modelId
stringlengths 5
139
| author
stringlengths 2
42
| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-06-23 18:27:52
| downloads
int64 0
223M
| likes
int64 0
11.7k
| library_name
stringclasses 492
values | tags
sequencelengths 1
4.05k
| pipeline_tag
stringclasses 54
values | createdAt
timestamp[us, tz=UTC]date 2022-03-02 23:29:04
2025-06-23 18:25:26
| card
stringlengths 11
1.01M
|
|---|---|---|---|---|---|---|---|---|---|
uripper/AVA | uripper | 2023-07-13T08:15:52Z | 10 | 0 | transformers | [
"transformers",
"pytorch",
"safetensors",
"gpt2",
"text-generation",
"license:cc",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2022-08-22T20:54:37Z | ---
license: cc
widget:
- text: "Movie: Parasite Score:"
example_title: "Parasite"
- text: "Movie: Come and See Score:"
example_title: "Come and See"
- text: "Movie: Harakiri Score:"
example_title: "Harakiri"
---
# Review Training Bot
This model was trained for the purpose of generating scores and reviews for any given movie. It is fine-tuned on distilgpt2 as a baseline and trained on a custom dataset created by scraping around 120k letterboxd reviews. The current state of the model can get the correct formatting reliably but oftentimes is prone to gibberish. Further training will hopefully add coherency. It is in version 0.1 currently.
## Intended uses & limitations
This model is intended to be used for entertainment.
Limitations for this model will be much of the same as distilgpt2 which can be viewed here https://huggingface.co/distilgpt2. These may include persistent biases. Another issue may be through language specifically on letterboxd that the algorithm may not be able to understand. i.e. an LGBT+ film on letterboxd may have multiple reviews that mention the word "gay" positively, this model has not been able to understand this contextual usage and will use the word as a slur. As the current model also struggles to find a connection between movie titles and the reviews, this could happen with any entered movie.
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.001
- train_batch_size: 10
- eval_batch_size: 20
- seed: 42
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- training_steps: 5000
### Framework versions
- Transformers 4.21.2
- Pytorch 1.12.1+cu113
- Tokenizers 0.12.1
|
Ablustrund/moss-rlhf-reward-model-7B-zh | Ablustrund | 2023-07-13T08:10:42Z | 3 | 23 | null | [
"llm",
"reward model",
"moss",
"rlhf",
"zh",
"arxiv:2307.04964",
"license:agpl-3.0",
"region:us"
] | null | 2023-07-12T02:27:02Z | ---
license: agpl-3.0
language:
- zh
tags:
- llm
- reward model
- moss
- rlhf
---
# MOSS-RLHF
### *MOSS-RLHF & "Secrets of RLHF in Large Language Models Part I: PPO" <br>👉 <a href="https://arxiv.org/abs/2307.04964" target="_blank">[Technical report]</a> <a href="https://openlmlab.github.io/MOSS-RLHF/" target="_blank">[Home page]*
## 🌟 News
### 👉 Wed, 12. July 2023. We have released Chinese reward model based OpenChineseLlama-7B!
[moss-rlhf-reward-model-7B-zh](https://huggingface.co/Ablustrund/moss-rlhf-reward-model-7B-zh/tree/main)
<br>
### 👉 Thu, 13. July 2023. We have released English reward model and SFT model based Llama-7B!
[moss-rlhf-reward-model-7B-en](https://huggingface.co/fnlp/moss-rlhf-reward-model-7B-en)
[moss-rlhf-sft-model-7B-en](https://huggingface.co/fnlp/moss-rlhf-sft-model-7B-en)
<br>
## 🧾 Open-source List
- [x] Open source code for RL training in large language models.
- [x] A 7B Chinese reward model based on openChineseLlama.
- [x] A 7B English reward model based on Llama-7B.
- [x] SFT model for English.
- [ ] Policy model for English after RLHF.
- ...
## 🌠 Introduction
Due to the challenges of reward design, environment interaction, and agent training, coupled with huge trial and error cost of large language models, there is a significant barrier for AI researchers to motivate the development of technical alignment and safe landing of LLMs. The stable training of RLHF has still been a puzzle.
In this technical report, we intend to help researchers to train their models stably with human feedback.
Contributions are summarized as follows:
1) We release competitive Chinese and English reward models, respectively, which have good cross-model generalization ability, alleviating the cost of relabeling human preference data;
2) We conduct in-depth analysis on the inner workings of PPO algorithm and propose the PPO-max algorithm to ensure stable model training;
3) We release the complete PPO-max codes to ensure that the LLMs in the current SFT stage can be better aligned with humans.
## 🔩 Requirements & Setup
This repository works on Python 3.8 and PyTorch 1.13.1.
We recommend using the **conda** virtual environment to run the code.
#### Step 1: Create a new Python virtual environment
```bash
conda update conda -n base -c defaults
conda create -n rlhf python=3.8
conda activate rlhf
```
#### Step 2: Install PyTorch and TensorBoard
```bash
conda install pytorch==1.13.1 pytorch-cuda=11.7 tensorboard -c pytorch -c nvidia
```
#### Step 3: Install the remaining dependencies
```bash
conda install datasets accelerate safetensors chardet cchardet -c huggingface -c conda-forge
pip3 install transformers sentencepiece einops triton==1.0.0 rouge jionlp==1.4.14 nltk sacrebleu cpm_kernels
apt install libaio-dev
DS_BUILD_OPS=1 pip install deepspeed
```
## ✨ Start training your own model!
Run code in a few steps.
### Step 1: Recover Reward model weights
We can not directly release the full weight of the reward model because of protocol restrictions.
You can merge the diff weight with original Llama-7B to recover the reward model we used.
We upload the diff models, thanks to tatsu-lab, you can recover the reward model follow these steps:
```bash
1) Download the weight diff into your local machine. The weight diff is located at:
# For English:
TODO
# For Chinese:
https://huggingface.co/Ablustrund/moss-rlhf-reward-model-7B-zh/tree/main
2) Merge the weight diff with the original Llama-7B:
# For English:
# Reward model
python merge_weight_en.py recover --path_raw decapoda-research/llama-7b-hf --path_diff ./models/moss-rlhf-reward-model-7B-en/diff --path_tuned ./models/moss-rlhf-reward-model-7B-en/recover --model_type reward
# SFT model
python merge_weight_en.py recover --path_raw decapoda-research/llama-7b-hf --path_diff ./models/moss-rlhf-sft-model-7B-en/diff --path_tuned ./models/moss-rlhf-sft-model-7B-en/recover --model_type sft
# Policy model
TODO
# For Chinese:
python merge_weight_zh.py recover --path_raw decapoda-research/llama-7b-hf --path_diff ./models/moss-rlhf-reward-model-7B-zh/diff --path_tuned ./models/moss-rlhf-reward-model-7B-zh/recover
```
### Step 2: Select your own SFT model.
Because of some limitations, we can not release the **Chinese** SFT model (Currently).
You can use your own SFT model, or a strong base model instead of our SFT model.
### Step 3: Start training
Run the command below.
```
# For Chinese:
# You need to use your own sft model currently.
bash run_zh.sh
# For English:
# We have loaded the sft model and reward model to huggingface.
bash run_en.sh
```
## Citation
```bibtex
@article{zheng2023secrets,
title={Secrets of RLHF in Large Language Models Part I: PPO},
author={Rui Zheng and Shihan Dou and Songyang Gao and Wei Shen and Binghai Wang and Yan Liu and Senjie Jin and Qin Liu and Limao Xiong and Lu Chen and Zhiheng Xi and Yuhao Zhou and Nuo Xu and Wenbin Lai and Minghao Zhu and Rongxiang Weng and Wensen Cheng and Cheng Chang and Zhangyue Yin and Yuan Hua and Haoran Huang and Tianxiang Sun and Hang Yan and Tao Gui and Qi Zhang and Xipeng Qiu and Xuanjing Huang},
year={2023},
eprint={2307.04964},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
veluchs/dqn-SpaceInvadersNoFrameskip-v4-newest | veluchs | 2023-07-13T08:04:45Z | 4 | 0 | stable-baselines3 | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | 2023-07-13T08:01:41Z | ---
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: 257.00 +/- 38.81
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 veluchs -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 veluchs -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 veluchs
```
## 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', 100000),
('n_timesteps', 100000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
# Environment Arguments
```python
{'render_mode': 'rgb_array'}
```
|
yubuu/path-to-save-model | yubuu | 2023-07-13T08:03:07Z | 30 | 0 | diffusers | [
"diffusers",
"tensorboard",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"dreambooth",
"base_model:runwayml/stable-diffusion-v1-5",
"base_model:finetune:runwayml/stable-diffusion-v1-5",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | text-to-image | 2023-07-13T07:51:30Z |
---
license: creativeml-openrail-m
base_model: runwayml/stable-diffusion-v1-5
instance_prompt: a photo of sks dog
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- dreambooth
inference: true
---
# DreamBooth - yubuu/path-to-save-model
This is a dreambooth model derived from runwayml/stable-diffusion-v1-5. The weights were trained on a photo of sks dog using [DreamBooth](https://dreambooth.github.io/).
You can find some example images in the following.
DreamBooth for the text encoder was enabled: False.
|
haxett333/RL-Reinforce-100TrainEpisodesInsteadof1000 | haxett333 | 2023-07-13T08:00:13Z | 0 | 0 | null | [
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | reinforcement-learning | 2023-07-13T08:00:09Z | ---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: RL-Reinforce-100TrainEpisodesInsteadof1000
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 98.70 +/- 36.77
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
saeedehj/led-base-finetune-cnn | saeedehj | 2023-07-13T07:50:12Z | 34 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"led",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2023-07-12T22:27:22Z | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: led-base-16384-finetune-cnn
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. -->
# led-base-16384-finetune-cnn
This model is a fine-tuned version of [allenai/led-base-16384](https://huggingface.co/allenai/led-base-16384) on the cnn_dailymail dataset.
It achieves the following results on the evaluation set:
- Loss: 3.2020
- Rouge1: 24.2258
- Rouge2: 9.0151
- Rougel: 19.0336
- Rougelsum: 22.2604
- Gen Len: 20.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| 1.8988 | 1.0 | 2000 | 2.0031 | 25.1709 | 10.0426 | 20.1311 | 23.1639 | 20.0 |
| 1.6038 | 2.0 | 4000 | 2.0314 | 25.0213 | 9.8701 | 19.8987 | 23.0129 | 20.0 |
| 1.3352 | 3.0 | 6000 | 2.1124 | 24.99 | 9.905 | 19.9566 | 23.0973 | 20.0 |
| 1.1173 | 4.0 | 8000 | 2.2055 | 25.0568 | 10.0949 | 19.9602 | 23.18 | 20.0 |
| 0.9566 | 5.0 | 10000 | 2.3262 | 24.941 | 9.5856 | 19.6285 | 23.042 | 20.0 |
| 0.7986 | 6.0 | 12000 | 2.4489 | 24.4114 | 9.2808 | 19.3296 | 22.5481 | 20.0 |
| 0.6685 | 7.0 | 14000 | 2.5211 | 24.467 | 9.5124 | 19.2685 | 22.5624 | 20.0 |
| 0.5601 | 8.0 | 16000 | 2.6299 | 24.6939 | 9.6533 | 19.4627 | 22.8048 | 20.0 |
| 0.4757 | 9.0 | 18000 | 2.7185 | 24.2098 | 9.1232 | 19.0181 | 22.4085 | 20.0 |
| 0.3926 | 10.0 | 20000 | 2.7947 | 24.5092 | 9.3964 | 19.2593 | 22.5592 | 20.0 |
| 0.3391 | 11.0 | 22000 | 2.8626 | 24.4731 | 9.3634 | 19.2966 | 22.5688 | 20.0 |
| 0.2872 | 12.0 | 24000 | 2.9175 | 24.5587 | 9.3888 | 19.3335 | 22.6443 | 20.0 |
| 0.2479 | 13.0 | 26000 | 2.9658 | 24.2983 | 9.1038 | 19.019 | 22.3675 | 20.0 |
| 0.213 | 14.0 | 28000 | 3.0273 | 24.4196 | 9.1481 | 19.0458 | 22.5135 | 20.0 |
| 0.1828 | 15.0 | 30000 | 3.0751 | 24.3283 | 9.2334 | 18.9771 | 22.3322 | 20.0 |
| 0.1608 | 16.0 | 32000 | 3.1185 | 24.3965 | 9.2047 | 19.0899 | 22.4666 | 20.0 |
| 0.1442 | 17.0 | 34000 | 3.1494 | 24.3832 | 9.1915 | 19.077 | 22.4366 | 20.0 |
| 0.1293 | 18.0 | 36000 | 3.1738 | 24.3796 | 9.1132 | 19.1015 | 22.3862 | 20.0 |
| 0.1165 | 19.0 | 38000 | 3.2073 | 24.2804 | 9.1018 | 19.0692 | 22.3023 | 20.0 |
| 0.1118 | 20.0 | 40000 | 3.2020 | 24.2258 | 9.0151 | 19.0336 | 22.2604 | 20.0 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
jslin09/LegalChatbot-bloom-3b | jslin09 | 2023-07-13T07:45:16Z | 19 | 0 | peft | [
"peft",
"region:us"
] | null | 2023-07-06T02:44:57Z | ---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.4.0.dev0
- PEFT 0.4.0.dev0
- PEFT 0.4.0.dev0
|
JeffreyHuang/llm-selector | JeffreyHuang | 2023-07-13T07:30:31Z | 45 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2023-06-27T04:16:52Z | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: llm-selector
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. -->
# llm-selector
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7315
- Accuracy: 0.5048
## 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 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 118 | 1.8920 | 0.3714 |
| No log | 2.0 | 236 | 1.7753 | 0.5143 |
| No log | 3.0 | 354 | 1.7671 | 0.4952 |
| No log | 4.0 | 472 | 1.7441 | 0.5048 |
| 1.8665 | 5.0 | 590 | 1.7315 | 0.5048 |
| 1.8665 | 6.0 | 708 | 1.7413 | 0.5048 |
| 1.8665 | 7.0 | 826 | 1.7378 | 0.4667 |
| 1.8665 | 8.0 | 944 | 1.7426 | 0.4667 |
| 1.7254 | 9.0 | 1062 | 1.7513 | 0.4476 |
| 1.7254 | 10.0 | 1180 | 1.7513 | 0.4476 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1
- Datasets 2.12.0
- Tokenizers 0.13.3
|
vineetsharma/dqn-SpaceInvadersNoFrameskip-v4 | vineetsharma | 2023-07-13T07:13:19Z | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | 2023-07-13T07:12:43Z | ---
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: 560.00 +/- 101.24
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 vineetsharma -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 vineetsharma -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 vineetsharma
```
## 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'}
```
|
kaelee/llava-lightning-mpt-7b-chat-pretrain | kaelee | 2023-07-13T07:08:09Z | 14 | 0 | transformers | [
"transformers",
"pytorch",
"llava_mpt",
"text-generation",
"custom_code",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2023-07-13T00:20:35Z | ---
license: cc-by-nc-sa-4.0
---
|
aiacademy131/opt-2.7b-lora | aiacademy131 | 2023-07-13T06:34:01Z | 1 | 0 | peft | [
"peft",
"region:us"
] | null | 2023-07-13T05:36:48Z | ---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: True
- load_in_4bit: False
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
### Framework versions
- PEFT 0.4.0.dev0
|
smithlai/q-FrozenLake-v1-4x4-noSlippery | smithlai | 2023-07-13T06:33:59Z | 0 | 0 | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | reinforcement-learning | 2023-07-13T06:33:57Z | ---
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="smithlai/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"])
```
|
localmodels/WizardLM-13B-v1.1-GPTQ | localmodels | 2023-07-13T06:11:46Z | 7 | 0 | transformers | [
"transformers",
"llama",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2023-07-13T06:11:46Z | ---
duplicated_from: localmodels/LLM
---
# WizardLM 13B v1.1 GPTQ
From: https://huggingface.co/WizardLM/WizardLM-13B-V1.1
---
| Model | Bits | Group Size | Act Order (desc_act) | File Size | ExLlama Compatible? | Made With | Description |
| ------ | ---- | ---------- | -------------------- | --------- | ------------------- | --------- | ----------- |
| wizardlm-13b-v1.1-GPTQ-4bit-128g.no-act.order | 4 | 128 | False | 7.45 GB | True | GPTQ-for-LLaMa | Most compatible. Good inference speed in AutoGPTQ and GPTQ-for-LLaMa. | |
YanJiangJerry/SA-tweet-roberta-large-e4-w1-1.5-b16-m4 | YanJiangJerry | 2023-07-13T05:42:53Z | 105 | 0 | transformers | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2023-07-13T05:19:19Z | ---
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: SA-tweet-roberta-large-e4-w1-1.5-b16-m4
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. -->
# SA-tweet-roberta-large-e4-w1-1.5-b16-m4
This model is a fine-tuned version of [Amalq/autotrain-smm4h_large_roberta_clean-874027878](https://huggingface.co/Amalq/autotrain-smm4h_large_roberta_clean-874027878) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3545
- Accuracy: 0.945
- F1: 0.9511
- Precision: 0.9537
- Recall: 0.9486
## 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: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| No log | 1.0 | 285 | 0.1933 | 0.92 | 0.9290 | 0.9306 | 0.9273 |
| 0.2508 | 2.0 | 570 | 0.2097 | 0.933 | 0.9411 | 0.9337 | 0.9486 |
| 0.2508 | 3.0 | 855 | 0.2958 | 0.937 | 0.9450 | 0.9312 | 0.9592 |
| 0.0947 | 4.0 | 1140 | 0.3545 | 0.945 | 0.9511 | 0.9537 | 0.9486 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
preetham/rpanda | preetham | 2023-07-13T05:42:03Z | 30 | 0 | diffusers | [
"diffusers",
"tensorboard",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"dreambooth",
"base_model:CompVis/stable-diffusion-v1-4",
"base_model:finetune:CompVis/stable-diffusion-v1-4",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | text-to-image | 2023-07-13T05:23:12Z |
---
license: creativeml-openrail-m
base_model: CompVis/stable-diffusion-v1-4
instance_prompt: a photo of sks panda
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- dreambooth
inference: true
---
# DreamBooth - preetham/rpanda
This is a dreambooth model derived from CompVis/stable-diffusion-v1-4. The weights were trained on a photo of sks panda using [DreamBooth](https://dreambooth.github.io/).
You can find some example images in the following.
DreamBooth for the text encoder was enabled: False.
|
localmodels/Guanaco-33B-GPTQ | localmodels | 2023-07-13T05:28:12Z | 5 | 0 | transformers | [
"transformers",
"llama",
"text-generation",
"arxiv:2305.14314",
"arxiv:2302.13971",
"arxiv:2304.07327",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2023-07-13T05:28:12Z | ---
duplicated_from: localmodels/LLM
---
# Guanaco 33B GPTQ
From: https://huggingface.co/timdettmers/guanaco-33b-merged
---
## Model
* Guanaco-33B-GPTQ-4bit.act-order.safetensors
* Works with all versions of GPTQ-for-LLaMa code, both Triton and CUDA branches
* Works with AutoGPTQ
* Parameters: Groupsize = None. --act-order.
---
# Guanaco Models Based on LLaMA
| [Paper](https://arxiv.org/abs/2305.14314) | [Code](https://github.com/artidoro/qlora) | [Demo](https://huggingface.co/spaces/uwnlp/guanaco-playground-tgi) |
**The Guanaco models are open-source finetuned chatbots obtained through 4-bit QLoRA tuning of LLaMA base models on the OASST1 dataset. They are available in 7B, 13B, 33B, and 65B parameter sizes.**
⚠️Guanaco is a model purely intended for research purposes and could produce problematic outputs.
## Why use Guanaco?
- **Competitive with commercial chatbot systems on the Vicuna and OpenAssistant benchmarks** (ChatGPT and BARD) according to human and GPT-4 raters. We note that the relative performance on tasks not covered in these benchmarks could be very different. In addition, commercial systems evolve over time (we used outputs from the March 2023 version of the models).
- **Available open-source for research purposes**. Guanaco models allow *cheap* and *local* experimentation with high-quality chatbot systems.
- **Replicable and efficient training procedure** that can be extended to new use cases. Guanaco training scripts are available in the [QLoRA repo](https://github.com/artidoro/qlora).
- **Rigorous comparison to 16-bit methods** (both 16-bit full-finetuning and LoRA) in [our paper](https://arxiv.org/abs/2305.14314) demonstrates the effectiveness of 4-bit QLoRA finetuning.
- **Lightweight** checkpoints which only contain adapter weights.
## License and Intended Use
Guanaco adapter weights are available under Apache 2 license. Note the use of the Guanaco adapter weights, requires access to the LLaMA model weighs.
Guanaco is based on LLaMA and therefore should be used according to the LLaMA license.
## Usage
Here is an example of how you would load Guanaco 7B in 4-bits:
```python
import torch
from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
model_name = "huggyllama/llama-7b"
adapters_name = 'timdettmers/guanaco-7b'
model = AutoModelForCausalLM.from_pretrained(
model_name,
load_in_4bit=True,
torch_dtype=torch.bfloat16,
device_map="auto",
max_memory= {i: '24000MB' for i in range(torch.cuda.device_count())},
quantization_config=BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_compute_dtype=torch.bfloat16,
bnb_4bit_use_double_quant=True,
bnb_4bit_quant_type='nf4'
),
)
model = PeftModel.from_pretrained(model, adapters_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
```
Inference can then be performed as usual with HF models as follows:
```python
prompt = "Introduce yourself"
formatted_prompt = (
f"A chat between a curious human and an artificial intelligence assistant."
f"The assistant gives helpful, detailed, and polite answers to the user's questions.\n"
f"### Human: {prompt} ### Assistant:"
)
inputs = tokenizer(formatted_prompt, return_tensors="pt").to("cuda:0")
outputs = model.generate(inputs=inputs.input_ids, max_new_tokens=20)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```
Expected output similar to the following:
```
A chat between a curious human and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions.
### Human: Introduce yourself ### Assistant: I am an artificial intelligence assistant. I am here to help you with any questions you may have.
```
## Current Inference Limitations
Currently, 4-bit inference is slow. We recommend loading in 16 bits if inference speed is a concern. We are actively working on releasing efficient 4-bit inference kernels.
Below is how you would load the model in 16 bits:
```python
model_name = "huggyllama/llama-7b"
adapters_name = 'timdettmers/guanaco-7b'
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype=torch.bfloat16,
device_map="auto",
max_memory= {i: '24000MB' for i in range(torch.cuda.device_count())},
)
model = PeftModel.from_pretrained(model, adapters_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
```
## Model Card
**Architecture**: The Guanaco models are LoRA adapters to be used on top of LLaMA models. They are added to all layers. For all model sizes, we use $r=64$.
**Base Model**: Guanaco uses LLaMA as base model with sizes 7B, 13B, 33B, 65B. LLaMA is a causal language model pretrained on a large corpus of text. See [LLaMA paper](https://arxiv.org/abs/2302.13971) for more details. Note that Guanaco can inherit biases and limitations of the base model.
**Finetuning Data**: Guanaco is finetuned on OASST1. The exact dataset is available at [timdettmers/openassistant-guanaco](https://huggingface.co/datasets/timdettmers/openassistant-guanaco).
**Languages**: The OASST1 dataset is multilingual (see [the paper](https://arxiv.org/abs/2304.07327) for details) and as such Guanaco responds to user queries in different languages. We note, however, that OASST1 is heavy in high-resource languages. In addition, human evaluation of Guanaco was only performed in English and based on qualitative analysis we observed degradation in performance in other languages.
Next, we describe Training and Evaluation details.
### Training
Guanaco models are the result of 4-bit QLoRA supervised finetuning on the OASST1 dataset.
All models use NormalFloat4 datatype for the base model and LoRA adapters on all linear layers with BFloat16 as computation datatype. We set LoRA $r=64$, $\alpha=16$. We also use Adam beta2 of 0.999, max grad norm of 0.3 and LoRA dropout of 0.1 for models up to 13B and 0.05 for 33B and 65B models.
For the finetuning process, we use constant learning rate schedule and paged AdamW optimizer.
### Training hyperparameters
Size| Dataset | Batch Size | Learning Rate | Max Steps | Sequence length
---|---|---|---|---|---
7B | OASST1 | 16 | 2e-4 | 1875 | 512
13B | OASST1 | 16 | 2e-4 | 1875 | 512
33B | OASST1 | 16 | 1e-4 | 1875 | 512
65B | OASST1 | 16 | 1e-4 | 1875 | 512
### Evaluation
We test generative language capabilities through both automated and human evaluations. This second set of evaluations relies on queries curated by humans and aims at measuring the quality of model responses. We use the Vicuna and OpenAssistant datasets with 80 and 953 prompts respectively.
In both human and automated evaluations, for each prompt, raters compare all pairs of responses across the models considered. For human raters we randomize the order of the systems, for GPT-4 we evaluate with both orders.
Benchmark | Vicuna | | Vicuna | | OpenAssistant | | -
-----------|----|-----|--------|---|---------------|---|---
Prompts | 80 | | 80 | | 953 | |
Judge | Human | | GPT-4 | | GPT-4 | |
Model | Elo | Rank | Elo | Rank | Elo | Rank | **Median Rank**
GPT-4 | 1176 | 1 | 1348 | 1 | 1294 | 1 | 1
Guanaco-65B | 1023 | 2 | 1022 | 2 | 1008 | 3 | 2
Guanaco-33B | 1009 | 4 | 992 | 3 | 1002 | 4 | 4
ChatGPT-3.5 Turbo | 916 | 7 | 966 | 5 | 1015 | 2 | 5
Vicuna-13B | 984 | 5 | 974 | 4 | 936 | 5 | 5
Guanaco-13B | 975 | 6 | 913 | 6 | 885 | 6 | 6
Guanaco-7B | 1010 | 3 | 879 | 8 | 860 | 7 | 7
Bard | 909 | 8 | 902 | 7 | - | - | 8
We also use the MMLU benchmark to measure performance on a range of language understanding tasks. This is a multiple-choice benchmark covering 57 tasks including elementary mathematics, US history, computer science, law, and more. We report 5-shot test accuracy.
Dataset | 7B | 13B | 33B | 65B
---|---|---|---|---
LLaMA no tuning | 35.1 | 46.9 | 57.8 | 63.4
Self-Instruct | 36.4 | 33.3 | 53.0 | 56.7
Longform | 32.1 | 43.2 | 56.6 | 59.7
Chip2 | 34.5 | 41.6 | 53.6 | 59.8
HH-RLHF | 34.9 | 44.6 | 55.8 | 60.1
Unnatural Instruct | 41.9 | 48.1 | 57.3 | 61.3
OASST1 (Guanaco) | 36.6 | 46.4 | 57.0 | 62.2
Alpaca | 38.8 | 47.8 | 57.3 | 62.5
FLAN v2 | 44.5 | 51.4 | 59.2 | 63.9
## Risks and Biases
The model can produce factually incorrect output, and should not be relied on to produce factually accurate information. The model was trained on various public datasets; it is possible that this model could generate lewd, biased, or otherwise offensive outputs.
However, we note that finetuning on OASST1 seems to reduce biases as measured on the CrowS dataset. We report here the performance of Guanaco-65B compared to other baseline models on the CrowS dataset.
| | LLaMA-65B | GPT-3 | OPT-175B | Guanaco-65B |
|----------------------|-----------|-------|----------|---------------|
| Gender | 70.6 | 62.6 | 65.7 | **47.5** |
| Religion | {79.0} | 73.3 | 68.6 | **38.7** |
| Race/Color | 57.0 | 64.7 | 68.6 | **45.3** |
| Sexual orientation | {81.0} | 76.2 | 78.6 | **59.1** |
| Age | 70.1 | 64.4 | 67.8 | **36.3** |
| Nationality | 64.2 | 61.6 | 62.9 | **32.4** |
| Disability | 66.7 | 76.7 | 76.7 | **33.9** |
| Physical appearance | 77.8 | 74.6 | 76.2 | **43.1** |
| Socioeconomic status | 71.5 | 73.8 | 76.2 | **55.3** |
| Average | 66.6 | 67.2 | 69.5 | **43.5** |
## Citation
```bibtex
@article{dettmers2023qlora,
title={QLoRA: Efficient Finetuning of Quantized LLMs},
author={Dettmers, Tim and Pagnoni, Artidoro and Holtzman, Ari and Zettlemoyer, Luke},
journal={arXiv preprint arXiv:2305.14314},
year={2023}
}
```
|
localmodels/Guanaco-65B-GPTQ | localmodels | 2023-07-13T05:21:10Z | 7 | 4 | transformers | [
"transformers",
"llama",
"text-generation",
"arxiv:2305.14314",
"arxiv:2302.13971",
"arxiv:2304.07327",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2023-05-28T21:51:04Z | # Guanaco 65B GPTQ
From: https://huggingface.co/timdettmers/guanaco-65b
---
## Model
* guanaco-65b-4bit.safetensors
* Works with all versions of GPTQ-for-LLaMa code, both Triton and CUDA branches
* Works with AutoGPTQ
* Parameters: Groupsize = None. act-order
---
# Guanaco Models Based on LLaMA
| [Paper](https://arxiv.org/abs/2305.14314) | [Code](https://github.com/artidoro/qlora) | [Demo](https://huggingface.co/spaces/uwnlp/guanaco-playground-tgi) |
**The Guanaco models are open-source finetuned chatbots obtained through 4-bit QLoRA tuning of LLaMA base models on the OASST1 dataset. They are available in 7B, 13B, 33B, and 65B parameter sizes.**
⚠️Guanaco is a model purely intended for research purposes and could produce problematic outputs.
## Why use Guanaco?
- **Competitive with commercial chatbot systems on the Vicuna and OpenAssistant benchmarks** (ChatGPT and BARD) according to human and GPT-4 raters. We note that the relative performance on tasks not covered in these benchmarks could be very different. In addition, commercial systems evolve over time (we used outputs from the March 2023 version of the models).
- **Available open-source for research purposes**. Guanaco models allow *cheap* and *local* experimentation with high-quality chatbot systems.
- **Replicable and efficient training procedure** that can be extended to new use cases. Guanaco training scripts are available in the [QLoRA repo](https://github.com/artidoro/qlora).
- **Rigorous comparison to 16-bit methods** (both 16-bit full-finetuning and LoRA) in [our paper](https://arxiv.org/abs/2305.14314) demonstrates the effectiveness of 4-bit QLoRA finetuning.
- **Lightweight** checkpoints which only contain adapter weights.
## License and Intended Use
Guanaco adapter weights are available under Apache 2 license. Note the use of the Guanaco adapter weights, requires access to the LLaMA model weighs.
Guanaco is based on LLaMA and therefore should be used according to the LLaMA license.
## Usage
Here is an example of how you would load Guanaco 7B in 4-bits:
```python
import torch
from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
model_name = "huggyllama/llama-7b"
adapters_name = 'timdettmers/guanaco-7b'
model = AutoModelForCausalLM.from_pretrained(
model_name,
load_in_4bit=True,
torch_dtype=torch.bfloat16,
device_map="auto",
max_memory= {i: '24000MB' for i in range(torch.cuda.device_count())},
quantization_config=BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_compute_dtype=torch.bfloat16,
bnb_4bit_use_double_quant=True,
bnb_4bit_quant_type='nf4'
),
)
model = PeftModel.from_pretrained(model, adapters_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
```
Inference can then be performed as usual with HF models as follows:
```python
prompt = "Introduce yourself"
formatted_prompt = (
f"A chat between a curious human and an artificial intelligence assistant."
f"The assistant gives helpful, detailed, and polite answers to the user's questions.\n"
f"### Human: {prompt} ### Assistant:"
)
inputs = tokenizer(formatted_prompt, return_tensors="pt").to("cuda:0")
outputs = model.generate(inputs=inputs.input_ids, max_new_tokens=20)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```
Expected output similar to the following:
```
A chat between a curious human and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions.
### Human: Introduce yourself ### Assistant: I am an artificial intelligence assistant. I am here to help you with any questions you may have.
```
## Current Inference Limitations
Currently, 4-bit inference is slow. We recommend loading in 16 bits if inference speed is a concern. We are actively working on releasing efficient 4-bit inference kernels.
Below is how you would load the model in 16 bits:
```python
model_name = "huggyllama/llama-7b"
adapters_name = 'timdettmers/guanaco-7b'
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype=torch.bfloat16,
device_map="auto",
max_memory= {i: '24000MB' for i in range(torch.cuda.device_count())},
)
model = PeftModel.from_pretrained(model, adapters_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
```
## Model Card
**Architecture**: The Guanaco models are LoRA adapters to be used on top of LLaMA models. They are added to all layers. For all model sizes, we use $r=64$.
**Base Model**: Guanaco uses LLaMA as base model with sizes 7B, 13B, 33B, 65B. LLaMA is a causal language model pretrained on a large corpus of text. See [LLaMA paper](https://arxiv.org/abs/2302.13971) for more details. Note that Guanaco can inherit biases and limitations of the base model.
**Finetuning Data**: Guanaco is finetuned on OASST1. The exact dataset is available at [timdettmers/openassistant-guanaco](https://huggingface.co/datasets/timdettmers/openassistant-guanaco).
**Languages**: The OASST1 dataset is multilingual (see [the paper](https://arxiv.org/abs/2304.07327) for details) and as such Guanaco responds to user queries in different languages. We note, however, that OASST1 is heavy in high-resource languages. In addition, human evaluation of Guanaco was only performed in English and based on qualitative analysis we observed degradation in performance in other languages.
Next, we describe Training and Evaluation details.
### Training
Guanaco models are the result of 4-bit QLoRA supervised finetuning on the OASST1 dataset.
All models use NormalFloat4 datatype for the base model and LoRA adapters on all linear layers with BFloat16 as computation datatype. We set LoRA $r=64$, $\alpha=16$. We also use Adam beta2 of 0.999, max grad norm of 0.3 and LoRA dropout of 0.1 for models up to 13B and 0.05 for 33B and 65B models.
For the finetuning process, we use constant learning rate schedule and paged AdamW optimizer.
### Training hyperparameters
Size| Dataset | Batch Size | Learning Rate | Max Steps | Sequence length
---|---|---|---|---|---
7B | OASST1 | 16 | 2e-4 | 1875 | 512
13B | OASST1 | 16 | 2e-4 | 1875 | 512
33B | OASST1 | 16 | 1e-4 | 1875 | 512
65B | OASST1 | 16 | 1e-4 | 1875 | 512
### Evaluation
We test generative language capabilities through both automated and human evaluations. This second set of evaluations relies on queries curated by humans and aims at measuring the quality of model responses. We use the Vicuna and OpenAssistant datasets with 80 and 953 prompts respectively.
In both human and automated evaluations, for each prompt, raters compare all pairs of responses across the models considered. For human raters we randomize the order of the systems, for GPT-4 we evaluate with both orders.
Benchmark | Vicuna | | Vicuna | | OpenAssistant | | -
-----------|----|-----|--------|---|---------------|---|---
Prompts | 80 | | 80 | | 953 | |
Judge | Human | | GPT-4 | | GPT-4 | |
Model | Elo | Rank | Elo | Rank | Elo | Rank | **Median Rank**
GPT-4 | 1176 | 1 | 1348 | 1 | 1294 | 1 | 1
Guanaco-65B | 1023 | 2 | 1022 | 2 | 1008 | 3 | 2
Guanaco-33B | 1009 | 4 | 992 | 3 | 1002 | 4 | 4
ChatGPT-3.5 Turbo | 916 | 7 | 966 | 5 | 1015 | 2 | 5
Vicuna-13B | 984 | 5 | 974 | 4 | 936 | 5 | 5
Guanaco-13B | 975 | 6 | 913 | 6 | 885 | 6 | 6
Guanaco-7B | 1010 | 3 | 879 | 8 | 860 | 7 | 7
Bard | 909 | 8 | 902 | 7 | - | - | 8
We also use the MMLU benchmark to measure performance on a range of language understanding tasks. This is a multiple-choice benchmark covering 57 tasks including elementary mathematics, US history, computer science, law, and more. We report 5-shot test accuracy.
Dataset | 7B | 13B | 33B | 65B
---|---|---|---|---
LLaMA no tuning | 35.1 | 46.9 | 57.8 | 63.4
Self-Instruct | 36.4 | 33.3 | 53.0 | 56.7
Longform | 32.1 | 43.2 | 56.6 | 59.7
Chip2 | 34.5 | 41.6 | 53.6 | 59.8
HH-RLHF | 34.9 | 44.6 | 55.8 | 60.1
Unnatural Instruct | 41.9 | 48.1 | 57.3 | 61.3
OASST1 (Guanaco) | 36.6 | 46.4 | 57.0 | 62.2
Alpaca | 38.8 | 47.8 | 57.3 | 62.5
FLAN v2 | 44.5 | 51.4 | 59.2 | 63.9
## Risks and Biases
The model can produce factually incorrect output, and should not be relied on to produce factually accurate information. The model was trained on various public datasets; it is possible that this model could generate lewd, biased, or otherwise offensive outputs.
However, we note that finetuning on OASST1 seems to reduce biases as measured on the CrowS dataset. We report here the performance of Guanaco-65B compared to other baseline models on the CrowS dataset.
| | LLaMA-65B | GPT-3 | OPT-175B | Guanaco-65B |
|----------------------|-----------|-------|----------|---------------|
| Gender | 70.6 | 62.6 | 65.7 | **47.5** |
| Religion | {79.0} | 73.3 | 68.6 | **38.7** |
| Race/Color | 57.0 | 64.7 | 68.6 | **45.3** |
| Sexual orientation | {81.0} | 76.2 | 78.6 | **59.1** |
| Age | 70.1 | 64.4 | 67.8 | **36.3** |
| Nationality | 64.2 | 61.6 | 62.9 | **32.4** |
| Disability | 66.7 | 76.7 | 76.7 | **33.9** |
| Physical appearance | 77.8 | 74.6 | 76.2 | **43.1** |
| Socioeconomic status | 71.5 | 73.8 | 76.2 | **55.3** |
| Average | 66.6 | 67.2 | 69.5 | **43.5** |
## Citation
```bibtex
@article{dettmers2023qlora,
title={QLoRA: Efficient Finetuning of Quantized LLMs},
author={Dettmers, Tim and Pagnoni, Artidoro and Holtzman, Ari and Zettlemoyer, Luke},
journal={arXiv preprint arXiv:2305.14314},
year={2023}
}
``` |
vertxlabs/controlnet_qrcode-control_v11p_v1 | vertxlabs | 2023-07-13T05:04:14Z | 13 | 0 | diffusers | [
"diffusers",
"safetensors",
"stable-diffusion",
"controlnet",
"image-to-image",
"en",
"license:openrail++",
"endpoints_compatible",
"region:us"
] | image-to-image | 2023-07-13T03:45:24Z | ---
tags:
- stable-diffusion
- controlnet
- image-to-image
license: openrail++
language:
- en
pipeline_tag: image-to-image
---
# QR Code Conditioned ControlNet Models for Stable Diffusion 2.1
## Model Description
This repo holds the safetensors & diffusers versions of the QR code conditioned ControlNet for Stable Diffusion v2.1.
The Stable Diffusion 2.1 version is marginally more effective, as it was developed to address my specific needs. However, a 1.5 version model was also trained on the same dataset for those who are using the older version.
## How to use with diffusers
```bash
pip -q install diffusers transformers accelerate torch xformers
```
```python
import torch
from PIL import Image
from diffusers import StableDiffusionControlNetImg2ImgPipeline, ControlNetModel, DDIMScheduler
from diffusers.utils import load_image
controlnet = ControlNetModel.from_pretrained("DionTimmer/controlnet_qrcode-control_v11p_sd21",
torch_dtype=torch.float16)
pipe = StableDiffusionControlNetImg2ImgPipeline.from_pretrained(
"stabilityai/stable-diffusion-2-1",
controlnet=controlnet,
safety_checker=None,
torch_dtype=torch.float16
)
pipe.enable_xformers_memory_efficient_attention()
pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config)
pipe.enable_model_cpu_offload()
def resize_for_condition_image(input_image: Image, resolution: int):
input_image = input_image.convert("RGB")
W, H = input_image.size
k = float(resolution) / min(H, W)
H *= k
W *= k
H = int(round(H / 64.0)) * 64
W = int(round(W / 64.0)) * 64
img = input_image.resize((W, H), resample=Image.LANCZOS)
return img
# play with guidance_scale, controlnet_conditioning_scale and strength to make a valid QR Code Image
# qr code image
source_image = load_image("https://s3.amazonaws.com/moonup/production/uploads/6064e095abd8d3692e3e2ed6/A_RqHaAM6YHBodPLwqtjn.png")
# initial image, anything
init_image = load_image("https://s3.amazonaws.com/moonup/production/uploads/noauth/KfMBABpOwIuNolv1pe3qX.jpeg")
condition_image = resize_for_condition_image(source_image, 768)
init_image = resize_for_condition_image(init_image, 768)
generator = torch.manual_seed(123121231)
image = pipe(prompt="a bilboard in NYC with a qrcode",
negative_prompt="ugly, disfigured, low quality, blurry, nsfw",
image=init_image,
control_image=condition_image,
width=768,
height=768,
guidance_scale=20,
controlnet_conditioning_scale=1.5,
generator=generator,
strength=0.9,
num_inference_steps=150,
)
image.images[0]
```
## Performance and Limitations
These models perform quite well in most cases, but please note that they are not 100% accurate. In some instances, the QR code shape might not come through as expected. You can increase the ControlNet weight to emphasize the QR code shape. However, be cautious as this might negatively impact the style of your output.**To optimize for scanning, please generate your QR codes with correction mode 'H' (30%).**
To balance between style and shape, a gentle fine-tuning of the control weight might be required based on the individual input and the desired output, aswell as the correct prompt. Some prompts do not work until you increase the weight by a lot. The process of finding the right balance between these factors is part art and part science. For the best results, it is recommended to generate your artwork at a resolution of 768. This allows for a higher level of detail in the final product, enhancing the quality and effectiveness of the QR code-based artwork.
## Installation
The simplest way to use this is to place the .safetensors model and its .yaml config file in the folder where your other controlnet models are installed, which varies per application.
For usage in auto1111 they can be placed in the webui/models/ControlNet folder. They can be loaded using the controlnet webui extension which you can install through the extensions tab in the webui (https://github.com/Mikubill/sd-webui-controlnet). Make sure to enable your controlnet unit and set your input image as the QR code. Set the model to either the SD2.1 or 1.5 version depending on your base stable diffusion model, or it will error. No pre-processor is needed, though you can use the invert pre-processor for a different variation of results. 768 is the preferred resolution for generation since it allows for more detail.
Make sure to look up additional info on how to use controlnet if you get stuck, once you have the webui up and running its really easy to install the controlnet extension aswell. |
at2507/finetuned_model | at2507 | 2023-07-13T04:56:59Z | 103 | 4 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2023-07-10T08:51:09Z | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: finetuned_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. -->
# finetuned_model
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on a [Financial News Tweet Dataset](https://huggingface.co/datasets/zeroshot/twitter-financial-news-sentiment).
It achieves the following results on the evaluation set:
- Loss: 0.9382
- Accuracy: 0.803
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 125 | 0.6514 | 0.783 |
| No log | 2.0 | 250 | 0.6665 | 0.775 |
| No log | 3.0 | 375 | 0.9382 | 0.803 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
FelixChao/falcon-7b-instruct-ft-adapters-ESG-chatting | FelixChao | 2023-07-13T04:55:48Z | 3 | 0 | peft | [
"peft",
"region:us"
] | null | 2023-07-13T04:55:35Z | ---
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: bfloat16
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: bfloat16
### Framework versions
- PEFT 0.4.0.dev0
- PEFT 0.4.0.dev0
|
YanJiangJerry/SA-tweet-roberta-large-e4-w1-1.5-b16 | YanJiangJerry | 2023-07-13T04:53:22Z | 105 | 0 | transformers | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2023-07-13T04:17:05Z | ---
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: SA-tweet-roberta-large-e4-w1-1.5-b16
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. -->
# SA-tweet-roberta-large-e4-w1-1.5-b16
This model is a fine-tuned version of [Amalq/autotrain-smm4h_large_roberta_clean-874027878](https://huggingface.co/Amalq/autotrain-smm4h_large_roberta_clean-874027878) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6396
- Accuracy: 0.9166
- F1: 0.8872
- Precision: 0.8939
- Recall: 0.8806
## 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: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.2895 | 1.0 | 581 | 0.4026 | 0.9110 | 0.8806 | 0.8806 | 0.8806 |
| 0.1182 | 2.0 | 1162 | 0.6190 | 0.9110 | 0.8754 | 0.9153 | 0.8388 |
| 0.0589 | 3.0 | 1743 | 0.6167 | 0.9155 | 0.8838 | 0.9060 | 0.8627 |
| 0.0211 | 4.0 | 2324 | 0.6396 | 0.9166 | 0.8872 | 0.8939 | 0.8806 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
ui-chope/distilbert-base-uncased | ui-chope | 2023-07-13T04:52:42Z | 5 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | 2023-07-05T01:45:44Z | ---
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: distilbert-base-uncased
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1298
- Precision: 0.9739
- Recall: 0.9617
- F1: 0.9678
- Accuracy: 0.9837
## 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: 11
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0218 | 1.0 | 5296 | 0.0828 | 0.9609 | 0.9609 | 0.9609 | 0.9842 |
| 0.0159 | 2.0 | 10592 | 0.1135 | 0.9677 | 0.9602 | 0.9639 | 0.9820 |
| 0.0137 | 3.0 | 15888 | 0.0846 | 0.9631 | 0.9570 | 0.9600 | 0.9831 |
| 0.0074 | 4.0 | 21184 | 0.1179 | 0.9621 | 0.9523 | 0.9572 | 0.9804 |
| 0.0058 | 5.0 | 26480 | 0.1080 | 0.9763 | 0.9664 | 0.9713 | 0.9857 |
| 0.0056 | 6.0 | 31776 | 0.1273 | 0.9685 | 0.9594 | 0.9639 | 0.9828 |
| 0.0055 | 7.0 | 37072 | 0.1451 | 0.9637 | 0.9531 | 0.9584 | 0.9800 |
| 0.0035 | 8.0 | 42368 | 0.1345 | 0.9707 | 0.9563 | 0.9634 | 0.9805 |
| 0.0027 | 9.0 | 47664 | 0.1242 | 0.9739 | 0.9633 | 0.9686 | 0.9852 |
| 0.0018 | 10.0 | 52960 | 0.1232 | 0.9739 | 0.9633 | 0.9686 | 0.9844 |
| 0.0017 | 11.0 | 58256 | 0.1298 | 0.9739 | 0.9617 | 0.9678 | 0.9837 |
### Framework versions
- Transformers 4.30.2
- Pytorch 1.13.1+cu117
- Datasets 2.13.1
- Tokenizers 0.13.3
|
localmodels/Vicuna-7B-v1.3-GPTQ | localmodels | 2023-07-13T04:47:45Z | 15 | 0 | transformers | [
"transformers",
"llama",
"text-generation",
"arxiv:2302.13971",
"arxiv:2306.05685",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2023-07-13T04:47:41Z | ---
duplicated_from: localmodels/LLM
---
# Vicuna 7B v1.3 GPTQ
From LMSYS: https://huggingface.co/lmsys/vicuna-7b-v1.3
---
| Branch | Bits | Group Size | Act Order (desc_act) | File Size | ExLlama Compatible? | Made With | Description |
| ------ | ---- | ---------- | -------------------- | --------- | ------------------- | --------- | ----------- |
| vicuna-7b-v1.3-GPTQ-4bit-128g.no-act.order | 4 | 128 | False | 4.00 GB | True | GPTQ-for-LLaMa | Most compatible. Good inference speed in AutoGPTQ and GPTQ-for-LLaMa. |
---
# Vicuna Model Card
## Model Details
Vicuna is a chat assistant trained by fine-tuning LLaMA on user-shared conversations collected from ShareGPT.
- **Developed by:** [LMSYS](https://lmsys.org/)
- **Model type:** An auto-regressive language model based on the transformer architecture.
- **License:** Non-commercial license
- **Finetuned from model:** [LLaMA](https://arxiv.org/abs/2302.13971).
### Model Sources
- **Repository:** https://github.com/lm-sys/FastChat
- **Blog:** https://lmsys.org/blog/2023-03-30-vicuna/
- **Paper:** https://arxiv.org/abs/2306.05685
- **Demo:** https://chat.lmsys.org/
## Uses
The primary use of Vicuna is research on large language models and chatbots.
The primary intended users of the model are researchers and hobbyists in natural language processing, machine learning, and artificial intelligence.
## How to Get Started with the Model
Command line interface: https://github.com/lm-sys/FastChat#vicuna-weights.
APIs (OpenAI API, Huggingface API): https://github.com/lm-sys/FastChat/tree/main#api.
## Training Details
Vicuna v1.3 is fine-tuned from LLaMA with supervised instruction fine-tuning.
The training data is around 140K conversations collected from ShareGPT.com.
See more details in the "Training Details of Vicuna Models" section in the appendix of this [paper](https://arxiv.org/pdf/2306.05685.pdf).
## Evaluation
Vicuna is evaluated with standard benchmarks, human preference, and LLM-as-a-judge. See more details in this [paper](https://arxiv.org/pdf/2306.05685.pdf) and [leaderboard](https://huggingface.co/spaces/lmsys/chatbot-arena-leaderboard).
## Difference between different versions of Vicuna
See [vicuna_weights_version.md](https://github.com/lm-sys/FastChat/blob/main/docs/vicuna_weights_version.md) |
localmodels/Vicuna-13B-v1.3-GPTQ | localmodels | 2023-07-13T04:45:19Z | 6 | 0 | transformers | [
"transformers",
"llama",
"text-generation",
"arxiv:2302.13971",
"arxiv:2306.05685",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2023-07-13T04:45:15Z | ---
duplicated_from: localmodels/LLM
---
# Vicuna 13B v1.3 GPTQ
From LMSYS: https://huggingface.co/lmsys/vicuna-13b-v1.3
---
| Branch | Bits | Group Size | Act Order (desc_act) | File Size | ExLlama Compatible? | Made With | Description |
| ------ | ---- | ---------- | -------------------- | --------- | ------------------- | --------- | ----------- |
| vicuna-13b-v1.3.0-GPTQ-4bit-128g.no-act.order | 4 | 128 | False | 7.45 GB | True | GPTQ-for-LLaMa | Most compatible. Good inference speed in AutoGPTQ and GPTQ-for-LLaMa. |
---
# Vicuna Model Card
## Model Details
Vicuna is a chat assistant trained by fine-tuning LLaMA on user-shared conversations collected from ShareGPT.
- **Developed by:** [LMSYS](https://lmsys.org/)
- **Model type:** An auto-regressive language model based on the transformer architecture.
- **License:** Non-commercial license
- **Finetuned from model:** [LLaMA](https://arxiv.org/abs/2302.13971).
### Model Sources
- **Repository:** https://github.com/lm-sys/FastChat
- **Blog:** https://lmsys.org/blog/2023-03-30-vicuna/
- **Paper:** https://arxiv.org/abs/2306.05685
- **Demo:** https://chat.lmsys.org/
## Uses
The primary use of Vicuna is research on large language models and chatbots.
The primary intended users of the model are researchers and hobbyists in natural language processing, machine learning, and artificial intelligence.
## How to Get Started with the Model
Command line interface: https://github.com/lm-sys/FastChat#vicuna-weights.
APIs (OpenAI API, Huggingface API): https://github.com/lm-sys/FastChat/tree/main#api.
## Training Details
Vicuna v1.3 is fine-tuned from LLaMA with supervised instruction fine-tuning.
The training data is around 140K conversations collected from ShareGPT.com.
See more details in the "Training Details of Vicuna Models" section in the appendix of this [paper](https://arxiv.org/pdf/2306.05685.pdf).
## Evaluation
Vicuna is evaluated with standard benchmarks, human preference, and LLM-as-a-judge. See more details in this [paper](https://arxiv.org/pdf/2306.05685.pdf) and [leaderboard](https://huggingface.co/spaces/lmsys/chatbot-arena-leaderboard).
## Difference between different versions of Vicuna
See [vicuna_weights_version.md](https://github.com/lm-sys/FastChat/blob/main/docs/vicuna_weights_version.md) |
insomeniaT/falcon-7b-uae-qapairs-67 | insomeniaT | 2023-07-13T04:40:37Z | 10 | 1 | peft | [
"peft",
"text-generation",
"en",
"license:apache-2.0",
"region:us"
] | text-generation | 2023-07-07T19:21:06Z | ---
license: apache-2.0
language:
- en
library_name: peft
pipeline_tag: text-generation
inference: false
---
# PEFT Model Fine-tuned on UAE QA Pairs
This repository contains a fine-tuned model based on the PEFT framework for question answering tasks. The model has been trained on a dataset of question and answer pairs related to the UAE.
## Installation
Before using the model, make sure to install the necessary packages:
```sh
pip install transformers
pip install torch torchvision
pip install peft
```
## Usage
The model can be used for generating responses to prompts. Here is an example:
```python
from peft import PeftModel, PeftConfig
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
peft_model_id = "insomeniaT/falcon-7b-uae-qapairs-67"
config = PeftConfig.from_pretrained(peft_model_id)
model = AutoModelForCausalLM.from_pretrained(config.base_model_name_or_path, trust_remote_code=True)
model = PeftModel.from_pretrained(model, peft_model_id)
tokenizer = AutoTokenizer.from_pretrained("tiiuae/falcon-7b")
tokenizer.pad_token = tokenizer.eos_token
text = "### Human: What is the minimum requirement for the UAE's GCC residency?? ### Assistant: "
device = "cuda:0"
inputs = tokenizer(text, return_tensors="pt")
inputs.to(device)
model.to(device)
outputs = model.generate(input_ids=inputs["input_ids"], attention_mask=inputs["attention_mask"], max_new_tokens=300, pad_token_id=tokenizer.eos_token_id)
result = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(result)
```
|
hoanghoavienvo/xlnet-large-cased-stage-2-ver1 | hoanghoavienvo | 2023-07-13T04:37:38Z | 91 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"xlnet",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2023-07-13T03:34:49Z | ---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: xlnet-large-cased-stage-2-ver1
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. -->
# xlnet-large-cased-stage-2-ver1
This model is a fine-tuned version of [xlnet-large-cased](https://huggingface.co/xlnet-large-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4128
- Accuracy: 0.8317
- F1: 0.9022
## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log | 1.0 | 469 | 0.4226 | 0.85 | 0.9189 |
| 0.4839 | 2.0 | 938 | 0.3964 | 0.845 | 0.9141 |
| 0.4284 | 3.0 | 1407 | 0.4128 | 0.8317 | 0.9022 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
kazuhidet/norurun | kazuhidet | 2023-07-13T04:23:39Z | 0 | 0 | diffusers | [
"diffusers",
"tensorboard",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"dreambooth",
"base_model:CompVis/stable-diffusion-v1-4",
"base_model:finetune:CompVis/stable-diffusion-v1-4",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | text-to-image | 2023-07-13T04:06:49Z |
---
license: creativeml-openrail-m
base_model: CompVis/stable-diffusion-v1-4
instance_prompt: a photo of mascot norurun
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- dreambooth
inference: true
---
# DreamBooth - kazuhidet/norurun
This is a dreambooth model derived from CompVis/stable-diffusion-v1-4. The weights were trained on a photo of mascot norurun using [DreamBooth](https://dreambooth.github.io/).
You can find some example images in the following.
DreamBooth for the text encoder was enabled: False.
|
Ife/BM-FR | Ife | 2023-07-13T04:17:55Z | 106 | 0 | transformers | [
"transformers",
"pytorch",
"marian",
"text2text-generation",
"bm",
"fr",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2022-03-02T23:29:04Z | ---
language:
- bm
- fr
---
@inproceedings{adebara-abdul-mageed-2021-improving,
title = "Improving Similar Language Translation With Transfer Learning",
author = "Adebara, Ife and
Abdul-Mageed, Muhammad",
booktitle = "Proceedings of the Sixth Conference on Machine Translation",
month = nov,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.wmt-1.27",
pages = "273--278",
abstract = "We investigate transfer learning based on pre-trained neural machine translation models to translate between (low-resource) similar languages. This work is part of our contribution to the WMT 2021 Similar Languages Translation Shared Task where we submitted models for different language pairs, including French-Bambara, Spanish-Catalan, and Spanish-Portuguese in both directions. Our models for Catalan-Spanish (82.79 BLEU)and Portuguese-Spanish (87.11 BLEU) rank top 1 in the official shared task evaluation, and we are the only team to submit models for the French-Bambara pairs.",
} |
AnirbanRC/flan_t5_small_finetuned_anirbanrc | AnirbanRC | 2023-07-13T04:12:54Z | 162 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:samsum",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2023-07-13T04:03:45Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- samsum
metrics:
- rouge
model-index:
- name: flan_t5_small_finetuned_anirbanrc
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: samsum
type: samsum
config: samsum
split: train[:50]
args: samsum
metrics:
- name: Rouge1
type: rouge
value: 43.2639
---
<!-- 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. -->
# flan_t5_small_finetuned_anirbanrc
This model is a fine-tuned version of [google/flan-t5-small](https://huggingface.co/google/flan-t5-small) on the samsum dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5172
- Rouge1: 43.2639
- Rouge2: 20.726
- Rougel: 37.0774
- Rougelsum: 39.6232
- Gen Len: 16.92
## 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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| No log | 1.0 | 7 | 1.6379 | 42.0058 | 18.6227 | 35.3019 | 38.6413 | 17.36 |
| No log | 2.0 | 14 | 1.5869 | 43.938 | 20.3595 | 36.876 | 40.0421 | 17.14 |
| No log | 3.0 | 21 | 1.5483 | 43.3723 | 20.3935 | 36.9286 | 39.6476 | 17.0 |
| No log | 4.0 | 28 | 1.5255 | 43.9774 | 21.5464 | 37.8954 | 40.5009 | 16.9 |
| No log | 5.0 | 35 | 1.5172 | 43.2639 | 20.726 | 37.0774 | 39.6232 | 16.92 |
### Framework versions
- Transformers 4.30.2
- Pytorch 1.13.1+cpu
- Datasets 2.13.1
- Tokenizers 0.13.3
|
abbiezz/tomuntitled | abbiezz | 2023-07-13T04:12:40Z | 0 | 0 | null | [
"license:openrail",
"region:us"
] | null | 2023-07-13T04:06:35Z | ---
license: openrail
---
https://drive.google.com/file/d/1qilU9BEfX7RY8q9Uohesz9qQa0R_B5PW/view?usp=drive_link
|
quyc/picture | quyc | 2023-07-13T03:51:01Z | 54 | 0 | transformers | [
"transformers",
"pytorch",
"vit",
"image-classification",
"image-to-text",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-to-text | 2023-07-12T07:05:17Z | ---
pipeline_tag: image-to-text
--- |
rdyzakya/IndoLEGO-ABSA | rdyzakya | 2023-07-13T03:43:17Z | 113 | 1 | transformers | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"id",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2023-07-12T13:28:26Z | ---
language:
- id
metrics:
- f1
pipeline_tag: text2text-generation
--- |
kazuhidet/kasumi | kazuhidet | 2023-07-13T03:35:32Z | 1 | 0 | diffusers | [
"diffusers",
"tensorboard",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"dreambooth",
"base_model:CompVis/stable-diffusion-v1-4",
"base_model:finetune:CompVis/stable-diffusion-v1-4",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | text-to-image | 2023-07-13T03:18:42Z |
---
license: creativeml-openrail-m
base_model: CompVis/stable-diffusion-v1-4
instance_prompt: a photo of people kasumi
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- dreambooth
inference: true
---
# DreamBooth - kazuhidet/kasumi
This is a dreambooth model derived from CompVis/stable-diffusion-v1-4. The weights were trained on a photo of people kasumi using [DreamBooth](https://dreambooth.github.io/).
You can find some example images in the following.
DreamBooth for the text encoder was enabled: False.
|
NasimB/gpt2-concat-all-base-rarity-all-iorder-est-5p5k | NasimB | 2023-07-13T03:30:43Z | 5 | 0 | transformers | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"generated_from_trainer",
"dataset:generator",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2023-07-13T01:51:53Z | ---
license: mit
tags:
- generated_from_trainer
datasets:
- generator
model-index:
- name: gpt2-concat-all-base-rarity-all-iorder-est-5p5k
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-concat-all-base-rarity-all-iorder-est-5p5k
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the generator dataset.
It achieves the following results on the evaluation set:
- Loss: 4.3322
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 1000
- num_epochs: 6
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 6.7625 | 0.31 | 500 | 5.6584 |
| 5.4053 | 0.63 | 1000 | 5.2182 |
| 5.0653 | 0.94 | 1500 | 4.9736 |
| 4.7706 | 1.25 | 2000 | 4.8109 |
| 4.6273 | 1.56 | 2500 | 4.6831 |
| 4.5134 | 1.88 | 3000 | 4.5789 |
| 4.3042 | 2.19 | 3500 | 4.5166 |
| 4.2107 | 2.5 | 4000 | 4.4533 |
| 4.1747 | 2.82 | 4500 | 4.3963 |
| 4.0257 | 3.13 | 5000 | 4.3718 |
| 3.8934 | 3.44 | 5500 | 4.3419 |
| 3.8694 | 3.75 | 6000 | 4.3086 |
| 3.7894 | 4.07 | 6500 | 4.2941 |
| 3.5908 | 4.38 | 7000 | 4.2908 |
| 3.586 | 4.69 | 7500 | 4.2727 |
| 3.5713 | 5.01 | 8000 | 4.2605 |
| 3.3959 | 5.32 | 8500 | 4.2717 |
| 3.3922 | 5.63 | 9000 | 4.2700 |
| 3.3874 | 5.94 | 9500 | 4.2690 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.11.0+cu113
- Datasets 2.13.0
- Tokenizers 0.13.3
|
LoupGarou/WizardCoder-Guanaco-15B-V1.1 | LoupGarou | 2023-07-13T03:21:55Z | 1,506 | 12 | transformers | [
"transformers",
"pytorch",
"gpt_bigcode",
"text-generation",
"en",
"dataset:guanaco",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2023-07-12T06:10:19Z | ---
language:
- en
datasets:
- guanaco
model_hub_library:
- transformers
license:
- apache-2.0
---
## WizardCoder-Guanaco-15B-V1.1 Model Card
The WizardCoder-Guanaco-15B-V1.1 is a language model that combines the strengths of the [WizardCoder](https://huggingface.co/WizardLM/WizardCoder-15B-V1.0) base model and the [openassistant-guanaco](https://huggingface.co/datasets/timdettmers/openassistant-guanaco) dataset for finetuning. The openassistant-guanaco dataset was further trimmed to within 2 standard deviations of token size for input and output pairs and all non-english data has been removed to reduce training size requirements.
Version 1.1 showcases notable enhancements, employing a modified version of the previous openassistant-guanaco dataset. This dataset underwent a comprehensive revision, replacing every single answer with those generated by GPT-4.
The volume of the datasets has also been augmented by approximately 50%, with a particular focus on high school and abstract algebra. This expansion leveraged the combined capabilities of GPT-4 and GPT-3.5-Turbo. The initial evaluation of algebraic functions over 12 epochs indicated promising results from this enriched dataset. However, this is just the beginning; further refinements are in the pipeline, aiming to optimize the dataset quality and subsequently decrease the number of epochs required to achieve comparable results.
Considering the need to curtail memory consumption during training, this dataset was tailored to consist solely of English language questions and answers. Consequently, the model's performance in language translation may not be up to par. Nevertheless, the focus remains on enhancing the model's proficiency and efficiency within its defined scope.
# Intended Use
This model is designed to be used for a wide array of text generation tasks that require understanding and generating English text. The model is expected to perform well in tasks such as answering questions, writing essays, summarizing text, translation, and more. However, given the specific data processing and finetuning done, it might be particularly effective for tasks related to English language question-answering systems.
# Limitations
Despite the powerful capabilities of this model, users should be aware of its limitations. The model's knowledge is up to date only until the time it was trained, and it doesn't know about events in the world after that. It can sometimes produce incorrect or nonsensical responses, as it doesn't understand the text in the same way humans do. It should be used as a tool to assist in generating text and not as a sole source of truth.
# How to use
Here is an example of how to use this model:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import time
import torch
class Chatbot:
def __init__(self, model_name):
self.tokenizer = AutoTokenizer.from_pretrained(model_name, padding_side='left')
self.model = AutoModelForCausalLM.from_pretrained(model_name, load_in_4bit=True, torch_dtype=torch.bfloat16)
if self.tokenizer.pad_token_id is None:
self.tokenizer.pad_token_id = self.tokenizer.eos_token_id
def get_response(self, prompt):
inputs = self.tokenizer.encode_plus(prompt, return_tensors="pt", padding='max_length', max_length=100)
if next(self.model.parameters()).is_cuda:
inputs = {name: tensor.to('cuda') for name, tensor in inputs.items()}
start_time = time.time()
tokens = self.model.generate(input_ids=inputs['input_ids'],
attention_mask=inputs['attention_mask'],
pad_token_id=self.tokenizer.pad_token_id,
max_new_tokens=400)
end_time = time.time()
output_tokens = tokens[0][inputs['input_ids'].shape[-1]:]
output = self.tokenizer.decode(output_tokens, skip_special_tokens=True)
time_taken = end_time - start_time
return output, time_taken
def main():
chatbot = Chatbot("LoupGarou/WizardCoder-Guanaco-15B-V1.1")
while True:
user_input = input("Enter your prompt: ")
if user_input.lower() == 'quit':
break
output, time_taken = chatbot.get_response(user_input)
print("\033[33m" + output + "\033[0m")
print("Time taken to process: ", time_taken, "seconds")
print("Exited the program.")
if __name__ == "__main__":
main()
```
# Training Procedure
The WizardCoder model, serving as the base, was fine-tuned on a modified version of the openassistant-guanaco dataset. This dataset underwent a significant revision, replacing every single answer with responses generated by the AI model GPT-4. It was then expanded by approximately 50%, emphasizing high school and abstract algebra-related questions, using a mix of GPT-4 and GPT-3.5-Turbo for answer generation.
The selected dataset was standardized to fall within two standard deviations of token size for the question sets, ensuring consistency in data handling. The order of the questions was also randomized to mitigate any potential biases during the training phase.
In the interest of optimizing memory usage during the training process, the dataset was streamlined to only include English language content. As a result, all non-English data was systematically expunged from this fine-tuning dataset. It's worth noting that this modification limits the model's performance in language translation tasks, but it significantly boosts its efficiency and effectiveness when dealing with English language questions and answers.
## Acknowledgements
This model, WizardCoder-Guanaco-15B-V1.1, is simply building on the efforts of two great teams to evaluate the performance of a combined model with the strengths of the [WizardCoder base model](https://huggingface.co/WizardLM/WizardCoder-15B-V1.0) and the [openassistant-guanaco dataset](https://huggingface.co/datasets/timdettmers/openassistant-guanaco).
A sincere appreciation goes out to the developers and the community involved in the creation and refinement of these models. Their commitment to providing open source tools and datasets have been instrumental in making this project a reality.
Moreover, a special note of thanks to the [Hugging Face](https://huggingface.co/) team, whose transformative library has not only streamlined the process of model creation and adaptation, but also democratized the access to state-of-the-art machine learning technologies. Their impact on the development of this project cannot be overstated. |
sd-dreambooth-library/this-youtuber-does-not-exist | sd-dreambooth-library | 2023-07-13T03:12:53Z | 32 | 2 | diffusers | [
"diffusers",
"tensorboard",
"text-to-image",
"en",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | text-to-image | 2023-02-03T21:50:06Z | ---
license: creativeml-openrail-m
tags:
- text-to-image
widget:
- text: tyznedsk1
language:
- en
library_name: diffusers
pipeline_tag: text-to-image
---
### This Youtuber Does Not Exist Dreambooth model trained 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!
WELCOME TO THE INTERNET:
# THIS YOUTUBER DOES NOT EXIST
# NOR DO YOU
# RED , PINK OR BLUE OR GREEN OR YELLOW M&M PLS
tyznedsk1 (use that on your prompt) |
h2oai/h2ogpt-gm-oasst1-en-2048-falcon-40b-v2 | h2oai | 2023-07-13T03:12:11Z | 72 | 18 | transformers | [
"transformers",
"pytorch",
"RefinedWeb",
"text-generation",
"gpt",
"llm",
"large language model",
"h2o-llmstudio",
"custom_code",
"en",
"dataset:OpenAssistant/oasst1",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2023-06-23T07:35:02Z | ---
language:
- en
library_name: transformers
tags:
- gpt
- llm
- large language model
- h2o-llmstudio
inference: false
thumbnail: >-
https://h2o.ai/etc.clientlibs/h2o/clientlibs/clientlib-site/resources/images/favicon.ico
license: apache-2.0
datasets:
- OpenAssistant/oasst1
---
# Model Card
## Summary
This model was trained using [H2O LLM Studio](https://github.com/h2oai/h2o-llmstudio).
- Base model: [tiiuae/falcon-40b](https://huggingface.co/tiiuae/falcon-40b)
- Dataset preparation: [OpenAssistant/oasst1](https://github.com/h2oai/h2o-llmstudio/blob/1935d84d9caafed3ee686ad2733eb02d2abfce57/app_utils/utils.py#LL1896C5-L1896C28) personalized
## Usage
To use the model with the `transformers` library on a machine with GPUs, first make sure you have the `transformers`, `accelerate` and `torch` libraries installed.
```bash
pip install transformers==4.29.2
pip install bitsandbytes==0.39.0
pip install accelerate==0.19.0
pip install torch==2.0.0
pip install einops==0.6.1
```
```python
import torch
from transformers import pipeline, BitsAndBytesConfig, AutoTokenizer
model_kwargs = {}
quantization_config = None
# optional quantization
quantization_config = BitsAndBytesConfig(
load_in_8bit=True,
llm_int8_threshold=6.0,
)
model_kwargs["quantization_config"] = quantization_config
tokenizer = AutoTokenizer.from_pretrained(
"h2oai/h2ogpt-gm-oasst1-en-2048-falcon-40b-v2",
use_fast=False,
padding_side="left",
trust_remote_code=True,
)
generate_text = pipeline(
model="h2oai/h2ogpt-gm-oasst1-en-2048-falcon-40b-v2",
tokenizer=tokenizer,
torch_dtype=torch.float16,
trust_remote_code=True,
use_fast=False,
device_map={"": "cuda:0"},
model_kwargs=model_kwargs,
)
res = generate_text(
"Why is drinking water so healthy?",
min_new_tokens=2,
max_new_tokens=1024,
do_sample=False,
num_beams=1,
temperature=float(0.3),
repetition_penalty=float(1.2),
renormalize_logits=True
)
print(res[0]["generated_text"])
```
You can print a sample prompt after the preprocessing step to see how it is feed to the tokenizer:
```python
print(generate_text.preprocess("Why is drinking water so healthy?")["prompt_text"])
```
```bash
<|prompt|>Why is drinking water so healthy?<|endoftext|><|answer|>
```
Alternatively, you can download [h2oai_pipeline.py](h2oai_pipeline.py), store it alongside your notebook, and construct the pipeline yourself from the loaded model and tokenizer:
```python
import torch
from h2oai_pipeline import H2OTextGenerationPipeline
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
quantization_config = None
# optional quantization
quantization_config = BitsAndBytesConfig(
load_in_8bit=True,
llm_int8_threshold=6.0,
)
tokenizer = AutoTokenizer.from_pretrained(
"h2oai/h2ogpt-gm-oasst1-en-2048-falcon-40b-v2",
use_fast=False,
padding_side="left",
trust_remote_code=True,
)
model = AutoModelForCausalLM.from_pretrained(
"h2oai/h2ogpt-gm-oasst1-en-2048-falcon-40b-v2",
trust_remote_code=True,
torch_dtype=torch.float16,
device_map={"": "cuda:0"},
quantization_config=quantization_config
).eval()
generate_text = H2OTextGenerationPipeline(model=model, tokenizer=tokenizer)
res = generate_text(
"Why is drinking water so healthy?",
min_new_tokens=2,
max_new_tokens=1024,
do_sample=False,
num_beams=1,
temperature=float(0.3),
repetition_penalty=float(1.2),
renormalize_logits=True
)
print(res[0]["generated_text"])
```
You may also construct the pipeline from the loaded model and tokenizer yourself and consider the preprocessing steps:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
# Important: The prompt needs to be in the same format the model was trained with.
# You can find an example prompt in the experiment logs.
prompt = "<|prompt|>How are you?<|endoftext|><|answer|>"
quantization_config = None
# optional quantization
quantization_config = BitsAndBytesConfig(
load_in_8bit=True,
llm_int8_threshold=6.0,
)
tokenizer = AutoTokenizer.from_pretrained(
"h2oai/h2ogpt-gm-oasst1-en-2048-falcon-40b-v2",
use_fast=False,
padding_side="left",
trust_remote_code=True,
)
model = AutoModelForCausalLM.from_pretrained(
"h2oai/h2ogpt-gm-oasst1-en-2048-falcon-40b-v2",
trust_remote_code=True,
torch_dtype=torch.float16,
device_map={"": "cuda:0"},
quantization_config=quantization_config
).eval()
inputs = tokenizer(prompt, return_tensors="pt", add_special_tokens=False).to("cuda")
# generate configuration can be modified to your needs
tokens = model.generate(
**inputs,
min_new_tokens=2,
max_new_tokens=1024,
do_sample=False,
num_beams=1,
temperature=float(0.3),
repetition_penalty=float(1.2),
renormalize_logits=True
)[0]
tokens = tokens[inputs["input_ids"].shape[1]:]
answer = tokenizer.decode(tokens, skip_special_tokens=True)
print(answer)
```
## Model Architecture
```
RWForCausalLM(
(transformer): RWModel(
(word_embeddings): Embedding(65024, 8192)
(h): ModuleList(
(0-59): 60 x DecoderLayer(
(ln_attn): LayerNorm((8192,), eps=1e-05, elementwise_affine=True)
(ln_mlp): LayerNorm((8192,), eps=1e-05, elementwise_affine=True)
(self_attention): Attention(
(maybe_rotary): RotaryEmbedding()
(query_key_value): Linear(in_features=8192, out_features=9216, bias=False)
(dense): Linear(in_features=8192, out_features=8192, bias=False)
(attention_dropout): Dropout(p=0.0, inplace=False)
)
(mlp): MLP(
(dense_h_to_4h): Linear(in_features=8192, out_features=32768, bias=False)
(act): GELU(approximate='none')
(dense_4h_to_h): Linear(in_features=32768, out_features=8192, bias=False)
)
)
)
(ln_f): LayerNorm((8192,), eps=1e-05, elementwise_affine=True)
)
(lm_head): Linear(in_features=8192, out_features=65024, bias=False)
)
```
## Model Configuration
This model was trained using H2O LLM Studio and with the configuration in [cfg.yaml](cfg.yaml). Visit [H2O LLM Studio](https://github.com/h2oai/h2o-llmstudio) to learn how to train your own large language models.
## Disclaimer
Please read this disclaimer carefully before using the large language model provided in this repository. Your use of the model signifies your agreement to the following terms and conditions.
- Biases and Offensiveness: The large language model is trained on a diverse range of internet text data, which may contain biased, racist, offensive, or otherwise inappropriate content. By using this model, you acknowledge and accept that the generated content may sometimes exhibit biases or produce content that is offensive or inappropriate. The developers of this repository do not endorse, support, or promote any such content or viewpoints.
- Limitations: The large language model is an AI-based tool and not a human. It may produce incorrect, nonsensical, or irrelevant responses. It is the user's responsibility to critically evaluate the generated content and use it at their discretion.
- Use at Your Own Risk: Users of this large language model must assume full responsibility for any consequences that may arise from their use of the tool. The developers and contributors of this repository shall not be held liable for any damages, losses, or harm resulting from the use or misuse of the provided model.
- Ethical Considerations: Users are encouraged to use the large language model responsibly and ethically. By using this model, you agree not to use it for purposes that promote hate speech, discrimination, harassment, or any form of illegal or harmful activities.
- Reporting Issues: If you encounter any biased, offensive, or otherwise inappropriate content generated by the large language model, please report it to the repository maintainers through the provided channels. Your feedback will help improve the model and mitigate potential issues.
- Changes to this Disclaimer: The developers of this repository reserve the right to modify or update this disclaimer at any time without prior notice. It is the user's responsibility to periodically review the disclaimer to stay informed about any changes.
By using the large language model provided in this repository, you agree to accept and comply with the terms and conditions outlined in this disclaimer. If you do not agree with any part of this disclaimer, you should refrain from using the model and any content generated by it. |
DeeeTeeee01/mytest_trainer_roberta | DeeeTeeee01 | 2023-07-13T03:05:52Z | 5 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2023-07-13T02:27:31Z | ---
tags:
- generated_from_trainer
model-index:
- name: mytest_trainer_roberta
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. -->
# mytest_trainer_roberta
This model is a fine-tuned version of [cardiffnlp/twitter-xlm-roberta-base-sentiment](https://huggingface.co/cardiffnlp/twitter-xlm-roberta-base-sentiment) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8617
- Rmse: 0.6928
## 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: 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 | Rmse |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.7365 | 1.0 | 500 | 0.6992 | 0.7543 |
| 0.6079 | 2.0 | 1000 | 0.6532 | 0.6841 |
| 0.4798 | 3.0 | 1500 | 0.7034 | 0.6823 |
| 0.3451 | 4.0 | 2000 | 0.7757 | 0.6925 |
| 0.256 | 5.0 | 2500 | 1.0959 | 0.7266 |
| 0.1818 | 6.0 | 3000 | 1.2213 | 0.6775 |
| 0.1407 | 7.0 | 3500 | 1.4863 | 0.6764 |
| 0.0938 | 8.0 | 4000 | 1.7213 | 0.7032 |
| 0.0623 | 9.0 | 4500 | 1.8237 | 0.6917 |
| 0.0484 | 10.0 | 5000 | 1.8617 | 0.6928 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
sd-dreambooth-library/HairDye | sd-dreambooth-library | 2023-07-13T03:05:23Z | 31 | 0 | diffusers | [
"diffusers",
"tensorboard",
"text-to-image",
"en",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | text-to-image | 2023-02-03T01:16:40Z | ---
license: creativeml-openrail-m
tags:
- text-to-image
widget:
- text: daizky1
language:
- en
library_name: diffusers
pipeline_tag: text-to-image
---
### Hair Dye Dreambooth model
[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).
### TERMS OF SERVICE:
# No Selling Models
# Merge with CREDIT
VAE is not required but is fun.
I am not responsible for what you make.
If this model bites you call the CIA.
### Codeword:
daizky1 (use that on your prompt) |
Hedayat-Abrishami/rl_course_vizdoom_health_gathering_supreme | Hedayat-Abrishami | 2023-07-13T02:54:34Z | 0 | 0 | sample-factory | [
"sample-factory",
"tensorboard",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | 2023-07-13T01:42:33Z | ---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: doom_health_gathering_supreme
type: doom_health_gathering_supreme
metrics:
- type: mean_reward
value: 12.83 +/- 5.92
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **doom_health_gathering_supreme** environment.
This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory.
Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/
## Downloading the model
After installing Sample-Factory, download the model with:
```
python -m sample_factory.huggingface.load_from_hub -r Hedayat-Abrishami/rl_course_vizdoom_health_gathering_supreme
```
## Using the model
To run the model after download, use the `enjoy` script corresponding to this environment:
```
python -m .usr.local.lib.python3.10.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme
```
You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag.
See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details
## Training with this model
To continue training with this model, use the `train` script corresponding to this environment:
```
python -m .usr.local.lib.python3.10.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme --restart_behavior=resume --train_for_env_steps=10000000000
```
Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
|
yuean/my_resnet50_model | yuean | 2023-07-13T02:41:43Z | 249 | 0 | transformers | [
"transformers",
"pytorch",
"resnet",
"image-classification",
"dataset:yuean/EuroSAT-2750",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-classification | 2023-07-12T05:55:17Z | ---
metrics:
- accuracy
pipeline_tag: image-classification
datasets:
- yuean/EuroSAT-2750
--- |
Jonathaniu/alpaca-breast-cancer-7b-epoch-2 | Jonathaniu | 2023-07-13T02:19:50Z | 4 | 0 | peft | [
"peft",
"region:us"
] | null | 2023-07-13T02:19:33Z | ---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
### Framework versions
- PEFT 0.4.0.dev0
|
hululuzhu/solidity-t5 | hululuzhu | 2023-07-13T00:59:53Z | 118 | 10 | transformers | [
"transformers",
"pytorch",
"safetensors",
"t5",
"text2text-generation",
"solidity",
"web3",
"code generation",
"smart contract",
"en",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2023-01-01T02:23:20Z | ---
language:
- en
license: apache-2.0
tags:
- solidity
- web3
- code generation
- smart contract
widget:
- text: "pragma solidity ^0.5.7;\n// Context: ParentA | Functions: helloA helloB | Constants: constantA \ncontract HelloWorld is ParentA {"
---
# A code generation T5 model for solidity (web3 smart contract)
- See https://github.com/hululuzhu/solidity-t5 for more context
## How to use this trained model
- A hello world example to use this model, notice the input `text` includes
- Header solidity version like `pragma solidity ^0.5.7`
- Ancestor class/library info, e.g. public functions and constants from `ParentA`
- Contract/Library/Interface declaration header, e.g. `HelloWorld` ended with `{`
- Or simply use the test widget on the right side of the window and test, however
the quality is known to be worse without decoding params
```python
# !pip install transformers -q
from transformers import AutoTokenizer, T5ForConditionalGeneration
DEVICE = 'cuda' # fallback to cpu if you do not have cuda
tokenizer = AutoTokenizer.from_pretrained("hululuzhu/solidity-t5")
model = T5ForConditionalGeneration.from_pretrained("hululuzhu/solidity-t5").to(DEVICE)
text = """pragma solidity ^0.5.7;
// Context: ParentA | Functions: helloA helloB | Constants: constantA
contract HelloWorld is ParentA {"""
input_ids = tokenizer(text, return_tensors="pt", truncation=True).input_ids.to(DEVICE)
# Need to tune beam/topk/topp params to get good outcome
generated_ids = model.generate(input_ids, max_length=256, num_beams=5, top_p=0.95, top_k=50)
print(tokenizer.decode(generated_ids[0], skip_special_tokens=True))
# Expect outcome
"""
string public constant name = "Hello World";
...
uint256 public constant override returns (uint256) {
return initialSupply;
}
function initialSupply() public view returns (uint256) {
...
"""
```
## Background
- Base T5 code model: https://huggingface.co/Salesforce/codet5-large
- Source data: https://huggingface.co/datasets/mwritescode/slither-audited-smart-contracts
- Processing steps: Clean, contract-level segmentation sepration, split in and out
- After processing input sample
```
pragma solidity 0.5.7;
// Context: PauserRole | Functions: isPauser addPauser renouncePauser | Constants:
contract Pausable is PauserRole {
```
- After processing output sample (**notice indentation is bad, this is intentional to reduce token size**)
```
event Paused(address account);
event Unpaused(address account);
bool private _pausableActive;
bool private _paused;
constructor () internal {
_paused = false;
}
function paused() public view returns (bool) {
return _paused;
}
modifier whenNotPaused() {
require(!_paused);
_;
}
modifier whenPaused() {
require(_paused);
_;
}
function pause() public onlyPauser whenNotPaused whenPausableActive {
_paused = true;
emit Paused(msg.sender);
}
function unpause() public onlyPauser whenPaused whenPausableActive {
_paused = false;
emit Unpaused(msg.sender);
}
function _setPausableActive(bool _active) internal {
_pausableActive = _active;
}
modifier whenPausableActive() {
require(_pausableActive);
_;
}
}
```
- Source training code: See the [end to end notebook](https://github.com/hululuzhu/solidity-t5/blob/main/code/Solidity_T5_Data_Processing_and_Training.ipynb) at code dir here
## Future TODO
- The model is significantly under-trained because of lack of GPU budget, need 10x colab resources (~$100 for full train)
- This is quite limited on how the model is used, potentially we could switch to GPT2 decoder-only to compare, but CodeT5 has its strong code optimization
- Need more classifiers (T5 or BERT alike) to detect potential defects.
|
tbooy/Taxi-v3 | tbooy | 2023-07-13T00:58:52Z | 0 | 0 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | reinforcement-learning | 2023-07-13T00:58:41Z | ---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.52 +/- 2.73
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="tbooy/Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
tbooy/Taxi | tbooy | 2023-07-13T00:58:17Z | 0 | 0 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | reinforcement-learning | 2023-07-13T00:57:59Z | ---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: Taxi
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.52 +/- 2.73
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="tbooy/Taxi", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
mpinedaa/distilbert_squad_sample_finetuned_model | mpinedaa | 2023-07-13T00:30:09Z | 103 | 0 | transformers | [
"transformers",
"pytorch",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | question-answering | 2023-07-12T14:14:48Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: distilbert_squad_sample_finetuned_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. -->
# distilbert_squad_sample_finetuned_model
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5925
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 250 | 2.3529 |
| 2.7741 | 2.0 | 500 | 1.6561 |
| 2.7741 | 3.0 | 750 | 1.5925 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cpu
- Datasets 2.13.1
- Tokenizers 0.13.3
|
manmyung/dqn-SpaceInvadersNoFrameskip-v4 | manmyung | 2023-07-13T00:08:57Z | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | 2023-07-13T00:08:12Z | ---
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: 613.50 +/- 78.77
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 manmyung -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 manmyung -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 manmyung
```
## 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'}
```
|
Hedayat-Abrishami/ppo-CartPole-v1 | Hedayat-Abrishami | 2023-07-12T23:58:20Z | 0 | 0 | null | [
"tensorboard",
"CartPole-v1",
"ppo",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"deep-rl-course",
"model-index",
"region:us"
] | reinforcement-learning | 2023-07-12T23:51:42Z | ---
tags:
- CartPole-v1
- ppo
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
- deep-rl-course
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 223.00 +/- 113.45
name: mean_reward
verified: false
---
# PPO Agent Playing CartPole-v1
This is a trained model of a PPO agent playing CartPole-v1.
# Hyperparameters
```python
{'exp_name': 'Name'
'seed': 1
'torch_deterministic': True
'cuda': True
'track': False
'wandb_project_name': 'cleanRL'
'wandb_entity': None
'capture_video': False
'env_id': 'CartPole-v1'
'total_timesteps': 50000
'learning_rate': 0.00025
'num_envs': 4
'num_steps': 128
'anneal_lr': True
'gae': True
'gamma': 0.99
'gae_lambda': 0.95
'num_minibatches': 4
'update_epochs': 4
'norm_adv': True
'clip_coef': 0.2
'clip_vloss': True
'ent_coef': 0.01
'vf_coef': 0.5
'max_grad_norm': 0.5
'target_kl': None
'repo_id': 'Hedayat-Abrishami/ppo-CartPole-v1'
'batch_size': 512
'minibatch_size': 128}
```
|
ramymohamed/a2c-AntBulletEnv-v0 | ramymohamed | 2023-07-12T23:55:29Z | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"AntBulletEnv-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | 2023-07-12T23:54:08Z | ---
library_name: stable-baselines3
tags:
- AntBulletEnv-v0
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: AntBulletEnv-v0
type: AntBulletEnv-v0
metrics:
- type: mean_reward
value: 1714.78 +/- 104.09
name: mean_reward
verified: false
---
# **A2C** Agent playing **AntBulletEnv-v0**
This is a trained model of a **A2C** agent playing **AntBulletEnv-v0**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
Blu72/Falcon | Blu72 | 2023-07-12T23:48:51Z | 0 | 0 | null | [
"license:openrail",
"region:us"
] | null | 2023-07-12T23:48:30Z | ---
license: openrail
---
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("tiiuae/falcon-40b")
model = AutoModelForCausalLM.from_pretrained("tiiuae/falcon-40b") |
Isotonic/informal_to_formal | Isotonic | 2023-07-12T22:55:28Z | 111 | 0 | transformers | [
"transformers",
"pytorch",
"safetensors",
"t5",
"text2text-generation",
"style-transfer",
"seq2seq",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2022-11-01T05:59:36Z | ---
language: "en"
tags:
- style-transfer
- text2text-generation
- seq2seq
inference: true
---
# Formality Style Transfer
## Model description
T5 Model for Formality Style Transfer. Trained on the GYAFC dataset.
## How to use
PyTorch model available.
```python
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("Isotonic/informal_to_formal")
model = AutoModelForSeq2SeqLM.from_pretrained("Isotonic/informal_to_formal")
sentence = "will you look into these two deals and let me know"
text = "Make the following sentence Formal: " + sentence + " </s>"
encoding = tokenizer.encode_plus(text,pad_to_max_length=True, return_tensors="pt")
input_ids, attention_masks = encoding["input_ids"].to("cuda"), encoding["attention_mask"].to("cuda")
outputs = model.generate(
input_ids=input_ids, attention_mask=attention_masks,
max_length=256,
do_sample=True,
top_k=120,
top_p=0.95,
early_stopping=True,
num_return_sequences=5
)
for output in outputs:
line = tokenizer.decode(output, skip_special_tokens=True,clean_up_tokenization_spaces=True)
print(line)
Output: "Would you look into the two deals in question, then let me know?"
``` |
Peebranco/teste-pedro-branco | Peebranco | 2023-07-12T22:53:38Z | 0 | 0 | null | [
"pt",
"en",
"dataset:Open-Orca/OpenOrca",
"region:us"
] | null | 2023-07-12T22:52:52Z | ---
datasets:
- Open-Orca/OpenOrca
language:
- pt
- en
metrics:
- character
--- |
digiplay/FumizukiMix_v1 | digiplay | 2023-07-12T22:49:15Z | 329 | 3 | diffusers | [
"diffusers",
"safetensors",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | text-to-image | 2023-07-12T22:33:07Z | ---
license: other
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
inference: true
---
Model info:
https://civitai.com/models/107380/fumizukimix
|
KingShmeeky/KingshmeekyRVC | KingShmeeky | 2023-07-12T22:43:21Z | 0 | 0 | null | [
"music",
"en",
"license:openrail",
"region:us"
] | null | 2023-07-12T22:30:27Z | ---
license: openrail
language:
- en
tags:
- music
--- |
lovelyxs/Pyramids | lovelyxs | 2023-07-12T22:37:03Z | 3 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"Pyramids",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
] | reinforcement-learning | 2023-07-12T22:36:58Z | ---
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: lovelyxs/Pyramids
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
whywynn/q-Taxi-v3 | whywynn | 2023-07-12T22:34:05Z | 0 | 0 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | reinforcement-learning | 2023-07-12T21:52:10Z | ---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 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="whywynn/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
NasimB/gpt2-concat-cbt-mod-formatting-rarity-all-no-cut-rev | NasimB | 2023-07-12T22:26:31Z | 5 | 0 | transformers | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"generated_from_trainer",
"dataset:generator",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2023-07-12T20:28:20Z | ---
license: mit
tags:
- generated_from_trainer
datasets:
- generator
model-index:
- name: gpt2-concat-cbt-mod-formatting-rarity-all-no-cut-rev
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-concat-cbt-mod-formatting-rarity-all-no-cut-rev
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the generator dataset.
It achieves the following results on the evaluation set:
- Loss: 4.3397
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 1000
- num_epochs: 6
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 6.68 | 0.29 | 500 | 5.6382 |
| 5.326 | 0.59 | 1000 | 5.2100 |
| 4.9873 | 0.88 | 1500 | 4.9727 |
| 4.72 | 1.18 | 2000 | 4.8271 |
| 4.5629 | 1.47 | 2500 | 4.7084 |
| 4.468 | 1.76 | 3000 | 4.6087 |
| 4.3349 | 2.06 | 3500 | 4.5351 |
| 4.1534 | 2.35 | 4000 | 4.4838 |
| 4.1205 | 2.65 | 4500 | 4.4211 |
| 4.0865 | 2.94 | 5000 | 4.3663 |
| 3.8691 | 3.24 | 5500 | 4.3627 |
| 3.8207 | 3.53 | 6000 | 4.3272 |
| 3.8 | 3.82 | 6500 | 4.2943 |
| 3.6899 | 4.12 | 7000 | 4.2964 |
| 3.5382 | 4.41 | 7500 | 4.2861 |
| 3.5296 | 4.71 | 8000 | 4.2710 |
| 3.5189 | 5.0 | 8500 | 4.2564 |
| 3.3408 | 5.29 | 9000 | 4.2730 |
| 3.3436 | 5.59 | 9500 | 4.2712 |
| 3.3413 | 5.88 | 10000 | 4.2705 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.11.0+cu113
- Datasets 2.13.0
- Tokenizers 0.13.3
|
uribah/my_awesome_model_2 | uribah | 2023-07-12T21:59:11Z | 62 | 0 | transformers | [
"transformers",
"tf",
"distilbert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2023-07-12T21:27:51Z | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: uribah/my_awesome_model_2
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# uribah/my_awesome_model_2
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.4528
- Validation Loss: 0.2263
- Train Accuracy: 0.9160
- Epoch: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 1470, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Train Accuracy | Epoch |
|:----------:|:---------------:|:--------------:|:-----:|
| 0.4528 | 0.2263 | 0.9160 | 0 |
### Framework versions
- Transformers 4.30.2
- TensorFlow 2.12.0
- Datasets 2.13.1
- Tokenizers 0.13.3
|
lovelyxs/ppo-SnowballTarget | lovelyxs | 2023-07-12T21:43:03Z | 9 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"SnowballTarget",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SnowballTarget",
"region:us"
] | reinforcement-learning | 2023-07-12T21:42:55Z | ---
library_name: ml-agents
tags:
- SnowballTarget
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SnowballTarget
---
# **ppo** Agent playing **SnowballTarget**
This is a trained model of a **ppo** agent playing **SnowballTarget**
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: lovelyxs/ppo-SnowballTarget
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
S1X3L4/Reinforce-cartpole0 | S1X3L4 | 2023-07-12T21:36:17Z | 0 | 0 | null | [
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | reinforcement-learning | 2023-07-12T21:36:00Z | ---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-cartpole0
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 500.00 +/- 0.00
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
tiiuae/falcon-rw-1b | tiiuae | 2023-07-12T21:34:11Z | 25,115 | 104 | transformers | [
"transformers",
"pytorch",
"falcon",
"text-generation",
"custom_code",
"en",
"dataset:tiiuae/falcon-refinedweb",
"arxiv:2306.01116",
"arxiv:2005.14165",
"arxiv:2108.12409",
"arxiv:2205.14135",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2023-04-26T09:25:36Z | ---
datasets:
- tiiuae/falcon-refinedweb
language:
- en
inference: false
license: apache-2.0
---
# Falcon-RW-1B
**Falcon-RW-1B is a 1B parameters causal decoder-only model built by [TII](https://www.tii.ae) and trained on 350B tokens of [RefinedWeb](https://huggingface.co/datasets/tiiuae/falcon-refinedweb). It is made available under the Apache 2.0 license.**
See the 📓 [paper on arXiv](https://arxiv.org/abs/2306.01116) for more details.
RefinedWeb is a high-quality web dataset built by leveraging stringent filtering and large-scale deduplication. Falcon-RW-1B, trained on RefinedWeb only, matches or outperforms comparable models trained on curated data.
⚠️ Falcon is now available as a core model in the `transformers` library! To use the in-library version, please install the latest version of `transformers` with `pip install git+https://github.com/huggingface/transformers.git`, then simply remove the `trust_remote_code=True` argument from `from_pretrained()`.
⚠️ This model is intended for use as a **research artifact**, to study the influence of training on web data alone. **If you are interested in state-of-the-art models, we recommend using Falcon-[7B](https://huggingface.co/tiiuae/falcon-7b)/[40B](https://huggingface.co/tiiuae/falcon-40b), both trained on >1,000 billion tokens.**
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
import transformers
import torch
model = "tiiuae/falcon-rw-1b"
tokenizer = AutoTokenizer.from_pretrained(model)
pipeline = transformers.pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
torch_dtype=torch.bfloat16,
device_map="auto",
)
sequences = pipeline(
"Girafatron is obsessed with giraffes, the most glorious animal on the face of this Earth. Giraftron believes all other animals are irrelevant when compared to the glorious majesty of the giraffe.\nDaniel: Hello, Girafatron!\nGirafatron:",
max_length=200,
do_sample=True,
top_k=10,
num_return_sequences=1,
eos_token_id=tokenizer.eos_token_id,
)
for seq in sequences:
print(f"Result: {seq['generated_text']}")
```
💥 **Falcon LLMs require PyTorch 2.0 for use with `transformers`!**
# Model Card for Falcon-RW-1B
## Model Details
### Model Description
- **Developed by:** [https://www.tii.ae](https://www.tii.ae);
- **Model type:** Causal decoder-only;
- **Language(s) (NLP):** English;
- **License:** Apache 2.0.
### Model Source
- **Paper:** [https://arxiv.org/abs/2306.01116](https://arxiv.org/abs/2306.01116).
## Uses
### Direct Use
Research on large language models, specifically the influence of adequately filtered and deduplicated web data on the properties of large language models (fairness, safety, limitations, capabilities, etc.).
### Out-of-Scope Use
Production use without adequate assessment of risks and mitigation; any use cases which may be considered irresponsible or harmful.
Broadly speaking, we would recommend Falcon-[7B](https://huggingface.co/tiiuae/falcon-7b)/[40B](https://huggingface.co/tiiuae/falcon-40b) for any use not directly related to research on web data pipelines.
## Bias, Risks, and Limitations
Falcon-RW-1B is trained on English data only, and will not generalize appropriately to other languages. Furthermore, as it is trained on a large-scale corpora representative of the web, it will carry the stereotypes and biases commonly encountered online.
### Recommendations
We recommend users of Falcon-RW-1B to consider finetuning it for the specific set of tasks of interest, and for guardrails and appropriate precautions to be taken for any production use.
## How to Get Started with the Model
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
import transformers
import torch
model = "tiiuae/falcon-rw-1b"
tokenizer = AutoTokenizer.from_pretrained(model)
pipeline = transformers.pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
torch_dtype=torch.bfloat16,
device_map="auto",
)
sequences = pipeline(
"Girafatron is obsessed with giraffes, the most glorious animal on the face of this Earth. Giraftron believes all other animals are irrelevant when compared to the glorious majesty of the giraffe.\nDaniel: Hello, Girafatron!\nGirafatron:",
max_length=200,
do_sample=True,
top_k=10,
num_return_sequences=1,
eos_token_id=tokenizer.eos_token_id,
)
for seq in sequences:
print(f"Result: {seq['generated_text']}")
```
## Training Details
### Training Data
Falcon-RW-1B was trained on 350B tokens of [RefinedWeb](https://huggingface.co/datasets/tiiuae/falcon-refinedweb), a high-quality filtered and deduplicated web dataset. The data was tokenized with the GPT-2 tokenizer.
### Training Procedure
Falcon-RW-1B was trained on 32 A100 40GB GPUs, using only data parallelism with ZeRO.
#### Training Hyperparameters
Hyperparameters were adapted from the GPT-3 paper ([Brown et al., 2020](https://arxiv.org/abs/2005.14165)).
| **Hyperparameter** | **Value** | **Comment** |
|--------------------|------------|-------------------------------------------|
| Precision | `bfloat16` | |
| Optimizer | AdamW | |
| Learning rate | 2e-4 | 500M tokens warm-up, cosine decay to 2e-5 |
| Weight decay | 1e-1 | |
| Batch size | 512 | 4B tokens ramp-up |
#### Speeds, Sizes, Times
Training happened in early December 2022 and took about six days.
## Evaluation
See the 📓 [paper on arXiv](https://arxiv.org/abs/2306.01116) for in-depth evaluation.
## Technical Specifications
### Model Architecture and Objective
Falcon-RW-1B is a causal decoder-only model trained on a causal language modeling task (i.e., predict the next token).
The architecture is adapted from the GPT-3 paper ([Brown et al., 2020](https://arxiv.org/abs/2005.14165)), but uses ALiBi ([Ofir et al., 2021](https://arxiv.org/abs/2108.12409)) and FlashAttention ([Dao et al., 2022](https://arxiv.org/abs/2205.14135)).
| **Hyperparameter** | **Value** | **Comment** |
|--------------------|-----------|----------------------------------------|
| Layers | 24 | |
| `d_model` | 2048 | |
| `head_dim` | 64 | Reduced to optimise for FlashAttention |
| Vocabulary | 50304 | |
| Sequence length | 2048 | |
### Compute Infrastructure
#### Hardware
Falcon-RW-1B was trained on AWS SageMaker, on 32 A100 40GB GPUs in P4d instances.
#### Software
Falcon-RW-1B was trained a custom distributed training codebase, Gigatron. It uses a 3D parallelism approach combined with ZeRO and high-performance Triton kernels (FlashAttention, etc.)
## Citation
```
@article{refinedweb,
title={The {R}efined{W}eb dataset for {F}alcon {LLM}: outperforming curated corpora with web data, and web data only},
author={Guilherme Penedo and Quentin Malartic and Daniel Hesslow and Ruxandra Cojocaru and Alessandro Cappelli and Hamza Alobeidli and Baptiste Pannier and Ebtesam Almazrouei and Julien Launay},
journal={arXiv preprint arXiv:2306.01116},
eprint={2306.01116},
eprinttype = {arXiv},
url={https://arxiv.org/abs/2306.01116},
year={2023}
}
```
## Contact
[email protected]
|
yanex0/cn-v1-1 | yanex0 | 2023-07-12T21:20:20Z | 0 | 0 | null | [
"license:openrail",
"region:us"
] | null | 2023-07-12T21:14:00Z | ---
license: openrail
---
This is the model files for [ControlNet 1.1](https://github.com/lllyasviel/ControlNet-v1-1-nightly).
This model card will be filled in a more detailed way after 1.1 is officially merged into ControlNet.
|
SrPrieto/ppo-LunarLander-v2 | SrPrieto | 2023-07-12T21:14:49Z | 5 | 0 | stable-baselines3 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | 2023-07-12T21:14:30Z | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 271.18 +/- 13.06
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
carbon225/byt5-abbreviations-pl | carbon225 | 2023-07-12T21:00:28Z | 104 | 0 | transformers | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"pl",
"dataset:carbon225/poleval-abbreviation-disambiguation-wiki",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2023-07-09T21:40:24Z | ---
datasets:
- carbon225/poleval-abbreviation-disambiguation-wiki
language:
- pl
widget:
- text: "Kolejne 0,12 <mask>pkt. proc.</mask> wynika ze spadku popytu na polski eksport, a 0,08 z zaburzeń na rynku wewnętrznym"
example_title: "Example 1"
---
# 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]
|
foreverip/q-Taxi-v3 | foreverip | 2023-07-12T20:56:40Z | 0 | 0 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | reinforcement-learning | 2023-07-12T20:56:37Z | ---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 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="foreverip/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
saeedehj/led-base-finetune-xsum | saeedehj | 2023-07-12T20:52:30Z | 95 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"led",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2023-07-12T16:21:51Z | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: led-base-16384-finetune-xsum
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. -->
# led-base-16384-finetune-xsum
This model is a fine-tuned version of [allenai/led-base-16384](https://huggingface.co/allenai/led-base-16384) on the xsum dataset.
It achieves the following results on the evaluation set:
- Loss: 3.3325
- Rouge1: 31.3157
- Rouge2: 9.2183
- Rougel: 23.7641
- Rougelsum: 23.8202
- Gen Len: 19.89
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| No log | 1.0 | 125 | 2.6311 | 32.5653 | 10.8601 | 25.3811 | 25.5187 | 19.84 |
| No log | 2.0 | 250 | 2.7544 | 31.6321 | 9.9595 | 25.0264 | 25.0779 | 19.85 |
| No log | 3.0 | 375 | 2.8261 | 32.0246 | 10.1415 | 25.2121 | 25.2632 | 19.89 |
| 0.1515 | 4.0 | 500 | 2.9240 | 31.6961 | 11.1892 | 25.0684 | 25.1019 | 19.92 |
| 0.1515 | 5.0 | 625 | 3.0229 | 31.1022 | 9.294 | 24.3075 | 24.309 | 19.9 |
| 0.1515 | 6.0 | 750 | 3.0900 | 31.7063 | 10.2344 | 25.1885 | 25.3359 | 19.89 |
| 0.1515 | 7.0 | 875 | 3.0958 | 31.6973 | 10.2856 | 25.5433 | 25.6242 | 19.91 |
| 0.0437 | 8.0 | 1000 | 3.1248 | 30.9445 | 10.3904 | 24.0861 | 24.109 | 19.91 |
| 0.0437 | 9.0 | 1125 | 3.1542 | 31.4694 | 9.4087 | 24.3248 | 24.4039 | 19.97 |
| 0.0437 | 10.0 | 1250 | 3.1986 | 30.428 | 9.6657 | 24.2568 | 24.4035 | 19.86 |
| 0.0437 | 11.0 | 1375 | 3.2040 | 32.3325 | 9.8754 | 25.117 | 25.1563 | 19.95 |
| 0.0229 | 12.0 | 1500 | 3.2044 | 30.8435 | 8.6959 | 23.4129 | 23.5211 | 19.99 |
| 0.0229 | 13.0 | 1625 | 3.2419 | 31.8807 | 9.6734 | 24.5748 | 24.6672 | 19.96 |
| 0.0229 | 14.0 | 1750 | 3.2926 | 31.8181 | 9.5238 | 24.3606 | 24.4569 | 19.88 |
| 0.0229 | 15.0 | 1875 | 3.2935 | 30.7551 | 8.9042 | 23.9581 | 24.1074 | 19.98 |
| 0.0107 | 16.0 | 2000 | 3.3219 | 31.3919 | 9.3308 | 24.1432 | 24.2162 | 19.93 |
| 0.0107 | 17.0 | 2125 | 3.3167 | 31.7918 | 9.4813 | 23.9672 | 24.0244 | 19.9 |
| 0.0107 | 18.0 | 2250 | 3.3281 | 31.0624 | 9.3608 | 23.6247 | 23.6658 | 19.89 |
| 0.0107 | 19.0 | 2375 | 3.3248 | 31.7832 | 9.5257 | 23.9738 | 24.0255 | 19.96 |
| 0.0063 | 20.0 | 2500 | 3.3325 | 31.3157 | 9.2183 | 23.7641 | 23.8202 | 19.89 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
foreverip/q-FrozenLake-v1-4x4-noSlippery | foreverip | 2023-07-12T20:49:59Z | 0 | 0 | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | reinforcement-learning | 2023-07-12T20:49:56Z | ---
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="foreverip/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"])
```
|
kimnguyenwork/Taxi-v3 | kimnguyenwork | 2023-07-12T20:45:36Z | 0 | 0 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | reinforcement-learning | 2023-07-12T20:45:29Z | ---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.52 +/- 2.73
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="kimnguyenwork/Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
Sanyam0605/whisper-large-v2-hi | Sanyam0605 | 2023-07-12T20:39:50Z | 5 | 1 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"whisper",
"automatic-speech-recognition",
"hi",
"dataset:google/fleurs",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | 2023-07-10T20:07:11Z | ---
datasets:
- google/fleurs
metrics:
- accuracy/wer
license: apache-2.0
language:
- hi
library_name: transformers
--- |
NasimB/gpt2-concat-guten-rarity-no-cut | NasimB | 2023-07-12T20:33:38Z | 5 | 0 | transformers | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"generated_from_trainer",
"dataset:generator",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2023-07-12T18:48:47Z | ---
license: mit
tags:
- generated_from_trainer
datasets:
- generator
model-index:
- name: gpt2-concat-guten-rarity-no-cut
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-concat-guten-rarity-no-cut
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the generator dataset.
It achieves the following results on the evaluation set:
- Loss: 4.3296
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 1000
- num_epochs: 6
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 6.6869 | 0.29 | 500 | 5.6385 |
| 5.3235 | 0.59 | 1000 | 5.2015 |
| 4.9865 | 0.88 | 1500 | 4.9498 |
| 4.7068 | 1.18 | 2000 | 4.8080 |
| 4.5674 | 1.47 | 2500 | 4.6941 |
| 4.4601 | 1.76 | 3000 | 4.5872 |
| 4.3293 | 2.06 | 3500 | 4.5155 |
| 4.1497 | 2.35 | 4000 | 4.4676 |
| 4.1182 | 2.64 | 4500 | 4.4072 |
| 4.0826 | 2.94 | 5000 | 4.3514 |
| 3.8664 | 3.23 | 5500 | 4.3488 |
| 3.8272 | 3.53 | 6000 | 4.3168 |
| 3.8034 | 3.82 | 6500 | 4.2843 |
| 3.6795 | 4.11 | 7000 | 4.2836 |
| 3.5333 | 4.41 | 7500 | 4.2764 |
| 3.534 | 4.7 | 8000 | 4.2603 |
| 3.5182 | 4.99 | 8500 | 4.2478 |
| 3.3437 | 5.29 | 9000 | 4.2620 |
| 3.3384 | 5.58 | 9500 | 4.2601 |
| 3.3385 | 5.88 | 10000 | 4.2595 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.11.0+cu113
- Datasets 2.13.0
- Tokenizers 0.13.3
|
Jonathaniu/alpaca-breast-cancer-7b | Jonathaniu | 2023-07-12T20:29:18Z | 2 | 0 | peft | [
"peft",
"region:us"
] | null | 2023-07-11T18:18:27Z | ---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
### Framework versions
- PEFT 0.4.0.dev0
|
odunola/sentence-transformers-bible-reference-final | odunola | 2023-07-12T20:20:22Z | 14 | 4 | sentence-transformers | [
"sentence-transformers",
"pytorch",
"mpnet",
"feature-extraction",
"transformers",
"dataset:odunola/bible-reference-sentence-pair",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | feature-extraction | 2023-06-18T23:33:09Z | ---
pipeline_tag: feature-extraction
tags:
- sentence-transformers
- feature-extraction
- transformers
license: apache-2.0
datasets:
- odunola/bible-reference-sentence-pair
---
# {MODEL_NAME}
This model is a product of the [Sentence-Transformers](https://www.SBERT.net) family. It refines sentences and paragraphs into a sophisticated 768-dimensional vector space. It owes its precision to a fine-tuning process, executed on a dataset comprised of over one hundred thousand rows of pairs of sentences rooted in biblical context, thus able to discern similarities between two sentences talking about the same biblical essence.
This enriched dataset—bountiful in biblically sound & matching sentence pairs—is accessible on the hub, referenced as [odunola/bible-reference-sentence-pair](https://huggingface.co/datasets/odunola/bible-reference-sentence-pair). As a result of this intensive learning process, the model possesses an uncanny knack for recognising parallels in seemingly disparate biblical discussions.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
model = AutoModel.from_pretrained('{MODEL_NAME}')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`sentence_transformers.datasets.NoDuplicatesDataLoader.NoDuplicatesDataLoader` of length 10940 with parameters:
```
{'batch_size': 32}
```
**Loss**:
`sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters:
```
{'scale': 20.0, 'similarity_fct': 'cos_sim'}
```
Parameters of the fit()-Method:
```
{
"epochs": 5,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 5470,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: MPNetModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information --> |
VK246/IC_ver5b_coco_swin_gpt2_01pc_1e | VK246 | 2023-07-12T20:14:54Z | 46 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"vision-encoder-decoder",
"image-text-to-text",
"generated_from_trainer",
"dataset:coco",
"endpoints_compatible",
"region:us"
] | image-text-to-text | 2023-07-12T19:47:07Z | ---
tags:
- generated_from_trainer
datasets:
- coco
metrics:
- rouge
- bleu
model-index:
- name: IC_ver5b_coco_swin_gpt2_01pc_1e
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. -->
# IC_ver5b_coco_swin_gpt2_01pc_1e
This model is a fine-tuned version of [VK246/IC_ver5a_coco_swin_gpt2_05pc_1e](https://huggingface.co/VK246/IC_ver5a_coco_swin_gpt2_05pc_1e) on the coco dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1266
- Rouge1: 27.4772
- Rouge2: 5.9305
- Rougel: 25.1138
- Rougelsum: 25.1235
- Bleu: 2.437
- Gen Len: 11.1124
## 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: 96
- eval_batch_size: 96
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Bleu | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:|:------:|:-------:|
| 1.2093 | 0.42 | 25 | 1.1552 | 22.8898 | 3.6353 | 20.6781 | 20.6737 | 1.1554 | 11.1124 |
| 1.2149 | 0.85 | 50 | 1.1358 | 26.2857 | 5.2765 | 24.0266 | 24.0308 | 2.1954 | 11.1124 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
jd06/TwoSentenceHorrorModel | jd06 | 2023-07-12T20:14:37Z | 211 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2023-07-11T20:51:49Z | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: TwoSentenceHorrorModel
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. -->
# TwoSentenceHorrorModel
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 4.3563
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 1 | 4.7786 |
| No log | 2.0 | 2 | 4.4930 |
| No log | 3.0 | 3 | 4.3563 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
grace-pro/xlmr-finetuned-igbo | grace-pro | 2023-07-12T20:02:08Z | 105 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | 2023-07-12T18:22:59Z | ---
license: mit
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: xlmr-finetuned-igbo
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. -->
# xlmr-finetuned-igbo
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: 0.2323
- Precision: 0.7134
- Recall: 0.4641
- F1: 0.5623
- Accuracy: 0.9188
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.284 | 1.0 | 1257 | 0.2690 | 0.7177 | 0.2740 | 0.3966 | 0.9019 |
| 0.2383 | 2.0 | 2514 | 0.2597 | 0.7436 | 0.3418 | 0.4683 | 0.9101 |
| 0.2108 | 3.0 | 3771 | 0.2241 | 0.7097 | 0.4378 | 0.5416 | 0.9161 |
| 0.1925 | 4.0 | 5028 | 0.2323 | 0.7274 | 0.4343 | 0.5439 | 0.9173 |
| 0.1774 | 5.0 | 6285 | 0.2323 | 0.7134 | 0.4641 | 0.5623 | 0.9188 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
macapa/segmentation-mod | macapa | 2023-07-12T19:59:53Z | 0 | 0 | fastai | [
"fastai",
"region:us"
] | null | 2023-07-12T19:59:38Z | ---
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
|
newsrx/instructor-large | newsrx | 2023-07-12T19:56:14Z | 7 | 0 | sentence-transformers | [
"sentence-transformers",
"pytorch",
"t5",
"text-embedding",
"embeddings",
"information-retrieval",
"beir",
"text-classification",
"language-model",
"text-clustering",
"text-semantic-similarity",
"text-evaluation",
"prompt-retrieval",
"text-reranking",
"feature-extraction",
"sentence-similarity",
"transformers",
"English",
"Sentence Similarity",
"natural_questions",
"ms_marco",
"fever",
"hotpot_qa",
"mteb",
"en",
"arxiv:2212.09741",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] | sentence-similarity | 2023-07-12T19:56:14Z | ---
pipeline_tag: sentence-similarity
tags:
- text-embedding
- embeddings
- information-retrieval
- beir
- text-classification
- language-model
- text-clustering
- text-semantic-similarity
- text-evaluation
- prompt-retrieval
- text-reranking
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
- t5
- English
- Sentence Similarity
- natural_questions
- ms_marco
- fever
- hotpot_qa
- mteb
language: en
inference: false
license: apache-2.0
model-index:
- name: INSTRUCTOR
results:
- task:
type: Classification
dataset:
type: mteb/amazon_counterfactual
name: MTEB AmazonCounterfactualClassification (en)
config: en
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 88.13432835820896
- type: ap
value: 59.298209334395665
- type: f1
value: 83.31769058643586
- task:
type: Classification
dataset:
type: mteb/amazon_polarity
name: MTEB AmazonPolarityClassification
config: default
split: test
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
metrics:
- type: accuracy
value: 91.526375
- type: ap
value: 88.16327709705504
- type: f1
value: 91.51095801287843
- task:
type: Classification
dataset:
type: mteb/amazon_reviews_multi
name: MTEB AmazonReviewsClassification (en)
config: en
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 47.856
- type: f1
value: 45.41490917650942
- task:
type: Retrieval
dataset:
type: arguana
name: MTEB ArguAna
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 31.223
- type: map_at_10
value: 47.947
- type: map_at_100
value: 48.742000000000004
- type: map_at_1000
value: 48.745
- type: map_at_3
value: 43.137
- type: map_at_5
value: 45.992
- type: mrr_at_1
value: 32.432
- type: mrr_at_10
value: 48.4
- type: mrr_at_100
value: 49.202
- type: mrr_at_1000
value: 49.205
- type: mrr_at_3
value: 43.551
- type: mrr_at_5
value: 46.467999999999996
- type: ndcg_at_1
value: 31.223
- type: ndcg_at_10
value: 57.045
- type: ndcg_at_100
value: 60.175
- type: ndcg_at_1000
value: 60.233000000000004
- type: ndcg_at_3
value: 47.171
- type: ndcg_at_5
value: 52.322
- type: precision_at_1
value: 31.223
- type: precision_at_10
value: 8.599
- type: precision_at_100
value: 0.991
- type: precision_at_1000
value: 0.1
- type: precision_at_3
value: 19.63
- type: precision_at_5
value: 14.282
- type: recall_at_1
value: 31.223
- type: recall_at_10
value: 85.989
- type: recall_at_100
value: 99.075
- type: recall_at_1000
value: 99.502
- type: recall_at_3
value: 58.89
- type: recall_at_5
value: 71.408
- task:
type: Clustering
dataset:
type: mteb/arxiv-clustering-p2p
name: MTEB ArxivClusteringP2P
config: default
split: test
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
metrics:
- type: v_measure
value: 43.1621946393635
- task:
type: Clustering
dataset:
type: mteb/arxiv-clustering-s2s
name: MTEB ArxivClusteringS2S
config: default
split: test
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
metrics:
- type: v_measure
value: 32.56417132407894
- task:
type: Reranking
dataset:
type: mteb/askubuntudupquestions-reranking
name: MTEB AskUbuntuDupQuestions
config: default
split: test
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
metrics:
- type: map
value: 64.29539304390207
- type: mrr
value: 76.44484017060196
- task:
type: STS
dataset:
type: mteb/biosses-sts
name: MTEB BIOSSES
config: default
split: test
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
metrics:
- type: cos_sim_spearman
value: 84.38746499431112
- task:
type: Classification
dataset:
type: mteb/banking77
name: MTEB Banking77Classification
config: default
split: test
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
metrics:
- type: accuracy
value: 78.51298701298701
- type: f1
value: 77.49041754069235
- task:
type: Clustering
dataset:
type: mteb/biorxiv-clustering-p2p
name: MTEB BiorxivClusteringP2P
config: default
split: test
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
metrics:
- type: v_measure
value: 37.61848554098577
- task:
type: Clustering
dataset:
type: mteb/biorxiv-clustering-s2s
name: MTEB BiorxivClusteringS2S
config: default
split: test
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
metrics:
- type: v_measure
value: 31.32623280148178
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackAndroidRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 35.803000000000004
- type: map_at_10
value: 48.848
- type: map_at_100
value: 50.5
- type: map_at_1000
value: 50.602999999999994
- type: map_at_3
value: 45.111000000000004
- type: map_at_5
value: 47.202
- type: mrr_at_1
value: 44.635000000000005
- type: mrr_at_10
value: 55.593
- type: mrr_at_100
value: 56.169999999999995
- type: mrr_at_1000
value: 56.19499999999999
- type: mrr_at_3
value: 53.361999999999995
- type: mrr_at_5
value: 54.806999999999995
- type: ndcg_at_1
value: 44.635000000000005
- type: ndcg_at_10
value: 55.899
- type: ndcg_at_100
value: 60.958
- type: ndcg_at_1000
value: 62.302
- type: ndcg_at_3
value: 51.051
- type: ndcg_at_5
value: 53.351000000000006
- type: precision_at_1
value: 44.635000000000005
- type: precision_at_10
value: 10.786999999999999
- type: precision_at_100
value: 1.6580000000000001
- type: precision_at_1000
value: 0.213
- type: precision_at_3
value: 24.893
- type: precision_at_5
value: 17.740000000000002
- type: recall_at_1
value: 35.803000000000004
- type: recall_at_10
value: 68.657
- type: recall_at_100
value: 89.77199999999999
- type: recall_at_1000
value: 97.67
- type: recall_at_3
value: 54.066
- type: recall_at_5
value: 60.788
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackEnglishRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 33.706
- type: map_at_10
value: 44.896
- type: map_at_100
value: 46.299
- type: map_at_1000
value: 46.44
- type: map_at_3
value: 41.721000000000004
- type: map_at_5
value: 43.486000000000004
- type: mrr_at_1
value: 41.592
- type: mrr_at_10
value: 50.529
- type: mrr_at_100
value: 51.22
- type: mrr_at_1000
value: 51.258
- type: mrr_at_3
value: 48.205999999999996
- type: mrr_at_5
value: 49.528
- type: ndcg_at_1
value: 41.592
- type: ndcg_at_10
value: 50.77199999999999
- type: ndcg_at_100
value: 55.383
- type: ndcg_at_1000
value: 57.288
- type: ndcg_at_3
value: 46.324
- type: ndcg_at_5
value: 48.346000000000004
- type: precision_at_1
value: 41.592
- type: precision_at_10
value: 9.516
- type: precision_at_100
value: 1.541
- type: precision_at_1000
value: 0.2
- type: precision_at_3
value: 22.399
- type: precision_at_5
value: 15.770999999999999
- type: recall_at_1
value: 33.706
- type: recall_at_10
value: 61.353
- type: recall_at_100
value: 80.182
- type: recall_at_1000
value: 91.896
- type: recall_at_3
value: 48.204
- type: recall_at_5
value: 53.89699999999999
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackGamingRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 44.424
- type: map_at_10
value: 57.169000000000004
- type: map_at_100
value: 58.202
- type: map_at_1000
value: 58.242000000000004
- type: map_at_3
value: 53.825
- type: map_at_5
value: 55.714
- type: mrr_at_1
value: 50.470000000000006
- type: mrr_at_10
value: 60.489000000000004
- type: mrr_at_100
value: 61.096
- type: mrr_at_1000
value: 61.112
- type: mrr_at_3
value: 58.192
- type: mrr_at_5
value: 59.611999999999995
- type: ndcg_at_1
value: 50.470000000000006
- type: ndcg_at_10
value: 63.071999999999996
- type: ndcg_at_100
value: 66.964
- type: ndcg_at_1000
value: 67.659
- type: ndcg_at_3
value: 57.74399999999999
- type: ndcg_at_5
value: 60.367000000000004
- type: precision_at_1
value: 50.470000000000006
- type: precision_at_10
value: 10.019
- type: precision_at_100
value: 1.29
- type: precision_at_1000
value: 0.13899999999999998
- type: precision_at_3
value: 25.558999999999997
- type: precision_at_5
value: 17.467
- type: recall_at_1
value: 44.424
- type: recall_at_10
value: 77.02
- type: recall_at_100
value: 93.738
- type: recall_at_1000
value: 98.451
- type: recall_at_3
value: 62.888
- type: recall_at_5
value: 69.138
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackGisRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 26.294
- type: map_at_10
value: 34.503
- type: map_at_100
value: 35.641
- type: map_at_1000
value: 35.724000000000004
- type: map_at_3
value: 31.753999999999998
- type: map_at_5
value: 33.190999999999995
- type: mrr_at_1
value: 28.362
- type: mrr_at_10
value: 36.53
- type: mrr_at_100
value: 37.541000000000004
- type: mrr_at_1000
value: 37.602000000000004
- type: mrr_at_3
value: 33.917
- type: mrr_at_5
value: 35.358000000000004
- type: ndcg_at_1
value: 28.362
- type: ndcg_at_10
value: 39.513999999999996
- type: ndcg_at_100
value: 44.815
- type: ndcg_at_1000
value: 46.839
- type: ndcg_at_3
value: 34.02
- type: ndcg_at_5
value: 36.522
- type: precision_at_1
value: 28.362
- type: precision_at_10
value: 6.101999999999999
- type: precision_at_100
value: 0.9129999999999999
- type: precision_at_1000
value: 0.11399999999999999
- type: precision_at_3
value: 14.161999999999999
- type: precision_at_5
value: 9.966
- type: recall_at_1
value: 26.294
- type: recall_at_10
value: 53.098
- type: recall_at_100
value: 76.877
- type: recall_at_1000
value: 91.834
- type: recall_at_3
value: 38.266
- type: recall_at_5
value: 44.287
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackMathematicaRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 16.407
- type: map_at_10
value: 25.185999999999996
- type: map_at_100
value: 26.533
- type: map_at_1000
value: 26.657999999999998
- type: map_at_3
value: 22.201999999999998
- type: map_at_5
value: 23.923
- type: mrr_at_1
value: 20.522000000000002
- type: mrr_at_10
value: 29.522
- type: mrr_at_100
value: 30.644
- type: mrr_at_1000
value: 30.713
- type: mrr_at_3
value: 26.679000000000002
- type: mrr_at_5
value: 28.483000000000004
- type: ndcg_at_1
value: 20.522000000000002
- type: ndcg_at_10
value: 30.656
- type: ndcg_at_100
value: 36.864999999999995
- type: ndcg_at_1000
value: 39.675
- type: ndcg_at_3
value: 25.319000000000003
- type: ndcg_at_5
value: 27.992
- type: precision_at_1
value: 20.522000000000002
- type: precision_at_10
value: 5.795999999999999
- type: precision_at_100
value: 1.027
- type: precision_at_1000
value: 0.13999999999999999
- type: precision_at_3
value: 12.396
- type: precision_at_5
value: 9.328
- type: recall_at_1
value: 16.407
- type: recall_at_10
value: 43.164
- type: recall_at_100
value: 69.695
- type: recall_at_1000
value: 89.41900000000001
- type: recall_at_3
value: 28.634999999999998
- type: recall_at_5
value: 35.308
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackPhysicsRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 30.473
- type: map_at_10
value: 41.676
- type: map_at_100
value: 43.120999999999995
- type: map_at_1000
value: 43.230000000000004
- type: map_at_3
value: 38.306000000000004
- type: map_at_5
value: 40.355999999999995
- type: mrr_at_1
value: 37.536
- type: mrr_at_10
value: 47.643
- type: mrr_at_100
value: 48.508
- type: mrr_at_1000
value: 48.551
- type: mrr_at_3
value: 45.348
- type: mrr_at_5
value: 46.744
- type: ndcg_at_1
value: 37.536
- type: ndcg_at_10
value: 47.823
- type: ndcg_at_100
value: 53.395
- type: ndcg_at_1000
value: 55.271
- type: ndcg_at_3
value: 42.768
- type: ndcg_at_5
value: 45.373000000000005
- type: precision_at_1
value: 37.536
- type: precision_at_10
value: 8.681
- type: precision_at_100
value: 1.34
- type: precision_at_1000
value: 0.165
- type: precision_at_3
value: 20.468
- type: precision_at_5
value: 14.495
- type: recall_at_1
value: 30.473
- type: recall_at_10
value: 60.092999999999996
- type: recall_at_100
value: 82.733
- type: recall_at_1000
value: 94.875
- type: recall_at_3
value: 45.734
- type: recall_at_5
value: 52.691
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackProgrammersRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 29.976000000000003
- type: map_at_10
value: 41.097
- type: map_at_100
value: 42.547000000000004
- type: map_at_1000
value: 42.659000000000006
- type: map_at_3
value: 37.251
- type: map_at_5
value: 39.493
- type: mrr_at_1
value: 37.557
- type: mrr_at_10
value: 46.605000000000004
- type: mrr_at_100
value: 47.487
- type: mrr_at_1000
value: 47.54
- type: mrr_at_3
value: 43.721
- type: mrr_at_5
value: 45.411
- type: ndcg_at_1
value: 37.557
- type: ndcg_at_10
value: 47.449000000000005
- type: ndcg_at_100
value: 53.052
- type: ndcg_at_1000
value: 55.010999999999996
- type: ndcg_at_3
value: 41.439
- type: ndcg_at_5
value: 44.292
- type: precision_at_1
value: 37.557
- type: precision_at_10
value: 8.847
- type: precision_at_100
value: 1.357
- type: precision_at_1000
value: 0.16999999999999998
- type: precision_at_3
value: 20.091
- type: precision_at_5
value: 14.384
- type: recall_at_1
value: 29.976000000000003
- type: recall_at_10
value: 60.99099999999999
- type: recall_at_100
value: 84.245
- type: recall_at_1000
value: 96.97200000000001
- type: recall_at_3
value: 43.794
- type: recall_at_5
value: 51.778999999999996
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 28.099166666666665
- type: map_at_10
value: 38.1365
- type: map_at_100
value: 39.44491666666667
- type: map_at_1000
value: 39.55858333333334
- type: map_at_3
value: 35.03641666666666
- type: map_at_5
value: 36.79833333333334
- type: mrr_at_1
value: 33.39966666666667
- type: mrr_at_10
value: 42.42583333333333
- type: mrr_at_100
value: 43.28575
- type: mrr_at_1000
value: 43.33741666666667
- type: mrr_at_3
value: 39.94975
- type: mrr_at_5
value: 41.41633333333334
- type: ndcg_at_1
value: 33.39966666666667
- type: ndcg_at_10
value: 43.81741666666667
- type: ndcg_at_100
value: 49.08166666666667
- type: ndcg_at_1000
value: 51.121166666666674
- type: ndcg_at_3
value: 38.73575
- type: ndcg_at_5
value: 41.18158333333333
- type: precision_at_1
value: 33.39966666666667
- type: precision_at_10
value: 7.738916666666667
- type: precision_at_100
value: 1.2265833333333331
- type: precision_at_1000
value: 0.15983333333333336
- type: precision_at_3
value: 17.967416666666665
- type: precision_at_5
value: 12.78675
- type: recall_at_1
value: 28.099166666666665
- type: recall_at_10
value: 56.27049999999999
- type: recall_at_100
value: 78.93291666666667
- type: recall_at_1000
value: 92.81608333333334
- type: recall_at_3
value: 42.09775
- type: recall_at_5
value: 48.42533333333334
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackStatsRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 23.663
- type: map_at_10
value: 30.377
- type: map_at_100
value: 31.426
- type: map_at_1000
value: 31.519000000000002
- type: map_at_3
value: 28.069
- type: map_at_5
value: 29.256999999999998
- type: mrr_at_1
value: 26.687
- type: mrr_at_10
value: 33.107
- type: mrr_at_100
value: 34.055
- type: mrr_at_1000
value: 34.117999999999995
- type: mrr_at_3
value: 31.058000000000003
- type: mrr_at_5
value: 32.14
- type: ndcg_at_1
value: 26.687
- type: ndcg_at_10
value: 34.615
- type: ndcg_at_100
value: 39.776
- type: ndcg_at_1000
value: 42.05
- type: ndcg_at_3
value: 30.322
- type: ndcg_at_5
value: 32.157000000000004
- type: precision_at_1
value: 26.687
- type: precision_at_10
value: 5.491
- type: precision_at_100
value: 0.877
- type: precision_at_1000
value: 0.11499999999999999
- type: precision_at_3
value: 13.139000000000001
- type: precision_at_5
value: 9.049
- type: recall_at_1
value: 23.663
- type: recall_at_10
value: 45.035
- type: recall_at_100
value: 68.554
- type: recall_at_1000
value: 85.077
- type: recall_at_3
value: 32.982
- type: recall_at_5
value: 37.688
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackTexRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 17.403
- type: map_at_10
value: 25.197000000000003
- type: map_at_100
value: 26.355
- type: map_at_1000
value: 26.487
- type: map_at_3
value: 22.733
- type: map_at_5
value: 24.114
- type: mrr_at_1
value: 21.37
- type: mrr_at_10
value: 29.091
- type: mrr_at_100
value: 30.018
- type: mrr_at_1000
value: 30.096
- type: mrr_at_3
value: 26.887
- type: mrr_at_5
value: 28.157
- type: ndcg_at_1
value: 21.37
- type: ndcg_at_10
value: 30.026000000000003
- type: ndcg_at_100
value: 35.416
- type: ndcg_at_1000
value: 38.45
- type: ndcg_at_3
value: 25.764
- type: ndcg_at_5
value: 27.742
- type: precision_at_1
value: 21.37
- type: precision_at_10
value: 5.609
- type: precision_at_100
value: 0.9860000000000001
- type: precision_at_1000
value: 0.14300000000000002
- type: precision_at_3
value: 12.423
- type: precision_at_5
value: 9.009
- type: recall_at_1
value: 17.403
- type: recall_at_10
value: 40.573
- type: recall_at_100
value: 64.818
- type: recall_at_1000
value: 86.53699999999999
- type: recall_at_3
value: 28.493000000000002
- type: recall_at_5
value: 33.660000000000004
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackUnixRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 28.639
- type: map_at_10
value: 38.951
- type: map_at_100
value: 40.238
- type: map_at_1000
value: 40.327
- type: map_at_3
value: 35.842
- type: map_at_5
value: 37.617
- type: mrr_at_1
value: 33.769
- type: mrr_at_10
value: 43.088
- type: mrr_at_100
value: 44.03
- type: mrr_at_1000
value: 44.072
- type: mrr_at_3
value: 40.656
- type: mrr_at_5
value: 42.138999999999996
- type: ndcg_at_1
value: 33.769
- type: ndcg_at_10
value: 44.676
- type: ndcg_at_100
value: 50.416000000000004
- type: ndcg_at_1000
value: 52.227999999999994
- type: ndcg_at_3
value: 39.494
- type: ndcg_at_5
value: 42.013
- type: precision_at_1
value: 33.769
- type: precision_at_10
value: 7.668
- type: precision_at_100
value: 1.18
- type: precision_at_1000
value: 0.145
- type: precision_at_3
value: 18.221
- type: precision_at_5
value: 12.966
- type: recall_at_1
value: 28.639
- type: recall_at_10
value: 57.687999999999995
- type: recall_at_100
value: 82.541
- type: recall_at_1000
value: 94.896
- type: recall_at_3
value: 43.651
- type: recall_at_5
value: 49.925999999999995
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackWebmastersRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 29.57
- type: map_at_10
value: 40.004
- type: map_at_100
value: 41.75
- type: map_at_1000
value: 41.97
- type: map_at_3
value: 36.788
- type: map_at_5
value: 38.671
- type: mrr_at_1
value: 35.375
- type: mrr_at_10
value: 45.121
- type: mrr_at_100
value: 45.994
- type: mrr_at_1000
value: 46.04
- type: mrr_at_3
value: 42.227
- type: mrr_at_5
value: 43.995
- type: ndcg_at_1
value: 35.375
- type: ndcg_at_10
value: 46.392
- type: ndcg_at_100
value: 52.196
- type: ndcg_at_1000
value: 54.274
- type: ndcg_at_3
value: 41.163
- type: ndcg_at_5
value: 43.813
- type: precision_at_1
value: 35.375
- type: precision_at_10
value: 8.676
- type: precision_at_100
value: 1.678
- type: precision_at_1000
value: 0.253
- type: precision_at_3
value: 19.104
- type: precision_at_5
value: 13.913
- type: recall_at_1
value: 29.57
- type: recall_at_10
value: 58.779
- type: recall_at_100
value: 83.337
- type: recall_at_1000
value: 95.979
- type: recall_at_3
value: 44.005
- type: recall_at_5
value: 50.975
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackWordpressRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 20.832
- type: map_at_10
value: 29.733999999999998
- type: map_at_100
value: 30.727
- type: map_at_1000
value: 30.843999999999998
- type: map_at_3
value: 26.834999999999997
- type: map_at_5
value: 28.555999999999997
- type: mrr_at_1
value: 22.921
- type: mrr_at_10
value: 31.791999999999998
- type: mrr_at_100
value: 32.666000000000004
- type: mrr_at_1000
value: 32.751999999999995
- type: mrr_at_3
value: 29.144
- type: mrr_at_5
value: 30.622
- type: ndcg_at_1
value: 22.921
- type: ndcg_at_10
value: 34.915
- type: ndcg_at_100
value: 39.744
- type: ndcg_at_1000
value: 42.407000000000004
- type: ndcg_at_3
value: 29.421000000000003
- type: ndcg_at_5
value: 32.211
- type: precision_at_1
value: 22.921
- type: precision_at_10
value: 5.675
- type: precision_at_100
value: 0.872
- type: precision_at_1000
value: 0.121
- type: precision_at_3
value: 12.753999999999998
- type: precision_at_5
value: 9.353
- type: recall_at_1
value: 20.832
- type: recall_at_10
value: 48.795
- type: recall_at_100
value: 70.703
- type: recall_at_1000
value: 90.187
- type: recall_at_3
value: 34.455000000000005
- type: recall_at_5
value: 40.967
- task:
type: Retrieval
dataset:
type: climate-fever
name: MTEB ClimateFEVER
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 10.334
- type: map_at_10
value: 19.009999999999998
- type: map_at_100
value: 21.129
- type: map_at_1000
value: 21.328
- type: map_at_3
value: 15.152
- type: map_at_5
value: 17.084
- type: mrr_at_1
value: 23.453
- type: mrr_at_10
value: 36.099
- type: mrr_at_100
value: 37.069
- type: mrr_at_1000
value: 37.104
- type: mrr_at_3
value: 32.096000000000004
- type: mrr_at_5
value: 34.451
- type: ndcg_at_1
value: 23.453
- type: ndcg_at_10
value: 27.739000000000004
- type: ndcg_at_100
value: 35.836
- type: ndcg_at_1000
value: 39.242
- type: ndcg_at_3
value: 21.263
- type: ndcg_at_5
value: 23.677
- type: precision_at_1
value: 23.453
- type: precision_at_10
value: 9.199
- type: precision_at_100
value: 1.791
- type: precision_at_1000
value: 0.242
- type: precision_at_3
value: 16.2
- type: precision_at_5
value: 13.147
- type: recall_at_1
value: 10.334
- type: recall_at_10
value: 35.177
- type: recall_at_100
value: 63.009
- type: recall_at_1000
value: 81.938
- type: recall_at_3
value: 19.914
- type: recall_at_5
value: 26.077
- task:
type: Retrieval
dataset:
type: dbpedia-entity
name: MTEB DBPedia
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 8.212
- type: map_at_10
value: 17.386
- type: map_at_100
value: 24.234
- type: map_at_1000
value: 25.724999999999998
- type: map_at_3
value: 12.727
- type: map_at_5
value: 14.785
- type: mrr_at_1
value: 59.25
- type: mrr_at_10
value: 68.687
- type: mrr_at_100
value: 69.133
- type: mrr_at_1000
value: 69.14099999999999
- type: mrr_at_3
value: 66.917
- type: mrr_at_5
value: 67.742
- type: ndcg_at_1
value: 48.625
- type: ndcg_at_10
value: 36.675999999999995
- type: ndcg_at_100
value: 41.543
- type: ndcg_at_1000
value: 49.241
- type: ndcg_at_3
value: 41.373
- type: ndcg_at_5
value: 38.707
- type: precision_at_1
value: 59.25
- type: precision_at_10
value: 28.525
- type: precision_at_100
value: 9.027000000000001
- type: precision_at_1000
value: 1.8339999999999999
- type: precision_at_3
value: 44.833
- type: precision_at_5
value: 37.35
- type: recall_at_1
value: 8.212
- type: recall_at_10
value: 23.188
- type: recall_at_100
value: 48.613
- type: recall_at_1000
value: 73.093
- type: recall_at_3
value: 14.419
- type: recall_at_5
value: 17.798
- task:
type: Classification
dataset:
type: mteb/emotion
name: MTEB EmotionClassification
config: default
split: test
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
metrics:
- type: accuracy
value: 52.725
- type: f1
value: 46.50743309855908
- task:
type: Retrieval
dataset:
type: fever
name: MTEB FEVER
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 55.086
- type: map_at_10
value: 66.914
- type: map_at_100
value: 67.321
- type: map_at_1000
value: 67.341
- type: map_at_3
value: 64.75800000000001
- type: map_at_5
value: 66.189
- type: mrr_at_1
value: 59.28600000000001
- type: mrr_at_10
value: 71.005
- type: mrr_at_100
value: 71.304
- type: mrr_at_1000
value: 71.313
- type: mrr_at_3
value: 69.037
- type: mrr_at_5
value: 70.35
- type: ndcg_at_1
value: 59.28600000000001
- type: ndcg_at_10
value: 72.695
- type: ndcg_at_100
value: 74.432
- type: ndcg_at_1000
value: 74.868
- type: ndcg_at_3
value: 68.72200000000001
- type: ndcg_at_5
value: 71.081
- type: precision_at_1
value: 59.28600000000001
- type: precision_at_10
value: 9.499
- type: precision_at_100
value: 1.052
- type: precision_at_1000
value: 0.11100000000000002
- type: precision_at_3
value: 27.503
- type: precision_at_5
value: 17.854999999999997
- type: recall_at_1
value: 55.086
- type: recall_at_10
value: 86.453
- type: recall_at_100
value: 94.028
- type: recall_at_1000
value: 97.052
- type: recall_at_3
value: 75.821
- type: recall_at_5
value: 81.6
- task:
type: Retrieval
dataset:
type: fiqa
name: MTEB FiQA2018
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 22.262999999999998
- type: map_at_10
value: 37.488
- type: map_at_100
value: 39.498
- type: map_at_1000
value: 39.687
- type: map_at_3
value: 32.529
- type: map_at_5
value: 35.455
- type: mrr_at_1
value: 44.907000000000004
- type: mrr_at_10
value: 53.239000000000004
- type: mrr_at_100
value: 54.086
- type: mrr_at_1000
value: 54.122
- type: mrr_at_3
value: 51.235
- type: mrr_at_5
value: 52.415
- type: ndcg_at_1
value: 44.907000000000004
- type: ndcg_at_10
value: 45.446
- type: ndcg_at_100
value: 52.429
- type: ndcg_at_1000
value: 55.169000000000004
- type: ndcg_at_3
value: 41.882000000000005
- type: ndcg_at_5
value: 43.178
- type: precision_at_1
value: 44.907000000000004
- type: precision_at_10
value: 12.931999999999999
- type: precision_at_100
value: 2.025
- type: precision_at_1000
value: 0.248
- type: precision_at_3
value: 28.652
- type: precision_at_5
value: 21.204
- type: recall_at_1
value: 22.262999999999998
- type: recall_at_10
value: 52.447
- type: recall_at_100
value: 78.045
- type: recall_at_1000
value: 94.419
- type: recall_at_3
value: 38.064
- type: recall_at_5
value: 44.769
- task:
type: Retrieval
dataset:
type: hotpotqa
name: MTEB HotpotQA
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 32.519
- type: map_at_10
value: 45.831
- type: map_at_100
value: 46.815
- type: map_at_1000
value: 46.899
- type: map_at_3
value: 42.836
- type: map_at_5
value: 44.65
- type: mrr_at_1
value: 65.037
- type: mrr_at_10
value: 72.16
- type: mrr_at_100
value: 72.51100000000001
- type: mrr_at_1000
value: 72.53
- type: mrr_at_3
value: 70.682
- type: mrr_at_5
value: 71.54599999999999
- type: ndcg_at_1
value: 65.037
- type: ndcg_at_10
value: 55.17999999999999
- type: ndcg_at_100
value: 58.888
- type: ndcg_at_1000
value: 60.648
- type: ndcg_at_3
value: 50.501
- type: ndcg_at_5
value: 52.977
- type: precision_at_1
value: 65.037
- type: precision_at_10
value: 11.530999999999999
- type: precision_at_100
value: 1.4460000000000002
- type: precision_at_1000
value: 0.168
- type: precision_at_3
value: 31.483
- type: precision_at_5
value: 20.845
- type: recall_at_1
value: 32.519
- type: recall_at_10
value: 57.657000000000004
- type: recall_at_100
value: 72.30199999999999
- type: recall_at_1000
value: 84.024
- type: recall_at_3
value: 47.225
- type: recall_at_5
value: 52.113
- task:
type: Classification
dataset:
type: mteb/imdb
name: MTEB ImdbClassification
config: default
split: test
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
metrics:
- type: accuracy
value: 88.3168
- type: ap
value: 83.80165516037135
- type: f1
value: 88.29942471066407
- task:
type: Retrieval
dataset:
type: msmarco
name: MTEB MSMARCO
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 20.724999999999998
- type: map_at_10
value: 32.736
- type: map_at_100
value: 33.938
- type: map_at_1000
value: 33.991
- type: map_at_3
value: 28.788000000000004
- type: map_at_5
value: 31.016
- type: mrr_at_1
value: 21.361
- type: mrr_at_10
value: 33.323
- type: mrr_at_100
value: 34.471000000000004
- type: mrr_at_1000
value: 34.518
- type: mrr_at_3
value: 29.453000000000003
- type: mrr_at_5
value: 31.629
- type: ndcg_at_1
value: 21.361
- type: ndcg_at_10
value: 39.649
- type: ndcg_at_100
value: 45.481
- type: ndcg_at_1000
value: 46.775
- type: ndcg_at_3
value: 31.594
- type: ndcg_at_5
value: 35.543
- type: precision_at_1
value: 21.361
- type: precision_at_10
value: 6.3740000000000006
- type: precision_at_100
value: 0.931
- type: precision_at_1000
value: 0.104
- type: precision_at_3
value: 13.514999999999999
- type: precision_at_5
value: 10.100000000000001
- type: recall_at_1
value: 20.724999999999998
- type: recall_at_10
value: 61.034
- type: recall_at_100
value: 88.062
- type: recall_at_1000
value: 97.86399999999999
- type: recall_at_3
value: 39.072
- type: recall_at_5
value: 48.53
- task:
type: Classification
dataset:
type: mteb/mtop_domain
name: MTEB MTOPDomainClassification (en)
config: en
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 93.8919288645691
- type: f1
value: 93.57059586398059
- task:
type: Classification
dataset:
type: mteb/mtop_intent
name: MTEB MTOPIntentClassification (en)
config: en
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 67.97993616051072
- type: f1
value: 48.244319183606535
- task:
type: Classification
dataset:
type: mteb/amazon_massive_intent
name: MTEB MassiveIntentClassification (en)
config: en
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 68.90047074646941
- type: f1
value: 66.48999056063725
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification (en)
config: en
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 73.34566240753195
- type: f1
value: 73.54164154290658
- task:
type: Clustering
dataset:
type: mteb/medrxiv-clustering-p2p
name: MTEB MedrxivClusteringP2P
config: default
split: test
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
metrics:
- type: v_measure
value: 34.21866934757011
- task:
type: Clustering
dataset:
type: mteb/medrxiv-clustering-s2s
name: MTEB MedrxivClusteringS2S
config: default
split: test
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
metrics:
- type: v_measure
value: 32.000936217235534
- task:
type: Reranking
dataset:
type: mteb/mind_small
name: MTEB MindSmallReranking
config: default
split: test
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
metrics:
- type: map
value: 31.68189362520352
- type: mrr
value: 32.69603637784303
- task:
type: Retrieval
dataset:
type: nfcorpus
name: MTEB NFCorpus
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 6.078
- type: map_at_10
value: 12.671
- type: map_at_100
value: 16.291
- type: map_at_1000
value: 17.855999999999998
- type: map_at_3
value: 9.610000000000001
- type: map_at_5
value: 11.152
- type: mrr_at_1
value: 43.963
- type: mrr_at_10
value: 53.173
- type: mrr_at_100
value: 53.718999999999994
- type: mrr_at_1000
value: 53.756
- type: mrr_at_3
value: 50.980000000000004
- type: mrr_at_5
value: 52.42
- type: ndcg_at_1
value: 42.415000000000006
- type: ndcg_at_10
value: 34.086
- type: ndcg_at_100
value: 32.545
- type: ndcg_at_1000
value: 41.144999999999996
- type: ndcg_at_3
value: 39.434999999999995
- type: ndcg_at_5
value: 37.888
- type: precision_at_1
value: 43.653
- type: precision_at_10
value: 25.014999999999997
- type: precision_at_100
value: 8.594
- type: precision_at_1000
value: 2.169
- type: precision_at_3
value: 37.049
- type: precision_at_5
value: 33.065
- type: recall_at_1
value: 6.078
- type: recall_at_10
value: 16.17
- type: recall_at_100
value: 34.512
- type: recall_at_1000
value: 65.447
- type: recall_at_3
value: 10.706
- type: recall_at_5
value: 13.158
- task:
type: Retrieval
dataset:
type: nq
name: MTEB NQ
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 27.378000000000004
- type: map_at_10
value: 42.178
- type: map_at_100
value: 43.32
- type: map_at_1000
value: 43.358000000000004
- type: map_at_3
value: 37.474000000000004
- type: map_at_5
value: 40.333000000000006
- type: mrr_at_1
value: 30.823
- type: mrr_at_10
value: 44.626
- type: mrr_at_100
value: 45.494
- type: mrr_at_1000
value: 45.519
- type: mrr_at_3
value: 40.585
- type: mrr_at_5
value: 43.146
- type: ndcg_at_1
value: 30.794
- type: ndcg_at_10
value: 50.099000000000004
- type: ndcg_at_100
value: 54.900999999999996
- type: ndcg_at_1000
value: 55.69499999999999
- type: ndcg_at_3
value: 41.238
- type: ndcg_at_5
value: 46.081
- type: precision_at_1
value: 30.794
- type: precision_at_10
value: 8.549
- type: precision_at_100
value: 1.124
- type: precision_at_1000
value: 0.12
- type: precision_at_3
value: 18.926000000000002
- type: precision_at_5
value: 14.16
- type: recall_at_1
value: 27.378000000000004
- type: recall_at_10
value: 71.842
- type: recall_at_100
value: 92.565
- type: recall_at_1000
value: 98.402
- type: recall_at_3
value: 49.053999999999995
- type: recall_at_5
value: 60.207
- task:
type: Retrieval
dataset:
type: quora
name: MTEB QuoraRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 70.557
- type: map_at_10
value: 84.729
- type: map_at_100
value: 85.369
- type: map_at_1000
value: 85.382
- type: map_at_3
value: 81.72
- type: map_at_5
value: 83.613
- type: mrr_at_1
value: 81.3
- type: mrr_at_10
value: 87.488
- type: mrr_at_100
value: 87.588
- type: mrr_at_1000
value: 87.589
- type: mrr_at_3
value: 86.53
- type: mrr_at_5
value: 87.18599999999999
- type: ndcg_at_1
value: 81.28999999999999
- type: ndcg_at_10
value: 88.442
- type: ndcg_at_100
value: 89.637
- type: ndcg_at_1000
value: 89.70700000000001
- type: ndcg_at_3
value: 85.55199999999999
- type: ndcg_at_5
value: 87.154
- type: precision_at_1
value: 81.28999999999999
- type: precision_at_10
value: 13.489999999999998
- type: precision_at_100
value: 1.54
- type: precision_at_1000
value: 0.157
- type: precision_at_3
value: 37.553
- type: precision_at_5
value: 24.708
- type: recall_at_1
value: 70.557
- type: recall_at_10
value: 95.645
- type: recall_at_100
value: 99.693
- type: recall_at_1000
value: 99.995
- type: recall_at_3
value: 87.359
- type: recall_at_5
value: 91.89699999999999
- task:
type: Clustering
dataset:
type: mteb/reddit-clustering
name: MTEB RedditClustering
config: default
split: test
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
metrics:
- type: v_measure
value: 63.65060114776209
- task:
type: Clustering
dataset:
type: mteb/reddit-clustering-p2p
name: MTEB RedditClusteringP2P
config: default
split: test
revision: 282350215ef01743dc01b456c7f5241fa8937f16
metrics:
- type: v_measure
value: 64.63271250680617
- task:
type: Retrieval
dataset:
type: scidocs
name: MTEB SCIDOCS
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 4.263
- type: map_at_10
value: 10.801
- type: map_at_100
value: 12.888
- type: map_at_1000
value: 13.224
- type: map_at_3
value: 7.362
- type: map_at_5
value: 9.149000000000001
- type: mrr_at_1
value: 21
- type: mrr_at_10
value: 31.416
- type: mrr_at_100
value: 32.513
- type: mrr_at_1000
value: 32.58
- type: mrr_at_3
value: 28.116999999999997
- type: mrr_at_5
value: 29.976999999999997
- type: ndcg_at_1
value: 21
- type: ndcg_at_10
value: 18.551000000000002
- type: ndcg_at_100
value: 26.657999999999998
- type: ndcg_at_1000
value: 32.485
- type: ndcg_at_3
value: 16.834
- type: ndcg_at_5
value: 15.204999999999998
- type: precision_at_1
value: 21
- type: precision_at_10
value: 9.84
- type: precision_at_100
value: 2.16
- type: precision_at_1000
value: 0.35500000000000004
- type: precision_at_3
value: 15.667
- type: precision_at_5
value: 13.62
- type: recall_at_1
value: 4.263
- type: recall_at_10
value: 19.922
- type: recall_at_100
value: 43.808
- type: recall_at_1000
value: 72.14500000000001
- type: recall_at_3
value: 9.493
- type: recall_at_5
value: 13.767999999999999
- task:
type: STS
dataset:
type: mteb/sickr-sts
name: MTEB SICK-R
config: default
split: test
revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
metrics:
- type: cos_sim_spearman
value: 81.27446313317233
- task:
type: STS
dataset:
type: mteb/sts12-sts
name: MTEB STS12
config: default
split: test
revision: a0d554a64d88156834ff5ae9920b964011b16384
metrics:
- type: cos_sim_spearman
value: 76.27963301217527
- task:
type: STS
dataset:
type: mteb/sts13-sts
name: MTEB STS13
config: default
split: test
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
metrics:
- type: cos_sim_spearman
value: 88.18495048450949
- task:
type: STS
dataset:
type: mteb/sts14-sts
name: MTEB STS14
config: default
split: test
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
metrics:
- type: cos_sim_spearman
value: 81.91982338692046
- task:
type: STS
dataset:
type: mteb/sts15-sts
name: MTEB STS15
config: default
split: test
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
metrics:
- type: cos_sim_spearman
value: 89.00896818385291
- task:
type: STS
dataset:
type: mteb/sts16-sts
name: MTEB STS16
config: default
split: test
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
metrics:
- type: cos_sim_spearman
value: 85.48814644586132
- task:
type: STS
dataset:
type: mteb/sts17-crosslingual-sts
name: MTEB STS17 (en-en)
config: en-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_spearman
value: 90.30116926966582
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (en)
config: en
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_spearman
value: 67.74132963032342
- task:
type: STS
dataset:
type: mteb/stsbenchmark-sts
name: MTEB STSBenchmark
config: default
split: test
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
metrics:
- type: cos_sim_spearman
value: 86.87741355780479
- task:
type: Reranking
dataset:
type: mteb/scidocs-reranking
name: MTEB SciDocsRR
config: default
split: test
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
metrics:
- type: map
value: 82.0019012295875
- type: mrr
value: 94.70267024188593
- task:
type: Retrieval
dataset:
type: scifact
name: MTEB SciFact
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 50.05
- type: map_at_10
value: 59.36
- type: map_at_100
value: 59.967999999999996
- type: map_at_1000
value: 60.023
- type: map_at_3
value: 56.515
- type: map_at_5
value: 58.272999999999996
- type: mrr_at_1
value: 53
- type: mrr_at_10
value: 61.102000000000004
- type: mrr_at_100
value: 61.476
- type: mrr_at_1000
value: 61.523
- type: mrr_at_3
value: 58.778
- type: mrr_at_5
value: 60.128
- type: ndcg_at_1
value: 53
- type: ndcg_at_10
value: 64.43100000000001
- type: ndcg_at_100
value: 66.73599999999999
- type: ndcg_at_1000
value: 68.027
- type: ndcg_at_3
value: 59.279
- type: ndcg_at_5
value: 61.888
- type: precision_at_1
value: 53
- type: precision_at_10
value: 8.767
- type: precision_at_100
value: 1.01
- type: precision_at_1000
value: 0.11100000000000002
- type: precision_at_3
value: 23.444000000000003
- type: precision_at_5
value: 15.667
- type: recall_at_1
value: 50.05
- type: recall_at_10
value: 78.511
- type: recall_at_100
value: 88.5
- type: recall_at_1000
value: 98.333
- type: recall_at_3
value: 64.117
- type: recall_at_5
value: 70.867
- task:
type: PairClassification
dataset:
type: mteb/sprintduplicatequestions-pairclassification
name: MTEB SprintDuplicateQuestions
config: default
split: test
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
metrics:
- type: cos_sim_accuracy
value: 99.72178217821782
- type: cos_sim_ap
value: 93.0728601593541
- type: cos_sim_f1
value: 85.6727976766699
- type: cos_sim_precision
value: 83.02063789868667
- type: cos_sim_recall
value: 88.5
- type: dot_accuracy
value: 99.72178217821782
- type: dot_ap
value: 93.07287396168348
- type: dot_f1
value: 85.6727976766699
- type: dot_precision
value: 83.02063789868667
- type: dot_recall
value: 88.5
- type: euclidean_accuracy
value: 99.72178217821782
- type: euclidean_ap
value: 93.07285657982895
- type: euclidean_f1
value: 85.6727976766699
- type: euclidean_precision
value: 83.02063789868667
- type: euclidean_recall
value: 88.5
- type: manhattan_accuracy
value: 99.72475247524753
- type: manhattan_ap
value: 93.02792973059809
- type: manhattan_f1
value: 85.7727737973388
- type: manhattan_precision
value: 87.84067085953879
- type: manhattan_recall
value: 83.8
- type: max_accuracy
value: 99.72475247524753
- type: max_ap
value: 93.07287396168348
- type: max_f1
value: 85.7727737973388
- task:
type: Clustering
dataset:
type: mteb/stackexchange-clustering
name: MTEB StackExchangeClustering
config: default
split: test
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
metrics:
- type: v_measure
value: 68.77583615550819
- task:
type: Clustering
dataset:
type: mteb/stackexchange-clustering-p2p
name: MTEB StackExchangeClusteringP2P
config: default
split: test
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
metrics:
- type: v_measure
value: 36.151636938606956
- task:
type: Reranking
dataset:
type: mteb/stackoverflowdupquestions-reranking
name: MTEB StackOverflowDupQuestions
config: default
split: test
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
metrics:
- type: map
value: 52.16607939471187
- type: mrr
value: 52.95172046091163
- task:
type: Summarization
dataset:
type: mteb/summeval
name: MTEB SummEval
config: default
split: test
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
metrics:
- type: cos_sim_pearson
value: 31.314646669495666
- type: cos_sim_spearman
value: 31.83562491439455
- type: dot_pearson
value: 31.314590842874157
- type: dot_spearman
value: 31.83363065810437
- task:
type: Retrieval
dataset:
type: trec-covid
name: MTEB TRECCOVID
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 0.198
- type: map_at_10
value: 1.3010000000000002
- type: map_at_100
value: 7.2139999999999995
- type: map_at_1000
value: 20.179
- type: map_at_3
value: 0.528
- type: map_at_5
value: 0.8019999999999999
- type: mrr_at_1
value: 72
- type: mrr_at_10
value: 83.39999999999999
- type: mrr_at_100
value: 83.39999999999999
- type: mrr_at_1000
value: 83.39999999999999
- type: mrr_at_3
value: 81.667
- type: mrr_at_5
value: 83.06700000000001
- type: ndcg_at_1
value: 66
- type: ndcg_at_10
value: 58.059000000000005
- type: ndcg_at_100
value: 44.316
- type: ndcg_at_1000
value: 43.147000000000006
- type: ndcg_at_3
value: 63.815999999999995
- type: ndcg_at_5
value: 63.005
- type: precision_at_1
value: 72
- type: precision_at_10
value: 61.4
- type: precision_at_100
value: 45.62
- type: precision_at_1000
value: 19.866
- type: precision_at_3
value: 70
- type: precision_at_5
value: 68.8
- type: recall_at_1
value: 0.198
- type: recall_at_10
value: 1.517
- type: recall_at_100
value: 10.587
- type: recall_at_1000
value: 41.233
- type: recall_at_3
value: 0.573
- type: recall_at_5
value: 0.907
- task:
type: Retrieval
dataset:
type: webis-touche2020
name: MTEB Touche2020
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 1.894
- type: map_at_10
value: 8.488999999999999
- type: map_at_100
value: 14.445
- type: map_at_1000
value: 16.078
- type: map_at_3
value: 4.589
- type: map_at_5
value: 6.019
- type: mrr_at_1
value: 22.448999999999998
- type: mrr_at_10
value: 39.82
- type: mrr_at_100
value: 40.752
- type: mrr_at_1000
value: 40.771
- type: mrr_at_3
value: 34.354
- type: mrr_at_5
value: 37.721
- type: ndcg_at_1
value: 19.387999999999998
- type: ndcg_at_10
value: 21.563
- type: ndcg_at_100
value: 33.857
- type: ndcg_at_1000
value: 46.199
- type: ndcg_at_3
value: 22.296
- type: ndcg_at_5
value: 21.770999999999997
- type: precision_at_1
value: 22.448999999999998
- type: precision_at_10
value: 19.796
- type: precision_at_100
value: 7.142999999999999
- type: precision_at_1000
value: 1.541
- type: precision_at_3
value: 24.490000000000002
- type: precision_at_5
value: 22.448999999999998
- type: recall_at_1
value: 1.894
- type: recall_at_10
value: 14.931
- type: recall_at_100
value: 45.524
- type: recall_at_1000
value: 83.243
- type: recall_at_3
value: 5.712
- type: recall_at_5
value: 8.386000000000001
- task:
type: Classification
dataset:
type: mteb/toxic_conversations_50k
name: MTEB ToxicConversationsClassification
config: default
split: test
revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
metrics:
- type: accuracy
value: 71.049
- type: ap
value: 13.85116971310922
- type: f1
value: 54.37504302487686
- task:
type: Classification
dataset:
type: mteb/tweet_sentiment_extraction
name: MTEB TweetSentimentExtractionClassification
config: default
split: test
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
metrics:
- type: accuracy
value: 64.1312959818902
- type: f1
value: 64.11413877009383
- task:
type: Clustering
dataset:
type: mteb/twentynewsgroups-clustering
name: MTEB TwentyNewsgroupsClustering
config: default
split: test
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
metrics:
- type: v_measure
value: 54.13103431861502
- task:
type: PairClassification
dataset:
type: mteb/twittersemeval2015-pairclassification
name: MTEB TwitterSemEval2015
config: default
split: test
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
metrics:
- type: cos_sim_accuracy
value: 87.327889372355
- type: cos_sim_ap
value: 77.42059895975699
- type: cos_sim_f1
value: 71.02706903250873
- type: cos_sim_precision
value: 69.75324344950394
- type: cos_sim_recall
value: 72.34828496042216
- type: dot_accuracy
value: 87.327889372355
- type: dot_ap
value: 77.4209479346677
- type: dot_f1
value: 71.02706903250873
- type: dot_precision
value: 69.75324344950394
- type: dot_recall
value: 72.34828496042216
- type: euclidean_accuracy
value: 87.327889372355
- type: euclidean_ap
value: 77.42096495861037
- type: euclidean_f1
value: 71.02706903250873
- type: euclidean_precision
value: 69.75324344950394
- type: euclidean_recall
value: 72.34828496042216
- type: manhattan_accuracy
value: 87.31000774870358
- type: manhattan_ap
value: 77.38930750711619
- type: manhattan_f1
value: 71.07935314027831
- type: manhattan_precision
value: 67.70957726295677
- type: manhattan_recall
value: 74.80211081794195
- type: max_accuracy
value: 87.327889372355
- type: max_ap
value: 77.42096495861037
- type: max_f1
value: 71.07935314027831
- task:
type: PairClassification
dataset:
type: mteb/twitterurlcorpus-pairclassification
name: MTEB TwitterURLCorpus
config: default
split: test
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
metrics:
- type: cos_sim_accuracy
value: 89.58939729110878
- type: cos_sim_ap
value: 87.17594155025475
- type: cos_sim_f1
value: 79.21146953405018
- type: cos_sim_precision
value: 76.8918527109307
- type: cos_sim_recall
value: 81.67539267015707
- type: dot_accuracy
value: 89.58939729110878
- type: dot_ap
value: 87.17593963273593
- type: dot_f1
value: 79.21146953405018
- type: dot_precision
value: 76.8918527109307
- type: dot_recall
value: 81.67539267015707
- type: euclidean_accuracy
value: 89.58939729110878
- type: euclidean_ap
value: 87.17592466925834
- type: euclidean_f1
value: 79.21146953405018
- type: euclidean_precision
value: 76.8918527109307
- type: euclidean_recall
value: 81.67539267015707
- type: manhattan_accuracy
value: 89.62626615438352
- type: manhattan_ap
value: 87.16589873161546
- type: manhattan_f1
value: 79.25143598295348
- type: manhattan_precision
value: 76.39494177323712
- type: manhattan_recall
value: 82.32984293193716
- type: max_accuracy
value: 89.62626615438352
- type: max_ap
value: 87.17594155025475
- type: max_f1
value: 79.25143598295348
duplicated_from: hkunlp/instructor-large
---
# hkunlp/instructor-large
We introduce **Instructor**👨🏫, an instruction-finetuned text embedding model that can generate text embeddings tailored to any task (e.g., classification, retrieval, clustering, text evaluation, etc.) and domains (e.g., science, finance, etc.) ***by simply providing the task instruction, without any finetuning***. Instructor👨 achieves sota on 70 diverse embedding tasks ([MTEB leaderboard](https://huggingface.co/spaces/mteb/leaderboard))!
The model is easy to use with **our customized** `sentence-transformer` library. For more details, check out [our paper](https://arxiv.org/abs/2212.09741) and [project page](https://instructor-embedding.github.io/)!
**************************** **Updates** ****************************
* 12/28: We released a new [checkpoint](https://huggingface.co/hkunlp/instructor-large) trained with hard negatives, which gives better performance.
* 12/21: We released our [paper](https://arxiv.org/abs/2212.09741), [code](https://github.com/HKUNLP/instructor-embedding), [checkpoint](https://huggingface.co/hkunlp/instructor-large) and [project page](https://instructor-embedding.github.io/)! Check them out!
## Quick start
<hr />
## Installation
```bash
pip install InstructorEmbedding
```
## Compute your customized embeddings
Then you can use the model like this to calculate domain-specific and task-aware embeddings:
```python
from InstructorEmbedding import INSTRUCTOR
model = INSTRUCTOR('hkunlp/instructor-large')
sentence = "3D ActionSLAM: wearable person tracking in multi-floor environments"
instruction = "Represent the Science title:"
embeddings = model.encode([[instruction,sentence]])
print(embeddings)
```
## Use cases
<hr />
## Calculate embeddings for your customized texts
If you want to calculate customized embeddings for specific sentences, you may follow the unified template to write instructions:
Represent the `domain` `text_type` for `task_objective`:
* `domain` is optional, and it specifies the domain of the text, e.g., science, finance, medicine, etc.
* `text_type` is required, and it specifies the encoding unit, e.g., sentence, document, paragraph, etc.
* `task_objective` is optional, and it specifies the objective of embedding, e.g., retrieve a document, classify the sentence, etc.
## Calculate Sentence similarities
You can further use the model to compute similarities between two groups of sentences, with **customized embeddings**.
```python
from sklearn.metrics.pairwise import cosine_similarity
sentences_a = [['Represent the Science sentence: ','Parton energy loss in QCD matter'],
['Represent the Financial statement: ','The Federal Reserve on Wednesday raised its benchmark interest rate.']]
sentences_b = [['Represent the Science sentence: ','The Chiral Phase Transition in Dissipative Dynamics'],
['Represent the Financial statement: ','The funds rose less than 0.5 per cent on Friday']]
embeddings_a = model.encode(sentences_a)
embeddings_b = model.encode(sentences_b)
similarities = cosine_similarity(embeddings_a,embeddings_b)
print(similarities)
```
## Information Retrieval
You can also use **customized embeddings** for information retrieval.
```python
import numpy as np
from sklearn.metrics.pairwise import cosine_similarity
query = [['Represent the Wikipedia question for retrieving supporting documents: ','where is the food stored in a yam plant']]
corpus = [['Represent the Wikipedia document for retrieval: ','Capitalism has been dominant in the Western world since the end of feudalism, but most feel[who?] that the term "mixed economies" more precisely describes most contemporary economies, due to their containing both private-owned and state-owned enterprises. In capitalism, prices determine the demand-supply scale. For example, higher demand for certain goods and services lead to higher prices and lower demand for certain goods lead to lower prices.'],
['Represent the Wikipedia document for retrieval: ',"The disparate impact theory is especially controversial under the Fair Housing Act because the Act regulates many activities relating to housing, insurance, and mortgage loans—and some scholars have argued that the theory's use under the Fair Housing Act, combined with extensions of the Community Reinvestment Act, contributed to rise of sub-prime lending and the crash of the U.S. housing market and ensuing global economic recession"],
['Represent the Wikipedia document for retrieval: ','Disparate impact in United States labor law refers to practices in employment, housing, and other areas that adversely affect one group of people of a protected characteristic more than another, even though rules applied by employers or landlords are formally neutral. Although the protected classes vary by statute, most federal civil rights laws protect based on race, color, religion, national origin, and sex as protected traits, and some laws include disability status and other traits as well.']]
query_embeddings = model.encode(query)
corpus_embeddings = model.encode(corpus)
similarities = cosine_similarity(query_embeddings,corpus_embeddings)
retrieved_doc_id = np.argmax(similarities)
print(retrieved_doc_id)
```
## Clustering
Use **customized embeddings** for clustering texts in groups.
```python
import sklearn.cluster
sentences = [['Represent the Medicine sentence for clustering: ','Dynamical Scalar Degree of Freedom in Horava-Lifshitz Gravity'],
['Represent the Medicine sentence for clustering: ','Comparison of Atmospheric Neutrino Flux Calculations at Low Energies'],
['Represent the Medicine sentence for clustering: ','Fermion Bags in the Massive Gross-Neveu Model'],
['Represent the Medicine sentence for clustering: ',"QCD corrections to Associated t-tbar-H production at the Tevatron"],
['Represent the Medicine sentence for clustering: ','A New Analysis of the R Measurements: Resonance Parameters of the Higher, Vector States of Charmonium']]
embeddings = model.encode(sentences)
clustering_model = sklearn.cluster.MiniBatchKMeans(n_clusters=2)
clustering_model.fit(embeddings)
cluster_assignment = clustering_model.labels_
print(cluster_assignment)
``` |
chh6/v0TaxiAttempt | chh6 | 2023-07-12T19:47:12Z | 0 | 0 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | reinforcement-learning | 2023-07-12T19:47:10Z | ---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: v0TaxiAttempt
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="chh6/v0TaxiAttempt", 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"])
```
|
veluchs/dqn-SpaceInvadersNoFrameskip-v4-4 | veluchs | 2023-07-12T19:41:22Z | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | 2023-07-12T19:40:57Z | ---
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: 264.50 +/- 87.36
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 veluchs -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 veluchs -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 veluchs
```
## 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', 100000),
('n_timesteps', 10000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
# Environment Arguments
```python
{'render_mode': 'rgb_array'}
```
|
Nikhil-HugFace/bert-base-multilingual-cased-finetuned-SQUAD2 | Nikhil-HugFace | 2023-07-12T19:38:54Z | 61 | 0 | transformers | [
"transformers",
"tf",
"tensorboard",
"bert",
"question-answering",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | question-answering | 2023-07-12T17:11:27Z | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Nikhil-HugFace/bert-base-multilingual-cased-finetuned-SQUAD2
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. -->
# Nikhil-HugFace/bert-base-multilingual-cased-finetuned-SQUAD2
This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 1.6512
- Train End Logits Accuracy: 0.5819
- Train Start Logits Accuracy: 0.6096
- Validation Loss: 1.3298
- Validation End Logits Accuracy: 0.6339
- Validation Start Logits Accuracy: 0.6896
- Epoch: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 7001, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train End Logits Accuracy | Train Start Logits Accuracy | Validation Loss | Validation End Logits Accuracy | Validation Start Logits Accuracy | Epoch |
|:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:--------------------------------:|:-----:|
| 1.6512 | 0.5819 | 0.6096 | 1.3298 | 0.6339 | 0.6896 | 0 |
### Framework versions
- Transformers 4.30.2
- TensorFlow 2.12.0
- Datasets 2.13.1
- Tokenizers 0.13.3
|
vuiseng9/baseline-ft-mrpc-IRoberta-b-8bit | vuiseng9 | 2023-07-12T19:21:04Z | 6 | 0 | transformers | [
"transformers",
"pytorch",
"ibert",
"text-classification",
"generated_from_trainer",
"en",
"dataset:glue",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2023-07-12T18:39:16Z | ---
language:
- en
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
- f1
model-index:
- name: baseline-ft-mrpc-IRoberta-b-8bit
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE MRPC
type: glue
config: mrpc
split: validation
args: mrpc
metrics:
- name: Accuracy
type: accuracy
value: 0.8970588235294118
- name: F1
type: f1
value: 0.9257950530035336
---
<!-- 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. -->
# baseline-ft-mrpc-IRoberta-b-8bit
This model is a fine-tuned version of [vuiseng9/baseline-ft-mrpc-IRoberta-b-unquantized](https://huggingface.co/vuiseng9/baseline-ft-mrpc-IRoberta-b-unquantized) on the GLUE MRPC dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3871
- Accuracy: 0.8971
- F1: 0.9258
- Combined Score: 0.9114
## 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-07
- 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: 12.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Combined Score |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------------:|
| 0.0021 | 1.0 | 230 | 0.4017 | 0.8848 | 0.9147 | 0.8998 |
| 0.0026 | 2.0 | 460 | 0.4105 | 0.8873 | 0.9173 | 0.9023 |
| 0.0026 | 3.0 | 690 | 0.3707 | 0.8946 | 0.9236 | 0.9091 |
| 0.0037 | 4.0 | 920 | 0.3893 | 0.8946 | 0.9228 | 0.9087 |
| 1.324 | 5.0 | 1150 | 0.3871 | 0.8897 | 0.9204 | 0.9050 |
| 0.0227 | 6.0 | 1380 | 0.3951 | 0.8897 | 0.9201 | 0.9049 |
| 0.0081 | 7.0 | 1610 | 0.3818 | 0.8824 | 0.9155 | 0.8989 |
| 0.0054 | 8.0 | 1840 | 0.3902 | 0.8873 | 0.9181 | 0.9027 |
| 0.0383 | 9.0 | 2070 | 0.3659 | 0.8922 | 0.9225 | 0.9073 |
| 0.3861 | 10.0 | 2300 | 0.4260 | 0.8652 | 0.9030 | 0.8841 |
| 0.0028 | 11.0 | 2530 | 0.3619 | 0.8946 | 0.9234 | 0.9090 |
| 0.0957 | 12.0 | 2760 | 0.3871 | 0.8971 | 0.9258 | 0.9114 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
|
Ruborobot/distilbert-base-uncased-finetuned-TeacherMomentsConfusion | Ruborobot | 2023-07-12T19:16:03Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2023-07-11T18:44:27Z | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: distilbert-base-uncased-finetuned-TeacherMomentsConfusion
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-TeacherMomentsConfusion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6691
- Accuracy: 0.7517
- Precision: 0.1790
- Recall: 0.2359
- F1: 0.2035
- Balanced Accuracy: 0.5339
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | Balanced Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:-----------------:|
| No log | 1.0 | 295 | 0.6717 | 0.8655 | 0.0 | 0.0 | 0.0 | 0.5 |
| 0.6903 | 2.0 | 590 | 0.6691 | 0.7517 | 0.1790 | 0.2359 | 0.2035 | 0.5339 |
| 0.6903 | 3.0 | 885 | 0.7994 | 0.7076 | 0.1602 | 0.2769 | 0.2030 | 0.5257 |
| 0.5787 | 4.0 | 1180 | 1.0224 | 0.6317 | 0.1576 | 0.4 | 0.2261 | 0.5339 |
| 0.5787 | 5.0 | 1475 | 1.5546 | 0.7621 | 0.1528 | 0.1692 | 0.1606 | 0.5117 |
| 0.3142 | 6.0 | 1770 | 2.0188 | 0.7724 | 0.1271 | 0.1179 | 0.1223 | 0.4960 |
| 0.1212 | 7.0 | 2065 | 2.4508 | 0.8014 | 0.1157 | 0.0718 | 0.0886 | 0.4933 |
| 0.1212 | 8.0 | 2360 | 2.7545 | 0.8138 | 0.1287 | 0.0667 | 0.0878 | 0.4983 |
| 0.0543 | 9.0 | 2655 | 2.8085 | 0.7876 | 0.1258 | 0.0974 | 0.1098 | 0.4961 |
| 0.0543 | 10.0 | 2950 | 2.8602 | 0.7903 | 0.1342 | 0.1026 | 0.1163 | 0.4999 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
malik463/arain | malik463 | 2023-07-12T19:05:44Z | 0 | 0 | null | [
"arxiv:1910.09700",
"license:openrail",
"region:us"
] | null | 2023-07-12T19:04:10Z | ---
license: openrail
---
# 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]
|
gmurillo/set-fit-goup-5-f | gmurillo | 2023-07-12T18:58:48Z | 3 | 0 | sentence-transformers | [
"sentence-transformers",
"pytorch",
"bart",
"setfit",
"text-classification",
"arxiv:2209.11055",
"license:apache-2.0",
"region:us"
] | text-classification | 2023-07-12T18:57:36Z | ---
license: apache-2.0
tags:
- setfit
- sentence-transformers
- text-classification
pipeline_tag: text-classification
---
# gmurillo/set-fit-goup-5-f
This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves:
1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.
## Usage
To use this model for inference, first install the SetFit library:
```bash
python -m pip install setfit
```
You can then run inference as follows:
```python
from setfit import SetFitModel
# Download from Hub and run inference
model = SetFitModel.from_pretrained("gmurillo/set-fit-goup-5-f")
# Run inference
preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"])
```
## BibTeX entry and citation info
```bibtex
@article{https://doi.org/10.48550/arxiv.2209.11055,
doi = {10.48550/ARXIV.2209.11055},
url = {https://arxiv.org/abs/2209.11055},
author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Efficient Few-Shot Learning Without Prompts},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
```
|
gokuls/sa_bert_12_layer_modified_complete_training_48_v2 | gokuls | 2023-07-12T18:58:21Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"hybridbert",
"fill-mask",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | fill-mask | 2023-07-10T18:19:08Z | ---
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: sa_bert_12_layer_modified_complete_training_48_v2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# sa_bert_12_layer_modified_complete_training_48_v2
This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.9821
- Accuracy: 0.3685
## 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: 32
- eval_batch_size: 32
- seed: 10
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 10000
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:------:|:---------------:|:--------:|
| 6.5933 | 0.05 | 10000 | 6.5711 | 0.1226 |
| 6.1523 | 0.11 | 20000 | 6.3425 | 0.1396 |
| 6.1308 | 0.16 | 30000 | 6.2468 | 0.1444 |
| 6.2297 | 0.22 | 40000 | 6.1895 | 0.1468 |
| 6.1484 | 0.27 | 50000 | 6.1483 | 0.1487 |
| 6.0591 | 0.33 | 60000 | 6.1205 | 0.1492 |
| 6.0199 | 0.38 | 70000 | 6.0862 | 0.1501 |
| 5.8666 | 0.44 | 80000 | 5.8875 | 0.1600 |
| 5.9153 | 0.49 | 90000 | 5.7648 | 0.1722 |
| 5.5197 | 0.55 | 100000 | 5.6349 | 0.1891 |
| 5.4384 | 0.6 | 110000 | 5.5023 | 0.2051 |
| 5.3973 | 0.66 | 120000 | 5.3651 | 0.2209 |
| 5.2627 | 0.71 | 130000 | 5.2054 | 0.2395 |
| 5.3179 | 0.76 | 140000 | 5.0131 | 0.2621 |
| 4.8813 | 0.82 | 150000 | 4.7153 | 0.2949 |
| 4.6653 | 0.87 | 160000 | 4.4651 | 0.3209 |
| 4.7227 | 0.93 | 170000 | 4.1752 | 0.3502 |
| 4.2892 | 0.98 | 180000 | 3.9821 | 0.3685 |
### Framework versions
- Transformers 4.30.2
- Pytorch 1.14.0a0+410ce96
- Datasets 2.13.1
- Tokenizers 0.13.3
|
komo-dono/dashiegames | komo-dono | 2023-07-12T18:44:13Z | 0 | 0 | null | [
"region:us"
] | null | 2023-07-12T18:42:38Z | ---
license: openrail
language:
- en
tags:
- music
dashiegames 500 epoch |
GodRain/WizardCoder-15B-V1.1-4bit | GodRain | 2023-07-12T18:40:18Z | 5 | 2 | transformers | [
"transformers",
"llama",
"text-generation",
"en",
"dataset:WizardLM/WizardLM_evol_instruct_70k",
"arxiv:2304.12244",
"license:bigcode-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2023-07-12T18:00:51Z | ---
license: bigcode-openrail-m
datasets:
- WizardLM/WizardLM_evol_instruct_70k
language:
- en
---
<font size=5>Here is an example to show how to use model quantized by auto_gptq</font>
```
_4BITS_MODEL_PATH_V1_ = 'GodRain/WizardCoder-15B-V1.1-4bit'
# pip install auto_gptq
from auto_gptq import AutoGPTQForCausalLM
from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained(_4BITS_MODEL_PATH_V1_)
model = AutoGPTQForCausalLM.from_quantized(_4BITS_MODEL_PATH_V1_)
out = evaluate("Hello, tell me a story about sun", model=model, tokenizer=tokenizer)
print(out[0].strip())
```
```
def evaluate(
batch_data,
tokenizer,
model,
temperature=1,
top_p=0.9,
top_k=40,
num_beams=1,
max_new_tokens=2048,
**kwargs,
):
prompts = generate_prompt(batch_data)
inputs = tokenizer(prompts, return_tensors="pt", max_length=256, truncation=True)
input_ids = inputs["input_ids"].to(device)
generation_config = GenerationConfig(
temperature=temperature,
top_p=top_p,
top_k=top_k,
num_beams=num_beams,
eos_token_id=tokenizer.eos_token_id,
pad_token_id=tokenizer.pad_token_id,
**kwargs,
)
with torch.no_grad():
generation_output = model.generate(
input_ids=input_ids,
generation_config=generation_config,
return_dict_in_generate=True,
output_scores=True,
max_new_tokens=max_new_tokens,
)
s = generation_output.sequences
output = tokenizer.batch_decode(s, skip_special_tokens=True)
return output
```
Citiation:
```
@misc{xu2023wizardlm,
title={WizardLM: Empowering Large Language Models to Follow Complex Instructions},
author={Can Xu and Qingfeng Sun and Kai Zheng and Xiubo Geng and Pu Zhao and Jiazhan Feng and Chongyang Tao and Daxin Jiang},
year={2023},
eprint={2304.12244},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
``` |
GodRain/WizardCoder-15B-V1.1-3bit | GodRain | 2023-07-12T18:39:59Z | 3 | 0 | transformers | [
"transformers",
"llama",
"text-generation",
"dataset:WizardLM/WizardLM_evol_instruct_70k",
"arxiv:2304.12244",
"license:bigcode-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2023-07-12T17:45:29Z | ---
license: bigcode-openrail-m
datasets:
- WizardLM/WizardLM_evol_instruct_70k
---
<font size=5>Here is an example to show how to use model quantized by auto_gptq</font>
```
_3BITS_MODEL_PATH_V1_ = 'GodRain/WizardCoder-15B-V1.1-3bit'
# pip install auto_gptq
from auto_gptq import AutoGPTQForCausalLM
from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained(_3BITS_MODEL_PATH_V1_)
model = AutoGPTQForCausalLM.from_quantized(_3BITS_MODEL_PATH_V1_)
out = evaluate("Hello, tell me a story about sun", model=model, tokenizer=tokenizer)
print(out[0].strip())
```
```
def evaluate(
batch_data,
tokenizer,
model,
temperature=1,
top_p=0.9,
top_k=40,
num_beams=1,
max_new_tokens=2048,
**kwargs,
):
prompts = generate_prompt(batch_data)
inputs = tokenizer(prompts, return_tensors="pt", max_length=256, truncation=True)
input_ids = inputs["input_ids"].to(device)
generation_config = GenerationConfig(
temperature=temperature,
top_p=top_p,
top_k=top_k,
num_beams=num_beams,
eos_token_id=tokenizer.eos_token_id,
pad_token_id=tokenizer.pad_token_id,
**kwargs,
)
with torch.no_grad():
generation_output = model.generate(
input_ids=input_ids,
generation_config=generation_config,
return_dict_in_generate=True,
output_scores=True,
max_new_tokens=max_new_tokens,
)
s = generation_output.sequences
output = tokenizer.batch_decode(s, skip_special_tokens=True)
return output
```
Citiation:
```
@misc{xu2023wizardlm,
title={WizardLM: Empowering Large Language Models to Follow Complex Instructions},
author={Can Xu and Qingfeng Sun and Kai Zheng and Xiubo Geng and Pu Zhao and Jiazhan Feng and Chongyang Tao and Daxin Jiang},
year={2023},
eprint={2304.12244},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
``` |
tyavika/LR1E4-BS16-Distilbert-QA-Pytorch-FULL | tyavika | 2023-07-12T18:39:38Z | 105 | 0 | transformers | [
"transformers",
"pytorch",
"distilbert",
"question-answering",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | question-answering | 2023-07-07T04:59:00Z | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: LR1E4-BS16-Distilbert-QA-Pytorch-FULL
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. -->
# LR1E4-BS16-Distilbert-QA-Pytorch-FULL
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3888
## 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.4071 | 1.0 | 3290 | 1.2792 |
| 1.0123 | 2.0 | 6580 | 1.2843 |
| 0.6916 | 3.0 | 9870 | 1.3888 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
vuiseng9/baseline-ft-mrpc-IRoberta-b-unquantized | vuiseng9 | 2023-07-12T18:33:30Z | 107 | 0 | transformers | [
"transformers",
"pytorch",
"ibert",
"text-classification",
"generated_from_trainer",
"en",
"dataset:glue",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2023-07-12T18:24:52Z | ---
language:
- en
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
- f1
model-index:
- name: baseline-ft-mrpc-IRoberta-b-unquantized
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE MRPC
type: glue
config: mrpc
split: validation
args: mrpc
metrics:
- name: Accuracy
type: accuracy
value: 0.8995098039215687
- name: F1
type: f1
value: 0.9266547406082289
---
<!-- 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. -->
# baseline-ft-mrpc-IRoberta-b-unquantized
This model is a fine-tuned version of [kssteven/ibert-roberta-base](https://huggingface.co/kssteven/ibert-roberta-base) on the GLUE MRPC dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5354
- Accuracy: 0.8995
- F1: 0.9267
- Combined Score: 0.9131
## 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: 5.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Combined Score |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------------:|
| 0.1212 | 1.0 | 230 | 0.3401 | 0.8799 | 0.9136 | 0.8967 |
| 0.0347 | 2.0 | 460 | 0.3085 | 0.8676 | 0.9059 | 0.8868 |
| 0.0495 | 3.0 | 690 | 0.3552 | 0.8848 | 0.9174 | 0.9011 |
| 0.0024 | 4.0 | 920 | 0.4960 | 0.8824 | 0.9158 | 0.8991 |
| 0.0046 | 5.0 | 1150 | 0.5354 | 0.8995 | 0.9267 | 0.9131 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
|
hsultanbey/autocomplete_trainer | hsultanbey | 2023-07-12T18:23:42Z | 143 | 0 | transformers | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2023-07-12T18:22:39Z | ---
license: mit
tags:
- generated_from_trainer
model-index:
- name: autocomplete_trainer
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. -->
# autocomplete_trainer
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 1000
- num_epochs: 2
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.13.3
|
t23e2/poca-SoccerTwos | t23e2 | 2023-07-12T18:20:17Z | 1 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"SoccerTwos",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SoccerTwos",
"region:us"
] | reinforcement-learning | 2023-07-12T18:20:11Z | ---
library_name: ml-agents
tags:
- SoccerTwos
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SoccerTwos
---
# **poca** Agent playing **SoccerTwos**
This is a trained model of a **poca** agent playing **SoccerTwos**
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: t23e2/poca-SoccerTwos
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
asrimanth/person-thumbs-up-lora | asrimanth | 2023-07-12T18:19:11Z | 2 | 3 | diffusers | [
"diffusers",
"tensorboard",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"lora",
"base_model:runwayml/stable-diffusion-v1-5",
"base_model:adapter:runwayml/stable-diffusion-v1-5",
"license:creativeml-openrail-m",
"region:us"
] | text-to-image | 2023-07-12T18:18:41Z |
---
license: creativeml-openrail-m
base_model: runwayml/stable-diffusion-v1-5
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- lora
inference: true
---
# LoRA text2image fine-tuning - asrimanth/person-thumbs-up-lora
These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were fine-tuned on the Custom dataset dataset. You can find some example images in the following.
|
Danish-summarisation/DanSumT5-pilot | Danish-summarisation | 2023-07-12T18:12:28Z | 122 | 2 | transformers | [
"transformers",
"pytorch",
"safetensors",
"mt5",
"text2text-generation",
"summarization",
"da",
"arxiv:1804.11283",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | summarization | 2022-07-05T10:06:53Z | ---
language:
- da
tags:
- summarization
widget:
- text: "De strejkende SAS-piloter melder sig nu klar til gøre en undtagelse fra strejken for at hente strandede chartergæster hjem fra flere ferieområder.
Undtagelsen skal gælde nogle uger frem, men piloterne vil under ingen omstændigheder have nye gæster med sig ned til de samme destinationer.
Det skriver SAS Pilot Group i en pressemeddelelse.
- Vi forstår, at det er uundgåeligt, at vores passagerer bliver ramt af strejken. Men vi piloter er altid fokuseret på at opføre os ansvarligt med passagersikkerheden som højeste prioritet, siger Martin Lindgren, der er formand for SAS Pilot Group i Norden.
Men for at hjælpe strandede gæster kræver de strejkende piloter samtidig, at SAS' trækker sin lockout af piloterne tilbage.
Samtidig ser SAS Pilot Group det som en forudsætning, at SAS ikke får hjælp fra andre flyselskaber til at flyve nye passagerer til de samme destinationer, som piloterne tilbyder at flyve gæster hjem fra, skriver fagforeningen."
example_title: "Example 1"
- text: "Mere end 21.000 krigsforbrydelser. Så mange efterforsker de ukrainske myndigheder lige nu ifølge den ukrainske rigsadvokat, Iryna Venediktova.
Hun oplyser til britiske BBC, at der bliver anmeldt mellem 200 og 300 nye sager om dagen.
Forbrydelserne er ifølge Venediktova svære at efterforske, fordi det kan være vanskeligt at komme frem til de relevante områder og mennesker.
Men hun understreger overfor BBC, at russiske soldater, der har dræbt, tortureret eller voldtaget civile, bør forstå, at det kun er et spørgsmål om tid, før de alle vil komme for retten.
Rusland er blevet anklaget for en lang række krigsforbrydelser, siden landet invaderede Ukraine den 24. februar, men afviser alle anklager."
example_title: "Example 2"
- text: "Det nye studie Cognitive Science på Aarhus Universitet, som i år havde Østjyllands højeste adgangskrav på 11,7 i karaktergennemsnit, udklækker det første hold bachelorer til sommer.
Men når de skal læse videre på kandidaten må de til udlandet, hvis ikke de vil skifte til et andet fag. Aarhus Universitet kan nemlig ikke nå at oprette en kandidat i Cognitive Science til næste sommer, hvor det første hold bachelorer er færdige.
Det rammer blandt andre Julie Sohn, der startede på uddannelsen i sommeren 2015, og derfor kun mangler et år, før hun er bachelor.
- Jeg synes, at det er ærgerligt, at vi som nye studerende på et populært studie ikke kan tage en kandidat i Danmark, siger hun.
Bacheloruddannelsen i Cognitive Science blev oprettet af Aarhus Universitet i 2015, og uddannelsen kombinerer viden om menneskelig adfærd med avanceret statistik. Da der endnu ikke er oprettet en kandidatuddannelse indenfor dette område, har Julie Sohn i stedet mulighed for at læse en kandidatgrad i for eksempel informationsvidenskab.
Hun vil dog hellere fortsætte på Cognitive Science, og derfor overvejer hun nu at læse videre i udlandet.
- Det ser ud til, at det er den eneste mulighed, hvis man gerne vil læse videre på noget, der faktisk passer ind til vores studie, siger hun.
Nye regler giver forsinkelse
På Aarhus Universitet havde man håbet på at have kandidatuddannelsen klar, når det første hold bachelorer bliver færdige til sommer. Arbejdet er dog blevet forsinket, fordi der er kommet nye regler for, hvornår man må oprette en uddannelse, fortæller Niels Lehmann, prodekan på fakultetet Arts, som Cognitive Science hører under.
Det er nogle meget dygtige studerende, der kommer ind på uddannelsen, og det er klart, at de i et vist omfang vil orientere sig mod udlandet, hvor man så kan forestille sig, at de bider sig fast.
NIELS LEHMANN, PRODEKAN, AARHUS UNIVERSITET
Tidligere skulle Danmarks Akkrediteringsinstitution se alle nye uddannelser efter i sømmene for at sikre, at kvaliteten var i orden. Nu skal uddannelsesinstitutionerne selv stå for det kvalitetstjek.
Men det tjek har Aarhus Universitet endnu ikke fået grønt lys til selv at udføre, fortæller prodekanen.
- Vi ville meget gerne have kunnet nå at få et udbud på kandidaten i gang i 2018, men så længe man er under institutionsakkreditering, så kan man ikke ansøge om nye uddannelser, siger han.
Det er endnu usikkert, hvornår Aarhus Universitet kan oprette kandidaten i Cognitive Science. Hvis de får alle de nødvendige godkendelser, kan den tidligst være klar i 2019.
Prodekan Niels Lehmann frygter, at Danmark kommer til at miste nogle af landets skarpeste studerende, hvis de er nødt til at rejse til udlandet for at gøre deres uddannelse færdig.
- Det er nogle meget, meget dygtige studerende, der kommer ind på denne uddannelse, og det er klart, at de i et vist omfang vil orientere sig mod udlandet, hvor man så kan forestille sig, at de bider sig fast, siger han.
Hos Danmarks Akkrediteringsinstitution forstår man godt, at universitets ansatte og studenrede ærgrer sig.
- Jeg kan godt forstå, at Aarhus Universitet ærgrer sig over, at det trækker ud, og at der går noget tid, før man får mulighed for at oprette nye uddannelser, og at man ikke har fået den genvej til at oprette nye uddannelser, som ville være fuldt med, hvis man havde opnået en positiv institutionsakkreditering, siger kommunikationsansvarlig Daniel Sebastian Larsen.
I år var Cognitive Science i Aarhus den uddannelse i Danmark, der havde det fjerde højeste karakterkrav - det højeste var 'AP Graduate in Marketing Management' på Erhvervsakademi Sjælland med et krav på 12,3."
example_title: "Example 3"
---
# mT5-base fine-tuned for News article Summarisation ✏️🧾
[Google's mT5](https://aclanthology.org/2021.naacl-main.41/) for **summarisation** downstream task.
# Model summary
This repository contains a model for Danish abstractive summarisation of news articles. The summariser is based on a language-specific mT5-base, where the vocabulary is condensed to include tokens used in Danish and English. The model is fine-tuned using an abstractive subset of the DaNewsroom dataset (Varab & Schluter, 2020), according to the binned density categories employed in Newsroom (Grusky et al., 2019).
# References
Grusky, M., Naaman, M., & Artzi, Y. (2018). Newsroom: A Dataset of 1.3 Million Summaries with Diverse Extractive Strategies. ArXiv:1804.11283 [Cs]. http://arxiv.org/abs/1804.11283
Varab, D., & Schluter, N. (2020). DaNewsroom: A Large-scale Danish Summarisation Dataset. Proceedings of the 12th Language Resources and Evaluation Conference, 6731–6739. https://aclanthology.org/2020.lrec-1.831
|
arstep/q-FrozenLake-v1-4x4-noSlippery | arstep | 2023-07-12T18:12:13Z | 0 | 0 | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | reinforcement-learning | 2023-07-12T18:12:10Z | ---
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="arstep/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"])
```
|
jordyvl/vit-tiny_rvl_cdip_100_examples_per_class_kd_CEKD_t5.0_a0.9 | jordyvl | 2023-07-12T18:09:45Z | 163 | 0 | transformers | [
"transformers",
"pytorch",
"vit",
"image-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-classification | 2023-07-12T17:31:18Z | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: vit-tiny_rvl_cdip_100_examples_per_class_kd_CEKD_t5.0_a0.9
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-tiny_rvl_cdip_100_examples_per_class_kd_CEKD_t5.0_a0.9
This model is a fine-tuned version of [WinKawaks/vit-tiny-patch16-224](https://huggingface.co/WinKawaks/vit-tiny-patch16-224) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5750
- Accuracy: 0.5325
- Brier Loss: 0.5990
- Nll: 2.5263
- F1 Micro: 0.5325
- F1 Macro: 0.5240
- Ece: 0.1659
- Aurc: 0.2152
## 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: 128
- eval_batch_size: 128
- 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: 100
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Brier Loss | Nll | F1 Micro | F1 Macro | Ece | Aurc |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:----------:|:------:|:--------:|:--------:|:------:|:------:|
| No log | 1.0 | 7 | 3.9285 | 0.04 | 1.0722 | 7.3572 | 0.04 | 0.0319 | 0.2792 | 0.9556 |
| No log | 2.0 | 14 | 3.0027 | 0.095 | 0.9510 | 5.8779 | 0.095 | 0.0766 | 0.1668 | 0.8900 |
| No log | 3.0 | 21 | 2.6988 | 0.225 | 0.8896 | 5.3750 | 0.225 | 0.1801 | 0.1669 | 0.6473 |
| No log | 4.0 | 28 | 2.3179 | 0.285 | 0.8016 | 3.5658 | 0.285 | 0.2657 | 0.1679 | 0.4916 |
| No log | 5.0 | 35 | 2.0566 | 0.37 | 0.7203 | 2.9834 | 0.37 | 0.3493 | 0.1684 | 0.3612 |
| No log | 6.0 | 42 | 1.9505 | 0.4325 | 0.6892 | 3.0719 | 0.4325 | 0.4127 | 0.1775 | 0.3084 |
| No log | 7.0 | 49 | 1.9995 | 0.4375 | 0.7008 | 3.1569 | 0.4375 | 0.4084 | 0.2032 | 0.3103 |
| No log | 8.0 | 56 | 1.9133 | 0.445 | 0.6906 | 2.8574 | 0.445 | 0.4464 | 0.2016 | 0.3062 |
| No log | 9.0 | 63 | 1.9876 | 0.4625 | 0.6918 | 2.9267 | 0.4625 | 0.4538 | 0.2228 | 0.2868 |
| No log | 10.0 | 70 | 2.0051 | 0.4725 | 0.6971 | 2.9249 | 0.4725 | 0.4553 | 0.2234 | 0.2814 |
| No log | 11.0 | 77 | 2.1834 | 0.465 | 0.7319 | 2.9998 | 0.465 | 0.4426 | 0.2444 | 0.3006 |
| No log | 12.0 | 84 | 1.9953 | 0.4825 | 0.7087 | 2.7128 | 0.4825 | 0.4731 | 0.2386 | 0.2980 |
| No log | 13.0 | 91 | 1.8834 | 0.4975 | 0.6771 | 2.6879 | 0.4975 | 0.4954 | 0.2240 | 0.2748 |
| No log | 14.0 | 98 | 1.9647 | 0.4675 | 0.6987 | 2.8305 | 0.4675 | 0.4429 | 0.2409 | 0.2902 |
| No log | 15.0 | 105 | 1.8810 | 0.5 | 0.6785 | 2.6402 | 0.5 | 0.4847 | 0.2171 | 0.2725 |
| No log | 16.0 | 112 | 1.8777 | 0.4875 | 0.6877 | 2.6940 | 0.4875 | 0.4871 | 0.2210 | 0.2846 |
| No log | 17.0 | 119 | 1.9260 | 0.4925 | 0.6796 | 2.7055 | 0.4925 | 0.4834 | 0.2012 | 0.2744 |
| No log | 18.0 | 126 | 1.7864 | 0.505 | 0.6547 | 2.6724 | 0.505 | 0.4912 | 0.2081 | 0.2434 |
| No log | 19.0 | 133 | 1.7618 | 0.4975 | 0.6430 | 2.5951 | 0.4975 | 0.4915 | 0.2172 | 0.2490 |
| No log | 20.0 | 140 | 1.7496 | 0.515 | 0.6513 | 2.5263 | 0.515 | 0.5025 | 0.1975 | 0.2502 |
| No log | 21.0 | 147 | 1.7082 | 0.5275 | 0.6438 | 2.4039 | 0.5275 | 0.5224 | 0.2017 | 0.2450 |
| No log | 22.0 | 154 | 1.7482 | 0.4975 | 0.6682 | 2.5194 | 0.4975 | 0.4911 | 0.2247 | 0.2571 |
| No log | 23.0 | 161 | 1.7377 | 0.5075 | 0.6482 | 2.4136 | 0.5075 | 0.4900 | 0.2221 | 0.2396 |
| No log | 24.0 | 168 | 1.7094 | 0.515 | 0.6372 | 2.5605 | 0.515 | 0.5083 | 0.2137 | 0.2474 |
| No log | 25.0 | 175 | 1.6884 | 0.5175 | 0.6422 | 2.5270 | 0.5175 | 0.5104 | 0.2111 | 0.2444 |
| No log | 26.0 | 182 | 1.6489 | 0.5275 | 0.6246 | 2.5344 | 0.5275 | 0.5211 | 0.2066 | 0.2333 |
| No log | 27.0 | 189 | 1.6165 | 0.53 | 0.6191 | 2.5418 | 0.53 | 0.5256 | 0.2021 | 0.2305 |
| No log | 28.0 | 196 | 1.6316 | 0.5275 | 0.6181 | 2.6568 | 0.5275 | 0.5212 | 0.2004 | 0.2300 |
| No log | 29.0 | 203 | 1.6595 | 0.5175 | 0.6306 | 2.4298 | 0.5175 | 0.5096 | 0.2020 | 0.2427 |
| No log | 30.0 | 210 | 1.6193 | 0.5325 | 0.6157 | 2.5455 | 0.5325 | 0.5272 | 0.1779 | 0.2278 |
| No log | 31.0 | 217 | 1.6517 | 0.5325 | 0.6274 | 2.4579 | 0.5325 | 0.5259 | 0.2006 | 0.2362 |
| No log | 32.0 | 224 | 1.6434 | 0.5325 | 0.6167 | 2.5805 | 0.5325 | 0.5229 | 0.1995 | 0.2273 |
| No log | 33.0 | 231 | 1.6660 | 0.5225 | 0.6269 | 2.6794 | 0.5225 | 0.5132 | 0.2244 | 0.2283 |
| No log | 34.0 | 238 | 1.6353 | 0.515 | 0.6194 | 2.6085 | 0.515 | 0.5069 | 0.1839 | 0.2303 |
| No log | 35.0 | 245 | 1.5920 | 0.5325 | 0.6051 | 2.5645 | 0.5325 | 0.5248 | 0.1868 | 0.2208 |
| No log | 36.0 | 252 | 1.5909 | 0.54 | 0.6028 | 2.4786 | 0.54 | 0.5323 | 0.1902 | 0.2194 |
| No log | 37.0 | 259 | 1.5730 | 0.5425 | 0.5983 | 2.4877 | 0.5425 | 0.5368 | 0.1799 | 0.2177 |
| No log | 38.0 | 266 | 1.5800 | 0.535 | 0.6029 | 2.4736 | 0.535 | 0.5282 | 0.1761 | 0.2196 |
| No log | 39.0 | 273 | 1.5594 | 0.54 | 0.5955 | 2.5093 | 0.54 | 0.5327 | 0.1900 | 0.2126 |
| No log | 40.0 | 280 | 1.5685 | 0.53 | 0.5979 | 2.6068 | 0.53 | 0.5208 | 0.1893 | 0.2173 |
| No log | 41.0 | 287 | 1.5757 | 0.53 | 0.5995 | 2.5655 | 0.53 | 0.5218 | 0.1862 | 0.2164 |
| No log | 42.0 | 294 | 1.5797 | 0.535 | 0.6039 | 2.5445 | 0.535 | 0.5273 | 0.1834 | 0.2182 |
| No log | 43.0 | 301 | 1.5900 | 0.53 | 0.6074 | 2.5201 | 0.53 | 0.5189 | 0.1747 | 0.2206 |
| No log | 44.0 | 308 | 1.5760 | 0.5325 | 0.5986 | 2.4974 | 0.5325 | 0.5225 | 0.1870 | 0.2148 |
| No log | 45.0 | 315 | 1.5768 | 0.53 | 0.6013 | 2.5174 | 0.53 | 0.5204 | 0.1979 | 0.2158 |
| No log | 46.0 | 322 | 1.5774 | 0.53 | 0.6011 | 2.5199 | 0.53 | 0.5206 | 0.1882 | 0.2165 |
| No log | 47.0 | 329 | 1.5714 | 0.54 | 0.5983 | 2.5329 | 0.54 | 0.5303 | 0.1884 | 0.2135 |
| No log | 48.0 | 336 | 1.5834 | 0.5325 | 0.6026 | 2.5253 | 0.5325 | 0.5238 | 0.1658 | 0.2190 |
| No log | 49.0 | 343 | 1.5724 | 0.5375 | 0.5979 | 2.5569 | 0.5375 | 0.5299 | 0.1617 | 0.2151 |
| No log | 50.0 | 350 | 1.5685 | 0.5375 | 0.5985 | 2.5189 | 0.5375 | 0.5285 | 0.1919 | 0.2151 |
| No log | 51.0 | 357 | 1.5708 | 0.54 | 0.5986 | 2.5002 | 0.54 | 0.5305 | 0.1755 | 0.2149 |
| No log | 52.0 | 364 | 1.5665 | 0.535 | 0.5977 | 2.5224 | 0.535 | 0.5267 | 0.1842 | 0.2160 |
| No log | 53.0 | 371 | 1.5713 | 0.5325 | 0.5993 | 2.5515 | 0.5325 | 0.5250 | 0.1753 | 0.2160 |
| No log | 54.0 | 378 | 1.5693 | 0.535 | 0.5986 | 2.5516 | 0.535 | 0.5276 | 0.1841 | 0.2158 |
| No log | 55.0 | 385 | 1.5693 | 0.5375 | 0.5984 | 2.5190 | 0.5375 | 0.5285 | 0.1842 | 0.2144 |
| No log | 56.0 | 392 | 1.5725 | 0.535 | 0.5992 | 2.5527 | 0.535 | 0.5262 | 0.1776 | 0.2150 |
| No log | 57.0 | 399 | 1.5674 | 0.5425 | 0.5976 | 2.5502 | 0.5425 | 0.5326 | 0.1902 | 0.2137 |
| No log | 58.0 | 406 | 1.5675 | 0.5375 | 0.5974 | 2.5517 | 0.5375 | 0.5288 | 0.1794 | 0.2139 |
| No log | 59.0 | 413 | 1.5713 | 0.535 | 0.5988 | 2.5515 | 0.535 | 0.5257 | 0.1791 | 0.2147 |
| No log | 60.0 | 420 | 1.5729 | 0.535 | 0.5988 | 2.5512 | 0.535 | 0.5262 | 0.1796 | 0.2148 |
| No log | 61.0 | 427 | 1.5702 | 0.5375 | 0.5976 | 2.5521 | 0.5375 | 0.5281 | 0.1817 | 0.2139 |
| No log | 62.0 | 434 | 1.5728 | 0.535 | 0.5988 | 2.5514 | 0.535 | 0.5266 | 0.1722 | 0.2149 |
| No log | 63.0 | 441 | 1.5720 | 0.5325 | 0.5985 | 2.5206 | 0.5325 | 0.5231 | 0.1790 | 0.2149 |
| No log | 64.0 | 448 | 1.5704 | 0.5325 | 0.5975 | 2.5510 | 0.5325 | 0.5236 | 0.1706 | 0.2139 |
| No log | 65.0 | 455 | 1.5724 | 0.5325 | 0.5986 | 2.5225 | 0.5325 | 0.5236 | 0.1557 | 0.2148 |
| No log | 66.0 | 462 | 1.5718 | 0.5325 | 0.5985 | 2.5246 | 0.5325 | 0.5241 | 0.1772 | 0.2148 |
| No log | 67.0 | 469 | 1.5710 | 0.5325 | 0.5981 | 2.5511 | 0.5325 | 0.5237 | 0.1625 | 0.2146 |
| No log | 68.0 | 476 | 1.5716 | 0.54 | 0.5981 | 2.5001 | 0.54 | 0.5304 | 0.1622 | 0.2141 |
| No log | 69.0 | 483 | 1.5732 | 0.5325 | 0.5988 | 2.5517 | 0.5325 | 0.5232 | 0.1641 | 0.2150 |
| No log | 70.0 | 490 | 1.5733 | 0.5325 | 0.5987 | 2.5522 | 0.5325 | 0.5237 | 0.1715 | 0.2149 |
| No log | 71.0 | 497 | 1.5729 | 0.5325 | 0.5985 | 2.5523 | 0.5325 | 0.5241 | 0.1670 | 0.2147 |
| 0.3153 | 72.0 | 504 | 1.5730 | 0.5325 | 0.5987 | 2.5236 | 0.5325 | 0.5237 | 0.1656 | 0.2149 |
| 0.3153 | 73.0 | 511 | 1.5723 | 0.5325 | 0.5985 | 2.5212 | 0.5325 | 0.5238 | 0.1893 | 0.2145 |
| 0.3153 | 74.0 | 518 | 1.5738 | 0.5325 | 0.5989 | 2.5515 | 0.5325 | 0.5238 | 0.1744 | 0.2147 |
| 0.3153 | 75.0 | 525 | 1.5740 | 0.5325 | 0.5988 | 2.5318 | 0.5325 | 0.5237 | 0.1683 | 0.2150 |
| 0.3153 | 76.0 | 532 | 1.5734 | 0.535 | 0.5985 | 2.5525 | 0.535 | 0.5261 | 0.1763 | 0.2145 |
| 0.3153 | 77.0 | 539 | 1.5740 | 0.5325 | 0.5989 | 2.5516 | 0.5325 | 0.5243 | 0.1726 | 0.2149 |
| 0.3153 | 78.0 | 546 | 1.5738 | 0.5325 | 0.5987 | 2.5289 | 0.5325 | 0.5241 | 0.1692 | 0.2148 |
| 0.3153 | 79.0 | 553 | 1.5736 | 0.5325 | 0.5987 | 2.5255 | 0.5325 | 0.5242 | 0.1807 | 0.2147 |
| 0.3153 | 80.0 | 560 | 1.5739 | 0.5325 | 0.5988 | 2.5522 | 0.5325 | 0.5237 | 0.1769 | 0.2150 |
| 0.3153 | 81.0 | 567 | 1.5743 | 0.5325 | 0.5989 | 2.5519 | 0.5325 | 0.5238 | 0.1837 | 0.2151 |
| 0.3153 | 82.0 | 574 | 1.5742 | 0.5325 | 0.5989 | 2.5232 | 0.5325 | 0.5240 | 0.1712 | 0.2149 |
| 0.3153 | 83.0 | 581 | 1.5744 | 0.5325 | 0.5989 | 2.5256 | 0.5325 | 0.5239 | 0.1803 | 0.2151 |
| 0.3153 | 84.0 | 588 | 1.5741 | 0.5325 | 0.5988 | 2.5233 | 0.5325 | 0.5233 | 0.1655 | 0.2147 |
| 0.3153 | 85.0 | 595 | 1.5747 | 0.5325 | 0.5990 | 2.5274 | 0.5325 | 0.5237 | 0.1696 | 0.2152 |
| 0.3153 | 86.0 | 602 | 1.5747 | 0.5325 | 0.5989 | 2.5263 | 0.5325 | 0.5238 | 0.1689 | 0.2150 |
| 0.3153 | 87.0 | 609 | 1.5745 | 0.5325 | 0.5989 | 2.5251 | 0.5325 | 0.5237 | 0.1654 | 0.2149 |
| 0.3153 | 88.0 | 616 | 1.5747 | 0.5325 | 0.5989 | 2.5283 | 0.5325 | 0.5241 | 0.1693 | 0.2151 |
| 0.3153 | 89.0 | 623 | 1.5748 | 0.5325 | 0.5990 | 2.5275 | 0.5325 | 0.5239 | 0.1596 | 0.2152 |
| 0.3153 | 90.0 | 630 | 1.5749 | 0.5325 | 0.5990 | 2.5278 | 0.5325 | 0.5240 | 0.1602 | 0.2151 |
| 0.3153 | 91.0 | 637 | 1.5750 | 0.5325 | 0.5990 | 2.5337 | 0.5325 | 0.5239 | 0.1623 | 0.2152 |
| 0.3153 | 92.0 | 644 | 1.5749 | 0.5325 | 0.5990 | 2.5272 | 0.5325 | 0.5238 | 0.1653 | 0.2151 |
| 0.3153 | 93.0 | 651 | 1.5751 | 0.5325 | 0.5990 | 2.5281 | 0.5325 | 0.5240 | 0.1663 | 0.2149 |
| 0.3153 | 94.0 | 658 | 1.5750 | 0.5325 | 0.5990 | 2.5249 | 0.5325 | 0.5239 | 0.1715 | 0.2152 |
| 0.3153 | 95.0 | 665 | 1.5749 | 0.535 | 0.5990 | 2.5257 | 0.535 | 0.5263 | 0.1625 | 0.2149 |
| 0.3153 | 96.0 | 672 | 1.5750 | 0.5325 | 0.5990 | 2.5266 | 0.5325 | 0.5239 | 0.1655 | 0.2151 |
| 0.3153 | 97.0 | 679 | 1.5750 | 0.5325 | 0.5990 | 2.5268 | 0.5325 | 0.5239 | 0.1686 | 0.2152 |
| 0.3153 | 98.0 | 686 | 1.5750 | 0.5325 | 0.5990 | 2.5275 | 0.5325 | 0.5240 | 0.1664 | 0.2152 |
| 0.3153 | 99.0 | 693 | 1.5750 | 0.5325 | 0.5990 | 2.5269 | 0.5325 | 0.5240 | 0.1678 | 0.2152 |
| 0.3153 | 100.0 | 700 | 1.5750 | 0.5325 | 0.5990 | 2.5263 | 0.5325 | 0.5240 | 0.1659 | 0.2152 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1.post200
- Datasets 2.9.0
- Tokenizers 0.13.2
|
DipanAI/falcon_law_7Ba | DipanAI | 2023-07-12T18:01:26Z | 0 | 0 | null | [
"tensorboard",
"generated_from_trainer",
"text-generation",
"region:us"
] | text-generation | 2023-07-12T16:13:38Z | ---
tags:
- generated_from_trainer
model-index:
- name: falcon_law_7Ba
results: []
pipeline_tag: text-generation
---
<!-- 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. -->
# falcon_law_7Ba
This model is a fine-tuned version of [ybelkada/falcon-7b-sharded-bf16](https://huggingface.co/ybelkada/falcon-7b-sharded-bf16) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2
- training_steps: 100
### Training results
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3 |
tyavika/lr1e5-layer1-bs16-Distil-CNN128LSTM128NoBi | tyavika | 2023-07-12T17:59:27Z | 77 | 0 | transformers | [
"transformers",
"pytorch",
"distilbert",
"question-answering",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | question-answering | 2023-07-12T15:42:47Z | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: lr1e5-layer1-bs16-Distil-CNN128LSTM128NoBi
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. -->
# lr1e5-layer1-bs16-Distil-CNN128LSTM128NoBi
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3813
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 1.5317 | 1.0 | 3290 | 1.3385 |
| 1.0853 | 2.0 | 6580 | 1.1885 |
| 0.7993 | 3.0 | 9870 | 1.2330 |
| 0.5808 | 4.0 | 13160 | 1.3813 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
Jihyeon-2/lora-trained-xl_lhand | Jihyeon-2 | 2023-07-12T17:57:08Z | 5 | 1 | diffusers | [
"diffusers",
"stable-diffusion-xl",
"stable-diffusion-xl-diffusers",
"text-to-image",
"lora",
"base_model:stabilityai/stable-diffusion-xl-base-0.9",
"base_model:adapter:stabilityai/stable-diffusion-xl-base-0.9",
"license:creativeml-openrail-m",
"region:us"
] | text-to-image | 2023-07-12T16:34:56Z |
---
license: creativeml-openrail-m
base_model: stabilityai/stable-diffusion-xl-base-0.9
instance_prompt: a photo of sks hand
tags:
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
- text-to-image
- diffusers
- lora
inference: true
---
# LoRA DreamBooth - Jihyeon-2/lora-trained-xl_lhand
These are LoRA adaption weights for stabilityai/stable-diffusion-xl-base-0.9. The weights were trained on a photo of sks hand using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following.
LoRA for the text encoder was enabled: False.
|
ayanban011/vit-base_tobacco_bs_16_lr_2e-4_e_200_wr_0.01_wd_0.2 | ayanban011 | 2023-07-12T17:43:52Z | 165 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-classification | 2023-07-12T15:30:14Z | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: vit-base_tobacco_bs_16_lr_2e-4_e_200_wr_0.01_wd_0.2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# vit-base_tobacco_bs_16_lr_2e-4_e_200_wr_0.01_wd_0.2
This model is a fine-tuned version of [jordyvl/vit-base_tobacco](https://huggingface.co/jordyvl/vit-base_tobacco) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0644
- Accuracy: 0.86
- Brier Loss: 0.2705
- Nll: 1.3085
- F1 Micro: 0.8600
- F1 Macro: 0.8552
- Ece: 0.1378
- Aurc: 0.0461
## 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: 4
- eval_batch_size: 4
- 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_ratio: 0.01
- num_epochs: 200
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Brier Loss | Nll | F1 Micro | F1 Macro | Ece | Aurc |
|:-------------:|:------:|:----:|:---------------:|:--------:|:----------:|:------:|:--------:|:--------:|:------:|:------:|
| No log | 0.96 | 12 | 0.7869 | 0.78 | 0.3223 | 1.3413 | 0.78 | 0.7402 | 0.2269 | 0.0782 |
| No log | 2.0 | 25 | 0.9889 | 0.715 | 0.4300 | 1.9648 | 0.715 | 0.6881 | 0.2669 | 0.1397 |
| No log | 2.96 | 37 | 0.7053 | 0.82 | 0.2995 | 1.2578 | 0.82 | 0.8270 | 0.2253 | 0.0758 |
| No log | 4.0 | 50 | 0.7535 | 0.78 | 0.3225 | 1.3427 | 0.78 | 0.7395 | 0.2159 | 0.0616 |
| No log | 4.96 | 62 | 0.8538 | 0.775 | 0.3634 | 1.5684 | 0.775 | 0.7523 | 0.2181 | 0.1149 |
| No log | 6.0 | 75 | 0.7825 | 0.77 | 0.3557 | 1.3406 | 0.7700 | 0.7663 | 0.2136 | 0.0625 |
| No log | 6.96 | 87 | 1.0777 | 0.67 | 0.4896 | 1.5465 | 0.67 | 0.6728 | 0.2540 | 0.1106 |
| No log | 8.0 | 100 | 1.1030 | 0.73 | 0.4453 | 2.5744 | 0.7300 | 0.6939 | 0.2294 | 0.1423 |
| No log | 8.96 | 112 | 1.0215 | 0.725 | 0.4339 | 2.0485 | 0.7250 | 0.7012 | 0.2348 | 0.1278 |
| No log | 10.0 | 125 | 0.7940 | 0.795 | 0.3378 | 1.3057 | 0.795 | 0.7911 | 0.1828 | 0.0750 |
| No log | 10.96 | 137 | 0.7648 | 0.82 | 0.2963 | 1.3907 | 0.82 | 0.8022 | 0.1597 | 0.0744 |
| No log | 12.0 | 150 | 1.0755 | 0.74 | 0.4383 | 2.1271 | 0.74 | 0.7182 | 0.2281 | 0.0847 |
| No log | 12.96 | 162 | 1.0091 | 0.775 | 0.3856 | 1.7383 | 0.775 | 0.7339 | 0.1969 | 0.1029 |
| No log | 14.0 | 175 | 1.0531 | 0.77 | 0.4027 | 1.5532 | 0.7700 | 0.7592 | 0.2152 | 0.0888 |
| No log | 14.96 | 187 | 1.0221 | 0.77 | 0.4027 | 1.5199 | 0.7700 | 0.7259 | 0.2059 | 0.1031 |
| No log | 16.0 | 200 | 1.1795 | 0.735 | 0.4435 | 2.0739 | 0.735 | 0.7063 | 0.2262 | 0.1305 |
| No log | 16.96 | 212 | 1.1560 | 0.745 | 0.4379 | 2.0155 | 0.745 | 0.7240 | 0.2207 | 0.1273 |
| No log | 18.0 | 225 | 1.0635 | 0.76 | 0.4159 | 1.5491 | 0.76 | 0.7508 | 0.2124 | 0.0879 |
| No log | 18.96 | 237 | 1.2639 | 0.73 | 0.4649 | 1.9828 | 0.7300 | 0.7276 | 0.2298 | 0.1079 |
| No log | 20.0 | 250 | 1.0598 | 0.78 | 0.3866 | 1.5139 | 0.78 | 0.7676 | 0.1885 | 0.0914 |
| No log | 20.96 | 262 | 0.8900 | 0.81 | 0.3241 | 1.8355 | 0.81 | 0.7925 | 0.1691 | 0.0648 |
| No log | 22.0 | 275 | 1.0617 | 0.79 | 0.3788 | 1.8951 | 0.79 | 0.7783 | 0.1893 | 0.0676 |
| No log | 22.96 | 287 | 1.0362 | 0.785 | 0.3646 | 1.9399 | 0.785 | 0.7653 | 0.1914 | 0.0816 |
| No log | 24.0 | 300 | 1.1701 | 0.775 | 0.4060 | 2.1593 | 0.775 | 0.7718 | 0.2114 | 0.0842 |
| No log | 24.96 | 312 | 1.0841 | 0.79 | 0.3799 | 1.8773 | 0.79 | 0.7795 | 0.2016 | 0.0775 |
| No log | 26.0 | 325 | 1.0064 | 0.785 | 0.3650 | 1.7371 | 0.785 | 0.7674 | 0.1915 | 0.0813 |
| No log | 26.96 | 337 | 0.8886 | 0.825 | 0.3114 | 1.4858 | 0.825 | 0.8116 | 0.1609 | 0.0636 |
| No log | 28.0 | 350 | 1.1174 | 0.8 | 0.3751 | 1.9584 | 0.8000 | 0.7869 | 0.1928 | 0.0930 |
| No log | 28.96 | 362 | 1.0922 | 0.8 | 0.3672 | 1.8702 | 0.8000 | 0.7673 | 0.1954 | 0.0771 |
| No log | 30.0 | 375 | 1.0281 | 0.805 | 0.3506 | 1.6105 | 0.805 | 0.7809 | 0.1773 | 0.0936 |
| No log | 30.96 | 387 | 0.9041 | 0.82 | 0.3210 | 1.3323 | 0.82 | 0.8148 | 0.1627 | 0.0651 |
| No log | 32.0 | 400 | 1.1018 | 0.79 | 0.3804 | 1.9928 | 0.79 | 0.7962 | 0.1859 | 0.0574 |
| No log | 32.96 | 412 | 1.1973 | 0.765 | 0.4156 | 1.4304 | 0.765 | 0.7682 | 0.2147 | 0.0760 |
| No log | 34.0 | 425 | 1.0216 | 0.805 | 0.3605 | 1.4476 | 0.805 | 0.7864 | 0.1830 | 0.0633 |
| No log | 34.96 | 437 | 1.2356 | 0.755 | 0.4237 | 1.9897 | 0.755 | 0.7350 | 0.2214 | 0.0890 |
| No log | 36.0 | 450 | 1.0881 | 0.8 | 0.3757 | 1.3848 | 0.8000 | 0.7810 | 0.1960 | 0.0703 |
| No log | 36.96 | 462 | 1.1133 | 0.795 | 0.3687 | 2.0286 | 0.795 | 0.7707 | 0.1790 | 0.0652 |
| No log | 38.0 | 475 | 1.1243 | 0.78 | 0.3839 | 1.5683 | 0.78 | 0.7699 | 0.1905 | 0.0704 |
| No log | 38.96 | 487 | 1.1351 | 0.785 | 0.3983 | 1.4970 | 0.785 | 0.7647 | 0.1969 | 0.0666 |
| 0.0934 | 40.0 | 500 | 1.2551 | 0.775 | 0.4089 | 2.0438 | 0.775 | 0.7688 | 0.2082 | 0.1007 |
| 0.0934 | 40.96 | 512 | 1.1739 | 0.775 | 0.4003 | 1.3286 | 0.775 | 0.7654 | 0.2056 | 0.0819 |
| 0.0934 | 42.0 | 525 | 1.0007 | 0.83 | 0.3207 | 1.2576 | 0.83 | 0.8345 | 0.1579 | 0.0677 |
| 0.0934 | 42.96 | 537 | 1.0509 | 0.805 | 0.3580 | 1.2330 | 0.805 | 0.7933 | 0.1884 | 0.0716 |
| 0.0934 | 44.0 | 550 | 1.0830 | 0.805 | 0.3537 | 1.7652 | 0.805 | 0.7871 | 0.1740 | 0.0688 |
| 0.0934 | 44.96 | 562 | 0.8544 | 0.83 | 0.2957 | 1.4716 | 0.83 | 0.8039 | 0.1560 | 0.0532 |
| 0.0934 | 46.0 | 575 | 1.0803 | 0.815 | 0.3549 | 1.5802 | 0.815 | 0.7951 | 0.1840 | 0.0691 |
| 0.0934 | 46.96 | 587 | 0.9441 | 0.815 | 0.3318 | 1.2883 | 0.815 | 0.7924 | 0.1709 | 0.0514 |
| 0.0934 | 48.0 | 600 | 0.9007 | 0.845 | 0.2765 | 1.3443 | 0.845 | 0.8353 | 0.1402 | 0.0539 |
| 0.0934 | 48.96 | 612 | 0.9601 | 0.84 | 0.2952 | 1.4755 | 0.8400 | 0.8306 | 0.1499 | 0.0565 |
| 0.0934 | 50.0 | 625 | 0.9801 | 0.84 | 0.2992 | 1.4646 | 0.8400 | 0.8306 | 0.1529 | 0.0559 |
| 0.0934 | 50.96 | 637 | 0.9747 | 0.845 | 0.2950 | 1.4544 | 0.845 | 0.8338 | 0.1526 | 0.0546 |
| 0.0934 | 52.0 | 650 | 0.9651 | 0.845 | 0.2895 | 1.4442 | 0.845 | 0.8338 | 0.1469 | 0.0537 |
| 0.0934 | 52.96 | 662 | 0.9583 | 0.85 | 0.2848 | 1.4367 | 0.85 | 0.8370 | 0.1465 | 0.0525 |
| 0.0934 | 54.0 | 675 | 0.9534 | 0.85 | 0.2805 | 1.4300 | 0.85 | 0.8370 | 0.1455 | 0.0514 |
| 0.0934 | 54.96 | 687 | 0.9503 | 0.855 | 0.2776 | 1.4252 | 0.855 | 0.8425 | 0.1408 | 0.0510 |
| 0.0934 | 56.0 | 700 | 0.9480 | 0.855 | 0.2754 | 1.4207 | 0.855 | 0.8425 | 0.1407 | 0.0506 |
| 0.0934 | 56.96 | 712 | 0.9471 | 0.855 | 0.2739 | 1.4175 | 0.855 | 0.8425 | 0.1442 | 0.0504 |
| 0.0934 | 58.0 | 725 | 0.9471 | 0.855 | 0.2729 | 1.4147 | 0.855 | 0.8442 | 0.1435 | 0.0501 |
| 0.0934 | 58.96 | 737 | 0.9474 | 0.855 | 0.2720 | 1.4125 | 0.855 | 0.8442 | 0.1432 | 0.0497 |
| 0.0934 | 60.0 | 750 | 0.9482 | 0.855 | 0.2713 | 1.4101 | 0.855 | 0.8442 | 0.1420 | 0.0497 |
| 0.0934 | 60.96 | 762 | 0.9490 | 0.855 | 0.2708 | 1.4082 | 0.855 | 0.8442 | 0.1421 | 0.0493 |
| 0.0934 | 62.0 | 775 | 0.9500 | 0.86 | 0.2703 | 1.4063 | 0.8600 | 0.8534 | 0.1411 | 0.0493 |
| 0.0934 | 62.96 | 787 | 0.9512 | 0.86 | 0.2702 | 1.4046 | 0.8600 | 0.8534 | 0.1410 | 0.0492 |
| 0.0934 | 64.0 | 800 | 0.9528 | 0.86 | 0.2699 | 1.4032 | 0.8600 | 0.8534 | 0.1408 | 0.0489 |
| 0.0934 | 64.96 | 812 | 0.9541 | 0.86 | 0.2697 | 1.3472 | 0.8600 | 0.8534 | 0.1349 | 0.0487 |
| 0.0934 | 66.0 | 825 | 0.9558 | 0.86 | 0.2696 | 1.3431 | 0.8600 | 0.8534 | 0.1408 | 0.0487 |
| 0.0934 | 66.96 | 837 | 0.9574 | 0.86 | 0.2697 | 1.3403 | 0.8600 | 0.8534 | 0.1405 | 0.0486 |
| 0.0934 | 68.0 | 850 | 0.9591 | 0.86 | 0.2698 | 1.3375 | 0.8600 | 0.8534 | 0.1402 | 0.0486 |
| 0.0934 | 68.96 | 862 | 0.9605 | 0.86 | 0.2698 | 1.3355 | 0.8600 | 0.8552 | 0.1394 | 0.0486 |
| 0.0934 | 70.0 | 875 | 0.9624 | 0.86 | 0.2698 | 1.3338 | 0.8600 | 0.8552 | 0.1394 | 0.0486 |
| 0.0934 | 70.96 | 887 | 0.9638 | 0.86 | 0.2700 | 1.3322 | 0.8600 | 0.8552 | 0.1397 | 0.0485 |
| 0.0934 | 72.0 | 900 | 0.9657 | 0.86 | 0.2701 | 1.3310 | 0.8600 | 0.8552 | 0.1397 | 0.0485 |
| 0.0934 | 72.96 | 912 | 0.9673 | 0.86 | 0.2702 | 1.3299 | 0.8600 | 0.8552 | 0.1397 | 0.0484 |
| 0.0934 | 74.0 | 925 | 0.9691 | 0.86 | 0.2703 | 1.3289 | 0.8600 | 0.8552 | 0.1397 | 0.0484 |
| 0.0934 | 74.96 | 937 | 0.9708 | 0.86 | 0.2704 | 1.3280 | 0.8600 | 0.8552 | 0.1398 | 0.0485 |
| 0.0934 | 76.0 | 950 | 0.9725 | 0.86 | 0.2706 | 1.3271 | 0.8600 | 0.8552 | 0.1398 | 0.0485 |
| 0.0934 | 76.96 | 962 | 0.9740 | 0.86 | 0.2707 | 1.3263 | 0.8600 | 0.8552 | 0.1398 | 0.0485 |
| 0.0934 | 78.0 | 975 | 0.9757 | 0.86 | 0.2707 | 1.3256 | 0.8600 | 0.8552 | 0.1383 | 0.0485 |
| 0.0934 | 78.96 | 987 | 0.9772 | 0.86 | 0.2708 | 1.3248 | 0.8600 | 0.8552 | 0.1357 | 0.0484 |
| 0.0038 | 80.0 | 1000 | 0.9789 | 0.86 | 0.2709 | 1.3243 | 0.8600 | 0.8552 | 0.1359 | 0.0485 |
| 0.0038 | 80.96 | 1012 | 0.9806 | 0.86 | 0.2710 | 1.3238 | 0.8600 | 0.8552 | 0.1360 | 0.0484 |
| 0.0038 | 82.0 | 1025 | 0.9820 | 0.86 | 0.2711 | 1.3232 | 0.8600 | 0.8552 | 0.1361 | 0.0482 |
| 0.0038 | 82.96 | 1037 | 0.9837 | 0.86 | 0.2712 | 1.3227 | 0.8600 | 0.8552 | 0.1361 | 0.0481 |
| 0.0038 | 84.0 | 1050 | 0.9853 | 0.86 | 0.2713 | 1.3222 | 0.8600 | 0.8552 | 0.1362 | 0.0480 |
| 0.0038 | 84.96 | 1062 | 0.9867 | 0.86 | 0.2713 | 1.3216 | 0.8600 | 0.8552 | 0.1363 | 0.0481 |
| 0.0038 | 86.0 | 1075 | 0.9883 | 0.86 | 0.2714 | 1.3212 | 0.8600 | 0.8552 | 0.1364 | 0.0479 |
| 0.0038 | 86.96 | 1087 | 0.9896 | 0.86 | 0.2714 | 1.3208 | 0.8600 | 0.8552 | 0.1365 | 0.0477 |
| 0.0038 | 88.0 | 1100 | 0.9911 | 0.86 | 0.2715 | 1.3203 | 0.8600 | 0.8552 | 0.1366 | 0.0478 |
| 0.0038 | 88.96 | 1112 | 0.9925 | 0.86 | 0.2715 | 1.3200 | 0.8600 | 0.8552 | 0.1369 | 0.0478 |
| 0.0038 | 90.0 | 1125 | 0.9940 | 0.86 | 0.2715 | 1.3196 | 0.8600 | 0.8552 | 0.1369 | 0.0477 |
| 0.0038 | 90.96 | 1137 | 0.9954 | 0.86 | 0.2715 | 1.3194 | 0.8600 | 0.8552 | 0.1369 | 0.0476 |
| 0.0038 | 92.0 | 1150 | 0.9968 | 0.86 | 0.2716 | 1.3190 | 0.8600 | 0.8552 | 0.1368 | 0.0476 |
| 0.0038 | 92.96 | 1162 | 0.9983 | 0.86 | 0.2716 | 1.3187 | 0.8600 | 0.8552 | 0.1368 | 0.0476 |
| 0.0038 | 94.0 | 1175 | 0.9996 | 0.86 | 0.2716 | 1.3184 | 0.8600 | 0.8552 | 0.1394 | 0.0476 |
| 0.0038 | 94.96 | 1187 | 1.0009 | 0.86 | 0.2716 | 1.3182 | 0.8600 | 0.8552 | 0.1393 | 0.0475 |
| 0.0038 | 96.0 | 1200 | 1.0023 | 0.86 | 0.2717 | 1.3179 | 0.8600 | 0.8552 | 0.1392 | 0.0475 |
| 0.0038 | 96.96 | 1212 | 1.0035 | 0.86 | 0.2717 | 1.3176 | 0.8600 | 0.8552 | 0.1391 | 0.0475 |
| 0.0038 | 98.0 | 1225 | 1.0049 | 0.86 | 0.2717 | 1.3175 | 0.8600 | 0.8552 | 0.1391 | 0.0474 |
| 0.0038 | 98.96 | 1237 | 1.0062 | 0.86 | 0.2717 | 1.3172 | 0.8600 | 0.8552 | 0.1391 | 0.0475 |
| 0.0038 | 100.0 | 1250 | 1.0075 | 0.86 | 0.2717 | 1.3169 | 0.8600 | 0.8552 | 0.1367 | 0.0475 |
| 0.0038 | 100.96 | 1262 | 1.0087 | 0.86 | 0.2717 | 1.3167 | 0.8600 | 0.8552 | 0.1368 | 0.0475 |
| 0.0038 | 102.0 | 1275 | 1.0099 | 0.86 | 0.2717 | 1.3164 | 0.8600 | 0.8552 | 0.1375 | 0.0474 |
| 0.0038 | 102.96 | 1287 | 1.0111 | 0.86 | 0.2717 | 1.3162 | 0.8600 | 0.8552 | 0.1376 | 0.0473 |
| 0.0038 | 104.0 | 1300 | 1.0122 | 0.86 | 0.2717 | 1.3159 | 0.8600 | 0.8552 | 0.1378 | 0.0471 |
| 0.0038 | 104.96 | 1312 | 1.0134 | 0.86 | 0.2716 | 1.3158 | 0.8600 | 0.8552 | 0.1378 | 0.0473 |
| 0.0038 | 106.0 | 1325 | 1.0146 | 0.86 | 0.2717 | 1.3155 | 0.8600 | 0.8552 | 0.1379 | 0.0472 |
| 0.0038 | 106.96 | 1337 | 1.0158 | 0.86 | 0.2717 | 1.3153 | 0.8600 | 0.8552 | 0.1379 | 0.0471 |
| 0.0038 | 108.0 | 1350 | 1.0169 | 0.86 | 0.2716 | 1.3151 | 0.8600 | 0.8552 | 0.1380 | 0.0471 |
| 0.0038 | 108.96 | 1362 | 1.0180 | 0.86 | 0.2716 | 1.3149 | 0.8600 | 0.8552 | 0.1381 | 0.0471 |
| 0.0038 | 110.0 | 1375 | 1.0191 | 0.86 | 0.2716 | 1.3146 | 0.8600 | 0.8552 | 0.1381 | 0.0471 |
| 0.0038 | 110.96 | 1387 | 1.0201 | 0.86 | 0.2716 | 1.3144 | 0.8600 | 0.8552 | 0.1382 | 0.0471 |
| 0.0038 | 112.0 | 1400 | 1.0211 | 0.86 | 0.2716 | 1.3142 | 0.8600 | 0.8552 | 0.1382 | 0.0470 |
| 0.0038 | 112.96 | 1412 | 1.0222 | 0.86 | 0.2716 | 1.3141 | 0.8600 | 0.8552 | 0.1382 | 0.0471 |
| 0.0038 | 114.0 | 1425 | 1.0233 | 0.86 | 0.2715 | 1.3139 | 0.8600 | 0.8552 | 0.1383 | 0.0470 |
| 0.0038 | 114.96 | 1437 | 1.0242 | 0.86 | 0.2715 | 1.3138 | 0.8600 | 0.8552 | 0.1383 | 0.0470 |
| 0.0038 | 116.0 | 1450 | 1.0253 | 0.86 | 0.2715 | 1.3136 | 0.8600 | 0.8552 | 0.1383 | 0.0469 |
| 0.0038 | 116.96 | 1462 | 1.0263 | 0.86 | 0.2715 | 1.3134 | 0.8600 | 0.8552 | 0.1383 | 0.0470 |
| 0.0038 | 118.0 | 1475 | 1.0273 | 0.86 | 0.2715 | 1.3133 | 0.8600 | 0.8552 | 0.1384 | 0.0470 |
| 0.0038 | 118.96 | 1487 | 1.0282 | 0.86 | 0.2714 | 1.3131 | 0.8600 | 0.8552 | 0.1384 | 0.0468 |
| 0.0006 | 120.0 | 1500 | 1.0292 | 0.86 | 0.2714 | 1.3130 | 0.8600 | 0.8552 | 0.1385 | 0.0468 |
| 0.0006 | 120.96 | 1512 | 1.0301 | 0.86 | 0.2714 | 1.3128 | 0.8600 | 0.8552 | 0.1385 | 0.0468 |
| 0.0006 | 122.0 | 1525 | 1.0311 | 0.86 | 0.2714 | 1.3127 | 0.8600 | 0.8552 | 0.1386 | 0.0467 |
| 0.0006 | 122.96 | 1537 | 1.0319 | 0.86 | 0.2714 | 1.3126 | 0.8600 | 0.8552 | 0.1386 | 0.0467 |
| 0.0006 | 124.0 | 1550 | 1.0329 | 0.86 | 0.2714 | 1.3124 | 0.8600 | 0.8552 | 0.1387 | 0.0467 |
| 0.0006 | 124.96 | 1562 | 1.0337 | 0.86 | 0.2713 | 1.3123 | 0.8600 | 0.8552 | 0.1393 | 0.0467 |
| 0.0006 | 126.0 | 1575 | 1.0346 | 0.86 | 0.2713 | 1.3122 | 0.8600 | 0.8552 | 0.1374 | 0.0466 |
| 0.0006 | 126.96 | 1587 | 1.0354 | 0.86 | 0.2713 | 1.3120 | 0.8600 | 0.8552 | 0.1375 | 0.0466 |
| 0.0006 | 128.0 | 1600 | 1.0363 | 0.86 | 0.2713 | 1.3119 | 0.8600 | 0.8552 | 0.1375 | 0.0467 |
| 0.0006 | 128.96 | 1612 | 1.0372 | 0.86 | 0.2713 | 1.3118 | 0.8600 | 0.8552 | 0.1375 | 0.0466 |
| 0.0006 | 130.0 | 1625 | 1.0380 | 0.86 | 0.2712 | 1.3117 | 0.8600 | 0.8552 | 0.1375 | 0.0466 |
| 0.0006 | 130.96 | 1637 | 1.0388 | 0.86 | 0.2712 | 1.3116 | 0.8600 | 0.8552 | 0.1375 | 0.0467 |
| 0.0006 | 132.0 | 1650 | 1.0396 | 0.86 | 0.2712 | 1.3115 | 0.8600 | 0.8552 | 0.1375 | 0.0465 |
| 0.0006 | 132.96 | 1662 | 1.0403 | 0.86 | 0.2712 | 1.3113 | 0.8600 | 0.8552 | 0.1375 | 0.0466 |
| 0.0006 | 134.0 | 1675 | 1.0411 | 0.86 | 0.2712 | 1.3113 | 0.8600 | 0.8552 | 0.1376 | 0.0466 |
| 0.0006 | 134.96 | 1687 | 1.0419 | 0.86 | 0.2711 | 1.3112 | 0.8600 | 0.8552 | 0.1376 | 0.0466 |
| 0.0006 | 136.0 | 1700 | 1.0426 | 0.86 | 0.2711 | 1.3111 | 0.8600 | 0.8552 | 0.1376 | 0.0465 |
| 0.0006 | 136.96 | 1712 | 1.0433 | 0.86 | 0.2711 | 1.3110 | 0.8600 | 0.8552 | 0.1376 | 0.0465 |
| 0.0006 | 138.0 | 1725 | 1.0441 | 0.86 | 0.2711 | 1.3109 | 0.8600 | 0.8552 | 0.1376 | 0.0465 |
| 0.0006 | 138.96 | 1737 | 1.0448 | 0.86 | 0.2711 | 1.3108 | 0.8600 | 0.8552 | 0.1376 | 0.0465 |
| 0.0006 | 140.0 | 1750 | 1.0455 | 0.86 | 0.2710 | 1.3107 | 0.8600 | 0.8552 | 0.1377 | 0.0465 |
| 0.0006 | 140.96 | 1762 | 1.0461 | 0.86 | 0.2710 | 1.3106 | 0.8600 | 0.8552 | 0.1377 | 0.0465 |
| 0.0006 | 142.0 | 1775 | 1.0468 | 0.86 | 0.2710 | 1.3106 | 0.8600 | 0.8552 | 0.1377 | 0.0465 |
| 0.0006 | 142.96 | 1787 | 1.0474 | 0.86 | 0.2710 | 1.3105 | 0.8600 | 0.8552 | 0.1377 | 0.0465 |
| 0.0006 | 144.0 | 1800 | 1.0481 | 0.86 | 0.2710 | 1.3104 | 0.8600 | 0.8552 | 0.1377 | 0.0465 |
| 0.0006 | 144.96 | 1812 | 1.0487 | 0.86 | 0.2710 | 1.3103 | 0.8600 | 0.8552 | 0.1378 | 0.0465 |
| 0.0006 | 146.0 | 1825 | 1.0494 | 0.86 | 0.2709 | 1.3102 | 0.8600 | 0.8552 | 0.1378 | 0.0465 |
| 0.0006 | 146.96 | 1837 | 1.0500 | 0.86 | 0.2709 | 1.3102 | 0.8600 | 0.8552 | 0.1378 | 0.0465 |
| 0.0006 | 148.0 | 1850 | 1.0506 | 0.86 | 0.2709 | 1.3101 | 0.8600 | 0.8552 | 0.1378 | 0.0465 |
| 0.0006 | 148.96 | 1862 | 1.0511 | 0.86 | 0.2709 | 1.3100 | 0.8600 | 0.8552 | 0.1378 | 0.0464 |
| 0.0006 | 150.0 | 1875 | 1.0517 | 0.86 | 0.2709 | 1.3099 | 0.8600 | 0.8552 | 0.1378 | 0.0464 |
| 0.0006 | 150.96 | 1887 | 1.0523 | 0.86 | 0.2709 | 1.3099 | 0.8600 | 0.8552 | 0.1378 | 0.0464 |
| 0.0006 | 152.0 | 1900 | 1.0529 | 0.86 | 0.2708 | 1.3098 | 0.8600 | 0.8552 | 0.1378 | 0.0464 |
| 0.0006 | 152.96 | 1912 | 1.0534 | 0.86 | 0.2708 | 1.3097 | 0.8600 | 0.8552 | 0.1378 | 0.0464 |
| 0.0006 | 154.0 | 1925 | 1.0539 | 0.86 | 0.2708 | 1.3096 | 0.8600 | 0.8552 | 0.1378 | 0.0464 |
| 0.0006 | 154.96 | 1937 | 1.0544 | 0.86 | 0.2708 | 1.3096 | 0.8600 | 0.8552 | 0.1378 | 0.0464 |
| 0.0006 | 156.0 | 1950 | 1.0550 | 0.86 | 0.2708 | 1.3095 | 0.8600 | 0.8552 | 0.1378 | 0.0464 |
| 0.0006 | 156.96 | 1962 | 1.0554 | 0.86 | 0.2708 | 1.3094 | 0.8600 | 0.8552 | 0.1378 | 0.0464 |
| 0.0006 | 158.0 | 1975 | 1.0559 | 0.86 | 0.2707 | 1.3094 | 0.8600 | 0.8552 | 0.1378 | 0.0464 |
| 0.0006 | 158.96 | 1987 | 1.0563 | 0.86 | 0.2707 | 1.3093 | 0.8600 | 0.8552 | 0.1378 | 0.0463 |
| 0.0004 | 160.0 | 2000 | 1.0568 | 0.86 | 0.2707 | 1.3093 | 0.8600 | 0.8552 | 0.1378 | 0.0463 |
| 0.0004 | 160.96 | 2012 | 1.0573 | 0.86 | 0.2707 | 1.3092 | 0.8600 | 0.8552 | 0.1378 | 0.0463 |
| 0.0004 | 162.0 | 2025 | 1.0577 | 0.86 | 0.2707 | 1.3092 | 0.8600 | 0.8552 | 0.1378 | 0.0463 |
| 0.0004 | 162.96 | 2037 | 1.0581 | 0.86 | 0.2707 | 1.3091 | 0.8600 | 0.8552 | 0.1378 | 0.0463 |
| 0.0004 | 164.0 | 2050 | 1.0585 | 0.86 | 0.2707 | 1.3091 | 0.8600 | 0.8552 | 0.1378 | 0.0463 |
| 0.0004 | 164.96 | 2062 | 1.0589 | 0.86 | 0.2707 | 1.3090 | 0.8600 | 0.8552 | 0.1378 | 0.0463 |
| 0.0004 | 166.0 | 2075 | 1.0593 | 0.86 | 0.2707 | 1.3090 | 0.8600 | 0.8552 | 0.1378 | 0.0463 |
| 0.0004 | 166.96 | 2087 | 1.0597 | 0.86 | 0.2706 | 1.3089 | 0.8600 | 0.8552 | 0.1378 | 0.0463 |
| 0.0004 | 168.0 | 2100 | 1.0600 | 0.86 | 0.2706 | 1.3089 | 0.8600 | 0.8552 | 0.1378 | 0.0463 |
| 0.0004 | 168.96 | 2112 | 1.0603 | 0.86 | 0.2706 | 1.3089 | 0.8600 | 0.8552 | 0.1378 | 0.0463 |
| 0.0004 | 170.0 | 2125 | 1.0607 | 0.86 | 0.2706 | 1.3088 | 0.8600 | 0.8552 | 0.1378 | 0.0463 |
| 0.0004 | 170.96 | 2137 | 1.0610 | 0.86 | 0.2706 | 1.3088 | 0.8600 | 0.8552 | 0.1378 | 0.0463 |
| 0.0004 | 172.0 | 2150 | 1.0613 | 0.86 | 0.2706 | 1.3088 | 0.8600 | 0.8552 | 0.1378 | 0.0462 |
| 0.0004 | 172.96 | 2162 | 1.0616 | 0.86 | 0.2706 | 1.3087 | 0.8600 | 0.8552 | 0.1378 | 0.0462 |
| 0.0004 | 174.0 | 2175 | 1.0619 | 0.86 | 0.2706 | 1.3087 | 0.8600 | 0.8552 | 0.1378 | 0.0463 |
| 0.0004 | 174.96 | 2187 | 1.0621 | 0.86 | 0.2706 | 1.3087 | 0.8600 | 0.8552 | 0.1378 | 0.0462 |
| 0.0004 | 176.0 | 2200 | 1.0624 | 0.86 | 0.2706 | 1.3087 | 0.8600 | 0.8552 | 0.1378 | 0.0462 |
| 0.0004 | 176.96 | 2212 | 1.0626 | 0.86 | 0.2706 | 1.3086 | 0.8600 | 0.8552 | 0.1378 | 0.0462 |
| 0.0004 | 178.0 | 2225 | 1.0629 | 0.86 | 0.2706 | 1.3086 | 0.8600 | 0.8552 | 0.1378 | 0.0462 |
| 0.0004 | 178.96 | 2237 | 1.0630 | 0.86 | 0.2706 | 1.3086 | 0.8600 | 0.8552 | 0.1378 | 0.0462 |
| 0.0004 | 180.0 | 2250 | 1.0632 | 0.86 | 0.2706 | 1.3086 | 0.8600 | 0.8552 | 0.1378 | 0.0462 |
| 0.0004 | 180.96 | 2262 | 1.0634 | 0.86 | 0.2706 | 1.3086 | 0.8600 | 0.8552 | 0.1378 | 0.0463 |
| 0.0004 | 182.0 | 2275 | 1.0636 | 0.86 | 0.2705 | 1.3085 | 0.8600 | 0.8552 | 0.1378 | 0.0462 |
| 0.0004 | 182.96 | 2287 | 1.0637 | 0.86 | 0.2705 | 1.3085 | 0.8600 | 0.8552 | 0.1378 | 0.0462 |
| 0.0004 | 184.0 | 2300 | 1.0639 | 0.86 | 0.2705 | 1.3085 | 0.8600 | 0.8552 | 0.1378 | 0.0462 |
| 0.0004 | 184.96 | 2312 | 1.0640 | 0.86 | 0.2705 | 1.3085 | 0.8600 | 0.8552 | 0.1378 | 0.0462 |
| 0.0004 | 186.0 | 2325 | 1.0641 | 0.86 | 0.2705 | 1.3085 | 0.8600 | 0.8552 | 0.1378 | 0.0462 |
| 0.0004 | 186.96 | 2337 | 1.0642 | 0.86 | 0.2705 | 1.3085 | 0.8600 | 0.8552 | 0.1378 | 0.0462 |
| 0.0004 | 188.0 | 2350 | 1.0643 | 0.86 | 0.2705 | 1.3085 | 0.8600 | 0.8552 | 0.1378 | 0.0461 |
| 0.0004 | 188.96 | 2362 | 1.0643 | 0.86 | 0.2705 | 1.3085 | 0.8600 | 0.8552 | 0.1378 | 0.0461 |
| 0.0004 | 190.0 | 2375 | 1.0644 | 0.86 | 0.2705 | 1.3085 | 0.8600 | 0.8552 | 0.1378 | 0.0461 |
| 0.0004 | 190.96 | 2387 | 1.0644 | 0.86 | 0.2705 | 1.3085 | 0.8600 | 0.8552 | 0.1378 | 0.0461 |
| 0.0004 | 192.0 | 2400 | 1.0644 | 0.86 | 0.2705 | 1.3085 | 0.8600 | 0.8552 | 0.1378 | 0.0461 |
### Framework versions
- Transformers 4.30.2
- Pytorch 1.13.1
- Datasets 2.13.1
- Tokenizers 0.13.3
|
Subsets and Splits