuned version of roberta-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.8382
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More informa... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 256\n* eval\\_batch\\_size: 256\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 20\n* mixed\\_p... | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #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: 256\n* eval\\_batch\... |
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... | osanseviero/ppo-LunarLander-v5 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-07-07T07:47:49+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 | Kalyana Virundhu Biryani is one of the best biryani in Chennai." We Serve various types of Biryani along with our special side-Dish. Order us"Phone: +91 8939234566 or visit our website
https://www.kalyanavirundhubiryani.com/
#biryanifamousinchennai
#biryanibestinchennai
#chennaibestbiryanihotel
#specialbiryaniin... | {} | kalyanavirundhubiryani/Best-Biryani-in-Chennai-Kalyana-virundhu-Biryani | null | [
"region:us"
] | null | 2022-07-07T07:48:29+00:00 | [] | [] | TAGS
#region-us
| Kalyana Virundhu Biryani is one of the best biryani in Chennai." We Serve various types of Biryani along with our special side-Dish. Order us"Phone: +91 8939234566 or visit our website
URL
#biryanifamousinchennai
#biryanibestinchennai
#chennaibestbiryanihotel
#specialbiryaniinchennai
#KalyanaVirundhuBiryani
| [] | [
"TAGS\n#region-us \n"
] |
null | null | Introduction
See https://github.com/k2-fsa/icefall/pull/330
and https://github.com/k2-fsa/icefall/pull/452
It has random combiner inside.
| {} | Zengwei/icefall-asr-librispeech-pruned-transducer-stateless5-B-2022-07-07 | null | [
"tensorboard",
"region:us"
] | null | 2022-07-07T08:00:28+00:00 | [] | [] | TAGS
#tensorboard #region-us
| Introduction
See URL
and URL
It has random combiner inside.
| [] | [
"TAGS\n#tensorboard #region-us \n"
] |
reinforcement-learning | stable-baselines3 |
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 ... | {"library_name": "stable-baselines3", "tags": ["LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "LunarLander-v2", "type": "LunarL... | osanseviero/ppo-LunarLander-v6 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-07-07T08:07:08+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... |
text-generation | transformers |
# rugpt3large\_mailqa
Model was finetuned with sequence length 1024 for 516000 steps on a dataset of otvet.mail.ru questions and answers. The raw dataset can be found [here](https://www.kaggle.com/datasets/atleast6characterss/otvetmailru-full). Beware that the data contains a good portion of toxic language, so the ans... | {"language": ["ru"], "tags": ["PyTorch", "Transformers"]} | its5Q/rugpt3large_mailqa | null | [
"transformers",
"pytorch",
"safetensors",
"gpt2",
"text-generation",
"PyTorch",
"Transformers",
"ru",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-07-07T08:12:17+00:00 | [] | [
"ru"
] | TAGS
#transformers #pytorch #safetensors #gpt2 #text-generation #PyTorch #Transformers #ru #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
# rugpt3large\_mailqa
Model was finetuned with sequence length 1024 for 516000 steps on a dataset of URL questions and answers. The raw dataset can be found here. Beware that the data contains a good portion of toxic language, so the answers can be unpredictable.
Jupyter notebook with an example of how to inference t... | [
"# rugpt3large\\_mailqa\nModel was finetuned with sequence length 1024 for 516000 steps on a dataset of URL questions and answers. The raw dataset can be found here. Beware that the data contains a good portion of toxic language, so the answers can be unpredictable.\n\nJupyter notebook with an example of how to inf... | [
"TAGS\n#transformers #pytorch #safetensors #gpt2 #text-generation #PyTorch #Transformers #ru #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"# rugpt3large\\_mailqa\nModel was finetuned with sequence length 1024 for 516000 steps on a dataset of URL questions and a... |
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... | osanseviero/ppo-LunarLander-v9 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-07-07T08:36:34+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... | osanseviero/ppo-LunarLander-v10 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-07-07T08:38:00+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... | osanseviero/ppo-LunarLander-v11 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-07-07T08:42:42+00:00 | [] | [] | TAGS
#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add you... |
text2text-generation | transformers | ## m2m100 fine-tuned on the ca_zh_wikipedia dataset for machine translation
## Table of Contents
<details>
<summary>Click to expand</summary>
- [Model description](#model-description)
- [Intended uses and limitations](#intended-use)
- [How to use](#how-to-use)
- [Training](#training)
- [Training data](#training-... | {"license": "cc-by-4.0"} | projecte-aina/m2m100_418M_ft_ca_zh | null | [
"transformers",
"pytorch",
"m2m_100",
"text2text-generation",
"license:cc-by-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-07T09:04:29+00:00 | [] | [] | TAGS
#transformers #pytorch #m2m_100 #text2text-generation #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #region-us
| m2m100 fine-tuned on the ca\_zh\_wikipedia dataset for machine translation
--------------------------------------------------------------------------
Table of Contents
-----------------
Click to expand
* Model description
* Intended uses and limitations
* How to use
* Training
+ Training data
+ Training procedur... | [
"### Training data\n\n\nAs a data for fine-tuning we used the ca\\_zh\\_wikipedia dataset extracted from Wikipedia.",
"### Training procedure",
"#### Tokenization\n\n\nThe original m2m100\\_418M model's sentencepiece tokenizer was used. The fine-tuning dataset that contained both simplified and traditional Chin... | [
"TAGS\n#transformers #pytorch #m2m_100 #text2text-generation #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training data\n\n\nAs a data for fine-tuning we used the ca\\_zh\\_wikipedia dataset extracted from Wikipedia.",
"### Training procedure",
"#### Tokenization\n\n\nTh... |
token-classification | transformers | # tner/twitter-roberta-base-dec2020-tweetner7-2020
This model is a fine-tuned version of [cardiffnlp/twitter-roberta-base-dec2020](https://huggingface.co/cardiffnlp/twitter-roberta-base-dec2020) on the
[tner/tweetner7](https://huggingface.co/datasets/tner/tweetner7) dataset (`train_2020` split).
Model fine-tuning is ... | {"datasets": ["tner/tweetner7"], "metrics": ["f1", "precision", "recall"], "pipeline_tag": "token-classification", "widget": [{"text": "Get the all-analog Classic Vinyl Edition of `Takin' Off` Album from {@herbiehancock@} via {@bluenoterecords@} link below: {{URL}}", "example_title": "NER Example 1"}], "model-index": [... | tner/twitter-roberta-base-dec2020-tweetner7-2020 | null | [
"transformers",
"pytorch",
"roberta",
"token-classification",
"dataset:tner/tweetner7",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-07T09:08:40+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #token-classification #dataset-tner/tweetner7 #model-index #autotrain_compatible #endpoints_compatible #region-us
| # tner/twitter-roberta-base-dec2020-tweetner7-2020
This model is a fine-tuned version of cardiffnlp/twitter-roberta-base-dec2020 on the
tner/tweetner7 dataset ('train_2020' split).
Model fine-tuning is done via T-NER's hyper-parameter search (see the repository
for more detail). It achieves the following results on t... | [
"# tner/twitter-roberta-base-dec2020-tweetner7-2020\n\nThis model is a fine-tuned version of cardiffnlp/twitter-roberta-base-dec2020 on the \ntner/tweetner7 dataset ('train_2020' split).\nModel fine-tuning is done via T-NER's hyper-parameter search (see the repository\nfor more detail). It achieves the following re... | [
"TAGS\n#transformers #pytorch #roberta #token-classification #dataset-tner/tweetner7 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# tner/twitter-roberta-base-dec2020-tweetner7-2020\n\nThis model is a fine-tuned version of cardiffnlp/twitter-roberta-base-dec2020 on the \ntner/tweetner7... |
token-classification | transformers | # tner/twitter-roberta-base-2019-90m-tweetner7-2020
This model is a fine-tuned version of [cardiffnlp/twitter-roberta-base-2019-90m](https://huggingface.co/cardiffnlp/twitter-roberta-base-2019-90m) on the
[tner/tweetner7](https://huggingface.co/datasets/tner/tweetner7) dataset (`train_2020` split).
Model fine-tuning ... | {"datasets": ["tner/tweetner7"], "metrics": ["f1", "precision", "recall"], "pipeline_tag": "token-classification", "widget": [{"text": "Get the all-analog Classic Vinyl Edition of `Takin' Off` Album from {@herbiehancock@} via {@bluenoterecords@} link below: {{URL}}", "example_title": "NER Example 1"}], "model-index": [... | tner/twitter-roberta-base-2019-90m-tweetner7-2020 | null | [
"transformers",
"pytorch",
"roberta",
"token-classification",
"dataset:tner/tweetner7",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-07T09:08:40+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #token-classification #dataset-tner/tweetner7 #model-index #autotrain_compatible #endpoints_compatible #region-us
| # tner/twitter-roberta-base-2019-90m-tweetner7-2020
This model is a fine-tuned version of cardiffnlp/twitter-roberta-base-2019-90m on the
tner/tweetner7 dataset ('train_2020' split).
Model fine-tuning is done via T-NER's hyper-parameter search (see the repository
for more detail). It achieves the following results on... | [
"# tner/twitter-roberta-base-2019-90m-tweetner7-2020\n\nThis model is a fine-tuned version of cardiffnlp/twitter-roberta-base-2019-90m on the \ntner/tweetner7 dataset ('train_2020' split).\nModel fine-tuning is done via T-NER's hyper-parameter search (see the repository\nfor more detail). It achieves the following ... | [
"TAGS\n#transformers #pytorch #roberta #token-classification #dataset-tner/tweetner7 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# tner/twitter-roberta-base-2019-90m-tweetner7-2020\n\nThis model is a fine-tuned version of cardiffnlp/twitter-roberta-base-2019-90m on the \ntner/tweetne... |
token-classification | transformers | # tner/twitter-roberta-base-2019-90m-tweetner7-2021
This model is a fine-tuned version of [cardiffnlp/twitter-roberta-base-2019-90m](https://huggingface.co/cardiffnlp/twitter-roberta-base-2019-90m) on the
[tner/tweetner7](https://huggingface.co/datasets/tner/tweetner7) dataset (`train_2021` split).
Model fine-tuning ... | {"datasets": ["tner/tweetner7"], "metrics": ["f1", "precision", "recall"], "pipeline_tag": "token-classification", "widget": [{"text": "Get the all-analog Classic Vinyl Edition of `Takin' Off` Album from {@herbiehancock@} via {@bluenoterecords@} link below: {{URL}}", "example_title": "NER Example 1"}], "model-index": [... | tner/twitter-roberta-base-2019-90m-tweetner7-2021 | null | [
"transformers",
"pytorch",
"roberta",
"token-classification",
"dataset:tner/tweetner7",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-07T09:10:40+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #token-classification #dataset-tner/tweetner7 #model-index #autotrain_compatible #endpoints_compatible #region-us
| # tner/twitter-roberta-base-2019-90m-tweetner7-2021
This model is a fine-tuned version of cardiffnlp/twitter-roberta-base-2019-90m on the
tner/tweetner7 dataset ('train_2021' split).
Model fine-tuning is done via T-NER's hyper-parameter search (see the repository
for more detail). It achieves the following results on... | [
"# tner/twitter-roberta-base-2019-90m-tweetner7-2021\n\nThis model is a fine-tuned version of cardiffnlp/twitter-roberta-base-2019-90m on the \ntner/tweetner7 dataset ('train_2021' split).\nModel fine-tuning is done via T-NER's hyper-parameter search (see the repository\nfor more detail). It achieves the following ... | [
"TAGS\n#transformers #pytorch #roberta #token-classification #dataset-tner/tweetner7 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# tner/twitter-roberta-base-2019-90m-tweetner7-2021\n\nThis model is a fine-tuned version of cardiffnlp/twitter-roberta-base-2019-90m on the \ntner/tweetne... |
token-classification | transformers | # tner/twitter-roberta-base-dec2020-tweetner7-2021
This model is a fine-tuned version of [cardiffnlp/twitter-roberta-base-dec2020](https://huggingface.co/cardiffnlp/twitter-roberta-base-dec2020) on the
[tner/tweetner7](https://huggingface.co/datasets/tner/tweetner7) dataset (`train_2021` split).
Model fine-tuning is ... | {"datasets": ["tner/tweetner7"], "metrics": ["f1", "precision", "recall"], "pipeline_tag": "token-classification", "widget": [{"text": "Get the all-analog Classic Vinyl Edition of `Takin' Off` Album from {@herbiehancock@} via {@bluenoterecords@} link below: {{URL}}", "example_title": "NER Example 1"}], "model-index": [... | tner/twitter-roberta-base-dec2020-tweetner7-2021 | null | [
"transformers",
"pytorch",
"roberta",
"token-classification",
"dataset:tner/tweetner7",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-07T09:11:09+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #token-classification #dataset-tner/tweetner7 #model-index #autotrain_compatible #endpoints_compatible #region-us
| # tner/twitter-roberta-base-dec2020-tweetner7-2021
This model is a fine-tuned version of cardiffnlp/twitter-roberta-base-dec2020 on the
tner/tweetner7 dataset ('train_2021' split).
Model fine-tuning is done via T-NER's hyper-parameter search (see the repository
for more detail). It achieves the following results on t... | [
"# tner/twitter-roberta-base-dec2020-tweetner7-2021\n\nThis model is a fine-tuned version of cardiffnlp/twitter-roberta-base-dec2020 on the \ntner/tweetner7 dataset ('train_2021' split).\nModel fine-tuning is done via T-NER's hyper-parameter search (see the repository\nfor more detail). It achieves the following re... | [
"TAGS\n#transformers #pytorch #roberta #token-classification #dataset-tner/tweetner7 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# tner/twitter-roberta-base-dec2020-tweetner7-2021\n\nThis model is a fine-tuned version of cardiffnlp/twitter-roberta-base-dec2020 on the \ntner/tweetner7... |
token-classification | transformers | # tner/twitter-roberta-base-2019-90m-tweetner7-all
This model is a fine-tuned version of [cardiffnlp/twitter-roberta-base-2019-90m](https://huggingface.co/cardiffnlp/twitter-roberta-base-2019-90m) on the
[tner/tweetner7](https://huggingface.co/datasets/tner/tweetner7) dataset (`train_all` split).
Model fine-tuning is... | {"datasets": ["tner/tweetner7"], "metrics": ["f1", "precision", "recall"], "pipeline_tag": "token-classification", "widget": [{"text": "Get the all-analog Classic Vinyl Edition of `Takin' Off` Album from {@herbiehancock@} via {@bluenoterecords@} link below: {{URL}}", "example_title": "NER Example 1"}], "model-index": [... | tner/twitter-roberta-base-2019-90m-tweetner7-all | null | [
"transformers",
"pytorch",
"roberta",
"token-classification",
"dataset:tner/tweetner7",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-07T09:12:18+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #token-classification #dataset-tner/tweetner7 #model-index #autotrain_compatible #endpoints_compatible #region-us
| # tner/twitter-roberta-base-2019-90m-tweetner7-all
This model is a fine-tuned version of cardiffnlp/twitter-roberta-base-2019-90m on the
tner/tweetner7 dataset ('train_all' split).
Model fine-tuning is done via T-NER's hyper-parameter search (see the repository
for more detail). It achieves the following results on t... | [
"# tner/twitter-roberta-base-2019-90m-tweetner7-all\n\nThis model is a fine-tuned version of cardiffnlp/twitter-roberta-base-2019-90m on the \ntner/tweetner7 dataset ('train_all' split).\nModel fine-tuning is done via T-NER's hyper-parameter search (see the repository\nfor more detail). It achieves the following re... | [
"TAGS\n#transformers #pytorch #roberta #token-classification #dataset-tner/tweetner7 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# tner/twitter-roberta-base-2019-90m-tweetner7-all\n\nThis model is a fine-tuned version of cardiffnlp/twitter-roberta-base-2019-90m on the \ntner/tweetner... |
token-classification | transformers | # tner/twitter-roberta-base-dec2020-tweetner7-all
This model is a fine-tuned version of [cardiffnlp/twitter-roberta-base-dec2020](https://huggingface.co/cardiffnlp/twitter-roberta-base-dec2020) on the
[tner/tweetner7](https://huggingface.co/datasets/tner/tweetner7) dataset (`train_all` split).
Model fine-tuning is do... | {"datasets": ["tner/tweetner7"], "metrics": ["f1", "precision", "recall"], "pipeline_tag": "token-classification", "widget": [{"text": "Get the all-analog Classic Vinyl Edition of `Takin' Off` Album from {@herbiehancock@} via {@bluenoterecords@} link below: {{URL}}", "example_title": "NER Example 1"}], "model-index": [... | tner/twitter-roberta-base-dec2020-tweetner7-all | null | [
"transformers",
"pytorch",
"roberta",
"token-classification",
"dataset:tner/tweetner7",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-07T09:12:58+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #token-classification #dataset-tner/tweetner7 #model-index #autotrain_compatible #endpoints_compatible #region-us
| # tner/twitter-roberta-base-dec2020-tweetner7-all
This model is a fine-tuned version of cardiffnlp/twitter-roberta-base-dec2020 on the
tner/tweetner7 dataset ('train_all' split).
Model fine-tuning is done via T-NER's hyper-parameter search (see the repository
for more detail). It achieves the following results on the... | [
"# tner/twitter-roberta-base-dec2020-tweetner7-all\n\nThis model is a fine-tuned version of cardiffnlp/twitter-roberta-base-dec2020 on the \ntner/tweetner7 dataset ('train_all' split).\nModel fine-tuning is done via T-NER's hyper-parameter search (see the repository\nfor more detail). It achieves the following resu... | [
"TAGS\n#transformers #pytorch #roberta #token-classification #dataset-tner/tweetner7 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# tner/twitter-roberta-base-dec2020-tweetner7-all\n\nThis model is a fine-tuned version of cardiffnlp/twitter-roberta-base-dec2020 on the \ntner/tweetner7 ... |
null | null | Introduction
See https://github.com/k2-fsa/icefall/pull/330
and https://github.com/k2-fsa/icefall/pull/452
It has random combiner inside.
| {} | Zengwei/icefall-asr-librispeech-pruned-transducer-stateless5-M-2022-07-07 | null | [
"tensorboard",
"region:us"
] | null | 2022-07-07T09:17:44+00:00 | [] | [] | TAGS
#tensorboard #region-us
| Introduction
See URL
and URL
It has random combiner inside.
| [] | [
"TAGS\n#tensorboard #region-us \n"
] |
text2text-generation | transformers |
# Model Trained Using AutoTrain
- Problem type: Summarization
- Model ID: 1101140174
- CO2 Emissions (in grams): 1.4118255120710663
## Validation Metrics
- Loss: 0.0049639358185231686
- Rouge1: 49.3333
- Rouge2: 26.6667
- RougeL: 49.3333
- RougeLsum: 49.3333
- Gen Len: 15.12
## Usage
You can use cURL to access th... | {"language": "unk", "tags": "autotrain", "datasets": ["zhifei/autotrain-data-chinese-title-summarization-8"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 1.4118255120710663} | zhifei/autotrain-chinese-title-summarization-8-1101140174 | null | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"autotrain",
"unk",
"dataset:zhifei/autotrain-data-chinese-title-summarization-8",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-07T09:19:46+00:00 | [] | [
"unk"
] | TAGS
#transformers #pytorch #mt5 #text2text-generation #autotrain #unk #dataset-zhifei/autotrain-data-chinese-title-summarization-8 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Model Trained Using AutoTrain
- Problem type: Summarization
- Model ID: 1101140174
- CO2 Emissions (in grams): 1.4118255120710663
## Validation Metrics
- Loss: 0.0049639358185231686
- Rouge1: 49.3333
- Rouge2: 26.6667
- RougeL: 49.3333
- RougeLsum: 49.3333
- Gen Len: 15.12
## Usage
You can use cURL to access th... | [
"# Model Trained Using AutoTrain\n\n- Problem type: Summarization\n- Model ID: 1101140174\n- CO2 Emissions (in grams): 1.4118255120710663",
"## Validation Metrics\n\n- Loss: 0.0049639358185231686\n- Rouge1: 49.3333\n- Rouge2: 26.6667\n- RougeL: 49.3333\n- RougeLsum: 49.3333\n- Gen Len: 15.12",
"## Usage\n\nYou ... | [
"TAGS\n#transformers #pytorch #mt5 #text2text-generation #autotrain #unk #dataset-zhifei/autotrain-data-chinese-title-summarization-8 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Model Trained Using AutoTrain\n\n- Problem type: Summarization\n- Model ... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# TestZee/t5-small-finetuned-custom-wion-test-BIG
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on a... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "TestZee/t5-small-finetuned-custom-wion-test-BIG", "results": []}]} | TestZee/t5-small-finetuned-custom-wion-test-BIG | null | [
"transformers",
"tf",
"t5",
"text2text-generation",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-07T09:30:30+00:00 | [] | [] | TAGS
#transformers #tf #t5 #text2text-generation #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| TestZee/t5-small-finetuned-custom-wion-test-BIG
===============================================
This model is a fine-tuned version of t5-small on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 1.1165
* Validation Loss: 0.4609
* Epoch: 29
Model description
--------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': 2e-05, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight\\_decay\\_rate': 0.01}\n* training\\_precision: float32",
... | [
"TAGS\n#transformers #tf #t5 #text2text-generation #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamW... |
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-ft500_6class
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingfa... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "distilbert-base-uncased-finetuned-ft500_6class", "results": []}]} | dminiotas05/distilbert-base-uncased-finetuned-ft500_6class | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-07T09:45:38+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-ft500\_6class
===============================================
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 1.5162
* Accuracy: 0.356
* F1: 0.3347
Model description
------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_b... |
text2text-generation | transformers |
This is a model for correcting spelling and grammar errors in Icelandic text. It is based on the pretrained ByT5 model (https://arxiv.org/abs/2105.13626) and finetuned on Icelandic error correction data along with synthetic error data. The model is trained using the HuggingFace and PyTorch libraries.
The model is tra... | {"language": "is", "license": "cc-by-sa-4.0", "tag": "text2text-generation", "pipeline_tag": "text2text-generation", "widget": [{"text": "\u00e9k var a\u00f0 bor\u00f0a\u00f0i maturinn min"}], "inference": {"parameters": {"max_length": 512}}} | mideind/yfirlestur-icelandic-correction-byt5 | null | [
"transformers",
"pytorch",
"safetensors",
"t5",
"text2text-generation",
"is",
"arxiv:2105.13626",
"license:cc-by-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-07-07T09:46:55+00:00 | [
"2105.13626"
] | [
"is"
] | TAGS
#transformers #pytorch #safetensors #t5 #text2text-generation #is #arxiv-2105.13626 #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
This is a model for correcting spelling and grammar errors in Icelandic text. It is based on the pretrained ByT5 model (URL and finetuned on Icelandic error correction data along with synthetic error data. The model is trained using the HuggingFace and PyTorch libraries.
The model is trained to correct a single sente... | [] | [
"TAGS\n#transformers #pytorch #safetensors #t5 #text2text-generation #is #arxiv-2105.13626 #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n"
] |
text2text-generation | transformers |
# Model Trained Using AutoTrain
- Problem type: Summarization
- Model ID: 1101340178
- CO2 Emissions (in grams): 1.565396518204961
## Validation Metrics
- Loss: 0.00012778821110259742
- Rouge1: 29.2308
- Rouge2: 0.0
- RougeL: 29.2308
- RougeLsum: 29.2308
- Gen Len: 18.4462
## Usage
You can use cURL to access this... | {"language": "unk", "tags": "autotrain", "datasets": ["zhifei/autotrain-data-autotrain-chinese-title-summarization-9"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 1.565396518204961} | zhifei/autotrain-autotrain-chinese-title-summarization-9-1101340178 | null | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"autotrain",
"unk",
"dataset:zhifei/autotrain-data-autotrain-chinese-title-summarization-9",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-07T09:48:04+00:00 | [] | [
"unk"
] | TAGS
#transformers #pytorch #mt5 #text2text-generation #autotrain #unk #dataset-zhifei/autotrain-data-autotrain-chinese-title-summarization-9 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Model Trained Using AutoTrain
- Problem type: Summarization
- Model ID: 1101340178
- CO2 Emissions (in grams): 1.565396518204961
## Validation Metrics
- Loss: 0.00012778821110259742
- Rouge1: 29.2308
- Rouge2: 0.0
- RougeL: 29.2308
- RougeLsum: 29.2308
- Gen Len: 18.4462
## Usage
You can use cURL to access this... | [
"# Model Trained Using AutoTrain\n\n- Problem type: Summarization\n- Model ID: 1101340178\n- CO2 Emissions (in grams): 1.565396518204961",
"## Validation Metrics\n\n- Loss: 0.00012778821110259742\n- Rouge1: 29.2308\n- Rouge2: 0.0\n- RougeL: 29.2308\n- RougeLsum: 29.2308\n- Gen Len: 18.4462",
"## Usage\n\nYou ca... | [
"TAGS\n#transformers #pytorch #mt5 #text2text-generation #autotrain #unk #dataset-zhifei/autotrain-data-autotrain-chinese-title-summarization-9 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Model Trained Using AutoTrain\n\n- Problem type: Summarization... |
tabular-classification | sklearn |
## Baseline Model trained on trainii_ac94u to apply classification on label
**Metrics of the best model:**
accuracy 0.361046
recall_macro 0.353192
precision_macro 0.240667
f1_macro 0.278231
Name: LogisticRegression(C=0.1, class_weight='balanced', max_iter=1000), dtype: float64
**S... | {"license": "apache-2.0", "library_name": "sklearn", "tags": ["tabular-classification", "baseline-trainer"]} | Fulccrum/trainii_ac94u-label-classification | null | [
"sklearn",
"tabular-classification",
"baseline-trainer",
"license:apache-2.0",
"region:us"
] | null | 2022-07-07T09:48:16+00:00 | [] | [] | TAGS
#sklearn #tabular-classification #baseline-trainer #license-apache-2.0 #region-us
|
## Baseline Model trained on trainii_ac94u to apply classification on label
Metrics of the best model:
accuracy 0.361046
recall_macro 0.353192
precision_macro 0.240667
f1_macro 0.278231
Name: LogisticRegression(C=0.1, class_weight='balanced', max_iter=1000), dtype: float64
See mod... | [
"## Baseline Model trained on trainii_ac94u to apply classification on label\n\nMetrics of the best model:\n\naccuracy 0.361046\n\nrecall_macro 0.353192\n\nprecision_macro 0.240667\n\nf1_macro 0.278231\n\nName: LogisticRegression(C=0.1, class_weight='balanced', max_iter=1000), dtype: fl... | [
"TAGS\n#sklearn #tabular-classification #baseline-trainer #license-apache-2.0 #region-us \n",
"## Baseline Model trained on trainii_ac94u to apply classification on label\n\nMetrics of the best model:\n\naccuracy 0.361046\n\nrecall_macro 0.353192\n\nprecision_macro 0.240667\n\nf1_macro ... |
null | null | # FgFlex: A flexible multitasking sequence-labeler for fine-grained sentiment analysis
| {} | pmch/fgflex | null | [
"region:us"
] | null | 2022-07-07T09:53:17+00:00 | [] | [] | TAGS
#region-us
| # FgFlex: A flexible multitasking sequence-labeler for fine-grained sentiment analysis
| [
"# FgFlex: A flexible multitasking sequence-labeler for fine-grained sentiment analysis"
] | [
"TAGS\n#region-us \n",
"# FgFlex: A flexible multitasking sequence-labeler for fine-grained sentiment analysis"
] |
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. -->
# discourse_classification
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "discourse_classification", "results": []}]} | Manishkalra/discourse_classification | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-07T10:13:57+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| discourse\_classification
=========================
This model is a fine-tuned version of distilbert-base-uncased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.7639
* Accuracy: 0.6649
* F1: 0.6649
Model description
-----------------
More information needed
Intended us... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_b... |
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