modelId
string | author
string | last_modified
timestamp[us, tz=UTC] | downloads
int64 | likes
int64 | library_name
string | tags
sequence | pipeline_tag
string | createdAt
timestamp[us, tz=UTC] | card
string |
|---|---|---|---|---|---|---|---|---|---|
Kamer/bert-base-uncased-eurlex | Kamer | 2023-09-02T08:14:26Z | 109 | 0 | transformers | [
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:nlpaueb/bert-base-uncased-eurlex",
"base_model:finetune:nlpaueb/bert-base-uncased-eurlex",
"license:cc-by-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2023-09-02T07:18:39Z | ---
license: cc-by-sa-4.0
base_model: nlpaueb/bert-base-uncased-eurlex
tags:
- generated_from_trainer
model-index:
- name: bert-base-uncased-eurlex
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-eurlex
This model is a fine-tuned version of [nlpaueb/bert-base-uncased-eurlex](https://huggingface.co/nlpaueb/bert-base-uncased-eurlex) on the None dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.4164
- eval_Accuracy: 0.9224
- eval_F1_macro: 0.9301
- eval_F1_class_0: 0.8941
- eval_F1_class_1: 0.9388
- eval_F1_class_2: 0.9412
- eval_F1_class_3: 0.9730
- eval_F1_class_4: 0.9148
- eval_F1_class_5: 0.9573
- eval_F1_class_6: 0.9399
- eval_F1_class_7: 0.9685
- eval_F1_class_8: 0.9630
- eval_F1_class_9: 0.9495
- eval_F1_class_10: 0.8574
- eval_F1_class_11: 0.9241
- eval_F1_class_12: 0.8677
- eval_F1_class_13: 0.9442
- eval_F1_class_14: 0.9055
- eval_F1_class_15: 0.9022
- eval_F1_class_16: 0.8929
- eval_F1_class_17: 0.9811
- eval_F1_class_18: 0.8870
- eval_F1_class_19: 1.0
- eval_runtime: 154.2922
- eval_samples_per_second: 32.918
- eval_steps_per_second: 4.116
- epoch: 0.52
- step: 3000
## 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
### Framework versions
- Transformers 4.32.0
- Pytorch 2.0.1+cu117
- Datasets 2.14.4
- Tokenizers 0.13.3
|
Xmm/led-large-16384-cnn_dailymail | Xmm | 2023-09-02T08:09:40Z | 98 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"led",
"text2text-generation",
"generated_from_trainer",
"dataset:cnn_dailymail",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2023-06-17T03:05:46Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- cnn_dailymail
metrics:
- rouge
model-index:
- name: led-large-16384-cnn_dailymail
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: cnn_dailymail
type: cnn_dailymail
config: 3.0.0
split: test
args: 3.0.0
metrics:
- name: Rouge1
type: rouge
value: 0.3869876274946419
---
<!-- 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. -->
# led-large-16384-cnn_dailymail
This model is a fine-tuned version of [allenai/led-base-16384](https://huggingface.co/allenai/led-base-16384) on the cnn_dailymail dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5544
- Rouge1: 0.3870
- Rouge2: 0.1736
- Rougel: 0.2599
- Rougelsum: 0.3653
## 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: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 64
- total_train_batch_size: 128
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:---------:|
| 1.9531 | 0.4 | 500 | 1.8639 | 0.3485 | 0.1441 | 0.2275 | 0.3288 |
| 1.9563 | 0.8 | 1000 | 1.8260 | 0.3538 | 0.1482 | 0.2315 | 0.3343 |
| 1.7176 | 1.2 | 1500 | 1.8208 | 0.3628 | 0.1527 | 0.2383 | 0.3433 |
| 1.7197 | 1.6 | 2000 | 1.8162 | 0.3696 | 0.1602 | 0.2434 | 0.3486 |
| 1.8086 | 2.0 | 2500 | 1.7924 | 0.3558 | 0.1533 | 0.2334 | 0.3361 |
| 1.2448 | 2.4 | 3000 | 1.8510 | 0.3703 | 0.1591 | 0.2447 | 0.3483 |
| 1.3574 | 2.8 | 3500 | 1.8277 | 0.3741 | 0.1593 | 0.2422 | 0.3540 |
| 1.0966 | 3.2 | 4000 | 1.8924 | 0.3682 | 0.1576 | 0.2424 | 0.3479 |
| 0.9938 | 3.6 | 4500 | 1.8957 | 0.3723 | 0.1599 | 0.2451 | 0.3511 |
| 1.0735 | 4.0 | 5000 | 1.8772 | 0.3653 | 0.1557 | 0.2399 | 0.3454 |
| 0.9106 | 4.4 | 5500 | 1.9401 | 0.3720 | 0.1585 | 0.2436 | 0.3504 |
| 1.015 | 4.8 | 6000 | 1.9320 | 0.3725 | 0.1570 | 0.2429 | 0.3515 |
| 1.7854 | 0.36 | 6500 | 1.7800 | 0.3624 | 0.1544 | 0.2390 | 0.3422 |
| 1.9079 | 0.39 | 7000 | 1.7629 | 0.3573 | 0.1553 | 0.2352 | 0.3370 |
| 1.7606 | 3.34 | 7500 | 1.6902 | 0.3783 | 0.1673 | 0.2521 | 0.3570 |
| 1.7571 | 3.57 | 8000 | 1.6563 | 0.3802 | 0.1691 | 0.2538 | 0.3587 |
| 1.6602 | 3.79 | 8500 | 1.6439 | 0.3814 | 0.1693 | 0.2548 | 0.3600 |
| 1.6614 | 4.01 | 9000 | 1.6312 | 0.3812 | 0.1691 | 0.2544 | 0.3599 |
| 1.668 | 4.24 | 9500 | 1.6189 | 0.3815 | 0.1689 | 0.2550 | 0.3603 |
| 1.6491 | 4.46 | 10000 | 1.6172 | 0.3799 | 0.1681 | 0.2540 | 0.3586 |
| 1.5994 | 4.68 | 10500 | 1.6132 | 0.3825 | 0.1702 | 0.2560 | 0.3610 |
| 1.6493 | 4.9 | 11000 | 1.6093 | 0.3828 | 0.1701 | 0.2561 | 0.3613 |
| 1.6769 | 5.13 | 11500 | 1.6074 | 0.3831 | 0.1706 | 0.2569 | 0.3619 |
| 1.6554 | 5.35 | 12000 | 1.6044 | 0.3817 | 0.1695 | 0.2559 | 0.3605 |
| 1.6155 | 5.57 | 12500 | 1.6010 | 0.3825 | 0.1700 | 0.2561 | 0.3608 |
| 1.5863 | 5.8 | 13000 | 1.5981 | 0.3829 | 0.1704 | 0.2569 | 0.3614 |
| 1.6306 | 6.02 | 13500 | 1.6004 | 0.3831 | 0.1702 | 0.2563 | 0.3618 |
| 1.6425 | 6.24 | 14000 | 1.5987 | 0.3821 | 0.1698 | 0.2561 | 0.3610 |
| 1.6863 | 6.46 | 14500 | 1.5876 | 0.3837 | 0.1710 | 0.2569 | 0.3622 |
| 1.6085 | 6.69 | 15000 | 1.5815 | 0.3836 | 0.1717 | 0.2573 | 0.3621 |
| 1.6267 | 6.91 | 15500 | 1.5792 | 0.3852 | 0.1722 | 0.2579 | 0.3633 |
| 1.5637 | 7.13 | 16000 | 1.5768 | 0.3830 | 0.1709 | 0.2568 | 0.3611 |
| 1.5586 | 7.36 | 16500 | 1.5740 | 0.3833 | 0.1706 | 0.2567 | 0.3617 |
| 1.5389 | 7.58 | 17000 | 1.5689 | 0.3858 | 0.1729 | 0.2590 | 0.3640 |
| 1.5694 | 7.8 | 17500 | 1.5645 | 0.3853 | 0.1731 | 0.2589 | 0.3636 |
| 1.5265 | 8.02 | 18000 | 1.5621 | 0.3871 | 0.1733 | 0.2596 | 0.3654 |
| 1.5273 | 8.25 | 18500 | 1.5624 | 0.3861 | 0.1726 | 0.2588 | 0.3646 |
| 1.5148 | 8.47 | 19000 | 1.5602 | 0.3866 | 0.1733 | 0.2592 | 0.3651 |
| 1.532 | 8.69 | 19500 | 1.5599 | 0.3859 | 0.1732 | 0.2593 | 0.3642 |
| 1.5113 | 8.92 | 20000 | 1.5602 | 0.3877 | 0.1748 | 0.2606 | 0.3658 |
| 1.5133 | 9.14 | 20500 | 1.5595 | 0.3855 | 0.1725 | 0.2587 | 0.3637 |
| 1.4875 | 9.36 | 21000 | 1.5572 | 0.3873 | 0.1741 | 0.2600 | 0.3654 |
| 1.5038 | 9.59 | 21500 | 1.5557 | 0.3860 | 0.1728 | 0.2590 | 0.3641 |
| 1.5062 | 9.81 | 22000 | 1.5544 | 0.3870 | 0.1736 | 0.2599 | 0.3653 |
### Framework versions
- Transformers 4.27.1
- Pytorch 2.0.0+cu118
- Datasets 2.10.1
- Tokenizers 0.13.2
|
maroti/dqn-SpaceInvadersNoFrameskip-v4 | maroti | 2023-09-02T08:07:44Z | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | 2023-09-02T08:07:09Z | ---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 507.00 +/- 124.00
name: mean_reward
verified: false
---
# **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
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga maroti -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga maroti -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga maroti
```
## Hyperparameters
```python
OrderedDict([('batch_size', 128),
('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', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
# Environment Arguments
```python
{'render_mode': 'rgb_array'}
```
|
Hemanth-thunder/kazuki_kurusu_lora_xl | Hemanth-thunder | 2023-09-02T08:02:49Z | 1 | 2 | diffusers | [
"diffusers",
"tensorboard",
"stable-diffusion-xl",
"stable-diffusion-xl-diffusers",
"text-to-image",
"lora",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0",
"license:openrail++",
"region:us"
] | text-to-image | 2023-09-02T06:23:41Z |
---
license: openrail++
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: a photo of a kazuki kurusu
tags:
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
- text-to-image
- diffusers
- lora
inference: true
---
# LoRA DreamBooth - Hemanth-thunder/lora-trained-xl-colab
These are LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained on a photo of a kazuki kurusu using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following.
LoRA for the text encoder was enabled: False.
Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
|
Johnlhugface/ppo-Huggy | Johnlhugface | 2023-09-02T07:55:02Z | 0 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"Huggy",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
] | reinforcement-learning | 2023-09-02T07:54:57Z | ---
library_name: ml-agents
tags:
- Huggy
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Huggy
---
# **ppo** Agent playing **Huggy**
This is a trained model of a **ppo** agent playing **Huggy**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog ๐ถ to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: Johnlhugface/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play ๐
|
02shanky/Reinforce-base1 | 02shanky | 2023-09-02T07:54:00Z | 0 | 0 | null | [
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | reinforcement-learning | 2023-09-02T07:53:56Z | ---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-base1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 176.70 +/- 77.98
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
StefanoCaloni/q-FrozenLake-v1-4x4-noSlippery | StefanoCaloni | 2023-09-02T07:42:36Z | 0 | 0 | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | reinforcement-learning | 2023-08-31T06:35:24Z | ---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="StefanoCaloni/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"])
```
|
StefanoCaloni/taxi | StefanoCaloni | 2023-09-02T07:42:24Z | 0 | 0 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | reinforcement-learning | 2023-08-31T06:40:39Z | ---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: taxi
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.56 +/- 2.71
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="StefanoCaloni/taxi", 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"])
```
|
Jakir057/finetuned-indian-food | Jakir057 | 2023-09-02T06:53:08Z | 192 | 0 | transformers | [
"transformers",
"pytorch",
"vit",
"image-classification",
"generated_from_trainer",
"base_model:google/vit-base-patch16-224-in21k",
"base_model:finetune:google/vit-base-patch16-224-in21k",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-classification | 2023-09-02T06:19:35Z | ---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- image-classification
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: finetuned-indian-food
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. -->
# finetuned-indian-food
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the indian_food_images dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0026
- Accuracy: 0.9996
## 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: 0.0002
- 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: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.7056 | 0.1 | 100 | 0.5113 | 0.8881 |
| 0.3027 | 0.21 | 200 | 0.1280 | 0.9796 |
| 0.2823 | 0.31 | 300 | 0.1580 | 0.9656 |
| 0.3273 | 0.42 | 400 | 0.0879 | 0.9837 |
| 0.1808 | 0.52 | 500 | 0.0812 | 0.9822 |
| 0.2101 | 0.63 | 600 | 0.0339 | 0.9937 |
| 0.1495 | 0.73 | 700 | 0.0568 | 0.9833 |
| 0.1296 | 0.84 | 800 | 0.0629 | 0.9844 |
| 0.1462 | 0.94 | 900 | 0.0886 | 0.9733 |
| 0.0519 | 1.04 | 1000 | 0.0544 | 0.9870 |
| 0.3192 | 1.15 | 1100 | 0.0892 | 0.9726 |
| 0.158 | 1.25 | 1200 | 0.0632 | 0.98 |
| 0.0266 | 1.36 | 1300 | 0.0233 | 0.9944 |
| 0.1832 | 1.46 | 1400 | 0.0292 | 0.9930 |
| 0.1212 | 1.57 | 1500 | 0.0489 | 0.9852 |
| 0.0994 | 1.67 | 1600 | 0.0142 | 0.9974 |
| 0.0219 | 1.78 | 1700 | 0.0277 | 0.9930 |
| 0.0664 | 1.88 | 1800 | 0.0158 | 0.9974 |
| 0.0834 | 1.99 | 1900 | 0.0124 | 0.9978 |
| 0.1093 | 2.09 | 2000 | 0.0140 | 0.9974 |
| 0.1726 | 2.19 | 2100 | 0.0147 | 0.9963 |
| 0.0476 | 2.3 | 2200 | 0.0058 | 0.9993 |
| 0.0257 | 2.4 | 2300 | 0.0424 | 0.9911 |
| 0.0215 | 2.51 | 2400 | 0.0076 | 0.9989 |
| 0.0748 | 2.61 | 2500 | 0.0099 | 0.9974 |
| 0.0059 | 2.72 | 2600 | 0.0053 | 0.9993 |
| 0.0527 | 2.82 | 2700 | 0.0149 | 0.9963 |
| 0.0203 | 2.93 | 2800 | 0.0041 | 0.9993 |
| 0.0791 | 3.03 | 2900 | 0.0033 | 0.9989 |
| 0.0389 | 3.13 | 3000 | 0.0033 | 0.9989 |
| 0.0459 | 3.24 | 3100 | 0.0044 | 0.9989 |
| 0.0276 | 3.34 | 3200 | 0.0031 | 0.9996 |
| 0.0139 | 3.45 | 3300 | 0.0028 | 0.9996 |
| 0.0076 | 3.55 | 3400 | 0.0055 | 0.9985 |
| 0.0097 | 3.66 | 3500 | 0.0027 | 0.9996 |
| 0.0193 | 3.76 | 3600 | 0.0026 | 0.9996 |
| 0.0471 | 3.87 | 3700 | 0.0027 | 0.9996 |
| 0.0282 | 3.97 | 3800 | 0.0027 | 0.9996 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
|
trieudemo11/llama_7b_attrb_cate_b6_l320_low_10 | trieudemo11 | 2023-09-02T06:41:20Z | 0 | 0 | peft | [
"peft",
"region:us"
] | null | 2023-09-02T06:41:05Z | ---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
rsions
- PEFT 0.6.0.dev0
|
NobodyExistsOnTheInternet/GiftedConvo13bLoraNoEconsE4 | NobodyExistsOnTheInternet | 2023-09-02T06:23:16Z | 0 | 0 | null | [
"license:mit",
"region:us"
] | null | 2023-09-02T05:20:49Z | ---
license: mit
---
Trained on Math Chain of thought, Chemistry and Physics domain knowledge, and chat
V1 |
gg-ai/twhin-bert-base-p-tuning | gg-ai | 2023-09-02T06:08:04Z | 1 | 0 | peft | [
"peft",
"region:us"
] | null | 2023-09-02T06:08:01Z | ---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.5.0
|
Hellstar1337/freyaLoRA | Hellstar1337 | 2023-09-02T05:45:06Z | 0 | 0 | null | [
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-09-02T05:41:39Z | ---
license: creativeml-openrail-m
---
|
Imxxn/AudioCourseU6-TextToSpeech | Imxxn | 2023-09-02T05:38:00Z | 80 | 0 | transformers | [
"transformers",
"pytorch",
"speecht5",
"text-to-audio",
"generated_from_trainer",
"text-to-speech",
"base_model:microsoft/speecht5_tts",
"base_model:finetune:microsoft/speecht5_tts",
"license:mit",
"endpoints_compatible",
"region:us"
] | text-to-speech | 2023-09-02T05:18:20Z | ---
license: mit
base_model: microsoft/speecht5_tts
tags:
- generated_from_trainer
model-index:
- name: AudioCourseU6-TextToSpeech
results: []
pipeline_tag: text-to-speech
---
<!-- 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. -->
# AudioCourseU6-TextToSpeech
This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 4
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 500
### Training results
### Framework versions
- Transformers 4.32.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3 |
NobodyExistsOnTheInternet/GiftedConvo13bLoraNoEcons | NobodyExistsOnTheInternet | 2023-09-02T05:34:07Z | 0 | 0 | null | [
"license:mit",
"region:us"
] | null | 2023-09-01T15:12:04Z | ---
license: mit
---
Trained on Math Chain of thought, Chemistry and Physics domain knowledge, and chat
V1 |
johaanm/test-planner-alpha-V5.9 | johaanm | 2023-09-02T05:14:35Z | 0 | 0 | peft | [
"peft",
"region:us"
] | null | 2023-09-02T05:14:31Z | ---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float16
### Framework versions
- PEFT 0.4.0
|
substratusai/weaviate-gorilla-v3 | substratusai | 2023-09-02T05:13:22Z | 8 | 2 | transformers | [
"transformers",
"pytorch",
"llama",
"text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2023-09-01T22:50:07Z | ## Prompt
```
{input}
{output}
```
Example: of entry used for finetuning
```
Your task is to write an API request for a new schema given the API reference and an example. The user command is: "Get me the details of 2 music tracks that are similar to the given vector." Here is the API reference for a query that will help with this command and an example of how to use it: {Get {JeopardyQuestion (limit: 2,nearVector: {vector: [-0.0125526935, -0.021168863, -0.01076519, ...]}}}}} Could you please formulate this query for the following schema? {"class": "Track","description": "A music track.","properties": [{"name": "trackId","dataType": ["uuid"],"description": "A unique identifier for each track.","moduleConfig": {"text2vec-transformers": {"skip": true,"vectorizeClassName": false,"vectorizePropertyName": false}}{"name": "title","dataType": ["text"],"description": "The title of the track.","moduleConfig": {"text2vec-transformers": {"skip": false,"vectorizeClassName": false,"vectorizePropertyName": false}}{"name": "duration","dataType": ["int"],"description": "The duration of the track in seconds.","moduleConfig": {"text2vec-transformers": {"skip": true,"vectorizeClassName": false,"vectorizePropertyName": false}}{"name": "artist","dataType": ["Artist"],"description": "The artist of the track.","moduleConfig": {"text2vec-transformers": {"skip": true,"vectorizeClassName": false,"vectorizePropertyName": false}}{"name": "album","dataType": ["Album"],"description": "The album of the track.","moduleConfig": {"text2vec-transformers": {"skip": true,"vectorizeClassName": false,"vectorizePropertyName": false}}}} VERY IMPORTANT! Please only output the GraphQL for the query and nothing else!
{ Get { Track ( limit: 2, nearVector: { vector: [-0.0125526935, -0.021168863, -0.01076519, ...] } ) { trackId title duration artist { artistId name } album { albumId title } } }}
``` |
gg-ai/roberta-peft-p-tuning | gg-ai | 2023-09-02T05:05:59Z | 1 | 0 | peft | [
"peft",
"region:us"
] | null | 2023-09-02T05:05:58Z | ---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.5.0
|
vita-group/llama-2-7b_magnitude_unstructured | vita-group | 2023-09-02T05:03:13Z | 9 | 0 | null | [
"license:mit",
"region:us"
] | null | 2023-09-01T15:03:37Z | ---
license: mit
---
# Compressed LLM Model Zone
The models are prepared by [Visual Informatics Group @ University of Texas at Austin (VITA-group)](https://vita-group.github.io/). Credits to Ajay Jaiswal, Zhenyu Zhang.
License: [MIT License](https://opensource.org/license/mit/)
Setup environment
```shell
pip install torch==2.0.0+cu117 torchvision==0.15.1+cu117 torchaudio==2.0.1 --index-url https://download.pytorch.org/whl/cu117
pip install transformers==4.31.0
pip install accelerate
```
How to use
```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
base_model = 'llama-2-7b'
comp_method = 'magnitude_unstructured'
comp_degree = 0.2
model_path = f'vita-group/{base_model}_{comp_method}'
model = AutoModelForCausalLM.from_pretrained(
model_path,
revision=f's{comp_degree}',
torch_dtype=torch.float16,
low_cpu_mem_usage=True,
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained('meta-llama/Llama-2-7b-hf')
input_ids = tokenizer('Hello! I am a VITA-compressed-LLM chatbot!', return_tensors='pt').input_ids
outputs = model.generate(input_ids)
print(tokenizer.decode(outputs[0]))
```
| | Base Model | Model Size | Compression Method | Compression Degree |
|---:|:-------------|:-------------|:----------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------|
| 0 | Llama-2 | 7b | [magnitude_unstructured](https://huggingface.co/vita-group/llama-2-7b_magnitude_unstructured) | [s0.1](https://huggingface.co/vita-group/llama-2-7b_magnitude_unstructured/tree/s0.1) |
| 1 | Llama-2 | 7b | [magnitude_unstructured](https://huggingface.co/vita-group/llama-2-7b_magnitude_unstructured) | [s0.2](https://huggingface.co/vita-group/llama-2-7b_magnitude_unstructured/tree/s0.2) |
| 2 | Llama-2 | 7b | [magnitude_unstructured](https://huggingface.co/vita-group/llama-2-7b_magnitude_unstructured) | [s0.3](https://huggingface.co/vita-group/llama-2-7b_magnitude_unstructured/tree/s0.3) |
| 3 | Llama-2 | 7b | [magnitude_unstructured](https://huggingface.co/vita-group/llama-2-7b_magnitude_unstructured) | [s0.5](https://huggingface.co/vita-group/llama-2-7b_magnitude_unstructured/tree/s0.5) |
| 4 | Llama-2 | 7b | [magnitude_unstructured](https://huggingface.co/vita-group/llama-2-7b_magnitude_unstructured) | [s0.6](https://huggingface.co/vita-group/llama-2-7b_magnitude_unstructured/tree/s0.6) |
| 5 | Llama-2 | 7b | [sparsegpt_unstructured](https://huggingface.co/vita-group/llama-2-7b_sparsegpt_unstructured) | [s0.1](https://huggingface.co/vita-group/llama-2-7b_sparsegpt_unstructured/tree/s0.1) |
| 6 | Llama-2 | 7b | [sparsegpt_unstructured](https://huggingface.co/vita-group/llama-2-7b_sparsegpt_unstructured) | [s0.2](https://huggingface.co/vita-group/llama-2-7b_sparsegpt_unstructured/tree/s0.2) |
| 7 | Llama-2 | 7b | [sparsegpt_unstructured](https://huggingface.co/vita-group/llama-2-7b_sparsegpt_unstructured) | [s0.3](https://huggingface.co/vita-group/llama-2-7b_sparsegpt_unstructured/tree/s0.3) |
| 8 | Llama-2 | 7b | [sparsegpt_unstructured](https://huggingface.co/vita-group/llama-2-7b_sparsegpt_unstructured) | [s0.5](https://huggingface.co/vita-group/llama-2-7b_sparsegpt_unstructured/tree/s0.5) |
| 9 | Llama-2 | 7b | [sparsegpt_unstructured](https://huggingface.co/vita-group/llama-2-7b_sparsegpt_unstructured) | [s0.6](https://huggingface.co/vita-group/llama-2-7b_sparsegpt_unstructured/tree/s0.6) |
| 10 | Llama-2 | 7b | [wanda_unstructured](https://huggingface.co/vita-group/llama-2-7b_wanda_unstructured) | [s0.1](https://huggingface.co/vita-group/llama-2-7b_wanda_unstructured/tree/s0.1) |
| 11 | Llama-2 | 7b | [wanda_unstructured](https://huggingface.co/vita-group/llama-2-7b_wanda_unstructured) | [s0.2](https://huggingface.co/vita-group/llama-2-7b_wanda_unstructured/tree/s0.2) |
| 12 | Llama-2 | 7b | [wanda_unstructured](https://huggingface.co/vita-group/llama-2-7b_wanda_unstructured) | [s0.3](https://huggingface.co/vita-group/llama-2-7b_wanda_unstructured/tree/s0.3) |
| 13 | Llama-2 | 7b | [wanda_unstructured](https://huggingface.co/vita-group/llama-2-7b_wanda_unstructured) | [s0.5](https://huggingface.co/vita-group/llama-2-7b_wanda_unstructured/tree/s0.5) |
| 14 | Llama-2 | 7b | [wanda_unstructured](https://huggingface.co/vita-group/llama-2-7b_wanda_unstructured) | [s0.6](https://huggingface.co/vita-group/llama-2-7b_wanda_unstructured/tree/s0.6) |
|
vita-group/llama-2-7b_sparsegpt_unstructured | vita-group | 2023-09-02T05:02:59Z | 19 | 0 | null | [
"license:mit",
"region:us"
] | null | 2023-09-01T15:05:45Z | ---
license: mit
---
# Compressed LLM Model Zone
The models are prepared by [Visual Informatics Group @ University of Texas at Austin (VITA-group)](https://vita-group.github.io/). Credits to Ajay Jaiswal, Zhenyu Zhang.
License: [MIT License](https://opensource.org/license/mit/)
Setup environment
```shell
pip install torch==2.0.0+cu117 torchvision==0.15.1+cu117 torchaudio==2.0.1 --index-url https://download.pytorch.org/whl/cu117
pip install transformers==4.31.0
pip install accelerate
```
How to use
```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
base_model = 'llama-2-7b'
comp_method = 'magnitude_unstructured'
comp_degree = 0.2
model_path = f'vita-group/{base_model}_{comp_method}'
model = AutoModelForCausalLM.from_pretrained(
model_path,
revision=f's{comp_degree}',
torch_dtype=torch.float16,
low_cpu_mem_usage=True,
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained('meta-llama/Llama-2-7b-hf')
input_ids = tokenizer('Hello! I am a VITA-compressed-LLM chatbot!', return_tensors='pt').input_ids
outputs = model.generate(input_ids)
print(tokenizer.decode(outputs[0]))
```
| | Base Model | Model Size | Compression Method | Compression Degree |
|---:|:-------------|:-------------|:----------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------|
| 0 | Llama-2 | 7b | [magnitude_unstructured](https://huggingface.co/vita-group/llama-2-7b_magnitude_unstructured) | [s0.1](https://huggingface.co/vita-group/llama-2-7b_magnitude_unstructured/tree/s0.1) |
| 1 | Llama-2 | 7b | [magnitude_unstructured](https://huggingface.co/vita-group/llama-2-7b_magnitude_unstructured) | [s0.2](https://huggingface.co/vita-group/llama-2-7b_magnitude_unstructured/tree/s0.2) |
| 2 | Llama-2 | 7b | [magnitude_unstructured](https://huggingface.co/vita-group/llama-2-7b_magnitude_unstructured) | [s0.3](https://huggingface.co/vita-group/llama-2-7b_magnitude_unstructured/tree/s0.3) |
| 3 | Llama-2 | 7b | [magnitude_unstructured](https://huggingface.co/vita-group/llama-2-7b_magnitude_unstructured) | [s0.5](https://huggingface.co/vita-group/llama-2-7b_magnitude_unstructured/tree/s0.5) |
| 4 | Llama-2 | 7b | [magnitude_unstructured](https://huggingface.co/vita-group/llama-2-7b_magnitude_unstructured) | [s0.6](https://huggingface.co/vita-group/llama-2-7b_magnitude_unstructured/tree/s0.6) |
| 5 | Llama-2 | 7b | [sparsegpt_unstructured](https://huggingface.co/vita-group/llama-2-7b_sparsegpt_unstructured) | [s0.1](https://huggingface.co/vita-group/llama-2-7b_sparsegpt_unstructured/tree/s0.1) |
| 6 | Llama-2 | 7b | [sparsegpt_unstructured](https://huggingface.co/vita-group/llama-2-7b_sparsegpt_unstructured) | [s0.2](https://huggingface.co/vita-group/llama-2-7b_sparsegpt_unstructured/tree/s0.2) |
| 7 | Llama-2 | 7b | [sparsegpt_unstructured](https://huggingface.co/vita-group/llama-2-7b_sparsegpt_unstructured) | [s0.3](https://huggingface.co/vita-group/llama-2-7b_sparsegpt_unstructured/tree/s0.3) |
| 8 | Llama-2 | 7b | [sparsegpt_unstructured](https://huggingface.co/vita-group/llama-2-7b_sparsegpt_unstructured) | [s0.5](https://huggingface.co/vita-group/llama-2-7b_sparsegpt_unstructured/tree/s0.5) |
| 9 | Llama-2 | 7b | [sparsegpt_unstructured](https://huggingface.co/vita-group/llama-2-7b_sparsegpt_unstructured) | [s0.6](https://huggingface.co/vita-group/llama-2-7b_sparsegpt_unstructured/tree/s0.6) |
| 10 | Llama-2 | 7b | [wanda_unstructured](https://huggingface.co/vita-group/llama-2-7b_wanda_unstructured) | [s0.1](https://huggingface.co/vita-group/llama-2-7b_wanda_unstructured/tree/s0.1) |
| 11 | Llama-2 | 7b | [wanda_unstructured](https://huggingface.co/vita-group/llama-2-7b_wanda_unstructured) | [s0.2](https://huggingface.co/vita-group/llama-2-7b_wanda_unstructured/tree/s0.2) |
| 12 | Llama-2 | 7b | [wanda_unstructured](https://huggingface.co/vita-group/llama-2-7b_wanda_unstructured) | [s0.3](https://huggingface.co/vita-group/llama-2-7b_wanda_unstructured/tree/s0.3) |
| 13 | Llama-2 | 7b | [wanda_unstructured](https://huggingface.co/vita-group/llama-2-7b_wanda_unstructured) | [s0.5](https://huggingface.co/vita-group/llama-2-7b_wanda_unstructured/tree/s0.5) |
| 14 | Llama-2 | 7b | [wanda_unstructured](https://huggingface.co/vita-group/llama-2-7b_wanda_unstructured) | [s0.6](https://huggingface.co/vita-group/llama-2-7b_wanda_unstructured/tree/s0.6) |
|
xiaoygv/xiaos | xiaoygv | 2023-09-02T04:56:48Z | 0 | 0 | asteroid | [
"asteroid",
"dataset:PygmalionAI/PIPPA",
"license:afl-3.0",
"region:us"
] | null | 2023-09-02T04:55:25Z | ---
license: afl-3.0
datasets:
- PygmalionAI/PIPPA
metrics:
- bleu
library_name: asteroid
--- |
minh21/results | minh21 | 2023-09-02T04:56:45Z | 0 | 0 | null | [
"generated_from_trainer",
"base_model:google/flan-t5-large",
"base_model:finetune:google/flan-t5-large",
"license:apache-2.0",
"region:us"
] | null | 2023-09-01T07:33:03Z | ---
license: apache-2.0
base_model: google/flan-t5-large
tags:
- generated_from_trainer
model-index:
- name: results
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. -->
# results
This model is a fine-tuned version of [google/flan-t5-large](https://huggingface.co/google/flan-t5-large) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 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: 3e-05
- train_batch_size: 16
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.0 | 1.0 | 860 | nan |
| 0.0 | 2.0 | 1720 | nan |
| 0.0 | 3.0 | 2580 | nan |
| 0.0 | 4.0 | 3440 | nan |
| 0.0 | 5.0 | 4300 | nan |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
|
Alexshan/Dreamshaper | Alexshan | 2023-09-02T04:44:16Z | 0 | 0 | null | [
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-09-02T04:36:41Z | ---
license: creativeml-openrail-m
---
|
cfchase/stable-diffusion-rhteddy | cfchase | 2023-09-02T04:30:11Z | 3 | 0 | diffusers | [
"diffusers",
"tensorboard",
"safetensors",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | text-to-image | 2023-08-21T02:50:44Z | ---
license: creativeml-openrail-m
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
inference: true
extra_gated_prompt: |-
This model is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage.
The CreativeML OpenRAIL License specifies:
1. You can't use the model to deliberately produce nor share illegal or harmful outputs or content
2. CompVis claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license
3. You may re-distribute the weights and use the model commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully)
Please read the full license carefully here: https://huggingface.co/spaces/CompVis/stable-diffusion-license
---
# Red Hat Teddy
## Fine Tuned from Stable Diffusion v1-5
This model was based on [runwayml/stable-diffusion-v1-5](https://huggingface.co/runwayml/stable-diffusion-v1-5) and finetuned to generate pictures of `rhteddy`.
### Diffusers
```py
from diffusers import StableDiffusionPipeline
import torch
model_id = "cfchase/stable-diffusion-rhteddy"
pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16)
pipe = pipe.to("cuda")
prompt = "a photo of rhteddy on the beach"
image = pipe(prompt).images[0]
image
```
|
erickdp/beto-base-peft-p-tuning | erickdp | 2023-09-02T04:12:54Z | 1 | 0 | peft | [
"peft",
"region:us"
] | null | 2023-09-02T04:12:52Z | ---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.5.0
|
intanm/baseline001-25L-20230901 | intanm | 2023-09-02T04:10:56Z | 120 | 0 | transformers | [
"transformers",
"pytorch",
"bert",
"question-answering",
"generated_from_trainer",
"base_model:intanm/mbert-squadv2",
"base_model:finetune:intanm/mbert-squadv2",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | question-answering | 2023-09-02T04:04:19Z | ---
license: apache-2.0
base_model: intanm/mbert-squadv2
tags:
- generated_from_trainer
model-index:
- name: baseline001-25L-20230901
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. -->
# baseline001-25L-20230901
This model is a fine-tuned version of [intanm/mbert-squadv2](https://huggingface.co/intanm/mbert-squadv2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 5.1941
## 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 50 | 3.1546 |
| No log | 2.0 | 100 | 3.2274 |
| No log | 3.0 | 150 | 3.5274 |
| No log | 4.0 | 200 | 3.9318 |
| No log | 5.0 | 250 | 4.2708 |
| No log | 6.0 | 300 | 4.8027 |
| No log | 7.0 | 350 | 4.9737 |
| No log | 8.0 | 400 | 5.0809 |
| No log | 9.0 | 450 | 5.1268 |
| 1.1872 | 10.0 | 500 | 5.1941 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
|
dt-and-vanilla-ardt/ardt-vanilla-robust_train_halfcheetah_level-0209_0247-99 | dt-and-vanilla-ardt | 2023-09-02T04:05:19Z | 32 | 0 | transformers | [
"transformers",
"pytorch",
"decision_transformer",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | null | 2023-09-02T01:49:10Z | ---
tags:
- generated_from_trainer
model-index:
- name: ardt-vanilla-robust_train_halfcheetah_level-0209_0247-99
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. -->
# ardt-vanilla-robust_train_halfcheetah_level-0209_0247-99
This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 64
- 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
- training_steps: 10000
### Training results
### Framework versions
- Transformers 4.29.2
- Pytorch 2.1.0.dev20230727+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
gg-ai/beto-base-peft-p-tuning-sentiment | gg-ai | 2023-09-02T04:00:53Z | 0 | 0 | peft | [
"peft",
"region:us"
] | null | 2023-09-02T04:00:51Z | ---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.5.0
|
Aniya/llama2-7b-instruction-gen | Aniya | 2023-09-02T03:56:02Z | 5 | 0 | transformers | [
"transformers",
"pytorch",
"llama",
"text-generation",
"license:llama2",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2023-08-29T12:58:11Z | ---
license: llama2
---
# Alpaca style Instruction generating
This model is used for creating the instructions, which we can then use to fine-tune the base model of Llama 2 to follow our instructions.
# Prompt๏ผ
Use the Input below to create an instruction, which could have been used to generate the input using an LLM. ๆ นๆฎไธ้ข็ๆๆฌ่พๅ
ฅ็ๆไธไธชๆไปค๏ผๅฏไปฅ็จๆฅ่ฎฉๅคง่จ่ฏญๆจกๅๆ นๆฎ่ฟๆกๆไปค็ๆ่ฟๆฎตๆๆฌใ
# Examples๏ผ
### 1.
Input๏ผ็็ฃๅญฆไน ๆฏไธ็งๆบๅจๅญฆไน ็ฑปๅ๏ผๅ
ถไธญๆ ่ฎฐ่ฟ็ๆฐๆฎ็จไบๅๅปบๅฏไปฅๅฏนๆชๆ ่ฎฐๆฐๆฎ่ฟ่ก้ขๆต็ๆจกๅใๅจ็็ฃๅญฆไน ไธญ๏ผ่ฎญ็ปๆฐๆฎ้็ฑๅทฒ็ป็จๆญฃ็กฎ็่พๅบๆ็ฎๆ ๅผๆ ่ฎฐ็ๆฐๆฎ็ปๆใ็ถๅ็ฎๆณไป่ฟไบๆ ่ฎฐๆฐๆฎไธญๅญฆไน ๏ผๅนถ่ฝๅคๅจๆฐ็ใไนๅๆช่ง่ฟ็ๆฐๆฎไธๅๅบ้ขๆตใ็็ฃๅญฆไน ้ๅธธๅจๅพๅ่ฏๅซ๏ผ่ช็ถ่ฏญ่จๅค็ไปฅๅๅคงๅคๆฐๆบๅจๅญฆไน ไปปๅกไธญไฝฟ็จใ
Response๏ผ่งฃ้ไปไนๆฏ็็ฃๅผๆบๅจๅญฆไน
### 2.
Input๏ผI see a lot of folks saying that minimum wage jobs are just intended for students. I wonder who they think makes their egg McMuffin at 8am or their chicken McNuggets at lunch? Folks who work a full-time job should be able to afford housing and food. Itโs not that hard.
Response๏ผๅๅคไปฅไธ่ฏ่ฎบ๏ผ็จไธ่ถ
่ฟ100ไธชๅ่ฏใ
" Minimum wages are just for students."
### 3.
Input๏ผGet ready for an anonymous cross-chain bridge coming to #MaxxChain courtesy of @BlockBlendIO! Transact with over 10 of the most prominent cryptocurrencies and receive #PWR through a single, untraceable transaction. Experience true DeFi freedom. Deployment slated for next week.
Response๏ผๅๅธไธๆกๆจๆ๏ผๅฎฃไผ ๆฐ็ๅ ๅฏ่ดงๅธๆกฅใ
### 4.
Input๏ผ Radial substructures have now been observed in a wide range of protoplanetary discs (PPDs), from young to old systems, however their formation is still an area of vigorous debate. Recent magnetohydrodynamic (MHD) simulations have shown that rings and gaps can form naturally in PPDs when non-ideal MHD effects are included. However these simulations employ ad-hoc approximations to the magnitudes of the magnetic diffusivities in order to facilitate ring growth. We replace the parametrisation of these terms with a simple chemical network and grain distribution model to calculate the non-ideal effects in a more self-consistent way. We use a range of grain distributions to simulate grain formation for different disc conditions. Including ambipolar diffusion, we find that large grain populations (> 1{\mu}m), and those including a population of very small polyaromatic hydrocarbons (PAHs) facilitate the growth of periodic, stable rings, while intermediate sized grains suppress ring formation. Including Ohmic diffusion removes the positive influence of PAHs, with only large grain populations still producing periodic ring and gap structures. These results relate closely to the degree of coupling between the magnetic field and the neutral disc material, quantified by the non-dimensional Elsasser number {\Lambda} (the ratio of magnetic forces to Coriolis force). For both the ambipolar-only and ambipolar-ohmic cases, if the total Elsasser number is initially of order unity along the disc mid-plane, ring and gap structures may develop.
Response๏ผๅๅปบไธ็ฏๅ
ณไบ่กๆ็ไธญ่พๅฐ็ปๆ็็ ็ฉถ่ฎบๆใ
|
gdhdp/xiao | gdhdp | 2023-09-02T03:52:50Z | 0 | 0 | diffusers | [
"diffusers",
"dataset:Open-Orca/OpenOrca",
"arxiv:1910.09700",
"license:openrail",
"region:us"
] | null | 2023-09-02T03:50:57Z | ---
license: openrail
datasets:
- Open-Orca/OpenOrca
library_name: diffusers
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Data Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
wangrongsheng/Baichuan-13B-Chat-sft-merge | wangrongsheng | 2023-09-02T03:38:05Z | 1 | 0 | peft | [
"peft",
"region:us"
] | null | 2023-09-02T03:36:26Z | ---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: float16
### Framework versions
- PEFT 0.4.0
|
dt-and-vanilla-ardt/ardt-vanilla-robust_train_walker2d_level-0209_0306-33 | dt-and-vanilla-ardt | 2023-09-02T03:36:52Z | 29 | 0 | transformers | [
"transformers",
"pytorch",
"decision_transformer",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | null | 2023-09-02T02:08:08Z | ---
tags:
- generated_from_trainer
model-index:
- name: ardt-vanilla-robust_train_walker2d_level-0209_0306-33
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. -->
# ardt-vanilla-robust_train_walker2d_level-0209_0306-33
This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 64
- 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
- training_steps: 10000
### Training results
### Framework versions
- Transformers 4.29.2
- Pytorch 2.1.0.dev20230727+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
unggulP/unggul | unggulP | 2023-09-02T03:19:04Z | 0 | 0 | keras | [
"keras",
"id",
"license:openrail",
"region:us"
] | null | 2023-09-02T03:16:12Z | ---
license: openrail
language:
- id
library_name: keras
--- |
dt-and-vanilla-ardt/ardt-vanilla-robust_train_hopper_level-0209_0334-99 | dt-and-vanilla-ardt | 2023-09-02T03:14:50Z | 32 | 0 | transformers | [
"transformers",
"pytorch",
"decision_transformer",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | null | 2023-09-02T02:35:16Z | ---
tags:
- generated_from_trainer
model-index:
- name: ardt-vanilla-robust_train_hopper_level-0209_0334-99
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. -->
# ardt-vanilla-robust_train_hopper_level-0209_0334-99
This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 64
- 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
- training_steps: 10000
### Training results
### Framework versions
- Transformers 4.29.2
- Pytorch 2.1.0.dev20230727+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
nightdude/config_81190 | nightdude | 2023-09-02T02:59:29Z | 0 | 0 | peft | [
"peft",
"region:us"
] | null | 2023-09-02T02:58:06Z | ---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.5.0.dev0
|
FelipeCasali-USP/lgpd_pii_identifier | FelipeCasali-USP | 2023-09-02T02:00:33Z | 10 | 3 | transformers | [
"transformers",
"distilbert",
"token-classification",
"pt",
"doi:10.57967/hf/1026",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | 2023-08-27T01:45:54Z | ---
language: pt
license: apache-2.0
widget:
- text: "123.456.789-0"
example_title: "CPF"
- text: "75528899000119"
example_title: "CNPJ (sem pontuaรงรฃo)"
- text: "Nome Completo"
example_title: "Felipe Casali Silva"
- text: "Dados diversos"
example_title: "Felipe Casali Silva, Teste, Rio de Janeiro, RJ"
---
# lgpd_pii_identifier : LGPD PII Identifier
lgpd_pii_identifier is a pre-trained NLP model to identify sensitive data in the scope of LGPD (Lei Geral de Proteรงรฃo de Dados)
The goal is to have a tool to identify document numbers like CNPJ, CPF, people's names and other kind of sensitive data, allowing companies to find and anonymize
data according to their businness needs, and governance rules.
## Applications
### Identify PII (Personal Identifiable Information) in the scope of LGPD
# WIP (Add image here)
## Usage
In order to use the model, you need to get the HuggingFace auth token. You can get it [here](https://huggingface.co/settings/token).
```python
from transformers import DistilBertModel, DistilBertTokenizer
import numpy as np
pred_mapper = {
0: "cnpj",
1: "cpf",
2: "nome",
3: "estado"
}
tokenizer = DistilBertTokenizer.from_pretrained("FelipeCasali-USP/lgpd_pii_identifier")
lgpd_pii_identifier = DistilBertModel.from_pretrained("FelipeCasali-USP/lgpd_pii_identifier")
tokens = tokenizer(["String to be analized"], return_tensors="pt",
padding=True, truncation=True, max_length=512)
lgpd_pii_identifier_outputs = lgpd_pii_identifier(**tokens)
preds = [pred_mapper[np.argmax(pred)] for pred in lgpd_pii_identifier_outputs.logits.cpu().detach().numpy()]
```
## Author
- [Felipe Casali](https://www.linkedin.com/in/felipecasali/)
## Paper
- Paper: WIP
- MBA thesis: [lgpd_pii_identifier: Proteรงรฃo de Dados Sensรญveis na Era da Inteligรชncia Artificial](WIP) |
DmatryMakeev/diamonic-v3 | DmatryMakeev | 2023-09-02T01:57:43Z | 31 | 0 | diffusers | [
"diffusers",
"text-to-image",
"stable-diffusion",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | text-to-image | 2023-09-02T01:53:06Z | ---
license: creativeml-openrail-m
tags:
- text-to-image
- stable-diffusion
---
### DIAMONIC_V3 Dreambooth model trained by DmatryMakeev with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook
Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb)
Sample pictures of this concept:
|
germanchelo/example_fastai | germanchelo | 2023-09-02T01:48:16Z | 0 | 0 | fastai | [
"fastai",
"region:us"
] | null | 2023-09-02T01:48:13Z | ---
tags:
- fastai
---
# Amazing!
๐ฅณ Congratulations on hosting your fastai model on the Hugging Face Hub!
# Some next steps
1. Fill out this model card with more information (see the template below and the [documentation here](https://huggingface.co/docs/hub/model-repos))!
2. Create a demo in Gradio or Streamlit using ๐ค Spaces ([documentation here](https://huggingface.co/docs/hub/spaces)).
3. Join the fastai community on the [Fastai Discord](https://discord.com/invite/YKrxeNn)!
Greetings fellow fastlearner ๐ค! Don't forget to delete this content from your model card.
---
# Model card
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
|
Laly/intel_image_classification_fastai | Laly | 2023-09-02T01:47:48Z | 0 | 0 | fastai | [
"fastai",
"region:us"
] | null | 2023-09-02T01:47:44Z | ---
tags:
- fastai
---
# Amazing!
๐ฅณ Congratulations on hosting your fastai model on the Hugging Face Hub!
# Some next steps
1. Fill out this model card with more information (see the template below and the [documentation here](https://huggingface.co/docs/hub/model-repos))!
2. Create a demo in Gradio or Streamlit using ๐ค Spaces ([documentation here](https://huggingface.co/docs/hub/spaces)).
3. Join the fastai community on the [Fastai Discord](https://discord.com/invite/YKrxeNn)!
Greetings fellow fastlearner ๐ค! Don't forget to delete this content from your model card.
---
# Model card
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
|
Isaacf/intel_image_classification_fastai | Isaacf | 2023-09-02T01:44:54Z | 0 | 0 | fastai | [
"fastai",
"region:us"
] | null | 2023-09-02T01:44:50Z | ---
tags:
- fastai
---
# Amazing!
๐ฅณ Congratulations on hosting your fastai model on the Hugging Face Hub!
# Some next steps
1. Fill out this model card with more information (see the template below and the [documentation here](https://huggingface.co/docs/hub/model-repos))!
2. Create a demo in Gradio or Streamlit using ๐ค Spaces ([documentation here](https://huggingface.co/docs/hub/spaces)).
3. Join the fastai community on the [Fastai Discord](https://discord.com/invite/YKrxeNn)!
Greetings fellow fastlearner ๐ค! Don't forget to delete this content from your model card.
---
# Model card
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
|
baibaibai/baini_VoiceBank | baibaibai | 2023-09-02T01:44:46Z | 0 | 0 | null | [
"UTAU",
"diffsinger",
"ja",
"zh",
"license:cc-by-nc-4.0",
"region:us"
] | null | 2023-09-01T13:36:24Z | ---
license: cc-by-nc-4.0
language:
- ja
- zh
tags:
- UTAU
- diffsinger
---
ไฝ ๅฅฝๆ่ฐขๆจไฝฟ็จ็ฝๆบบ็ๆญๅฃฐๆฐๆฎๅบใ
ไฝฟ็จ่ง็บฆ๏ผ
1.็ฆๆญข็จไบ ๅฎๆ ๆฟๆฒป๏ผ็ญ็ญ๏ผไปปไฝ่ฟๅๆณๅพๅ
ๅฎน็ๅไฝใ
2.็ฆๆญขๅฐๆฌ้ณๆบไฝฟ็จไบๅคงไผ้ท็น็ธๅ
ณๅ
ๅฎน็ๅไฝ๏ผไพๅฆ๏ผไธบๆไธ่ฏ่กไธบ็ไบบๆ็ฉ่ฟ่กไบๅ๏ผ
3.ๅ
่ฎธ็จไบ้ๅไธ็จ้ไธไธ่ฟๅ่ง็บฆ็ๅไฝใ
4.็จไบๅไธ็จ้ๆ่
็ๅฉ๏ผ้่ฆๅ้ณๆบ็ฎก็่
็ณ่ฏทๆๆใไธ่ฌๆฅ่ฏด้ฝๆฏๅ
่ดน็ใ
5.ไฝฟ็จๆฌ้ณๆบ๏ผไธ่ฎบๆฏๅไธ่ฟๆฏ้ๅ๏ผไธ่ฎบๆฏไธปๅฑ่ฟๆฏๅๅฃฐ็ญ่ไฝ๏ผ้ฝๅบๆธ
ๆฐๆไบ็ๆ ๆณจๅฃฐๅบๅไปฅๅๆๅฑไฝ็ฝฎ๏ผไพ๏ผๅๅฃฐ๏ผ็ฝๆบบ)
6.ๆฌ้ณๆบ็otoๆไปถไปฅๅwavๅ๏ผwavๆไปถ๏ผๅผๆๆจกๅๆไปถ็ญ็ญ๏ผ่ฟ็ฑป็ฑๆฌ้ณๆบ้
ๅธ็ๅ
ๅฎนไปฅๅ้
ๅธๅ
ๅฎนไบๆฌก็ๆ็็ฉๅ๏ผๆช่ฏดๆๅ
่ฎธไบๆฌก้
ๅธ็๏ผ้ฝ่งไธบไธๅ
่ฎธไบๆฌก้
ๅธ๏ผไนไธๅ
่ฎธ็จไบ่ฟๅๆญค่ง็บฆ็ๅไฝใ๏ผๆณจ๏ผไฝฟ็จๆฌ้ณๆบๆ้
ๅธ็ๅ
ๅฎน๏ผ่ฟ่กaiๆ่
ๅ
ถไป็็ฑปไผผ็จๅบ็่ฎญ็ปๅญฆไน ็ญ็ญ๏ผๆๅ
ถไป่ฟไผผ็่กไธบใๅฑไบๅฏนๅทฒ้
ๅธๅ
ๅฎน็ไบๆฌก็ๆ็ฉ๏ผไธๅ
่ฎธไปฅไปปไฝๅฝขๅผ็ๅๅ๏ผ
7.ๆดไธๅ
่ฎธ๏ผๅฐๆญค้ณๆบไธๅ
ถไป้ณๆบไบๆฌกๆผๆฅ่ตทๆฅๅฝๅไธบๆฐ็้ณๆบใไนไธๅ
่ฎธๅถไฝ่ฏฅ้ณๆบ็ไบ็ง๏ผๅฆๆๆไบ็ง้ๆฑ๏ผ่ฏทไปฅ้ณ่ฒ็ๅไนๅๅธ๏ผไพๅฆ๏ผ็ฝๆบบยท็ง้ฃ๏ผใไธๅ
่ฎธๅฏน้ณๆบ็ๆไปถ่ฟ่กไบๆฌกไฟฎๆนๅไปฅๆฐ็ๅฝๅๅๅธใ
้่ฆ่ฟๅ่ง็บฆ็ไบๆ
๏ผๆ่
่ง็บฆๆฒกๆๅๆ็ฝ็ไบๆ
๏ผ่ฏทๅจ่ฏขๅฃฐๅบ็ฎก็่
๏ผๅฃฐๅบ็ฎก็่
ๆฅๆๆฌ่ง็บฆ็ๆ็ป่งฃ้ๆใ
ๆญๆๅบ็กไฟกๆฏ๏ผ
ๆญๆไฟกๆฏ๏ผ
ๅงๅ๏ผ็ฝๆบบ
ๆงๅซ๏ผ็ท
็ๆฅ๏ผ7ๆ20ๆฅ
ไธญไนไบบ๏ผๅฐ็ฝ่๏ผๅฐ้จๅคฉ๏ผ
ๅฃฐๅบ็ฎก็่
/็ๆๅฝๅฑ๏ผๅฐ็ฝ่่ https://space.bilibili.com/207917768
้่ฟ้ฎ็ฎฑ่็ณปๆ๏ผ[email protected]๏ผๆๅฏ่ฝๅชๆฏๅถๅฐ็ไธ็ผ๏ผ |
Dagaviri/intel_image_classification_fastai | Dagaviri | 2023-09-02T01:33:22Z | 0 | 0 | fastai | [
"fastai",
"region:us"
] | null | 2023-09-02T01:33:18Z | ---
tags:
- fastai
---
# Amazing!
๐ฅณ Congratulations on hosting your fastai model on the Hugging Face Hub!
# Some next steps
1. Fill out this model card with more information (see the template below and the [documentation here](https://huggingface.co/docs/hub/model-repos))!
2. Create a demo in Gradio or Streamlit using ๐ค Spaces ([documentation here](https://huggingface.co/docs/hub/spaces)).
3. Join the fastai community on the [Fastai Discord](https://discord.com/invite/YKrxeNn)!
Greetings fellow fastlearner ๐ค! Don't forget to delete this content from your model card.
---
# Model card
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
|
Jdex01/intel_image_classification_fastai | Jdex01 | 2023-09-02T01:31:58Z | 0 | 0 | fastai | [
"fastai",
"region:us"
] | null | 2023-09-02T01:31:54Z | ---
tags:
- fastai
---
# Amazing!
๐ฅณ Congratulations on hosting your fastai model on the Hugging Face Hub!
# Some next steps
1. Fill out this model card with more information (see the template below and the [documentation here](https://huggingface.co/docs/hub/model-repos))!
2. Create a demo in Gradio or Streamlit using ๐ค Spaces ([documentation here](https://huggingface.co/docs/hub/spaces)).
3. Join the fastai community on the [Fastai Discord](https://discord.com/invite/YKrxeNn)!
Greetings fellow fastlearner ๐ค! Don't forget to delete this content from your model card.
---
# Model card
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
|
dacuervo1/intel_image_classification_fastai | dacuervo1 | 2023-09-02T01:30:29Z | 0 | 0 | fastai | [
"fastai",
"region:us"
] | null | 2023-09-02T01:30:25Z | ---
tags:
- fastai
---
# Amazing!
๐ฅณ Congratulations on hosting your fastai model on the Hugging Face Hub!
# Some next steps
1. Fill out this model card with more information (see the template below and the [documentation here](https://huggingface.co/docs/hub/model-repos))!
2. Create a demo in Gradio or Streamlit using ๐ค Spaces ([documentation here](https://huggingface.co/docs/hub/spaces)).
3. Join the fastai community on the [Fastai Discord](https://discord.com/invite/YKrxeNn)!
Greetings fellow fastlearner ๐ค! Don't forget to delete this content from your model card.
---
# Model card
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
|
lauracata/intel_image_classification_fastai | lauracata | 2023-09-02T01:30:23Z | 0 | 0 | fastai | [
"fastai",
"region:us"
] | null | 2023-09-02T01:30:18Z | ---
tags:
- fastai
---
# Amazing!
๐ฅณ Congratulations on hosting your fastai model on the Hugging Face Hub!
# Some next steps
1. Fill out this model card with more information (see the template below and the [documentation here](https://huggingface.co/docs/hub/model-repos))!
2. Create a demo in Gradio or Streamlit using ๐ค Spaces ([documentation here](https://huggingface.co/docs/hub/spaces)).
3. Join the fastai community on the [Fastai Discord](https://discord.com/invite/YKrxeNn)!
Greetings fellow fastlearner ๐ค! Don't forget to delete this content from your model card.
---
# Model card
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
|
AshtakaOOf/itpiki | AshtakaOOf | 2023-09-02T01:30:11Z | 0 | 1 | null | [
"license:cc-by-nc-sa-4.0",
"region:us"
] | null | 2023-06-13T20:31:18Z | ---
license: cc-by-nc-sa-4.0
---
# MOVED HERE: [AshtakaOOf/ash-networks](https://huggingface.co/AshtakaOOf/ash-networks) |
AshtakaOOf/csm-bg | AshtakaOOf | 2023-09-02T01:29:49Z | 0 | 4 | null | [
"art",
"anime",
"chainsaw-man",
"loha",
"text-to-image",
"license:cc-by-nc-sa-4.0",
"region:us"
] | text-to-image | 2023-05-25T00:35:39Z | ---
license: cc-by-nc-sa-4.0
pipeline_tag: text-to-image
thumbnail: "https://media.discordapp.net/attachments/1102319457180856411/1113586206836535396/00145-2181918707.png"
tags:
- art
- anime
- chainsaw-man
- loha
---
# MOVED HERE: [AshtakaOOf/ash-networks](https://huggingface.co/AshtakaOOf/ash-networks)
<details id="Dropdown">
<summary style="font-size: 1.10em"><strong>Old README.md</strong> (click to open the dropdown)</summary>
<p align="center", style="font-size: 2.8rem; font-weight: bold; color: #f6df00;">Chainsaw Man LoHa</p>
<p align="center", style="font-size: 1.2rem; font-weight: bold; color: #bf5a5f;">Use at 0.6 to 0.8 weight for best quality</p>
<p align="center"><img src="https://media.discordapp.net/attachments/1102319457180856411/1113583145128833135/00118-1819962614.png" width="512"> <img src="https://media.discordapp.net/attachments/1102319457180856411/1113586206836535396/00145-2181918707.png" width="512"></p>
<p align="center">Trained by AshtakaOOf</p>
<p align="center">Dataset provided by High Speed Rail Enjoyer</p>
# Main Token:
```
M,A,P
```
# Characters Tokens:
```
denji_(chainsaw_man)
makima_(chainsaw_man)
power_(chainsaw_man)
hayakawa_aki
himeno_(chainsaw_man)
higashiyama_kobeni
```
# Examples:
```
1boy, male focus, solo, shirt, smile, teeth, sky, blonde hair, cloud, sharp teeth, white shirt, upper body, day, brown eyes, spiked hair, looking at viewer, cloudy sky, red eyes, grin, ((masterpiece)), denji_(chainsaw_man)
```
```
1girl, horns, solo, teeth, sharp teeth, sky, open mouth, cross-shaped pupils, long hair, cloud, hair over one eye, looking at viewer, demon horns, blue sky, day, outdoors, yellow eyes, hood, blonde hair, anime coloring, red horns, cloudy sky, jacket, ((masterpiece)), power_(chainsaw_man), M,A,P
```
```
1girl, solo, breasts, looking_at_viewer, short_hair, bangs, shirt, black_hair, holding, white_shirt, food, necktie, collared_shirt, indoors, medium_hair, cup, v, eyepatch, table, plant, black_necktie, plate, bowl, cigarette, chopsticks, spoon, noodles, holdingcigarette, ramen, restaurant, himeno(chainsaw_man), M, A, P
```
</details> |
JmGarzonv/intel_image_classification_fastai | JmGarzonv | 2023-09-02T01:27:35Z | 0 | 0 | fastai | [
"fastai",
"region:us"
] | null | 2023-09-02T01:27:24Z | ---
tags:
- fastai
---
# Amazing!
๐ฅณ Congratulations on hosting your fastai model on the Hugging Face Hub!
# Some next steps
1. Fill out this model card with more information (see the template below and the [documentation here](https://huggingface.co/docs/hub/model-repos))!
2. Create a demo in Gradio or Streamlit using ๐ค Spaces ([documentation here](https://huggingface.co/docs/hub/spaces)).
3. Join the fastai community on the [Fastai Discord](https://discord.com/invite/YKrxeNn)!
Greetings fellow fastlearner ๐ค! Don't forget to delete this content from your model card.
---
# Model card
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
|
KingKazma/xsum_gpt2_p_tuning_500_4_50000_6_e0_s6789_v4_l4_v100 | KingKazma | 2023-09-02T01:26:10Z | 0 | 0 | peft | [
"peft",
"region:us"
] | null | 2023-09-02T01:26:08Z | ---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.6.0.dev0
|
KhalfounMehdi/dermatology_vit | KhalfounMehdi | 2023-09-02T01:24:24Z | 193 | 1 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"autotrain",
"dataset:KhalfounMehdi/dermatology_anomaly_detection_vit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-classification | 2023-09-02T01:23:50Z |
---
tags:
- autotrain
- image-classification
widget:
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg
example_title: Tiger
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg
example_title: Teapot
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg
example_title: Palace
datasets:
- KhalfounMehdi/dermatology_anomaly_detection_vit
---
# Model Trained Using AutoTrain
- Problem type: Image Classification
## Validation Metricsg
No validation metrics available
|
justina/undersampled-review-clf | justina | 2023-09-02T01:20:18Z | 109 | 0 | transformers | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"generated_from_trainer",
"dataset:justina/yelp_boba_reviews",
"base_model:cardiffnlp/twitter-roberta-base-sentiment-latest",
"base_model:finetune:cardiffnlp/twitter-roberta-base-sentiment-latest",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2023-09-02T00:44:50Z | ---
base_model: cardiffnlp/twitter-roberta-base-sentiment-latest
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: undersampled-review-clf
results: []
datasets:
- justina/yelp_boba_reviews
---
<!-- 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. -->
# undersampled-review-clf
This model is a fine-tuned version of [cardiffnlp/twitter-roberta-base-sentiment-latest](https://huggingface.co/cardiffnlp/twitter-roberta-base-sentiment-latest) on
[justina/yelp-boba-reviews](https://huggingface.co/datasets/justina/yelp_boba_reviews) dataset. Undersampling techniques were used to optimize the model for predicting
Yelp review ratings.
It achieves the following results on the evaluation set:
- Loss: 0.4412
- F1 Macro: 0.7799
- Aucpr Macro: 0.8286
- Accuracy: 0.8464
## 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: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 Macro | Aucpr Macro | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-----------:|:--------:|
| 0.9348 | 1.22 | 100 | 0.7286 | 0.6132 | 0.6244 | 0.6962 |
| 0.7438 | 2.44 | 200 | 0.7857 | 0.6232 | 0.6215 | 0.6735 |
| 0.6275 | 3.66 | 300 | 0.8317 | 0.5976 | 0.6092 | 0.6778 |
| 0.5561 | 4.88 | 400 | 0.8176 | 0.6200 | 0.6238 | 0.6868 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3 |
sam2ai/falcon-1b-odia-lora-pt | sam2ai | 2023-09-02T01:07:21Z | 2 | 0 | peft | [
"peft",
"region:us"
] | null | 2023-09-02T00:57:09Z | ---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: True
- load_in_4bit: False
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
### Framework versions
- PEFT 0.6.0.dev0
|
aibatik/batik_bakaran | aibatik | 2023-09-02T00:57:41Z | 29 | 0 | diffusers | [
"diffusers",
"tensorboard",
"safetensors",
"text-to-image",
"license:unknown",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | text-to-image | 2023-09-02T00:40:30Z | ---
license: unknown
pipeline_tag: text-to-image
library_name: diffusers
--- |
nightdude/config_8119 | nightdude | 2023-09-02T00:53:19Z | 0 | 0 | peft | [
"peft",
"region:us"
] | null | 2023-09-02T00:52:13Z | ---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.5.0.dev0
|
peppe243439/my_awesome_model | peppe243439 | 2023-09-02T00:50:02Z | 105 | 0 | transformers | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:imdb",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2023-09-02T00:32:27Z | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- imdb
model-index:
- name: my_awesome_model
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. -->
# my_awesome_model
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Framework versions
- Transformers 4.32.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
|
KingKazma/xsum_t5-small_lora_500_4_1000_8_e1_s6789_v4_l4_r4 | KingKazma | 2023-09-02T00:40:29Z | 0 | 0 | peft | [
"peft",
"region:us"
] | null | 2023-09-02T00:40:25Z | ---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.6.0.dev0
|
placeholdereet/Lfgc | placeholdereet | 2023-09-02T00:39:18Z | 0 | 0 | null | [
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-08-14T15:23:18Z | ---
license: creativeml-openrail-m
---
|
KingKazma/xsum_t5-small_lora_500_4_150_8_e2_s6789_v4_l4_r4 | KingKazma | 2023-09-02T00:23:01Z | 0 | 0 | peft | [
"peft",
"region:us"
] | null | 2023-09-02T00:14:32Z | ---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.6.0.dev0
|
KingKazma/xsum_t5-small_lora_500_4_150_8_e1_s6789_v4_l4_r4 | KingKazma | 2023-09-02T00:22:54Z | 0 | 0 | peft | [
"peft",
"region:us"
] | null | 2023-09-02T00:14:24Z | ---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.6.0.dev0
|
stfamod/fine-tuned-bert-financial-sentiment-analysis | stfamod | 2023-09-02T00:06:19Z | 124 | 1 | transformers | [
"transformers",
"pytorch",
"bert",
"text-classification",
"sentiment",
"sentiment-analysis",
"financial",
"fine-tuned",
"fine-tuned-bert",
"bert-uncased",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2023-09-01T23:06:32Z | ---
license: mit
tags:
- sentiment
- sentiment-analysis
- financial
- fine-tuned
- fine-tuned-bert
- bert-uncased
---
### Model Overview:
This NLP model is fine-tuned with a focus on analyzing sentiment in financial text and news headlines. It was fine-tuned using the [bert-base-uncased](https://huggingface.co/bert-base-uncased) model on the [financial_phrasebank](https://huggingface.co/datasets/financial_phrasebank) and [auditor_sentiment](https://huggingface.co/datasets/FinanceInc/auditor_sentiment) datasets.
**Accuracies:** \
**financial_phrasebank:** 0.993\
**auditor_senitment:** 0.974
### Training Hyperparameters:
**Learning Rate:** 2e-05\
**Train Batch Size:** 16\
**Eval Batch Size:** 16\
**Random Seed:** 42\
**Optimizer:** AdamW-betas(0.9, 0.999)\
**Learning Rate Scheduler:** Linear\
**Number of Epochs:** 6\
**Number of Warmup Steps:** 0.2 * Number of Training Steps
### How To Use:
```
from transformers import pipeline
pipe = pipeline("sentiment-analysis", model="mstafam/fine-tuned-bert-financial-sentimental-analysis")
text = "Example company has seen a 5% increase in revenue this quarter."
print(pipe(text))
[{'label': 'Positive', 'score': 0.9993795156478882}]
``` |
ardt-multipart/ardt-multipart-robust_train_halfcheetah_level-0109_2225-66 | ardt-multipart | 2023-09-01T23:46:16Z | 31 | 0 | transformers | [
"transformers",
"pytorch",
"decision_transformer",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | null | 2023-09-01T21:27:13Z | ---
tags:
- generated_from_trainer
model-index:
- name: ardt-multipart-robust_train_halfcheetah_level-0109_2225-66
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. -->
# ardt-multipart-robust_train_halfcheetah_level-0109_2225-66
This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 64
- 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
- training_steps: 10000
### Training results
### Framework versions
- Transformers 4.29.2
- Pytorch 2.1.0.dev20230727+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
frankkuete/electra-large-cuad-qa | frankkuete | 2023-09-01T23:40:06Z | 114 | 1 | transformers | [
"transformers",
"pytorch",
"electra",
"question-answering",
"generated_from_trainer",
"legal",
"en",
"dataset:cuad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | question-answering | 2023-06-02T15:09:45Z | ---
license: apache-2.0
tags:
- generated_from_trainer
- legal
datasets:
- cuad
model-index:
- name: electra-large
results: []
language:
- en
widget:
- text: "Highlight the parts (if any) of this contract related to 'Document Name' that should be reviewed by a lawyer. Details: The name of the contract"
context: "AGREEMENT FOR MARKETING AND BRAND DEVELOPMENT SERVICES \n This AGREEMENT FOR MARKETING AND BRAND DEVELOPMENT SERVICES (โAgreementโ), effective as of December 28, 2014 (โEffective Dateโ), is by and between Aquarius Cannabis Inc, a Nevada C-Corporation, with offices located at 2214 Clarendon Street, Suite 230, Woodland Hills, CA 91367 (โAquariusโ), and Sysco Pancho LLC, a Washington limited liability company, with offices located at 6262 cambell rd peshastin wa 98847 (โClientโ). 1.Marketing and Brand Development Services. Aquarius will perform services for Client in connection with the planning, provision, creation and/or placing of branding, research, advertising, marketing, consulting, creative and/or digital services for Client, during the Term, as provided in the attached (Attachment A) Statement of Work (โSOWโ), incorporated herein by reference (such services are collectively referred to as โServicesโ). During the term of this agreement, Client may wish to assign additional projects, products, or services to Aquarius beyond the Services outlined in the SOW (โOut-of-Scope Assignmentsโ). Aquarius agrees to accept such Out-of-Scope Assignments only upon a separate written agreement with Client regarding additional compensation to be paid to Aquarius and other relevant terms and conditions. Nothing in this Agreement will be deemed to require Aquarius to undertake any act or perform any services which in its good faith judgment would be misleading, false, libelous, unlawful, in breach of a contract, or otherwise prejudicial to Clientโs or Aquariusโs interests. 2.Subcontractors. Client acknowledges that Aquarius may, in the rendition of the Services hereunder, engage third party suppliers and other vendors and subcontractors (โSubcontractorsโ) from time to time to provide certain services. Aquarius shall supervise such services and endeavor to guard against any loss to Client as the result of the failure of Subcontractors to properly execute their commitments, but Aquarius shall not be responsible for their failure, acts or omissions, except where such failure, acts or omissions are due to Aquariusโs negligence or willful misconduct. If Client enters into arrangements with third party vendors, subcontractors or suppliers regarding the provision of materials or services (โPreferred Suppliersโ) and requests that Aquarius utilize such Preferred Suppliers in the discharge of Aquariusโs obligations hereunder, Client remains solely responsible for such Preferred Suppliers. 3.Client Approval of Materials. Aquarius shall submit to Client for its approval all elements of any materials to be produced or placed hereunder, including, but not limited to, all copy, layouts, slogans, websites artworks, graphic materials, and photography (collectively, โMaterialsโ). Submission for prior approval of Materials will not be required to the extent that they are preliminary only. 4.Services to Clientโs Designees. Should Client request Aquarius to make purchases for or render services to any parent, subsidiary, or affiliate of Client (โClient Affiliateโ), Client and such Client Affiliate shall be jointly and severally liable to Aquarius even though Aquarius may render invoices to, or in the name of, such Client Affiliate."
- text: "Highlight the parts (if any) of this contract related to 'Agreement Date' that should be reviewed by a lawyer. Details: The date of the contract"
context: "AGREEMENT FOR MARKETING AND BRAND DEVELOPMENT SERVICES \n This AGREEMENT FOR MARKETING AND BRAND DEVELOPMENT SERVICES (โAgreementโ), effective as of December 28, 2014 (โEffective Dateโ), is by and between Aquarius Cannabis Inc, a Nevada C-Corporation, with offices located at 2214 Clarendon Street, Suite 230, Woodland Hills, CA 91367 (โAquariusโ), and Sysco Pancho LLC, a Washington limited liability company, with offices located at 6262 cambell rd peshastin wa 98847 (โClientโ). 1.Marketing and Brand Development Services. Aquarius will perform services for Client in connection with the planning, provision, creation and/or placing of branding, research, advertising, marketing, consulting, creative and/or digital services for Client, during the Term, as provided in the attached (Attachment A) Statement of Work (โSOWโ), incorporated herein by reference (such services are collectively referred to as โServicesโ). During the term of this agreement, Client may wish to assign additional projects, products, or services to Aquarius beyond the Services outlined in the SOW (โOut-of-Scope Assignmentsโ). Aquarius agrees to accept such Out-of-Scope Assignments only upon a separate written agreement with Client regarding additional compensation to be paid to Aquarius and other relevant terms and conditions. Nothing in this Agreement will be deemed to require Aquarius to undertake any act or perform any services which in its good faith judgment would be misleading, false, libelous, unlawful, in breach of a contract, or otherwise prejudicial to Clientโs or Aquariusโs interests. 2.Subcontractors. Client acknowledges that Aquarius may, in the rendition of the Services hereunder, engage third party suppliers and other vendors and subcontractors (โSubcontractorsโ) from time to time to provide certain services. Aquarius shall supervise such services and endeavor to guard against any loss to Client as the result of the failure of Subcontractors to properly execute their commitments, but Aquarius shall not be responsible for their failure, acts or omissions, except where such failure, acts or omissions are due to Aquariusโs negligence or willful misconduct. If Client enters into arrangements with third party vendors, subcontractors or suppliers regarding the provision of materials or services (โPreferred Suppliersโ) and requests that Aquarius utilize such Preferred Suppliers in the discharge of Aquariusโs obligations hereunder, Client remains solely responsible for such Preferred Suppliers. 3.Client Approval of Materials. Aquarius shall submit to Client for its approval all elements of any materials to be produced or placed hereunder, including, but not limited to, all copy, layouts, slogans, websites artworks, graphic materials, and photography (collectively, โMaterialsโ). Submission for prior approval of Materials will not be required to the extent that they are preliminary only. 4.Services to Clientโs Designees. Should Client request Aquarius to make purchases for or render services to any parent, subsidiary, or affiliate of Client (โClient Affiliateโ), Client and such Client Affiliate shall be jointly and severally liable to Aquarius even though Aquarius may render invoices to, or in the name of, such Client Affiliate."
- text: "Highlight the parts (if any) of this contract related to 'Parties' that should be reviewed by a lawyer. Details: The two or more parties who signed the contract"
context: "AGREEMENT FOR MARKETING AND BRAND DEVELOPMENT SERVICES \n This AGREEMENT FOR MARKETING AND BRAND DEVELOPMENT SERVICES (โAgreementโ), effective as of December 28, 2014 (โEffective Dateโ), is by and between Aquarius Cannabis Inc, a Nevada C-Corporation, with offices located at 2214 Clarendon Street, Suite 230, Woodland Hills, CA 91367 (โAquariusโ), and Sysco Pancho LLC, a Washington limited liability company, with offices located at 6262 cambell rd peshastin wa 98847 (โClientโ). 1.Marketing and Brand Development Services. Aquarius will perform services for Client in connection with the planning, provision, creation and/or placing of branding, research, advertising, marketing, consulting, creative and/or digital services for Client, during the Term, as provided in the attached (Attachment A) Statement of Work (โSOWโ), incorporated herein by reference (such services are collectively referred to as โServicesโ). During the term of this agreement, Client may wish to assign additional projects, products, or services to Aquarius beyond the Services outlined in the SOW (โOut-of-Scope Assignmentsโ). Aquarius agrees to accept such Out-of-Scope Assignments only upon a separate written agreement with Client regarding additional compensation to be paid to Aquarius and other relevant terms and conditions. Nothing in this Agreement will be deemed to require Aquarius to undertake any act or perform any services which in its good faith judgment would be misleading, false, libelous, unlawful, in breach of a contract, or otherwise prejudicial to Clientโs or Aquariusโs interests. 2.Subcontractors. Client acknowledges that Aquarius may, in the rendition of the Services hereunder, engage third party suppliers and other vendors and subcontractors (โSubcontractorsโ) from time to time to provide certain services. Aquarius shall supervise such services and endeavor to guard against any loss to Client as the result of the failure of Subcontractors to properly execute their commitments, but Aquarius shall not be responsible for their failure, acts or omissions, except where such failure, acts or omissions are due to Aquariusโs negligence or willful misconduct. If Client enters into arrangements with third party vendors, subcontractors or suppliers regarding the provision of materials or services (โPreferred Suppliersโ) and requests that Aquarius utilize such Preferred Suppliers in the discharge of Aquariusโs obligations hereunder, Client remains solely responsible for such Preferred Suppliers. 3.Client Approval of Materials. Aquarius shall submit to Client for its approval all elements of any materials to be produced or placed hereunder, including, but not limited to, all copy, layouts, slogans, websites artworks, graphic materials, and photography (collectively, โMaterialsโ). Submission for prior approval of Materials will not be required to the extent that they are preliminary only. 4.Services to Clientโs Designees. Should Client request Aquarius to make purchases for or render services to any parent, subsidiary, or affiliate of Client (โClient Affiliateโ), Client and such Client Affiliate shall be jointly and severally liable to Aquarius even though Aquarius may render invoices to, or in the name of, such Client Affiliate."
---
<!-- 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-large
This model is a fine-tuned version of [google/electra-large-discriminator](https://huggingface.co/google/electra-large-discriminator) on the cuad dataset for the question-answering task.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
Contract Understanding Atticus Dataset (CUAD) is an extractive question-answering dataset on legal contracts proposed by the
Atticus Project, a non-profit organization of legal experts, designed with the help of many experts in the legal field.
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 8
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 6.0
### Training results
### Framework versions
- Transformers 4.27.4
- Pytorch 2.0.0+cu117
- Datasets 2.11.0
- Tokenizers 0.13.2 |
dt-and-vanilla-ardt/ardt-vanilla-robust_train_halfcheetah_level-0109_2214-33 | dt-and-vanilla-ardt | 2023-09-01T23:31:49Z | 31 | 0 | transformers | [
"transformers",
"pytorch",
"decision_transformer",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | null | 2023-09-01T21:16:41Z | ---
tags:
- generated_from_trainer
model-index:
- name: ardt-vanilla-robust_train_halfcheetah_level-0109_2214-33
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. -->
# ardt-vanilla-robust_train_halfcheetah_level-0109_2214-33
This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 64
- 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
- training_steps: 10000
### Training results
### Framework versions
- Transformers 4.29.2
- Pytorch 2.1.0.dev20230727+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
mihirtw/med-train-llama | mihirtw | 2023-09-01T23:17:57Z | 10 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"gpt",
"llm",
"large language model",
"h2o-llmstudio",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2023-09-01T21:43:00Z | ---
language:
- en
library_name: transformers
tags:
- gpt
- llm
- large language model
- h2o-llmstudio
inference: true
thumbnail: https://h2o.ai/etc.clientlibs/h2o/clientlibs/clientlib-site/resources/images/favicon.ico
---
# Model Card
## Summary
This model was trained using [H2O LLM Studio](https://github.com/h2oai/h2o-llmstudio).
- Base model: [h2oai/h2ogpt-4096-llama2-7b](https://huggingface.co/h2oai/h2ogpt-4096-llama2-7b)
## Usage
To use the model with the `transformers` library on a machine with GPUs, first make sure you have the `transformers` library installed.
```bash
pip install transformers==4.31.0
```
Also make sure you are providing your huggingface token to the pipeline if the model is lying in a private repo.
- Either leave `token=True` in the `pipeline` and login to hugginface_hub by running
```python
import huggingface_hub
huggingface_hub.login(<ACCES_TOKEN>)
```
- Or directly pass your <ACCES_TOKEN> to `token` in the `pipeline`
```python
from transformers import pipeline
generate_text = pipeline(
model="mihirtw/med-train-llama",
torch_dtype="auto",
trust_remote_code=True,
use_fast=True,
device_map={"": "cuda:0"},
token=True,
)
res = generate_text(
"Why is drinking water so healthy?",
min_new_tokens=2,
max_new_tokens=256,
do_sample=False,
num_beams=1,
temperature=float(0.3),
repetition_penalty=float(1.2),
renormalize_logits=True
)
print(res[0]["generated_text"])
```
You can print a sample prompt after the preprocessing step to see how it is feed to the tokenizer:
```python
print(generate_text.preprocess("Why is drinking water so healthy?")["prompt_text"])
```
```bash
<|prompt|>Why is drinking water so healthy?</s><|answer|>
```
Alternatively, you can download [h2oai_pipeline.py](h2oai_pipeline.py), store it alongside your notebook, and construct the pipeline yourself from the loaded model and tokenizer. If the model and the tokenizer are fully supported in the `transformers` package, this will allow you to set `trust_remote_code=False`.
```python
from h2oai_pipeline import H2OTextGenerationPipeline
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained(
"mihirtw/med-train-llama",
use_fast=True,
padding_side="left",
trust_remote_code=True,
)
model = AutoModelForCausalLM.from_pretrained(
"mihirtw/med-train-llama",
torch_dtype="auto",
device_map={"": "cuda:0"},
trust_remote_code=True,
)
generate_text = H2OTextGenerationPipeline(model=model, tokenizer=tokenizer)
res = generate_text(
"Why is drinking water so healthy?",
min_new_tokens=2,
max_new_tokens=256,
do_sample=False,
num_beams=1,
temperature=float(0.3),
repetition_penalty=float(1.2),
renormalize_logits=True
)
print(res[0]["generated_text"])
```
You may also construct the pipeline from the loaded model and tokenizer yourself and consider the preprocessing steps:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "mihirtw/med-train-llama" # either local folder or huggingface model name
# Important: The prompt needs to be in the same format the model was trained with.
# You can find an example prompt in the experiment logs.
prompt = "<|prompt|>How are you?</s><|answer|>"
tokenizer = AutoTokenizer.from_pretrained(
model_name,
use_fast=True,
trust_remote_code=True,
)
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype="auto",
device_map={"": "cuda:0"},
trust_remote_code=True,
)
model.cuda().eval()
inputs = tokenizer(prompt, return_tensors="pt", add_special_tokens=False).to("cuda")
# generate configuration can be modified to your needs
tokens = model.generate(
input_ids=inputs["input_ids"],
attention_mask=inputs["attention_mask"],
min_new_tokens=2,
max_new_tokens=256,
do_sample=False,
num_beams=1,
temperature=float(0.3),
repetition_penalty=float(1.2),
renormalize_logits=True
)[0]
tokens = tokens[inputs["input_ids"].shape[1]:]
answer = tokenizer.decode(tokens, skip_special_tokens=True)
print(answer)
```
## Quantization and sharding
You can load the models using quantization by specifying ```load_in_8bit=True``` or ```load_in_4bit=True```. Also, sharding on multiple GPUs is possible by setting ```device_map=auto```.
## Model Architecture
```
LlamaForCausalLM(
(model): LlamaModel(
(embed_tokens): Embedding(32000, 4096, padding_idx=0)
(layers): ModuleList(
(0-31): 32 x LlamaDecoderLayer(
(self_attn): LlamaAttention(
(q_proj): Linear(in_features=4096, out_features=4096, bias=False)
(k_proj): Linear(in_features=4096, out_features=4096, bias=False)
(v_proj): Linear(in_features=4096, out_features=4096, bias=False)
(o_proj): Linear(in_features=4096, out_features=4096, bias=False)
(rotary_emb): LlamaRotaryEmbedding()
)
(mlp): LlamaMLP(
(gate_proj): Linear(in_features=4096, out_features=11008, bias=False)
(up_proj): Linear(in_features=4096, out_features=11008, bias=False)
(down_proj): Linear(in_features=11008, out_features=4096, bias=False)
(act_fn): SiLUActivation()
)
(input_layernorm): LlamaRMSNorm()
(post_attention_layernorm): LlamaRMSNorm()
)
)
(norm): LlamaRMSNorm()
)
(lm_head): Linear(in_features=4096, out_features=32000, bias=False)
)
```
## Model Configuration
This model was trained using H2O LLM Studio and with the configuration in [cfg.yaml](cfg.yaml). Visit [H2O LLM Studio](https://github.com/h2oai/h2o-llmstudio) to learn how to train your own large language models.
## Disclaimer
Please read this disclaimer carefully before using the large language model provided in this repository. Your use of the model signifies your agreement to the following terms and conditions.
- Biases and Offensiveness: The large language model is trained on a diverse range of internet text data, which may contain biased, racist, offensive, or otherwise inappropriate content. By using this model, you acknowledge and accept that the generated content may sometimes exhibit biases or produce content that is offensive or inappropriate. The developers of this repository do not endorse, support, or promote any such content or viewpoints.
- Limitations: The large language model is an AI-based tool and not a human. It may produce incorrect, nonsensical, or irrelevant responses. It is the user's responsibility to critically evaluate the generated content and use it at their discretion.
- Use at Your Own Risk: Users of this large language model must assume full responsibility for any consequences that may arise from their use of the tool. The developers and contributors of this repository shall not be held liable for any damages, losses, or harm resulting from the use or misuse of the provided model.
- Ethical Considerations: Users are encouraged to use the large language model responsibly and ethically. By using this model, you agree not to use it for purposes that promote hate speech, discrimination, harassment, or any form of illegal or harmful activities.
- Reporting Issues: If you encounter any biased, offensive, or otherwise inappropriate content generated by the large language model, please report it to the repository maintainers through the provided channels. Your feedback will help improve the model and mitigate potential issues.
- Changes to this Disclaimer: The developers of this repository reserve the right to modify or update this disclaimer at any time without prior notice. It is the user's responsibility to periodically review the disclaimer to stay informed about any changes.
By using the large language model provided in this repository, you agree to accept and comply with the terms and conditions outlined in this disclaimer. If you do not agree with any part of this disclaimer, you should refrain from using the model and any content generated by it. |
C57Box/bert-finetuned-squad | C57Box | 2023-09-01T23:06:55Z | 116 | 0 | transformers | [
"transformers",
"pytorch",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"base_model:google-bert/bert-base-cased",
"base_model:finetune:google-bert/bert-base-cased",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | question-answering | 2023-09-01T20:52:03Z | ---
license: apache-2.0
base_model: bert-base-cased
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: bert-finetuned-squad
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-finetuned-squad
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
### Framework versions
- Transformers 4.32.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
|
AltamashAhmed/TTS_speecht5_finetuned_voxpopuli_it | AltamashAhmed | 2023-09-01T23:03:49Z | 75 | 0 | transformers | [
"transformers",
"pytorch",
"speecht5",
"text-to-audio",
"generated_from_trainer",
"it",
"dataset:facebook/voxpopuli",
"endpoints_compatible",
"region:us"
] | text-to-audio | 2023-08-31T18:27:02Z | ---
language:
- it
base_model: SpeechT5
tags:
- generated_from_trainer
datasets:
- facebook/voxpopuli
model-index:
- name: microsoft/speecht5_tts
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. -->
# microsoft/speecht5_tts
This model is a fine-tuned version of [SpeechT5](https://huggingface.co/SpeechT5) on the facebook/voxpopuli dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4873
## 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: 4
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 5000
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.5472 | 6.13 | 1000 | 0.5091 |
| 0.5229 | 12.26 | 2000 | 0.4946 |
| 0.5122 | 18.39 | 3000 | 0.4898 |
| 0.5159 | 24.52 | 4000 | 0.4889 |
| 0.511 | 30.65 | 5000 | 0.4873 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
|
lsoni/bert-finetuned-ner-word-embedding | lsoni | 2023-09-01T23:01:03Z | 105 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | 2023-08-31T14:37:06Z | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-finetuned-ner-word-embedding
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-finetuned-ner-word-embedding
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the combined training dataset(tweetner7(train_2021)+augmented dataset(train_2021) using word embedding technique).
It achieves the following results on the evaluation set:
- Loss: 0.5502
- Precision: 0.6522
- Recall: 0.4973
- F1: 0.5643
- Accuracy: 0.8615
## 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: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.7144 | 1.0 | 624 | 0.5837 | 0.7042 | 0.4422 | 0.5433 | 0.8601 |
| 0.5257 | 2.0 | 1248 | 0.5522 | 0.6575 | 0.4803 | 0.5551 | 0.8610 |
| 0.4564 | 3.0 | 1872 | 0.5502 | 0.6522 | 0.4973 | 0.5643 | 0.8615 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.12.1
- Datasets 2.10.1
- Tokenizers 0.12.1
|
Whybother/version-3 | Whybother | 2023-09-01T22:58:56Z | 47 | 0 | diffusers | [
"diffusers",
"text-to-image",
"stable-diffusion",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | text-to-image | 2023-09-01T22:56:11Z | ---
license: creativeml-openrail-m
tags:
- text-to-image
- stable-diffusion
---
### Version_3 Dreambooth model trained by Whybother with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook
Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb)
Sample pictures of this concept:
|
KingKazma/xsum_gpt2_p_tuning_500_4_50000_6_e-1_s6789_v4_l4_v100 | KingKazma | 2023-09-01T22:56:15Z | 1 | 0 | peft | [
"peft",
"region:us"
] | null | 2023-09-01T22:56:14Z | ---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.6.0.dev0
|
U-sama/wav2vec2-base-demo-colab | U-sama | 2023-09-01T22:49:04Z | 105 | 0 | transformers | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"base_model:facebook/wav2vec2-base",
"base_model:finetune:facebook/wav2vec2-base",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | 2023-09-01T20:37:29Z | ---
license: apache-2.0
base_model: facebook/wav2vec2-base
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-base-demo-colab
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-demo-colab
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.4908
- eval_wer: 0.3950
- eval_runtime: 62.3995
- eval_samples_per_second: 26.923
- eval_steps_per_second: 3.365
- epoch: 12.0
- step: 1500
## 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: 32
- 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: 30
### Framework versions
- Transformers 4.32.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
|
hmcgovern/gpt-j-6b_text_detection | hmcgovern | 2023-09-01T22:46:19Z | 0 | 0 | null | [
"generated_from_trainer",
"base_model:EleutherAI/gpt-j-6b",
"base_model:finetune:EleutherAI/gpt-j-6b",
"license:apache-2.0",
"region:us"
] | null | 2023-09-01T18:24:54Z | ---
license: apache-2.0
base_model: EleutherAI/gpt-j-6b
tags:
- generated_from_trainer
model-index:
- name: gpt-j-6b_text_detection
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. -->
# gpt-j-6b_text_detection
This model is a fine-tuned version of [EleutherAI/gpt-j-6b](https://huggingface.co/EleutherAI/gpt-j-6b) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0529
## 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: 1
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- total_eval_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.2716 | 1.0 | 384 | 0.0529 |
### Framework versions
- Transformers 4.32.0.dev0
- Pytorch 2.0.1+cu117
- Datasets 2.14.4
- Tokenizers 0.13.3
|
ardt-multipart/ardt-multipart-robust_train_walker2d_level-0109_2144-33 | ardt-multipart | 2023-09-01T22:37:32Z | 31 | 0 | transformers | [
"transformers",
"pytorch",
"decision_transformer",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | null | 2023-09-01T20:46:28Z | ---
tags:
- generated_from_trainer
model-index:
- name: ardt-multipart-robust_train_walker2d_level-0109_2144-33
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. -->
# ardt-multipart-robust_train_walker2d_level-0109_2144-33
This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 64
- 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
- training_steps: 10000
### Training results
### Framework versions
- Transformers 4.29.2
- Pytorch 2.1.0.dev20230727+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
Whybother/private | Whybother | 2023-09-01T22:23:34Z | 1 | 1 | diffusers | [
"diffusers",
"safetensors",
"text-to-image",
"stable-diffusion",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | text-to-image | 2023-09-01T22:19:37Z | ---
license: creativeml-openrail-m
tags:
- text-to-image
- stable-diffusion
---
### Private() Dreambooth model trained by Whybother with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook
Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb)
Sample pictures of this concept:
|
marcdemory/SDXL-lora-MADeMory-v1-0-1 | marcdemory | 2023-09-01T22:18:40Z | 2 | 1 | diffusers | [
"diffusers",
"tensorboard",
"stable-diffusion-xl",
"stable-diffusion-xl-diffusers",
"text-to-image",
"lora",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0",
"license:openrail++",
"region:us"
] | text-to-image | 2023-09-01T14:25:12Z |
---
license: openrail++
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: photo of a MADeMory person
tags:
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
- text-to-image
- diffusers
- lora
inference: true
---
# LoRA DreamBooth - marcdemory/SDXL-lora-MADeMory-v1-0-1
These are LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained on photo of a MADeMory person using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following.
LoRA for the text encoder was enabled: False.
Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
|
Yaxin1992/codellama-13b-multi-3500 | Yaxin1992 | 2023-09-01T21:28:14Z | 0 | 0 | null | [
"generated_from_trainer",
"base_model:codellama/CodeLlama-34b-hf",
"base_model:finetune:codellama/CodeLlama-34b-hf",
"license:llama2",
"region:us"
] | null | 2023-08-31T18:01:45Z | ---
license: llama2
base_model: codellama/CodeLlama-34b-hf
tags:
- generated_from_trainer
model-index:
- name: codellama-13b-multi-3500
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. -->
# codellama-13b-multi-3500
This model is a fine-tuned version of [codellama/CodeLlama-34b-hf](https://huggingface.co/codellama/CodeLlama-34b-hf) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 3000
### Training results
### Framework versions
- Transformers 4.33.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
|
ardt-multipart/ardt-multipart-robust_train_halfcheetah_level-0109_2000-33 | ardt-multipart | 2023-09-01T21:25:13Z | 31 | 0 | transformers | [
"transformers",
"pytorch",
"decision_transformer",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | null | 2023-09-01T19:01:43Z | ---
tags:
- generated_from_trainer
model-index:
- name: ardt-multipart-robust_train_halfcheetah_level-0109_2000-33
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. -->
# ardt-multipart-robust_train_halfcheetah_level-0109_2000-33
This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 64
- 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
- training_steps: 10000
### Training results
### Framework versions
- Transformers 4.29.2
- Pytorch 2.1.0.dev20230727+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
dreamboat26/bert-finetuned-ner | dreamboat26 | 2023-09-01T21:20:54Z | 61 | 0 | transformers | [
"transformers",
"tf",
"bert",
"token-classification",
"generated_from_keras_callback",
"base_model:google-bert/bert-base-cased",
"base_model:finetune:google-bert/bert-base-cased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | 2023-09-01T20:47:01Z | ---
license: apache-2.0
base_model: bert-base-cased
tags:
- generated_from_keras_callback
model-index:
- name: dreamboat26/bert-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. -->
# dreamboat26/bert-finetuned-ner
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0219
- Validation Loss: 0.0516
- Epoch: 2
## Model description
Find the entities (such as persons, locations, or organizations) in a sentence. This can be formulated as attributing a label to each token by having one class per entity and one class for โno entity.โ
## Intended uses & limitations
Academic Use
## 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': 2634, '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: mixed_float16
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 0.0216 | 0.0516 | 0 |
| 0.0222 | 0.0516 | 1 |
| 0.0219 | 0.0516 | 2 |
### Framework versions
- Transformers 4.32.1
- TensorFlow 2.12.0
- Datasets 2.14.4
- Tokenizers 0.13.3
|
ardt-multipart/ardt-multipart-robust_train_hopper_level-0109_2125-99 | ardt-multipart | 2023-09-01T21:09:35Z | 31 | 0 | transformers | [
"transformers",
"pytorch",
"decision_transformer",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | null | 2023-09-01T20:26:47Z | ---
tags:
- generated_from_trainer
model-index:
- name: ardt-multipart-robust_train_hopper_level-0109_2125-99
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. -->
# ardt-multipart-robust_train_hopper_level-0109_2125-99
This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 64
- 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
- training_steps: 10000
### Training results
### Framework versions
- Transformers 4.29.2
- Pytorch 2.1.0.dev20230727+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
ameerazam08/person_train | ameerazam08 | 2023-09-01T20:58:04Z | 35 | 0 | diffusers | [
"diffusers",
"safetensors",
"stable-diffusion-xl",
"stable-diffusion-xl-diffusers",
"text-to-image",
"lora",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0",
"license:openrail++",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | text-to-image | 2023-09-01T19:46:33Z |
---
license: openrail++
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: a photo of dkjfgkj
tags:
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
- text-to-image
- diffusers
- lora
inference: true
---
# LoRA DreamBooth - ameerazam08/person_train
These are LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained on a photo of dkjfgkj using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following.
LoRA for the text encoder was enabled: False.
Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
|
Syedian123/rachel | Syedian123 | 2023-09-01T20:48:52Z | 2 | 2 | diffusers | [
"diffusers",
"safetensors",
"text-to-image",
"stable-diffusion",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | text-to-image | 2023-09-01T20:43:17Z | ---
license: creativeml-openrail-m
tags:
- text-to-image
- stable-diffusion
---
### Rachel Dreambooth model trained by Syedian123 with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook
Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb)
Sample pictures of this concept:
|
gustavodemoura/ppo-LunarLander-v2 | gustavodemoura | 2023-09-01T20:42:45Z | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | 2023-09-01T20:19:30Z | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 280.60 +/- 20.06
name: mean_reward
verified: false
---
# **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
...
```
|
cmvgia/loratest2 | cmvgia | 2023-09-01T20:37:01Z | 0 | 0 | peft | [
"peft",
"region:us"
] | null | 2023-09-01T20:36:59Z | ---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.6.0.dev0
|
ardt-multipart/ardt-multipart-robust_train_hopper_level-0109_2041-66 | ardt-multipart | 2023-09-01T20:25:15Z | 32 | 0 | transformers | [
"transformers",
"pytorch",
"decision_transformer",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | null | 2023-09-01T19:43:05Z | ---
tags:
- generated_from_trainer
model-index:
- name: ardt-multipart-robust_train_hopper_level-0109_2041-66
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. -->
# ardt-multipart-robust_train_hopper_level-0109_2041-66
This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 64
- 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
- training_steps: 10000
### Training results
### Framework versions
- Transformers 4.29.2
- Pytorch 2.1.0.dev20230727+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
Ajani/lesson-summarization | Ajani | 2023-09-01T20:14:28Z | 3 | 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 | 2023-05-23T17:25:55Z | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: lesson-summarization
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. -->
# lesson-summarization
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.0801
## 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: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 200
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:-----:|:---------------:|
| 2.8198 | 3.12 | 200 | 2.8048 |
| 2.5358 | 6.25 | 400 | 2.6645 |
| 2.333 | 9.38 | 600 | 2.6123 |
| 2.2096 | 12.5 | 800 | 2.5807 |
| 2.0783 | 15.62 | 1000 | 2.5703 |
| 1.9919 | 18.75 | 1200 | 2.5653 |
| 1.89 | 21.88 | 1400 | 2.5602 |
| 1.7865 | 25.0 | 1600 | 2.5650 |
| 1.7149 | 28.12 | 1800 | 2.5812 |
| 1.6651 | 31.25 | 2000 | 2.5813 |
| 1.5662 | 34.38 | 2200 | 2.5997 |
| 1.5333 | 37.5 | 2400 | 2.6097 |
| 1.4336 | 40.62 | 2600 | 2.6389 |
| 1.3986 | 43.75 | 2800 | 2.6564 |
| 1.352 | 46.88 | 3000 | 2.6720 |
| 1.3072 | 50.0 | 3200 | 2.6863 |
| 1.2773 | 53.12 | 3400 | 2.6931 |
| 1.2079 | 56.25 | 3600 | 2.7350 |
| 1.1768 | 59.38 | 3800 | 2.7521 |
| 1.1749 | 62.5 | 4000 | 2.7553 |
| 1.0857 | 65.62 | 4200 | 2.7921 |
| 1.0883 | 68.75 | 4400 | 2.7840 |
| 1.0307 | 71.88 | 4600 | 2.8110 |
| 1.0255 | 75.0 | 4800 | 2.8365 |
| 0.9992 | 78.12 | 5000 | 2.8358 |
| 0.9516 | 81.25 | 5200 | 2.8554 |
| 0.9363 | 84.38 | 5400 | 2.8742 |
| 0.91 | 87.5 | 5600 | 2.8923 |
| 0.895 | 90.62 | 5800 | 2.9057 |
| 0.8371 | 93.75 | 6000 | 2.9234 |
| 0.8588 | 96.88 | 6200 | 2.9443 |
| 0.8237 | 100.0 | 6400 | 2.9612 |
| 0.8147 | 103.12 | 6600 | 2.9633 |
| 0.7936 | 106.25 | 6800 | 2.9641 |
| 0.7883 | 109.38 | 7000 | 2.9711 |
| 0.7589 | 112.5 | 7200 | 2.9744 |
| 0.7277 | 115.62 | 7400 | 2.9879 |
| 0.7505 | 118.75 | 7600 | 2.9974 |
| 0.705 | 121.88 | 7800 | 3.0033 |
| 0.7111 | 125.0 | 8000 | 3.0032 |
| 0.7005 | 128.12 | 8200 | 3.0055 |
| 0.6961 | 131.25 | 8400 | 3.0168 |
| 0.6543 | 134.38 | 8600 | 3.0339 |
| 0.6482 | 137.5 | 8800 | 3.0312 |
| 0.6807 | 140.62 | 9000 | 3.0393 |
| 0.6365 | 143.75 | 9200 | 3.0413 |
| 0.648 | 146.88 | 9400 | 3.0461 |
| 0.6275 | 150.0 | 9600 | 3.0454 |
| 0.6284 | 153.12 | 9800 | 3.0552 |
| 0.6062 | 156.25 | 10000 | 3.0514 |
| 0.6312 | 159.38 | 10200 | 3.0487 |
| 0.6244 | 162.5 | 10400 | 3.0525 |
| 0.5792 | 165.62 | 10600 | 3.0547 |
| 0.5997 | 168.75 | 10800 | 3.0491 |
| 0.5972 | 171.88 | 11000 | 3.0542 |
| 0.5891 | 175.0 | 11200 | 3.0624 |
| 0.582 | 178.12 | 11400 | 3.0717 |
| 0.5934 | 181.25 | 11600 | 3.0683 |
| 0.5803 | 184.38 | 11800 | 3.0761 |
| 0.5724 | 187.5 | 12000 | 3.0777 |
| 0.6015 | 190.62 | 12200 | 3.0784 |
| 0.5874 | 193.75 | 12400 | 3.0792 |
| 0.5531 | 196.88 | 12600 | 3.0801 |
| 0.5863 | 200.0 | 12800 | 3.0801 |
### Framework versions
- Transformers 4.28.0
- Pytorch 1.13.1+cu116
- Datasets 2.12.0
- Tokenizers 0.13.3
|
trieudemo11/llama_7b_attrb_cate_8m_1 | trieudemo11 | 2023-09-01T20:12:01Z | 5 | 0 | peft | [
"peft",
"region:us"
] | null | 2023-09-01T20:11:47Z | ---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
|
KatMarie/wav2vec2-large-xls-r-300m-euskera2.1-colab | KatMarie | 2023-09-01T20:11:37Z | 7 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"base_model:facebook/wav2vec2-xls-r-300m",
"base_model:finetune:facebook/wav2vec2-xls-r-300m",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | 2023-08-17T17:51:50Z | ---
license: apache-2.0
base_model: facebook/wav2vec2-xls-r-300m
tags:
- generated_from_trainer
datasets:
- common_voice
metrics:
- wer
model-index:
- name: wav2vec2-large-xls-r-300m-euskera2.1-colab
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: common_voice
type: common_voice
config: eu
split: test
args: eu
metrics:
- name: Wer
type: wer
value: 0.2787176738226604
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-300m-euskera2.1-colab
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2291
- Wer: 0.2787
## 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: 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: 6
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 2.1152 | 1.49 | 700 | 0.3510 | 0.4973 |
| 0.1969 | 2.98 | 1400 | 0.2552 | 0.3643 |
| 0.1027 | 4.47 | 2100 | 0.2379 | 0.3108 |
| 0.0648 | 5.96 | 2800 | 0.2291 | 0.2787 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
|
Frorozcol/speecht5_finetuned_voxpopuli_nl | Frorozcol | 2023-09-01T20:10:46Z | 76 | 0 | transformers | [
"transformers",
"pytorch",
"speecht5",
"text-to-audio",
"generated_from_trainer",
"dataset:voxpopuli",
"base_model:microsoft/speecht5_tts",
"base_model:finetune:microsoft/speecht5_tts",
"license:mit",
"endpoints_compatible",
"region:us"
] | text-to-audio | 2023-08-31T16:01:04Z | ---
license: mit
base_model: microsoft/speecht5_tts
tags:
- generated_from_trainer
datasets:
- voxpopuli
model-index:
- name: speecht5_finetuned_voxpopuli_nl
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. -->
# speecht5_finetuned_voxpopuli_nl
This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the voxpopuli dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4642
## 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: 4
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 4000
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.4622 | 105.26 | 1000 | 0.4629 |
| 0.4376 | 210.53 | 2000 | 0.4654 |
| 0.4274 | 315.79 | 3000 | 0.4635 |
| 0.422 | 421.05 | 4000 | 0.4642 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
|
trieudemo11/llama_7b_attrb_cate_b6_l320_low_9 | trieudemo11 | 2023-09-01T20:10:05Z | 0 | 0 | peft | [
"peft",
"region:us"
] | null | 2023-09-01T20:09:51Z | ---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
rsions
- PEFT 0.6.0.dev0
|
anik424/SD_xl_base_madras_checks | anik424 | 2023-09-01T20:06:23Z | 1 | 2 | diffusers | [
"diffusers",
"text-to-image",
"autotrain",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:finetune:stabilityai/stable-diffusion-xl-base-1.0",
"region:us"
] | text-to-image | 2023-08-30T18:11:49Z |
---
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: Photo of madras check pattern"
tags:
- text-to-image
- diffusers
- autotrain
inference: true
---
|
DavidHuggingFace1/Concept-Phrase | DavidHuggingFace1 | 2023-09-01T20:04:55Z | 0 | 0 | peft | [
"peft",
"endpoints_compatible",
"region:us"
] | null | 2023-09-01T19:57:16Z | ---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.5.0
- PEFT 0.5.0
|
KingKazma/xsum_t5-small_lora_500_2_300_8_e2_s6789_v4_l4_r4 | KingKazma | 2023-09-01T19:55:08Z | 0 | 0 | peft | [
"peft",
"region:us"
] | null | 2023-09-01T19:50:00Z | ---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.6.0.dev0
|
KingKazma/xsum_t5-small_lora_500_2_300_8_e1_s6789_v4_l4_r4 | KingKazma | 2023-09-01T19:54:55Z | 1 | 0 | peft | [
"peft",
"region:us"
] | null | 2023-09-01T19:45:45Z | ---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.6.0.dev0
|
LarryAIDraw/ScyllaOnlySchool | LarryAIDraw | 2023-09-01T19:52:13Z | 0 | 0 | null | [
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-09-01T19:48:05Z | ---
license: creativeml-openrail-m
---
https://civitai.com/models/26602/scylla-azur-lane-school-uniform-and-2in1-and |
KatMarie/wav2vec2-large-xls-r-300m-eu | KatMarie | 2023-09-01T19:47:42Z | 5 | 0 | transformers | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"base_model:facebook/wav2vec2-xls-r-300m",
"base_model:finetune:facebook/wav2vec2-xls-r-300m",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | 2023-08-30T20:11:45Z | ---
license: apache-2.0
base_model: facebook/wav2vec2-xls-r-300m
tags:
- generated_from_trainer
datasets:
- common_voice
metrics:
- wer
model-index:
- name: wav2vec2-large-xls-r-300m-eu
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: common_voice
type: common_voice
config: eu
split: test
args: eu
metrics:
- name: Wer
type: wer
value: 0.4967706508380619
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-300m-eu
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4355
- Wer: 0.4968
## 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: 0.003
- 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: 6
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.5405 | 0.85 | 400 | 0.8115 | 0.8949 |
| 0.5355 | 1.7 | 800 | 0.6292 | 0.7331 |
| 0.405 | 2.56 | 1200 | 0.5805 | 0.6699 |
| 0.3261 | 3.41 | 1600 | 0.5308 | 0.6513 |
| 0.2496 | 4.26 | 2000 | 0.4755 | 0.5850 |
| 0.1878 | 5.11 | 2400 | 0.4926 | 0.5448 |
| 0.1342 | 5.96 | 2800 | 0.4355 | 0.4968 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
|
zapadaz/ppo-Huggy | zapadaz | 2023-09-01T19:43:25Z | 0 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"Huggy",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
] | reinforcement-learning | 2023-09-01T19:43:21Z | ---
library_name: ml-agents
tags:
- Huggy
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Huggy
---
# **ppo** Agent playing **Huggy**
This is a trained model of a **ppo** agent playing **Huggy**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog ๐ถ to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: zapadaz/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play ๐
|
ardt-multipart/ardt-multipart-robust_train_hopper_level-0109_2000-33 | ardt-multipart | 2023-09-01T19:41:40Z | 31 | 0 | transformers | [
"transformers",
"pytorch",
"decision_transformer",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | null | 2023-09-01T19:01:16Z | ---
tags:
- generated_from_trainer
model-index:
- name: ardt-multipart-robust_train_hopper_level-0109_2000-33
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. -->
# ardt-multipart-robust_train_hopper_level-0109_2000-33
This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 64
- 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
- training_steps: 10000
### Training results
### Framework versions
- Transformers 4.29.2
- Pytorch 2.1.0.dev20230727+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
swl-models/Yorunohitsuji-v1.1 | swl-models | 2023-09-01T19:32:15Z | 0 | 0 | null | [
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-09-01T16:18:36Z | ---
license: creativeml-openrail-m
---
|
santyzenith/amazon_es_reviews | santyzenith | 2023-09-01T19:19:25Z | 105 | 0 | transformers | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"generated_from_trainer",
"dataset:amazon_reviews_multi",
"base_model:BSC-LT/roberta-base-bne",
"base_model:finetune:BSC-LT/roberta-base-bne",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2023-09-01T18:52:13Z | ---
license: apache-2.0
base_model: BSC-TeMU/roberta-base-bne
tags:
- generated_from_trainer
datasets:
- amazon_reviews_multi
metrics:
- accuracy
model-index:
- name: amazon_es_reviews
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: amazon_reviews_multi
type: amazon_reviews_multi
config: es
split: validation
args: es
metrics:
- name: Accuracy
type: accuracy
value: 0.9295
---
<!-- 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. -->
# amazon_es_reviews
This model is a fine-tuned version of [BSC-TeMU/roberta-base-bne](https://huggingface.co/BSC-TeMU/roberta-base-bne) on the amazon_reviews_multi dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2246
- Accuracy: 0.9295
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.1939 | 1.0 | 938 | 0.2086 | 0.93 |
| 0.0999 | 2.0 | 1876 | 0.2246 | 0.9295 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
|
ameerazam08/sd_train | ameerazam08 | 2023-09-01T18:38:04Z | 30 | 0 | diffusers | [
"diffusers",
"safetensors",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"dreambooth",
"base_model:runwayml/stable-diffusion-v1-5",
"base_model:finetune:runwayml/stable-diffusion-v1-5",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | text-to-image | 2023-09-01T18:23:53Z |
---
license: creativeml-openrail-m
base_model: runwayml/stable-diffusion-v1-5
instance_prompt: a photo of sks dog
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- dreambooth
inference: true
---
# DreamBooth - ameerazam08/sd_train
This is a dreambooth model derived from runwayml/stable-diffusion-v1-5. The weights were trained on a photo of sks dog using [DreamBooth](https://dreambooth.github.io/).
You can find some example images in the following.
DreamBooth for the text encoder was enabled: False.
|
facebook/mms-tts-tzo-dialect_chamula | facebook | 2023-09-01T18:25:20Z | 107 | 0 | transformers | [
"transformers",
"pytorch",
"safetensors",
"vits",
"text-to-audio",
"mms",
"text-to-speech",
"arxiv:2305.13516",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
] | text-to-speech | 2023-09-01T18:25:04Z |
---
license: cc-by-nc-4.0
tags:
- mms
- vits
pipeline_tag: text-to-speech
---
# Massively Multilingual Speech (MMS): Tzotzil Text-to-Speech
This repository contains the **Tzotzil (tzo-dialect_chamula)** language text-to-speech (TTS) model checkpoint.
This model is part of Facebook's [Massively Multilingual Speech](https://arxiv.org/abs/2305.13516) project, aiming to
provide speech technology across a diverse range of languages. You can find more details about the supported languages
and their ISO 639-3 codes in the [MMS Language Coverage Overview](https://dl.fbaipublicfiles.com/mms/misc/language_coverage_mms.html),
and see all MMS-TTS checkpoints on the Hugging Face Hub: [facebook/mms-tts](https://huggingface.co/models?sort=trending&search=facebook%2Fmms-tts).
MMS-TTS is available in the ๐ค Transformers library from version 4.33 onwards.
## Model Details
VITS (**V**ariational **I**nference with adversarial learning for end-to-end **T**ext-to-**S**peech) is an end-to-end
speech synthesis model that predicts a speech waveform conditional on an input text sequence. It is a conditional variational
autoencoder (VAE) comprised of a posterior encoder, decoder, and conditional prior.
A set of spectrogram-based acoustic features are predicted by the flow-based module, which is formed of a Transformer-based
text encoder and multiple coupling layers. The spectrogram is decoded using a stack of transposed convolutional layers,
much in the same style as the HiFi-GAN vocoder. Motivated by the one-to-many nature of the TTS problem, where the same text
input can be spoken in multiple ways, the model also includes a stochastic duration predictor, which allows the model to
synthesise speech with different rhythms from the same input text.
The model is trained end-to-end with a combination of losses derived from variational lower bound and adversarial training.
To improve the expressiveness of the model, normalizing flows are applied to the conditional prior distribution. During
inference, the text encodings are up-sampled based on the duration prediction module, and then mapped into the
waveform using a cascade of the flow module and HiFi-GAN decoder. Due to the stochastic nature of the duration predictor,
the model is non-deterministic, and thus requires a fixed seed to generate the same speech waveform.
For the MMS project, a separate VITS checkpoint is trained on each langauge.
## Usage
MMS-TTS is available in the ๐ค Transformers library from version 4.33 onwards. To use this checkpoint,
first install the latest version of the library:
```
pip install --upgrade transformers accelerate
```
Then, run inference with the following code-snippet:
```python
from transformers import VitsModel, AutoTokenizer
import torch
model = VitsModel.from_pretrained("facebook/mms-tts-tzo-dialect_chamula")
tokenizer = AutoTokenizer.from_pretrained("facebook/mms-tts-tzo-dialect_chamula")
text = "some example text in the Tzotzil language"
inputs = tokenizer(text, return_tensors="pt")
with torch.no_grad():
output = model(**inputs).waveform
```
The resulting waveform can be saved as a `.wav` file:
```python
import scipy
scipy.io.wavfile.write("techno.wav", rate=model.config.sampling_rate, data=output)
```
Or displayed in a Jupyter Notebook / Google Colab:
```python
from IPython.display import Audio
Audio(output, rate=model.config.sampling_rate)
```
## BibTex citation
This model was developed by Vineel Pratap et al. from Meta AI. If you use the model, consider citing the MMS paper:
```
@article{pratap2023mms,
title={Scaling Speech Technology to 1,000+ Languages},
author={Vineel Pratap and Andros Tjandra and Bowen Shi and Paden Tomasello and Arun Babu and Sayani Kundu and Ali Elkahky and Zhaoheng Ni and Apoorv Vyas and Maryam Fazel-Zarandi and Alexei Baevski and Yossi Adi and Xiaohui Zhang and Wei-Ning Hsu and Alexis Conneau and Michael Auli},
journal={arXiv},
year={2023}
}
```
## License
The model is licensed as **CC-BY-NC 4.0**.
|
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