pipeline_tag stringclasses 48
values | library_name stringclasses 198
values | text stringlengths 1 900k | metadata stringlengths 2 438k | id stringlengths 5 122 | last_modified null | tags listlengths 1 1.84k | sha null | created_at stringlengths 25 25 | arxiv listlengths 0 201 | languages listlengths 0 1.83k | tags_str stringlengths 17 9.34k | text_str stringlengths 0 389k | text_lists listlengths 0 722 | processed_texts listlengths 1 723 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
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. -->
# wac2vec-lllfantomlll
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-bas... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wac2vec-lllfantomlll", "results": []}]} | lllFaNToMlll/wac2vec-lllfantomlll | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-06-11T10:42:50+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wac2vec-lllfantomlll
====================
This model is a fine-tuned version of facebook/wav2vec2-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5560
* Wer: 0.3417
Model description
-----------------
More information needed
Intended uses & limitations
------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps:... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 8... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# lmv2ubiai-pan8doc-06-11
This model is a fine-tuned version of [microsoft/layoutlmv2-base-uncased](https://huggingface.co/microso... | {"license": "cc-by-nc-sa-4.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "lmv2ubiai-pan8doc-06-11", "results": []}]} | Sebabrata/lmv2ubiai-pan8doc-06-11 | null | [
"transformers",
"pytorch",
"tensorboard",
"layoutlmv2",
"token-classification",
"generated_from_trainer",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-11T10:46:22+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #layoutlmv2 #token-classification #generated_from_trainer #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us
| lmv2ubiai-pan8doc-06-11
=======================
This model is a fine-tuned version of microsoft/layoutlmv2-base-uncased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.9633
* Dob Precision: 1.0
* Dob Recall: 1.0
* Dob F1: 1.0
* Dob Number: 2
* Fname Precision: 0.6667
* Fname ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 4e-05\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: constant\n* num\\_epochs: 30",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #layoutlmv2 #token-classification #generated_from_trainer #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 4e-05\n* tra... |
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. -->
# albert-base-v2-finetuned-filtered-0609
This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "precision", "recall", "f1"], "model-index": [{"name": "albert-base-v2-finetuned-filtered-0609", "results": []}]} | YeRyeongLee/albert-base-v2-finetuned-filtered-0609 | null | [
"transformers",
"pytorch",
"albert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-11T10:46:52+00:00 | [] | [] | TAGS
#transformers #pytorch #albert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| albert-base-v2-finetuned-filtered-0609
======================================
This model is a fine-tuned version of albert-base-v2 on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2062
* Accuracy: 0.9723
* Precision: 0.9724
* Recall: 0.9723
* F1: 0.9723
Model description
-... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: ... | [
"TAGS\n#transformers #pytorch #albert #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: 5e-05\n* train\\_batch\\_size: 8\n*... |
text-generation | transformers |
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1303333944360869888/DcCZ... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/dekotale/1654949168644/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/dekotale | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-11T11:04:17+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Dekotale
@dekotale
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
-------------
... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation | transformers |
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1192394634305134593/kWwF... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/adrianramy/1654949574810/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/adrianramy | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-11T11:12:20+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Adri
@adrianramy
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"
] |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-de
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-b... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["xtreme"], "metrics": ["f1"], "model-index": [{"name": "xlm-roberta-base-finetuned-panx-de", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "xtreme", "type": "xtreme", "args": "PAN-X.de"}, "me... | Akshat/xlm-roberta-base-finetuned-panx-de | null | [
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-11T11:19:48+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-panx-de
==================================
This model is a fine-tuned version of xlm-roberta-base on the xtreme dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1405
* F1: 0.8611
Model description
-----------------
More information needed
Intended uses & l... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 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 #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_... |
reinforcement-learning | stable-baselines3 |
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framewor... | {"library_name": "stable-baselines3", "tags": ["SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "DQN", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "SpaceInvadersNoFram... | DavidCollier/SpaceInvader | null | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-11T11:39:28+00:00 | [] | [] | TAGS
#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# DQN Agent playing SpaceInvadersNoFrameskip-v4
This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4
using the stable-baselines3 library
and the RL Zoo.
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents... | [
"# DQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framework for Stable Baselines3\nreinforcement learning agents,\nwith hyperparameter optimization and pre-tra... | [
"TAGS\n#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# DQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo... |
reinforcement-learning | stable-baselines3 |
# **PPO** Agent playing **FrozenLake-v1**
This is a trained model of a **PPO** agent playing **FrozenLake-v1**
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 im... | {"library_name": "stable-baselines3", "tags": ["FrozenLake-v1", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "FrozenLake-v1", "type": "FrozenLa... | antonioricciardi/FrozenLake-v1 | null | [
"stable-baselines3",
"FrozenLake-v1",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-11T12:06:48+00:00 | [] | [] | TAGS
#stable-baselines3 #FrozenLake-v1 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing FrozenLake-v1
This is a trained model of a PPO agent playing FrozenLake-v1
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# PPO Agent playing FrozenLake-v1\nThis is a trained model of a PPO agent playing FrozenLake-v1\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #FrozenLake-v1 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing FrozenLake-v1\nThis is a trained model of a PPO agent playing FrozenLake-v1\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your c... |
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. -->
# roberta-base-bne-finetuned_personality_multi_4
This model is a fine-tuned version of [BSC-TeMU/roberta-base-bne](https://hugging... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "roberta-base-bne-finetuned_personality_multi_4", "results": []}]} | titi7242229/roberta-base-bne-finetuned_personality_multi_4 | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-11T12:23:50+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| roberta-base-bne-finetuned\_personality\_multi\_4
=================================================
This model is a fine-tuned version of BSC-TeMU/roberta-base-bne on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 4.1709
* Accuracy: 0.3470
Model description
-----------------
... | [
"### 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: 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: 20",
"### Trai... | [
"TAGS\n#transformers #pytorch #tensorboard #roberta #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: 0.0001\n* train\\_bat... |
reinforcement-learning | stable-baselines3 |
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framewor... | {"library_name": "stable-baselines3", "tags": ["SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "DQN", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "SpaceInvadersNoFram... | send-it/dqn-SpaceInvadersNoFrameskip-v4 | null | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-11T12:30:29+00:00 | [] | [] | TAGS
#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# DQN Agent playing SpaceInvadersNoFrameskip-v4
This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4
using the stable-baselines3 library
and the RL Zoo.
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents... | [
"# DQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framework for Stable Baselines3\nreinforcement learning agents,\nwith hyperparameter optimization and pre-tra... | [
"TAGS\n#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# DQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo... |
feature-extraction | transformers | 在bert-base-chinese基础上进行新闻语料库的增量预训练的模型，token采用的是bert-base-chinese
Model
模型导出时将生成 config.json 和 pytorch_model.bin 参数文件
Tokenizer
这是一个将纯文本转换为编码的过程。注意，Tokenizer 并不涉及将词转化为词向量的过程，仅仅是将纯文本分词，添加[MASK]标记、[SEP]、[CLS]标记，并转换为字典索引。Tokenizer 类导出时将分为三个文件
vocab.txt 词典文件，每一行为一个词或词的一部分
special_tokens_map.json 特殊标记的定义方式
tokenizer_config.j... | {} | LDD/bert_mlm_new2 | null | [
"transformers",
"pytorch",
"bert",
"feature-extraction",
"endpoints_compatible",
"region:us"
] | null | 2022-06-11T12:35:53+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #feature-extraction #endpoints_compatible #region-us
| 在bert-base-chinese基础上进行新闻语料库的增量预训练的模型，token采用的是bert-base-chinese
Model
模型导出时将生成 URL 和 pytorch_model.bin 参数文件
Tokenizer
这是一个将纯文本转换为编码的过程。注意，Tokenizer 并不涉及将词转化为词向量的过程，仅仅是将纯文本分词，添加[MASK]标记、[SEP]、[CLS]标记，并转换为字典索引。Tokenizer 类导出时将分为三个文件
URL 词典文件，每一行为一个词或词的一部分
special_tokens_map.json 特殊标记的定义方式
tokenizer_config.json 配置文件，主要存储特... | [] | [
"TAGS\n#transformers #pytorch #bert #feature-extraction #endpoints_compatible #region-us \n"
] |
text-generation | transformers |
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1382014203796553732/DFDi... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/nosuba_13/1654954852706/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/nosuba_13 | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-11T12:40:23+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Noel
@nosuba\_13
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
-------------
... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
reinforcement-learning | 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... | neeenway/ppo-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-11T12:43:03+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 |
# Bengali Word2Vec Model
This is a pre-trained word2vec model for Bengali language.
This model is build for [bnlp](https://github.com/sagorbrur/bnlp) package.
## Datasets
- [Wikipedia dump datasets](https://dumps.wikimedia.org/bnwiki/latest/)
## Training details
- Word2Vec word embedding dimension = 100, min_count=... | {"license": "mit"} | sagorsarker/bangla_word2vec | null | [
"license:mit",
"region:us"
] | null | 2022-06-11T12:44:00+00:00 | [] | [] | TAGS
#license-mit #region-us
|
# Bengali Word2Vec Model
This is a pre-trained word2vec model for Bengali language.
This model is build for bnlp package.
## Datasets
- Wikipedia dump datasets
## Training details
- Word2Vec word embedding dimension = 100, min_count=5, window=5, epochs=10
## Usage
- 'pip install -U bnlp_toolkit'
- Generate Vector ... | [
"# Bengali Word2Vec Model\nThis is a pre-trained word2vec model for Bengali language.\n\nThis model is build for bnlp package.",
"## Datasets\n- Wikipedia dump datasets",
"## Training details\n- Word2Vec word embedding dimension = 100, min_count=5, window=5, epochs=10",
"## Usage\n- 'pip install -U bnlp_toolk... | [
"TAGS\n#license-mit #region-us \n",
"# Bengali Word2Vec Model\nThis is a pre-trained word2vec model for Bengali language.\n\nThis model is build for bnlp package.",
"## Datasets\n- Wikipedia dump datasets",
"## Training details\n- Word2Vec word embedding dimension = 100, min_count=5, window=5, epochs=10",
"... |
null | null | # Bangla FastText Model
This is a FastText pre-trained model for the Bengali language.
This model is build for [bnlp](https://github.com/sagorbrur/bnlp) package.
## Datasets
- [Wikipedia dump datasets](https://dumps.wikimedia.org/bnwiki/latest/)
## Training Details
- Fasttext trained with total words = 20M, vocab si... | {"license": "mit"} | sagorsarker/bangla-fasttext | null | [
"license:mit",
"region:us"
] | null | 2022-06-11T12:48:49+00:00 | [] | [] | TAGS
#license-mit #region-us
| # Bangla FastText Model
This is a FastText pre-trained model for the Bengali language.
This model is build for bnlp package.
## Datasets
- Wikipedia dump datasets
## Training Details
- Fasttext trained with total words = 20M, vocab size = 1171011, epoch=50, embedding dimension = 300
## Evaluation Details
- training... | [
"# Bangla FastText Model\nThis is a FastText pre-trained model for the Bengali language.\n\nThis model is build for bnlp package.",
"## Datasets\n- Wikipedia dump datasets",
"## Training Details\n- Fasttext trained with total words = 20M, vocab size = 1171011, epoch=50, embedding dimension = 300",
"## Evaluat... | [
"TAGS\n#license-mit #region-us \n",
"# Bangla FastText Model\nThis is a FastText pre-trained model for the Bengali language.\n\nThis model is build for bnlp package.",
"## Datasets\n- Wikipedia dump datasets",
"## Training Details\n- Fasttext trained with total words = 20M, vocab size = 1171011, epoch=50, emb... |
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-cola
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/di... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["matthews_correlation"], "model-index": [{"name": "distilbert-base-uncased-finetuned-cola", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "ar... | tuni/distilbert-base-uncased-finetuned-cola | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-11T12:50:38+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-cola
======================================
This model is a fine-tuned version of distilbert-base-uncased on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.7035
* Matthews Correlation: 0.5324
Model description
-----------------
More informa... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3.785228097724678e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 16\n* seed: 28\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3"... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning... |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-squad
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/d... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "distilbert-base-uncased-finetuned-squad", "results": []}]} | seomh/distilbert-base-uncased-finetuned-squad | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-06-11T13:04:20+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-squad
=======================================
This model is a fine-tuned version of distilbert-base-uncased on the squad dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0083
Model description
-----------------
More information needed
Intended uses ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_s... |
tabular-classification | keras |
### Keras Implementation of Structured data learning with TabTransformer
This repo contains the trained model of [Structured data learning with TabTransformer](https://keras.io/examples/structured_data/tabtransformer/#define-dataset-metadata).
The full credit goes to: [Khalid Salama](https://www.linkedin.com/in/khali... | {"library_name": "keras", "tags": ["tabular-classification", "transformer"]} | keras-io/tab_transformer | null | [
"keras",
"tensorboard",
"tabular-classification",
"transformer",
"has_space",
"region:us"
] | null | 2022-06-11T13:12:07+00:00 | [] | [] | TAGS
#keras #tensorboard #tabular-classification #transformer #has_space #region-us
|
### Keras Implementation of Structured data learning with TabTransformer
This repo contains the trained model of Structured data learning with TabTransformer.
The full credit goes to: Khalid Salama
Spaces Link:
### Model summary:
- The trained model uses self-attention based Transformers structure following by mul... | [
"### Keras Implementation of Structured data learning with TabTransformer\nThis repo contains the trained model of Structured data learning with TabTransformer.\nThe full credit goes to: Khalid Salama\n\nSpaces Link:",
"### Model summary:\n- The trained model uses self-attention based Transformers structure follo... | [
"TAGS\n#keras #tensorboard #tabular-classification #transformer #has_space #region-us \n",
"### Keras Implementation of Structured data learning with TabTransformer\nThis repo contains the trained model of Structured data learning with TabTransformer.\nThe full credit goes to: Khalid Salama\n\nSpaces Link:",
"#... |
null | null | ## Bangla Glove Vectors
This is a collection of pre-trained glove vectors for the Bengali language. You can find details training in [this](https://github.com/sagorbrur/GloVe-Bengali) repository.
This model is build for [bnlp](https://github.com/sagorbrur/bnlp) package.
## Datasets
- [Wikipedia dump datasets](https:... | {"license": "mit"} | sagorsarker/bangla-glove-vectors | null | [
"license:mit",
"region:us"
] | null | 2022-06-11T13:14:02+00:00 | [] | [] | TAGS
#license-mit #region-us
| ## Bangla Glove Vectors
This is a collection of pre-trained glove vectors for the Bengali language. You can find details training in this repository.
This model is build for bnlp package.
## Datasets
- Wikipedia dump datasets
## Model Details
- wikipedia+crawl_news_articles (39M(39055685) tokens, 0.18M(178152) voca... | [
"## Bangla Glove Vectors\nThis is a collection of pre-trained glove vectors for the Bengali language. You can find details training in this repository.\n\nThis model is build for bnlp package.",
"## Datasets\n- Wikipedia dump datasets",
"## Model Details\n- wikipedia+crawl_news_articles (39M(39055685) tokens, 0... | [
"TAGS\n#license-mit #region-us \n",
"## Bangla Glove Vectors\nThis is a collection of pre-trained glove vectors for the Bengali language. You can find details training in this repository.\n\nThis model is build for bnlp package.",
"## Datasets\n- Wikipedia dump datasets",
"## Model Details\n- wikipedia+crawl_... |
reinforcement-learning | stable-baselines3 |
# **PPO** Agent playing **CarRacing-v0**
This is a trained model of a **PPO** agent playing **CarRacing-v0**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 impo... | {"library_name": "stable-baselines3", "tags": ["CarRacing-v0", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "CarRacing-v0", "type": "CarRacing-... | antonioricciardi/CarRacing-v0 | null | [
"stable-baselines3",
"CarRacing-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-11T13:26:00+00:00 | [] | [] | TAGS
#stable-baselines3 #CarRacing-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing CarRacing-v0
This is a trained model of a PPO agent playing CarRacing-v0
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# PPO Agent playing CarRacing-v0\nThis is a trained model of a PPO agent playing CarRacing-v0\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #CarRacing-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing CarRacing-v0\nThis is a trained model of a PPO agent playing CarRacing-v0\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code... |
unconditional-image-generation | transformers |
# Hugging NFT: frames
## Disclaimer
All rights belong to their owners. Models and datasets can be removed from the site at the request of the copyright
holder.
## Model description
LightWeight GAN model for unconditional generation.
NFT collection available [here](https://opensea.io/collection/frames).
Dataset i... | {"license": "mit", "tags": ["huggingnft", "nft", "huggan", "gan", "image", "images", "unconditional-image-generation"], "datasets": ["huggingnft/frames"]} | huggingnft/frames | null | [
"transformers",
"huggingnft",
"nft",
"huggan",
"gan",
"image",
"images",
"unconditional-image-generation",
"dataset:huggingnft/frames",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-06-11T13:58:47+00:00 | [] | [] | TAGS
#transformers #huggingnft #nft #huggan #gan #image #images #unconditional-image-generation #dataset-huggingnft/frames #license-mit #endpoints_compatible #region-us
|
# Hugging NFT: frames
## Disclaimer
All rights belong to their owners. Models and datasets can be removed from the site at the request of the copyright
holder.
## Model description
LightWeight GAN model for unconditional generation.
NFT collection available here.
Dataset is available here.
Check Space: link.
P... | [
"# Hugging NFT: frames",
"## Disclaimer\n\nAll rights belong to their owners. Models and datasets can be removed from the site at the request of the copyright\nholder.",
"## Model description\n\nLightWeight GAN model for unconditional generation.\n\nNFT collection available here.\n\nDataset is available here.\n... | [
"TAGS\n#transformers #huggingnft #nft #huggan #gan #image #images #unconditional-image-generation #dataset-huggingnft/frames #license-mit #endpoints_compatible #region-us \n",
"# Hugging NFT: frames",
"## Disclaimer\n\nAll rights belong to their owners. Models and datasets can be removed from the site at the re... |
null | null | See <https://github.com/k2-fsa/icefall/pull/389>
| {} | Zengwei/icefall-asr-librispeech-conv-emformer-transducer-stateless-2022-06-11 | null | [
"tensorboard",
"region:us"
] | null | 2022-06-11T14:22:15+00:00 | [] | [] | TAGS
#tensorboard #region-us
| See <URL
| [] | [
"TAGS\n#tensorboard #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. -->
# bertweet-base-finetuned-filtered-0609
This model is a fine-tuned version of [vinai/bertweet-base](https://huggingface.co/vinai/b... | {"tags": ["generated_from_trainer"], "metrics": ["accuracy", "precision", "recall", "f1"], "model-index": [{"name": "bertweet-base-finetuned-filtered-0609", "results": []}]} | YeRyeongLee/bertweet-base-finetuned-filtered-0609 | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-11T14:37:42+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| bertweet-base-finetuned-filtered-0609
=====================================
This model is a fine-tuned version of vinai/bertweet-base on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5397
* Accuracy: 0.9299
* Precision: 0.9297
* Recall: 0.9299
* F1: 0.9298
Model descriptio... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: ... | [
"TAGS\n#transformers #pytorch #roberta #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_siz... |
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. -->
# m2m100_418M-evaluated-en-to-ar-2000instancesopus-leaningRate2e-05-batchSize16-20epoch-1
This model is a fine-tuned version of [f... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["opus100"], "metrics": ["bleu"], "model-index": [{"name": "m2m100_418M-evaluated-en-to-ar-2000instancesopus-leaningRate2e-05-batchSize16-20epoch-1", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}... | abdoutony207/m2m100_418M-evaluated-en-to-ar-2000instancesopus-leaningRate2e-05-batchSize16-20epoch-1 | null | [
"transformers",
"pytorch",
"tensorboard",
"m2m_100",
"text2text-generation",
"generated_from_trainer",
"dataset:opus100",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-11T14:56:17+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #m2m_100 #text2text-generation #generated_from_trainer #dataset-opus100 #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| m2m100\_418M-evaluated-en-to-ar-2000instancesopus-leaningRate2e-05-batchSize16-20epoch-1
========================================================================================
This model is a fine-tuned version of facebook/m2m100\_418M on the opus100 dataset.
It achieves the following results on the evaluation set:... | [
"### 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: 20\n* mixed\\_pre... | [
"TAGS\n#transformers #pytorch #tensorboard #m2m_100 #text2text-generation #generated_from_trainer #dataset-opus100 #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rat... |
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. -->
# evangeloc/t5-small-finetuned-xsum
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown data... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "evangeloc/t5-small-finetuned-xsum", "results": []}]} | evangeloc/t5-small-finetuned-xsum | null | [
"transformers",
"pytorch",
"tf",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-11T15:05:22+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #tensorboard #safetensors #t5 #text2text-generation #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| evangeloc/t5-small-finetuned-xsum
=================================
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: 2.7203
* Validation Loss: 2.4006
* Train Rouge1: 28.1689
* Train Rouge2: 7.9798
* Train Rougel: 22.6998
* T... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': 2e-05, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight\\_decay\\_rate': 0.01}\n* training\\_precision: float32",
... | [
"TAGS\n#transformers #pytorch #tf #tensorboard #safetensors #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... |
automatic-speech-recognition | transformers |
# wav2vec2-ksponspeech
This model is a fine-tuned version of [Wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the None dataset.
It achieves the following results on the evaluation set:
- **WER(Word Error Rate)** for Third party test data : 0.373
**For improving WER:**
- Numeric... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-ksponspeech", "results": []}]} | Taeham/wav2vec2-ksponspeech | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-06-11T15:31:06+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
|
# wav2vec2-ksponspeech
This model is a fine-tuned version of Wav2vec2-large-xlsr-53 on the None dataset.
It achieves the following results on the evaluation set:
- WER(Word Error Rate) for Third party test data : 0.373
For improving WER:
- Numeric / Character Unification
- Decoding the word with the correct nota... | [
"# wav2vec2-ksponspeech\n\nThis model is a fine-tuned version of Wav2vec2-large-xlsr-53 on the None dataset. \nIt achieves the following results on the evaluation set:\n\n- WER(Word Error Rate) for Third party test data : 0.373\n\nFor improving WER:\n- Numeric / Character Unification\n- Decoding the word with the ... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"# wav2vec2-ksponspeech\n\nThis model is a fine-tuned version of Wav2vec2-large-xlsr-53 on the None dataset. \nIt achieves the following results on the evaluatio... |
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-large-cnn-aprischa
This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/facebook/bart-lar... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "bart-large-cnn-aprischa", "results": []}]} | aprischa/bart-large-cnn-aprischa | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-11T15:53:31+00:00 | [] | [] | TAGS
#transformers #pytorch #bart #text2text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| bart-large-cnn-aprischa
=======================
This model is a fine-tuned version of facebook/bart-large-cnn on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3589
* Rouge1: 66.7098
* Rouge2: 57.7992
* Rougel: 63.2231
* Rougelsum: 65.9009
* Gen Len: 141.198
Model descripti... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-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* num\\_epochs: 2\n* mixed\\_precis... | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 2\n* eval\\_... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-base-librispeech100h-google-colab
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-base-librispeech100h-google-colab", "results": []}]} | zoha/wav2vec2-base-librispeech100h-google-colab | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-06-11T16:07:29+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-base-librispeech100h-google-colab
==========================================
This model is a fine-tuned version of facebook/wav2vec2-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1156
* Wer: 0.0756
Model description
-----------------
More information neede... | [
"### 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* lr\\_scheduler\\_warmup\\_steps... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 1... |
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-large-cnn-aprischa2
This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/facebook/bart-la... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "bart-large-cnn-aprischa2", "results": []}]} | aprischa/bart-large-cnn-aprischa2 | null | [
"transformers",
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-11T16:40:18+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bart #text2text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| bart-large-cnn-aprischa2
========================
This model is a fine-tuned version of facebook/bart-large-cnn on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3425
* Rouge1: 65.7088
* Rouge2: 56.6701
* Rougel: 62.1926
* Rougelsum: 64.7727
* Gen Len: 140.8469
Model descri... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-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* num\\_epochs: 3\n* mixed\\_precis... | [
"TAGS\n#transformers #pytorch #tensorboard #bart #text2text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size:... |
null | null | crypto Trust**wallet customer service Support Number +**1-**818-869-**2884 | {"license": "artistic-2.0"} | trustwallet/22.00 | null | [
"license:artistic-2.0",
"region:us"
] | null | 2022-06-11T17:00:13+00:00 | [] | [] | TAGS
#license-artistic-2.0 #region-us
| crypto Trustwallet customer service Support Number +1-818-869-2884 | [] | [
"TAGS\n#license-artistic-2.0 #region-us \n"
] |
reinforcement-learning | stable-baselines3 |
# **DQN** Agent playing **MountainCar-v0**
This is a trained model of a **DQN** agent playing **MountainCar-v0**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 ... | {"library_name": "stable-baselines3", "tags": ["MountainCar-v0", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "DQN", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "MountainCar-v0", "type": "Mounta... | Galeros/dqn-mountaincar-v0-local | null | [
"stable-baselines3",
"MountainCar-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-11T17:38:19+00:00 | [] | [] | TAGS
#stable-baselines3 #MountainCar-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# DQN Agent playing MountainCar-v0
This is a trained model of a DQN agent playing MountainCar-v0
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# DQN Agent playing MountainCar-v0\nThis is a trained model of a DQN agent playing MountainCar-v0\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #MountainCar-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# DQN Agent playing MountainCar-v0\nThis is a trained model of a DQN agent playing MountainCar-v0\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. -->
# distilbert-base-uncased-finetuned-cola
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/di... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["matthews_correlation"], "model-index": [{"name": "distilbert-base-uncased-finetuned-cola", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "ar... | lindeberg/distilbert-base-uncased-finetuned-cola | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-11T17:50:58+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-cola
======================================
This model is a fine-tuned version of distilbert-base-uncased on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4949
* Matthews Correlation: 0.4497
Model description
-----------------
More informa... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning... |
text-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/1529956155937759233/Nyn1... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/elonmusk-iamjohnoliver-neiltyson/1654974044761/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/elonmusk-iamjohnoliver-neiltyson | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-11T17:54:15+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI CYBORG
Elon Musk & John Oliver & Neil deGrasse Tyson
@elonmusk-iamjohnoliver-neiltyson
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 dev... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
reinforcement-learning | stable-baselines3 |
# **DQN** Agent playing **MountainCar-v0**
This is a trained model of a **DQN** agent playing **MountainCar-v0**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 ... | {"library_name": "stable-baselines3", "tags": ["MountainCar-v0", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "DQN", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "MountainCar-v0", "type": "Mounta... | Galeros/dqn-mountaincar-v0-zoo | null | [
"stable-baselines3",
"MountainCar-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-11T17:55:20+00:00 | [] | [] | TAGS
#stable-baselines3 #MountainCar-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# DQN Agent playing MountainCar-v0
This is a trained model of a DQN agent playing MountainCar-v0
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# DQN Agent playing MountainCar-v0\nThis is a trained model of a DQN agent playing MountainCar-v0\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #MountainCar-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# DQN Agent playing MountainCar-v0\nThis is a trained model of a DQN agent playing MountainCar-v0\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add you... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# opus-mt-en-ar-evaluated-en-to-ar-1000instancesopus-leaningRate2e-05-batchSize8-11epoch-3
This model is a fine-tuned version of [... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["opus100"], "metrics": ["bleu"], "model-index": [{"name": "opus-mt-en-ar-evaluated-en-to-ar-1000instancesopus-leaningRate2e-05-batchSize8-11epoch-3", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Mo... | meghazisofiane/opus-mt-en-ar-evaluated-en-to-ar-1000instancesopus-leaningRate2e-05-batchSize8-11epoch-3 | null | [
"transformers",
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"generated_from_trainer",
"dataset:opus100",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-11T18:16:47+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #marian #text2text-generation #generated_from_trainer #dataset-opus100 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| opus-mt-en-ar-evaluated-en-to-ar-1000instancesopus-leaningRate2e-05-batchSize8-11epoch-3
========================================================================================
This model is a fine-tuned version of Helsinki-NLP/opus-mt-en-ar on the opus100 dataset.
It achieves the following results on the evaluation... | [
"### 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: 11\n* mixed\\_preci... | [
"TAGS\n#transformers #pytorch #tensorboard #marian #text2text-generation #generated_from_trainer #dataset-opus100 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning... |
null | null | git lfs install
git clone https://github.com/nneonneo/2048-ai.git | {} | JClementC/test | null | [
"region:us"
] | null | 2022-06-11T18:19:48+00:00 | [] | [] | TAGS
#region-us
| git lfs install
git clone URL | [] | [
"TAGS\n#region-us \n"
] |
text-generation | transformers |
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1098660288193269762/n5v9... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/mdoukmas/1654976150184/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/mdoukmas | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-11T18:34:24+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Maya Dukmasova
@mdoukmas
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"
] |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# opus-mt-en-ar-evaluated-en-to-ar-2000instancesopus-leaningRate2e-05-batchSize8-11epoch-3
This model is a fine-tuned version of [... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["opus100"], "metrics": ["bleu"], "model-index": [{"name": "opus-mt-en-ar-evaluated-en-to-ar-2000instancesopus-leaningRate2e-05-batchSize8-11epoch-3", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Mo... | meghazisofiane/opus-mt-en-ar-evaluated-en-to-ar-2000instancesopus-leaningRate2e-05-batchSize8-11epoch-3 | null | [
"transformers",
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"generated_from_trainer",
"dataset:opus100",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-11T18:41:31+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #marian #text2text-generation #generated_from_trainer #dataset-opus100 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| opus-mt-en-ar-evaluated-en-to-ar-2000instancesopus-leaningRate2e-05-batchSize8-11epoch-3
========================================================================================
This model is a fine-tuned version of Helsinki-NLP/opus-mt-en-ar on the opus100 dataset.
It achieves the following results on the evaluation... | [
"### 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: 11\n* mixed\\_preci... | [
"TAGS\n#transformers #pytorch #tensorboard #marian #text2text-generation #generated_from_trainer #dataset-opus100 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning... |
text-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/378800000836981162/b683f... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/rshowerthoughts-stephenking/1654976942704/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/rshowerthoughts-stephenking | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-11T18:42:08+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI CYBORG
Stephen King & Showerthoughts
@rshowerthoughts-stephenking
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the ... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
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/1271404115042676736/PAIb... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/conanobrien-mikemancini-wendymolyneux/1654977049172/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/conanobrien-mikemancini-wendymolyneux | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-11T18:46:43+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI CYBORG
mike mancini & Conan O'Brien & Wendy Molyneux
@conanobrien-mikemancini-wendymolyneux
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 wa... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
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. -->
# mT5_multilingual_XLSum-finetune-ar-xlsum
This model is a fine-tuned version of [csebuetnlp/mT5_multilingual_XLSum](https://huggi... | {"tags": ["summarization", "mT5_multilingual_XLSum", "mt5", "abstractive summarization", "ar", "xlsum", "generated_from_trainer"], "datasets": ["xlsum"], "model-index": [{"name": "mT5_multilingual_XLSum-finetune-ar-xlsum", "results": []}]} | ahmeddbahaa/mT5_multilingual_XLSum-finetune-ar-xlsum | null | [
"transformers",
"pytorch",
"tensorboard",
"mt5",
"text2text-generation",
"summarization",
"mT5_multilingual_XLSum",
"abstractive summarization",
"ar",
"xlsum",
"generated_from_trainer",
"dataset:xlsum",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"regio... | null | 2022-06-11T18:48:24+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #mt5 #text2text-generation #summarization #mT5_multilingual_XLSum #abstractive summarization #ar #xlsum #generated_from_trainer #dataset-xlsum #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| mT5\_multilingual\_XLSum-finetune-ar-xlsum
==========================================
This model is a fine-tuned version of csebuetnlp/mT5\_multilingual\_XLSum on the xlsum dataset.
It achieves the following results on the evaluation set:
* Loss: 3.2497
* Rouge-1: 32.52
* Rouge-2: 14.71
* Rouge-l: 27.88
* Gen Len: ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* gradient\\_accumulation\\_steps: 16\n* total\\_train\\_batch\\_size: 64\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
"TAGS\n#transformers #pytorch #tensorboard #mt5 #text2text-generation #summarization #mT5_multilingual_XLSum #abstractive summarization #ar #xlsum #generated_from_trainer #dataset-xlsum #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe fo... |
reinforcement-learning | stable-baselines3 |
# **DQN** Agent playing **MountainCar-v0**
This is a trained model of a **DQN** agent playing **MountainCar-v0**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 ... | {"library_name": "stable-baselines3", "tags": ["MountainCar-v0", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "DQN", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "MountainCar-v0", "type": "Mounta... | Galeros/dqn-mountaincar-v0-zoo-mimick | null | [
"stable-baselines3",
"MountainCar-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-11T18:55:00+00:00 | [] | [] | TAGS
#stable-baselines3 #MountainCar-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# DQN Agent playing MountainCar-v0
This is a trained model of a DQN agent playing MountainCar-v0
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# DQN Agent playing MountainCar-v0\nThis is a trained model of a DQN agent playing MountainCar-v0\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #MountainCar-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# DQN Agent playing MountainCar-v0\nThis is a trained model of a DQN agent playing MountainCar-v0\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add you... |
text-generation | transformers |
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1529956155937759233/Nyn1... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/elonmusk-rshowerthoughts-stephenking/1654978546952/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/elonmusk-rshowerthoughts-stephenking | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-11T19:04:06+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI CYBORG
Elon Musk & Stephen King & Showerthoughts
@elonmusk-rshowerthoughts-stephenking
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 dev... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
reinforcement-learning | null |
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="tjscollins/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional a... | {"tags": ["FrozenLake-v1-4x4", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-FrozenLake-v1-4x4-noSlippery", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "FrozenLake-v1-4x4", "type": "FrozenLake-v1-4x4"}, "m... | tjscollins/q-FrozenLake-v1-4x4-noSlippery | null | [
"FrozenLake-v1-4x4",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-06-11T19:24:19+00:00 | [] | [] | TAGS
#FrozenLake-v1-4x4 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing FrozenLake-v1
This is a trained model of a Q-Learning agent playing FrozenLake-v1 .
## Usage
| [
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] | [
"TAGS\n#FrozenLake-v1-4x4 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] |
token-classification | transformers | This model is a BERT-based Location Mention Recognition model that is adopted from the [TLLMR4CM GitHub](https://github.com/rsuwaileh/TLLMR4CM/).
The model is trained using Hurricane Dorian 2019 event (training, development, and test data are used for training) from [IDRISI-R dataset](https://github.com/rsuwaileh/IDRIS... | {} | rsuwaileh/IDRISI-LMR-HD-TB | null | [
"transformers",
"pytorch",
"bert",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-11T19:26:44+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #token-classification #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 is trained using Hurricane Dorian 2019 event (training, development, and test data are used for training) from IDRISI-R dataset under the Type-based LMR mode and using the random version of the data.
You ca... | [] | [
"TAGS\n#transformers #pytorch #bert #token-classification #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. -->
# bert-finetuned-squad
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the squa... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "bert-finetuned-squad", "results": []}]} | MyMild/bert-finetuned-squad | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-06-11T19:26:57+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us
|
# bert-finetuned-squad
This model is a fine-tuned version of bert-base-cased on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
T... | [
"# bert-finetuned-squad\n\nThis model is a fine-tuned version of bert-base-cased on the squad dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"#... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us \n",
"# bert-finetuned-squad\n\nThis model is a fine-tuned version of bert-base-cased on the squad dataset.",
"## Model description\n\nMore information... |
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 is trained using Hurricane Dorian 2019 event (training, development, and test data are used for training) from [IDRISI-R dataset](https://github.com/rsuwaileh/IDRIS... | {} | rsuwaileh/IDRISI-LMR-HD-TL | null | [
"transformers",
"pytorch",
"bert",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-11T19:30:24+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #token-classification #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 is trained using Hurricane Dorian 2019 event (training, development, and test data are used for training) from IDRISI-R dataset under the Type-less LMR mode and using the random version of the data.
You can... | [] | [
"TAGS\n#transformers #pytorch #bert #token-classification #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 is trained using Hurricane Dorian 2019 event (training data is used for training) from [IDRISI-R dataset](https://github.com/rsuwaileh/IDRISI) under the Type-less L... | {} | rsuwaileh/IDRISI-LMR-HD-TL-partition | null | [
"transformers",
"pytorch",
"bert",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-11T19:30:51+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #token-classification #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 is trained using Hurricane Dorian 2019 event (training data is used for training) from IDRISI-R dataset under the Type-less LMR mode and using the random version of the data.
You can download this data in B... | [] | [
"TAGS\n#transformers #pytorch #bert #token-classification #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 is trained using Hurricane Dorian 2019 event (only the training data is used for training) from [IDRISI-R dataset](https://github.com/rsuwaileh/IDRISI) under the Ty... | {} | rsuwaileh/IDRISI-LMR-HD-TB-partition | null | [
"transformers",
"pytorch",
"bert",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-11T19:32:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #token-classification #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 is trained using Hurricane Dorian 2019 event (only the training data is used for training) from IDRISI-R dataset under the Type-based LMR mode and using the random version of the data.
You can download this... | [] | [
"TAGS\n#transformers #pytorch #bert #token-classification #autotrain_compatible #endpoints_compatible #region-us \n"
] |
reinforcement-learning | null |
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="tjscollins/q-FrozenLake-v1-4x4-slippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional att... | {"tags": ["FrozenLake-v1-4x4-4x4", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-FrozenLake-v1-4x4-slippery", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "FrozenLake-v1-4x4-4x4", "type": "FrozenLake-v1-4x4... | tjscollins/q-FrozenLake-v1-4x4-slippery | null | [
"FrozenLake-v1-4x4-4x4",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-06-11T19:32:22+00:00 | [] | [] | TAGS
#FrozenLake-v1-4x4-4x4 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing FrozenLake-v1
This is a trained model of a Q-Learning agent playing FrozenLake-v1 .
## Usage
| [
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] | [
"TAGS\n#FrozenLake-v1-4x4-4x4 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] |
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. -->
# mt5-base-finetune-ar-xlsum
This model is a fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) on th... | {"license": "apache-2.0", "tags": ["summarization", "mT5_multilingual_XLSum", "mt5", "abstractive summarization", "ar", "xlsum", "generated_from_trainer"], "datasets": ["xlsum"], "model-index": [{"name": "mt5-base-finetune-ar-xlsum", "results": []}]} | ahmeddbahaa/mt5-base-finetune-ar-xlsum | null | [
"transformers",
"pytorch",
"tensorboard",
"mt5",
"text2text-generation",
"summarization",
"mT5_multilingual_XLSum",
"abstractive summarization",
"ar",
"xlsum",
"generated_from_trainer",
"dataset:xlsum",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generat... | null | 2022-06-11T19:41:00+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #mt5 #text2text-generation #summarization #mT5_multilingual_XLSum #abstractive summarization #ar #xlsum #generated_from_trainer #dataset-xlsum #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| mt5-base-finetune-ar-xlsum
==========================
This model is a fine-tuned version of google/mt5-base on the xlsum dataset.
It achieves the following results on the evaluation set:
* Loss: 3.2546
* Rouge-1: 22.2
* Rouge-2: 9.57
* Rouge-l: 20.26
* Gen Len: 19.0
* Bertscore: 71.43
Model description
----------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* gradient\\_accumulation\\_steps: 16\n* total\\_train\\_batch\\_size: 64\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
"TAGS\n#transformers #pytorch #tensorboard #mt5 #text2text-generation #summarization #mT5_multilingual_XLSum #abstractive summarization #ar #xlsum #generated_from_trainer #dataset-xlsum #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperpa... |
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. -->
# music-generation
This model a trained from scratch version of [distilgpt2](https://huggingface.co/distilgpt2) on a dataset where... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "music-generation", "results": []}]} | DancingIguana/music-generation | null | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-06-11T19:47:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
# music-generation
This model a trained from scratch version of distilgpt2 on a dataset where the text represents musical notes. The dataset consists of one stream of notes from MIDI files (the stream with most notes), where all of the melodies were transposed either to C major or A minor. Also, the BPM of the song... | [
"# music-generation\n\nThis model a trained from scratch version of distilgpt2 on a dataset where the text represents musical notes. The dataset consists of one stream of notes from MIDI files (the stream with most notes), where all of the melodies were transposed either to C major or A minor. Also, the BPM of the ... | [
"TAGS\n#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"# music-generation\n\nThis model a trained from scratch version of distilgpt2 on a dataset where the text... |
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="tjscollins/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)... | {"tags": ["Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-Taxi-v3", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Taxi-v3", "type": "Taxi-v3"}, "metrics": [{"type": "mean_reward", "value": "12.00 +... | tjscollins/q-Taxi-v3 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-06-11T20:00:50+00:00 | [] | [] | TAGS
#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing Taxi-v3
This is a trained model of a Q-Learning agent playing Taxi-v3 .
## Usage
| [
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] | [
"TAGS\n#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# opus-mt-en-ar-evaluated-en-to-ar-4000instances-opus-leaningRate2e-05-batchSize8-11-action-1
This model is a fine-tuned version o... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["opus100"], "metrics": ["bleu"], "model-index": [{"name": "opus-mt-en-ar-evaluated-en-to-ar-4000instances-opus-leaningRate2e-05-batchSize8-11-action-1", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language... | meghazisofiane/opus-mt-en-ar-evaluated-en-to-ar-4000instances-opus-leaningRate2e-05-batchSize8-11-action-1 | null | [
"transformers",
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"generated_from_trainer",
"dataset:opus100",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-11T20:33:30+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #marian #text2text-generation #generated_from_trainer #dataset-opus100 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| opus-mt-en-ar-evaluated-en-to-ar-4000instances-opus-leaningRate2e-05-batchSize8-11-action-1
===========================================================================================
This model is a fine-tuned version of Helsinki-NLP/opus-mt-en-ar on the opus100 dataset.
It achieves the following results on the eval... | [
"### 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: 11",
"### Trainin... | [
"TAGS\n#transformers #pytorch #tensorboard #marian #text2text-generation #generated_from_trainer #dataset-opus100 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning... |
reinforcement-learning | null |
# **Q-Learning** Agent playing **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="tjscollins/q-Taxi-v3-broken-eval-seed", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_sl... | {"tags": ["Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-Taxi-v3-broken-eval-seed", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Taxi-v3", "type": "Taxi-v3"}, "metrics": [{"type": "mean_reward", ... | tjscollins/q-Taxi-v3-broken-eval-seed | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-06-11T20:38:44+00:00 | [] | [] | TAGS
#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing Taxi-v3
This is a trained model of a Q-Learning agent playing Taxi-v3 .
## Usage
| [
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] | [
"TAGS\n#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# opus-mt-en-ar-evaluated-en-to-ar-4000instances-un_multi-leaningRate2e-05-batchSize8-11-action-1
This model is a fine-tuned versi... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["un_multi"], "metrics": ["bleu"], "model-index": [{"name": "opus-mt-en-ar-evaluated-en-to-ar-4000instances-un_multi-leaningRate2e-05-batchSize8-11-action-1", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Lan... | meghazisofiane/opus-mt-en-ar-evaluated-en-to-ar-4000instances-un_multi-leaningRate2e-05-batchSize8-11-action-1 | null | [
"transformers",
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"generated_from_trainer",
"dataset:un_multi",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-11T21:15:42+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #marian #text2text-generation #generated_from_trainer #dataset-un_multi #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| opus-mt-en-ar-evaluated-en-to-ar-4000instances-un\_multi-leaningRate2e-05-batchSize8-11-action-1
================================================================================================
This model is a fine-tuned version of Helsinki-NLP/opus-mt-en-ar on the un\_multi dataset.
It achieves the following results... | [
"### 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: 11",
"### Trainin... | [
"TAGS\n#transformers #pytorch #tensorboard #marian #text2text-generation #generated_from_trainer #dataset-un_multi #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* learnin... |
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. -->
# finetune_iapp_thaiqa
This model is a fine-tuned version of [airesearch/wangchanberta-base-att-spm-uncased](https://huggingface.c... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "finetune_iapp_thaiqa", "results": []}]} | MyMild/finetune_iapp_thaiqa | null | [
"transformers",
"pytorch",
"tensorboard",
"camembert",
"question-answering",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | null | 2022-06-11T22:05:08+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #camembert #question-answering #generated_from_trainer #endpoints_compatible #region-us
|
# finetune_iapp_thaiqa
This model is a fine-tuned version of airesearch/wangchanberta-base-att-spm-uncased on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
##... | [
"# finetune_iapp_thaiqa\n\nThis model is a fine-tuned version of airesearch/wangchanberta-base-att-spm-uncased 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",
... | [
"TAGS\n#transformers #pytorch #tensorboard #camembert #question-answering #generated_from_trainer #endpoints_compatible #region-us \n",
"# finetune_iapp_thaiqa\n\nThis model is a fine-tuned version of airesearch/wangchanberta-base-att-spm-uncased on the None dataset.",
"## Model description\n\nMore information ... |
null | null | # MedicalNet
This repository contains a Pytorch implementation of [Med3D: Transfer Learning for 3D Medical Image Analysis](https://arxiv.org/abs/1904.00625).
Many studies have shown that the performance on deep learning is significantly affected by volume of training data. The MedicalNet project aggregated the dataset... | {"language": ["en"], "license": "mit", "tags": ["MedicalNet", "medical images", "medical", "3D", "Med3D"], "datasets": ["MRBrainS18"], "metrics": [], "thumbnail": "https://github.com/Tencent/MedicalNet/blob/master/images/logo.png?raw=true"} | TencentMedicalNet/MedicalNet-Resnet10 | null | [
"MedicalNet",
"medical images",
"medical",
"3D",
"Med3D",
"en",
"dataset:MRBrainS18",
"arxiv:1904.00625",
"license:mit",
"region:us"
] | null | 2022-06-11T22:12:06+00:00 | [
"1904.00625"
] | [
"en"
] | TAGS
#MedicalNet #medical images #medical #3D #Med3D #en #dataset-MRBrainS18 #arxiv-1904.00625 #license-mit #region-us
| # MedicalNet
This repository contains a Pytorch implementation of Med3D: Transfer Learning for 3D Medical Image Analysis.
Many studies have shown that the performance on deep learning is significantly affected by volume of training data. The MedicalNet project aggregated the dataset with diverse modalities, target org... | [
"# MedicalNet\nThis repository contains a Pytorch implementation of Med3D: Transfer Learning for 3D Medical Image Analysis. \nMany studies have shown that the performance on deep learning is significantly affected by volume of training data. The MedicalNet project aggregated the dataset with diverse modalities, tar... | [
"TAGS\n#MedicalNet #medical images #medical #3D #Med3D #en #dataset-MRBrainS18 #arxiv-1904.00625 #license-mit #region-us \n",
"# MedicalNet\nThis repository contains a Pytorch implementation of Med3D: Transfer Learning for 3D Medical Image Analysis. \nMany studies have shown that the performance on deep learning ... |
fill-mask | transformers | Size of the model is **48MB**
Example of usage:
```
tokenizer = AutoTokenizer.from_pretrained("Abhijnan/assamese_albert")
model = AutoModel.from_pretrained("Abhijnan/assamese_albert")
assamese_text = 'সঁচাই পৃথিৱীখন যে ইমান সুন্দৰ'
assamese_token = tokenizer(assamese_text ,padding='longest', return_tensors="pt")
pri... | {} | Abhijnan/AxomiyaBERTa | null | [
"transformers",
"pytorch",
"albert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-11T22:16:12+00:00 | [] | [] | TAGS
#transformers #pytorch #albert #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| Size of the model is 48MB
Example of usage:
| [] | [
"TAGS\n#transformers #pytorch #albert #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n"
] |
reinforcement-learning | stable-baselines3 |
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```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... | 745H1N/LunarLander-v2-PPO-optuna | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-11T22:30:32+00:00 | [] | [] | TAGS
#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add you... |
reinforcement-learning | stable-baselines3 |
# **DQN** Agent playing **LunarLander-v2**
This is a trained model of a **DQN** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```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": "DQN", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "LunarLander-v2", "type": "LunarL... | 745H1N/LunarLander-v2-DQN-optuna | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-11T22:36:25+00:00 | [] | [] | TAGS
#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# DQN Agent playing LunarLander-v2
This is a trained model of a DQN agent playing LunarLander-v2
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# DQN Agent playing LunarLander-v2\nThis is a trained model of a DQN 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",
"# DQN Agent playing LunarLander-v2\nThis is a trained model of a DQN agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add you... |
reinforcement-learning | stable-baselines3 |
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 ... | {"library_name": "stable-baselines3", "tags": ["LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "LunarLander-v2", "type": "LunarL... | DLWCMD/TEST2ppo-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-11T22:38:43+00:00 | [] | [] | TAGS
#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add you... |
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/1500274766195793921/bA4s... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/laserboat999/1654991516445/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/laserboat999 | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-11T22:49:07+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
donald boat
@laserboat999
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
-------... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation | transformers |
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1273429972229804032/_kkJ... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/cancer_blood69/1654992058711/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/cancer_blood69 | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-11T22:58:19+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
cancer\_blood69 (reanimated decaying corpse)
@cancer\_blood69
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
null | null | # MedicalNet
This repository contains a Pytorch implementation of [Med3D: Transfer Learning for 3D Medical Image Analysis](https://arxiv.org/abs/1904.00625).
Many studies have shown that the performance on deep learning is significantly affected by volume of training data. The MedicalNet project aggregated the dataset... | {"language": ["en"], "license": "mit", "tags": ["MedicalNet", "medical images", "medical", "3D", "Med3D"], "datasets": ["MRBrainS18"], "metrics": [], "thumbnail": "https://github.com/Tencent/MedicalNet/blob/master/images/logo.png?raw=true"} | TencentMedicalNet/MedicalNet-Resnet18 | null | [
"MedicalNet",
"medical images",
"medical",
"3D",
"Med3D",
"en",
"dataset:MRBrainS18",
"arxiv:1904.00625",
"license:mit",
"region:us"
] | null | 2022-06-11T23:26:59+00:00 | [
"1904.00625"
] | [
"en"
] | TAGS
#MedicalNet #medical images #medical #3D #Med3D #en #dataset-MRBrainS18 #arxiv-1904.00625 #license-mit #region-us
| # MedicalNet
This repository contains a Pytorch implementation of Med3D: Transfer Learning for 3D Medical Image Analysis.
Many studies have shown that the performance on deep learning is significantly affected by volume of training data. The MedicalNet project aggregated the dataset with diverse modalities, target org... | [
"# MedicalNet\nThis repository contains a Pytorch implementation of Med3D: Transfer Learning for 3D Medical Image Analysis. \nMany studies have shown that the performance on deep learning is significantly affected by volume of training data. The MedicalNet project aggregated the dataset with diverse modalities, tar... | [
"TAGS\n#MedicalNet #medical images #medical #3D #Med3D #en #dataset-MRBrainS18 #arxiv-1904.00625 #license-mit #region-us \n",
"# MedicalNet\nThis repository contains a Pytorch implementation of Med3D: Transfer Learning for 3D Medical Image Analysis. \nMany studies have shown that the performance on deep learning ... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# opus-mt-en-ar-evaluated-en-to-ar-2000instances-un_multi-leaningRate2e-05-batchSize8-11-action-1
This model is a fine-tuned versi... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["un_multi"], "metrics": ["bleu"], "model-index": [{"name": "opus-mt-en-ar-evaluated-en-to-ar-2000instances-un_multi-leaningRate2e-05-batchSize8-11-action-1", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Lan... | meghazisofiane/opus-mt-en-ar-evaluated-en-to-ar-2000instances-un_multi-leaningRate2e-05-batchSize8-11-action-1 | null | [
"transformers",
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"generated_from_trainer",
"dataset:un_multi",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-11T23:34:12+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #marian #text2text-generation #generated_from_trainer #dataset-un_multi #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| opus-mt-en-ar-evaluated-en-to-ar-2000instances-un\_multi-leaningRate2e-05-batchSize8-11-action-1
================================================================================================
This model is a fine-tuned version of Helsinki-NLP/opus-mt-en-ar on the un\_multi dataset.
It achieves the following results... | [
"### 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: 11",
"### Trainin... | [
"TAGS\n#transformers #pytorch #tensorboard #marian #text2text-generation #generated_from_trainer #dataset-un_multi #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* learnin... |
null | null | # MedicalNet
This repository contains a Pytorch implementation of [Med3D: Transfer Learning for 3D Medical Image Analysis](https://arxiv.org/abs/1904.00625).
Many studies have shown that the performance on deep learning is significantly affected by volume of training data. The MedicalNet project aggregated the dataset... | {"language": ["en"], "license": "mit", "tags": ["MedicalNet", "medical images", "medical", "3D", "Med3D"], "datasets": ["MRBrainS18"], "metrics": [], "thumbnail": "https://github.com/Tencent/MedicalNet/blob/master/images/logo.png?raw=true"} | TencentMedicalNet/MedicalNet-Resnet34 | null | [
"MedicalNet",
"medical images",
"medical",
"3D",
"Med3D",
"en",
"dataset:MRBrainS18",
"arxiv:1904.00625",
"license:mit",
"region:us"
] | null | 2022-06-11T23:34:54+00:00 | [
"1904.00625"
] | [
"en"
] | TAGS
#MedicalNet #medical images #medical #3D #Med3D #en #dataset-MRBrainS18 #arxiv-1904.00625 #license-mit #region-us
| # MedicalNet
This repository contains a Pytorch implementation of Med3D: Transfer Learning for 3D Medical Image Analysis.
Many studies have shown that the performance on deep learning is significantly affected by volume of training data. The MedicalNet project aggregated the dataset with diverse modalities, target org... | [
"# MedicalNet\nThis repository contains a Pytorch implementation of Med3D: Transfer Learning for 3D Medical Image Analysis. \nMany studies have shown that the performance on deep learning is significantly affected by volume of training data. The MedicalNet project aggregated the dataset with diverse modalities, tar... | [
"TAGS\n#MedicalNet #medical images #medical #3D #Med3D #en #dataset-MRBrainS18 #arxiv-1904.00625 #license-mit #region-us \n",
"# MedicalNet\nThis repository contains a Pytorch implementation of Med3D: Transfer Learning for 3D Medical Image Analysis. \nMany studies have shown that the performance on deep learning ... |
null | null | # MedicalNet
This repository contains a Pytorch implementation of [Med3D: Transfer Learning for 3D Medical Image Analysis](https://arxiv.org/abs/1904.00625).
Many studies have shown that the performance on deep learning is significantly affected by volume of training data. The MedicalNet project aggregated the dataset... | {"language": ["en"], "license": "mit", "tags": ["MedicalNet", "medical images", "medical", "3D", "Med3D"], "datasets": ["MRBrainS18"], "metrics": [], "thumbnail": "https://github.com/Tencent/MedicalNet/blob/master/images/logo.png?raw=true"} | TencentMedicalNet/MedicalNet-Resnet50 | null | [
"MedicalNet",
"medical images",
"medical",
"3D",
"Med3D",
"en",
"dataset:MRBrainS18",
"arxiv:1904.00625",
"license:mit",
"region:us"
] | null | 2022-06-11T23:35:55+00:00 | [
"1904.00625"
] | [
"en"
] | TAGS
#MedicalNet #medical images #medical #3D #Med3D #en #dataset-MRBrainS18 #arxiv-1904.00625 #license-mit #region-us
| # MedicalNet
This repository contains a Pytorch implementation of Med3D: Transfer Learning for 3D Medical Image Analysis.
Many studies have shown that the performance on deep learning is significantly affected by volume of training data. The MedicalNet project aggregated the dataset with diverse modalities, target org... | [
"# MedicalNet\nThis repository contains a Pytorch implementation of Med3D: Transfer Learning for 3D Medical Image Analysis. \nMany studies have shown that the performance on deep learning is significantly affected by volume of training data. The MedicalNet project aggregated the dataset with diverse modalities, tar... | [
"TAGS\n#MedicalNet #medical images #medical #3D #Med3D #en #dataset-MRBrainS18 #arxiv-1904.00625 #license-mit #region-us \n",
"# MedicalNet\nThis repository contains a Pytorch implementation of Med3D: Transfer Learning for 3D Medical Image Analysis. \nMany studies have shown that the performance on deep learning ... |
null | null | # MedicalNet
This repository contains a Pytorch implementation of [Med3D: Transfer Learning for 3D Medical Image Analysis](https://arxiv.org/abs/1904.00625).
Many studies have shown that the performance on deep learning is significantly affected by volume of training data. The MedicalNet project aggregated the dataset... | {"language": ["en"], "license": "mit", "tags": ["MedicalNet", "medical images", "medical", "3D", "Med3D"], "datasets": ["MRBrainS18"], "metrics": [], "thumbnail": "https://github.com/Tencent/MedicalNet/blob/master/images/logo.png?raw=true"} | TencentMedicalNet/MedicalNet-Resnet101 | null | [
"MedicalNet",
"medical images",
"medical",
"3D",
"Med3D",
"en",
"dataset:MRBrainS18",
"arxiv:1904.00625",
"license:mit",
"region:us"
] | null | 2022-06-11T23:52:14+00:00 | [
"1904.00625"
] | [
"en"
] | TAGS
#MedicalNet #medical images #medical #3D #Med3D #en #dataset-MRBrainS18 #arxiv-1904.00625 #license-mit #region-us
| # MedicalNet
This repository contains a Pytorch implementation of Med3D: Transfer Learning for 3D Medical Image Analysis.
Many studies have shown that the performance on deep learning is significantly affected by volume of training data. The MedicalNet project aggregated the dataset with diverse modalities, target org... | [
"# MedicalNet\nThis repository contains a Pytorch implementation of Med3D: Transfer Learning for 3D Medical Image Analysis. \nMany studies have shown that the performance on deep learning is significantly affected by volume of training data. The MedicalNet project aggregated the dataset with diverse modalities, tar... | [
"TAGS\n#MedicalNet #medical images #medical #3D #Med3D #en #dataset-MRBrainS18 #arxiv-1904.00625 #license-mit #region-us \n",
"# MedicalNet\nThis repository contains a Pytorch implementation of Med3D: Transfer Learning for 3D Medical Image Analysis. \nMany studies have shown that the performance on deep learning ... |
null | null | # MedicalNet
This repository contains a Pytorch implementation of [Med3D: Transfer Learning for 3D Medical Image Analysis](https://arxiv.org/abs/1904.00625).
Many studies have shown that the performance on deep learning is significantly affected by volume of training data. The MedicalNet project aggregated the dataset... | {"language": ["en"], "license": "mit", "tags": ["MedicalNet", "medical images", "medical", "3D", "Med3D"], "datasets": ["MRBrainS18"], "metrics": [], "thumbnail": "https://github.com/Tencent/MedicalNet/blob/master/images/logo.png?raw=true"} | TencentMedicalNet/MedicalNet-Resnet152 | null | [
"MedicalNet",
"medical images",
"medical",
"3D",
"Med3D",
"en",
"dataset:MRBrainS18",
"arxiv:1904.00625",
"license:mit",
"region:us"
] | null | 2022-06-11T23:52:56+00:00 | [
"1904.00625"
] | [
"en"
] | TAGS
#MedicalNet #medical images #medical #3D #Med3D #en #dataset-MRBrainS18 #arxiv-1904.00625 #license-mit #region-us
| # MedicalNet
This repository contains a Pytorch implementation of Med3D: Transfer Learning for 3D Medical Image Analysis.
Many studies have shown that the performance on deep learning is significantly affected by volume of training data. The MedicalNet project aggregated the dataset with diverse modalities, target org... | [
"# MedicalNet\nThis repository contains a Pytorch implementation of Med3D: Transfer Learning for 3D Medical Image Analysis. \nMany studies have shown that the performance on deep learning is significantly affected by volume of training data. The MedicalNet project aggregated the dataset with diverse modalities, tar... | [
"TAGS\n#MedicalNet #medical images #medical #3D #Med3D #en #dataset-MRBrainS18 #arxiv-1904.00625 #license-mit #region-us \n",
"# MedicalNet\nThis repository contains a Pytorch implementation of Med3D: Transfer Learning for 3D Medical Image Analysis. \nMany studies have shown that the performance on deep learning ... |
null | null | # MedicalNet
This repository contains a Pytorch implementation of [Med3D: Transfer Learning for 3D Medical Image Analysis](https://arxiv.org/abs/1904.00625).
Many studies have shown that the performance on deep learning is significantly affected by volume of training data. The MedicalNet project aggregated the dataset... | {"language": ["en"], "license": "mit", "tags": ["MedicalNet", "medical images", "medical", "3D", "Med3D"], "datasets": ["MRBrainS18"], "metrics": [], "thumbnail": "https://github.com/Tencent/MedicalNet/blob/master/images/logo.png?raw=true"} | TencentMedicalNet/MedicalNet-Resnet200 | null | [
"MedicalNet",
"medical images",
"medical",
"3D",
"Med3D",
"en",
"dataset:MRBrainS18",
"arxiv:1904.00625",
"license:mit",
"region:us"
] | null | 2022-06-11T23:53:17+00:00 | [
"1904.00625"
] | [
"en"
] | TAGS
#MedicalNet #medical images #medical #3D #Med3D #en #dataset-MRBrainS18 #arxiv-1904.00625 #license-mit #region-us
| # MedicalNet
This repository contains a Pytorch implementation of Med3D: Transfer Learning for 3D Medical Image Analysis.
Many studies have shown that the performance on deep learning is significantly affected by volume of training data. The MedicalNet project aggregated the dataset with diverse modalities, target org... | [
"# MedicalNet\nThis repository contains a Pytorch implementation of Med3D: Transfer Learning for 3D Medical Image Analysis. \nMany studies have shown that the performance on deep learning is significantly affected by volume of training data. The MedicalNet project aggregated the dataset with diverse modalities, tar... | [
"TAGS\n#MedicalNet #medical images #medical #3D #Med3D #en #dataset-MRBrainS18 #arxiv-1904.00625 #license-mit #region-us \n",
"# MedicalNet\nThis repository contains a Pytorch implementation of Med3D: Transfer Learning for 3D Medical Image Analysis. \nMany studies have shown that the performance on deep learning ... |
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. -->
# MIX2_ja-en_helsinki
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-ja-en](https://huggingface.co/Helsinki-NLP/opus-... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "MIX2_ja-en_helsinki", "results": []}]} | twieland/MIX2_ja-en_helsinki | null | [
"transformers",
"pytorch",
"marian",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-12T00:01:47+00:00 | [] | [] | TAGS
#transformers #pytorch #marian #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| MIX2\_ja-en\_helsinki
=====================
This model is a fine-tuned version of Helsinki-NLP/opus-mt-ja-en on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 1.4929
* Otaku Benchmark VN BLEU: 20.21
* Otaku Benchmark LN BLEU: 13.29
* Otaku Benchmark MANGA BLEU: 19.07
Model de... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 96\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: 4\n* mixed\\_pre... | [
"TAGS\n#transformers #pytorch #marian #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 96... |
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. -->
# arabert2arabert-finetuned-ar-wikilingua
This model is a fine-tuned version of [](https://huggingface.co/) on the wiki_lingua dat... | {"tags": ["summarization", "ar", "encoder-decoder", "arabert", "arabert2arabert", "Abstractive Summarization", "generated_from_trainer"], "datasets": ["wiki_lingua"], "model-index": [{"name": "arabert2arabert-finetuned-ar-wikilingua", "results": []}]} | ahmeddbahaa/arabert2arabert-finetuned-ar-wikilingua | null | [
"transformers",
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"summarization",
"ar",
"arabert",
"arabert2arabert",
"Abstractive Summarization",
"generated_from_trainer",
"dataset:wiki_lingua",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-12T00:03:07+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #encoder-decoder #text2text-generation #summarization #ar #arabert #arabert2arabert #Abstractive Summarization #generated_from_trainer #dataset-wiki_lingua #autotrain_compatible #endpoints_compatible #region-us
| arabert2arabert-finetuned-ar-wikilingua
=======================================
This model is a fine-tuned version of [](URL on the wiki\_lingua dataset.
It achieves the following results on the evaluation set:
* Loss: 4.6877
* Rouge-1: 13.2
* Rouge-2: 3.43
* Rouge-l: 12.45
* Gen Len: 20.0
* Bertscore: 64.88
Mode... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 16\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
"TAGS\n#transformers #pytorch #tensorboard #encoder-decoder #text2text-generation #summarization #ar #arabert #arabert2arabert #Abstractive Summarization #generated_from_trainer #dataset-wiki_lingua #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperp... |
reinforcement-learning | stable-baselines3 |
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framewor... | {"library_name": "stable-baselines3", "tags": ["SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "DQN", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "SpaceInvadersNoFram... | bguan/SpaceInvadersNoFrameskip-v4 | null | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-12T00:04:38+00:00 | [] | [] | TAGS
#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# DQN Agent playing SpaceInvadersNoFrameskip-v4
This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4
using the stable-baselines3 library
and the RL Zoo.
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents... | [
"# DQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framework for Stable Baselines3\nreinforcement learning agents,\nwith hyperparameter optimization and pre-tra... | [
"TAGS\n#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# DQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo... |
null | transformers |
# Empathic Conversations: Dialog Acts
Model owner(s): Ryan Guan, [rguan@seas.upenn.edu](mailto:rguan@seas.upenn.edu)
Associated paper:
## Model description
### Related models
- wwbproj/empathic_conversations_empathy
- wwbproj/empathic_conversations_emotion
- wwbproj/empathic_conversations_emotional_polarity
- wwbpr... | {"language": ["en"]} | wwbproj/empathic_conversations_dialog_acts | null | [
"transformers",
"pytorch",
"roberta",
"en",
"endpoints_compatible",
"region:us"
] | null | 2022-06-12T00:45:44+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #roberta #en #endpoints_compatible #region-us
|
# Empathic Conversations: Dialog Acts
Model owner(s): Ryan Guan, rguan@URL
Associated paper:
## Model description
### Related models
- wwbproj/empathic_conversations_empathy
- wwbproj/empathic_conversations_emotion
- wwbproj/empathic_conversations_emotional_polarity
- wwbproj/empathic_conversations_self_disclosure
... | [
"# Empathic Conversations: Dialog Acts\nModel owner(s): Ryan Guan, rguan@URL\nAssociated paper:",
"## Model description",
"### Related models\n- wwbproj/empathic_conversations_empathy\n- wwbproj/empathic_conversations_emotion\n- wwbproj/empathic_conversations_emotional_polarity\n- wwbproj/empathic_conversations... | [
"TAGS\n#transformers #pytorch #roberta #en #endpoints_compatible #region-us \n",
"# Empathic Conversations: Dialog Acts\nModel owner(s): Ryan Guan, rguan@URL\nAssociated paper:",
"## Model description",
"### Related models\n- wwbproj/empathic_conversations_empathy\n- wwbproj/empathic_conversations_emotion\n- ... |
text-generation | transformers | > THIS MODEL IS INTENDED FOR RESEARCH PURPOSES ONLY
# Kekbot Mini
Based on a `distilgpt2` model, fine-tuned to a select subset (65k<= messages) of Art Union's general-chat channel chat history.
### Limits and biases
As this is trained on chat history, it is possible that discriminatory or even offensive materials to ... | {"language": ["en"], "license": "cc-by-nc-sa-4.0", "metrics": ["accuracy"], "co2_eq_emissions": {"emissions": "10", "source": "mlco2.github.io", "training_type": "fine-tuning", "geographical_location": "West Java, Indonesia", "hardware_used": "1 T4"}, "widget": [{"text": "You: \"Hey kekbot! Whats up?\"\\nKekbot: \"", "... | spuun/kekbot-mini | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"en",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-12T02:40:33+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #en #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| > THIS MODEL IS INTENDED FOR RESEARCH PURPOSES ONLY
# Kekbot Mini
Based on a 'distilgpt2' model, fine-tuned to a select subset (65k<= messages) of Art Union's general-chat channel chat history.
### Limits and biases
As this is trained on chat history, it is possible that discriminatory or even offensive materials to ... | [
"# Kekbot Mini\n\nBased on a 'distilgpt2' model, fine-tuned to a select subset (65k<= messages) of Art Union's general-chat channel chat history.",
"### Limits and biases\nAs this is trained on chat history, it is possible that discriminatory or even offensive materials to be outputted. \nAuthor holds his ground ... | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #en #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Kekbot Mini\n\nBased on a 'distilgpt2' model, fine-tuned to a select subset (65k<= messages) of Art Union's general-chat channel chat history.... |
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/1144053838459969536/lv3y... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/tayplaysgaymes/1655006196516/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/tayplaysgaymes | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-12T02:55:39+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Tay
@tayplaysgaymes
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
-------------... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
fill-mask | transformers |
* Continue pre-training RoBERTa-base using discharge summaries from MIMIC-III datasets.
* Details can be found in the following paper
> Xiang Dai and Ilias Chalkidis and Sune Darkner and Desmond Elliott. 2022. Revisiting Transformer-based Models for Long Document Classification. (https://arxiv.org/abs/2204.06683)
*... | {"language": ["English"], "license": "cc-by-4.0", "tags": ["Clinical notes", "Discharge summaries", "RoBERTa"], "datasets": ["MIMIC-III"]} | xdai/mimic_roberta_base | null | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"Clinical notes",
"Discharge summaries",
"RoBERTa",
"dataset:MIMIC-III",
"arxiv:2204.06683",
"license:cc-by-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-12T03:12:20+00:00 | [
"2204.06683"
] | [
"English"
] | TAGS
#transformers #pytorch #roberta #fill-mask #Clinical notes #Discharge summaries #RoBERTa #dataset-MIMIC-III #arxiv-2204.06683 #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #region-us
| * Continue pre-training RoBERTa-base using discharge summaries from MIMIC-III datasets.
* Details can be found in the following paper
>
> Xiang Dai and Ilias Chalkidis and Sune Darkner and Desmond Elliott. 2022. Revisiting Transformer-based Models for Long Document Classification. (URL
>
>
>
* Important hyper-... | [] | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #Clinical notes #Discharge summaries #RoBERTa #dataset-MIMIC-III #arxiv-2204.06683 #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
fill-mask | transformers |
* Continue pre-training RoBERTa-base using discharge summaries from MIMIC-III datasets.
* Details can be found in the following paper
> Xiang Dai and Ilias Chalkidis and Sune Darkner and Desmond Elliott. 2022. Revisiting Transformer-based Models for Long Document Classification. (https://arxiv.org/abs/2204.06683)
*... | {"language": "en", "license": "cc-by-4.0", "tags": ["Clinical notes", "Discharge summaries", "longformer"], "datasets": ["MIMIC-III"]} | xdai/mimic_longformer_base | null | [
"transformers",
"pytorch",
"longformer",
"fill-mask",
"Clinical notes",
"Discharge summaries",
"en",
"dataset:MIMIC-III",
"arxiv:2204.06683",
"license:cc-by-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-12T03:47:58+00:00 | [
"2204.06683"
] | [
"en"
] | TAGS
#transformers #pytorch #longformer #fill-mask #Clinical notes #Discharge summaries #en #dataset-MIMIC-III #arxiv-2204.06683 #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #region-us
| * Continue pre-training RoBERTa-base using discharge summaries from MIMIC-III datasets.
* Details can be found in the following paper
>
> Xiang Dai and Ilias Chalkidis and Sune Darkner and Desmond Elliott. 2022. Revisiting Transformer-based Models for Long Document Classification. (URL
>
>
>
* Important hyper-... | [] | [
"TAGS\n#transformers #pytorch #longformer #fill-mask #Clinical notes #Discharge summaries #en #dataset-MIMIC-III #arxiv-2204.06683 #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
reinforcement-learning | stable-baselines3 |
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framewor... | {"library_name": "stable-baselines3", "tags": ["SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "DQN", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "SpaceInvadersNoFram... | bguan/SpaceInvadersNoFrameskip-v4-2Msteps | null | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-12T04:15:25+00:00 | [] | [] | TAGS
#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# DQN Agent playing SpaceInvadersNoFrameskip-v4
This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4
using the stable-baselines3 library
and the RL Zoo.
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents... | [
"# DQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framework for Stable Baselines3\nreinforcement learning agents,\nwith hyperparameter optimization and pre-tra... | [
"TAGS\n#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# DQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo... |
time-series-forecasting | keras |
## Model description
Demonstrates timeseries forecasting using a [LSTM model.](https://keras.io/api/layers/recurrent_layers/lstm/)
## Full credits to:
* [Prabhanshu Attri](https://prabhanshu.com/github)
* [Yashika Sharma](https://github.com/yashika51)
* [Kristi Takach](https://github.com/ktakattack)
* [Falak Shah]... | {"library_name": "keras", "tags": ["time-series", "time-series-forecasting"]} | keras-io/timeseries_forecasting_for_weather | null | [
"keras",
"tensorboard",
"time-series",
"time-series-forecasting",
"has_space",
"region:us"
] | null | 2022-06-12T04:30:57+00:00 | [] | [] | TAGS
#keras #tensorboard #time-series #time-series-forecasting #has_space #region-us
| Model description
-----------------
Demonstrates timeseries forecasting using a LSTM model.
Full credits to:
----------------
* Prabhanshu Attri
* Yashika Sharma
* Kristi Takach
* Falak Shah
* Keras Example
Data Preprocessing
------------------
Here we are picking ~300,000 data points for training. Observatio... | [
"### 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 #time-series #time-series-forecasting #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"
] |
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. -->
# mbert2mbert-finetuned-ar-wikilingua
This model is a fine-tuned version of [](https://huggingface.co/) on the wiki_lingua dataset... | {"tags": ["summarization", "ar", "encoder-decoder", "mbert", "mbert2mbert", "Abstractive Summarization", "generated_from_trainer"], "datasets": ["wiki_lingua"], "model-index": [{"name": "mbert2mbert-finetuned-ar-wikilingua", "results": []}]} | eslamxm/mbert2mbert-finetuned-ar-wikilingua | null | [
"transformers",
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"summarization",
"ar",
"mbert",
"mbert2mbert",
"Abstractive Summarization",
"generated_from_trainer",
"dataset:wiki_lingua",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-12T05:43:14+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #encoder-decoder #text2text-generation #summarization #ar #mbert #mbert2mbert #Abstractive Summarization #generated_from_trainer #dataset-wiki_lingua #autotrain_compatible #endpoints_compatible #region-us
|
# mbert2mbert-finetuned-ar-wikilingua
This model is a fine-tuned version of [](URL on the wiki_lingua dataset.
It achieves the following results on the evaluation set:
- Loss: 3.6753
- Rouge-1: 15.19
- Rouge-2: 5.45
- Rouge-l: 14.64
- Gen Len: 20.0
- Bertscore: 67.86
## Model description
More information needed
... | [
"# mbert2mbert-finetuned-ar-wikilingua\n\nThis model is a fine-tuned version of [](URL on the wiki_lingua dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 3.6753\n- Rouge-1: 15.19\n- Rouge-2: 5.45\n- Rouge-l: 14.64\n- Gen Len: 20.0\n- Bertscore: 67.86",
"## Model description\n\nMore inf... | [
"TAGS\n#transformers #pytorch #tensorboard #encoder-decoder #text2text-generation #summarization #ar #mbert #mbert2mbert #Abstractive Summarization #generated_from_trainer #dataset-wiki_lingua #autotrain_compatible #endpoints_compatible #region-us \n",
"# mbert2mbert-finetuned-ar-wikilingua\n\nThis model is a fin... |
text-generation | transformers |
# Miles Prower DialoGPT Model | {"tags": ["conversational"]} | Averium/DialoGPT-medium-TailsBot | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-12T05:46:09+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Miles Prower DialoGPT Model | [
"# Miles Prower DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Miles Prower DialoGPT Model"
] |
text-to-speech | transformers |
# Common Voice it Vits
Train on [Mozzila Common voice](https://commonvoice.mozilla.org/) v9.0 it with [Coqui VITS](https://github.com/coqui-ai/TTS)
```
# Coqui tts sha commit coquitts: 0cf3265a4686d7e856bd472cdaf1572d61cab2b8
PYTORCH_CUDA_ALLOC_CONF="max_split_size_mb:25" CUDA_VISIBLE_DEVICES=1 python recipes/common... | {"language": ["it"], "tags": ["text-to-speech"], "datasets": ["mozilla-foundation/common_voice_9_0"], "model-index": [{"name": "vits-commonvoice9.0", "results": []}]} | z-uo/vits-commonvoice9.0 | null | [
"transformers",
"tensorboard",
"text-to-speech",
"it",
"dataset:mozilla-foundation/common_voice_9_0",
"endpoints_compatible",
"region:us"
] | null | 2022-06-12T06:07:07+00:00 | [] | [
"it"
] | TAGS
#transformers #tensorboard #text-to-speech #it #dataset-mozilla-foundation/common_voice_9_0 #endpoints_compatible #region-us
|
# Common Voice it Vits
Train on Mozzila Common voice v9.0 it with Coqui VITS
| [
"# Common Voice it Vits\n\nTrain on Mozzila Common voice v9.0 it with Coqui VITS"
] | [
"TAGS\n#transformers #tensorboard #text-to-speech #it #dataset-mozilla-foundation/common_voice_9_0 #endpoints_compatible #region-us \n",
"# Common Voice it Vits\n\nTrain on Mozzila Common voice v9.0 it with Coqui VITS"
] |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# opus-mt-en-ro-finetuned-en-to-ro
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-ro](https://huggingface.co/Helsi... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["wmt16"], "metrics": ["bleu"], "model-index": [{"name": "opus-mt-en-ro-finetuned-en-to-ro", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "wmt16", "type": "wmt16", "a... | Dwayne/opus-mt-en-ro-finetuned-en-to-ro | null | [
"transformers",
"pytorch",
"marian",
"text2text-generation",
"generated_from_trainer",
"dataset:wmt16",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-12T07:01:01+00:00 | [] | [] | TAGS
#transformers #pytorch #marian #text2text-generation #generated_from_trainer #dataset-wmt16 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| opus-mt-en-ro-finetuned-en-to-ro
================================
This model is a fine-tuned version of Helsinki-NLP/opus-mt-en-ro on the wmt16 dataset.
It achieves the following results on the evaluation set:
* Loss: 1.2889
* Bleu: 28.0591
* Gen Len: 34.043
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: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1\n* mixed\\_prec... | [
"TAGS\n#transformers #pytorch #marian #text2text-generation #generated_from_trainer #dataset-wmt16 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\... |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# vishvamahadevan/distilbert-base-uncased-finetuned-squad
This model is a fine-tuned version of [distilbert-base-uncased](https://huggin... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "vishvamahadevan/distilbert-base-uncased-finetuned-squad", "results": []}]} | vishvamahadevan/distilbert-base-uncased-finetuned-squad | null | [
"transformers",
"tf",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-06-12T07:07:48+00:00 | [] | [] | TAGS
#transformers #tf #tensorboard #distilbert #question-answering #generated_from_keras_callback #license-apache-2.0 #endpoints_compatible #region-us
| vishvamahadevan/distilbert-base-uncased-finetuned-squad
=======================================================
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.9560
* Validation Loss: 1.1174
* Epoch: 1
Mo... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'learning\\_rate': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_steps': 11064, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': False, 'na... | [
"TAGS\n#transformers #tf #tensorboard #distilbert #question-answering #generated_from_keras_callback #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'learning\\_rate': {'class\\... |
reinforcement-learning | stable-baselines3 |
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framewor... | {"library_name": "stable-baselines3", "tags": ["SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "DQN", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "SpaceInvadersNoFram... | ironbar/dqn-SpaceInvadersNoFrameskip-v4-1M-steps | null | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-12T07:15:30+00:00 | [] | [] | TAGS
#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# DQN Agent playing SpaceInvadersNoFrameskip-v4
This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4
using the stable-baselines3 library
and the RL Zoo.
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents... | [
"# DQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framework for Stable Baselines3\nreinforcement learning agents,\nwith hyperparameter optimization and pre-tra... | [
"TAGS\n#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# DQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo... |
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. -->
# ainize-kobart-news-eb-finetuned-meetings-papers
This model is a fine-tuned version of [ainize/kobart-news](https://huggingface.c... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "ainize-kobart-news-eb-finetuned-meetings-papers", "results": []}]} | eunbeee/ainize-kobart-news-eb-finetuned-meetings-papers | null | [
"transformers",
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-12T07:37:23+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bart #text2text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| ainize-kobart-news-eb-finetuned-meetings-papers
===============================================
This model is a fine-tuned version of ainize/kobart-news on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3289
* Rouge1: 17.3988
* Rouge2: 7.0454
* Rougel: 17.3877
* Rougelsum: 17.4... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5\n* mixed\\_precis... | [
"TAGS\n#transformers #pytorch #tensorboard #bart #text2text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size:... |
translation | transformers |
## Model Details
- **Developed by:** İlhami SEL
- **Model type:** Turkish-English Machine Translation -- Transformer Based(6 Layer)
- **Language:** Turkish - English
- **Resources for more information:** Sel, İ. , Üzen, H. & Hanbay, D. (2021). Creating a Parallel Corpora for Turkish-English Academic Translations . ... | {"language": ["tr", "en"], "license": "apache-2.0", "tags": ["translation"], "datasets": ["Parallel Corpora for Turkish-English Academic Translations"], "metrics": ["bleu", "sacrebleu"]} | ilhami/Tr_En_AcademicTranslation | null | [
"transformers",
"pytorch",
"marian",
"text2text-generation",
"translation",
"tr",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-12T08:19:05+00:00 | [] | [
"tr",
"en"
] | TAGS
#transformers #pytorch #marian #text2text-generation #translation #tr #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
## Model Details
- Developed by: İlhami SEL
- Model type: Turkish-English Machine Translation -- Transformer Based(6 Layer)
- Language: Turkish - English
- Resources for more information: Sel, İ. , Üzen, H. & Hanbay, D. (2021). Creating a Parallel Corpora for Turkish-English Academic Translations . Computer Science... | [
"## Model Details\n\n- Developed by: İlhami SEL\n- Model type: Turkish-English Machine Translation -- Transformer Based(6 Layer)\n- Language: Turkish - English\n- Resources for more information: Sel, İ. , Üzen, H. & Hanbay, D. (2021). Creating a Parallel Corpora for Turkish-English Academic Translations . Computer... | [
"TAGS\n#transformers #pytorch #marian #text2text-generation #translation #tr #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"## Model Details\n\n- Developed by: İlhami SEL\n- Model type: Turkish-English Machine Translation -- Transformer Based(6 Layer)\n- Language: Turkish - ... |
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/1342130927737176064/SiNG... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/bosstjanz/1655026050127/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/bosstjanz | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-12T08:26:54+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Zrimškow
@bosstjanz
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"
] |
null | keras |
## Model description
This repo contains the model and the notebook for implementing Message Passing Neural Network (MPNN) to predict a molecular property known as blood-brain barrier permeability (BBBP). [Message-passing neural network (MPNN) for molecular property prediction](https://keras.io/examples/graph/mpnn-mol... | {"library_name": "keras"} | keras-io/MPNN-for-molecular-property-prediction | null | [
"keras",
"tensorboard",
"has_space",
"region:us"
] | null | 2022-06-12T08:33:48+00:00 | [] | [] | TAGS
#keras #tensorboard #has_space #region-us
| Model description
-----------------
This repo contains the model and the notebook for implementing Message Passing Neural Network (MPNN) to predict a molecular property known as blood-brain barrier permeability (BBBP). Message-passing neural network (MPNN) for molecular property prediction.
Full credits go to Alexa... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n\nModel Plot\n----------",
"### View Model Demo\n\n\n!Model Demo\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----------",
"### View Model Demo\n\n\n!Model Demo\n\n\n\nView Model Plot\n!Model Image"
] |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mt5-base-finetuned-xsum-RAW_data_prep_2021_12_26___t22027_162754.csv__google_mt5_base
This model is a fine-tuned version of [goo... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "mt5-base-finetuned-xsum-RAW_data_prep_2021_12_26___t22027_162754.csv__google_mt5_base", "results": []}]} | nestoralvaro/mt5-base-finetuned-xsum-RAW_data_prep_2021_12_26___t22027_162754.csv__google_mt5_base | null | [
"transformers",
"pytorch",
"tensorboard",
"mt5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-12T08:42:52+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #mt5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| mt5-base-finetuned-xsum-RAW\_data\_prep\_2021\_12\_26\_\_\_t22027\_162754.csv\_\_google\_mt5\_base
==================================================================================================
This model is a fine-tuned version of google/mt5-base on an unknown dataset.
It achieves the following results on the ev... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1\n* mixed\\_precis... | [
"TAGS\n#transformers #pytorch #tensorboard #mt5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rat... |
image-segmentation | transformers |
# SegFormer (b0-sized) model fine-tuned on sidewalk-semantic dataset
SegFormer model fine-tuned on segments/sidewalk-semantic at resolution 512x512. It was introduced in the paper [SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers](https://arxiv.org/abs/2105.15203) by Xie et al. and f... | {"license": "apache-2.0", "tags": ["vision", "image-segmentation"], "datasets": ["segments/sidewalk-semantic"]} | chainyo/segformer-sidewalk | null | [
"transformers",
"pytorch",
"safetensors",
"segformer",
"vision",
"image-segmentation",
"dataset:segments/sidewalk-semantic",
"arxiv:2105.15203",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-06-12T08:44:06+00:00 | [
"2105.15203"
] | [] | TAGS
#transformers #pytorch #safetensors #segformer #vision #image-segmentation #dataset-segments/sidewalk-semantic #arxiv-2105.15203 #license-apache-2.0 #endpoints_compatible #region-us
|
# SegFormer (b0-sized) model fine-tuned on sidewalk-semantic dataset
SegFormer model fine-tuned on segments/sidewalk-semantic at resolution 512x512. It was introduced in the paper SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers by Xie et al. and first released in this repository.
... | [
"# SegFormer (b0-sized) model fine-tuned on sidewalk-semantic dataset\n\nSegFormer model fine-tuned on segments/sidewalk-semantic at resolution 512x512. It was introduced in the paper SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers by Xie et al. and first released in this reposito... | [
"TAGS\n#transformers #pytorch #safetensors #segformer #vision #image-segmentation #dataset-segments/sidewalk-semantic #arxiv-2105.15203 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# SegFormer (b0-sized) model fine-tuned on sidewalk-semantic dataset\n\nSegFormer model fine-tuned on segments/sidewalk... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mt5-base-finetuned-xsum-RAW_data_prep_2021_12_26___t55_403.csv__google_mt5_base
This model is a fine-tuned version of [google/mt... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "mt5-base-finetuned-xsum-RAW_data_prep_2021_12_26___t55_403.csv__google_mt5_base", "results": []}]} | nestoralvaro/mt5-base-finetuned-xsum-RAW_data_prep_2021_12_26___t55_403.csv__google_mt5_base | null | [
"transformers",
"pytorch",
"tensorboard",
"mt5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-12T09:01:09+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #mt5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| mt5-base-finetuned-xsum-RAW\_data\_prep\_2021\_12\_26\_\_\_t55\_403.csv\_\_google\_mt5\_base
============================================================================================
This model is a fine-tuned version of google/mt5-base on an unknown dataset.
It achieves the following results on the evaluation set... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1\n* mixed\\_precis... | [
"TAGS\n#transformers #pytorch #tensorboard #mt5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rat... |
translation | transformers |
## Model Details
- **Developed by:** İlhami SEL
- **Model type:** Mbart Finetune Machine Translation
- **Language:** Turkish - English
- **Resources for more information:** Sel, İ. , Üzen, H. & Hanbay, D. (2021). Creating a Parallel Corpora for Turkish-English Academic Translations . Computer Science , 5th Internati... | {"language": ["tr", "en"], "license": "apache-2.0", "tags": ["translation"], "datasets": ["Parallel Corpora for Turkish-English Academic Translations"], "metrics": ["bleu", "sacrebleu"]} | ilhami/Tr_En-MbartFinetune | null | [
"transformers",
"pytorch",
"mbart",
"text2text-generation",
"translation",
"tr",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-12T09:02:23+00:00 | [] | [
"tr",
"en"
] | TAGS
#transformers #pytorch #mbart #text2text-generation #translation #tr #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
## Model Details
- Developed by: İlhami SEL
- Model type: Mbart Finetune Machine Translation
- Language: Turkish - English
- Resources for more information: Sel, İ. , Üzen, H. & Hanbay, D. (2021). Creating a Parallel Corpora for Turkish-English Academic Translations . Computer Science , 5th International Artificial ... | [
"## Model Details\n\n- Developed by: İlhami SEL\n- Model type: Mbart Finetune Machine Translation\n- Language: Turkish - English\n- Resources for more information: Sel, İ. , Üzen, H. & Hanbay, D. (2021). Creating a Parallel Corpora for Turkish-English Academic Translations . Computer Science , 5th International Art... | [
"TAGS\n#transformers #pytorch #mbart #text2text-generation #translation #tr #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"## Model Details\n\n- Developed by: İlhami SEL\n- Model type: Mbart Finetune Machine Translation\n- Language: Turkish - English\n- Resources for more inf... |
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/1184073162520031232/V6DO... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/manfightdragon/1655029573001/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/manfightdragon | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-12T09:23:38+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Lance McDonald
@manfightdragon
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
--... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
reinforcement-learning | stable-baselines3 |
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framewor... | {"library_name": "stable-baselines3", "tags": ["SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "DQN", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "SpaceInvadersNoFram... | DavidCollier/dqn-SpaceInvadersNoFrameskip-v4 | null | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-12T10:04:27+00:00 | [] | [] | TAGS
#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# DQN Agent playing SpaceInvadersNoFrameskip-v4
This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4
using the stable-baselines3 library
and the RL Zoo.
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents... | [
"# DQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framework for Stable Baselines3\nreinforcement learning agents,\nwith hyperparameter optimization and pre-tra... | [
"TAGS\n#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# DQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo... |
reinforcement-learning | stable-baselines3 |
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framewor... | {"library_name": "stable-baselines3", "tags": ["SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "DQN", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "SpaceInvadersNoFram... | mgfrantz/dql-SpaceInvadersNoFrameskip-v4 | null | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-12T10:12:58+00:00 | [] | [] | TAGS
#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# DQN Agent playing SpaceInvadersNoFrameskip-v4
This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4
using the stable-baselines3 library
and the RL Zoo.
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents... | [
"# DQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framework for Stable Baselines3\nreinforcement learning agents,\nwith hyperparameter optimization and pre-tra... | [
"TAGS\n#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# DQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo... |
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... | tauseefr84/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 | 2022-06-12T11:23:32+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-emotion
=========================================
This model is a fine-tuned version of distilbert-base-uncased on the emotion dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5268
* Accuracy: 0.838
* F1: 0.8228
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: 1",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learn... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-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... | YuryK/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 | 2022-06-12T11:47:26+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: 0.1669
* Accuracy: 0.933
* F1: 0.9333
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: 3",
"### 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... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.