modelId
stringlengths 5
139
| author
stringlengths 2
42
| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-08-29 12:28:39
| downloads
int64 0
223M
| likes
int64 0
11.7k
| library_name
stringclasses 526
values | tags
listlengths 1
4.05k
| pipeline_tag
stringclasses 55
values | createdAt
timestamp[us, tz=UTC]date 2022-03-02 23:29:04
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| card
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|---|---|---|---|---|---|---|---|---|---|
huggingtweets/manfightdragon
|
huggingtweets
| 2022-06-12T10:26:35Z | 106 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-06-12T10:23:38Z |
---
language: en
thumbnail: http://www.huggingtweets.com/manfightdragon/1655029573001/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<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/V6DOEeLp_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Lance McDonald</div>
<div style="text-align: center; font-size: 14px;">@manfightdragon</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from Lance McDonald.
| Data | Lance McDonald |
| --- | --- |
| Tweets downloaded | 3249 |
| Retweets | 209 |
| Short tweets | 214 |
| Tweets kept | 2826 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3pc794z5/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @manfightdragon's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2t8940p5) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2t8940p5/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/manfightdragon')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
z-uo/vits-commonvoice9.0
|
z-uo
| 2022-06-12T09:46:23Z | 1 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"text-to-speech",
"it",
"dataset:mozilla-foundation/common_voice_9_0",
"endpoints_compatible",
"region:us"
] |
text-to-speech
| 2022-06-12T07:07:07Z |
---
tags:
- text-to-speech
language:
- it
model-index:
- name: vits-commonvoice9.0
results: []
datasets:
- mozilla-foundation/common_voice_9_0
---
# 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_voice/vits/train_vits.py
CUDA_VISIBLE_DEVICES=0 tts-server --model_path "/run/media/opensuse/Barracuda/Models/TTS_new/trained_common_voice/vits_vctk-June-05-2022_03+45PM-0cf3265a/best_model.pth" --config_path "/run/media/opensuse/Barracuda/Models/TTS_new/trained_common_voice/vits_vctk-June-05-2022_03+45PM-0cf3265a/config.json"
```
|
ironbar/dqn-SpaceInvadersNoFrameskip-v4-1M-steps
|
ironbar
| 2022-06-12T08:16:08Z | 11 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-06-12T08:15:30Z |
---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- metrics:
- type: mean_reward
value: 629.50 +/- 140.06
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
---
# **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 framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
```
# Download model and save it into the logs/ folder
python -m utils.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga ironbar -f logs/
python enjoy.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python train.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m utils.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga ironbar
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 1000000.0),
('optimize_memory_usage', True),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
|
MyMild/finetune_iapp_thaiqa
|
MyMild
| 2022-06-12T07:52:39Z | 57 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"camembert",
"question-answering",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-06-11T23:05:08Z |
---
tags:
- generated_from_trainer
model-index:
- name: finetune_iapp_thaiqa
results: []
---
<!-- 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.co/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
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.2
- num_epochs: 2
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.15.0
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.10.3
|
bguan/SpaceInvadersNoFrameskip-v4
|
bguan
| 2022-06-12T01:05:09Z | 4 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-06-12T01:04:38Z |
---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- metrics:
- type: mean_reward
value: 255.00 +/- 93.83
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
---
# **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 framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
```
# Download model and save it into the logs/ folder
python -m utils.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga bguan -f logs/
python enjoy.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python train.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m utils.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga bguan
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 500000),
('optimize_memory_usage', True),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
|
meghazisofiane/opus-mt-en-ar-evaluated-en-to-ar-2000instances-un_multi-leaningRate2e-05-batchSize8-11-action-1
|
meghazisofiane
| 2022-06-12T00:44:37Z | 7 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"generated_from_trainer",
"dataset:un_multi",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-06-12T00:34:12Z |
---
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:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: un_multi
type: un_multi
args: ar-en
metrics:
- name: Bleu
type: bleu
value: 53.0137
---
<!-- 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 version of [Helsinki-NLP/opus-mt-en-ar](https://huggingface.co/Helsinki-NLP/opus-mt-en-ar) on the un_multi dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1873
- Bleu: 53.0137
- Meteor: 0.5005
- Gen Len: 25.845
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 11
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Meteor | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|
| 0.6585 | 0.5 | 100 | 0.2085 | 52.5874 | 0.4969 | 25.485 |
| 0.1802 | 1.0 | 200 | 0.1788 | 52.9434 | 0.4982 | 25.1725 |
| 0.1501 | 1.5 | 300 | 0.1683 | 53.6994 | 0.5033 | 25.625 |
| 0.1454 | 2.0 | 400 | 0.1706 | 53.3946 | 0.5005 | 25.6675 |
| 0.1193 | 2.5 | 500 | 0.1774 | 53.2011 | 0.4982 | 25.58 |
| 0.1194 | 3.0 | 600 | 0.1741 | 53.8651 | 0.5026 | 25.5775 |
| 0.1002 | 3.5 | 700 | 0.1878 | 53.1332 | 0.5005 | 25.8975 |
| 0.0979 | 4.0 | 800 | 0.1881 | 52.5989 | 0.4974 | 25.485 |
| 0.0807 | 4.5 | 900 | 0.1873 | 53.0137 | 0.5005 | 25.845 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.12.1
|
TencentMedicalNet/MedicalNet-Resnet10
|
TencentMedicalNet
| 2022-06-12T00:26:42Z | 0 | 4 | null |
[
"MedicalNet",
"medical images",
"medical",
"3D",
"Med3D",
"en",
"dataset:MRBrainS18",
"arxiv:1904.00625",
"license:mit",
"region:us"
] | null | 2022-06-11T23:12:06Z |
---
license: mit
datasets:
- MRBrainS18
language:
- en
metrics:
-
tags:
- MedicalNet
- medical images
- medical
- 3D
- Med3D
thumbnail: "https://github.com/Tencent/MedicalNet/blob/master/images/logo.png?raw=true"
---
# 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 with diverse modalities, target organs, and pathologies to to build relatively large datasets. Based on this dataset, a series of 3D-ResNet pre-trained models and corresponding transfer-learning training code are provided.
### License
MedicalNet is released under the MIT License (refer to the LICENSE file for detailso).
### Citing MedicalNet
If you use this code or pre-trained models, please cite the following:
```
@article{chen2019med3d,
title={Med3D: Transfer Learning for 3D Medical Image Analysis},
author={Chen, Sihong and Ma, Kai and Zheng, Yefeng},
journal={arXiv preprint arXiv:1904.00625},
year={2019}
}
```
### Update(2019/07/30)
We uploaded 4 pre-trained models based on more datasets (23 datasets).
```
Model name : parameters settings
resnet_10_23dataset.pth: --model resnet --model_depth 10 --resnet_shortcut B
resnet_18_23dataset.pth: --model resnet --model_depth 18 --resnet_shortcut A
resnet_34_23dataset.pth: --model resnet --model_depth 34 --resnet_shortcut A
resnet_50_23dataset.pth: --model resnet --model_depth 50 --resnet_shortcut B
```
Hugging Face repository contribution by:
[Rafael Zimmer](https://www.github.com/rzimmerdev)
|
huggingtweets/cancer_blood69
|
huggingtweets
| 2022-06-12T00:01:54Z | 105 | 1 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-06-11T23:58:19Z |
---
language: en
thumbnail: http://www.huggingtweets.com/cancer_blood69/1654992058711/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<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/_kkJmwqw_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">cancer_blood69 (reanimated decaying corpse)</div>
<div style="text-align: center; font-size: 14px;">@cancer_blood69</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from cancer_blood69 (reanimated decaying corpse).
| Data | cancer_blood69 (reanimated decaying corpse) |
| --- | --- |
| Tweets downloaded | 3237 |
| Retweets | 215 |
| Short tweets | 381 |
| Tweets kept | 2641 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3cav70ew/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @cancer_blood69's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/sp5449e2) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/sp5449e2/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/cancer_blood69')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
DLWCMD/TEST2ppo-LunarLander-v2
|
DLWCMD
| 2022-06-11T23:39:16Z | 1 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-06-11T23:38:43Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 263.13 +/- 22.16
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
---
# **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 import load_from_hub
...
```
|
745H1N/LunarLander-v2-DQN-optuna
|
745H1N
| 2022-06-11T23:36:51Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-06-11T23:36:25Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- metrics:
- type: mean_reward
value: -140.18 +/- 41.67
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
---
# **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 import load_from_hub
...
```
|
aprischa/bart-large-cnn-aprischa2
|
aprischa
| 2022-06-11T23:27:38Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-06-11T17:40:18Z |
---
license: mit
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: bart-large-cnn-aprischa2
results: []
---
<!-- 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-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 description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:--------:|
| 0.3772 | 1.0 | 5403 | 0.3586 | 65.7702 | 56.7968 | 62.264 | 64.8605 | 140.268 |
| 0.316 | 2.0 | 10806 | 0.3421 | 64.8238 | 55.8837 | 61.3245 | 63.8894 | 140.7472 |
| 0.2397 | 3.0 | 16209 | 0.3425 | 65.7088 | 56.6701 | 62.1926 | 64.7727 | 140.8469 |
### Framework versions
- Transformers 4.19.4
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
meghazisofiane/opus-mt-en-ar-evaluated-en-to-ar-2000instancesopus-leaningRate2e-05-batchSize8-11epoch-3
|
meghazisofiane
| 2022-06-11T21:27:25Z | 107 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"generated_from_trainer",
"dataset:opus100",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-06-11T19:41:31Z |
---
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:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: opus100
type: opus100
args: ar-en
metrics:
- name: Bleu
type: bleu
value: 26.2629
---
<!-- 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 [Helsinki-NLP/opus-mt-en-ar](https://huggingface.co/Helsinki-NLP/opus-mt-en-ar) on the opus100 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1959
- Bleu: 26.2629
- Meteor: 0.1703
- Gen Len: 11.0925
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 11
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Meteor | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|
| 1.0519 | 0.5 | 100 | 0.1985 | 27.3525 | 0.1815 | 11.0725 |
| 0.1947 | 1.0 | 200 | 0.1902 | 26.9728 | 0.1789 | 10.82 |
| 0.1489 | 1.5 | 300 | 0.1910 | 27.7003 | 0.1811 | 10.975 |
| 0.1665 | 2.0 | 400 | 0.1905 | 26.3739 | 0.1772 | 11.1075 |
| 0.1321 | 2.5 | 500 | 0.1926 | 26.752 | 0.1772 | 10.975 |
| 0.1271 | 3.0 | 600 | 0.1927 | 27.3663 | 0.1751 | 10.9725 |
| 0.1105 | 3.5 | 700 | 0.1952 | 27.134 | 0.1738 | 10.9975 |
| 0.109 | 4.0 | 800 | 0.1959 | 26.2629 | 0.1703 | 11.0925 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.12.1
|
MyMild/bert-finetuned-squad
|
MyMild
| 2022-06-11T21:24:26Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-06-11T20:26:57Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: bert-finetuned-squad
results: []
---
<!-- 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 squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.15.0
- Pytorch 1.11.0+cu113
- Datasets 1.17.0
- Tokenizers 0.10.3
|
lindeberg/distilbert-base-uncased-finetuned-cola
|
lindeberg
| 2022-06-11T21:10:06Z | 106 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-06-11T18:50:58Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- matthews_correlation
model-index:
- name: distilbert-base-uncased-finetuned-cola
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
args: cola
metrics:
- name: Matthews Correlation
type: matthews_correlation
value: 0.4496664370323995
---
<!-- 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/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 information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.5231 | 1.0 | 535 | 0.4949 | 0.4497 |
### Framework versions
- Transformers 4.19.4
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
huggingtweets/rshowerthoughts-stephenking
|
huggingtweets
| 2022-06-11T19:50:01Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-06-11T19:42:08Z |
---
language: en
thumbnail: http://www.huggingtweets.com/rshowerthoughts-stephenking/1654976942704/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<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/b683f7509ec792c3e481ead332940cdc_400x400.jpeg')">
</div>
<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/641699738224455680/L_ji6ClT_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Stephen King & Showerthoughts</div>
<div style="text-align: center; font-size: 14px;">@rshowerthoughts-stephenking</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from Stephen King & Showerthoughts.
| Data | Stephen King | Showerthoughts |
| --- | --- | --- |
| Tweets downloaded | 3230 | 3200 |
| Retweets | 780 | 0 |
| Short tweets | 202 | 0 |
| Tweets kept | 2248 | 3200 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2bn3s9yg/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @rshowerthoughts-stephenking's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2waq2b3w) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2waq2b3w/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/rshowerthoughts-stephenking')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
huggingtweets/mdoukmas
|
huggingtweets
| 2022-06-11T19:35:54Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-06-11T19:34:24Z |
---
language: en
thumbnail: http://www.huggingtweets.com/mdoukmas/1654976150184/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<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/n5v9daol_400x400.png')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Maya Dukmasova</div>
<div style="text-align: center; font-size: 14px;">@mdoukmas</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from Maya Dukmasova.
| Data | Maya Dukmasova |
| --- | --- |
| Tweets downloaded | 3241 |
| Retweets | 896 |
| Short tweets | 158 |
| Tweets kept | 2187 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2jwhv7l5/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @mdoukmas's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/25v3pmsy) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/25v3pmsy/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/mdoukmas')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
meghazisofiane/opus-mt-en-ar-evaluated-en-to-ar-1000instancesopus-leaningRate2e-05-batchSize8-11epoch-3
|
meghazisofiane
| 2022-06-11T19:25:04Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"generated_from_trainer",
"dataset:opus100",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-06-11T19:16:47Z |
---
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:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: opus100
type: opus100
args: ar-en
metrics:
- name: Bleu
type: bleu
value: 21.3028
---
<!-- 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 [Helsinki-NLP/opus-mt-en-ar](https://huggingface.co/Helsinki-NLP/opus-mt-en-ar) on the opus100 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1421
- Bleu: 21.3028
- Meteor: 0.1285
- Gen Len: 9.975
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 11
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Meteor | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|
| 1.0508 | 1.0 | 100 | 0.1413 | 27.9009 | 0.1416 | 8.85 |
| 0.1253 | 2.0 | 200 | 0.1372 | 23.11 | 0.1345 | 9.855 |
| 0.1017 | 3.0 | 300 | 0.1390 | 21.7885 | 0.1364 | 9.97 |
| 0.0868 | 4.0 | 400 | 0.1378 | 21.3889 | 0.1314 | 9.835 |
| 0.0754 | 5.0 | 500 | 0.1398 | 22.198 | 0.132 | 9.675 |
| 0.0667 | 6.0 | 600 | 0.1396 | 20.8645 | 0.1308 | 10.055 |
| 0.0604 | 7.0 | 700 | 0.1408 | 20.289 | 0.1303 | 10.53 |
| 0.0553 | 8.0 | 800 | 0.1414 | 21.7023 | 0.1293 | 10.005 |
| 0.0518 | 9.0 | 900 | 0.1421 | 21.3028 | 0.1285 | 9.975 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.12.1
|
huggingtweets/elonmusk-iamjohnoliver-neiltyson
|
huggingtweets
| 2022-06-11T19:00:50Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-06-11T18:54:15Z |
---
language: en
thumbnail: http://www.huggingtweets.com/elonmusk-iamjohnoliver-neiltyson/1654974044761/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<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/Nyn1HZWF_400x400.jpg')">
</div>
<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/1393958859/main_400x400.jpg')">
</div>
<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/74188698/NeilTysonOriginsA-Crop_400x400.jpg')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Elon Musk & John Oliver & Neil deGrasse Tyson</div>
<div style="text-align: center; font-size: 14px;">@elonmusk-iamjohnoliver-neiltyson</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from Elon Musk & John Oliver & Neil deGrasse Tyson.
| Data | Elon Musk | John Oliver | Neil deGrasse Tyson |
| --- | --- | --- | --- |
| Tweets downloaded | 3200 | 636 | 3237 |
| Retweets | 147 | 122 | 10 |
| Short tweets | 954 | 9 | 87 |
| Tweets kept | 2099 | 505 | 3140 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/14h905cr/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @elonmusk-iamjohnoliver-neiltyson's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3gcc5ko3) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3gcc5ko3/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/elonmusk-iamjohnoliver-neiltyson')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
aprischa/bart-large-cnn-aprischa
|
aprischa
| 2022-06-11T17:21:57Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bart",
"text2text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-06-11T16:53:31Z |
---
license: mit
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: bart-large-cnn-aprischa
results: []
---
<!-- 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-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 description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:--------:|
| 0.369 | 1.0 | 5403 | 0.3835 | 66.0604 | 56.9948 | 62.4967 | 65.265 | 141.1126 |
| 0.2985 | 2.0 | 10806 | 0.3589 | 66.7098 | 57.7992 | 63.2231 | 65.9009 | 141.198 |
### Framework versions
- Transformers 4.19.4
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
DancingIguana/codeparrot-ds
|
DancingIguana
| 2022-06-11T16:58:04Z | 107 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-06-08T21:56:49Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: codeparrot-ds
results: []
---
<!-- 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. -->
# codeparrot-ds
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 1000
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.19.4
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
abdoutony207/m2m100_418M-evaluated-en-to-ar-2000instancesopus-leaningRate2e-05-batchSize16-20epoch-1
|
abdoutony207
| 2022-06-11T16:26:19Z | 106 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"m2m_100",
"text2text-generation",
"generated_from_trainer",
"dataset:opus100",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-06-11T15:56:17Z |
---
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:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: opus100
type: opus100
args: ar-en
metrics:
- name: Bleu
type: bleu
value: 13.1835
---
<!-- 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 [facebook/m2m100_418M](https://huggingface.co/facebook/m2m100_418M) on the opus100 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3640
- Bleu: 13.1835
- Meteor: 0.1189
- Gen Len: 17.72
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Meteor | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|
| 6.1776 | 1.0 | 100 | 3.8904 | 10.5866 | 0.0995 | 16.64 |
| 2.4531 | 2.0 | 200 | 1.0928 | 12.3452 | 0.1108 | 17.0575 |
| 0.512 | 3.0 | 300 | 0.3625 | 10.5224 | 0.0982 | 17.2575 |
| 0.1924 | 4.0 | 400 | 0.3342 | 12.4242 | 0.1098 | 16.6325 |
| 0.1227 | 5.0 | 500 | 0.3403 | 13.0526 | 0.1185 | 17.3475 |
| 0.0889 | 6.0 | 600 | 0.3481 | 13.1323 | 0.1133 | 17.815 |
| 0.0651 | 7.0 | 700 | 0.3601 | 12.6684 | 0.1133 | 17.3525 |
| 0.0533 | 8.0 | 800 | 0.3640 | 13.1835 | 0.1189 | 17.72 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.12.1
|
IshanKumar/molecular_generation
|
IshanKumar
| 2022-06-11T14:27:39Z | 0 | 0 |
keras
|
[
"keras",
"tensorboard",
"tf-keras",
"mol_gen",
"region:us"
] | null | 2022-06-02T19:30:33Z |
---
library_name: keras
tags:
- mol_gen
---
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'learning_rate': 0.0005, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
- training_precision: float32
## Training Metrics
| Epochs | Train Loss |
|--- |--- |
| 1| 68866.578|
| 2| 68818.219|
| 3| 68850.844|
| 4| 68829.688|
| 5| 68840.258|
| 6| 68813.281|
| 7| 68809.414|
| 8| 68815.312|
| 9| 68805.641|
| 10| 68803.672|
## Model Plot
<details>
<summary>View Model Plot</summary>
</details>
|
antonioricciardi/CarRacing-v0
|
antonioricciardi
| 2022-06-11T14:26:51Z | 2 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"CarRacing-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-06-11T14:26:00Z |
---
library_name: stable-baselines3
tags:
- CarRacing-v0
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: -75.94 +/- 1.29
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CarRacing-v0
type: CarRacing-v0
---
# **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 import load_from_hub
...
```
|
neeenway/ppo-LunarLander-v2
|
neeenway
| 2022-06-11T13:43:31Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-06-11T13:43:03Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: ppo
results:
- metrics:
- type: mean_reward
value: 240.31 +/- 12.46
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
---
# **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 import load_from_hub
...
```
|
marieke93/MiniLM-evidence-types
|
marieke93
| 2022-06-11T13:32:27Z | 14,142 | 18 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-06-07T14:19:25Z |
---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: MiniLM-evidence-types
results: []
---
<!-- 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. -->
# MiniLM-evidence-types
This model is a fine-tuned version of [microsoft/MiniLM-L12-H384-uncased](https://huggingface.co/microsoft/MiniLM-L12-H384-uncased) on the evidence types dataset.
It achieved the following results on the evaluation set:
- Loss: 1.8672
- Macro f1: 0.3726
- Weighted f1: 0.7030
- Accuracy: 0.7161
- Balanced accuracy: 0.3616
## Training and evaluation data
The data set, as well as the code that was used to fine tune this model can be found in the GitHub repository [BA-Thesis-Information-Science-Persuasion-Strategies](https://github.com/mariekevdh/BA-Thesis-Information-Science-Persuasion-Strategies)
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Macro f1 | Weighted f1 | Accuracy | Balanced accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-----------:|:--------:|:-----------------:|
| 1.4106 | 1.0 | 250 | 1.2698 | 0.1966 | 0.6084 | 0.6735 | 0.2195 |
| 1.1437 | 2.0 | 500 | 1.0985 | 0.3484 | 0.6914 | 0.7116 | 0.3536 |
| 0.9714 | 3.0 | 750 | 1.0901 | 0.2606 | 0.6413 | 0.6446 | 0.2932 |
| 0.8382 | 4.0 | 1000 | 1.0197 | 0.2764 | 0.7024 | 0.7237 | 0.2783 |
| 0.7192 | 5.0 | 1250 | 1.0895 | 0.2847 | 0.6824 | 0.6963 | 0.2915 |
| 0.6249 | 6.0 | 1500 | 1.1296 | 0.3487 | 0.6888 | 0.6948 | 0.3377 |
| 0.5336 | 7.0 | 1750 | 1.1515 | 0.3591 | 0.6982 | 0.7024 | 0.3496 |
| 0.4694 | 8.0 | 2000 | 1.1962 | 0.3626 | 0.7185 | 0.7314 | 0.3415 |
| 0.4058 | 9.0 | 2250 | 1.3313 | 0.3121 | 0.6920 | 0.7085 | 0.3033 |
| 0.3746 | 10.0 | 2500 | 1.3993 | 0.3628 | 0.6976 | 0.7047 | 0.3495 |
| 0.3267 | 11.0 | 2750 | 1.5078 | 0.3560 | 0.6958 | 0.7055 | 0.3464 |
| 0.2939 | 12.0 | 3000 | 1.5875 | 0.3685 | 0.6968 | 0.7062 | 0.3514 |
| 0.2677 | 13.0 | 3250 | 1.6470 | 0.3606 | 0.6976 | 0.7070 | 0.3490 |
| 0.2425 | 14.0 | 3500 | 1.7164 | 0.3714 | 0.7069 | 0.7207 | 0.3551 |
| 0.2301 | 15.0 | 3750 | 1.8151 | 0.3597 | 0.6975 | 0.7123 | 0.3466 |
| 0.2268 | 16.0 | 4000 | 1.7838 | 0.3940 | 0.7034 | 0.7123 | 0.3869 |
| 0.201 | 17.0 | 4250 | 1.8328 | 0.3725 | 0.6964 | 0.7062 | 0.3704 |
| 0.1923 | 18.0 | 4500 | 1.8788 | 0.3708 | 0.7019 | 0.7154 | 0.3591 |
| 0.1795 | 19.0 | 4750 | 1.8574 | 0.3752 | 0.7031 | 0.7161 | 0.3619 |
| 0.1713 | 20.0 | 5000 | 1.8672 | 0.3726 | 0.7030 | 0.7161 | 0.3616 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
marieke93/BERT-evidence-types
|
marieke93
| 2022-06-11T13:32:10Z | 107 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-06-08T11:54:50Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: BERT-evidence-types
results: []
---
<!-- 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-evidence-types
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the evidence types dataset.
It achieves the following results on the evaluation set:
- Loss: 2.8008
- Macro f1: 0.4227
- Weighted f1: 0.6976
- Accuracy: 0.7154
- Balanced accuracy: 0.3876
## Training and evaluation data
The data set, as well as the code that was used to fine tune this model can be found in the GitHub repository [BA-Thesis-Information-Science-Persuasion-Strategies](https://github.com/mariekevdh/BA-Thesis-Information-Science-Persuasion-Strategies)
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Macro f1 | Weighted f1 | Accuracy | Balanced accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-----------:|:--------:|:-----------------:|
| 1.1148 | 1.0 | 125 | 1.0531 | 0.2566 | 0.6570 | 0.6705 | 0.2753 |
| 0.7546 | 2.0 | 250 | 0.9725 | 0.3424 | 0.6947 | 0.7002 | 0.3334 |
| 0.4757 | 3.0 | 375 | 1.1375 | 0.3727 | 0.7113 | 0.7184 | 0.3680 |
| 0.2637 | 4.0 | 500 | 1.3585 | 0.3807 | 0.6836 | 0.6910 | 0.3805 |
| 0.1408 | 5.0 | 625 | 1.6605 | 0.3785 | 0.6765 | 0.6872 | 0.3635 |
| 0.0856 | 6.0 | 750 | 1.9703 | 0.3802 | 0.6890 | 0.7047 | 0.3704 |
| 0.0502 | 7.0 | 875 | 2.1245 | 0.4067 | 0.6995 | 0.7169 | 0.3751 |
| 0.0265 | 8.0 | 1000 | 2.2676 | 0.3756 | 0.6816 | 0.6925 | 0.3647 |
| 0.0147 | 9.0 | 1125 | 2.4286 | 0.4052 | 0.6887 | 0.7062 | 0.3803 |
| 0.0124 | 10.0 | 1250 | 2.5773 | 0.4084 | 0.6853 | 0.7040 | 0.3695 |
| 0.0111 | 11.0 | 1375 | 2.5941 | 0.4146 | 0.6915 | 0.7085 | 0.3834 |
| 0.0076 | 12.0 | 1500 | 2.6124 | 0.4157 | 0.6936 | 0.7078 | 0.3863 |
| 0.0067 | 13.0 | 1625 | 2.7050 | 0.4139 | 0.6925 | 0.7108 | 0.3798 |
| 0.0087 | 14.0 | 1750 | 2.6695 | 0.4252 | 0.7009 | 0.7169 | 0.3920 |
| 0.0056 | 15.0 | 1875 | 2.7357 | 0.4257 | 0.6985 | 0.7161 | 0.3868 |
| 0.0054 | 16.0 | 2000 | 2.7389 | 0.4249 | 0.6955 | 0.7116 | 0.3890 |
| 0.0051 | 17.0 | 2125 | 2.7767 | 0.4197 | 0.6967 | 0.7146 | 0.3863 |
| 0.004 | 18.0 | 2250 | 2.7947 | 0.4211 | 0.6977 | 0.7154 | 0.3876 |
| 0.0041 | 19.0 | 2375 | 2.8030 | 0.4204 | 0.6953 | 0.7131 | 0.3855 |
| 0.0042 | 20.0 | 2500 | 2.8008 | 0.4227 | 0.6976 | 0.7154 | 0.3876 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
send-it/dqn-SpaceInvadersNoFrameskip-v4
|
send-it
| 2022-06-11T13:31:04Z | 7 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-06-11T13:30:29Z |
---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- metrics:
- type: mean_reward
value: 558.50 +/- 102.18
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
---
# **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 framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
```
# Download model and save it into the logs/ folder
python -m utils.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga send-it -f logs/
python enjoy.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python train.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m utils.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga send-it
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 1000000.0),
('optimize_memory_usage', True),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
|
antonioricciardi/FrozenLake-v1
|
antonioricciardi
| 2022-06-11T13:06:56Z | 2 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"FrozenLake-v1",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-06-11T13:06:48Z |
---
library_name: stable-baselines3
tags:
- FrozenLake-v1
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1
type: FrozenLake-v1
---
# **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 import load_from_hub
...
```
|
Sebabrata/lmv2ubiai-pan8doc-06-11
|
Sebabrata
| 2022-06-11T12:25:03Z | 78 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"layoutlmv2",
"token-classification",
"generated_from_trainer",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-06-11T11:46:22Z |
---
license: cc-by-nc-sa-4.0
tags:
- generated_from_trainer
model-index:
- name: lmv2ubiai-pan8doc-06-11
results: []
---
<!-- 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/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 Recall: 1.0
- Fname F1: 0.8
- Fname Number: 2
- Name Precision: 1.0
- Name Recall: 1.0
- Name F1: 1.0
- Name Number: 2
- Pan Precision: 1.0
- Pan Recall: 1.0
- Pan F1: 1.0
- Pan Number: 2
- Overall Precision: 0.8889
- Overall Recall: 1.0
- Overall F1: 0.9412
- Overall Accuracy: 0.9821
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 4e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- num_epochs: 30
### Training results
| Training Loss | Epoch | Step | Validation Loss | Dob Precision | Dob Recall | Dob F1 | Dob Number | Fname Precision | Fname Recall | Fname F1 | Fname Number | Name Precision | Name Recall | Name F1 | Name Number | Pan Precision | Pan Recall | Pan F1 | Pan Number | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:-------------:|:----------:|:------:|:----------:|:---------------:|:------------:|:--------:|:------------:|:--------------:|:-----------:|:-------:|:-----------:|:-------------:|:----------:|:------:|:----------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 2.1195 | 1.0 | 6 | 1.7519 | 0.0 | 0.0 | 0.0 | 2 | 0.0 | 0.0 | 0.0 | 2 | 0.0 | 0.0 | 0.0 | 2 | 0.0 | 0.0 | 0.0 | 2 | 0.0 | 0.0 | 0.0 | 0.7857 |
| 1.6994 | 2.0 | 12 | 1.5117 | 0.0 | 0.0 | 0.0 | 2 | 0.0 | 0.0 | 0.0 | 2 | 0.0 | 0.0 | 0.0 | 2 | 0.0 | 0.0 | 0.0 | 2 | 0.0 | 0.0 | 0.0 | 0.7857 |
| 1.5521 | 3.0 | 18 | 1.4130 | 0.0 | 0.0 | 0.0 | 2 | 0.0 | 0.0 | 0.0 | 2 | 0.0 | 0.0 | 0.0 | 2 | 0.0 | 0.0 | 0.0 | 2 | 0.0 | 0.0 | 0.0 | 0.7857 |
| 1.4726 | 4.0 | 24 | 1.3410 | 0.0 | 0.0 | 0.0 | 2 | 0.0 | 0.0 | 0.0 | 2 | 0.0 | 0.0 | 0.0 | 2 | 0.0 | 0.0 | 0.0 | 2 | 0.0 | 0.0 | 0.0 | 0.7857 |
| 1.395 | 5.0 | 30 | 1.2693 | 0.0 | 0.0 | 0.0 | 2 | 0.0 | 0.0 | 0.0 | 2 | 0.0 | 0.0 | 0.0 | 2 | 0.0 | 0.0 | 0.0 | 2 | 0.0 | 0.0 | 0.0 | 0.7857 |
| 1.3131 | 6.0 | 36 | 1.2079 | 1.0 | 1.0 | 1.0 | 2 | 0.1667 | 0.5 | 0.25 | 2 | 0.0 | 0.0 | 0.0 | 2 | 0.0 | 0.0 | 0.0 | 2 | 0.3 | 0.375 | 0.3333 | 0.8929 |
| 1.2474 | 7.0 | 42 | 1.1495 | 1.0 | 1.0 | 1.0 | 2 | 0.2 | 0.5 | 0.2857 | 2 | 0.0 | 0.0 | 0.0 | 2 | 1.0 | 1.0 | 1.0 | 2 | 0.4167 | 0.625 | 0.5 | 0.9286 |
| 1.1869 | 8.0 | 48 | 1.0942 | 1.0 | 1.0 | 1.0 | 2 | 0.2 | 0.5 | 0.2857 | 2 | 0.0 | 0.0 | 0.0 | 2 | 1.0 | 1.0 | 1.0 | 2 | 0.4167 | 0.625 | 0.5 | 0.9286 |
| 1.1369 | 9.0 | 54 | 1.0453 | 1.0 | 1.0 | 1.0 | 2 | 0.4 | 1.0 | 0.5714 | 2 | 0.0 | 0.0 | 0.0 | 2 | 1.0 | 1.0 | 1.0 | 2 | 0.5455 | 0.75 | 0.6316 | 0.9464 |
| 1.0882 | 10.0 | 60 | 1.0054 | 1.0 | 1.0 | 1.0 | 2 | 0.5 | 1.0 | 0.6667 | 2 | 0.5 | 0.5 | 0.5 | 2 | 1.0 | 1.0 | 1.0 | 2 | 0.7 | 0.875 | 0.7778 | 0.9643 |
| 1.0482 | 11.0 | 66 | 0.9633 | 1.0 | 1.0 | 1.0 | 2 | 0.6667 | 1.0 | 0.8 | 2 | 1.0 | 1.0 | 1.0 | 2 | 1.0 | 1.0 | 1.0 | 2 | 0.8889 | 1.0 | 0.9412 | 0.9821 |
| 1.017 | 12.0 | 72 | 0.9368 | 1.0 | 1.0 | 1.0 | 2 | 0.6667 | 1.0 | 0.8 | 2 | 1.0 | 1.0 | 1.0 | 2 | 1.0 | 1.0 | 1.0 | 2 | 0.8889 | 1.0 | 0.9412 | 0.9643 |
| 0.9825 | 13.0 | 78 | 0.9139 | 1.0 | 1.0 | 1.0 | 2 | 0.6667 | 1.0 | 0.8 | 2 | 1.0 | 1.0 | 1.0 | 2 | 1.0 | 1.0 | 1.0 | 2 | 0.8889 | 1.0 | 0.9412 | 0.9821 |
| 0.9459 | 14.0 | 84 | 0.8837 | 1.0 | 1.0 | 1.0 | 2 | 0.6667 | 1.0 | 0.8 | 2 | 1.0 | 1.0 | 1.0 | 2 | 1.0 | 1.0 | 1.0 | 2 | 0.8889 | 1.0 | 0.9412 | 0.9643 |
| 0.9155 | 15.0 | 90 | 0.8472 | 1.0 | 1.0 | 1.0 | 2 | 1.0 | 1.0 | 1.0 | 2 | 0.5 | 0.5 | 0.5 | 2 | 1.0 | 1.0 | 1.0 | 2 | 0.875 | 0.875 | 0.875 | 0.9643 |
| 0.8819 | 16.0 | 96 | 0.8231 | 1.0 | 1.0 | 1.0 | 2 | 1.0 | 1.0 | 1.0 | 2 | 0.5 | 0.5 | 0.5 | 2 | 1.0 | 1.0 | 1.0 | 2 | 0.875 | 0.875 | 0.875 | 0.9643 |
| 0.8523 | 17.0 | 102 | 0.7957 | 1.0 | 1.0 | 1.0 | 2 | 1.0 | 1.0 | 1.0 | 2 | 0.6667 | 1.0 | 0.8 | 2 | 1.0 | 1.0 | 1.0 | 2 | 0.8889 | 1.0 | 0.9412 | 0.9821 |
| 0.8251 | 18.0 | 108 | 0.7681 | 1.0 | 1.0 | 1.0 | 2 | 1.0 | 1.0 | 1.0 | 2 | 0.5 | 0.5 | 0.5 | 2 | 1.0 | 1.0 | 1.0 | 2 | 0.875 | 0.875 | 0.875 | 0.9643 |
| 0.7982 | 19.0 | 114 | 0.7533 | 1.0 | 1.0 | 1.0 | 2 | 1.0 | 1.0 | 1.0 | 2 | 0.5 | 0.5 | 0.5 | 2 | 1.0 | 1.0 | 1.0 | 2 | 0.875 | 0.875 | 0.875 | 0.9643 |
| 0.7762 | 20.0 | 120 | 0.7283 | 1.0 | 1.0 | 1.0 | 2 | 1.0 | 1.0 | 1.0 | 2 | 0.5 | 0.5 | 0.5 | 2 | 1.0 | 1.0 | 1.0 | 2 | 0.875 | 0.875 | 0.875 | 0.9643 |
| 0.7558 | 21.0 | 126 | 0.7114 | 1.0 | 1.0 | 1.0 | 2 | 1.0 | 1.0 | 1.0 | 2 | 0.5 | 0.5 | 0.5 | 2 | 1.0 | 1.0 | 1.0 | 2 | 0.875 | 0.875 | 0.875 | 0.9643 |
| 0.7346 | 22.0 | 132 | 0.6889 | 1.0 | 1.0 | 1.0 | 2 | 1.0 | 1.0 | 1.0 | 2 | 0.5 | 0.5 | 0.5 | 2 | 1.0 | 1.0 | 1.0 | 2 | 0.875 | 0.875 | 0.875 | 0.9643 |
| 0.7116 | 23.0 | 138 | 0.6697 | 1.0 | 1.0 | 1.0 | 2 | 1.0 | 1.0 | 1.0 | 2 | 0.5 | 0.5 | 0.5 | 2 | 1.0 | 1.0 | 1.0 | 2 | 0.875 | 0.875 | 0.875 | 0.9643 |
| 0.6898 | 24.0 | 144 | 0.6593 | 1.0 | 1.0 | 1.0 | 2 | 1.0 | 1.0 | 1.0 | 2 | 0.5 | 0.5 | 0.5 | 2 | 1.0 | 1.0 | 1.0 | 2 | 0.875 | 0.875 | 0.875 | 0.9643 |
| 0.6748 | 25.0 | 150 | 0.6356 | 1.0 | 1.0 | 1.0 | 2 | 1.0 | 1.0 | 1.0 | 2 | 0.5 | 0.5 | 0.5 | 2 | 1.0 | 1.0 | 1.0 | 2 | 0.875 | 0.875 | 0.875 | 0.9643 |
| 0.6487 | 26.0 | 156 | 0.6142 | 1.0 | 1.0 | 1.0 | 2 | 1.0 | 1.0 | 1.0 | 2 | 0.5 | 0.5 | 0.5 | 2 | 1.0 | 1.0 | 1.0 | 2 | 0.875 | 0.875 | 0.875 | 0.9643 |
| 0.6312 | 27.0 | 162 | 0.6008 | 1.0 | 1.0 | 1.0 | 2 | 1.0 | 1.0 | 1.0 | 2 | 0.5 | 0.5 | 0.5 | 2 | 1.0 | 1.0 | 1.0 | 2 | 0.875 | 0.875 | 0.875 | 0.9643 |
| 0.6156 | 28.0 | 168 | 0.5855 | 1.0 | 1.0 | 1.0 | 2 | 1.0 | 1.0 | 1.0 | 2 | 0.5 | 0.5 | 0.5 | 2 | 1.0 | 1.0 | 1.0 | 2 | 0.875 | 0.875 | 0.875 | 0.9643 |
| 0.5961 | 29.0 | 174 | 0.5625 | 1.0 | 1.0 | 1.0 | 2 | 1.0 | 1.0 | 1.0 | 2 | 0.5 | 0.5 | 0.5 | 2 | 1.0 | 1.0 | 1.0 | 2 | 0.875 | 0.875 | 0.875 | 0.9643 |
| 0.5781 | 30.0 | 180 | 0.5553 | 1.0 | 1.0 | 1.0 | 2 | 1.0 | 1.0 | 1.0 | 2 | 0.5 | 0.5 | 0.5 | 2 | 1.0 | 1.0 | 1.0 | 2 | 0.875 | 0.875 | 0.875 | 0.9643 |
### Framework versions
- Transformers 4.20.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
huggingtweets/adrianramy
|
huggingtweets
| 2022-06-11T12:12:59Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-06-11T12:12:20Z |
---
language: en
thumbnail: http://www.huggingtweets.com/adrianramy/1654949574810/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<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/kWwF0YSv_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Adri</div>
<div style="text-align: center; font-size: 14px;">@adrianramy</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from Adri.
| Data | Adri |
| --- | --- |
| Tweets downloaded | 3050 |
| Retweets | 1585 |
| Short tweets | 275 |
| Tweets kept | 1190 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/30dqbz5d/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @adrianramy's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/16tp54yl) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/16tp54yl/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/adrianramy')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
huggingtweets/dekotale
|
huggingtweets
| 2022-06-11T12:08:52Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-06-11T12:04:17Z |
---
language: en
thumbnail: http://www.huggingtweets.com/dekotale/1654949168644/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<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/DcCZvOOS_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Dekotale</div>
<div style="text-align: center; font-size: 14px;">@dekotale</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from Dekotale.
| Data | Dekotale |
| --- | --- |
| Tweets downloaded | 3125 |
| Retweets | 1528 |
| Short tweets | 433 |
| Tweets kept | 1164 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1l1uql9a/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @dekotale's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/fv8rmutq) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/fv8rmutq/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/dekotale')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
Jawaher/LIAR-fake-news-roberta-base
|
Jawaher
| 2022-06-11T11:12:24Z | 103 | 1 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"roberta",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-06-11T05:40:13Z |
A pre-trained Roberta masked language model (MLM) trained on around 12K fake news dataset called LIAR. The perplexity of the original pre-trained Roberta model on the dataset is 5.957 and the perplexity of the adapted model is 3.918.
|
Theivaprakasham/layoutlmv3-finetuned-wildreceipt
|
Theivaprakasham
| 2022-06-11T09:14:40Z | 28 | 3 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"layoutlmv3",
"token-classification",
"generated_from_trainer",
"dataset:wild_receipt",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-06-11T07:21:14Z |
---
tags:
- generated_from_trainer
datasets:
- wild_receipt
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: layoutlmv3-finetuned-wildreceipt
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: wild_receipt
type: wild_receipt
args: WildReceipt
metrics:
- name: Precision
type: precision
value: 0.877212237618329
- name: Recall
type: recall
value: 0.8798678959680749
- name: F1
type: f1
value: 0.8785380599065679
- name: Accuracy
type: accuracy
value: 0.9249204782274871
---
<!-- 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. -->
# layoutlmv3-finetuned-wildreceipt
This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the wild_receipt dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3108
- Precision: 0.8772
- Recall: 0.8799
- F1: 0.8785
- Accuracy: 0.9249
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
The WildReceipt dataset consists of 1740 receipt images, and contains 25 key information categories, and a total of about 69000 text boxes. 1268 and 472 images are used for training and testing respectively to train the LayoutLMv3 model for Key Information Extraction.
## Training procedure
The training code: https://github.com/Theivaprakasham/layoutlmv3/blob/main/training_codes/LayoutLMv3_training_WildReceipts_dataset.ipynb
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 4000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 0.32 | 100 | 1.3143 | 0.6709 | 0.2679 | 0.3829 | 0.6700 |
| No log | 0.63 | 200 | 0.8814 | 0.6478 | 0.5195 | 0.5766 | 0.7786 |
| No log | 0.95 | 300 | 0.6568 | 0.7205 | 0.6491 | 0.6829 | 0.8303 |
| No log | 1.26 | 400 | 0.5618 | 0.7544 | 0.7072 | 0.7300 | 0.8519 |
| 1.0284 | 1.58 | 500 | 0.5003 | 0.7802 | 0.7566 | 0.7682 | 0.8687 |
| 1.0284 | 1.89 | 600 | 0.4454 | 0.7941 | 0.7679 | 0.7807 | 0.8748 |
| 1.0284 | 2.21 | 700 | 0.4314 | 0.8142 | 0.7928 | 0.8033 | 0.8852 |
| 1.0284 | 2.52 | 800 | 0.3870 | 0.8172 | 0.8200 | 0.8186 | 0.8953 |
| 1.0284 | 2.84 | 900 | 0.3629 | 0.8288 | 0.8369 | 0.8329 | 0.9025 |
| 0.4167 | 3.15 | 1000 | 0.3537 | 0.8540 | 0.8200 | 0.8366 | 0.9052 |
| 0.4167 | 3.47 | 1100 | 0.3383 | 0.8438 | 0.8285 | 0.8361 | 0.9063 |
| 0.4167 | 3.79 | 1200 | 0.3403 | 0.8297 | 0.8493 | 0.8394 | 0.9062 |
| 0.4167 | 4.1 | 1300 | 0.3271 | 0.8428 | 0.8545 | 0.8487 | 0.9110 |
| 0.4167 | 4.42 | 1400 | 0.3182 | 0.8491 | 0.8518 | 0.8504 | 0.9131 |
| 0.2766 | 4.73 | 1500 | 0.3111 | 0.8491 | 0.8539 | 0.8515 | 0.9129 |
| 0.2766 | 5.05 | 1600 | 0.3177 | 0.8397 | 0.8620 | 0.8507 | 0.9124 |
| 0.2766 | 5.36 | 1700 | 0.3091 | 0.8676 | 0.8548 | 0.8612 | 0.9191 |
| 0.2766 | 5.68 | 1800 | 0.3080 | 0.8508 | 0.8645 | 0.8576 | 0.9162 |
| 0.2766 | 5.99 | 1900 | 0.3059 | 0.8492 | 0.8662 | 0.8576 | 0.9163 |
| 0.2114 | 6.31 | 2000 | 0.3184 | 0.8536 | 0.8657 | 0.8596 | 0.9147 |
| 0.2114 | 6.62 | 2100 | 0.3161 | 0.8583 | 0.8713 | 0.8648 | 0.9184 |
| 0.2114 | 6.94 | 2200 | 0.3055 | 0.8707 | 0.8682 | 0.8694 | 0.9220 |
| 0.2114 | 7.26 | 2300 | 0.3004 | 0.8689 | 0.8745 | 0.8717 | 0.9219 |
| 0.2114 | 7.57 | 2400 | 0.3111 | 0.8701 | 0.8720 | 0.8711 | 0.9211 |
| 0.174 | 7.89 | 2500 | 0.3130 | 0.8599 | 0.8741 | 0.8669 | 0.9198 |
| 0.174 | 8.2 | 2600 | 0.3034 | 0.8661 | 0.8748 | 0.8704 | 0.9219 |
| 0.174 | 8.52 | 2700 | 0.3005 | 0.8799 | 0.8673 | 0.8736 | 0.9225 |
| 0.174 | 8.83 | 2800 | 0.3043 | 0.8687 | 0.8804 | 0.8745 | 0.9240 |
| 0.174 | 9.15 | 2900 | 0.3121 | 0.8776 | 0.8704 | 0.8740 | 0.9242 |
| 0.1412 | 9.46 | 3000 | 0.3131 | 0.8631 | 0.8755 | 0.8692 | 0.9204 |
| 0.1412 | 9.78 | 3100 | 0.3067 | 0.8715 | 0.8773 | 0.8744 | 0.9233 |
| 0.1412 | 10.09 | 3200 | 0.3021 | 0.8751 | 0.8812 | 0.8782 | 0.9248 |
| 0.1412 | 10.41 | 3300 | 0.3092 | 0.8651 | 0.8808 | 0.8729 | 0.9228 |
| 0.1412 | 10.73 | 3400 | 0.3084 | 0.8776 | 0.8749 | 0.8762 | 0.9237 |
| 0.1254 | 11.04 | 3500 | 0.3156 | 0.8738 | 0.8785 | 0.8761 | 0.9237 |
| 0.1254 | 11.36 | 3600 | 0.3131 | 0.8723 | 0.8818 | 0.8770 | 0.9244 |
| 0.1254 | 11.67 | 3700 | 0.3108 | 0.8778 | 0.8781 | 0.8780 | 0.9250 |
| 0.1254 | 11.99 | 3800 | 0.3097 | 0.8778 | 0.8771 | 0.8775 | 0.9239 |
| 0.1254 | 12.3 | 3900 | 0.3115 | 0.8785 | 0.8801 | 0.8793 | 0.9251 |
| 0.111 | 12.62 | 4000 | 0.3108 | 0.8772 | 0.8799 | 0.8785 | 0.9249 |
### Framework versions
- Transformers 4.20.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
OTQ/q-Taxi-v3
|
OTQ
| 2022-06-11T08:10:17Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-06-11T08:10:10Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3
results:
- metrics:
- type: mean_reward
value: 7.50 +/- 2.78
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
---
# **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="/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"])
```
|
orzhan/t5-long-extract
|
orzhan
| 2022-06-11T07:20:59Z | 105 | 1 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"feature-extraction",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2022-03-02T23:29:05Z |
T5-small model fine-tuned for extractive summarization on long documents.
Repository: [GitHub](https://github.com/orzhan/t5-long-extract)
|
orzhan/rut5-base-detox-v2
|
orzhan
| 2022-06-11T07:18:47Z | 8 | 1 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"PyTorch",
"Transformers",
"ru",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-05-25T06:51:41Z |
---
language:
- ru
tags:
- PyTorch
- Transformers
---
# rut5-base-detox-v2
Model was fine-tuned from sberbank-ai/ruT5-base on parallel detoxification corpus.
* Task: `text2text generation`
* Type: `encoder-decoder`
* Tokenizer: `bpe`
* Dict size: `32 101`
* Num Parameters: `222 M`
|
huggingtweets/waffle_64
|
huggingtweets
| 2022-06-11T04:39:14Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-06-11T04:35:42Z |
---
language: en
thumbnail: http://www.huggingtweets.com/waffle_64/1654922313776/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<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/1534033778787639296/a9JUby19_400x400.png')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">🧇 Werewaffle🐺LOU NATION🐺</div>
<div style="text-align: center; font-size: 14px;">@waffle_64</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from 🧇 Werewaffle🐺LOU NATION🐺.
| Data | 🧇 Werewaffle🐺LOU NATION🐺 |
| --- | --- |
| Tweets downloaded | 3249 |
| Retweets | 110 |
| Short tweets | 217 |
| Tweets kept | 2922 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1rq6yndm/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @waffle_64's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/ucwnzfby) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/ucwnzfby/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/waffle_64')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
ablam/distilgpt2_fine_tuned_gcode
|
ablam
| 2022-06-11T03:52:00Z | 9 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-05-11T01:09:05Z |
---
tags:
- generated_from_trainer
model-index:
- name: distilgpt2_fine_tuned_gcode
results: []
---
<!-- 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. -->
# distilgpt2_fine_tuned_gcode
This model is a fine-tuned version of [congcongwang/distilgpt2_fine_tuned_coder](https://huggingface.co/congcongwang/distilgpt2_fine_tuned_coder) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 4.1670
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.1
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 4.1754 | 1.0 | 52144 | 4.1670 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.1
- Datasets 2.1.0
- Tokenizers 0.10.3
|
tclong/wav2vec2-base-vios-commonvoice-1
|
tclong
| 2022-06-11T03:01:54Z | 106 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-06-10T11:09:14Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-base-vios-commonvoice-1
results: []
---
<!-- 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-vios-commonvoice-1
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8913
- Wer: 0.3621
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 3.4706 | 0.55 | 500 | 3.4725 | 1.0 |
| 3.202 | 1.1 | 1000 | 2.7555 | 1.0008 |
| 1.0507 | 1.66 | 1500 | 1.0481 | 0.6196 |
| 0.7325 | 2.21 | 2000 | 0.8120 | 0.4958 |
| 0.599 | 2.76 | 2500 | 0.7035 | 0.4447 |
| 0.5224 | 3.31 | 3000 | 0.6761 | 0.4078 |
| 0.4844 | 3.86 | 3500 | 0.6688 | 0.4011 |
| 0.4234 | 4.42 | 4000 | 0.6080 | 0.3729 |
| 0.4237 | 4.97 | 4500 | 0.5953 | 0.3556 |
| 0.3986 | 5.52 | 5000 | 0.6054 | 0.3478 |
| 0.3554 | 6.07 | 5500 | 0.6193 | 0.3479 |
| 0.3446 | 6.62 | 6000 | 0.5809 | 0.3302 |
| 0.3104 | 7.17 | 6500 | 0.5713 | 0.3283 |
| 0.3166 | 7.73 | 7000 | 0.5593 | 0.3133 |
| 0.2938 | 8.28 | 7500 | 0.5645 | 0.3081 |
| 0.3061 | 8.83 | 8000 | 0.5508 | 0.3020 |
| 0.2986 | 9.38 | 8500 | 0.5462 | 0.3024 |
| 0.2939 | 9.93 | 9000 | 0.5544 | 0.3028 |
| 0.2633 | 10.49 | 9500 | 0.5496 | 0.3024 |
| 0.2683 | 11.04 | 10000 | 0.5439 | 0.2946 |
| 0.2714 | 11.59 | 10500 | 0.5524 | 0.2947 |
| 0.2354 | 12.14 | 11000 | 0.5267 | 0.2918 |
| 0.2488 | 12.69 | 11500 | 0.5728 | 0.2938 |
| 0.2479 | 13.25 | 12000 | 0.5802 | 0.2951 |
| 0.245 | 13.8 | 12500 | 0.5571 | 0.2890 |
| 0.2422 | 14.35 | 13000 | 0.5531 | 0.2871 |
| 0.2369 | 14.9 | 13500 | 0.5453 | 0.2860 |
| 0.2345 | 15.45 | 14000 | 0.5452 | 0.2847 |
| 0.2507 | 16.0 | 14500 | 0.5536 | 0.2884 |
| 0.2454 | 16.56 | 15000 | 0.5577 | 0.2871 |
| 0.2729 | 17.11 | 15500 | 0.6019 | 0.2931 |
| 0.2743 | 17.66 | 16000 | 0.5619 | 0.2905 |
| 0.3031 | 18.21 | 16500 | 0.6401 | 0.3006 |
| 0.315 | 18.76 | 17000 | 0.6044 | 0.2990 |
| 0.4025 | 19.32 | 17500 | 0.6739 | 0.3304 |
| 0.4915 | 19.87 | 18000 | 0.7267 | 0.3472 |
| 0.5539 | 20.42 | 18500 | 0.8078 | 0.3483 |
| 0.7138 | 20.97 | 19000 | 0.9362 | 0.3765 |
| 0.5766 | 21.52 | 19500 | 0.7921 | 0.3392 |
| 0.688 | 22.08 | 20000 | 0.8833 | 0.3693 |
| 0.6964 | 22.63 | 20500 | 0.9137 | 0.3469 |
| 0.7389 | 23.18 | 21000 | 0.9379 | 0.3460 |
| 0.7851 | 23.73 | 21500 | 1.0438 | 0.3653 |
| 0.7619 | 24.28 | 22000 | 0.9313 | 0.3873 |
| 0.7175 | 24.83 | 22500 | 0.8668 | 0.3789 |
| 0.6842 | 25.39 | 23000 | 0.8243 | 0.3761 |
| 0.6941 | 25.94 | 23500 | 0.8557 | 0.3804 |
| 0.7167 | 26.49 | 24000 | 0.8618 | 0.3875 |
| 0.721 | 27.04 | 24500 | 0.8686 | 0.3764 |
| 0.6949 | 27.59 | 25000 | 0.8773 | 0.3690 |
| 0.727 | 28.15 | 25500 | 0.8769 | 0.3666 |
| 0.7363 | 28.7 | 26000 | 0.8867 | 0.3634 |
| 0.7157 | 29.25 | 26500 | 0.8895 | 0.3626 |
| 0.7385 | 29.8 | 27000 | 0.8913 | 0.3621 |
### Framework versions
- Transformers 4.19.3
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
enoriega/rule_learning_margin_1mm
|
enoriega
| 2022-06-11T02:04:28Z | 22 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"generated_from_trainer",
"dataset:enoriega/odinsynth_dataset",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-06-10T01:52:07Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- enoriega/odinsynth_dataset
model-index:
- name: rule_learning_margin_1mm
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# rule_learning_margin_1mm
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the enoriega/odinsynth_dataset dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3806
- Margin Accuracy: 0.8239
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 2000
- total_train_batch_size: 8000
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Margin Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------------:|
| 0.6482 | 0.16 | 20 | 0.6494 | 0.7263 |
| 0.5151 | 0.32 | 40 | 0.5088 | 0.7792 |
| 0.4822 | 0.48 | 60 | 0.4429 | 0.8045 |
| 0.4472 | 0.64 | 80 | 0.4265 | 0.8107 |
| 0.4352 | 0.8 | 100 | 0.4155 | 0.8132 |
| 0.4335 | 0.96 | 120 | 0.4128 | 0.8116 |
| 0.4113 | 1.12 | 140 | 0.4119 | 0.8142 |
| 0.4186 | 1.28 | 160 | 0.4075 | 0.8120 |
| 0.42 | 1.44 | 180 | 0.4072 | 0.8123 |
| 0.4175 | 1.6 | 200 | 0.4080 | 0.8130 |
| 0.4097 | 1.76 | 220 | 0.4031 | 0.8128 |
| 0.397 | 1.92 | 240 | 0.4004 | 0.8130 |
| 0.4115 | 2.08 | 260 | 0.3979 | 0.8136 |
| 0.4108 | 2.24 | 280 | 0.3940 | 0.8167 |
| 0.4125 | 2.4 | 300 | 0.3879 | 0.8218 |
| 0.4117 | 2.56 | 320 | 0.3848 | 0.8217 |
| 0.3967 | 2.72 | 340 | 0.3818 | 0.8231 |
| 0.3947 | 2.88 | 360 | 0.3813 | 0.8240 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0
- Datasets 2.2.1
- Tokenizers 0.12.1
|
huggingtweets/yomancuso
|
huggingtweets
| 2022-06-11T01:08:18Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-06-11T01:08:10Z |
---
language: en
thumbnail: https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<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/1490538004607385602/laSBwC6u_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Davey Wavey</div>
<div style="text-align: center; font-size: 14px;">@yomancuso</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from Davey Wavey.
| Data | Davey Wavey |
| --- | --- |
| Tweets downloaded | 3176 |
| Retweets | 1207 |
| Short tweets | 485 |
| Tweets kept | 1484 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2i0ci708/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @yomancuso's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3mexojoq) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3mexojoq/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/yomancuso')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
huggingtweets/froliki2108
|
huggingtweets
| 2022-06-11T00:04:16Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-06-11T00:02:55Z |
---
language: en
thumbnail: http://www.huggingtweets.com/froliki2108/1654905851117/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<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/1447692349493100549/1PV2c-PJ_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Froliki💉💉💉</div>
<div style="text-align: center; font-size: 14px;">@froliki2108</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from Froliki💉💉💉.
| Data | Froliki💉💉💉 |
| --- | --- |
| Tweets downloaded | 2223 |
| Retweets | 1133 |
| Short tweets | 229 |
| Tweets kept | 861 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2tug3miv/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @froliki2108's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3otsf5pj) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3otsf5pj/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/froliki2108')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
nateraw/modelcard-creator-demo
|
nateraw
| 2022-06-10T23:58:39Z | 0 | 0 |
pytorch
|
[
"pytorch",
"modelcards",
"autogenerated-modelcard",
"en",
"dataset:beans",
"arxiv:1810.03993",
"arxiv:1910.09700",
"license:mit",
"region:us"
] | null | 2022-06-10T23:40:23Z |
---
language:
- en
license: mit
library_name: pytorch
tags:
- modelcards
- autogenerated-modelcard
datasets:
- beans
metrics:
- accuracy
---
# modelcard-creator-demo
## Table of Contents
- [Model Details](#model-details)
- [How To Get Started With the Model](#how-to-get-started-with-the-model)
- [Uses](#uses)
- [Direct Use](#direct-use)
- [Downstream Use](#downstream-use)
- [Misuse and Out of Scope Use](#misuse-and-out-of-scope-use)
- [Limitations and Biases](#limitations-and-biases)
- [Training](#training)
- [Training Data](#training-data)
- [Training Procedure](#training-procedure)
- [Evaluation Results](#evaluation-results)
- [Environmental Impact](#environmental-impact)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Model Details
<!-- Give an overview of your model, the relevant research paper, who trained it, etc. -->
This isn't really a model, it's just a test repo to see if the [model card creator](https://huggingface.co/spaces/nateraw/modelcard-creator) works!
- Developed by: Nathan Raw
- Language(s):
- License: modelcard-creator-demo is licensed under the mit license
- Resources for more information:
- [Research Paper](https://arxiv.org/pdf/1810.03993.pdf)
- [GitHub Repo](https://github.com/nateraw/modelcards)
## How to Get Started with the Model
Use the code below to get started with the model.
```python
# A nice code snippet here that describes how to use the model...
```
## Uses
#### Direct Use
<!-- Describe what kind of tasks this model can be used for directly or problems it can solve. -->
[More Information Needed]
#### Downstream Use
<!-- Describe how this model could be leveraged by a downstream model (if applicable) -->
[More Information Needed]
#### Misuse and Out-of-scope Use
<!-- Describe ways in which this model ***should not*** be used. -->
[More Information Needed]
## Limitations and Biases
<!-- Describe limitations and biases of this model or models of it's type. -->
**CONTENT WARNING: Readers should be aware this section contains content that is disturbing, offensive, and can propogate historical and current stereotypes.**
[More Information Needed]
## Training
#### Training Data
<!-- Describe the dataset used to train this model. -->
<!-- Refer to data card if dataset is provided and exists on the hub -->
See the data card for additional information.
#### Training Procedure
<!-- Describe the preprocessing, hardware used, training hyperparameters, etc. -->
[More Information Needed]
## Evaluation Results
<!-- Describe evaluation results of this model across any datasets it was evaluated on. -->
[More Information Needed]
## Environmental Impact
<!-- Provide information to document the environmental impact of this model -->
You can estimate carbon emissions using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700)
- **Hardware Type:**
- **Hours used:**
- **Cloud Provider:**
- **Compute Region:**
- **Carbon Emitted:**
## Citation Information
```bibtex
@inproceedings{Mitchell_2019,
doi = {10.1145/3287560.3287596},
url = {https://doi.org/10.1145%2F3287560.3287596},
year = 2019,
month = {jan},
publisher = {{ACM}
},
author = {Margaret Mitchell and Simone Wu and Andrew Zaldivar and Parker Barnes and Lucy Vasserman and Ben Hutchinson and Elena Spitzer and Inioluwa Deborah Raji and Timnit Gebru},
title = {Model Cards for Model Reporting},
booktitle = {Proceedings of the Conference on Fairness, Accountability, and Transparency}
}
```
|
ahmeddbahaa/t5-arabic-base-finetuned-wikilingua-ar
|
ahmeddbahaa
| 2022-06-10T23:54:52Z | 12 | 1 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"summarization",
"mt5",
"ar",
"abstractive summarization",
"generated_from_trainer",
"dataset:wiki_lingua",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
summarization
| 2022-06-10T15:19:23Z |
---
license: apache-2.0
tags:
- summarization
- mt5
- ar
- abstractive summarization
- generated_from_trainer
datasets:
- wiki_lingua
model-index:
- name: t5-arabic-base-finetuned-wikilingua-ar
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-arabic-base-finetuned-wikilingua-ar
This model is a fine-tuned version of [bakrianoo/t5-arabic-base](https://huggingface.co/bakrianoo/t5-arabic-base) on the wiki_lingua dataset.
It achieves the following results on the evaluation set:
- Loss: 3.2735
- Rouge-1: 20.72
- Rouge-2: 7.63
- Rouge-l: 18.75
- Gen Len: 18.74
- Bertscore: 70.79
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 250
- num_epochs: 8
- label_smoothing_factor: 0.1
### Training results
### Framework versions
- Transformers 4.19.3
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
yiyanghkust/finbert-fls
|
yiyanghkust
| 2022-06-10T23:20:05Z | 164,311 | 22 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"financial-text-analysis",
"forward-looking-statement",
"en",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-05-12T01:33:03Z |
---
language: "en"
tags:
- financial-text-analysis
- forward-looking-statement
widget:
- text: "We expect the age of our fleet to enhance availability and reliability due to reduced downtime for repairs. "
---
Forward-looking statements (FLS) inform investors of managers’ beliefs and opinions about firm's future events or results. Identifying forward-looking statements from corporate reports can assist investors in financial analysis. FinBERT-FLS is a FinBERT model fine-tuned on 3,500 manually annotated sentences from Management Discussion and Analysis section of annual reports of Russell 3000 firms.
**Input**: A financial text.
**Output**: Specific-FLS , Non-specific FLS, or Not-FLS.
# How to use
You can use this model with Transformers pipeline for forward-looking statement classification.
```python
# tested in transformers==4.18.0
from transformers import BertTokenizer, BertForSequenceClassification, pipeline
finbert = BertForSequenceClassification.from_pretrained('yiyanghkust/finbert-fls',num_labels=3)
tokenizer = BertTokenizer.from_pretrained('yiyanghkust/finbert-fls')
nlp = pipeline("text-classification", model=finbert, tokenizer=tokenizer)
results = nlp('We expect the age of our fleet to enhance availability and reliability due to reduced downtime for repairs.')
print(results) # [{'label': 'Specific FLS', 'score': 0.77278733253479}]
```
Visit [FinBERT.AI](https://finbert.ai/) for more details on the recent development of FinBERT.
|
huggingtweets/jedwill1999
|
huggingtweets
| 2022-06-10T23:10:10Z | 106 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-06-10T23:09:22Z |
---
language: en
thumbnail: http://www.huggingtweets.com/jedwill1999/1654902604867/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<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/1510152678919135250/lfEmlEGJ_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">a local</div>
<div style="text-align: center; font-size: 14px;">@jedwill1999</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from a local.
| Data | a local |
| --- | --- |
| Tweets downloaded | 3246 |
| Retweets | 1080 |
| Short tweets | 525 |
| Tweets kept | 1641 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1qsnsp6t/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @jedwill1999's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/mjjc73pu) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/mjjc73pu/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/jedwill1999')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
public-data/MangaLineExtraction_PyTorch
|
public-data
| 2022-06-10T23:01:13Z | 0 | 1 | null |
[
"region:us"
] | null | 2022-06-10T22:58:25Z |
# MangaLineExtraction_PyTorch
- https://github.com/ljsabc/MangaLineExtraction_PyTorch
|
huggingtweets/boopysaur
|
huggingtweets
| 2022-06-10T22:57:09Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-06-10T22:56:08Z |
---
language: en
thumbnail: http://www.huggingtweets.com/boopysaur/1654901824865/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<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/1476816918879297559/2jt_Rt2L_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">boop ♡</div>
<div style="text-align: center; font-size: 14px;">@boopysaur</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from boop ♡.
| Data | boop ♡ |
| --- | --- |
| Tweets downloaded | 920 |
| Retweets | 162 |
| Short tweets | 128 |
| Tweets kept | 630 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/398l195g/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @boopysaur's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3te0suw6) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3te0suw6/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/boopysaur')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
facebook/roberta-hate-speech-dynabench-r3-target
|
facebook
| 2022-06-10T22:34:01Z | 7 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"text-classification",
"en",
"arxiv:2012.15761",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-06-10T22:10:40Z |
---
language: en
---
# LFTW R3 Target
The R3 Target model from [Learning from the Worst: Dynamically Generated Datasets to Improve Online Hate Detection](https://arxiv.org/abs/2012.15761)
## Citation Information
```bibtex
@inproceedings{vidgen2021lftw,
title={Learning from the Worst: Dynamically Generated Datasets to Improve Online Hate Detection},
author={Bertie Vidgen and Tristan Thrush and Zeerak Waseem and Douwe Kiela},
booktitle={ACL},
year={2021}
}
```
Thanks to Kushal Tirumala and Adina Williams for helping the authors put the model on the hub!
|
mmillet/distilrubert-tiny-cased-conversational-v1_single_finetuned_on_cedr_augmented
|
mmillet
| 2022-06-10T20:27:38Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-06-10T20:14:44Z |
---
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: distilrubert-tiny-cased-conversational-v1_single_finetuned_on_cedr_augmented
results: []
---
<!-- 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. -->
# distilrubert-tiny-cased-conversational-v1_single_finetuned_on_cedr_augmented
This model is a fine-tuned version of [DeepPavlov/distilrubert-tiny-cased-conversational-v1](https://huggingface.co/DeepPavlov/distilrubert-tiny-cased-conversational-v1) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5908
- Accuracy: 0.8653
- F1: 0.8656
- Precision: 0.8665
- Recall: 0.8653
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-06
- lr_scheduler_type: linear
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.9172 | 1.0 | 69 | 0.5124 | 0.8246 | 0.8220 | 0.8271 | 0.8246 |
| 0.4709 | 2.0 | 138 | 0.4279 | 0.8528 | 0.8505 | 0.8588 | 0.8528 |
| 0.3194 | 3.0 | 207 | 0.3770 | 0.8737 | 0.8727 | 0.8740 | 0.8737 |
| 0.2459 | 4.0 | 276 | 0.3951 | 0.8685 | 0.8682 | 0.8692 | 0.8685 |
| 0.1824 | 5.0 | 345 | 0.4005 | 0.8831 | 0.8834 | 0.8841 | 0.8831 |
| 0.1515 | 6.0 | 414 | 0.4356 | 0.8800 | 0.8797 | 0.8801 | 0.8800 |
| 0.1274 | 7.0 | 483 | 0.4642 | 0.8727 | 0.8726 | 0.8731 | 0.8727 |
| 0.0833 | 8.0 | 552 | 0.5226 | 0.8633 | 0.8627 | 0.8631 | 0.8633 |
| 0.073 | 9.0 | 621 | 0.5327 | 0.8695 | 0.8686 | 0.8692 | 0.8695 |
| 0.0575 | 10.0 | 690 | 0.5908 | 0.8653 | 0.8656 | 0.8665 | 0.8653 |
### Framework versions
- Transformers 4.19.3
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
huggingtweets/ninjasexparty
|
huggingtweets
| 2022-06-10T19:56:27Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-06-10T19:56:18Z |
---
language: en
thumbnail: https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<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/1446572046679302144/jF9HS_Yd_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Ninja Sex Party</div>
<div style="text-align: center; font-size: 14px;">@ninjasexparty</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from Ninja Sex Party.
| Data | Ninja Sex Party |
| --- | --- |
| Tweets downloaded | 3250 |
| Retweets | 631 |
| Short tweets | 439 |
| Tweets kept | 2180 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1ik0ji2l/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @ninjasexparty's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1jyhmzsa) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1jyhmzsa/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/ninjasexparty')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
FritzOS/TEdetection_distilBERT_mLM_V5
|
FritzOS
| 2022-06-10T19:43:24Z | 63 | 0 |
transformers
|
[
"transformers",
"tf",
"distilbert",
"fill-mask",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-06-10T19:43:11Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: TEdetection_distilBERT_mLM_V5
results: []
---
<!-- 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. -->
# TEdetection_distilBERT_mLM_V5
This model is a fine-tuned version of [FritzOS/TEdetection_distiBERT_mLM_V2](https://huggingface.co/FritzOS/TEdetection_distiBERT_mLM_V2) on an unknown dataset.
It achieves the following results on the evaluation set:
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 5e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5e-05, 'decay_steps': 208018, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '__passive_serialization__': True}, 'warmup_steps': 1000, 'power': 1.0, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
### Framework versions
- Transformers 4.19.3
- TensorFlow 2.8.2
- Datasets 2.2.2
- Tokenizers 0.12.1
|
huggingtweets/jana_aych_ess
|
huggingtweets
| 2022-06-10T19:22:06Z | 98 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-06-10T19:21:00Z |
---
language: en
thumbnail: http://www.huggingtweets.com/jana_aych_ess/1654888920998/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<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/1169751139409117185/BU60y7P5_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Jana 'All Cops Are Bastards' H-S (they/them)</div>
<div style="text-align: center; font-size: 14px;">@jana_aych_ess</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from Jana 'All Cops Are Bastards' H-S (they/them).
| Data | Jana 'All Cops Are Bastards' H-S (they/them) |
| --- | --- |
| Tweets downloaded | 3234 |
| Retweets | 343 |
| Short tweets | 148 |
| Tweets kept | 2743 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3q5i1d01/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @jana_aych_ess's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3uy7dmw6) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3uy7dmw6/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/jana_aych_ess')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
huggingtweets/malzliebchen
|
huggingtweets
| 2022-06-10T18:29:39Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-06-10T18:26:43Z |
---
language: en
thumbnail: http://www.huggingtweets.com/malzliebchen/1654885748305/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<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/1521909233024913408/4QsF2YzM_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Malzbeard's Severed Head</div>
<div style="text-align: center; font-size: 14px;">@malzliebchen</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from Malzbeard's Severed Head.
| Data | Malzbeard's Severed Head |
| --- | --- |
| Tweets downloaded | 3247 |
| Retweets | 41 |
| Short tweets | 486 |
| Tweets kept | 2720 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/e1wzn1e5/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @malzliebchen's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/38g20s6n) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/38g20s6n/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/malzliebchen')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
Clody0071/camembert-base-finetuned-paraphrase
|
Clody0071
| 2022-06-10T18:05:49Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"camembert",
"text-classification",
"generated_from_trainer",
"dataset:pawsx",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-06-10T16:20:01Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- pawsx
metrics:
- accuracy
- f1
model-index:
- name: camembert-base-finetuned-paraphrase
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: pawsx
type: pawsx
args: fr
metrics:
- name: Accuracy
type: accuracy
value: 0.9085
- name: F1
type: f1
value: 0.9088724090678741
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# camembert-base-finetuned-paraphrase
This model is a fine-tuned version of [camembert-base](https://huggingface.co/camembert-base) on the pawsx dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2708
- Accuracy: 0.9085
- F1: 0.9089
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.3918 | 1.0 | 772 | 0.3211 | 0.869 | 0.8696 |
| 0.2103 | 2.0 | 1544 | 0.2448 | 0.9075 | 0.9077 |
| 0.1622 | 3.0 | 2316 | 0.2577 | 0.9055 | 0.9059 |
| 0.1344 | 4.0 | 3088 | 0.2708 | 0.9085 | 0.9089 |
### Framework versions
- Transformers 4.19.3
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
meln1k/dqn-SpaceInvadersNoFrameskip-v4
|
meln1k
| 2022-06-10T17:30:42Z | 5 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-06-10T17:30:14Z |
---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- metrics:
- type: mean_reward
value: 817.50 +/- 327.32
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
---
# **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 framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
```
# Download model and save it into the logs/ folder
python -m utils.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga meln1k -f logs/
python enjoy.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python train.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m utils.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga meln1k
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 1000000.0),
('optimize_memory_usage', True),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
|
income/bpr-base-msmarco-contriever
|
income
| 2022-06-10T17:16:00Z | 2 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2022-06-10T17:11:14Z |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
model = AutoModel.from_pretrained('{MODEL_NAME}')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 6653 with parameters:
```
{'batch_size': 75, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`bpr_loss.BPRLossFunction`
Parameters of the fit()-Method:
```
{
"epochs": 10,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'transformers.optimization.AdamW'>",
"optimizer_params": {
"correct_bias": false,
"eps": 1e-06,
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 1000,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 350, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information -->
|
juancopi81/mt5-small-finetuned-amazon-en-es
|
juancopi81
| 2022-06-10T15:58:27Z | 61 | 0 |
transformers
|
[
"transformers",
"tf",
"mt5",
"text2text-generation",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-06-10T13:57:35Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: juancopi81/mt5-small-finetuned-amazon-en-es
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# juancopi81/mt5-small-finetuned-amazon-en-es
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 4.1238
- Validation Loss: 3.4046
- Epoch: 7
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5.6e-05, 'decay_steps': 9672, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 10.2166 | 4.4331 | 0 |
| 6.0386 | 3.8849 | 1 |
| 5.2369 | 3.6628 | 2 |
| 4.7882 | 3.5569 | 3 |
| 4.5111 | 3.4850 | 4 |
| 4.3250 | 3.4330 | 5 |
| 4.1930 | 3.4163 | 6 |
| 4.1238 | 3.4046 | 7 |
### Framework versions
- Transformers 4.19.3
- TensorFlow 2.8.2
- Datasets 2.2.2
- Tokenizers 0.12.1
|
vasudevgupta/speech_jax_wav2vec2-large-lv60_100h
|
vasudevgupta
| 2022-06-10T15:53:32Z | 8 | 0 |
transformers
|
[
"transformers",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-05-29T21:03:05Z |
* Evaluation Notebook: https://colab.research.google.com/drive/1dV1Z3WajMCYMjNZab98CEEcg3FTbtONO?usp=sharing
* Training Code: https://github.com/vasudevgupta7/speech-jax/blob/main/projects/asr/train_wav2vec2.py
Following results are obtained with `adce555df7402dc63f8f4d9d14cb286f4b9d4107`
| dataset | WER |
|------------------------|-------|
| Librispeech-test-clean | 5.5 % |
|
Clody0071/distilbert-base-multilingual-cased-finetuned-similarite
|
Clody0071
| 2022-06-10T15:25:52Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:pawsx",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-06-10T14:33:47Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- pawsx
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-multilingual-cased-finetuned-similarite
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: pawsx
type: pawsx
args: fr
metrics:
- name: Accuracy
type: accuracy
value: 0.7995
- name: F1
type: f1
value: 0.7994565743967147
---
<!-- 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-multilingual-cased-finetuned-similarite
This model is a fine-tuned version of [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) on the pawsx dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4781
- Accuracy: 0.7995
- F1: 0.7995
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.5343 | 1.0 | 772 | 0.4879 | 0.7705 | 0.7714 |
| 0.3523 | 2.0 | 1544 | 0.4781 | 0.7995 | 0.7995 |
### Framework versions
- Transformers 4.19.3
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
adalbertojunior/clip-rpt
|
adalbertojunior
| 2022-06-10T14:35:02Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"vision-text-dual-encoder",
"feature-extraction",
"generated_from_trainer",
"dataset:ydshieh/coco_dataset_script",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2022-06-10T12:46:52Z |
---
tags:
- generated_from_trainer
datasets:
- ydshieh/coco_dataset_script
model-index:
- name: clip-roberta-finetuned
results: []
---
<!-- 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. -->
# clip-roberta-finetuned
This model is a fine-tuned version of [./models/clip-roberta](https://huggingface.co/./models/clip-roberta) on the ydshieh/coco_dataset_script 2017 dataset.
It achieves the following results on the evaluation set:
- Loss: 2.7269
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 256
- eval_batch_size: 256
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 1.18.4
- Tokenizers 0.11.6
|
ahmeddbahaa/mt5-base-finetuned-wikilingua-ar
|
ahmeddbahaa
| 2022-06-10T13:00:43Z | 13 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"mt5",
"text2text-generation",
"summarization",
"ar",
"abstractive summarization",
"generated_from_trainer",
"dataset:wiki_lingua",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
summarization
| 2022-06-10T02:40:53Z |
---
license: apache-2.0
tags:
- summarization
- mt5
- ar
- abstractive summarization
- generated_from_trainer
datasets:
- wiki_lingua
model-index:
- name: mt5-base-finetuned-wikilingua-ar
results: []
---
<!-- 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-wikilingua-ar
This model is a fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) on the wiki_lingua dataset.
It achieves the following results on the evaluation set:
- Loss: 3.4936
- Rouge-1: 20.79
- Rouge-2: 7.6
- Rouge-l: 18.81
- Gen Len: 18.73
- Bertscore: 70.87
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 250
- num_epochs: 8
- label_smoothing_factor: 0.1
### Training results
### Framework versions
- Transformers 4.19.3
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
adi1494/distilbert-base-uncased-finetuned-squad
|
adi1494
| 2022-06-10T12:39:00Z | 62 | 0 |
transformers
|
[
"transformers",
"tf",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-06-10T06:38:11Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: adi1494/distilbert-base-uncased-finetuned-squad
results: []
---
<!-- 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. -->
# adi1494/distilbert-base-uncased-finetuned-squad
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 1.5671
- Validation Loss: 1.2217
- Epoch: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 5532, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 1.5671 | 1.2217 | 0 |
### Framework versions
- Transformers 4.19.3
- TensorFlow 2.8.2
- Datasets 2.2.2
- Tokenizers 0.12.1
|
FabianWillner/distilbert-base-uncased-finetuned-squad-finetuned-triviaqa
|
FabianWillner
| 2022-06-10T11:54:41Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-06-10T09:44:08Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: distilbert-base-uncased-finetuned-squad-finetuned-triviaqa
results: []
---
<!-- 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-finetuned-triviaqa
This model is a fine-tuned version of [FabianWillner/distilbert-base-uncased-finetuned-squad](https://huggingface.co/FabianWillner/distilbert-base-uncased-finetuned-squad) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9583
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 0.9722 | 1.0 | 11195 | 0.9665 |
| 0.7558 | 2.0 | 22390 | 0.9583 |
### Framework versions
- Transformers 4.19.3
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
mmillet/distilrubert-2ndfinetune-epru
|
mmillet
| 2022-06-10T10:52:26Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-06-10T10:49:55Z |
---
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: distilrubert-2ndfinetune-epru
results: []
---
<!-- 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. -->
# distilrubert-2ndfinetune-epru
This model is a fine-tuned version of [mmillet/distilrubert-tiny-cased-conversational-v1_best_finetuned_emotion_experiment_augmented_anger_fear](https://huggingface.co/mmillet/distilrubert-tiny-cased-conversational-v1_best_finetuned_emotion_experiment_augmented_anger_fear) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3531
- Accuracy: 0.9054
- F1: 0.9034
- Precision: 0.9074
- Recall: 0.9054
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-06
- lr_scheduler_type: linear
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.4716 | 1.0 | 11 | 0.2851 | 0.8986 | 0.8945 | 0.9029 | 0.8986 |
| 0.2842 | 2.0 | 22 | 0.3041 | 0.8851 | 0.8796 | 0.8816 | 0.8851 |
| 0.167 | 3.0 | 33 | 0.2996 | 0.8986 | 0.8914 | 0.8997 | 0.8986 |
| 0.1527 | 4.0 | 44 | 0.2443 | 0.9189 | 0.9163 | 0.9222 | 0.9189 |
| 0.0926 | 5.0 | 55 | 0.2777 | 0.9054 | 0.9016 | 0.9059 | 0.9054 |
| 0.0897 | 6.0 | 66 | 0.3081 | 0.9122 | 0.9080 | 0.9147 | 0.9122 |
| 0.0438 | 7.0 | 77 | 0.3332 | 0.8986 | 0.8952 | 0.8993 | 0.8986 |
| 0.0433 | 8.0 | 88 | 0.3480 | 0.8851 | 0.8859 | 0.8896 | 0.8851 |
| 0.0398 | 9.0 | 99 | 0.3531 | 0.9054 | 0.9034 | 0.9074 | 0.9054 |
### Framework versions
- Transformers 4.19.3
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
shivigupta/dqn-SpaceInvadersNoFrameskip-v4
|
shivigupta
| 2022-06-10T10:11:07Z | 3 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-06-10T10:10:35Z |
---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- metrics:
- type: mean_reward
value: 374.00 +/- 214.89
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
---
# **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 framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
```
# Download model and save it into the logs/ folder
python -m utils.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga shivigupta -f logs/
python enjoy.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python train.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m utils.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga shivigupta
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 100000.0),
('optimize_memory_usage', True),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
|
huggingtweets/atrioc
|
huggingtweets
| 2022-06-10T09:05:36Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-06-10T08:58:33Z |
---
language: en
thumbnail: http://www.huggingtweets.com/atrioc/1654851931751/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<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/1522249702837657603/1jNZf3aB_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Atrioc</div>
<div style="text-align: center; font-size: 14px;">@atrioc</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from Atrioc.
| Data | Atrioc |
| --- | --- |
| Tweets downloaded | 3205 |
| Retweets | 746 |
| Short tweets | 502 |
| Tweets kept | 1957 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2zlbp16x/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @atrioc's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3oldn78j) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3oldn78j/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/atrioc')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
flood/distilbert-base-uncased-finetuned-clinc
|
flood
| 2022-06-10T07:21:47Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:clinc_oos",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-06-10T07:19:14Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- clinc_oos
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased-finetuned-clinc
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: clinc_oos
type: clinc_oos
args: plus
metrics:
- name: Accuracy
type: accuracy
value: 0.9161290322580645
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-clinc
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the clinc_oos dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7793
- Accuracy: 0.9161
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 48
- eval_batch_size: 48
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 4.2926 | 1.0 | 318 | 3.2834 | 0.7374 |
| 2.6259 | 2.0 | 636 | 1.8736 | 0.8303 |
| 1.5511 | 3.0 | 954 | 1.1612 | 0.8913 |
| 1.0185 | 4.0 | 1272 | 0.8625 | 0.91 |
| 0.8046 | 5.0 | 1590 | 0.7793 | 0.9161 |
### Framework versions
- Transformers 4.19.3
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
flood/pegasus-samsum
|
flood
| 2022-06-10T07:00:06Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"generated_from_trainer",
"dataset:samsum",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-06-10T06:24:51Z |
---
tags:
- generated_from_trainer
datasets:
- samsum
model-index:
- name: pegasus-samsum
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# pegasus-samsum
This model is a fine-tuned version of [google/pegasus-cnn_dailymail](https://huggingface.co/google/pegasus-cnn_dailymail) on the samsum dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4814
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.7052 | 0.54 | 500 | 1.4814 |
### Framework versions
- Transformers 4.19.3
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
alibaba-pai/pai-dkplm-financial-base-zh
|
alibaba-pai
| 2022-06-10T06:49:32Z | 4 | 1 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"pretraining",
"fill-mask",
"zh",
"arxiv:2205.00258",
"arxiv:2112.01047",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-06-10T06:28:43Z |
---
language: zh
pipeline_tag: fill-mask
widget:
- text: "根据新闻报道，三大[MASK]数午后集体涨超1％。"
- text: "用各种途径支持中小[MASK]企业融资。"
tags:
- bert
license: apache-2.0
---
## Chinese DKPLM (Decomposable Knowledge-enhanced Pre-trained Language Model) for the financial domain
For Chinese natural language processing in specific domains, we provide **Chinese DKPLM (Decomposable Knowledge-enhanced Pre-trained Language Model)** for the financial domain named **pai-dkplm-financial-base-zh**, from our AAAI 2021 paper named **DKPLM: Decomposable Knowledge-enhanced Pre-trained Language Model for Natural Language Understanding**.
This repository is developed based on the EasyNLP framework: [https://github.com/alibaba/EasyNLP](https://github.com/alibaba/EasyNLP ) developed by the Alibaba PAI team.
## Citation
If you find the resource is useful, please cite the following papers in your work.
- For the EasyNLP framework:
```
@article{easynlp,
title = {EasyNLP: A Comprehensive and Easy-to-use Toolkit for Natural Language Processing}, publisher = {arXiv},
author = {Wang, Chengyu and Qiu, Minghui and Zhang, Taolin and Liu, Tingting and Li, Lei and Wang, Jianing and Wang, Ming and Huang, Jun and Lin, Wei},
url = {https://arxiv.org/abs/2205.00258},
year = {2022}
}
```
- For DKPLM:
```
@article{dkplm,
title = {DKPLM: Decomposable Knowledge-enhanced Pre-trained Language Model for Natural Language Understanding},
author = {Zhang, Taolin and Wang, Chengyu and Hu, Nan and Qiu, Minghui and Tang, Chengguang and He, Xiaofeng and Huang, Jun},
url = {https://arxiv.org/abs/2112.01047},
publisher = {arXiv},
year = {2021}
}
```
|
huggingtweets/macarena_olona
|
huggingtweets
| 2022-06-10T06:32:02Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-06-10T06:10:00Z |
---
language: en
thumbnail: http://www.huggingtweets.com/macarena_olona/1654842717478/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<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/1535020786007916545/po7DO1ln_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Macarena Olona</div>
<div style="text-align: center; font-size: 14px;">@macarena_olona</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from Macarena Olona.
| Data | Macarena Olona |
| --- | --- |
| Tweets downloaded | 3245 |
| Retweets | 1797 |
| Short tweets | 225 |
| Tweets kept | 1223 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1yx7hguo/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @macarena_olona's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2i64c9y6) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2i64c9y6/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/macarena_olona')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
HrayrM/distilbert-base-uncased-distilled-clinc
|
HrayrM
| 2022-06-10T06:31:28Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:clinc_oos",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-06-10T05:50:40Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- clinc_oos
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased-distilled-clinc
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: clinc_oos
type: clinc_oos
args: plus
metrics:
- name: Accuracy
type: accuracy
value: 0.9429032258064516
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-distilled-clinc
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the clinc_oos dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3209
- Accuracy: 0.9429
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 48
- eval_batch_size: 48
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 8
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 3.0228 | 1.0 | 318 | 2.2545 | 0.7548 |
| 1.7605 | 2.0 | 636 | 1.2040 | 0.8513 |
| 0.959 | 3.0 | 954 | 0.6910 | 0.9123 |
| 0.5707 | 4.0 | 1272 | 0.4821 | 0.9294 |
| 0.3877 | 5.0 | 1590 | 0.3890 | 0.9394 |
| 0.3025 | 6.0 | 1908 | 0.3476 | 0.9410 |
| 0.258 | 7.0 | 2226 | 0.3264 | 0.9432 |
| 0.2384 | 8.0 | 2544 | 0.3209 | 0.9429 |
### Framework versions
- Transformers 4.13.0
- Pytorch 1.10.0
- Datasets 2.2.2
- Tokenizers 0.10.3
|
twieland/MIX1_ja-en_helsinki
|
twieland
| 2022-06-10T05:49:30Z | 20 | 0 |
transformers
|
[
"transformers",
"pytorch",
"marian",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-06-09T13:37:39Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: MIX1_ja-en_helsinki
results: []
---
<!-- 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. -->
# MIX1_ja-en_helsinki
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-ja-en](https://huggingface.co/Helsinki-NLP/opus-mt-ja-en) on a combination of Visual Novel, Light Novel, and Subtitle data. A total of ~10MM lines of training data were used.
It achieves the following results on the evaluation set:
- Loss: 1.7947
- Otaku Benchmark VN BLEU: 17.78
- Otaku Benchmark LN BLEU: 11.80
- Otaku Benchmark MANGA BLEU: 13.66
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:------:|:---------------:|
| 2.7495 | 0.01 | 2000 | 2.5989 |
| 2.5415 | 0.03 | 4000 | 2.4746 |
| 2.4409 | 0.04 | 6000 | 2.4731 |
| 2.3743 | 0.05 | 8000 | 2.4012 |
| 2.3254 | 0.06 | 10000 | 2.3904 |
| 2.2857 | 0.08 | 12000 | 2.3649 |
| 2.2448 | 0.09 | 14000 | 2.3188 |
| 2.2158 | 0.1 | 16000 | 2.2975 |
| 2.193 | 0.11 | 18000 | 2.2756 |
| 2.1669 | 0.13 | 20000 | 2.2852 |
| 2.144 | 0.14 | 22000 | 2.2689 |
| 2.1222 | 0.15 | 24000 | 2.2721 |
| 2.1045 | 0.16 | 26000 | 2.2489 |
| 2.0885 | 0.18 | 28000 | 2.2359 |
| 2.0732 | 0.19 | 30000 | 2.2771 |
| 2.0584 | 0.2 | 32000 | 2.2582 |
| 2.0471 | 0.21 | 34000 | 2.2093 |
| 2.0369 | 0.23 | 36000 | 2.1768 |
| 2.0241 | 0.24 | 38000 | 2.1884 |
| 2.0196 | 0.25 | 40000 | 2.2025 |
| 2.004 | 0.27 | 42000 | 2.1507 |
| 1.9936 | 0.28 | 44000 | 2.1668 |
| 1.9869 | 0.29 | 46000 | 2.1432 |
| 1.9735 | 0.3 | 48000 | 2.1662 |
| 1.9651 | 0.32 | 50000 | 2.1824 |
| 1.9551 | 0.33 | 52000 | 2.1608 |
| 1.9485 | 0.34 | 54000 | 2.1322 |
| 1.9421 | 0.35 | 56000 | 2.1476 |
| 1.9303 | 0.37 | 58000 | 2.0994 |
| 1.9236 | 0.38 | 60000 | 2.1182 |
| 1.9183 | 0.39 | 62000 | 2.1305 |
| 1.9108 | 0.4 | 64000 | 2.1469 |
| 1.9051 | 0.42 | 66000 | 2.1414 |
| 1.9018 | 0.43 | 68000 | 2.1089 |
| 1.8959 | 0.44 | 70000 | 2.0908 |
| 1.886 | 0.46 | 72000 | 2.0968 |
| 1.8802 | 0.47 | 74000 | 2.0503 |
| 1.8713 | 0.48 | 76000 | 2.0542 |
| 1.8648 | 0.49 | 78000 | 2.0990 |
| 1.8599 | 0.51 | 80000 | 2.1112 |
| 1.8563 | 0.52 | 82000 | 2.1007 |
| 1.8541 | 0.53 | 84000 | 2.0849 |
| 1.845 | 0.54 | 86000 | 2.0831 |
| 1.8448 | 0.56 | 88000 | 2.0560 |
| 1.8342 | 0.57 | 90000 | 2.0349 |
| 1.8344 | 0.58 | 92000 | 2.0301 |
| 1.8291 | 0.59 | 94000 | 2.0300 |
| 1.819 | 0.61 | 96000 | 2.0378 |
| 1.8154 | 0.62 | 98000 | 2.0197 |
| 1.82 | 0.63 | 100000 | 2.0463 |
| 1.8081 | 0.64 | 102000 | 2.0077 |
| 1.8046 | 0.66 | 104000 | 2.0101 |
| 1.7978 | 0.67 | 106000 | 2.0150 |
| 1.7934 | 0.68 | 108000 | 2.0215 |
| 1.7904 | 0.7 | 110000 | 2.0278 |
| 1.7871 | 0.71 | 112000 | 2.0588 |
| 1.779 | 0.72 | 114000 | 2.0062 |
| 1.7784 | 0.73 | 116000 | 2.0300 |
| 1.7749 | 0.75 | 118000 | 1.9664 |
| 1.7691 | 0.76 | 120000 | 2.0033 |
| 1.7622 | 0.77 | 122000 | 1.9983 |
| 1.7587 | 0.78 | 124000 | 2.0030 |
| 1.755 | 0.8 | 126000 | 1.9955 |
| 1.7531 | 0.81 | 128000 | 1.9764 |
| 1.7439 | 0.82 | 130000 | 1.9942 |
| 1.7406 | 0.83 | 132000 | 2.0221 |
| 1.7385 | 0.85 | 134000 | 1.9835 |
| 1.7332 | 0.86 | 136000 | 1.9967 |
| 1.7332 | 0.87 | 138000 | 2.0247 |
| 1.7309 | 0.88 | 140000 | 1.9817 |
| 1.7248 | 0.9 | 142000 | 2.0063 |
| 1.7209 | 0.91 | 144000 | 1.9583 |
| 1.7154 | 0.92 | 146000 | 1.9779 |
| 1.7153 | 0.94 | 148000 | 1.9478 |
| 1.7094 | 0.95 | 150000 | 1.9706 |
| 1.7061 | 0.96 | 152000 | 1.9605 |
| 1.7017 | 0.97 | 154000 | 1.9447 |
| 1.6965 | 0.99 | 156000 | 1.9419 |
| 1.6929 | 1.0 | 158000 | 1.9589 |
| 1.6628 | 1.01 | 160000 | 1.9383 |
| 1.6535 | 1.02 | 162000 | 1.9487 |
| 1.6495 | 1.04 | 164000 | 1.9400 |
| 1.6516 | 1.05 | 166000 | 1.9353 |
| 1.6513 | 1.06 | 168000 | 1.9253 |
| 1.6518 | 1.07 | 170000 | 1.9132 |
| 1.6491 | 1.09 | 172000 | 1.9076 |
| 1.6453 | 1.1 | 174000 | 1.9192 |
| 1.6426 | 1.11 | 176000 | 1.9191 |
| 1.6353 | 1.13 | 178000 | 1.9367 |
| 1.6352 | 1.14 | 180000 | 1.9218 |
| 1.6304 | 1.15 | 182000 | 1.9305 |
| 1.6299 | 1.16 | 184000 | 1.9072 |
| 1.6263 | 1.18 | 186000 | 1.9211 |
| 1.6284 | 1.19 | 188000 | 1.9037 |
| 1.6237 | 1.2 | 190000 | 1.8951 |
| 1.6231 | 1.21 | 192000 | 1.8998 |
| 1.6184 | 1.23 | 194000 | 1.8960 |
| 1.6153 | 1.24 | 196000 | 1.8776 |
| 1.6122 | 1.25 | 198000 | 1.8747 |
| 1.6109 | 1.26 | 200000 | 1.8951 |
| 1.6072 | 1.28 | 202000 | 1.8705 |
| 1.6094 | 1.29 | 204000 | 1.8903 |
| 1.6063 | 1.3 | 206000 | 1.8660 |
| 1.599 | 1.31 | 208000 | 1.8696 |
| 1.5931 | 1.33 | 210000 | 1.8598 |
| 1.5943 | 1.34 | 212000 | 1.8760 |
| 1.5906 | 1.35 | 214000 | 1.8833 |
| 1.5858 | 1.37 | 216000 | 1.8645 |
| 1.5873 | 1.38 | 218000 | 1.8620 |
| 1.5842 | 1.39 | 220000 | 1.8632 |
| 1.5808 | 1.4 | 222000 | 1.8782 |
| 1.5756 | 1.42 | 224000 | 1.8627 |
| 1.5728 | 1.43 | 226000 | 1.8649 |
| 1.5709 | 1.44 | 228000 | 1.8735 |
| 1.5704 | 1.45 | 230000 | 1.8630 |
| 1.5659 | 1.47 | 232000 | 1.8598 |
| 1.5637 | 1.48 | 234000 | 1.8519 |
| 1.5628 | 1.49 | 236000 | 1.8569 |
| 1.5559 | 1.5 | 238000 | 1.8401 |
| 1.5532 | 1.52 | 240000 | 1.8528 |
| 1.557 | 1.53 | 242000 | 1.8637 |
| 1.5499 | 1.54 | 244000 | 1.8701 |
| 1.5476 | 1.55 | 246000 | 1.8423 |
| 1.5502 | 1.57 | 248000 | 1.8320 |
| 1.5469 | 1.58 | 250000 | 1.8542 |
| 1.5382 | 1.59 | 252000 | 1.8526 |
| 1.5396 | 1.61 | 254000 | 1.8537 |
| 1.528 | 1.62 | 256000 | 1.8248 |
| 1.532 | 1.63 | 258000 | 1.8322 |
| 1.5269 | 1.64 | 260000 | 1.8381 |
| 1.5269 | 1.66 | 262000 | 1.8389 |
| 1.5269 | 1.67 | 264000 | 1.8445 |
| 1.525 | 1.68 | 266000 | 1.8232 |
| 1.5175 | 1.69 | 268000 | 1.8561 |
| 1.5172 | 1.71 | 270000 | 1.8342 |
| 1.5174 | 1.72 | 272000 | 1.8167 |
| 1.5114 | 1.73 | 274000 | 1.8281 |
| 1.5094 | 1.74 | 276000 | 1.8164 |
| 1.5083 | 1.76 | 278000 | 1.8317 |
| 1.5047 | 1.77 | 280000 | 1.8207 |
| 1.5045 | 1.78 | 282000 | 1.8155 |
| 1.497 | 1.8 | 284000 | 1.8275 |
| 1.4996 | 1.81 | 286000 | 1.8152 |
| 1.497 | 1.82 | 288000 | 1.8137 |
| 1.4967 | 1.83 | 290000 | 1.8109 |
| 1.4936 | 1.85 | 292000 | 1.8037 |
| 1.4867 | 1.86 | 294000 | 1.7955 |
| 1.4859 | 1.87 | 296000 | 1.8181 |
| 1.4869 | 1.88 | 298000 | 1.7999 |
| 1.4811 | 1.9 | 300000 | 1.8062 |
| 1.4831 | 1.91 | 302000 | 1.8042 |
| 1.4791 | 1.92 | 304000 | 1.8020 |
| 1.4797 | 1.93 | 306000 | 1.7972 |
| 1.483 | 1.95 | 308000 | 1.8044 |
| 1.4748 | 1.96 | 310000 | 1.8036 |
| 1.4772 | 1.97 | 312000 | 1.7958 |
| 1.4708 | 1.98 | 314000 | 1.7967 |
| 1.4743 | 2.0 | 316000 | 1.7947 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
ritheshSree/animal-classifier
|
ritheshSree
| 2022-06-10T05:38:54Z | 115 | 1 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2022-06-10T05:21:44Z |
---
tags:
- image-classification
- pytorch
- huggingpics
metrics:
- accuracy
model-index:
- name: animal-classifier
results:
- task:
name: Image Classification
type: image-classification
metrics:
- name: Accuracy
type: accuracy
value: 1.0
---
# animal-classifier
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics).
## Example Images
#### cat
#### dog
#### snake
#### tiger
|
alibaba-pai/pai-bert-tiny-zh
|
alibaba-pai
| 2022-06-10T02:34:43Z | 272 | 6 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"fill-mask",
"zh",
"arxiv:2205.00258",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-06-09T03:45:15Z |
---
language: zh
pipeline_tag: fill-mask
widget:
- text: "中国的首都是北[MASK]。"
- text: "牛奶是[MASK]色的。"
tags:
- bert
license: apache-2.0
---
## Alibaba PAI BERT Tiny Chinese
This project provides Chinese pre-trained language models and various types of NLP tools. The models are pre-trained on the large-scale corpora hosted by the Alibaba PAI team. It is developed based on the EasyNLP framework (https://github.com/alibaba/EasyNLP).
## Citation
If you find the resource is useful, please cite the following paper in your work:
```
@article{easynlp,
title = {EasyNLP: A Comprehensive and Easy-to-use Toolkit for Natural Language Processing},
publisher = {arXiv},
author = {Wang, Chengyu and Qiu, Minghui and Zhang, Taolin and Liu, Tingting and Li, Lei and Wang, Jianing and Wang, Ming and Huang, Jun and Lin, Wei},
url = {https://arxiv.org/abs/2205.00258},
year = {2022}
}
```
|
huggingtweets/loganpaul
|
huggingtweets
| 2022-06-10T02:29:07Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-06-10T02:27:26Z |
---
language: en
thumbnail: http://www.huggingtweets.com/loganpaul/1654828143127/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<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/1401837042934468611/okzqIoMb_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Logan Paul</div>
<div style="text-align: center; font-size: 14px;">@loganpaul</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from Logan Paul.
| Data | Logan Paul |
| --- | --- |
| Tweets downloaded | 3245 |
| Retweets | 170 |
| Short tweets | 318 |
| Tweets kept | 2757 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/wj9pph5f/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @loganpaul's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1sqzuxgo) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1sqzuxgo/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/loganpaul')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
BigSalmon/InformalToFormalLincoln51
|
BigSalmon
| 2022-06-10T02:22:40Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-06-10T02:03:20Z |
```
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("BigSalmon/InformalToFormalLincoln51")
model = AutoModelForCausalLM.from_pretrained("BigSalmon/InformalToFormalLincoln51")
```
```
How To Make Prompt:
informal english: i am very ready to do that just that.
Translated into the Style of Abraham Lincoln: you can assure yourself of my readiness to work toward this end.
Translated into the Style of Abraham Lincoln: please be assured that i am most ready to undertake this laborious task.
***
informal english: space is huge and needs to be explored.
Translated into the Style of Abraham Lincoln: space awaits traversal, a new world whose boundaries are endless.
Translated into the Style of Abraham Lincoln: space is a ( limitless / boundless ) expanse, a vast virgin domain awaiting exploration.
***
informal english: corn fields are all across illinois, visible once you leave chicago.
Translated into the Style of Abraham Lincoln: corn fields ( permeate illinois / span the state of illinois / ( occupy / persist in ) all corners of illinois / line the horizon of illinois / envelop the landscape of illinois ), manifesting themselves visibly as one ventures beyond chicago.
informal english:
```
```
infill: chrome extensions [MASK] accomplish everyday tasks.
Translated into the Style of Abraham Lincoln: chrome extensions ( expedite the ability to / unlock the means to more readily ) accomplish everyday tasks.
infill: at a time when nintendo has become inflexible, [MASK] consoles that are tethered to a fixed iteration, sega diligently curates its legacy of classic video games on handheld devices.
Translated into the Style of Abraham Lincoln: at a time when nintendo has become inflexible, ( stubbornly [MASK] on / firmly set on / unyielding in its insistence on ) consoles that are tethered to a fixed iteration, sega diligently curates its legacy of classic video games on handheld devices.
infill:
```
```
Essay Intro (Warriors vs. Rockets in Game 7):
text: eagerly anticipated by fans, game 7's are the highlight of the post-season.
text: ever-building in suspense, game 7's have the crowd captivated.
***
Essay Intro (South Korean TV Is Becoming Popular):
text: maturing into a bona fide paragon of programming, south korean television ( has much to offer / entertains without fail / never disappoints ).
text: increasingly held in critical esteem, south korean television continues to impress.
text: at the forefront of quality content, south korea is quickly achieving celebrity status.
***
Essay Intro (
```
```
Search: What is the definition of Checks and Balances?
https://en.wikipedia.org/wiki/Checks_and_balances
Checks and Balances is the idea of having a system where each and every action in government should be subject to one or more checks that would not allow one branch or the other to overly dominate.
https://www.harvard.edu/glossary/Checks_and_Balances
Checks and Balances is a system that allows each branch of government to limit the powers of the other branches in order to prevent abuse of power
https://www.law.cornell.edu/library/constitution/Checks_and_Balances
Checks and Balances is a system of separation through which branches of government can control the other, thus preventing excess power.
***
Search: What is the definition of Separation of Powers?
https://en.wikipedia.org/wiki/Separation_of_powers
The separation of powers is a principle in government, whereby governmental powers are separated into different branches, each with their own set of powers, that are prevent one branch from aggregating too much power.
https://www.yale.edu/tcf/Separation_of_Powers.html
Separation of Powers is the division of governmental functions between the executive, legislative and judicial branches, clearly demarcating each branch's authority, in the interest of ensuring that individual liberty or security is not undermined.
***
Search: What is the definition of Connection of Powers?
https://en.wikipedia.org/wiki/Connection_of_powers
Connection of Powers is a feature of some parliamentary forms of government where different branches of government are intermingled, typically the executive and legislative branches.
https://simple.wikipedia.org/wiki/Connection_of_powers
The term Connection of Powers describes a system of government in which there is overlap between different parts of the government.
***
Search: What is the definition of
```
```
Search: What are phrase synonyms for "second-guess"?
https://www.powerthesaurus.org/second-guess/synonyms
Shortest to Longest:
- feel dubious about
- raise an eyebrow at
- wrinkle their noses at
- cast a jaundiced eye at
- teeter on the fence about
***
Search: What are phrase synonyms for "mean to newbies"?
https://www.powerthesaurus.org/mean_to_newbies/synonyms
Shortest to Longest:
- readiness to balk at rookies
- absence of tolerance for novices
- hostile attitude toward newcomers
***
Search: What are phrase synonyms for "make use of"?
https://www.powerthesaurus.org/make_use_of/synonyms
Shortest to Longest:
- call upon
- glean value from
- reap benefits from
- derive utility from
- seize on the merits of
- draw on the strength of
- tap into the potential of
***
Search: What are phrase synonyms for "hurting itself"?
https://www.powerthesaurus.org/hurting_itself/synonyms
Shortest to Longest:
- erring
- slighting itself
- forfeiting its integrity
- doing itself a disservice
- evincing a lack of backbone
***
Search: What are phrase synonyms for "
```
```
- nebraska
- unicamerical legislature
- different from federal house and senate
text: featuring a unicameral legislature, nebraska's political system stands in stark contrast to the federal model, comprised of a house and senate.
***
-
```
```
original: sports teams are profitable for owners. [MASK], their valuations experience a dramatic uptick.
infill: sports teams are profitable for owners. ( accumulating vast sums / stockpiling treasure / realizing benefits / cashing in / registering robust financials / scoring on balance sheets ), their valuations experience a dramatic uptick.
***
original:
```
```
wordy: classical music is becoming less popular more and more.
Translate into Concise Text: interest in classic music is fading.
***
wordy:
```
```
sweet: savvy voters ousted him.
longer: voters who were informed delivered his defeat.
***
sweet:
```
```
1: commercial space company spacex plans to launch a whopping 52 flights in 2022.
2: spacex, a commercial space company, intends to undertake a total of 52 flights in 2022.
3: in 2022, commercial space company spacex has its sights set on undertaking 52 flights.
4: 52 flights are in the pipeline for 2022, according to spacex, a commercial space company.
5: a commercial space company, spacex aims to conduct 52 flights in 2022.
***
1:
```
Keywords to sentences or sentence.
```
ngos are characterized by:
□ voluntary citizens' group that is organized on a local, national or international level
□ encourage political participation
□ often serve humanitarian functions
□ work for social, economic, or environmental change
***
what are the drawbacks of living near an airbnb?
□ noise
□ parking
□ traffic
□ security
□ strangers
***
```
```
original: musicals generally use spoken dialogue as well as songs to convey the story. operas are usually fully sung.
adapted: musicals generally use spoken dialogue as well as songs to convey the story. ( in a stark departure / on the other hand / in contrast / by comparison / at odds with this practice / far from being alike / in defiance of this standard / running counter to this convention ), operas are usually fully sung.
***
original: akoya and tahitian are types of pearls. akoya pearls are mostly white, and tahitian pearls are naturally dark.
adapted: akoya and tahitian are types of pearls. ( a far cry from being indistinguishable / easily distinguished / on closer inspection / setting them apart / not to be mistaken for one another / hardly an instance of mere synonymy / differentiating the two ), akoya pearls are mostly white, and tahitian pearls are naturally dark.
***
original:
```
```
original: had trouble deciding.
translated into journalism speak: wrestled with the question, agonized over the matter, furrowed their brows in contemplation.
***
original:
```
```
input: not loyal
1800s english: ( two-faced / inimical / perfidious / duplicitous / mendacious / double-dealing / shifty ).
***
input:
```
|
huggingtweets/wickdedaccount
|
huggingtweets
| 2022-06-10T02:20:32Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-06-10T02:17:51Z |
---
language: en
thumbnail: http://www.huggingtweets.com/wickdedaccount/1654827628283/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<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/1353151127026597889/Yarj5Kfr_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">pp</div>
<div style="text-align: center; font-size: 14px;">@wickdedaccount</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from pp.
| Data | pp |
| --- | --- |
| Tweets downloaded | 1028 |
| Retweets | 822 |
| Short tweets | 119 |
| Tweets kept | 87 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1of8kmw1/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @wickdedaccount's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2q4m95l8) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2q4m95l8/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/wickdedaccount')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
huggingtweets/burkevillemama
|
huggingtweets
| 2022-06-10T02:15:58Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-06-10T02:15:51Z |
---
language: en
thumbnail: https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<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/1367879964733804547/buUeka0V_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Bree</div>
<div style="text-align: center; font-size: 14px;">@burkevillemama</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from Bree.
| Data | Bree |
| --- | --- |
| Tweets downloaded | 2994 |
| Retweets | 805 |
| Short tweets | 201 |
| Tweets kept | 1988 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/82nbekwu/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @burkevillemama's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3gdpxbzc) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3gdpxbzc/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/burkevillemama')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
huggingtweets/wick_is_tired
|
huggingtweets
| 2022-06-10T01:42:38Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-06-10T01:41:57Z |
---
language: en
thumbnail: http://www.huggingtweets.com/wick_is_tired/1654825353897/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<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/1381121023567917058/JyYfOsKC_400x400.png')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">IntroWick</div>
<div style="text-align: center; font-size: 14px;">@wick_is_tired</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from IntroWick.
| Data | IntroWick |
| --- | --- |
| Tweets downloaded | 257 |
| Retweets | 29 |
| Short tweets | 77 |
| Tweets kept | 151 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/az5xmdyn/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @wick_is_tired's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/lxj96tnp) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/lxj96tnp/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/wick_is_tired')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
25khattab/vit_test_1_95
|
25khattab
| 2022-06-10T01:40:54Z | 55 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2022-06-10T01:40:38Z |
---
tags:
- image-classification
- pytorch
- huggingpics
metrics:
- accuracy
model-index:
- name: vit_test_1_95
results:
- task:
name: Image Classification
type: image-classification
metrics:
- name: Accuracy
type: accuracy
value: 0.9501661062240601
---
# vit_test_1_95
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics).
## Example Images
|
huggingtweets/artificialbuttr
|
huggingtweets
| 2022-06-10T01:39:43Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-06-10T01:37:50Z |
---
language: en
thumbnail: http://www.huggingtweets.com/artificialbuttr/1654825134207/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<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/1485413658351968256/NUVesGCM_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">artificialbutter</div>
<div style="text-align: center; font-size: 14px;">@artificialbuttr</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from artificialbutter.
| Data | artificialbutter |
| --- | --- |
| Tweets downloaded | 785 |
| Retweets | 129 |
| Short tweets | 407 |
| Tweets kept | 249 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1ypylns0/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @artificialbuttr's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1phf128l) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1phf128l/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/artificialbuttr')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
HrayrM/distilbert-base-uncased-finetuned-clinc
|
HrayrM
| 2022-06-10T01:17:59Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:clinc_oos",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-06-10T00:50:05Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- clinc_oos
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased-finetuned-clinc
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: clinc_oos
type: clinc_oos
args: plus
metrics:
- name: Accuracy
type: accuracy
value: 0.9135483870967742
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-clinc
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the clinc_oos dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7771
- Accuracy: 0.9135
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 48
- eval_batch_size: 48
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 4.2843 | 1.0 | 318 | 3.2793 | 0.7448 |
| 2.6208 | 2.0 | 636 | 1.8750 | 0.8297 |
| 1.5453 | 3.0 | 954 | 1.1565 | 0.8919 |
| 1.0141 | 4.0 | 1272 | 0.8628 | 0.9090 |
| 0.795 | 5.0 | 1590 | 0.7771 | 0.9135 |
### Framework versions
- Transformers 4.13.0
- Pytorch 1.10.0
- Datasets 2.2.2
- Tokenizers 0.10.3
|
nestoralvaro/mt5-base-finetuned-xsum-data_prep_2021_12_26___t1_7.csv___topic_text_google_mt5_base
|
nestoralvaro
| 2022-06-10T00:52:35Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"mt5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-06-09T23:49:44Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: mt5-base-finetuned-xsum-data_prep_2021_12_26___t1_7.csv___topic_text_google_mt5_base
results: []
---
<!-- 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-data_prep_2021_12_26___t1_7.csv___topic_text_google_mt5_base
This model is a fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: nan
- Rouge1: 2.8146
- Rouge2: 0.6707
- Rougel: 2.8187
- Rougelsum: 2.8098
- Gen Len: 6.4901
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| 0.0 | 1.0 | 3869 | nan | 2.8146 | 0.6707 | 2.8187 | 2.8098 | 6.4901 |
### Framework versions
- Transformers 4.19.3
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
UBC-NLP/turjuman
|
UBC-NLP
| 2022-06-10T00:24:37Z | 32 | 7 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"arxiv:2206.03933",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-05-10T22:07:50Z |
<p align="center">
<br>
<img src="https://github.com/UBC-NLP/turjuman/raw/master//images/turjuman_logo.png"/>
<br>
<p>
<img src="https://github.com/UBC-NLP/turjuman/raw/master/images/turjuman.png" alt="AraT5" width="50%" height="50%" align="right"/>
Turjuman is a neural machine translation toolkit. It translates from 20 languages into Modern Standard Arabic (MSA). Turjuman is described in this paper:
[**TURJUMAN: A Public Toolkit for Neural Arabic Machine Translation**](https://arxiv.org/abs/2206.03933).
Turjuman exploits our [AraT5 model](https://github.com/UBC-NLP/araT5). This endows Turjuman with a powerful ability to decode into Arabic. The toolkit offers the possibility of employing a number of diverse decoding methods, making it suited for acquiring paraphrases for the MSA translations as an added value.
**Github**: [https://github.com/UBC-NLP/turjuman](https://github.com/UBC-NLP/turjuman)
**Demo**: [https://demos.dlnlp.ai/turjuman](https://demos.dlnlp.ai/turjuman)
**Paper**: [https://arxiv.org/abs/2206.03933](https://arxiv.org/abs/2206.03933)
## License
turjuman(-py) is Apache-2.0 licensed. The license applies to the pre-trained models as well.
## Citation
If you use TURJUMAN toolkit or the pre-trained models for your scientific publication, or if you find the resources in this repository useful, please cite our paper as follows (to be updated):
```
@inproceedings{nagoudi-osact5-2022-turjuman,
title={TURJUMAN: A Public Toolkit for Neural Arabic Machine Translation},
author={Nagoudi, El Moatez Billah and Elmadany, AbdelRahim and Abdul-Mageed, Muhammad},
booktitle = "Proceedings of the 5th Workshop on Open-Source Arabic Corpora and Processing Tools (OSACT5)",
month = "June",
year = "2022",
address = "Marseille, France",
publisher = "European Language Resource Association",
}
```
|
ajtamayoh/NLP-CIC-WFU_Clinical_Cases_NER_Paragraph_Tokenized_mBERT_cased_fine_tuned
|
ajtamayoh
| 2022-06-09T23:31:56Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-06-09T23:02:35Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: NLP-CIC-WFU_Clinical_Cases_NER_Paragraph_Tokenized_mBERT_cased_fine_tuned
results: []
---
<!-- 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. -->
# NLP-CIC-WFU_Clinical_Cases_NER_Paragraph_Tokenized_mBERT_cased_fine_tuned
This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0537
- Precision: 0.8585
- Recall: 0.7101
- F1: 0.7773
- Accuracy: 0.9893
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 7
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0693 | 1.0 | 514 | 0.0416 | 0.9485 | 0.6492 | 0.7708 | 0.9884 |
| 0.0367 | 2.0 | 1028 | 0.0396 | 0.9391 | 0.6710 | 0.7827 | 0.9892 |
| 0.0283 | 3.0 | 1542 | 0.0385 | 0.9388 | 0.6889 | 0.7947 | 0.9899 |
| 0.0222 | 4.0 | 2056 | 0.0422 | 0.9456 | 0.6790 | 0.7904 | 0.9898 |
| 0.0182 | 5.0 | 2570 | 0.0457 | 0.9349 | 0.6925 | 0.7956 | 0.9901 |
| 0.013 | 6.0 | 3084 | 0.0484 | 0.8947 | 0.7062 | 0.7894 | 0.9899 |
| 0.0084 | 7.0 | 3598 | 0.0537 | 0.8585 | 0.7101 | 0.7773 | 0.9893 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
pm390/dqn-SpaceInvadersNoFrameskip-v4
|
pm390
| 2022-06-09T22:03:09Z | 2 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-06-09T22:02:36Z |
---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- metrics:
- type: mean_reward
value: 374.00 +/- 214.89
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
---
# **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 framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
```
# Download model and save it into the logs/ folder
python -m utils.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga pm390 -f logs/
python enjoy.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python train.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m utils.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga pm390
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('max_grad_norm', 6),
('n_timesteps', 100000.0),
('optimize_memory_usage', True),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
|
nthakur/contriever-base-msmarco
|
nthakur
| 2022-06-09T22:01:51Z | 1,072 | 1 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2022-06-09T21:50:15Z |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# nthakur/contriever-base-msmarco
This is a port of the [Contriever MSMARCO Model](https://huggingface.co/facebook/contriever-msmarco) to [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('nthakur/contriever-base-msmarco')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('nthakur/contriever-base-msmarco')
model = AutoModel.from_pretrained('nthakur/contriever-base-msmarco')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=nthakur/contriever-base-msmarco)
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 509, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
Have a look at: [Contriever Model](https://github.com/facebookresearch/contriever).
<!--- Describe where people can find more information -->
|
fbadine/uk_ireland_accent_classification
|
fbadine
| 2022-06-09T20:07:40Z | 8 | 1 |
tf-keras
|
[
"tf-keras",
"tensorboard",
"license:apache-2.0",
"region:us"
] | null | 2022-03-09T16:53:02Z |
---
license: apache-2.0
---
## UK & Ireland Accent Classification Model
This model classifies UK & Ireland accents using feature extraction from [Yamnet](https://tfhub.dev/google/yamnet/1).
### Yamnet Model
Yamnet is an audio event classifier trained on the AudioSet dataset to predict audio events from the AudioSet ontology. It is available on TensorFlow Hub.
Yamnet accepts a 1-D tensor of audio samples with a sample rate of 16 kHz.
As output, the model returns a 3-tuple:
- Scores of shape `(N, 521)` representing the scores of the 521 classes.
- Embeddings of shape `(N, 1024)`.
- The log-mel spectrogram of the entire audio frame.
We will use the embeddings, which are the features extracted from the audio samples, as the input to our dense model.
For more detailed information about Yamnet, please refer to its [TensorFlow Hub](https://tfhub.dev/google/yamnet/1) page.
### Dense Model
The dense model that we used consists of:
- An input layer which is embedding output of the Yamnet classifier.
- 4 dense hidden layers and 4 dropout layers.
- An output dense layer.
<details>
<summary>View Model Plot</summary>
</details>
---
## Results
The model achieved the following results:
Results | Training | Validation
-----------|-----------|------------
Accuracy | 55% | 51%
AUC | 0.9090 | 0.8911
d-prime | 1.887 | 1.743
And the confusion matrix for the validation set is:
---
## Dataset
The dataset used is the
[Crowdsourced high-quality UK and Ireland English Dialect speech data set](https://openslr.org/83/)
which consists of a total of 17,877 high-quality audio wav files.
This dataset includes over 31 hours of recording from 120 vounteers who self-identify as
native speakers of Southern England, Midlands, Northern England, Wales, Scotland and Ireland.
For more info, please refer to the above link or to the following paper:
[Open-source Multi-speaker Corpora of the English Accents in the British Isles](https://aclanthology.org/2020.lrec-1.804.pdf)
---
## How to use
Having already installed `huggingface_hub` using:
`pip install -U -q huggingface_hub`
Use the following in your code:
`from huggingface_hub import from_pretrained_keras`
`model = from_pretrained_keras("fbadine/uk_ireland_accent_classification")`
---
## Demo
A demo is available in [HuggingFace Spaces](https://huggingface.co/spaces/fbadine/uk_ireland_accent_classification)
|
q2-jlbar/segformer-b0-finetuned-brooks-or-dunn
|
q2-jlbar
| 2022-06-09T19:47:36Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"segformer",
"vision",
"image-segmentation",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
image-segmentation
| 2022-06-09T18:20:04Z |
---
license: apache-2.0
tags:
- vision
- image-segmentation
- generated_from_trainer
model-index:
- name: segformer-b0-finetuned-brooks-or-dunn
results: []
---
<!-- 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. -->
# segformer-b0-finetuned-brooks-or-dunn
This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the q2-jlbar/BrooksOrDunn dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1158
- Mean Iou: nan
- Mean Accuracy: nan
- Overall Accuracy: nan
- Per Category Iou: [nan, nan]
- Per Category Accuracy: [nan, nan]
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 6e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Per Category Iou | Per Category Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:----------------:|:---------------------:|
| 0.5153 | 4.0 | 20 | 0.5276 | nan | nan | nan | [nan, nan] | [nan, nan] |
| 0.4082 | 8.0 | 40 | 0.3333 | nan | nan | nan | [nan, nan] | [nan, nan] |
| 0.3157 | 12.0 | 60 | 0.2773 | nan | nan | nan | [nan, nan] | [nan, nan] |
| 0.2911 | 16.0 | 80 | 0.2389 | nan | nan | nan | [nan, nan] | [nan, nan] |
| 0.2395 | 20.0 | 100 | 0.1982 | nan | nan | nan | [nan, nan] | [nan, nan] |
| 0.2284 | 24.0 | 120 | 0.1745 | nan | nan | nan | [nan, nan] | [nan, nan] |
| 0.1818 | 28.0 | 140 | 0.1595 | nan | nan | nan | [nan, nan] | [nan, nan] |
| 0.1549 | 32.0 | 160 | 0.1556 | nan | nan | nan | [nan, nan] | [nan, nan] |
| 0.1351 | 36.0 | 180 | 0.1387 | nan | nan | nan | [nan, nan] | [nan, nan] |
| 0.1254 | 40.0 | 200 | 0.1263 | nan | nan | nan | [nan, nan] | [nan, nan] |
| 0.1412 | 44.0 | 220 | 0.1190 | nan | nan | nan | [nan, nan] | [nan, nan] |
| 0.1179 | 48.0 | 240 | 0.1158 | nan | nan | nan | [nan, nan] | [nan, nan] |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0
- Datasets 2.2.2
- Tokenizers 0.12.1
|
nbroad/jplu-xlm-r-ner-40-lang
|
nbroad
| 2022-06-09T17:51:49Z | 4 | 1 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-04-27T15:22:16Z |
pytorch version of [jplu/tf-xlm-r-ner-40-lang](https://huggingface.co/jplu/tf-xlm-r-ner-40-lang)
|
veb/twitch-distilbert-base-uncased-finetuned-sst-2-english
|
veb
| 2022-06-09T17:33:12Z | 7 | 0 |
transformers
|
[
"transformers",
"tf",
"distilbert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-06-09T16:58:37Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: veb/twitch-distilbert-base-uncased-finetuned-sst-2-english
results: []
---
<!-- 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. -->
# veb/twitch-distilbert-base-uncased-finetuned-sst-2-english
This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.3074
- Train Sparse Categorical Accuracy: 0.9219
- Validation Loss: 0.1151
- Validation Sparse Categorical Accuracy: 1.0
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'learning_rate': 5e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Sparse Categorical Accuracy | Validation Loss | Validation Sparse Categorical Accuracy | Epoch |
|:----------:|:---------------------------------:|:---------------:|:--------------------------------------:|:-----:|
| 1.0992 | 0.6094 | 0.3072 | 1.0 | 0 |
| 0.3921 | 0.7812 | 0.2903 | 1.0 | 1 |
| 0.3074 | 0.9219 | 0.1151 | 1.0 | 2 |
### Framework versions
- Transformers 4.19.2
- TensorFlow 2.7.0
- Datasets 2.2.2
- Tokenizers 0.12.1
|
ksabeh/bert-base-uncased-attribute-correction-mlm
|
ksabeh
| 2022-06-09T17:23:14Z | 61 | 0 |
transformers
|
[
"transformers",
"tf",
"bert",
"question-answering",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-06-09T09:08:11Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: ksabeh/bert-base-uncased-mlm-electronics-attribute-correction
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# ksabeh/bert-base-uncased-mlm-electronics-attribute-correction
This model is a fine-tuned version of [ksabeh/bert-base-uncased-mlm-electronics](https://huggingface.co/ksabeh/bert-base-uncased-mlm-electronics) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0524
- Validation Loss: 0.0520
- Epoch: 1
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 36848, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 0.1459 | 0.0678 | 0 |
| 0.0524 | 0.0520 | 1 |
### Framework versions
- Transformers 4.18.0
- TensorFlow 2.6.4
- Datasets 2.1.0
- Tokenizers 0.12.1
|
tclong/wav2vec2-base-vios-commonvoice
|
tclong
| 2022-06-09T17:17:08Z | 77 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-06-08T18:03:39Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-base-vios-commonvoice
results: []
---
<!-- 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-vios-commonvoice
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3823
- Wer: 0.2401
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 15
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 1.2268 | 0.66 | 500 | 0.8746 | 0.5939 |
| 0.8728 | 1.32 | 1000 | 0.6435 | 0.4554 |
| 0.6899 | 1.99 | 1500 | 0.5655 | 0.3995 |
| 0.5842 | 2.65 | 2000 | 0.5267 | 0.3694 |
| 0.5371 | 3.31 | 2500 | 0.4980 | 0.3431 |
| 0.4921 | 3.97 | 3000 | 0.4781 | 0.3276 |
| 0.4508 | 4.64 | 3500 | 0.4434 | 0.3134 |
| 0.433 | 5.3 | 4000 | 0.4348 | 0.2963 |
| 0.404 | 5.96 | 4500 | 0.4248 | 0.2874 |
| 0.3834 | 6.62 | 5000 | 0.4163 | 0.2775 |
| 0.3784 | 7.28 | 5500 | 0.4104 | 0.2751 |
| 0.3669 | 7.95 | 6000 | 0.4143 | 0.2724 |
| 0.3462 | 8.61 | 6500 | 0.4131 | 0.2699 |
| 0.3364 | 9.27 | 7000 | 0.4070 | 0.2617 |
| 0.3249 | 9.93 | 7500 | 0.4076 | 0.2603 |
| 0.3154 | 10.6 | 8000 | 0.3998 | 0.2577 |
| 0.3117 | 11.26 | 8500 | 0.3930 | 0.2505 |
| 0.3101 | 11.92 | 9000 | 0.4003 | 0.2492 |
| 0.298 | 12.58 | 9500 | 0.3960 | 0.2496 |
| 0.2968 | 13.24 | 10000 | 0.3877 | 0.2469 |
| 0.29 | 13.91 | 10500 | 0.3870 | 0.2456 |
| 0.2921 | 14.57 | 11000 | 0.3823 | 0.2401 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
huggingtweets/medscape
|
huggingtweets
| 2022-06-09T16:30:23Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-06-09T16:29:41Z |
---
language: en
thumbnail: http://www.huggingtweets.com/medscape/1654792218439/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<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/1401919208133378050/l2MKtnC7_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Medscape</div>
<div style="text-align: center; font-size: 14px;">@medscape</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from Medscape.
| Data | Medscape |
| --- | --- |
| Tweets downloaded | 3250 |
| Retweets | 16 |
| Short tweets | 2 |
| Tweets kept | 3232 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/mn0jpyr0/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @medscape's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3n6qbw51) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3n6qbw51/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/medscape')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
YaYaB/SpaceInvadersNoFrameskip-v4-1
|
YaYaB
| 2022-06-09T16:24:57Z | 3 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-06-09T16:23:40Z |
---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- metrics:
- type: mean_reward
value: 511.00 +/- 164.98
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
---
# **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 framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
```
# Download model and save it into the logs/ folder
python -m utils.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga YaYaB -f logs/
python enjoy.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python train.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m utils.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga YaYaB
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 1000000.0),
('optimize_memory_usage', True),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
|
huggingtweets/elrichmc
|
huggingtweets
| 2022-06-09T16:04:04Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-06-09T16:01:27Z |
---
language: en
thumbnail: http://www.huggingtweets.com/elrichmc/1654790629445/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<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/1484686785812832263/Beh-qGPk_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">ElRichMC</div>
<div style="text-align: center; font-size: 14px;">@elrichmc</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from ElRichMC.
| Data | ElRichMC |
| --- | --- |
| Tweets downloaded | 3245 |
| Retweets | 203 |
| Short tweets | 618 |
| Tweets kept | 2424 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1jeok5aq/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @elrichmc's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/28fmqsme) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/28fmqsme/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/elrichmc')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
ksabeh/roberta-base-attribute-correction-mlm-titles
|
ksabeh
| 2022-06-09T15:44:28Z | 5 | 0 |
transformers
|
[
"transformers",
"tf",
"roberta",
"question-answering",
"generated_from_keras_callback",
"license:mit",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-06-09T08:42:02Z |
---
license: mit
tags:
- generated_from_keras_callback
model-index:
- name: ksabeh/roberta-base-attribute-correction-mlm-titles-2
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# ksabeh/roberta-base-attribute-correction-mlm-titles-2
This model is a fine-tuned version of [ksabeh/roberta-base-attribute-correction-mlm](https://huggingface.co/ksabeh/roberta-base-attribute-correction-mlm) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0822
- Validation Loss: 0.0914
- Epoch: 1
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 23870, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 0.2007 | 0.1023 | 0 |
| 0.0822 | 0.0914 | 1 |
### Framework versions
- Transformers 4.18.0
- TensorFlow 2.6.4
- Datasets 2.1.0
- Tokenizers 0.12.1
|
buio/attention_mil_classification
|
buio
| 2022-06-09T15:10:38Z | 0 | 0 |
keras
|
[
"keras",
"tensorboard",
"tf-keras",
"computer-vision",
"classification",
"multiple-instance-learning ",
"region:us"
] | null | 2022-06-09T14:46:43Z |
---
library_name: keras
tags:
- computer-vision
- classification
- 'multiple-instance-learning '
---
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'learning_rate': 0.001, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
- training_precision: float32
## Training Metrics
| Epochs | Train Loss | Train Accuracy | Validation Loss | Validation Accuracy |
|--- |--- |--- |--- |--- |
| 1| 0.315| 0.915| 0.066| 0.983|
| 2| 0.089| 0.982| 0.049| 0.99|
| 3| 0.078| 0.987| 0.084| 0.983|
| 4| 0.059| 0.983| 0.033| 0.993|
| 5| 0.042| 0.99| 0.053| 0.99|
| 6| 0.042| 0.996| 0.019| 0.993|
| 7| 0.013| 0.999| 0.067| 0.987|
| 8| 0.055| 0.988| 0.049| 0.99|
| 9| 0.005| 1.0| 0.039| 0.993|
| 10| 0.005| 1.0| 0.038| 0.99|
| 11| 0.039| 0.995| 0.214| 0.97|
| 12| 0.008| 1.0| 0.039| 0.99|
| 13| 0.002| 1.0| 0.047| 0.993|
| 14| 0.016| 0.999| 0.057| 0.99|
| 15| 0.046| 0.993| 0.026| 0.997|
| 16| 0.002| 1.0| 0.06| 0.99|
## Model Plot
<details>
<summary>View Model Plot</summary>
</details>
|
veb/twitch-roberta-base-sentiment-latest
|
veb
| 2022-06-09T14:34:50Z | 5 | 0 |
transformers
|
[
"transformers",
"tf",
"roberta",
"text-classification",
"generated_from_keras_callback",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-06-09T05:14:29Z |
---
tags:
- generated_from_keras_callback
model-index:
- name: veb/twitch-roberta-base-sentiment-latest
results: []
---
<!-- 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. -->
# veb/twitch-roberta-base-sentiment-latest
This model is a fine-tuned version of [cardiffnlp/twitter-roberta-base-sentiment-latest](https://huggingface.co/cardiffnlp/twitter-roberta-base-sentiment-latest) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 1.0941
- Train Sparse Categorical Accuracy: 0.375
- Validation Loss: 1.0186
- Validation Sparse Categorical Accuracy: 0.3333
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'learning_rate': 5e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Sparse Categorical Accuracy | Validation Loss | Validation Sparse Categorical Accuracy | Epoch |
|:----------:|:---------------------------------:|:---------------:|:--------------------------------------:|:-----:|
| 1.1272 | 0.3281 | 1.0190 | 0.3333 | 0 |
| 1.1254 | 0.2969 | 1.1164 | 0.0 | 1 |
| 1.0941 | 0.375 | 1.0186 | 0.3333 | 2 |
### Framework versions
- Transformers 4.19.2
- TensorFlow 2.7.0
- Datasets 2.2.2
- Tokenizers 0.12.1
|
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