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question-answering | transformers |
# xtremedistil-l6-h256-uncased for QA
This is a [xtremedistil-l6-h256-uncased](https://huggingface.co/microsoft/xtremedistil-l6-h256-uncased) model, fine-tuned using the [NaturalQuestionsShort](https://research.google/pubs/pub47761/) dataset from the [MRQA Shared Task 2019](https://github.com/mrqa/MRQA-Shared-Task-2... | {"language": "en", "license": "mit", "tags": ["natural-questions-short", "question-answering"]} | Be-Lo/xtremedistil-l6-h256-uncased-natural-questions-short | null | [
"transformers",
"pytorch",
"bert",
"question-answering",
"natural-questions-short",
"en",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-07-22T13:29:38+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bert #question-answering #natural-questions-short #en #license-mit #endpoints_compatible #region-us
|
# xtremedistil-l6-h256-uncased for QA
This is a xtremedistil-l6-h256-uncased model, fine-tuned using the NaturalQuestionsShort dataset from the MRQA Shared Task 2019 repository.
## Overview
Language model: xtremedistil-l6-h256-uncased
Language: English
Downstream-task: Extractive QA
Training data: NaturalQues... | [
"# xtremedistil-l6-h256-uncased for QA \n\nThis is a xtremedistil-l6-h256-uncased model, fine-tuned using the NaturalQuestionsShort dataset from the MRQA Shared Task 2019 repository.",
"## Overview\nLanguage model: xtremedistil-l6-h256-uncased \nLanguage: English \nDownstream-task: Extractive QA \nTraining dat... | [
"TAGS\n#transformers #pytorch #bert #question-answering #natural-questions-short #en #license-mit #endpoints_compatible #region-us \n",
"# xtremedistil-l6-h256-uncased for QA \n\nThis is a xtremedistil-l6-h256-uncased model, fine-tuned using the NaturalQuestionsShort dataset from the MRQA Shared Task 2019 reposit... |
null | diffusers |
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# ddpm-ema-flower-64
## Model description
This diffusion model is trained with the [🤗 Diffusers](https://github.com/hugg... | {"language": "en", "license": "apache-2.0", "library_name": "diffusers", "tags": [], "datasets": "huggan/smithsonian_butterflies_subset", "metrics": []} | mrm8488/ddpm-ema-butterflies-128 | null | [
"diffusers",
"tensorboard",
"en",
"dataset:huggan/smithsonian_butterflies_subset",
"license:apache-2.0",
"has_space",
"diffusers:DDPMPipeline",
"region:us"
] | null | 2022-07-22T13:42:15+00:00 | [] | [
"en"
] | TAGS
#diffusers #tensorboard #en #dataset-huggan/smithsonian_butterflies_subset #license-apache-2.0 #has_space #diffusers-DDPMPipeline #region-us
|
# ddpm-ema-flower-64
## Model description
This diffusion model is trained with the Diffusers library
on the 'huggan/smithsonian_butterflies_subset' dataset.
## Intended uses & limitations
#### How to use
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Trai... | [
"# ddpm-ema-flower-64",
"## Model description\n\nThis diffusion model is trained with the Diffusers library \non the 'huggan/smithsonian_butterflies_subset' dataset.",
"## Intended uses & limitations",
"#### How to use",
"#### Limitations and bias\n\n[TODO: provide examples of latent issues and potential r... | [
"TAGS\n#diffusers #tensorboard #en #dataset-huggan/smithsonian_butterflies_subset #license-apache-2.0 #has_space #diffusers-DDPMPipeline #region-us \n",
"# ddpm-ema-flower-64",
"## Model description\n\nThis diffusion model is trained with the Diffusers library \non the 'huggan/smithsonian_butterflies_subset' d... |
text-generation | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# distilgpt_new2_0080
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set... | {"tags": ["generated_from_keras_callback"], "model-index": [{"name": "distilgpt_new2_0080", "results": []}]} | bigmorning/distilgpt_new2_0080 | null | [
"transformers",
"tf",
"gpt2",
"text-generation",
"generated_from_keras_callback",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-22T14:14:45+00:00 | [] | [] | TAGS
#transformers #tf #gpt2 #text-generation #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| distilgpt\_new2\_0080
=====================
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 2.5463
* Validation Loss: 2.4318
* Epoch: 79
Model description
-----------------
More information needed
Intended uses & limitations
---... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': 2e-05, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight\\_decay\\_rate': 0.01}\n* training\\_precision: float32",
... | [
"TAGS\n#transformers #tf #gpt2 #text-generation #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\... |
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... | reachrkr/dqn-SpaceInvadersNoFrameskip-v4 | null | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-07-22T14:16:27+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 | 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... | EvanMath/new-PPO-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-07-22T14:23:36+00:00 | [] | [] | TAGS
#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add you... |
text-classification | transformers |
# XLM-RoBERTa-Urdu-Classification
This [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) text classification model trained on Urdu sentiment [data-set](https://huggingface.co/datasets/hassan4830/urdu-binary-classification-data) performs binary sentiment classification on any given Urdu sentence. The model h... | {"language": "ur", "license": "afl-3.0"} | Aimlab/xlm-roberta-base-finetuned-urdu | null | [
"transformers",
"pytorch",
"xlm-roberta",
"text-classification",
"ur",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-22T14:55:47+00:00 | [] | [
"ur"
] | TAGS
#transformers #pytorch #xlm-roberta #text-classification #ur #license-afl-3.0 #autotrain_compatible #endpoints_compatible #region-us
|
# XLM-RoBERTa-Urdu-Classification
This xlm-roberta-base text classification model trained on Urdu sentiment data-set performs binary sentiment classification on any given Urdu sentence. The model has been fine-tuned for better results in manageable time frames.
## Model description
XLM-RoBERTa is a scaled cross-lin... | [
"# XLM-RoBERTa-Urdu-Classification\n\nThis xlm-roberta-base text classification model trained on Urdu sentiment data-set performs binary sentiment classification on any given Urdu sentence. The model has been fine-tuned for better results in manageable time frames.",
"## Model description\n\nXLM-RoBERTa is a scal... | [
"TAGS\n#transformers #pytorch #xlm-roberta #text-classification #ur #license-afl-3.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# XLM-RoBERTa-Urdu-Classification\n\nThis xlm-roberta-base text classification model trained on Urdu sentiment data-set performs binary sentiment classification on any ... |
question-answering | transformers | learning_rate:
- 1e-5
train_batchsize:
- 16
epochs:
- 2
weight_decay
- 0.01
optimizer
- Adam
datasets:
- squad
metrics
- EM:10.307414104882
- F1:42.10389032370503
| {} | cyr19/distilbert-base-uncased_2-epochs-squad | null | [
"transformers",
"pytorch",
"distilbert",
"question-answering",
"endpoints_compatible",
"region:us"
] | null | 2022-07-22T15:02:31+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #question-answering #endpoints_compatible #region-us
| learning_rate:
- 1e-5
train_batchsize:
- 16
epochs:
- 2
weight_decay
- 0.01
optimizer
- Adam
datasets:
- squad
metrics
- EM:10.307414104882
- F1:42.10389032370503
| [] | [
"TAGS\n#transformers #pytorch #distilbert #question-answering #endpoints_compatible #region-us \n"
] |
text-generation | null |
# HPV chatbot model
| {"tags": ["conversational"]} | Jingna/test_hpv_discord | null | [
"conversational",
"region:us"
] | null | 2022-07-22T15:24:20+00:00 | [] | [] | TAGS
#conversational #region-us
|
# HPV chatbot model
| [
"# HPV chatbot model"
] | [
"TAGS\n#conversational #region-us \n",
"# HPV chatbot model"
] |
null | diffusers |
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# ddpm-ema-butterflies-64
## Model description
This diffusion model is trained with the [🤗 Diffusers](https://github.com... | {"language": "en", "license": "apache-2.0", "library_name": "diffusers", "tags": [], "datasets": "huggan/smithsonian_butterflies_subset", "metrics": []} | ceyda/ddpm-ema-butterflies-64 | null | [
"diffusers",
"tensorboard",
"en",
"dataset:huggan/smithsonian_butterflies_subset",
"license:apache-2.0",
"diffusers:DDPMPipeline",
"region:us"
] | null | 2022-07-22T15:36:47+00:00 | [] | [
"en"
] | TAGS
#diffusers #tensorboard #en #dataset-huggan/smithsonian_butterflies_subset #license-apache-2.0 #diffusers-DDPMPipeline #region-us
|
# ddpm-ema-butterflies-64
## Model description
This diffusion model is trained with the Diffusers library
on the 'huggan/smithsonian_butterflies_subset' dataset. Using this script
## Intended uses & limitations
#### How to use
#### Limitations and bias
[TODO: provide examples of latent issues and potential... | [
"# ddpm-ema-butterflies-64",
"## Model description\n\nThis diffusion model is trained with the Diffusers library \non the 'huggan/smithsonian_butterflies_subset' dataset. Using this script",
"## Intended uses & limitations",
"#### How to use",
"#### Limitations and bias\n\n[TODO: provide examples of latent... | [
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"# ddpm-ema-butterflies-64",
"## Model description\n\nThis diffusion model is trained with the Diffusers library \non the 'huggan/smithsonian_butterflies_subset' dataset... |
text-classification | transformers |
# Auditor Review Sentiment Model
This model has been finetuned from the proprietary version of [FinBERT](https://huggingface.co/FinanceInc/finbert-pretrain) trained internally using demo.org proprietary dataset of auditor evaluation of sentiment.
FinBERT is a BERT model pre-trained on a large corpora of financial ... | {"language": "en", "tags": ["autotrain", "DEV"], "datasets": ["rajistics/autotrain-data-auditor-sentiment", "FinanceInc/auditor_sentiment"], "widget": [{"text": "Operating profit jumped to EUR 47 million from EUR 6.6 million"}], "co2_eq_emissions": 3.165771608457648, "model-index": [{"name": "auditor_sentiment_finetune... | FinanceInc/auditor_sentiment_finetuned | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain",
"DEV",
"en",
"dataset:rajistics/autotrain-data-auditor-sentiment",
"dataset:FinanceInc/auditor_sentiment",
"model-index",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-07-22T15:42:57+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bert #text-classification #autotrain #DEV #en #dataset-rajistics/autotrain-data-auditor-sentiment #dataset-FinanceInc/auditor_sentiment #model-index #co2_eq_emissions #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# Auditor Review Sentiment Model
This model has been finetuned from the proprietary version of FinBERT trained internally using URL proprietary dataset of auditor evaluation of sentiment.
FinBERT is a BERT model pre-trained on a large corpora of financial texts. The purpose is to enhance financial NLP research and... | [
"# Auditor Review Sentiment Model\n\nThis model has been finetuned from the proprietary version of FinBERT trained internally using URL proprietary dataset of auditor evaluation of sentiment. \n\nFinBERT is a BERT model pre-trained on a large corpora of financial texts. The purpose is to enhance financial NLP rese... | [
"TAGS\n#transformers #pytorch #bert #text-classification #autotrain #DEV #en #dataset-rajistics/autotrain-data-auditor-sentiment #dataset-FinanceInc/auditor_sentiment #model-index #co2_eq_emissions #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# Auditor Review Sentiment Model\n\nThis mod... |
null | null | Language model:xtremedistil-l12-h384-uncased
Language: English
Training data: hotpot_qa
Eval data: hotpot_qa
Code: See an example QA pipeline on Haystack
EM:46.4
F1:64.6
GroupId:105 | {} | llei/xtremedistil-l12-h384-uncased-HotpotQA | null | [
"region:us"
] | null | 2022-07-22T15:44:04+00:00 | [] | [] | TAGS
#region-us
| Language model:xtremedistil-l12-h384-uncased
Language: English
Training data: hotpot_qa
Eval data: hotpot_qa
Code: See an example QA pipeline on Haystack
EM:46.4
F1:64.6
GroupId:105 | [] | [
"TAGS\n#region-us \n"
] |
feature-extraction | transformers | # relbert/roberta-large-semeval2012-average-no-mask-prompt-a-triplet
RelBERT fine-tuned from [roberta-large](https://huggingface.co/roberta-large) on
[semeval2012](https://huggingface.co/datasets/semeval2012).
Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more... | {"datasets": ["semeval2012"], "model-index": [{"name": "relbert/roberta-large-semeval2012-average-no-mask-prompt-a-triplet", "results": [{"task": {"type": "sorting-task", "name": "Relation Mapping"}, "dataset": {"name": "Relation Mapping", "type": "relation-mapping", "args": "relbert/relation_mapping"}, "metrics": [{"t... | research-backup/roberta-large-semeval2012-average-no-mask-prompt-a-triplet | null | [
"transformers",
"pytorch",
"roberta",
"feature-extraction",
"dataset:semeval2012",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-07-22T15:45:11+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #feature-extraction #dataset-semeval2012 #model-index #endpoints_compatible #region-us
| # relbert/roberta-large-semeval2012-average-no-mask-prompt-a-triplet
RelBERT fine-tuned from roberta-large on
semeval2012.
Fine-tuning is done via RelBERT library (see the repository for more detail).
It achieves the following results on the relation understanding tasks:
- Analogy Question (dataset, full result):
... | [
"# relbert/roberta-large-semeval2012-average-no-mask-prompt-a-triplet\n\nRelBERT fine-tuned from roberta-large on \nsemeval2012.\nFine-tuning is done via RelBERT library (see the repository for more detail).\nIt achieves the following results on the relation understanding tasks:\n- Analogy Question (dataset, full ... | [
"TAGS\n#transformers #pytorch #roberta #feature-extraction #dataset-semeval2012 #model-index #endpoints_compatible #region-us \n",
"# relbert/roberta-large-semeval2012-average-no-mask-prompt-a-triplet\n\nRelBERT fine-tuned from roberta-large on \nsemeval2012.\nFine-tuning is done via RelBERT library (see the rep... |
feature-extraction | transformers | # relbert/roberta-large-semeval2012-average-no-mask-prompt-b-triplet
RelBERT fine-tuned from [roberta-large](https://huggingface.co/roberta-large) on
[semeval2012](https://huggingface.co/datasets/semeval2012).
Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more... | {"datasets": ["semeval2012"], "model-index": [{"name": "relbert/roberta-large-semeval2012-average-no-mask-prompt-b-triplet", "results": [{"task": {"type": "sorting-task", "name": "Relation Mapping"}, "dataset": {"name": "Relation Mapping", "type": "relation-mapping", "args": "relbert/relation_mapping"}, "metrics": [{"t... | research-backup/roberta-large-semeval2012-average-no-mask-prompt-b-triplet | null | [
"transformers",
"pytorch",
"roberta",
"feature-extraction",
"dataset:semeval2012",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-07-22T15:47:24+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #feature-extraction #dataset-semeval2012 #model-index #endpoints_compatible #region-us
| # relbert/roberta-large-semeval2012-average-no-mask-prompt-b-triplet
RelBERT fine-tuned from roberta-large on
semeval2012.
Fine-tuning is done via RelBERT library (see the repository for more detail).
It achieves the following results on the relation understanding tasks:
- Analogy Question (dataset, full result):
... | [
"# relbert/roberta-large-semeval2012-average-no-mask-prompt-b-triplet\n\nRelBERT fine-tuned from roberta-large on \nsemeval2012.\nFine-tuning is done via RelBERT library (see the repository for more detail).\nIt achieves the following results on the relation understanding tasks:\n- Analogy Question (dataset, full ... | [
"TAGS\n#transformers #pytorch #roberta #feature-extraction #dataset-semeval2012 #model-index #endpoints_compatible #region-us \n",
"# relbert/roberta-large-semeval2012-average-no-mask-prompt-b-triplet\n\nRelBERT fine-tuned from roberta-large on \nsemeval2012.\nFine-tuning is done via RelBERT library (see the rep... |
feature-extraction | transformers | # relbert/roberta-large-semeval2012-average-no-mask-prompt-c-triplet
RelBERT fine-tuned from [roberta-large](https://huggingface.co/roberta-large) on
[semeval2012](https://huggingface.co/datasets/semeval2012).
Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more... | {"datasets": ["semeval2012"], "model-index": [{"name": "relbert/roberta-large-semeval2012-average-no-mask-prompt-c-triplet", "results": [{"task": {"type": "sorting-task", "name": "Relation Mapping"}, "dataset": {"name": "Relation Mapping", "type": "relation-mapping", "args": "relbert/relation_mapping"}, "metrics": [{"t... | research-backup/roberta-large-semeval2012-average-no-mask-prompt-c-triplet | null | [
"transformers",
"pytorch",
"roberta",
"feature-extraction",
"dataset:semeval2012",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-07-22T15:49:45+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #feature-extraction #dataset-semeval2012 #model-index #endpoints_compatible #region-us
| # relbert/roberta-large-semeval2012-average-no-mask-prompt-c-triplet
RelBERT fine-tuned from roberta-large on
semeval2012.
Fine-tuning is done via RelBERT library (see the repository for more detail).
It achieves the following results on the relation understanding tasks:
- Analogy Question (dataset, full result):
... | [
"# relbert/roberta-large-semeval2012-average-no-mask-prompt-c-triplet\n\nRelBERT fine-tuned from roberta-large on \nsemeval2012.\nFine-tuning is done via RelBERT library (see the repository for more detail).\nIt achieves the following results on the relation understanding tasks:\n- Analogy Question (dataset, full ... | [
"TAGS\n#transformers #pytorch #roberta #feature-extraction #dataset-semeval2012 #model-index #endpoints_compatible #region-us \n",
"# relbert/roberta-large-semeval2012-average-no-mask-prompt-c-triplet\n\nRelBERT fine-tuned from roberta-large on \nsemeval2012.\nFine-tuning is done via RelBERT library (see the rep... |
feature-extraction | transformers | # relbert/roberta-large-semeval2012-average-no-mask-prompt-d-triplet
RelBERT fine-tuned from [roberta-large](https://huggingface.co/roberta-large) on
[semeval2012](https://huggingface.co/datasets/semeval2012).
Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more... | {"datasets": ["semeval2012"], "model-index": [{"name": "relbert/roberta-large-semeval2012-average-no-mask-prompt-d-triplet", "results": [{"task": {"type": "sorting-task", "name": "Relation Mapping"}, "dataset": {"name": "Relation Mapping", "type": "relation-mapping", "args": "relbert/relation_mapping"}, "metrics": [{"t... | research-backup/roberta-large-semeval2012-average-no-mask-prompt-d-triplet | null | [
"transformers",
"pytorch",
"roberta",
"feature-extraction",
"dataset:semeval2012",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-07-22T15:52:00+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #feature-extraction #dataset-semeval2012 #model-index #endpoints_compatible #region-us
| # relbert/roberta-large-semeval2012-average-no-mask-prompt-d-triplet
RelBERT fine-tuned from roberta-large on
semeval2012.
Fine-tuning is done via RelBERT library (see the repository for more detail).
It achieves the following results on the relation understanding tasks:
- Analogy Question (dataset, full result):
... | [
"# relbert/roberta-large-semeval2012-average-no-mask-prompt-d-triplet\n\nRelBERT fine-tuned from roberta-large on \nsemeval2012.\nFine-tuning is done via RelBERT library (see the repository for more detail).\nIt achieves the following results on the relation understanding tasks:\n- Analogy Question (dataset, full ... | [
"TAGS\n#transformers #pytorch #roberta #feature-extraction #dataset-semeval2012 #model-index #endpoints_compatible #region-us \n",
"# relbert/roberta-large-semeval2012-average-no-mask-prompt-d-triplet\n\nRelBERT fine-tuned from roberta-large on \nsemeval2012.\nFine-tuning is done via RelBERT library (see the rep... |
question-answering | transformers |
Model based on distilbert-base-uncased model trained on natural question short dataset.
Trained for one episode with AdamW optimizer and learning rate of 5e-03 and no warmup steps.
We achieved a f1 score of 32.67 and an em score of 10.35 | {"language": ["en"], "tags": ["question-answering"], "datasets": ["nq", "natural-question", "natural-question-short"], "metrics": ["squad"]} | dl4nlp/distilbert-base-uncased-nq-short | null | [
"transformers",
"pytorch",
"distilbert",
"question-answering",
"en",
"dataset:nq",
"dataset:natural-question",
"dataset:natural-question-short",
"endpoints_compatible",
"region:us"
] | null | 2022-07-22T15:52:33+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #distilbert #question-answering #en #dataset-nq #dataset-natural-question #dataset-natural-question-short #endpoints_compatible #region-us
|
Model based on distilbert-base-uncased model trained on natural question short dataset.
Trained for one episode with AdamW optimizer and learning rate of 5e-03 and no warmup steps.
We achieved a f1 score of 32.67 and an em score of 10.35 | [] | [
"TAGS\n#transformers #pytorch #distilbert #question-answering #en #dataset-nq #dataset-natural-question #dataset-natural-question-short #endpoints_compatible #region-us \n"
] |
feature-extraction | transformers | # relbert/roberta-large-semeval2012-average-no-mask-prompt-e-triplet
RelBERT fine-tuned from [roberta-large](https://huggingface.co/roberta-large) on
[semeval2012](https://huggingface.co/datasets/semeval2012).
Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more... | {"datasets": ["semeval2012"], "model-index": [{"name": "relbert/roberta-large-semeval2012-average-no-mask-prompt-e-triplet", "results": [{"task": {"type": "sorting-task", "name": "Relation Mapping"}, "dataset": {"name": "Relation Mapping", "type": "relation-mapping", "args": "relbert/relation_mapping"}, "metrics": [{"t... | research-backup/roberta-large-semeval2012-average-no-mask-prompt-e-triplet | null | [
"transformers",
"pytorch",
"roberta",
"feature-extraction",
"dataset:semeval2012",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-07-22T15:54:16+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #feature-extraction #dataset-semeval2012 #model-index #endpoints_compatible #region-us
| # relbert/roberta-large-semeval2012-average-no-mask-prompt-e-triplet
RelBERT fine-tuned from roberta-large on
semeval2012.
Fine-tuning is done via RelBERT library (see the repository for more detail).
It achieves the following results on the relation understanding tasks:
- Analogy Question (dataset, full result):
... | [
"# relbert/roberta-large-semeval2012-average-no-mask-prompt-e-triplet\n\nRelBERT fine-tuned from roberta-large on \nsemeval2012.\nFine-tuning is done via RelBERT library (see the repository for more detail).\nIt achieves the following results on the relation understanding tasks:\n- Analogy Question (dataset, full ... | [
"TAGS\n#transformers #pytorch #roberta #feature-extraction #dataset-semeval2012 #model-index #endpoints_compatible #region-us \n",
"# relbert/roberta-large-semeval2012-average-no-mask-prompt-e-triplet\n\nRelBERT fine-tuned from roberta-large on \nsemeval2012.\nFine-tuning is done via RelBERT library (see the rep... |
null | null | Language model:
xtremedistil-l12-h384-uncased
Language: English
Downstream-task: xtremedistil-l12-h384-uncased
Training data: hotpot_qa
Eval data: hotpot_qa
EM:
F1:
GroupID:105 | {} | DL4NLP-Group105/xtremedistil-l12-h384-uncased-hotpot_qa | null | [
"region:us"
] | null | 2022-07-22T15:55:10+00:00 | [] | [] | TAGS
#region-us
| Language model:
xtremedistil-l12-h384-uncased
Language: English
Downstream-task: xtremedistil-l12-h384-uncased
Training data: hotpot_qa
Eval data: hotpot_qa
EM:
F1:
GroupID:105 | [] | [
"TAGS\n#region-us \n"
] |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-finetuned-ner
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["conll2003"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "bert-finetuned-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "conll2003", "type": "c... | shamweel/bert-finetuned-ner | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-22T15:59:26+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| bert-finetuned-ner
==================
This model is a fine-tuned version of bert-base-cased on the conll2003 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0684
* Precision: 0.9313
* Recall: 0.9483
* F1: 0.9397
* Accuracy: 0.9857
Model description
-----------------
More information ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning... |
image-classification | keras |
## Model description
ShiftViT is a variation of the Vision Transformer (ViT) where the attention operation has been replaced with a shifting operation.
ShiftViT model was proposed as part of the paper [When Shift Operation Meets Vision Transformer: An Extremely Simple Alternative to Attention Mechanism](https://arxi... | {"library_name": "keras", "tags": ["ShiftVit", "image-classification"]} | keras-io/shiftvit | null | [
"keras",
"tensorboard",
"ShiftVit",
"image-classification",
"arxiv:2201.10801",
"arxiv:2103.14030",
"has_space",
"region:us"
] | null | 2022-07-22T16:28:23+00:00 | [
"2201.10801",
"2103.14030"
] | [] | TAGS
#keras #tensorboard #ShiftVit #image-classification #arxiv-2201.10801 #arxiv-2103.14030 #has_space #region-us
| Model description
-----------------
ShiftViT is a variation of the Vision Transformer (ViT) where the attention operation has been replaced with a shifting operation.
ShiftViT model was proposed as part of the paper When Shift Operation Meets Vision Transformer: An Extremely Simple Alternative to Attention Mechanis... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n\nModel Plot\n----------\n\n\n\nView Model Plot\n!Model Image\n\n\n\nCredits:\n--------\n\n\n* HF Contribution: Shivalika Singh\n* Full credits to original Keras example by Aritra Roy Gosthipaty and Ritwik Raha\n* Check... | [
"TAGS\n#keras #tensorboard #ShiftVit #image-classification #arxiv-2201.10801 #arxiv-2103.14030 #has_space #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n\nModel Plot\n----------\n\n\n\nView Model Plot\n!Model Image\n\n\n\nCredits:\n--------\n\n\n* ... |
document-question-answering | transformers |
# LayoutLMv3: DocVQA Replication WIP
See experiments code: <https://github.com/redthing1/layoutlm_experiments>
| {} | xhyi/layoutlmv3_docvqa_t11c5000 | null | [
"transformers",
"pytorch",
"safetensors",
"layoutlmv3",
"document-question-answering",
"endpoints_compatible",
"region:us"
] | null | 2022-07-22T16:35:34+00:00 | [] | [] | TAGS
#transformers #pytorch #safetensors #layoutlmv3 #document-question-answering #endpoints_compatible #region-us
|
# LayoutLMv3: DocVQA Replication WIP
See experiments code: <URL
| [
"# LayoutLMv3: DocVQA Replication WIP\n\nSee experiments code: <URL"
] | [
"TAGS\n#transformers #pytorch #safetensors #layoutlmv3 #document-question-answering #endpoints_compatible #region-us \n",
"# LayoutLMv3: DocVQA Replication WIP\n\nSee experiments code: <URL"
] |
text-classification | transformers |
Forward-looking statements (FLS) inform investors of managers’ beliefs and opinions about firm's future events or results. Identifying forward-looking statements from corporate reports can assist investors in financial analysis. FinBERT-FLS is a FinBERT model fine-tuned on 3,500 manually annotated sentences from Manag... | {"language": "en", "tags": ["financial-text-analysis", "forward-looking-statement"], "widget": [{"text": "We expect the age of our fleet to enhance availability and reliability due to reduced downtime for repairs. "}]} | FinanceInc/finbert_fls | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"financial-text-analysis",
"forward-looking-statement",
"en",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-07-22T16:40:26+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bert #text-classification #financial-text-analysis #forward-looking-statement #en #autotrain_compatible #endpoints_compatible #has_space #region-us
|
Forward-looking statements (FLS) inform investors of managers’ beliefs and opinions about firm's future events or results. Identifying forward-looking statements from corporate reports can assist investors in financial analysis. FinBERT-FLS is a FinBERT model fine-tuned on 3,500 manually annotated sentences from Manag... | [
"# How to use \nYou can use this model with Transformers pipeline for forward-looking statement classification.\n\n\nVisit FinBERT.AI for more details on the recent development of FinBERT."
] | [
"TAGS\n#transformers #pytorch #bert #text-classification #financial-text-analysis #forward-looking-statement #en #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# How to use \nYou can use this model with Transformers pipeline for forward-looking statement classification.\n\n\nVisit FinBERT... |
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/1241879678455078914/e2Ed... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/deepleffen-tsm_leffen/1658512231427/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/deepleffen-tsm_leffen | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-22T16:49:13+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI CYBORG
Deep Leffen Bot & TSM FTX Leffen
@deepleffen-tsm\_leffen
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
reinforcement-learning | 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"]} | heriosousa/testpyramidsrnd | null | [
"ml-agents",
"tensorboard",
"onnx",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
] | null | 2022-07-22T16:50:26+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... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-distilled-clinc
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/d... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["clinc_oos"], "metrics": ["accuracy"], "model-index": [{"name": "distilbert-base-uncased-distilled-clinc", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "clinc_oos", "type": "clinc_oos",... | ManqingLiu/distilbert-base-uncased-distilled-clinc | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:clinc_oos",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-22T16:52:14+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-clinc_oos #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-distilled-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: 0.0990
* Accuracy: 0.9390
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: 10",
"### Train... | [
"TAGS\n#transformers #pytorch #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* learning\\_rate:... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# platzi-test
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unkn... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "platzi-test", "results": []}]} | osanseviero/platzi-test | null | [
"transformers",
"tf",
"distilbert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-22T17:11:18+00:00 | [] | [] | TAGS
#transformers #tf #distilbert #text-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# platzi-test
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information need... | [
"# platzi-test\n\nThis model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.\nIt achieves the following results on the evaluation set:",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\... | [
"TAGS\n#transformers #tf #distilbert #text-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# platzi-test\n\nThis model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.\nIt achieves the following results on the ... |
feature-extraction | transformers | # relbert/roberta-large-semeval2012-average-prompt-e-triplet
RelBERT fine-tuned from [roberta-large](https://huggingface.co/roberta-large) on
[semeval2012](https://huggingface.co/datasets/semeval2012).
Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail)... | {"datasets": ["semeval2012"], "model-index": [{"name": "relbert/roberta-large-semeval2012-average-prompt-e-triplet", "results": [{"task": {"type": "sorting-task", "name": "Relation Mapping"}, "dataset": {"name": "Relation Mapping", "type": "relation-mapping", "args": "relbert/relation_mapping"}, "metrics": [{"type": "a... | research-backup/roberta-large-semeval2012-average-prompt-e-triplet | null | [
"transformers",
"pytorch",
"roberta",
"feature-extraction",
"dataset:semeval2012",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-07-22T17:25:55+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #feature-extraction #dataset-semeval2012 #model-index #endpoints_compatible #region-us
| # relbert/roberta-large-semeval2012-average-prompt-e-triplet
RelBERT fine-tuned from roberta-large on
semeval2012.
Fine-tuning is done via RelBERT library (see the repository for more detail).
It achieves the following results on the relation understanding tasks:
- Analogy Question (dataset, full result):
- Accu... | [
"# relbert/roberta-large-semeval2012-average-prompt-e-triplet\n\nRelBERT fine-tuned from roberta-large on \nsemeval2012.\nFine-tuning is done via RelBERT library (see the repository for more detail).\nIt achieves the following results on the relation understanding tasks:\n- Analogy Question (dataset, full result):... | [
"TAGS\n#transformers #pytorch #roberta #feature-extraction #dataset-semeval2012 #model-index #endpoints_compatible #region-us \n",
"# relbert/roberta-large-semeval2012-average-prompt-e-triplet\n\nRelBERT fine-tuned from roberta-large on \nsemeval2012.\nFine-tuning is done via RelBERT library (see the repository ... |
image-classification | transformers |
# pond
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics).
... | {"tags": ["image-classification", "pytorch", "huggingpics"], "metrics": ["accuracy"]} | SummerChiam/pond | null | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-22T17:26:03+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# pond
Autogenerated by HuggingPics️
Create your own image classifier for anything by running the demo on Google Colab.
Report any issues with the demo at the github repo.
## Example Images
#### Algae0
!Algae0
#### Boiling0
!Boiling0
#### BoilingNight0
!BoilingNight0
#### Normal0
!Normal0
#### NormalCe... | [
"# pond\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nReport any issues with the demo at the github repo.",
"## Example Images",
"#### Algae0\n\n!Algae0",
"#### Boiling0\n\n!Boiling0",
"#### BoilingNight0\n\n!BoilingNight0",
"##... | [
"TAGS\n#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# pond\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nReport any issues with the... |
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. -->
# auditor-test
This model is a fine-tuned version of [demo-org/finbert-pretrain](https://huggingface.co/demo-org/finbert-pretrain)... | {"tags": ["generated_from_trainer", "PROD"], "model-index": [{"name": "auditor-test", "results": []}]} | rajistics/auditor-test | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"PROD",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-22T17:41:17+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #text-classification #generated_from_trainer #PROD #autotrain_compatible #endpoints_compatible #region-us
|
# auditor-test
This model is a fine-tuned version of demo-org/finbert-pretrain on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters... | [
"# auditor-test\n\nThis model is a fine-tuned version of demo-org/finbert-pretrain on an unknown dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
... | [
"TAGS\n#transformers #pytorch #bert #text-classification #generated_from_trainer #PROD #autotrain_compatible #endpoints_compatible #region-us \n",
"# auditor-test\n\nThis model is a fine-tuned version of demo-org/finbert-pretrain on an unknown dataset.",
"## Model description\n\nMore information needed",
"## ... |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-finetuned-squad
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the squa... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "bert-finetuned-squad", "results": []}]} | tmgondal/bert-finetuned-squad | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-07-22T17:44:10+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us
|
# bert-finetuned-squad
This model is a fine-tuned version of bert-base-cased on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
T... | [
"# bert-finetuned-squad\n\nThis model is a fine-tuned version of bert-base-cased on the squad dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"#... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us \n",
"# bert-finetuned-squad\n\nThis model is a fine-tuned version of bert-base-cased on the squad dataset.",
"## Model description\n\nMore information... |
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/1241879678455078914/e2Ed... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/deepleffen-falco-tsm_leffen/1658517045179/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/deepleffen-falco-tsm_leffen | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-22T18:09:51+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI CYBORG
Deep Leffen Bot & nick & TSM FTX Leffen
@deepleffen-falco-tsm\_leffen
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, ch... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
reinforcement-learning | stable-baselines3 |
# **PPO** Agent playing **CarRacing-v0**
This is a trained model of a **PPO** agent playing **CarRacing-v0**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 impo... | {"library_name": "stable-baselines3", "tags": ["CarRacing-v0", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "CarRacing-v0", "type": "CarRacing-... | masterdezign/ppo-CarRacing-v0-10M | null | [
"stable-baselines3",
"CarRacing-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-07-22T18:27:11+00:00 | [] | [] | TAGS
#stable-baselines3 #CarRacing-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing CarRacing-v0
This is a trained model of a PPO agent playing CarRacing-v0
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# PPO Agent playing CarRacing-v0\nThis is a trained model of a PPO agent playing CarRacing-v0\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #CarRacing-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing CarRacing-v0\nThis is a trained model of a PPO agent playing CarRacing-v0\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code... |
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... | Reshalkin/ppo-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-07-22T19:21:43+00:00 | [] | [] | TAGS
#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# PPO Agent playing LunarLander-v2\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... |
question-answering | transformers |
xtremedistil-l12-h384-uncased model trained on a subset of "Natural Questions Short".
Done for the "Deep Learning for Natural Language Processing" course at TU Darmstadt.
Group 69.
Squad metric:
- 'exact_match': 40.217
- 'f1': 62.3873 | {"language": "en", "license": "mit", "tags": ["question-answering"]} | nyorain/xtremedistil-l12-h384-uncased-natural-questions | null | [
"transformers",
"pytorch",
"bert",
"question-answering",
"en",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-07-22T20:06:32+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bert #question-answering #en #license-mit #endpoints_compatible #region-us
|
xtremedistil-l12-h384-uncased model trained on a subset of "Natural Questions Short".
Done for the "Deep Learning for Natural Language Processing" course at TU Darmstadt.
Group 69.
Squad metric:
- 'exact_match': 40.217
- 'f1': 62.3873 | [] | [
"TAGS\n#transformers #pytorch #bert #question-answering #en #license-mit #endpoints_compatible #region-us \n"
] |
question-answering | transformers | language:
- en
tags:
- Question Answering
- QA
datasets:
- SQuAD
models:
- microsoft/xtremedistil-l12-h384-uncased
metrics:
- SQuAD
hyper-parameters:
- learning rate: `5e-5`
- badge size: 16
- epochs: 1
Score:
- EM: 0.07613971637955648
- F1: 1.5494283569738803
Group:
- 97 | {} | nclskfm/SQuAD-xtremedistil-l12-h384-uncased | null | [
"transformers",
"pytorch",
"bert",
"question-answering",
"endpoints_compatible",
"region:us"
] | null | 2022-07-22T20:07:52+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #question-answering #endpoints_compatible #region-us
| language:
- en
tags:
- Question Answering
- QA
datasets:
- SQuAD
models:
- microsoft/xtremedistil-l12-h384-uncased
metrics:
- SQuAD
hyper-parameters:
- learning rate: '5e-5'
- badge size: 16
- epochs: 1
Score:
- EM: 0.07613971637955648
- F1: 1.5494283569738803
Group:
- 97 | [] | [
"TAGS\n#transformers #pytorch #bert #question-answering #endpoints_compatible #region-us \n"
] |
question-answering | transformers |
This model fine-tuned `huawei-noahTinyBERT_General_6L_768` on `HotpotQA`.
| EM | F1 |
|------------|----------|
| 31.552419 | 53.535072 |
| {"language": ["en"], "license": "apache-2.0", "tags": ["tag1", "tag2"], "datasets": ["HotpotQA"], "metrics": ["SQuad"]} | DL4NLP-Group4/huawei-noahTinyBERT_General_6L_768_HotpotQA | null | [
"transformers",
"pytorch",
"bert",
"question-answering",
"tag1",
"tag2",
"en",
"dataset:HotpotQA",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-07-22T20:29:10+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bert #question-answering #tag1 #tag2 #en #dataset-HotpotQA #license-apache-2.0 #endpoints_compatible #region-us
| This model fine-tuned 'huawei-noahTinyBERT\_General\_6L\_768' on 'HotpotQA'.
| [] | [
"TAGS\n#transformers #pytorch #bert #question-answering #tag1 #tag2 #en #dataset-HotpotQA #license-apache-2.0 #endpoints_compatible #region-us \n"
] |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-300m-en-libri-more-steps
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggi... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["librispeech_asr"], "model-index": [{"name": "wav2vec2-large-xls-r-300m-en-libri-more-steps", "results": []}]} | tsrivatsav/wav2vec2-large-xls-r-300m-en-libri-more-steps | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:librispeech_asr",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-07-22T20:32:47+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-librispeech_asr #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-large-xls-r-300m-en-libri-more-steps
=============================================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the librispeech\_asr dataset.
It achieves the following results on the evaluation set:
* Loss: 1.7624
* Wer: 0.8772
* Cer: 0.3762
Model description
----... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.001\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-librispeech_asr #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.001\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/732596482336002049/JYMrr... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/leadermcconnell/1664399993131/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/leadermcconnell | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-22T21:05:57+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Leader McConnell
@leadermcconnell
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-classification | transformers |
<!-- 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. -->
# DepressionAnalysis
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncase... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "DepressionAnalysis", "results": []}]} | sanskar/DepressionAnalysis | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-22T21:06:20+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| DepressionAnalysis
==================
This model is a fine-tuned version of distilbert-base-uncased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4023
* Accuracy: 0.8367
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: 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* lr\\_scheduler\\_warmup\\_steps... | [
"TAGS\n#transformers #pytorch #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: ... |
reinforcement-learning | stable-baselines3 |
# **A2C** Agent playing **AntBulletEnv-v0**
This is a trained model of a **A2C** agent playing **AntBulletEnv-v0**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb... | {"library_name": "stable-baselines3", "tags": ["AntBulletEnv-v0", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "A2C", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "AntBulletEnv-v0", "type": "AntB... | trtd56/a2c-AntBulletEnv-v0 | null | [
"stable-baselines3",
"AntBulletEnv-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-07-22T21:11:51+00:00 | [] | [] | TAGS
#stable-baselines3 #AntBulletEnv-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# A2C Agent playing AntBulletEnv-v0
This is a trained model of a A2C agent playing AntBulletEnv-v0
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# A2C Agent playing AntBulletEnv-v0\nThis is a trained model of a A2C agent playing AntBulletEnv-v0\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #AntBulletEnv-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# A2C Agent playing AntBulletEnv-v0\nThis is a trained model of a A2C agent playing AntBulletEnv-v0\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add ... |
text-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/666311094256971779/rhb7q... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true", "widget": [{"text": "My dream is"}]} | huggingtweets/luciengreaves-seanhannity | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-22T21:49:32+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI CYBORG
Lucien Greaves & Sean Hannity
@luciengreaves-seanhannity
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
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. -->
# roberta-base-spanish-squades-modelo1
This model is a fine-tuned version of [IIC/roberta-base-spanish-squades](https://huggingfac... | {"tags": ["generated_from_trainer"], "datasets": ["becasv2"], "model-index": [{"name": "roberta-base-spanish-squades-modelo1", "results": []}]} | Evelyn18/roberta-base-spanish-squades-modelo1 | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"question-answering",
"generated_from_trainer",
"dataset:becasv2",
"endpoints_compatible",
"region:us"
] | null | 2022-07-22T21:55:11+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #question-answering #generated_from_trainer #dataset-becasv2 #endpoints_compatible #region-us
| roberta-base-spanish-squades-modelo1
====================================
This model is a fine-tuned version of IIC/roberta-base-spanish-squades on the becasv2 dataset.
It achieves the following results on the evaluation set:
* Loss: 5.7001
Model description
-----------------
More information needed
Intended ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 11\n* eval\\_batch\\_size: 11\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",
"### Trai... | [
"TAGS\n#transformers #pytorch #tensorboard #roberta #question-answering #generated_from_trainer #dataset-becasv2 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 11\n* eval\\_ba... |
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/59437078/icon-200x200_40... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/hillaryclinton-maddow-speakerpelosi/1658531793071/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/hillaryclinton-maddow-speakerpelosi | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-22T22:14:30+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI CYBORG
Rachel Maddow MSNBC & Nancy Pelosi & Hillary Clinton
@hillaryclinton-maddow-speakerpelosi
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 mod... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-base-spanish-squades-modelo2
This model is a fine-tuned version of [IIC/roberta-base-spanish-squades](https://huggingfac... | {"tags": ["generated_from_trainer"], "datasets": ["becasv2"], "model-index": [{"name": "roberta-base-spanish-squades-modelo2", "results": []}]} | Evelyn18/roberta-base-spanish-squades-modelo2 | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"question-answering",
"generated_from_trainer",
"dataset:becasv2",
"endpoints_compatible",
"region:us"
] | null | 2022-07-22T22:15:30+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #question-answering #generated_from_trainer #dataset-becasv2 #endpoints_compatible #region-us
| roberta-base-spanish-squades-modelo2
====================================
This model is a fine-tuned version of IIC/roberta-base-spanish-squades on the becasv2 dataset.
It achieves the following results on the evaluation set:
* Loss: 2.4358
Model description
-----------------
More information needed
Intended ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 11\n* eval\\_batch\\_size: 11\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 6",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #roberta #question-answering #generated_from_trainer #dataset-becasv2 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 11\n* eval\\_bat... |
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/666311094256971779/rhb7q... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/luciengreaves-pontifex/1658534403996/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/luciengreaves-pontifex | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-22T22:57:54+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI CYBORG
Lucien Greaves & Pope Francis
@luciengreaves-pontifex
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 r... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
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"]} | SamuelMYoussef/testpyramidsrnd | null | [
"ml-agents",
"tensorboard",
"onnx",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
] | null | 2022-07-22T23:12:54+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... |
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="Chris1/q-FrozenLake-v1-8x8-Slippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attribu... | {"tags": ["FrozenLake-v1-8x8", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-FrozenLake-v1-8x8-Slippery", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "FrozenLake-v1-8x8", "type": "FrozenLake-v1-8x8"}, "met... | Chris1/q-FrozenLake-v1-8x8-Slippery | null | [
"FrozenLake-v1-8x8",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-07-22T23:32:49+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"
] |
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. -->
# rule_learning_margin_3mm_many_negatives_spanpred_attention
This model is a fine-tuned version of [enoriega/rule_softmatching](ht... | {"tags": ["generated_from_trainer"], "datasets": ["enoriega/odinsynth_dataset"], "model-index": [{"name": "rule_learning_margin_3mm_many_negatives_spanpred_attention", "results": []}]} | enoriega/rule_learning_margin_3mm_many_negatives_spanpred_attention | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"generated_from_trainer",
"dataset:enoriega/odinsynth_dataset",
"endpoints_compatible",
"region:us"
] | null | 2022-07-23T00:01:07+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #generated_from_trainer #dataset-enoriega/odinsynth_dataset #endpoints_compatible #region-us
| rule\_learning\_margin\_3mm\_many\_negatives\_spanpred\_attention
=================================================================
This model is a fine-tuned version of enoriega/rule\_softmatching on the enoriega/odinsynth\_dataset dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2196
* ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* gradient\\_accumulation\\_steps: 2000\n* total\\_train\\_batch\\_size: 8000\n* optimizer: Adam with betas=(0.9,0.999) and epsi... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #generated_from_trainer #dataset-enoriega/odinsynth_dataset #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: 4\n* eval\\_batch\\_... |
null | keras |
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
| Hyperparameters | Value |
| :-- | :-- |
| na... | {"library_name": "keras", "tags": ["ShiftVit", "Image Classification"]} | shivi/shiftViT-Model | null | [
"keras",
"tensorboard",
"ShiftVit",
"Image Classification",
"region:us"
] | null | 2022-07-23T00:59:31+00:00 | [] | [] | TAGS
#keras #tensorboard #ShiftVit #Image Classification #region-us
| Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following h... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n\nModel Plot\n----------\n\n\n\nView Model Plot\n!Model Image"
] | [
"TAGS\n#keras #tensorboard #ShiftVit #Image Classification #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n\nModel Plot\n----------\n\n\n\nView Model Plot\n!Model Image"
] |
sentence-similarity | sentence-transformers |
# ONNX convert all-MiniLM-L6-v2
## Conversion of [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L12-v2)
This is a [sentence-transformers](https://www.SBERT.net) ONNX model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks... | {"language": "en", "license": "apache-2.0", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers", "onnx"], "pipeline_tag": "sentence-similarity"} | vamsibanda/sbert-all-MiniLM-L6-with-pooler | null | [
"sentence-transformers",
"onnx",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-07-23T02:55:24+00:00 | [] | [
"en"
] | TAGS
#sentence-transformers #onnx #bert #feature-extraction #sentence-similarity #transformers #en #license-apache-2.0 #endpoints_compatible #region-us
|
# ONNX convert all-MiniLM-L6-v2
## Conversion of sentence-transformers/all-MiniLM-L6-v2
This is a sentence-transformers ONNX model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search. This custom model takes 'last_hidden_state' and 'poole... | [
"# ONNX convert all-MiniLM-L6-v2",
"## Conversion of sentence-transformers/all-MiniLM-L6-v2\nThis is a sentence-transformers ONNX model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search. This custom model takes 'last_hidden_state' ... | [
"TAGS\n#sentence-transformers #onnx #bert #feature-extraction #sentence-similarity #transformers #en #license-apache-2.0 #endpoints_compatible #region-us \n",
"# ONNX convert all-MiniLM-L6-v2",
"## Conversion of sentence-transformers/all-MiniLM-L6-v2\nThis is a sentence-transformers ONNX model: It maps sentence... |
text-classification | transformers |
## Validation Metrics
- Loss: 0.6447726488113403
- Accuracy: 0.8263473053892215
- Macro F1: 0.7776555055392036
- Micro F1: 0.8263473053892215
- Weighted F1: 0.8161511591973788
- Macro Precision: 0.8273504273504274
- Micro Precision: 0.8263473053892215
- Weighted Precision: 0.8266697374481806
- Macro Recall: 0.7615518... | {"language": "en", "tags": "autotrain", "datasets": ["Shenzy/autotrain-data-sentence_classification"], "widget": [{"text": "An unusual hierarchy in the section near the top where the design seems to prioritise running time over a compacted artist name."}], "co2_eq_emissions": 0.00986494387043499} | Shenzy/Sentence_Classification4DesignTutor | null | [
"transformers",
"pytorch",
"safetensors",
"roberta",
"text-classification",
"autotrain",
"en",
"dataset:Shenzy/autotrain-data-sentence_classification",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-23T02:58:41+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #safetensors #roberta #text-classification #autotrain #en #dataset-Shenzy/autotrain-data-sentence_classification #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
## Validation Metrics
- Loss: 0.6447726488113403
- Accuracy: 0.8263473053892215
- Macro F1: 0.7776555055392036
- Micro F1: 0.8263473053892215
- Weighted F1: 0.8161511591973788
- Macro Precision: 0.8273504273504274
- Micro Precision: 0.8263473053892215
- Weighted Precision: 0.8266697374481806
- Macro Recall: 0.7615518... | [
"## Validation Metrics\n\n- Loss: 0.6447726488113403\n- Accuracy: 0.8263473053892215\n- Macro F1: 0.7776555055392036\n- Micro F1: 0.8263473053892215\n- Weighted F1: 0.8161511591973788\n- Macro Precision: 0.8273504273504274\n- Micro Precision: 0.8263473053892215\n- Weighted Precision: 0.8266697374481806\n- Macro Rec... | [
"TAGS\n#transformers #pytorch #safetensors #roberta #text-classification #autotrain #en #dataset-Shenzy/autotrain-data-sentence_classification #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"## Validation Metrics\n\n- Loss: 0.6447726488113403\n- Accuracy: 0.8263473053892215\n- Macro... |
sentence-similarity | sentence-transformers |
# ONNX convert all-MiniLM-L12-v2
## Conversion of [sentence-transformers/all-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L12-v2)
This is a [sentence-transformers](https://www.SBERT.net) ONNX model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tas... | {"language": "en", "license": "apache-2.0", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers", "onnx"], "pipeline_tag": "sentence-similarity"} | vamsibanda/sbert-all-MiniLM-L12-with-pooler | null | [
"sentence-transformers",
"onnx",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-07-23T03:04:24+00:00 | [] | [
"en"
] | TAGS
#sentence-transformers #onnx #bert #feature-extraction #sentence-similarity #transformers #en #license-apache-2.0 #endpoints_compatible #region-us
|
# ONNX convert all-MiniLM-L12-v2
## Conversion of sentence-transformers/all-MiniLM-L12-v2
This is a sentence-transformers ONNX model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search. This custom model takes 'last_hidden_state' and 'poo... | [
"# ONNX convert all-MiniLM-L12-v2",
"## Conversion of sentence-transformers/all-MiniLM-L12-v2\nThis is a sentence-transformers ONNX model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search. This custom model takes 'last_hidden_state... | [
"TAGS\n#sentence-transformers #onnx #bert #feature-extraction #sentence-similarity #transformers #en #license-apache-2.0 #endpoints_compatible #region-us \n",
"# ONNX convert all-MiniLM-L12-v2",
"## Conversion of sentence-transformers/all-MiniLM-L12-v2\nThis is a sentence-transformers ONNX model: It maps senten... |
text-generation | transformers |
# text_generation_bangla_model
BanglaCLM dataset:
- OSCAR: 12.84GB
- Wikipedia dump: 6.24GB
- ProthomAlo: 3.92GB
- Kalerkantho: 3.24GB
## Model description
- context size : 128
## Training and evaluation data
The BanglaCLM data set is divided into a training set (90%)and a validation set (10%).
## Training... | {} | shahidul034/text_generation_bangla_model | null | [
"transformers",
"pytorch",
"tf",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-23T03:19:19+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# text_generation_bangla_model
BanglaCLM dataset:
- OSCAR: 12.84GB
- Wikipedia dump: 6.24GB
- ProthomAlo: 3.92GB
- Kalerkantho: 3.24GB
## Model description
- context size : 128
## Training and evaluation data
The BanglaCLM data set is divided into a training set (90%)and a validation set (10%).
## Training... | [
"# text_generation_bangla_model\nBanglaCLM dataset: \n\n- OSCAR: 12.84GB\n\n- Wikipedia dump: 6.24GB\n\n- ProthomAlo: 3.92GB\n\n- Kalerkantho: 3.24GB",
"## Model description\n\n- context size : 128",
"## Training and evaluation data\nThe BanglaCLM data set is divided into a training set (90%)and a validation se... | [
"TAGS\n#transformers #pytorch #tf #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# text_generation_bangla_model\nBanglaCLM dataset: \n\n- OSCAR: 12.84GB\n\n- Wikipedia dump: 6.24GB\n\n- ProthomAlo: 3.92GB\n\n- Kalerkantho: 3.24GB",
"## Model descri... |
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/1377062766314467332/2hyq... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/aoc-kamalaharris/1658551469874/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/aoc-kamalaharris | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-23T03:34:58+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI CYBORG
Kamala Harris & Alexandria Ocasio-Cortez
@aoc-kamalaharris
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 ... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
fill-mask | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# distilbert_final_0005
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation s... | {"tags": ["generated_from_keras_callback"], "model-index": [{"name": "distilbert_final_0005", "results": []}]} | bigmorning/distilbert_final_0005 | null | [
"transformers",
"tf",
"distilbert",
"fill-mask",
"generated_from_keras_callback",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-23T04:03:57+00:00 | [] | [] | TAGS
#transformers #tf #distilbert #fill-mask #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #region-us
| distilbert\_final\_0005
=======================
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.9295
* Validation Loss: 0.9157
* Epoch: 4
Model description
-----------------
More information needed
Intended uses & limitations
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': 2e-05, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight\\_decay\\_rate': 0.01}\n* training\\_precision: float32",
... | [
"TAGS\n#transformers #tf #distilbert #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': 2e-05, 'decay': 0.... |
text-generation | transformers |
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/501717583846842368/psd9a... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/kremlinrussia_e/1658555307462/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/kremlinrussia_e | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-23T04:29:27+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
President of Russia
@kremlinrussia\_e
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 ... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
null | keras | ## Tensorflow Keras Implementation of Character-level Recurrent Sequence-to-Sequence Model
This repo contains code using the model. [Character-level recurrent sequence-to-sequence model](https://keras.io/examples/nlp/lstm_seq2seq/).
Credits: [fchollet](https://twitter.com/fchollet) - Original Author
HF Contribution:... | {"tags": ["seq2seq", "character-level", "machine translation"]} | keras-io/cl_s2s | null | [
"keras",
"seq2seq",
"character-level",
"machine translation",
"has_space",
"region:us"
] | null | 2022-07-23T04:30:30+00:00 | [] | [] | TAGS
#keras #seq2seq #character-level #machine translation #has_space #region-us
| ## Tensorflow Keras Implementation of Character-level Recurrent Sequence-to-Sequence Model
This repo contains code using the model. Character-level recurrent sequence-to-sequence model.
Credits: fchollet - Original Author
HF Contribution: Rishav Chandra Varma
## Background Information
### Introduction
This exam... | [
"## Tensorflow Keras Implementation of Character-level Recurrent Sequence-to-Sequence Model\n\nThis repo contains code using the model. Character-level recurrent sequence-to-sequence model.\n\nCredits: fchollet - Original Author\n\nHF Contribution: Rishav Chandra Varma",
"## Background Information",
"### Introd... | [
"TAGS\n#keras #seq2seq #character-level #machine translation #has_space #region-us \n",
"## Tensorflow Keras Implementation of Character-level Recurrent Sequence-to-Sequence Model\n\nThis repo contains code using the model. Character-level recurrent sequence-to-sequence model.\n\nCredits: fchollet - Original Auth... |
null | null | SculptDiffusion is a custom diffusion model trained by @jags111.
It can be used to create wonderful sculpture style outputs as it is trained on a variety of real world sculptures and 3d objects in a variety of materials and textures .
To use it you can use "sculptdiffusion" as a selection in the DD version.
If you c... | {"license": "mit"} | jags/sculptdiffusion | null | [
"license:mit",
"region:us"
] | null | 2022-07-23T04:35:31+00:00 | [] | [] | TAGS
#license-mit #region-us
| SculptDiffusion is a custom diffusion model trained by @jags111.
It can be used to create wonderful sculpture style outputs as it is trained on a variety of real world sculptures and 3d objects in a variety of materials and textures .
To use it you can use "sculptdiffusion" as a selection in the DD version.
If you c... | [] | [
"TAGS\n#license-mit #region-us \n"
] |
token-classification | transformers |
# Model Trained Using AutoTrain
- Problem type: Entity Extraction
- Model ID: 1169643336
- CO2 Emissions (in grams): 0.004032656988228696
## Validation Metrics
- Loss: 0.677674412727356
- Accuracy: 0.8129095674967235
- Precision: 0.4424778761061947
- Recall: 0.4844961240310077
- F1: 0.4625346901017577
## Usage
Yo... | {"language": "en", "tags": "autotrain", "datasets": ["Shenzy2/autotrain-data-NER4DesignTutor"], "widget": [{"text": "Why is the username the largest part of each card?"}], "co2_eq_emissions": 0.004032656988228696} | Shenzy2/NER4DesignTutor | null | [
"transformers",
"pytorch",
"bert",
"token-classification",
"autotrain",
"en",
"dataset:Shenzy2/autotrain-data-NER4DesignTutor",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-23T05:04:57+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bert #token-classification #autotrain #en #dataset-Shenzy2/autotrain-data-NER4DesignTutor #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoTrain
- Problem type: Entity Extraction
- Model ID: 1169643336
- CO2 Emissions (in grams): 0.004032656988228696
## Validation Metrics
- Loss: 0.677674412727356
- Accuracy: 0.8129095674967235
- Precision: 0.4424778761061947
- Recall: 0.4844961240310077
- F1: 0.4625346901017577
## Usage
Yo... | [
"# Model Trained Using AutoTrain\n\n- Problem type: Entity Extraction\n- Model ID: 1169643336\n- CO2 Emissions (in grams): 0.004032656988228696",
"## Validation Metrics\n\n- Loss: 0.677674412727356\n- Accuracy: 0.8129095674967235\n- Precision: 0.4424778761061947\n- Recall: 0.4844961240310077\n- F1: 0.462534690101... | [
"TAGS\n#transformers #pytorch #bert #token-classification #autotrain #en #dataset-Shenzy2/autotrain-data-NER4DesignTutor #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoTrain\n\n- Problem type: Entity Extraction\n- Model ID: 1169643336\n- CO2 Emissions (in ... |
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. -->
# distilbert_final_0010
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation s... | {"tags": ["generated_from_keras_callback"], "model-index": [{"name": "distilbert_final_0010", "results": []}]} | bigmorning/distilbert_final_0010 | null | [
"transformers",
"tf",
"distilbert",
"fill-mask",
"generated_from_keras_callback",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-23T05:14:24+00:00 | [] | [] | TAGS
#transformers #tf #distilbert #fill-mask #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #region-us
| distilbert\_final\_0010
=======================
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.9257
* Validation Loss: 0.9120
* Epoch: 9
Model description
-----------------
More information needed
Intended uses & limitations
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': 2e-05, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight\\_decay\\_rate': 0.01}\n* training\\_precision: float32",
... | [
"TAGS\n#transformers #tf #distilbert #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': 2e-05, 'decay': 0.... |
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... | marice/ppo-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-07-23T05:28:56+00:00 | [] | [] | TAGS
#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add you... |
fill-mask | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# test-trainer
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the glue dat... | {"language": ["ja"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "model-index": [{"name": "test-trainer", "results": []}]} | planhanasan/test-trainer | null | [
"transformers",
"pytorch",
"tensorboard",
"camembert",
"fill-mask",
"generated_from_trainer",
"ja",
"dataset:glue",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-23T05:45:29+00:00 | [] | [
"ja"
] | TAGS
#transformers #pytorch #tensorboard #camembert #fill-mask #generated_from_trainer #ja #dataset-glue #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# test-trainer
This model is a fine-tuned version of bert-base-uncased on the glue dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The foll... | [
"# test-trainer\n\nThis model is a fine-tuned version of bert-base-uncased on the glue dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Trai... | [
"TAGS\n#transformers #pytorch #tensorboard #camembert #fill-mask #generated_from_trainer #ja #dataset-glue #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# test-trainer\n\nThis model is a fine-tuned version of bert-base-uncased on the glue dataset.",
"## Model description\n\nMo... |
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. -->
# distilbert_complete
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set... | {"tags": ["generated_from_keras_callback"], "model-index": [{"name": "distilbert_complete", "results": []}]} | bigmorning/distilbert_complete | null | [
"transformers",
"tf",
"distilbert",
"fill-mask",
"generated_from_keras_callback",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-23T05:48:12+00:00 | [] | [] | TAGS
#transformers #tf #distilbert #fill-mask #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #region-us
| distilbert\_complete
====================
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.9232
* Validation Loss: 0.9128
* Epoch: 12
Model description
-----------------
More information needed
Intended uses & limitations
-----... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': 2e-05, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight\\_decay\\_rate': 0.01}\n* training\\_precision: float32",
... | [
"TAGS\n#transformers #tf #distilbert #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': 2e-05, 'decay': 0.... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# pegasus-samsum
This model is a fine-tuned version of [google/pegasus-cnn_dailymail](https://huggingface.co/google/pegasus-cnn_da... | {"tags": ["generated_from_trainer"], "datasets": ["samsum"], "model-index": [{"name": "pegasus-samsum", "results": []}]} | Someman/pegasus-samsum | null | [
"transformers",
"pytorch",
"tensorboard",
"pegasus",
"text2text-generation",
"generated_from_trainer",
"dataset:samsum",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-23T06:30:10+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #pegasus #text2text-generation #generated_from_trainer #dataset-samsum #autotrain_compatible #endpoints_compatible #region-us
| pegasus-samsum
==============
This model is a fine-tuned version of google/pegasus-cnn\_dailymail on the samsum dataset.
It achieves the following results on the evaluation set:
* Loss: 1.4884
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\n* seed: 42\n* gradient\\_accumulation\\_steps: 16\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=... | [
"TAGS\n#transformers #pytorch #tensorboard #pegasus #text2text-generation #generated_from_trainer #dataset-samsum #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\... |
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. -->
# Millad
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "Millad", "results": []}]} | Siyong/M | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-07-23T06:38:42+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| Millad
======
This model is a fine-tuned version of facebook/wav2vec2-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 3.2265
* Wer: 0.5465
* Cer: 0.3162
Model description
-----------------
More information needed
Intended uses & limitations
--------------------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps:... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 8\n* eval\\_ba... |
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/676614171849453568/AZd1B... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/vgdunkey-vgdunkeybot/1658611112335/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/vgdunkey-vgdunkeybot | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-23T07:20:40+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI CYBORG
dunkey & dunkey bot
@vgdunkey-vgdunkeybot
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.
Tra... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-end2end-questions-generation
This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the squad_mod... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad_modified_for_t5_qg"], "model-index": [{"name": "t5-end2end-questions-generation", "results": []}]} | shiulian/t5-end2end-questions-generation | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:squad_modified_for_t5_qg",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-23T07:34:55+00:00 | [] | [] | TAGS
#transformers #pytorch #t5 #text2text-generation #generated_from_trainer #dataset-squad_modified_for_t5_qg #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| t5-end2end-questions-generation
===============================
This model is a fine-tuned version of t5-base on the squad\_modified\_for\_t5\_qg dataset.
It achieves the following results on the evaluation set:
* Loss: 1.5679
Model description
-----------------
More information needed
Intended uses & limitat... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* gradient\\_accumulation\\_steps: 16\n* total\\_train\\_batch\\_size: 64\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #generated_from_trainer #dataset-squad_modified_for_t5_qg #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n... |
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="Chris1/q-FrozenLake-v1-8x8-no_slippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attr... | {"tags": ["FrozenLake-v1-8x8-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-FrozenLake-v1-8x8-no_slippery", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "FrozenLake-v1-8x8-no_slippery", "type":... | Chris1/q-FrozenLake-v1-8x8-no_slippery | null | [
"FrozenLake-v1-8x8-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-07-23T07:47:59+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 | 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... | rebolforces/ppo-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-07-23T08:28:37+00:00 | [] | [] | TAGS
#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add you... |
null | null | this is test | {} | Sarmila/layoutlmv2-finetuned-funsd-v2 | null | [
"region:us"
] | null | 2022-07-23T08:50:36+00:00 | [] | [] | TAGS
#region-us
| this is test | [] | [
"TAGS\n#region-us \n"
] |
sentence-similarity | sentence-transformers |
# all-mpnet-base-v2
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.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers]... | {"language": "en", "license": "apache-2.0", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity"], "datasets": ["s2orc", "flax-sentence-embeddings/stackexchange_xml", "MS Marco", "gooaq", "yahoo_answers_topics", "code_search_net", "search_qa", "eli5", "snli", "multi_nli", "wikihow", "natural_qu... | valurank/headline_similarities | null | [
"sentence-transformers",
"pytorch",
"mpnet",
"feature-extraction",
"sentence-similarity",
"en",
"arxiv:1904.06472",
"arxiv:2102.07033",
"arxiv:2104.08727",
"arxiv:1704.05179",
"arxiv:1810.09305",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-07-23T09:21:35+00:00 | [
"1904.06472",
"2102.07033",
"2104.08727",
"1704.05179",
"1810.09305"
] | [
"en"
] | TAGS
#sentence-transformers #pytorch #mpnet #feature-extraction #sentence-similarity #en #arxiv-1904.06472 #arxiv-2102.07033 #arxiv-2104.08727 #arxiv-1704.05179 #arxiv-1810.09305 #license-apache-2.0 #endpoints_compatible #has_space #region-us
| all-mpnet-base-v2
=================
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 se... | [
"### Pre-training\n\n\nWe use the pretrained 'microsoft/mpnet-base' model. Please refer to the model card for more detailed information about the pre-training procedure.",
"### Fine-tuning\n\n\nWe fine-tune the model using a contrastive objective. Formally, we compute the cosine similarity from each possible sent... | [
"TAGS\n#sentence-transformers #pytorch #mpnet #feature-extraction #sentence-similarity #en #arxiv-1904.06472 #arxiv-2102.07033 #arxiv-2104.08727 #arxiv-1704.05179 #arxiv-1810.09305 #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"### Pre-training\n\n\nWe use the pretrained 'microsoft/mpnet-ba... |
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. -->
# Millad_Customer_RN
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base)... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "Millad_Customer_RN", "results": []}]} | Siyong/MC_RN | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-07-23T09:22:37+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| Millad\_Customer\_RN
====================
This model is a fine-tuned version of facebook/wav2vec2-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 4.5635
* Wer: 0.8113
* Cer: 0.4817
Model description
-----------------
More information needed
Intended uses & limitation... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps:... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 8\n* eval\\_ba... |
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. -->
# robot22
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16... | {"license": "apache-2.0", "tags": ["image-classification", "generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "robot22", "results": []}]} | sudo-s/robot22 | null | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-23T09:34:24+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #vit #image-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| robot22
=======
This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the sudo-s/herbier\_mesuem6 dataset.
It achieves the following results on the evaluation set:
* Loss: 2.5674
* Accuracy: 0.5077
Model description
-----------------
More information needed
Intended uses & limitations
---... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0002\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1\n* mixed\\_prec... | [
"TAGS\n#transformers #pytorch #tensorboard #vit #image-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0002\n* train\\_batch\... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# MilladRN
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the No... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "MilladRN", "results": []}]} | Siyong/M_RN | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-07-23T09:59:34+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| MilladRN
========
This model is a fine-tuned version of facebook/wav2vec2-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 3.4355
* Wer: 0.4907
* Cer: 0.2802
Model description
-----------------
More information needed
Intended uses & limitations
----------------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps:... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 8\n* eval\\_ba... |
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. -->
# google-mt5-small-ibn-Shaddad-v1
This model is a fine-tuned version of [google/t5-v1_1-small](https://huggingface.co/google/t5-v1... | {"license": "apache-2.0", "tags": ["Poet", "generated_from_trainer"], "model-index": [{"name": "google-mt5-small-ibn-Shaddad-v1", "results": []}]} | Ahmed007/google-mt5-small-ibn-Shaddad-v1 | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"Poet",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-23T10:02:34+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #Poet #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| google-mt5-small-ibn-Shaddad-v1
===============================
This model is a fine-tuned version of google/t5-v1\_1-small on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0015
Model description
-----------------
More information needed
Intended uses & limitations
---... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5.6e-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",
"### Trai... | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #Poet #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\... |
text-generation | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# turkishReviews-ds-finetuned
This model is a fine-tuned version of [kmkarakaya/turkishReviews-ds](https://huggingface.co/kmkarakaya/tur... | {"license": "mit", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "turkishReviews-ds-finetuned", "results": []}]} | kmkarakaya/turkishReviews-ds-finetuned | null | [
"transformers",
"tf",
"gpt2",
"text-generation",
"generated_from_keras_callback",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-23T11:35:33+00:00 | [] | [] | TAGS
#transformers #tf #gpt2 #text-generation #generated_from_keras_callback #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# turkishReviews-ds-finetuned
This model is a fine-tuned version of kmkarakaya/turkishReviews-ds on an unknown dataset.
It achieves the following results on the evaluation set:
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
... | [
"# turkishReviews-ds-finetuned\n\nThis model is a fine-tuned version of kmkarakaya/turkishReviews-ds on an unknown dataset.\nIt achieves the following results on the evaluation set:",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training a... | [
"TAGS\n#transformers #tf #gpt2 #text-generation #generated_from_keras_callback #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# turkishReviews-ds-finetuned\n\nThis model is a fine-tuned version of kmkarakaya/turkishReviews-ds on an unknown dataset.\nIt achieve... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# deberta-v3-large-finetuned-synthetic-multi-class
This model is a fine-tuned version of [microsoft/deberta-v3-large](https://hugg... | {"license": "mit", "tags": ["text-classification", "generated_from_trainer"], "metrics": ["f1", "precision", "recall"], "model-index": [{"name": "deberta-v3-large-finetuned-synthetic-multi-class", "results": []}]} | domenicrosati/deberta-v3-large-finetuned-synthetic-multi-class | null | [
"transformers",
"pytorch",
"tensorboard",
"deberta-v2",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-23T12:02:38+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #deberta-v2 #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| deberta-v3-large-finetuned-synthetic-multi-class
================================================
This model is a fine-tuned version of microsoft/deberta-v3-large on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0223
* F1: 0.9961
* Precision: 0.9961
* Recall: 0.9961
Model ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 6e-06\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: ... | [
"TAGS\n#transformers #pytorch #tensorboard #deberta-v2 #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 6e-06\n* train\\_batch\\_... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# tiny-bert-sst2-distilled
This model is a fine-tuned version of [google/bert_uncased_L-2_H-128_A-2](https://huggingface.co/google... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "model-index": [{"name": "tiny-bert-sst2-distilled", "results": []}]} | ilana/tiny-bert-sst2-distilled | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-23T12:46:33+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# tiny-bert-sst2-distilled
This model is a fine-tuned version of google/bert_uncased_L-2_H-128_A-2 on the glue dataset.
It achieves the following results on the evaluation set:
- eval_loss: 3.0017
- eval_accuracy: 0.7477
- eval_runtime: 0.3985
- eval_samples_per_second: 2188.296
- eval_steps_per_second: 17.567
- ep... | [
"# tiny-bert-sst2-distilled\n\nThis model is a fine-tuned version of google/bert_uncased_L-2_H-128_A-2 on the glue dataset.\nIt achieves the following results on the evaluation set:\n- eval_loss: 3.0017\n- eval_accuracy: 0.7477\n- eval_runtime: 0.3985\n- eval_samples_per_second: 2188.296\n- eval_steps_per_second: 1... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# tiny-bert-sst2-distilled\n\nThis model is a fine-tuned version of google/bert_uncased_L-2_H-128_A-2 on the glue dataset.\nI... |
automatic-speech-recognition | transformers | # exp_w2v2r_de_vp-100k_accent_germany-5_austria-5_s3
Fine-tuned [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (de)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When usin... | {"language": ["de"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "de"], "datasets": ["mozilla-foundation/common_voice_7_0"]} | jonatasgrosman/exp_w2v2r_de_vp-100k_accent_germany-5_austria-5_s3 | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"de",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-07-23T13:22:07+00:00 | [] | [
"de"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #de #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #endpoints_compatible #region-us
| # exp_w2v2r_de_vp-100k_accent_germany-5_austria-5_s3
Fine-tuned facebook/wav2vec2-large-100k-voxpopuli for speech recognition using the train split of Common Voice 7.0 (de).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the HuggingSound tool.
| [
"# exp_w2v2r_de_vp-100k_accent_germany-5_austria-5_s3\n\nFine-tuned facebook/wav2vec2-large-100k-voxpopuli for speech recognition using the train split of Common Voice 7.0 (de).\nWhen using this model, make sure that your speech input is sampled at 16kHz.\n\nThis model has been fine-tuned by the HuggingSound tool."... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #de #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# exp_w2v2r_de_vp-100k_accent_germany-5_austria-5_s3\n\nFine-tuned facebook/wav2vec2-large-100k-voxpopuli for speech recognition using t... |
automatic-speech-recognition | transformers | # exp_w2v2r_de_vp-100k_accent_germany-5_austria-5_s803
Fine-tuned [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (de)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When us... | {"language": ["de"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "de"], "datasets": ["mozilla-foundation/common_voice_7_0"]} | jonatasgrosman/exp_w2v2r_de_vp-100k_accent_germany-5_austria-5_s803 | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"de",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-07-23T13:32:41+00:00 | [] | [
"de"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #de #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #endpoints_compatible #region-us
| # exp_w2v2r_de_vp-100k_accent_germany-5_austria-5_s803
Fine-tuned facebook/wav2vec2-large-100k-voxpopuli for speech recognition using the train split of Common Voice 7.0 (de).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the HuggingSound tool.
| [
"# exp_w2v2r_de_vp-100k_accent_germany-5_austria-5_s803\n\nFine-tuned facebook/wav2vec2-large-100k-voxpopuli for speech recognition using the train split of Common Voice 7.0 (de).\nWhen using this model, make sure that your speech input is sampled at 16kHz.\n\nThis model has been fine-tuned by the HuggingSound tool... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #de #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# exp_w2v2r_de_vp-100k_accent_germany-5_austria-5_s803\n\nFine-tuned facebook/wav2vec2-large-100k-voxpopuli for speech recognition using... |
automatic-speech-recognition | transformers | # exp_w2v2r_de_vp-100k_accent_germany-5_austria-5_s95
Fine-tuned [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (de)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When usi... | {"language": ["de"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "de"], "datasets": ["mozilla-foundation/common_voice_7_0"]} | jonatasgrosman/exp_w2v2r_de_vp-100k_accent_germany-5_austria-5_s95 | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"de",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-07-23T13:37:36+00:00 | [] | [
"de"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #de #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #endpoints_compatible #region-us
| # exp_w2v2r_de_vp-100k_accent_germany-5_austria-5_s95
Fine-tuned facebook/wav2vec2-large-100k-voxpopuli for speech recognition using the train split of Common Voice 7.0 (de).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the HuggingSound tool.
| [
"# exp_w2v2r_de_vp-100k_accent_germany-5_austria-5_s95\n\nFine-tuned facebook/wav2vec2-large-100k-voxpopuli for speech recognition using the train split of Common Voice 7.0 (de).\nWhen using this model, make sure that your speech input is sampled at 16kHz.\n\nThis model has been fine-tuned by the HuggingSound tool.... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #de #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# exp_w2v2r_de_vp-100k_accent_germany-5_austria-5_s95\n\nFine-tuned facebook/wav2vec2-large-100k-voxpopuli for speech recognition using ... |
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... | th1s1s1t/ppo-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-07-23T13:41:01+00:00 | [] | [] | TAGS
#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# PPO Agent playing LunarLander-v2\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... |
automatic-speech-recognition | transformers | # exp_w2v2r_de_vp-100k_accent_germany-0_austria-10_s103
Fine-tuned [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (de)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When u... | {"language": ["de"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "de"], "datasets": ["mozilla-foundation/common_voice_7_0"]} | jonatasgrosman/exp_w2v2r_de_vp-100k_accent_germany-0_austria-10_s103 | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"de",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-07-23T13:42:13+00:00 | [] | [
"de"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #de #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #endpoints_compatible #region-us
| # exp_w2v2r_de_vp-100k_accent_germany-0_austria-10_s103
Fine-tuned facebook/wav2vec2-large-100k-voxpopuli for speech recognition using the train split of Common Voice 7.0 (de).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the HuggingSound tool.
| [
"# exp_w2v2r_de_vp-100k_accent_germany-0_austria-10_s103\n\nFine-tuned facebook/wav2vec2-large-100k-voxpopuli for speech recognition using the train split of Common Voice 7.0 (de).\nWhen using this model, make sure that your speech input is sampled at 16kHz.\n\nThis model has been fine-tuned by the HuggingSound too... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #de #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# exp_w2v2r_de_vp-100k_accent_germany-0_austria-10_s103\n\nFine-tuned facebook/wav2vec2-large-100k-voxpopuli for speech recognition usin... |
image-classification | transformers |
# Check_Gum_Teeth
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https://github.com/nateraw/hugg... | {"tags": ["image-classification", "pytorch", "huggingpics"], "metrics": ["accuracy"]} | steven123/Check_Gum_Teeth | null | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-23T13:50:33+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# Check_Gum_Teeth
Autogenerated by HuggingPics️
Create your own image classifier for anything by running the demo on Google Colab.
Report any issues with the demo at the github repo.
## Example Images
#### Bad_Gum
!Bad_Gum
#### Good_Gum
!Good_Gum | [
"# Check_Gum_Teeth\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nReport any issues with the demo at the github repo.",
"## Example Images",
"#### Bad_Gum\n\n!Bad_Gum",
"#### Good_Gum\n\n!Good_Gum"
] | [
"TAGS\n#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# Check_Gum_Teeth\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nReport any issu... |
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. -->
# results
This model is a fine-tuned version of [gagan3012/k2t](https://huggingface.co/gagan3012/k2t) on an unknown dataset.
It ac... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "results", "results": []}]} | nishita/results | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-23T14:21:06+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| results
=======
This model is a fine-tuned version of gagan3012/k2t on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5481
* Rouge1: 65.0534
* Rouge2: 45.7092
* Rougel: 55.8222
* Rougelsum: 57.1866
* Gen Len: 17.8061
Model description
-----------------
More information ne... | [
"### 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* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05... |
reinforcement-learning | stable-baselines3 |
# **A2C** Agent playing **AntBulletEnv-v0**
This is a trained model of a **A2C** agent playing **AntBulletEnv-v0**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb... | {"library_name": "stable-baselines3", "tags": ["AntBulletEnv-v0", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "A2C", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "AntBulletEnv-v0", "type": "AntB... | srini98/a2c-AntBulletEnv-v0 | null | [
"stable-baselines3",
"AntBulletEnv-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-07-23T14:41:36+00:00 | [] | [] | TAGS
#stable-baselines3 #AntBulletEnv-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# A2C Agent playing AntBulletEnv-v0
This is a trained model of a A2C agent playing AntBulletEnv-v0
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# A2C Agent playing AntBulletEnv-v0\nThis is a trained model of a A2C agent playing AntBulletEnv-v0\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #AntBulletEnv-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# A2C Agent playing AntBulletEnv-v0\nThis is a trained model of a A2C agent playing AntBulletEnv-v0\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add ... |
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. -->
# MilladRN
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the No... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "MilladRN", "results": []}]} | Siyong/M_RN_LM | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-07-23T15:39:57+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| MilladRN
========
This model is a fine-tuned version of facebook/wav2vec2-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 3.4355
* Wer: 0.4907
* Cer: 0.2802
Model description
-----------------
More information needed
Intended uses & limitations
----------------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps:... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 8\n* eval\\_ba... |
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-small-juman-unigram
This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset.
It achieves the ... | {"tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "bert-small-juman-unigram", "results": []}]} | schnell/bert-small-juman-unigram | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-23T15:53:56+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| bert-small-juman-unigram
========================
This model is a fine-tuned version of [](URL on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 1.4490
* Accuracy: 0.6911
Model description
-----------------
More information needed
Intended uses & limitations
---------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 256\n* eval\\_batch\\_size: 8\n* seed: 42\n* distributed\\_type: multi-GPU\n* num\\_devices: 3\n* total\\_train\\_batch\\_size: 768\n* total\\_eval\\_batch\\_size: 24\n... | [
"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: 0.0001\n* train\\_batch\\_size: 256\n* eval\\_batch\\_... |
text-generation | transformers |
# DialoGPT-small-EvilMortyTheBot | {"tags": ["conversational"]} | xander-cross/DialoGPT-small-EvilMortyTheBot | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-23T15:55:02+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# DialoGPT-small-EvilMortyTheBot | [
"# DialoGPT-small-EvilMortyTheBot"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# DialoGPT-small-EvilMortyTheBot"
] |
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. -->
# wav2vec-base-Millad_TIMIT
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec-base-Millad_TIMIT", "results": []}]} | Siyong/MT_LM | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-07-23T15:55:38+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec-base-Millad\_TIMIT
==========================
This model is a fine-tuned version of facebook/wav2vec2-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 1.3772
* Wer: 0.6859
* Cer: 0.3217
Model description
-----------------
More information needed
Intended uses ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps:... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 8\n* eval\\_ba... |
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. -->
# Millad_Customer_RN
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base)... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "Millad_Customer_RN", "results": []}]} | Siyong/MC_RN_LM | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-07-23T16:08:32+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| Millad\_Customer\_RN
====================
This model is a fine-tuned version of facebook/wav2vec2-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 4.5635
* Wer: 0.8113
* Cer: 0.4817
Model description
-----------------
More information needed
Intended uses & limitation... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps:... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 8\n* eval\\_ba... |
text2text-generation | transformers |
# Model Trained Using AutoTrain
- Problem type: Summarization
- Model ID: 1170943400
- CO2 Emissions (in grams): 25.718350806012065
## Validation Metrics
- Loss: 2.569204092025757
- Rouge1: 21.072
- Rouge2: 6.2072
- RougeL: 18.9156
- RougeLsum: 18.8997
- Gen Len: 10.7165
## Usage
You can use cURL to access this m... | {"language": "en", "tags": "autotrain", "datasets": ["jcashmoney123/autotrain-data-amazon-summarization"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 25.718350806012065} | jcashmoney123/autotrain-amazon-summarization-1170943400 | null | [
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"autotrain",
"en",
"dataset:jcashmoney123/autotrain-data-amazon-summarization",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-23T16:53:26+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #pegasus #text2text-generation #autotrain #en #dataset-jcashmoney123/autotrain-data-amazon-summarization #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoTrain
- Problem type: Summarization
- Model ID: 1170943400
- CO2 Emissions (in grams): 25.718350806012065
## Validation Metrics
- Loss: 2.569204092025757
- Rouge1: 21.072
- Rouge2: 6.2072
- RougeL: 18.9156
- RougeLsum: 18.8997
- Gen Len: 10.7165
## Usage
You can use cURL to access this m... | [
"# Model Trained Using AutoTrain\n\n- Problem type: Summarization\n- Model ID: 1170943400\n- CO2 Emissions (in grams): 25.718350806012065",
"## Validation Metrics\n\n- Loss: 2.569204092025757\n- Rouge1: 21.072\n- Rouge2: 6.2072\n- RougeL: 18.9156\n- RougeLsum: 18.8997\n- Gen Len: 10.7165",
"## Usage\n\nYou can ... | [
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain #en #dataset-jcashmoney123/autotrain-data-amazon-summarization #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoTrain\n\n- Problem type: Summarization\n- Model ID: 1170943400\n- CO2 Emis... |
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. -->
# modeversion1_m7_e4
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-b... | {"license": "apache-2.0", "tags": ["image-classification", "generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "modeversion1_m7_e4", "results": []}]} | sudo-s/modeversion1_m7_e4 | null | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-23T17:20:54+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #vit #image-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| modeversion1\_m7\_e4
====================
This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the sudo-s/herbier\_mesuem7 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0902
* Accuracy: 0.9731
Model description
-----------------
More information needed
Inten... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0002\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 4\n* mixed\\_prec... | [
"TAGS\n#transformers #pytorch #tensorboard #vit #image-classification #generated_from_trainer #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: 0.0002\n* train\\_batch\... |
text2text-generation | transformers |
# Model Trained Using AutoTrain
- Problem type: Summarization
- Model ID: 1171143428
- CO2 Emissions (in grams): 5.4331208624177245
## Validation Metrics
- Loss: 2.5859596729278564
- Rouge1: 19.3601
- Rouge2: 4.6055
- RougeL: 17.4309
- RougeLsum: 17.4621
- Gen Len: 15.2938
## Usage
You can use cURL to access this... | {"language": "unk", "tags": "autotrain", "datasets": ["jcashmoney123/autotrain-data-amz"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 5.4331208624177245} | jcashmoney123/autotrain-amz-1171143428 | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"autotrain",
"unk",
"dataset:jcashmoney123/autotrain-data-amz",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-23T17:27:51+00:00 | [] | [
"unk"
] | TAGS
#transformers #pytorch #bart #text2text-generation #autotrain #unk #dataset-jcashmoney123/autotrain-data-amz #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoTrain
- Problem type: Summarization
- Model ID: 1171143428
- CO2 Emissions (in grams): 5.4331208624177245
## Validation Metrics
- Loss: 2.5859596729278564
- Rouge1: 19.3601
- Rouge2: 4.6055
- RougeL: 17.4309
- RougeLsum: 17.4621
- Gen Len: 15.2938
## Usage
You can use cURL to access this... | [
"# Model Trained Using AutoTrain\n\n- Problem type: Summarization\n- Model ID: 1171143428\n- CO2 Emissions (in grams): 5.4331208624177245",
"## Validation Metrics\n\n- Loss: 2.5859596729278564\n- Rouge1: 19.3601\n- Rouge2: 4.6055\n- RougeL: 17.4309\n- RougeLsum: 17.4621\n- Gen Len: 15.2938",
"## Usage\n\nYou ca... | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #autotrain #unk #dataset-jcashmoney123/autotrain-data-amz #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoTrain\n\n- Problem type: Summarization\n- Model ID: 1171143428\n- CO2 Emissions (in grams): 5... |
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-cars
This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imagefolder"], "metrics": ["accuracy"], "model-index": [{"name": "swin-tiny-patch4-window7-224-finetuned-cars", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "i... | Isaacks/swin-tiny-patch4-window7-224-finetuned-cars | null | [
"transformers",
"pytorch",
"tensorboard",
"swin",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-23T17:46:57+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-cars
===========================================
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: 0.2192
* Accuracy: 0.9135
Model description
------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsilo... | [
"TAGS\n#transformers #pytorch #tensorboard #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... |
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. -->
# XSum_t5-small_800_adafactor
This model is a fine-tuned version of [/content/XSum_t5-small_800_adafactor/checkpoint-11000](https:... | {"tags": ["generated_from_trainer"], "datasets": ["xsum"], "metrics": ["rouge"], "model-index": [{"name": "XSum_t5-small_800_adafactor", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "xsum", "type": "xsum", "args": "default"}, "metrics": [{"... | oMateos2020/XSum_t5-small_800_adafactor | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:xsum",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-23T18:08:32+00:00 | [] | [] | TAGS
#transformers #pytorch #t5 #text2text-generation #generated_from_trainer #dataset-xsum #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| XSum\_t5-small\_800\_adafactor
==============================
This model is a fine-tuned version of /content/XSum\_t5-small\_800\_adafactor/checkpoint-11000 on the xsum dataset.
It achieves the following results on the evaluation set:
* Loss: 2.1714
* Rouge1: 33.022
* Rouge2: 11.9979
* Rougel: 26.7476
* Rougelsum: ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 25\n* eval\\_batch\\_size: 25\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2\n* mixed\\_pre... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #generated_from_trainer #dataset-xsum #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.00... |
null | sklearn |
# Model description
[More Information Needed]
## Intended uses & limitations
[More Information Needed]
## Training Procedure
### Hyperparameters
The model is trained with below hyperparameters.
<details>
<summary> Click to expand </summary>
| Hyperparameters | Value |
| :-- | :-- |
| aggressive_elimination | F... | {"library_name": "sklearn"} | osanseviero/hf_hub_example-f7d1d7e5-f207-4eef-99bb-57408d604e2b | null | [
"sklearn",
"region:us"
] | null | 2022-07-23T20:04:37+00:00 | [] | [] | TAGS
#sklearn #region-us
| Model description
=================
Intended uses & limitations
---------------------------
Training Procedure
------------------
### Hyperparameters
The model is trained with below hyperparameters.
Click to expand
### Model Plot
The model plot is below.
#sk-container-id-1 {color: black;background-co... | [
"### 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 #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."
] |
null | sklearn |
# Model description
This is a HistGradientBoostingClassifier model trained on breast cancer dataset. It's trained with Halving Grid Search Cross Validation, with parameter grids on max_leaf_nodes and max_depth.
## Intended uses & limitations
This model is not ready to be used in production.
## Training Procedure
... | {"library_name": "sklearn"} | osanseviero/hf_hub_example-821defb0-1482-4d27-884d-4359bfad704f | null | [
"sklearn",
"region:us"
] | null | 2022-07-23T20:08:18+00:00 | [] | [] | TAGS
#sklearn #region-us
| Model description
=================
This is a HistGradientBoostingClassifier model trained on breast cancer dataset. It's trained with Halving Grid Search Cross Validation, with parameter grids on max\_leaf\_nodes and max\_depth.
Intended uses & limitations
---------------------------
This model is not ready to b... | [
"### 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 #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."
] |
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/676614171849453568/AZd1B... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/vgdunkey-vgdunkeybot-videobotdunkey/1658610683659/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/vgdunkey-vgdunkeybot-videobotdunkey | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-23T20:10:35+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI CYBORG
dunkey & dunkey bot & dunkey bot
@vgdunkey-vgdunkeybot-videobotdunkey
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, ch... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
null | sklearn |
# Model description
This is a HistGradientBoostingClassifier model trained on breast cancer dataset. It's trained with Halving Grid Search Cross Validation, with parameter grids on max_leaf_nodes and max_depth.
## Intended uses & limitations
This model is not ready to be used in production.
## Training Procedure
... | {"library_name": "sklearn"} | osanseviero/hf_hub_example-023f3150-3eae-45a4-bd3c-7a95639e10e0 | null | [
"sklearn",
"region:us"
] | null | 2022-07-23T20:11:49+00:00 | [] | [] | TAGS
#sklearn #region-us
| Model description
=================
This is a HistGradientBoostingClassifier model trained on breast cancer dataset. It's trained with Halving Grid Search Cross Validation, with parameter grids on max\_leaf\_nodes and max\_depth.
Intended uses & limitations
---------------------------
This model is not ready to b... | [
"### 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 #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."
] |
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