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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-base-librispeech-demo-colab
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/faceb... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-base-librispeech-demo-colab", "results": []}]} | khanhnguyen/wav2vec2-base-librispeech-demo-colab | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-05-18T04:56:33+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
|
# wav2vec2-base-librispeech-demo-colab
This model is a fine-tuned version of facebook/wav2vec2-base on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Train... | [
"# wav2vec2-base-librispeech-demo-colab\n\nThis model is a fine-tuned version of facebook/wav2vec2-base on the None dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Tra... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"# wav2vec2-base-librispeech-demo-colab\n\nThis model is a fine-tuned version of facebook/wav2vec2-base on the None dataset.",
"## Model descriptio... |
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
| {"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... | makram/TEST2ppo-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-05-18T05:12:34+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\n This is a trained model of a PPO agent playing LunarLander-v2 using the stable-baselines3 library.\n \n ## Usage (with Stable-baselines3)\n TODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\n This is a trained model of a PPO agent playing LunarLander-v2 using the stable-baselines3 library.\n \n ## Usage (with Stable-baselines3)\n TODO: Ad... |
text-generation | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# MariaZafar/gpt2-finetuned-wikitext2
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset.
I... | {"license": "mit", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "MariaZafar/gpt2-finetuned-wikitext2", "results": []}]} | MariaZafar/gpt2-finetuned-wikitext2 | null | [
"transformers",
"tf",
"gpt2",
"text-generation",
"generated_from_keras_callback",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-05-18T05:16:52+00:00 | [] | [] | TAGS
#transformers #tf #gpt2 #text-generation #generated_from_keras_callback #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| MariaZafar/gpt2-finetuned-wikitext2
===================================
This model is a fine-tuned version of gpt2 on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.7785
* Validation Loss: 3.7004
* Epoch: 49
Model description
-----------------
More information neede... | [
"### 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 #gpt2 #text-generation #generated_from_keras_callback #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay... |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Callmenicky/bert-finetuned-squad
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "Callmenicky/bert-finetuned-squad", "results": []}]} | Callmenicky/bert-finetuned-squad | null | [
"transformers",
"tf",
"bert",
"question-answering",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-05-18T05:41:55+00:00 | [] | [] | TAGS
#transformers #tf #bert #question-answering #generated_from_keras_callback #license-apache-2.0 #endpoints_compatible #region-us
| Callmenicky/bert-finetuned-squad
================================
This model is a fine-tuned version of bert-base-cased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.7907
* Epoch: 1
Model description
-----------------
More information needed
Intended uses & li... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_steps': 11090, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle':... | [
"TAGS\n#transformers #tf #bert #question-answering #generated_from_keras_callback #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': ... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# silviacamplani/bert-finetuned-ner
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on a... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "silviacamplani/bert-finetuned-ner", "results": []}]} | silviacamplani/bert-finetuned-ner | null | [
"transformers",
"tf",
"bert",
"token-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-18T05:58:50+00:00 | [] | [] | TAGS
#transformers #tf #bert #token-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| silviacamplani/bert-finetuned-ner
=================================
This model is a fine-tuned version of bert-base-cased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.0270
* Validation Loss: 0.0563
* Epoch: 2
Model description
-----------------
More information... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_steps': 2631, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': ... | [
"TAGS\n#transformers #tf #bert #token-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_... |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# ksabeh/distilbert-base-uncased-mlm-electronics-attribute-correction-qa-mlm
This model is a fine-tuned version of [ksabeh/distilbert-ba... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "ksabeh/distilbert-base-uncased-mlm-electronics-attribute-correction-qa-mlm", "results": []}]} | ksabeh/distilbert-attribute-correction-mlm | null | [
"transformers",
"tf",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-05-18T06:21:37+00:00 | [] | [] | TAGS
#transformers #tf #tensorboard #distilbert #question-answering #generated_from_keras_callback #license-apache-2.0 #endpoints_compatible #region-us
| ksabeh/distilbert-base-uncased-mlm-electronics-attribute-correction-qa-mlm
==========================================================================
This model is a fine-tuned version of ksabeh/distilbert-base-uncased-mlm-electronics on an unknown dataset.
It achieves the following results on the evaluation set:
*... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'learning\\_rate': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_steps': 36794, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': False, 'na... | [
"TAGS\n#transformers #tf #tensorboard #distilbert #question-answering #generated_from_keras_callback #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'learning\\_rate': {'class\\... |
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. -->
# Mohammed245/bert-base-uncased-finetuned-ED_BERT_test
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "Mohammed245/bert-base-uncased-finetuned-ED_BERT_test", "results": []}]} | Mohammed245/bert-base-uncased-finetuned-ED_BERT_test | null | [
"transformers",
"tf",
"tensorboard",
"bert",
"fill-mask",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-18T06:34:13+00:00 | [] | [] | TAGS
#transformers #tf #tensorboard #bert #fill-mask #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| Mohammed245/bert-base-uncased-finetuned-ED\_BERT\_test
======================================================
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: 4.9663
* Validation Loss: 5.2474
* Epoch: 1
Model desc... | [
"### 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 #tensorboard #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\... |
token-classification | transformers | <h1>Model description</h1>
This is a fine-tuned BioBERT model for extracting primary and secondary outcomes from articles reporting clinical trials.
This model is a version of https://huggingface.co/aakorolyova/primary_outcome_extraction. We have not annotated any secondary outcome during the related PhD project. To ... | {} | aakorolyova/primary_and_secondary_outcome_extraction | null | [
"transformers",
"pytorch",
"tf",
"bert",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-18T07:04:32+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #bert #token-classification #autotrain_compatible #endpoints_compatible #region-us
| <h1>Model description</h1>
This is a fine-tuned BioBERT model for extracting primary and secondary outcomes from articles reporting clinical trials.
This model is a version of URL We have not annotated any secondary outcome during the related PhD project. To be able to extract secondary outcomes, we manually annotate... | [] | [
"TAGS\n#transformers #pytorch #tf #bert #token-classification #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-classification | transformers |
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 879428192
- CO2 Emissions (in grams): 13.170344687762716
## Validation Metrics
- Loss: 0.06465228646993637
- Accuracy: 0.9796652588768966
- Precision: 0.9843385538153949
- Recall: 0.993943472409152
- AUC: 0.9855992605071237
- F1: 0.98... | {"language": "unk", "tags": "autotrain", "datasets": ["nthanhha26/autotrain-data-test-project"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 13.170344687762716} | nthanhha26/autotrain-test-project-879428192 | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"autotrain",
"unk",
"dataset:nthanhha26/autotrain-data-test-project",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-18T07:21:51+00:00 | [] | [
"unk"
] | TAGS
#transformers #pytorch #roberta #text-classification #autotrain #unk #dataset-nthanhha26/autotrain-data-test-project #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 879428192
- CO2 Emissions (in grams): 13.170344687762716
## Validation Metrics
- Loss: 0.06465228646993637
- Accuracy: 0.9796652588768966
- Precision: 0.9843385538153949
- Recall: 0.993943472409152
- AUC: 0.9855992605071237
- F1: 0.98... | [
"# Model Trained Using AutoTrain\n\n- Problem type: Binary Classification\n- Model ID: 879428192\n- CO2 Emissions (in grams): 13.170344687762716",
"## Validation Metrics\n\n- Loss: 0.06465228646993637\n- Accuracy: 0.9796652588768966\n- Precision: 0.9843385538153949\n- Recall: 0.993943472409152\n- AUC: 0.985599260... | [
"TAGS\n#transformers #pytorch #roberta #text-classification #autotrain #unk #dataset-nthanhha26/autotrain-data-test-project #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoTrain\n\n- Problem type: Binary Classification\n- Model ID: 879428192\n- CO2 Emission... |
null | null | Ambient bamboo’s strand woven panels are 2-3X harder than comparable wood surfaces and have been tested for indoor air quality safety, with consistent test results showing essentially no off-gassing. They’re also priced affordably, giving you premium quality at wholesale costs. Come to Ambient Bamboo and enjoy nationwi... | {} | ambientbp/largest-selection-of-bamboo-plywood-on-the-web-for-cabinets | null | [
"region:us"
] | null | 2022-05-18T07:22:17+00:00 | [] | [] | TAGS
#region-us
| Ambient bamboo’s strand woven panels are 2-3X harder than comparable wood surfaces and have been tested for indoor air quality safety, with consistent test results showing essentially no off-gassing. They’re also priced affordably, giving you premium quality at wholesale costs. Come to Ambient Bamboo and enjoy nationwi... | [] | [
"TAGS\n#region-us \n"
] |
token-classification | transformers | <h1>Model description</h1>
This is a fine-tuned BioBERT model for extracting reported outcomes (i.e. those for which results are presented) from articles reporting clinical trials.
This is the second version of the model; the original model development was reported in:
Anna Koroleva, Sanjay Kamath, Patrick Paroubek. ... | {} | aakorolyova/reported_outcome_extraction | null | [
"transformers",
"pytorch",
"bert",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-18T07:32:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #token-classification #autotrain_compatible #endpoints_compatible #region-us
| <h1>Model description</h1>
This is a fine-tuned BioBERT model for extracting reported outcomes (i.e. those for which results are presented) from articles reporting clinical trials.
This is the second version of the model; the original model development was reported in:
Anna Koroleva, Sanjay Kamath, Patrick Paroubek. ... | [] | [
"TAGS\n#transformers #pytorch #bert #token-classification #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-classification | transformers | <h1>Model description</h1>
This is a fine-tuned BioBERT model for text pair classification, namely for identifying pairs of clinical trial outcomes' mentions that refeer to the same outcome (e.g. "overall survival in patients with oesophageal squamous cell carcinoma and PD-L1 combined positive score (CPS) of 10 or mor... | {} | aakorolyova/outcome_similarity | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-18T07:43:29+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us
| <h1>Model description</h1>
This is a fine-tuned BioBERT model for text pair classification, namely for identifying pairs of clinical trial outcomes' mentions that refeer to the same outcome (e.g. "overall survival in patients with oesophageal squamous cell carcinoma and PD-L1 combined positive score (CPS) of 10 or mor... | [] | [
"TAGS\n#transformers #pytorch #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us \n"
] |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-liepa-1-percent
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook... | {"language": ["lt"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "common_voice", "generated_from_trainer"], "model-index": [{"name": "wav2vec2-liepa-1-percent", "results": []}]} | birgermoell/wav2vec2-liepa-1-percent | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"common_voice",
"generated_from_trainer",
"lt",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-05-18T08:14:06+00:00 | [] | [
"lt"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #common_voice #generated_from_trainer #lt #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-liepa-1-percent
========================
This model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the COMMON\_VOICE - LT dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5774
* Wer: 0.5079
Model description
-----------------
More information needed
Intended ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #common_voice #generated_from_trainer #lt #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_si... |
null | null | BUY HOUSES ANYWHERE IN TUCSON ARIZONA
We work differently at Synrgy Home Offer. We [buy houses](https://www.synrgyhomeoffer.com/) in ANY CONDITION in Arizona. There are no commissions or fees and no obligation whatsoever. It doesn’t matter what condition the house is in, or even if there are tenants in there that you ... | {} | synrgyhomeoffer/synrgyhomeoffer | null | [
"region:us"
] | null | 2022-05-18T09:24:19+00:00 | [] | [] | TAGS
#region-us
| BUY HOUSES ANYWHERE IN TUCSON ARIZONA
We work differently at Synrgy Home Offer. We buy houses in ANY CONDITION in Arizona. There are no commissions or fees and no obligation whatsoever. It doesn’t matter what condition the house is in, or even if there are tenants in there that you can’t get rid of… don’t worry about ... | [] | [
"TAGS\n#region-us \n"
] |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-300m-singlish-colab
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingfac... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["li_singlish"], "model-index": [{"name": "wav2vec2-large-xls-r-300m-singlish-colab", "results": []}]} | RuiqianLi/wav2vec2-large-xls-r-300m-singlish-colab | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:li_singlish",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-05-18T09:52:17+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-li_singlish #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-large-xls-r-300m-singlish-colab
========================================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the li\_singlish dataset.
It achieves the following results on the evaluation set:
* Loss: 0.7199
* Wer: 0.3337
Model description
-----------------
More informa... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* tr... |
text2text-generation | transformers | Details can be found [here](https://github.com/IBM/quality-controlled-paraphrase-generation) | {} | ibm/qcpg-questions | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-05-18T09:56:12+00:00 | [] | [] | TAGS
#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| Details can be found here | [] | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text2text-generation | transformers |
# Model Card for qcpg-sentences
# Model Details
# Quality Controlled Paraphrase Generation (ACL 2022)
> Paraphrase generation has been widely used in various downstream tasks. Most tasks benefit mainly from high quality paraphrases, namely those that are semantically similar to, yet linguistically diverse from, t... | {"license": "apache-2.0", "tags": ["text-2-text-generation", "t5", "augmentation", "paraphrase", "paraphrasing"]} | ibm/qcpg-sentences | null | [
"transformers",
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"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"regi... | null | 2022-05-18T09:57:00+00:00 | [
"2203.10940",
"1910.09700"
] | [] | TAGS
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|
# Model Card for qcpg-sentences
# Model Details
# Quality Controlled Paraphrase Generation (ACL 2022)
> Paraphrase generation has been widely used in various downstream tasks. Most tasks benefit mainly from high quality paraphrases, namely those that are semantically similar to, yet linguistically diverse from, t... | [
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"# Model Details",
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null | null | Test BERT-BiLSTM-CRF Page for the model | {} | gcelikmasat/BERT-biLSTM-CRF | null | [
"region:us"
] | null | 2022-05-18T09:58:07+00:00 | [] | [] | TAGS
#region-us
| Test BERT-BiLSTM-CRF Page for the model | [] | [
"TAGS\n#region-us \n"
] |
text-classification | transformers | Fine-tuned model based on
#XLM-RoBERTa (large-sized model)
Data for finetuning:
Italian vaccine stance data: 781 training tweets and 281 evaluation tweets
#BibTeX entry and citation info
to be added | {} | FrGes/xlm-roberta-large-finetuned-EUJAV-datasetA | null | [
"transformers",
"pytorch",
"xlm-roberta",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-18T10:05:34+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #text-classification #autotrain_compatible #endpoints_compatible #region-us
| Fine-tuned model based on
#XLM-RoBERTa (large-sized model)
Data for finetuning:
Italian vaccine stance data: 781 training tweets and 281 evaluation tweets
#BibTeX entry and citation info
to be added | [] | [
"TAGS\n#transformers #pytorch #xlm-roberta #text-classification #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-classification | transformers | Fine-tuned model based on
#XLM-RoBERTa (large-sized model)
Data for finetuning:
Italian vaccine stance data: 1042 training tweets and 348 evaluation tweets
#BibTeX entry and citation info
to be added | {} | FrGes/xlm-roberta-large-finetuned-EUJAV-datasetAB | null | [
"transformers",
"pytorch",
"xlm-roberta",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-18T10:18:53+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #text-classification #autotrain_compatible #endpoints_compatible #region-us
| Fine-tuned model based on
#XLM-RoBERTa (large-sized model)
Data for finetuning:
Italian vaccine stance data: 1042 training tweets and 348 evaluation tweets
#BibTeX entry and citation info
to be added | [] | [
"TAGS\n#transformers #pytorch #xlm-roberta #text-classification #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# paraphrase-multilingual-mpnet-base-v2-tuned-smartcat
This model is a fine-tuned version of [sentence-transformers/paraphrase-mul... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "paraphrase-multilingual-mpnet-base-v2-tuned-smartcat", "results": []}]} | steysie/paraphrase-multilingual-mpnet-base-v2-tuned-smartcat | null | [
"transformers",
"pytorch",
"xlm-roberta",
"text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-18T10:54:56+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| paraphrase-multilingual-mpnet-base-v2-tuned-smartcat
====================================================
This model is a fine-tuned version of sentence-transformers/paraphrase-multilingual-mpnet-base-v2 on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0000
Model description... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 6\n* eval\\_batch\\_size: 6\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Training... | [
"TAGS\n#transformers #pytorch #xlm-roberta #text-generation #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: 6\n... |
fill-mask | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# CodeBerta-finetuned-react
This model is a fine-tuned version of [huggingface/CodeBERTa-small-v1](https://huggingface.co/huggingf... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "CodeBerta-finetuned-react", "results": []}]} | EddieChen372/CodeBerta-finetuned-react | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"fill-mask",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-18T11:25:31+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| CodeBerta-finetuned-react
=========================
This model is a fine-tuned version of huggingface/CodeBERTa-small-v1 on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 2.7887
Model description
-----------------
More information needed
Intended uses & limitations
------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0\n* mixed\\_pr... | [
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"### 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\\... |
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-protagonist-english
This model is a fine-tuned version of [Jean-Baptiste/roberta-large-ner-english](https://huggi... | {"tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "bert-finetuned-protagonist-english", "results": []}]} | airi/bert-finetuned-protagonist-english | null | [
"transformers",
"pytorch",
"roberta",
"token-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-18T12:02:48+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #token-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| bert-finetuned-protagonist-english
==================================
This model is a fine-tuned version of Jean-Baptiste/roberta-large-ner-english on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0630
* Precision: 0.8646
* Recall: 0.8839
* F1: 0.8742
* Accuracy: 0.9876
Mode... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Training... | [
"TAGS\n#transformers #pytorch #roberta #token-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_si... |
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
| {"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... | amrahmed/TEST2ppo-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-05-18T12:05:32+00:00 | [] | [] | TAGS
#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2 using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# PPO Agent playing LunarLander-v2\n This is a trained model of a PPO agent playing LunarLander-v2 using the stable-baselines3 library.\n \n ## Usage (with Stable-baselines3)\n TODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\n This is a trained model of a PPO agent playing LunarLander-v2 using the stable-baselines3 library.\n \n ## Usage (with Stable-baselines3)\n TODO: Ad... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-base-timit-demo-google-colab
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/face... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-base-timit-demo-google-colab", "results": []}]} | zoha/wav2vec2-base-timit-demo-google-colab | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-05-18T12:11:37+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-base-timit-demo-google-colab
=====================================
This model is a fine-tuned version of facebook/wav2vec2-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5035
* Wer: 0.3346
Model description
-----------------
More information needed
Intend... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps:... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 8... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# juancopi81/bert-finetuned-ner
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an un... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "base_model": "bert-base-cased", "model-index": [{"name": "juancopi81/bert-finetuned-ner", "results": []}]} | juancopi81/bert-finetuned-ner | null | [
"transformers",
"tf",
"bert",
"token-classification",
"generated_from_keras_callback",
"base_model:bert-base-cased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-18T12:21:00+00:00 | [] | [] | TAGS
#transformers #tf #bert #token-classification #generated_from_keras_callback #base_model-bert-base-cased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| juancopi81/bert-finetuned-ner
=============================
This model is a fine-tuned version of bert-base-cased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.0269
* Validation Loss: 0.0528
* Epoch: 2
Model description
-----------------
More information needed
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_steps': 2631, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': ... | [
"TAGS\n#transformers #tf #bert #token-classification #generated_from_keras_callback #base_model-bert-base-cased #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': 'Ad... |
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": []}]} | EddieChen372/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 | 2022-05-18T12:58:35+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... |
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
| {"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... | sinhprous/ppo-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-05-18T13:03:59+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\n This is a trained model of a PPO agent playing LunarLander-v2 using the stable-baselines3 library.\n \n ## Usage (with Stable-baselines3)\n TODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\n This is a trained model of a PPO agent playing LunarLander-v2 using the stable-baselines3 library.\n \n ## Usage (with Stable-baselines3)\n TODO: Ad... |
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-common_voice-tr-demo-dist
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.c... | {"language": ["tr"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "common_voice", "generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-common_voice-tr-demo-dist", "results": []}]} | cromz22/wav2vec2-common_voice-tr-demo-dist | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"common_voice",
"generated_from_trainer",
"tr",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-05-18T13:17:53+00:00 | [] | [
"tr"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #common_voice #generated_from_trainer #tr #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-common\_voice-tr-demo-dist
===================================
This model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the COMMON\_VOICE - TR dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3848
* Wer: 0.3242
Model description
-----------------
More informat... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 8\n* seed: 42\n* distributed\\_type: multi-GPU\n* num\\_devices: 4\n* total\\_train\\_batch\\_size: 16\n* total\\_eval\\_batch\\_size: 32\n* o... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #common_voice #generated_from_trainer #tr #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003... |
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
| {"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... | fabiochiu/PPO-LunarLander-v2-1M | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-05-18T13:36:01+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\n This is a trained model of a PPO agent playing LunarLander-v2 using the stable-baselines3 library.\n \n ## Usage (with Stable-baselines3)\n TODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\n This is a trained model of a PPO agent playing LunarLander-v2 using the stable-baselines3 library.\n \n ## Usage (with Stable-baselines3)\n TODO: Ad... |
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-protagonist-english-pc
This model is a fine-tuned version of [Jean-Baptiste/roberta-large-ner-english](https://hu... | {"tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "bert-finetuned-protagonist-english-pc", "results": []}]} | airi/bert-finetuned-protagonist-english-pc | null | [
"transformers",
"pytorch",
"roberta",
"token-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-18T14:31:14+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #token-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| bert-finetuned-protagonist-english-pc
=====================================
This model is a fine-tuned version of Jean-Baptiste/roberta-large-ner-english on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0351
* Precision: 0.9513
* Recall: 0.9598
* F1: 0.9556
* Accuracy: 0.9919
... | [
"### 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: 2",
"### Training... | [
"TAGS\n#transformers #pytorch #roberta #token-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_si... |
reinforcement-learning | stable-baselines3 |
# **DQN** Agent playing **LunarLander-v2**
This is a trained model of a **DQN** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
| {"library_name": "stable-baselines3", "tags": ["LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "DQN", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "LunarLander-v2", "type": "LunarL... | fabiochiu/DQN-LunarLander-v2-500k | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-05-18T14:31:34+00:00 | [] | [] | TAGS
#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# DQN Agent playing LunarLander-v2
This is a trained model of a DQN agent playing LunarLander-v2 using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# DQN Agent playing LunarLander-v2\n This is a trained model of a DQN agent playing LunarLander-v2 using the stable-baselines3 library.\n \n ## Usage (with Stable-baselines3)\n TODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# DQN Agent playing LunarLander-v2\n This is a trained model of a DQN agent playing LunarLander-v2 using the stable-baselines3 library.\n \n ## Usage (with Stable-baselines3)\n TODO: Ad... |
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"], "model-index": [{"name": "distilbert-base-uncased-finetuned-squad", "results": []}]} | haunt224/distilbert-base-uncased-finetuned-squad | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-05-18T14:50:55+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #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 an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 2.7507
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: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #question-answering #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: 2e-05\n* train\\_batch\\_size: 16\n* eval... |
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. -->
# hubert-base-cc-finetuned-forum
This model is a fine-tuned version of [SZTAKI-HLT/hubert-base-cc](https://huggingface.co/SZTAKI-H... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "hubert-base-cc-finetuned-forum", "results": []}]} | papsebestyen/hubert-base-cc-finetuned-forum | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-18T15:09:40+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| hubert-base-cc-finetuned-forum
==============================
This model is a fine-tuned version of SZTAKI-HLT/hubert-base-cc on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 2.4746
Model description
-----------------
More information needed
Intended uses & limitations
-... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0\n* mixed\\_pr... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\... |
image-classification | transformers |
# Convolutional Vision Transformer (CvT)
CvT-w24 model pre-trained on ImageNet-22k and fine-tuned on ImageNet-1k at resolution 384x384. It was introduced in the paper [CvT: Introducing Convolutions to Vision Transformers](https://arxiv.org/abs/2103.15808) by Wu et al. and first released in [this repository](https://g... | {"license": "apache-2.0", "tags": ["vision", "image-classification"], "datasets": ["imagenet-1k"], "widget": [{"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg", "example_title": "Tiger"}, {"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg", "example... | microsoft/cvt-w24-384-22k | null | [
"transformers",
"pytorch",
"tf",
"cvt",
"image-classification",
"vision",
"dataset:imagenet-1k",
"arxiv:2103.15808",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-18T16:17:26+00:00 | [
"2103.15808"
] | [] | TAGS
#transformers #pytorch #tf #cvt #image-classification #vision #dataset-imagenet-1k #arxiv-2103.15808 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# Convolutional Vision Transformer (CvT)
CvT-w24 model pre-trained on ImageNet-22k and fine-tuned on ImageNet-1k at resolution 384x384. It was introduced in the paper CvT: Introducing Convolutions to Vision Transformers by Wu et al. and first released in this repository.
Disclaimer: The team releasing CvT did not w... | [
"# Convolutional Vision Transformer (CvT)\n\nCvT-w24 model pre-trained on ImageNet-22k and fine-tuned on ImageNet-1k at resolution 384x384. It was introduced in the paper CvT: Introducing Convolutions to Vision Transformers by Wu et al. and first released in this repository. \n\nDisclaimer: The team releasing CvT d... | [
"TAGS\n#transformers #pytorch #tf #cvt #image-classification #vision #dataset-imagenet-1k #arxiv-2103.15808 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# Convolutional Vision Transformer (CvT)\n\nCvT-w24 model pre-trained on ImageNet-22k and fine-tuned on ImageNet-1k at resolu... |
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. -->
# deep-pavlov-full-2
This model is a fine-tuned version of [DeepPavlov/rubert-base-cased](https://huggingface.co/DeepPavlov/rubert... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "deep-pavlov-full-2", "results": []}]} | ruselkomp/deep-pavlov-full-2 | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"question-answering",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | null | 2022-05-18T16:20:01+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #endpoints_compatible #region-us
| deep-pavlov-full-2
==================
This model is a fine-tuned version of DeepPavlov/rubert-base-cased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 1.0892
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: 18\n* eval\\_batch\\_size: 18\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #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: 18\n* eval\\_batch\\_size: 18\n* see... |
token-classification | spacy | RoBERTA based Social determinants of health NER with a standard ontology extension interface (SNOMED_CT and CUI)
| Feature | Description |
| --- | --- |
| **Name** | `en_sdoh_roberta_cui` |
| **Version** | `0.0.0` |
| **spaCy** | `>=3.2.1,<3.3.0` |
| **Default Pipeline** | `transformer`, `ner`, `sdoh_cui` |
| **Compo... | {"language": ["en"], "license": "mit", "tags": ["spacy", "token-classification"], "widget": [{"text": "She lives in Oakland with her bf and commutes to work by bus.", "example_title": "SDOH NER example 1"}, {"text": "There are some logistical barriers, as she lives in Oakland and works at the VA, commuting by bus", "ex... | dlituiev/en_sdoh_roberta_cui | null | [
"spacy",
"token-classification",
"en",
"license:mit",
"model-index",
"region:us"
] | null | 2022-05-18T16:34:08+00:00 | [] | [
"en"
] | TAGS
#spacy #token-classification #en #license-mit #model-index #region-us
| RoBERTA based Social determinants of health NER with a standard ontology extension interface (SNOMED\_CT and CUI)
### Label Scheme
View label scheme (52 labels for 1 components)
### Accuracy
| [
"### Label Scheme\n\n\n\nView label scheme (52 labels for 1 components)",
"### Accuracy"
] | [
"TAGS\n#spacy #token-classification #en #license-mit #model-index #region-us \n",
"### Label Scheme\n\n\n\nView label scheme (52 labels for 1 components)",
"### Accuracy"
] |
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
| {"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... | sidcodes/ppo-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-05-18T16:36:00+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\n This is a trained model of a PPO agent playing LunarLander-v2 using the stable-baselines3 library.\n \n ## Usage (with Stable-baselines3)\n TODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\n This is a trained model of a PPO agent playing LunarLander-v2 using the stable-baselines3 library.\n \n ## Usage (with Stable-baselines3)\n TODO: Ad... |
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
| {"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... | nonoise/test1_PPO-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-05-18T16:53:08+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\n This is a trained model of a PPO agent playing LunarLander-v2 using the stable-baselines3 library.\n \n ## Usage (with Stable-baselines3)\n TODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\n This is a trained model of a PPO agent playing LunarLander-v2 using the stable-baselines3 library.\n \n ## Usage (with Stable-baselines3)\n TODO: Ad... |
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
| {"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... | OverFitter/ppo-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-05-18T17:01:43+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\n This is a trained model of a PPO agent playing LunarLander-v2 using the stable-baselines3 library.\n \n ## Usage (with Stable-baselines3)\n TODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\n This is a trained model of a PPO agent playing LunarLander-v2 using the stable-baselines3 library.\n \n ## Usage (with Stable-baselines3)\n TODO: Ad... |
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
| {"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... | DenisKochetov/TESTppo-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-05-18T17:07:56+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\n This is a trained model of a PPO agent playing LunarLander-v2 using the stable-baselines3 library.\n \n ## Usage (with Stable-baselines3)\n TODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\n This is a trained model of a PPO agent playing LunarLander-v2 using the stable-baselines3 library.\n \n ## Usage (with Stable-baselines3)\n TODO: Ad... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-base-timit-demo-google-colab
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/face... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-base-timit-demo-google-colab", "results": []}]} | zakria/wav2vec2-base-timit-demo-google-colab | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-05-18T17:42:25+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-base-timit-demo-google-colab
=====================================
This model is a fine-tuned version of facebook/wav2vec2-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5093
* Wer: 0.3413
Model description
-----------------
More information needed
Intend... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps:... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 8... |
text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# gpt2-medium-chunked-eos
This model is a fine-tuned version of [gpt2-medium](https://huggingface.co/gpt2-medium) on an unknown da... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "gpt2-medium-chunked-eos", "results": []}]} | Dizzykong/gpt2-medium-chunked-eos | null | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-05-18T17:44:23+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# gpt2-medium-chunked-eos
This model is a fine-tuned version of gpt2-medium on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
T... | [
"# gpt2-medium-chunked-eos\n\nThis model is a fine-tuned version of gpt2-medium on an unknown dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"#... | [
"TAGS\n#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# gpt2-medium-chunked-eos\n\nThis model is a fine-tuned version of gpt2-medium on an unknown dataset.",
"## Model description\n\nMore ... |
null | 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. -->
# custom-resnet50d2
This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset.
It achieves the following r... | {"tags": ["generated_from_keras_callback"], "model-index": [{"name": "custom-resnet50d2", "results": []}]} | FreddeFrallan/custom-resnet50d2 | null | [
"transformers",
"tf",
"PostTransformation",
"generated_from_keras_callback",
"endpoints_compatible",
"region:us"
] | null | 2022-05-18T17:49:23+00:00 | [] | [] | TAGS
#transformers #tf #PostTransformation #generated_from_keras_callback #endpoints_compatible #region-us
|
# custom-resnet50d2
This model is a fine-tuned version of [](URL on an unknown dataset.
It achieves the following results on the evaluation set:
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Trai... | [
"# custom-resnet50d2\n\nThis model is a fine-tuned version of [](URL on an unknown dataset.\nIt achieves the following results on the evaluation set:",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore infor... | [
"TAGS\n#transformers #tf #PostTransformation #generated_from_keras_callback #endpoints_compatible #region-us \n",
"# custom-resnet50d2\n\nThis model is a fine-tuned version of [](URL on an unknown dataset.\nIt achieves the following results on the evaluation set:",
"## Model description\n\nMore information need... |
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. -->
# MariaZafar/bert-base-cased-finetuned-wikitext2
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-bas... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "MariaZafar/bert-base-cased-finetuned-wikitext2", "results": []}]} | MariaZafar/bert-base-cased-finetuned-wikitext2 | null | [
"transformers",
"tf",
"tensorboard",
"bert",
"fill-mask",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-18T17:51:18+00:00 | [] | [] | TAGS
#transformers #tf #tensorboard #bert #fill-mask #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| MariaZafar/bert-base-cased-finetuned-wikitext2
==============================================
This model is a fine-tuned version of bert-base-cased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 3.9493
* Validation Loss: 4.7051
* 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 #tensorboard #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\... |
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
| {"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... | DenisKochetov/TEST2ppo-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-05-18T18:09:09+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\n This is a trained model of a PPO agent playing LunarLander-v2 using the stable-baselines3 library.\n \n ## Usage (with Stable-baselines3)\n TODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\n This is a trained model of a PPO agent playing LunarLander-v2 using the stable-baselines3 library.\n \n ## Usage (with Stable-baselines3)\n TODO: Ad... |
text-generation | transformers | ```
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("BigSalmon/InformalToFormalLincoln45")
model = AutoModelForCausalLM.from_pretrained("BigSalmon/InformalToFormalLincoln45")
```
```
How To Make Prompt:
informal english: i am very ready to do that just that.
Tra... | {} | BigSalmon/InformalToFormalLincoln45 | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-05-18T18:59:29+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
Keywords to sentences or sentence.
| [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n"
] |
text-classification | transformers |
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 882328335
- CO2 Emissions (in grams): 0.21373468108000182
## Validation Metrics
- Loss: 0.2641160488128662
- Accuracy: 0.9128
- Precision: 0.9444444444444444
- Recall: 0.8772
- AUC: 0.9709556000000001
- F1: 0.9095810866860223
## Usag... | {"language": "unk", "tags": "autotrain", "datasets": ["priyamm/autotrain-data-KeywordExtraction"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 0.21373468108000182} | priyamm/autotrain-KeywordExtraction-882328335 | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain",
"unk",
"dataset:priyamm/autotrain-data-KeywordExtraction",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-18T19:17:16+00:00 | [] | [
"unk"
] | TAGS
#transformers #pytorch #bert #text-classification #autotrain #unk #dataset-priyamm/autotrain-data-KeywordExtraction #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 882328335
- CO2 Emissions (in grams): 0.21373468108000182
## Validation Metrics
- Loss: 0.2641160488128662
- Accuracy: 0.9128
- Precision: 0.9444444444444444
- Recall: 0.8772
- AUC: 0.9709556000000001
- F1: 0.9095810866860223
## Usag... | [
"# Model Trained Using AutoTrain\n\n- Problem type: Binary Classification\n- Model ID: 882328335\n- CO2 Emissions (in grams): 0.21373468108000182",
"## Validation Metrics\n\n- Loss: 0.2641160488128662\n- Accuracy: 0.9128\n- Precision: 0.9444444444444444\n- Recall: 0.8772\n- AUC: 0.9709556000000001\n- F1: 0.909581... | [
"TAGS\n#transformers #pytorch #bert #text-classification #autotrain #unk #dataset-priyamm/autotrain-data-KeywordExtraction #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoTrain\n\n- Problem type: Binary Classification\n- Model ID: 882328335\n- CO2 Emissions... |
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
| {"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... | coldfir3/TEST2ppo-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-05-18T19:29:05+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\n This is a trained model of a PPO agent playing LunarLander-v2 using the stable-baselines3 library.\n \n ## Usage (with Stable-baselines3)\n TODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\n This is a trained model of a PPO agent playing LunarLander-v2 using the stable-baselines3 library.\n \n ## Usage (with Stable-baselines3)\n TODO: Ad... |
text-classification | transformers | Model used by Memes-CS (Metric for Evaluating Model Efficiency in Summarization).
Part of my bachelor's thesis.
Šimon Zvára | {} | SimonZvara/Memes-CS_1.0 | null | [
"transformers",
"pytorch",
"xlm-roberta",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-18T20:39:36+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #text-classification #autotrain_compatible #endpoints_compatible #region-us
| Model used by Memes-CS (Metric for Evaluating Model Efficiency in Summarization).
Part of my bachelor's thesis.
Šimon Zvára | [] | [
"TAGS\n#transformers #pytorch #xlm-roberta #text-classification #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text2text-generation | transformers | <!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# pegasus-multi_news-headline_depreciated
This model is a fine-tuned version of [google/pegasus-multi_news](https://huggingface.co/g... | {"license": "other", "tags": ["generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "pegasus_multi_news-headline_depreciated", "results": []}]} | valurank/pegasus-multi_news-headline | null | [
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"generated_from_trainer",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-18T21:07:13+00:00 | [] | [] | TAGS
#transformers #pytorch #pegasus #text2text-generation #generated_from_trainer #license-other #autotrain_compatible #endpoints_compatible #region-us
| pegasus-multi\_news-headline\_depreciated
=========================================
This model is a fine-tuned version of google/pegasus-multi\_news on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 1.4421
* Rouge1: 41.616
* Rouge2: 22.922
* Rougel: 35.2189
* Rougelsum: 35.3561... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3\n* mixed\\_precis... | [
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #generated_from_trainer #license-other #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 1\n* ev... |
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
| {"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... | Ourfairduke/Lunarland | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-05-18T21:08:09+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\n This is a trained model of a PPO agent playing LunarLander-v2 using the stable-baselines3 library.\n \n ## Usage (with Stable-baselines3)\n TODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\n This is a trained model of a PPO agent playing LunarLander-v2 using the stable-baselines3 library.\n \n ## Usage (with Stable-baselines3)\n TODO: Ad... |
text2text-generation | transformers |
Fine-tuned T5 small model for use as a frame semantic parser in the [Frame Semantic Transformer](https://github.com/chanind/frame-semantic-transformer) project. This model is trained on data from [FrameNet](https://framenet2.icsi.berkeley.edu/).
### Usage
This is meant to be used a part of [Frame Semantic Transforme... | {"license": "apache-2.0"} | chanind/frame-semantic-transformer-small | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-05-18T21:14:49+00:00 | [] | [] | TAGS
#transformers #pytorch #t5 #text2text-generation #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| Fine-tuned T5 small model for use as a frame semantic parser in the Frame Semantic Transformer project. This model is trained on data from FrameNet.
### Usage
This is meant to be used a part of Frame Semantic Transformer. See that project for usage instructions.
### Tasks
This model is trained to perform 3 task... | [
"### Usage\n\n\nThis is meant to be used a part of Frame Semantic Transformer. See that project for usage instructions.",
"### Tasks\n\n\nThis model is trained to perform 3 tasks related to semantic frame parsing:\n\n\n1. Identify frame trigger locations in the text\n2. Classify the frame given a trigger location... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Usage\n\n\nThis is meant to be used a part of Frame Semantic Transformer. See that project for usage instructions.",
"### Tasks\n\n\nThis model ... |
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": ["cifar100"], "metrics": ["accuracy"], "model-index": [{"name": "vit-base-beans-demo-v5", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "Cifar100", "type": "cifa... | Ahmed9275/Vit-Cifar100 | null | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:cifar100",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-18T21:16:08+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #vit #image-classification #generated_from_trainer #dataset-cifar100 #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 Cifar100 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4420
* Accuracy: 0.8985
Model description
-----------------
More information needed
Intended uses & ... | [
"### 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 #tensorboard #vit #image-classification #generated_from_trainer #dataset-cifar100 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\... |
text-classification | transformers |
## Citation
Please star the [GRS GitHub repo](https://github.com/imohammad12/GRS) and cite the paper if you found our model useful:
```
@inproceedings{dehghan-etal-2022-grs,
title = "{GRS}: Combining Generation and Revision in Unsupervised Sentence Simplification",
author = "Dehghan, Mohammad and
Kumar... | {"language": "en", "tags": "grs"} | imohammad12/GRS-complex-simple-classifier-DeBerta | null | [
"transformers",
"pytorch",
"deberta",
"text-classification",
"grs",
"en",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-18T21:37:49+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #deberta #text-classification #grs #en #autotrain_compatible #endpoints_compatible #region-us
|
Please star the GRS GitHub repo and cite the paper if you found our model useful:
| [] | [
"TAGS\n#transformers #pytorch #deberta #text-classification #grs #en #autotrain_compatible #endpoints_compatible #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
| {"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... | Ourfairduke/Lunarland1M | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-05-18T21:57:13+00:00 | [] | [] | TAGS
#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2 using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# PPO Agent playing LunarLander-v2\n This is a trained model of a PPO agent playing LunarLander-v2 using the stable-baselines3 library.\n \n ## Usage (with Stable-baselines3)\n TODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\n This is a trained model of a PPO agent playing LunarLander-v2 using the stable-baselines3 library.\n \n ## Usage (with Stable-baselines3)\n TODO: Ad... |
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
| {"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... | Chris1/TEST1ppo-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-05-18T22:23:37+00:00 | [] | [] | TAGS
#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2 using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# PPO Agent playing LunarLander-v2\n This is a trained model of a PPO agent playing LunarLander-v2 using the stable-baselines3 library.\n \n ## Usage (with Stable-baselines3)\n TODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\n This is a trained model of a PPO agent playing LunarLander-v2 using the stable-baselines3 library.\n \n ## Usage (with Stable-baselines3)\n TODO: Ad... |
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
| {"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... | Aladin/PPO_lunar_lander_v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-05-18T22:45:46+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\n This is a trained model of a PPO agent playing LunarLander-v2 using the stable-baselines3 library.\n \n ## Usage (with Stable-baselines3)\n TODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\n This is a trained model of a PPO agent playing LunarLander-v2 using the stable-baselines3 library.\n \n ## Usage (with Stable-baselines3)\n TODO: Ad... |
text2text-generation | transformers |
## Citation
Please star the [GRS GitHub repo](https://github.com/imohammad12/GRS) and cite the paper if you found our model useful:
```
@inproceedings{dehghan-etal-2022-grs,
title = "{GRS}: Combining Generation and Revision in Unsupervised Sentence Simplification",
author = "Dehghan, Mohammad and
Kumar... | {"language": "en", "tags": "grs"} | imohammad12/GRS-Constrained-Paraphrasing-Bart | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"grs",
"en",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-18T23:14:51+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bart #text2text-generation #grs #en #autotrain_compatible #endpoints_compatible #region-us
|
Please star the GRS GitHub repo and cite the paper if you found our model useful:
| [] | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #grs #en #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-classification | transformers |
## Citation
Please star the [GRS GitHub repo](https://github.com/imohammad12/GRS) and cite the paper if you found our model useful:
```
@inproceedings{dehghan-etal-2022-grs,
title = "{GRS}: Combining Generation and Revision in Unsupervised Sentence Simplification",
author = "Dehghan, Mohammad and
Kumar... | {"language": "en", "tags": "grs"} | imohammad12/GRS-Grammar-Checker-DeBerta | null | [
"transformers",
"pytorch",
"deberta",
"text-classification",
"grs",
"en",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-19T00:01:25+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #deberta #text-classification #grs #en #autotrain_compatible #endpoints_compatible #region-us
|
Please star the GRS GitHub repo and cite the paper if you found our model useful:
| [] | [
"TAGS\n#transformers #pytorch #deberta #text-classification #grs #en #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# pegasus-cnn_dailymail_2
This model is a fine-tuned version of [google/pegasus-cnn_dailymail](https://huggingface.co/google/pegas... | {"tags": ["generated_from_trainer"], "datasets": ["cnn_dailymail"], "model-index": [{"name": "pegasus-cnn_dailymail_2", "results": []}]} | alk/pegasus-cnn_dailymail_2 | null | [
"transformers",
"pytorch",
"tensorboard",
"pegasus",
"text2text-generation",
"generated_from_trainer",
"dataset:cnn_dailymail",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-19T00:11:50+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #pegasus #text2text-generation #generated_from_trainer #dataset-cnn_dailymail #autotrain_compatible #endpoints_compatible #region-us
| pegasus-cnn\_dailymail\_2
=========================
This model is a fine-tuned version of google/pegasus-cnn\_dailymail on the cnn\_dailymail dataset.
It achieves the following results on the evaluation set:
* Loss: 1.4308
Model description
-----------------
More information needed
Intended uses & limitations... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\n* seed: 42\n* gradient\\_accumulation\\_steps: 16\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=... | [
"TAGS\n#transformers #pytorch #tensorboard #pegasus #text2text-generation #generated_from_trainer #dataset-cnn_dailymail #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_... |
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/1497272349636239361/L-9J... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/barterblex/1652924018963/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/barterblex | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-05-19T00:32:35+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Dr. Negative B
@barterblex
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"
] |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# clementgyj/bert-finetuned-squad
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an ... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "clementgyj/bert-finetuned-squad", "results": []}]} | clementgyj/bert-finetuned-squad | null | [
"transformers",
"tf",
"bert",
"question-answering",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-05-19T00:55:34+00:00 | [] | [] | TAGS
#transformers #tf #bert #question-answering #generated_from_keras_callback #license-apache-2.0 #endpoints_compatible #region-us
| clementgyj/bert-finetuned-squad
===============================
This model is a fine-tuned version of bert-base-cased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.6741
* Epoch: 2
Model description
-----------------
More information needed
Intended uses & limi... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'inner\\_optimizer': {'class\\_name': 'AdamWeightDecay', 'config': {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\... | [
"TAGS\n#transformers #tf #bert #question-answering #generated_from_keras_callback #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'inner\\_optimizer': {'class\\_name': 'AdamWeightDecay', 'confi... |
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-food101-demo-v5
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google... | {"license": "apache-2.0", "tags": ["image-classification", "generated_from_trainer"], "datasets": ["food101"], "metrics": ["accuracy"], "model-index": [{"name": "vit-base-food101-demo-v5", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "food101", "type": "food... | eslamxm/vit-base-food101 | null | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:food101",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-19T01:41:57+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #vit #image-classification #generated_from_trainer #dataset-food101 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| vit-base-food101-demo-v5
========================
This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the food101 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5493
* Accuracy: 0.8539
Model description
-----------------
More information needed
Intended uses... | [
"### 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 #tensorboard #vit #image-classification #generated_from_trainer #dataset-food101 #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\\_... |
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-xls-r-300m-phoneme
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-xls-r-300m-phoneme", "results": []}]} | vitouphy/wav2vec2-xls-r-300m-phoneme | null | [
"transformers",
"pytorch",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-05-19T02:03:57+00:00 | [] | [] | TAGS
#transformers #pytorch #safetensors #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #has_space #region-us
| wav2vec2-xls-r-300m-phoneme
===========================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3327
* Cer: 0.1332
Model description
-----------------
More information needed
Intended uses & limi... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1... | [
"TAGS\n#transformers #pytorch #safetensors #wav2vec2 #automatic-speech-recognition #generated_from_trainer #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: 3e-05\n* train\\_batch... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-imdb
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) ... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imdb"], "model-index": [{"name": "distilbert-imdb", "results": []}]} | wooglee/distilbert-imdb | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-19T02:12:24+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-imdb #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-imdb
===============
This model is a fine-tuned version of distilbert-base-uncased on the imdb dataset.
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
------------------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1",
"### Trai... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #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: 5e-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/751118197126991873/eSXub... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/lightcrypto-sergeynazarov/1652931465147/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/lightcrypto-sergeynazarov | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-05-19T02:37:09+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI CYBORG
Sergey Nazarov & light
@lightcrypto-sergeynazarov
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 repor... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# rob2rand_chen
This model was trained from scratch on the None dataset.
## Model description
More information needed
## Intend... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "rob2rand_chen", "results": []}]} | imamnurby/rob2rand_chen | null | [
"transformers",
"pytorch",
"encoder-decoder",
"text2text-generation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-19T03:32:14+00:00 | [] | [] | TAGS
#transformers #pytorch #encoder-decoder #text2text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
|
# rob2rand_chen
This model was trained from scratch on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparamete... | [
"# rob2rand_chen\n\nThis model was trained from scratch on the None dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameter... | [
"TAGS\n#transformers #pytorch #encoder-decoder #text2text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"# rob2rand_chen\n\nThis model was trained from scratch on the None dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitat... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mT5_multilingual_XLSum-finetuned-summarization
This model is a fine-tuned version of [csebuetnlp/mT5_multilingual_XLSum](https:/... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "mT5_multilingual_XLSum-finetuned-summarization", "results": []}]} | GiordanoB/mT5_multilingual_XLSum-finetuned-summarization | null | [
"transformers",
"pytorch",
"tensorboard",
"mt5",
"text2text-generation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-05-19T04:08:50+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #mt5 #text2text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# mT5_multilingual_XLSum-finetuned-summarization
This model is a fine-tuned version of csebuetnlp/mT5_multilingual_XLSum on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Traini... | [
"# mT5_multilingual_XLSum-finetuned-summarization\n\nThis model is a fine-tuned version of csebuetnlp/mT5_multilingual_XLSum on an unknown dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore informa... | [
"TAGS\n#transformers #pytorch #tensorboard #mt5 #text2text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# mT5_multilingual_XLSum-finetuned-summarization\n\nThis model is a fine-tuned version of csebuetnlp/mT5_multilingual_XLSum on an un... |
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. -->
# opus-mt-en-de-finetuned-de-to-en
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-de](https://huggingface.co/Helsi... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["wmt14"], "model-index": [{"name": "opus-mt-en-de-finetuned-de-to-en", "results": []}]} | PontifexMaximus/opus-mt-en-de-finetuned-de-to-en | null | [
"transformers",
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"generated_from_trainer",
"dataset:wmt14",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-19T04:48:17+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #marian #text2text-generation #generated_from_trainer #dataset-wmt14 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# opus-mt-en-de-finetuned-de-to-en
This model is a fine-tuned version of Helsinki-NLP/opus-mt-en-de on the wmt14 dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Trai... | [
"# opus-mt-en-de-finetuned-de-to-en\n\nThis model is a fine-tuned version of Helsinki-NLP/opus-mt-en-de on the wmt14 dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Tr... | [
"TAGS\n#transformers #pytorch #tensorboard #marian #text2text-generation #generated_from_trainer #dataset-wmt14 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# opus-mt-en-de-finetuned-de-to-en\n\nThis model is a fine-tuned version of Helsinki-NLP/opus-mt-en-de on the wmt14 datas... |
sentence-similarity | sentence-transformers |
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when ... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity", "inference": false} | NFflow/healthcare_27.03.2021-27.03.2022_redditflow | null | [
"sentence-transformers",
"pytorch",
"distilbert",
"feature-extraction",
"sentence-similarity",
"transformers",
"region:us"
] | null | 2022-05-19T05:11:11+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #region-us
|
# {MODEL_NAME}
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers installed:
Then you can u... | [
"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\n... | [
"TAGS\n#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #region-us \n",
"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",... |
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. -->
# opus-mt-tr-en-finetuned-tr-to-en
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-tr-en](https://huggingface.co/Helsi... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["opus_infopankki"], "metrics": ["bleu"], "model-index": [{"name": "opus-mt-tr-en-finetuned-tr-to-en", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "opus_infopankki",... | PontifexMaximus/TurkishTranslator | null | [
"transformers",
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"generated_from_trainer",
"dataset:opus_infopankki",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-05-19T06:10:55+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #marian #text2text-generation #generated_from_trainer #dataset-opus_infopankki #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
| opus-mt-tr-en-finetuned-tr-to-en
================================
This model is a fine-tuned version of Helsinki-NLP/opus-mt-tr-en on the opus\_infopankki dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6924
* Bleu: 54.7617
* Gen Len: 13.5501
Model description
-----------------
More ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-06\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 16\n* mixed\\_pre... | [
"TAGS\n#transformers #pytorch #tensorboard #marian #text2text-generation #generated_from_trainer #dataset-opus_infopankki #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 traini... |
token-classification | transformers | This model can be used for sentence compression (aka extractive sentence summarization).
It predicts for each word, whether the word can be dropped from the sentence without severely affecting its meaning.
The resulting sentences are often ungrammatical, but they still can be useful.
The model is [rubert-tiny2]() fi... | {} | cointegrated/rubert-tiny2-sentence-compression | null | [
"transformers",
"pytorch",
"safetensors",
"bert",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-19T06:19:01+00:00 | [] | [] | TAGS
#transformers #pytorch #safetensors #bert #token-classification #autotrain_compatible #endpoints_compatible #region-us
| This model can be used for sentence compression (aka extractive sentence summarization).
It predicts for each word, whether the word can be dropped from the sentence without severely affecting its meaning.
The resulting sentences are often ungrammatical, but they still can be useful.
The model is [rubert-tiny2]() fi... | [] | [
"TAGS\n#transformers #pytorch #safetensors #bert #token-classification #autotrain_compatible #endpoints_compatible #region-us \n"
] |
fill-mask | transformers | Just a dummy first model | {} | varunpatrikar/dummy-model | null | [
"transformers",
"pytorch",
"camembert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-19T06:19:38+00:00 | [] | [] | TAGS
#transformers #pytorch #camembert #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| Just a dummy first model | [] | [
"TAGS\n#transformers #pytorch #camembert #fill-mask #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
```
pickle_model = load_from_hub(repo_id="ThomasSimonini/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery et... | {"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 +/... | ThomasSimonini/q-Taxi-v3 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-05-19T06:24:26+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"
] |
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. -->
# opus-mt-en-ro-finetuned-ro-to-en
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-ro](https://huggingface.co/Helsi... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["wmt16"], "model-index": [{"name": "opus-mt-en-ro-finetuned-ro-to-en", "results": []}]} | PontifexMaximus/opus-mt-en-ro-finetuned-ro-to-en | null | [
"transformers",
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"generated_from_trainer",
"dataset:wmt16",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-19T06:27:51+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #marian #text2text-generation #generated_from_trainer #dataset-wmt16 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# opus-mt-en-ro-finetuned-ro-to-en
This model is a fine-tuned version of Helsinki-NLP/opus-mt-en-ro on the wmt16 dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Trai... | [
"# opus-mt-en-ro-finetuned-ro-to-en\n\nThis model is a fine-tuned version of Helsinki-NLP/opus-mt-en-ro on the wmt16 dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Tr... | [
"TAGS\n#transformers #pytorch #tensorboard #marian #text2text-generation #generated_from_trainer #dataset-wmt16 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# opus-mt-en-ro-finetuned-ro-to-en\n\nThis model is a fine-tuned version of Helsinki-NLP/opus-mt-en-ro on the wmt16 datas... |
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)
from huggingface_sb3 import load_from_hub
checkpoint = load_from_hub(rep... | {"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... | XGBooster/LunarLanderPPO | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-05-19T06:29:22+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)
from huggingface_sb3 import load_from_hub
checkpoint = load_from_hub(repo_id="XGBooster/LunarLanderPPO",
filename="{MODEL FILENAME}.zi... | [
"# PPO Agent playing LunarLander-v2\n This is a trained model of a PPO agent playing LunarLander-v2 using the stable-baselines3 library.\n \n ## Usage (with Stable-baselines3)\n from huggingface_sb3 import load_from_hub\n \n checkpoint = load_from_hub(repo_id=\"XGBooster/LunarLanderPPO\",\n\tfilename=\"{MODEL... | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\n This is a trained model of a PPO agent playing LunarLander-v2 using the stable-baselines3 library.\n \n ## Usage (with Stable-baselines3)\n from hug... |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# CherylTSW/bert-finetuned-squad
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an u... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "CherylTSW/bert-finetuned-squad", "results": []}]} | CherylTSW/bert-finetuned-squad | null | [
"transformers",
"tf",
"bert",
"question-answering",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-05-19T06:35:46+00:00 | [] | [] | TAGS
#transformers #tf #bert #question-answering #generated_from_keras_callback #license-apache-2.0 #endpoints_compatible #region-us
| CherylTSW/bert-finetuned-squad
==============================
This model is a fine-tuned version of bert-base-cased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.6669
* Epoch: 2
Model description
-----------------
More information needed
Intended uses & limita... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'inner\\_optimizer': {'class\\_name': 'AdamWeightDecay', 'config': {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\... | [
"TAGS\n#transformers #tf #bert #question-answering #generated_from_keras_callback #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'inner\\_optimizer': {'class\\_name': 'AdamWeightDecay', 'confi... |
question-answering | null |
**task**: `question-answering`
Fixed parameters:
* **model_name_or_path**: `distilbert-base-uncased-distilled-squad`
* **dataset**:
* **path**: `squad`
* **name**: `None`
* **calibration_split**: `train`
* **eval_split**: `validation`
* **data_keys**: `{'question': 'question', 'context': 'context'... | {"tags": ["distilbert"], "datasets": ["squad"], "metrics": ["exact_match", "f1"], "pipeline_tag": "question-answering"} | fxmarty/donotdelete2 | null | [
"tensorboard",
"distilbert",
"question-answering",
"dataset:squad",
"region:us"
] | null | 2022-05-19T06:38:16+00:00 | [] | [] | TAGS
#tensorboard #distilbert #question-answering #dataset-squad #region-us
|
task: 'question-answering'
Fixed parameters:
* model_name_or_path: 'distilbert-base-uncased-distilled-squad'
* dataset:
* path: 'squad'
* name: 'None'
* calibration_split: 'train'
* eval_split: 'validation'
* data_keys: '{'question': 'question', 'context': 'context'}'
* ref_keys: '['answers']'... | [
"## Evaluation\nBelow, time metrics for\n* Batch size: 8\n* Input length: 128\n| quantization_approach | operators_to_quantize | | latency_mean (original, ms) | latency_mean (optimized, ms) | | throughput (original, /s) | throughput (optimized, /s) | | exact_match (original) | exact_match (optimized) | ... | [
"TAGS\n#tensorboard #distilbert #question-answering #dataset-squad #region-us \n",
"## Evaluation\nBelow, time metrics for\n* Batch size: 8\n* Input length: 128\n| quantization_approach | operators_to_quantize | | latency_mean (original, ms) | latency_mean (optimized, ms) | | throughput (original, /s) | t... |
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... | awilli/bert-finetuned-ner | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-19T06:51:30+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| bert-finetuned-ner
==================
This model is a fine-tuned version of bert-base-cased on the conll2003 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0673
* Precision: 0.9295
* Recall: 0.9458
* F1: 0.9376
* Accuracy: 0.9848
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... |
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-powo_mgh_pt
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingfac... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "distilbert-base-uncased-finetuned-powo_mgh_pt", "results": []}]} | ViktorDo/distilbert-base-uncased-finetuned-powo_mgh_pt | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-19T06:54:22+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-powo\_mgh\_pt
===============================================
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: 1.0128
Model description
-----------------
More information needed
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0\n* mixed\\_pr... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_siz... |
text2text-generation | transformers |
This repository contains a language-specific mT5-base, where the vocabulary is condensed to include tokens used in Danish and English. | {"language": ["da"]} | sarakolding/daT5-base | null | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"da",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-05-19T07:03:45+00:00 | [] | [
"da"
] | TAGS
#transformers #pytorch #mt5 #text2text-generation #da #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
This repository contains a language-specific mT5-base, where the vocabulary is condensed to include tokens used in Danish and English. | [] | [
"TAGS\n#transformers #pytorch #mt5 #text2text-generation #da #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
null | transformers | Fine-tuned KB BERT for identifying compounded introductions in the Riksdagen corpus | {} | jesperjmb/CompundedIntros | null | [
"transformers",
"pytorch",
"bert",
"next-sentence-prediction",
"endpoints_compatible",
"region:us"
] | null | 2022-05-19T07:05:17+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #next-sentence-prediction #endpoints_compatible #region-us
| Fine-tuned KB BERT for identifying compounded introductions in the Riksdagen corpus | [] | [
"TAGS\n#transformers #pytorch #bert #next-sentence-prediction #endpoints_compatible #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
| {"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... | shintaro/test-2ppo-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-05-19T07:43:06+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\n This is a trained model of a PPO agent playing LunarLander-v2 using the stable-baselines3 library.\n \n ## Usage (with Stable-baselines3)\n TODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\n This is a trained model of a PPO agent playing LunarLander-v2 using the stable-baselines3 library.\n \n ## Usage (with Stable-baselines3)\n TODO: Ad... |
text2text-generation | transformers |
# Model description
This model is [t5-base](https://huggingface.co/t5-base) fine-tuned on the [190k Medium Articles](https://www.kaggle.com/datasets/fabiochiusano/medium-articles) dataset for predicting article tags using the article textual content as input. While usually formulated as a multi-label classification p... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "widget": [{"text": "Python is a high-level, interpreted, general-purpose programming language. Its design philosophy emphasizes code readability with the use of significant indentation. Python is dynamically-typed and garbage-collected.", "example_title": "... | fabiochiu/t5-base-tag-generation | null | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-05-19T07:45:13+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #safetensors #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
# Model description
This model is t5-base fine-tuned on the 190k Medium Articles dataset for predicting article tags using the article textual content as input. While usually formulated as a multi-label classification problem, this model deals with _tag generation_ as a text2text generation task (inspiration from tex... | [
"# Model description\n\nThis model is t5-base fine-tuned on the 190k Medium Articles dataset for predicting article tags using the article textual content as input. While usually formulated as a multi-label classification problem, this model deals with _tag generation_ as a text2text generation task (inspiration fr... | [
"TAGS\n#transformers #pytorch #tensorboard #safetensors #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"# Model description\n\nThis model is t5-base fine-tuned on the 190k Medium Articles datas... |
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. -->
# Boglinger/mt5-small-klex
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on an unkno... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "Boglinger/mt5-small-klex", "results": []}]} | Boglinger/mt5-small-klex | null | [
"transformers",
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"text2text-generation",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-05-19T08:17:01+00:00 | [] | [] | TAGS
#transformers #tf #mt5 #text2text-generation #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| Boglinger/mt5-small-klex
========================
This model is a fine-tuned version of google/mt5-small on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 4.7908
* Validation Loss: 3.2086
* Epoch: 19
Model description
-----------------
More information needed
Intend... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 5.6e-05, 'decay\\_steps': 2344, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle'... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam... |
text2text-generation | transformers | # UnifiedQA-Reddit-SYAC
This is an abstractive title answering (TA) / clickbait spoiling model.
This is a variant of [allenai/unifiedqa-t5-large](https://huggingface.co/allenai/unifiedqa-t5-large), fine-tuned on the Reddit SYAC dataset.
The model was trained as part of my masters thesis:
_Abstractive title answ... | {"language": "en"} | marksverdhei/unifiedqa-large-reddit-syac | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"en",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-05-19T08:28:21+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #en #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| UnifiedQA-Reddit-SYAC
=====================
This is an abstractive title answering (TA) / clickbait spoiling model.
This is a variant of allenai/unifiedqa-t5-large, fine-tuned on the Reddit SYAC dataset.
The model was trained as part of my masters thesis:
*Abstractive title answering for clickbait content*
... | [
"### Disinformation\n\n\nThis model has the proven capability of generating, and hallucinating false information. \n\nAny use of a TA system such as this one should be with knowledge of this risk.\n\n\nPerformance\n-----------",
"### Intrinsic\n\n\nThe following scores is the result of intrinsic evaluation on th... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #en #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"### Disinformation\n\n\nThis model has the proven capability of generating, and hallucinating false information. \n\nAny use of a TA system such as this o... |
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/1522099366592352257/qhlV... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/pmadhavv/1652952613201/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/pmadhavv | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-05-19T08:29:26+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Madhav Patel
@pmadhavv
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-classification | transformers |
# RePublic
### Model description
RePublic (<u>re</u>putation analyzer for <u>public</u> service organizations) is a Dutch BERT model based on BERTje (De Vries, 2019). The model was designed to predict the sentiment in Dutch-language news article text about public agencies. RePublic was developed by [CLiPS](https://ww... | {"language": ["nl"], "tags": ["text classification", "sentiment analysis", "domain adaptation"], "pipeline_tag": "text-classification", "widget": [{"text": "De NMBS heeft recent de airconditioning in alle treinen vernieuwd.", "example_title": "POS-NMBS"}, {"text": "De wegenwerken langs de E34 blijven al maanden aanhoud... | clips/republic | null | [
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"bert",
"text-classification",
"text classification",
"sentiment analysis",
"domain adaptation",
"nl",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-19T08:56:10+00:00 | [] | [
"nl"
] | TAGS
#transformers #pytorch #bert #text-classification #text classification #sentiment analysis #domain adaptation #nl #autotrain_compatible #endpoints_compatible #region-us
| RePublic
========
### Model description
RePublic (reputation analyzer for public service organizations) is a Dutch BERT model based on BERTje (De Vries, 2019). The model was designed to predict the sentiment in Dutch-language news article text about public agencies. RePublic was developed by CLiPS in collaboration ... | [
"### Model description\n\n\nRePublic (reputation analyzer for public service organizations) is a Dutch BERT model based on BERTje (De Vries, 2019). The model was designed to predict the sentiment in Dutch-language news article text about public agencies. RePublic was developed by CLiPS in collaboration with Prof. D... | [
"TAGS\n#transformers #pytorch #bert #text-classification #text classification #sentiment analysis #domain adaptation #nl #autotrain_compatible #endpoints_compatible #region-us \n",
"### Model description\n\n\nRePublic (reputation analyzer for public service organizations) is a Dutch BERT model based on BERTje (De... |
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
| {"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... | sulimovp/TEST2ppo-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-05-19T09:09:52+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\n This is a trained model of a PPO agent playing LunarLander-v2 using the stable-baselines3 library.\n \n ## Usage (with Stable-baselines3)\n TODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\n This is a trained model of a PPO agent playing LunarLander-v2 using the stable-baselines3 library.\n \n ## Usage (with Stable-baselines3)\n TODO: Ad... |
fill-mask | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-uncased-scratch-powo_mgh_pt
This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset.
It ... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "bert-base-uncased-scratch-powo_mgh_pt", "results": []}]} | ViktorDo/bert-base-uncased-scratch-powo_mgh_pt | null | [
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"tensorboard",
"bert",
"fill-mask",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-19T09:37:18+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| bert-base-uncased-scratch-powo\_mgh\_pt
=======================================
This model is a fine-tuned version of [](URL on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 4.5901
Model description
-----------------
More information needed
Intended uses & limitations
----... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 5\n* eval\\_batch\\_size: 16\n* seed: 42\n* gradient\\_accumulation\\_steps: 8\n* total\\_train\\_batch\\_size: 40\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 5\n* eval\\_batch\\_siz... |
reinforcement-learning | stable-baselines3 |
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
| {"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... | Yozh2/ppo-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-05-19T09:51:09+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\n This is a trained model of a PPO agent playing LunarLander-v2 using the stable-baselines3 library.\n \n ## Usage (with Stable-baselines3)\n TODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\n This is a trained model of a PPO agent playing LunarLander-v2 using the stable-baselines3 library.\n \n ## Usage (with Stable-baselines3)\n TODO: Ad... |
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. -->
# test_trainer
This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "test_trainer", "results": []}]} | vijaygoriya/test_trainer | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-19T10:03:06+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| test\_trainer
=============
This model is a fine-tuned version of bert-base-multilingual-cased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.9646
* Accuracy: 0.8171
Model description
-----------------
More information needed
Intended uses & limitations
-------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Traini... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 8\n* e... |
text-generation | transformers |
# mawaidhaChatbot Model | {"tags": ["conversational"]} | okwach/mawaidhaChatbot | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-05-19T10:18:50+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# mawaidhaChatbot Model | [
"# mawaidhaChatbot Model"
] | [
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"# mawaidhaChatbot 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. -->
# Boglinger/mt5-small-german-finetune-mlsum-klex
This model is a fine-tuned version of [ml6team/mt5-small-german-finetune-mlsum](https:/... | {"tags": ["generated_from_keras_callback"], "model-index": [{"name": "Boglinger/mt5-small-german-finetune-mlsum-klex", "results": []}]} | Boglinger/mt5-small-german-finetune-mlsum-klex | null | [
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"text2text-generation",
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"autotrain_compatible",
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"region:us"
] | null | 2022-05-19T10:48:37+00:00 | [] | [] | TAGS
#transformers #tf #t5 #text2text-generation #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| Boglinger/mt5-small-german-finetune-mlsum-klex
==============================================
This model is a fine-tuned version of ml6team/mt5-small-german-finetune-mlsum on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 3.7473
* Validation Loss: 3.3362
* Epoch: 9
Mode... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 5.6e-05, 'decay\\_steps': 744, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle':... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learni... |
text-generation | transformers |
<h1 style='text-align: center '>BLOOM LM</h1>
<h2 style='text-align: center '><em>BigScience Large Open-science Open-access Multilingual Language Model</em> </h2>
<h3 style='text-align: center '>Model Card</h3>
<img src="https://s3.amazonaws.com/moonup/production/uploads/1657124309515-5f17f0a0925b9863e28ad517.png" a... | {"language": ["ak", "ar", "as", "bm", "bn", "ca", "code", "en", "es", "eu", "fon", "fr", "gu", "hi", "id", "ig", "ki", "kn", "lg", "ln", "ml", "mr", "ne", "nso", "ny", "or", "pa", "pt", "rn", "rw", "sn", "st", "sw", "ta", "te", "tn", "ts", "tum", "tw", "ur", "vi", "wo", "xh", "yo", "zh", "zhs", "zht", "zu"], "license":... | bigscience/bloom-560m | null | [
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========
*BigScience Large Open-science Open-access Multilingual Language Model*
-----------------------------------------------------------------------
### Model Card
Version 1.0 / 26.May.2022
Model Card for Bloom-560m
=========================
Table of Contents
-----------------
1. ... | [
"### Model Card\n\n\n\nVersion 1.0 / 26.May.2022\n\n\nModel Card for Bloom-560m\n=========================\n\n\nTable of Contents\n-----------------\n\n\n1. Model Details\n2. Uses\n3. Bias, Risks, and Limitations\n4. Recommendations\n5. Training Data\n6. Evaluation\n7. Environmental Impact\n8. Technica... | [
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text-generation | transformers |
<h1 style='text-align: center '>BLOOM LM</h1>
<h2 style='text-align: center '><em>BigScience Large Open-science Open-access Multilingual Language Model</em> </h2>
<h3 style='text-align: center '>Model Card</h3>
<img src="https://s3.amazonaws.com/moonup/production/uploads/1657124309515-5f17f0a0925b9863e28ad517.png" a... | {"language": ["ak", "ar", "as", "bm", "bn", "ca", "code", "en", "es", "eu", "fon", "fr", "gu", "hi", "id", "ig", "ki", "kn", "lg", "ln", "ml", "mr", "ne", "nso", "ny", "or", "pa", "pt", "rn", "rw", "sn", "st", "sw", "ta", "te", "tn", "ts", "tum", "tw", "ur", "vi", "wo", "xh", "yo", "zh", "zhs", "zht", "zu"], "license":... | bigscience/bloom-1b1 | null | [
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#transformers #pytorch #jax #onnx #safetensors #bloom #text-generation #ak #ar #as #bm #bn #ca #code #en #es #eu #fon #fr #gu #hi #id #ig #ki #kn #lg #ln #ml #mr #ne #nso #ny #or #pa #pt #rn #rw #sn #st #sw #ta #te #tn #ts #tum #tw #ur #vi #wo #xh #yo #zh #zhs #zht #zu #arxiv-1909.08053 #arxiv-2110.02861 #arxiv-21... | BLOOM LM
========
*BigScience Large Open-science Open-access Multilingual Language Model*
-----------------------------------------------------------------------
### Model Card
Version 1.0 / 26.May.2022
Table of Contents
-----------------
1. Model Details
2. Uses
3. Training Data
4. Risks and Li... | [
"### Model Card\n\n\n\nVersion 1.0 / 26.May.2022\n\n\nTable of Contents\n-----------------\n\n\n1. Model Details\n2. Uses\n3. Training Data\n4. Risks and Limitations\n5. Evaluation\n6. Recommendations\n7. Glossary and Calculations\n8. More Information\n9. Model Card Authors\n\n\nModel Details\n--------... | [
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text-generation | transformers |
<h1 style='text-align: center '>BLOOM LM</h1>
<h2 style='text-align: center '><em>BigScience Large Open-science Open-access Multilingual Language Model</em> </h2>
<h3 style='text-align: center '>Model Card</h3>
<img src="https://s3.amazonaws.com/moonup/production/uploads/1657124309515-5f17f0a0925b9863e28ad517.png" a... | {"language": ["ak", "ar", "as", "bm", "bn", "ca", "code", "en", "es", "eu", "fon", "fr", "gu", "hi", "id", "ig", "ki", "kn", "lg", "ln", "ml", "mr", "ne", "nso", "ny", "or", "pa", "pt", "rn", "rw", "sn", "st", "sw", "ta", "te", "tn", "ts", "tum", "tw", "ur", "vi", "wo", "xh", "yo", "zh", "zhs", "zht", "zu"], "license":... | bigscience/bloom-1b7 | null | [
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... | null | 2022-05-19T10:52:06+00:00 | [
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... | TAGS
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========
*BigScience Large Open-science Open-access Multilingual Language Model*
-----------------------------------------------------------------------
### Model Card
Version 1.0 / 26.May.2022
Model Card for Bloom-1b7
========================
Table of Contents
-----------------
1. Mo... | [
"### Model Card\n\n\n\nVersion 1.0 / 26.May.2022\n\n\nModel Card for Bloom-1b7\n========================\n\n\nTable of Contents\n-----------------\n\n\n1. Model Details\n2. Uses\n3. Bias, Risks, and Limitations\n4. Recommendations\n5. Training Data\n6. Evaluation\n7. Environmental Impact\n8. Technical ... | [
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text-generation | transformers |
<h1 style='text-align: center '>BLOOM LM</h1>
<h2 style='text-align: center '><em>BigScience Large Open-science Open-access Multilingual Language Model</em> </h2>
<h3 style='text-align: center '>Model Card</h3>
<img src="https://s3.amazonaws.com/moonup/production/uploads/1657124309515-5f17f0a0925b9863e28ad517.png" a... | {"language": ["ak", "ar", "as", "bm", "bn", "ca", "code", "en", "es", "eu", "fon", "fr", "gu", "hi", "id", "ig", "ki", "kn", "lg", "ln", "ml", "mr", "ne", "nso", "ny", "or", "pa", "pt", "rn", "rw", "sn", "st", "sw", "ta", "te", "tn", "ts", "tum", "tw", "ur", "vi", "wo", "xh", "yo", "zh", "zhs", "zht", "zu"], "license":... | bigscience/bloom-3b | null | [
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... | null | 2022-05-19T10:52:27+00:00 | [
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... | TAGS
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========
*BigScience Large Open-science Open-access Multilingual Language Model*
-----------------------------------------------------------------------
### Model Card
Version 1.0 / 26.May.2022
Table of Contents
-----------------
1. Model Details
2. Uses
3. Training Data
4. Risks and Li... | [
"### Model Card\n\n\n\nVersion 1.0 / 26.May.2022\n\n\nTable of Contents\n-----------------\n\n\n1. Model Details\n2. Uses\n3. Training Data\n4. Risks and Limitations\n5. Evaluation\n6. Recommendations\n7. Glossary and Calculations\n8. More Information\n9. Model Card Authors\n\n\nModel Details\n--------... | [
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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... | chans/PPO-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-05-19T10:52:40+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... |
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