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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
text-generation | transformers | ## GPT Neo 125m fine-tuned
#### Pushing model to repo
1. Login to hugging face,
```
from huggingface_hub import notebook_login
notebook_login()
```
2. Then push model to repo.
```
model.push_to_hub("gpt-neo-125m-finetuned", use_temp_dir=True)
tokenizer.push_to_hub("gpt-neo-125m-finetuned", use_temp_dir=True)
```
--... | {} | ytling/gpt-neo-125m-finetuned | null | [
"transformers",
"pytorch",
"gpt_neo",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-18T01:51:58+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt_neo #text-generation #autotrain_compatible #endpoints_compatible #region-us
| ## GPT Neo 125m fine-tuned
#### Pushing model to repo
1. Login to hugging face,
2. Then push model to repo.
---
#### Using the Model
Load the model along with the tokenizer:
To use model, pass text, the loaded model and tokenizer into the 'gpt_model()' function,
returns dict containing predicted entities wit... | [
"## GPT Neo 125m fine-tuned",
"#### Pushing model to repo\n\n1. Login to hugging face,\n\n\n2. Then push model to repo.\n\n\n---",
"#### Using the Model\nLoad the model along with the tokenizer:\n\n\nTo use model, pass text, the loaded model and tokenizer into the 'gpt_model()' function, \n\n\nreturns dict cont... | [
"TAGS\n#transformers #pytorch #gpt_neo #text-generation #autotrain_compatible #endpoints_compatible #region-us \n",
"## GPT Neo 125m fine-tuned",
"#### Pushing model to repo\n\n1. Login to hugging face,\n\n\n2. Then push model to repo.\n\n\n---",
"#### Using the Model\nLoad the model along with the tokenizer:... |
fill-mask | transformers | # Chinese Whole Word Masking RoBERTa Miniatures
## Model description
This is the set of 6 Chinese Whole Word Masking RoBERTa models pre-trained by [UER-py](https://github.com/dbiir/UER-py/), which is introduced in [this paper](https://arxiv.org/abs/1909.05658). Besides, the models could also be pre-trained by [Tencen... | {"language": "zh", "datasets": "CLUECorpusSmall", "widget": [{"text": "\u5317\u4eac\u662f[MASK]\u56fd\u7684\u9996\u90fd\u3002"}]} | uer/roberta-tiny-wwm-chinese-cluecorpussmall | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"zh",
"dataset:CLUECorpusSmall",
"arxiv:1909.05658",
"arxiv:2212.06385",
"arxiv:1908.08962",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-18T02:46:21+00:00 | [
"1909.05658",
"2212.06385",
"1908.08962"
] | [
"zh"
] | TAGS
#transformers #pytorch #bert #fill-mask #zh #dataset-CLUECorpusSmall #arxiv-1909.05658 #arxiv-2212.06385 #arxiv-1908.08962 #autotrain_compatible #endpoints_compatible #region-us
| Chinese Whole Word Masking RoBERTa Miniatures
=============================================
Model description
-----------------
This is the set of 6 Chinese Whole Word Masking RoBERTa models pre-trained by UER-py, which is introduced in this paper. Besides, the models could also be pre-trained by TencentPretrain in... | [
"### BibTeX entry and citation info"
] | [
"TAGS\n#transformers #pytorch #bert #fill-mask #zh #dataset-CLUECorpusSmall #arxiv-1909.05658 #arxiv-2212.06385 #arxiv-1908.08962 #autotrain_compatible #endpoints_compatible #region-us \n",
"### BibTeX entry and citation info"
] |
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. -->
# phobert-base-finetuned-imdb
This model is a fine-tuned version of [vinai/phobert-base](https://huggingface.co/vinai/phobert-base... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "phobert-base-finetuned-imdb", "results": []}]} | MadridMaverick/phobert-base-finetuned-imdb | null | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-18T02:55:03+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| phobert-base-finetuned-imdb
===========================
This model is a fine-tuned version of vinai/phobert-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 1.2510
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: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Traini... | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* se... |
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. -->
# bart-base-finetuned-squad2
This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base)... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad_v2"], "model-index": [{"name": "bart-base-finetuned-squad2", "results": []}]} | ChuVN/bart-base-finetuned-squad2 | null | [
"transformers",
"pytorch",
"tensorboard",
"bart",
"question-answering",
"generated_from_trainer",
"dataset:squad_v2",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-07-18T03:13:07+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bart #question-answering #generated_from_trainer #dataset-squad_v2 #license-apache-2.0 #endpoints_compatible #region-us
| bart-base-finetuned-squad2
==========================
This model is a fine-tuned version of facebook/bart-base on the squad\_v2 dataset.
It achieves the following results on the evaluation set:
* Loss: 1.0446
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: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #bart #question-answering #generated_from_trainer #dataset-squad_v2 #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size... |
reinforcement-learning | null |
# **Q-Learning** Agent playing **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="spacestar1705/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False e... | {"tags": ["Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-Taxi-v3", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Taxi-v3", "type": "Taxi-v3"}, "metrics": [{"type": "mean_reward", "value": "7.56 +/... | spacestar1705/q-Taxi-v3 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-07-18T04:31:17+00:00 | [] | [] | TAGS
#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing Taxi-v3
This is a trained model of a Q-Learning agent playing Taxi-v3 .
## Usage
| [
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] | [
"TAGS\n#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] |
fill-mask | transformers | # Chinese Whole Word Masking RoBERTa Miniatures
## Model description
This is the set of 6 Chinese Whole Word Masking RoBERTa models pre-trained by [UER-py](https://github.com/dbiir/UER-py/), which is introduced in [this paper](https://arxiv.org/abs/1909.05658). Besides, the models could also be pre-trained by [Tencen... | {"language": "zh", "datasets": "CLUECorpusSmall", "widget": [{"text": "\u5317\u4eac\u662f[MASK]\u56fd\u7684\u9996\u90fd\u3002"}]} | uer/roberta-mini-wwm-chinese-cluecorpussmall | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"zh",
"dataset:CLUECorpusSmall",
"arxiv:1909.05658",
"arxiv:2212.06385",
"arxiv:1908.08962",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-18T04:36:20+00:00 | [
"1909.05658",
"2212.06385",
"1908.08962"
] | [
"zh"
] | TAGS
#transformers #pytorch #bert #fill-mask #zh #dataset-CLUECorpusSmall #arxiv-1909.05658 #arxiv-2212.06385 #arxiv-1908.08962 #autotrain_compatible #endpoints_compatible #region-us
| Chinese Whole Word Masking RoBERTa Miniatures
=============================================
Model description
-----------------
This is the set of 6 Chinese Whole Word Masking RoBERTa models pre-trained by UER-py, which is introduced in this paper. Besides, the models could also be pre-trained by TencentPretrain in... | [
"### BibTeX entry and citation info"
] | [
"TAGS\n#transformers #pytorch #bert #fill-mask #zh #dataset-CLUECorpusSmall #arxiv-1909.05658 #arxiv-2212.06385 #arxiv-1908.08962 #autotrain_compatible #endpoints_compatible #region-us \n",
"### BibTeX entry and citation info"
] |
fill-mask | transformers | # Chinese Whole Word Masking RoBERTa Miniatures
## Model description
This is the set of 6 Chinese Whole Word Masking RoBERTa models pre-trained by [UER-py](https://github.com/dbiir/UER-py/), which is introduced in [this paper](https://arxiv.org/abs/1909.05658). Besides, the models could also be pre-trained by [Tencen... | {"language": "zh", "datasets": "CLUECorpusSmall", "widget": [{"text": "\u5317\u4eac\u662f[MASK]\u56fd\u7684\u9996\u90fd\u3002"}]} | uer/roberta-small-wwm-chinese-cluecorpussmall | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"zh",
"dataset:CLUECorpusSmall",
"arxiv:1909.05658",
"arxiv:2212.06385",
"arxiv:1908.08962",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-18T04:41:35+00:00 | [
"1909.05658",
"2212.06385",
"1908.08962"
] | [
"zh"
] | TAGS
#transformers #pytorch #bert #fill-mask #zh #dataset-CLUECorpusSmall #arxiv-1909.05658 #arxiv-2212.06385 #arxiv-1908.08962 #autotrain_compatible #endpoints_compatible #region-us
| Chinese Whole Word Masking RoBERTa Miniatures
=============================================
Model description
-----------------
This is the set of 6 Chinese Whole Word Masking RoBERTa models pre-trained by UER-py, which is introduced in this paper. Besides, the models could also be pre-trained by TencentPretrain in... | [
"### BibTeX entry and citation info"
] | [
"TAGS\n#transformers #pytorch #bert #fill-mask #zh #dataset-CLUECorpusSmall #arxiv-1909.05658 #arxiv-2212.06385 #arxiv-1908.08962 #autotrain_compatible #endpoints_compatible #region-us \n",
"### BibTeX entry and citation info"
] |
fill-mask | transformers | # Chinese Whole Word Masking RoBERTa Miniatures
## Model description
This is the set of 6 Chinese Whole Word Masking RoBERTa models pre-trained by [UER-py](https://github.com/dbiir/UER-py/), which is introduced in [this paper](https://arxiv.org/abs/1909.05658). Besides, the models could also be pre-trained by [Tencen... | {"language": "zh", "datasets": "CLUECorpusSmall", "widget": [{"text": "\u5317\u4eac\u662f[MASK]\u56fd\u7684\u9996\u90fd\u3002"}]} | uer/roberta-medium-wwm-chinese-cluecorpussmall | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"zh",
"dataset:CLUECorpusSmall",
"arxiv:1909.05658",
"arxiv:2212.06385",
"arxiv:1908.08962",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-18T04:45:40+00:00 | [
"1909.05658",
"2212.06385",
"1908.08962"
] | [
"zh"
] | TAGS
#transformers #pytorch #bert #fill-mask #zh #dataset-CLUECorpusSmall #arxiv-1909.05658 #arxiv-2212.06385 #arxiv-1908.08962 #autotrain_compatible #endpoints_compatible #region-us
| Chinese Whole Word Masking RoBERTa Miniatures
=============================================
Model description
-----------------
This is the set of 6 Chinese Whole Word Masking RoBERTa models pre-trained by UER-py, which is introduced in this paper. Besides, the models could also be pre-trained by TencentPretrain in... | [
"### BibTeX entry and citation info"
] | [
"TAGS\n#transformers #pytorch #bert #fill-mask #zh #dataset-CLUECorpusSmall #arxiv-1909.05658 #arxiv-2212.06385 #arxiv-1908.08962 #autotrain_compatible #endpoints_compatible #region-us \n",
"### BibTeX entry and citation info"
] |
fill-mask | transformers | # Chinese Whole Word Masking RoBERTa Miniatures
## Model description
This is the set of 6 Chinese Whole Word Masking RoBERTa models pre-trained by [UER-py](https://github.com/dbiir/UER-py/), which is introduced in [this paper](https://arxiv.org/abs/1909.05658). Besides, the models could also be pre-trained by [Tencen... | {"language": "zh", "datasets": "CLUECorpusSmall", "widget": [{"text": "\u5317\u4eac\u662f[MASK]\u56fd\u7684\u9996\u90fd\u3002"}]} | uer/roberta-base-wwm-chinese-cluecorpussmall | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"zh",
"dataset:CLUECorpusSmall",
"arxiv:1909.05658",
"arxiv:2212.06385",
"arxiv:1908.08962",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-18T04:49:07+00:00 | [
"1909.05658",
"2212.06385",
"1908.08962"
] | [
"zh"
] | TAGS
#transformers #pytorch #bert #fill-mask #zh #dataset-CLUECorpusSmall #arxiv-1909.05658 #arxiv-2212.06385 #arxiv-1908.08962 #autotrain_compatible #endpoints_compatible #region-us
| Chinese Whole Word Masking RoBERTa Miniatures
=============================================
Model description
-----------------
This is the set of 6 Chinese Whole Word Masking RoBERTa models pre-trained by UER-py, which is introduced in this paper. Besides, the models could also be pre-trained by TencentPretrain in... | [
"### BibTeX entry and citation info"
] | [
"TAGS\n#transformers #pytorch #bert #fill-mask #zh #dataset-CLUECorpusSmall #arxiv-1909.05658 #arxiv-2212.06385 #arxiv-1908.08962 #autotrain_compatible #endpoints_compatible #region-us \n",
"### BibTeX entry and citation info"
] |
fill-mask | transformers | # Chinese Whole Word Masking RoBERTa Miniatures
## Model description
This is the set of 6 Chinese Whole Word Masking RoBERTa models pre-trained by [UER-py](https://github.com/dbiir/UER-py/), which is introduced in [this paper](https://arxiv.org/abs/1909.05658). Besides, the models could also be pre-trained by [Tencen... | {"language": "zh", "datasets": "CLUECorpusSmall", "widget": [{"text": "\u5317\u4eac\u662f[MASK]\u56fd\u7684\u9996\u90fd\u3002"}]} | uer/roberta-large-wwm-chinese-cluecorpussmall | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"zh",
"dataset:CLUECorpusSmall",
"arxiv:1909.05658",
"arxiv:2212.06385",
"arxiv:1908.08962",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-18T04:51:04+00:00 | [
"1909.05658",
"2212.06385",
"1908.08962"
] | [
"zh"
] | TAGS
#transformers #pytorch #bert #fill-mask #zh #dataset-CLUECorpusSmall #arxiv-1909.05658 #arxiv-2212.06385 #arxiv-1908.08962 #autotrain_compatible #endpoints_compatible #region-us
| Chinese Whole Word Masking RoBERTa Miniatures
=============================================
Model description
-----------------
This is the set of 6 Chinese Whole Word Masking RoBERTa models pre-trained by UER-py, which is introduced in this paper. Besides, the models could also be pre-trained by TencentPretrain in... | [
"### BibTeX entry and citation info"
] | [
"TAGS\n#transformers #pytorch #bert #fill-mask #zh #dataset-CLUECorpusSmall #arxiv-1909.05658 #arxiv-2212.06385 #arxiv-1908.08962 #autotrain_compatible #endpoints_compatible #region-us \n",
"### BibTeX entry and citation info"
] |
automatic-speech-recognition | transformers | # Fine-tuned XLSR-53 large model for speech recognition in Chinese
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Chinese using the train and validation splits of [Common Voice 6.1](https://huggingface.co/datasets/common_voice), [CSS10](https://github.com/Kyubyo... | {"language": "zh", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice"], "metrics": ["wer", "cer"], "model-index": [{"name": "XLSR Wav2Vec2 Chinese (zh-CN) by wbbbbb", "results": [{"task": {"type": "automatic-speech-recognition", "na... | wbbbbb/wav2vec2-large-chinese-zh-cn | null | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"wav2vec2",
"pretraining",
"audio",
"automatic-speech-recognition",
"speech",
"xlsr-fine-tuning-week",
"zh",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-07-18T05:21:56+00:00 | [] | [
"zh"
] | TAGS
#transformers #pytorch #tensorboard #safetensors #wav2vec2 #pretraining #audio #automatic-speech-recognition #speech #xlsr-fine-tuning-week #zh #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us
| Fine-tuned XLSR-53 large model for speech recognition in Chinese
================================================================
Fine-tuned facebook/wav2vec2-large-xlsr-53 on Chinese using the train and validation splits of Common Voice 6.1, CSS10 and ST-CMDS.
When using this model, make sure that your speech input ... | [] | [
"TAGS\n#transformers #pytorch #tensorboard #safetensors #wav2vec2 #pretraining #audio #automatic-speech-recognition #speech #xlsr-fine-tuning-week #zh #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us \n"
] |
reinforcement-learning | stable-baselines3 |
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 ... | {"library_name": "stable-baselines3", "tags": ["LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "LunarLander-v2", "type": "LunarL... | Yuan99/ppo-LunarLander-v1 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-07-18T05:38:12+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 | WORK IN PROGRESS AND NOT YET RELEASED! I'm still working out the optimum settings :) | {"license": "cc-by-sa-4.0"} | KaliYuga/textilediffusion | null | [
"license:cc-by-sa-4.0",
"region:us"
] | null | 2022-07-18T06:21:23+00:00 | [] | [] | TAGS
#license-cc-by-sa-4.0 #region-us
| WORK IN PROGRESS AND NOT YET RELEASED! I'm still working out the optimum settings :) | [] | [
"TAGS\n#license-cc-by-sa-4.0 #region-us \n"
] |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["emotion"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "distilbert-base-uncased-finetuned-emotion", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "emotion", "type": "emotion... | fumakurata/distilbert-base-uncased-finetuned-emotion | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-18T06:22:20+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-emotion
=========================================
This model is a fine-tuned version of distilbert-base-uncased on the emotion dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5401
* Accuracy: 0.834
* F1: 0.8172
Model description
-----------------
Mor... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 192\n* eval\\_batch\\_size: 192\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 #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learn... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["emotion"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "distilbert-base-uncased-finetuned-emotion", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "emotion", "type": "emotion... | Prafuld3/distilbert-base-uncased-finetuned-emotion | null | [
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"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-18T06:29:28+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-emotion
=========================================
This model is a fine-tuned version of distilbert-base-uncased on the emotion dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2185
* Accuracy: 0.923
* F1: 0.9232
Model description
-----------------
Mor... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learn... |
text-generation | transformers |
# <span style="color:red"><b>WARNING:</b> This is an <b>intermediary checkpoint</b> and WIP project. It is not fully trained yet. You might want to use [Bloom-1B3](https://huggingface.co/bigscience/bloom-1b3) if you want a model that has completed training. This model is a distilled version of [Bloom-1B3](https://hugg... | {"language": ["ak", "ar", "as", "bm", "bn", "ca", "code", "en", "es", "eu", "fon", "fr", "gu", "hi", "id", "ig", "ki", "kn", "lg", "ln", "ml", "mr", "ne", "nso", "ny", "or", "pa", "pt", "rn", "rw", "sn", "st", "sw", "ta", "te", "tn", "ts", "tum", "tw", "ur", "vi", "wo", "xh", "yo", "zh", "zhs", "zht", "zu"], "license":... | bigscience/distill-bloom-1b3 | null | [
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... | null | 2022-07-18T06:59:47+00:00 | [
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... | TAGS
#transformers #pytorch #safetensors #bloom #text-generation #ak #ar #as #bm #bn #ca #code #en #es #eu #fon #fr #gu #hi #id #ig #ki #kn #lg #ln #ml #mr #ne #nso #ny #or #pa #pt #rn #rw #sn #st #sw #ta #te #tn #ts #tum #tw #ur #vi #wo #xh #yo #zh #zhs #zht #zu #arxiv-1909.08053 #arxiv-2110.02861 #arxiv-2108.12409 #l... | **WARNING:** This is an **intermediary checkpoint** and WIP project. It is not fully trained yet. You might want to use Bloom-1B3 if you want a model that has completed training. This model is a distilled version of Bloom-1B3
==============================================================================================... | [
"### Model Card\n\n\n\nVersion 1.0 / 18.Jul.2022\n\n\nTable of Contents\n-----------------\n\n\n1. Model Details\n2. Uses\n3. Training Data\n4. Risks and Limitations\n5. Evaluation\n6. Recommendations\n7. Glossary and Calculations\n8. More Information\n9. Model Card Authors\n\n\nModel Details\n--------... | [
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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-pegasus-finetuned
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluat... | {"tags": ["generated_from_trainer"], "metrics": ["sacrebleu"], "model-index": [{"name": "t5-pegasus-finetuned", "results": []}]} | fqw/t5-pegasus-finetuned | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-18T07:01:38+00:00 | [] | [] | TAGS
#transformers #pytorch #bart #text2text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
|
# t5-pegasus-finetuned
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 4.8469
- Sacrebleu: 1.6544
- Rouge 1: 0.0646
- Rouge 2: 0.0076
- Rouge L: 0.0629
- Bleu 1: 0.2040
- Bleu 2: 0.0959
- Bleu 3: 0.0484
- Bleu 4: 0.0287
- Meteor: 0.0872
- G... | [
"# t5-pegasus-finetuned\n\nThis model was trained from scratch on an unknown dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 4.8469\n- Sacrebleu: 1.6544\n- Rouge 1: 0.0646\n- Rouge 2: 0.0076\n- Rouge L: 0.0629\n- Bleu 1: 0.2040\n- Bleu 2: 0.0959\n- Bleu 3: 0.0484\n- Bleu 4: 0.0287\n- Met... | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"# t5-pegasus-finetuned\n\nThis model was trained from scratch on an unknown dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 4.8469\n- Sacrebleu... |
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-wandb-week-3-complaints-classifier-1500
This model is a fine-tuned version of [distilbert-base-uncased](... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["consumer-finance-complaints"], "model-index": [{"name": "distilbert-base-uncased-wandb-week-3-complaints-classifier-1500", "results": []}]} | Kayvane/distilbert-base-uncased-wandb-week-3-complaints-classifier-1500 | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:consumer-finance-complaints",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-18T07:15:34+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-consumer-finance-complaints #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# distilbert-base-uncased-wandb-week-3-complaints-classifier-1500
This model is a fine-tuned version of distilbert-base-uncased on the consumer-finance-complaints dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More i... | [
"# distilbert-base-uncased-wandb-week-3-complaints-classifier-1500\n\nThis model is a fine-tuned version of distilbert-base-uncased on the consumer-finance-complaints dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and eva... | [
"TAGS\n#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-consumer-finance-complaints #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# distilbert-base-uncased-wandb-week-3-complaints-classifier-1500\n\nThis model is a fine-tuned version of di... |
text-generation | transformers |
# <span style="color:red"><b>WARNING:</b> This is an <b>intermediary checkpoint</b> and WIP project. It is not fully trained yet. You might want to use [Bloom-1B3](https://huggingface.co/bigscience/bloom-1b3) if you want a model that has completed training. This model is a distilled version of [Bloom-1B3](https://hugg... | {"language": ["ak", "ar", "as", "bm", "bn", "ca", "code", "en", "es", "eu", "fon", "fr", "gu", "hi", "id", "ig", "ki", "kn", "lg", "ln", "ml", "mr", "ne", "nso", "ny", "or", "pa", "pt", "rn", "rw", "sn", "st", "sw", "ta", "te", "tn", "ts", "tum", "tw", "ur", "vi", "wo", "xh", "yo", "zh", "zhs", "zht", "zu"], "license":... | bigscience/distill-bloom-1b3-10x | null | [
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... | null | 2022-07-18T07:49:12+00:00 | [
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... | TAGS
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===========================================================================... | [
"### Model Card\n\n\n\nVersion 1.0 / 18.Jul.2022\n\n\nTable of Contents\n-----------------\n\n\n1. Model Details\n2. Uses\n3. Training Data\n4. Risks and Limitations\n5. Evaluation\n6. Recommendations\n7. Glossary and Calculations\n8. More Information\n9. Model Card Authors\n\n\nModel Details\n--------... | [
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image-classification | transformers |
# housing-categories
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/h... | {"tags": ["image-classification", "pytorch", "huggingpics"], "metrics": ["accuracy"]} | Albe/housing-categories | null | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-18T07:57:44+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# housing-categories
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
#### caravan
!caravan
#### castle
!castle
#### farm
!farm
#### tree house
!tree house
#### yurt
!y... | [
"# housing-categories\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",
"#### caravan\n\n!caravan",
"#### castle\n\n!castle",
"#### farm\n\n!farm",
"#### tre... | [
"TAGS\n#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# housing-categories\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nReport any i... |
token-classification | transformers |
This model is a BERT-based Location Mention Recognition model that is adopted from the [TLLMR4CM GitHub](https://github.com/rsuwaileh/TLLMR4CM/). The model identifies the toponyms' spans in text and predicts their location types. The location type can be coarse-grained (e.g., country, city, etc.) and fine-grained (e.g... | {"license": "apache-2.0"} | rsuwaileh/IDRISI-LMR-EN-random-typebased | null | [
"transformers",
"pytorch",
"bert",
"token-classification",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-18T08:01:01+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #token-classification #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
This model is a BERT-based Location Mention Recognition model that is adopted from the TLLMR4CM GitHub. The model identifies the toponyms' spans in text and predicts their location types. The location type can be coarse-grained (e.g., country, city, etc.) and fine-grained (e.g., street, POI, etc.)
The model is traine... | [] | [
"TAGS\n#transformers #pytorch #bert #token-classification #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# TestZee/t5-small-finetuned-kaggle-data-t5-v2.0
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "TestZee/t5-small-finetuned-kaggle-data-t5-v2.0", "results": []}]} | TestZee/t5-small-finetuned-kaggle-data-t5-v2.0 | null | [
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"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-18T08:03:47+00:00 | [] | [] | TAGS
#transformers #tf #tensorboard #t5 #text2text-generation #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| TestZee/t5-small-finetuned-kaggle-data-t5-v2.0
==============================================
This model is a fine-tuned version of t5-small on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 1.8648
* Validation Loss: 1.7172
* Train Rouge1: 24.1639
* Train Rouge2: 13.1314
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': 2e-05, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight\\_decay\\_rate': 0.01}\n* training\\_precision: float32",
... | [
"TAGS\n#transformers #tf #tensorboard #t5 #text2text-generation #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# CV_bn_trained_on_Validation
This model is a fine-tuned version of [./content/CV_bn_trained_on_Validation/checkpoint-6000](https:... | {"tags": ["generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "CV_bn_trained_on_Validation", "results": []}]} | Lancelot53/CV_bn_trained_on_Validation | null | [
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"tensorboard",
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"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"endpoints_compatible",
"region:us"
] | null | 2022-07-18T08:06:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #endpoints_compatible #region-us
| CV\_bn\_trained\_on\_Validation
===============================
This model is a fine-tuned version of ./content/CV\_bn\_trained\_on\_Validation/checkpoint-6000 on the common\_voice dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2181
* Wer: 0.3385
Model description
-----------------
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 7.5e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 7.5e-05\n* train\\_batch\\_size... |
fill-mask | transformers | # Neural Language Models for Nineteenth-Century English: bert_1760_1850
## Introduction
BERT model trained on a large historical dataset of books in English, published between 1760-1850 and comprised of ~1.3 billion tokens.
- Data paper: http://doi.org/10.5334/johd.48
- Github repository: https://github.com/Living-... | {} | Livingwithmachines/bert_1760_1850 | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-18T08:22:18+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| # Neural Language Models for Nineteenth-Century English: bert_1760_1850
## Introduction
BERT model trained on a large historical dataset of books in English, published between 1760-1850 and comprised of ~1.3 billion tokens.
- Data paper: URL
- Github repository: URL
## License
The models are released under open l... | [
"# Neural Language Models for Nineteenth-Century English: bert_1760_1850",
"## Introduction\n\nBERT model trained on a large historical dataset of books in English, published between 1760-1850 and comprised of ~1.3 billion tokens. \n\n- Data paper: URL\n- Github repository: URL",
"## License\n\nThe models are r... | [
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"# Neural Language Models for Nineteenth-Century English: bert_1760_1850",
"## Introduction\n\nBERT model trained on a large historical dataset of books in English, published between 1760-1850 and comprise... |
token-classification | transformers |
This model is a BERT-based Location Mention Recognition model that is adopted from the [TLLMR4CM GitHub](https://github.com/rsuwaileh/TLLMR4CM/). The model identifies the toponyms' spans in the text without predicting their location types.
The model is trained using the training splits of all events from [IDRISI-R d... | {"license": "apache-2.0"} | rsuwaileh/IDRISI-LMR-EN-random-typeless | null | [
"transformers",
"pytorch",
"bert",
"token-classification",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-18T08:22:59+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #token-classification #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
This model is a BERT-based Location Mention Recognition model that is adopted from the TLLMR4CM GitHub. The model identifies the toponyms' spans in the text without predicting their location types.
The model is trained using the training splits of all events from IDRISI-R dataset under the 'Type-less' LMR mode and u... | [] | [
"TAGS\n#transformers #pytorch #bert #token-classification #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
token-classification | transformers |
This model is a BERT-based Location Mention Recognition model that is adopted from the [TLLMR4CM GitHub](https://github.com/rsuwaileh/TLLMR4CM/). The model identifies the toponyms' spans in the text without predicting their location types.
The model is trained using the training splits of all events from [IDRISI-R d... | {"license": "apache-2.0"} | rsuwaileh/IDRISI-LMR-EN-timebased-typeless | null | [
"transformers",
"pytorch",
"bert",
"token-classification",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-18T08:25:35+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #token-classification #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
This model is a BERT-based Location Mention Recognition model that is adopted from the TLLMR4CM GitHub. The model identifies the toponyms' spans in the text without predicting their location types.
The model is trained using the training splits of all events from IDRISI-R dataset under the 'Type-less' LMR mode and u... | [] | [
"TAGS\n#transformers #pytorch #bert #token-classification #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
token-classification | transformers |
This model is a BERT-based Location Mention Recognition model that is adopted from the [TLLMR4CM GitHub](https://github.com/rsuwaileh/TLLMR4CM/). The model identifies the toponyms' spans in the text and predicts their location types. The location type can be coarse-grained (e.g., country, city, etc.) and fine-grained ... | {"license": "apache-2.0"} | rsuwaileh/IDRISI-LMR-EN-timebased-typebased | null | [
"transformers",
"pytorch",
"bert",
"token-classification",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-18T08:27:01+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #token-classification #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
This model is a BERT-based Location Mention Recognition model that is adopted from the TLLMR4CM GitHub. The model identifies the toponyms' spans in the text and predicts their location types. The location type can be coarse-grained (e.g., country, city, etc.) and fine-grained (e.g., street, POI, etc.)
The model is tr... | [] | [
"TAGS\n#transformers #pytorch #bert #token-classification #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
fill-mask | transformers | # Neural Language Models for Nineteenth-Century English: bert_1760_1900
## Introduction
BERT model trained on a large historical dataset of books in English, published between 1760-1900 and comprised of ~5.1 billion tokens.
- Data paper: http://doi.org/10.5334/johd.48
- Github repository: https://github.com/Living-... | {} | Livingwithmachines/bert_1760_1900 | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-18T08:28:01+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| # Neural Language Models for Nineteenth-Century English: bert_1760_1900
## Introduction
BERT model trained on a large historical dataset of books in English, published between 1760-1900 and comprised of ~5.1 billion tokens.
- Data paper: URL
- Github repository: URL
## License
The models are released under open l... | [
"# Neural Language Models for Nineteenth-Century English: bert_1760_1900",
"## Introduction\n\nBERT model trained on a large historical dataset of books in English, published between 1760-1900 and comprised of ~5.1 billion tokens. \n\n- Data paper: URL\n- Github repository: URL",
"## License\n\nThe models are r... | [
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"## Introduction\n\nBERT model trained on a large historical dataset of books in English, published between 1760-1900 and comprise... |
fill-mask | transformers | # Neural Language Models for Nineteenth-Century English: bert_1850_1875
## Introduction
BERT model trained on a large historical dataset of books in English, published between 1850-1875 and comprised of ~1.3 billion tokens.
- Data paper: http://doi.org/10.5334/johd.48
- Github repository: https://github.com/Living-... | {} | Livingwithmachines/bert_1850_1875 | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-18T08:31:40+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| # Neural Language Models for Nineteenth-Century English: bert_1850_1875
## Introduction
BERT model trained on a large historical dataset of books in English, published between 1850-1875 and comprised of ~1.3 billion tokens.
- Data paper: URL
- Github repository: URL
## License
The models are released under open l... | [
"# Neural Language Models for Nineteenth-Century English: bert_1850_1875",
"## Introduction\n\nBERT model trained on a large historical dataset of books in English, published between 1850-1875 and comprised of ~1.3 billion tokens. \n\n- Data paper: URL\n- Github repository: URL",
"## License\n\nThe models are r... | [
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"## Introduction\n\nBERT model trained on a large historical dataset of books in English, published between 1850-1875 and comprise... |
fill-mask | transformers | # Neural Language Models for Nineteenth-Century English: bert_1875_1890
## Introduction
BERT model trained on a large historical dataset of books in English, published between 1875-1890 and comprised of ~1.3 billion tokens.
- Data paper: http://doi.org/10.5334/johd.48
- Github repository: https://github.com/Living-... | {} | Livingwithmachines/bert_1875_1890 | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-18T08:35:12+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| # Neural Language Models for Nineteenth-Century English: bert_1875_1890
## Introduction
BERT model trained on a large historical dataset of books in English, published between 1875-1890 and comprised of ~1.3 billion tokens.
- Data paper: URL
- Github repository: URL
## License
The models are released under open l... | [
"# Neural Language Models for Nineteenth-Century English: bert_1875_1890",
"## Introduction\n\nBERT model trained on a large historical dataset of books in English, published between 1875-1890 and comprised of ~1.3 billion tokens. \n\n- Data paper: URL\n- Github repository: URL",
"## License\n\nThe models are r... | [
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"# Neural Language Models for Nineteenth-Century English: bert_1875_1890",
"## Introduction\n\nBERT model trained on a large historical dataset of books in English, published between 1875-1890 and comprise... |
fill-mask | transformers | # Neural Language Models for Nineteenth-Century English: bert_1890_1900
## Introduction
BERT model trained on a large historical dataset of books in English, published between 1890-1900 and comprised of ~1.1 billion tokens.
- Data paper: http://doi.org/10.5334/johd.48
- Github repository: https://github.com/Living-... | {} | Livingwithmachines/bert_1890_1900 | null | [
"transformers",
"pytorch",
"safetensors",
"bert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-18T08:38:46+00:00 | [] | [] | TAGS
#transformers #pytorch #safetensors #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| # Neural Language Models for Nineteenth-Century English: bert_1890_1900
## Introduction
BERT model trained on a large historical dataset of books in English, published between 1890-1900 and comprised of ~1.1 billion tokens.
- Data paper: URL
- Github repository: URL
## License
The models are released under open l... | [
"# Neural Language Models for Nineteenth-Century English: bert_1890_1900",
"## Introduction\n\nBERT model trained on a large historical dataset of books in English, published between 1890-1900 and comprised of ~1.1 billion tokens. \n\n- Data paper: URL\n- Github repository: URL",
"## License\n\nThe models are r... | [
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"# Neural Language Models for Nineteenth-Century English: bert_1890_1900",
"## Introduction\n\nBERT model trained on a large historical dataset of books in English, published between 1890-1900... |
text-classification | transformers | distilbert Binary Text Classifier
This distilbert based text classification model trained on imdb dataset performs binary sentiment classification on any given sentence.
The model has been fine tuned for better results in manageable time frames.
LABEL0 - Negative
LABEL1 - Positive | {"license": "afl-3.0"} | hassan4830/distil-bert-uncased-finetuned-english | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-18T08:42:35+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #text-classification #license-afl-3.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert Binary Text Classifier
This distilbert based text classification model trained on imdb dataset performs binary sentiment classification on any given sentence.
The model has been fine tuned for better results in manageable time frames.
LABEL0 - Negative
LABEL1 - Positive | [] | [
"TAGS\n#transformers #pytorch #distilbert #text-classification #license-afl-3.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-squad
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/d... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad_v2"], "model-index": [{"name": "distilbert-base-uncased-finetuned-squad", "results": []}]} | MMVos/distilbert-base-uncased-finetuned-squad | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:squad_v2",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-07-18T08:52:16+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-squad_v2 #license-apache-2.0 #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-squad
=======================================
This model is a fine-tuned version of distilbert-base-uncased on the squad\_v2 dataset.
It achieves the following results on the evaluation set:
* Loss: 1.4214
Model description
-----------------
More information needed
Intended u... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-squad_v2 #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# dpovedano/distilbert-base-uncased-finetuned-ner
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "dpovedano/distilbert-base-uncased-finetuned-ner", "results": []}]} | dpovedano/distilbert-base-uncased-finetuned-ner | null | [
"transformers",
"tf",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-18T09:05:44+00:00 | [] | [] | TAGS
#transformers #tf #tensorboard #distilbert #token-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| dpovedano/distilbert-base-uncased-finetuned-ner
===============================================
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.0285
* Validation Loss: 0.0612
* Train Precision: 0.9222
* Tra... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_steps': 2631, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': ... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightD... |
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. -->
# bert-base-cased_conll2003-CRF-first-ner
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["conll2003"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "bert-base-cased_conll2003-CRF-first-ner", "results": []}]} | jordyvl/bert-base-cased_conll2003-CRF-first-ner | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"generated_from_trainer",
"dataset:conll2003",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-07-18T09:50:20+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #endpoints_compatible #region-us
| bert-base-cased\_conll2003-CRF-first-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: 0.0546
* Precision: 0.6483
* Recall: 0.3940
* F1: 0.4902
* Accuracy: 0.9225
Model descr... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 2\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: ... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 2\n* eval\\_batch... |
text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# opt-350m-finetuned-stack
This model is a fine-tuned version of [facebook/opt-350m](https://huggingface.co/facebook/opt-350m) on ... | {"license": "other", "tags": ["generated_from_trainer"], "model-index": [{"name": "opt-350m-finetuned-stack", "results": []}]} | pritoms/opt-350m-finetuned-stack | null | [
"transformers",
"pytorch",
"tensorboard",
"opt",
"text-generation",
"generated_from_trainer",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-18T09:53:56+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #opt #text-generation #generated_from_trainer #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# opt-350m-finetuned-stack
This model is a fine-tuned version of facebook/opt-350m on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparamete... | [
"# opt-350m-finetuned-stack\n\nThis model is a fine-tuned version of facebook/opt-350m on the None dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",... | [
"TAGS\n#transformers #pytorch #tensorboard #opt #text-generation #generated_from_trainer #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# opt-350m-finetuned-stack\n\nThis model is a fine-tuned version of facebook/opt-350m on the None dataset.",
"## Model d... |
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... | julmarti/ppo-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-07-18T10:06:23+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 |
<!-- 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. -->
# hf-model-0
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on th... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "hf-model-0", "results": []}]} | semy/hf-model-0 | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-18T10:20:24+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| hf-model-0
==========
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: 0.7158
* Accuracy: 0.45
* F1: 0.45
Model description
-----------------
More information needed
Intended uses & limitations
-----------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Traini... | [
"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: ... |
text-classification | generic |
# Text Classification repository template
This is a template repository for Text Classification to support generic inference with Hugging Face Hub generic Inference API. There are two required steps:
1. Specify the requirements by defining a `requirements.txt` file.
2. Implement the `pipeline.py` `__init__` and `__c... | {"library_name": "generic", "tags": ["text-classification"]} | philschmid/custom-pipeline-text-classification | null | [
"generic",
"text-classification",
"region:us"
] | null | 2022-07-18T11:21:29+00:00 | [] | [] | TAGS
#generic #text-classification #region-us
|
# Text Classification repository template
This is a template repository for Text Classification to support generic inference with Hugging Face Hub generic Inference API. There are two required steps:
1. Specify the requirements by defining a 'URL' file.
2. Implement the 'URL' '__init__' and '__call__' methods. These... | [
"# Text Classification repository template\n\nThis is a template repository for Text Classification to support generic inference with Hugging Face Hub generic Inference API. There are two required steps:\n\n1. Specify the requirements by defining a 'URL' file.\n2. Implement the 'URL' '__init__' and '__call__' metho... | [
"TAGS\n#generic #text-classification #region-us \n",
"# Text Classification repository template\n\nThis is a template repository for Text Classification to support generic inference with Hugging Face Hub generic Inference API. There are two required steps:\n\n1. Specify the requirements by defining a 'URL' file.\... |
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-xsum-finetuned-paws-parasci
This model is a fine-tuned version of [domenicrosati/pegasus-xsum-finetuned-paws](https://hu... | {"tags": ["paraphrasing", "generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "pegasus-xsum-finetuned-paws-parasci", "results": []}]} | domenicrosati/pegasus-xsum-finetuned-paws-parasci | null | [
"transformers",
"pytorch",
"tensorboard",
"pegasus",
"text2text-generation",
"paraphrasing",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-18T11:24:37+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #pegasus #text2text-generation #paraphrasing #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| pegasus-xsum-finetuned-paws-parasci
===================================
This model is a fine-tuned version of domenicrosati/pegasus-xsum-finetuned-paws on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 3.2256
* Rouge1: 61.8854
* Rouge2: 43.1061
* Rougel: 57.421
* Rougelsum: 57.... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 4000\n* mixed... | [
"TAGS\n#transformers #pytorch #tensorboard #pegasus #text2text-generation #paraphrasing #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\\_... |
text-classification | transformers |
# bert-multilingual-uncased-intelligence-headlines
This a bert-base-multilingual-uncased model fine-tuned to perform classification of news headlines according to an intelligence taxonomy.
### Authors
The [NLP Odyssey](https://github.com/nlpodyssey/) Authors | {"language": ["multilingual", "af", "sq", "ar", "an", "hy", "ast", "az", "ba", "eu", "bar", "be", "bn", "inc", "bs", "br", "bg", "my", "ca", "ceb", "ce", "zh", "cv", "hr", "cs", "da", "nl", "en", "et", "fi", "fr", "gl", "ka", "de", "el", "gu", "ht", "he", "hi", "hu", "is", "io", "id", "ga", "it", "ja", "jv", "kn", "kk"... | nlpodyssey/bert-multilingual-uncased-intelligence-headlines | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"multilingual",
"af",
"sq",
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"ast",
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"ceb",
"ce",
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"cv",
"hr",
"cs",
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"en",
"et",
"fi"... | null | 2022-07-18T11:30:34+00:00 | [] | [
"multilingual",
"af",
"sq",
"ar",
"an",
"hy",
"ast",
"az",
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"eu",
"bar",
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"ce",
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"cv",
"hr",
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"da",
"nl",
"en",
"et",
"fi",
"fr",
"gl",
"ka",
"de",
"el",
"gu",
"ht",
"he",
... | TAGS
#transformers #pytorch #bert #text-classification #multilingual #af #sq #ar #an #hy #ast #az #ba #eu #bar #be #bn #inc #bs #br #bg #my #ca #ceb #ce #zh #cv #hr #cs #da #nl #en #et #fi #fr #gl #ka #de #el #gu #ht #he #hi #hu #is #io #id #ga #it #ja #jv #kn #kk #ky #ko #la #lv #lt #roa #nds #lm #mk #mg #ms #ml #mr #... |
# bert-multilingual-uncased-intelligence-headlines
This a bert-base-multilingual-uncased model fine-tuned to perform classification of news headlines according to an intelligence taxonomy.
### Authors
The NLP Odyssey Authors | [
"# bert-multilingual-uncased-intelligence-headlines\n\nThis a bert-base-multilingual-uncased model fine-tuned to perform classification of news headlines according to an intelligence taxonomy.",
"### Authors\n\nThe NLP Odyssey Authors"
] | [
"TAGS\n#transformers #pytorch #bert #text-classification #multilingual #af #sq #ar #an #hy #ast #az #ba #eu #bar #be #bn #inc #bs #br #bg #my #ca #ceb #ce #zh #cv #hr #cs #da #nl #en #et #fi #fr #gl #ka #de #el #gu #ht #he #hi #hu #is #io #id #ga #it #ja #jv #kn #kk #ky #ko #la #lv #lt #roa #nds #lm #mk #mg #ms #ml... |
text-classification | transformers |
# Finetuned-Distilbert-needmining (uncased)
This model is a finetuned version of the [Distilbert base model](https://huggingface.co/distilbert-base-uncased). It was
trained to predict need-containing sentences from amazon product reviews.
## Model description
This mode is part of ongoing research, after the publica... | {"language": "en", "license": "apache-2.0", "tags": ["distilbert", "needmining"], "metric": ["f1"]} | svenstahlmann/finetuned-distilbert-needmining | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"needmining",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-18T11:50:37+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #distilbert #text-classification #needmining #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| Finetuned-Distilbert-needmining (uncased)
=========================================
This model is a finetuned version of the Distilbert base model. It was
trained to predict need-containing sentences from amazon product reviews.
Model description
-----------------
This mode is part of ongoing research, after the ... | [
"### How to use\n\n\nYou can use this model directly with a pipeline for text classification:",
"### Limitations and bias\n\n\nWe are not aware of any bias in the training data.\n\n\nTraining data\n-------------\n\n\nThe training was done on a dataset of 6400 sentences. The sentences were taken from product revie... | [
"TAGS\n#transformers #pytorch #distilbert #text-classification #needmining #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### How to use\n\n\nYou can use this model directly with a pipeline for text classification:",
"### Limitations and bias\n\n\nWe are not aware of any bi... |
token-classification | transformers |
## Token Classification
Classifies Gro's items and metrics
| **tag** | **token** |
|---------------------------------|-----------|
|B-ITEM | BEGINNING ITEM|
|I-ITEM | INSIDE ITEM|
|B-METRIC |BEGINNING METRIC |
|I-METRIC | INSIDE METRIC|
|O | OUTSIDE |
---
### Training: Sc... | {"language": "en", "tags": ["bert", "token-classification", "sequence-tagger-model"], "widget": [{"text": "Total exports of maize"}]} | Wanjiru/autotrain_gro_ner | null | [
"transformers",
"pytorch",
"bert",
"token-classification",
"sequence-tagger-model",
"en",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-18T11:54:37+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bert #token-classification #sequence-tagger-model #en #autotrain_compatible #endpoints_compatible #region-us
| Token Classification
--------------------
Classifies Gro's items and metrics
---
### Training: Script to train this model
The following Flair script was used to train this model:
---
| [
"### Training: Script to train this model\n\n\nThe following Flair script was used to train this model:\n\n\n\n\n---"
] | [
"TAGS\n#transformers #pytorch #bert #token-classification #sequence-tagger-model #en #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training: Script to train this model\n\n\nThe following Flair script was used to train this model:\n\n\n\n\n---"
] |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# pegasus-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": []}]} | andreaschandra/pegasus-samsum | null | [
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"generated_from_trainer",
"dataset:samsum",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-18T12:58:11+00:00 | [] | [] | TAGS
#transformers #pytorch #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.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparam... | [
"# pegasus-samsum\n\nThis model is a fine-tuned version of google/pegasus-cnn_dailymail on the samsum dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedur... | [
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #generated_from_trainer #dataset-samsum #autotrain_compatible #endpoints_compatible #region-us \n",
"# pegasus-samsum\n\nThis model is a fine-tuned version of google/pegasus-cnn_dailymail on the samsum dataset.",
"## Model description\n\nMore informat... |
null | transformers |
# Nowcasting CNN
## Model description
3d conv model, that takes in different data streams
architecture is roughly
1. satellite image time series goes into many 3d convolution layers.
2. nwp time series goes into many 3d convolution layers.
3. Final convolutional layer goes to full co... | {"license": "mit", "tags": ["nowcasting", "forecasting", "timeseries", "remote-sensing"]} | openclimatefix/nowcasting_cnn_v3 | null | [
"transformers",
"pytorch",
"nowcasting",
"forecasting",
"timeseries",
"remote-sensing",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-07-18T14:51:49+00:00 | [] | [] | TAGS
#transformers #pytorch #nowcasting #forecasting #timeseries #remote-sensing #license-mit #endpoints_compatible #region-us
|
# Nowcasting CNN
## Model description
3d conv model, that takes in different data streams
architecture is roughly
1. satellite image time series goes into many 3d convolution layers.
2. nwp time series goes into many 3d convolution layers.
3. Final convolutional layer goes to full co... | [
"# Nowcasting CNN",
"## Model description\n\n3d conv model, that takes in different data streams\n\n architecture is roughly\n 1. satellite image time series goes into many 3d convolution layers.\n 2. nwp time series goes into many 3d convolution layers.\n 3. Final convolutional layer ... | [
"TAGS\n#transformers #pytorch #nowcasting #forecasting #timeseries #remote-sensing #license-mit #endpoints_compatible #region-us \n",
"# Nowcasting CNN",
"## Model description\n\n3d conv model, that takes in different data streams\n\n architecture is roughly\n 1. satellite image time series goes i... |
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-modelo-robertav0
This model is a fine-tuned version of [IIC/roberta-base-spanish-squades](https://h... | {"tags": ["generated_from_trainer"], "datasets": ["becasv2"], "model-index": [{"name": "roberta-base-spanish-squades-modelo-robertav0", "results": []}]} | Evelyn18/roberta-base-spanish-squades-modelo-robertav0 | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"question-answering",
"generated_from_trainer",
"dataset:becasv2",
"endpoints_compatible",
"region:us"
] | null | 2022-07-18T14:52:15+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #question-answering #generated_from_trainer #dataset-becasv2 #endpoints_compatible #region-us
| roberta-base-spanish-squades-modelo-robertav0
=============================================
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: 2.7628
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: 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",
"### Train... | [
"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: 2e-05\n* train\\_batch\\_size: 11\n* eval\\_bat... |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-squad_2_512_1
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingf... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad_v2"], "model-index": [{"name": "distilbert-base-uncased-finetuned-squad_2_512_1", "results": []}]} | raisinbl/distilbert-base-uncased-finetuned-squad_2_512_1 | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:squad_v2",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-07-18T15:03:11+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-squad_v2 #license-apache-2.0 #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-squad\_2\_512\_1
==================================================
This model is a fine-tuned version of distilbert-base-uncased on the squad\_v2 dataset.
It achieves the following results on the evaluation set:
* Loss: 1.3225
Model description
-----------------
More informati... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 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 #distilbert #question-answering #generated_from_trainer #dataset-squad_v2 #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\... |
reinforcement-learning | stable-baselines3 |
# **PPO** Agent playing **Walker2DBulletEnv-v0**
This is a trained model of a **PPO** agent playing **Walker2DBulletEnv-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 hugg... | {"library_name": "stable-baselines3", "tags": ["Walker2DBulletEnv-v0", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Walker2DBulletEnv-v0", "ty... | masterdezign/ppo-Walker2DBulletEnv-v0 | null | [
"stable-baselines3",
"Walker2DBulletEnv-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-07-18T15:03:33+00:00 | [] | [] | TAGS
#stable-baselines3 #Walker2DBulletEnv-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing Walker2DBulletEnv-v0
This is a trained model of a PPO agent playing Walker2DBulletEnv-v0
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# PPO Agent playing Walker2DBulletEnv-v0\nThis is a trained model of a PPO agent playing Walker2DBulletEnv-v0\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #Walker2DBulletEnv-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing Walker2DBulletEnv-v0\nThis is a trained model of a PPO agent playing Walker2DBulletEnv-v0\nusing the stable-baselines3 library.",
"## Usage (with Stable-baseline... |
reinforcement-learning | null |
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes ... | {"tags": ["FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-FrozenLake-v1-4x4-noSlippery", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "FrozenLake-v1-4x4-no_slippery", "type": ... | AliMMZ/q-FrozenLake-v1-4x4-noSlippery | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-07-18T15:07:09+00:00 | [] | [] | TAGS
#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing FrozenLake-v1
This is a trained model of a Q-Learning agent playing FrozenLake-v1 .
## Usage
| [
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] | [
"TAGS\n#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] |
reinforcement-learning | null |
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="AliMMZ/second_RL", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=Fa... | {"tags": ["FrozenLake-v1-8x8", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "second_RL", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "FrozenLake-v1-8x8", "type": "FrozenLake-v1-8x8"}, "metrics": [{"type": "m... | AliMMZ/second_RL | null | [
"FrozenLake-v1-8x8",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-07-18T15:09:41+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"
] |
reinforcement-learning | null |
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: https://github.com/huggingface/deep-rl-class/tree/main/unit5
| {"tags": ["CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class"], "model-index": [{"name": "CartPole", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "CartPole-v1", "type": "CartPole-v1"}, "metrics": [{"type": "mean... | bothrajat/CartPole | null | [
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | null | 2022-07-18T15:19:20+00:00 | [] | [] | TAGS
#CartPole-v1 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us
|
# Reinforce Agent playing CartPole-v1
This is a trained model of a Reinforce agent playing CartPole-v1 .
To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: URL
| [
"# Reinforce Agent playing CartPole-v1\n This is a trained model of a Reinforce agent playing CartPole-v1 .\n To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: URL"
] | [
"TAGS\n#CartPole-v1 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us \n",
"# Reinforce Agent playing CartPole-v1\n This is a trained model of a Reinforce agent playing CartPole-v1 .\n To learn to use this model and train yours check Unit 5 of the Deep Reinforcemen... |
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-wandb-week-3-complaints-classifier-1024
This model is a fine-tuned version of [distilbert-base-uncased](... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["consumer-finance-complaints"], "metrics": ["accuracy", "f1", "recall", "precision"], "model-index": [{"name": "distilbert-base-uncased-wandb-week-3-complaints-classifier-1024", "results": [{"task": {"type": "text-classification", "name": "Text ... | Kayvane/distilbert-base-uncased-wandb-week-3-complaints-classifier-1024 | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:consumer-finance-complaints",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-18T15:27:54+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-consumer-finance-complaints #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-wandb-week-3-complaints-classifier-1024
===============================================================
This model is a fine-tuned version of distilbert-base-uncased on the consumer-finance-complaints dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5664
* Accuracy... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2.9291066722689668e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler... | [
"TAGS\n#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-consumer-finance-complaints #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... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bart-finetuned-yelpreviews
This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base)... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "facebook/bart-base", "model-index": [{"name": "bart-finetuned-yelpreviews", "results": []}]} | eliolio/bart-finetuned-yelpreviews | null | [
"transformers",
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"generated_from_trainer",
"base_model:facebook/bart-base",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-07-18T15:33:55+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bart #text2text-generation #generated_from_trainer #base_model-facebook/bart-base #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| bart-finetuned-yelpreviews
==========================
This model is a fine-tuned version of facebook/bart-base on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 3.4346
Model description
-----------------
More information needed
Intended uses & limitations
----------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n... |
text-classification | transformers |
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 1148842296
- CO2 Emissions (in grams): 0.027846282970913613
## Validation Metrics
- Loss: 0.4816772937774658
- Accuracy: 0.864
- Macro F1: 0.865050349743783
- Micro F1: 0.864
- Weighted F1: 0.865050349743783
- Macro Precision: 0.... | {"language": "es", "tags": "xerox", "datasets": ["erixxdp/autotrain-data-gsemodel"], "widget": [{"text": "Debo de levantarme temprano para hacer ejercicio"}], "co2_eq_emissions": 0.027846282970913613} | erickdp/gs3n-roberta-model | null | [
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"es",
"dataset:erixxdp/autotrain-data-gsemodel",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-18T15:34:31+00:00 | [] | [
"es"
] | TAGS
#transformers #pytorch #roberta #text-classification #xerox #es #dataset-erixxdp/autotrain-data-gsemodel #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 1148842296
- CO2 Emissions (in grams): 0.027846282970913613
## Validation Metrics
- Loss: 0.4816772937774658
- Accuracy: 0.864
- Macro F1: 0.865050349743783
- Micro F1: 0.864
- Weighted F1: 0.865050349743783
- Macro Precision: 0.... | [
"# Model Trained Using AutoTrain\n\n- Problem type: Multi-class Classification\n- Model ID: 1148842296\n- CO2 Emissions (in grams): 0.027846282970913613",
"## Validation Metrics\n\n- Loss: 0.4816772937774658\n- Accuracy: 0.864\n- Macro F1: 0.865050349743783\n- Micro F1: 0.864\n- Weighted F1: 0.865050349743783\n- ... | [
"TAGS\n#transformers #pytorch #roberta #text-classification #xerox #es #dataset-erixxdp/autotrain-data-gsemodel #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoTrain\n\n- Problem type: Multi-class Classification\n- Model ID: 1148842296\n- CO2 Emissions (in ... |
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. -->
# distilroberta-base-wandb-week-3-complaints-classifier-1024
This model is a fine-tuned version of [distilroberta-base](https://hu... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["consumer-finance-complaints"], "metrics": ["accuracy", "f1", "recall", "precision"], "model-index": [{"name": "distilroberta-base-wandb-week-3-complaints-classifier-1024", "results": [{"task": {"type": "text-classification", "name": "Text Class... | Kayvane/distilroberta-base-wandb-week-3-complaints-classifier-1024 | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"generated_from_trainer",
"dataset:consumer-finance-complaints",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-18T16:43:13+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #text-classification #generated_from_trainer #dataset-consumer-finance-complaints #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilroberta-base-wandb-week-3-complaints-classifier-1024
==========================================================
This model is a fine-tuned version of distilroberta-base on the consumer-finance-complaints dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5351
* Accuracy: 0.8280
* F1: ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 9.027176214786854e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\... | [
"TAGS\n#transformers #pytorch #roberta #text-classification #generated_from_trainer #dataset-consumer-finance-complaints #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* l... |
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. -->
# topic_classification_01
This model is a fine-tuned version of [microsoft/xtremedistil-l6-h256-uncased](https://huggingface.co/microsof... | {"license": "mit", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "topic_classification_01", "results": []}]} | jonaskoenig/topic_classification_01 | null | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-18T16:58:13+00:00 | [] | [] | TAGS
#transformers #tf #bert #text-classification #generated_from_keras_callback #license-mit #autotrain_compatible #endpoints_compatible #region-us
| topic\_classification\_01
=========================
This model is a fine-tuned version of microsoft/xtremedistil-l6-h256-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.0306
* Train Binary Crossentropy: 0.5578
* Epoch: 9
Model description
-----------------
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'learning\\_rate': 3e-05, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}\n* training\\_precision: float32",
"### Training results",
"### Framework... | [
"TAGS\n#transformers #tf #bert #text-classification #generated_from_keras_callback #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'learning\\_rate': 3e-05, 'deca... |
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-modelo-robertav1
This model is a fine-tuned version of [IIC/roberta-base-spanish-squades](https://h... | {"tags": ["generated_from_trainer"], "datasets": ["becasv2"], "model-index": [{"name": "roberta-base-spanish-squades-modelo-robertav1", "results": []}]} | Evelyn18/roberta-base-spanish-squades-modelo-robertav1 | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"question-answering",
"generated_from_trainer",
"dataset:becasv2",
"endpoints_compatible",
"region:us"
] | null | 2022-07-18T17:53:50+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #question-answering #generated_from_trainer #dataset-becasv2 #endpoints_compatible #region-us
| roberta-base-spanish-squades-modelo-robertav1
=============================================
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 ... | [
"### 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... | [
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"### 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... |
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. -->
# bert-small2bert-small-finetuned-cnn_daily_mail-summarization-finetuned-multi-news
This model is a fine-tuned version of [mrm8488... | {"license": "apache-2.0", "tags": ["summarisation", "generated_from_trainer"], "datasets": ["multi_news"], "metrics": ["rouge"], "model-index": [{"name": "bert-small2bert-small-finetuned-cnn_daily_mail-summarization-finetuned-multi-news", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequen... | Atharvgarg/bert-small2bert-small-finetuned-cnn_daily_mail-summarization-finetuned-multi-news | null | [
"transformers",
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
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"generated_from_trainer",
"dataset:multi_news",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-18T18:13:08+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #encoder-decoder #text2text-generation #summarisation #generated_from_trainer #dataset-multi_news #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| bert-small2bert-small-finetuned-cnn\_daily\_mail-summarization-finetuned-multi-news
===================================================================================
This model is a fine-tuned version of mrm8488/bert-small2bert-small-finetuned-cnn\_daily\_mail-summarization on the multi\_news dataset.
It achieves t... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5.6e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 4",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #encoder-decoder #text2text-generation #summarisation #generated_from_trainer #dataset-multi_news #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used durin... |
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. -->
# bart-large-mnli-finetuned-emotion
This model is a fine-tuned version of [facebook/bart-large-mnli](https://huggingface.co/facebo... | {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "bart-large-mnli-finetuned-emotion", "results": []}]} | Eleven/bart-large-mnli-finetuned-emotion | null | [
"transformers",
"pytorch",
"tensorboard",
"bart",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-18T18:19:13+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bart #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# bart-large-mnli-finetuned-emotion
This model is a fine-tuned version of facebook/bart-large-mnli on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Trai... | [
"# bart-large-mnli-finetuned-emotion\n\nThis model is a fine-tuned version of facebook/bart-large-mnli 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",
"## Tr... | [
"TAGS\n#transformers #pytorch #tensorboard #bart #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# bart-large-mnli-finetuned-emotion\n\nThis model is a fine-tuned version of facebook/bart-large-mnli on an unknown dataset.",
"## Model descrip... |
unconditional-image-generation | diffusers |
# Denoising Diffusion Probabilistic Models (DDPM)
**Paper**: [Denoising Diffusion Probabilistic Models](https://arxiv.org/abs/2006.11239)
**Authors**: Jonathan Ho, Ajay Jain, Pieter Abbeel
**Abstract**:
*We present high quality image synthesis results using diffusion probabilistic models, a class of latent variabl... | {"license": "apache-2.0", "tags": ["pytorch", "diffusers", "unconditional-image-generation"]} | google/ddpm-ema-bedroom-256 | null | [
"diffusers",
"pytorch",
"unconditional-image-generation",
"arxiv:2006.11239",
"license:apache-2.0",
"diffusers:DDPMPipeline",
"region:us"
] | null | 2022-07-18T18:49:13+00:00 | [
"2006.11239"
] | [] | TAGS
#diffusers #pytorch #unconditional-image-generation #arxiv-2006.11239 #license-apache-2.0 #diffusers-DDPMPipeline #region-us
|
# Denoising Diffusion Probabilistic Models (DDPM)
Paper: Denoising Diffusion Probabilistic Models
Authors: Jonathan Ho, Ajay Jain, Pieter Abbeel
Abstract:
*We present high quality image synthesis results using diffusion probabilistic models, a class of latent variable models inspired by considerations from nonequi... | [
"# Denoising Diffusion Probabilistic Models (DDPM)\n\nPaper: Denoising Diffusion Probabilistic Models\n\nAuthors: Jonathan Ho, Ajay Jain, Pieter Abbeel\n\nAbstract:\n\n*We present high quality image synthesis results using diffusion probabilistic models, a class of latent variable models inspired by considerations ... | [
"TAGS\n#diffusers #pytorch #unconditional-image-generation #arxiv-2006.11239 #license-apache-2.0 #diffusers-DDPMPipeline #region-us \n",
"# Denoising Diffusion Probabilistic Models (DDPM)\n\nPaper: Denoising Diffusion Probabilistic Models\n\nAuthors: Jonathan Ho, Ajay Jain, Pieter Abbeel\n\nAbstract:\n\n*We prese... |
image-classification | timm |
# EfficientFormer-L1
## Table of Contents
- [EfficientFormer-L1](#-model_id--defaultmymodelname-true)
- [Table of Contents](#table-of-contents)
- [Model Details](#model-details)
- [How to Get Started with the Model](#how-to-get-started-with-the-model)
- [Uses](#uses)
- [Direct Use](#direct-use)
- ... | {"language": ["en"], "license": "apache-2.0", "library_name": "timm", "tags": ["mobile", "vison", "image-classification"], "datasets": ["imagenet-1k"], "metrics": ["accuracy"]} | NimaBoscarino/efficientformer-l1-300 | null | [
"timm",
"pytorch",
"coreml",
"onnx",
"mobile",
"vison",
"image-classification",
"en",
"dataset:imagenet-1k",
"arxiv:2206.01191",
"license:apache-2.0",
"region:us"
] | null | 2022-07-18T19:08:43+00:00 | [
"2206.01191"
] | [
"en"
] | TAGS
#timm #pytorch #coreml #onnx #mobile #vison #image-classification #en #dataset-imagenet-1k #arxiv-2206.01191 #license-apache-2.0 #region-us
|
# EfficientFormer-L1
## Table of Contents
- EfficientFormer-L1
- Table of Contents
- Model Details
- How to Get Started with the Model
- Uses
- Direct Use
- Downstream Use
- Misuse and Out-of-scope Use
- Limitations and Biases
- Training
- Training Data
- Training Procedure
... | [
"# EfficientFormer-L1",
"## Table of Contents\n- EfficientFormer-L1\n - Table of Contents\n - Model Details\n - How to Get Started with the Model\n - Uses\n - Direct Use\n - Downstream Use\n - Misuse and Out-of-scope Use\n - Limitations and Biases\n - Training\n - Training Data\n - ... | [
"TAGS\n#timm #pytorch #coreml #onnx #mobile #vison #image-classification #en #dataset-imagenet-1k #arxiv-2206.01191 #license-apache-2.0 #region-us \n",
"# EfficientFormer-L1",
"## Table of Contents\n- EfficientFormer-L1\n - Table of Contents\n - Model Details\n - How to Get Started with the Model\n - Uses\n... |
translation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# opus-mt-es-en-scielo
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-es-en](https://huggingface.co/Helsinki-NLP/opus... | {"license": "apache-2.0", "tags": ["translation", "generated_from_trainer"], "datasets": ["scielo"], "metrics": ["bleu"], "model-index": [{"name": "opus-mt-es-en-scielo", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "scielo", "type": "sciel... | domenicrosati/opus-mt-es-en-scielo | null | [
"transformers",
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"translation",
"generated_from_trainer",
"dataset:scielo",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-18T19:16:36+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #marian #text2text-generation #translation #generated_from_trainer #dataset-scielo #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| opus-mt-es-en-scielo
====================
This model is a fine-tuned version of Helsinki-NLP/opus-mt-es-en on the scielo dataset.
It achieves the following results on the evaluation set:
* Loss: 1.2593
* Bleu: 40.8788
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\n* mixed\\_pr... | [
"TAGS\n#transformers #pytorch #tensorboard #marian #text2text-generation #translation #generated_from_trainer #dataset-scielo #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... |
image-classification | timm |
# EfficientFormer-L3
## Table of Contents
- [EfficientFormer-L3](#-model_id--defaultmymodelname-true)
- [Table of Contents](#table-of-contents)
- [Model Details](#model-details)
- [How to Get Started with the Model](#how-to-get-started-with-the-model)
- [Uses](#uses)
- [Direct Use](#direct-use)
- ... | {"language": ["en"], "license": "apache-2.0", "library_name": "timm", "tags": ["mobile", "vison", "image-classification"], "datasets": ["imagenet-1k"], "metrics": ["accuracy"]} | NimaBoscarino/efficientformer-l3-300 | null | [
"timm",
"coreml",
"onnx",
"mobile",
"vison",
"image-classification",
"en",
"dataset:imagenet-1k",
"arxiv:2206.01191",
"license:apache-2.0",
"region:us"
] | null | 2022-07-18T19:31:10+00:00 | [
"2206.01191"
] | [
"en"
] | TAGS
#timm #coreml #onnx #mobile #vison #image-classification #en #dataset-imagenet-1k #arxiv-2206.01191 #license-apache-2.0 #region-us
|
# EfficientFormer-L3
## Table of Contents
- EfficientFormer-L3
- Table of Contents
- Model Details
- How to Get Started with the Model
- Uses
- Direct Use
- Downstream Use
- Misuse and Out-of-scope Use
- Limitations and Biases
- Training
- Training Data
- Training Procedure
... | [
"# EfficientFormer-L3",
"## Table of Contents\n- EfficientFormer-L3\n - Table of Contents\n - Model Details\n - How to Get Started with the Model\n - Uses\n - Direct Use\n - Downstream Use\n - Misuse and Out-of-scope Use\n - Limitations and Biases\n - Training\n - Training Data\n - ... | [
"TAGS\n#timm #coreml #onnx #mobile #vison #image-classification #en #dataset-imagenet-1k #arxiv-2206.01191 #license-apache-2.0 #region-us \n",
"# EfficientFormer-L3",
"## Table of Contents\n- EfficientFormer-L3\n - Table of Contents\n - Model Details\n - How to Get Started with the Model\n - Uses\n - D... |
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-wandb-week-3-complaints-classifier-512
This model is a fine-tuned version of [distilbert-base-uncased](h... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["consumer-finance-complaints"], "metrics": ["accuracy", "f1", "recall", "precision"], "model-index": [{"name": "distilbert-base-uncased-wandb-week-3-complaints-classifier-512", "results": [{"task": {"type": "text-classification", "name": "Text C... | Kayvane/distilbert-base-uncased-wandb-week-3-complaints-classifier-512 | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:consumer-finance-complaints",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-18T19:33:45+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-consumer-finance-complaints #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-wandb-week-3-complaints-classifier-512
==============================================================
This model is a fine-tuned version of distilbert-base-uncased on the consumer-finance-complaints dataset.
It achieves the following results on the evaluation set:
* Loss: 1.0839
* Accuracy: ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0007879237562376572\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\... | [
"TAGS\n#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-consumer-finance-complaints #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... |
image-classification | timm |
# EfficientFormer-L7
## Table of Contents
- [EfficientFormer-L7](#-model_id--defaultmymodelname-true)
- [Table of Contents](#table-of-contents)
- [Model Details](#model-details)
- [How to Get Started with the Model](#how-to-get-started-with-the-model)
- [Uses](#uses)
- [Direct Use](#direct-use)
- ... | {"language": ["en"], "license": "apache-2.0", "library_name": "timm", "tags": ["mobile", "vison", "image-classification"], "datasets": ["imagenet-1k"], "metrics": ["accuracy"]} | NimaBoscarino/efficientformer-l7-300 | null | [
"timm",
"coreml",
"onnx",
"mobile",
"vison",
"image-classification",
"en",
"dataset:imagenet-1k",
"arxiv:2206.01191",
"license:apache-2.0",
"region:us"
] | null | 2022-07-18T19:42:32+00:00 | [
"2206.01191"
] | [
"en"
] | TAGS
#timm #coreml #onnx #mobile #vison #image-classification #en #dataset-imagenet-1k #arxiv-2206.01191 #license-apache-2.0 #region-us
|
# EfficientFormer-L7
## Table of Contents
- EfficientFormer-L7
- Table of Contents
- Model Details
- How to Get Started with the Model
- Uses
- Direct Use
- Downstream Use
- Misuse and Out-of-scope Use
- Limitations and Biases
- Training
- Training Data
- Training Procedure
... | [
"# EfficientFormer-L7",
"## Table of Contents\n- EfficientFormer-L7\n - Table of Contents\n - Model Details\n - How to Get Started with the Model\n - Uses\n - Direct Use\n - Downstream Use\n - Misuse and Out-of-scope Use\n - Limitations and Biases\n - Training\n - Training Data\n - ... | [
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"# EfficientFormer-L7",
"## Table of Contents\n- EfficientFormer-L7\n - Table of Contents\n - Model Details\n - How to Get Started with the Model\n - Uses\n - D... |
text-to-image | diffusers |
# High-Resolution Image Synthesis with Latent Diffusion Models (LDM)
**Paper**: [High-Resolution Image Synthesis with Latent Diffusion Models (LDM)s](https://arxiv.org/abs/2112.10752)
**Abstract**:
*By decomposing the image formation process into a sequential application of denoising autoencoders, diffusion models... | {"license": "apache-2.0", "tags": ["pytorch", "diffusers", "text-to-image"]} | CompVis/ldm-text2im-large-256 | null | [
"diffusers",
"pytorch",
"text-to-image",
"arxiv:2112.10752",
"license:apache-2.0",
"has_space",
"diffusers:LDMTextToImagePipeline",
"region:us"
] | null | 2022-07-18T19:58:25+00:00 | [
"2112.10752"
] | [] | TAGS
#diffusers #pytorch #text-to-image #arxiv-2112.10752 #license-apache-2.0 #has_space #diffusers-LDMTextToImagePipeline #region-us
|
# High-Resolution Image Synthesis with Latent Diffusion Models (LDM)
Paper: High-Resolution Image Synthesis with Latent Diffusion Models (LDM)s
Abstract:
*By decomposing the image formation process into a sequential application of denoising autoencoders, diffusion models (DMs) achieve state-of-the-art synthesis re... | [
"# High-Resolution Image Synthesis with Latent Diffusion Models (LDM)\n\nPaper: High-Resolution Image Synthesis with Latent Diffusion Models (LDM)s\n\nAbstract:\n\n*By decomposing the image formation process into a sequential application of denoising autoencoders, diffusion models (DMs) achieve state-of-the-art syn... | [
"TAGS\n#diffusers #pytorch #text-to-image #arxiv-2112.10752 #license-apache-2.0 #has_space #diffusers-LDMTextToImagePipeline #region-us \n",
"# High-Resolution Image Synthesis with Latent Diffusion Models (LDM)\n\nPaper: High-Resolution Image Synthesis with Latent Diffusion Models (LDM)s\n\nAbstract:\n\n*By decom... |
automatic-speech-recognition | nemo |
# NVIDIA Conformer-CTC Large (Catalan)
<style>
img {
display: inline;
}
</style>
| [](#model-architecture)
| [](#model-architecture)
| [![La... | {"language": ["ca"], "license": "cc-by-4.0", "library_name": "nemo", "tags": ["automatic-speech-recognition", "speech", "audio", "CTC", "Conformer", "Transformer", "pytorch", "NeMo", "hf-asr-leaderboard", "Riva"], "datasets": ["mozilla-foundation/common_voice_9_0"], "model-index": [{"name": "stt_ca_conformer_ctc_large"... | nvidia/stt_ca_conformer_ctc_large | null | [
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"license:cc-by-4.0",
"model-index",
"region:us"
] | null | 2022-07-18T20:18:35+00:00 | [
"2005.08100"
] | [
"ca"
] | TAGS
#nemo #automatic-speech-recognition #speech #audio #CTC #Conformer #Transformer #pytorch #NeMo #hf-asr-leaderboard #Riva #ca #dataset-mozilla-foundation/common_voice_9_0 #arxiv-2005.08100 #license-cc-by-4.0 #model-index #region-us
| NVIDIA Conformer-CTC Large (Catalan)
====================================
img {
display: inline;
}
| 
| 
| 
|  |
This model transcribes speech into lowercase Catalan a... | [
"### Automatically instantiate the model",
"### Transcribing using Python\n\n\nFirst, let's get a sample\n\n\nThen simply do:",
"### Transcribing many audio files",
"### Input\n\n\nThis model accepts 16 kHz mono-channel Audio (wav files) as input.",
"### Output\n\n\nThis model provides transcribed speech as... | [
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"### Automatically instantiate the model",
"### Transcribing using Py... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-portuguese-cased_harem-sm-first-ner
This model is a fine-tuned version of [neuralmind/bert-base-portuguese-cased](http... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["harem"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "bert-base-portuguese-cased_harem-sm-first-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "harem"... | jordyvl/bert-base-portuguese-cased_harem-selective-sm-first-ner | null | [
"transformers",
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"tensorboard",
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"dataset:harem",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-18T20:25:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-harem #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| bert-base-portuguese-cased\_harem-sm-first-ner
==============================================
This model is a fine-tuned version of neuralmind/bert-base-portuguese-cased on the harem dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1952
* Precision: 0.7456
* Recall: 0.8053
* F1: 0.7743
* ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 2\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: ... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e... |
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. -->
# test_model
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1", "precision", "recall"], "model-index": [{"name": "test_model", "results": []}]} | natalierobbins/test_model | null | [
"transformers",
"pytorch",
"distilbert",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-18T20:25:43+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| test\_model
===========
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0749
* Accuracy: 0.9720
* F1: 0.9698
* Precision: 0.9710
* Recall: 0.9720
Model description
-----------------
More information needed
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1",
"### Traini... | [
"TAGS\n#transformers #pytorch #distilbert #token-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:... |
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. -->
# bert-base-portuguese-cased_harem-selective-CRF-first-ner
This model is a fine-tuned version of [neuralmind/bert-base-portuguese-... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["harem"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "bert-base-portuguese-cased_harem-selective-CRF-first-ner", "results": []}]} | jordyvl/bert-base-portuguese-cased_harem-selective-CRF-first-ner | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"generated_from_trainer",
"dataset:harem",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-07-18T21:14:19+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #generated_from_trainer #dataset-harem #license-mit #endpoints_compatible #region-us
| bert-base-portuguese-cased\_harem-selective-CRF-first-ner
=========================================================
This model is a fine-tuned version of neuralmind/bert-base-portuguese-cased on the harem dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2045
* Precision: 0.5352
* Recall: ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 2\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: ... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #generated_from_trainer #dataset-harem #license-mit #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 2\... |
automatic-speech-recognition | nemo |
# NVIDIA Conformer-Transducer Large (Catalan)
<style>
img {
display: inline;
}
</style>
| [](#model-architecture)
| [](#model-archite... | {"language": ["ca"], "license": "cc-by-4.0", "library_name": "nemo", "tags": ["automatic-speech-recognition", "speech", "audio", "Transducer", "Conformer", "Transformer", "pytorch", "NeMo", "hf-asr-leaderboard"], "datasets": ["mozilla-foundation/common_voice_9_0"], "model-index": [{"name": "stt_ca_conformer_transducer_... | nvidia/stt_ca_conformer_transducer_large | null | [
"nemo",
"automatic-speech-recognition",
"speech",
"audio",
"Transducer",
"Conformer",
"Transformer",
"pytorch",
"NeMo",
"hf-asr-leaderboard",
"ca",
"dataset:mozilla-foundation/common_voice_9_0",
"arxiv:2005.08100",
"license:cc-by-4.0",
"model-index",
"region:us"
] | null | 2022-07-18T21:15:51+00:00 | [
"2005.08100"
] | [
"ca"
] | TAGS
#nemo #automatic-speech-recognition #speech #audio #Transducer #Conformer #Transformer #pytorch #NeMo #hf-asr-leaderboard #ca #dataset-mozilla-foundation/common_voice_9_0 #arxiv-2005.08100 #license-cc-by-4.0 #model-index #region-us
| NVIDIA Conformer-Transducer Large (Catalan)
===========================================
img {
display: inline;
}
| 
| 
| 
This model transcribes speech into lowercase Catalan alphabet including spaces, dashes and ap... | [
"### Automatically instantiate the model",
"### Transcribing using Python\n\n\nFirst, let's get a sample\n\n\nThen simply do:",
"### Transcribing many audio files",
"### Input\n\n\nThis model accepts 16 kHz mono-channel Audio (wav files) as input.",
"### Output\n\n\nThis model provides transcribed speech as... | [
"TAGS\n#nemo #automatic-speech-recognition #speech #audio #Transducer #Conformer #Transformer #pytorch #NeMo #hf-asr-leaderboard #ca #dataset-mozilla-foundation/common_voice_9_0 #arxiv-2005.08100 #license-cc-by-4.0 #model-index #region-us \n",
"### Automatically instantiate the model",
"### Transcribing using P... |
sentence-similarity | sentence-transformers |
# ronanki/ml_use_512_MNR_10-2022-07-17_14-22-50
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 512 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Us... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity"], "pipeline_tag": "sentence-similarity"} | ronanki/ml_use_512_MNR_10-2022-07-17_14-22-50 | null | [
"sentence-transformers",
"pytorch",
"distilbert",
"feature-extraction",
"sentence-similarity",
"endpoints_compatible",
"region:us"
] | null | 2022-07-18T21:16:09+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #endpoints_compatible #region-us
|
# ronanki/ml_use_512_MNR_10-2022-07-17_14-22-50
This is a sentence-transformers model: It maps sentences & paragraphs to a 512 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-transfor... | [
"# ronanki/ml_use_512_MNR_10-2022-07-17_14-22-50\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 512 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentenc... | [
"TAGS\n#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #endpoints_compatible #region-us \n",
"# ronanki/ml_use_512_MNR_10-2022-07-17_14-22-50\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 512 dimensional dense vector space and can be used for ta... |
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-becas1
This model is a fine-tuned version of [IIC/roberta-base-spanish-squades](https://huggingface... | {"tags": ["generated_from_trainer"], "datasets": ["becasv2"], "model-index": [{"name": "roberta-base-spanish-squades-becas1", "results": []}]} | Evelyn18/roberta-base-spanish-squades-becas1 | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"question-answering",
"generated_from_trainer",
"dataset:becasv2",
"endpoints_compatible",
"region:us"
] | null | 2022-07-18T22:14:18+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #question-answering #generated_from_trainer #dataset-becasv2 #endpoints_compatible #region-us
| roberta-base-spanish-squades-becas1
===================================
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: 2.4402
Model description
-----------------
More information needed
Intended us... | [
"### 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... | [
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"### 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/1549758722989334529/v73A... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/repmtg/1667322161718/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/repmtg | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-18T22:54:59+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Rep. Marjorie Taylor Greene🇺🇸
@repmtg
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.
Trainin... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
sentence-similarity | sentence-transformers |
# ONNX convert all-roberta-large-v1
## Conversion of [sentence-transformers/all-roberta-large-v1](https://huggingface.co/sentence-transformers/all-roberta-large-v1)
## Usage (HuggingFace Optimum)
Using this model becomes easy when you have [optimum](https://github.com/huggingface/optimum) installed:
```
python -m pip... | {"language": "en", "license": "apache-2.0", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers", "onnx"], "pipeline_tag": "sentence-similarity"} | vamsibanda/sbert-all-roberta-large-v1-with-pooler | null | [
"sentence-transformers",
"onnx",
"roberta",
"feature-extraction",
"sentence-similarity",
"transformers",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-07-18T23:43:14+00:00 | [] | [
"en"
] | TAGS
#sentence-transformers #onnx #roberta #feature-extraction #sentence-similarity #transformers #en #license-apache-2.0 #endpoints_compatible #region-us
|
# ONNX convert all-roberta-large-v1
## Conversion of sentence-transformers/all-roberta-large-v1
## Usage (HuggingFace Optimum)
Using this model becomes easy when you have optimum installed:
Then you can use the model like this:
| [
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"## Conversion of sentence-transformers/all-roberta-large-v1",
"## Usage (HuggingFace Optimum)\nUsing this model becomes easy when you have optimum installed:\n\nThen you can use the model like this:"
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"## Conversion of sentence-transformers/all-roberta-large-v1",
"## Usage (HuggingFace Optimum)\nUsing this mo... |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-squad_2_384_1
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingf... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad_v2"], "model-index": [{"name": "distilbert-base-uncased-finetuned-squad_2_384_1", "results": []}]} | raisinbl/distilbert-base-uncased-finetuned-squad_2_384_1 | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:squad_v2",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-07-18T23:49:30+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-squad_v2 #license-apache-2.0 #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-squad\_2\_384\_1
==================================================
This model is a fine-tuned version of distilbert-base-uncased on the squad\_v2 dataset.
It achieves the following results on the evaluation set:
* Loss: 1.3787
Model description
-----------------
More informati... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 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 #distilbert #question-answering #generated_from_trainer #dataset-squad_v2 #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\... |
summarization | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-small-finetuned-cnn-news
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the cnn_dailyma... | {"license": "apache-2.0", "tags": ["summarization", "generated_from_trainer"], "datasets": ["cnn_dailymail"], "metrics": ["rouge"], "model-index": [{"name": "t5-small-finetuned-cnn-news", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "cnn_da... | shivaniNK8/t5-small-finetuned-cnn-news | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"summarization",
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"dataset:cnn_dailymail",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-19T00:48:34+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #summarization #generated_from_trainer #dataset-cnn_dailymail #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| t5-small-finetuned-cnn-news
===========================
This model is a fine-tuned version of t5-small on the cnn\_dailymail dataset.
It achieves the following results on the evaluation set:
* Loss: 1.8412
* Rouge1: 24.7231
* Rouge2: 12.292
* Rougel: 20.5347
* Rougelsum: 23.4668
Model description
----------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.00056\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #summarization #generated_from_trainer #dataset-cnn_dailymail #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameter... |
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/1475314622332764161/tzLI... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/yashar/1658196662556/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/yashar | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-19T00:50:50+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Yashar Ali
@yashar
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"
] |
image-to-text | transformers |
# Donut (base-sized model, fine-tuned on CORD)
Donut model fine-tuned on CORD. It was introduced in the paper [OCR-free Document Understanding Transformer](https://arxiv.org/abs/2111.15664) by Geewok et al. and first released in [this repository](https://github.com/clovaai/donut).
Disclaimer: The team releasing Don... | {"license": "mit", "tags": ["donut", "image-to-text", "vision"]} | naver-clova-ix/donut-base-finetuned-cord-v2 | null | [
"transformers",
"pytorch",
"vision-encoder-decoder",
"donut",
"image-to-text",
"vision",
"arxiv:2111.15664",
"license:mit",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-07-19T00:53:24+00:00 | [
"2111.15664"
] | [] | TAGS
#transformers #pytorch #vision-encoder-decoder #donut #image-to-text #vision #arxiv-2111.15664 #license-mit #endpoints_compatible #has_space #region-us
|
# Donut (base-sized model, fine-tuned on CORD)
Donut model fine-tuned on CORD. It was introduced in the paper OCR-free Document Understanding Transformer by Geewok et al. and first released in this repository.
Disclaimer: The team releasing Donut did not write a model card for this model so this model card has been... | [
"# Donut (base-sized model, fine-tuned on CORD) \n\nDonut model fine-tuned on CORD. It was introduced in the paper OCR-free Document Understanding Transformer by Geewok et al. and first released in this repository.\n\nDisclaimer: The team releasing Donut did not write a model card for this model so this model card ... | [
"TAGS\n#transformers #pytorch #vision-encoder-decoder #donut #image-to-text #vision #arxiv-2111.15664 #license-mit #endpoints_compatible #has_space #region-us \n",
"# Donut (base-sized model, fine-tuned on CORD) \n\nDonut model fine-tuned on CORD. It was introduced in the paper OCR-free Document Understanding Tra... |
feature-extraction | transformers |
# Taiyi-CLIP-Roberta-large-326M-Chinese
- Main Page:[Fengshenbang](https://fengshenbang-lm.com/)
- Github: [Fengshenbang-LM](https://github.com/IDEA-CCNL/Fengshenbang-LM)
## 简介 Brief Introduction
首个开源的中文CLIP模型，1.23亿图文对上进行预训练的文本端RoBERTa-large。
The first open source Chinese CLIP, pre-training on 123M image-text pair... | {"license": "apache-2.0", "tags": ["clip", "zh", "image-text", "feature-extraction"], "pipeline_tag": "feature-extraction"} | IDEA-CCNL/Taiyi-CLIP-Roberta-large-326M-Chinese | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"clip",
"zh",
"image-text",
"feature-extraction",
"arxiv:2209.02970",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-07-19T02:01:55+00:00 | [
"2209.02970"
] | [] | TAGS
#transformers #pytorch #bert #text-classification #clip #zh #image-text #feature-extraction #arxiv-2209.02970 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| Taiyi-CLIP-Roberta-large-326M-Chinese
=====================================
* Main Page:Fengshenbang
* Github: Fengshenbang-LM
简介 Brief Introduction
---------------------
首个开源的中文CLIP模型，1.23亿图文对上进行预训练的文本端RoBERTa-large。
The first open source Chinese CLIP, pre-training on 123M image-text pairs, the text encoder: R... | [
"### 下游效果 Performance\n\n\nZero-Shot Classification\n\n\n\nZero-Shot Text-to-Image Retrieval\n\n\n\n使用 Usage\n--------\n\n\n引用 Citation\n-----------\n\n\n如果您在您的工作中使用了我们的模型，可以引用我们的论文：\n\n\nIf you are using the resource for your work, please cite the our paper:\n\n\n也可以引用我们的网站:\n\n\nYou can also cite our website:"
] | [
"TAGS\n#transformers #pytorch #bert #text-classification #clip #zh #image-text #feature-extraction #arxiv-2209.02970 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### 下游效果 Performance\n\n\nZero-Shot Classification\n\n\n\nZero-Shot Text-to-Image Retrieval\n\n\n\n使用 Usa... |
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-pegasus-finetuned_test
This model was trained from scratch on an unknown dataset.
It achieves the following results on the ev... | {"tags": ["generated_from_trainer"], "metrics": ["sacrebleu"], "model-index": [{"name": "t5-pegasus-finetuned_test", "results": []}]} | fqw/t5-pegasus-finetuned_test | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-19T02:32:58+00:00 | [] | [] | TAGS
#transformers #pytorch #bart #text2text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| t5-pegasus-finetuned\_test
==========================
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 6.0045
* Sacrebleu: 0.8737
* Rouge 1: 0.0237
* Rouge 2: 0.0
* Rouge L: 0.0232
* Bleu 1: 0.1444
* Bleu 2: 0.0447
* Bleu 3: 0.0175
* Bleu 4:... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 64\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 256\n* optimizer: Adam with betas=(0.9,0.999) and epsil... | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 128\n* eval\\_batch\\_siz... |
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. -->
# ai-light-dance_singing3_ft_wav2vec2-large-xlsr-53-v1-5gram
This model is a fine-tuned version of [gary109/ai-light-dance_singing... | {"license": "apache-2.0", "tags": ["automatic-speech-recognition", "gary109/AI_Light_Dance", "generated_from_trainer"], "model-index": [{"name": "ai-light-dance_singing3_ft_wav2vec2-large-xlsr-53-v1-5gram", "results": []}]} | gary109/ai-light-dance_singing3_ft_wav2vec2-large-xlsr-53-v1-5gram | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"gary109/AI_Light_Dance",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-07-19T02:33:21+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #gary109/AI_Light_Dance #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| ai-light-dance\_singing3\_ft\_wav2vec2-large-xlsr-53-v1-5gram
=============================================================
This model is a fine-tuned version of gary109/ai-light-dance\_singing3\_ft\_wav2vec2-large-xlsr-53-v1-5gram on the GARY109/AI\_LIGHT\_DANCE - ONSET-SINGING3 dataset.
It achieves the following re... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-07\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #gary109/AI_Light_Dance #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: 1e-07\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. -->
# distilroberta-base-wandb-week-3-complaints-classifier-512
This model is a fine-tuned version of [distilroberta-base](https://hug... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["consumer-finance-complaints"], "metrics": ["accuracy", "f1", "recall", "precision"], "model-index": [{"name": "distilroberta-base-wandb-week-3-complaints-classifier-512", "results": [{"task": {"type": "text-classification", "name": "Text Classi... | Kayvane/distilroberta-base-wandb-week-3-complaints-classifier-512 | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"generated_from_trainer",
"dataset:consumer-finance-complaints",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-19T02:40:55+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #text-classification #generated_from_trainer #dataset-consumer-finance-complaints #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilroberta-base-wandb-week-3-complaints-classifier-512
=========================================================
This model is a fine-tuned version of distilroberta-base on the consumer-finance-complaints dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6004
* Accuracy: 0.8038
* F1: 0.... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1.7835312622444155e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler... | [
"TAGS\n#transformers #pytorch #roberta #text-classification #generated_from_trainer #dataset-consumer-finance-complaints #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* l... |
text-classification | transformers | COVID-RadBERT was trained to detect the presence or absence of COVID-19 within radiology reports, along an "uncertain" diagnostic when further medical tests are required.
## Citation
```bibtex
@article{chambon_cook_langlotz_2022,
title={Improved fine-tuning of in-domain transformer model for inferring COVID-19 pr... | {"language": ["en"], "license": "mit", "tags": ["text-classification", "pytorch", "transformers", "uncased", "radiology", "biomedical", "covid-19", "covid19"], "widget": [{"text": "procedure: single ap view of the chest comparison: none findings: no surgical hardware nor tubes. lungs, pleura: low lung volumes, bilatera... | StanfordAIMI/covid-radbert | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"uncased",
"radiology",
"biomedical",
"covid-19",
"covid19",
"en",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-07-19T02:44:46+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bert #text-classification #uncased #radiology #biomedical #covid-19 #covid19 #en #license-mit #endpoints_compatible #region-us
| COVID-RadBERT was trained to detect the presence or absence of COVID-19 within radiology reports, along an "uncertain" diagnostic when further medical tests are required.
| [] | [
"TAGS\n#transformers #pytorch #bert #text-classification #uncased #radiology #biomedical #covid-19 #covid19 #en #license-mit #endpoints_compatible #region-us \n"
] |
null | null | Field blue flowers and bright stars ethereal in holy lighting | {} | DsVuin/Flower | null | [
"region:us"
] | null | 2022-07-19T02:45:37+00:00 | [] | [] | TAGS
#region-us
| Field blue flowers and bright stars ethereal in holy lighting | [] | [
"TAGS\n#region-us \n"
] |
null | keras |
## Model description
**This model is implementation of the distillation recipe proposed in DeiT.**
Visit Keras example on [Distilling Vision Transformers](https://keras.io/examples/vision/deit/).
Full credits to: [Sayak Paul](https://twitter.com/RisingSayak)
In the original Vision Transformers (ViT) p... | {"library_name": "keras"} | keras-io/deit | null | [
"keras",
"tensorboard",
"has_space",
"region:us"
] | null | 2022-07-19T03:33:19+00:00 | [] | [] | TAGS
#keras #tensorboard #has_space #region-us
| Model description
-----------------
This model is implementation of the distillation recipe proposed in DeiT.
Visit Keras example on Distilling Vision Transformers.
Full credits to: Sayak Paul
In the original Vision Transformers (ViT) paper (Dosovitskiy et al.), the authors concluded that to perform on par wit... | [
"### 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 #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"
] |
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-wandb-week-3-complaints-classifier-256
This model is a fine-tuned version of [distilbert-base-uncased](h... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["consumer-finance-complaints"], "metrics": ["accuracy", "f1", "recall", "precision"], "model-index": [{"name": "distilbert-base-uncased-wandb-week-3-complaints-classifier-256", "results": [{"task": {"type": "text-classification", "name": "Text C... | Kayvane/distilbert-base-uncased-wandb-week-3-complaints-classifier-256 | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:consumer-finance-complaints",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-19T04:06:36+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-consumer-finance-complaints #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-wandb-week-3-complaints-classifier-256
==============================================================
This model is a fine-tuned version of distilbert-base-uncased on the consumer-finance-complaints dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5453
* Accuracy: ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 4.097565552226687e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\... | [
"TAGS\n#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-consumer-finance-complaints #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... |
text2text-generation | transformers |
# Model Trained Using AutoTrain
- Problem type: Summarization
- Model ID: 1149642380
- CO2 Emissions (in grams): 4.803822525731932
## Validation Metrics
- Loss: 1.1474181413650513
- Rouge1: 57.8827
- Rouge2: 46.6881
- RougeL: 56.4209
- RougeLsum: 56.4665
- Gen Len: 18.0731
## Usage
You can use cURL to access this... | {"language": "en", "tags": "autotrain", "datasets": ["vencortexTeam/autotrain-data-CompanyDescription"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 4.803822525731932} | vencortexTeam/autotrain-CompanyDescription-1149642380 | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"autotrain",
"en",
"dataset:vencortexTeam/autotrain-data-CompanyDescription",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-19T04:44:45+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bart #text2text-generation #autotrain #en #dataset-vencortexTeam/autotrain-data-CompanyDescription #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoTrain
- Problem type: Summarization
- Model ID: 1149642380
- CO2 Emissions (in grams): 4.803822525731932
## Validation Metrics
- Loss: 1.1474181413650513
- Rouge1: 57.8827
- Rouge2: 46.6881
- RougeL: 56.4209
- RougeLsum: 56.4665
- Gen Len: 18.0731
## Usage
You can use cURL to access this... | [
"# Model Trained Using AutoTrain\n\n- Problem type: Summarization\n- Model ID: 1149642380\n- CO2 Emissions (in grams): 4.803822525731932",
"## Validation Metrics\n\n- Loss: 1.1474181413650513\n- Rouge1: 57.8827\n- Rouge2: 46.6881\n- RougeL: 56.4209\n- RougeLsum: 56.4665\n- Gen Len: 18.0731",
"## Usage\n\nYou ca... | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #autotrain #en #dataset-vencortexTeam/autotrain-data-CompanyDescription #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoTrain\n\n- Problem type: Summarization\n- Model ID: 1149642380\n- CO2 Emissions... |
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"]} | SimingSiming/testpyramidsrnd | null | [
"ml-agents",
"tensorboard",
"onnx",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
] | null | 2022-07-19T05:58:21+00:00 | [] | [] | TAGS
#ml-agents #tensorboard #onnx #unity-ml-agents #deep-reinforcement-learning #reinforcement-learning #ML-Agents-Pyramids #region-us
|
# ppo Agent playing Pyramids
This is a trained model of a ppo agent playing Pyramids using the Unity ML-Agents Library.
## Usage (with ML-Agents)
The Documentation: URL
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the trainin... | [
"# ppo Agent playing Pyramids\n This is a trained model of a ppo agent playing Pyramids using the Unity ML-Agents Library.\n \n ## Usage (with ML-Agents)\n The Documentation: URL\n We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:\n\n\n ### Resume the t... | [
"TAGS\n#ml-agents #tensorboard #onnx #unity-ml-agents #deep-reinforcement-learning #reinforcement-learning #ML-Agents-Pyramids #region-us \n",
"# ppo Agent playing Pyramids\n This is a trained model of a ppo agent playing Pyramids using the Unity ML-Agents Library.\n \n ## Usage (with ML-Agents)\n The Documen... |
fill-mask | transformers |
# Erlangshen-DeBERTa-v2-97M-Chinese
- Main Page:[Fengshenbang](https://fengshenbang-lm.com/)
- Github: [Fengshenbang-LM](https://github.com/IDEA-CCNL/Fengshenbang-LM)
## 简介 Brief Introduction
善于处理NLU任务，采用全词掩码的，中文版的0.97亿参数DeBERTa-v2-Base。
Good at solving NLU tasks, adopting Whole Word Masking, Chinese DeBERTa-v2-Ba... | {"language": ["zh"], "license": "apache-2.0", "tags": ["DeBERTa"], "inference": true, "widget": [{"text": "\u751f\u6d3b\u7684\u771f\u8c1b\u662f[MASK]\u3002"}]} | IDEA-CCNL/Erlangshen-DeBERTa-v2-97M-Chinese | null | [
"transformers",
"pytorch",
"safetensors",
"deberta-v2",
"fill-mask",
"DeBERTa",
"zh",
"arxiv:2209.02970",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-19T06:38:30+00:00 | [
"2209.02970"
] | [
"zh"
] | TAGS
#transformers #pytorch #safetensors #deberta-v2 #fill-mask #DeBERTa #zh #arxiv-2209.02970 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| Erlangshen-DeBERTa-v2-97M-Chinese
=================================
* Main Page:Fengshenbang
* Github: Fengshenbang-LM
简介 Brief Introduction
---------------------
善于处理NLU任务，采用全词掩码的，中文版的0.97亿参数DeBERTa-v2-Base。
Good at solving NLU tasks, adopting Whole Word Masking, Chinese DeBERTa-v2-Base with 97M parameters.
... | [
"### 下游任务 Performance\n\n\n我们展示了下列下游任务的结果（dev集）：\n\n\nWe present the results (dev set) on the following tasks:\n\n\n\n使用 Usage\n--------\n\n\n引用 Citation\n-----------\n\n\n如果您在您的工作中使用了我们的模型，可以引用我们的论文：\n\n\nIf you are using the resource for your work, please cite the our paper:\n\n\n也可以引用我们的网站:\n\n\nYou can also cit... | [
"TAGS\n#transformers #pytorch #safetensors #deberta-v2 #fill-mask #DeBERTa #zh #arxiv-2209.02970 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### 下游任务 Performance\n\n\n我们展示了下列下游任务的结果（dev集）：\n\n\nWe present the results (dev set) on the following tasks:\n\n\n\n使用 Usage\n--------\... |
object-detection | null |
# unicorn_track_tiny_rt_mask
## Table of Contents
- [unicorn_track_tiny_rt_mask](#-model_id--defaultmymodelname-true)
- [Table of Contents](#table-of-contents)
- [Model Details](#model-details)
- [Uses](#uses)
- [Direct Use](#direct-use)
- [Evaluation Results](#evaluation-results)
<model_details>
## ... | {"license": "mit", "tags": ["object-detection", "object-tracking", "video", "video-object-segmentation"], "inference": false} | NimaBoscarino/unicorn_track_tiny_rt_mask | null | [
"object-detection",
"object-tracking",
"video",
"video-object-segmentation",
"arxiv:2111.12085",
"license:mit",
"region:us"
] | null | 2022-07-19T06:59:43+00:00 | [
"2111.12085"
] | [] | TAGS
#object-detection #object-tracking #video #video-object-segmentation #arxiv-2111.12085 #license-mit #region-us
|
# unicorn_track_tiny_rt_mask
## Table of Contents
- unicorn_track_tiny_rt_mask
- Table of Contents
- Model Details
- Uses
- Direct Use
- Evaluation Results
<model_details>
## Model Details
Unicorn accomplishes the great unification of the network architecture and the learning paradigm for four track... | [
"# unicorn_track_tiny_rt_mask",
"## Table of Contents\n- unicorn_track_tiny_rt_mask\n - Table of Contents\n - Model Details\n - Uses\n - Direct Use\n - Evaluation Results\n\n<model_details>",
"## Model Details\n\nUnicorn accomplishes the great unification of the network architecture and the learning pa... | [
"TAGS\n#object-detection #object-tracking #video #video-object-segmentation #arxiv-2111.12085 #license-mit #region-us \n",
"# unicorn_track_tiny_rt_mask",
"## Table of Contents\n- unicorn_track_tiny_rt_mask\n - Table of Contents\n - Model Details\n - Uses\n - Direct Use\n - Evaluation Results\n\n<model... |
reinforcement-learning | null |
# **Q-Learning** Agent playing **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="AliMMZ/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
e... | {"tags": ["Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-Taxi-v3", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Taxi-v3", "type": "Taxi-v3"}, "metrics": [{"type": "mean_reward", "value": "7.56 +/... | AliMMZ/q-Taxi-v3 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-07-19T07:00:13+00:00 | [] | [] | TAGS
#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing Taxi-v3
This is a trained model of a Q-Learning agent playing Taxi-v3 .
## Usage
| [
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] | [
"TAGS\n#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] |
reinforcement-learning | null |
# **Q-Learning** Agent playing **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="AliMMZ/q-Taxi-v3A", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
... | {"tags": ["Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-Taxi-v3A", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Taxi-v3", "type": "Taxi-v3"}, "metrics": [{"type": "mean_reward", "value": "7.56 +... | AliMMZ/q-Taxi-v3A | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-07-19T07:24:28+00:00 | [] | [] | TAGS
#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing Taxi-v3
This is a trained model of a Q-Learning agent playing Taxi-v3 .
## Usage
| [
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] | [
"TAGS\n#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] |
unconditional-image-generation | diffusers |
# Score-Based Generative Modeling through Stochastic Differential Equations (SDE)
**Paper**: [Score-Based Generative Modeling through Stochastic Differential Equations](https://arxiv.org/abs/2011.13456)
**Authors**: Yang Song, Jascha Sohl-Dickstein, Diederik P. Kingma, Abhishek Kumar, Stefano Ermon, Ben Poole
**Abs... | {"license": "apache-2.0", "tags": ["pytorch", "diffusers", "unconditional-image-generation"]} | google/ncsnpp-ffhq-1024 | null | [
"diffusers",
"pytorch",
"unconditional-image-generation",
"arxiv:2011.13456",
"license:apache-2.0",
"diffusers:ScoreSdeVePipeline",
"region:us"
] | null | 2022-07-19T07:50:21+00:00 | [
"2011.13456"
] | [] | TAGS
#diffusers #pytorch #unconditional-image-generation #arxiv-2011.13456 #license-apache-2.0 #diffusers-ScoreSdeVePipeline #region-us
|
# Score-Based Generative Modeling through Stochastic Differential Equations (SDE)
Paper: Score-Based Generative Modeling through Stochastic Differential Equations
Authors: Yang Song, Jascha Sohl-Dickstein, Diederik P. Kingma, Abhishek Kumar, Stefano Ermon, Ben Poole
Abstract:
*Creating noise from data is easy; cre... | [
"# Score-Based Generative Modeling through Stochastic Differential Equations (SDE)\n\nPaper: Score-Based Generative Modeling through Stochastic Differential Equations\n\nAuthors: Yang Song, Jascha Sohl-Dickstein, Diederik P. Kingma, Abhishek Kumar, Stefano Ermon, Ben Poole\n\nAbstract:\n\n*Creating noise from data ... | [
"TAGS\n#diffusers #pytorch #unconditional-image-generation #arxiv-2011.13456 #license-apache-2.0 #diffusers-ScoreSdeVePipeline #region-us \n",
"# Score-Based Generative Modeling through Stochastic Differential Equations (SDE)\n\nPaper: Score-Based Generative Modeling through Stochastic Differential Equations\n\nA... |
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-banking77-classification
This model is a fine-tuned version of [distilbert-base-uncased](https://hugging... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["banking77"], "metrics": ["accuracy"], "widget": [{"text": "Can I track the card you sent to me? ", "example_title": "Card Arrival Example"}, {"text": "Can you explain your exchange rate policy to me?", "example_title": "Exchange Rate Example"}, {"text... | nickprock/distilbert-base-uncased-banking77-classification | null | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:banking77",
"base_model:distilbert-base-uncased",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-07-19T07:51:52+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #safetensors #distilbert #text-classification #generated_from_trainer #dataset-banking77 #base_model-distilbert-base-uncased #license-mit #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
| distilbert-base-uncased-banking77-classification
================================================
This model is a fine-tuned version of distilbert-base-uncased on the banking77 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3152
* Accuracy: 0.9240
* F1 Score: 0.9243
Model description
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 20",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #safetensors #distilbert #text-classification #generated_from_trainer #dataset-banking77 #base_model-distilbert-base-uncased #license-mit #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### Training hyperparameters\n\n\nThe following ... |
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. -->
# twitter-roberta-base-mar2022-finetuned-emotion
This model is a fine-tuned version of [cardiffnlp/twitter-roberta-base-mar2022](h... | {"tags": ["generated_from_trainer"], "datasets": ["tweet_eval"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "twitter-roberta-base-mar2022-finetuned-emotion", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "tweet_eval", "type": "tweet_eval", "args": ... | Tomas23/twitter-roberta-base-mar2022-finetuned-emotion | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"generated_from_trainer",
"dataset:tweet_eval",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-07-19T07:54:06+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #text-classification #generated_from_trainer #dataset-tweet_eval #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
| twitter-roberta-base-mar2022-finetuned-emotion
==============================================
This model is a fine-tuned version of cardiffnlp/twitter-roberta-base-mar2022 on the tweet\_eval dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5146
* Accuracy: 0.8191
* F1: 0.8171
Model desc... | [
"### 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: 2",
"### Traini... | [
"TAGS\n#transformers #pytorch #roberta #text-classification #generated_from_trainer #dataset-tweet_eval #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* t... |
unconditional-image-generation | diffusers |
# Score-Based Generative Modeling through Stochastic Differential Equations (SDE)
**Paper**: [Score-Based Generative Modeling through Stochastic Differential Equations](https://arxiv.org/abs/2011.13456)
**Authors**: Yang Song, Jascha Sohl-Dickstein, Diederik P. Kingma, Abhishek Kumar, Stefano Ermon, Ben Poole
**Abs... | {"license": "apache-2.0", "tags": ["pytorch", "diffusers", "unconditional-image-generation"]} | google/ncsnpp-bedroom-256 | null | [
"diffusers",
"pytorch",
"unconditional-image-generation",
"arxiv:2011.13456",
"license:apache-2.0",
"diffusers:ScoreSdeVePipeline",
"region:us"
] | null | 2022-07-19T08:08:11+00:00 | [
"2011.13456"
] | [] | TAGS
#diffusers #pytorch #unconditional-image-generation #arxiv-2011.13456 #license-apache-2.0 #diffusers-ScoreSdeVePipeline #region-us
|
# Score-Based Generative Modeling through Stochastic Differential Equations (SDE)
Paper: Score-Based Generative Modeling through Stochastic Differential Equations
Authors: Yang Song, Jascha Sohl-Dickstein, Diederik P. Kingma, Abhishek Kumar, Stefano Ermon, Ben Poole
Abstract:
*Creating noise from data is easy; cre... | [
"# Score-Based Generative Modeling through Stochastic Differential Equations (SDE)\n\nPaper: Score-Based Generative Modeling through Stochastic Differential Equations\n\nAuthors: Yang Song, Jascha Sohl-Dickstein, Diederik P. Kingma, Abhishek Kumar, Stefano Ermon, Ben Poole\n\nAbstract:\n\n*Creating noise from data ... | [
"TAGS\n#diffusers #pytorch #unconditional-image-generation #arxiv-2011.13456 #license-apache-2.0 #diffusers-ScoreSdeVePipeline #region-us \n",
"# Score-Based Generative Modeling through Stochastic Differential Equations (SDE)\n\nPaper: Score-Based Generative Modeling through Stochastic Differential Equations\n\nA... |
unconditional-image-generation | diffusers |
# Score-Based Generative Modeling through Stochastic Differential Equations (SDE)
**Paper**: [Score-Based Generative Modeling through Stochastic Differential Equations](https://arxiv.org/abs/2011.13456)
**Authors**: Yang Song, Jascha Sohl-Dickstein, Diederik P. Kingma, Abhishek Kumar, Stefano Ermon, Ben Poole
**Abs... | {"license": "apache-2.0", "tags": ["pytorch", "diffusers", "unconditional-image-generation"]} | google/ncsnpp-celebahq-256 | null | [
"diffusers",
"pytorch",
"unconditional-image-generation",
"arxiv:2011.13456",
"license:apache-2.0",
"diffusers:ScoreSdeVePipeline",
"region:us"
] | null | 2022-07-19T08:10:04+00:00 | [
"2011.13456"
] | [] | TAGS
#diffusers #pytorch #unconditional-image-generation #arxiv-2011.13456 #license-apache-2.0 #diffusers-ScoreSdeVePipeline #region-us
|
# Score-Based Generative Modeling through Stochastic Differential Equations (SDE)
Paper: Score-Based Generative Modeling through Stochastic Differential Equations
Authors: Yang Song, Jascha Sohl-Dickstein, Diederik P. Kingma, Abhishek Kumar, Stefano Ermon, Ben Poole
Abstract:
*Creating noise from data is easy; cre... | [
"# Score-Based Generative Modeling through Stochastic Differential Equations (SDE)\n\nPaper: Score-Based Generative Modeling through Stochastic Differential Equations\n\nAuthors: Yang Song, Jascha Sohl-Dickstein, Diederik P. Kingma, Abhishek Kumar, Stefano Ermon, Ben Poole\n\nAbstract:\n\n*Creating noise from data ... | [
"TAGS\n#diffusers #pytorch #unconditional-image-generation #arxiv-2011.13456 #license-apache-2.0 #diffusers-ScoreSdeVePipeline #region-us \n",
"# Score-Based Generative Modeling through Stochastic Differential Equations (SDE)\n\nPaper: Score-Based Generative Modeling through Stochastic Differential Equations\n\nA... |
unconditional-image-generation | diffusers |
# Score-Based Generative Modeling through Stochastic Differential Equations (SDE)
**Paper**: [Score-Based Generative Modeling through Stochastic Differential Equations](https://arxiv.org/abs/2011.13456)
**Authors**: Yang Song, Jascha Sohl-Dickstein, Diederik P. Kingma, Abhishek Kumar, Stefano Ermon, Ben Poole
**Abs... | {"license": "apache-2.0", "tags": ["pytorch", "diffusers", "unconditional-image-generation"]} | google/ncsnpp-church-256 | null | [
"diffusers",
"pytorch",
"unconditional-image-generation",
"arxiv:2011.13456",
"license:apache-2.0",
"diffusers:ScoreSdeVePipeline",
"region:us"
] | null | 2022-07-19T08:13:12+00:00 | [
"2011.13456"
] | [] | TAGS
#diffusers #pytorch #unconditional-image-generation #arxiv-2011.13456 #license-apache-2.0 #diffusers-ScoreSdeVePipeline #region-us
|
# Score-Based Generative Modeling through Stochastic Differential Equations (SDE)
Paper: Score-Based Generative Modeling through Stochastic Differential Equations
Authors: Yang Song, Jascha Sohl-Dickstein, Diederik P. Kingma, Abhishek Kumar, Stefano Ermon, Ben Poole
Abstract:
*Creating noise from data is easy; cre... | [
"# Score-Based Generative Modeling through Stochastic Differential Equations (SDE)\n\nPaper: Score-Based Generative Modeling through Stochastic Differential Equations\n\nAuthors: Yang Song, Jascha Sohl-Dickstein, Diederik P. Kingma, Abhishek Kumar, Stefano Ermon, Ben Poole\n\nAbstract:\n\n*Creating noise from data ... | [
"TAGS\n#diffusers #pytorch #unconditional-image-generation #arxiv-2011.13456 #license-apache-2.0 #diffusers-ScoreSdeVePipeline #region-us \n",
"# Score-Based Generative Modeling through Stochastic Differential Equations (SDE)\n\nPaper: Score-Based Generative Modeling through Stochastic Differential Equations\n\nA... |
unconditional-image-generation | diffusers |
# Score-Based Generative Modeling through Stochastic Differential Equations (SDE)
**Paper**: [Score-Based Generative Modeling through Stochastic Differential Equations](https://arxiv.org/abs/2011.13456)
**Authors**: Yang Song, Jascha Sohl-Dickstein, Diederik P. Kingma, Abhishek Kumar, Stefano Ermon, Ben Poole
**Abs... | {"license": "apache-2.0", "tags": ["pytorch", "diffusers", "unconditional-image-generation"]} | google/ncsnpp-ffhq-256 | null | [
"diffusers",
"pytorch",
"unconditional-image-generation",
"arxiv:2011.13456",
"license:apache-2.0",
"diffusers:ScoreSdeVePipeline",
"region:us"
] | null | 2022-07-19T08:14:34+00:00 | [
"2011.13456"
] | [] | TAGS
#diffusers #pytorch #unconditional-image-generation #arxiv-2011.13456 #license-apache-2.0 #diffusers-ScoreSdeVePipeline #region-us
|
# Score-Based Generative Modeling through Stochastic Differential Equations (SDE)
Paper: Score-Based Generative Modeling through Stochastic Differential Equations
Authors: Yang Song, Jascha Sohl-Dickstein, Diederik P. Kingma, Abhishek Kumar, Stefano Ermon, Ben Poole
Abstract:
*Creating noise from data is easy; cre... | [
"# Score-Based Generative Modeling through Stochastic Differential Equations (SDE)\n\nPaper: Score-Based Generative Modeling through Stochastic Differential Equations\n\nAuthors: Yang Song, Jascha Sohl-Dickstein, Diederik P. Kingma, Abhishek Kumar, Stefano Ermon, Ben Poole\n\nAbstract:\n\n*Creating noise from data ... | [
"TAGS\n#diffusers #pytorch #unconditional-image-generation #arxiv-2011.13456 #license-apache-2.0 #diffusers-ScoreSdeVePipeline #region-us \n",
"# Score-Based Generative Modeling through Stochastic Differential Equations (SDE)\n\nPaper: Score-Based Generative Modeling through Stochastic Differential Equations\n\nA... |
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