modelId
stringlengths 5
139
| author
stringlengths 2
42
| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-08-30 00:39:23
| 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
2025-08-30 00:39:08
| card
stringlengths 11
1.01M
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|---|---|---|---|---|---|---|---|---|---|
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
...
```
|
DavidCollier/SpaceInvader
|
DavidCollier
| 2022-06-11T12:40:06Z | 4 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-06-11T12:39:28Z |
---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- metrics:
- type: mean_reward
value: 15.50 +/- 12.54
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 DavidCollier -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 DavidCollier
```
## 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', 10000.0),
('optimize_memory_usage', True),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
|
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
|
shivarama23/swin-tiny-patch4-window7-224-finetuned-image_quality
|
shivarama23
| 2022-06-11T11:54:49Z | 85 | 1 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"swin",
"image-classification",
"generated_from_trainer",
"dataset:image_folder",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2022-06-11T11:41:01Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- image_folder
metrics:
- accuracy
model-index:
- name: swin-tiny-patch4-window7-224-finetuned-image_quality
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: image_folder
type: image_folder
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9090909090909091
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# swin-tiny-patch4-window7-224-finetuned-image_quality
This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the image_folder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5242
- Accuracy: 0.9091
## 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: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 1 | 0.6762 | 0.6364 |
| No log | 2.0 | 2 | 0.6309 | 0.7273 |
| No log | 3.0 | 3 | 0.6095 | 0.6364 |
| No log | 4.0 | 4 | 0.5775 | 0.6364 |
| No log | 5.0 | 5 | 0.5443 | 0.8182 |
| No log | 6.0 | 6 | 0.5242 | 0.9091 |
| No log | 7.0 | 7 | 0.5149 | 0.8182 |
| No log | 8.0 | 8 | 0.5094 | 0.8182 |
| No log | 9.0 | 9 | 0.5038 | 0.8182 |
| 0.4095 | 10.0 | 10 | 0.4992 | 0.8182 |
### Framework versions
- Transformers 4.19.4
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
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.
|
Gbartee/Gbartee2
|
Gbartee
| 2022-06-11T08:57:03Z | 0 | 0 | null |
[
"license:bigscience-bloom-rail-1.0",
"region:us"
] | null | 2022-06-11T08:57:03Z |
---
license: bigscience-bloom-rail-1.0
---
|
huggingtweets/gustholomulers
|
huggingtweets
| 2022-06-11T07:53:54Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-06-11T07:50:54Z |
---
language: en
thumbnail: http://www.huggingtweets.com/gustholomulers/1654934015981/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/1535477036353040384/tXI_s1Yi_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">soppy</div>
<div style="text-align: center; font-size: 14px;">@gustholomulers</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 soppy.
| Data | soppy |
| --- | --- |
| Tweets downloaded | 1482 |
| Retweets | 55 |
| Short tweets | 329 |
| Tweets kept | 1098 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1nhfbopf/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 @gustholomulers's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3p5yu4wm) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3p5yu4wm/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/gustholomulers')
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)
|
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`
|
titi7242229/roberta-base-bne-finetuned_personality_multi_2
|
titi7242229
| 2022-06-11T06:21:27Z | 104 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-06-11T05:27:06Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: roberta-base-bne-finetuned_personality_multi_2
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. -->
# roberta-base-bne-finetuned_personality_multi_2
This model is a fine-tuned version of [BSC-TeMU/roberta-base-bne](https://huggingface.co/BSC-TeMU/roberta-base-bne) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.2983
- Accuracy: 0.5429
## 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: 1e-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
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.3256 | 1.0 | 125 | 2.2642 | 0.2161 |
| 1.815 | 2.0 | 250 | 1.9569 | 0.3919 |
| 1.614 | 3.0 | 375 | 1.7264 | 0.5014 |
| 1.1718 | 4.0 | 500 | 1.6387 | 0.5239 |
| 1.135 | 5.0 | 625 | 1.6259 | 0.5245 |
| 0.5637 | 6.0 | 750 | 1.6443 | 0.5372 |
| 0.3672 | 7.0 | 875 | 1.7146 | 0.5326 |
| 0.3249 | 8.0 | 1000 | 1.8099 | 0.5297 |
| 0.1791 | 9.0 | 1125 | 1.8888 | 0.5285 |
| 0.2175 | 10.0 | 1250 | 1.9228 | 0.5326 |
| 0.0465 | 11.0 | 1375 | 1.9753 | 0.5435 |
| 0.1154 | 12.0 | 1500 | 2.1102 | 0.5256 |
| 0.0745 | 13.0 | 1625 | 2.1319 | 0.5429 |
| 0.0281 | 14.0 | 1750 | 2.1743 | 0.5360 |
| 0.0173 | 15.0 | 1875 | 2.2087 | 0.5441 |
| 0.0269 | 16.0 | 2000 | 2.2456 | 0.5424 |
| 0.0107 | 17.0 | 2125 | 2.2685 | 0.5458 |
| 0.0268 | 18.0 | 2250 | 2.2893 | 0.5383 |
| 0.0245 | 19.0 | 2375 | 2.2943 | 0.5418 |
| 0.0156 | 20.0 | 2500 | 2.2983 | 0.5429 |
### Framework versions
- Transformers 4.19.4
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
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
|
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/tonebot_
|
huggingtweets
| 2022-06-11T00:15:41Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-06-11T00:14:25Z |
---
language: en
thumbnail: http://www.huggingtweets.com/tonebot_/1654906535396/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/1447253318380793858/VVNhWBGI_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">tone bot</div>
<div style="text-align: center; font-size: 14px;">@tonebot_</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 tone bot.
| Data | tone bot |
| --- | --- |
| Tweets downloaded | 3250 |
| Retweets | 0 |
| Short tweets | 537 |
| Tweets kept | 2713 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2ot29sc5/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 @tonebot_'s tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3g614pb8) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3g614pb8/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/tonebot_')
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/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-r1-target
|
facebook
| 2022-06-10T22:36:34Z | 4 | 1 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"text-classification",
"en",
"arxiv:2012.15761",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-06-10T21:32:03Z |
---
language: en
---
# LFTW R1 Target
The R1 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!
|
luisrqe/cubucetapenis
|
luisrqe
| 2022-06-10T21:08:15Z | 0 | 0 | null |
[
"region:us"
] | null | 2022-06-10T20:52:33Z |
git lfs install
https://www.novinhavideosporno.com/wp-content/uploads/2018/11/a-maior-buceta-do-mundo-e-a-mais-escrota-tambem.jpg
https://www.xvideos-tv.com/wp-content/uploads/2021/11/buceta-da-novinha-sendo-arrombada-por-varios-machos-272x180.jpg
http://cdn.xvideos-br.com/media/imagens/10501.jpg
https://upload.wikimedia.org/wikipedia/commons/thumb/a/ac/Sidoka_photoshoot.jpg/800px-Sidoka_photoshoot.jpg
https://rapforte.com/wp-content/uploads/2021/08/Doka.jpg
https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcR2pWEwhp9tl7CDcHd7ELiKLpUPXkhCm4zmCwZGerHYh7CY8WxsGnOSACYussZdIF283so&usqp=CAU
git clone https://huggingface.co/luisrqe/cubucetapenis
|
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)
|
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 -->
|
louisdeco/camembert-base-finetuned-LineCause
|
louisdeco
| 2022-06-10T16:35:03Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"camembert",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-06-10T13:11:32Z |
---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- recall
model-index:
- name: camembert-base-finetuned-LineCause
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. -->
# camembert-base-finetuned-LineCause
This model is a fine-tuned version of [camembert-base](https://huggingface.co/camembert-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0001
- Accuracy: 1.0
- F1: 1.0
- Recall: 1.0
## 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:
- learning_rate: 2e-05
- train_batch_size: 50
- eval_batch_size: 50
- 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 | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---:|:------:|
| 0.0428 | 1.0 | 4409 | 0.0002 | 1.0 | 1.0 | 1.0 |
| 0.0009 | 2.0 | 8818 | 0.0001 | 1.0 | 1.0 | 1.0 |
### Framework versions
- Transformers 4.19.3
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
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
|
OTQ/q-FrozenLake-v1-4x4-noSlippery
|
OTQ
| 2022-06-10T15:14:57Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-06-10T15:14:51Z |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
---
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (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"])
```
|
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_multilingual_XLSum-finetuned-wikilingua-ar
|
ahmeddbahaa
| 2022-06-10T14:19:32Z | 10 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"mt5",
"text2text-generation",
"summarization",
"mT5_multilingual_XLSum",
"abstractive summarization",
"ar",
"generated_from_trainer",
"dataset:wiki_lingua",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
summarization
| 2022-06-10T02:47:03Z |
---
tags:
- summarization
- mT5_multilingual_XLSum
- mt5
- abstractive summarization
- ar
- generated_from_trainer
datasets:
- wiki_lingua
model-index:
- name: mT5_multilingual_XLSum-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_multilingual_XLSum-finetuned-wikilingua-ar
This model is a fine-tuned version of [csebuetnlp/mT5_multilingual_XLSum](https://huggingface.co/csebuetnlp/mT5_multilingual_XLSum) on the wiki_lingua dataset.
It achieves the following results on the evaluation set:
- Loss: 3.5540
- Rouge-1: 27.46
- Rouge-2: 9.0
- Rouge-l: 22.59
- Gen Len: 43.41
- Bertscore: 73.7
## 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:
- 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
|
google/muril-base-cased
|
google
| 2022-06-10T13:33:04Z | 10,230 | 35 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"arxiv:2103.10730",
"arxiv:1810.04805",
"arxiv:1911.02116",
"arxiv:2003.11080",
"arxiv:2009.05166",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:05Z |
---
thumbnail: https://huggingface.co/front/thumbnails/google.png
license: apache-2.0
---
MuRIL: Multilingual Representations for Indian Languages
===
MuRIL is a BERT model pre-trained on 17 Indian languages and their transliterated counterparts. We have released the pre-trained model (with the MLM layer intact, enabling masked word predictions) in this repository. We have also released the encoder on [TFHub](https://tfhub.dev/google/MuRIL/1) with an additional pre-processing module, that processes raw text into the expected input format for the encoder. You can find more details on MuRIL in this [paper](http://arxiv.org/abs/2103.10730).
## Overview
This model uses a BERT base architecture [1] pretrained from scratch using the
Wikipedia [2], Common Crawl [3], PMINDIA [4] and Dakshina [5] corpora for 17 [6]
Indian languages.
We use a training paradigm similar to multilingual bert, with a few
modifications as listed:
* We include translation and transliteration segment pairs in training as
well.
* We keep an exponent value of 0.3 and not 0.7 for upsampling, shown to
enhance low-resource performance. [7]
See the Training section for more details.
## Training
The MuRIL model is pre-trained on monolingual segments as well as parallel
segments as detailed below :
* Monolingual Data : We make use of publicly available corpora from Wikipedia
and Common Crawl for 17 Indian languages.
* Parallel Data : We have two types of parallel data :
* Translated Data : We obtain translations of the above monolingual
corpora using the Google NMT pipeline. We feed translated segment pairs
as input. We also make use of the publicly available PMINDIA corpus.
* Transliterated Data : We obtain transliterations of Wikipedia using the
IndicTrans [8] library. We feed transliterated segment pairs as input.
We also make use of the publicly available Dakshina dataset.
We keep an exponent value of 0.3 to calculate duplication multiplier values for
upsampling of lower resourced languages and set dupe factors accordingly. Note,
we limit transliterated pairs to Wikipedia only.
The model was trained using a self-supervised masked language modeling task. We
do whole word masking with a maximum of 80 predictions. The model was trained
for 1000K steps, with a batch size of 4096, and a max sequence length of 512.
### Trainable parameters
All parameters in the module are trainable, and fine-tuning all parameters is
the recommended practice.
## Uses & Limitations
This model is intended to be used for a variety of downstream NLP tasks for
Indian languages. This model is trained on transliterated data as well, a
phenomomenon commonly observed in the Indian context. This model is not expected
to perform well on languages other than the ones used in pretraining, i.e. 17
Indian languages.
## Evaluation
We provide the results of fine-tuning this model on a set of downstream tasks.<br/>
We choose these tasks from the XTREME benchmark, with evaluation done on Indian language test-sets.<br/>
We also transliterate the test-sets and evaluate on the same.<br/>
We use the same fine-tuning setting as is used by [9], except for TyDiQA, where we use additional SQuAD v1.1 English training data, similar to [10].<br/>
For Tatoeba, we do not fine-tune the model, and use the pooled_output of the last layer as the sentence embedding.<br/>
All results are computed in a zero-shot setting, with English being the high resource training set language.
* Shown below are results on datasets from the XTREME benchmark (in %)
<br/>
PANX (F1) | ml | ta | te | en | bn | hi | mr | ur | Average
:-------- | ----: | ----: | ----: | ----: | ----: | ----: | ----: | ----: | ------:
mBERT | 54.77 | 51.24 | 50.16 | 84.40 | 68.59 | 65.13 | 58.44 | 31.36 | 58.01
MuRIL | 75.74 | 71.86 | 64.99 | 84.43 | 85.97 | 78.09 | 74.63 | 85.07 | 77.60
<br/>
UDPOS (F1) | en | hi | mr | ta | te | ur | Average
:--------- | ----: | ----: | ----: | ----: | ----: | ----: | ------:
mBERT | 95.35 | 66.09 | 71.27 | 59.58 | 76.98 | 57.85 | 71.19
MuRIL | 95.55 | 64.47 | 82.95 | 62.57 | 85.63 | 58.93 | 75.02
<br/>
XNLI (Accuracy) | en | hi | ur | Average
:-------------- | ----: | ----: | ----: | ------:
mBERT | 81.72 | 60.52 | 58.20 | 66.81
MuRIL | 83.85 | 70.66 | 67.70 | 74.07
<br/>
Tatoeba (Accuracy) | ml | ta | te | bn | hi | mr | ur | Average
:----------------- | ----: | ----: | ----: | ----: | ----: | ----: | ----: | ------:
mBERT | 20.23 | 12.38 | 14.96 | 12.80 | 27.80 | 18.00 | 22.70 | 18.41
MuRIL | 26.35 | 36.81 | 17.52 | 20.20 | 31.50 | 26.60 | 17.10 | 25.15
<br/>
XQUAD (F1/EM) | en | hi | Average
:------------ | ----------: | ----------: | ----------:
mBERT | 83.85/72.86 | 58.46/43.53 | 71.15/58.19
MuRIL | 84.31/72.94 | 73.93/58.32 | 79.12/65.63
<br/>
MLQA (F1/EM) | en | hi | Average
:----------- | ----------: | ----------: | ----------:
mBERT | 80.39/67.30 | 50.28/35.18 | 65.34/51.24
MuRIL | 80.28/67.37 | 67.34/50.22 | 73.81/58.80
<br/>
TyDiQA (F1/EM) | en | bn | te | Average
:---------------- | ----------: | ----------: | ----------: | ----------:
mBERT | 75.21/65.00 | 60.62/45.13 | 53.55/44.54 | 63.13/51.66
MuRIL | 74.10/64.55 | 78.03/66.37 | 73.95/46.94 | 75.36/59.28
* Shown below are results on the transliterated versions of the above
test-sets.
PANX (F1) | ml_tr | ta_tr | te_tr | bn_tr | hi_tr | mr_tr | ur_tr | Average
:-------- | ----: | ----: | ----: | ----: | ----: | ----: | ----: | ------:
mBERT | 7.53 | 1.04 | 8.24 | 41.77 | 25.46 | 8.34 | 7.30 | 14.24
MuRIL | 63.39 | 7.00 | 53.62 | 72.94 | 69.75 | 68.77 | 68.41 | 57.70
<br/>
UDPOS (F1) | hi_tr | mr_tr | ta_tr | te_tr | ur_tr | Average
:--------- | ----: | ----: | ----: | ----: | ----: | ------:
mBERT | 25.00 | 33.67 | 24.02 | 36.21 | 22.07 | 28.20
MuRIL | 63.09 | 67.19 | 58.40 | 65.30 | 56.49 | 62.09
<br/>
XNLI (Accuracy) | hi_tr | ur_tr | Average
:-------------- | ----: | ----: | ------:
mBERT | 39.6 | 38.86 | 39.23
MuRIL | 68.24 | 61.16 | 64.70
<br/>
Tatoeba (Accuracy) | ml_tr | ta_tr | te_tr | bn_tr | hi_tr | mr_tr | ur_tr | Average
:----------------- | ----: | ----: | ----: | ----: | ----: | ----: | ----: | ------:
mBERT | 2.18 | 1.95 | 5.13 | 1.80 | 3.00 | 2.40 | 2.30 | 2.68
MuRIL | 10.33 | 11.07 | 11.54 | 8.10 | 14.90 | 7.20 | 13.70 | 10.98
<br/>
## References
\[1]: Jacob Devlin, Ming-Wei Chang, Kenton Lee, Kristina Toutanova. [BERT:
Pre-training of Deep Bidirectional Transformers for Language
Understanding](https://arxiv.org/abs/1810.04805). arXiv preprint
arXiv:1810.04805, 2018.
\[2]: [Wikipedia](https://www.tensorflow.org/datasets/catalog/wikipedia)
\[3]: [Common Crawl](http://commoncrawl.org/the-data/)
\[4]:
[PMINDIA](http://lotus.kuee.kyoto-u.ac.jp/WAT/indic-multilingual/index.html)
\[5]: [Dakshina](https://github.com/google-research-datasets/dakshina)
\[6]: Assamese (as), Bengali (bn), English (en), Gujarati (gu), Hindi (hi),
Kannada (kn), Kashmiri (ks), Malayalam (ml), Marathi (mr), Nepali (ne), Oriya
(or), Punjabi (pa), Sanskrit (sa), Sindhi (sd), Tamil (ta), Telugu (te) and Urdu
(ur).
\[7]: Conneau, Alexis, et al.
[Unsupervised cross-lingual representation learning at scale](https://arxiv.org/pdf/1911.02116.pdf).
arXiv preprint arXiv:1911.02116 (2019).
\[8]: [IndicTrans](https://github.com/libindic/indic-trans)
\[9]: Hu, J., Ruder, S., Siddhant, A., Neubig, G., Firat, O., & Johnson, M.
(2020). [Xtreme: A massively multilingual multi-task benchmark for evaluating
cross-lingual generalization.](https://arxiv.org/pdf/2003.11080.pdf) arXiv
preprint arXiv:2003.11080.
\[10]: Fang, Y., Wang, S., Gan, Z., Sun, S., & Liu, J. (2020).
[FILTER: An Enhanced Fusion Method for Cross-lingual Language Understanding.](https://arxiv.org/pdf/2009.05166.pdf)
arXiv preprint arXiv:2009.05166.
## Citation
If you find MuRIL useful in your applications, please cite the following paper:
```
@misc{khanuja2021muril,
title={MuRIL: Multilingual Representations for Indian Languages},
author={Simran Khanuja and Diksha Bansal and Sarvesh Mehtani and Savya Khosla and Atreyee Dey and Balaji Gopalan and Dilip Kumar Margam and Pooja Aggarwal and Rajiv Teja Nagipogu and Shachi Dave and Shruti Gupta and Subhash Chandra Bose Gali and Vish Subramanian and Partha Talukdar},
year={2021},
eprint={2103.10730},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
## Contact
Please mail your queries/feedback to muril-contact@google.com.
|
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
|
becher/t5-small-finetuned-arxiv
|
becher
| 2022-06-10T12:28:48Z | 106 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-06-10T11:59:37Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: t5-small-finetuned-arxiv
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-small-finetuned-arxiv
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.1559
- Rouge1: 37.854
- Rouge2: 20.4934
- Rougel: 33.9992
- Rougelsum: 33.9943
- Gen Len: 15.847
## 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
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|:-------:|:---------:|:-------:|
| 2.3848 | 1.0 | 3564 | 2.1559 | 37.854 | 20.4934 | 33.9992 | 33.9943 | 15.847 |
### Framework versions
- Transformers 4.19.3
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
stig/distilbert-base-uncased-finetuned
|
stig
| 2022-06-10T10:59:39Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-06-10T09:59:19Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: distilbert-base-uncased-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. -->
# distilbert-base-uncased-finetuned
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8627
## 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: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.0255 | 1.0 | 2312 | 1.9202 |
| 1.7483 | 2.0 | 4624 | 1.8437 |
| 1.5733 | 3.0 | 6936 | 1.8627 |
### 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)])
```
|
YaYaB/SpaceInvadersNoFrameskip-v4-2
|
YaYaB
| 2022-06-10T09:16:18Z | 4 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-06-10T09:15:44Z |
---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- metrics:
- type: mean_reward
value: 556.00 +/- 162.23
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', 64),
('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', 10000000.0),
('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)
|
TurkuNLP/bert-large-finnish-cased-v1
|
TurkuNLP
| 2022-06-10T08:46:17Z | 152 | 2 |
transformers
|
[
"transformers",
"pytorch",
"fi",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-06-10T07:53:16Z |
---
license: apache-2.0
language: fi
---
This is the large variant of FinBERT (TurkuNLP/bert-base-finnish-cased-v1). The training data is exactly the same.
|
flood/distilbert-base-uncased-distilled-clinc
|
flood
| 2022-06-10T08:03:08Z | 77 | 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
| 2022-06-10T07:59:25Z |
---
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: 0.9309677419354838
---
<!-- 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: 0.0389
- Accuracy: 0.9310
## 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6206 | 1.0 | 318 | 0.3251 | 0.6610 |
| 0.2571 | 2.0 | 636 | 0.1366 | 0.8584 |
| 0.1392 | 3.0 | 954 | 0.0813 | 0.9081 |
| 0.0967 | 4.0 | 1272 | 0.0598 | 0.9152 |
| 0.0779 | 5.0 | 1590 | 0.0503 | 0.9229 |
| 0.0675 | 6.0 | 1908 | 0.0451 | 0.9271 |
| 0.0615 | 7.0 | 2226 | 0.0425 | 0.9326 |
| 0.058 | 8.0 | 2544 | 0.0403 | 0.9316 |
| 0.0557 | 9.0 | 2862 | 0.0393 | 0.9306 |
| 0.0544 | 10.0 | 3180 | 0.0389 | 0.9310 |
### Framework versions
- Transformers 4.19.3
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
juns/imdb_finetuned_distilbert-base-uncased-finetuned-sst-2-english
|
juns
| 2022-06-10T07:37:10Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-18T07:05:06Z |
imdb_finetuned_distilbert-base-uncased-finetuned-sst-2-english for boostcamp ai tech 3
|
Intel/MiniLM-L12-H384-uncased-mrpc
|
Intel
| 2022-06-10T07:06:45Z | 220 | 1 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"en",
"dataset:glue",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-06-10T06:55:25Z |
---
language:
- en
license: mit
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
- f1
model-index:
- name: MiniLM-L12-H384-uncased-mrpc
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE MRPC
type: glue
args: mrpc
metrics:
- name: Accuracy
type: accuracy
value: 0.875
- name: F1
type: f1
value: 0.9097345132743363
---
<!-- 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-L12-H384-uncased-mrpc
This model is a fine-tuned version of [microsoft/MiniLM-L12-H384-uncased](https://huggingface.co/microsoft/MiniLM-L12-H384-uncased) on the GLUE MRPC dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4319
- Accuracy: 0.875
- F1: 0.9097
- Combined Score: 0.8924
## 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: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5.0
### Training results
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0+cu102
- 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
|
jayeshgar/dqn-SpaceInvadersNoFrameskip-v4
|
jayeshgar
| 2022-06-10T06:54:27Z | 2 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-06-10T06:53:42Z |
---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- metrics:
- type: mean_reward
value: 653.00 +/- 114.70
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 jayeshgar -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 jayeshgar
```
## 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)])
```
|
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)
|
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
|
flood/xlm-roberta-base-finetuned-panx-de
|
flood
| 2022-06-10T04:39:15Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-04-13T17:46:31Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-de
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xtreme
type: xtreme
args: PAN-X.de
metrics:
- name: F1
type: f1
value: 0.8633935674508466
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-de
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1344
- F1: 0.8634
## 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: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2588 | 1.0 | 525 | 0.1676 | 0.8194 |
| 0.1318 | 2.0 | 1050 | 0.1326 | 0.8513 |
| 0.084 | 3.0 | 1575 | 0.1344 | 0.8634 |
### Framework versions
- Transformers 4.19.3
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
RuiqianLi/malaya-speech_Mrbrown_finetune1
|
RuiqianLi
| 2022-06-10T02:23:06Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:uob_singlish",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-06-09T09:01:56Z |
---
tags:
- generated_from_trainer
datasets:
- uob_singlish
model-index:
- name: malaya-speech_Mrbrown_finetune1
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. -->
# malaya-speech_Mrbrown_finetune1
This model is a fine-tuned version of [malay-huggingface/wav2vec2-xls-r-300m-mixed](https://huggingface.co/malay-huggingface/wav2vec2-xls-r-300m-mixed) on the uob_singlish dataset.
## This time use self-made dataset(cut the audio of "https://www.youtube.com/watch?v=a2ZOTD3R7JI" into slices and write the corresponding transcript, totally 4 mins), get really bad fine-tuning result, that may mean the training/fine-tuning dataset must be high quality/at least several hours? Or maybe is because the learning rate is set too high(0.01) ? Still searching for the important factors.
It achieves the following results on the evaluation set:
- Loss: 3.8458
- Wer: 1.01
## 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: 0.01
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 100
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:----:|
| 0.3186 | 20.0 | 200 | 4.2225 | 1.13 |
| 0.4911 | 40.0 | 400 | 4.0427 | 0.99 |
| 0.9014 | 60.0 | 600 | 5.3285 | 1.04 |
| 1.0955 | 80.0 | 800 | 3.6922 | 1.02 |
| 0.7533 | 100.0 | 1000 | 3.8458 | 1.01 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu113
- Datasets 1.18.3
- Tokenizers 0.10.3
|
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/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)
|
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",
}
```
|
kjunelee/distilbert-base-uncased-finetuned-emotion
|
kjunelee
| 2022-06-10T00:24:32Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-06-10T00:03:16Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.931
- name: F1
type: f1
value: 0.9313235272564213
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1595
- Accuracy: 0.931
- F1: 0.9313
## 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: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log | 1.0 | 125 | 0.1873 | 0.924 | 0.9234 |
| 0.1992 | 2.0 | 250 | 0.1649 | 0.929 | 0.9293 |
| 0.1992 | 3.0 | 375 | 0.1595 | 0.931 | 0.9313 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0
- Datasets 2.2.3.dev0
- Tokenizers 0.12.1
|
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
|
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
|
huggingtweets/midudev
|
huggingtweets
| 2022-06-09T18:48:30Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-06-09T18:33:17Z |
---
language: en
thumbnail: http://www.huggingtweets.com/midudev/1654800505422/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/1526668354609680384/r85fytOs_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">🔴 EN DIRECTO twitch.tv/midudev</div>
<div style="text-align: center; font-size: 14px;">@midudev</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 🔴 EN DIRECTO twitch.tv/midudev.
| Data | 🔴 EN DIRECTO twitch.tv/midudev |
| --- | --- |
| Tweets downloaded | 3246 |
| Retweets | 824 |
| Short tweets | 163 |
| Tweets kept | 2259 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/11iwoc6b/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 @midudev's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/s48ktc1m) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/s48ktc1m/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/midudev')
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)
|
bookpanda/wangchanberta-base-att-spm-uncased-finetuned-imdb
|
bookpanda
| 2022-06-09T18:17:16Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"camembert",
"fill-mask",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-05-28T08:22:04Z |
---
tags:
- generated_from_trainer
model-index:
- name: wangchanberta-base-att-spm-uncased-finetuned-imdb
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. -->
# wangchanberta-base-att-spm-uncased-finetuned-imdb
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.
It achieves the following results on the evaluation set:
- Loss: 0.0810
## 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: 5
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 0.1831 | 1.0 | 4826 | 0.1542 |
| 0.1 | 2.0 | 9652 | 0.1075 |
| 0.0946 | 3.0 | 14478 | 0.0443 |
| 0.0618 | 4.0 | 19304 | 0.0830 |
| 0.0783 | 5.0 | 24130 | 0.0810 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.11.0+cu113
- Datasets 1.17.0
- Tokenizers 0.10.3
|
kabelomalapane/En-Ts
|
kabelomalapane
| 2022-06-09T17:33:20Z | 69 | 0 |
transformers
|
[
"transformers",
"pytorch",
"marian",
"text2text-generation",
"translation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
translation
| 2022-06-09T16:33:13Z |
---
license: apache-2.0
tags:
- translation
- generated_from_trainer
metrics:
- bleu
model-index:
- name: En-Ts
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. -->
# En-Ts
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-ts](https://huggingface.co/Helsinki-NLP/opus-mt-en-ts) on the None dataset.
It achieves the following results on the evaluation set:
Before training:
- Loss: 3.17
- Bleu: 14.513
After Training
- Loss: 1.3320
- Bleu: 36.7687
## 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: 32
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 6
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|
| 1.7082 | 1.0 | 5929 | 1.6902 | 32.1311 |
| 1.4606 | 2.0 | 11858 | 1.4996 | 34.1129 |
| 1.3182 | 3.0 | 17787 | 1.4107 | 35.7428 |
| 1.2543 | 4.0 | 23716 | 1.3631 | 36.2009 |
| 1.2116 | 5.0 | 29645 | 1.3389 | 36.5876 |
| 1.1723 | 6.0 | 35574 | 1.3320 | 36.7481 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- 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
|
FritzOS/TEdetection_distiBERT_NER_V4
|
FritzOS
| 2022-06-09T16:36:54Z | 5 | 0 |
transformers
|
[
"transformers",
"tf",
"distilbert",
"token-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-06-09T16:36:37Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: TEdetection_distiBERT_NER_V4
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_distiBERT_NER_V4
This model is a fine-tuned version of [FritzOS/TEdetection_distiBERT_mLM_V2_shuffleplus3](https://huggingface.co/FritzOS/TEdetection_distiBERT_mLM_V2_shuffleplus3) 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': 2e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-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 4.19.2
- TensorFlow 2.8.2
- 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)
|
XGBooster/dqn-SpaceInvadersNoFrameskip-v4
|
XGBooster
| 2022-06-09T16:03:42Z | 2 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-06-09T16:03:00Z |
---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- metrics:
- type: mean_reward
value: 744.00 +/- 231.20
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 XGBooster -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 XGBooster
```
## 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', 10000000.0),
('optimize_memory_usage', True),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
|
buio/vq-vae
|
buio
| 2022-06-09T15:06:33Z | 0 | 0 |
keras
|
[
"keras",
"tf-keras",
"computer-vision",
"generative",
"variational-autoencoder",
"vq-vae",
"region:us"
] | null | 2022-06-09T15:04:32Z |
---
library_name: keras
tags:
- computer-vision
- generative
- variational-autoencoder
- vq-vae
---
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training Metrics
Model history needed
## Model Plot
<details>
<summary>View Model Plot</summary>
</details>
|
EmileEsmaili/gpt2-p4k
|
EmileEsmaili
| 2022-06-09T14:55:23Z | 9 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-06-05T17:16:58Z |
---
license: mit
tags:
- generated_from_trainer
model-index:
- name: gpt2-p4k
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. -->
# gpt2-p4k
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) 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: 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: 3.0
### Training results
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Tokenizers 0.12.1
|
chanifrusydi/distillbert-finetuned-ner
|
chanifrusydi
| 2022-06-09T14:34:38Z | 18 | 1 |
transformers
|
[
"transformers",
"tf",
"distilbert",
"token-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-06-06T02:43:03Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: chanifrusydi/distillbert-finetuned-ner
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. -->
# chanifrusydi/distillbert-finetuned-ner
This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0168
- Validation Loss: 0.0691
- Epoch: 4
## 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': 2e-05, 'decay_steps': 4385, '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 |
|:----------:|:---------------:|:-----:|
| 0.2046 | 0.0929 | 0 |
| 0.0638 | 0.0732 | 1 |
| 0.0376 | 0.0668 | 2 |
| 0.0241 | 0.0707 | 3 |
| 0.0168 | 0.0691 | 4 |
### Framework versions
- Transformers 4.19.2
- TensorFlow 2.8.2
- Datasets 2.2.2
- Tokenizers 0.12.1
|
Khaled002/Yy
|
Khaled002
| 2022-06-09T14:22:32Z | 0 | 0 | null |
[
"license:bsd-3-clause-clear",
"region:us"
] | null | 2022-06-09T14:22:32Z |
---
license: bsd-3-clause-clear
---
|
ricardo-filho/bert_base_tcm_0.6
|
ricardo-filho
| 2022-06-09T14:15:12Z | 14 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-06-03T18:39:06Z |
---
license: mit
tags:
- generated_from_trainer
model-index:
- name: bert_base_tcm_0.6
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_base_tcm_0.6
This model is a fine-tuned version of [neuralmind/bert-base-portuguese-cased](https://huggingface.co/neuralmind/bert-base-portuguese-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0193
- Criterio Julgamento Precision: 0.8875
- Criterio Julgamento Recall: 0.8659
- Criterio Julgamento F1: 0.8765
- Criterio Julgamento Number: 82
- Data Sessao Precision: 0.7571
- Data Sessao Recall: 0.9636
- Data Sessao F1: 0.848
- Data Sessao Number: 55
- Modalidade Licitacao Precision: 0.9394
- Modalidade Licitacao Recall: 0.9718
- Modalidade Licitacao F1: 0.9553
- Modalidade Licitacao Number: 319
- Numero Exercicio Precision: 0.9172
- Numero Exercicio Recall: 0.9688
- Numero Exercicio F1: 0.9422
- Numero Exercicio Number: 160
- Objeto Licitacao Precision: 0.4659
- Objeto Licitacao Recall: 0.7069
- Objeto Licitacao F1: 0.5616
- Objeto Licitacao Number: 58
- Valor Objeto Precision: 0.8333
- Valor Objeto Recall: 0.9211
- Valor Objeto F1: 0.875
- Valor Objeto Number: 38
- Overall Precision: 0.8537
- Overall Recall: 0.9340
- Overall F1: 0.8920
- Overall Accuracy: 0.9951
## 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: 10.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Criterio Julgamento Precision | Criterio Julgamento Recall | Criterio Julgamento F1 | Criterio Julgamento Number | Data Sessao Precision | Data Sessao Recall | Data Sessao F1 | Data Sessao Number | Modalidade Licitacao Precision | Modalidade Licitacao Recall | Modalidade Licitacao F1 | Modalidade Licitacao Number | Numero Exercicio Precision | Numero Exercicio Recall | Numero Exercicio F1 | Numero Exercicio Number | Objeto Licitacao Precision | Objeto Licitacao Recall | Objeto Licitacao F1 | Objeto Licitacao Number | Valor Objeto Precision | Valor Objeto Recall | Valor Objeto F1 | Valor Objeto Number | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:-----------------------------:|:--------------------------:|:----------------------:|:--------------------------:|:---------------------:|:------------------:|:--------------:|:------------------:|:------------------------------:|:---------------------------:|:-----------------------:|:---------------------------:|:--------------------------:|:-----------------------:|:-------------------:|:-----------------------:|:--------------------------:|:-----------------------:|:-------------------:|:-----------------------:|:----------------------:|:-------------------:|:---------------:|:-------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 0.0252 | 1.0 | 1963 | 0.0202 | 0.8022 | 0.8902 | 0.8439 | 82 | 0.7391 | 0.9273 | 0.8226 | 55 | 0.9233 | 0.9812 | 0.9514 | 319 | 0.8966 | 0.975 | 0.9341 | 160 | 0.4730 | 0.6034 | 0.5303 | 58 | 0.7083 | 0.8947 | 0.7907 | 38 | 0.8327 | 0.9298 | 0.8786 | 0.9948 |
| 0.0191 | 2.0 | 3926 | 0.0226 | 0.8554 | 0.8659 | 0.8606 | 82 | 0.5641 | 0.4 | 0.4681 | 55 | 0.9572 | 0.9812 | 0.9690 | 319 | 0.9273 | 0.9563 | 0.9415 | 160 | 0.3770 | 0.3966 | 0.3866 | 58 | 0.8571 | 0.7895 | 0.8219 | 38 | 0.8620 | 0.8596 | 0.8608 | 0.9951 |
| 0.0137 | 3.0 | 5889 | 0.0193 | 0.8875 | 0.8659 | 0.8765 | 82 | 0.7571 | 0.9636 | 0.848 | 55 | 0.9394 | 0.9718 | 0.9553 | 319 | 0.9172 | 0.9688 | 0.9422 | 160 | 0.4659 | 0.7069 | 0.5616 | 58 | 0.8333 | 0.9211 | 0.875 | 38 | 0.8537 | 0.9340 | 0.8920 | 0.9951 |
| 0.0082 | 4.0 | 7852 | 0.0210 | 0.8780 | 0.8780 | 0.8780 | 82 | 0.7966 | 0.8545 | 0.8246 | 55 | 0.9512 | 0.9781 | 0.9645 | 319 | 0.9023 | 0.9812 | 0.9401 | 160 | 0.5385 | 0.6034 | 0.5691 | 58 | 0.9 | 0.9474 | 0.9231 | 38 | 0.8810 | 0.9256 | 0.9027 | 0.9963 |
| 0.0048 | 5.0 | 9815 | 0.0222 | 0.8261 | 0.9268 | 0.8736 | 82 | 0.7969 | 0.9273 | 0.8571 | 55 | 0.9512 | 0.9781 | 0.9645 | 319 | 0.9231 | 0.975 | 0.9483 | 160 | 0.6515 | 0.7414 | 0.6935 | 58 | 0.875 | 0.9211 | 0.8974 | 38 | 0.8867 | 0.9452 | 0.9150 | 0.9964 |
| 0.0044 | 6.0 | 11778 | 0.0262 | 0.8276 | 0.8780 | 0.8521 | 82 | 0.7681 | 0.9636 | 0.8548 | 55 | 0.9541 | 0.9781 | 0.9659 | 319 | 0.9235 | 0.9812 | 0.9515 | 160 | 0.5263 | 0.6897 | 0.5970 | 58 | 0.9211 | 0.9211 | 0.9211 | 38 | 0.8722 | 0.9396 | 0.9047 | 0.9959 |
| 0.0042 | 7.0 | 13741 | 0.0246 | 0.8523 | 0.9146 | 0.8824 | 82 | 0.7656 | 0.8909 | 0.8235 | 55 | 0.9509 | 0.9718 | 0.9612 | 319 | 0.9118 | 0.9688 | 0.9394 | 160 | 0.5938 | 0.6552 | 0.6230 | 58 | 0.8974 | 0.9211 | 0.9091 | 38 | 0.8815 | 0.9298 | 0.9050 | 0.9960 |
| 0.0013 | 8.0 | 15704 | 0.0294 | 0.8295 | 0.8902 | 0.8588 | 82 | 0.7391 | 0.9273 | 0.8226 | 55 | 0.9543 | 0.9812 | 0.9675 | 319 | 0.9070 | 0.975 | 0.9398 | 160 | 0.6094 | 0.6724 | 0.6393 | 58 | 0.875 | 0.9211 | 0.8974 | 38 | 0.8765 | 0.9368 | 0.9056 | 0.9961 |
| 0.0019 | 9.0 | 17667 | 0.0303 | 0.8690 | 0.8902 | 0.8795 | 82 | 0.8305 | 0.8909 | 0.8596 | 55 | 0.9538 | 0.9718 | 0.9627 | 319 | 0.9290 | 0.9812 | 0.9544 | 160 | 0.6441 | 0.6552 | 0.6496 | 58 | 0.9211 | 0.9211 | 0.9211 | 38 | 0.9019 | 0.9298 | 0.9156 | 0.9961 |
| 0.0007 | 10.0 | 19630 | 0.0295 | 0.8488 | 0.8902 | 0.8690 | 82 | 0.7903 | 0.8909 | 0.8376 | 55 | 0.9571 | 0.9781 | 0.9674 | 319 | 0.9181 | 0.9812 | 0.9486 | 160 | 0.6393 | 0.6724 | 0.6555 | 58 | 0.9211 | 0.9211 | 0.9211 | 38 | 0.8938 | 0.9340 | 0.9135 | 0.9962 |
### Framework versions
- Transformers 4.20.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
sschellhammer/SciTweets_SciBert
|
sschellhammer
| 2022-06-09T14:03:30Z | 97 | 1 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"license:cc-by-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-06-04T06:16:44Z |
---
license: cc-by-4.0
widget:
- text: "Study: Shifts in electricity generation spur net job growth, but coal jobs decline - via @DukeU https://www.eurekalert.org/news-releases/637217"
example_title: "All categories"
- text: "Shifts in electricity generation spur net job growth, but coal jobs decline"
example_title: "Only Cat 1.1"
- text: "Study on impacts of electricity generation shift via @DukeU https://www.eurekalert.org/news-releases/637217"
example_title: "Only Cat 1.2 and 1.3"
- text: "@DukeU received grant for research on electricity generation shift"
example_title: "Only Cat 1.3"
---
This SciBert-based multi-label classifier, trained as part of the work "SciTweets - A Dataset and Annotation Framework for Detecting Scientific Online Discourse", distinguishes three different forms of science-relatedness for Tweets. See details at https://github.com/AI-4-Sci/SciTweets .
|
YeRyeongLee/electra-base-discriminator-finetuned-filtered-0609
|
YeRyeongLee
| 2022-06-09T14:00:07Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"electra",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-06-09T07:24:13Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: electra-base-discriminator-finetuned-filtered-0609
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. -->
# electra-base-discriminator-finetuned-filtered-0609
This model is a fine-tuned version of [google/electra-base-discriminator](https://huggingface.co/google/electra-base-discriminator) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1933
- Accuracy: 0.9745
- Precision: 0.9747
- Recall: 0.9745
- F1: 0.9746
## 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
- lr_scheduler_warmup_steps: 1000
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.238 | 1.0 | 3180 | 0.1861 | 0.9682 | 0.9686 | 0.9682 | 0.9682 |
| 0.1827 | 2.0 | 6360 | 0.2262 | 0.9645 | 0.9648 | 0.9645 | 0.9644 |
| 0.1326 | 3.0 | 9540 | 0.1904 | 0.9711 | 0.9716 | 0.9711 | 0.9712 |
| 0.1575 | 4.0 | 12720 | 0.2065 | 0.9676 | 0.9680 | 0.9676 | 0.9676 |
| 0.1224 | 5.0 | 15900 | 0.2666 | 0.9557 | 0.9571 | 0.9557 | 0.9558 |
| 0.1083 | 6.0 | 19080 | 0.1697 | 0.9752 | 0.9754 | 0.9752 | 0.9752 |
| 0.0792 | 7.0 | 22260 | 0.1684 | 0.9742 | 0.9744 | 0.9742 | 0.9742 |
| 0.0751 | 8.0 | 25440 | 0.1784 | 0.9723 | 0.9726 | 0.9723 | 0.9723 |
| 0.0572 | 9.0 | 28620 | 0.1868 | 0.9736 | 0.9737 | 0.9736 | 0.9736 |
| 0.0593 | 10.0 | 31800 | 0.1933 | 0.9745 | 0.9747 | 0.9745 | 0.9746 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.9.1+cu111
- Datasets 1.16.1
- Tokenizers 0.12.1
|
Nehc/FakeMobile
|
Nehc
| 2022-06-09T13:44:35Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"ru",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-06-07T18:05:08Z |
---
language:
- ru
widget:
- text: "[CLS] Какая абонентская плата на тарифе Позвони маме? [SEP]"
metrics:
- loss: 0.704381
- accuracy: 1.000000
---
Start from 'DeepPavlov/rubert-base-cased' and finetuning on DUMBOT fake data (http://dumbot.ru/Home/MobileOperatorRate).
100 epoch
on progress...
|
i8pxgd2s/q-Taxi-v3
|
i8pxgd2s
| 2022-06-09T13:26:49Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-06-09T13:26:40Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3
results:
- metrics:
- type: mean_reward
value: 7.56 +/- 2.71
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="i8pxgd2s/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"])
```
|
victorlee071200/bert-base-cased-finetuned-squad_v2
|
victorlee071200
| 2022-06-09T13:16:06Z | 8 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad_v2",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-06-08T17:41:30Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad_v2
model-index:
- name: bert-base-cased-finetuned-squad_v2
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-base-cased-finetuned-squad_v2
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the squad_v2 dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3226
## 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: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 1.03 | 1.0 | 8255 | 1.1334 |
| 0.7511 | 2.0 | 16510 | 1.1299 |
| 0.5376 | 3.0 | 24765 | 1.3226 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
huggingtweets/bbclaurakt
|
huggingtweets
| 2022-06-09T12:48:19Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-06-09T12:47:22Z |
---
language: en
thumbnail: http://www.huggingtweets.com/bbclaurakt/1654778894531/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/1533553176619716608/4klYwjkC_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">Laura Kuenssberg Translator</div>
<div style="text-align: center; font-size: 14px;">@bbclaurakt</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 Laura Kuenssberg Translator.
| Data | Laura Kuenssberg Translator |
| --- | --- |
| Tweets downloaded | 2063 |
| Retweets | 23 |
| Short tweets | 135 |
| Tweets kept | 1905 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/37mk0av7/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 @bbclaurakt's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3a8gt7bb) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3a8gt7bb/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/bbclaurakt')
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)
|
bubblecookie/t5-small-finetuned-cnndm-samsum
|
bubblecookie
| 2022-06-09T12:40:46Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:cnn_dailymail",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-06-08T10:21:22Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- cnn_dailymail
metrics:
- rouge
model-index:
- name: t5-small-finetuned-cnndm-samsum
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: cnn_dailymail
type: cnn_dailymail
args: 3.0.0
metrics:
- name: Rouge1
type: rouge
value: 24.5996
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-small-finetuned-cnndm-samsum
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the cnn_dailymail dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6422
- Rouge1: 24.5996
- Rouge2: 11.817
- Rougel: 20.3346
- Rougelsum: 23.2155
- Gen Len: 18.9999
## 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: 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 |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:|
| 1.8078 | 1.0 | 71779 | 1.6422 | 24.5996 | 11.817 | 20.3346 | 23.2155 | 18.9999 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
assamim/mt5-pukulenam-summarization
|
assamim
| 2022-06-09T12:19:33Z | 61 | 0 |
transformers
|
[
"transformers",
"tf",
"mt5",
"text2text-generation",
"generated_from_keras_callback",
"Summarization",
"mT5",
"dataset:csebuetnlp/xlsum",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-06-08T15:08:51Z |
---
tags:
- generated_from_keras_callback
- Summarization
- mT5
datasets:
- csebuetnlp/xlsum
model-index:
- name: assamim/mt5-pukulenam-summarization
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. -->
# assamim/mt5-pukulenam-summarization
This model is a fine-tuned version of [csebuetnlp/mT5_multilingual_XLSum](https://huggingface.co/csebuetnlp/mT5_multilingual_XLSum) on an [csebuetnlp/xlsum](https://huggingface.co/datasets/csebuetnlp/xlsum) dataset
## Using this model in `transformers` (tested on 4.19.2)
```python
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import re
news = """
Anggota Unit Perlindungan Rakyat Kurdi di kota Rabia, pada perbatasan Irak-Suriah. Pasukan Kurdi Irak dilaporkan sudah menguasai kembali kota Rabia meskipun banyak korban jatuh. Pejabat senior Kurdi Irak mengatakan pasukan Kurdi Peshmerga mencatat kemajuan lewat serangan dini hari di Rabia. Sementara itu, milisi ISIS berusaha memukul mundur pasukan Kurdi Suriah di bagian lain perbatasan. Hal ini terjadi saat koalisi pimpinan Amerika terus melanjutkan serangan udara terhadap sasaran ISIS di Suriah dan Irak. Hari Selasa (30 September) dilaporkan juga terjadi serangkaian serangan bom di ibu kota Irak, Baghdad dan kota suci Syiah, Karbala. Dalam perkembangan terpisah, sejumlah tank Turki berada di bukit di sepanjang perbatasan dekat kota Kobane, Suriah setelah sejumlah bom mengenai wilayah Turki saat terjadi bentrokan dengan milisi ISIS dan pejuang Kurdi. Pemerintah Turki diperkirakan akan menyampaikan mosi ke parlemen, agar menyetujui aksi militer terhadap ISIS di Irak dan Suriah.
"""
tokenizer = AutoTokenizer.from_pretrained("assamim/mt5-pukulenam-summarization")
model = AutoModelForSeq2SeqLM.from_pretrained("assamim/mt5-pukulenam-summarization", from_tf=True)
WHITESPACE_HANDLER = lambda k: re.sub('\s+', ' ', re.sub('\n+', ' ', k.strip()))
input_ids = tokenizer.encode(WHITESPACE_HANDLER(news1), return_tensors='pt')
summary_ids = model.generate(input_ids,
min_length=20,
max_length=200,
num_beams=7,
repetition_penalty=2.5,
length_penalty=1.0,
early_stopping=True,
no_repeat_ngram_size=2,
use_cache=True,
do_sample = True,
temperature = 0.8,
top_k = 50,
top_p = 0.95)
summary_text = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
print(summary_text)
```
### Framework versions
- Transformers 4.19.2
- TensorFlow 2.8.2
- Datasets 2.2.2
- Tokenizers 0.12.1
|
nestoralvaro/mt5-base-finetuned-xsum-data_prep_2021_12_26___t404_2980.csv___topic_text_google_mt5_base
|
nestoralvaro
| 2022-06-09T11:54:52Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"mt5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-06-09T05:36:35Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: mt5-base-finetuned-xsum-data_prep_2021_12_26___t404_2980.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___t404_2980.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: 0.8441
- Rouge2: 0.0894
- Rougel: 0.8428
- Rougelsum: 0.844
- Gen Len: 6.338
## 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 | 89332 | nan | 0.8441 | 0.0894 | 0.8428 | 0.844 | 6.338 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
FritzOS/TEdetection_distiBERT_mLM_V2_shuffleplus3
|
FritzOS
| 2022-06-09T11:28:40Z | 5 | 0 |
transformers
|
[
"transformers",
"tf",
"distilbert",
"fill-mask",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-06-09T11:28:25Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: TEdetection_distiBERT_mLM_V2_shuffleplus3
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_distiBERT_mLM_V2_shuffleplus3
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:
## 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 4.19.2
- TensorFlow 2.8.2
- Datasets 2.2.2
- Tokenizers 0.12.1
|
YaYaB/dqn-SpaceInvadersNoFrameskip-v4
|
YaYaB
| 2022-06-09T11:24:49Z | 7 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-06-09T11:24:10Z |
---
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 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', 10000.0),
('optimize_memory_usage', True),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
|
mbazaNLP/kinyarwanda-tts-model
|
mbazaNLP
| 2022-06-09T11:16:13Z | 0 | 2 |
fastpitch
|
[
"fastpitch",
"waveglow",
"text-to-speech",
"rw",
"dataset:mbazaNLP/kinyarwanda-tts-dataset",
"region:us"
] |
text-to-speech
| 2022-06-01T03:42:31Z |
---
library_name: fastpitch
task: text-to-speech
tags:
- fastpitch
- waveglow
- text-to-speech
language: rw
datasets:
- mbazaNLP/kinyarwanda-tts-dataset
widget:
- text: "Muraho neza, murakaza neza mu Rwanda."
example_title: "Muraho neza, murakaza neza mu Rwanda."
---
**Model card - Kinyarwanda TTS model**
**Model details**
- Kinyarwanda Text to Speech model
- Developed by [Digital Umuganda](digitalumuganda.com), [Arxia](https://www.arxia.com/home.html) and [Zevo Tech](https://zevo-tech.com/)
- Model based from: Fastspeech and Waveglow
- License: Mozilla 2.0 License
- Feedback on the model: samuel@digitalumuganda.com
**Metrics**
- We use Mean Opinion Score (MOS) to evaluate the model with a maximum score being 5
|Test Corpus|MOS|
|-----------|---|
|Custom phrases|3|
**Challenges**
- The model does not always capture the Kinyarwanda tones
**Recommendations**
- Use a tonal dictionary to train future models
- Add a numbers and symbols Dictionary
- Create a code-switching dictionary containing foreign words used in Kinyarwanda
|
huggingtweets/politifact
|
huggingtweets
| 2022-06-09T11:14:17Z | 4 | 1 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-06-09T11:13:06Z |
---
language: en
thumbnail: http://www.huggingtweets.com/politifact/1654773253130/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/1286766140115517441/8rq6ZxZm_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">PolitiFact</div>
<div style="text-align: center; font-size: 14px;">@politifact</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 PolitiFact.
| Data | PolitiFact |
| --- | --- |
| Tweets downloaded | 3250 |
| Retweets | 680 |
| Short tweets | 14 |
| Tweets kept | 2556 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1vfo2t7i/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 @politifact's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/7h3iptm6) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/7h3iptm6/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/politifact')
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_V4
|
FritzOS
| 2022-06-09T11:12:10Z | 5 | 0 |
transformers
|
[
"transformers",
"tf",
"distilbert",
"fill-mask",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-06-09T11:11:56Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: TEdetection_distilBERT_mLM_V4
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_V4
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: 0.0181
- Validation Loss: 0.0215
- 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': '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
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 0.0181 | 0.0215 | 0 |
### Framework versions
- Transformers 4.19.2
- TensorFlow 2.8.2
- Datasets 2.2.2
- Tokenizers 0.12.1
|
huggingtweets/aylesim
|
huggingtweets
| 2022-06-09T11:10:26Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-06-09T11:10:17Z |
---
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/1513156868612448256/2nXWRcn5_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">mira</div>
<div style="text-align: center; font-size: 14px;">@aylesim</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 mira.
| Data | mira |
| --- | --- |
| Tweets downloaded | 3215 |
| Retweets | 255 |
| Short tweets | 765 |
| Tweets kept | 2195 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3buhour0/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 @aylesim's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/c2a7aq5o) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/c2a7aq5o/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/aylesim')
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)
|
mbazaNLP/kinyarwanda-coqui-stt-model
|
mbazaNLP
| 2022-06-09T11:09:26Z | 0 | 0 | null |
[
"tflite",
"Coqui",
"Deepspeech",
"LSTM",
"automatic-speech-recognition",
"rw",
"dataset:commonvoice",
"arxiv:1412.5567",
"license:apache-2.0",
"region:us"
] |
automatic-speech-recognition
| 2022-05-27T08:23:47Z |
---
language: "rw"
thumbnail:
pipeline_tag: automatic-speech-recognition
tags:
- Coqui
- Deepspeech
- LSTM
license: "apache-2.0"
datasets:
- commonvoice
metrics:
- wer
---
**Model card - Kinyarwanda coqui STT model**
**Model details**
- Kinyarwanda Speech to text model
- Developed by [Digital Umuganda](digitalumuganda.com)
- Model based from: Baidu Deepspeech end to end RNN model
- paper: [deepspeech end to end STT](https://arxiv.org/pdf/1412.5567.pdf)
- Documentation on model: [deepspeech documentation](https://deepspeech.readthedocs.io/)
- License: Mozilla 2.0 License
- Feedback on the model: samuel@digitalumuganda.com
**Intended use cases**
- Intended to be used for
- simple keyword spotting
- simple transcribing
- transfer learning for better kinyarwanda and african language models
- Intended to be used by:
- App developpers
- various organizations who want to transcribe kinyarwanda recordings
- ML researchers
- other researchers in Kinyarwanda and tech usage in kinyarwanda (e.g. Linguists, journalists)
- Not intended to be used as:
- a fully fledged voice assistant
- voice recognition application
- Multiple languages STT
- language detection
**Factors**
- Anti-bias: these are bias that can influence the accuracy of the model
- Gender
- accents and dialects
- age
- Voice quality: factors that can influence the accuracy of the model
- Background noise
- short sentences
- Voice format: voices must be converted to the wav format
- wav format
**Metrics**
- word error rate on the Common Voice Kinyarwanda test set
|Test Corpus|WER|
|-----------|---|
|Common Voice|39.1\%|
**Training data**
- [common voice crowdsource website](https://commonvoice.mozilla.org/en/datasets)
**Evaluation data**
- [common voice crowdsource website](https://commonvoice.mozilla.org/en/datasets)
|
i8pxgd2s/q-FrozenLake-v1-4x4-Slippery
|
i8pxgd2s
| 2022-06-09T10:29:25Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-06-09T10:29:18Z |
---
tags:
- FrozenLake-v1-4x4
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-Slippery
results:
- metrics:
- type: mean_reward
value: 0.75 +/- 0.43
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4
type: FrozenLake-v1-4x4
---
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="i8pxgd2s/q-FrozenLake-v1-4x4-Slippery", 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"])
```
|
huggingtweets/osanseviero
|
huggingtweets
| 2022-06-09T10:20:54Z | 105 | 1 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-06-09T10:15:42Z |
---
language: en
thumbnail: http://www.huggingtweets.com/osanseviero/1654769951427/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/1106315906165157889/0Hxb1ESL_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">Omar Sanseviero</div>
<div style="text-align: center; font-size: 14px;">@osanseviero</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 Omar Sanseviero.
| Data | Omar Sanseviero |
| --- | --- |
| Tweets downloaded | 3244 |
| Retweets | 1158 |
| Short tweets | 224 |
| Tweets kept | 1862 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/29bkab0t/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 @osanseviero's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1s35jikq) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1s35jikq/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/osanseviero')
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)
|
louisdeco/camembert-base-finetuned-ICDCode_5
|
louisdeco
| 2022-06-09T10:18:38Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"camembert",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-06-08T08:47:52Z |
---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- recall
model-index:
- name: camembert-base-finetuned-ICDCode_5
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. -->
# camembert-base-finetuned-ICDCode_5
This model is a fine-tuned version of [camembert-base](https://huggingface.co/camembert-base) on the None dataset. It has been trained on a corpus of death certificate. One ICDCode is given for a given cause of death or commorbidities. As it is an important task to be able to predict these ICDCode, I shave trained this model for 8 epochs on 400 000 death causes. Pre-processing of noisy data points was mandatory before tokenization. It allows us to get this accuracy.
It achieves the following results on the evaluation set:
- Loss: 0.6574
- Accuracy: 0.8964
- F1: 0.8750
- Recall: 0.8964
## 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: 50
- eval_batch_size: 50
- 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 | F1 | Recall |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:------:|
| 3.7466 | 1.0 | 4411 | 1.9448 | 0.7201 | 0.6541 | 0.7201 |
| 1.5264 | 2.0 | 8822 | 1.2045 | 0.8134 | 0.7691 | 0.8134 |
| 1.0481 | 3.0 | 13233 | 0.9473 | 0.8513 | 0.8149 | 0.8513 |
| 0.8304 | 4.0 | 17644 | 0.8098 | 0.8718 | 0.8427 | 0.8718 |
| 0.7067 | 5.0 | 22055 | 0.7352 | 0.8834 | 0.8574 | 0.8834 |
| 0.6285 | 6.0 | 26466 | 0.6911 | 0.8898 | 0.8659 | 0.8898 |
| 0.5779 | 7.0 | 30877 | 0.6641 | 0.8958 | 0.8741 | 0.8958 |
| 0.549 | 8.0 | 35288 | 0.6574 | 0.8964 | 0.8750 | 0.8964 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
Kiwipirate/q-FrozenLake-v1-4x4-noSlippery
|
Kiwipirate
| 2022-06-09T10:04:19Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-06-09T10:04:12Z |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
---
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="Kiwipirate/q-FrozenLake-v1-4x4-noSlippery", 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"])
```
|
yogeshkulkarni/MidcurveNN
|
yogeshkulkarni
| 2022-06-09T09:47:16Z | 0 | 0 | null |
[
"arxiv:1904.0429",
"region:us"
] | null | 2022-06-06T10:55:33Z |
# MidcurveNN
Midcurve by Neural Networks
---
license: apache-2.0
---
## Description
- Goal: Given a 2D closed shape (closed polygon) find its midcurve (polyline, closed or open)
- Input: set of points or set of connected lines, non-intersecting, simple, convex, closed polygon
- Output: another set of points or set of connected lines, open/branched polygons possible
## ToDos
- Based on code at https://github.com/yogeshhk/MidcurveNN/tree/master/src/simpleencoderdecoder prepare Trainer class to train model using dataset uploaded here. Push model here
- Prepare Gradio demo space here as well as inferencing API which takes profile image and generates midcurve image
## Publications/Talks
- Vixra paper MidcurveNN: Encoder-Decoder Neural Network for Computing Midcurve of a Thin Polygon, viXra.org e-Print archive, viXra:1904.0429 http://vixra.org/abs/1904.0429
- ODSC proposal https://confengine.com/odsc-india-2019/proposal/10090/midcurvenn-encoder-decoder-neural-network-for-computing-midcurve-of-a-thin-polygon
- CAD Conference 2021, Barcelona, pages 223-225 http://www.cad-conference.net/files/CAD21/CAD21_223-225.pdf
- CAD & Applications 2022 Journal paper 19(6) http://www.cad-journal.net/files/vol_19/CAD_19(6)_2022_1154-1161.pdf
- Google Developers Dev Library https://devlibrary.withgoogle.com/products/ml/repos/yogeshhk-MidcurveNN
## Citation
```
@article{MidcurveNN,
doi = {https://doi.org/10.14733/cadaps.2022.1154-1161},
url = {https://www.cad-journal.net/files/vol_19/CAD_19(6)_2022_1154-1161.pdf},
author = {Kulkarni, Yogesh H.},
keywords = {Midcurve, Encoder-Decoder, Neural Network},
title = {MidcurveNN: Neural Network for Computing Midcurve of a Thin Polygon},
publisher = {CAD Solutions, LLC},
journal={Computer-Aided Design & Applications},
volume={19},
issue={6},
pages={1154-1161},
year = {2022}
}
```
|
Skil-Internal/bart-paraphrase-finetuned-xsum-v5
|
Skil-Internal
| 2022-06-09T09:42:05Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-06-09T09:13:35Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: bart-paraphrase-finetuned-xsum-v5
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-paraphrase-finetuned-xsum-v5
This model is a fine-tuned version of [eugenesiow/bart-paraphrase](https://huggingface.co/eugenesiow/bart-paraphrase) 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: 2e-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: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| No log | 1.0 | 263 | 0.4728 | 38.7072 | 38.5333 | 38.6391 | 38.6212 | 7.0513 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
RalphX1/TEST2ppo-LunarLander-v2
|
RalphX1
| 2022-06-09T09:01:27Z | 2 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-06-09T09:01:00Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 270.09 +/- 19.04
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
...
```
|
Skil-Internal/bart-paraphrase-finetuned-xsum-v4
|
Skil-Internal
| 2022-06-09T08:52:10Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-06-09T07:40:27Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: bart-paraphrase-finetuned-xsum-v4
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-paraphrase-finetuned-xsum-v4
This model is a fine-tuned version of [eugenesiow/bart-paraphrase](https://huggingface.co/eugenesiow/bart-paraphrase) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1765
- Rouge1: 49.972
- Rouge2: 49.85
- Rougel: 49.9165
- Rougelsum: 49.7819
- Gen Len: 8.3061
## 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: 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: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| No log | 1.0 | 263 | 0.5050 | 47.9628 | 47.7085 | 47.8625 | 47.772 | 6.9639 |
| 0.676 | 2.0 | 526 | 0.5793 | 49.6085 | 49.3495 | 49.5196 | 49.4173 | 7.4715 |
| 0.676 | 3.0 | 789 | 0.7011 | 49.8635 | 49.6937 | 49.8155 | 49.6604 | 7.576 |
| 0.322 | 4.0 | 1052 | 0.7585 | 49.8851 | 49.7578 | 49.8526 | 49.6977 | 7.6654 |
| 0.322 | 5.0 | 1315 | 0.6615 | 49.861 | 49.7185 | 49.7978 | 49.6669 | 8.3023 |
| 0.2828 | 6.0 | 1578 | 0.6233 | 49.916 | 49.7819 | 49.8861 | 49.7384 | 7.6084 |
| 0.2828 | 7.0 | 1841 | 0.9380 | 49.916 | 49.7819 | 49.8861 | 49.7384 | 8.2433 |
| 0.2073 | 8.0 | 2104 | 0.8497 | 49.9624 | 49.8355 | 49.91 | 49.7666 | 7.6331 |
| 0.2073 | 9.0 | 2367 | 0.7715 | 49.972 | 49.85 | 49.9165 | 49.7819 | 7.9772 |
| 0.1744 | 10.0 | 2630 | 1.1765 | 49.972 | 49.85 | 49.9165 | 49.7819 | 8.3061 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
edonath/pegasus-samsum
|
edonath
| 2022-06-09T07:56:49Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"pegasus",
"text2text-generation",
"generated_from_trainer",
"dataset:samsum",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-04-15T21:05:00Z |
---
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: 1.4841
## 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 |
|:-------------:|:-----:|:----:|:---------------:|
| 1.7073 | 0.54 | 500 | 1.4841 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 1.16.1
- Tokenizers 0.12.1
|
hugoguh/dqn-SpaceInvadersNoFrameskip-v4
|
hugoguh
| 2022-06-09T07:55:06Z | 2 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-06-09T07:48:40Z |
---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- metrics:
- type: mean_reward
value: 927.00 +/- 301.22
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 hugoguh -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 hugoguh
```
## 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', 10000000.0),
('optimize_memory_usage', True),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
|
nestoralvaro/mt5-base-finetuned-xsum-data_prep_2021_12_26___t8_54.csv___topic_text_google_mt5_base
|
nestoralvaro
| 2022-06-09T06:59:53Z | 102 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"mt5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-06-09T05:34:16Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: mt5-base-finetuned-xsum-data_prep_2021_12_26___t8_54.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___t8_54.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: 1.4678
- Rouge2: 0.1841
- Rougel: 1.4748
- Rougelsum: 1.4701
- Gen Len: 6.4874
## 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 | 10645 | nan | 1.4678 | 0.1841 | 1.4748 | 1.4701 | 6.4874 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
YeRyeongLee/bert-base-uncased-finetuned-filtered-0609
|
YeRyeongLee
| 2022-06-09T06:54:36Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-06-09T04:49:10Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: bert-base-uncased-finetuned-filtered-0609
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-base-uncased-finetuned-filtered-0609
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1749
- Accuracy: 0.9789
- Precision: 0.9790
- Recall: 0.9789
- F1: 0.9789
## 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
- lr_scheduler_warmup_steps: 1000
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.1671 | 1.0 | 3180 | 0.1735 | 0.9632 | 0.9648 | 0.9632 | 0.9635 |
| 0.1384 | 2.0 | 6360 | 0.1120 | 0.9736 | 0.9738 | 0.9736 | 0.9736 |
| 0.1064 | 3.0 | 9540 | 0.1880 | 0.9635 | 0.9647 | 0.9635 | 0.9635 |
| 0.0823 | 4.0 | 12720 | 0.1495 | 0.9758 | 0.9759 | 0.9758 | 0.9757 |
| 0.0426 | 5.0 | 15900 | 0.1766 | 0.9742 | 0.9746 | 0.9742 | 0.9743 |
| 0.0254 | 6.0 | 19080 | 0.1724 | 0.9777 | 0.9778 | 0.9777 | 0.9777 |
| 0.0257 | 7.0 | 22260 | 0.1760 | 0.9764 | 0.9767 | 0.9764 | 0.9764 |
| 0.0017 | 8.0 | 25440 | 0.1672 | 0.9786 | 0.9787 | 0.9786 | 0.9786 |
| 0.0077 | 9.0 | 28620 | 0.1894 | 0.9789 | 0.9791 | 0.9789 | 0.9789 |
| 0.0014 | 10.0 | 31800 | 0.1749 | 0.9789 | 0.9790 | 0.9789 | 0.9789 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.9.1+cu111
- Datasets 1.16.1
- Tokenizers 0.12.1
|
huggingtweets/itsnovaherev2
|
huggingtweets
| 2022-06-09T03:53:35Z | 104 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-06-09T03:53:27Z |
---
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/1253734967923798018/FJ7AvxLN_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">ItsNovaHere</div>
<div style="text-align: center; font-size: 14px;">@itsnovaherev2</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 ItsNovaHere.
| Data | ItsNovaHere |
| --- | --- |
| Tweets downloaded | 588 |
| Retweets | 409 |
| Short tweets | 67 |
| Tweets kept | 112 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2tz4bf7d/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 @itsnovaherev2's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/35es3xf7) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/35es3xf7/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/itsnovaherev2')
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/verizon
|
huggingtweets
| 2022-06-09T00:33:36Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-06-08T23:20:44Z |
---
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/1496892874276880389/ndAolYWm_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">Verizon</div>
<div style="text-align: center; font-size: 14px;">@verizon</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 Verizon.
| Data | Verizon |
| --- | --- |
| Tweets downloaded | 3246 |
| Retweets | 408 |
| Short tweets | 188 |
| Tweets kept | 2650 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2rssnlth/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 @verizon's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/17qcsqw6) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/17qcsqw6/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/verizon')
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/beepunz
|
huggingtweets
| 2022-06-08T23:51:59Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-06-08T23:50:21Z |
---
language: en
thumbnail: http://www.huggingtweets.com/beepunz/1654732293963/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/942050096837005317/u5sbn8VY_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">BeePunz</div>
<div style="text-align: center; font-size: 14px;">@beepunz</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 BeePunz.
| Data | BeePunz |
| --- | --- |
| Tweets downloaded | 3218 |
| Retweets | 1775 |
| Short tweets | 336 |
| Tweets kept | 1107 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/84kgxhyn/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 @beepunz's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2analnwj) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2analnwj/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/beepunz')
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)
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Subsets and Splits
Filtered Qwen2.5 Distill Models
Identifies specific configurations of models by filtering cards that contain 'distill', 'qwen2.5', '7b' while excluding certain base models and incorrect model ID patterns, uncovering unique model variants.
Filtered Model Cards Count
Finds the count of entries with specific card details that include 'distill', 'qwen2.5', '7b' but exclude certain base models, revealing valuable insights about the dataset's content distribution.
Filtered Distill Qwen 7B Models
Filters for specific card entries containing 'distill', 'qwen', and '7b', excluding certain strings and patterns, to identify relevant model configurations.
Filtered Qwen-7b Model Cards
The query performs a detailed filtering based on specific keywords and excludes certain entries, which could be useful for identifying a specific subset of cards but does not provide deeper insights or trends.
Filtered Qwen 7B Model Cards
The query filters for specific terms related to "distilled" or "distill", "qwen", and "7b" in the 'card' column but excludes certain base models, providing a limited set of entries for further inspection.
Qwen 7B Distilled Models
The query provides a basic filtering of records to find specific card names that include keywords related to distilled Qwen 7b models, excluding a particular base model, which gives limited insight but helps in focusing on relevant entries.
Qwen 7B Distilled Model Cards
The query filters data based on specific keywords in the modelId and card fields, providing limited insight primarily useful for locating specific entries rather than revealing broad patterns or trends.
Qwen 7B Distilled Models
Finds all entries containing the terms 'distilled', 'qwen', and '7b' in a case-insensitive manner, providing a filtered set of records but without deeper analysis.
Distilled Qwen 7B Models
The query filters for specific model IDs containing 'distilled', 'qwen', and '7b', providing a basic retrieval of relevant entries but without deeper analysis or insight.
Filtered Model Cards with Distill Qwen2.
Filters and retrieves records containing specific keywords in the card description while excluding certain phrases, providing a basic count of relevant entries.
Filtered Model Cards with Distill Qwen 7
The query filters specific variations of card descriptions containing 'distill', 'qwen', and '7b' while excluding a particular base model, providing limited but specific data retrieval.
Distill Qwen 7B Model Cards
The query filters and retrieves rows where the 'card' column contains specific keywords ('distill', 'qwen', and '7b'), providing a basic filter result that can help in identifying specific entries.