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question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-large-uncased-whole-word-masking-finetuned-squad-finetuned-squad
This model is a fine-tuned version of [bert-large-uncased-... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "bert-large-uncased-whole-word-masking-finetuned-squad-finetuned-squad", "results": []}]} | Jiqing/bert-large-uncased-whole-word-masking-finetuned-squad-finetuned-squad | null | [
"transformers",
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"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-08-12T08:22:04+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us
|
# bert-large-uncased-whole-word-masking-finetuned-squad-finetuned-squad
This model is a fine-tuned version of bert-large-uncased-whole-word-masking-finetuned-squad on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluatio... | [
"# bert-large-uncased-whole-word-masking-finetuned-squad-finetuned-squad\n\nThis model is a fine-tuned version of bert-large-uncased-whole-word-masking-finetuned-squad on the squad dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Tr... | [
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translation | transformers |
# Model Card for T5 11B - fp16
# Table of Contents
1. [Model Details](#model-details)
2. [Uses](#use... | {"language": ["en", "fr", "ro", "de"], "license": "apache-2.0", "tags": ["summarization", "translation"], "datasets": ["c4"], "inference": false} | ybelkada/t5-11b-sharded | null | [
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"arxiv:1808.09121",
"arxiv:1810.12885",
"arxiv:1905.10044",
"arxiv:1910.0... | null | 2022-08-12T08:26:58+00:00 | [
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# Model Card for T5 11B - fp16
!model image
# Table of Contents
1. Model Details
2. Uses
3. Bias, Risks, and Limitations
4. Training Details
5. Evaluation
6. Environmental Impact
7. Citation
8. Model Card Authors
9. How To Get Started With the Model
# Model Details
## Model Description
The developers of the Tex... | [
"# Model Card for T5 11B - fp16\n\n!model image",
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"# Model Details",
"## Model Descriptio... | [
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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. -->
# xls-r-uzbek-cv10
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-x... | {"language": ["uz"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "mozilla-foundation/common_voice_10_0", "generated_from_trainer"], "datasets": ["common_voice_10_0"], "base_model": "facebook/wav2vec2-xls-r-300m", "model-index": [{"name": "xls-r-uzbek-cv10", "results": []}]} | vodiylik/xls-r-uzbek-cv10-full | null | [
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"license:apache-2.0",
"endpoin... | null | 2022-08-12T08:51:59+00:00 | [] | [
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] | TAGS
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| xls-r-uzbek-cv10
================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON\_VOICE\_10\_0 - UZ dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2491
* Wer: 0.2588
* Cer: 0.0513
Model description
-----------------
More informati... | [
"### 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... | [
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"### Training... |
translation | transformers | # opus-mt-tc-big-itc-itc
## Table of Contents
- [Model Details](#model-details)
- [Uses](#uses)
- [Risks, Limitations and Biases](#risks-limitations-and-biases)
- [How to Get Started With the Model](#how-to-get-started-with-the-model)
- [Training](#training)
- [Evaluation](#evaluation)
- [Citation Information](#citati... | {"language": ["ast", "ca", "es", "fr", "gl", "it", "lad", "oc", "pms", "pt", "ro"], "license": "cc-by-4.0", "tags": ["translation", "opus-mt-tc"], "model-index": [{"name": "opus-mt-tc-big-itc-itc", "results": [{"task": {"type": "translation", "name": "Translation ast-cat"}, "dataset": {"name": "flores101-devtest", "typ... | Helsinki-NLP/opus-mt-tc-big-itc-itc | null | [
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"autotrain_compatible",
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"has_s... | null | 2022-08-12T09:02:43+00:00 | [] | [
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| opus-mt-tc-big-itc-itc
======================
Table of Contents
-----------------
* Model Details
* Uses
* Risks, Limitations and Biases
* How to Get Started With the Model
* Training
* Evaluation
* Citation Information
* Acknowledgements
Model Details
-------------
Neural machine translation model for translat... | [] | [
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] |
text-classification | transformers |
# Danish Offensive Text Detection based on XLM-Roberta-Base
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on a dataset consisting of approximately 5 million Facebook comments on [DR](https://dr.dk/)'s public Facebook pages. The labels have been automatically generat... | {"license": "apache-2.0", "widget": [{"text": "Din store idiot"}], "base_model": "xlm-roberta-base"} | alexandrainst/da-offensive-detection-base | null | [
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"safetensors",
"xlm-roberta",
"text-classification",
"base_model:xlm-roberta-base",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-12T09:04:35+00:00 | [] | [] | TAGS
#transformers #pytorch #safetensors #xlm-roberta #text-classification #base_model-xlm-roberta-base #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| Danish Offensive Text Detection based on XLM-Roberta-Base
=========================================================
This model is a fine-tuned version of xlm-roberta-base on a dataset consisting of approximately 5 million Facebook comments on DR's public Facebook pages. The labels have been automatically generated us... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* gradient\\_accumulation\\_steps: 1\n* total\\_train\\_batch\\_size: 32\n* seed: 4242\n* optimizer: Adam with betas=(0.9,0.999) and epsil... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* tra... |
translation | transformers | # opus-mt-tc-big-gmw-gmw
## Table of Contents
- [Model Details](#model-details)
- [Uses](#uses)
- [Risks, Limitations and Biases](#risks-limitations-and-biases)
- [How to Get Started With the Model](#how-to-get-started-with-the-model)
- [Training](#training)
- [Evaluation](#evaluation)
- [Citation Information](#citati... | {"language": ["af", "de", "en", "fy", "gos", "hrx", "lb", "multilingual", "nds", "nl"], "license": "cc-by-4.0", "tags": ["translation", "opus-mt-tc"], "model-index": [{"name": "opus-mt-tc-big-gmw-gmw", "results": [{"task": {"type": "translation", "name": "Translation deu-eng"}, "dataset": {"name": "news-test2008", "typ... | Helsinki-NLP/opus-mt-tc-big-gmw-gmw | null | [
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"model-index",
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"gos",
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"multilingual",
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| opus-mt-tc-big-gmw-gmw
======================
Table of Contents
-----------------
* Model Details
* Uses
* Risks, Limitations and Biases
* How to Get Started With the Model
* Training
* Evaluation
* Citation Information
* Acknowledgements
Model Details
-------------
Neural machine translation model for translat... | [] | [
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] |
reinforcement-learning | null |
# **Q-Learning** Agent playing **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="marii/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
en... | {"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 +/... | marii/q-Taxi-v3 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-08-12T09:28:16+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"
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"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] |
translation | transformers | # opus-mt-tc-big-gmq-gmq
## Table of Contents
- [Model Details](#model-details)
- [Uses](#uses)
- [Risks, Limitations and Biases](#risks-limitations-and-biases)
- [How to Get Started With the Model](#how-to-get-started-with-the-model)
- [Training](#training)
- [Evaluation](#evaluation)
- [Citation Information](#citati... | {"language": ["da", "is", "nb", "nn", "sv"], "license": "cc-by-4.0", "tags": ["translation", "opus-mt-tc"], "model-index": [{"name": "opus-mt-tc-big-gmq-gmq", "results": [{"task": {"type": "translation", "name": "Translation isl-swe"}, "dataset": {"name": "europeana2021", "type": "europeana2021", "args": "isl-swe"}, "m... | Helsinki-NLP/opus-mt-tc-big-gmq-gmq | null | [
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"region:us"
] | null | 2022-08-12T09:30:28+00:00 | [] | [
"da",
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] | TAGS
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| opus-mt-tc-big-gmq-gmq
======================
Table of Contents
-----------------
* Model Details
* Uses
* Risks, Limitations and Biases
* How to Get Started With the Model
* Training
* Evaluation
* Citation Information
* Acknowledgements
Model Details
-------------
Neural machine translation model for translat... | [] | [
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] |
token-classification | transformers | # tner/roberta-large-ontonotes5
This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on the
[tner/ontonotes5](https://huggingface.co/datasets/tner/ontonotes5) dataset.
Model fine-tuning is done via [T-NER](https://github.com/asahi417/tner)'s hyper-parameter search (see the repos... | {"datasets": ["tner/ontonotes5"], "metrics": ["f1", "precision", "recall"], "pipeline_tag": "token-classification", "widget": [{"text": "Jacob Collier is a Grammy awarded artist from England.", "example_title": "NER Example 1"}], "model-index": [{"name": "tner/roberta-large-ontonotes5", "results": [{"task": {"type": "t... | tner/roberta-large-ontonotes5 | null | [
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"region:us"
] | null | 2022-08-12T09:33:41+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #token-classification #dataset-tner/ontonotes5 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
| # tner/roberta-large-ontonotes5
This model is a fine-tuned version of roberta-large on the
tner/ontonotes5 dataset.
Model fine-tuning is done via T-NER's hyper-parameter search (see the repository
for more detail). It achieves the following results on the test set:
- F1 (micro): 0.908632361399938
- Precision (micro):... | [
"# tner/roberta-large-ontonotes5\n\nThis model is a fine-tuned version of roberta-large on the \ntner/ontonotes5 dataset.\nModel fine-tuning is done via T-NER's hyper-parameter search (see the repository\nfor more detail). It achieves the following results on the test set:\n- F1 (micro): 0.908632361399938\n- Precis... | [
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"# tner/roberta-large-ontonotes5\n\nThis model is a fine-tuned version of roberta-large on the \ntner/ontonotes5 dataset.\nModel fine-tuning is ... |
token-classification | transformers | # tner/roberta-large-mit-movie-trivia
This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on the
[tner/mit_movie_trivia](https://huggingface.co/datasets/tner/mit_movie_trivia) dataset.
Model fine-tuning is done via [T-NER](https://github.com/asahi417/tner)'s hyper-parameter sea... | {"datasets": ["tner/mit_movie_trivia"], "metrics": ["f1", "precision", "recall"], "pipeline_tag": "token-classification", "widget": [{"text": "Jacob Collier is a Grammy awarded artist from England.", "example_title": "NER Example 1"}], "model-index": [{"name": "tner/roberta-large-mit-movie-trivia", "results": [{"task":... | tner/roberta-large-mit-movie-trivia | null | [
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| # tner/roberta-large-mit-movie-trivia
This model is a fine-tuned version of roberta-large on the
tner/mit_movie_trivia dataset.
Model fine-tuning is done via T-NER's hyper-parameter search (see the repository
for more detail). It achieves the following results on the test set:
- F1 (micro): 0.7284025200655909
- Preci... | [
"# tner/roberta-large-mit-movie-trivia\n\nThis model is a fine-tuned version of roberta-large on the \ntner/mit_movie_trivia dataset.\nModel fine-tuning is done via T-NER's hyper-parameter search (see the repository\nfor more detail). It achieves the following results on the test set:\n- F1 (micro): 0.7284025200655... | [
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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. -->
# BACnet-Klassifizierung-Sanitaertechnik-bert-base-german-cased
This model is a fine-tuned version of [bert-base-german-cased](htt... | {"language": ["de"], "license": "mit", "tags": ["generated_from_trainer"], "metrics": ["f1"], "model-index": [{"name": "BACnet-Klassifizierung-Sanitaertechnik-bert-base-german-cased", "results": []}]} | cm-mueller/BACnet-Klassifizierung-Sanitaertechnik | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"de",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-12T09:39:09+00:00 | [] | [
"de"
] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #de #license-mit #autotrain_compatible #endpoints_compatible #region-us
| BACnet-Klassifizierung-Sanitaertechnik-bert-base-german-cased
=============================================================
This model is a fine-tuned version of bert-base-german-cased on the gart-labor "klassifizierung\_sanitaer\_v2" dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0039
... | [
"### 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* gradient\\_accumulation\\_steps: 16\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #de #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_si... |
token-classification | transformers | # tner/deberta-v3-large-mit-restaurant
This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) on the
[tner/mit_restaurant](https://huggingface.co/datasets/tner/mit_restaurant) dataset.
Model fine-tuning is done via [T-NER](https://github.com/asahi417/tner... | {"datasets": ["tner/mit_restaurant"], "metrics": ["f1", "precision", "recall"], "pipeline_tag": "token-classification", "widget": [{"text": "Jacob Collier is a Grammy awarded artist from England.", "example_title": "NER Example 1"}], "model-index": [{"name": "tner/deberta-v3-large-mit-restaurant", "results": [{"task": ... | tner/deberta-v3-large-mit-restaurant | null | [
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#transformers #pytorch #deberta-v2 #token-classification #dataset-tner/mit_restaurant #model-index #autotrain_compatible #endpoints_compatible #region-us
| # tner/deberta-v3-large-mit-restaurant
This model is a fine-tuned version of microsoft/deberta-v3-large on the
tner/mit_restaurant dataset.
Model fine-tuning is done via T-NER's hyper-parameter search (see the repository
for more detail). It achieves the following results on the test set:
- F1 (micro): 0.815889029003... | [
"# tner/deberta-v3-large-mit-restaurant\n\nThis model is a fine-tuned version of microsoft/deberta-v3-large on the \ntner/mit_restaurant dataset.\nModel fine-tuning is done via T-NER's hyper-parameter search (see the repository\nfor more detail). It achieves the following results on the test set:\n- F1 (micro): 0.8... | [
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token-classification | transformers | # tner/roberta-large-ttc
This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on the
[tner/ttc](https://huggingface.co/datasets/tner/ttc) dataset.
Model fine-tuning is done via [T-NER](https://github.com/asahi417/tner)'s hyper-parameter search (see the repository
for more detail... | {"datasets": ["tner/ttc"], "metrics": ["f1", "precision", "recall"], "pipeline_tag": "token-classification", "widget": [{"text": "Jacob Collier is a Grammy awarded artist from England.", "example_title": "NER Example 1"}], "model-index": [{"name": "tner/roberta-large-ttc", "results": [{"task": {"type": "token-classific... | tner/roberta-large-ttc | null | [
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] | null | 2022-08-12T09:49:56+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #token-classification #dataset-tner/ttc #model-index #autotrain_compatible #endpoints_compatible #region-us
| # tner/roberta-large-ttc
This model is a fine-tuned version of roberta-large on the
tner/ttc dataset.
Model fine-tuning is done via T-NER's hyper-parameter search (see the repository
for more detail). It achieves the following results on the test set:
- F1 (micro): 0.8314534321624235
- Precision (micro): 0.8269230769... | [
"# tner/roberta-large-ttc\n\nThis model is a fine-tuned version of roberta-large on the \ntner/ttc dataset.\nModel fine-tuning is done via T-NER's hyper-parameter search (see the repository\nfor more detail). It achieves the following results on the test set:\n- F1 (micro): 0.8314534321624235\n- Precision (micro): ... | [
"TAGS\n#transformers #pytorch #roberta #token-classification #dataset-tner/ttc #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# tner/roberta-large-ttc\n\nThis model is a fine-tuned version of roberta-large on the \ntner/ttc dataset.\nModel fine-tuning is done via T-NER's hyper-parameter... |
translation | transformers | # opus-mt-tc-big-zls-itc
## Table of Contents
- [Model Details](#model-details)
- [Uses](#uses)
- [Risks, Limitations and Biases](#risks-limitations-and-biases)
- [How to Get Started With the Model](#how-to-get-started-with-the-model)
- [Training](#training)
- [Evaluation](#evaluation)
- [Citation Information](#citati... | {"language": ["bg", "es", "fr", "hr", "it", "mk", "pt", "ro", "sh", "sl", "sr"], "license": "cc-by-4.0", "tags": ["translation", "opus-mt-tc"], "language_bcp47": ["sr_Cyrl", "sr_Latn"], "model-index": [{"name": "opus-mt-tc-big-zls-itc", "results": [{"task": {"type": "translation", "name": "Translation bul-fra"}, "datas... | Helsinki-NLP/opus-mt-tc-big-zls-itc | null | [
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| opus-mt-tc-big-zls-itc
======================
Table of Contents
-----------------
* Model Details
* Uses
* Risks, Limitations and Biases
* How to Get Started With the Model
* Training
* Evaluation
* Citation Information
* Acknowledgements
Model Details
-------------
Neural machine translation model for translat... | [] | [
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] |
translation | transformers | # opus-mt-tc-big-gmq-itc
## Table of Contents
- [Model Details](#model-details)
- [Uses](#uses)
- [Risks, Limitations and Biases](#risks-limitations-and-biases)
- [How to Get Started With the Model](#how-to-get-started-with-the-model)
- [Training](#training)
- [Evaluation](#evaluation)
- [Citation Information](#citati... | {"language": ["ca", "da", "es", "fr", "gl", "is", "it", "nb", "pt", "ro", "sv"], "license": "cc-by-4.0", "tags": ["translation", "opus-mt-tc"], "model-index": [{"name": "opus-mt-tc-big-gmq-itc", "results": [{"task": {"type": "translation", "name": "Translation dan-cat"}, "dataset": {"name": "flores101-devtest", "type":... | Helsinki-NLP/opus-mt-tc-big-gmq-itc | null | [
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| opus-mt-tc-big-gmq-itc
======================
Table of Contents
-----------------
* Model Details
* Uses
* Risks, Limitations and Biases
* How to Get Started With the Model
* Training
* Evaluation
* Citation Information
* Acknowledgements
Model Details
-------------
Neural machine translation model for translat... | [] | [
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] |
text-classification | transformers |
# Danish Offensive Text Detection based on ELECTRA-small
This model is a fine-tuned version of [Maltehb/aelaectra-danish-electra-small-cased](https://huggingface.co/Maltehb/aelaectra-danish-electra-small-cased) on a dataset consisting of approximately 5 million Facebook comments on [DR](https://dr.dk/)'s public Faceb... | {"license": "apache-2.0", "widget": [{"text": "Din store idiot"}], "base_model": "Maltehb/aelaectra-danish-electra-small-cased"} | alexandrainst/da-offensive-detection-small | null | [
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"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-08-12T10:20:57+00:00 | [] | [] | TAGS
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| Danish Offensive Text Detection based on ELECTRA-small
======================================================
This model is a fine-tuned version of Maltehb/aelaectra-danish-electra-small-cased on a dataset consisting of approximately 5 million Facebook comments on DR's public Facebook pages. The labels have been auto... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* gradient\\_accumulation\\_steps: 1\n* total\\_train\\_batch\\_size: 32\n* seed: 4242\n* optimizer: Adam with betas=(0.9,0.999) and epsil... | [
"TAGS\n#transformers #pytorch #electra #text-classification #base_model-Maltehb/aelaectra-danish-electra-small-cased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learnin... |
translation | transformers | # opus-mt-tc-big-itc-ar
## Table of Contents
- [Model Details](#model-details)
- [Uses](#uses)
- [Risks, Limitations and Biases](#risks-limitations-and-biases)
- [How to Get Started With the Model](#how-to-get-started-with-the-model)
- [Training](#training)
- [Evaluation](#evaluation)
- [Citation Information](#citatio... | {"language": ["ar", "ca", "es", "fr", "gl", "it", "pt", "ro"], "license": "cc-by-4.0", "tags": ["translation", "opus-mt-tc"], "model-index": [{"name": "opus-mt-tc-big-itc-ar", "results": [{"task": {"type": "translation", "name": "Translation cat-ara"}, "dataset": {"name": "flores101-devtest", "type": "flores_101", "arg... | Helsinki-NLP/opus-mt-tc-big-itc-ar | null | [
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] | null | 2022-08-12T10:32:09+00:00 | [] | [
"ar",
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] | TAGS
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| opus-mt-tc-big-itc-ar
=====================
Table of Contents
-----------------
* Model Details
* Uses
* Risks, Limitations and Biases
* How to Get Started With the Model
* Training
* Evaluation
* Citation Information
* Acknowledgements
Model Details
-------------
Neural machine translation model for translatin... | [] | [
"TAGS\n#transformers #pytorch #tf #safetensors #marian #text2text-generation #translation #opus-mt-tc #ar #ca #es #fr #gl #it #pt #ro #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# BACnet-Klassifizierung-Raumlufttechnik-bert-base-german-cased
This model is a fine-tuned version of [bert-base-german-cased](htt... | {"language": ["de"], "license": "mit", "tags": ["generated_from_trainer"], "metrics": ["f1"], "model-index": [{"name": "BACnet-Klassifizierung-Raumlufttechnik-bert-base-german-cased", "results": []}]} | cm-mueller/BACnet-Klassifizierung-Raumlufttechnik | null | [
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"region:us"
] | null | 2022-08-12T10:35:27+00:00 | [] | [
"de"
] | TAGS
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| BACnet-Klassifizierung-Raumlufttechnik-bert-base-german-cased
=============================================================
This model is a fine-tuned version of bert-base-german-cased on the gart-labor "klassifizierung\_rlt\_v2" dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0597
* F1:... | [
"### 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* gradient\\_accumulation\\_steps: 16\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #de #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_si... |
token-classification | transformers | # tner/deberta-v3-large-mit-movie-trivia
This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) on the
[tner/mit_movie_trivia](https://huggingface.co/datasets/tner/mit_movie_trivia) dataset.
Model fine-tuning is done via [T-NER](https://github.com/asahi41... | {"datasets": ["tner/mit_movie_trivia"], "metrics": ["f1", "precision", "recall"], "pipeline_tag": "token-classification", "widget": [{"text": "Jacob Collier is a Grammy awarded artist from England.", "example_title": "NER Example 1"}], "model-index": [{"name": "tner/deberta-v3-large-mit-movie-trivia", "results": [{"tas... | tner/deberta-v3-large-mit-movie-trivia | null | [
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"region:us"
] | null | 2022-08-12T10:41:52+00:00 | [] | [] | TAGS
#transformers #pytorch #deberta-v2 #token-classification #dataset-tner/mit_movie_trivia #model-index #autotrain_compatible #endpoints_compatible #region-us
| # tner/deberta-v3-large-mit-movie-trivia
This model is a fine-tuned version of microsoft/deberta-v3-large on the
tner/mit_movie_trivia dataset.
Model fine-tuning is done via T-NER's hyper-parameter search (see the repository
for more detail). It achieves the following results on the test set:
- F1 (micro): 0.73244781... | [
"# tner/deberta-v3-large-mit-movie-trivia\n\nThis model is a fine-tuned version of microsoft/deberta-v3-large on the \ntner/mit_movie_trivia dataset.\nModel fine-tuning is done via T-NER's hyper-parameter search (see the repository\nfor more detail). It achieves the following results on the test set:\n- F1 (micro):... | [
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"# tner/deberta-v3-large-mit-movie-trivia\n\nThis model is a fine-tuned version of microsoft/deberta-v3-large on the \ntner/mit_movie_trivia datas... |
translation | transformers | # opus-mt-tc-big-ar-gmq
## Table of Contents
- [Model Details](#model-details)
- [Uses](#uses)
- [Risks, Limitations and Biases](#risks-limitations-and-biases)
- [How to Get Started With the Model](#how-to-get-started-with-the-model)
- [Training](#training)
- [Evaluation](#evaluation)
- [Citation Information](#citatio... | {"language": ["ar", "da", "nb", "sv"], "license": "cc-by-4.0", "tags": ["translation", "opus-mt-tc"], "model-index": [{"name": "opus-mt-tc-big-ar-gmq", "results": [{"task": {"type": "translation", "name": "Translation ara-dan"}, "dataset": {"name": "flores101-devtest", "type": "flores_101", "args": "ara dan devtest"}, ... | Helsinki-NLP/opus-mt-tc-big-ar-gmq | null | [
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] | null | 2022-08-12T10:48:46+00:00 | [] | [
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| opus-mt-tc-big-ar-gmq
=====================
Table of Contents
-----------------
* Model Details
* Uses
* Risks, Limitations and Biases
* How to Get Started With the Model
* Training
* Evaluation
* Citation Information
* Acknowledgements
Model Details
-------------
Neural machine translation model for translatin... | [] | [
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] |
translation | transformers | # opus-mt-tc-big-zle-itc
## Table of Contents
- [Model Details](#model-details)
- [Uses](#uses)
- [Risks, Limitations and Biases](#risks-limitations-and-biases)
- [How to Get Started With the Model](#how-to-get-started-with-the-model)
- [Training](#training)
- [Evaluation](#evaluation)
- [Citation Information](#citati... | {"language": ["be", "ca", "es", "fr", "gl", "it", "pt", "ro", "ru", "uk"], "license": "cc-by-4.0", "tags": ["translation", "opus-mt-tc"], "model-index": [{"name": "opus-mt-tc-big-zle-itc", "results": [{"task": {"type": "translation", "name": "Translation bel-cat"}, "dataset": {"name": "flores101-devtest", "type": "flor... | Helsinki-NLP/opus-mt-tc-big-zle-itc | null | [
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| opus-mt-tc-big-zle-itc
======================
Table of Contents
-----------------
* Model Details
* Uses
* Risks, Limitations and Biases
* How to Get Started With the Model
* Training
* Evaluation
* Citation Information
* Acknowledgements
Model Details
-------------
Neural machine translation model for translat... | [] | [
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] |
translation | transformers | # opus-mt-tc-big-itc-he
## Table of Contents
- [Model Details](#model-details)
- [Uses](#uses)
- [Risks, Limitations and Biases](#risks-limitations-and-biases)
- [How to Get Started With the Model](#how-to-get-started-with-the-model)
- [Training](#training)
- [Evaluation](#evaluation)
- [Citation Information](#citatio... | {"language": ["ca", "es", "fr", "gl", "he", "it", "pt", "ro"], "license": "cc-by-4.0", "tags": ["translation", "opus-mt-tc"], "model-index": [{"name": "opus-mt-tc-big-itc-he", "results": [{"task": {"type": "translation", "name": "Translation cat-heb"}, "dataset": {"name": "flores101-devtest", "type": "flores_101", "arg... | Helsinki-NLP/opus-mt-tc-big-itc-he | null | [
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"region:us"
] | null | 2022-08-12T11:28:06+00:00 | [] | [
"ca",
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"fr",
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#transformers #pytorch #tf #safetensors #marian #text2text-generation #translation #opus-mt-tc #ca #es #fr #gl #he #it #pt #ro #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
| opus-mt-tc-big-itc-he
=====================
Table of Contents
-----------------
* Model Details
* Uses
* Risks, Limitations and Biases
* How to Get Started With the Model
* Training
* Evaluation
* Citation Information
* Acknowledgements
Model Details
-------------
Neural machine translation model for translatin... | [] | [
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] |
text-classification | transformers | --alpha_ce 5.0 --alpha_mlm 2.0 --alpha_cos 0.0 --alpha_act 1.0 --alpha_clm 0.0 --mlm \ | {} | alishudi/distil_wo_cos | null | [
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"distilbert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-12T11:45:08+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #text-classification #autotrain_compatible #endpoints_compatible #region-us
| --alpha_ce 5.0 --alpha_mlm 2.0 --alpha_cos 0.0 --alpha_act 1.0 --alpha_clm 0.0 --mlm \ | [] | [
"TAGS\n#transformers #pytorch #distilbert #text-classification #autotrain_compatible #endpoints_compatible #region-us \n"
] |
translation | transformers | # opus-mt-tc-big-gmq-zlw
## Table of Contents
- [Model Details](#model-details)
- [Uses](#uses)
- [Risks, Limitations and Biases](#risks-limitations-and-biases)
- [How to Get Started With the Model](#how-to-get-started-with-the-model)
- [Training](#training)
- [Evaluation](#evaluation)
- [Citation Information](#citati... | {"language": ["cs", "da", "nb", "pl", "sv"], "license": "cc-by-4.0", "tags": ["translation", "opus-mt-tc"], "model-index": [{"name": "opus-mt-tc-big-gmq-zlw", "results": [{"task": {"type": "translation", "name": "Translation dan-ces"}, "dataset": {"name": "flores101-devtest", "type": "flores_101", "args": "dan ces devt... | Helsinki-NLP/opus-mt-tc-big-gmq-zlw | null | [
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"autotrain_compatible",
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] | null | 2022-08-12T11:46:57+00:00 | [] | [
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"sv"
] | TAGS
#transformers #pytorch #tf #safetensors #marian #text2text-generation #translation #opus-mt-tc #cs #da #nb #pl #sv #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
| opus-mt-tc-big-gmq-zlw
======================
Table of Contents
-----------------
* Model Details
* Uses
* Risks, Limitations and Biases
* How to Get Started With the Model
* Training
* Evaluation
* Citation Information
* Acknowledgements
Model Details
-------------
Neural machine translation model for translat... | [] | [
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] |
translation | transformers | # opus-mt-tc-big-zh-ja
## Table of Contents
- [Model Details](#model-details)
- [Uses](#uses)
- [Risks, Limitations and Biases](#risks-limitations-and-biases)
- [How to Get Started With the Model](#how-to-get-started-with-the-model)
- [Training](#training)
- [Evaluation](#evaluation)
- [Citation Information](#citation... | {"language": ["ja", "zh"], "license": "cc-by-4.0", "tags": ["translation", "opus-mt-tc"], "model-index": [{"name": "opus-mt-tc-big-zh-ja", "results": [{"task": {"type": "translation", "name": "Translation zho-jpn"}, "dataset": {"name": "tatoeba-test-v2021-08-07", "type": "tatoeba_mt", "args": "zho-jpn"}, "metrics": [{"... | Helsinki-NLP/opus-mt-tc-big-zh-ja | null | [
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"license:cc-by-4.0",
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"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-08-12T12:06:10+00:00 | [] | [
"ja",
"zh"
] | TAGS
#transformers #pytorch #tf #safetensors #marian #text2text-generation #translation #opus-mt-tc #ja #zh #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
| opus-mt-tc-big-zh-ja
====================
Table of Contents
-----------------
* Model Details
* Uses
* Risks, Limitations and Biases
* How to Get Started With the Model
* Training
* Evaluation
* Citation Information
* Acknowledgements
Model Details
-------------
Neural machine translation model for translating ... | [] | [
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] |
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. -->
# BACnet-Klassifizierung-Heizungstechnik-bert-base-german-cased
This model is a fine-tuned version of [bert-base-german-cased](htt... | {"language": ["de"], "license": "mit", "tags": ["generated_from_trainer"], "metrics": ["f1"], "model-index": [{"name": "BACnet-Klassifizierung-Heizungstechnik-bert-base-german-cased", "results": []}]} | cm-mueller/BACnet-Klassifizierung-Heizungstechnik | null | [
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"bert",
"text-classification",
"generated_from_trainer",
"de",
"license:mit",
"autotrain_compatible",
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"region:us"
] | null | 2022-08-12T12:14:46+00:00 | [] | [
"de"
] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #de #license-mit #autotrain_compatible #endpoints_compatible #region-us
| BACnet-Klassifizierung-Heizungstechnik-bert-base-german-cased
=============================================================
This model is a fine-tuned version of bert-base-german-cased on the gart-labor "klassifizierung\_heizung\_v2" dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0798
*... | [
"### 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* gradient\\_accumulation\\_steps: 16\n* total\\_train\\_batch\\_size: 128\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: 5e-05\n* train\\_batch\\_si... |
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. -->
# BACnet-Klassifizierung-Kaeltettechnik-bert-base-german-cased
This model is a fine-tuned version of [bert-base-german-cased](http... | {"language": ["de"], "license": "mit", "tags": ["generated_from_trainer"], "metrics": ["f1"], "model-index": [{"name": "BACnet-Klassifizierung-Kaeltettechnik-bert-base-german-cased", "results": []}]} | cm-mueller/BACnet-Klassifizierung-Kaeltettechnik | null | [
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"de",
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"region:us"
] | null | 2022-08-12T12:22:59+00:00 | [] | [
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| BACnet-Klassifizierung-Kaeltettechnik-bert-base-german-cased
============================================================
This model is a fine-tuned version of bert-base-german-cased on the gart-labor "klassifizierung\_kaelte\_v2" dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0466
* F1... | [
"### 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* gradient\\_accumulation\\_steps: 16\n* total\\_train\\_batch\\_size: 128\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: 5e-05\n* train\\_batch\\_si... |
translation | transformers | # opus-mt-tc-big-itc-tr
## Table of Contents
- [Model Details](#model-details)
- [Uses](#uses)
- [Risks, Limitations and Biases](#risks-limitations-and-biases)
- [How to Get Started With the Model](#how-to-get-started-with-the-model)
- [Training](#training)
- [Evaluation](#evaluation)
- [Citation Information](#citatio... | {"language": ["ca", "es", "fr", "gl", "it", "oc", "pt", "ro", "tr"], "license": "cc-by-4.0", "tags": ["translation", "opus-mt-tc"], "model-index": [{"name": "opus-mt-tc-big-itc-tr", "results": [{"task": {"type": "translation", "name": "Translation cat-tur"}, "dataset": {"name": "flores101-devtest", "type": "flores_101"... | Helsinki-NLP/opus-mt-tc-big-itc-tr | null | [
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] | TAGS
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| opus-mt-tc-big-itc-tr
=====================
Table of Contents
-----------------
* Model Details
* Uses
* Risks, Limitations and Biases
* How to Get Started With the Model
* Training
* Evaluation
* Citation Information
* Acknowledgements
Model Details
-------------
Neural machine translation model for translatin... | [] | [
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] |
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. -->
# BACnet-Klassifizierung-Gewerke-bert-base-german-cased
This model is a fine-tuned version of [bert-base-german-cased](https://hug... | {"language": ["de"], "license": "mit", "tags": ["generated_from_trainer", "BACnet"], "metrics": ["f1"], "widget": [{"text": "11004KAE901KL1ST15"}, {"text": "Heizkreis Nord Ost"}, {"text": "Raumluftqualitaet RLT Sporthalle"}, {"text": "11004ELT002IS011MW22"}, {"text": "Beschreibung: Abschaltung durch Stoppwert erreicht ... | cm-mueller/BACnet-Klassifizierung-Gewerke | null | [
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"text-classification",
"generated_from_trainer",
"BACnet",
"de",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-12T12:36:16+00:00 | [] | [
"de"
] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #BACnet #de #license-mit #autotrain_compatible #endpoints_compatible #region-us
| BACnet-Klassifizierung-Gewerke-bert-base-german-cased
=====================================================
This model is a fine-tuned version of bert-base-german-cased on the gart-labor "klassifizierung\_gewerke" dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0394
* F1: [0.96296296 0.8... | [
"### 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* gradient\\_accumulation\\_steps: 16\n* total\\_train\\_batch\\_size: 128\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: 5e-05\n* train\\_ba... |
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. -->
# VanessaSchenkel/padrao-unicamp-finetuned-news_commentary
This model is a fine-tuned version of [unicamp-dl/translation-en-pt-t5](https... | {"tags": ["generated_from_keras_callback"], "model-index": [{"name": "VanessaSchenkel/padrao-unicamp-finetuned-news_commentary", "results": []}]} | VanessaSchenkel/padrao-unicamp-finetuned-news_commentary | null | [
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"region:us"
] | null | 2022-08-12T12:40:38+00:00 | [] | [] | TAGS
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| VanessaSchenkel/padrao-unicamp-finetuned-news\_commentary
=========================================================
This model is a fine-tuned version of unicamp-dl/translation-en-pt-t5 on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 1.4840
* Validation Loss: 1.2138
* T... | [
"### 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",
... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDe... |
reinforcement-learning | ml-agents |
# **sac** Agent playing **Worm**
This is a trained model of a **sac** agent playing **Worm** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutor... | {"library_name": "ml-agents", "tags": ["unity-ml-agents", "ml-agents", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Worm"]} | mrm8488/Worm_poca | null | [
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"onnx",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Worm",
"region:us"
] | null | 2022-08-12T12:43:15+00:00 | [] | [] | TAGS
#ml-agents #tensorboard #onnx #unity-ml-agents #deep-reinforcement-learning #reinforcement-learning #ML-Agents-Worm #region-us
|
# sac Agent playing Worm
This is a trained model of a sac agent playing Worm using the Unity ML-Agents Library.
## Usage (with ML-Agents)
The Documentation: URL
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
#... | [
"# sac Agent playing Worm\n This is a trained model of a sac agent playing Worm using the Unity ML-Agents Library.\n \n ## Usage (with ML-Agents)\n The Documentation: URL\n We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:\n\n\n ### Resume the training\... | [
"TAGS\n#ml-agents #tensorboard #onnx #unity-ml-agents #deep-reinforcement-learning #reinforcement-learning #ML-Agents-Worm #region-us \n",
"# sac Agent playing Worm\n This is a trained model of a sac agent playing Worm using the Unity ML-Agents Library.\n \n ## Usage (with ML-Agents)\n The Documentation: URL\... |
translation | transformers | # opus-mt-tc-big-gmq-he
## Table of Contents
- [Model Details](#model-details)
- [Uses](#uses)
- [Risks, Limitations and Biases](#risks-limitations-and-biases)
- [How to Get Started With the Model](#how-to-get-started-with-the-model)
- [Training](#training)
- [Evaluation](#evaluation)
- [Citation Information](#citatio... | {"language": ["da", "he", "sv"], "license": "cc-by-4.0", "tags": ["translation", "opus-mt-tc"], "model-index": [{"name": "opus-mt-tc-big-gmq-he", "results": [{"task": {"type": "translation", "name": "Translation dan-heb"}, "dataset": {"name": "flores101-devtest", "type": "flores_101", "args": "dan heb devtest"}, "metri... | Helsinki-NLP/opus-mt-tc-big-gmq-he | null | [
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"region:us"
] | null | 2022-08-12T12:44:04+00:00 | [] | [
"da",
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] | TAGS
#transformers #pytorch #tf #safetensors #marian #text2text-generation #translation #opus-mt-tc #da #he #sv #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
| opus-mt-tc-big-gmq-he
=====================
Table of Contents
-----------------
* Model Details
* Uses
* Risks, Limitations and Biases
* How to Get Started With the Model
* Training
* Evaluation
* Citation Information
* Acknowledgements
Model Details
-------------
Neural machine translation model for translatin... | [] | [
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] |
text-classification | transformers | ## Arabic MARBERT News Article Classification Model
#### Model description
**arabic-MARBERT-news-article-classification Model** is a news article classification model that was built by fine-tuning the [MARBERT](https://huggingface.co/UBC-NLP/MARBERT) model. For the fine-tuning, I used [SANAD: Single-Label Arabic News A... | {"language": ["ar"], "tags": ["text classification", "news"], "widget": [{"text": "\u0623\u062e\u0637\u0631\u062a \u0634\u0631\u0643\u0629 \u0623\u0631\u0627\u0645\u0643\u0648 \u0627\u0644\u0633\u0639\u0648\u062f\u064a\u0629 4 \u0639\u0644\u0649 \u0627\u0644\u0623\u0642\u0644 \u0645\u0646 \u0627\u0644\u0645\u0634\u062a... | Ammar-alhaj-ali/arabic-MARBERT-news-article-classification | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"text classification",
"news",
"ar",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-08-12T12:55:59+00:00 | [] | [
"ar"
] | TAGS
#transformers #pytorch #bert #text-classification #text classification #news #ar #autotrain_compatible #endpoints_compatible #has_space #region-us
| ## Arabic MARBERT News Article Classification Model
#### Model description
arabic-MARBERT-news-article-classification Model is a news article classification model that was built by fine-tuning the MARBERT model. For the fine-tuning, I used SANAD: Single-Label Arabic News Articles Dataset that includes 7 labels(Culture,... | [
"## Arabic MARBERT News Article Classification Model",
"#### Model description\narabic-MARBERT-news-article-classification Model is a news article classification model that was built by fine-tuning the MARBERT model. For the fine-tuning, I used SANAD: Single-Label Arabic News Articles Dataset that includes 7 labe... | [
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"## Arabic MARBERT News Article Classification Model",
"#### Model description\narabic-MARBERT-news-article-classification Model is a news article classi... |
translation | transformers | # opus-mt-tc-big-ar-itc
## Table of Contents
- [Model Details](#model-details)
- [Uses](#uses)
- [Risks, Limitations and Biases](#risks-limitations-and-biases)
- [How to Get Started With the Model](#how-to-get-started-with-the-model)
- [Training](#training)
- [Evaluation](#evaluation)
- [Citation Information](#citatio... | {"language": ["ar", "ca", "es", "fr", "gl", "it", "pt", "ro"], "license": "cc-by-4.0", "tags": ["translation", "opus-mt-tc"], "model-index": [{"name": "opus-mt-tc-big-ar-itc", "results": [{"task": {"type": "translation", "name": "Translation ara-cat"}, "dataset": {"name": "flores101-devtest", "type": "flores_101", "arg... | Helsinki-NLP/opus-mt-tc-big-ar-itc | null | [
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"ro"
] | TAGS
#transformers #pytorch #tf #safetensors #marian #text2text-generation #translation #opus-mt-tc #ar #ca #es #fr #gl #it #pt #ro #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
| opus-mt-tc-big-ar-itc
=====================
Table of Contents
-----------------
* Model Details
* Uses
* Risks, Limitations and Biases
* How to Get Started With the Model
* Training
* Evaluation
* Citation Information
* Acknowledgements
Model Details
-------------
Neural machine translation model for translatin... | [] | [
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] |
token-classification | spacy | | Feature | Description |
| --- | --- |
| **Name** | `en_reciparse_model` |
| **Version** | `0.0.0` |
| **spaCy** | `>=3.3.1,<3.4.0` |
| **Default Pipeline** | `tok2vec`, `ner` |
| **Components** | `tok2vec`, `ner` |
| **Vectors** | 0 keys, 0 unique vectors (0 dimensions) |
| **Sources** | n/a |
| **License** | n/a |
|... | {"language": ["en"], "license": "mit", "tags": ["spacy", "token-classification"], "widget": [{"text": "Season the chicken inside and out with salt and pepper, really getting into all of the crevices. Let the chicken hang out for at least 1 hour at room temperature, which will help the meat absorb the salt. If you can s... | victorialslocum/en_reciparse_model | null | [
"spacy",
"token-classification",
"en",
"license:mit",
"has_space",
"region:us"
] | null | 2022-08-12T13:03:13+00:00 | [] | [
"en"
] | TAGS
#spacy #token-classification #en #license-mit #has_space #region-us
|
### Label Scheme
View label scheme (1 labels for 1 components)
### Accuracy
| [
"### Label Scheme\n\n\n\nView label scheme (1 labels for 1 components)",
"### Accuracy"
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"TAGS\n#spacy #token-classification #en #license-mit #has_space #region-us \n",
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"### Accuracy"
] |
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-IMDB_distilbert
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-IMDB_distilbert", "results": []}]} | Billwzl/distilbert-base-uncased-IMDB_distilbert | null | [
"transformers",
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"distilbert",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-12T13:06:42+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-IMDB\_distilbert
========================================
This model is a fine-tuned version of distilbert-base-uncased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 2.6232
Model description
-----------------
More information needed
Intended uses... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 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",
"### Train... | [
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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: 32\n* eval... |
translation | transformers | # opus-mt-tc-big-itc-bat
## Table of Contents
- [Model Details](#model-details)
- [Uses](#uses)
- [Risks, Limitations and Biases](#risks-limitations-and-biases)
- [How to Get Started With the Model](#how-to-get-started-with-the-model)
- [Training](#training)
- [Evaluation](#evaluation)
- [Citation Information](#citati... | {"language": ["ca", "es", "fr", "gl", "it", "lt", "lv", "pt"], "license": "cc-by-4.0", "tags": ["translation", "opus-mt-tc"], "model-index": [{"name": "opus-mt-tc-big-itc-bat", "results": [{"task": {"type": "translation", "name": "Translation cat-lav"}, "dataset": {"name": "flores101-devtest", "type": "flores_101", "ar... | Helsinki-NLP/opus-mt-tc-big-itc-bat | null | [
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"opus-mt-tc",
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"pt",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-08-12T13:20:41+00:00 | [] | [
"ca",
"es",
"fr",
"gl",
"it",
"lt",
"lv",
"pt"
] | TAGS
#transformers #pytorch #tf #marian #text2text-generation #translation #opus-mt-tc #ca #es #fr #gl #it #lt #lv #pt #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
| opus-mt-tc-big-itc-bat
======================
Table of Contents
-----------------
* Model Details
* Uses
* Risks, Limitations and Biases
* How to Get Started With the Model
* Training
* Evaluation
* Citation Information
* Acknowledgements
Model Details
-------------
Neural machine translation model for translat... | [] | [
"TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #opus-mt-tc #ca #es #fr #gl #it #lt #lv #pt #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #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. -->
# BART_corrector
This model is a fine-tuned version of [ainize/bart-base-cnn](https://huggingface.co/ainize/bart-base-cnn) on a ho... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "BART_corrector", "results": []}]} | qBob/BART_corrector | null | [
"transformers",
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-12T13:22:31+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bart #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| BART\_corrector
===============
This model is a fine-tuned version of ainize/bart-base-cnn on a homemade dataset. Each sample of the dataset is an english sentence that has been duplicated 10 times and where random errors (7%) were added.
It achieves the following results on the evaluation set:
* Loss: 0.0025
* R... | [
"### 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: 4\n* mixed\\_precis... | [
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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\... |
translation | transformers | # opus-mt-tc-big-gmq-ar
## Table of Contents
- [Model Details](#model-details)
- [Uses](#uses)
- [Risks, Limitations and Biases](#risks-limitations-and-biases)
- [How to Get Started With the Model](#how-to-get-started-with-the-model)
- [Training](#training)
- [Evaluation](#evaluation)
- [Citation Information](#citatio... | {"language": ["ar", "da", "sv"], "license": "cc-by-4.0", "tags": ["translation", "opus-mt-tc"], "model-index": [{"name": "opus-mt-tc-big-gmq-ar", "results": [{"task": {"type": "translation", "name": "Translation dan-ara"}, "dataset": {"name": "flores101-devtest", "type": "flores_101", "args": "dan ara devtest"}, "metri... | Helsinki-NLP/opus-mt-tc-big-gmq-ar | null | [
"transformers",
"pytorch",
"tf",
"safetensors",
"marian",
"text2text-generation",
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"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-08-12T13:35:33+00:00 | [] | [
"ar",
"da",
"sv"
] | TAGS
#transformers #pytorch #tf #safetensors #marian #text2text-generation #translation #opus-mt-tc #ar #da #sv #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
| opus-mt-tc-big-gmq-ar
=====================
Table of Contents
-----------------
* Model Details
* Uses
* Risks, Limitations and Biases
* How to Get Started With the Model
* Training
* Evaluation
* Citation Information
* Acknowledgements
Model Details
-------------
Neural machine translation model for translatin... | [] | [
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] |
image-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# swin-tiny-patch4-window7-224-finetuned-eurosat
This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](ht... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imagefolder"], "metrics": ["accuracy"], "model-index": [{"name": "swin-tiny-patch4-window7-224-finetuned-eurosat", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type":... | XC/swin-tiny-patch4-window7-224-finetuned-eurosat | null | [
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"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-12T13:36:18+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #swin #image-classification #generated_from_trainer #dataset-imagefolder #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| swin-tiny-patch4-window7-224-finetuned-eurosat
==============================================
This model is a fine-tuned version of microsoft/swin-tiny-patch4-window7-224 on the imagefolder dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0483
* Accuracy: 0.9811
Model description
------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 64\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
"TAGS\n#transformers #pytorch #tensorboard #swin #image-classification #generated_from_trainer #dataset-imagefolder #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* learni... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["emotion"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "distilbert-base-uncased-finetuned-emotion", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "emotion", "type": "emotion... | stevevee0101/distilbert-base-uncased-finetuned-emotion | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
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"generated_from_trainer",
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"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-12T13:45:32+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-emotion
=========================================
This model is a fine-tuned version of distilbert-base-uncased on the emotion dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1582
* Accuracy: 0.936
* F1: 0.9362
Model description
-----------------
Mor... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learn... |
null | null | Jjj | {} | NikoAwayyy/Hovno | null | [
"region:us"
] | null | 2022-08-12T13:49:23+00:00 | [] | [] | TAGS
#region-us
| Jjj | [] | [
"TAGS\n#region-us \n"
] |
translation | transformers | # opus-mt-tc-big-zls-de
## Table of Contents
- [Model Details](#model-details)
- [Uses](#uses)
- [Risks, Limitations and Biases](#risks-limitations-and-biases)
- [How to Get Started With the Model](#how-to-get-started-with-the-model)
- [Training](#training)
- [Evaluation](#evaluation)
- [Citation Information](#citatio... | {"language": ["bg", "de", "hr", "mk", "sh", "sl", "sr"], "license": "cc-by-4.0", "tags": ["translation", "opus-mt-tc"], "language_bcp47": ["sr_Cyrl", "sr_Latn"], "model-index": [{"name": "opus-mt-tc-big-zls-de", "results": [{"task": {"type": "translation", "name": "Translation bul-deu"}, "dataset": {"name": "flores101-... | Helsinki-NLP/opus-mt-tc-big-zls-de | null | [
"transformers",
"pytorch",
"tf",
"safetensors",
"marian",
"text2text-generation",
"translation",
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"sl",
"sr",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-08-12T13:51:09+00:00 | [] | [
"bg",
"de",
"hr",
"mk",
"sh",
"sl",
"sr"
] | TAGS
#transformers #pytorch #tf #safetensors #marian #text2text-generation #translation #opus-mt-tc #bg #de #hr #mk #sh #sl #sr #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
| opus-mt-tc-big-zls-de
=====================
Table of Contents
-----------------
* Model Details
* Uses
* Risks, Limitations and Biases
* How to Get Started With the Model
* Training
* Evaluation
* Citation Information
* Acknowledgements
Model Details
-------------
Neural machine translation model for translatin... | [] | [
"TAGS\n#transformers #pytorch #tf #safetensors #marian #text2text-generation #translation #opus-mt-tc #bg #de #hr #mk #sh #sl #sr #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# cv_bn_bestModel_1
This model is a fine-tuned version of [Sameen53/facebook_large_CV_bn3](https://huggingface.co/Sameen53/faceboo... | {"tags": ["generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "cv_bn_bestModel_1", "results": []}]} | Sameen53/cv_bn_bestModel_1 | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"endpoints_compatible",
"region:us"
] | null | 2022-08-12T13:54:07+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #endpoints_compatible #region-us
| cv\_bn\_bestModel\_1
====================
This model is a fine-tuned version of Sameen53/facebook\_large\_CV\_bn3 on the common\_voice dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1780
* Wer: 0.2315
Model description
-----------------
More information needed
Intended uses & limi... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-06\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 #generated_from_trainer #dataset-common_voice #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-06\n* train\\_batch\\_size: 16\n* eval\\_... |
translation | transformers | # opus-mt-tc-big-gmq-tr
## Table of Contents
- [Model Details](#model-details)
- [Uses](#uses)
- [Risks, Limitations and Biases](#risks-limitations-and-biases)
- [How to Get Started With the Model](#how-to-get-started-with-the-model)
- [Training](#training)
- [Evaluation](#evaluation)
- [Citation Information](#citatio... | {"language": ["da", "nb", "sv", "tr"], "license": "cc-by-4.0", "tags": ["translation", "opus-mt-tc"], "model-index": [{"name": "opus-mt-tc-big-gmq-tr", "results": [{"task": {"type": "translation", "name": "Translation dan-tur"}, "dataset": {"name": "flores101-devtest", "type": "flores_101", "args": "dan tur devtest"}, ... | Helsinki-NLP/opus-mt-tc-big-gmq-tr | null | [
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"tf",
"safetensors",
"marian",
"text2text-generation",
"translation",
"opus-mt-tc",
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"sv",
"tr",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-08-12T14:06:04+00:00 | [] | [
"da",
"nb",
"sv",
"tr"
] | TAGS
#transformers #pytorch #tf #safetensors #marian #text2text-generation #translation #opus-mt-tc #da #nb #sv #tr #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
| opus-mt-tc-big-gmq-tr
=====================
Table of Contents
-----------------
* Model Details
* Uses
* Risks, Limitations and Biases
* How to Get Started With the Model
* Training
* Evaluation
* Citation Information
* Acknowledgements
Model Details
-------------
Neural machine translation model for translatin... | [] | [
"TAGS\n#transformers #pytorch #tf #safetensors #marian #text2text-generation #translation #opus-mt-tc #da #nb #sv #tr #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
text-generation | transformers |
## Model description
Purely for research
### Abstract
LindaGold-v1 | {"language": ["en"], "license": "apache-2.0", "tags": ["convAI", "conversational", "facebook"], "datasets": ["blended_skill_talk"], "metrics": ["perplexity"]} | TheodoreAinsley/LindaGold | null | [
"transformers",
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"blenderbot",
"text2text-generation",
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"conversational",
"facebook",
"en",
"dataset:blended_skill_talk",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-12T14:06:07+00:00 | [] | [
"en"
] | TAGS
#transformers #tf #blenderbot #text2text-generation #convAI #conversational #facebook #en #dataset-blended_skill_talk #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
## Model description
Purely for research
### Abstract
LindaGold-v1 | [
"## Model description\n\nPurely for research",
"### Abstract\n\n\nLindaGold-v1"
] | [
"TAGS\n#transformers #tf #blenderbot #text2text-generation #convAI #conversational #facebook #en #dataset-blended_skill_talk #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"## Model description\n\nPurely for research",
"### Abstract\n\n\nLindaGold-v1"
] |
translation | transformers | # opus-mt-tc-big-de-es
## Table of Contents
- [Model Details](#model-details)
- [Uses](#uses)
- [Risks, Limitations and Biases](#risks-limitations-and-biases)
- [How to Get Started With the Model](#how-to-get-started-with-the-model)
- [Training](#training)
- [Evaluation](#evaluation)
- [Citation Information](#citation... | {"language": ["de", "es"], "license": "cc-by-4.0", "tags": ["translation", "opus-mt-tc"], "model-index": [{"name": "opus-mt-tc-big-de-es", "results": [{"task": {"type": "translation", "name": "Translation deu-spa"}, "dataset": {"name": "flores101-devtest", "type": "flores_101", "args": "deu spa devtest"}, "metrics": [{... | Helsinki-NLP/opus-mt-tc-big-de-es | null | [
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"safetensors",
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"text2text-generation",
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"de",
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"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-08-12T14:21:06+00:00 | [] | [
"de",
"es"
] | TAGS
#transformers #pytorch #tf #safetensors #marian #text2text-generation #translation #opus-mt-tc #de #es #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
| opus-mt-tc-big-de-es
====================
Table of Contents
-----------------
* Model Details
* Uses
* Risks, Limitations and Biases
* How to Get Started With the Model
* Training
* Evaluation
* Citation Information
* Acknowledgements
Model Details
-------------
Neural machine translation model for translating ... | [] | [
"TAGS\n#transformers #pytorch #tf #safetensors #marian #text2text-generation #translation #opus-mt-tc #de #es #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
translation | transformers | # opus-mt-tc-big-he-itc
## Table of Contents
- [Model Details](#model-details)
- [Uses](#uses)
- [Risks, Limitations and Biases](#risks-limitations-and-biases)
- [How to Get Started With the Model](#how-to-get-started-with-the-model)
- [Training](#training)
- [Evaluation](#evaluation)
- [Citation Information](#citatio... | {"language": ["ca", "es", "fr", "gl", "he", "it", "pt", "ro"], "license": "cc-by-4.0", "tags": ["translation", "opus-mt-tc"], "model-index": [{"name": "opus-mt-tc-big-he-itc", "results": [{"task": {"type": "translation", "name": "Translation heb-cat"}, "dataset": {"name": "flores101-devtest", "type": "flores_101", "arg... | Helsinki-NLP/opus-mt-tc-big-he-itc | null | [
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"marian",
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"opus-mt-tc",
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"pt",
"ro",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-08-12T14:35:46+00:00 | [] | [
"ca",
"es",
"fr",
"gl",
"he",
"it",
"pt",
"ro"
] | TAGS
#transformers #pytorch #tf #safetensors #marian #text2text-generation #translation #opus-mt-tc #ca #es #fr #gl #he #it #pt #ro #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
| opus-mt-tc-big-he-itc
=====================
Table of Contents
-----------------
* Model Details
* Uses
* Risks, Limitations and Biases
* How to Get Started With the Model
* Training
* Evaluation
* Citation Information
* Acknowledgements
Model Details
-------------
Neural machine translation model for translatin... | [] | [
"TAGS\n#transformers #pytorch #tf #safetensors #marian #text2text-generation #translation #opus-mt-tc #ca #es #fr #gl #he #it #pt #ro #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
text2text-generation | transformers |
# 日本語T5事前学習済みモデル
This is a T5 (Text-to-Text Transfer Transformer) model pretrained on Japanese corpus.
次の日本語コーパス（約100GB）を用いて事前学習を行ったT5 (Text-to-Text Transfer Transformer) [v1.1アーキテクチャ](https://github.com/google-research/text-to-text-transfer-transformer/blob/main/released_checkpoints.md)のモデルです。
* [Wikipedia](http... | {"language": ["ja"], "license": "cc-by-sa-4.0", "tags": ["t5", "text2text-generation", "seq2seq"], "datasets": ["wikipedia", "oscar", "cc100"]} | sonoisa/t5-base-japanese-v1.1 | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"seq2seq",
"ja",
"dataset:wikipedia",
"dataset:oscar",
"dataset:cc100",
"license:cc-by-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-12T14:41:22+00:00 | [] | [
"ja"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #seq2seq #ja #dataset-wikipedia #dataset-oscar #dataset-cc100 #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# 日本語T5事前学習済みモデル
This is a T5 (Text-to-Text Transfer Transformer) model pretrained on Japanese corpus.
次の日本語コーパス（約100GB）を用いて事前学習を行ったT5 (Text-to-Text Transfer Transformer) v1.1アーキテクチャのモデルです。
* Wikipediaの日本語ダンプデータ (2022年6月27日時点のもの)
* OSCARの日本語コーパス
* CC-100の日本語コーパス
このモデルは事前学習のみを行なったものであり、特定のタスクに利用するにはファインチューニングする必要... | [
"# 日本語T5事前学習済みモデル\n\nThis is a T5 (Text-to-Text Transfer Transformer) model pretrained on Japanese corpus.\n\n次の日本語コーパス（約100GB）を用いて事前学習を行ったT5 (Text-to-Text Transfer Transformer) v1.1アーキテクチャのモデルです。 \n\n* Wikipediaの日本語ダンプデータ (2022年6月27日時点のもの)\n* OSCARの日本語コーパス\n* CC-100の日本語コーパス\n\nこのモデルは事前学習のみを行なったものであり、特定のタスクに利用するには... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #seq2seq #ja #dataset-wikipedia #dataset-oscar #dataset-cc100 #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# 日本語T5事前学習済みモデル\n\nThis is a T5 (Text-to-Text Transfer Transformer) model pretrained ... |
translation | transformers | # opus-mt-tc-big-de-gmq
## Table of Contents
- [Model Details](#model-details)
- [Uses](#uses)
- [Risks, Limitations and Biases](#risks-limitations-and-biases)
- [How to Get Started With the Model](#how-to-get-started-with-the-model)
- [Training](#training)
- [Evaluation](#evaluation)
- [Citation Information](#citatio... | {"language": ["da", "de", "is", "nb", "sv"], "license": "cc-by-4.0", "tags": ["translation", "opus-mt-tc"], "model-index": [{"name": "opus-mt-tc-big-de-gmq", "results": [{"task": {"type": "translation", "name": "Translation deu-dan"}, "dataset": {"name": "flores101-devtest", "type": "flores_101", "args": "deu dan devte... | Helsinki-NLP/opus-mt-tc-big-de-gmq | null | [
"transformers",
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"safetensors",
"marian",
"text2text-generation",
"translation",
"opus-mt-tc",
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"nb",
"sv",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-08-12T14:55:24+00:00 | [] | [
"da",
"de",
"is",
"nb",
"sv"
] | TAGS
#transformers #pytorch #tf #safetensors #marian #text2text-generation #translation #opus-mt-tc #da #de #is #nb #sv #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
| opus-mt-tc-big-de-gmq
=====================
Table of Contents
-----------------
* Model Details
* Uses
* Risks, Limitations and Biases
* How to Get Started With the Model
* Training
* Evaluation
* Citation Information
* Acknowledgements
Model Details
-------------
Neural machine translation model for translatin... | [] | [
"TAGS\n#transformers #pytorch #tf #safetensors #marian #text2text-generation #translation #opus-mt-tc #da #de #is #nb #sv #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
reinforcement-learning | stable-baselines3 |
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 ... | {"library_name": "stable-baselines3", "tags": ["LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "LunarLander-v2", "type": "LunarL... | danielmaxwell/ppo-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-08-12T14:57:41+00:00 | [] | [] | TAGS
#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add you... |
reinforcement-learning | stable-baselines3 |
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framewor... | {"library_name": "stable-baselines3", "tags": ["SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "DQN", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "SpaceInvadersNoFram... | MerlinTK/dqn-SpaceInvadersNoFrameskip-v4 | null | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-08-12T15:00:05+00:00 | [] | [] | TAGS
#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# DQN Agent playing SpaceInvadersNoFrameskip-v4
This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4
using the stable-baselines3 library
and the RL Zoo.
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents... | [
"# DQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framework for Stable Baselines3\nreinforcement learning agents,\nwith hyperparameter optimization and pre-tra... | [
"TAGS\n#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# DQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo... |
tabular-classification | sklearn |
# Model description
This is a DecisionTreeClassifier model built for Kaggle Tabular Playground Series August 2022, trained on supersoaker production failures dataset.
## Intended uses & limitations
This model is not ready to be used in production.
## Training Procedure
### Hyperparameters
The model is trained wi... | {"library_name": "sklearn", "tags": ["sklearn", "skops", "tabular-classification"], "widget": {"structuredData": {"attribute_0": ["material_7", "material_7", "material_7"], "attribute_1": ["material_8", "material_8", "material_6"], "attribute_2": [5, 5, 6], "attribute_3": [8, 8, 9], "loading": [154.02, 108.73, 99.84], ... | scikit-learn/tabular-playground | null | [
"sklearn",
"skops",
"tabular-classification",
"has_space",
"region:us"
] | null | 2022-08-12T15:08:16+00:00 | [] | [] | TAGS
#sklearn #skops #tabular-classification #has_space #region-us
| Model description
=================
This is a DecisionTreeClassifier model built for Kaggle Tabular Playground Series August 2022, trained on supersoaker production failures dataset.
Intended uses & limitations
---------------------------
This model is not ready to be used in production.
Training Procedure
----... | [
"### Hyperparameters\n\n\nThe model is trained with below hyperparameters.\n\n\n\n Click to expand",
"### Model Plot\n\n\nThe model plot is below."
] | [
"TAGS\n#sklearn #skops #tabular-classification #has_space #region-us \n",
"### Hyperparameters\n\n\nThe model is trained with below hyperparameters.\n\n\n\n Click to expand",
"### Model Plot\n\n\nThe model plot is below."
] |
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. -->
# roberta_large-chunking_0812_v0
This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on an... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "roberta_large-chunking_0812_v0", "results": []}]} | mariolinml/roberta_large-chunking_0812_v0 | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"token-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-12T15:09:06+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| roberta\_large-chunking\_0812\_v0
=================================
This model is a fine-tuned version of roberta-large on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3382
* Precision: 0.8195
* Recall: 0.8350
* F1: 0.8272
* Accuracy: 0.9106
Model description
------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #roberta #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_si... |
null | transformers | <h1>Transformer Encoder for Social Science (TESS)</h1>
TESS is a deep neural network model intended for social science related NLP tasks. The model is developed by Haosen Ge, In Young Park, Xuancheng Qian, and Grace Zeng.
We demonstrate in two validation tests that TESS outperforms BERT and RoBERTa by 16.7\% on aver... | {"license": "mit"} | hsge/TESS_768_v1 | null | [
"transformers",
"pytorch",
"albert",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-08-12T15:11:48+00:00 | [] | [] | TAGS
#transformers #pytorch #albert #license-mit #endpoints_compatible #region-us
| Transformer Encoder for Social Science (TESS)
=============================================
TESS is a deep neural network model intended for social science related NLP tasks. The model is developed by Haosen Ge, In Young Park, Xuancheng Qian, and Grace Zeng.
We demonstrate in two validation tests that TESS outperfo... | [] | [
"TAGS\n#transformers #pytorch #albert #license-mit #endpoints_compatible #region-us \n"
] |
translation | transformers | # opus-mt-tc-big-he-gmq
## Table of Contents
- [Model Details](#model-details)
- [Uses](#uses)
- [Risks, Limitations and Biases](#risks-limitations-and-biases)
- [How to Get Started With the Model](#how-to-get-started-with-the-model)
- [Training](#training)
- [Evaluation](#evaluation)
- [Citation Information](#citatio... | {"language": ["da", "he", "nb", "sv"], "license": "cc-by-4.0", "tags": ["translation", "opus-mt-tc"], "model-index": [{"name": "opus-mt-tc-big-he-gmq", "results": [{"task": {"type": "translation", "name": "Translation heb-dan"}, "dataset": {"name": "flores101-devtest", "type": "flores_101", "args": "heb dan devtest"}, ... | Helsinki-NLP/opus-mt-tc-big-he-gmq | null | [
"transformers",
"pytorch",
"tf",
"safetensors",
"marian",
"text2text-generation",
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"nb",
"sv",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-08-12T15:14:29+00:00 | [] | [
"da",
"he",
"nb",
"sv"
] | TAGS
#transformers #pytorch #tf #safetensors #marian #text2text-generation #translation #opus-mt-tc #da #he #nb #sv #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
| opus-mt-tc-big-he-gmq
=====================
Table of Contents
-----------------
* Model Details
* Uses
* Risks, Limitations and Biases
* How to Get Started With the Model
* Training
* Evaluation
* Citation Information
* Acknowledgements
Model Details
-------------
Neural machine translation model for translatin... | [] | [
"TAGS\n#transformers #pytorch #tf #safetensors #marian #text2text-generation #translation #opus-mt-tc #da #he #nb #sv #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
translation | transformers | # opus-mt-tc-big-itc-eu
## Table of Contents
- [Model Details](#model-details)
- [Uses](#uses)
- [Risks, Limitations and Biases](#risks-limitations-and-biases)
- [How to Get Started With the Model](#how-to-get-started-with-the-model)
- [Training](#training)
- [Evaluation](#evaluation)
- [Citation Information](#citatio... | {"language": ["es", "eu"], "license": "cc-by-4.0", "tags": ["translation", "opus-mt-tc"], "model-index": [{"name": "opus-mt-tc-big-itc-eu", "results": [{"task": {"type": "translation", "name": "Translation spa-eus"}, "dataset": {"name": "tatoeba-test-v2021-08-07", "type": "tatoeba_mt", "args": "spa-eus"}, "metrics": [{... | Helsinki-NLP/opus-mt-tc-big-itc-eu | null | [
"transformers",
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"tf",
"safetensors",
"marian",
"text2text-generation",
"translation",
"opus-mt-tc",
"es",
"eu",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-08-12T15:30:35+00:00 | [] | [
"es",
"eu"
] | TAGS
#transformers #pytorch #tf #safetensors #marian #text2text-generation #translation #opus-mt-tc #es #eu #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
| opus-mt-tc-big-itc-eu
=====================
Table of Contents
-----------------
* Model Details
* Uses
* Risks, Limitations and Biases
* How to Get Started With the Model
* Training
* Evaluation
* Citation Information
* Acknowledgements
Model Details
-------------
Neural machine translation model for translatin... | [] | [
"TAGS\n#transformers #pytorch #tf #safetensors #marian #text2text-generation #translation #opus-mt-tc #es #eu #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
text2text-generation | transformers |
# idT5 for Indonesian Question Generation and Question Answering
[idT5](https://huggingface.co/muchad/idt5-base) (Indonesian version of [mT5](https://huggingface.co/google/mt5-base)) is fine-tuned on 30% of [translated SQuAD v2.0](https://github.com/Wikidepia/indonesian_datasets/tree/master/question-answering/squad) ... | {"language": "id", "license": "apache-2.0", "tags": ["question-generation", "multitask-model", "idt5"], "datasets": ["SQuADv2.0"], "widget": [{"text": "generate question: <hl> Dua orang <hl> pengembara berjalan di sepanjang jalan yang berdebu dan tandus di hari yang sangat panas. Tidak lama kemudian, mereka menemukan s... | muchad/idt5-qa-qg | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"question-generation",
"multitask-model",
"idt5",
"id",
"dataset:SQuADv2.0",
"arxiv:2302.00856",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-08-12T15:32:11+00:00 | [
"2302.00856"
] | [
"id"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #question-generation #multitask-model #idt5 #id #dataset-SQuADv2.0 #arxiv-2302.00856 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
# idT5 for Indonesian Question Generation and Question Answering
idT5 (Indonesian version of mT5) is fine-tuned on 30% of translated SQuAD v2.0 for Question Generation and Question Answering tasks.
## Live Demo
* Question Generation: URL
* Question Answering: t.me/caritahubot
## Requirements
## Usage
#### Questi... | [
"# idT5 for Indonesian Question Generation and Question Answering\n\nidT5 (Indonesian version of mT5) is fine-tuned on 30% of translated SQuAD v2.0 for Question Generation and Question Answering tasks.",
"## Live Demo\n* Question Generation: URL\n* Question Answering: t.me/caritahubot",
"## Requirements",
"##... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #question-generation #multitask-model #idt5 #id #dataset-SQuADv2.0 #arxiv-2302.00856 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"# idT5 for Indonesian Question Generation and Question... |
translation | transformers | # opus-mt-tc-big-fi-zls
## Table of Contents
- [Model Details](#model-details)
- [Uses](#uses)
- [Risks, Limitations and Biases](#risks-limitations-and-biases)
- [How to Get Started With the Model](#how-to-get-started-with-the-model)
- [Training](#training)
- [Evaluation](#evaluation)
- [Citation Information](#citatio... | {"language": ["bg", "fi", "hr", "sl", "sr"], "license": "cc-by-4.0", "tags": ["translation", "opus-mt-tc"], "language_bcp47": ["sr_Cyrl"], "model-index": [{"name": "opus-mt-tc-big-fi-zls", "results": [{"task": {"type": "translation", "name": "Translation fin-bul"}, "dataset": {"name": "flores101-devtest", "type": "flor... | Helsinki-NLP/opus-mt-tc-big-fi-zls | null | [
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"marian",
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"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-08-12T15:46:33+00:00 | [] | [
"bg",
"fi",
"hr",
"sl",
"sr"
] | TAGS
#transformers #pytorch #tf #safetensors #marian #text2text-generation #translation #opus-mt-tc #bg #fi #hr #sl #sr #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
| opus-mt-tc-big-fi-zls
=====================
Table of Contents
-----------------
* Model Details
* Uses
* Risks, Limitations and Biases
* How to Get Started With the Model
* Training
* Evaluation
* Citation Information
* Acknowledgements
Model Details
-------------
Neural machine translation model for translatin... | [] | [
"TAGS\n#transformers #pytorch #tf #safetensors #marian #text2text-generation #translation #opus-mt-tc #bg #fi #hr #sl #sr #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
reinforcement-learning | stable-baselines3 |
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 ... | {"library_name": "stable-baselines3", "tags": ["LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "LunarLander-v2", "type": "LunarL... | bdokmeci/ppo-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-08-12T15:55:04+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... |
translation | transformers | # opus-mt-tc-big-fa-itc
## Table of Contents
- [Model Details](#model-details)
- [Uses](#uses)
- [Risks, Limitations and Biases](#risks-limitations-and-biases)
- [How to Get Started With the Model](#how-to-get-started-with-the-model)
- [Training](#training)
- [Evaluation](#evaluation)
- [Citation Information](#citatio... | {"language": ["fa", "fr", "pt", "ro"], "license": "cc-by-4.0", "tags": ["translation", "opus-mt-tc"], "model-index": [{"name": "opus-mt-tc-big-fa-itc", "results": [{"task": {"type": "translation", "name": "Translation fas-fra"}, "dataset": {"name": "flores101-devtest", "type": "flores_101", "args": "fas fra devtest"}, ... | Helsinki-NLP/opus-mt-tc-big-fa-itc | null | [
"transformers",
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"tf",
"safetensors",
"marian",
"text2text-generation",
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"pt",
"ro",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-08-12T16:02:21+00:00 | [] | [
"fa",
"fr",
"pt",
"ro"
] | TAGS
#transformers #pytorch #tf #safetensors #marian #text2text-generation #translation #opus-mt-tc #fa #fr #pt #ro #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
| opus-mt-tc-big-fa-itc
=====================
Table of Contents
-----------------
* Model Details
* Uses
* Risks, Limitations and Biases
* How to Get Started With the Model
* Training
* Evaluation
* Citation Information
* Acknowledgements
Model Details
-------------
Neural machine translation model for translatin... | [] | [
"TAGS\n#transformers #pytorch #tf #safetensors #marian #text2text-generation #translation #opus-mt-tc #fa #fr #pt #ro #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
image-classification | transformers | This is a on-going work of developing a deep neural network for the plant species Identification. Intitally, a Pre-trained model called "ConvNext"used which is built on top of Transformer Model, where a checkpoint called " https://huggingface.co/facebook/convnext-tiny-224#convnext-tiny-sized-model" used by FaceBook now... | {} | nsarker/convnext-tiny-finetune-plantspecies | null | [
"transformers",
"pytorch",
"convnext",
"image-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-12T16:05:42+00:00 | [] | [] | TAGS
#transformers #pytorch #convnext #image-classification #autotrain_compatible #endpoints_compatible #region-us
| This is a on-going work of developing a deep neural network for the plant species Identification. Intitally, a Pre-trained model called "ConvNext"used which is built on top of Transformer Model, where a checkpoint called " URL used by FaceBook now has been fine-tuned for this particular dataset. An API allows to access... | [] | [
"TAGS\n#transformers #pytorch #convnext #image-classification #autotrain_compatible #endpoints_compatible #region-us \n"
] |
translation | transformers | # opus-mt-tc-big-fa-gmq
## Table of Contents
- [Model Details](#model-details)
- [Uses](#uses)
- [Risks, Limitations and Biases](#risks-limitations-and-biases)
- [How to Get Started With the Model](#how-to-get-started-with-the-model)
- [Training](#training)
- [Evaluation](#evaluation)
- [Citation Information](#citatio... | {"language": ["da", "fa"], "license": "cc-by-4.0", "tags": ["translation", "opus-mt-tc"], "model-index": [{"name": "opus-mt-tc-big-fa-gmq", "results": [{"task": {"type": "translation", "name": "Translation fas-dan"}, "dataset": {"name": "flores101-devtest", "type": "flores_101", "args": "fas dan devtest"}, "metrics": [... | Helsinki-NLP/opus-mt-tc-big-fa-gmq | null | [
"transformers",
"pytorch",
"tf",
"safetensors",
"marian",
"text2text-generation",
"translation",
"opus-mt-tc",
"da",
"fa",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-08-12T16:17:15+00:00 | [] | [
"da",
"fa"
] | TAGS
#transformers #pytorch #tf #safetensors #marian #text2text-generation #translation #opus-mt-tc #da #fa #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
| opus-mt-tc-big-fa-gmq
=====================
Table of Contents
-----------------
* Model Details
* Uses
* Risks, Limitations and Biases
* How to Get Started With the Model
* Training
* Evaluation
* Citation Information
* Acknowledgements
Model Details
-------------
Neural machine translation model for translatin... | [] | [
"TAGS\n#transformers #pytorch #tf #safetensors #marian #text2text-generation #translation #opus-mt-tc #da #fa #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
translation | transformers | # opus-mt-tc-big-eu-itc
## Table of Contents
- [Model Details](#model-details)
- [Uses](#uses)
- [Risks, Limitations and Biases](#risks-limitations-and-biases)
- [How to Get Started With the Model](#how-to-get-started-with-the-model)
- [Training](#training)
- [Evaluation](#evaluation)
- [Citation Information](#citatio... | {"language": ["es", "eu"], "license": "cc-by-4.0", "tags": ["translation", "opus-mt-tc"], "model-index": [{"name": "opus-mt-tc-big-eu-itc", "results": [{"task": {"type": "translation", "name": "Translation eus-spa"}, "dataset": {"name": "tatoeba-test-v2021-08-07", "type": "tatoeba_mt", "args": "eus-spa"}, "metrics": [{... | Helsinki-NLP/opus-mt-tc-big-eu-itc | null | [
"transformers",
"pytorch",
"tf",
"safetensors",
"marian",
"text2text-generation",
"translation",
"opus-mt-tc",
"es",
"eu",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-08-12T16:31:56+00:00 | [] | [
"es",
"eu"
] | TAGS
#transformers #pytorch #tf #safetensors #marian #text2text-generation #translation #opus-mt-tc #es #eu #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
| opus-mt-tc-big-eu-itc
=====================
Table of Contents
-----------------
* Model Details
* Uses
* Risks, Limitations and Biases
* How to Get Started With the Model
* Training
* Evaluation
* Citation Information
* Acknowledgements
Model Details
-------------
Neural machine translation model for translatin... | [] | [
"TAGS\n#transformers #pytorch #tf #safetensors #marian #text2text-generation #translation #opus-mt-tc #es #eu #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
tabular-classification | sklearn |
# Model description
This is a DecisionTreeClassifier model built for Kaggle Tabular Playground Series August 2022, trained on supersoaker production failures dataset.
## Intended uses & limitations
This model is not ready to be used in production.
## Training Procedure
### Hyperparameters
The model is trained wi... | {"library_name": "sklearn", "tags": ["sklearn", "skops", "tabular-classification"], "widget": {"structuredData": {"attribute_0": ["material_7", "material_7", "material_7"], "attribute_1": ["material_6", "material_5", "material_6"], "attribute_2": [6, 6, 6], "attribute_3": [9, 6, 9], "loading": [101.52, 91.34, 167.03], ... | demo-org/tabular-playground | null | [
"sklearn",
"skops",
"tabular-classification",
"region:us"
] | null | 2022-08-12T17:03:12+00:00 | [] | [] | TAGS
#sklearn #skops #tabular-classification #region-us
| Model description
=================
This is a DecisionTreeClassifier model built for Kaggle Tabular Playground Series August 2022, trained on supersoaker production failures dataset.
Intended uses & limitations
---------------------------
This model is not ready to be used in production.
Training Procedure
----... | [
"### Hyperparameters\n\n\nThe model is trained with below hyperparameters.\n\n\n\n Click to expand \n\n\n```\n SimpleImputer(), ['loading']),\n ('numerical_missing_value_imputer',\n SimpleImputer(),\n ['lo... | [
"TAGS\n#sklearn #skops #tabular-classification #region-us \n",
"### Hyperparameters\n\n\nThe model is trained with below hyperparameters.\n\n\n\n Click to expand \n\n\n```\n SimpleImputer(), ['loading']),\n ('numerical_missing_value_imputer',\n ... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# camembert-base-finetuned-avec-symbole-dd
This model is a fine-tuned version of [camembert-base](https://huggingface.co/camembert... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "camembert-base-finetuned-avec-symbole-dd", "results": []}]} | ZhiyuanQiu/camembert-base-finetuned-avec-symbole-dd | null | [
"transformers",
"pytorch",
"tensorboard",
"camembert",
"token-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-12T17:42:39+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #camembert #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| camembert-base-finetuned-avec-symbole-dd
========================================
This model is a fine-tuned version of camembert-base on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2583
* Precision: 0.8906
* Recall: 0.9204
* F1: 0.9053
* Accuracy: 0.9319
Model descripti... | [
"### 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 #camembert #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_... |
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... | mdround/ppo-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-08-12T17:53:31+00:00 | [] | [] | TAGS
#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add you... |
text-generation | transformers |
<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/1556081004699435010/Qvh2... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/20pointsbot-apesahoy-nsp_gpt2/1660331471256/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/20pointsbot-apesahoy-nsp_gpt2 | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-12T18:02:44+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI CYBORG
20 Points Ahead Bot & Humongous Ape MP & Ninja Sex Party but AI
@20pointsbot-apesahoy-nsp\_gpt2
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 t... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation | transformers |
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1556081004699435010/Qvh2... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/20pointsbot-apesahoy-chai_ste-deepfanfiction-nsp_gpt2-pldroneoperated/1660333381797/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/20pointsbot-apesahoy-chai_ste-deepfanfiction-nsp_gpt2-pldroneoperated | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-12T18:41:45+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI CYBORG
20 Points Ahead Bot & Humongous Ape MP & ste & Deep Fanfiction & Ninja Sex Party but AI & PLDroneOperated
@20pointsbot-apesahoy-chai\_ste-deepfanfiction-nsp\_gpt2-pldroneoperated
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
---------... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation | transformers |
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1196519479364268034/5Qpn... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/apesahoy-chai_ste-deepfanfiction-nsp_gpt2-pldroneoperated/1660334711576/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/apesahoy-chai_ste-deepfanfiction-nsp_gpt2-pldroneoperated | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-12T18:58:42+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI CYBORG
Humongous Ape MP & ste & Deep Fanfiction & Ninja Sex Party but AI & PLDroneOperated
@apesahoy-chai\_ste-deepfanfiction-nsp\_gpt2-pldroneoperated
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 foll... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
token-classification | transformers | # tner/roberta-large-fin
This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on the
[tner/fin](https://huggingface.co/datasets/tner/fin) dataset.
Model fine-tuning is done via [T-NER](https://github.com/asahi417/tner)'s hyper-parameter search (see the repository
for more detail... | {"datasets": ["fin"], "metrics": ["f1", "precision", "recall"], "pipeline_tag": "token-classification", "widget": [{"text": "Jacob Collier is a Grammy awarded artist from England.", "example_title": "NER Example 1"}], "model-index": [{"name": "tner/roberta-large-fin", "results": [{"task": {"type": "token-classification... | tner/roberta-large-fin | null | [
"transformers",
"pytorch",
"roberta",
"token-classification",
"dataset:fin",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-12T19:28:39+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #token-classification #dataset-fin #model-index #autotrain_compatible #endpoints_compatible #region-us
| # tner/roberta-large-fin
This model is a fine-tuned version of roberta-large on the
tner/fin dataset.
Model fine-tuning is done via T-NER's hyper-parameter search (see the repository
for more detail). It achieves the following results on the test set:
- F1 (micro): 0.6988727858293075
- Precision (micro): 0.7161716171... | [
"# tner/roberta-large-fin\n\nThis model is a fine-tuned version of roberta-large on the \ntner/fin dataset.\nModel fine-tuning is done via T-NER's hyper-parameter search (see the repository\nfor more detail). It achieves the following results on the test set:\n- F1 (micro): 0.6988727858293075\n- Precision (micro): ... | [
"TAGS\n#transformers #pytorch #roberta #token-classification #dataset-fin #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# tner/roberta-large-fin\n\nThis model is a fine-tuned version of roberta-large on the \ntner/fin dataset.\nModel fine-tuning is done via T-NER's hyper-parameter sear... |
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"} | spacemanidol/esci-all-distilbert-base-uncased-5e-5 | null | [
"sentence-transformers",
"pytorch",
"distilbert",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2022-08-12T19:47:08+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us
|
# {MODEL_NAME}
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers installed:
Then you can u... | [
"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\n... | [
"TAGS\n#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us \n",
"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering... |
reinforcement-learning | null |
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="rebolforces/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional ... | {"tags": ["FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-FrozenLake-v1-4x4-noSlippery", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "FrozenLake-v1-4x4-no_slippery", "type": ... | rebolforces/q-FrozenLake-v1-4x4-noSlippery | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-08-12T20:22:20+00:00 | [] | [] | TAGS
#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing FrozenLake-v1
This is a trained model of a Q-Learning agent playing FrozenLake-v1 .
## Usage
| [
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] | [
"TAGS\n#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# camembert-base-finetuned-sans-symbole-dd
This model is a fine-tuned version of [camembert-base](https://huggingface.co/camembert... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "camembert-base-finetuned-sans-symbole-dd", "results": []}]} | ZhiyuanQiu/camembert-base-finetuned-sans-symbole-dd | null | [
"transformers",
"pytorch",
"tensorboard",
"camembert",
"token-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-12T21:10:53+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #camembert #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| camembert-base-finetuned-sans-symbole-dd
========================================
This model is a fine-tuned version of camembert-base on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2642
* Precision: 0.8856
* Recall: 0.9176
* F1: 0.9013
* Accuracy: 0.9364
Model descripti... | [
"### 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: 4",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #camembert #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_... |
token-classification | transformers | # tner/deberta-v3-large-fin
This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) on the
[tner/fin](https://huggingface.co/datasets/tner/fin) dataset.
Model fine-tuning is done via [T-NER](https://github.com/asahi417/tner)'s hyper-parameter search (see t... | {"datasets": ["tner/fin"], "metrics": ["f1", "precision", "recall"], "pipeline_tag": "token-classification", "widget": [{"text": "Jacob Collier is a Grammy awarded artist from England.", "example_title": "NER Example 1"}], "model-index": [{"name": "tner/deberta-v3-large-fin", "results": [{"task": {"type": "token-classi... | tner/deberta-v3-large-fin | null | [
"transformers",
"pytorch",
"deberta-v2",
"token-classification",
"dataset:tner/fin",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-12T21:13:20+00:00 | [] | [] | TAGS
#transformers #pytorch #deberta-v2 #token-classification #dataset-tner/fin #model-index #autotrain_compatible #endpoints_compatible #region-us
| # tner/deberta-v3-large-fin
This model is a fine-tuned version of microsoft/deberta-v3-large on the
tner/fin dataset.
Model fine-tuning is done via T-NER's hyper-parameter search (see the repository
for more detail). It achieves the following results on the test set:
- F1 (micro): 0.7060755336617406
- Precision (micr... | [
"# tner/deberta-v3-large-fin\n\nThis model is a fine-tuned version of microsoft/deberta-v3-large on the \ntner/fin dataset.\nModel fine-tuning is done via T-NER's hyper-parameter search (see the repository\nfor more detail). It achieves the following results on the test set:\n- F1 (micro): 0.7060755336617406\n- Pre... | [
"TAGS\n#transformers #pytorch #deberta-v2 #token-classification #dataset-tner/fin #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# tner/deberta-v3-large-fin\n\nThis model is a fine-tuned version of microsoft/deberta-v3-large on the \ntner/fin dataset.\nModel fine-tuning is done via T-NE... |
null | 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-xlsr-korean-speech-emotion-recognition
This model is a fine-tuned version of [jungjongho/wav2vec2-large-xlsr-korean-dem... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "wav2vec2-xlsr-korean-speech-emotion-recognition", "results": []}]} | jungjongho/wav2vec2-xlsr-korean-speech-emotion-recognition | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-08-12T22:49:28+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-xlsr-korean-speech-emotion-recognition
===============================================
This model is a fine-tuned version of jungjongho/wav2vec2-large-xlsr-korean-demo-colab\_epoch15 on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6651
* Accuracy: 0.7667
Model desc... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 2\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 4\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #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: 2\n* eval\\_batch\\_size: 2\n* ... |
token-classification | transformers | # tner/deberta-v3-large-bionlp2004
This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) on the
[tner/bionlp2004](https://huggingface.co/datasets/tner/bionlp2004) dataset.
Model fine-tuning is done via [T-NER](https://github.com/asahi417/tner)'s hyper-pa... | {"datasets": ["tner/bionlp2004"], "metrics": ["f1", "precision", "recall"], "pipeline_tag": "token-classification", "widget": [{"text": "Jacob Collier is a Grammy awarded artist from England.", "example_title": "NER Example 1"}], "model-index": [{"name": "tner/deberta-v3-large-bionlp2004", "results": [{"task": {"type":... | tner/deberta-v3-large-bionlp2004 | null | [
"transformers",
"pytorch",
"deberta-v2",
"token-classification",
"dataset:tner/bionlp2004",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-12T22:58:35+00:00 | [] | [] | TAGS
#transformers #pytorch #deberta-v2 #token-classification #dataset-tner/bionlp2004 #model-index #autotrain_compatible #endpoints_compatible #region-us
| # tner/deberta-v3-large-bionlp2004
This model is a fine-tuned version of microsoft/deberta-v3-large on the
tner/bionlp2004 dataset.
Model fine-tuning is done via T-NER's hyper-parameter search (see the repository
for more detail). It achieves the following results on the test set:
- F1 (micro): 0.758624442267929
- Pr... | [
"# tner/deberta-v3-large-bionlp2004\n\nThis model is a fine-tuned version of microsoft/deberta-v3-large on the \ntner/bionlp2004 dataset.\nModel fine-tuning is done via T-NER's hyper-parameter search (see the repository\nfor more detail). It achieves the following results on the test set:\n- F1 (micro): 0.758624442... | [
"TAGS\n#transformers #pytorch #deberta-v2 #token-classification #dataset-tner/bionlp2004 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# tner/deberta-v3-large-bionlp2004\n\nThis model is a fine-tuned version of microsoft/deberta-v3-large on the \ntner/bionlp2004 dataset.\nModel fine-tu... |
reinforcement-learning | null |
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="rebolforces/q-FrozenLake-v1-4x4-Slippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional at... | {"tags": ["FrozenLake-v1-4x4", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-FrozenLake-v1-4x4-Slippery", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "FrozenLake-v1-4x4", "type": "FrozenLake-v1-4x4"}, "met... | rebolforces/q-FrozenLake-v1-4x4-Slippery | null | [
"FrozenLake-v1-4x4",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-08-12T23:23:53+00:00 | [] | [] | TAGS
#FrozenLake-v1-4x4 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing FrozenLake-v1
This is a trained model of a Q-Learning agent playing FrozenLake-v1 .
## Usage
| [
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] | [
"TAGS\n#FrozenLake-v1-4x4 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] |
reinforcement-learning | null |
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="rebolforces/q-FrozenLake-v1-4x4", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes ... | {"tags": ["FrozenLake-v1-4x4", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-FrozenLake-v1-4x4", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "FrozenLake-v1-4x4", "type": "FrozenLake-v1-4x4"}, "metrics": [{... | rebolforces/q-FrozenLake-v1-4x4 | null | [
"FrozenLake-v1-4x4",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-08-12T23:29:12+00:00 | [] | [] | TAGS
#FrozenLake-v1-4x4 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing FrozenLake-v1
This is a trained model of a Q-Learning agent playing FrozenLake-v1 .
## Usage
| [
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] | [
"TAGS\n#FrozenLake-v1-4x4 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["emotion"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "distilbert-base-uncased-finetuned-emotion", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "emotion", "type": "emotion... | nakayankuro/distilbert-base-uncased-finetuned-emotion | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-13T00:30:22+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-emotion
=========================================
This model is a fine-tuned version of distilbert-base-uncased on the emotion dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2198
* Accuracy: 0.924
* F1: 0.9239
Model description
-----------------
Mor... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learn... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-clinc
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/d... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["clinc_oos"], "metrics": ["accuracy"], "model-index": [{"name": "distilbert-base-uncased-finetuned-clinc", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "clinc_oos", "type": "clinc_oos",... | yokoe/distilbert-base-uncased-finetuned-clinc | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:clinc_oos",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-13T00:37:07+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-clinc_oos #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-clinc
=======================================
This model is a fine-tuned version of distilbert-base-uncased on the clinc\_oos dataset.
It achieves the following results on the evaluation set:
* Loss: 0.7720
* Accuracy: 0.9184
Model description
-----------------
More information... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 48\n* eval\\_batch\\_size: 48\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-clinc_oos #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* lea... |
reinforcement-learning | null |
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="butchland/q-FrozenLake-v1-4x4-noSlippery-iter2", filename="q-learning.pkl")
# Don't forget to check if you need to add additio... | {"tags": ["FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-FrozenLake-v1-4x4-noSlippery-iter2", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "FrozenLake-v1-4x4-no_slippery", "t... | butchland/q-FrozenLake-v1-4x4-noSlippery-iter2 | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-08-13T00:59:54+00:00 | [] | [] | TAGS
#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing FrozenLake-v1
This is a trained model of a Q-Learning agent playing FrozenLake-v1 .
## Usage
| [
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] | [
"TAGS\n#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] |
reinforcement-learning | null |
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="butchland/q-FrozenLake-v1-4x4-slippery-work1", filename="q-learning.pkl")
# Don't forget to check if you need to add additiona... | {"tags": ["FrozenLake-v1-4x4", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-FrozenLake-v1-4x4-slippery-work1", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "FrozenLake-v1-4x4", "type": "FrozenLake-v1-4x4"}... | butchland/q-FrozenLake-v1-4x4-slippery-work1 | null | [
"FrozenLake-v1-4x4",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-08-13T01:15:04+00:00 | [] | [] | TAGS
#FrozenLake-v1-4x4 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing FrozenLake-v1
This is a trained model of a Q-Learning agent playing FrozenLake-v1 .
## Usage
| [
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] | [
"TAGS\n#FrozenLake-v1-4x4 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] |
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. -->
# dna_bert_6_kmers-finetuned
This model is a fine-tuned version of [armheb/DNA_bert_6](https://huggingface.co/armheb/DNA_bert_6) o... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "dna_bert_6_kmers-finetuned", "results": []}]} | Mozart-coder/dna_bert_6_kmers-finetuned | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-13T04:23:00+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| dna\_bert\_6\_kmers-finetuned
=============================
This model is a fine-tuned version of armheb/DNA\_bert\_6 on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0034
Model description
-----------------
More information needed
Intended uses & limitations
---------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10\n* mixed\\_pre... | [
"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: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_si... |
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/885547010186559489/qicTb... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/xelanater/1660409759216/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/xelanater | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-13T04:50:28+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Xelanater
@xelanater
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
------------... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation | transformers |
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1511972083835838465/M96V... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/vitamoonshadow/1660371232802/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/vitamoonshadow | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-13T04:53:59+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Vita!
@vitamoonshadow
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
-----------... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
reinforcement-learning | null |
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="yogeshkulkarni/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add addition... | {"tags": ["FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-FrozenLake-v1-4x4-noSlippery", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "FrozenLake-v1-4x4-no_slippery", "type": ... | yogeshkulkarni/q-FrozenLake-v1-4x4-noSlippery | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-08-13T04:57:22+00:00 | [] | [] | TAGS
#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing FrozenLake-v1
This is a trained model of a Q-Learning agent playing FrozenLake-v1 .
## Usage
| [
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] | [
"TAGS\n#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] |
reinforcement-learning | null |
# **Q-Learning** Agent playing **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="yogeshkulkarni/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False ... | {"tags": ["Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-Taxi-v3", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Taxi-v3", "type": "Taxi-v3"}, "metrics": [{"type": "mean_reward", "value": "7.48 +/... | yogeshkulkarni/q-Taxi-v3 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-08-13T04:59:08+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"
] |
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-issues-128
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased)... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "bert-base-uncased-issues-128", "results": []}]} | Shenghao1993/bert-base-uncased-issues-128 | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-13T05:25:06+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| bert-base-uncased-issues-128
============================
This model is a fine-tuned version of bert-base-uncased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 1.2503
Model description
-----------------
More information needed
Intended uses & limitations
---------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 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: 16",
"### Traini... | [
"TAGS\n#transformers #pytorch #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: 5e-05\n* train\\_batch\\_size: 32\n* eval\\_bat... |
text-generation | transformers |
# ESを書くAI
Japanese GPT-2 modelをファインチューニングしました。<br>
就活のESを書くAIで、IT業界のESに絞ってトレーニングをしました。
The model was trained using code from Github repository [rinnakk/japanese-pretrained-models](https://github.com/rinnakk/japanese-pretrained-models) by [rinna Co., Ltd.](https://corp.rinna.co.jp/)
| {"language": "ja", "license": "mit", "tags": ["ja", "japanese", "gpt2", "text-generation", "lm", "nlp"], "widget": [{"text": "\u5fa1\u793e"}]} | huranokuma/es_IT | null | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"ja",
"japanese",
"lm",
"nlp",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-13T05:48:54+00:00 | [] | [
"ja"
] | TAGS
#transformers #pytorch #tensorboard #gpt2 #text-generation #ja #japanese #lm #nlp #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# ESを書くAI
Japanese GPT-2 modelをファインチューニングしました。<br>
就活のESを書くAIで、IT業界のESに絞ってトレーニングをしました。
The model was trained using code from Github repository rinnakk/japanese-pretrained-models by rinna Co., Ltd.
| [
"# ESを書くAI\nJapanese GPT-2 modelをファインチューニングしました。<br>\n就活のESを書くAIで、IT業界のESに絞ってトレーニングをしました。\n\nThe model was trained using code from Github repository rinnakk/japanese-pretrained-models by rinna Co., Ltd."
] | [
"TAGS\n#transformers #pytorch #tensorboard #gpt2 #text-generation #ja #japanese #lm #nlp #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# ESを書くAI\nJapanese GPT-2 modelをファインチューニングしました。<br>\n就活のESを書くAIで、IT業界のESに絞ってトレーニングをしました。\n\nThe model was trained using code... |
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. -->
# mojtaba767/bert-base-parsbert-uncased-finetuned-imdb-m2
This model is a fine-tuned version of [HooshvareLab/bert-base-parsbert-uncased... | {"tags": ["generated_from_keras_callback"], "model-index": [{"name": "mojtaba767/bert-base-parsbert-uncased-finetuned-imdb-m2", "results": []}]} | mojtaba767/bert-base-parsbert-uncased-finetuned-imdb-m2 | null | [
"transformers",
"tf",
"bert",
"fill-mask",
"generated_from_keras_callback",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-13T06:19:37+00:00 | [] | [] | TAGS
#transformers #tf #bert #fill-mask #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #region-us
| mojtaba767/bert-base-parsbert-uncased-finetuned-imdb-m2
=======================================================
This model is a fine-tuned version of HooshvareLab/bert-base-parsbert-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 3.4126
* Validation Loss: 3.4258... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'WarmUp', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_schedule\\_fn': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_le... | [
"TAGS\n#transformers #tf #bert #fill-mask #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'WarmUp... |
reinforcement-learning | null |
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="butchland/q-FrozenLake-v1-8x8-nonslippery-work1", filename="q-learning.pkl")
# Don't forget to check if you need to add additi... | {"tags": ["FrozenLake-v1-8x8-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-FrozenLake-v1-8x8-nonslippery-work1", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "FrozenLake-v1-8x8-no_slippery", "... | butchland/q-FrozenLake-v1-8x8-nonslippery-work1 | null | [
"FrozenLake-v1-8x8-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-08-13T06:30:25+00:00 | [] | [] | TAGS
#FrozenLake-v1-8x8-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing FrozenLake-v1
This is a trained model of a Q-Learning agent playing FrozenLake-v1 .
## Usage
| [
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] | [
"TAGS\n#FrozenLake-v1-8x8-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] |
reinforcement-learning | null |
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="butchland/q-FrozenLake-v1-8x8-slippery-work1", filename="q-learning.pkl")
# Don't forget to check if you need to add additiona... | {"tags": ["FrozenLake-v1-8x8", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-FrozenLake-v1-8x8-slippery-work1", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "FrozenLake-v1-8x8", "type": "FrozenLake-v1-8x8"}... | butchland/q-FrozenLake-v1-8x8-slippery-work1 | null | [
"FrozenLake-v1-8x8",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-08-13T06:41:20+00:00 | [] | [] | TAGS
#FrozenLake-v1-8x8 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing FrozenLake-v1
This is a trained model of a Q-Learning agent playing FrozenLake-v1 .
## Usage
| [
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] | [
"TAGS\n#FrozenLake-v1-8x8 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["emotion"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "distilbert-base-uncased-finetuned-emotion", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "emotion", "type": "emotion... | bengeisler/distilbert-base-uncased-finetuned-emotion | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-13T07:15:32+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-emotion
=========================================
This model is a fine-tuned version of distilbert-base-uncased on the emotion dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2168
* Accuracy: 0.9285
* F1: 0.9285
Model description
-----------------
Mo... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learn... |
reinforcement-learning | stable-baselines3 |
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framewor... | {"library_name": "stable-baselines3", "tags": ["SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "DQN", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "SpaceInvadersNoFram... | yogeshkulkarni/dqn-SpaceInvadersNoFrameskip-v4 | null | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-08-13T08:17:15+00:00 | [] | [] | TAGS
#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# DQN Agent playing SpaceInvadersNoFrameskip-v4
This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4
using the stable-baselines3 library
and the RL Zoo.
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents... | [
"# DQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framework for Stable Baselines3\nreinforcement learning agents,\nwith hyperparameter optimization and pre-tra... | [
"TAGS\n#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# DQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo... |
reinforcement-learning | ml-agents |
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a comple... | {"library_name": "ml-agents", "tags": ["unity-ml-agents", "ml-agents", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Pyramids"]} | yogeshkulkarni/testpyramidsrnd | null | [
"ml-agents",
"tensorboard",
"onnx",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
] | null | 2022-08-13T08:50:05+00:00 | [] | [] | TAGS
#ml-agents #tensorboard #onnx #unity-ml-agents #deep-reinforcement-learning #reinforcement-learning #ML-Agents-Pyramids #region-us
|
# ppo Agent playing Pyramids
This is a trained model of a ppo agent playing Pyramids using the Unity ML-Agents Library.
## Usage (with ML-Agents)
The Documentation: URL
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the trainin... | [
"# ppo Agent playing Pyramids\n This is a trained model of a ppo agent playing Pyramids using the Unity ML-Agents Library.\n \n ## Usage (with ML-Agents)\n The Documentation: URL\n We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:\n\n\n ### Resume the t... | [
"TAGS\n#ml-agents #tensorboard #onnx #unity-ml-agents #deep-reinforcement-learning #reinforcement-learning #ML-Agents-Pyramids #region-us \n",
"# ppo Agent playing Pyramids\n This is a trained model of a ppo agent playing Pyramids using the Unity ML-Agents Library.\n \n ## Usage (with ML-Agents)\n The Documen... |
fill-mask | transformers | ## RoBERTa Latin model, version 3 --> model card not finished yet
This is a Latin RoBERTa-based LM model, version 3.
The intention of the Transformer-based LM is twofold: on the one hand, it will be used for the evaluation of HTR results; on the other, it should be used as a decoder for the TrOCR architecture.
The t... | {} | pstroe/roberta-base-latin-cased3 | null | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"arxiv:2009.10053",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-13T08:54:45+00:00 | [
"2009.10053"
] | [] | TAGS
#transformers #pytorch #roberta #fill-mask #arxiv-2009.10053 #autotrain_compatible #endpoints_compatible #region-us
| ## RoBERTa Latin model, version 3 --> model card not finished yet
This is a Latin RoBERTa-based LM model, version 3.
The intention of the Transformer-based LM is twofold: on the one hand, it will be used for the evaluation of HTR results; on the other, it should be used as a decoder for the TrOCR architecture.
The t... | [
"## RoBERTa Latin model, version 3 --> model card not finished yet\n\nThis is a Latin RoBERTa-based LM model, version 3.\n\nThe intention of the Transformer-based LM is twofold: on the one hand, it will be used for the evaluation of HTR results; on the other, it should be used as a decoder for the TrOCR architectur... | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #arxiv-2009.10053 #autotrain_compatible #endpoints_compatible #region-us \n",
"## RoBERTa Latin model, version 3 --> model card not finished yet\n\nThis is a Latin RoBERTa-based LM model, version 3.\n\nThe intention of the Transformer-based LM is twofold: on the o... |
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-newsroom-cnn-adam8bit-bs4x64acc
This model is a fine-tuned version of [oMateos2020/pegasus-newsroom-cnn-adam8bit-bs16x64... | {"tags": ["generated_from_trainer"], "datasets": ["cnn_dailymail"], "metrics": ["rouge"], "model-index": [{"name": "pegasus-newsroom-cnn-adam8bit-bs4x64acc", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "cnn_dailymail", "type": "cnn_dailyma... | oMateos2020/pegasus-newsroom-cnn-adam8bit-bs4x64acc | null | [
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"generated_from_trainer",
"dataset:cnn_dailymail",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-13T09:39:50+00:00 | [] | [] | TAGS
#transformers #pytorch #pegasus #text2text-generation #generated_from_trainer #dataset-cnn_dailymail #model-index #autotrain_compatible #endpoints_compatible #region-us
| pegasus-newsroom-cnn-adam8bit-bs4x64acc
=======================================
This model is a fine-tuned version of oMateos2020/pegasus-newsroom-cnn-adam8bit-bs16x64acc on the cnn\_dailymail dataset.
It achieves the following results on the evaluation set:
* Loss: 2.8608
* Rouge1: 44.2881
* Rouge2: 21.5487
* Roug... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 6.4e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 32\n* seed: 42\n* gradient\\_accumulation\\_steps: 64\n* total\\_train\\_batch\\_size: 256\n* optimizer: Adam with betas=(0.9,0.999) and epsi... | [
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #generated_from_trainer #dataset-cnn_dailymail #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 6.4e-05\n* train\... |
reinforcement-learning | null |
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: https://github.com/huggingface/deep-rl-class/tree/main/unit5
| {"tags": ["CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class"], "model-index": [{"name": "Reinforce-CartPole-v1", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "CartPole-v1", "type": "CartPole-v1"}, "metrics": [{... | yogeshkulkarni/Reinforce-CartPole-v1 | null | [
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | null | 2022-08-13T09:40:35+00:00 | [] | [] | TAGS
#CartPole-v1 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us
|
# Reinforce Agent playing CartPole-v1
This is a trained model of a Reinforce agent playing CartPole-v1 .
To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: URL
| [
"# Reinforce Agent playing CartPole-v1\n This is a trained model of a Reinforce agent playing CartPole-v1 .\n To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: URL"
] | [
"TAGS\n#CartPole-v1 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us \n",
"# Reinforce Agent playing CartPole-v1\n This is a trained model of a Reinforce agent playing CartPole-v1 .\n To learn to use this model and train yours check Unit 5 of the Deep Reinforcemen... |
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. -->
# mojtaba767/bert-base-parsbert-uncased-finetuned-imdb-m-test
This model is a fine-tuned version of [HooshvareLab/bert-base-parsbert-unc... | {"tags": ["generated_from_keras_callback"], "model-index": [{"name": "mojtaba767/bert-base-parsbert-uncased-finetuned-imdb-m-test", "results": []}]} | mojtaba767/bert-base-parsbert-uncased-finetuned-imdb-m-test | null | [
"transformers",
"tf",
"bert",
"fill-mask",
"generated_from_keras_callback",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-13T09:45:01+00:00 | [] | [] | TAGS
#transformers #tf #bert #fill-mask #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #region-us
| mojtaba767/bert-base-parsbert-uncased-finetuned-imdb-m-test
===========================================================
This model is a fine-tuned version of HooshvareLab/bert-base-parsbert-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.0879
* Validation Loss... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'WarmUp', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_schedule\\_fn': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_le... | [
"TAGS\n#transformers #tf #bert #fill-mask #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'WarmUp... |
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