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| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-07-28 00:48:09
| downloads
int64 0
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| likes
int64 0
11.7k
| library_name
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| pipeline_tag
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ckandemir/xlm-roberta-base-finetuned-panx-all
|
ckandemir
| 2023-09-02T14:31:52Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"base_model:FacebookAI/xlm-roberta-base",
"base_model:finetune:FacebookAI/xlm-roberta-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2023-09-02T13:34:37Z |
---
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-all
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. -->
# xlm-roberta-base-finetuned-panx-all
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1723
- F1: 0.8549
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.3018 | 1.0 | 835 | 0.1952 | 0.8121 |
| 0.1575 | 2.0 | 1670 | 0.1776 | 0.8404 |
| 0.1017 | 3.0 | 2505 | 0.1723 | 0.8549 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
|
Lenouche/JoueurDuGrenier
|
Lenouche
| 2023-09-02T14:31:09Z | 0 | 0 | null |
[
"fr",
"license:openrail",
"region:us"
] | null | 2023-08-13T23:02:23Z |
---
license: openrail
language:
- fr
---
|
Lenouche/MrBidouille
|
Lenouche
| 2023-09-02T14:29:22Z | 0 | 0 | null |
[
"fr",
"license:openrail",
"region:us"
] | null | 2023-08-16T20:37:30Z |
---
language:
- fr
license: openrail
---
|
Lenouche/PPWorld
|
Lenouche
| 2023-09-02T14:29:04Z | 0 | 0 | null |
[
"fr",
"license:openrail",
"region:us"
] | null | 2023-08-16T23:14:29Z |
---
language:
- fr
license: openrail
---
|
Lenouche/GiaTechAndGaming
|
Lenouche
| 2023-09-02T14:28:46Z | 0 | 0 | null |
[
"fr",
"license:openrail",
"region:us"
] | null | 2023-08-17T01:44:54Z |
---
language:
- fr
license: openrail
---
|
Lenouche/SebDuGrenier
|
Lenouche
| 2023-09-02T14:28:23Z | 0 | 0 | null |
[
"fr",
"license:openrail",
"region:us"
] | null | 2023-08-17T15:16:22Z |
---
language:
- fr
type de modèle:
- voix
epochs:
- 300
version de modèle:
- RVC.v2
license: openrail
---
|
Zevin2023/MoC-IQA
|
Zevin2023
| 2023-09-02T14:28:05Z | 0 | 0 | null |
[
"aa",
"license:openrail",
"region:us"
] | null | 2023-09-02T14:02:17Z |
---
license: openrail
language:
- aa
metrics:
- accuracy
---
|
Lenouche/TevIciJapon
|
Lenouche
| 2023-09-02T14:27:59Z | 0 | 0 | null |
[
"fr",
"license:openrail",
"region:us"
] | null | 2023-08-17T18:47:02Z |
---
language:
- fr
license: openrail
---
|
Lenouche/PaulMirabel
|
Lenouche
| 2023-09-02T14:27:32Z | 0 | 0 | null |
[
"fr",
"license:openrail",
"region:us"
] | null | 2023-08-28T23:47:56Z |
---
language:
- fr
license: openrail
---
|
Lenouche/DefendIntelligence
|
Lenouche
| 2023-09-02T14:26:44Z | 0 | 0 | null |
[
"fr",
"license:openrail",
"region:us"
] | null | 2023-08-31T00:44:45Z |
---
language:
- fr
license: openrail
---
|
SymeCloud/Llama2-7b-Chat-GGUF
|
SymeCloud
| 2023-09-02T14:25:41Z | 1 | 2 |
transformers
|
[
"transformers",
"llama",
"code",
"llama-2",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2023-09-02T11:59:57Z |
---
license: apache-2.0
language:
- en
tags:
- code
- llama-2
---
# Llama2 Chat 7B - GGUF
- Model creator: [Meta](https://huggingface.co/meta-llama)
- Original model: [Llama 2 7b Chat GGML](https://huggingface.co/TheBloke/Llama-2-7B-Chat-GGML)
<!-- README_GGUF.md-about-gguf start -->
### About GGUF
GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp.
The key benefit of GGUF is that it is a extensible, future-proof format which stores more information about the model as metadata. It also includes significantly improved tokenization code, including for the first time full support for special tokens. This should improve performance, especially with models that use new special tokens and implement custom prompt templates.
* [llama.cpp](https://github.com/ggerganov/llama.cpp)
|
NiscR/Pyramids-1
|
NiscR
| 2023-09-02T13:53:42Z | 5 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"Pyramids",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
] |
reinforcement-learning
| 2023-09-02T13:53:36Z |
---
library_name: ml-agents
tags:
- Pyramids
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Pyramids
---
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://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 🐶 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: NiscR/Pyramids-1
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
VinayHajare/ppo-LunarLander-v2
|
VinayHajare
| 2023-09-02T13:51:21Z | 5 | 3 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-09-02T06:37:42Z |
---
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: 263.26 +/- 19.25
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)
```python
# !pip gymnasium huggingface-sb3 stable_baselines3[extra]
import gymnasium as gym
from huggingface_sb3 import load_from_hub
from stable_baselines3 import PPO
from stable_baselines3.common.vec_env import DummyVecEnv
from stable_baselines3.common.evaluation import evaluate_policy
from stable_baselines3.common.monitor import Monitor
repo_id = "VinayHajare/ppo-LunarLander-v2"
filename = "ppo-LunarLander-v2.zip"
eval_env = gym.make("LunarLander-v2", render_mode="human")
checkpoint = load_from_hub(repo_id, filename)
model = PPO.load(checkpoint,print_system_info=True)
mean_reward, std_reward = evaluate_policy(model,eval_env, n_eval_episodes=10, deterministic=True)
print(f"mean_reward={mean_reward:.2f} +/- {std_reward}")
# Enjoy trained agent
observation, info = eval_env.reset()
for _ in range(1000):
action, _states = model.predict(observation, deterministic=True)
observation, rewards, terminated, truncated, info = eval_env.step(action)
eval_env.render()
```
|
venetis/roberta-base-finetuned-3d-sentiment
|
venetis
| 2023-09-02T13:41:28Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-09-01T07:49:37Z |
---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: roberta-base-finetuned-3d-sentiment
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-base-finetuned-3d-sentiment
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5817
- Accuracy: 0.7753
- Precision: 0.7757
- Recall: 0.7753
- F1: 0.7745
## 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
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 6381
- num_epochs: 7
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.7758 | 1.0 | 1595 | 0.7691 | 0.7069 | 0.7256 | 0.7069 | 0.7052 |
| 0.5496 | 2.0 | 3190 | 0.6961 | 0.7255 | 0.7441 | 0.7255 | 0.7252 |
| 0.4856 | 3.0 | 4785 | 0.6451 | 0.7368 | 0.7562 | 0.7368 | 0.7328 |
| 0.4257 | 4.0 | 6380 | 0.5817 | 0.7753 | 0.7757 | 0.7753 | 0.7745 |
| 0.351 | 5.0 | 7975 | 0.6637 | 0.7633 | 0.7717 | 0.7633 | 0.7637 |
| 0.2551 | 6.0 | 9570 | 0.7646 | 0.7696 | 0.7738 | 0.7696 | 0.7699 |
| 0.1845 | 7.0 | 11165 | 0.8529 | 0.7674 | 0.7730 | 0.7674 | 0.7680 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu117
- Datasets 2.10.1
- Tokenizers 0.13.3
|
Kamer/NoDuplicates
|
Kamer
| 2023-09-02T13:27:46Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"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
| 2023-09-01T16:09:33Z |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: NoDuplicates
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. -->
# NoDuplicates
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4279
- Accuracy: 0.9128
- F1 Macro: 0.8384
- F1 Class 0: 0.9406
- F1 Class 1: 0.3333
- F1 Class 2: 0.9127
- F1 Class 3: 0.6471
- F1 Class 4: 0.8254
- F1 Class 5: 0.8293
- F1 Class 6: 0.8767
- F1 Class 7: 0.7606
- F1 Class 8: 0.7500
- F1 Class 9: 0.9878
- F1 Class 10: 0.9444
- F1 Class 11: 0.9630
- F1 Class 12: 0.9265
- F1 Class 13: 0.8980
- F1 Class 14: 0.8444
- F1 Class 15: 0.8132
- F1 Class 16: 0.7778
- F1 Class 17: 0.9651
- F1 Class 18: 0.9574
- F1 Class 19: 0.8148
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Macro | F1 Class 0 | F1 Class 1 | F1 Class 2 | F1 Class 3 | F1 Class 4 | F1 Class 5 | F1 Class 6 | F1 Class 7 | F1 Class 8 | F1 Class 9 | F1 Class 10 | F1 Class 11 | F1 Class 12 | F1 Class 13 | F1 Class 14 | F1 Class 15 | F1 Class 16 | F1 Class 17 | F1 Class 18 | F1 Class 19 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:----------:|:----------:|:----------:|:----------:|:----------:|:----------:|:----------:|:----------:|:----------:|:----------:|:-----------:|:-----------:|:-----------:|:-----------:|:-----------:|:-----------:|:-----------:|:-----------:|:-----------:|:-----------:|
| 1.4862 | 0.27 | 300 | 0.8201 | 0.7845 | 0.4484 | 0.8675 | 0.0 | 0.8627 | 0.0 | 0.6733 | 0.0 | 0.6627 | 0.0 | 0.0 | 0.9862 | 0.1935 | 0.9600 | 0.8299 | 0.0833 | 0.2353 | 0.24 | 0.0400 | 0.8852 | 0.9451 | 0.5033 |
| 0.7269 | 0.53 | 600 | 0.5951 | 0.8491 | 0.6504 | 0.9048 | 0.0 | 0.8567 | 0.0 | 0.7596 | 0.6111 | 0.6887 | 0.0 | 0.0 | 0.9877 | 0.8033 | 0.9286 | 0.8798 | 0.9167 | 0.74 | 0.6857 | 0.5823 | 0.9506 | 0.9485 | 0.7640 |
| 0.5429 | 0.8 | 900 | 0.5375 | 0.8637 | 0.7086 | 0.8904 | 0.0 | 0.8589 | 0.0 | 0.7254 | 0.7805 | 0.8215 | 0.6769 | 0.0 | 0.9877 | 0.7833 | 1.0 | 0.9022 | 0.9130 | 0.7912 | 0.7733 | 0.7048 | 0.9032 | 0.9474 | 0.7119 |
| 0.4594 | 1.06 | 1200 | 0.5110 | 0.8805 | 0.7113 | 0.9099 | 0.0 | 0.8925 | 0.0 | 0.7706 | 0.7391 | 0.8139 | 0.4091 | 0.0 | 0.9908 | 0.8785 | 1.0 | 0.8983 | 0.8936 | 0.8090 | 0.7556 | 0.7907 | 0.9529 | 0.9574 | 0.7647 |
| 0.3484 | 1.33 | 1500 | 0.4679 | 0.8951 | 0.7667 | 0.9180 | 0.0 | 0.9080 | 0.6957 | 0.8 | 0.7619 | 0.8299 | 0.6875 | 0.0 | 0.9908 | 0.8909 | 1.0 | 0.9196 | 0.9130 | 0.8172 | 0.7865 | 0.7527 | 0.9398 | 0.9474 | 0.7755 |
| 0.3744 | 1.59 | 1800 | 0.4359 | 0.8951 | 0.7774 | 0.9290 | 0.0 | 0.8815 | 0.8462 | 0.8049 | 0.7805 | 0.8449 | 0.7059 | 0.0 | 0.9908 | 0.9346 | 1.0 | 0.9143 | 0.8980 | 0.8387 | 0.7475 | 0.7179 | 0.9647 | 0.9583 | 0.7895 |
| 0.3514 | 1.86 | 2100 | 0.5161 | 0.8903 | 0.7592 | 0.9109 | 0.0 | 0.8973 | 0.6429 | 0.7603 | 0.7907 | 0.8571 | 0.7077 | 0.0 | 0.9908 | 0.9346 | 1.0 | 0.8971 | 0.8936 | 0.7042 | 0.7324 | 0.7857 | 0.9595 | 0.9574 | 0.7609 |
| 0.3111 | 2.12 | 2400 | 0.4327 | 0.9080 | 0.8027 | 0.9283 | 0.3333 | 0.9141 | 0.7407 | 0.8207 | 0.8095 | 0.8622 | 0.7606 | 0.0 | 0.9908 | 0.9298 | 0.9630 | 0.9215 | 0.9167 | 0.8041 | 0.8 | 0.8132 | 0.9651 | 0.9574 | 0.8224 |
| 0.2088 | 2.39 | 2700 | 0.4356 | 0.9128 | 0.8452 | 0.9386 | 0.3333 | 0.9058 | 0.8462 | 0.8265 | 0.8 | 0.8562 | 0.7429 | 0.7500 | 0.9893 | 0.9346 | 0.9630 | 0.9322 | 0.8936 | 0.8205 | 0.8372 | 0.7765 | 0.9651 | 0.9574 | 0.8350 |
| 0.2317 | 2.65 | 3000 | 0.4294 | 0.9137 | 0.8217 | 0.9365 | 0.3333 | 0.9102 | 0.625 | 0.8243 | 0.8293 | 0.875 | 0.8056 | 0.3333 | 0.9893 | 0.9444 | 0.9630 | 0.9284 | 0.8980 | 0.8478 | 0.8471 | 0.7816 | 0.9651 | 0.9574 | 0.8400 |
| 0.1816 | 2.92 | 3300 | 0.4279 | 0.9128 | 0.8384 | 0.9406 | 0.3333 | 0.9127 | 0.6471 | 0.8254 | 0.8293 | 0.8767 | 0.7606 | 0.7500 | 0.9878 | 0.9444 | 0.9630 | 0.9265 | 0.8980 | 0.8444 | 0.8132 | 0.7778 | 0.9651 | 0.9574 | 0.8148 |
### Framework versions
- Transformers 4.32.0
- Pytorch 2.0.1+cu117
- Datasets 2.14.4
- Tokenizers 0.13.3
|
chillpixel/blacklight-makeup-sdxl-lora
|
chillpixel
| 2023-09-02T13:15:34Z | 651 | 8 |
diffusers
|
[
"diffusers",
"art",
"style",
"sdxl",
"lora",
"stable diffusion",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"blacklight",
"makeup",
"neon",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0",
"region:us"
] |
text-to-image
| 2023-08-26T22:37:20Z |
---
library_name: diffusers
pipeline_tag: text-to-image
base_model: stabilityai/stable-diffusion-xl-base-1.0
tags:
- art
- style
- sdxl
- lora
- stable diffusion
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- blacklight
- makeup
- neon
inference: true
---
# Blacklight Makeup — SDXL LoRA
## <span style="color: orange;">Blacklight Makeup</span> is a fun art style for SDXL
**Difficulty**: <span style="color: indianred;">*Advanced*</span> (not for beginners)
**Blacklight makeup** is a mesmerizing art style that I have come to enjoy for its *creativity* and *boldness*. The magic lies in its ability to transform a simple canvas, such as the human face and body, into a vibrant and otherworldly masterpiece under the enchanting glow of ultraviolet light. The way the colors pop and come to life creates an almost surreal experience for both the creator and the audience. It's like stepping into a dreamlike realm.
I hope that Blacklight Makeup's radiant glow inspires you to experiment, to challenge norms, and to create beauty that transcends the ordinary!
### What's new in Version 2?
I've retrained it with *improved captions and parameters*, which brings:
- simpler trigger words: `blacklight makeup`
- better output quality
- reduced file size
- improved compatibility with other LoRAs
### What's next?
Enhancing the dataset while also experimenting with new training techniques.
### How to use:
**Example prompt:** `Portrait of woman with blacklight makeup, fantasy, highly detailed, digital painting, artstation, concept art, sharp focus, illustration, art by Tony Sart and artgerm and randy vargas`
- trigger words: `blacklight makeup`
- **combine with other LoRAs for extra fun!**
- `<lora:blacklight_makeup_v2:1>`
- **2:3** — 832x1248
- **16:9** — 1360x768
- **1:1** — 1024x1024
#### HuggingFace🤗 Diffusers
```python
from diffusers import StableDiffusionXLPipeline, EulerDiscreteScheduler
import torch
pipe = StableDiffusionXLPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
torch_dtype=torch.float16,
variant="fp16",
use_safetensors=True,
)
pipe.scheduler = EulerDiscreteScheduler.from_config(
pipe.scheduler.config, use_karras_sigmas=True
)
pipe.to("cuda")
pipe.load_lora_weights(
"chillpixel/blacklight-makeup-sdxl-lora",
weight_name="blacklight_makeup_v2.safetensors",
)
image = pipe(
prompt="Portrait of woman with blacklight makeup, fantasy, highly detailed, digital painting, artstation, concept art, sharp focus, illustration, art by Tony Sart and artgerm and randy vargas",
num_inference_steps=35,
guidance_scale=6,
width=832,
height=1248,
).images[0]
```
#### Also, available at:
- [LoRA the Explorer](https://huggingface.co/spaces/multimodalart/LoraTheExplorer)
- [CivitAI](https://civitai.com/models/134643/blacklight-makeup-sdxl-lora)
- [Tensor.Art](https://tensor.art/models/630245562870045528)
- [Ko-Fi](https://ko-fi.com/s/9d846bf374)
I really hope you enjoy this LoRA — and if you do, ***please click the "like" button!***
I will release a new model every time somebody [buys me a coffee on Ko-Fi](https://ko-fi.com/chillpixel).
Want to hire me to train SDXL? I'm open to innovation and marketing opportunities. Contact me at [email protected]
|
Ahmedhisham/social_bias_Bert
|
Ahmedhisham
| 2023-09-02T13:10:27Z | 61 | 0 |
transformers
|
[
"transformers",
"tf",
"distilbert",
"text-classification",
"generated_from_keras_callback",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-09-02T12:32:03Z |
---
tags:
- generated_from_keras_callback
model-index:
- name: social_bias_Bert
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. -->
# social_bias_Bert
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: None
- training_precision: float32
### Training results
### Framework versions
- Transformers 4.32.1
- TensorFlow 2.12.0
- Tokenizers 0.13.3
|
ckandemir/xlm-roberta-base-finetuned-panx-de-fr
|
ckandemir
| 2023-09-02T13:04:30Z | 124 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"base_model:FacebookAI/xlm-roberta-base",
"base_model:finetune:FacebookAI/xlm-roberta-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2023-09-02T12:13:02Z |
---
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-de-fr
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. -->
# xlm-roberta-base-finetuned-panx-de-fr
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1669
- F1: 0.8604
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.3059 | 1.0 | 715 | 0.1894 | 0.8169 |
| 0.148 | 2.0 | 1430 | 0.1663 | 0.8473 |
| 0.0932 | 3.0 | 2145 | 0.1669 | 0.8604 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
|
astroid19/ppo-LunarLander-v2
|
astroid19
| 2023-09-02T12:46:19Z | 1 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-09-02T12:45:58Z |
---
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: 284.82 +/- 21.66
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
...
```
|
HorcruxNo13/swinv2-small-patch4-window8-256-finetuned-eurosat
|
HorcruxNo13
| 2023-09-02T12:44:00Z | 146 | 0 |
transformers
|
[
"transformers",
"pytorch",
"swinv2",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:microsoft/swinv2-small-patch4-window8-256",
"base_model:finetune:microsoft/swinv2-small-patch4-window8-256",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-09-02T12:25:25Z |
---
license: apache-2.0
base_model: microsoft/swinv2-small-patch4-window8-256
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: swinv2-small-patch4-window8-256-finetuned-eurosat
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: validation
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.7333333333333333
---
<!-- 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. -->
# swinv2-small-patch4-window8-256-finetuned-eurosat
This model is a fine-tuned version of [microsoft/swinv2-small-patch4-window8-256](https://huggingface.co/microsoft/swinv2-small-patch4-window8-256) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5868
- Accuracy: 0.7333
## 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: 0.05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 8 | 1.1951 | 0.2667 |
| 5.0901 | 2.0 | 16 | 1.4301 | 0.7333 |
| 2.785 | 3.0 | 24 | 1.1514 | 0.2667 |
| 0.8599 | 4.0 | 32 | 0.5810 | 0.7333 |
| 0.6058 | 5.0 | 40 | 0.5868 | 0.7333 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
|
Kamer/eliminare
|
Kamer
| 2023-09-02T12:42:03Z | 107 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"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
| 2023-09-02T09:01:13Z |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
model-index:
- name: eliminare
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. -->
# eliminare
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- eval_loss: 2.0155
- eval_Accuracy: 0.4159
- eval_F1_macro: 0.1352
- eval_F1_class_0: 0.8025
- eval_F1_class_1: 0.2857
- eval_F1_class_2: 0.5222
- eval_F1_class_3: 0.0
- eval_F1_class_4: 0.0593
- eval_F1_class_5: 0.0
- eval_F1_class_6: 0.0194
- eval_F1_class_7: 0.0
- eval_F1_class_8: 0.0
- eval_F1_class_9: 0.8555
- eval_F1_class_10: 0.0
- eval_F1_class_11: 0.0
- eval_F1_class_12: 0.1262
- eval_F1_class_13: 0.0
- eval_F1_class_14: 0.0
- eval_F1_class_15: 0.0323
- eval_F1_class_16: 0.0
- eval_F1_class_17: 0.0
- eval_F1_class_18: 0.0
- eval_F1_class_19: 0.0
- eval_runtime: 16.4701
- eval_samples_per_second: 68.609
- eval_steps_per_second: 8.622
- epoch: 0.23
- 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: 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
|
mademuhas/qlora-cabrita-joao
|
mademuhas
| 2023-09-02T12:32:23Z | 0 | 0 | null |
[
"generated_from_trainer",
"base_model:tiiuae/falcon-7b",
"base_model:finetune:tiiuae/falcon-7b",
"license:apache-2.0",
"region:us"
] | null | 2023-09-02T12:32:17Z |
---
license: apache-2.0
base_model: tiiuae/falcon-7b
tags:
- generated_from_trainer
model-index:
- name: qlora-cabrita-joao
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. -->
# qlora-cabrita-joao
This model is a fine-tuned version of [tiiuae/falcon-7b](https://huggingface.co/tiiuae/falcon-7b) 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: 0.0002
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.33.0.dev0
- Pytorch 2.0.0
- Datasets 2.14.4
- Tokenizers 0.13.3
|
simlamkr1/llama2-simtestmodel1
|
simlamkr1
| 2023-09-02T12:32:06Z | 0 | 0 |
peft
|
[
"peft",
"pytorch",
"llama",
"region:us"
] | null | 2023-09-01T13:56:00Z |
---
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: False
- bnb_4bit_compute_dtype: float16
### Framework versions
- PEFT 0.6.0.dev0
|
penguinman73/xlm-roberta-base-finetuned-panx-en
|
penguinman73
| 2023-09-02T12:25:02Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"base_model:FacebookAI/xlm-roberta-base",
"base_model:finetune:FacebookAI/xlm-roberta-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2023-09-02T12:22:08Z |
---
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-en
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. -->
# xlm-roberta-base-finetuned-panx-en
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4028
- F1: 0.6831
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 1.1353 | 1.0 | 50 | 0.6267 | 0.5068 |
| 0.5283 | 2.0 | 100 | 0.4369 | 0.6552 |
| 0.358 | 3.0 | 150 | 0.4028 | 0.6831 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
|
rrozb/dqn-SpaceInvadersNoFrameskip-v4
|
rrozb
| 2023-09-02T12:22:17Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-09-02T12:21:54Z |
---
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: 597.00 +/- 109.80
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 rrozb -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 rrozb -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 rrozb
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 1000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
# Environment Arguments
```python
{'render_mode': 'rgb_array'}
```
|
penguinman73/xlm-roberta-base-finetuned-panx-it
|
penguinman73
| 2023-09-02T12:21:56Z | 136 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"base_model:FacebookAI/xlm-roberta-base",
"base_model:finetune:FacebookAI/xlm-roberta-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2023-09-02T12:18:42Z |
---
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-it
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. -->
# xlm-roberta-base-finetuned-panx-it
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2368
- F1: 0.8232
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.8358 | 1.0 | 70 | 0.3188 | 0.7261 |
| 0.2864 | 2.0 | 140 | 0.2533 | 0.7911 |
| 0.1938 | 3.0 | 210 | 0.2368 | 0.8232 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
|
penguinman73/xlm-roberta-base-finetuned-panx-fr
|
penguinman73
| 2023-09-02T12:18:32Z | 124 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"base_model:FacebookAI/xlm-roberta-base",
"base_model:finetune:FacebookAI/xlm-roberta-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2023-09-02T12:13:41Z |
---
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-fr
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. -->
# xlm-roberta-base-finetuned-panx-fr
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2760
- F1: 0.8452
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.5839 | 1.0 | 191 | 0.3623 | 0.7527 |
| 0.2607 | 2.0 | 382 | 0.2836 | 0.8238 |
| 0.1745 | 3.0 | 573 | 0.2760 | 0.8452 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
|
penguinman73/xlm-roberta-base-finetuned-panx-de-fr
|
penguinman73
| 2023-09-02T12:12:18Z | 114 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"base_model:FacebookAI/xlm-roberta-base",
"base_model:finetune:FacebookAI/xlm-roberta-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2023-09-02T11:58:38Z |
---
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-de-fr
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. -->
# xlm-roberta-base-finetuned-panx-de-fr
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1623
- F1: 0.8603
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2891 | 1.0 | 715 | 0.1813 | 0.8232 |
| 0.1482 | 2.0 | 1430 | 0.1586 | 0.8462 |
| 0.0959 | 3.0 | 2145 | 0.1623 | 0.8603 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
|
darthruebezahl/alicia02092023
|
darthruebezahl
| 2023-09-02T12:09:23Z | 29 | 0 |
diffusers
|
[
"diffusers",
"tensorboard",
"text-to-image",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-09-02T12:07:42Z |
---
license: creativeml-openrail-m
tags:
- text-to-image
widget:
- text: Alicia02092023
---
### Alicia02092023 Dreambooth model trained by darthruebezahl with [Hugging Face Dreambooth Training Space](https://huggingface.co/spaces/multimodalart/dreambooth-training) with the v1-5 base model
You run your new concept via `diffusers` [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb). Don't forget to use the concept prompts!
Sample pictures of:
Alicia02092023 (use that on your prompt)
|
fkc294/xlm-roberta-base-finetuned-panx-de
|
fkc294
| 2023-09-02T11:56:53Z | 124 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"base_model:FacebookAI/xlm-roberta-base",
"base_model:finetune:FacebookAI/xlm-roberta-base",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2023-09-02T11:06:08Z |
---
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-de
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xtreme
type: xtreme
config: PAN-X.de
split: validation
args: PAN-X.de
metrics:
- name: F1
type: f1
value: 0.8646808510638297
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-de
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1361
- F1: 0.8647
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2595 | 1.0 | 525 | 0.1540 | 0.8302 |
| 0.1265 | 2.0 | 1050 | 0.1493 | 0.8468 |
| 0.0806 | 3.0 | 1575 | 0.1361 | 0.8647 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
|
abeiler/huggingface-goatLora-goatV10-fullData-withAutoInference
|
abeiler
| 2023-09-02T11:54:04Z | 84 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"llama",
"text-generation",
"generated_from_trainer",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"8-bit",
"region:us"
] |
text-generation
| 2023-09-01T03:25:58Z |
---
tags:
- generated_from_trainer
model-index:
- name: huggingface-goatLora-goatV10-fullData-withAutoInference
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. -->
# huggingface-goatLora-goatV10-fullData-withAutoInference
This model is a fine-tuned version of [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-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: 0.0003
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0
- Datasets 2.12.0
- Tokenizers 0.13.3
|
sharoz/gpt2-medium-custom-functions-dataset-python
|
sharoz
| 2023-09-02T11:52:46Z | 170 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"generated_from_trainer",
"base_model:openai-community/gpt2-medium",
"base_model:finetune:openai-community/gpt2-medium",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-09-02T11:39:39Z |
---
license: mit
base_model: gpt2-medium
tags:
- generated_from_trainer
model-index:
- name: gpt2-medium-custom-functions-dataset-python
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# gpt2-medium-custom-functions-dataset-python
This model is a fine-tuned version of [gpt2-medium](https://huggingface.co/gpt2-medium) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4735
## 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.6637 | 0.02 | 1 | 2.1553 |
| 2.4565 | 0.05 | 2 | 2.0239 |
| 2.1968 | 0.07 | 3 | 1.9137 |
| 2.2327 | 0.09 | 4 | 1.8194 |
| 2.0672 | 0.12 | 5 | 1.7425 |
| 1.9292 | 0.14 | 6 | 1.6721 |
| 1.8293 | 0.16 | 7 | 1.6049 |
| 1.71 | 0.19 | 8 | 1.5385 |
| 1.8569 | 0.21 | 9 | 1.4786 |
| 1.7208 | 0.23 | 10 | 1.4236 |
| 1.6461 | 0.26 | 11 | 1.3770 |
| 1.6146 | 0.28 | 12 | 1.3361 |
| 1.5799 | 0.3 | 13 | 1.3006 |
| 1.515 | 0.33 | 14 | 1.2690 |
| 1.4448 | 0.35 | 15 | 1.2488 |
| 1.2871 | 0.37 | 16 | 1.2254 |
| 1.6566 | 0.4 | 17 | 1.1972 |
| 1.4823 | 0.42 | 18 | 1.1638 |
| 1.4655 | 0.44 | 19 | 1.1379 |
| 1.3227 | 0.47 | 20 | 1.1172 |
| 1.4135 | 0.49 | 21 | 1.0973 |
| 1.4835 | 0.51 | 22 | 1.0784 |
| 1.401 | 0.53 | 23 | 1.0607 |
| 1.3294 | 0.56 | 24 | 1.0455 |
| 1.4781 | 0.58 | 25 | 1.0302 |
| 1.1167 | 0.6 | 26 | 1.0153 |
| 1.3876 | 0.63 | 27 | 1.0017 |
| 1.1708 | 0.65 | 28 | 0.9911 |
| 1.2199 | 0.67 | 29 | 0.9833 |
| 1.2328 | 0.7 | 30 | 0.9709 |
| 1.5262 | 0.72 | 31 | 0.9599 |
| 1.1906 | 0.74 | 32 | 0.9501 |
| 1.191 | 0.77 | 33 | 0.9404 |
| 1.0422 | 0.79 | 34 | 0.9291 |
| 1.277 | 0.81 | 35 | 0.9183 |
| 1.1522 | 0.84 | 36 | 0.9092 |
| 1.1841 | 0.86 | 37 | 0.9006 |
| 1.2538 | 0.88 | 38 | 0.8931 |
| 1.1318 | 0.91 | 39 | 0.8862 |
| 1.012 | 0.93 | 40 | 0.8807 |
| 1.0553 | 0.95 | 41 | 0.8753 |
| 1.0566 | 0.98 | 42 | 0.8691 |
| 1.1235 | 1.0 | 43 | 0.8638 |
| 1.1207 | 1.02 | 44 | 0.8591 |
| 1.0835 | 1.05 | 45 | 0.8544 |
| 1.3731 | 1.07 | 46 | 0.8505 |
| 0.9843 | 1.09 | 47 | 0.8450 |
| 0.9201 | 1.12 | 48 | 0.8385 |
| 1.0392 | 1.14 | 49 | 0.8340 |
| 1.1158 | 1.16 | 50 | 0.8297 |
| 0.8518 | 1.19 | 51 | 0.8247 |
| 0.8871 | 1.21 | 52 | 0.8185 |
| 1.0378 | 1.23 | 53 | 0.8124 |
| 1.1116 | 1.26 | 54 | 0.8083 |
| 1.0364 | 1.28 | 55 | 0.8043 |
| 0.8949 | 1.3 | 56 | 0.7988 |
| 1.068 | 1.33 | 57 | 0.7925 |
| 0.9319 | 1.35 | 58 | 0.7859 |
| 0.7654 | 1.37 | 59 | 0.7818 |
| 0.8887 | 1.4 | 60 | 0.7787 |
| 1.0294 | 1.42 | 61 | 0.7748 |
| 1.1351 | 1.44 | 62 | 0.7711 |
| 0.998 | 1.47 | 63 | 0.7689 |
| 1.1106 | 1.49 | 64 | 0.7679 |
| 0.9606 | 1.51 | 65 | 0.7660 |
| 0.9273 | 1.53 | 66 | 0.7628 |
| 0.9725 | 1.56 | 67 | 0.7595 |
| 1.0205 | 1.58 | 68 | 0.7569 |
| 1.0131 | 1.6 | 69 | 0.7549 |
| 0.9203 | 1.63 | 70 | 0.7530 |
| 0.898 | 1.65 | 71 | 0.7508 |
| 0.817 | 1.67 | 72 | 0.7478 |
| 0.9439 | 1.7 | 73 | 0.7447 |
| 1.079 | 1.72 | 74 | 0.7427 |
| 0.9806 | 1.74 | 75 | 0.7398 |
| 1.261 | 1.77 | 76 | 0.7369 |
| 1.0824 | 1.79 | 77 | 0.7340 |
| 0.9523 | 1.81 | 78 | 0.7317 |
| 0.9734 | 1.84 | 79 | 0.7300 |
| 1.0786 | 1.86 | 80 | 0.7302 |
| 0.8675 | 1.88 | 81 | 0.7298 |
| 0.851 | 1.91 | 82 | 0.7279 |
| 1.066 | 1.93 | 83 | 0.7254 |
| 1.137 | 1.95 | 84 | 0.7239 |
| 1.1387 | 1.98 | 85 | 0.7224 |
| 0.739 | 2.0 | 86 | 0.7207 |
| 0.8809 | 2.02 | 87 | 0.7192 |
| 1.0253 | 2.05 | 88 | 0.7178 |
| 0.8942 | 2.07 | 89 | 0.7160 |
| 0.8436 | 2.09 | 90 | 0.7134 |
| 0.8356 | 2.12 | 91 | 0.7115 |
| 0.9951 | 2.14 | 92 | 0.7110 |
| 0.7637 | 2.16 | 93 | 0.7098 |
| 0.722 | 2.19 | 94 | 0.7087 |
| 1.023 | 2.21 | 95 | 0.7072 |
| 0.7015 | 2.23 | 96 | 0.7044 |
| 0.8949 | 2.26 | 97 | 0.7017 |
| 0.9573 | 2.28 | 98 | 0.6996 |
| 0.8989 | 2.3 | 99 | 0.6987 |
| 0.9738 | 2.33 | 100 | 0.6983 |
| 0.8317 | 2.35 | 101 | 0.6970 |
| 0.9778 | 2.37 | 102 | 0.6951 |
| 0.7919 | 2.4 | 103 | 0.6924 |
| 0.653 | 2.42 | 104 | 0.6898 |
| 0.9133 | 2.44 | 105 | 0.6873 |
| 0.8521 | 2.47 | 106 | 0.6841 |
| 0.8673 | 2.49 | 107 | 0.6808 |
| 0.8792 | 2.51 | 108 | 0.6777 |
| 0.8635 | 2.53 | 109 | 0.6747 |
| 1.0299 | 2.56 | 110 | 0.6719 |
| 0.7554 | 2.58 | 111 | 0.6694 |
| 0.9195 | 2.6 | 112 | 0.6671 |
| 0.8374 | 2.63 | 113 | 0.6649 |
| 0.8847 | 2.65 | 114 | 0.6628 |
| 0.938 | 2.67 | 115 | 0.6615 |
| 0.8967 | 2.7 | 116 | 0.6603 |
| 0.8264 | 2.72 | 117 | 0.6594 |
| 0.9195 | 2.74 | 118 | 0.6591 |
| 0.8584 | 2.77 | 119 | 0.6588 |
| 0.8058 | 2.79 | 120 | 0.6578 |
| 1.0978 | 2.81 | 121 | 0.6560 |
| 0.7889 | 2.84 | 122 | 0.6544 |
| 0.7865 | 2.86 | 123 | 0.6527 |
| 0.8553 | 2.88 | 124 | 0.6507 |
| 0.9134 | 2.91 | 125 | 0.6486 |
| 0.7911 | 2.93 | 126 | 0.6463 |
| 0.9675 | 2.95 | 127 | 0.6439 |
| 0.761 | 2.98 | 128 | 0.6417 |
| 0.6347 | 3.0 | 129 | 0.6394 |
| 0.7608 | 3.02 | 130 | 0.6368 |
| 0.7563 | 3.05 | 131 | 0.6352 |
| 0.8059 | 3.07 | 132 | 0.6333 |
| 0.8825 | 3.09 | 133 | 0.6320 |
| 0.7952 | 3.12 | 134 | 0.6307 |
| 0.9209 | 3.14 | 135 | 0.6299 |
| 0.8556 | 3.16 | 136 | 0.6295 |
| 0.8613 | 3.19 | 137 | 0.6289 |
| 0.7908 | 3.21 | 138 | 0.6288 |
| 0.7728 | 3.23 | 139 | 0.6285 |
| 0.707 | 3.26 | 140 | 0.6280 |
| 0.8353 | 3.28 | 141 | 0.6270 |
| 0.9482 | 3.3 | 142 | 0.6265 |
| 0.726 | 3.33 | 143 | 0.6260 |
| 0.7594 | 3.35 | 144 | 0.6250 |
| 0.9403 | 3.37 | 145 | 0.6237 |
| 0.8986 | 3.4 | 146 | 0.6218 |
| 0.7309 | 3.42 | 147 | 0.6204 |
| 0.8011 | 3.44 | 148 | 0.6197 |
| 0.7373 | 3.47 | 149 | 0.6193 |
| 0.6195 | 3.49 | 150 | 0.6174 |
| 0.8668 | 3.51 | 151 | 0.6154 |
| 0.8096 | 3.53 | 152 | 0.6136 |
| 0.9364 | 3.56 | 153 | 0.6116 |
| 0.7081 | 3.58 | 154 | 0.6105 |
| 0.7799 | 3.6 | 155 | 0.6091 |
| 0.7862 | 3.63 | 156 | 0.6090 |
| 0.7221 | 3.65 | 157 | 0.6097 |
| 0.7605 | 3.67 | 158 | 0.6090 |
| 0.7481 | 3.7 | 159 | 0.6071 |
| 0.776 | 3.72 | 160 | 0.6045 |
| 0.9396 | 3.74 | 161 | 0.6022 |
| 0.7166 | 3.77 | 162 | 0.6001 |
| 0.709 | 3.79 | 163 | 0.5985 |
| 0.8412 | 3.81 | 164 | 0.5970 |
| 0.7692 | 3.84 | 165 | 0.5956 |
| 0.7621 | 3.86 | 166 | 0.5942 |
| 0.7832 | 3.88 | 167 | 0.5930 |
| 0.7455 | 3.91 | 168 | 0.5919 |
| 0.7888 | 3.93 | 169 | 0.5913 |
| 0.7197 | 3.95 | 170 | 0.5908 |
| 0.7936 | 3.98 | 171 | 0.5900 |
| 0.5976 | 4.0 | 172 | 0.5890 |
| 0.6375 | 4.02 | 173 | 0.5874 |
| 0.7342 | 4.05 | 174 | 0.5859 |
| 0.644 | 4.07 | 175 | 0.5845 |
| 0.7232 | 4.09 | 176 | 0.5831 |
| 0.7743 | 4.12 | 177 | 0.5819 |
| 0.8015 | 4.14 | 178 | 0.5808 |
| 0.7475 | 4.16 | 179 | 0.5801 |
| 0.7005 | 4.19 | 180 | 0.5797 |
| 0.7032 | 4.21 | 181 | 0.5795 |
| 0.8204 | 4.23 | 182 | 0.5789 |
| 0.7674 | 4.26 | 183 | 0.5787 |
| 0.7219 | 4.28 | 184 | 0.5781 |
| 0.624 | 4.3 | 185 | 0.5771 |
| 0.7429 | 4.33 | 186 | 0.5755 |
| 0.6445 | 4.35 | 187 | 0.5730 |
| 0.7782 | 4.37 | 188 | 0.5712 |
| 0.7882 | 4.4 | 189 | 0.5698 |
| 0.7005 | 4.42 | 190 | 0.5687 |
| 0.7509 | 4.44 | 191 | 0.5678 |
| 0.6764 | 4.47 | 192 | 0.5671 |
| 0.6529 | 4.49 | 193 | 0.5667 |
| 0.6101 | 4.51 | 194 | 0.5668 |
| 0.8211 | 4.53 | 195 | 0.5674 |
| 0.7529 | 4.56 | 196 | 0.5667 |
| 0.8615 | 4.58 | 197 | 0.5651 |
| 0.8099 | 4.6 | 198 | 0.5641 |
| 0.7145 | 4.63 | 199 | 0.5635 |
| 0.7437 | 4.65 | 200 | 0.5632 |
| 0.873 | 4.67 | 201 | 0.5631 |
| 0.7937 | 4.7 | 202 | 0.5620 |
| 0.7493 | 4.72 | 203 | 0.5608 |
| 0.7614 | 4.74 | 204 | 0.5596 |
| 0.6642 | 4.77 | 205 | 0.5585 |
| 0.5854 | 4.79 | 206 | 0.5576 |
| 0.6442 | 4.81 | 207 | 0.5572 |
| 0.859 | 4.84 | 208 | 0.5562 |
| 0.6627 | 4.86 | 209 | 0.5553 |
| 0.8024 | 4.88 | 210 | 0.5540 |
| 0.7443 | 4.91 | 211 | 0.5526 |
| 0.6725 | 4.93 | 212 | 0.5520 |
| 0.749 | 4.95 | 213 | 0.5521 |
| 0.7687 | 4.98 | 214 | 0.5521 |
| 0.5998 | 5.0 | 215 | 0.5522 |
| 0.7578 | 5.02 | 216 | 0.5526 |
| 0.7074 | 5.05 | 217 | 0.5536 |
| 0.5647 | 5.07 | 218 | 0.5543 |
| 0.7475 | 5.09 | 219 | 0.5539 |
| 0.5776 | 5.12 | 220 | 0.5523 |
| 0.7232 | 5.14 | 221 | 0.5507 |
| 0.6487 | 5.16 | 222 | 0.5491 |
| 0.6446 | 5.19 | 223 | 0.5477 |
| 0.8951 | 5.21 | 224 | 0.5467 |
| 0.7706 | 5.23 | 225 | 0.5460 |
| 0.6351 | 5.26 | 226 | 0.5453 |
| 0.7336 | 5.28 | 227 | 0.5445 |
| 0.6329 | 5.3 | 228 | 0.5436 |
| 0.5795 | 5.33 | 229 | 0.5430 |
| 0.7553 | 5.35 | 230 | 0.5428 |
| 0.6959 | 5.37 | 231 | 0.5430 |
| 0.5945 | 5.4 | 232 | 0.5427 |
| 0.6274 | 5.42 | 233 | 0.5422 |
| 0.7024 | 5.44 | 234 | 0.5414 |
| 0.8223 | 5.47 | 235 | 0.5402 |
| 0.6441 | 5.49 | 236 | 0.5386 |
| 0.749 | 5.51 | 237 | 0.5368 |
| 0.6654 | 5.53 | 238 | 0.5357 |
| 0.8781 | 5.56 | 239 | 0.5346 |
| 0.7139 | 5.58 | 240 | 0.5340 |
| 0.587 | 5.6 | 241 | 0.5339 |
| 0.8308 | 5.63 | 242 | 0.5340 |
| 0.5613 | 5.65 | 243 | 0.5334 |
| 0.7108 | 5.67 | 244 | 0.5330 |
| 0.6884 | 5.7 | 245 | 0.5322 |
| 0.6955 | 5.72 | 246 | 0.5310 |
| 0.5989 | 5.74 | 247 | 0.5301 |
| 0.7517 | 5.77 | 248 | 0.5295 |
| 0.6765 | 5.79 | 249 | 0.5291 |
| 0.6223 | 5.81 | 250 | 0.5285 |
| 0.6694 | 5.84 | 251 | 0.5277 |
| 0.6235 | 5.86 | 252 | 0.5267 |
| 0.6591 | 5.88 | 253 | 0.5259 |
| 0.6832 | 5.91 | 254 | 0.5251 |
| 0.7346 | 5.93 | 255 | 0.5246 |
| 0.6574 | 5.95 | 256 | 0.5242 |
| 0.704 | 5.98 | 257 | 0.5236 |
| 0.7269 | 6.0 | 258 | 0.5234 |
| 0.6097 | 6.02 | 259 | 0.5231 |
| 0.5369 | 6.05 | 260 | 0.5224 |
| 0.7094 | 6.07 | 261 | 0.5214 |
| 0.608 | 6.09 | 262 | 0.5207 |
| 0.6112 | 6.12 | 263 | 0.5200 |
| 0.6414 | 6.14 | 264 | 0.5192 |
| 0.6254 | 6.16 | 265 | 0.5186 |
| 0.8219 | 6.19 | 266 | 0.5184 |
| 0.6536 | 6.21 | 267 | 0.5183 |
| 0.601 | 6.23 | 268 | 0.5184 |
| 0.672 | 6.26 | 269 | 0.5182 |
| 0.6646 | 6.28 | 270 | 0.5179 |
| 0.7228 | 6.3 | 271 | 0.5179 |
| 0.6542 | 6.33 | 272 | 0.5182 |
| 0.6003 | 6.35 | 273 | 0.5185 |
| 0.4799 | 6.37 | 274 | 0.5195 |
| 0.7062 | 6.4 | 275 | 0.5203 |
| 0.7557 | 6.42 | 276 | 0.5199 |
| 0.7419 | 6.44 | 277 | 0.5189 |
| 0.5468 | 6.47 | 278 | 0.5179 |
| 0.6142 | 6.49 | 279 | 0.5168 |
| 0.5953 | 6.51 | 280 | 0.5161 |
| 0.602 | 6.53 | 281 | 0.5152 |
| 0.6168 | 6.56 | 282 | 0.5146 |
| 0.815 | 6.58 | 283 | 0.5141 |
| 0.7738 | 6.6 | 284 | 0.5138 |
| 0.64 | 6.63 | 285 | 0.5136 |
| 0.6377 | 6.65 | 286 | 0.5133 |
| 0.7254 | 6.67 | 287 | 0.5131 |
| 0.6416 | 6.7 | 288 | 0.5128 |
| 0.6555 | 6.72 | 289 | 0.5123 |
| 0.6812 | 6.74 | 290 | 0.5118 |
| 0.7116 | 6.77 | 291 | 0.5113 |
| 0.6046 | 6.79 | 292 | 0.5104 |
| 0.7386 | 6.81 | 293 | 0.5095 |
| 0.733 | 6.84 | 294 | 0.5088 |
| 0.6579 | 6.86 | 295 | 0.5081 |
| 0.5418 | 6.88 | 296 | 0.5076 |
| 0.5853 | 6.91 | 297 | 0.5071 |
| 0.6488 | 6.93 | 298 | 0.5070 |
| 0.5726 | 6.95 | 299 | 0.5069 |
| 0.5821 | 6.98 | 300 | 0.5068 |
| 0.9157 | 7.0 | 301 | 0.5068 |
| 0.6769 | 7.02 | 302 | 0.5061 |
| 0.7632 | 7.05 | 303 | 0.5049 |
| 0.7479 | 7.07 | 304 | 0.5037 |
| 0.5632 | 7.09 | 305 | 0.5028 |
| 0.6493 | 7.12 | 306 | 0.5015 |
| 0.6517 | 7.14 | 307 | 0.5007 |
| 0.6944 | 7.16 | 308 | 0.5000 |
| 0.5862 | 7.19 | 309 | 0.4996 |
| 0.6161 | 7.21 | 310 | 0.4993 |
| 0.6396 | 7.23 | 311 | 0.4988 |
| 0.5506 | 7.26 | 312 | 0.4985 |
| 0.7518 | 7.28 | 313 | 0.4982 |
| 0.7445 | 7.3 | 314 | 0.4977 |
| 0.6228 | 7.33 | 315 | 0.4974 |
| 0.5555 | 7.35 | 316 | 0.4968 |
| 0.7457 | 7.37 | 317 | 0.4964 |
| 0.579 | 7.4 | 318 | 0.4961 |
| 0.528 | 7.42 | 319 | 0.4956 |
| 0.5286 | 7.44 | 320 | 0.4953 |
| 0.591 | 7.47 | 321 | 0.4952 |
| 0.5903 | 7.49 | 322 | 0.4953 |
| 0.6155 | 7.51 | 323 | 0.4955 |
| 0.5907 | 7.53 | 324 | 0.4954 |
| 0.6028 | 7.56 | 325 | 0.4949 |
| 0.5852 | 7.58 | 326 | 0.4943 |
| 0.6156 | 7.6 | 327 | 0.4934 |
| 0.582 | 7.63 | 328 | 0.4925 |
| 0.6091 | 7.65 | 329 | 0.4918 |
| 0.5877 | 7.67 | 330 | 0.4912 |
| 0.7017 | 7.7 | 331 | 0.4908 |
| 0.6496 | 7.72 | 332 | 0.4905 |
| 0.6089 | 7.74 | 333 | 0.4903 |
| 0.5807 | 7.77 | 334 | 0.4901 |
| 0.5553 | 7.79 | 335 | 0.4897 |
| 0.8058 | 7.81 | 336 | 0.4894 |
| 0.6147 | 7.84 | 337 | 0.4892 |
| 0.6289 | 7.86 | 338 | 0.4891 |
| 0.5883 | 7.88 | 339 | 0.4891 |
| 0.6048 | 7.91 | 340 | 0.4890 |
| 0.6411 | 7.93 | 341 | 0.4889 |
| 0.5575 | 7.95 | 342 | 0.4887 |
| 0.6509 | 7.98 | 343 | 0.4884 |
| 0.764 | 8.0 | 344 | 0.4882 |
| 0.6364 | 8.02 | 345 | 0.4880 |
| 0.561 | 8.05 | 346 | 0.4880 |
| 0.5949 | 8.07 | 347 | 0.4878 |
| 0.6904 | 8.09 | 348 | 0.4874 |
| 0.647 | 8.12 | 349 | 0.4868 |
| 0.6374 | 8.14 | 350 | 0.4862 |
| 0.7048 | 8.16 | 351 | 0.4859 |
| 0.6085 | 8.19 | 352 | 0.4854 |
| 0.5246 | 8.21 | 353 | 0.4852 |
| 0.531 | 8.23 | 354 | 0.4849 |
| 0.4605 | 8.26 | 355 | 0.4844 |
| 0.6132 | 8.28 | 356 | 0.4839 |
| 0.6378 | 8.3 | 357 | 0.4835 |
| 0.7885 | 8.33 | 358 | 0.4831 |
| 0.6008 | 8.35 | 359 | 0.4827 |
| 0.7118 | 8.37 | 360 | 0.4823 |
| 0.6792 | 8.4 | 361 | 0.4821 |
| 0.6317 | 8.42 | 362 | 0.4819 |
| 0.5942 | 8.44 | 363 | 0.4817 |
| 0.6184 | 8.47 | 364 | 0.4815 |
| 0.5902 | 8.49 | 365 | 0.4813 |
| 0.5353 | 8.51 | 366 | 0.4812 |
| 0.685 | 8.53 | 367 | 0.4812 |
| 0.5232 | 8.56 | 368 | 0.4811 |
| 0.6393 | 8.58 | 369 | 0.4812 |
| 0.5685 | 8.6 | 370 | 0.4812 |
| 0.6234 | 8.63 | 371 | 0.4813 |
| 0.5456 | 8.65 | 372 | 0.4810 |
| 0.6159 | 8.67 | 373 | 0.4807 |
| 0.6575 | 8.7 | 374 | 0.4804 |
| 0.5769 | 8.72 | 375 | 0.4803 |
| 0.5939 | 8.74 | 376 | 0.4801 |
| 0.5721 | 8.77 | 377 | 0.4800 |
| 0.5283 | 8.79 | 378 | 0.4797 |
| 0.5275 | 8.81 | 379 | 0.4795 |
| 0.5907 | 8.84 | 380 | 0.4794 |
| 0.6058 | 8.86 | 381 | 0.4792 |
| 0.7202 | 8.88 | 382 | 0.4790 |
| 0.6811 | 8.91 | 383 | 0.4787 |
| 0.5979 | 8.93 | 384 | 0.4785 |
| 0.5572 | 8.95 | 385 | 0.4783 |
| 0.5893 | 8.98 | 386 | 0.4781 |
| 0.6796 | 9.0 | 387 | 0.4779 |
| 0.5412 | 9.02 | 388 | 0.4780 |
| 0.5453 | 9.05 | 389 | 0.4781 |
| 0.7475 | 9.07 | 390 | 0.4782 |
| 0.6222 | 9.09 | 391 | 0.4781 |
| 0.5177 | 9.12 | 392 | 0.4778 |
| 0.6182 | 9.14 | 393 | 0.4775 |
| 0.6124 | 9.16 | 394 | 0.4772 |
| 0.6485 | 9.19 | 395 | 0.4769 |
| 0.5852 | 9.21 | 396 | 0.4765 |
| 0.5656 | 9.23 | 397 | 0.4761 |
| 0.6162 | 9.26 | 398 | 0.4758 |
| 0.6965 | 9.28 | 399 | 0.4755 |
| 0.5342 | 9.3 | 400 | 0.4753 |
| 0.718 | 9.33 | 401 | 0.4751 |
| 0.5089 | 9.35 | 402 | 0.4750 |
| 0.5738 | 9.37 | 403 | 0.4748 |
| 0.5612 | 9.4 | 404 | 0.4746 |
| 0.5628 | 9.42 | 405 | 0.4744 |
| 0.6512 | 9.44 | 406 | 0.4743 |
| 0.6717 | 9.47 | 407 | 0.4742 |
| 0.5937 | 9.49 | 408 | 0.4741 |
| 0.5906 | 9.51 | 409 | 0.4741 |
| 0.529 | 9.53 | 410 | 0.4741 |
| 0.6554 | 9.56 | 411 | 0.4741 |
| 0.5074 | 9.58 | 412 | 0.4741 |
| 0.6997 | 9.6 | 413 | 0.4741 |
| 0.5573 | 9.63 | 414 | 0.4741 |
| 0.6113 | 9.65 | 415 | 0.4741 |
| 0.5129 | 9.67 | 416 | 0.4741 |
| 0.5428 | 9.7 | 417 | 0.4740 |
| 0.5363 | 9.72 | 418 | 0.4739 |
| 0.5862 | 9.74 | 419 | 0.4739 |
| 0.6119 | 9.77 | 420 | 0.4738 |
| 0.6698 | 9.79 | 421 | 0.4738 |
| 0.5966 | 9.81 | 422 | 0.4737 |
| 0.5309 | 9.84 | 423 | 0.4737 |
| 0.5924 | 9.86 | 424 | 0.4736 |
| 0.6133 | 9.88 | 425 | 0.4736 |
| 0.6869 | 9.91 | 426 | 0.4736 |
| 0.5508 | 9.93 | 427 | 0.4735 |
| 0.6858 | 9.95 | 428 | 0.4735 |
| 0.5681 | 9.98 | 429 | 0.4735 |
| 0.7834 | 10.0 | 430 | 0.4735 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
|
abeiler/goatV10-testData-withAutoInference-withS3SafeTens
|
abeiler
| 2023-09-02T11:46:08Z | 75 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"llama",
"text-generation",
"generated_from_trainer",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"8-bit",
"region:us"
] |
text-generation
| 2023-09-02T11:45:47Z |
---
tags:
- generated_from_trainer
model-index:
- name: goatV10-testData-withAutoInference-withS3SafeTens
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. -->
# goatV10-testData-withAutoInference-withS3SafeTens
This model is a fine-tuned version of [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-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: 0.0003
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0
- Datasets 2.12.0
- Tokenizers 0.13.3
|
casque/FilmVelvia3
|
casque
| 2023-09-02T11:34:13Z | 0 | 1 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-09-02T11:32:49Z |
---
license: creativeml-openrail-m
---
|
Mustain/line_fujiki3
|
Mustain
| 2023-09-02T11:20:10Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-09-02T11:20:04Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.6.0.dev0
|
dwitidibyajyoti/fine_tune_layoutmlv3_model
|
dwitidibyajyoti
| 2023-09-02T11:15:36Z | 77 | 0 |
transformers
|
[
"transformers",
"pytorch",
"layoutlmv3",
"token-classification",
"generated_from_trainer",
"base_model:microsoft/layoutlmv3-base",
"base_model:finetune:microsoft/layoutlmv3-base",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2023-08-30T09:45:10Z |
---
license: cc-by-nc-sa-4.0
base_model: microsoft/layoutlmv3-base
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: test
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. -->
# test
This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2763
- Precision: 0.5109
- Recall: 0.6026
- F1: 0.5529
- Accuracy: 0.9222
## 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: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 1000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 8.33 | 100 | 0.6800 | 0.3371 | 0.3846 | 0.3593 | 0.7682 |
| No log | 16.67 | 200 | 0.3088 | 0.5204 | 0.6538 | 0.5795 | 0.9156 |
| No log | 25.0 | 300 | 0.2142 | 0.5326 | 0.6282 | 0.5765 | 0.9305 |
| No log | 33.33 | 400 | 0.2301 | 0.5795 | 0.6538 | 0.6145 | 0.9288 |
| 0.4115 | 41.67 | 500 | 0.2426 | 0.5618 | 0.6410 | 0.5988 | 0.9272 |
| 0.4115 | 50.0 | 600 | 0.4171 | 0.6190 | 0.6667 | 0.6420 | 0.8924 |
| 0.4115 | 58.33 | 700 | 0.2265 | 0.5393 | 0.6154 | 0.5749 | 0.9371 |
| 0.4115 | 66.67 | 800 | 0.2869 | 0.5506 | 0.6282 | 0.5868 | 0.9156 |
| 0.4115 | 75.0 | 900 | 0.2633 | 0.5568 | 0.6282 | 0.5904 | 0.9272 |
| 0.0231 | 83.33 | 1000 | 0.2763 | 0.5109 | 0.6026 | 0.5529 | 0.9222 |
### Framework versions
- Transformers 4.33.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
|
KhalfounMehdi/vit_musculoskeletal_abnormality_detection_mura_224px_16bs_20ep
|
KhalfounMehdi
| 2023-09-02T11:10:51Z | 191 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"autotrain",
"dataset:KhalfounMehdi/mura_dataset_processed_224px_train_val",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-09-02T11:10:27Z |
---
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/mura_dataset_processed_224px_train_val
---
# Model Trained Using AutoTrain
- Problem type: Image Classification
## Validation Metricsg
loss: 0.5185230374336243
f1: 0.8211164615658998
precision: 0.7175810473815462
recall: 0.9595664860358483
auc: 0.7988417458585272
accuracy: 0.749312671832042
|
yaohuacn/a2c-PandaReachDense-v3
|
yaohuacn
| 2023-09-02T11:10:11Z | 2 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"PandaReachDense-v3",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-09-02T11:05:12Z |
---
library_name: stable-baselines3
tags:
- PandaReachDense-v3
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachDense-v3
type: PandaReachDense-v3
metrics:
- type: mean_reward
value: -0.19 +/- 0.08
name: mean_reward
verified: false
---
# **A2C** Agent playing **PandaReachDense-v3**
This is a trained model of a **A2C** agent playing **PandaReachDense-v3**
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
...
```
|
VuongQuoc/longformer_sciq
|
VuongQuoc
| 2023-09-02T11:06:18Z | 97 | 0 |
transformers
|
[
"transformers",
"pytorch",
"longformer",
"multiple-choice",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] |
multiple-choice
| 2023-08-29T02:34:13Z |
---
tags:
- generated_from_trainer
model-index:
- name: longformer_sciq
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. -->
# longformer_sciq
This model is a fine-tuned version of [VuongQuoc/longformer_sciq](https://huggingface.co/VuongQuoc/longformer_sciq) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5326
## 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-06
- train_batch_size: 2
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.2
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.1931 | 0.2 | 20 | 0.7457 |
| 0.7677 | 0.4 | 40 | 0.7063 |
| 1.0391 | 0.6 | 60 | 0.6745 |
| 1.2915 | 0.8 | 80 | 0.6316 |
| 1.1399 | 1.0 | 100 | 0.6652 |
| 0.9975 | 1.2 | 120 | 0.6134 |
| 0.9232 | 1.4 | 140 | 0.5561 |
| 0.8026 | 1.6 | 160 | 0.5422 |
| 0.7188 | 1.8 | 180 | 0.5370 |
| 0.7272 | 2.0 | 200 | 0.5326 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.0+cpu
- Datasets 2.1.0
- Tokenizers 0.13.3
|
aigrils2/primitive0-diffuser
|
aigrils2
| 2023-09-02T11:05:44Z | 29 | 0 |
diffusers
|
[
"diffusers",
"safetensors",
"text-to-image",
"stable-diffusion",
"lora",
"base_model:wangjun/majicmix-realistic-v6",
"base_model:adapter:wangjun/majicmix-realistic-v6",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-09-02T10:20:37Z |
---
base_model: wangjun/majicmix-realistic-v6
tags:
- text-to-image
- stable-diffusion
- lora
- diffusers
pipeline_tag: text-to-image
---
|
madroid/onnx-whisper
|
madroid
| 2023-09-02T11:02:02Z | 0 | 0 | null |
[
"onnx",
"whisper",
"openai",
"license:apache-2.0",
"region:us"
] | null | 2023-09-02T07:14:04Z |
---
license: apache-2.0
tags:
- whisper
- onnx
- openai
---
|
casque/majicmixRealistic_betterV2V25
|
casque
| 2023-09-02T11:00:36Z | 0 | 1 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-09-02T10:43:18Z |
---
license: creativeml-openrail-m
---
|
Tharun2003/tharun-3
|
Tharun2003
| 2023-09-02T10:57:42Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-09-02T10:53:06Z |
---
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: False
- bnb_4bit_compute_dtype: float16
### Framework versions
- PEFT 0.6.0.dev0
|
JanSt/gbert-base-finetuned-twitter
|
JanSt
| 2023-09-02T10:57:40Z | 8 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"fill-mask",
"generated_from_trainer",
"base_model:deepset/gbert-base",
"base_model:finetune:deepset/gbert-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2023-08-24T10:58:07Z |
---
license: mit
base_model: deepset/gbert-base
tags:
- generated_from_trainer
model-index:
- name: gbert-base-finetuned-twitter
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. -->
# gbert-base-finetuned-twitter
This model is a fine-tuned version of [deepset/gbert-base](https://huggingface.co/deepset/gbert-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7380
## 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: 192
- eval_batch_size: 192
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|
| 2.194 | 1.0 | 4180 | 1.9622 |
| 2.0075 | 2.0 | 8360 | 1.8813 |
| 1.9429 | 3.0 | 12540 | 1.8339 |
| 1.8985 | 4.0 | 16720 | 1.8057 |
| 1.8676 | 5.0 | 20900 | 1.7801 |
| 1.8446 | 6.0 | 25080 | 1.7793 |
| 1.829 | 7.0 | 29260 | 1.7580 |
| 1.815 | 8.0 | 33440 | 1.7445 |
| 1.8048 | 9.0 | 37620 | 1.7319 |
| 1.7997 | 10.0 | 41800 | 1.7331 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.0.1+cu117
- Datasets 2.14.4
- Tokenizers 0.13.3
|
andrewcho92/helloworld
|
andrewcho92
| 2023-09-02T10:33:10Z | 0 | 0 | null |
[
"text-generation",
"en",
"license:openrail",
"region:us"
] |
text-generation
| 2023-09-02T10:14:37Z |
---
license: openrail
language:
- en
pipeline_tag: text-generation
---
|
adimazuz/texi-v3
|
adimazuz
| 2023-09-02T10:30:56Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-09-02T10:30:54Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: texi-v3
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="adimazuz/texi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
adimazuz/q-FrozenLake-v1-4x4-noSlippery
|
adimazuz
| 2023-09-02T10:23:17Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-09-02T10:23:15Z |
---
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="adimazuz/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"])
```
|
jigglesaw/finetuning-sentiment-model-3000-samples
|
jigglesaw
| 2023-09-02T10:16:22Z | 106 | 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",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-09-02T08:56:24Z |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- imdb
metrics:
- accuracy
- f1
model-index:
- name: finetuning-sentiment-model-3000-samples
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: imdb
type: imdb
config: plain_text
split: test
args: plain_text
metrics:
- name: Accuracy
type: accuracy
value: 0.8666666666666667
- name: F1
type: f1
value: 0.870967741935484
---
<!-- 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. -->
# finetuning-sentiment-model-3000-samples
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3394
- Accuracy: 0.8667
- F1: 0.8710
## 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
### Framework versions
- Transformers 4.32.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
|
gg4ever/trOCR-final
|
gg4ever
| 2023-09-02T10:15:40Z | 126 | 0 |
transformers
|
[
"transformers",
"pytorch",
"vision-encoder-decoder",
"image-text-to-text",
"image-to-text",
"ko",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
image-to-text
| 2023-08-22T11:31:10Z |
---
license: apache-2.0
language:
- ko
metrics:
- cer
- wer
pipeline_tag: image-to-text
---
# trOCR-final
fine-tuned for VisionEncoderDecoderModel(encoder , decoder)
encoder = 'facebook/deit-base-distilled-patch16-384'
decoder = 'klue/roberta-base'
## How to Get Started with the Model
```python
from transformers import VisionEncoderDecoderModel,AutoTokenizer, TrOCRProcessor
import torch
from PIL import Image
device = torch.device('cuda') # change 'cuda' if you need.
image_path='(your image path)'
image = Image.open(image_path)
#model can be .jpg or .png
#hugging face download: https://huggingface.co/gg4ever/trOCR-final
processor = TrOCRProcessor.from_pretrained("microsoft/trocr-base-handwritten")
trocr_model = "gg4ever/trOCR-final"
model = VisionEncoderDecoderModel.from_pretrained(trocr_model).to(device)
tokenizer = AutoTokenizer.from_pretrained(trocr_model)
pixel_values = (processor(image, return_tensors="pt").pixel_values).to(device)
generated_ids = model.generate(pixel_values)
generated_text = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(generated_text)
```
## Training Details
### Training Data
1M words generated by TextRecognitionDataGenerator(trdg) : https://github.com/Belval/TextRecognitionDataGenerator/blob/master/trdg/run.py
1.1M words from AI-hub OCR words dataset : https://aihub.or.kr/aihubdata/data/view.do?currMenu=115&topMenu=100&dataSetSn=81
### Training Hyperparameters
|hyperparameters|values|
|-----------------------------|-------|
|predict_with_generate|True|
|evaluation_strategy|"steps"|
|per_device_train_batch_size|32|
|per_device_eval_batch_size|32|
|num_train_epochs|2|
|fp16|True|
|learning_rate|4e-5|
|eval_stept|10000|
|warmup_steps|20000|
|weight_decay|0.01|
|
muralee491/murale
|
muralee491
| 2023-09-02T10:14:33Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-09-02T10:12:40Z |
---
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: False
- bnb_4bit_compute_dtype: float16
### Framework versions
- PEFT 0.6.0.dev0
|
StefanoCaloni/dqn-SpaceInvaders
|
StefanoCaloni
| 2023-09-02T10:04:52Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-09-02T08:32:06Z |
---
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: 299.00 +/- 68.26
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 StefanoCaloni -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 StefanoCaloni -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 StefanoCaloni
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 10000),
('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', 10000),
('n_timesteps', 100000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 100),
('train_freq', 4),
('normalize', False)])
```
# Environment Arguments
```python
{'render_mode': 'rgb_array'}
```
|
KhalfounMehdi/mura_vit_224
|
KhalfounMehdi
| 2023-09-02T10:01:11Z | 192 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"autotrain",
"dataset:KhalfounMehdi/mura_dataset_processed_224px_train_val",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-09-02T06:30:20Z |
---
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/mura_dataset_processed_224px_train_val
metrics:
- accuracy
---
# Model Trained Using AutoTrain
- Problem type: Image Classification
## Validation Metrics
{'accuracy': 0.7795551112221945,
'recall': 0.9037098791162984,
'precision': 0.7690670450514366,
'f1': 0.83096972019931,
'total_time_in_seconds': 81.18831510400014,
'samples_per_second': 49.28049060846776,
'latency_in_seconds': 0.020292005774556397}
|
nichelia/example100
|
nichelia
| 2023-09-02T09:40:53Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-09-02T09:40:51Z |
---
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
|
utnah/ckpt
|
utnah
| 2023-09-02T09:33:43Z | 0 | 2 | null |
[
"license:openrail",
"region:us"
] | null | 2022-10-31T12:34:09Z |
---
license: openrail
---
Модели весов для StableDiffusion в формате ckpt.
Для быстрой загрузки в [Google Colab](https://colab.research.google.com/drive/1TC4SSLncPWytSPvquR6Y4-U7wZRfAXrV)
[](https://colab.research.google.com/drive/1TC4SSLncPWytSPvquR6Y4-U7wZRfAXrV)
|
MP-1961/vit-base-patch16-224-finetuned-flower
|
MP-1961
| 2023-09-02T09:13:52Z | 163 | 0 |
transformers
|
[
"transformers",
"pytorch",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-09-02T09:03:21Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
model-index:
- name: vit-base-patch16-224-finetuned-flower
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. -->
# vit-base-patch16-224-finetuned-flower
This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
### Framework versions
- Transformers 4.24.0
- Pytorch 2.0.1+cu118
- Datasets 2.7.1
- Tokenizers 0.13.3
|
franziskaM/b25-wav2vec2-large-xls-r-romansh-colab
|
franziskaM
| 2023-09-02T08:58:53Z | 7 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice_13_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-09-01T10:20:50Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- common_voice_13_0
metrics:
- wer
model-index:
- name: b25-wav2vec2-large-xls-r-romansh-colab
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: common_voice_13_0
type: common_voice_13_0
config: rm-vallader
split: test
args: rm-vallader
metrics:
- name: Wer
type: wer
value: 0.24149976711690732
---
<!-- 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. -->
# b25-wav2vec2-large-xls-r-romansh-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_13_0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3303
- Wer: 0.2415
## 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: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 6.1605 | 3.05 | 400 | 2.9535 | 1.0 |
| 2.9451 | 6.11 | 800 | 2.9092 | 1.0 |
| 1.7795 | 9.16 | 1200 | 0.4982 | 0.4951 |
| 0.4094 | 12.21 | 1600 | 0.3883 | 0.3575 |
| 0.2374 | 15.27 | 2000 | 0.3151 | 0.2876 |
| 0.1674 | 18.32 | 2400 | 0.3284 | 0.2783 |
| 0.1385 | 21.37 | 2800 | 0.3408 | 0.2641 |
| 0.1133 | 24.43 | 3200 | 0.3355 | 0.2538 |
| 0.1015 | 27.48 | 3600 | 0.3303 | 0.2415 |
### Framework versions
- Transformers 4.26.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
|
922-Narra/llama-2-7b-chat-tagalog-v0.3a-gguf
|
922-Narra
| 2023-09-02T08:24:02Z | 21 | 1 | null |
[
"gguf",
"license:llama2",
"endpoints_compatible",
"region:us"
] | null | 2023-09-01T10:32:37Z |
---
license: llama2
---
GGUFs of [l27b-chat-tagalog-v0.3a](https://huggingface.co/922-Narra/llama-2-7b-chat-tagalog-v0.3a). (Primarily tested and run with Koboldcpp v1.41+).
QLora (hf and GGML) [here](https://huggingface.co/922-Narra/tagalog-lm-lora-tests/tree/main/llama-2-7b-chat-tagalog-0.3a).
|
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
|
Ori/lama-2-13b-peft-2wikihop-strategyqa-retrieval-at1
|
Ori
| 2023-09-02T08:09:57Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"region:us"
] | null | 2023-09-02T08:05:43Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.5.0.dev0
|
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 🐶 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 👀
|
squarelike/Gugugo-koja-1.3B-V0.95
|
squarelike
| 2023-09-02T07:31:26Z | 67 | 2 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"gpt_neox",
"text-generation",
"translation",
"ja",
"ko",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
translation
| 2023-08-31T14:17:12Z |
---
license: apache-2.0
language:
- ja
- ko
pipeline_tag: translation
---
[https://github.com/jwj7140/Gugugo](https://github.com/jwj7140/Gugugo)
Prompt Template:
```
### 한국어: {sentence}</끝>
### 일본어:
```
```
### 일본어: {sentence}</끝>
### 한국어:
```
|
shiveshnavin/my-dreambooth
|
shiveshnavin
| 2023-09-02T07:01:51Z | 2 | 0 |
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
| 2023-09-02T05:01:54Z |
---
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: photo of shivesh
tags:
- text-to-image
- diffusers
- autotrain
inference: true
---
# DreamBooth trained by AutoTrain
Text encoder was not trained.
|
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
|
dt-and-vanilla-ardt/ardt-vanilla-robust_train_walker2d_level-0209_0608-99
|
dt-and-vanilla-ardt
| 2023-09-02T06:36:38Z | 31 | 0 |
transformers
|
[
"transformers",
"pytorch",
"decision_transformer",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | null | 2023-09-02T05:10:31Z |
---
tags:
- generated_from_trainer
model-index:
- name: ardt-vanilla-robust_train_walker2d_level-0209_0608-99
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_walker2d_level-0209_0608-99
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
|
GyanPrakashKushwaha/Sentiment-Analysis
|
GyanPrakashKushwaha
| 2023-09-02T06:26:34Z | 0 | 0 | null |
[
"license:bigscience-openrail-m",
"region:us"
] | null | 2023-09-02T06:26:34Z |
---
license: bigscience-openrail-m
---
|
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
|
budecosystem/genz-70b
|
budecosystem
| 2023-09-02T06:03:21Z | 2,642 | 30 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-08-21T11:36:04Z |
---
language:
- en
library_name: transformers
pipeline_tag: text-generation
---
---
<div align="center"><h1 align="center">~ GenZ ~</h1><img src="https://raw.githubusercontent.com/BudEcosystem/GenZ/main/assets/genz-logo.png" width=150></div>
<p align="center"><i>Democratizing access to LLMs for the open-source community.<br>Let's advance AI, together. </i></p>
---
## Introduction 🎉
Welcome to **GenZ**, an advanced Large Language Model (LLM) fine-tuned on the foundation of Meta's open-source Llama V2 70B parameter model. At Bud Ecosystem, we believe in the power of open-source collaboration to drive the advancement of technology at an accelerated pace. Our vision is to democratize access to fine-tuned LLMs, and to that end, we will be releasing a series of models across different parameter counts (7B, 13B, and 70B) and quantizations (32-bit and 4-bit) for the open-source community to use, enhance, and build upon.
<p align="center"><img src="https://raw.githubusercontent.com/BudEcosystem/GenZ/main/assets/mt_bench_compare.png" width="500"></p>
The smaller quantization version of our models makes them more accessible, enabling their use even on personal computers. This opens up a world of possibilities for developers, researchers, and enthusiasts to experiment with these models and contribute to the collective advancement of language model technology.
GenZ isn't just a powerful text generator—it's a sophisticated AI assistant, capable of understanding and responding to user prompts with high-quality responses. We've taken the robust capabilities of Llama V2 and fine-tuned them to offer a more user-focused experience. Whether you're seeking informative responses or engaging interactions, GenZ is designed to deliver.
And this isn't the end. It's just the beginning of a journey towards creating more advanced, more efficient, and more accessible language models. We invite you to join us on this exciting journey. 🚀
---
<h2>Milestone Releases ️🏁</h2>
**[21 August 2023]**
[_GenZ-70B_](https://huggingface.co/budecosystem/genz-70b) : We're excited to announce the release of our Genz 70BB model. Experience the advancements by downloading the model from [HuggingFace](https://huggingface.co/budecosystem/genz-70b).
**[27 July 2023]**
[_GenZ-13B V2 (ggml)_](https://huggingface.co/budecosystem/genz-13b-v2-ggml) : Announcing our GenZ-13B v2 with ggml. This variant of GenZ can run inferencing using only CPU and without the need of GPU. Download the model from [HuggingFace](https://huggingface.co/budecosystem/genz-13b-v2-ggml).
**[27 July 2023]**
[_GenZ-13B V2 (4-bit)_](https://huggingface.co/budecosystem/genz-13b-v2-4bit) : Announcing our GenZ-13B v2 with 4-bit quantisation. Enabling inferencing with much lesser GPU memory than the 32-bit variant. Download the model from [HuggingFace](https://huggingface.co/budecosystem/genz-13b-v2-4bit).
**[26 July 2023]**
[_GenZ-13B V2_](https://huggingface.co/budecosystem/genz-13b-v2) : We're excited to announce the release of our Genz 13B v2 model, a step forward with improved evaluation results compared to v1. Experience the advancements by downloading the model from [HuggingFace](https://huggingface.co/budecosystem/genz-13b-v2).
**[20 July 2023]**
[_GenZ-13B_](https://huggingface.co/budecosystem/genz-13b) : We marked an important milestone with the release of the Genz 13B model. The journey began here, and you can partake in it by downloading the model from [Hugging Face](https://huggingface.co/budecosystem/genz-13b).
---
<h2>Evaluations 🎯</h2>
Evaluating our model is a key part of our fine-tuning process. It helps us understand how our model is performing and how it stacks up against other models. Here's a look at some of the key evaluations for GenZ 70B:
<h3>Benchmark Comparison</h3>
We've compared GenZ models to understand the improvements our fine-tuning has achieved.
| Model Name | MT Bench | MMLU | Human Eval | BBH |
|:----------:|:--------:|:----:|:----------:|:----:|
| Genz 13B | 6.12 | 53.62| 17.68 | 37.76|
| Genz 13B v2| 6.79 | 53.68| 21.95 | 38.1 |
| Genz 70B | 7.33 | 70.32| 37.8 |54.69 |
<h3>MT Bench Score</h3>
A key evaluation metric we use is the MT Bench score. This score provides a comprehensive assessment of our model's performance across a range of tasks.
<p align="center"><img src="https://raw.githubusercontent.com/BudEcosystem/GenZ/main/assets/mt_bench_score.png" width="500"></p>
---
<h2>Getting Started on Hugging Face 🤗</h2>
Getting up and running with our models on Hugging Face is a breeze. Follow these steps:
<h3>1️⃣ : Import necessary modules</h3>
Start by importing the necessary modules from the ‘transformers’ library and ‘torch’.
```python
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("budecosystem/genz-70b", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("budecosystem/genz-70b", torch_dtype=torch.bfloat16, rope_scaling={"type": "dynamic", "factor": 2})
prompt = "### User:\nWrite a python flask code for login management\n\n### Assistant:\n"
inputs = tokenizer(prompt, return_tensors="pt")
sample = model.generate(**inputs, max_length=128)
print(tokenizer.decode(sample[0]))
```
Want to interact with the model in a more intuitive way? We have a Gradio interface set up for that. Head over to our GitHub page, clone the repository, and run the ‘generate.py’ script to try it out. Happy experimenting! 😄
<h2>Why Use GenZ? 💡</h2>
You might be wondering, "Why should I choose GenZ over a pretrained model?" The answer lies in the extra mile we've gone to fine-tune our models.
While pretrained models are undeniably powerful, GenZ brings something extra to the table. We've fine-tuned it with curated datasets, which means it has additional skills and capabilities beyond what a pretrained model can offer. Whether you need it for a simple task or a complex project, GenZ is up for the challenge.
What's more, we are committed to continuously enhancing GenZ. We believe in the power of constant learning and improvement. That's why we'll be regularly fine-tuning our models with various curated datasets to make them even better. Our goal is to reach the state of the art and beyond - and we're committed to staying the course until we get there.
But don't just take our word for it. We've provided detailed evaluations and performance details in a later section, so you can see the difference for yourself.
Choose GenZ and join us on this journey. Together, we can push the boundaries of what's possible with large language models.
---
<h2>Model Card for GenZ 70B 📄</h2>
Here's a quick overview of everything you need to know about GenZ 70B.
<h3>Model Details:</h3>
- Developed by: Bud Ecosystem
- Base pretrained model type: Llama V2 70B
- Model Architecture: GenZ 70B, fine-tuned on Llama V2 70B, is an auto-regressive language model that employs an optimized transformer architecture. The fine-tuning process for GenZ 70B leveraged Supervised Fine-Tuning (SFT)
- License: The model is available for commercial use under a custom commercial license. For more information, please visit: [Meta AI Model and Library Downloads](https://ai.meta.com/resources/models-and-libraries/llama-downloads/)
---
<h2>Intended Use 💼</h2>
When we created GenZ 70B, we had a clear vision of how it could be used to push the boundaries of what's possible with large language models. We also understand the importance of using such models responsibly. Here's a brief overview of the intended and out-of-scope uses for GenZ 70B.
<h3>Direct Use</h3>
GenZ 70B is designed to be a powerful tool for research on large language models. It's also an excellent foundation for further specialization and fine-tuning for specific use cases, such as:
- Text summarization
- Text generation
- Chatbot creation
- And much more!
<h3>Out-of-Scope Use 🚩</h3>
While GenZ 70B is versatile, there are certain uses that are out of scope:
- Production use without adequate assessment of risks and mitigation
- Any use cases which may be considered irresponsible or harmful
- Use in any manner that violates applicable laws or regulations, including trade compliance laws
- Use in any other way that is prohibited by the Acceptable Use Policy and Licensing Agreement for Llama 2
Remember, GenZ 70B, like any large language model, is trained on a large-scale corpora representative of the web, and therefore, may carry the stereotypes and biases commonly encountered online.
<h3>Recommendations 🧠</h3>
We recommend users of GenZ 70B to consider fine-tuning it for the specific set of tasks of interest. Appropriate precautions and guardrails should be taken for any production use. Using GenZ 70B responsibly is key to unlocking its full potential while maintaining a safe and respectful environment.
---
<h2>Training Details 📚</h2>
When fine-tuning GenZ 70B, we took a meticulous approach to ensure we were building on the solid base of the pretrained Llama V2 70B model in the most effective way. Here's a look at the key details of our training process:
<h3>Fine-Tuning Training Data</h3>
For the fine-tuning process, we used a carefully curated mix of datasets. These included data from OpenAssistant, an instruction fine-tuning dataset, and Thought Source for the Chain Of Thought (CoT) approach. This diverse mix of data sources helped us enhance the model's capabilities across a range of tasks.
<h3>Hyperparameters</h3>
Here are the hyperparameters we used for fine-tuning:
| Hyperparameter | Value |
| -------------- | ----- |
| Warmup Ratio | 0.04 |
| Learning Rate Scheduler Type | Cosine |
| Learning Rate | 2e-5 |
| Number of Training Epochs | 3 |
| Per Device Training Batch Size | 4 |
| Gradient Accumulation Steps | 4 |
| Precision | FP16 |
| Optimizer | AdamW |
---
<h2>Looking Ahead 👀</h2>
We're excited about the journey ahead with GenZ. We're committed to continuously improving and enhancing our models, and we're excited to see what the open-source community will build with them. We believe in the power of collaboration, and we can't wait to see what we can achieve together.
Remember, we're just getting started. This is just the beginning of a journey that we believe will revolutionize the world of large language models. We invite you to join us on this exciting journey. Together, we can push the boundaries of what's possible with AI. 🚀
---
Check the GitHub for the code -> [GenZ](https://raw.githubusercontent.com/BudEcosystem/GenZ)
|
miaoyh32/roberta-large-peft-p-tuning
|
miaoyh32
| 2023-09-02T05:46:27Z | 1 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-22T01:47:27Z |
---
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
---
|
jmhessel/cosmo-v2-7b
|
jmhessel
| 2023-09-02T05:39:26Z | 3 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-09-02T05:39:20Z |
---
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: False
- bnb_4bit_compute_dtype: float16
### Framework versions
- PEFT 0.5.0
|
sushanadhikari/animal_detection
|
sushanadhikari
| 2023-09-02T05:31:02Z | 223 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-09-02T05:30:56Z |
---
tags:
- image-classification
- pytorch
- huggingpics
metrics:
- accuracy
model-index:
- name: animal_detection
results:
- task:
name: Image Classification
type: image-classification
metrics:
- name: Accuracy
type: accuracy
value: 0.9909909963607788
---
# animal_detection
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics).
## Example Images
#### cat
#### dog
#### girl
#### gold fish
#### house
|
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
|
dt-and-vanilla-ardt/ardt-vanilla-robust_train_walker2d_level-0209_0437-66
|
dt-and-vanilla-ardt
| 2023-09-02T05:08:32Z | 35 | 0 |
transformers
|
[
"transformers",
"pytorch",
"decision_transformer",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | null | 2023-09-02T03:38:44Z |
---
tags:
- generated_from_trainer
model-index:
- name: ardt-vanilla-robust_train_walker2d_level-0209_0437-66
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-vanilla-robust_train_walker2d_level-0209_0437-66
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
|
vita-group/llama-2-7b_wanda_unstructured
|
vita-group
| 2023-09-02T05:03:35Z | 10 | 0 | null |
[
"license:mit",
"region:us"
] | null | 2023-09-01T15:05:46Z |
---
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) |
|
spssspss0712/my_awesome_swag_model
|
spssspss0712
| 2023-09-02T04:40:41Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"multiple-choice",
"generated_from_trainer",
"dataset:swag",
"base_model:google-bert/bert-base-uncased",
"base_model:finetune:google-bert/bert-base-uncased",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
multiple-choice
| 2023-09-02T03:10:29Z |
---
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
datasets:
- swag
metrics:
- accuracy
model-index:
- name: my_awesome_swag_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_swag_model
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the swag dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0292
- Accuracy: 0.7910
## 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.7701 | 1.0 | 4597 | 0.5856 | 0.7695 |
| 0.3699 | 2.0 | 9194 | 0.6229 | 0.7873 |
| 0.1533 | 3.0 | 13791 | 1.0292 | 0.7910 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
|
rpi-zhar2/RSNA
|
rpi-zhar2
| 2023-09-02T04:36:27Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-09-02T04:33:33Z |
vits_model_1.pth => train loss: 0.442 && valid loss: 0.556
|
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
```
|
Imxxn/AudioCourseU4-MusicClassification
|
Imxxn
| 2023-09-02T04:21:50Z | 162 | 0 |
transformers
|
[
"transformers",
"pytorch",
"hubert",
"audio-classification",
"generated_from_trainer",
"dataset:marsyas/gtzan",
"base_model:ntu-spml/distilhubert",
"base_model:finetune:ntu-spml/distilhubert",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
audio-classification
| 2023-09-02T01:42:30Z |
---
license: apache-2.0
base_model: ntu-spml/distilhubert
tags:
- generated_from_trainer
datasets:
- marsyas/gtzan
metrics:
- accuracy
model-index:
- name: AudioCourseU4-MusicClassification
results:
- task:
name: Audio Classification
type: audio-classification
dataset:
name: GTZAN
type: marsyas/gtzan
config: all
split: train
args: all
metrics:
- name: Accuracy
type: accuracy
value: 0.88
---
<!-- 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. -->
# AudioCourseU4-MusicClassification
This model is a fine-tuned version of [ntu-spml/distilhubert](https://huggingface.co/ntu-spml/distilhubert) on the GTZAN dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8804
- Accuracy: 0.88
## 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: 8e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 15
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.7993 | 1.0 | 225 | 1.5770 | 0.4 |
| 1.0767 | 2.0 | 450 | 0.9900 | 0.7 |
| 0.8292 | 3.0 | 675 | 0.8554 | 0.73 |
| 0.5892 | 4.0 | 900 | 0.8991 | 0.74 |
| 0.1584 | 5.0 | 1125 | 0.8473 | 0.78 |
| 0.0082 | 6.0 | 1350 | 0.9282 | 0.8 |
| 0.0094 | 7.0 | 1575 | 1.0036 | 0.82 |
| 0.0581 | 8.0 | 1800 | 1.2186 | 0.82 |
| 0.0021 | 9.0 | 2025 | 1.0192 | 0.83 |
| 0.0011 | 10.0 | 2250 | 0.8804 | 0.88 |
| 0.002 | 11.0 | 2475 | 1.1519 | 0.83 |
| 0.0009 | 12.0 | 2700 | 0.9439 | 0.87 |
| 0.0006 | 13.0 | 2925 | 1.1227 | 0.84 |
| 0.0008 | 14.0 | 3150 | 1.0344 | 0.86 |
| 0.0006 | 15.0 | 3375 | 1.0209 | 0.86 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
|
kwholee/distilbert-base-uncased-finetuned-emotion
|
kwholee
| 2023-09-02T04:05:10Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-09-02T03:28:15Z |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
config: split
split: validation
args: split
metrics:
- name: Accuracy
type: accuracy
value: 0.926
- name: F1
type: f1
value: 0.9259991388364199
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2102
- Accuracy: 0.926
- F1: 0.9260
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8314 | 1.0 | 250 | 0.3108 | 0.9085 | 0.9077 |
| 0.2446 | 2.0 | 500 | 0.2102 | 0.926 | 0.9260 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- 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
|
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
|
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
|
ardt-multipart/ardt-multipart-robust_train_walker2d_level-0209_0140-99
|
ardt-multipart
| 2023-09-02T02:43:07Z | 31 | 0 |
transformers
|
[
"transformers",
"pytorch",
"decision_transformer",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | null | 2023-09-02T00:42:39Z |
---
tags:
- generated_from_trainer
model-index:
- name: ardt-multipart-robust_train_walker2d_level-0209_0140-99
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_walker2d_level-0209_0140-99
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
|
ardt-multipart/ardt-multipart-robust_train_halfcheetah_level-0209_0046-99
|
ardt-multipart
| 2023-09-02T02:02:23Z | 31 | 0 |
transformers
|
[
"transformers",
"pytorch",
"decision_transformer",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | null | 2023-09-01T23:48:11Z |
---
tags:
- generated_from_trainer
model-index:
- name: ardt-multipart-robust_train_halfcheetah_level-0209_0046-99
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_halfcheetah_level-0209_0046-99
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
|
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 pontuação)"
- 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 Proteção 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: Proteção de Dados Sensíveis na Era da Inteligência Artificial](WIP)
|
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!
🥳 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 🤗 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 🤝! 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!
🥳 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 🤗 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 🤝! 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
---
你好感谢您使用白溺的歌声数据库。
使用规约：
1.禁止用于 宗教 政治，等等，任何违反法律内容的创作。
2.禁止将本音源使用于大众雷点相关内容的创作（例如：为有不良行为的人或物进行二创）
3.允许用于非商业用途且不违反规约的创作。
4.用于商业用途或者盈利，需要向音源管理者申请授权。一般来说都是免费的。
5.使用本音源，不论是商业还是非商，不论是主唱还是和声等职位，都应清晰明了的标注声库名以及所属位置（例：和声：白溺)
6.本音源的oto文件以及wav名，wav文件，引擎模型文件等等，这类由本音源配布的内容以及配布内容二次生成的物品，未说明允许二次配布的，都视为不允许二次配布，也不允许用于违反此规约的创作。（注：使用本音源所配布的内容，进行ai或者其他的类似程序的训练学习等等，或其他近似的行为。属于对已配布内容的二次生成物，不允许以任何形式的分发）
7.更不允许，将此音源与其他音源二次拼接起来命名为新的音源。也不允许制作该音源的亚种，如果有亚种需求，请以音色的名义发布（例如：白溺·秋风）。不允许对音源的文件进行二次修改后以新的命名发布。
需要违反规约的事情，或者规约没有写明白的事情，请咨询声库管理者，声库管理者拥有本规约的最终解释权。
歌手基础信息：
歌手信息：
姓名：白溺
性别：男
生日：7月20日
中之人：小白菌（小雨天）
声库管理者/版权归属：小白菌菌 https://space.bilibili.com/207917768
通过邮箱联系我：[email protected]（我可能只是偶尔看一眼）
|
javidjamae/autotrain-movie-sentiment-86557143111
|
javidjamae
| 2023-09-02T01:44:36Z | 108 | 0 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"deberta",
"text-classification",
"autotrain",
"en",
"dataset:javidjamae/autotrain-data-movie-sentiment",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-09-02T01:43:36Z |
---
tags:
- autotrain
- text-classification
language:
- en
widget:
- text: "I love AutoTrain"
datasets:
- javidjamae/autotrain-data-movie-sentiment
co2_eq_emissions:
emissions: 0.0061768979977510595
---
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 86557143111
- CO2 Emissions (in grams): 0.0062
## Validation Metrics
- Loss: 0.736
- Accuracy: 0.747
- Precision: 0.669
- Recall: 0.993
- AUC: 0.932
- F1: 0.800
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/javidjamae/autotrain-movie-sentiment-86557143111
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("javidjamae/autotrain-movie-sentiment-86557143111", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("javidjamae/autotrain-movie-sentiment-86557143111", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
```
|
Jgutierrez90/intel_image_classification_fastai
|
Jgutierrez90
| 2023-09-02T01:44:09Z | 0 | 0 |
fastai
|
[
"fastai",
"region:us"
] | null | 2023-09-02T01:44:06Z |
---
tags:
- fastai
---
# Amazing!
🥳 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 🤗 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 🤝! 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
|
felipelondo/intel_image_classification_fastai
|
felipelondo
| 2023-09-02T01:42:19Z | 0 | 0 |
fastai
|
[
"fastai",
"region:us"
] | null | 2023-09-02T01:42:14Z |
---
tags:
- fastai
---
# Amazing!
🥳 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 🤗 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 🤝! 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
|
Andresf7/intel_image_classification_fastai
|
Andresf7
| 2023-09-02T01:40:03Z | 0 | 0 |
fastai
|
[
"fastai",
"region:us"
] | null | 2023-09-02T01:39:59Z |
---
tags:
- fastai
---
# Amazing!
🥳 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 🤗 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 🤝! 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
|
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!
🥳 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 🤗 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 🤝! 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!
🥳 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 🤗 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 🤝! 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/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>
|
dfelorza/intel_image_classification_fastai
|
dfelorza
| 2023-09-02T01:28:28Z | 0 | 0 |
fastai
|
[
"fastai",
"region:us"
] | null | 2023-09-02T01:28:24Z |
---
tags:
- fastai
---
# Amazing!
🥳 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 🤗 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 🤝! 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
|
DavidOsorio/intel_image_classification_fastai
|
DavidOsorio
| 2023-09-02T01:26:54Z | 0 | 0 |
fastai
|
[
"fastai",
"region:us"
] | null | 2023-09-02T01:26:50Z |
---
tags:
- fastai
---
# Amazing!
🥳 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 🤗 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 🤝! 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
|
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Filtered Qwen-7b Model Cards
The query performs a detailed filtering based on specific keywords and excludes certain entries, which could be useful for identifying a specific subset of cards but does not provide deeper insights or trends.
Filtered Qwen 7B Model Cards
The query filters for specific terms related to "distilled" or "distill", "qwen", and "7b" in the 'card' column but excludes certain base models, providing a limited set of entries for further inspection.
Qwen 7B Distilled Models
The query provides a basic filtering of records to find specific card names that include keywords related to distilled Qwen 7b models, excluding a particular base model, which gives limited insight but helps in focusing on relevant entries.
Qwen 7B Distilled Model Cards
The query filters data based on specific keywords in the modelId and card fields, providing limited insight primarily useful for locating specific entries rather than revealing broad patterns or trends.
Qwen 7B Distilled Models
Finds all entries containing the terms 'distilled', 'qwen', and '7b' in a case-insensitive manner, providing a filtered set of records but without deeper analysis.
Distilled Qwen 7B Models
The query filters for specific model IDs containing 'distilled', 'qwen', and '7b', providing a basic retrieval of relevant entries but without deeper analysis or insight.
Filtered Model Cards with Distill Qwen2.
Filters and retrieves records containing specific keywords in the card description while excluding certain phrases, providing a basic count of relevant entries.
Filtered Model Cards with Distill Qwen 7
The query filters specific variations of card descriptions containing 'distill', 'qwen', and '7b' while excluding a particular base model, providing limited but specific data retrieval.
Distill Qwen 7B Model Cards
The query filters and retrieves rows where the 'card' column contains specific keywords ('distill', 'qwen', and '7b'), providing a basic filter result that can help in identifying specific entries.