modelId
stringlengths 5
139
| author
stringlengths 2
42
| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-08-30 06:27:36
| downloads
int64 0
223M
| likes
int64 0
11.7k
| library_name
stringclasses 527
values | tags
listlengths 1
4.05k
| pipeline_tag
stringclasses 55
values | createdAt
timestamp[us, tz=UTC]date 2022-03-02 23:29:04
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| card
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|---|---|---|---|---|---|---|---|---|---|
Promiseve/Taxi-v3
|
Promiseve
| 2023-09-04T04:13:29Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-09-04T04:13:27Z |
---
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: 7.48 +/- 2.78
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="Promiseve/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"])
```
|
Cardist/My_AGNEWS
|
Cardist
| 2023-09-04T04:07:28Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-09-04T03:24:41Z |
<style>
.step {
color: #CCBBFF;
}
.title {
color: #FF5511;
font-size:30px;
}
.second-title {
color: #FFCC22;
font-size:25px;
}
</style>
<div class = "title">專案名稱</div>
## AG_news classification
<div class = "second-title"> 專案描述 </div>
- ### 這是一個使用 BERT 模型進行文本分類的範例實作。它展示了如何在 AG News 資料集上訓練並評估基於 BERT 的模型。
<div class = "second-title"> 安裝與使用 </div>
### 安裝相依套件
建議pipenv建置虛擬環境
```
pip install pipenv (先安裝pipenv)
pipenv shell (建置虛擬環境)
pipenv install (已有Pipfile檔案則可直接使用以下指令安裝所需套件)
```
如果上述安裝完成則不需以下分別安裝，總之需確保你已經安裝以下套件：
- torch
- numpy
- pandas
- transformers
- scikit-learn
- tqdm
使用以下指令透過pipenv安裝相依套件：
```
pipenv install pandas numpy torch transformers scikit-learn tqdm
```
<div class = "second-title">下載專案</div>
使用以下命令從GitHub下載專案：
```
git clone https://github.com/elmmaple/AG_news.git
```
<div class = "second-title">執行專案</div>
執行專案的方法：
1. 在終端中cd切至AG_news資料夾底下。
2. 執行Python檔案，這是執行主要程式碼的入口，指令如下
```
python classfication_use_BertForSequenceClassification.py(BertForSequenceClassification版本)
or
python classification_use_BertModel.py(BertModel + 分類器版本)
```
<div class = "second-title">數據集</div>
- 格式 csv
- 下載並準備 AG News 數據集。將訓練和測試數據放入 'data' 資料夾中。
- 數據來源 https://www.kaggle.com/datasets/amananandrai/ag-news-classification-dataset?fbclid=IwAR2VmOFQDNVGL4wrczn8-cK1rJzESm2OHwFCu10xPlSNIhuEGqefK0De6xY
----
<div class = "second-title">執行程式流程</div>
程式將會載入 BERT 模型和分詞器
定義 TextClassification 模型(如用BertForSequenceClassification則不需額外寫分類器)
加載訓練和測試數據集
設定優化器和損失函數
進行模型訓練
評估模型在測試集上的表現
訓練和評估流程
<div class = "second-title">程式碼結構</div>
<div class = "step">資料載入：
使用 pandas 載入train.csv和test.csv</div>
pd.read_csv(XXX_FILE_PATH)
<div class = "step"> 模型建立 </div>
加載預訓練的BERT模型。定義 TextClassification 模型，該模型在 BERT 的基礎上添加了全連接層進行分類
tokenizer = BertTokenizerFast.from_pretrained('bert-base-uncased')
<div class = "step"> 數據處理與訓練： </div>
創建 AGNewsDataset 類別，處理文本數據，進行分詞並準備成模型可接受的格式。
使用 DataLoader 加載訓練數據，定義優化器和損失函數。
進行多個 epoch 的訓練，計算損失並進行反向傳播優化
<div class = "step"> 模型評估： </div>
創建測試數據集並使用 DataLoader 加載。在模型評估模式下，對測試數據進行預測，計算精確度、召回率和 F1 分數等指標。
<div class = "step"> 結果： </div>
根據測試數據集的預測結果，以下是模型的性能指標：
精確度（Precision）：{precision}
召回率（Recall）：{recall}
測試準確度（Test Accuracy）：{accuracy}
F1 分數：{f1}
|
Promiseve/q-FrozenLake-v1-4x4-noSlippery
|
Promiseve
| 2023-09-04T04:03:09Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-09-04T04:03:07Z |
---
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="Promiseve/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"])
```
|
zpschang/my_awesome_food_model
|
zpschang
| 2023-09-04T04:01:10Z | 193 | 0 |
transformers
|
[
"transformers",
"pytorch",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:food101",
"base_model:google/vit-base-patch16-224-in21k",
"base_model:finetune:google/vit-base-patch16-224-in21k",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-09-04T03:20:59Z |
---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
datasets:
- food101
metrics:
- accuracy
model-index:
- name: my_awesome_food_model
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: food101
type: food101
config: default
split: train[:5000]
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.886
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# my_awesome_food_model
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the food101 dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6523
- Accuracy: 0.886
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.7479 | 0.99 | 62 | 2.5911 | 0.799 |
| 1.8771 | 2.0 | 125 | 1.8183 | 0.87 |
| 1.6422 | 2.98 | 186 | 1.6523 | 0.886 |
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu117
- Datasets 2.14.4
- Tokenizers 0.13.3
|
Ebitendon/ppo-LunarLander-v2
|
Ebitendon
| 2023-09-04T03:40:36Z | 5 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-09-03T11:06:59Z |
---
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: 253.19 +/- 65.19
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
...
```
|
bigmorning/whisper_input_decoder_no_lob_with_force__0015
|
bigmorning
| 2023-09-04T03:36:18Z | 56 | 0 |
transformers
|
[
"transformers",
"tf",
"whisper",
"automatic-speech-recognition",
"generated_from_keras_callback",
"base_model:openai/whisper-tiny",
"base_model:finetune:openai/whisper-tiny",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-09-04T03:36:09Z |
---
license: apache-2.0
base_model: openai/whisper-tiny
tags:
- generated_from_keras_callback
model-index:
- name: whisper_input_decoder_no_lob_with_force__0015
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. -->
# whisper_input_decoder_no_lob_with_force__0015
This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 4.1748
- Train Accuracy: 0.0130
- Train Wermet: 0.7090
- Validation Loss: 3.3528
- Validation Accuracy: 0.0127
- Validation Wermet: 0.7856
- Epoch: 14
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 1e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Train Wermet | Validation Loss | Validation Accuracy | Validation Wermet | Epoch |
|:----------:|:--------------:|:------------:|:---------------:|:-------------------:|:-----------------:|:-----:|
| 5.4192 | 0.0107 | 1.1095 | 3.9799 | 0.0114 | 0.9502 | 0 |
| 4.7193 | 0.0116 | 0.8751 | 3.9335 | 0.0114 | 0.9414 | 1 |
| 4.6743 | 0.0117 | 0.8498 | 3.9949 | 0.0112 | 0.9682 | 2 |
| 4.6508 | 0.0117 | 0.8454 | 3.8900 | 0.0114 | 0.9453 | 3 |
| 4.6308 | 0.0118 | 0.8337 | 3.8853 | 0.0114 | 0.9495 | 4 |
| 4.6102 | 0.0118 | 0.8215 | 3.8884 | 0.0115 | 0.9205 | 5 |
| 4.5940 | 0.0118 | 0.8132 | 3.8409 | 0.0116 | 0.9007 | 6 |
| 4.5703 | 0.0119 | 0.7971 | 3.8224 | 0.0116 | 0.9098 | 7 |
| 4.5470 | 0.0120 | 0.7822 | 3.8013 | 0.0116 | 0.8938 | 8 |
| 4.5219 | 0.0120 | 0.7679 | 3.7776 | 0.0117 | 0.8829 | 9 |
| 4.4859 | 0.0121 | 0.7519 | 3.7360 | 0.0118 | 0.8411 | 10 |
| 4.4408 | 0.0123 | 0.7412 | 3.6972 | 0.0118 | 0.8593 | 11 |
| 4.3774 | 0.0124 | 0.7240 | 3.6035 | 0.0121 | 0.8234 | 12 |
| 4.2906 | 0.0127 | 0.7168 | 3.5057 | 0.0123 | 0.8130 | 13 |
| 4.1748 | 0.0130 | 0.7090 | 3.3528 | 0.0127 | 0.7856 | 14 |
### Framework versions
- Transformers 4.33.0.dev0
- TensorFlow 2.13.0
- Tokenizers 0.13.3
|
Sethblocks/WizardLM-13B-v1.2-GGUF
|
Sethblocks
| 2023-09-04T03:24:45Z | 3 | 0 | null |
[
"gguf",
"license:llama2",
"endpoints_compatible",
"region:us"
] | null | 2023-09-04T03:04:34Z |
---
license: llama2
---
# GGUF conversion of WizardLM-13B-V1.2 by WizardLM
|
bigmorning/whisper_input_decoder_no_lob_with_force__0010
|
bigmorning
| 2023-09-04T03:23:06Z | 59 | 0 |
transformers
|
[
"transformers",
"tf",
"whisper",
"automatic-speech-recognition",
"generated_from_keras_callback",
"base_model:openai/whisper-tiny",
"base_model:finetune:openai/whisper-tiny",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-09-04T03:22:57Z |
---
license: apache-2.0
base_model: openai/whisper-tiny
tags:
- generated_from_keras_callback
model-index:
- name: whisper_input_decoder_no_lob_with_force__0010
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. -->
# whisper_input_decoder_no_lob_with_force__0010
This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 4.5219
- Train Accuracy: 0.0120
- Train Wermet: 0.7679
- Validation Loss: 3.7776
- Validation Accuracy: 0.0117
- Validation Wermet: 0.8829
- Epoch: 9
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 1e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Train Wermet | Validation Loss | Validation Accuracy | Validation Wermet | Epoch |
|:----------:|:--------------:|:------------:|:---------------:|:-------------------:|:-----------------:|:-----:|
| 5.4192 | 0.0107 | 1.1095 | 3.9799 | 0.0114 | 0.9502 | 0 |
| 4.7193 | 0.0116 | 0.8751 | 3.9335 | 0.0114 | 0.9414 | 1 |
| 4.6743 | 0.0117 | 0.8498 | 3.9949 | 0.0112 | 0.9682 | 2 |
| 4.6508 | 0.0117 | 0.8454 | 3.8900 | 0.0114 | 0.9453 | 3 |
| 4.6308 | 0.0118 | 0.8337 | 3.8853 | 0.0114 | 0.9495 | 4 |
| 4.6102 | 0.0118 | 0.8215 | 3.8884 | 0.0115 | 0.9205 | 5 |
| 4.5940 | 0.0118 | 0.8132 | 3.8409 | 0.0116 | 0.9007 | 6 |
| 4.5703 | 0.0119 | 0.7971 | 3.8224 | 0.0116 | 0.9098 | 7 |
| 4.5470 | 0.0120 | 0.7822 | 3.8013 | 0.0116 | 0.8938 | 8 |
| 4.5219 | 0.0120 | 0.7679 | 3.7776 | 0.0117 | 0.8829 | 9 |
### Framework versions
- Transformers 4.33.0.dev0
- TensorFlow 2.13.0
- Tokenizers 0.13.3
|
MouseTrap/maow-maow-machine-v3
|
MouseTrap
| 2023-09-04T03:18:02Z | 30 | 0 |
diffusers
|
[
"diffusers",
"safetensors",
"pytorch",
"stable-diffusion",
"text-to-image",
"diffusion-models-class",
"dreambooth-hackathon",
"animal",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-09-04T03:12:00Z |
---
license: creativeml-openrail-m
tags:
- pytorch
- diffusers
- stable-diffusion
- text-to-image
- diffusion-models-class
- dreambooth-hackathon
- animal
widget:
- text: a drawing of Mr. Maow Maow cat in outer space
---
# DreamBooth model for the Mr. Maow Maow concept trained by MouseTrap on the MouseTrap/maow-maow-dataset-v3 dataset.
This is a Stable Diffusion model fine-tuned on the Mr. Maow Maow drawings using DreamBooth. It can be used by modifying the `instance_prompt`: **a drawing of Mr. Maow Maow cat**
## Description
Introducing Maow Maow Machine v3!
This is a Stable Diffusion model fine-tuned on Mr. Maow Maow drawings by MouseTrap (me).
## Usage
```python
from diffusers import StableDiffusionPipeline
pipeline = StableDiffusionPipeline.from_pretrained('MouseTrap/maow-maow-machine-v3')
image = pipeline().images[0]
image
```
|
SafetyMary/rl_course_unit_1
|
SafetyMary
| 2023-09-04T02:47:55Z | 6 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-09-04T02:44:03Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO_MlpPolicy_model
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 267.06 +/- 17.69
name: mean_reward
verified: false
---
# **PPO_MlpPolicy_model** Agent playing **LunarLander-v2**
This is a trained model of a **PPO_MlpPolicy_model** 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
...
```
|
michaelveino/ppo-Huggy
|
michaelveino
| 2023-09-04T02:35:38Z | 5 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"Huggy",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
] |
reinforcement-learning
| 2023-09-04T02:35:33Z |
---
library_name: ml-agents
tags:
- Huggy
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Huggy
---
# **ppo** Agent playing **Huggy**
This is a trained model of a **ppo** agent playing **Huggy**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: michaelveino/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
wangrongsheng/m3ebase
|
wangrongsheng
| 2023-09-04T02:34:02Z | 6 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"safetensors",
"bert",
"embedding",
"text-embedding",
"zh",
"en",
"region:us"
] | null | 2023-09-04T02:26:47Z |
---
language:
- zh
- en
tags:
- embedding
- text-embedding
library_name: sentence-transformers
---
# 🅜 M3E Models
[m3e-small](https://huggingface.co/moka-ai/m3e-small) | [m3e-base](https://huggingface.co/moka-ai/m3e-base)
M3E 是 Moka Massive Mixed Embedding 的缩写
- Moka，此模型由 MokaAI 训练，开源和评测，训练脚本使用 [uniem](https://github.com/wangyuxinwhy/uniem/blob/main/scripts/train_m3e.py) ，评测 BenchMark 使用 [MTEB-zh](https://github.com/wangyuxinwhy/uniem/tree/main/mteb-zh)
- Massive，此模型通过**千万级** (2200w+) 的中文句对数据集进行训练
- Mixed，此模型支持中英双语的同质文本相似度计算，异质文本检索等功能，未来还会支持代码检索
- Embedding，此模型是文本嵌入模型，可以将自然语言转换成稠密的向量
## 🆕 更新说明
- 2023.06.24，添加微调 M3E 的教程 [notebook](https://github.com/wangyuxinwhy/uniem/blob/main/examples/finetune.ipynb)，几行代码，更佳适配！<a target="_blank" href="https://colab.research.google.com/github/wangyuxinwhy/uniem/blob/main/examples/finetune.ipynb">
<img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
</a>
- 2023.06.14，添加了三个中文开源文本嵌入模型到评测中，包括 UER, ErLangShen, DMetaSoul
- 2023.06.08，添加检索任务的评测结果，在 T2Ranking 1W 中文数据集上，m3e-base 在 ndcg@10 上达到了 0.8004，超过了 openai-ada-002 的 0.7786
- 2023.06.07，添加文本分类任务的评测结果，在 6 种文本分类数据集上，m3e-base 在 accuracy 上达到了 0.6157，超过了 openai-ada-002 的 0.5956
## ⚖️ 模型对比
| | 参数数量 | 维度 | 中文 | 英文 | s2s | s2p | s2c | 开源 | 兼容性 | s2s Acc | s2p ndcg@10 |
| --------- | -------- | -------- | -------- | -------- | -------- | -------- | -------- | ---- | ---------- | ------------ | -------- |
| m3e-small | 24M | 512 | 是 | 否 | 是 | 否 | 否 | 是 | 优 | 0.5834 | 0.7262 |
| m3e-base | 110M | 768 | 是 | 是 | 是 | 是 | 否 | 是 | 优 | **0.6157** | **0.8004** |
| text2vec | 110M | 768 | 是 | 否 | 是 | 否 | 否 | 是 | 优 | 0.5755 | 0.6346 |
| openai-ada-002 | 未知 | 1536 | 是 | 是 | 是 | 是 | 是 | 否 | 优 | 0.5956 | 0.7786 |
说明：
- s2s, 即 sentence to sentence ，代表了同质文本之间的嵌入能力，适用任务：文本相似度，重复问题检测，文本分类等
- s2p, 即 sentence to passage ，代表了异质文本之间的嵌入能力，适用任务：文本检索，GPT 记忆模块等
- s2c, 即 sentence to code ，代表了自然语言和程序语言之间的嵌入能力，适用任务：代码检索
- 兼容性，代表了模型在开源社区中各种项目被支持的程度，由于 m3e 和 text2vec 都可以直接通过 sentence-transformers 直接使用，所以和 openai 在社区的支持度上相当
- ACC & ndcg@10，详情见下方的评测
Tips:
- 使用场景主要是中文，少量英文的情况，建议使用 m3e 系列的模型
- 多语言使用场景，并且不介意数据隐私的话，我建议使用 openai text-embedding-ada-002
- 代码检索场景，推荐使用 openai text-embedding-ada-002
- 文本检索场景，请使用具备文本检索能力的模型，只在 S2S 上训练的文本嵌入模型，没有办法完成文本检索任务
## 🔧 使用 M3E
您需要先安装 sentence-transformers
```bash
pip install -U sentence-transformers
```
安装完成后，您可以使用以下代码来使用 M3E Models
```python
from sentence_transformers import SentenceTransformer
model = SentenceTransformer('moka-ai/m3e-base')
#Our sentences we like to encode
sentences = [
'* Moka 此文本嵌入模型由 MokaAI 训练并开源，训练脚本使用 uniem',
'* Massive 此文本嵌入模型通过**千万级**的中文句对数据集进行训练',
'* Mixed 此文本嵌入模型支持中英双语的同质文本相似度计算，异质文本检索等功能，未来还会支持代码检索，ALL in one'
]
#Sentences are encoded by calling model.encode()
embeddings = model.encode(sentences)
#Print the embeddings
for sentence, embedding in zip(sentences, embeddings):
print("Sentence:", sentence)
print("Embedding:", embedding)
print("")
```
M3E 系列的所有模型在设计的时候就考虑到完全兼容 [sentence-transformers](https://www.sbert.net/) ，所以你可以通过**替换名称字符串**的方式在所有支持 sentence-transformers 的项目中**无缝**使用 M3E Models，比如 [chroma](https://docs.trychroma.com/getting-started), [guidance](https://github.com/microsoft/guidance), [semantic-kernel](https://github.com/microsoft/semantic-kernel) 。
## 🎨 微调模型
`uniem` 提供了非常易用的 finetune 接口，几行代码，即刻适配！
```python
from datasets import load_dataset
from uniem.finetuner import FineTuner
dataset = load_dataset('shibing624/nli_zh', 'STS-B')
# 指定训练的模型为 m3e-small
finetuner = FineTuner.from_pretrained('moka-ai/m3e-small', dataset=dataset)
finetuner.run(epochs=1)
```
详见 [uniem 微调教程](https://github.com/wangyuxinwhy/uniem/blob/main/examples/finetune.ipynb)
<a target="_blank" href="https://colab.research.google.com/github/wangyuxinwhy/uniem/blob/main/examples/finetune.ipynb">
<img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
</a>
## ➿ 训练方案
M3E 使用 in-batch 负采样的对比学习的方式在句对数据集进行训练，为了保证 in-batch 负采样的效果，我们使用 A100 80G 来最大化 batch-size，并在共计 2200W+ 的句对数据集上训练了 1 epoch。训练脚本使用 [uniem](https://github.com/wangyuxinwhy/uniem/blob/main/scripts/train_m3e.py)，您可以在这里查看具体细节。
## 🌟 特性
- 中文训练集，M3E 在大规模句对数据集上的训练，包含中文百科，金融，医疗，法律，新闻，学术等多个领域共计 2200W 句对样本，数据集详见 [M3E 数据集](#M3E数据集)
- 英文训练集，M3E 使用 MEDI 145W 英文三元组数据集进行训练，数据集详见 [MEDI 数据集](https://drive.google.com/file/d/1vZ5c2oJNonGOvXzppNg5mHz24O6jcc52/view)，此数据集由 [instructor team](https://github.com/HKUNLP/instructor-embedding) 提供
- 指令数据集，M3E 使用了 300W + 的指令微调数据集，这使得 M3E 对文本编码的时候可以遵从指令，这部分的工作主要被启发于 [instructor-embedding](https://github.com/HKUNLP/instructor-embedding)
- 基础模型，M3E 使用 hfl 实验室的 [Roberta](https://huggingface.co/hfl/chinese-roberta-wwm-ext) 系列模型进行训练，目前提供 small 和 base 两个版本，大家则需选用
- ALL IN ONE，M3E 旨在提供一个 ALL IN ONE 的文本嵌入模型，不仅支持同质句子相似度判断，还支持异质文本检索，你只需要一个模型就可以覆盖全部的应用场景，未来还会支持代码检索
## 💯 MTEB-zh 评测
- 评测模型，[text2vec](https://github.com/shibing624/text2vec), m3e-base, m3e-small, openai text-embedding-ada-002, [DMetaSoul](https://huggingface.co/DMetaSoul/sbert-chinese-general-v2), [UER](https://huggingface.co/uer/sbert-base-chinese-nli), [ErLangShen](https://huggingface.co/IDEA-CCNL/Erlangshen-SimCSE-110M-Chinese)
- 评测脚本，具体参考 [MTEB-zh] (https://github.com/wangyuxinwhy/uniem/blob/main/mteb-zh)
### 文本分类
- 数据集选择，选择开源在 HuggingFace 上的 6 种文本分类数据集，包括新闻、电商评论、股票评论、长文本等
- 评测方式，使用 MTEB 的方式进行评测，报告 Accuracy。
| | text2vec | m3e-small | m3e-base | openai | DMetaSoul | uer | erlangshen |
| ----------------- | -------- | --------- | -------- | ------ | ----------- | ------- | ----------- |
| TNews | 0.43 | 0.4443 | **0.4827** | 0.4594 | 0.3084 | 0.3539 | 0.4361 |
| JDIphone | 0.8214 | 0.8293 | **0.8533** | 0.746 | 0.7972 | 0.8283 | 0.8356 |
| GubaEastmony | 0.7472 | 0.712 | 0.7621 | 0.7574 | 0.735 | 0.7534 | **0.7787** |
| TYQSentiment | 0.6099 | 0.6596 | **0.7188** | 0.68 | 0.6437 | 0.6662 | 0.6444 |
| StockComSentiment | 0.4307 | 0.4291 | 0.4363 | **0.4819** | 0.4309 | 0.4555 | 0.4482 |
| IFlyTek | 0.414 | 0.4263 | 0.4409 | **0.4486** | 0.3969 | 0.3762 | 0.4241 |
| Average | 0.5755 | 0.5834 | **0.6157** | 0.5956 | 0.552016667 | 0.57225 | 0.594516667 |
### 检索排序
#### T2Ranking 1W
- 数据集选择，使用 [T2Ranking](https://github.com/THUIR/T2Ranking/tree/main) 数据集，由于 T2Ranking 的数据集太大，openai 评测起来的时间成本和 api 费用有些高，所以我们只选择了 T2Ranking 中的前 10000 篇文章
- 评测方式，使用 MTEB 的方式进行评测，报告 map@1, map@10, mrr@1, mrr@10, ndcg@1, ndcg@10
- 注意！从实验结果和训练方式来看，除了 M3E 模型和 openai 模型外，其余模型都没有做检索任务的训练，所以结果仅供参考。
| | text2vec | openai-ada-002 | m3e-small | m3e-base | DMetaSoul | uer | erlangshen |
| ------- | -------- | -------------- | --------- | -------- | --------- | ------- | ---------- |
| map@1 | 0.4684 | 0.6133 | 0.5574 | **0.626** | 0.25203 | 0.08647 | 0.25394 |
| map@10 | 0.5877 | 0.7423 | 0.6878 | **0.7656** | 0.33312 | 0.13008 | 0.34714 |
| mrr@1 | 0.5345 | 0.6931 | 0.6324 | **0.7047** | 0.29258 | 0.10067 | 0.29447 |
| mrr@10 | 0.6217 | 0.7668 | 0.712 | **0.7841** | 0.36287 | 0.14516 | 0.3751 |
| ndcg@1 | 0.5207 | 0.6764 | 0.6159 | **0.6881** | 0.28358 | 0.09748 | 0.28578 |
| ndcg@10 | 0.6346 | 0.7786 | 0.7262 | **0.8004** | 0.37468 | 0.15783 | 0.39329 |
#### T2Ranking
- 数据集选择，使用 T2Ranking，刨除 openai-ada-002 模型后，我们对剩余的三个模型，进行 T2Ranking 10W 和 T2Ranking 50W 的评测。（T2Ranking 评测太耗内存了... 128G 都不行）
- 评测方式，使用 MTEB 的方式进行评测，报告 ndcg@10
| | text2vec | m3e-small | m3e-base |
| ------- | -------- | --------- | -------- |
| t2r-1w | 0.6346 | 0.72621 | **0.8004** |
| t2r-10w | 0.44644 | 0.5251 | **0.6263** |
| t2r-50w | 0.33482 | 0.38626 | **0.47364** |
说明：
- 检索排序对于 text2vec 并不公平，因为 text2vec 在训练的时候没有使用过检索相关的数据集，所以没有办法很好的完成检索任务也是正常的。
## 📂 M3E数据集
如果您想要使用这些数据集，你可以在 [uniem process_zh_datasets](https://github.com/wangyuxinwhy/uniem/blob/main/scripts/process_zh_datasets.py) 中找到加载 huggingface 数据集的脚本，非 huggingface 数据集需要您根据下方提供的链接自行下载和处理。
| 数据集名称 | 领域 | 数量 | 任务类型 | Prompt | 质量 | 数据提供者 | 说明 | 是否开源/研究使用 | 是否商用 | 脚本 | Done | URL | 是否同质 |
| -------------------- | ---- | --------- | ----------------- | ------ | ---- | ------------------------------------------------------------ | ------------------------------------------------------------ | ----------------- | -------- | ---- | ---- | ------------------------------------------------------------ | -------- |
| cmrc2018 | 百科 | 14,363 | 问答 | 问答 | 优 | Yiming Cui, Ting Liu, Wanxiang Che, Li Xiao, Zhipeng Chen, Wentao Ma, Shijin Wang, Guoping Hu | https://github.com/ymcui/cmrc2018/blob/master/README_CN.md 专家标注的基于维基百科的中文阅读理解数据集，将问题和上下文视为正例 | 是 | 否 | 是 | 是 | https://huggingface.co/datasets/cmrc2018 | 否 |
| belle_2m | 百科 | 2,000,000 | 指令微调 | 无 | 优 | LianjiaTech/BELLE | belle 的指令微调数据集，使用 self instruct 方法基于 gpt3.5 生成 | 是 | 否 | 是 | 是 | https://huggingface.co/datasets/BelleGroup/train_2M_CN | 否 |
| firefily | 百科 | 1,649,399 | 指令微调 | 无 | 优 | YeungNLP | Firefly（流萤） 是一个开源的中文对话式大语言模型，使用指令微调（Instruction Tuning）在中文数据集上进行调优。使用了词表裁剪、ZeRO等技术，有效降低显存消耗和提高训练效率。 在训练中，我们使用了更小的模型参数量，以及更少的计算资源。 | 未说明 | 未说明 | 是 | 是 | https://huggingface.co/datasets/YeungNLP/firefly-train-1.1M | 否 |
| alpaca_gpt4 | 百科 | 48,818 | 指令微调 | 无 | 优 | Baolin Peng, Chunyuan Li, Pengcheng He, Michel Galley, Jianfeng Gao | 本数据集是参考Alpaca方法基于GPT4得到的self-instruct数据，约5万条。 | 是 | 否 | 是 | 是 | https://huggingface.co/datasets/shibing624/alpaca-zh | 否 |
| zhihu_kol | 百科 | 1,006,218 | 问答 | 问答 | 优 | wangrui6 | 知乎问答 | 未说明 | 未说明 | 是 | 是 | https://huggingface.co/datasets/wangrui6/Zhihu-KOL | 否 |
| hc3_chinese | 百科 | 39,781 | 问答 | 问答 | 良 | Hello-SimpleAI | 问答数据，包括人工回答和 GPT 回答 | 是 | 未说明 | 是 | 是 | https://huggingface.co/datasets/Hello-SimpleAI/HC3-Chinese | 否 |
| amazon_reviews_multi | 电商 | 210,000 | 问答 文本分类 | 摘要 | 优 | 亚马逊 | 亚马逊产品评论数据集 | 是 | 否 | 是 | 是 | https://huggingface.co/datasets/amazon_reviews_multi/viewer/zh/train?row=8 | 否 |
| mlqa | 百科 | 85,853 | 问答 | 问答 | 良 | patrickvonplaten | 一个用于评估跨语言问答性能的基准数据集 | 是 | 未说明 | 是 | 是 | https://huggingface.co/datasets/mlqa/viewer/mlqa-translate-train.zh/train?p=2 | 否 |
| xlsum | 新闻 | 93,404 | 摘要 | 摘要 | 良 | BUET CSE NLP Group | BBC的专业注释文章摘要对 | 是 | 否 | 是 | 是 | https://huggingface.co/datasets/csebuetnlp/xlsum/viewer/chinese_simplified/train?row=259 | 否 |
| ocnli | 口语 | 17,726 | 自然语言推理 | 推理 | 良 | Thomas Wolf | 自然语言推理数据集 | 是 | 否 | 是 | 是 | https://huggingface.co/datasets/clue/viewer/ocnli | 是 |
| BQ | 金融 | 60,000 | 文本分类 | 相似 | 良 | Intelligent Computing Research Center, Harbin Institute of Technology(Shenzhen) | http://icrc.hitsz.edu.cn/info/1037/1162.htm BQ 语料库包含来自网上银行自定义服务日志的 120，000 个问题对。它分为三部分：100，000 对用于训练，10，000 对用于验证，10，000 对用于测试。 数据提供者： 哈尔滨工业大学（深圳）智能计算研究中心 | 是 | 否 | 是 | 是 | https://huggingface.co/datasets/shibing624/nli_zh/viewer/BQ | 是 |
| lcqmc | 口语 | 149,226 | 文本分类 | 相似 | 良 | Ming Xu | 哈工大文本匹配数据集，LCQMC 是哈尔滨工业大学在自然语言处理国际顶会 COLING2018 构建的问题语义匹配数据集，其目标是判断两个问题的语义是否相同 | 是 | 否 | 是 | 是 | https://huggingface.co/datasets/shibing624/nli_zh/viewer/LCQMC/train | 是 |
| paws-x | 百科 | 23,576 | 文本分类 | 相似 | 优 | Bhavitvya Malik | PAWS Wiki中的示例 | 是 | 是 | 是 | 是 | https://huggingface.co/datasets/paws-x/viewer/zh/train | 是 |
| wiki_atomic_edit | 百科 | 1,213,780 | 平行语义 | 相似 | 优 | abhishek thakur | 基于中文维基百科的编辑记录收集的数据集 | 未说明 | 未说明 | 是 | 是 | https://huggingface.co/datasets/wiki_atomic_edits | 是 |
| chatmed_consult | 医药 | 549,326 | 问答 | 问答 | 优 | Wei Zhu | 真实世界的医学相关的问题，使用 gpt3.5 进行回答 | 是 | 否 | 是 | 是 | https://huggingface.co/datasets/michaelwzhu/ChatMed_Consult_Dataset | 否 |
| webqa | 百科 | 42,216 | 问答 | 问答 | 优 | suolyer | 百度于2016年开源的数据集，数据来自于百度知道；格式为一个问题多篇意思基本一致的文章，分为人为标注以及浏览器检索；数据整体质量中，因为混合了很多检索而来的文章 | 是 | 未说明 | 是 | 是 | https://huggingface.co/datasets/suolyer/webqa/viewer/suolyer--webqa/train?p=3 | 否 |
| dureader_robust | 百科 | 65,937 | 机器阅读理解 问答 | 问答 | 优 | 百度 | DuReader robust旨在利用真实应用中的数据样本来衡量阅读理解模型的鲁棒性，评测模型的过敏感性、过稳定性以及泛化能力，是首个中文阅读理解鲁棒性数据集。 | 是 | 是 | 是 | 是 | https://huggingface.co/datasets/PaddlePaddle/dureader_robust/viewer/plain_text/train?row=96 | 否 |
| csl | 学术 | 395,927 | 语料 | 摘要 | 优 | Yudong Li, Yuqing Zhang, Zhe Zhao, Linlin Shen, Weijie Liu, Weiquan Mao and Hui Zhang | 提供首个中文科学文献数据集（CSL），包含 396,209 篇中文核心期刊论文元信息 （标题、摘要、关键词、学科、门类）。CSL 数据集可以作为预训练语料，也可以构建许多NLP任务，例如文本摘要（标题预测）、 关键词生成和文本分类等。 | 是 | 是 | 是 | 是 | https://huggingface.co/datasets/neuclir/csl | 否 |
| miracl-corpus | 百科 | 4,934,368 | 语料 | 摘要 | 优 | MIRACL | The corpus for each language is prepared from a Wikipedia dump, where we keep only the plain text and discard images, tables, etc. Each article is segmented into multiple passages using WikiExtractor based on natural discourse units (e.g., \n\n in the wiki markup). Each of these passages comprises a "document" or unit of retrieval. We preserve the Wikipedia article title of each passage. | 是 | 是 | 是 | 是 | https://huggingface.co/datasets/miracl/miracl-corpus | 否 |
| lawzhidao | 法律 | 36,368 | 问答 | 问答 | 优 | 和鲸社区-Ustinian | 百度知道清洗后的法律问答 | 是 | 是 | 否 | 是 | https://www.heywhale.com/mw/dataset/5e953ca8e7ec38002d02fca7/content | 否 |
| CINLID | 成语 | 34,746 | 平行语义 | 相似 | 优 | 高长宽 | 中文成语语义推理数据集（Chinese Idioms Natural Language Inference Dataset）收集了106832条由人工撰写的成语对（含少量歇后语、俗语等短文本），通过人工标注的方式进行平衡分类，标签为entailment、contradiction和neutral，支持自然语言推理（NLI）的任务。 | 是 | 否 | 否 | 是 | https://www.luge.ai/#/luge/dataDetail?id=39 | 是 |
| DuSQL | SQL | 25,003 | NL2SQL | SQL | 优 | 百度 | DuSQL是一个面向实际应用的数据集，包含200个数据库，覆盖了164个领域，问题覆盖了匹配、计算、推理等实际应用中常见形式。该数据集更贴近真实应用场景，要求模型领域无关、问题无关，且具备计算推理等能力。 | 是 | 否 | 否 | 是 | https://www.luge.ai/#/luge/dataDetail?id=13 | 否 |
| Zhuiyi-NL2SQL | SQL | 45,918 | NL2SQL | SQL | 优 | 追一科技 刘云峰 | NL2SQL是一个多领域的简单数据集，其主要包含匹配类型问题。该数据集主要验证模型的泛化能力，其要求模型具有较强的领域泛化能力、问题泛化能力。 | 是 | 否 | 否 | 是 | https://www.luge.ai/#/luge/dataDetail?id=12 | 否 |
| Cspider | SQL | 7,785 | NL2SQL | SQL | 优 | 西湖大学 张岳 | CSpider是一个多语言数据集，其问题以中文表达，数据库以英文存储，这种双语模式在实际应用中也非常常见，尤其是数据库引擎对中文支持不好的情况下。该数据集要求模型领域无关、问题无关，且能够实现多语言匹配。 | 是 | 否 | 否 | 是 | https://www.luge.ai/#/luge/dataDetail?id=11 | 否 |
| news2016zh | 新闻 | 2,507,549 | 语料 | 摘要 | 良 | Bright Xu | 包含了250万篇新闻。新闻来源涵盖了6.3万个媒体，含标题、关键词、描述、正文。 | 是 | 是 | 否 | 是 | https://github.com/brightmart/nlp_chinese_corpus | 否 |
| baike2018qa | 百科 | 1,470,142 | 问答 | 问答 | 良 | Bright Xu | 含有150万个预先过滤过的、高质量问题和答案，每个问题属于一个类别。总共有492个类别，其中频率达到或超过10次的类别有434个。 | 是 | 是 | 否 | 是 | https://github.com/brightmart/nlp_chinese_corpus | 否 |
| webtext2019zh | 百科 | 4,258,310 | 问答 | 问答 | 优 | Bright Xu | 含有410万个预先过滤过的、高质量问题和回复。每个问题属于一个【话题】，总共有2.8万个各式话题，话题包罗万象。 | 是 | 是 | 否 | 是 | https://github.com/brightmart/nlp_chinese_corpus | 否 |
| SimCLUE | 百科 | 775,593 | 平行语义 | 相似 | 良 | 数据集合，请在 simCLUE 中查看 | 整合了中文领域绝大多数可用的开源的语义相似度和自然语言推理的数据集，并重新做了数据拆分和整理。 | 是 | 否 | 否 | 是 | https://github.com/CLUEbenchmark/SimCLUE | 是 |
| Chinese-SQuAD | 新闻 | 76,449 | 机器阅读理解 | 问答 | 优 | junzeng-pluto | 中文机器阅读理解数据集，通过机器翻译加人工校正的方式从原始Squad转换而来 | 是 | 否 | 否 | 是 | https://github.com/pluto-junzeng/ChineseSquad | 否 |
## 🗓️ 计划表
- [x] 完成 MTEB 中文评测 BenchMark, [MTEB-zh](https://github.com/wangyuxinwhy/uniem/tree/main/mteb-zh)
- [x] 完成 Large 模型的训练和开源
- [x] 完成 Finetuner ，允许更优雅的微调
- [ ] 完成支持代码检索的模型
- [ ] 对 M3E 数据集进行清洗，保留高质量的部分，组成 m3e-hq，并在 huggingface 上开源
- [ ] 在 m3e-hq 的数据集上补充 hard negative 的样本及相似度分数，组成 m3e-hq-with-score，并在 huggingface 上开源
- [ ] 在 m3e-hq-with-score 上通过 [cosent loss](https://github.com/wangyuxinwhy/uniem/blob/main/uniem/criteria.py#LL24C39-L24C39) loss 进行训练并开源模型，CoSent 原理参考这篇[博客](https://kexue.fm/archives/8847)
- [ ] 开源商用版本的 M3E models
## 🙏 致谢
感谢开源社区提供的中文语料，感谢所有在此工作中提供帮助的人们，希望中文社区越来越好，共勉！
## 📜 License
M3E models 使用的数据集中包括大量非商用的数据集，所以 M3E models 也是非商用的，仅供研究使用。不过我们已经在 M3E 数据集上标识了商用和非商用的数据集，您可以根据自己的需求自行训练。
## Citation
Please cite this model using the following format:
```
@software {Moka Massive Mixed Embedding,
author = {Wang Yuxin,Sun Qingxuan,He sicheng},
title = {M3E: Moka Massive Mixed Embedding Model},
year = {2023}
}
```
|
Bazaar/cv_rock_classification
|
Bazaar
| 2023-09-04T02:12:53Z | 195 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-09-04T02:06:25Z |
---
tags:
- image-classification
- pytorch
- huggingpics
metrics:
- accuracy
model-index:
- name: cv_rock_classification
results:
- task:
name: Image Classification
type: image-classification
metrics:
- name: Accuracy
type: accuracy
value: 0.8327974081039429
---
# cv_rock_classification
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics).
## Example Images
#### Basalt
#### Coal
#### Granite
#### Limestone
#### Marble
#### Quartzite
#### Sandstone
|
michaelveino/ppo-LunarLander-v2
|
michaelveino
| 2023-09-04T01:28:06Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-09-04T01:27:37Z |
---
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: 273.83 +/- 14.65
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
...
```
|
actionpace/Stheno-Inverted-L2-13B
|
actionpace
| 2023-09-04T01:20:02Z | 5 | 0 | null |
[
"gguf",
"en",
"license:other",
"endpoints_compatible",
"region:us"
] | null | 2023-09-01T12:24:20Z |
---
license: other
language:
- en
---
**Some of my own quants:**
**Source:** [Sao10K](https://huggingface.co/Sao10K)
**Source Model:** [Stheno-Inverted-L2-13B](https://huggingface.co/Sao10K/Stheno-Inverted-L2-13B)
**Source models for Sao10K/Stheno-Inverted-L2-13B (Merge)**
- [elinas/chronos-13b-v2](https://huggingface.co/elinas/chronos-13b-v2) ([Ref](https://huggingface.co/actionpace/chronos-13b-v2))
- [jondurbin/airoboros-l2-13b-2.1](https://huggingface.co/jondurbin/airoboros-l2-13b-2.1)
- [NousResearch/Nous-Hermes-Llama2-13b](https://huggingface.co/NousResearch/Nous-Hermes-Llama2-13b) ([Ref](https://huggingface.co/actionpace/Nous-Hermes-Llama2-13b))
- [nRuaif/Kimiko-v2](https://huggingface.co/nRuaif/Kimiko-v2) (Lora)
- [CalderaAI/13B-Legerdemain-L2](https://huggingface.co/CalderaAI/13B-Legerdemain-L2)
- [lemonilia/limarp-llama2-v2](https://huggingface.co/lemonilia/limarp-llama2-v2) (Lora)
- [ehartford/WizardLM-1.0-Uncensored-Llama2-13b](https://huggingface.co/ehartford/WizardLM-1.0-Uncensored-Llama2-13b)
- [Henk717/spring-dragon](https://huggingface.co/Henk717/spring-dragon)
|
profetize/test-trainer
|
profetize
| 2023-09-04T01:18:11Z | 8 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"generated_from_trainer",
"base_model:openai-community/gpt2",
"base_model:finetune:openai-community/gpt2",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-08-31T23:00:34Z |
---
license: mit
base_model: gpt2
tags:
- generated_from_trainer
model-index:
- name: test-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. -->
# test-trainer
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.1937
## 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 |
|:-------------:|:-----:|:----:|:---------------:|
| 3.3178 | 1.0 | 2621 | 3.2188 |
| 3.2638 | 2.0 | 5242 | 3.1988 |
| 3.2344 | 3.0 | 7863 | 3.1937 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
|
actionpace/Medusa-13b
|
actionpace
| 2023-09-04T01:12:20Z | 4 | 0 | null |
[
"gguf",
"en",
"license:other",
"endpoints_compatible",
"region:us"
] | null | 2023-09-03T14:29:56Z |
---
license: other
language:
- en
---
**Some of my own quants:**
* Medusa-13b_Q5_1_4K.gguf
* Medusa-13b_Q5_1_8K.gguf
**Source:** [Sao10K](https://huggingface.co/Sao10K)
**Source Model:** [Medusa-13b](https://huggingface.co/Sao10K/Medusa-13b)
**Source models for Sao10K/Medusa-13b (Merge)**
- [TheBloke/Llama-2-13B-fp16](https://huggingface.co/TheBloke/Llama-2-13B-fp16)
- [elinas/chronos-13b-v2](https://huggingface.co/elinas/chronos-13b-v2) ([Ref](https://huggingface.co/actionpace/chronos-13b-v2))
- [NousResearch/Nous-Hermes-Llama2-13b](https://huggingface.co/NousResearch/Nous-Hermes-Llama2-13b) ([Ref](https://huggingface.co/actionpace/Nous-Hermes-Llama2-13b))
- [jondurbin/airoboros-l2-13b-gpt4-m2.0](https://huggingface.co/jondurbin/airoboros-l2-13b-gpt4-m2.0)
- [Open-Orca/OpenOrca-Platypus2-13B](https://huggingface.co/Open-Orca/OpenOrca-Platypus2-13B) ([Ref](https://huggingface.co/actionpace/OpenOrca-Platypus2-13B))
- [TheBloke/Kimiko-13B-fp16](https://huggingface.co/TheBloke/Kimiko-13B-fp16) (nRuaif, Original deleted)
- [stabilityai/StableBeluga-13B](https://huggingface.co/stabilityai/StableBeluga-13B)
|
actionpace/Mythical-Destroyer-L2-13B
|
actionpace
| 2023-09-04T01:05:07Z | 2 | 0 | null |
[
"gguf",
"en",
"license:other",
"endpoints_compatible",
"region:us"
] | null | 2023-09-03T12:38:58Z |
---
license: other
language:
- en
---
**Some of my own quants:**
* Mythical-Destroyer-L2-13B_Q5_1_4K.gguf
* Mythical-Destroyer-L2-13B_Q5_1_8K.gguf
**Source:** [Sao10K](https://huggingface.co/Sao10K)
**Source Model:** [Mythical-Destroyer-L2-13B](https://huggingface.co/Sao10K/Mythical-Destroyer-L2-13B)
**Source models for Sao10K/Mythical-Destroyer-L2-13B (Merge)**
- [TheBloke/Llama-2-13B-fp16](https://huggingface.co/TheBloke/Llama-2-13B-fp16)
- [Gryphe/MythoMax-L2-13b](https://huggingface.co/Gryphe/MythoMax-L2-13b) ([Ref](https://huggingface.co/actionpace/MythoMax-L2-13b))
- [totally-not-an-llm/PuddleJumper-13b](https://huggingface.co/totally-not-an-llm/PuddleJumper-13b)
- [TheBloke/Llama-2-13B-Chat-fp16](https://huggingface.co/TheBloke/Llama-2-13B-Chat-fp16)
- [rombodawg/LosslessMegaCoder-llama2-13b-mini](https://huggingface.co/rombodawg/LosslessMegaCoder-llama2-13b-mini)
- [The-Face-Of-Goonery/Chronos-Beluga-v2-13bfp16](https://huggingface.co/The-Face-Of-Goonery/Chronos-Beluga-v2-13bfp16)
**Models utilizing Sao10K/Mythical-Destroyer-L2-13B**
- [The-Face-Of-Goonery/Huginn-13b-V4](https://huggingface.co/The-Face-Of-Goonery/Huginn-13b-V4) (Merge)
|
Delcos/GB
|
Delcos
| 2023-09-04T01:03:10Z | 15 | 1 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-08-29T02:57:05Z |
---
language:
- en
library_name: transformers
pipeline_tag: text-generation
---
<h2>Why are you here? 👀</h2>
If you don't know what this is then I don't recommend using it.
---
|
GeorgeOk/Llama-2-13B-chat-GGUF
|
GeorgeOk
| 2023-09-04T00:33:23Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-09-04T00:00:41Z |
This is a repo with llama 2 ggml model converted to gguf format. Original files are from https://huggingface.co/TheBloke/Llama-2-13B-chat-GGML
|
actionpace/chronoboros-grad-l2-13B
|
actionpace
| 2023-09-04T00:08:03Z | 1 | 0 | null |
[
"gguf",
"en",
"license:other",
"endpoints_compatible",
"region:us"
] | null | 2023-09-01T21:19:53Z |
---
license: other
language:
- en
---
**Some of my own quants:**
* chronoboros-grad-l2-13B_Q5_1.gguf
**Source:** [kingbri](https://huggingface.co/kingbri)
**Source Model:** [chronoboros-grad-l2-13B](https://huggingface.co/kingbri/chronoboros-grad-l2-13B)
**Source models for kingbri/chronoboros-grad-l2-13B (Merge)**
- [elinas/chronos-13b-v2](https://huggingface.co/elinas/chronos-13b-v2) ([Ref](https://huggingface.co/actionpace/chronos-13b-v2))
- [jondurbin/airoboros-l2-13b-gpt4-2.0](https://huggingface.co/jondurbin/airoboros-l2-13b-gpt4-2.0) ([Ref](https://huggingface.co/actionpace/airoboros-l2-13b-gpt4-2.0))
|
BadTiger/badtiger_llama2
|
BadTiger
| 2023-09-04T00:05:51Z | 0 | 0 | null |
[
"generated_from_trainer",
"base_model:meta-llama/Llama-2-7b-chat-hf",
"base_model:finetune:meta-llama/Llama-2-7b-chat-hf",
"region:us"
] | null | 2023-09-02T07:37:18Z |
---
base_model: meta-llama/Llama-2-7b-chat-hf
tags:
- generated_from_trainer
model-index:
- name: results
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# results
This model is a fine-tuned version of [meta-llama/Llama-2-7b-chat-hf](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- lr_scheduler_warmup_ratio: 0.03
- training_steps: 5000
### Training results
### Framework versions
- Transformers 4.32.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
|
MatthisHoules/rat-t5-base-grounded-qdmr
|
MatthisHoules
| 2023-09-04T00:04:53Z | 104 | 0 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:google-t5/t5-base",
"base_model:finetune:google-t5/t5-base",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-09-04T00:04:12Z |
---
license: apache-2.0
base_model: t5-base
tags:
- generated_from_trainer
model-index:
- name: rat-t5-base-grounded-qdmr
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. -->
# rat-t5-base-grounded-qdmr
This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1484
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 1
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 2
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 20000
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.7653 | 0.23 | 500 | 0.3142 |
| 0.3222 | 0.46 | 1000 | 0.2488 |
| 0.251 | 0.69 | 1500 | 0.2092 |
| 0.2087 | 0.92 | 2000 | 0.1796 |
| 0.1651 | 1.16 | 2500 | 0.1590 |
| 0.1454 | 1.39 | 3000 | 0.1455 |
| 0.1359 | 1.62 | 3500 | 0.1484 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
|
jaober/Reinforce-Pixelcopter
|
jaober
| 2023-09-04T00:04:05Z | 0 | 0 | null |
[
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-09-03T20:25:13Z |
---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-Pixelcopter
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 12.50 +/- 5.37
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
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
|
flytech/custom
|
flytech
| 2023-09-03T23:56:56Z | 0 | 0 | null |
[
"generated_from_trainer",
"base_model:openai-community/gpt2-xl",
"base_model:finetune:openai-community/gpt2-xl",
"license:mit",
"region:us"
] | null | 2023-09-01T07:54:33Z |
---
license: mit
base_model: gpt2-xl
tags:
- generated_from_trainer
model-index:
- name: custom
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. -->
# custom
This model is a fine-tuned version of [gpt2-xl](https://huggingface.co/gpt2-xl) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3280
## 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: 0.001
- train_batch_size: 32
- eval_batch_size: 48
- seed: 42
- gradient_accumulation_steps: 2
- 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: 0.05
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.5844 | 0.68 | 200 | 1.4102 |
| 1.4081 | 1.37 | 400 | 1.3670 |
| 1.3422 | 2.05 | 600 | 1.3424 |
| 1.2711 | 2.73 | 800 | 1.3310 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
|
rkla/minetester-treechop_shaped-v0-ppo_gymnasium_seed1-seed1
|
rkla
| 2023-09-03T23:31:52Z | 0 | 0 |
minetest-baselines
|
[
"minetest-baselines",
"tensorboard",
"minetester-treechop_shaped-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-09-03T23:31:50Z |
---
tags:
- minetester-treechop_shaped-v0
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: minetest-baselines
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: minetester-treechop_shaped-v0
type: minetester-treechop_shaped-v0
metrics:
- type: mean_reward
value: 10.55 +/- 0.00
name: mean_reward
verified: false
---
#**PPO** Agent Playing **minetester-treechop_shaped-v0**
This is a trained model of a PPO agent playing minetester-treechop_shaped-v0.
The model was trained by using
[minetest-baselines](https://github.com/EleutherAI/minetest-baselines).
## Command to reproduce the training
```bash
python -m minetest_baselines.train --algo ppo --exp-name ppo_gymnasium_seed1 --seed 1 --capture-video --track --wandb-entity rkla --save-model --upload-model --hf-entity rkla --total-timesteps 500000 --num-envs 8
```
# Hyperparameters
```python
{'anneal_lr': True,
'batch_size': 1024,
'capture_video': True,
'clip_coef': 0.1,
'cuda': True,
'ent_coef': 0.01,
'env_id': 'minetester-treechop_shaped-v0',
'exp_name': 'ppo_gymnasium_seed1',
'gae_lambda': 0.95,
'gamma': 0.99,
'hf_entity': 'rkla',
'learning_rate': 0.00025,
'max_grad_norm': 0.5,
'minibatch_size': 256,
'norm_adv': True,
'num_envs': 8,
'num_minibatches': 4,
'num_steps': 128,
'num_updates': 488,
'save_model': True,
'seed': 1,
'target_kl': None,
'torch_deterministic': True,
'total_timesteps': 500000,
'track': True,
'update_epochs': 4,
'upload_model': True,
'vf_coef': 0.5,
'wandb_entity': 'rkla',
'wandb_project_name': 'minetest-baselines'}
```
|
actionpace/chronos-13b-v2
|
actionpace
| 2023-09-03T23:22:46Z | 0 | 0 | null |
[
"gguf",
"en",
"license:other",
"endpoints_compatible",
"region:us"
] | null | 2023-09-01T19:58:13Z |
---
license: other
language:
- en
---
**Some of my own quants:**
* chronos-13b-v2_Q5_1.gguf
**Source:** [elinas](https://huggingface.co/elinas)
**Source Model:** [chronos-13b-v2](https://huggingface.co/elinas/chronos-13b-v2)
**Models utilizing elinas/chronos-13b-v2**
- [Sao10K/Medusa-13b](https://huggingface.co/Sao10K/Medusa-13b) ([Ref](https://huggingface.co/actionpace/Medusa-13b)) (Merge)
- [Sao10K/Stheno-L2-13B](https://huggingface.co/Sao10K/Stheno-L2-13B) ([Ref](https://huggingface.co/actionpace/Stheno-L2-13B)) (Merge)
- [Doctor-Shotgun/Chronohermes-Grad-L2-13b](https://huggingface.co/Doctor-Shotgun/Chronohermes-Grad-L2-13b) ([Ref](https://huggingface.co/actionpace/Chronohermes-Grad-L2-13b)) (Merge)
|
actionpace/Platypus2-13B
|
actionpace
| 2023-09-03T23:20:17Z | 2 | 0 | null |
[
"gguf",
"en",
"license:other",
"endpoints_compatible",
"region:us"
] | null | 2023-09-01T17:53:29Z |
---
license: other
language:
- en
---
**Some of my own quants:**
* Platypus2-13B_Q5_1.gguf
**Source:** [garage-bAInd](https://huggingface.co/garage-bAInd)
**Source Model:** [Platypus2-13B](https://huggingface.co/garage-bAInd/Platypus2-13B)
|
lifeofcoding/mastermax-llama-7b
|
lifeofcoding
| 2023-09-03T23:19:24Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"dataset:timdettmers/openassistant-guanaco",
"dataset:databricks/databricks-dolly-15k",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-09-02T22:12:17Z |
---
license: apache-2.0
datasets:
- timdettmers/openassistant-guanaco
- databricks/databricks-dolly-15k
---
# Mastermax Llama 7B
This is a a Llama2 7B base model that was fined tuned on additional datasets, in attempts improve performance.
## How to use with HugginFace pipeline
```
from transformers import AutoModelForCausalLM, AutoTokenizer, pineline
model = AutoModelForCausalLM.from_pretrained(
"lifeofcoding/mastermax-llama-7b",
load_in_4bit=True)
tokenizer = AutoTokenizer.from_pretrained("lifeofcoding/mastermax-llama-7b", trust_remote_code=True)
# Generate text using the pipeline
pipe = pipeline(task="text-generation",
model=model,
tokenizer=tokenizer,
max_length=200)
result = pipe(f"<s>[INST] {prompt} [/INST]")
generated_text = result[0]['generated_text']
```
- **Developed by:** [LifeOfCoding](https://www.livingthecode.life/)
- **Finetuned from model:** [GuardRail Llama2 7B Sharded](https://huggingface.co/guardrail/llama-2-7b-guanaco-instruct-sharded)
|
altierispeixoto/ppo-LunarLander-v2
|
altierispeixoto
| 2023-09-03T23:17:33Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-09-03T23:17:13Z |
---
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: 240.76 +/- 44.50
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
...
```
|
actionpace/OpenOrca-Platypus2-13B
|
actionpace
| 2023-09-03T23:16:00Z | 0 | 0 | null |
[
"gguf",
"en",
"license:other",
"endpoints_compatible",
"region:us"
] | null | 2023-08-28T00:15:26Z |
---
license: other
language:
- en
---
**Some of my own quants:**
* OpenOrca-Platypus2-13B_Q5_1.gguf
**Source:** [Open-Orca](https://huggingface.co/Open-Orca)
**Source Model:** [OpenOrca-Platypus2-13B](https://huggingface.co/Open-Orca/OpenOrca-Platypus2-13B)
**Models utilizing Open-Orca/OpenOrca-Platypus2-13B**
- [Sao10K/Medusa-13b](https://huggingface.co/Sao10K/Medusa-13b) ([Ref](https://huggingface.co/actionpace/Medusa-13b)) (Merge)
- [chargoddard/Chronorctypus-Limarobormes-13b](https://huggingface.co/chargoddard/Chronorctypus-Limarobormes-13b) ([Ref](https://huggingface.co/actionpace/Chronorctypus-Limarobormes-13b)) (Merge)
- [uukuguy/speechless-llama2-13b](https://huggingface.co/uukuguy/speechless-llama2-13b) ([Ref](https://huggingface.co/actionpace/speechless-llama2-13b)) (Merge)
- [uukuguy/speechless-llama2-hermes-orca-platypus-13b](https://huggingface.co/uukuguy/speechless-llama2-hermes-orca-platypus-13b) ([Ref](https://huggingface.co/actionpace/speechless-llama2-hermes-orca-platypus-13b)) (Merge)
- [uukuguy/speechless-llama2-hermes-orca-platypus-wizardlm-13b](https://huggingface.co/uukuguy/speechless-llama2-hermes-orca-platypus-wizardlm-13b) ([Ref](https://huggingface.co/actionpace/speechless-llama2-hermes-orca-platypus-wizardlm-13b)) (Merge)
- [uukuguy/speechless-llama2-luban-orca-platypus-13b](https://huggingface.co/uukuguy/speechless-llama2-luban-orca-platypus-13b) ([Ref](https://huggingface.co/actionpace/speechless-llama2-luban-orca-platypus-13b)) (Merge)
|
areshi/sdxl-mk1
|
areshi
| 2023-09-03T23:09:08Z | 3 | 0 |
diffusers
|
[
"diffusers",
"text-to-image",
"stable-diffusion",
"lora",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0",
"license:creativeml-openrail-m",
"region:us"
] |
text-to-image
| 2023-09-03T23:08:43Z |
---
license: creativeml-openrail-m
tags:
- text-to-image
- stable-diffusion
- lora
- diffusers
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: <s0><s1>
inference: false
---
# sdxl-mk1 LoRA by [asronline](https://replicate.com/asronline)
### Generate Mortal Kombat 1 fighters and character skins
>
## Inference with Replicate API
Grab your replicate token [here](https://replicate.com/account)
```bash
pip install replicate
export REPLICATE_API_TOKEN=r8_*************************************
```
```py
import replicate
output = replicate.run(
"sdxl-mk1@sha256:7ad4307597a51cbe61a568e835f8f475d33b97b0987117c5a89cbf0e699eb69c",
input={"prompt": "In the style of MK1, scorpion in the style of a dark and mysterious ninja costume, black and misty details, fiery eyes and hands, skull as his head"}
)
print(output)
```
You may also do inference via the API with Node.js or curl, and locally with COG and Docker, [check out the Replicate API page for this model](https://replicate.com/asronline/sdxl-mk1/api)
## Inference with 🧨 diffusers
Replicate SDXL LoRAs are trained with Pivotal Tuning, which combines training a concept via Dreambooth LoRA with training a new token with Textual Inversion.
As `diffusers` doesn't yet support textual inversion for SDXL, we will use cog-sdxl `TokenEmbeddingsHandler` class.
The trigger tokens for your prompt will be `<s0><s1>`
```shell
pip install diffusers transformers accelerate safetensors huggingface_hub
git clone https://github.com/replicate/cog-sdxl cog_sdxl
```
```py
import torch
from huggingface_hub import hf_hub_download
from diffusers import DiffusionPipeline
from cog_sdxl.dataset_and_utils import TokenEmbeddingsHandler
from diffusers.models import AutoencoderKL
pipe = DiffusionPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
torch_dtype=torch.float16,
variant="fp16",
).to("cuda")
load_lora_weights("areshi/sdxl-mk1", weight_name="lora.safetensors")
text_encoders = [pipe.text_encoder, pipe.text_encoder_2]
tokenizers = [pipe.tokenizer, pipe.tokenizer_2]
embedding_path = hf_hub_download(repo_id="areshi/sdxl-mk1", filename="embeddings.pti", repo_type="model")
embhandler = TokenEmbeddingsHandler(text_encoders, tokenizers)
embhandler.load_embeddings(embedding_path)
prompt="In the style of MK1, scorpion in the style of a dark and mysterious ninja costume, black and misty details, fiery eyes and hands, skull as his head"
images = pipe(
prompt,
cross_attention_kwargs={"scale": 0.8},
).images
#your output image
images[0]
```
|
jaober/poca-SoccerTwos
|
jaober
| 2023-09-03T23:08:29Z | 0 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"SoccerTwos",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SoccerTwos",
"region:us"
] |
reinforcement-learning
| 2023-09-03T22:54:17Z |
---
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: jaober/poca-SoccerTwos
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
Bytte/TestedLunar
|
Bytte
| 2023-09-03T23:04:05Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-09-03T23:03:34Z |
---
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: 282.51 +/- 16.84
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
...
```
|
actionpace/Synthia-13B
|
actionpace
| 2023-09-03T23:01:14Z | 3 | 0 | null |
[
"gguf",
"en",
"license:other",
"endpoints_compatible",
"region:us"
] | null | 2023-08-28T23:20:08Z |
---
license: other
language:
- en
---
Some of my own quants:
* Synthia-13B_Q5_1.gguf
Source: [migtissera](https://huggingface.co/migtissera)
Source Model: [Synthia-13B](https://huggingface.co/migtissera/Synthia-13B)
|
Danielbrdz/CodeBarcenas-7b
|
Danielbrdz
| 2023-09-03T22:50:29Z | 1,498 | 0 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"en",
"license:llama2",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-09-03T22:10:59Z |
---
license: llama2
language:
- en
---
CodeBarcenas
Model specialized in the Python language
Based on the model: WizardLM/WizardCoder-Python-7B-V1.0
And trained with the dataset: mlabonne/Evol-Instruct-Python-26k
Made with ❤️ in Guadalupe, Nuevo Leon, Mexico 🇲🇽
|
Teera/lilt-en-funsd
|
Teera
| 2023-09-03T22:41:57Z | 106 | 0 |
transformers
|
[
"transformers",
"pytorch",
"lilt",
"token-classification",
"generated_from_trainer",
"dataset:funsd-layoutlmv3",
"base_model:SCUT-DLVCLab/lilt-roberta-en-base",
"base_model:finetune:SCUT-DLVCLab/lilt-roberta-en-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2023-09-03T22:13:49Z |
---
license: mit
base_model: SCUT-DLVCLab/lilt-roberta-en-base
tags:
- generated_from_trainer
datasets:
- funsd-layoutlmv3
model-index:
- name: lilt-en-funsd
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. -->
# lilt-en-funsd
This model is a fine-tuned version of [SCUT-DLVCLab/lilt-roberta-en-base](https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base) on the funsd-layoutlmv3 dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7487
- Answer: {'precision': 0.8851674641148325, 'recall': 0.9057527539779682, 'f1': 0.8953418027828192, 'number': 817}
- Header: {'precision': 0.65625, 'recall': 0.5294117647058824, 'f1': 0.586046511627907, 'number': 119}
- Question: {'precision': 0.8825088339222615, 'recall': 0.9275766016713092, 'f1': 0.9044816659121775, 'number': 1077}
- Overall Precision: 0.8731
- Overall Recall: 0.8952
- Overall F1: 0.8840
- Overall Accuracy: 0.7977
## 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
- training_steps: 2500
### Training results
| Training Loss | Epoch | Step | Validation Loss | Answer | Header | Question | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 0.4352 | 10.53 | 200 | 0.9574 | {'precision': 0.8385167464114832, 'recall': 0.8580171358629131, 'f1': 0.8481548699334543, 'number': 817} | {'precision': 0.5673076923076923, 'recall': 0.4957983193277311, 'f1': 0.5291479820627802, 'number': 119} | {'precision': 0.8394534585824082, 'recall': 0.9127205199628597, 'f1': 0.8745551601423487, 'number': 1077} | 0.8257 | 0.8659 | 0.8453 | 0.7896 |
| 0.0467 | 21.05 | 400 | 1.3446 | {'precision': 0.8343057176196033, 'recall': 0.8751529987760098, 'f1': 0.8542413381123058, 'number': 817} | {'precision': 0.5789473684210527, 'recall': 0.46218487394957986, 'f1': 0.514018691588785, 'number': 119} | {'precision': 0.8543859649122807, 'recall': 0.904363974001857, 'f1': 0.8786648624267027, 'number': 1077} | 0.8337 | 0.8664 | 0.8497 | 0.7969 |
| 0.0125 | 31.58 | 600 | 1.4274 | {'precision': 0.8556461001164144, 'recall': 0.8996328029375765, 'f1': 0.8770883054892601, 'number': 817} | {'precision': 0.568, 'recall': 0.5966386554621849, 'f1': 0.5819672131147541, 'number': 119} | {'precision': 0.8916211293260473, 'recall': 0.9090064995357474, 'f1': 0.9002298850574713, 'number': 1077} | 0.8573 | 0.8867 | 0.8718 | 0.8010 |
| 0.0071 | 42.11 | 800 | 1.4147 | {'precision': 0.865265760197775, 'recall': 0.8567931456548348, 'f1': 0.8610086100861009, 'number': 817} | {'precision': 0.6888888888888889, 'recall': 0.5210084033613446, 'f1': 0.5933014354066986, 'number': 119} | {'precision': 0.8798206278026905, 'recall': 0.9108635097493036, 'f1': 0.8950729927007299, 'number': 1077} | 0.8654 | 0.8659 | 0.8657 | 0.8055 |
| 0.0067 | 52.63 | 1000 | 1.5877 | {'precision': 0.8747016706443914, 'recall': 0.8971848225214198, 'f1': 0.8858006042296073, 'number': 817} | {'precision': 0.6074766355140186, 'recall': 0.5462184873949579, 'f1': 0.575221238938053, 'number': 119} | {'precision': 0.8936363636363637, 'recall': 0.9127205199628597, 'f1': 0.9030776297657327, 'number': 1077} | 0.8709 | 0.8847 | 0.8778 | 0.8080 |
| 0.003 | 63.16 | 1200 | 1.5406 | {'precision': 0.875, 'recall': 0.8996328029375765, 'f1': 0.8871454435727218, 'number': 817} | {'precision': 0.584070796460177, 'recall': 0.5546218487394958, 'f1': 0.5689655172413793, 'number': 119} | {'precision': 0.8858695652173914, 'recall': 0.9080779944289693, 'f1': 0.8968363136176066, 'number': 1077} | 0.8649 | 0.8838 | 0.8742 | 0.8183 |
| 0.0011 | 73.68 | 1400 | 1.6131 | {'precision': 0.8686987104337632, 'recall': 0.9069767441860465, 'f1': 0.8874251497005988, 'number': 817} | {'precision': 0.7011494252873564, 'recall': 0.5126050420168067, 'f1': 0.5922330097087377, 'number': 119} | {'precision': 0.8821966341895483, 'recall': 0.924791086350975, 'f1': 0.9029918404351769, 'number': 1077} | 0.8690 | 0.8932 | 0.8809 | 0.8111 |
| 0.0008 | 84.21 | 1600 | 1.7487 | {'precision': 0.8851674641148325, 'recall': 0.9057527539779682, 'f1': 0.8953418027828192, 'number': 817} | {'precision': 0.65625, 'recall': 0.5294117647058824, 'f1': 0.586046511627907, 'number': 119} | {'precision': 0.8825088339222615, 'recall': 0.9275766016713092, 'f1': 0.9044816659121775, 'number': 1077} | 0.8731 | 0.8952 | 0.8840 | 0.7977 |
| 0.0007 | 94.74 | 1800 | 1.8317 | {'precision': 0.8605990783410138, 'recall': 0.9143206854345165, 'f1': 0.886646884272997, 'number': 817} | {'precision': 0.6095238095238096, 'recall': 0.5378151260504201, 'f1': 0.5714285714285715, 'number': 119} | {'precision': 0.9026876737720111, 'recall': 0.904363974001857, 'f1': 0.9035250463821892, 'number': 1077} | 0.8699 | 0.8867 | 0.8782 | 0.7934 |
| 0.0005 | 105.26 | 2000 | 1.8600 | {'precision': 0.8669778296382731, 'recall': 0.9094247246022031, 'f1': 0.8876941457586618, 'number': 817} | {'precision': 0.6428571428571429, 'recall': 0.5294117647058824, 'f1': 0.5806451612903226, 'number': 119} | {'precision': 0.8928247048138056, 'recall': 0.9127205199628597, 'f1': 0.9026629935720845, 'number': 1077} | 0.8701 | 0.8887 | 0.8793 | 0.7897 |
| 0.0005 | 115.79 | 2200 | 1.7672 | {'precision': 0.8781362007168458, 'recall': 0.8996328029375765, 'f1': 0.8887545344619106, 'number': 817} | {'precision': 0.6568627450980392, 'recall': 0.5630252100840336, 'f1': 0.6063348416289592, 'number': 119} | {'precision': 0.8939256572982774, 'recall': 0.9155060352831941, 'f1': 0.9045871559633027, 'number': 1077} | 0.8756 | 0.8882 | 0.8819 | 0.8074 |
| 0.0002 | 126.32 | 2400 | 1.8044 | {'precision': 0.8604382929642446, 'recall': 0.9130966952264382, 'f1': 0.8859857482185273, 'number': 817} | {'precision': 0.6391752577319587, 'recall': 0.5210084033613446, 'f1': 0.5740740740740741, 'number': 119} | {'precision': 0.9022140221402214, 'recall': 0.9080779944289693, 'f1': 0.9051365108745951, 'number': 1077} | 0.8721 | 0.8872 | 0.8796 | 0.8000 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
|
substratusai/weaviate-gorilla-v4-schema-split
|
substratusai
| 2023-09-03T21:55:40Z | 7 | 1 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"dataset:weaviate/WeaviateGraphQLGorilla-SchemaSplit-Train",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-09-03T19:32:16Z |
---
datasets:
- weaviate/WeaviateGraphQLGorilla-SchemaSplit-Train
---
## Dataset
Finetuned on: https://huggingface.co/datasets/weaviate/WeaviateGraphQLGorilla-SchemaSplit-Train
## Prompt template
```
## Instruction
Your task is to write GraphQL for the Natural Language Query provided. Use the provided API reference and Schema to generate the GraphQL. The GraphQL should be valid for Weaviate.
Only use the API reference to understand the syntax of the request.
## Natural Language Query
{nlcommand}
## Schema
{schema}
## API reference
{apiRef}
## Answer
{output}
```
## Example usage
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "substratusai/weaviate-gorilla-v4-schema-split"
model = AutoModelForCausalLM.from_pretrained(
model_id,
load_in_4bit=True,
device_map='auto',
)
tokenizer = AutoTokenizer.from_pretrained(model_id)
text = """
## Instruction
Your task is to write GraphQL for the Natural Language Query provided. Use the provided API reference and Schema to generate the GraphQL. The GraphQL should be valid for Weaviate.
Only use the API reference to understand the syntax of the request.
## Natural Language Query
```text Get me the top 10 historical events related to 'World War II', and show the event name, description, year, significant impact, and the names and populations of the involved countries. ```
## Schema
{ "classes": [ { "class": "HistoricalEvent", "description": "Information about historical events", "vectorIndexType": "hnsw", "vectorizer": "text2vec-transformers", "properties": [ { "name": "eventName", "dataType": ["text"], "description": "Name of the historical event" }, { "name": "description", "dataType": ["text"], "description": "Detailed description of the event" }, { "name": "year", "dataType": ["int"], "description": "Year the event occurred" }, { "name": "hadSignificantImpact", "dataType": ["boolean"], "description": "Whether the event had a significant impact" }, { "name": "involvedCountries", "dataType": ["Country"], "description": "Countries involved in the event" }{ "class": "Country", "description": "Information about countries", "vectorIndexType": "hnsw", "vectorizer": "text2vec-transformers", "properties": [ { "name": "countryName", "dataType": ["text"], "description": "Name of the country" }, { "name": "population", "dataType": ["int"], "description": "Population of the country" }}}
## API reference
1. Limit BM25 search results Limit the results[] You can limit the number of results returned by a `bm25` search, - to a fixed number, using the `limit: <N>` operator - to the first N "drops" in `score`, using the `autocut` operator `autocut` can be combined with `limit: N`, which would limit autocut's input to the first `N` objects. Limiting the number of results Use the `limit` argument to specify the maximum number of results that should be returned: ```graphql { Get { JeopardyQuestion( bm25: { query: "safety" }, limit: 3 ) { question answer _additional { score } } } } ```
## Answer
```graphql
"""
device = "cuda:0"
inputs = tokenizer(text, return_tensors="pt").to(device)
# this was needed due to a issue with model not taking token_type_ids
# inputs.pop("token_type_ids")
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```
|
KawaiiApp/anythinv3-vae-handler
|
KawaiiApp
| 2023-09-03T21:14:16Z | 26 | 0 |
diffusers
|
[
"diffusers",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"en",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-06-25T07:53:19Z |
---
language:
- en
license: creativeml-openrail-m
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
inference: true
pipeline_tag: text-to-image
---
# Inference API Documentation
## Overview
The Inference API allows you to make inference requests to perform image processing tasks using a remote service. This API supports various operations and requires specific input parameters.
## Base URL
The base URL for the Inference API is: `<hf_endpoint_url>`
## Authentication
The Inference API requires authentication using a bearer token. Include the token in the `Authorization` header of your requests.
## Request Format
Send a POST request to the endpoint URL with the following JSON payload:
```json
{
"inputs": "<positive_prompt>",
"negative_prompt": "<negative_prompt>",
"height": <height>,
"width": <width>,
"guidance_scale": <guidance_scale>,
"inference_steps" : <inference_steps>,
}
```
## Request Parameters
| Parameter | Type | Required | Description |
|-------------------|----------|----------|----------------------------------------------------|
| inputs | string | Yes | The positive prompt for the inference. |
| negative_prompt | string | No | The negative prompt for the inference (optional). |
| height | integer | Yes | The height of the image. |
| width | integer | Yes | The width of the image. |
| guidance_scale | float | Yes | The guidance scale for the inference. |
| inference_steps | integer | No | The steps for inference.(25 default) |
## Response Format
The API response will be a JSON object with the following structure:
```json
{
"image": "<base64_encoded_image>"
}
```
## Response Format
The API response will be a JSON object with the following structure:
| Field | Type | Description |
|--------|--------|------------------------------------------------|
| image | string | The base64-encoded image generated by the API. |
## Example Request
### Here's an example request using Python:
```python
import requests
url = '<hf_endpoint_url>'
token = '<hf_token>'
requestData = {
'inputs': 'Positve prompt',
'negative_prompt': 'Negative prompt goes here',
'height': 512,
'width': 512,
'guidance_scale': 7.5,
'inference_steps': 50,
}
headers = {
'Authorization': 'Bearer ' + token,
'Content-Type': 'application/json'
}
response = requests.post(url, json=requestData, headers=headers)
print(response.json())
```
### Here's an example request using JavaScript:
```js
const endpointURL = '<hf_endpoint_url>';
const hfToken = '<hf_token>';
const requestData = {
s: 'Positve prompt',
negative_prompt: 'Negative prompt goes here',
height: 512,
width: 512,
guidance_scale: 7.5,
inference_steps: 50,
};
const headers = {
'Authorization': `Bearer ${hfToken}`,
'Content-Type': 'application/json'
};
fetch(endpointURL, {
method: 'POST',
body: JSON.stringify(requestData),
headers: headers
})
.then(response => response.json())
.then(data => console.log(data))
.catch(error => console.error(error));
```
|
Chirag051/ppo-SnowballTarget
|
Chirag051
| 2023-09-03T21:00:22Z | 0 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"SnowballTarget",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SnowballTarget",
"region:us"
] |
reinforcement-learning
| 2023-09-03T21:00:19Z |
---
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: Chirag051/ppo-SnowballTarget
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
zares/word2vec_tr
|
zares
| 2023-09-03T20:46:54Z | 0 | 0 | null |
[
"word2vec",
"turkish",
"model",
"wikipedia",
"tr",
"dataset:wikipedia",
"license:apache-2.0",
"region:us"
] | null | 2023-09-03T20:30:47Z |
---
license: apache-2.0
datasets:
- wikipedia
language:
- tr
tags:
- word2vec
- turkish
- model
- wikipedia
---
|
acdg1214/ppo-Pyramids
|
acdg1214
| 2023-09-03T20:44:55Z | 9 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"Pyramids",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
] |
reinforcement-learning
| 2023-09-03T20:43:23Z |
---
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: acdg1214/ppo-Pyramids
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
garcianacho/MedLlama-2-7B-GGUF
|
garcianacho
| 2023-09-03T20:41:42Z | 25 | 2 |
transformers
|
[
"transformers",
"gguf",
"medical",
"en",
"dataset:medalpaca/medical_meadow_medqa",
"endpoints_compatible",
"region:us"
] | null | 2023-09-02T21:11:24Z |
---
datasets:
- medalpaca/medical_meadow_medqa
language:
- en
library_name: transformers
tags:
- medical
---
Quantized files of MedLLama-2-7b to be used with llamma.cpp
|
tomasito12/ppo-Huggy
|
tomasito12
| 2023-09-03T20:37:02Z | 2 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"Huggy",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
] |
reinforcement-learning
| 2023-09-03T20:33:19Z |
---
library_name: ml-agents
tags:
- Huggy
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Huggy
---
# **ppo** Agent playing **Huggy**
This is a trained model of a **ppo** agent playing **Huggy**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: tomasito12/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
jcrkn/wav2vec2-large-xls-r-300m-breton-colab_lr
|
jcrkn
| 2023-09-03T20:31:42Z | 8 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice_13_0",
"base_model:facebook/wav2vec2-xls-r-300m",
"base_model:finetune:facebook/wav2vec2-xls-r-300m",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-09-03T13:20:43Z |
---
license: apache-2.0
base_model: facebook/wav2vec2-xls-r-300m
tags:
- generated_from_trainer
datasets:
- common_voice_13_0
metrics:
- wer
model-index:
- name: wav2vec2-large-xls-r-300m-breton-colab_lr
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: common_voice_13_0
type: common_voice_13_0
config: br
split: test
args: br
metrics:
- name: Wer
type: wer
value: 1.0
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-300m-breton-colab_lr
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice_13_0 dataset.
It achieves the following results on the evaluation set:
- Loss: 3.3053
- Wer: 1.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:---:|
| 14.0739 | 0.67 | 200 | 7.9939 | 1.0 |
| 4.5637 | 1.34 | 400 | 3.9380 | 1.0 |
| 3.2975 | 2.02 | 600 | 3.1902 | 1.0 |
| 3.0457 | 2.69 | 800 | 3.1215 | 1.0 |
| 2.8262 | 3.36 | 1000 | 3.1005 | 1.0 |
| 2.2082 | 4.03 | 1200 | 3.2135 | 1.0 |
| 1.7804 | 4.71 | 1400 | 3.3053 | 1.0 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
|
Luosoy/firstModle
|
Luosoy
| 2023-09-03T20:31:21Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-09-03T20:31:00Z |
---
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: 244.56 +/- 19.86
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
...
```
|
Youssef320/1L-BERT-finetuned-newcode
|
Youssef320
| 2023-09-03T20:30:01Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-09-03T19:19:02Z |
---
tags:
- generated_from_trainer
model-index:
- name: 1L-BERT-finetuned-newcode
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. -->
# 1L-BERT-finetuned-newcode
This model is a fine-tuned version of [Youssef320/LSTM-finetuned-50label-15epoch](https://huggingface.co/Youssef320/LSTM-finetuned-50label-15epoch) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.0668
- Top 1 Macro F1 Score: 0.1385
- Top 1 Weighted F1score: 0.1902
- Top 3 Macro F1 Score: 0.2868
- Top3 3 Weighted F1 Score : 0.3758
## 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: 64
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 32
- total_train_batch_size: 2048
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Top 1 Macro F1 Score | Top 1 Weighted F1score | Top 3 Macro F1 Score | Top3 3 Weighted F1 Score |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|:----------------------:|:--------------------:|:-------------------------:|
| 2.8006 | 0.13 | 32 | 3.1069 | 0.1217 | 0.1745 | 0.2614 | 0.3568 |
| 2.8197 | 0.26 | 64 | 3.0678 | 0.1271 | 0.1797 | 0.2715 | 0.3654 |
| 2.7933 | 0.4 | 96 | 3.0518 | 0.1279 | 0.1819 | 0.2714 | 0.3661 |
| 2.7852 | 0.53 | 128 | 3.0463 | 0.1322 | 0.1853 | 0.2787 | 0.3699 |
| 2.7847 | 0.66 | 160 | 3.0273 | 0.1294 | 0.1817 | 0.2773 | 0.3725 |
| 2.7899 | 0.79 | 192 | 3.0185 | 0.1316 | 0.1877 | 0.2769 | 0.3717 |
| 2.793 | 0.93 | 224 | 3.0140 | 0.1332 | 0.1864 | 0.2794 | 0.3731 |
| 2.6818 | 1.06 | 256 | 3.0629 | 0.1345 | 0.1879 | 0.2829 | 0.3739 |
| 2.6676 | 1.19 | 288 | 3.0798 | 0.1335 | 0.1867 | 0.2806 | 0.3724 |
| 2.6859 | 1.32 | 320 | 3.0595 | 0.1320 | 0.1845 | 0.2787 | 0.3704 |
| 2.6939 | 1.45 | 352 | 3.0650 | 0.1321 | 0.1841 | 0.2801 | 0.3710 |
| 2.7114 | 1.59 | 384 | 3.0594 | 0.1319 | 0.1841 | 0.2823 | 0.3735 |
| 2.7414 | 1.72 | 416 | 3.0475 | 0.1340 | 0.1864 | 0.2788 | 0.3710 |
| 2.7102 | 1.85 | 448 | 3.0464 | 0.1347 | 0.1883 | 0.2817 | 0.3741 |
| 2.7537 | 1.98 | 480 | 3.0270 | 0.1344 | 0.1879 | 0.2794 | 0.3736 |
| 2.616 | 2.12 | 512 | 3.0929 | 0.1361 | 0.1883 | 0.2798 | 0.3706 |
| 2.621 | 2.25 | 544 | 3.0821 | 0.1347 | 0.1867 | 0.2785 | 0.3709 |
| 2.6409 | 2.38 | 576 | 3.0870 | 0.1352 | 0.1873 | 0.2806 | 0.3705 |
| 2.6904 | 2.51 | 608 | 3.0735 | 0.1349 | 0.1867 | 0.2854 | 0.3748 |
| 2.6531 | 2.64 | 640 | 3.0732 | 0.1357 | 0.1895 | 0.2820 | 0.3731 |
| 2.6643 | 2.78 | 672 | 3.0677 | 0.1374 | 0.1896 | 0.2814 | 0.3729 |
| 2.6948 | 2.91 | 704 | 3.0668 | 0.1385 | 0.1902 | 0.2868 | 0.3758 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.12.1+cu102
- Datasets 2.0.0
- Tokenizers 0.11.0
|
MatthisHoules/checkpoints
|
MatthisHoules
| 2023-09-03T20:06:24Z | 104 | 0 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:google-t5/t5-base",
"base_model:finetune:google-t5/t5-base",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-09-03T20:01:32Z |
---
license: apache-2.0
base_model: t5-base
tags:
- generated_from_trainer
model-index:
- name: checkpoints
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. -->
# checkpoints
This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1363
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 1
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 2
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 20000
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.6612 | 0.23 | 500 | 0.3126 |
| 0.3142 | 0.46 | 1000 | 0.2436 |
| 0.2439 | 0.69 | 1500 | 0.2003 |
| 0.2004 | 0.92 | 2000 | 0.1803 |
| 0.1602 | 1.16 | 2500 | 0.1616 |
| 0.1417 | 1.39 | 3000 | 0.1579 |
| 0.1332 | 1.62 | 3500 | 0.1478 |
| 0.1331 | 1.85 | 4000 | 0.1351 |
| 0.1149 | 2.08 | 4500 | 0.1363 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
|
dwitidibyajyoti/layoutmlv3_sunday_sep3_v5
|
dwitidibyajyoti
| 2023-09-03T19:58:04Z | 75 | 0 |
transformers
|
[
"transformers",
"pytorch",
"layoutlmv3",
"token-classification",
"generated_from_trainer",
"base_model:microsoft/layoutlmv3-base",
"base_model:finetune:microsoft/layoutlmv3-base",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2023-09-03T17:54:22Z |
---
license: cc-by-nc-sa-4.0
base_model: microsoft/layoutlmv3-base
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: layoutmlv3_sunday_sep3_v5
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# layoutmlv3_sunday_sep3_v5
This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1630
- Precision: 0.6867
- Recall: 0.7308
- F1: 0.7081
- Accuracy: 0.9570
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 1000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 8.33 | 100 | 0.5150 | 0.5139 | 0.4744 | 0.4933 | 0.8460 |
| No log | 16.67 | 200 | 0.2462 | 0.5053 | 0.6154 | 0.5549 | 0.9387 |
| No log | 25.0 | 300 | 0.1973 | 0.6471 | 0.7051 | 0.6748 | 0.9536 |
| No log | 33.33 | 400 | 0.1617 | 0.6667 | 0.7179 | 0.6914 | 0.9520 |
| 0.3718 | 41.67 | 500 | 0.1630 | 0.6867 | 0.7308 | 0.7081 | 0.9570 |
| 0.3718 | 50.0 | 600 | 0.2247 | 0.5106 | 0.6154 | 0.5581 | 0.9073 |
| 0.3718 | 58.33 | 700 | 0.3364 | 0.5393 | 0.6154 | 0.5749 | 0.8907 |
| 0.3718 | 66.67 | 800 | 0.1783 | 0.5435 | 0.6410 | 0.5882 | 0.9454 |
| 0.3718 | 75.0 | 900 | 0.2255 | 0.5263 | 0.6410 | 0.5780 | 0.9305 |
| 0.0196 | 83.33 | 1000 | 0.2781 | 0.5158 | 0.6282 | 0.5665 | 0.9123 |
### Framework versions
- Transformers 4.33.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
|
gretchypr/SpaceInvader
|
gretchypr
| 2023-09-03T19:55:11Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-09-03T19:54:31Z |
---
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: 659.50 +/- 237.90
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 gretchypr -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 gretchypr -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 gretchypr
```
## 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'}
```
|
dkqjrm/20230903230355
|
dkqjrm
| 2023-09-03T19:32:49Z | 106 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:super_glue",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-09-03T14:04:14Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- super_glue
metrics:
- accuracy
model-index:
- name: '20230903230355'
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. -->
# 20230903230355
This model is a fine-tuned version of [bert-large-cased](https://huggingface.co/bert-large-cased) on the super_glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6499
- Accuracy: 0.5
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 16
- eval_batch_size: 8
- seed: 11
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 80.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| No log | 1.0 | 340 | 0.6198 | 0.5 |
| 0.6345 | 2.0 | 680 | 0.6217 | 0.5 |
| 0.6271 | 3.0 | 1020 | 0.6081 | 0.5 |
| 0.6271 | 4.0 | 1360 | 0.6146 | 0.5 |
| 0.6166 | 5.0 | 1700 | 0.6180 | 0.5 |
| 0.619 | 6.0 | 2040 | 0.6220 | 0.5 |
| 0.619 | 7.0 | 2380 | 0.6023 | 0.5 |
| 0.605 | 8.0 | 2720 | 0.5987 | 0.5 |
| 0.5863 | 9.0 | 3060 | 0.6086 | 0.5016 |
| 0.5863 | 10.0 | 3400 | 0.6292 | 0.5047 |
| 0.5789 | 11.0 | 3740 | 0.6150 | 0.5016 |
| 0.5716 | 12.0 | 4080 | 0.5969 | 0.5 |
| 0.5716 | 13.0 | 4420 | 0.6045 | 0.5 |
| 0.5599 | 14.0 | 4760 | 0.6281 | 0.4969 |
| 0.5555 | 15.0 | 5100 | 0.6021 | 0.5 |
| 0.5555 | 16.0 | 5440 | 0.6161 | 0.5 |
| 0.553 | 17.0 | 5780 | 0.6050 | 0.5 |
| 0.5412 | 18.0 | 6120 | 0.6483 | 0.4984 |
| 0.5412 | 19.0 | 6460 | 0.6169 | 0.5 |
| 0.5403 | 20.0 | 6800 | 0.6287 | 0.5 |
| 0.5349 | 21.0 | 7140 | 0.6369 | 0.5 |
| 0.5349 | 22.0 | 7480 | 0.6163 | 0.5 |
| 0.5341 | 23.0 | 7820 | 0.6180 | 0.4984 |
| 0.5264 | 24.0 | 8160 | 0.6171 | 0.5 |
| 0.5265 | 25.0 | 8500 | 0.6289 | 0.5 |
| 0.5265 | 26.0 | 8840 | 0.6161 | 0.5 |
| 0.5218 | 27.0 | 9180 | 0.6542 | 0.4984 |
| 0.5204 | 28.0 | 9520 | 0.6246 | 0.5 |
| 0.5204 | 29.0 | 9860 | 0.6192 | 0.5 |
| 0.5164 | 30.0 | 10200 | 0.6213 | 0.5 |
| 0.5136 | 31.0 | 10540 | 0.6256 | 0.5 |
| 0.5136 | 32.0 | 10880 | 0.6605 | 0.5 |
| 0.5113 | 33.0 | 11220 | 0.6310 | 0.5 |
| 0.5101 | 34.0 | 11560 | 0.6348 | 0.5 |
| 0.5101 | 35.0 | 11900 | 0.6392 | 0.5 |
| 0.5095 | 36.0 | 12240 | 0.6291 | 0.5 |
| 0.5058 | 37.0 | 12580 | 0.6399 | 0.5 |
| 0.5058 | 38.0 | 12920 | 0.6546 | 0.5 |
| 0.5022 | 39.0 | 13260 | 0.6294 | 0.5 |
| 0.5009 | 40.0 | 13600 | 0.6348 | 0.5 |
| 0.5009 | 41.0 | 13940 | 0.6261 | 0.5 |
| 0.5005 | 42.0 | 14280 | 0.6442 | 0.5 |
| 0.4952 | 43.0 | 14620 | 0.6338 | 0.5 |
| 0.4952 | 44.0 | 14960 | 0.6358 | 0.5 |
| 0.5019 | 45.0 | 15300 | 0.6387 | 0.5 |
| 0.4968 | 46.0 | 15640 | 0.6383 | 0.5 |
| 0.4968 | 47.0 | 15980 | 0.6361 | 0.5 |
| 0.4972 | 48.0 | 16320 | 0.6428 | 0.4984 |
| 0.4947 | 49.0 | 16660 | 0.6308 | 0.5 |
| 0.4958 | 50.0 | 17000 | 0.6443 | 0.5 |
| 0.4958 | 51.0 | 17340 | 0.6520 | 0.5 |
| 0.4926 | 52.0 | 17680 | 0.6491 | 0.5 |
| 0.4942 | 53.0 | 18020 | 0.6400 | 0.5 |
| 0.4942 | 54.0 | 18360 | 0.6373 | 0.5 |
| 0.4895 | 55.0 | 18700 | 0.6579 | 0.5 |
| 0.4908 | 56.0 | 19040 | 0.6611 | 0.5 |
| 0.4908 | 57.0 | 19380 | 0.6474 | 0.5 |
| 0.4916 | 58.0 | 19720 | 0.6537 | 0.5 |
| 0.492 | 59.0 | 20060 | 0.6507 | 0.5 |
| 0.492 | 60.0 | 20400 | 0.6582 | 0.5 |
| 0.4855 | 61.0 | 20740 | 0.6578 | 0.5 |
| 0.4874 | 62.0 | 21080 | 0.6498 | 0.5 |
| 0.4874 | 63.0 | 21420 | 0.6445 | 0.5 |
| 0.485 | 64.0 | 21760 | 0.6470 | 0.5 |
| 0.4889 | 65.0 | 22100 | 0.6483 | 0.5 |
| 0.4889 | 66.0 | 22440 | 0.6412 | 0.5 |
| 0.4778 | 67.0 | 22780 | 0.6437 | 0.5 |
| 0.4862 | 68.0 | 23120 | 0.6509 | 0.5 |
| 0.4862 | 69.0 | 23460 | 0.6491 | 0.5 |
| 0.4834 | 70.0 | 23800 | 0.6485 | 0.5 |
| 0.4802 | 71.0 | 24140 | 0.6444 | 0.5 |
| 0.4802 | 72.0 | 24480 | 0.6460 | 0.5 |
| 0.4818 | 73.0 | 24820 | 0.6500 | 0.5 |
| 0.4815 | 74.0 | 25160 | 0.6549 | 0.5 |
| 0.4804 | 75.0 | 25500 | 0.6577 | 0.5 |
| 0.4804 | 76.0 | 25840 | 0.6533 | 0.5 |
| 0.4812 | 77.0 | 26180 | 0.6516 | 0.5 |
| 0.4801 | 78.0 | 26520 | 0.6513 | 0.5 |
| 0.4801 | 79.0 | 26860 | 0.6519 | 0.5 |
| 0.48 | 80.0 | 27200 | 0.6499 | 0.5 |
### Framework versions
- Transformers 4.26.1
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
roemmele/falcon-40b-loss-score
|
roemmele
| 2023-09-03T19:30:51Z | 0 | 0 | null |
[
"endpoints_compatible",
"region:us"
] | null | 2023-07-06T17:35:22Z |
This is a fork of [tiiuae/falcon-40b](https://huggingface.co/tiiuae/falcon-40b), with a custom endpoint handler (handler.py) that returns the model loss score of a given input text.
|
sandeeppathania23/photography
|
sandeeppathania23
| 2023-09-03T19:05:46Z | 1 | 0 |
diffusers
|
[
"diffusers",
"text-to-image",
"autotrain",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:finetune:stabilityai/stable-diffusion-xl-base-1.0",
"region:us"
] |
text-to-image
| 2023-09-03T17:04:56Z |
---
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: photo of sunny
tags:
- text-to-image
- diffusers
- autotrain
inference: true
---
# DreamBooth trained by AutoTrain
Text encoder was not trained.
|
gArthur98/Roberta-capstone_2
|
gArthur98
| 2023-09-03T18:56:14Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"text-classification",
"generated_from_trainer",
"base_model:FacebookAI/roberta-base",
"base_model:finetune:FacebookAI/roberta-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-09-03T16:06:01Z |
---
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: Roberta-capstone_2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Roberta-capstone_2
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4013
- Accuracy: 0.9206
## 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
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.3164 | 1.0 | 625 | 0.2393 | 0.9086 |
| 0.2111 | 2.0 | 1250 | 0.2155 | 0.916 |
| 0.1568 | 3.0 | 1875 | 0.3493 | 0.9136 |
| 0.0714 | 4.0 | 2500 | 0.3669 | 0.9192 |
| 0.0398 | 5.0 | 3125 | 0.4013 | 0.9206 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
|
Rozminzamha/detr-resnet-50_finetuned-normal-vs-disabled
|
Rozminzamha
| 2023-09-03T18:44:28Z | 187 | 0 |
transformers
|
[
"transformers",
"pytorch",
"detr",
"object-detection",
"generated_from_trainer",
"base_model:facebook/detr-resnet-50",
"base_model:finetune:facebook/detr-resnet-50",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
object-detection
| 2023-09-01T12:26:22Z |
---
license: apache-2.0
base_model: facebook/detr-resnet-50
tags:
- generated_from_trainer
model-index:
- name: detr-resnet-50_finetuned-normal-vs-disabled
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. -->
# detr-resnet-50_finetuned-normal-vs-disabled
This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
### Training results
### Framework versions
- Transformers 4.32.1
- Pytorch 1.13.1+cpu
- Datasets 2.14.4
- Tokenizers 0.13.3
|
Avishka99/finetuned-pegasus-summarization
|
Avishka99
| 2023-09-03T18:32:24Z | 102 | 0 |
transformers
|
[
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"generated_from_trainer",
"dataset:samsum",
"base_model:google/pegasus-cnn_dailymail",
"base_model:finetune:google/pegasus-cnn_dailymail",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-09-03T17:04:35Z |
---
base_model: google/pegasus-cnn_dailymail
tags:
- generated_from_trainer
datasets:
- samsum
model-index:
- name: finetuned-pegasus-summarization
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuned-pegasus-summarization
This model is a fine-tuned version of [google/pegasus-cnn_dailymail](https://huggingface.co/google/pegasus-cnn_dailymail) on the samsum dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4880
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.6315 | 0.54 | 500 | 1.4880 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
|
bigmorning/whisper_input_decoder_no_lob__0145
|
bigmorning
| 2023-09-03T18:15:24Z | 59 | 0 |
transformers
|
[
"transformers",
"tf",
"whisper",
"automatic-speech-recognition",
"generated_from_keras_callback",
"base_model:openai/whisper-tiny",
"base_model:finetune:openai/whisper-tiny",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-09-03T18:15:16Z |
---
license: apache-2.0
base_model: openai/whisper-tiny
tags:
- generated_from_keras_callback
model-index:
- name: whisper_input_decoder_no_lob__0145
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. -->
# whisper_input_decoder_no_lob__0145
This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0321
- Train Accuracy: 0.0359
- Train Wermet: 0.0347
- Validation Loss: 1.4821
- Validation Accuracy: 0.0209
- Validation Wermet: 0.3368
- Epoch: 144
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 1e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Train Wermet | Validation Loss | Validation Accuracy | Validation Wermet | Epoch |
|:----------:|:--------------:|:------------:|:---------------:|:-------------------:|:-----------------:|:-----:|
| 5.4122 | 0.0107 | 0.9328 | 3.9759 | 0.0114 | 0.9606 | 0 |
| 4.7176 | 0.0116 | 0.8683 | 3.9404 | 0.0114 | 0.9334 | 1 |
| 4.6750 | 0.0117 | 0.8478 | 3.9211 | 0.0115 | 0.9237 | 2 |
| 4.6511 | 0.0117 | 0.8413 | 3.8864 | 0.0115 | 0.9331 | 3 |
| 4.6294 | 0.0118 | 0.8270 | 3.8729 | 0.0115 | 0.9228 | 4 |
| 4.6134 | 0.0118 | 0.8199 | 3.8690 | 0.0114 | 0.9451 | 5 |
| 4.5980 | 0.0118 | 0.8102 | 3.8491 | 0.0115 | 0.9152 | 6 |
| 4.5759 | 0.0119 | 0.7890 | 3.8366 | 0.0116 | 0.8691 | 7 |
| 4.5518 | 0.0120 | 0.7694 | 3.8081 | 0.0116 | 0.9013 | 8 |
| 4.5219 | 0.0121 | 0.7591 | 3.7734 | 0.0118 | 0.8383 | 9 |
| 4.4761 | 0.0122 | 0.7400 | 3.7156 | 0.0120 | 0.8125 | 10 |
| 4.4139 | 0.0125 | 0.7257 | 3.6311 | 0.0121 | 0.8188 | 11 |
| 4.3113 | 0.0128 | 0.7127 | 3.5089 | 0.0124 | 0.8008 | 12 |
| 4.1608 | 0.0132 | 0.7088 | 3.3587 | 0.0127 | 0.7742 | 13 |
| 3.9595 | 0.0138 | 0.7012 | 3.1493 | 0.0132 | 0.7718 | 14 |
| 3.7188 | 0.0145 | 0.6820 | 2.8784 | 0.0139 | 0.7292 | 15 |
| 3.4775 | 0.0153 | 0.6678 | 2.6716 | 0.0144 | 0.7074 | 16 |
| 3.2575 | 0.0160 | 0.6481 | 2.4980 | 0.0149 | 0.6764 | 17 |
| 3.0615 | 0.0167 | 0.6314 | 2.3456 | 0.0153 | 0.6476 | 18 |
| 2.8715 | 0.0174 | 0.6094 | 2.2090 | 0.0158 | 0.6210 | 19 |
| 2.6930 | 0.0181 | 0.5931 | 2.0918 | 0.0162 | 0.5992 | 20 |
| 2.5383 | 0.0187 | 0.5739 | 1.9769 | 0.0166 | 0.5791 | 21 |
| 2.3952 | 0.0193 | 0.5512 | 1.9042 | 0.0168 | 0.5589 | 22 |
| 2.2427 | 0.0201 | 0.5333 | 1.8028 | 0.0172 | 0.5394 | 23 |
| 2.1236 | 0.0206 | 0.5174 | 1.7434 | 0.0174 | 0.5240 | 24 |
| 2.0315 | 0.0211 | 0.4978 | 1.6755 | 0.0177 | 0.5084 | 25 |
| 1.9066 | 0.0217 | 0.4773 | 1.6534 | 0.0178 | 0.4947 | 26 |
| 1.8279 | 0.0221 | 0.4596 | 1.5606 | 0.0182 | 0.4788 | 27 |
| 1.7325 | 0.0227 | 0.4412 | 1.5173 | 0.0184 | 0.4667 | 28 |
| 1.6416 | 0.0232 | 0.4199 | 1.4733 | 0.0186 | 0.4511 | 29 |
| 1.5702 | 0.0236 | 0.4028 | 1.4519 | 0.0187 | 0.4442 | 30 |
| 1.4787 | 0.0241 | 0.3839 | 1.4213 | 0.0188 | 0.4322 | 31 |
| 1.4238 | 0.0244 | 0.3700 | 1.3971 | 0.0190 | 0.4272 | 32 |
| 1.3561 | 0.0249 | 0.3594 | 1.3499 | 0.0192 | 0.4171 | 33 |
| 1.2828 | 0.0254 | 0.3431 | 1.3555 | 0.0192 | 0.4097 | 34 |
| 1.2318 | 0.0257 | 0.3277 | 1.3183 | 0.0194 | 0.4035 | 35 |
| 1.1668 | 0.0262 | 0.3201 | 1.3068 | 0.0195 | 0.3978 | 36 |
| 1.1571 | 0.0261 | 0.3105 | 1.2901 | 0.0195 | 0.3916 | 37 |
| 1.0812 | 0.0267 | 0.2989 | 1.2720 | 0.0197 | 0.3860 | 38 |
| 1.0134 | 0.0273 | 0.2863 | 1.2593 | 0.0197 | 0.3777 | 39 |
| 0.9986 | 0.0273 | 0.2769 | 1.2629 | 0.0198 | 0.3754 | 40 |
| 0.9322 | 0.0279 | 0.2653 | 1.2320 | 0.0199 | 0.3694 | 41 |
| 0.9021 | 0.0281 | 0.2552 | 1.2308 | 0.0200 | 0.3651 | 42 |
| 0.8583 | 0.0284 | 0.2444 | 1.2199 | 0.0200 | 0.3614 | 43 |
| 0.8101 | 0.0288 | 0.2355 | 1.2120 | 0.0200 | 0.3597 | 44 |
| 0.8045 | 0.0288 | 0.2299 | 1.2023 | 0.0201 | 0.3567 | 45 |
| 0.7823 | 0.0290 | 0.2213 | 1.2075 | 0.0201 | 0.3529 | 46 |
| 0.7186 | 0.0296 | 0.2107 | 1.1917 | 0.0202 | 0.3530 | 47 |
| 0.6949 | 0.0298 | 0.2028 | 1.1926 | 0.0202 | 0.3465 | 48 |
| 0.6669 | 0.0300 | 0.1943 | 1.1902 | 0.0203 | 0.3446 | 49 |
| 0.6125 | 0.0305 | 0.1842 | 1.1892 | 0.0203 | 0.3437 | 50 |
| 0.5926 | 0.0307 | 0.1778 | 1.2058 | 0.0203 | 0.3450 | 51 |
| 0.6055 | 0.0305 | 0.1738 | 1.1859 | 0.0203 | 0.3394 | 52 |
| 0.5828 | 0.0307 | 0.1653 | 1.1921 | 0.0203 | 0.3379 | 53 |
| 0.5507 | 0.0311 | 0.1569 | 1.1906 | 0.0204 | 0.3385 | 54 |
| 0.5050 | 0.0315 | 0.1485 | 1.1834 | 0.0205 | 0.3361 | 55 |
| 0.4878 | 0.0316 | 0.1447 | 1.1815 | 0.0205 | 0.3329 | 56 |
| 0.4825 | 0.0317 | 0.1410 | 1.2096 | 0.0204 | 0.3359 | 57 |
| 0.4987 | 0.0315 | 0.1374 | 1.2000 | 0.0204 | 0.3352 | 58 |
| 0.4576 | 0.0319 | 0.1305 | 1.1868 | 0.0205 | 0.3329 | 59 |
| 0.4185 | 0.0323 | 0.1215 | 1.2043 | 0.0205 | 0.3322 | 60 |
| 0.3889 | 0.0326 | 0.1156 | 1.1853 | 0.0206 | 0.3302 | 61 |
| 0.3790 | 0.0327 | 0.1101 | 1.2028 | 0.0205 | 0.3316 | 62 |
| 0.4072 | 0.0324 | 0.1110 | 1.2502 | 0.0203 | 0.3309 | 63 |
| 0.3519 | 0.0330 | 0.1020 | 1.1959 | 0.0206 | 0.3284 | 64 |
| 0.3861 | 0.0326 | 0.1034 | 1.1885 | 0.0206 | 0.3271 | 65 |
| 0.3789 | 0.0326 | 0.0961 | 1.1969 | 0.0206 | 0.3298 | 66 |
| 0.3233 | 0.0332 | 0.0905 | 1.1922 | 0.0207 | 0.3280 | 67 |
| 0.2956 | 0.0335 | 0.0854 | 1.2003 | 0.0207 | 0.3296 | 68 |
| 0.2666 | 0.0339 | 0.0796 | 1.2141 | 0.0207 | 0.3252 | 69 |
| 0.3181 | 0.0333 | 0.0813 | 1.2133 | 0.0207 | 0.3302 | 70 |
| 0.3032 | 0.0335 | 0.0770 | 1.2170 | 0.0207 | 0.3315 | 71 |
| 0.2746 | 0.0337 | 0.0741 | 1.2180 | 0.0207 | 0.3299 | 72 |
| 0.2549 | 0.0339 | 0.0705 | 1.2496 | 0.0206 | 0.3308 | 73 |
| 0.2529 | 0.0339 | 0.0685 | 1.2239 | 0.0207 | 0.3321 | 74 |
| 0.2427 | 0.0340 | 0.0671 | 1.2351 | 0.0207 | 0.3292 | 75 |
| 0.2166 | 0.0343 | 0.0623 | 1.2361 | 0.0207 | 0.3313 | 76 |
| 0.2030 | 0.0345 | 0.0585 | 1.2462 | 0.0207 | 0.3312 | 77 |
| 0.2126 | 0.0344 | 0.0566 | 1.2441 | 0.0207 | 0.3313 | 78 |
| 0.2166 | 0.0343 | 0.0569 | 1.2506 | 0.0207 | 0.3334 | 79 |
| 0.2088 | 0.0344 | 0.0562 | 1.2557 | 0.0207 | 0.3389 | 80 |
| 0.2212 | 0.0342 | 0.0560 | 1.2652 | 0.0207 | 0.3334 | 81 |
| 0.2256 | 0.0343 | 0.0543 | 1.2543 | 0.0207 | 0.3393 | 82 |
| 0.1915 | 0.0346 | 0.0501 | 1.2540 | 0.0207 | 0.3299 | 83 |
| 0.1544 | 0.0350 | 0.0443 | 1.2676 | 0.0207 | 0.3347 | 84 |
| 0.1567 | 0.0350 | 0.0435 | 1.2740 | 0.0207 | 0.3375 | 85 |
| 0.1329 | 0.0352 | 0.0405 | 1.2833 | 0.0207 | 0.3398 | 86 |
| 0.1261 | 0.0353 | 0.0392 | 1.3088 | 0.0206 | 0.3397 | 87 |
| 0.1547 | 0.0350 | 0.0438 | 1.2933 | 0.0207 | 0.3279 | 88 |
| 0.1288 | 0.0352 | 0.0377 | 1.2985 | 0.0208 | 0.3371 | 89 |
| 0.1400 | 0.0351 | 0.0391 | 1.3020 | 0.0208 | 0.3329 | 90 |
| 0.1077 | 0.0355 | 0.0351 | 1.3118 | 0.0207 | 0.3344 | 91 |
| 0.0982 | 0.0355 | 0.0348 | 1.3274 | 0.0207 | 0.3352 | 92 |
| 0.1041 | 0.0355 | 0.0356 | 1.3251 | 0.0208 | 0.3362 | 93 |
| 0.1573 | 0.0350 | 0.0364 | 1.3173 | 0.0208 | 0.3362 | 94 |
| 0.1478 | 0.0350 | 0.0365 | 1.3125 | 0.0208 | 0.3367 | 95 |
| 0.0929 | 0.0356 | 0.0336 | 1.3256 | 0.0208 | 0.3351 | 96 |
| 0.1106 | 0.0354 | 0.0341 | 1.3289 | 0.0208 | 0.3428 | 97 |
| 0.0916 | 0.0355 | 0.0322 | 1.3384 | 0.0208 | 0.3383 | 98 |
| 0.0708 | 0.0358 | 0.0313 | 1.3525 | 0.0208 | 0.3333 | 99 |
| 0.1271 | 0.0352 | 0.0326 | 1.3447 | 0.0208 | 0.3336 | 100 |
| 0.0963 | 0.0355 | 0.0329 | 1.3481 | 0.0208 | 0.3288 | 101 |
| 0.0698 | 0.0358 | 0.0294 | 1.3748 | 0.0207 | 0.3341 | 102 |
| 0.0844 | 0.0356 | 0.0313 | 1.3615 | 0.0208 | 0.3380 | 103 |
| 0.1045 | 0.0354 | 0.0319 | 1.3519 | 0.0208 | 0.3357 | 104 |
| 0.0877 | 0.0355 | 0.0326 | 1.3760 | 0.0207 | 0.3402 | 105 |
| 0.0713 | 0.0357 | 0.0303 | 1.3742 | 0.0208 | 0.3392 | 106 |
| 0.0686 | 0.0358 | 0.0292 | 1.3920 | 0.0207 | 0.3353 | 107 |
| 0.0466 | 0.0360 | 0.0287 | 1.3964 | 0.0208 | 0.3342 | 108 |
| 0.0353 | 0.0361 | 0.0258 | 1.3977 | 0.0208 | 0.3339 | 109 |
| 0.0369 | 0.0361 | 0.0269 | 1.4132 | 0.0208 | 0.3298 | 110 |
| 0.0601 | 0.0359 | 0.0304 | 1.4104 | 0.0208 | 0.3438 | 111 |
| 0.1244 | 0.0352 | 0.0332 | 1.3939 | 0.0207 | 0.3349 | 112 |
| 0.1102 | 0.0353 | 0.0301 | 1.3826 | 0.0208 | 0.3421 | 113 |
| 0.1199 | 0.0352 | 0.0311 | 1.3783 | 0.0208 | 0.3368 | 114 |
| 0.0576 | 0.0358 | 0.0281 | 1.3972 | 0.0208 | 0.3340 | 115 |
| 0.0363 | 0.0360 | 0.0255 | 1.3984 | 0.0208 | 0.3314 | 116 |
| 0.0260 | 0.0362 | 0.0260 | 1.4108 | 0.0208 | 0.3327 | 117 |
| 0.0291 | 0.0361 | 0.0265 | 1.4222 | 0.0208 | 0.3376 | 118 |
| 0.0252 | 0.0361 | 0.0249 | 1.4481 | 0.0208 | 0.3327 | 119 |
| 0.0855 | 0.0356 | 0.0283 | 1.4200 | 0.0208 | 0.3303 | 120 |
| 0.0947 | 0.0355 | 0.0287 | 1.4148 | 0.0208 | 0.3291 | 121 |
| 0.0561 | 0.0358 | 0.0250 | 1.4208 | 0.0208 | 0.3292 | 122 |
| 0.0675 | 0.0357 | 0.0272 | 1.4145 | 0.0208 | 0.3402 | 123 |
| 0.0340 | 0.0360 | 0.0276 | 1.4617 | 0.0207 | 0.3460 | 124 |
| 0.0213 | 0.0362 | 0.0285 | 1.4502 | 0.0208 | 0.3374 | 125 |
| 0.0369 | 0.0360 | 0.0287 | 1.4512 | 0.0208 | 0.3483 | 126 |
| 0.0717 | 0.0357 | 0.0310 | 1.4271 | 0.0208 | 0.3314 | 127 |
| 0.0434 | 0.0359 | 0.0242 | 1.4545 | 0.0208 | 0.3355 | 128 |
| 0.0541 | 0.0358 | 0.0281 | 1.4461 | 0.0208 | 0.3298 | 129 |
| 0.0281 | 0.0361 | 0.0265 | 1.4912 | 0.0207 | 0.3358 | 130 |
| 0.0438 | 0.0359 | 0.0260 | 1.4805 | 0.0208 | 0.3327 | 131 |
| 0.1140 | 0.0352 | 0.0304 | 1.4342 | 0.0208 | 0.3370 | 132 |
| 0.0611 | 0.0358 | 0.0330 | 1.4374 | 0.0209 | 0.3405 | 133 |
| 0.0319 | 0.0360 | 0.0320 | 1.4453 | 0.0209 | 0.3382 | 134 |
| 0.0208 | 0.0361 | 0.0290 | 1.4533 | 0.0209 | 0.3382 | 135 |
| 0.0242 | 0.0361 | 0.0311 | 1.4630 | 0.0209 | 0.3409 | 136 |
| 0.0185 | 0.0361 | 0.0315 | 1.4696 | 0.0209 | 0.3468 | 137 |
| 0.0416 | 0.0359 | 0.0294 | 1.4782 | 0.0209 | 0.3362 | 138 |
| 0.0844 | 0.0355 | 0.0333 | 1.4643 | 0.0208 | 0.3339 | 139 |
| 0.0708 | 0.0356 | 0.0316 | 1.4601 | 0.0208 | 0.3416 | 140 |
| 0.0509 | 0.0358 | 0.0303 | 1.4652 | 0.0209 | 0.3417 | 141 |
| 0.0279 | 0.0360 | 0.0314 | 1.4720 | 0.0209 | 0.3376 | 142 |
| 0.0467 | 0.0358 | 0.0314 | 1.4730 | 0.0209 | 0.3361 | 143 |
| 0.0321 | 0.0359 | 0.0347 | 1.4821 | 0.0209 | 0.3368 | 144 |
### Framework versions
- Transformers 4.33.0.dev0
- TensorFlow 2.13.0
- Tokenizers 0.13.3
|
mqraitem/dreambooth_aimals
|
mqraitem
| 2023-09-03T18:06:28Z | 29 | 0 |
diffusers
|
[
"diffusers",
"safetensors",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"dreambooth",
"base_model:runwayml/stable-diffusion-v1-5",
"base_model:finetune:runwayml/stable-diffusion-v1-5",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-09-03T17:59:23Z |
---
license: creativeml-openrail-m
base_model: runwayml/stable-diffusion-v1-5
instance_prompt: a_photo_of_an_animal
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- dreambooth
inference: true
---
# DreamBooth - mqraitem/dreambooth_aimals
This is a dreambooth model derived from runwayml/stable-diffusion-v1-5. The weights were trained on a_photo_of_an_animal using [DreamBooth](https://dreambooth.github.io/).
You can find some example images in the following.
DreamBooth for the text encoder was enabled: False.
|
bigmorning/whisper_input_decoder_no_lob__0140
|
bigmorning
| 2023-09-03T18:02:19Z | 59 | 0 |
transformers
|
[
"transformers",
"tf",
"whisper",
"automatic-speech-recognition",
"generated_from_keras_callback",
"base_model:openai/whisper-tiny",
"base_model:finetune:openai/whisper-tiny",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-09-03T18:02:13Z |
---
license: apache-2.0
base_model: openai/whisper-tiny
tags:
- generated_from_keras_callback
model-index:
- name: whisper_input_decoder_no_lob__0140
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. -->
# whisper_input_decoder_no_lob__0140
This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0844
- Train Accuracy: 0.0355
- Train Wermet: 0.0333
- Validation Loss: 1.4643
- Validation Accuracy: 0.0208
- Validation Wermet: 0.3339
- Epoch: 139
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 1e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Train Wermet | Validation Loss | Validation Accuracy | Validation Wermet | Epoch |
|:----------:|:--------------:|:------------:|:---------------:|:-------------------:|:-----------------:|:-----:|
| 5.4122 | 0.0107 | 0.9328 | 3.9759 | 0.0114 | 0.9606 | 0 |
| 4.7176 | 0.0116 | 0.8683 | 3.9404 | 0.0114 | 0.9334 | 1 |
| 4.6750 | 0.0117 | 0.8478 | 3.9211 | 0.0115 | 0.9237 | 2 |
| 4.6511 | 0.0117 | 0.8413 | 3.8864 | 0.0115 | 0.9331 | 3 |
| 4.6294 | 0.0118 | 0.8270 | 3.8729 | 0.0115 | 0.9228 | 4 |
| 4.6134 | 0.0118 | 0.8199 | 3.8690 | 0.0114 | 0.9451 | 5 |
| 4.5980 | 0.0118 | 0.8102 | 3.8491 | 0.0115 | 0.9152 | 6 |
| 4.5759 | 0.0119 | 0.7890 | 3.8366 | 0.0116 | 0.8691 | 7 |
| 4.5518 | 0.0120 | 0.7694 | 3.8081 | 0.0116 | 0.9013 | 8 |
| 4.5219 | 0.0121 | 0.7591 | 3.7734 | 0.0118 | 0.8383 | 9 |
| 4.4761 | 0.0122 | 0.7400 | 3.7156 | 0.0120 | 0.8125 | 10 |
| 4.4139 | 0.0125 | 0.7257 | 3.6311 | 0.0121 | 0.8188 | 11 |
| 4.3113 | 0.0128 | 0.7127 | 3.5089 | 0.0124 | 0.8008 | 12 |
| 4.1608 | 0.0132 | 0.7088 | 3.3587 | 0.0127 | 0.7742 | 13 |
| 3.9595 | 0.0138 | 0.7012 | 3.1493 | 0.0132 | 0.7718 | 14 |
| 3.7188 | 0.0145 | 0.6820 | 2.8784 | 0.0139 | 0.7292 | 15 |
| 3.4775 | 0.0153 | 0.6678 | 2.6716 | 0.0144 | 0.7074 | 16 |
| 3.2575 | 0.0160 | 0.6481 | 2.4980 | 0.0149 | 0.6764 | 17 |
| 3.0615 | 0.0167 | 0.6314 | 2.3456 | 0.0153 | 0.6476 | 18 |
| 2.8715 | 0.0174 | 0.6094 | 2.2090 | 0.0158 | 0.6210 | 19 |
| 2.6930 | 0.0181 | 0.5931 | 2.0918 | 0.0162 | 0.5992 | 20 |
| 2.5383 | 0.0187 | 0.5739 | 1.9769 | 0.0166 | 0.5791 | 21 |
| 2.3952 | 0.0193 | 0.5512 | 1.9042 | 0.0168 | 0.5589 | 22 |
| 2.2427 | 0.0201 | 0.5333 | 1.8028 | 0.0172 | 0.5394 | 23 |
| 2.1236 | 0.0206 | 0.5174 | 1.7434 | 0.0174 | 0.5240 | 24 |
| 2.0315 | 0.0211 | 0.4978 | 1.6755 | 0.0177 | 0.5084 | 25 |
| 1.9066 | 0.0217 | 0.4773 | 1.6534 | 0.0178 | 0.4947 | 26 |
| 1.8279 | 0.0221 | 0.4596 | 1.5606 | 0.0182 | 0.4788 | 27 |
| 1.7325 | 0.0227 | 0.4412 | 1.5173 | 0.0184 | 0.4667 | 28 |
| 1.6416 | 0.0232 | 0.4199 | 1.4733 | 0.0186 | 0.4511 | 29 |
| 1.5702 | 0.0236 | 0.4028 | 1.4519 | 0.0187 | 0.4442 | 30 |
| 1.4787 | 0.0241 | 0.3839 | 1.4213 | 0.0188 | 0.4322 | 31 |
| 1.4238 | 0.0244 | 0.3700 | 1.3971 | 0.0190 | 0.4272 | 32 |
| 1.3561 | 0.0249 | 0.3594 | 1.3499 | 0.0192 | 0.4171 | 33 |
| 1.2828 | 0.0254 | 0.3431 | 1.3555 | 0.0192 | 0.4097 | 34 |
| 1.2318 | 0.0257 | 0.3277 | 1.3183 | 0.0194 | 0.4035 | 35 |
| 1.1668 | 0.0262 | 0.3201 | 1.3068 | 0.0195 | 0.3978 | 36 |
| 1.1571 | 0.0261 | 0.3105 | 1.2901 | 0.0195 | 0.3916 | 37 |
| 1.0812 | 0.0267 | 0.2989 | 1.2720 | 0.0197 | 0.3860 | 38 |
| 1.0134 | 0.0273 | 0.2863 | 1.2593 | 0.0197 | 0.3777 | 39 |
| 0.9986 | 0.0273 | 0.2769 | 1.2629 | 0.0198 | 0.3754 | 40 |
| 0.9322 | 0.0279 | 0.2653 | 1.2320 | 0.0199 | 0.3694 | 41 |
| 0.9021 | 0.0281 | 0.2552 | 1.2308 | 0.0200 | 0.3651 | 42 |
| 0.8583 | 0.0284 | 0.2444 | 1.2199 | 0.0200 | 0.3614 | 43 |
| 0.8101 | 0.0288 | 0.2355 | 1.2120 | 0.0200 | 0.3597 | 44 |
| 0.8045 | 0.0288 | 0.2299 | 1.2023 | 0.0201 | 0.3567 | 45 |
| 0.7823 | 0.0290 | 0.2213 | 1.2075 | 0.0201 | 0.3529 | 46 |
| 0.7186 | 0.0296 | 0.2107 | 1.1917 | 0.0202 | 0.3530 | 47 |
| 0.6949 | 0.0298 | 0.2028 | 1.1926 | 0.0202 | 0.3465 | 48 |
| 0.6669 | 0.0300 | 0.1943 | 1.1902 | 0.0203 | 0.3446 | 49 |
| 0.6125 | 0.0305 | 0.1842 | 1.1892 | 0.0203 | 0.3437 | 50 |
| 0.5926 | 0.0307 | 0.1778 | 1.2058 | 0.0203 | 0.3450 | 51 |
| 0.6055 | 0.0305 | 0.1738 | 1.1859 | 0.0203 | 0.3394 | 52 |
| 0.5828 | 0.0307 | 0.1653 | 1.1921 | 0.0203 | 0.3379 | 53 |
| 0.5507 | 0.0311 | 0.1569 | 1.1906 | 0.0204 | 0.3385 | 54 |
| 0.5050 | 0.0315 | 0.1485 | 1.1834 | 0.0205 | 0.3361 | 55 |
| 0.4878 | 0.0316 | 0.1447 | 1.1815 | 0.0205 | 0.3329 | 56 |
| 0.4825 | 0.0317 | 0.1410 | 1.2096 | 0.0204 | 0.3359 | 57 |
| 0.4987 | 0.0315 | 0.1374 | 1.2000 | 0.0204 | 0.3352 | 58 |
| 0.4576 | 0.0319 | 0.1305 | 1.1868 | 0.0205 | 0.3329 | 59 |
| 0.4185 | 0.0323 | 0.1215 | 1.2043 | 0.0205 | 0.3322 | 60 |
| 0.3889 | 0.0326 | 0.1156 | 1.1853 | 0.0206 | 0.3302 | 61 |
| 0.3790 | 0.0327 | 0.1101 | 1.2028 | 0.0205 | 0.3316 | 62 |
| 0.4072 | 0.0324 | 0.1110 | 1.2502 | 0.0203 | 0.3309 | 63 |
| 0.3519 | 0.0330 | 0.1020 | 1.1959 | 0.0206 | 0.3284 | 64 |
| 0.3861 | 0.0326 | 0.1034 | 1.1885 | 0.0206 | 0.3271 | 65 |
| 0.3789 | 0.0326 | 0.0961 | 1.1969 | 0.0206 | 0.3298 | 66 |
| 0.3233 | 0.0332 | 0.0905 | 1.1922 | 0.0207 | 0.3280 | 67 |
| 0.2956 | 0.0335 | 0.0854 | 1.2003 | 0.0207 | 0.3296 | 68 |
| 0.2666 | 0.0339 | 0.0796 | 1.2141 | 0.0207 | 0.3252 | 69 |
| 0.3181 | 0.0333 | 0.0813 | 1.2133 | 0.0207 | 0.3302 | 70 |
| 0.3032 | 0.0335 | 0.0770 | 1.2170 | 0.0207 | 0.3315 | 71 |
| 0.2746 | 0.0337 | 0.0741 | 1.2180 | 0.0207 | 0.3299 | 72 |
| 0.2549 | 0.0339 | 0.0705 | 1.2496 | 0.0206 | 0.3308 | 73 |
| 0.2529 | 0.0339 | 0.0685 | 1.2239 | 0.0207 | 0.3321 | 74 |
| 0.2427 | 0.0340 | 0.0671 | 1.2351 | 0.0207 | 0.3292 | 75 |
| 0.2166 | 0.0343 | 0.0623 | 1.2361 | 0.0207 | 0.3313 | 76 |
| 0.2030 | 0.0345 | 0.0585 | 1.2462 | 0.0207 | 0.3312 | 77 |
| 0.2126 | 0.0344 | 0.0566 | 1.2441 | 0.0207 | 0.3313 | 78 |
| 0.2166 | 0.0343 | 0.0569 | 1.2506 | 0.0207 | 0.3334 | 79 |
| 0.2088 | 0.0344 | 0.0562 | 1.2557 | 0.0207 | 0.3389 | 80 |
| 0.2212 | 0.0342 | 0.0560 | 1.2652 | 0.0207 | 0.3334 | 81 |
| 0.2256 | 0.0343 | 0.0543 | 1.2543 | 0.0207 | 0.3393 | 82 |
| 0.1915 | 0.0346 | 0.0501 | 1.2540 | 0.0207 | 0.3299 | 83 |
| 0.1544 | 0.0350 | 0.0443 | 1.2676 | 0.0207 | 0.3347 | 84 |
| 0.1567 | 0.0350 | 0.0435 | 1.2740 | 0.0207 | 0.3375 | 85 |
| 0.1329 | 0.0352 | 0.0405 | 1.2833 | 0.0207 | 0.3398 | 86 |
| 0.1261 | 0.0353 | 0.0392 | 1.3088 | 0.0206 | 0.3397 | 87 |
| 0.1547 | 0.0350 | 0.0438 | 1.2933 | 0.0207 | 0.3279 | 88 |
| 0.1288 | 0.0352 | 0.0377 | 1.2985 | 0.0208 | 0.3371 | 89 |
| 0.1400 | 0.0351 | 0.0391 | 1.3020 | 0.0208 | 0.3329 | 90 |
| 0.1077 | 0.0355 | 0.0351 | 1.3118 | 0.0207 | 0.3344 | 91 |
| 0.0982 | 0.0355 | 0.0348 | 1.3274 | 0.0207 | 0.3352 | 92 |
| 0.1041 | 0.0355 | 0.0356 | 1.3251 | 0.0208 | 0.3362 | 93 |
| 0.1573 | 0.0350 | 0.0364 | 1.3173 | 0.0208 | 0.3362 | 94 |
| 0.1478 | 0.0350 | 0.0365 | 1.3125 | 0.0208 | 0.3367 | 95 |
| 0.0929 | 0.0356 | 0.0336 | 1.3256 | 0.0208 | 0.3351 | 96 |
| 0.1106 | 0.0354 | 0.0341 | 1.3289 | 0.0208 | 0.3428 | 97 |
| 0.0916 | 0.0355 | 0.0322 | 1.3384 | 0.0208 | 0.3383 | 98 |
| 0.0708 | 0.0358 | 0.0313 | 1.3525 | 0.0208 | 0.3333 | 99 |
| 0.1271 | 0.0352 | 0.0326 | 1.3447 | 0.0208 | 0.3336 | 100 |
| 0.0963 | 0.0355 | 0.0329 | 1.3481 | 0.0208 | 0.3288 | 101 |
| 0.0698 | 0.0358 | 0.0294 | 1.3748 | 0.0207 | 0.3341 | 102 |
| 0.0844 | 0.0356 | 0.0313 | 1.3615 | 0.0208 | 0.3380 | 103 |
| 0.1045 | 0.0354 | 0.0319 | 1.3519 | 0.0208 | 0.3357 | 104 |
| 0.0877 | 0.0355 | 0.0326 | 1.3760 | 0.0207 | 0.3402 | 105 |
| 0.0713 | 0.0357 | 0.0303 | 1.3742 | 0.0208 | 0.3392 | 106 |
| 0.0686 | 0.0358 | 0.0292 | 1.3920 | 0.0207 | 0.3353 | 107 |
| 0.0466 | 0.0360 | 0.0287 | 1.3964 | 0.0208 | 0.3342 | 108 |
| 0.0353 | 0.0361 | 0.0258 | 1.3977 | 0.0208 | 0.3339 | 109 |
| 0.0369 | 0.0361 | 0.0269 | 1.4132 | 0.0208 | 0.3298 | 110 |
| 0.0601 | 0.0359 | 0.0304 | 1.4104 | 0.0208 | 0.3438 | 111 |
| 0.1244 | 0.0352 | 0.0332 | 1.3939 | 0.0207 | 0.3349 | 112 |
| 0.1102 | 0.0353 | 0.0301 | 1.3826 | 0.0208 | 0.3421 | 113 |
| 0.1199 | 0.0352 | 0.0311 | 1.3783 | 0.0208 | 0.3368 | 114 |
| 0.0576 | 0.0358 | 0.0281 | 1.3972 | 0.0208 | 0.3340 | 115 |
| 0.0363 | 0.0360 | 0.0255 | 1.3984 | 0.0208 | 0.3314 | 116 |
| 0.0260 | 0.0362 | 0.0260 | 1.4108 | 0.0208 | 0.3327 | 117 |
| 0.0291 | 0.0361 | 0.0265 | 1.4222 | 0.0208 | 0.3376 | 118 |
| 0.0252 | 0.0361 | 0.0249 | 1.4481 | 0.0208 | 0.3327 | 119 |
| 0.0855 | 0.0356 | 0.0283 | 1.4200 | 0.0208 | 0.3303 | 120 |
| 0.0947 | 0.0355 | 0.0287 | 1.4148 | 0.0208 | 0.3291 | 121 |
| 0.0561 | 0.0358 | 0.0250 | 1.4208 | 0.0208 | 0.3292 | 122 |
| 0.0675 | 0.0357 | 0.0272 | 1.4145 | 0.0208 | 0.3402 | 123 |
| 0.0340 | 0.0360 | 0.0276 | 1.4617 | 0.0207 | 0.3460 | 124 |
| 0.0213 | 0.0362 | 0.0285 | 1.4502 | 0.0208 | 0.3374 | 125 |
| 0.0369 | 0.0360 | 0.0287 | 1.4512 | 0.0208 | 0.3483 | 126 |
| 0.0717 | 0.0357 | 0.0310 | 1.4271 | 0.0208 | 0.3314 | 127 |
| 0.0434 | 0.0359 | 0.0242 | 1.4545 | 0.0208 | 0.3355 | 128 |
| 0.0541 | 0.0358 | 0.0281 | 1.4461 | 0.0208 | 0.3298 | 129 |
| 0.0281 | 0.0361 | 0.0265 | 1.4912 | 0.0207 | 0.3358 | 130 |
| 0.0438 | 0.0359 | 0.0260 | 1.4805 | 0.0208 | 0.3327 | 131 |
| 0.1140 | 0.0352 | 0.0304 | 1.4342 | 0.0208 | 0.3370 | 132 |
| 0.0611 | 0.0358 | 0.0330 | 1.4374 | 0.0209 | 0.3405 | 133 |
| 0.0319 | 0.0360 | 0.0320 | 1.4453 | 0.0209 | 0.3382 | 134 |
| 0.0208 | 0.0361 | 0.0290 | 1.4533 | 0.0209 | 0.3382 | 135 |
| 0.0242 | 0.0361 | 0.0311 | 1.4630 | 0.0209 | 0.3409 | 136 |
| 0.0185 | 0.0361 | 0.0315 | 1.4696 | 0.0209 | 0.3468 | 137 |
| 0.0416 | 0.0359 | 0.0294 | 1.4782 | 0.0209 | 0.3362 | 138 |
| 0.0844 | 0.0355 | 0.0333 | 1.4643 | 0.0208 | 0.3339 | 139 |
### Framework versions
- Transformers 4.33.0.dev0
- TensorFlow 2.13.0
- Tokenizers 0.13.3
|
abeiler/goatV9-wAI-noS3-wTrToConMocon
|
abeiler
| 2023-09-03T17:57:51Z | 77 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"llama",
"text-generation",
"generated_from_trainer",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"8-bit",
"region:us"
] |
text-generation
| 2023-09-03T17:57:30Z |
---
tags:
- generated_from_trainer
model-index:
- name: goatV9-wAI-noS3-wTrToConMocon
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. -->
# goatV9-wAI-noS3-wTrToConMocon
This model is a fine-tuned version of [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0
- Datasets 2.12.0
- Tokenizers 0.13.3
|
airsat/dalle-mini
|
airsat
| 2023-09-03T17:56:01Z | 5 | 1 |
transformers
|
[
"transformers",
"jax",
"dallebart",
"text-to-image",
"en",
"arxiv:2102.08981",
"arxiv:2012.09841",
"arxiv:1910.13461",
"arxiv:1910.09700",
"license:apache-2.0",
"co2_eq_emissions",
"region:us"
] |
text-to-image
| 2023-09-02T20:06:33Z |
---
inference: false
co2_eq_emissions:
emissions: 7540
source: MLCo2 Machine Learning Impact calculator
geographical_location: East USA
hardware_used: TPU v3-8
tags:
- text-to-image
license: apache-2.0
language: en
model-index:
- name: dalle-mini
results: []
pipeline_tag: text-to-image
---
# DALL·E Mini Model Card
This model card focuses on the model associated with the DALL·E mini space on Hugging Face, available [here](https://huggingface.co/spaces/dalle-mini/dalle-mini). The app is called “dalle-mini”, but incorporates “[DALL·E Mini](https://wandb.ai/dalle-mini/dalle-mini/reports/DALL-E-mini-Generate-images-from-any-text-prompt--VmlldzoyMDE4NDAy)’’ and “[DALL·E Mega](https://wandb.ai/dalle-mini/dalle-mini/reports/DALL-E-Mega-Training-Journal--VmlldzoxODMxMDI2)” models (further details on this distinction forthcoming).
The DALL·E Mega model is the largest version of DALLE Mini. For more information specific to DALL·E Mega, see the [DALL·E Mega model card](https://huggingface.co/dalle-mini/dalle-mega).
## Model Details
* **Developed by:** Boris Dayma, Suraj Patil, Pedro Cuenca, Khalid Saifullah, Tanishq Abraham, Phúc Lê, Luke, Luke Melas, Ritobrata Ghosh
* **Model type:** Transformer-based text-to-image generation model
* **Language(s):** English
* **License:** Apache 2.0
* **Model Description:** This is a model that can be used to generate images based on text prompts. As the model developers wrote in the [project report](https://wandb.ai/dalle-mini/dalle-mini/reports/DALL-E-mini-Generate-images-from-any-text-prompt--VmlldzoyMDE4NDAy) about DALL·E mini, “OpenAI had the first impressive model for generating images with [DALL·E](https://openai.com/blog/dall-e/). DALL·E mini is an attempt at reproducing those results with an open-source model.”
* **Resources for more information:** See OpenAI’s website for more information about [DALL·E](https://openai.com/blog/dall-e/), including the [DALL·E model card](https://github.com/openai/DALL-E/blob/master/model_card.md). See the [project report](https://wandb.ai/dalle-mini/dalle-mini/reports/DALL-E-mini-Generate-images-from-any-text-prompt--VmlldzoyMDE4NDAy) for more information from the model’s developers. To learn more about DALL·E Mega, see the DALL·E Mega [training journal](https://wandb.ai/dalle-mini/dalle-mini/reports/DALL-E-Mega-Training--VmlldzoxODMxMDI2#training-parameters).
* **Cite as:**
```bib text
@misc{Dayma_DALL·E_Mini_2021,
author = {Dayma, Boris and Patil, Suraj and Cuenca, Pedro and Saifullah, Khalid and Abraham, Tanishq and Lê Khắc, Phúc and Melas, Luke and Ghosh, Ritobrata},
doi = {10.5281/zenodo.5146400},
month = {7},
title = {DALL·E Mini},
url = {https://github.com/borisdayma/dalle-mini},
year = {2021}
}
```
## Uses
### Direct Use
The model is intended to be used to generate images based on text prompts for research and personal consumption. Intended uses include supporting creativity, creating humorous content, and providing generations for people curious about the model’s behavior. Intended uses exclude those described in the [Misuse and Out-of-Scope Use](#misuse-malicious-use-and-out-of-scope-use) section.
### Downstream Use
The model could also be used for downstream use cases, including:
* Research efforts, such as probing and better understanding the limitations and biases of generative models to further improve the state of science
* Development of educational or creative tools
* Generation of artwork and use in design and artistic processes.
* Other uses that are newly discovered by users. This currently includes poetry illustration (give a poem as prompt), fan art (putting a character in various other visual universes), visual puns, fairy tale illustrations (give a fantasy situation as prompt), concept mashups (applying a texture to something completely different), style transfers (portraits in the style of), … We hope you will find your own application!
Downstream uses exclude the uses described in [Misuse and Out-of-Scope Use](#misuse-malicious-use-and-out-of-scope-use).
### Misuse, Malicious Use, and Out-of-Scope Use
The model should not be used to intentionally create or disseminate images that create hostile or alienating environments for people. This includes generating images that people would foreseeably find disturbing, distressing, or offensive; or content that propagates historical or current stereotypes.
#### Out-of-Scope Use
The model was not trained to be factual or true representations of people or events, and therefore using the model to generate such content is out-of-scope for the abilities of this model.
#### Misuse and Malicious Use
Using the model to generate content that is cruel to individuals is a misuse of this model. This includes:
* Generating demeaning, dehumanizing, or otherwise harmful representations of people or their environments, cultures, religions, etc.
* Intentionally promoting or propagating discriminatory content or harmful stereotypes.
* Impersonating individuals without their consent.
* Sexual content without consent of the people who might see it.
* Mis- and disinformation
* Representations of egregious violence and gore
* Sharing of copyrighted or licensed material in violation of its terms of use.
* Sharing content that is an alteration of copyrighted or licensed material in violation of its terms of use.
## Limitations and Bias
### Limitations
The model developers discuss the limitations of the model further in the DALL·E Mini [technical report](https://wandb.ai/dalle-mini/dalle-mini/reports/DALL-E-Mini-Explained-with-Demo--Vmlldzo4NjIxODA):
* Faces and people in general are not generated properly.
* Animals are usually unrealistic.
* It is hard to predict where the model excels or falls short…Good prompt engineering will lead to the best results.
* The model has only been trained with English descriptions and will not perform as well in other languages
### Bias
**CONTENT WARNING: Readers should be aware this section contains content that is disturbing, offensive, and can propagate historical and current stereotypes.**
The model was trained on unfiltered data from the Internet, limited to pictures with English descriptions. Text and images from communities and cultures using other languages were not utilized. This affects all output of the model, with white and Western culture asserted as a default, and the model’s ability to generate content using non-English prompts is observably lower quality than prompts in English.
While the capabilities of image generation models are impressive, they may also reinforce or exacerbate societal biases. The extent and nature of the biases of DALL·E Mini and DALL·E Mega models have yet to be fully documented, but initial testing demonstrates that they may generate images that contain negative stereotypes against minoritized groups. Work to analyze the nature and extent of the models’ biases and limitations is ongoing.
Our current analyses demonstrate that:
* Images generated by the model can include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups.
* When the model generates images with people in them, it tends to output people who we perceive to be white, while people of color are underrepresented.
* Images generated by the model can contain biased content that depicts power differentials between people of color and people who are white, with white people in positions of privilege.
* The model is generally only usable for generating images based on text in English, limiting accessibility of the model for non-English speakers and potentially contributing to the biases in images generated by the model.
The [technical report](https://wandb.ai/dalle-mini/dalle-mini/reports/DALL-E-Mini-Explained-with-Demo--Vmlldzo4NjIxODA) discusses these issues in more detail, and also highlights potential sources of bias in the model development process.
### Limitations and Bias Recommendations
* Users (both direct and downstream) should be made aware of the biases and limitations.
* Content that is potentially problematic should be filtered out, e.g., via automated models that detect violence or pornography.
* Further work on this model should include methods for balanced and just representations of people and cultures, for example, by curating the training dataset to be both diverse and inclusive.
## Training
### Training Data
The model developers used 3 datasets for the model:
* [Conceptual Captions Dataset](https://aclanthology.org/P18-1238/), which contains 3 million image and caption pairs.
* [Conceptual 12M](https://arxiv.org/abs/2102.08981), which contains 12 million image and caption pairs.
* The [OpenAI subset](https://github.com/openai/CLIP/blob/main/data/yfcc100m.md) of [YFCC100M](https://multimediacommons.wordpress.com/yfcc100m-core-dataset/), which contains about 15 million images and that we further sub-sampled to 2 million images due to limitations in storage space. They used both title and description as caption and removed html tags, new lines and extra spaces.
For fine-tuning the image encoder, a subset of 2 million images were used.
All images (about 15 million) were used for training the Seq2Seq model.
### Training Procedure
As described further in the [technical report](https://wandb.ai/dalle-mini/dalle-mini/reports/DALL-E-Mini-Explained-with-Demo--Vmlldzo4NjIxODA#our-dall-e-model-architecture) for DALL·E Mini, during training, images and descriptions are both available and pass through the system as follows:
* Images are encoded through a [VQGAN](https://arxiv.org/abs/2012.09841) encoder, which turns images into a sequence of tokens.
* Descriptions are encoded through a [BART](https://arxiv.org/abs/1910.13461) encoder.
* The output of the BART encoder and encoded images are fed through the BART decoder, which is an auto-regressive model whose goal is to predict the next token.
* Loss is the [softmax cross-entropy](https://wandb.ai/sauravm/Activation-Functions/reports/Activation-Functions-Softmax--VmlldzoxNDU1Njgy#%F0%9F%93%A2-softmax-+-cross-entropy-loss-(caution:-math-alert)) between the model prediction logits and the actual image encodings from the VQGAN.
The simplified training procedure for DALL·E Mega is as follows:
* **Hardware:** 1 pod TPU v3-256 = 32 nodes of TPU VM v3-8 (8 TPU per node) = 256 TPU v3
* **Optimizer:** Distributed Shampoo
* **Model Partition Specificiations:** 8 model parallel x 32 data parallel
* **Batch:** 44 samples per model x 32 data parallel x 3 gradient accumulation steps = 4224 increasing samples per update
* **Learning rate:** warmup to 0.0001 for 10,000 steps and then kept constant until plateau
* Gradient checkpointing used on each Encoder/Decoder layer (ie, MHA + FFN)
* Distributed Shampoo + Normformer Optimizations have proved to be effective and efficiently scaling this model.
* It should also be noted that the learning rate and other parameters are sometimes adjusted on the fly, and batch size increased over time as well.
There is more information about the full procedure and technical material in the DALL·E Mega [training journal](https://wandb.ai/dalle-mini/dalle-mini/reports/DALL-E-Mega-Training--VmlldzoxODMxMDI2#training-parameters).
## Evaluation Results
The model developers discuss their results extensively in their [technical report](https://wandb.ai/dalle-mini/dalle-mini/reports/DALL-E-Mini-Explained-with-Demo--Vmlldzo4NjIxODA#the-results-of-our-dall-e-experiment) for DALL·E Mini, which provides comparisons between DALL·E Mini’s results with [DALL·E-pytorch](https://github.com/lucidrains/DALLE-pytorch), OpenAI’s [DALL·E](https://openai.com/blog/dall-e/), and models consisting of a generator coupled with the [CLIP neural network model](https://openai.com/blog/clip/).
For evaluation results related to DALL·E Mega, see this [technical report](https://wandb.ai/dalle-mini/dalle-mini/reports/DALL-E-mini-Generate-images-from-any-text-prompt--VmlldzoyMDE4NDAy).
## Environmental Impact
### DALL·E Mini Estimated Emissions
*The model is 27 times smaller than the original DALL·E and was trained on a single TPU v3-8 for only 3 days.*
Based on that information, we estimate the following CO2 emissions using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). The hardware, runtime, cloud provider, and compute region were utilized to estimate the carbon impact.
* **Hardware Type:** TPU v3-8
* **Hours used:** 72 (3 days)
* **Cloud Provider:** GCP (as mentioned in the technical report)
* **Compute Region:** us-east1 (provided by model developers)
* **Carbon Emitted (Power consumption x Time x Carbon produced based on location of power grid):** 30.16 kg CO2 eq.
### DALL·E Mega Estimated Emissions
DALL·E Mega is still training. So far, as on June 9, 2022, the model developers report that DALL·E Mega has been training for about 40-45 days on a TPU v3-256. Using those numbers, we estimate the following CO2 emissions using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). The hardware, runtime, cloud provider, and compute region were utilized to estimate the carbon impact.
* **Hardware Type:** TPU v3-256
* **Hours used:** 960 - 1080 hours (40-45 days)
* **Cloud Provider:** Unknown
* **Compute Region:** Unknown
* **Carbon Emitted (Power consumption x Time x Carbon produced based on location of power grid):** Unknown
## Citation
```bibtext
@misc{Dayma_DALL·E_Mini_2021,
author = {Dayma, Boris and Patil, Suraj and Cuenca, Pedro and Saifullah, Khalid and Abraham, Tanishq and Lê Khắc, Phúc and Melas, Luke and Ghosh, Ritobrata},
doi = {10.5281/zenodo.5146400},
month = {7},
title = {DALL·E Mini},
url = {https://github.com/borisdayma/dalle-mini},
year = {2021}
}
```
*This model card was written by: Boris Dayma, Margaret Mitchell, Ezi Ozoani, Marissa Gerchick, Irene Solaiman, Clémentine Fourrier, Sasha Luccioni, Emily Witko, Nazneen Rajani, and Julian Herrera.*
|
naresh4u/llama2-qlora-finetunined-french
|
naresh4u
| 2023-09-03T17:55:06Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-09-03T17:54:58Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float16
### Framework versions
- PEFT 0.6.0.dev0
|
bigmorning/whisper_input_decoder_no_lob__0135
|
bigmorning
| 2023-09-03T17:49:14Z | 59 | 0 |
transformers
|
[
"transformers",
"tf",
"whisper",
"automatic-speech-recognition",
"generated_from_keras_callback",
"base_model:openai/whisper-tiny",
"base_model:finetune:openai/whisper-tiny",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-09-03T17:49:06Z |
---
license: apache-2.0
base_model: openai/whisper-tiny
tags:
- generated_from_keras_callback
model-index:
- name: whisper_input_decoder_no_lob__0135
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. -->
# whisper_input_decoder_no_lob__0135
This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0319
- Train Accuracy: 0.0360
- Train Wermet: 0.0320
- Validation Loss: 1.4453
- Validation Accuracy: 0.0209
- Validation Wermet: 0.3382
- Epoch: 134
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 1e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Train Wermet | Validation Loss | Validation Accuracy | Validation Wermet | Epoch |
|:----------:|:--------------:|:------------:|:---------------:|:-------------------:|:-----------------:|:-----:|
| 5.4122 | 0.0107 | 0.9328 | 3.9759 | 0.0114 | 0.9606 | 0 |
| 4.7176 | 0.0116 | 0.8683 | 3.9404 | 0.0114 | 0.9334 | 1 |
| 4.6750 | 0.0117 | 0.8478 | 3.9211 | 0.0115 | 0.9237 | 2 |
| 4.6511 | 0.0117 | 0.8413 | 3.8864 | 0.0115 | 0.9331 | 3 |
| 4.6294 | 0.0118 | 0.8270 | 3.8729 | 0.0115 | 0.9228 | 4 |
| 4.6134 | 0.0118 | 0.8199 | 3.8690 | 0.0114 | 0.9451 | 5 |
| 4.5980 | 0.0118 | 0.8102 | 3.8491 | 0.0115 | 0.9152 | 6 |
| 4.5759 | 0.0119 | 0.7890 | 3.8366 | 0.0116 | 0.8691 | 7 |
| 4.5518 | 0.0120 | 0.7694 | 3.8081 | 0.0116 | 0.9013 | 8 |
| 4.5219 | 0.0121 | 0.7591 | 3.7734 | 0.0118 | 0.8383 | 9 |
| 4.4761 | 0.0122 | 0.7400 | 3.7156 | 0.0120 | 0.8125 | 10 |
| 4.4139 | 0.0125 | 0.7257 | 3.6311 | 0.0121 | 0.8188 | 11 |
| 4.3113 | 0.0128 | 0.7127 | 3.5089 | 0.0124 | 0.8008 | 12 |
| 4.1608 | 0.0132 | 0.7088 | 3.3587 | 0.0127 | 0.7742 | 13 |
| 3.9595 | 0.0138 | 0.7012 | 3.1493 | 0.0132 | 0.7718 | 14 |
| 3.7188 | 0.0145 | 0.6820 | 2.8784 | 0.0139 | 0.7292 | 15 |
| 3.4775 | 0.0153 | 0.6678 | 2.6716 | 0.0144 | 0.7074 | 16 |
| 3.2575 | 0.0160 | 0.6481 | 2.4980 | 0.0149 | 0.6764 | 17 |
| 3.0615 | 0.0167 | 0.6314 | 2.3456 | 0.0153 | 0.6476 | 18 |
| 2.8715 | 0.0174 | 0.6094 | 2.2090 | 0.0158 | 0.6210 | 19 |
| 2.6930 | 0.0181 | 0.5931 | 2.0918 | 0.0162 | 0.5992 | 20 |
| 2.5383 | 0.0187 | 0.5739 | 1.9769 | 0.0166 | 0.5791 | 21 |
| 2.3952 | 0.0193 | 0.5512 | 1.9042 | 0.0168 | 0.5589 | 22 |
| 2.2427 | 0.0201 | 0.5333 | 1.8028 | 0.0172 | 0.5394 | 23 |
| 2.1236 | 0.0206 | 0.5174 | 1.7434 | 0.0174 | 0.5240 | 24 |
| 2.0315 | 0.0211 | 0.4978 | 1.6755 | 0.0177 | 0.5084 | 25 |
| 1.9066 | 0.0217 | 0.4773 | 1.6534 | 0.0178 | 0.4947 | 26 |
| 1.8279 | 0.0221 | 0.4596 | 1.5606 | 0.0182 | 0.4788 | 27 |
| 1.7325 | 0.0227 | 0.4412 | 1.5173 | 0.0184 | 0.4667 | 28 |
| 1.6416 | 0.0232 | 0.4199 | 1.4733 | 0.0186 | 0.4511 | 29 |
| 1.5702 | 0.0236 | 0.4028 | 1.4519 | 0.0187 | 0.4442 | 30 |
| 1.4787 | 0.0241 | 0.3839 | 1.4213 | 0.0188 | 0.4322 | 31 |
| 1.4238 | 0.0244 | 0.3700 | 1.3971 | 0.0190 | 0.4272 | 32 |
| 1.3561 | 0.0249 | 0.3594 | 1.3499 | 0.0192 | 0.4171 | 33 |
| 1.2828 | 0.0254 | 0.3431 | 1.3555 | 0.0192 | 0.4097 | 34 |
| 1.2318 | 0.0257 | 0.3277 | 1.3183 | 0.0194 | 0.4035 | 35 |
| 1.1668 | 0.0262 | 0.3201 | 1.3068 | 0.0195 | 0.3978 | 36 |
| 1.1571 | 0.0261 | 0.3105 | 1.2901 | 0.0195 | 0.3916 | 37 |
| 1.0812 | 0.0267 | 0.2989 | 1.2720 | 0.0197 | 0.3860 | 38 |
| 1.0134 | 0.0273 | 0.2863 | 1.2593 | 0.0197 | 0.3777 | 39 |
| 0.9986 | 0.0273 | 0.2769 | 1.2629 | 0.0198 | 0.3754 | 40 |
| 0.9322 | 0.0279 | 0.2653 | 1.2320 | 0.0199 | 0.3694 | 41 |
| 0.9021 | 0.0281 | 0.2552 | 1.2308 | 0.0200 | 0.3651 | 42 |
| 0.8583 | 0.0284 | 0.2444 | 1.2199 | 0.0200 | 0.3614 | 43 |
| 0.8101 | 0.0288 | 0.2355 | 1.2120 | 0.0200 | 0.3597 | 44 |
| 0.8045 | 0.0288 | 0.2299 | 1.2023 | 0.0201 | 0.3567 | 45 |
| 0.7823 | 0.0290 | 0.2213 | 1.2075 | 0.0201 | 0.3529 | 46 |
| 0.7186 | 0.0296 | 0.2107 | 1.1917 | 0.0202 | 0.3530 | 47 |
| 0.6949 | 0.0298 | 0.2028 | 1.1926 | 0.0202 | 0.3465 | 48 |
| 0.6669 | 0.0300 | 0.1943 | 1.1902 | 0.0203 | 0.3446 | 49 |
| 0.6125 | 0.0305 | 0.1842 | 1.1892 | 0.0203 | 0.3437 | 50 |
| 0.5926 | 0.0307 | 0.1778 | 1.2058 | 0.0203 | 0.3450 | 51 |
| 0.6055 | 0.0305 | 0.1738 | 1.1859 | 0.0203 | 0.3394 | 52 |
| 0.5828 | 0.0307 | 0.1653 | 1.1921 | 0.0203 | 0.3379 | 53 |
| 0.5507 | 0.0311 | 0.1569 | 1.1906 | 0.0204 | 0.3385 | 54 |
| 0.5050 | 0.0315 | 0.1485 | 1.1834 | 0.0205 | 0.3361 | 55 |
| 0.4878 | 0.0316 | 0.1447 | 1.1815 | 0.0205 | 0.3329 | 56 |
| 0.4825 | 0.0317 | 0.1410 | 1.2096 | 0.0204 | 0.3359 | 57 |
| 0.4987 | 0.0315 | 0.1374 | 1.2000 | 0.0204 | 0.3352 | 58 |
| 0.4576 | 0.0319 | 0.1305 | 1.1868 | 0.0205 | 0.3329 | 59 |
| 0.4185 | 0.0323 | 0.1215 | 1.2043 | 0.0205 | 0.3322 | 60 |
| 0.3889 | 0.0326 | 0.1156 | 1.1853 | 0.0206 | 0.3302 | 61 |
| 0.3790 | 0.0327 | 0.1101 | 1.2028 | 0.0205 | 0.3316 | 62 |
| 0.4072 | 0.0324 | 0.1110 | 1.2502 | 0.0203 | 0.3309 | 63 |
| 0.3519 | 0.0330 | 0.1020 | 1.1959 | 0.0206 | 0.3284 | 64 |
| 0.3861 | 0.0326 | 0.1034 | 1.1885 | 0.0206 | 0.3271 | 65 |
| 0.3789 | 0.0326 | 0.0961 | 1.1969 | 0.0206 | 0.3298 | 66 |
| 0.3233 | 0.0332 | 0.0905 | 1.1922 | 0.0207 | 0.3280 | 67 |
| 0.2956 | 0.0335 | 0.0854 | 1.2003 | 0.0207 | 0.3296 | 68 |
| 0.2666 | 0.0339 | 0.0796 | 1.2141 | 0.0207 | 0.3252 | 69 |
| 0.3181 | 0.0333 | 0.0813 | 1.2133 | 0.0207 | 0.3302 | 70 |
| 0.3032 | 0.0335 | 0.0770 | 1.2170 | 0.0207 | 0.3315 | 71 |
| 0.2746 | 0.0337 | 0.0741 | 1.2180 | 0.0207 | 0.3299 | 72 |
| 0.2549 | 0.0339 | 0.0705 | 1.2496 | 0.0206 | 0.3308 | 73 |
| 0.2529 | 0.0339 | 0.0685 | 1.2239 | 0.0207 | 0.3321 | 74 |
| 0.2427 | 0.0340 | 0.0671 | 1.2351 | 0.0207 | 0.3292 | 75 |
| 0.2166 | 0.0343 | 0.0623 | 1.2361 | 0.0207 | 0.3313 | 76 |
| 0.2030 | 0.0345 | 0.0585 | 1.2462 | 0.0207 | 0.3312 | 77 |
| 0.2126 | 0.0344 | 0.0566 | 1.2441 | 0.0207 | 0.3313 | 78 |
| 0.2166 | 0.0343 | 0.0569 | 1.2506 | 0.0207 | 0.3334 | 79 |
| 0.2088 | 0.0344 | 0.0562 | 1.2557 | 0.0207 | 0.3389 | 80 |
| 0.2212 | 0.0342 | 0.0560 | 1.2652 | 0.0207 | 0.3334 | 81 |
| 0.2256 | 0.0343 | 0.0543 | 1.2543 | 0.0207 | 0.3393 | 82 |
| 0.1915 | 0.0346 | 0.0501 | 1.2540 | 0.0207 | 0.3299 | 83 |
| 0.1544 | 0.0350 | 0.0443 | 1.2676 | 0.0207 | 0.3347 | 84 |
| 0.1567 | 0.0350 | 0.0435 | 1.2740 | 0.0207 | 0.3375 | 85 |
| 0.1329 | 0.0352 | 0.0405 | 1.2833 | 0.0207 | 0.3398 | 86 |
| 0.1261 | 0.0353 | 0.0392 | 1.3088 | 0.0206 | 0.3397 | 87 |
| 0.1547 | 0.0350 | 0.0438 | 1.2933 | 0.0207 | 0.3279 | 88 |
| 0.1288 | 0.0352 | 0.0377 | 1.2985 | 0.0208 | 0.3371 | 89 |
| 0.1400 | 0.0351 | 0.0391 | 1.3020 | 0.0208 | 0.3329 | 90 |
| 0.1077 | 0.0355 | 0.0351 | 1.3118 | 0.0207 | 0.3344 | 91 |
| 0.0982 | 0.0355 | 0.0348 | 1.3274 | 0.0207 | 0.3352 | 92 |
| 0.1041 | 0.0355 | 0.0356 | 1.3251 | 0.0208 | 0.3362 | 93 |
| 0.1573 | 0.0350 | 0.0364 | 1.3173 | 0.0208 | 0.3362 | 94 |
| 0.1478 | 0.0350 | 0.0365 | 1.3125 | 0.0208 | 0.3367 | 95 |
| 0.0929 | 0.0356 | 0.0336 | 1.3256 | 0.0208 | 0.3351 | 96 |
| 0.1106 | 0.0354 | 0.0341 | 1.3289 | 0.0208 | 0.3428 | 97 |
| 0.0916 | 0.0355 | 0.0322 | 1.3384 | 0.0208 | 0.3383 | 98 |
| 0.0708 | 0.0358 | 0.0313 | 1.3525 | 0.0208 | 0.3333 | 99 |
| 0.1271 | 0.0352 | 0.0326 | 1.3447 | 0.0208 | 0.3336 | 100 |
| 0.0963 | 0.0355 | 0.0329 | 1.3481 | 0.0208 | 0.3288 | 101 |
| 0.0698 | 0.0358 | 0.0294 | 1.3748 | 0.0207 | 0.3341 | 102 |
| 0.0844 | 0.0356 | 0.0313 | 1.3615 | 0.0208 | 0.3380 | 103 |
| 0.1045 | 0.0354 | 0.0319 | 1.3519 | 0.0208 | 0.3357 | 104 |
| 0.0877 | 0.0355 | 0.0326 | 1.3760 | 0.0207 | 0.3402 | 105 |
| 0.0713 | 0.0357 | 0.0303 | 1.3742 | 0.0208 | 0.3392 | 106 |
| 0.0686 | 0.0358 | 0.0292 | 1.3920 | 0.0207 | 0.3353 | 107 |
| 0.0466 | 0.0360 | 0.0287 | 1.3964 | 0.0208 | 0.3342 | 108 |
| 0.0353 | 0.0361 | 0.0258 | 1.3977 | 0.0208 | 0.3339 | 109 |
| 0.0369 | 0.0361 | 0.0269 | 1.4132 | 0.0208 | 0.3298 | 110 |
| 0.0601 | 0.0359 | 0.0304 | 1.4104 | 0.0208 | 0.3438 | 111 |
| 0.1244 | 0.0352 | 0.0332 | 1.3939 | 0.0207 | 0.3349 | 112 |
| 0.1102 | 0.0353 | 0.0301 | 1.3826 | 0.0208 | 0.3421 | 113 |
| 0.1199 | 0.0352 | 0.0311 | 1.3783 | 0.0208 | 0.3368 | 114 |
| 0.0576 | 0.0358 | 0.0281 | 1.3972 | 0.0208 | 0.3340 | 115 |
| 0.0363 | 0.0360 | 0.0255 | 1.3984 | 0.0208 | 0.3314 | 116 |
| 0.0260 | 0.0362 | 0.0260 | 1.4108 | 0.0208 | 0.3327 | 117 |
| 0.0291 | 0.0361 | 0.0265 | 1.4222 | 0.0208 | 0.3376 | 118 |
| 0.0252 | 0.0361 | 0.0249 | 1.4481 | 0.0208 | 0.3327 | 119 |
| 0.0855 | 0.0356 | 0.0283 | 1.4200 | 0.0208 | 0.3303 | 120 |
| 0.0947 | 0.0355 | 0.0287 | 1.4148 | 0.0208 | 0.3291 | 121 |
| 0.0561 | 0.0358 | 0.0250 | 1.4208 | 0.0208 | 0.3292 | 122 |
| 0.0675 | 0.0357 | 0.0272 | 1.4145 | 0.0208 | 0.3402 | 123 |
| 0.0340 | 0.0360 | 0.0276 | 1.4617 | 0.0207 | 0.3460 | 124 |
| 0.0213 | 0.0362 | 0.0285 | 1.4502 | 0.0208 | 0.3374 | 125 |
| 0.0369 | 0.0360 | 0.0287 | 1.4512 | 0.0208 | 0.3483 | 126 |
| 0.0717 | 0.0357 | 0.0310 | 1.4271 | 0.0208 | 0.3314 | 127 |
| 0.0434 | 0.0359 | 0.0242 | 1.4545 | 0.0208 | 0.3355 | 128 |
| 0.0541 | 0.0358 | 0.0281 | 1.4461 | 0.0208 | 0.3298 | 129 |
| 0.0281 | 0.0361 | 0.0265 | 1.4912 | 0.0207 | 0.3358 | 130 |
| 0.0438 | 0.0359 | 0.0260 | 1.4805 | 0.0208 | 0.3327 | 131 |
| 0.1140 | 0.0352 | 0.0304 | 1.4342 | 0.0208 | 0.3370 | 132 |
| 0.0611 | 0.0358 | 0.0330 | 1.4374 | 0.0209 | 0.3405 | 133 |
| 0.0319 | 0.0360 | 0.0320 | 1.4453 | 0.0209 | 0.3382 | 134 |
### Framework versions
- Transformers 4.33.0.dev0
- TensorFlow 2.13.0
- Tokenizers 0.13.3
|
MEgooneh/ppo-LunarLander-v2
|
MEgooneh
| 2023-09-03T17:47:49Z | 5 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-09-03T17:47:27Z |
---
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: 246.64 +/- 29.26
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
...
```
|
bigmorning/whisper_input_decoder_no_lob__0130
|
bigmorning
| 2023-09-03T17:36:00Z | 59 | 0 |
transformers
|
[
"transformers",
"tf",
"whisper",
"automatic-speech-recognition",
"generated_from_keras_callback",
"base_model:openai/whisper-tiny",
"base_model:finetune:openai/whisper-tiny",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-09-03T17:35:54Z |
---
license: apache-2.0
base_model: openai/whisper-tiny
tags:
- generated_from_keras_callback
model-index:
- name: whisper_input_decoder_no_lob__0130
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. -->
# whisper_input_decoder_no_lob__0130
This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0541
- Train Accuracy: 0.0358
- Train Wermet: 0.0281
- Validation Loss: 1.4461
- Validation Accuracy: 0.0208
- Validation Wermet: 0.3298
- Epoch: 129
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 1e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Train Wermet | Validation Loss | Validation Accuracy | Validation Wermet | Epoch |
|:----------:|:--------------:|:------------:|:---------------:|:-------------------:|:-----------------:|:-----:|
| 5.4122 | 0.0107 | 0.9328 | 3.9759 | 0.0114 | 0.9606 | 0 |
| 4.7176 | 0.0116 | 0.8683 | 3.9404 | 0.0114 | 0.9334 | 1 |
| 4.6750 | 0.0117 | 0.8478 | 3.9211 | 0.0115 | 0.9237 | 2 |
| 4.6511 | 0.0117 | 0.8413 | 3.8864 | 0.0115 | 0.9331 | 3 |
| 4.6294 | 0.0118 | 0.8270 | 3.8729 | 0.0115 | 0.9228 | 4 |
| 4.6134 | 0.0118 | 0.8199 | 3.8690 | 0.0114 | 0.9451 | 5 |
| 4.5980 | 0.0118 | 0.8102 | 3.8491 | 0.0115 | 0.9152 | 6 |
| 4.5759 | 0.0119 | 0.7890 | 3.8366 | 0.0116 | 0.8691 | 7 |
| 4.5518 | 0.0120 | 0.7694 | 3.8081 | 0.0116 | 0.9013 | 8 |
| 4.5219 | 0.0121 | 0.7591 | 3.7734 | 0.0118 | 0.8383 | 9 |
| 4.4761 | 0.0122 | 0.7400 | 3.7156 | 0.0120 | 0.8125 | 10 |
| 4.4139 | 0.0125 | 0.7257 | 3.6311 | 0.0121 | 0.8188 | 11 |
| 4.3113 | 0.0128 | 0.7127 | 3.5089 | 0.0124 | 0.8008 | 12 |
| 4.1608 | 0.0132 | 0.7088 | 3.3587 | 0.0127 | 0.7742 | 13 |
| 3.9595 | 0.0138 | 0.7012 | 3.1493 | 0.0132 | 0.7718 | 14 |
| 3.7188 | 0.0145 | 0.6820 | 2.8784 | 0.0139 | 0.7292 | 15 |
| 3.4775 | 0.0153 | 0.6678 | 2.6716 | 0.0144 | 0.7074 | 16 |
| 3.2575 | 0.0160 | 0.6481 | 2.4980 | 0.0149 | 0.6764 | 17 |
| 3.0615 | 0.0167 | 0.6314 | 2.3456 | 0.0153 | 0.6476 | 18 |
| 2.8715 | 0.0174 | 0.6094 | 2.2090 | 0.0158 | 0.6210 | 19 |
| 2.6930 | 0.0181 | 0.5931 | 2.0918 | 0.0162 | 0.5992 | 20 |
| 2.5383 | 0.0187 | 0.5739 | 1.9769 | 0.0166 | 0.5791 | 21 |
| 2.3952 | 0.0193 | 0.5512 | 1.9042 | 0.0168 | 0.5589 | 22 |
| 2.2427 | 0.0201 | 0.5333 | 1.8028 | 0.0172 | 0.5394 | 23 |
| 2.1236 | 0.0206 | 0.5174 | 1.7434 | 0.0174 | 0.5240 | 24 |
| 2.0315 | 0.0211 | 0.4978 | 1.6755 | 0.0177 | 0.5084 | 25 |
| 1.9066 | 0.0217 | 0.4773 | 1.6534 | 0.0178 | 0.4947 | 26 |
| 1.8279 | 0.0221 | 0.4596 | 1.5606 | 0.0182 | 0.4788 | 27 |
| 1.7325 | 0.0227 | 0.4412 | 1.5173 | 0.0184 | 0.4667 | 28 |
| 1.6416 | 0.0232 | 0.4199 | 1.4733 | 0.0186 | 0.4511 | 29 |
| 1.5702 | 0.0236 | 0.4028 | 1.4519 | 0.0187 | 0.4442 | 30 |
| 1.4787 | 0.0241 | 0.3839 | 1.4213 | 0.0188 | 0.4322 | 31 |
| 1.4238 | 0.0244 | 0.3700 | 1.3971 | 0.0190 | 0.4272 | 32 |
| 1.3561 | 0.0249 | 0.3594 | 1.3499 | 0.0192 | 0.4171 | 33 |
| 1.2828 | 0.0254 | 0.3431 | 1.3555 | 0.0192 | 0.4097 | 34 |
| 1.2318 | 0.0257 | 0.3277 | 1.3183 | 0.0194 | 0.4035 | 35 |
| 1.1668 | 0.0262 | 0.3201 | 1.3068 | 0.0195 | 0.3978 | 36 |
| 1.1571 | 0.0261 | 0.3105 | 1.2901 | 0.0195 | 0.3916 | 37 |
| 1.0812 | 0.0267 | 0.2989 | 1.2720 | 0.0197 | 0.3860 | 38 |
| 1.0134 | 0.0273 | 0.2863 | 1.2593 | 0.0197 | 0.3777 | 39 |
| 0.9986 | 0.0273 | 0.2769 | 1.2629 | 0.0198 | 0.3754 | 40 |
| 0.9322 | 0.0279 | 0.2653 | 1.2320 | 0.0199 | 0.3694 | 41 |
| 0.9021 | 0.0281 | 0.2552 | 1.2308 | 0.0200 | 0.3651 | 42 |
| 0.8583 | 0.0284 | 0.2444 | 1.2199 | 0.0200 | 0.3614 | 43 |
| 0.8101 | 0.0288 | 0.2355 | 1.2120 | 0.0200 | 0.3597 | 44 |
| 0.8045 | 0.0288 | 0.2299 | 1.2023 | 0.0201 | 0.3567 | 45 |
| 0.7823 | 0.0290 | 0.2213 | 1.2075 | 0.0201 | 0.3529 | 46 |
| 0.7186 | 0.0296 | 0.2107 | 1.1917 | 0.0202 | 0.3530 | 47 |
| 0.6949 | 0.0298 | 0.2028 | 1.1926 | 0.0202 | 0.3465 | 48 |
| 0.6669 | 0.0300 | 0.1943 | 1.1902 | 0.0203 | 0.3446 | 49 |
| 0.6125 | 0.0305 | 0.1842 | 1.1892 | 0.0203 | 0.3437 | 50 |
| 0.5926 | 0.0307 | 0.1778 | 1.2058 | 0.0203 | 0.3450 | 51 |
| 0.6055 | 0.0305 | 0.1738 | 1.1859 | 0.0203 | 0.3394 | 52 |
| 0.5828 | 0.0307 | 0.1653 | 1.1921 | 0.0203 | 0.3379 | 53 |
| 0.5507 | 0.0311 | 0.1569 | 1.1906 | 0.0204 | 0.3385 | 54 |
| 0.5050 | 0.0315 | 0.1485 | 1.1834 | 0.0205 | 0.3361 | 55 |
| 0.4878 | 0.0316 | 0.1447 | 1.1815 | 0.0205 | 0.3329 | 56 |
| 0.4825 | 0.0317 | 0.1410 | 1.2096 | 0.0204 | 0.3359 | 57 |
| 0.4987 | 0.0315 | 0.1374 | 1.2000 | 0.0204 | 0.3352 | 58 |
| 0.4576 | 0.0319 | 0.1305 | 1.1868 | 0.0205 | 0.3329 | 59 |
| 0.4185 | 0.0323 | 0.1215 | 1.2043 | 0.0205 | 0.3322 | 60 |
| 0.3889 | 0.0326 | 0.1156 | 1.1853 | 0.0206 | 0.3302 | 61 |
| 0.3790 | 0.0327 | 0.1101 | 1.2028 | 0.0205 | 0.3316 | 62 |
| 0.4072 | 0.0324 | 0.1110 | 1.2502 | 0.0203 | 0.3309 | 63 |
| 0.3519 | 0.0330 | 0.1020 | 1.1959 | 0.0206 | 0.3284 | 64 |
| 0.3861 | 0.0326 | 0.1034 | 1.1885 | 0.0206 | 0.3271 | 65 |
| 0.3789 | 0.0326 | 0.0961 | 1.1969 | 0.0206 | 0.3298 | 66 |
| 0.3233 | 0.0332 | 0.0905 | 1.1922 | 0.0207 | 0.3280 | 67 |
| 0.2956 | 0.0335 | 0.0854 | 1.2003 | 0.0207 | 0.3296 | 68 |
| 0.2666 | 0.0339 | 0.0796 | 1.2141 | 0.0207 | 0.3252 | 69 |
| 0.3181 | 0.0333 | 0.0813 | 1.2133 | 0.0207 | 0.3302 | 70 |
| 0.3032 | 0.0335 | 0.0770 | 1.2170 | 0.0207 | 0.3315 | 71 |
| 0.2746 | 0.0337 | 0.0741 | 1.2180 | 0.0207 | 0.3299 | 72 |
| 0.2549 | 0.0339 | 0.0705 | 1.2496 | 0.0206 | 0.3308 | 73 |
| 0.2529 | 0.0339 | 0.0685 | 1.2239 | 0.0207 | 0.3321 | 74 |
| 0.2427 | 0.0340 | 0.0671 | 1.2351 | 0.0207 | 0.3292 | 75 |
| 0.2166 | 0.0343 | 0.0623 | 1.2361 | 0.0207 | 0.3313 | 76 |
| 0.2030 | 0.0345 | 0.0585 | 1.2462 | 0.0207 | 0.3312 | 77 |
| 0.2126 | 0.0344 | 0.0566 | 1.2441 | 0.0207 | 0.3313 | 78 |
| 0.2166 | 0.0343 | 0.0569 | 1.2506 | 0.0207 | 0.3334 | 79 |
| 0.2088 | 0.0344 | 0.0562 | 1.2557 | 0.0207 | 0.3389 | 80 |
| 0.2212 | 0.0342 | 0.0560 | 1.2652 | 0.0207 | 0.3334 | 81 |
| 0.2256 | 0.0343 | 0.0543 | 1.2543 | 0.0207 | 0.3393 | 82 |
| 0.1915 | 0.0346 | 0.0501 | 1.2540 | 0.0207 | 0.3299 | 83 |
| 0.1544 | 0.0350 | 0.0443 | 1.2676 | 0.0207 | 0.3347 | 84 |
| 0.1567 | 0.0350 | 0.0435 | 1.2740 | 0.0207 | 0.3375 | 85 |
| 0.1329 | 0.0352 | 0.0405 | 1.2833 | 0.0207 | 0.3398 | 86 |
| 0.1261 | 0.0353 | 0.0392 | 1.3088 | 0.0206 | 0.3397 | 87 |
| 0.1547 | 0.0350 | 0.0438 | 1.2933 | 0.0207 | 0.3279 | 88 |
| 0.1288 | 0.0352 | 0.0377 | 1.2985 | 0.0208 | 0.3371 | 89 |
| 0.1400 | 0.0351 | 0.0391 | 1.3020 | 0.0208 | 0.3329 | 90 |
| 0.1077 | 0.0355 | 0.0351 | 1.3118 | 0.0207 | 0.3344 | 91 |
| 0.0982 | 0.0355 | 0.0348 | 1.3274 | 0.0207 | 0.3352 | 92 |
| 0.1041 | 0.0355 | 0.0356 | 1.3251 | 0.0208 | 0.3362 | 93 |
| 0.1573 | 0.0350 | 0.0364 | 1.3173 | 0.0208 | 0.3362 | 94 |
| 0.1478 | 0.0350 | 0.0365 | 1.3125 | 0.0208 | 0.3367 | 95 |
| 0.0929 | 0.0356 | 0.0336 | 1.3256 | 0.0208 | 0.3351 | 96 |
| 0.1106 | 0.0354 | 0.0341 | 1.3289 | 0.0208 | 0.3428 | 97 |
| 0.0916 | 0.0355 | 0.0322 | 1.3384 | 0.0208 | 0.3383 | 98 |
| 0.0708 | 0.0358 | 0.0313 | 1.3525 | 0.0208 | 0.3333 | 99 |
| 0.1271 | 0.0352 | 0.0326 | 1.3447 | 0.0208 | 0.3336 | 100 |
| 0.0963 | 0.0355 | 0.0329 | 1.3481 | 0.0208 | 0.3288 | 101 |
| 0.0698 | 0.0358 | 0.0294 | 1.3748 | 0.0207 | 0.3341 | 102 |
| 0.0844 | 0.0356 | 0.0313 | 1.3615 | 0.0208 | 0.3380 | 103 |
| 0.1045 | 0.0354 | 0.0319 | 1.3519 | 0.0208 | 0.3357 | 104 |
| 0.0877 | 0.0355 | 0.0326 | 1.3760 | 0.0207 | 0.3402 | 105 |
| 0.0713 | 0.0357 | 0.0303 | 1.3742 | 0.0208 | 0.3392 | 106 |
| 0.0686 | 0.0358 | 0.0292 | 1.3920 | 0.0207 | 0.3353 | 107 |
| 0.0466 | 0.0360 | 0.0287 | 1.3964 | 0.0208 | 0.3342 | 108 |
| 0.0353 | 0.0361 | 0.0258 | 1.3977 | 0.0208 | 0.3339 | 109 |
| 0.0369 | 0.0361 | 0.0269 | 1.4132 | 0.0208 | 0.3298 | 110 |
| 0.0601 | 0.0359 | 0.0304 | 1.4104 | 0.0208 | 0.3438 | 111 |
| 0.1244 | 0.0352 | 0.0332 | 1.3939 | 0.0207 | 0.3349 | 112 |
| 0.1102 | 0.0353 | 0.0301 | 1.3826 | 0.0208 | 0.3421 | 113 |
| 0.1199 | 0.0352 | 0.0311 | 1.3783 | 0.0208 | 0.3368 | 114 |
| 0.0576 | 0.0358 | 0.0281 | 1.3972 | 0.0208 | 0.3340 | 115 |
| 0.0363 | 0.0360 | 0.0255 | 1.3984 | 0.0208 | 0.3314 | 116 |
| 0.0260 | 0.0362 | 0.0260 | 1.4108 | 0.0208 | 0.3327 | 117 |
| 0.0291 | 0.0361 | 0.0265 | 1.4222 | 0.0208 | 0.3376 | 118 |
| 0.0252 | 0.0361 | 0.0249 | 1.4481 | 0.0208 | 0.3327 | 119 |
| 0.0855 | 0.0356 | 0.0283 | 1.4200 | 0.0208 | 0.3303 | 120 |
| 0.0947 | 0.0355 | 0.0287 | 1.4148 | 0.0208 | 0.3291 | 121 |
| 0.0561 | 0.0358 | 0.0250 | 1.4208 | 0.0208 | 0.3292 | 122 |
| 0.0675 | 0.0357 | 0.0272 | 1.4145 | 0.0208 | 0.3402 | 123 |
| 0.0340 | 0.0360 | 0.0276 | 1.4617 | 0.0207 | 0.3460 | 124 |
| 0.0213 | 0.0362 | 0.0285 | 1.4502 | 0.0208 | 0.3374 | 125 |
| 0.0369 | 0.0360 | 0.0287 | 1.4512 | 0.0208 | 0.3483 | 126 |
| 0.0717 | 0.0357 | 0.0310 | 1.4271 | 0.0208 | 0.3314 | 127 |
| 0.0434 | 0.0359 | 0.0242 | 1.4545 | 0.0208 | 0.3355 | 128 |
| 0.0541 | 0.0358 | 0.0281 | 1.4461 | 0.0208 | 0.3298 | 129 |
### Framework versions
- Transformers 4.33.0.dev0
- TensorFlow 2.13.0
- Tokenizers 0.13.3
|
Gpaiva/NERDE-base
|
Gpaiva
| 2023-09-03T17:30:31Z | 114 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:nerde",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-07-24T02:13:00Z |
---
tags:
- generated_from_trainer
datasets:
- nerde
widget:
- text: "Considerando-se os argumentos elencados pela Peticionária, infere-se que a CNH Industrial detém legítimo interesse pelo caso em epígrafe, visto que pode ser afetada pela decisão a ser adotada pelo Cade sobre a Operação, constatação que autoriza o enquadramento do pleito nas hipóteses previstas no artigo 50 da Lei nº 12.529/2011."
- text: "Em análise dos autos verifica-se a existência de documentos contra Aurélio de Paula, datados de 04 de março de 2010, 19 de março de 2010 e 05 de outubro de 2010; contra Bianchini Indústria de Plásticos Ltda., Igon Bernardelli, datados de 19 de março de 2010; contra a Nasato Indústria de Plásticos Eireli e Osmair Nasato, datados de 04 de março de 2010 e 05 de outubro de 2010; contra TWB Indústria e Comércio de Produtos Plásticos Ltda. e Waldir Dezotti, datados de 04 de março de 2010 e 05 de outubro de 2010, podendo-se concluir que a conduta ocorreu de forma contínua na maioria dos casos, pelo menos ao longo do ano de 2010, questões que serão melhor analisadas após o fim da instrução processual."
inference:
parameters:
aggregation_strategy: "max"
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: NERDE-base
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: nerde
type: nerde
args: NERDE
metrics:
- name: Precision
type: precision
value: 0.9118601747815231
- name: Recall
type: recall
value: 0.9152882205513785
- name: F1
type: f1
value: 0.9135709818636648
- name: Accuracy
type: accuracy
value: 0.9841962132484992
---
<!-- 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. -->
# NERDE-base
This model is a fine-tuned version of [pierreguillou/bert-base-cased-pt-lenerbr](https://huggingface.co/pierreguillou/bert-base-cased-pt-lenerbr) on the nerde dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1246
- Precision: 0.9119
- Recall: 0.9153
- F1: 0.9136
- Accuracy: 0.9842
## 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 | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2466 | 1.0 | 541 | 0.1003 | 0.8515 | 0.8822 | 0.8666 | 0.9782 |
| 0.0608 | 2.0 | 1082 | 0.0855 | 0.8990 | 0.9083 | 0.9036 | 0.9837 |
| 0.0411 | 3.0 | 1623 | 0.1006 | 0.9078 | 0.9103 | 0.9090 | 0.9837 |
| 0.0266 | 4.0 | 2164 | 0.1052 | 0.9023 | 0.9163 | 0.9092 | 0.9828 |
| 0.0191 | 5.0 | 2705 | 0.1060 | 0.9112 | 0.9183 | 0.9147 | 0.9847 |
| 0.0153 | 6.0 | 3246 | 0.1152 | 0.9052 | 0.9098 | 0.9075 | 0.9831 |
| 0.0124 | 7.0 | 3787 | 0.1209 | 0.9029 | 0.9185 | 0.9107 | 0.9835 |
| 0.0083 | 8.0 | 4328 | 0.1176 | 0.9072 | 0.9163 | 0.9117 | 0.9844 |
| 0.0077 | 9.0 | 4869 | 0.1240 | 0.9080 | 0.9201 | 0.9140 | 0.9844 |
| 0.0051 | 10.0 | 5410 | 0.1246 | 0.9119 | 0.9153 | 0.9136 | 0.9842 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.12.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
MAG1965/jeff-milton-model-face
|
MAG1965
| 2023-09-03T17:27:58Z | 30 | 0 |
diffusers
|
[
"diffusers",
"text-to-image",
"stable-diffusion",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-09-03T17:16:05Z |
---
license: creativeml-openrail-m
tags:
- text-to-image
- stable-diffusion
---
### Jeff-Milton-model-face Dreambooth model trained by MAG1965 with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook
Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb)
Sample pictures of this concept:
|
bhenrym14/airoboros-l2-13b-2.1-YaRN-64k-LoRA
|
bhenrym14
| 2023-09-03T17:24:01Z | 0 | 1 | null |
[
"safetensors",
"dataset:jondurbin/airoboros-2.1",
"region:us"
] | null | 2023-09-03T17:18:51Z |
---
datasets:
- jondurbin/airoboros-2.1
---
# Extended Context (via YaRN) Finetune of Llama-2-13b with airoboros-2.1 (LoRA)
## Overview
This is a finetune of [NousResearch/Yarn-Llama-2-13b-64k](https://huggingface.co/NousResearch/Yarn-Llama-2-13b-64k). This starting point is Llama-2-13b with additional pretraining done with YaRN scaling applied to RoPE to extend the useful context length to 64k tokens. Starting with this model, I performed instruction tuning with [Jon Durbin's Airoboros 2.1 dataset](https://huggingface.co/datasets/jondurbin/airoboros-2.1), with same scaling approach applied.
**This is a (merged) QLoRA fine-tune (rank 64)**.
The finetune was performed with 1x RTX 6000 Ada (~18 hours).
For full model card, including benchmarks, see the model card of the fp16 [merged model](https://huggingface.co/bhenrym14/airoboros-l2-13b-2.1-YaRN-64k)
|
bigmorning/whisper_input_decoder_no_lob__0125
|
bigmorning
| 2023-09-03T17:22:58Z | 59 | 0 |
transformers
|
[
"transformers",
"tf",
"whisper",
"automatic-speech-recognition",
"generated_from_keras_callback",
"base_model:openai/whisper-tiny",
"base_model:finetune:openai/whisper-tiny",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-09-03T17:22:49Z |
---
license: apache-2.0
base_model: openai/whisper-tiny
tags:
- generated_from_keras_callback
model-index:
- name: whisper_input_decoder_no_lob__0125
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. -->
# whisper_input_decoder_no_lob__0125
This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0340
- Train Accuracy: 0.0360
- Train Wermet: 0.0276
- Validation Loss: 1.4617
- Validation Accuracy: 0.0207
- Validation Wermet: 0.3460
- Epoch: 124
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 1e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Train Wermet | Validation Loss | Validation Accuracy | Validation Wermet | Epoch |
|:----------:|:--------------:|:------------:|:---------------:|:-------------------:|:-----------------:|:-----:|
| 5.4122 | 0.0107 | 0.9328 | 3.9759 | 0.0114 | 0.9606 | 0 |
| 4.7176 | 0.0116 | 0.8683 | 3.9404 | 0.0114 | 0.9334 | 1 |
| 4.6750 | 0.0117 | 0.8478 | 3.9211 | 0.0115 | 0.9237 | 2 |
| 4.6511 | 0.0117 | 0.8413 | 3.8864 | 0.0115 | 0.9331 | 3 |
| 4.6294 | 0.0118 | 0.8270 | 3.8729 | 0.0115 | 0.9228 | 4 |
| 4.6134 | 0.0118 | 0.8199 | 3.8690 | 0.0114 | 0.9451 | 5 |
| 4.5980 | 0.0118 | 0.8102 | 3.8491 | 0.0115 | 0.9152 | 6 |
| 4.5759 | 0.0119 | 0.7890 | 3.8366 | 0.0116 | 0.8691 | 7 |
| 4.5518 | 0.0120 | 0.7694 | 3.8081 | 0.0116 | 0.9013 | 8 |
| 4.5219 | 0.0121 | 0.7591 | 3.7734 | 0.0118 | 0.8383 | 9 |
| 4.4761 | 0.0122 | 0.7400 | 3.7156 | 0.0120 | 0.8125 | 10 |
| 4.4139 | 0.0125 | 0.7257 | 3.6311 | 0.0121 | 0.8188 | 11 |
| 4.3113 | 0.0128 | 0.7127 | 3.5089 | 0.0124 | 0.8008 | 12 |
| 4.1608 | 0.0132 | 0.7088 | 3.3587 | 0.0127 | 0.7742 | 13 |
| 3.9595 | 0.0138 | 0.7012 | 3.1493 | 0.0132 | 0.7718 | 14 |
| 3.7188 | 0.0145 | 0.6820 | 2.8784 | 0.0139 | 0.7292 | 15 |
| 3.4775 | 0.0153 | 0.6678 | 2.6716 | 0.0144 | 0.7074 | 16 |
| 3.2575 | 0.0160 | 0.6481 | 2.4980 | 0.0149 | 0.6764 | 17 |
| 3.0615 | 0.0167 | 0.6314 | 2.3456 | 0.0153 | 0.6476 | 18 |
| 2.8715 | 0.0174 | 0.6094 | 2.2090 | 0.0158 | 0.6210 | 19 |
| 2.6930 | 0.0181 | 0.5931 | 2.0918 | 0.0162 | 0.5992 | 20 |
| 2.5383 | 0.0187 | 0.5739 | 1.9769 | 0.0166 | 0.5791 | 21 |
| 2.3952 | 0.0193 | 0.5512 | 1.9042 | 0.0168 | 0.5589 | 22 |
| 2.2427 | 0.0201 | 0.5333 | 1.8028 | 0.0172 | 0.5394 | 23 |
| 2.1236 | 0.0206 | 0.5174 | 1.7434 | 0.0174 | 0.5240 | 24 |
| 2.0315 | 0.0211 | 0.4978 | 1.6755 | 0.0177 | 0.5084 | 25 |
| 1.9066 | 0.0217 | 0.4773 | 1.6534 | 0.0178 | 0.4947 | 26 |
| 1.8279 | 0.0221 | 0.4596 | 1.5606 | 0.0182 | 0.4788 | 27 |
| 1.7325 | 0.0227 | 0.4412 | 1.5173 | 0.0184 | 0.4667 | 28 |
| 1.6416 | 0.0232 | 0.4199 | 1.4733 | 0.0186 | 0.4511 | 29 |
| 1.5702 | 0.0236 | 0.4028 | 1.4519 | 0.0187 | 0.4442 | 30 |
| 1.4787 | 0.0241 | 0.3839 | 1.4213 | 0.0188 | 0.4322 | 31 |
| 1.4238 | 0.0244 | 0.3700 | 1.3971 | 0.0190 | 0.4272 | 32 |
| 1.3561 | 0.0249 | 0.3594 | 1.3499 | 0.0192 | 0.4171 | 33 |
| 1.2828 | 0.0254 | 0.3431 | 1.3555 | 0.0192 | 0.4097 | 34 |
| 1.2318 | 0.0257 | 0.3277 | 1.3183 | 0.0194 | 0.4035 | 35 |
| 1.1668 | 0.0262 | 0.3201 | 1.3068 | 0.0195 | 0.3978 | 36 |
| 1.1571 | 0.0261 | 0.3105 | 1.2901 | 0.0195 | 0.3916 | 37 |
| 1.0812 | 0.0267 | 0.2989 | 1.2720 | 0.0197 | 0.3860 | 38 |
| 1.0134 | 0.0273 | 0.2863 | 1.2593 | 0.0197 | 0.3777 | 39 |
| 0.9986 | 0.0273 | 0.2769 | 1.2629 | 0.0198 | 0.3754 | 40 |
| 0.9322 | 0.0279 | 0.2653 | 1.2320 | 0.0199 | 0.3694 | 41 |
| 0.9021 | 0.0281 | 0.2552 | 1.2308 | 0.0200 | 0.3651 | 42 |
| 0.8583 | 0.0284 | 0.2444 | 1.2199 | 0.0200 | 0.3614 | 43 |
| 0.8101 | 0.0288 | 0.2355 | 1.2120 | 0.0200 | 0.3597 | 44 |
| 0.8045 | 0.0288 | 0.2299 | 1.2023 | 0.0201 | 0.3567 | 45 |
| 0.7823 | 0.0290 | 0.2213 | 1.2075 | 0.0201 | 0.3529 | 46 |
| 0.7186 | 0.0296 | 0.2107 | 1.1917 | 0.0202 | 0.3530 | 47 |
| 0.6949 | 0.0298 | 0.2028 | 1.1926 | 0.0202 | 0.3465 | 48 |
| 0.6669 | 0.0300 | 0.1943 | 1.1902 | 0.0203 | 0.3446 | 49 |
| 0.6125 | 0.0305 | 0.1842 | 1.1892 | 0.0203 | 0.3437 | 50 |
| 0.5926 | 0.0307 | 0.1778 | 1.2058 | 0.0203 | 0.3450 | 51 |
| 0.6055 | 0.0305 | 0.1738 | 1.1859 | 0.0203 | 0.3394 | 52 |
| 0.5828 | 0.0307 | 0.1653 | 1.1921 | 0.0203 | 0.3379 | 53 |
| 0.5507 | 0.0311 | 0.1569 | 1.1906 | 0.0204 | 0.3385 | 54 |
| 0.5050 | 0.0315 | 0.1485 | 1.1834 | 0.0205 | 0.3361 | 55 |
| 0.4878 | 0.0316 | 0.1447 | 1.1815 | 0.0205 | 0.3329 | 56 |
| 0.4825 | 0.0317 | 0.1410 | 1.2096 | 0.0204 | 0.3359 | 57 |
| 0.4987 | 0.0315 | 0.1374 | 1.2000 | 0.0204 | 0.3352 | 58 |
| 0.4576 | 0.0319 | 0.1305 | 1.1868 | 0.0205 | 0.3329 | 59 |
| 0.4185 | 0.0323 | 0.1215 | 1.2043 | 0.0205 | 0.3322 | 60 |
| 0.3889 | 0.0326 | 0.1156 | 1.1853 | 0.0206 | 0.3302 | 61 |
| 0.3790 | 0.0327 | 0.1101 | 1.2028 | 0.0205 | 0.3316 | 62 |
| 0.4072 | 0.0324 | 0.1110 | 1.2502 | 0.0203 | 0.3309 | 63 |
| 0.3519 | 0.0330 | 0.1020 | 1.1959 | 0.0206 | 0.3284 | 64 |
| 0.3861 | 0.0326 | 0.1034 | 1.1885 | 0.0206 | 0.3271 | 65 |
| 0.3789 | 0.0326 | 0.0961 | 1.1969 | 0.0206 | 0.3298 | 66 |
| 0.3233 | 0.0332 | 0.0905 | 1.1922 | 0.0207 | 0.3280 | 67 |
| 0.2956 | 0.0335 | 0.0854 | 1.2003 | 0.0207 | 0.3296 | 68 |
| 0.2666 | 0.0339 | 0.0796 | 1.2141 | 0.0207 | 0.3252 | 69 |
| 0.3181 | 0.0333 | 0.0813 | 1.2133 | 0.0207 | 0.3302 | 70 |
| 0.3032 | 0.0335 | 0.0770 | 1.2170 | 0.0207 | 0.3315 | 71 |
| 0.2746 | 0.0337 | 0.0741 | 1.2180 | 0.0207 | 0.3299 | 72 |
| 0.2549 | 0.0339 | 0.0705 | 1.2496 | 0.0206 | 0.3308 | 73 |
| 0.2529 | 0.0339 | 0.0685 | 1.2239 | 0.0207 | 0.3321 | 74 |
| 0.2427 | 0.0340 | 0.0671 | 1.2351 | 0.0207 | 0.3292 | 75 |
| 0.2166 | 0.0343 | 0.0623 | 1.2361 | 0.0207 | 0.3313 | 76 |
| 0.2030 | 0.0345 | 0.0585 | 1.2462 | 0.0207 | 0.3312 | 77 |
| 0.2126 | 0.0344 | 0.0566 | 1.2441 | 0.0207 | 0.3313 | 78 |
| 0.2166 | 0.0343 | 0.0569 | 1.2506 | 0.0207 | 0.3334 | 79 |
| 0.2088 | 0.0344 | 0.0562 | 1.2557 | 0.0207 | 0.3389 | 80 |
| 0.2212 | 0.0342 | 0.0560 | 1.2652 | 0.0207 | 0.3334 | 81 |
| 0.2256 | 0.0343 | 0.0543 | 1.2543 | 0.0207 | 0.3393 | 82 |
| 0.1915 | 0.0346 | 0.0501 | 1.2540 | 0.0207 | 0.3299 | 83 |
| 0.1544 | 0.0350 | 0.0443 | 1.2676 | 0.0207 | 0.3347 | 84 |
| 0.1567 | 0.0350 | 0.0435 | 1.2740 | 0.0207 | 0.3375 | 85 |
| 0.1329 | 0.0352 | 0.0405 | 1.2833 | 0.0207 | 0.3398 | 86 |
| 0.1261 | 0.0353 | 0.0392 | 1.3088 | 0.0206 | 0.3397 | 87 |
| 0.1547 | 0.0350 | 0.0438 | 1.2933 | 0.0207 | 0.3279 | 88 |
| 0.1288 | 0.0352 | 0.0377 | 1.2985 | 0.0208 | 0.3371 | 89 |
| 0.1400 | 0.0351 | 0.0391 | 1.3020 | 0.0208 | 0.3329 | 90 |
| 0.1077 | 0.0355 | 0.0351 | 1.3118 | 0.0207 | 0.3344 | 91 |
| 0.0982 | 0.0355 | 0.0348 | 1.3274 | 0.0207 | 0.3352 | 92 |
| 0.1041 | 0.0355 | 0.0356 | 1.3251 | 0.0208 | 0.3362 | 93 |
| 0.1573 | 0.0350 | 0.0364 | 1.3173 | 0.0208 | 0.3362 | 94 |
| 0.1478 | 0.0350 | 0.0365 | 1.3125 | 0.0208 | 0.3367 | 95 |
| 0.0929 | 0.0356 | 0.0336 | 1.3256 | 0.0208 | 0.3351 | 96 |
| 0.1106 | 0.0354 | 0.0341 | 1.3289 | 0.0208 | 0.3428 | 97 |
| 0.0916 | 0.0355 | 0.0322 | 1.3384 | 0.0208 | 0.3383 | 98 |
| 0.0708 | 0.0358 | 0.0313 | 1.3525 | 0.0208 | 0.3333 | 99 |
| 0.1271 | 0.0352 | 0.0326 | 1.3447 | 0.0208 | 0.3336 | 100 |
| 0.0963 | 0.0355 | 0.0329 | 1.3481 | 0.0208 | 0.3288 | 101 |
| 0.0698 | 0.0358 | 0.0294 | 1.3748 | 0.0207 | 0.3341 | 102 |
| 0.0844 | 0.0356 | 0.0313 | 1.3615 | 0.0208 | 0.3380 | 103 |
| 0.1045 | 0.0354 | 0.0319 | 1.3519 | 0.0208 | 0.3357 | 104 |
| 0.0877 | 0.0355 | 0.0326 | 1.3760 | 0.0207 | 0.3402 | 105 |
| 0.0713 | 0.0357 | 0.0303 | 1.3742 | 0.0208 | 0.3392 | 106 |
| 0.0686 | 0.0358 | 0.0292 | 1.3920 | 0.0207 | 0.3353 | 107 |
| 0.0466 | 0.0360 | 0.0287 | 1.3964 | 0.0208 | 0.3342 | 108 |
| 0.0353 | 0.0361 | 0.0258 | 1.3977 | 0.0208 | 0.3339 | 109 |
| 0.0369 | 0.0361 | 0.0269 | 1.4132 | 0.0208 | 0.3298 | 110 |
| 0.0601 | 0.0359 | 0.0304 | 1.4104 | 0.0208 | 0.3438 | 111 |
| 0.1244 | 0.0352 | 0.0332 | 1.3939 | 0.0207 | 0.3349 | 112 |
| 0.1102 | 0.0353 | 0.0301 | 1.3826 | 0.0208 | 0.3421 | 113 |
| 0.1199 | 0.0352 | 0.0311 | 1.3783 | 0.0208 | 0.3368 | 114 |
| 0.0576 | 0.0358 | 0.0281 | 1.3972 | 0.0208 | 0.3340 | 115 |
| 0.0363 | 0.0360 | 0.0255 | 1.3984 | 0.0208 | 0.3314 | 116 |
| 0.0260 | 0.0362 | 0.0260 | 1.4108 | 0.0208 | 0.3327 | 117 |
| 0.0291 | 0.0361 | 0.0265 | 1.4222 | 0.0208 | 0.3376 | 118 |
| 0.0252 | 0.0361 | 0.0249 | 1.4481 | 0.0208 | 0.3327 | 119 |
| 0.0855 | 0.0356 | 0.0283 | 1.4200 | 0.0208 | 0.3303 | 120 |
| 0.0947 | 0.0355 | 0.0287 | 1.4148 | 0.0208 | 0.3291 | 121 |
| 0.0561 | 0.0358 | 0.0250 | 1.4208 | 0.0208 | 0.3292 | 122 |
| 0.0675 | 0.0357 | 0.0272 | 1.4145 | 0.0208 | 0.3402 | 123 |
| 0.0340 | 0.0360 | 0.0276 | 1.4617 | 0.0207 | 0.3460 | 124 |
### Framework versions
- Transformers 4.33.0.dev0
- TensorFlow 2.13.0
- Tokenizers 0.13.3
|
bigmorning/whisper_input_decoder_no_lob__0120
|
bigmorning
| 2023-09-03T17:09:54Z | 59 | 0 |
transformers
|
[
"transformers",
"tf",
"whisper",
"automatic-speech-recognition",
"generated_from_keras_callback",
"base_model:openai/whisper-tiny",
"base_model:finetune:openai/whisper-tiny",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-09-03T17:09:46Z |
---
license: apache-2.0
base_model: openai/whisper-tiny
tags:
- generated_from_keras_callback
model-index:
- name: whisper_input_decoder_no_lob__0120
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. -->
# whisper_input_decoder_no_lob__0120
This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0252
- Train Accuracy: 0.0361
- Train Wermet: 0.0249
- Validation Loss: 1.4481
- Validation Accuracy: 0.0208
- Validation Wermet: 0.3327
- Epoch: 119
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 1e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Train Wermet | Validation Loss | Validation Accuracy | Validation Wermet | Epoch |
|:----------:|:--------------:|:------------:|:---------------:|:-------------------:|:-----------------:|:-----:|
| 5.4122 | 0.0107 | 0.9328 | 3.9759 | 0.0114 | 0.9606 | 0 |
| 4.7176 | 0.0116 | 0.8683 | 3.9404 | 0.0114 | 0.9334 | 1 |
| 4.6750 | 0.0117 | 0.8478 | 3.9211 | 0.0115 | 0.9237 | 2 |
| 4.6511 | 0.0117 | 0.8413 | 3.8864 | 0.0115 | 0.9331 | 3 |
| 4.6294 | 0.0118 | 0.8270 | 3.8729 | 0.0115 | 0.9228 | 4 |
| 4.6134 | 0.0118 | 0.8199 | 3.8690 | 0.0114 | 0.9451 | 5 |
| 4.5980 | 0.0118 | 0.8102 | 3.8491 | 0.0115 | 0.9152 | 6 |
| 4.5759 | 0.0119 | 0.7890 | 3.8366 | 0.0116 | 0.8691 | 7 |
| 4.5518 | 0.0120 | 0.7694 | 3.8081 | 0.0116 | 0.9013 | 8 |
| 4.5219 | 0.0121 | 0.7591 | 3.7734 | 0.0118 | 0.8383 | 9 |
| 4.4761 | 0.0122 | 0.7400 | 3.7156 | 0.0120 | 0.8125 | 10 |
| 4.4139 | 0.0125 | 0.7257 | 3.6311 | 0.0121 | 0.8188 | 11 |
| 4.3113 | 0.0128 | 0.7127 | 3.5089 | 0.0124 | 0.8008 | 12 |
| 4.1608 | 0.0132 | 0.7088 | 3.3587 | 0.0127 | 0.7742 | 13 |
| 3.9595 | 0.0138 | 0.7012 | 3.1493 | 0.0132 | 0.7718 | 14 |
| 3.7188 | 0.0145 | 0.6820 | 2.8784 | 0.0139 | 0.7292 | 15 |
| 3.4775 | 0.0153 | 0.6678 | 2.6716 | 0.0144 | 0.7074 | 16 |
| 3.2575 | 0.0160 | 0.6481 | 2.4980 | 0.0149 | 0.6764 | 17 |
| 3.0615 | 0.0167 | 0.6314 | 2.3456 | 0.0153 | 0.6476 | 18 |
| 2.8715 | 0.0174 | 0.6094 | 2.2090 | 0.0158 | 0.6210 | 19 |
| 2.6930 | 0.0181 | 0.5931 | 2.0918 | 0.0162 | 0.5992 | 20 |
| 2.5383 | 0.0187 | 0.5739 | 1.9769 | 0.0166 | 0.5791 | 21 |
| 2.3952 | 0.0193 | 0.5512 | 1.9042 | 0.0168 | 0.5589 | 22 |
| 2.2427 | 0.0201 | 0.5333 | 1.8028 | 0.0172 | 0.5394 | 23 |
| 2.1236 | 0.0206 | 0.5174 | 1.7434 | 0.0174 | 0.5240 | 24 |
| 2.0315 | 0.0211 | 0.4978 | 1.6755 | 0.0177 | 0.5084 | 25 |
| 1.9066 | 0.0217 | 0.4773 | 1.6534 | 0.0178 | 0.4947 | 26 |
| 1.8279 | 0.0221 | 0.4596 | 1.5606 | 0.0182 | 0.4788 | 27 |
| 1.7325 | 0.0227 | 0.4412 | 1.5173 | 0.0184 | 0.4667 | 28 |
| 1.6416 | 0.0232 | 0.4199 | 1.4733 | 0.0186 | 0.4511 | 29 |
| 1.5702 | 0.0236 | 0.4028 | 1.4519 | 0.0187 | 0.4442 | 30 |
| 1.4787 | 0.0241 | 0.3839 | 1.4213 | 0.0188 | 0.4322 | 31 |
| 1.4238 | 0.0244 | 0.3700 | 1.3971 | 0.0190 | 0.4272 | 32 |
| 1.3561 | 0.0249 | 0.3594 | 1.3499 | 0.0192 | 0.4171 | 33 |
| 1.2828 | 0.0254 | 0.3431 | 1.3555 | 0.0192 | 0.4097 | 34 |
| 1.2318 | 0.0257 | 0.3277 | 1.3183 | 0.0194 | 0.4035 | 35 |
| 1.1668 | 0.0262 | 0.3201 | 1.3068 | 0.0195 | 0.3978 | 36 |
| 1.1571 | 0.0261 | 0.3105 | 1.2901 | 0.0195 | 0.3916 | 37 |
| 1.0812 | 0.0267 | 0.2989 | 1.2720 | 0.0197 | 0.3860 | 38 |
| 1.0134 | 0.0273 | 0.2863 | 1.2593 | 0.0197 | 0.3777 | 39 |
| 0.9986 | 0.0273 | 0.2769 | 1.2629 | 0.0198 | 0.3754 | 40 |
| 0.9322 | 0.0279 | 0.2653 | 1.2320 | 0.0199 | 0.3694 | 41 |
| 0.9021 | 0.0281 | 0.2552 | 1.2308 | 0.0200 | 0.3651 | 42 |
| 0.8583 | 0.0284 | 0.2444 | 1.2199 | 0.0200 | 0.3614 | 43 |
| 0.8101 | 0.0288 | 0.2355 | 1.2120 | 0.0200 | 0.3597 | 44 |
| 0.8045 | 0.0288 | 0.2299 | 1.2023 | 0.0201 | 0.3567 | 45 |
| 0.7823 | 0.0290 | 0.2213 | 1.2075 | 0.0201 | 0.3529 | 46 |
| 0.7186 | 0.0296 | 0.2107 | 1.1917 | 0.0202 | 0.3530 | 47 |
| 0.6949 | 0.0298 | 0.2028 | 1.1926 | 0.0202 | 0.3465 | 48 |
| 0.6669 | 0.0300 | 0.1943 | 1.1902 | 0.0203 | 0.3446 | 49 |
| 0.6125 | 0.0305 | 0.1842 | 1.1892 | 0.0203 | 0.3437 | 50 |
| 0.5926 | 0.0307 | 0.1778 | 1.2058 | 0.0203 | 0.3450 | 51 |
| 0.6055 | 0.0305 | 0.1738 | 1.1859 | 0.0203 | 0.3394 | 52 |
| 0.5828 | 0.0307 | 0.1653 | 1.1921 | 0.0203 | 0.3379 | 53 |
| 0.5507 | 0.0311 | 0.1569 | 1.1906 | 0.0204 | 0.3385 | 54 |
| 0.5050 | 0.0315 | 0.1485 | 1.1834 | 0.0205 | 0.3361 | 55 |
| 0.4878 | 0.0316 | 0.1447 | 1.1815 | 0.0205 | 0.3329 | 56 |
| 0.4825 | 0.0317 | 0.1410 | 1.2096 | 0.0204 | 0.3359 | 57 |
| 0.4987 | 0.0315 | 0.1374 | 1.2000 | 0.0204 | 0.3352 | 58 |
| 0.4576 | 0.0319 | 0.1305 | 1.1868 | 0.0205 | 0.3329 | 59 |
| 0.4185 | 0.0323 | 0.1215 | 1.2043 | 0.0205 | 0.3322 | 60 |
| 0.3889 | 0.0326 | 0.1156 | 1.1853 | 0.0206 | 0.3302 | 61 |
| 0.3790 | 0.0327 | 0.1101 | 1.2028 | 0.0205 | 0.3316 | 62 |
| 0.4072 | 0.0324 | 0.1110 | 1.2502 | 0.0203 | 0.3309 | 63 |
| 0.3519 | 0.0330 | 0.1020 | 1.1959 | 0.0206 | 0.3284 | 64 |
| 0.3861 | 0.0326 | 0.1034 | 1.1885 | 0.0206 | 0.3271 | 65 |
| 0.3789 | 0.0326 | 0.0961 | 1.1969 | 0.0206 | 0.3298 | 66 |
| 0.3233 | 0.0332 | 0.0905 | 1.1922 | 0.0207 | 0.3280 | 67 |
| 0.2956 | 0.0335 | 0.0854 | 1.2003 | 0.0207 | 0.3296 | 68 |
| 0.2666 | 0.0339 | 0.0796 | 1.2141 | 0.0207 | 0.3252 | 69 |
| 0.3181 | 0.0333 | 0.0813 | 1.2133 | 0.0207 | 0.3302 | 70 |
| 0.3032 | 0.0335 | 0.0770 | 1.2170 | 0.0207 | 0.3315 | 71 |
| 0.2746 | 0.0337 | 0.0741 | 1.2180 | 0.0207 | 0.3299 | 72 |
| 0.2549 | 0.0339 | 0.0705 | 1.2496 | 0.0206 | 0.3308 | 73 |
| 0.2529 | 0.0339 | 0.0685 | 1.2239 | 0.0207 | 0.3321 | 74 |
| 0.2427 | 0.0340 | 0.0671 | 1.2351 | 0.0207 | 0.3292 | 75 |
| 0.2166 | 0.0343 | 0.0623 | 1.2361 | 0.0207 | 0.3313 | 76 |
| 0.2030 | 0.0345 | 0.0585 | 1.2462 | 0.0207 | 0.3312 | 77 |
| 0.2126 | 0.0344 | 0.0566 | 1.2441 | 0.0207 | 0.3313 | 78 |
| 0.2166 | 0.0343 | 0.0569 | 1.2506 | 0.0207 | 0.3334 | 79 |
| 0.2088 | 0.0344 | 0.0562 | 1.2557 | 0.0207 | 0.3389 | 80 |
| 0.2212 | 0.0342 | 0.0560 | 1.2652 | 0.0207 | 0.3334 | 81 |
| 0.2256 | 0.0343 | 0.0543 | 1.2543 | 0.0207 | 0.3393 | 82 |
| 0.1915 | 0.0346 | 0.0501 | 1.2540 | 0.0207 | 0.3299 | 83 |
| 0.1544 | 0.0350 | 0.0443 | 1.2676 | 0.0207 | 0.3347 | 84 |
| 0.1567 | 0.0350 | 0.0435 | 1.2740 | 0.0207 | 0.3375 | 85 |
| 0.1329 | 0.0352 | 0.0405 | 1.2833 | 0.0207 | 0.3398 | 86 |
| 0.1261 | 0.0353 | 0.0392 | 1.3088 | 0.0206 | 0.3397 | 87 |
| 0.1547 | 0.0350 | 0.0438 | 1.2933 | 0.0207 | 0.3279 | 88 |
| 0.1288 | 0.0352 | 0.0377 | 1.2985 | 0.0208 | 0.3371 | 89 |
| 0.1400 | 0.0351 | 0.0391 | 1.3020 | 0.0208 | 0.3329 | 90 |
| 0.1077 | 0.0355 | 0.0351 | 1.3118 | 0.0207 | 0.3344 | 91 |
| 0.0982 | 0.0355 | 0.0348 | 1.3274 | 0.0207 | 0.3352 | 92 |
| 0.1041 | 0.0355 | 0.0356 | 1.3251 | 0.0208 | 0.3362 | 93 |
| 0.1573 | 0.0350 | 0.0364 | 1.3173 | 0.0208 | 0.3362 | 94 |
| 0.1478 | 0.0350 | 0.0365 | 1.3125 | 0.0208 | 0.3367 | 95 |
| 0.0929 | 0.0356 | 0.0336 | 1.3256 | 0.0208 | 0.3351 | 96 |
| 0.1106 | 0.0354 | 0.0341 | 1.3289 | 0.0208 | 0.3428 | 97 |
| 0.0916 | 0.0355 | 0.0322 | 1.3384 | 0.0208 | 0.3383 | 98 |
| 0.0708 | 0.0358 | 0.0313 | 1.3525 | 0.0208 | 0.3333 | 99 |
| 0.1271 | 0.0352 | 0.0326 | 1.3447 | 0.0208 | 0.3336 | 100 |
| 0.0963 | 0.0355 | 0.0329 | 1.3481 | 0.0208 | 0.3288 | 101 |
| 0.0698 | 0.0358 | 0.0294 | 1.3748 | 0.0207 | 0.3341 | 102 |
| 0.0844 | 0.0356 | 0.0313 | 1.3615 | 0.0208 | 0.3380 | 103 |
| 0.1045 | 0.0354 | 0.0319 | 1.3519 | 0.0208 | 0.3357 | 104 |
| 0.0877 | 0.0355 | 0.0326 | 1.3760 | 0.0207 | 0.3402 | 105 |
| 0.0713 | 0.0357 | 0.0303 | 1.3742 | 0.0208 | 0.3392 | 106 |
| 0.0686 | 0.0358 | 0.0292 | 1.3920 | 0.0207 | 0.3353 | 107 |
| 0.0466 | 0.0360 | 0.0287 | 1.3964 | 0.0208 | 0.3342 | 108 |
| 0.0353 | 0.0361 | 0.0258 | 1.3977 | 0.0208 | 0.3339 | 109 |
| 0.0369 | 0.0361 | 0.0269 | 1.4132 | 0.0208 | 0.3298 | 110 |
| 0.0601 | 0.0359 | 0.0304 | 1.4104 | 0.0208 | 0.3438 | 111 |
| 0.1244 | 0.0352 | 0.0332 | 1.3939 | 0.0207 | 0.3349 | 112 |
| 0.1102 | 0.0353 | 0.0301 | 1.3826 | 0.0208 | 0.3421 | 113 |
| 0.1199 | 0.0352 | 0.0311 | 1.3783 | 0.0208 | 0.3368 | 114 |
| 0.0576 | 0.0358 | 0.0281 | 1.3972 | 0.0208 | 0.3340 | 115 |
| 0.0363 | 0.0360 | 0.0255 | 1.3984 | 0.0208 | 0.3314 | 116 |
| 0.0260 | 0.0362 | 0.0260 | 1.4108 | 0.0208 | 0.3327 | 117 |
| 0.0291 | 0.0361 | 0.0265 | 1.4222 | 0.0208 | 0.3376 | 118 |
| 0.0252 | 0.0361 | 0.0249 | 1.4481 | 0.0208 | 0.3327 | 119 |
### Framework versions
- Transformers 4.33.0.dev0
- TensorFlow 2.13.0
- Tokenizers 0.13.3
|
ibrahimciko/a2c-PandaReachDense-v3
|
ibrahimciko
| 2023-09-03T17:08:22Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"PandaReachDense-v3",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-09-03T17:02:52Z |
---
library_name: stable-baselines3
tags:
- PandaReachDense-v3
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachDense-v3
type: PandaReachDense-v3
metrics:
- type: mean_reward
value: -0.14 +/- 0.10
name: mean_reward
verified: false
---
# **A2C** Agent playing **PandaReachDense-v3**
This is a trained model of a **A2C** agent playing **PandaReachDense-v3**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
Aznor/llama2-7b-MeetingBank-Instruct-tune-v2
|
Aznor
| 2023-09-03T17:07:56Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-09-03T17:07:54Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: float16
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: float16
### Framework versions
- PEFT 0.5.0
- PEFT 0.5.0
|
bigmorning/whisper_input_decoder_no_lob__0115
|
bigmorning
| 2023-09-03T16:56:47Z | 59 | 0 |
transformers
|
[
"transformers",
"tf",
"whisper",
"automatic-speech-recognition",
"generated_from_keras_callback",
"base_model:openai/whisper-tiny",
"base_model:finetune:openai/whisper-tiny",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-09-03T16:56:41Z |
---
license: apache-2.0
base_model: openai/whisper-tiny
tags:
- generated_from_keras_callback
model-index:
- name: whisper_input_decoder_no_lob__0115
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. -->
# whisper_input_decoder_no_lob__0115
This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.1199
- Train Accuracy: 0.0352
- Train Wermet: 0.0311
- Validation Loss: 1.3783
- Validation Accuracy: 0.0208
- Validation Wermet: 0.3368
- Epoch: 114
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 1e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Train Wermet | Validation Loss | Validation Accuracy | Validation Wermet | Epoch |
|:----------:|:--------------:|:------------:|:---------------:|:-------------------:|:-----------------:|:-----:|
| 5.4122 | 0.0107 | 0.9328 | 3.9759 | 0.0114 | 0.9606 | 0 |
| 4.7176 | 0.0116 | 0.8683 | 3.9404 | 0.0114 | 0.9334 | 1 |
| 4.6750 | 0.0117 | 0.8478 | 3.9211 | 0.0115 | 0.9237 | 2 |
| 4.6511 | 0.0117 | 0.8413 | 3.8864 | 0.0115 | 0.9331 | 3 |
| 4.6294 | 0.0118 | 0.8270 | 3.8729 | 0.0115 | 0.9228 | 4 |
| 4.6134 | 0.0118 | 0.8199 | 3.8690 | 0.0114 | 0.9451 | 5 |
| 4.5980 | 0.0118 | 0.8102 | 3.8491 | 0.0115 | 0.9152 | 6 |
| 4.5759 | 0.0119 | 0.7890 | 3.8366 | 0.0116 | 0.8691 | 7 |
| 4.5518 | 0.0120 | 0.7694 | 3.8081 | 0.0116 | 0.9013 | 8 |
| 4.5219 | 0.0121 | 0.7591 | 3.7734 | 0.0118 | 0.8383 | 9 |
| 4.4761 | 0.0122 | 0.7400 | 3.7156 | 0.0120 | 0.8125 | 10 |
| 4.4139 | 0.0125 | 0.7257 | 3.6311 | 0.0121 | 0.8188 | 11 |
| 4.3113 | 0.0128 | 0.7127 | 3.5089 | 0.0124 | 0.8008 | 12 |
| 4.1608 | 0.0132 | 0.7088 | 3.3587 | 0.0127 | 0.7742 | 13 |
| 3.9595 | 0.0138 | 0.7012 | 3.1493 | 0.0132 | 0.7718 | 14 |
| 3.7188 | 0.0145 | 0.6820 | 2.8784 | 0.0139 | 0.7292 | 15 |
| 3.4775 | 0.0153 | 0.6678 | 2.6716 | 0.0144 | 0.7074 | 16 |
| 3.2575 | 0.0160 | 0.6481 | 2.4980 | 0.0149 | 0.6764 | 17 |
| 3.0615 | 0.0167 | 0.6314 | 2.3456 | 0.0153 | 0.6476 | 18 |
| 2.8715 | 0.0174 | 0.6094 | 2.2090 | 0.0158 | 0.6210 | 19 |
| 2.6930 | 0.0181 | 0.5931 | 2.0918 | 0.0162 | 0.5992 | 20 |
| 2.5383 | 0.0187 | 0.5739 | 1.9769 | 0.0166 | 0.5791 | 21 |
| 2.3952 | 0.0193 | 0.5512 | 1.9042 | 0.0168 | 0.5589 | 22 |
| 2.2427 | 0.0201 | 0.5333 | 1.8028 | 0.0172 | 0.5394 | 23 |
| 2.1236 | 0.0206 | 0.5174 | 1.7434 | 0.0174 | 0.5240 | 24 |
| 2.0315 | 0.0211 | 0.4978 | 1.6755 | 0.0177 | 0.5084 | 25 |
| 1.9066 | 0.0217 | 0.4773 | 1.6534 | 0.0178 | 0.4947 | 26 |
| 1.8279 | 0.0221 | 0.4596 | 1.5606 | 0.0182 | 0.4788 | 27 |
| 1.7325 | 0.0227 | 0.4412 | 1.5173 | 0.0184 | 0.4667 | 28 |
| 1.6416 | 0.0232 | 0.4199 | 1.4733 | 0.0186 | 0.4511 | 29 |
| 1.5702 | 0.0236 | 0.4028 | 1.4519 | 0.0187 | 0.4442 | 30 |
| 1.4787 | 0.0241 | 0.3839 | 1.4213 | 0.0188 | 0.4322 | 31 |
| 1.4238 | 0.0244 | 0.3700 | 1.3971 | 0.0190 | 0.4272 | 32 |
| 1.3561 | 0.0249 | 0.3594 | 1.3499 | 0.0192 | 0.4171 | 33 |
| 1.2828 | 0.0254 | 0.3431 | 1.3555 | 0.0192 | 0.4097 | 34 |
| 1.2318 | 0.0257 | 0.3277 | 1.3183 | 0.0194 | 0.4035 | 35 |
| 1.1668 | 0.0262 | 0.3201 | 1.3068 | 0.0195 | 0.3978 | 36 |
| 1.1571 | 0.0261 | 0.3105 | 1.2901 | 0.0195 | 0.3916 | 37 |
| 1.0812 | 0.0267 | 0.2989 | 1.2720 | 0.0197 | 0.3860 | 38 |
| 1.0134 | 0.0273 | 0.2863 | 1.2593 | 0.0197 | 0.3777 | 39 |
| 0.9986 | 0.0273 | 0.2769 | 1.2629 | 0.0198 | 0.3754 | 40 |
| 0.9322 | 0.0279 | 0.2653 | 1.2320 | 0.0199 | 0.3694 | 41 |
| 0.9021 | 0.0281 | 0.2552 | 1.2308 | 0.0200 | 0.3651 | 42 |
| 0.8583 | 0.0284 | 0.2444 | 1.2199 | 0.0200 | 0.3614 | 43 |
| 0.8101 | 0.0288 | 0.2355 | 1.2120 | 0.0200 | 0.3597 | 44 |
| 0.8045 | 0.0288 | 0.2299 | 1.2023 | 0.0201 | 0.3567 | 45 |
| 0.7823 | 0.0290 | 0.2213 | 1.2075 | 0.0201 | 0.3529 | 46 |
| 0.7186 | 0.0296 | 0.2107 | 1.1917 | 0.0202 | 0.3530 | 47 |
| 0.6949 | 0.0298 | 0.2028 | 1.1926 | 0.0202 | 0.3465 | 48 |
| 0.6669 | 0.0300 | 0.1943 | 1.1902 | 0.0203 | 0.3446 | 49 |
| 0.6125 | 0.0305 | 0.1842 | 1.1892 | 0.0203 | 0.3437 | 50 |
| 0.5926 | 0.0307 | 0.1778 | 1.2058 | 0.0203 | 0.3450 | 51 |
| 0.6055 | 0.0305 | 0.1738 | 1.1859 | 0.0203 | 0.3394 | 52 |
| 0.5828 | 0.0307 | 0.1653 | 1.1921 | 0.0203 | 0.3379 | 53 |
| 0.5507 | 0.0311 | 0.1569 | 1.1906 | 0.0204 | 0.3385 | 54 |
| 0.5050 | 0.0315 | 0.1485 | 1.1834 | 0.0205 | 0.3361 | 55 |
| 0.4878 | 0.0316 | 0.1447 | 1.1815 | 0.0205 | 0.3329 | 56 |
| 0.4825 | 0.0317 | 0.1410 | 1.2096 | 0.0204 | 0.3359 | 57 |
| 0.4987 | 0.0315 | 0.1374 | 1.2000 | 0.0204 | 0.3352 | 58 |
| 0.4576 | 0.0319 | 0.1305 | 1.1868 | 0.0205 | 0.3329 | 59 |
| 0.4185 | 0.0323 | 0.1215 | 1.2043 | 0.0205 | 0.3322 | 60 |
| 0.3889 | 0.0326 | 0.1156 | 1.1853 | 0.0206 | 0.3302 | 61 |
| 0.3790 | 0.0327 | 0.1101 | 1.2028 | 0.0205 | 0.3316 | 62 |
| 0.4072 | 0.0324 | 0.1110 | 1.2502 | 0.0203 | 0.3309 | 63 |
| 0.3519 | 0.0330 | 0.1020 | 1.1959 | 0.0206 | 0.3284 | 64 |
| 0.3861 | 0.0326 | 0.1034 | 1.1885 | 0.0206 | 0.3271 | 65 |
| 0.3789 | 0.0326 | 0.0961 | 1.1969 | 0.0206 | 0.3298 | 66 |
| 0.3233 | 0.0332 | 0.0905 | 1.1922 | 0.0207 | 0.3280 | 67 |
| 0.2956 | 0.0335 | 0.0854 | 1.2003 | 0.0207 | 0.3296 | 68 |
| 0.2666 | 0.0339 | 0.0796 | 1.2141 | 0.0207 | 0.3252 | 69 |
| 0.3181 | 0.0333 | 0.0813 | 1.2133 | 0.0207 | 0.3302 | 70 |
| 0.3032 | 0.0335 | 0.0770 | 1.2170 | 0.0207 | 0.3315 | 71 |
| 0.2746 | 0.0337 | 0.0741 | 1.2180 | 0.0207 | 0.3299 | 72 |
| 0.2549 | 0.0339 | 0.0705 | 1.2496 | 0.0206 | 0.3308 | 73 |
| 0.2529 | 0.0339 | 0.0685 | 1.2239 | 0.0207 | 0.3321 | 74 |
| 0.2427 | 0.0340 | 0.0671 | 1.2351 | 0.0207 | 0.3292 | 75 |
| 0.2166 | 0.0343 | 0.0623 | 1.2361 | 0.0207 | 0.3313 | 76 |
| 0.2030 | 0.0345 | 0.0585 | 1.2462 | 0.0207 | 0.3312 | 77 |
| 0.2126 | 0.0344 | 0.0566 | 1.2441 | 0.0207 | 0.3313 | 78 |
| 0.2166 | 0.0343 | 0.0569 | 1.2506 | 0.0207 | 0.3334 | 79 |
| 0.2088 | 0.0344 | 0.0562 | 1.2557 | 0.0207 | 0.3389 | 80 |
| 0.2212 | 0.0342 | 0.0560 | 1.2652 | 0.0207 | 0.3334 | 81 |
| 0.2256 | 0.0343 | 0.0543 | 1.2543 | 0.0207 | 0.3393 | 82 |
| 0.1915 | 0.0346 | 0.0501 | 1.2540 | 0.0207 | 0.3299 | 83 |
| 0.1544 | 0.0350 | 0.0443 | 1.2676 | 0.0207 | 0.3347 | 84 |
| 0.1567 | 0.0350 | 0.0435 | 1.2740 | 0.0207 | 0.3375 | 85 |
| 0.1329 | 0.0352 | 0.0405 | 1.2833 | 0.0207 | 0.3398 | 86 |
| 0.1261 | 0.0353 | 0.0392 | 1.3088 | 0.0206 | 0.3397 | 87 |
| 0.1547 | 0.0350 | 0.0438 | 1.2933 | 0.0207 | 0.3279 | 88 |
| 0.1288 | 0.0352 | 0.0377 | 1.2985 | 0.0208 | 0.3371 | 89 |
| 0.1400 | 0.0351 | 0.0391 | 1.3020 | 0.0208 | 0.3329 | 90 |
| 0.1077 | 0.0355 | 0.0351 | 1.3118 | 0.0207 | 0.3344 | 91 |
| 0.0982 | 0.0355 | 0.0348 | 1.3274 | 0.0207 | 0.3352 | 92 |
| 0.1041 | 0.0355 | 0.0356 | 1.3251 | 0.0208 | 0.3362 | 93 |
| 0.1573 | 0.0350 | 0.0364 | 1.3173 | 0.0208 | 0.3362 | 94 |
| 0.1478 | 0.0350 | 0.0365 | 1.3125 | 0.0208 | 0.3367 | 95 |
| 0.0929 | 0.0356 | 0.0336 | 1.3256 | 0.0208 | 0.3351 | 96 |
| 0.1106 | 0.0354 | 0.0341 | 1.3289 | 0.0208 | 0.3428 | 97 |
| 0.0916 | 0.0355 | 0.0322 | 1.3384 | 0.0208 | 0.3383 | 98 |
| 0.0708 | 0.0358 | 0.0313 | 1.3525 | 0.0208 | 0.3333 | 99 |
| 0.1271 | 0.0352 | 0.0326 | 1.3447 | 0.0208 | 0.3336 | 100 |
| 0.0963 | 0.0355 | 0.0329 | 1.3481 | 0.0208 | 0.3288 | 101 |
| 0.0698 | 0.0358 | 0.0294 | 1.3748 | 0.0207 | 0.3341 | 102 |
| 0.0844 | 0.0356 | 0.0313 | 1.3615 | 0.0208 | 0.3380 | 103 |
| 0.1045 | 0.0354 | 0.0319 | 1.3519 | 0.0208 | 0.3357 | 104 |
| 0.0877 | 0.0355 | 0.0326 | 1.3760 | 0.0207 | 0.3402 | 105 |
| 0.0713 | 0.0357 | 0.0303 | 1.3742 | 0.0208 | 0.3392 | 106 |
| 0.0686 | 0.0358 | 0.0292 | 1.3920 | 0.0207 | 0.3353 | 107 |
| 0.0466 | 0.0360 | 0.0287 | 1.3964 | 0.0208 | 0.3342 | 108 |
| 0.0353 | 0.0361 | 0.0258 | 1.3977 | 0.0208 | 0.3339 | 109 |
| 0.0369 | 0.0361 | 0.0269 | 1.4132 | 0.0208 | 0.3298 | 110 |
| 0.0601 | 0.0359 | 0.0304 | 1.4104 | 0.0208 | 0.3438 | 111 |
| 0.1244 | 0.0352 | 0.0332 | 1.3939 | 0.0207 | 0.3349 | 112 |
| 0.1102 | 0.0353 | 0.0301 | 1.3826 | 0.0208 | 0.3421 | 113 |
| 0.1199 | 0.0352 | 0.0311 | 1.3783 | 0.0208 | 0.3368 | 114 |
### Framework versions
- Transformers 4.33.0.dev0
- TensorFlow 2.13.0
- Tokenizers 0.13.3
|
Kamer/Day2
|
Kamer
| 2023-09-03T16:55:59Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:nlpaueb/legal-bert-base-uncased",
"base_model:finetune:nlpaueb/legal-bert-base-uncased",
"license:cc-by-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-09-03T14:46:47Z |
---
license: cc-by-sa-4.0
base_model: nlpaueb/legal-bert-base-uncased
tags:
- generated_from_trainer
model-index:
- name: Day2
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. -->
# Day2
This model is a fine-tuned version of [nlpaueb/legal-bert-base-uncased](https://huggingface.co/nlpaueb/legal-bert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.4956
- eval_Accuracy: 0.9035
- eval_F1_macro: 0.5229
- eval_F1_class_0: 0.0
- eval_F1_class_1: 0.0
- eval_F1_class_2: 0.9289
- eval_F1_class_3: 0.6667
- eval_F1_class_4: 0.9910
- eval_F1_class_5: 0.6667
- eval_F1_class_6: 0.0
- eval_F1_class_7: 0.5
- eval_F1_class_8: 0.0
- eval_F1_class_9: 0.0
- eval_F1_class_10: 0.0
- eval_F1_class_11: 0.9
- eval_F1_class_12: 0.6
- eval_F1_class_13: 0.9645
- eval_F1_class_14: 0.7500
- eval_F1_class_15: 0.8667
- eval_F1_class_16: 0.9302
- eval_F1_class_17: 1.0
- eval_F1_class_18: 0.8605
- eval_F1_class_19: 0.6667
- eval_F1_class_20: 0.9508
- eval_F1_class_21: 0.7692
- eval_F1_class_22: 0.0
- eval_F1_class_23: 0.6667
- eval_F1_class_24: 0.8333
- eval_F1_class_25: 0.5714
- eval_F1_class_26: 0.9231
- eval_F1_class_27: 0.9828
- eval_F1_class_28: 0.0
- eval_F1_class_29: 0.9500
- eval_F1_class_30: 0.0
- eval_F1_class_31: 0.0
- eval_F1_class_32: 0.5
- eval_F1_class_33: 0.5
- eval_F1_class_34: 0.8571
- eval_F1_class_35: 0.8547
- eval_F1_class_36: 0.8727
- eval_F1_class_37: 0.5
- eval_F1_class_38: 0.8837
- eval_F1_class_39: 0.7619
- eval_F1_class_40: 0.0
- eval_F1_class_41: 0.4444
- eval_F1_class_42: 0.0
- eval_F1_class_43: 0.0
- eval_F1_class_44: 0.0
- eval_F1_class_45: 0.7500
- eval_F1_class_46: 0.3333
- eval_F1_class_47: 0.7556
- eval_F1_class_48: 0.8679
- eval_F1_class_49: 1.0
- eval_F1_class_50: 0.8571
- eval_F1_class_51: 0.0
- eval_F1_class_52: 0.0
- eval_F1_class_53: 0.8
- eval_F1_class_54: 0.4
- eval_F1_class_55: 0.0
- eval_F1_class_56: 0.9082
- eval_F1_class_57: 0.0
- eval_F1_class_58: 0.6667
- eval_F1_class_59: 1.0
- eval_F1_class_60: 0.0
- eval_F1_class_61: 0.0
- eval_F1_class_62: 0.9747
- eval_F1_class_63: 0.0
- eval_F1_class_64: 0.8966
- eval_F1_class_65: 1.0
- eval_F1_class_66: 0.8
- eval_F1_class_67: 0.0
- eval_F1_class_68: 0.0
- eval_F1_class_69: 0.0
- eval_F1_class_70: 1.0
- eval_runtime: 61.7038
- eval_samples_per_second: 32.235
- eval_steps_per_second: 2.026
- epoch: 6.64
- step: 3300
## 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
### Framework versions
- Transformers 4.32.0
- Pytorch 2.0.1+cu117
- Datasets 2.14.4
- Tokenizers 0.13.3
|
mqraitem/dreambooth_celeba
|
mqraitem
| 2023-09-03T16:37:07Z | 29 | 1 |
diffusers
|
[
"diffusers",
"safetensors",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"dreambooth",
"base_model:runwayml/stable-diffusion-v1-5",
"base_model:finetune:runwayml/stable-diffusion-v1-5",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-09-03T16:27:26Z |
---
license: creativeml-openrail-m
base_model: runwayml/stable-diffusion-v1-5
instance_prompt: a_photo_of_a_celebrity
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- dreambooth
inference: true
---
# DreamBooth - mqraitem/dreambooth_celeba
This is a dreambooth model derived from runwayml/stable-diffusion-v1-5. The weights were trained on a_photo_of_a_celebrity using [DreamBooth](https://dreambooth.github.io/).
You can find some example images in the following.
DreamBooth for the text encoder was enabled: False.
|
bigmorning/whisper_input_decoder_no_lob__0105
|
bigmorning
| 2023-09-03T16:30:40Z | 59 | 0 |
transformers
|
[
"transformers",
"tf",
"whisper",
"automatic-speech-recognition",
"generated_from_keras_callback",
"base_model:openai/whisper-tiny",
"base_model:finetune:openai/whisper-tiny",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-09-03T16:30:33Z |
---
license: apache-2.0
base_model: openai/whisper-tiny
tags:
- generated_from_keras_callback
model-index:
- name: whisper_input_decoder_no_lob__0105
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. -->
# whisper_input_decoder_no_lob__0105
This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.1045
- Train Accuracy: 0.0354
- Train Wermet: 0.0319
- Validation Loss: 1.3519
- Validation Accuracy: 0.0208
- Validation Wermet: 0.3357
- Epoch: 104
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 1e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Train Wermet | Validation Loss | Validation Accuracy | Validation Wermet | Epoch |
|:----------:|:--------------:|:------------:|:---------------:|:-------------------:|:-----------------:|:-----:|
| 5.4122 | 0.0107 | 0.9328 | 3.9759 | 0.0114 | 0.9606 | 0 |
| 4.7176 | 0.0116 | 0.8683 | 3.9404 | 0.0114 | 0.9334 | 1 |
| 4.6750 | 0.0117 | 0.8478 | 3.9211 | 0.0115 | 0.9237 | 2 |
| 4.6511 | 0.0117 | 0.8413 | 3.8864 | 0.0115 | 0.9331 | 3 |
| 4.6294 | 0.0118 | 0.8270 | 3.8729 | 0.0115 | 0.9228 | 4 |
| 4.6134 | 0.0118 | 0.8199 | 3.8690 | 0.0114 | 0.9451 | 5 |
| 4.5980 | 0.0118 | 0.8102 | 3.8491 | 0.0115 | 0.9152 | 6 |
| 4.5759 | 0.0119 | 0.7890 | 3.8366 | 0.0116 | 0.8691 | 7 |
| 4.5518 | 0.0120 | 0.7694 | 3.8081 | 0.0116 | 0.9013 | 8 |
| 4.5219 | 0.0121 | 0.7591 | 3.7734 | 0.0118 | 0.8383 | 9 |
| 4.4761 | 0.0122 | 0.7400 | 3.7156 | 0.0120 | 0.8125 | 10 |
| 4.4139 | 0.0125 | 0.7257 | 3.6311 | 0.0121 | 0.8188 | 11 |
| 4.3113 | 0.0128 | 0.7127 | 3.5089 | 0.0124 | 0.8008 | 12 |
| 4.1608 | 0.0132 | 0.7088 | 3.3587 | 0.0127 | 0.7742 | 13 |
| 3.9595 | 0.0138 | 0.7012 | 3.1493 | 0.0132 | 0.7718 | 14 |
| 3.7188 | 0.0145 | 0.6820 | 2.8784 | 0.0139 | 0.7292 | 15 |
| 3.4775 | 0.0153 | 0.6678 | 2.6716 | 0.0144 | 0.7074 | 16 |
| 3.2575 | 0.0160 | 0.6481 | 2.4980 | 0.0149 | 0.6764 | 17 |
| 3.0615 | 0.0167 | 0.6314 | 2.3456 | 0.0153 | 0.6476 | 18 |
| 2.8715 | 0.0174 | 0.6094 | 2.2090 | 0.0158 | 0.6210 | 19 |
| 2.6930 | 0.0181 | 0.5931 | 2.0918 | 0.0162 | 0.5992 | 20 |
| 2.5383 | 0.0187 | 0.5739 | 1.9769 | 0.0166 | 0.5791 | 21 |
| 2.3952 | 0.0193 | 0.5512 | 1.9042 | 0.0168 | 0.5589 | 22 |
| 2.2427 | 0.0201 | 0.5333 | 1.8028 | 0.0172 | 0.5394 | 23 |
| 2.1236 | 0.0206 | 0.5174 | 1.7434 | 0.0174 | 0.5240 | 24 |
| 2.0315 | 0.0211 | 0.4978 | 1.6755 | 0.0177 | 0.5084 | 25 |
| 1.9066 | 0.0217 | 0.4773 | 1.6534 | 0.0178 | 0.4947 | 26 |
| 1.8279 | 0.0221 | 0.4596 | 1.5606 | 0.0182 | 0.4788 | 27 |
| 1.7325 | 0.0227 | 0.4412 | 1.5173 | 0.0184 | 0.4667 | 28 |
| 1.6416 | 0.0232 | 0.4199 | 1.4733 | 0.0186 | 0.4511 | 29 |
| 1.5702 | 0.0236 | 0.4028 | 1.4519 | 0.0187 | 0.4442 | 30 |
| 1.4787 | 0.0241 | 0.3839 | 1.4213 | 0.0188 | 0.4322 | 31 |
| 1.4238 | 0.0244 | 0.3700 | 1.3971 | 0.0190 | 0.4272 | 32 |
| 1.3561 | 0.0249 | 0.3594 | 1.3499 | 0.0192 | 0.4171 | 33 |
| 1.2828 | 0.0254 | 0.3431 | 1.3555 | 0.0192 | 0.4097 | 34 |
| 1.2318 | 0.0257 | 0.3277 | 1.3183 | 0.0194 | 0.4035 | 35 |
| 1.1668 | 0.0262 | 0.3201 | 1.3068 | 0.0195 | 0.3978 | 36 |
| 1.1571 | 0.0261 | 0.3105 | 1.2901 | 0.0195 | 0.3916 | 37 |
| 1.0812 | 0.0267 | 0.2989 | 1.2720 | 0.0197 | 0.3860 | 38 |
| 1.0134 | 0.0273 | 0.2863 | 1.2593 | 0.0197 | 0.3777 | 39 |
| 0.9986 | 0.0273 | 0.2769 | 1.2629 | 0.0198 | 0.3754 | 40 |
| 0.9322 | 0.0279 | 0.2653 | 1.2320 | 0.0199 | 0.3694 | 41 |
| 0.9021 | 0.0281 | 0.2552 | 1.2308 | 0.0200 | 0.3651 | 42 |
| 0.8583 | 0.0284 | 0.2444 | 1.2199 | 0.0200 | 0.3614 | 43 |
| 0.8101 | 0.0288 | 0.2355 | 1.2120 | 0.0200 | 0.3597 | 44 |
| 0.8045 | 0.0288 | 0.2299 | 1.2023 | 0.0201 | 0.3567 | 45 |
| 0.7823 | 0.0290 | 0.2213 | 1.2075 | 0.0201 | 0.3529 | 46 |
| 0.7186 | 0.0296 | 0.2107 | 1.1917 | 0.0202 | 0.3530 | 47 |
| 0.6949 | 0.0298 | 0.2028 | 1.1926 | 0.0202 | 0.3465 | 48 |
| 0.6669 | 0.0300 | 0.1943 | 1.1902 | 0.0203 | 0.3446 | 49 |
| 0.6125 | 0.0305 | 0.1842 | 1.1892 | 0.0203 | 0.3437 | 50 |
| 0.5926 | 0.0307 | 0.1778 | 1.2058 | 0.0203 | 0.3450 | 51 |
| 0.6055 | 0.0305 | 0.1738 | 1.1859 | 0.0203 | 0.3394 | 52 |
| 0.5828 | 0.0307 | 0.1653 | 1.1921 | 0.0203 | 0.3379 | 53 |
| 0.5507 | 0.0311 | 0.1569 | 1.1906 | 0.0204 | 0.3385 | 54 |
| 0.5050 | 0.0315 | 0.1485 | 1.1834 | 0.0205 | 0.3361 | 55 |
| 0.4878 | 0.0316 | 0.1447 | 1.1815 | 0.0205 | 0.3329 | 56 |
| 0.4825 | 0.0317 | 0.1410 | 1.2096 | 0.0204 | 0.3359 | 57 |
| 0.4987 | 0.0315 | 0.1374 | 1.2000 | 0.0204 | 0.3352 | 58 |
| 0.4576 | 0.0319 | 0.1305 | 1.1868 | 0.0205 | 0.3329 | 59 |
| 0.4185 | 0.0323 | 0.1215 | 1.2043 | 0.0205 | 0.3322 | 60 |
| 0.3889 | 0.0326 | 0.1156 | 1.1853 | 0.0206 | 0.3302 | 61 |
| 0.3790 | 0.0327 | 0.1101 | 1.2028 | 0.0205 | 0.3316 | 62 |
| 0.4072 | 0.0324 | 0.1110 | 1.2502 | 0.0203 | 0.3309 | 63 |
| 0.3519 | 0.0330 | 0.1020 | 1.1959 | 0.0206 | 0.3284 | 64 |
| 0.3861 | 0.0326 | 0.1034 | 1.1885 | 0.0206 | 0.3271 | 65 |
| 0.3789 | 0.0326 | 0.0961 | 1.1969 | 0.0206 | 0.3298 | 66 |
| 0.3233 | 0.0332 | 0.0905 | 1.1922 | 0.0207 | 0.3280 | 67 |
| 0.2956 | 0.0335 | 0.0854 | 1.2003 | 0.0207 | 0.3296 | 68 |
| 0.2666 | 0.0339 | 0.0796 | 1.2141 | 0.0207 | 0.3252 | 69 |
| 0.3181 | 0.0333 | 0.0813 | 1.2133 | 0.0207 | 0.3302 | 70 |
| 0.3032 | 0.0335 | 0.0770 | 1.2170 | 0.0207 | 0.3315 | 71 |
| 0.2746 | 0.0337 | 0.0741 | 1.2180 | 0.0207 | 0.3299 | 72 |
| 0.2549 | 0.0339 | 0.0705 | 1.2496 | 0.0206 | 0.3308 | 73 |
| 0.2529 | 0.0339 | 0.0685 | 1.2239 | 0.0207 | 0.3321 | 74 |
| 0.2427 | 0.0340 | 0.0671 | 1.2351 | 0.0207 | 0.3292 | 75 |
| 0.2166 | 0.0343 | 0.0623 | 1.2361 | 0.0207 | 0.3313 | 76 |
| 0.2030 | 0.0345 | 0.0585 | 1.2462 | 0.0207 | 0.3312 | 77 |
| 0.2126 | 0.0344 | 0.0566 | 1.2441 | 0.0207 | 0.3313 | 78 |
| 0.2166 | 0.0343 | 0.0569 | 1.2506 | 0.0207 | 0.3334 | 79 |
| 0.2088 | 0.0344 | 0.0562 | 1.2557 | 0.0207 | 0.3389 | 80 |
| 0.2212 | 0.0342 | 0.0560 | 1.2652 | 0.0207 | 0.3334 | 81 |
| 0.2256 | 0.0343 | 0.0543 | 1.2543 | 0.0207 | 0.3393 | 82 |
| 0.1915 | 0.0346 | 0.0501 | 1.2540 | 0.0207 | 0.3299 | 83 |
| 0.1544 | 0.0350 | 0.0443 | 1.2676 | 0.0207 | 0.3347 | 84 |
| 0.1567 | 0.0350 | 0.0435 | 1.2740 | 0.0207 | 0.3375 | 85 |
| 0.1329 | 0.0352 | 0.0405 | 1.2833 | 0.0207 | 0.3398 | 86 |
| 0.1261 | 0.0353 | 0.0392 | 1.3088 | 0.0206 | 0.3397 | 87 |
| 0.1547 | 0.0350 | 0.0438 | 1.2933 | 0.0207 | 0.3279 | 88 |
| 0.1288 | 0.0352 | 0.0377 | 1.2985 | 0.0208 | 0.3371 | 89 |
| 0.1400 | 0.0351 | 0.0391 | 1.3020 | 0.0208 | 0.3329 | 90 |
| 0.1077 | 0.0355 | 0.0351 | 1.3118 | 0.0207 | 0.3344 | 91 |
| 0.0982 | 0.0355 | 0.0348 | 1.3274 | 0.0207 | 0.3352 | 92 |
| 0.1041 | 0.0355 | 0.0356 | 1.3251 | 0.0208 | 0.3362 | 93 |
| 0.1573 | 0.0350 | 0.0364 | 1.3173 | 0.0208 | 0.3362 | 94 |
| 0.1478 | 0.0350 | 0.0365 | 1.3125 | 0.0208 | 0.3367 | 95 |
| 0.0929 | 0.0356 | 0.0336 | 1.3256 | 0.0208 | 0.3351 | 96 |
| 0.1106 | 0.0354 | 0.0341 | 1.3289 | 0.0208 | 0.3428 | 97 |
| 0.0916 | 0.0355 | 0.0322 | 1.3384 | 0.0208 | 0.3383 | 98 |
| 0.0708 | 0.0358 | 0.0313 | 1.3525 | 0.0208 | 0.3333 | 99 |
| 0.1271 | 0.0352 | 0.0326 | 1.3447 | 0.0208 | 0.3336 | 100 |
| 0.0963 | 0.0355 | 0.0329 | 1.3481 | 0.0208 | 0.3288 | 101 |
| 0.0698 | 0.0358 | 0.0294 | 1.3748 | 0.0207 | 0.3341 | 102 |
| 0.0844 | 0.0356 | 0.0313 | 1.3615 | 0.0208 | 0.3380 | 103 |
| 0.1045 | 0.0354 | 0.0319 | 1.3519 | 0.0208 | 0.3357 | 104 |
### Framework versions
- Transformers 4.33.0.dev0
- TensorFlow 2.13.0
- Tokenizers 0.13.3
|
ngkk/KiraVF
|
ngkk
| 2023-09-03T16:23:22Z | 0 | 0 | null |
[
"fr",
"license:openrail",
"region:us"
] | null | 2023-08-29T10:15:10Z |
---
license: openrail
language:
- fr
---
|
ngkk/madaravf
|
ngkk
| 2023-09-03T16:20:03Z | 0 | 0 | null |
[
"fr",
"license:openrail",
"region:us"
] | null | 2023-08-28T16:13:22Z |
---
license: openrail
language:
- fr
---
|
ngkk/itachiVF500EpochsRVC1
|
ngkk
| 2023-09-03T16:19:13Z | 0 | 0 | null |
[
"fr",
"license:openrail",
"region:us"
] | null | 2023-09-03T10:28:08Z |
---
license: openrail
language:
- fr
---
|
bigmorning/whisper_input_decoder_no_lob__0100
|
bigmorning
| 2023-09-03T16:17:33Z | 59 | 0 |
transformers
|
[
"transformers",
"tf",
"whisper",
"automatic-speech-recognition",
"generated_from_keras_callback",
"base_model:openai/whisper-tiny",
"base_model:finetune:openai/whisper-tiny",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-09-03T16:17:27Z |
---
license: apache-2.0
base_model: openai/whisper-tiny
tags:
- generated_from_keras_callback
model-index:
- name: whisper_input_decoder_no_lob__0100
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. -->
# whisper_input_decoder_no_lob__0100
This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0708
- Train Accuracy: 0.0358
- Train Wermet: 0.0313
- Validation Loss: 1.3525
- Validation Accuracy: 0.0208
- Validation Wermet: 0.3333
- Epoch: 99
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 1e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Train Wermet | Validation Loss | Validation Accuracy | Validation Wermet | Epoch |
|:----------:|:--------------:|:------------:|:---------------:|:-------------------:|:-----------------:|:-----:|
| 5.4122 | 0.0107 | 0.9328 | 3.9759 | 0.0114 | 0.9606 | 0 |
| 4.7176 | 0.0116 | 0.8683 | 3.9404 | 0.0114 | 0.9334 | 1 |
| 4.6750 | 0.0117 | 0.8478 | 3.9211 | 0.0115 | 0.9237 | 2 |
| 4.6511 | 0.0117 | 0.8413 | 3.8864 | 0.0115 | 0.9331 | 3 |
| 4.6294 | 0.0118 | 0.8270 | 3.8729 | 0.0115 | 0.9228 | 4 |
| 4.6134 | 0.0118 | 0.8199 | 3.8690 | 0.0114 | 0.9451 | 5 |
| 4.5980 | 0.0118 | 0.8102 | 3.8491 | 0.0115 | 0.9152 | 6 |
| 4.5759 | 0.0119 | 0.7890 | 3.8366 | 0.0116 | 0.8691 | 7 |
| 4.5518 | 0.0120 | 0.7694 | 3.8081 | 0.0116 | 0.9013 | 8 |
| 4.5219 | 0.0121 | 0.7591 | 3.7734 | 0.0118 | 0.8383 | 9 |
| 4.4761 | 0.0122 | 0.7400 | 3.7156 | 0.0120 | 0.8125 | 10 |
| 4.4139 | 0.0125 | 0.7257 | 3.6311 | 0.0121 | 0.8188 | 11 |
| 4.3113 | 0.0128 | 0.7127 | 3.5089 | 0.0124 | 0.8008 | 12 |
| 4.1608 | 0.0132 | 0.7088 | 3.3587 | 0.0127 | 0.7742 | 13 |
| 3.9595 | 0.0138 | 0.7012 | 3.1493 | 0.0132 | 0.7718 | 14 |
| 3.7188 | 0.0145 | 0.6820 | 2.8784 | 0.0139 | 0.7292 | 15 |
| 3.4775 | 0.0153 | 0.6678 | 2.6716 | 0.0144 | 0.7074 | 16 |
| 3.2575 | 0.0160 | 0.6481 | 2.4980 | 0.0149 | 0.6764 | 17 |
| 3.0615 | 0.0167 | 0.6314 | 2.3456 | 0.0153 | 0.6476 | 18 |
| 2.8715 | 0.0174 | 0.6094 | 2.2090 | 0.0158 | 0.6210 | 19 |
| 2.6930 | 0.0181 | 0.5931 | 2.0918 | 0.0162 | 0.5992 | 20 |
| 2.5383 | 0.0187 | 0.5739 | 1.9769 | 0.0166 | 0.5791 | 21 |
| 2.3952 | 0.0193 | 0.5512 | 1.9042 | 0.0168 | 0.5589 | 22 |
| 2.2427 | 0.0201 | 0.5333 | 1.8028 | 0.0172 | 0.5394 | 23 |
| 2.1236 | 0.0206 | 0.5174 | 1.7434 | 0.0174 | 0.5240 | 24 |
| 2.0315 | 0.0211 | 0.4978 | 1.6755 | 0.0177 | 0.5084 | 25 |
| 1.9066 | 0.0217 | 0.4773 | 1.6534 | 0.0178 | 0.4947 | 26 |
| 1.8279 | 0.0221 | 0.4596 | 1.5606 | 0.0182 | 0.4788 | 27 |
| 1.7325 | 0.0227 | 0.4412 | 1.5173 | 0.0184 | 0.4667 | 28 |
| 1.6416 | 0.0232 | 0.4199 | 1.4733 | 0.0186 | 0.4511 | 29 |
| 1.5702 | 0.0236 | 0.4028 | 1.4519 | 0.0187 | 0.4442 | 30 |
| 1.4787 | 0.0241 | 0.3839 | 1.4213 | 0.0188 | 0.4322 | 31 |
| 1.4238 | 0.0244 | 0.3700 | 1.3971 | 0.0190 | 0.4272 | 32 |
| 1.3561 | 0.0249 | 0.3594 | 1.3499 | 0.0192 | 0.4171 | 33 |
| 1.2828 | 0.0254 | 0.3431 | 1.3555 | 0.0192 | 0.4097 | 34 |
| 1.2318 | 0.0257 | 0.3277 | 1.3183 | 0.0194 | 0.4035 | 35 |
| 1.1668 | 0.0262 | 0.3201 | 1.3068 | 0.0195 | 0.3978 | 36 |
| 1.1571 | 0.0261 | 0.3105 | 1.2901 | 0.0195 | 0.3916 | 37 |
| 1.0812 | 0.0267 | 0.2989 | 1.2720 | 0.0197 | 0.3860 | 38 |
| 1.0134 | 0.0273 | 0.2863 | 1.2593 | 0.0197 | 0.3777 | 39 |
| 0.9986 | 0.0273 | 0.2769 | 1.2629 | 0.0198 | 0.3754 | 40 |
| 0.9322 | 0.0279 | 0.2653 | 1.2320 | 0.0199 | 0.3694 | 41 |
| 0.9021 | 0.0281 | 0.2552 | 1.2308 | 0.0200 | 0.3651 | 42 |
| 0.8583 | 0.0284 | 0.2444 | 1.2199 | 0.0200 | 0.3614 | 43 |
| 0.8101 | 0.0288 | 0.2355 | 1.2120 | 0.0200 | 0.3597 | 44 |
| 0.8045 | 0.0288 | 0.2299 | 1.2023 | 0.0201 | 0.3567 | 45 |
| 0.7823 | 0.0290 | 0.2213 | 1.2075 | 0.0201 | 0.3529 | 46 |
| 0.7186 | 0.0296 | 0.2107 | 1.1917 | 0.0202 | 0.3530 | 47 |
| 0.6949 | 0.0298 | 0.2028 | 1.1926 | 0.0202 | 0.3465 | 48 |
| 0.6669 | 0.0300 | 0.1943 | 1.1902 | 0.0203 | 0.3446 | 49 |
| 0.6125 | 0.0305 | 0.1842 | 1.1892 | 0.0203 | 0.3437 | 50 |
| 0.5926 | 0.0307 | 0.1778 | 1.2058 | 0.0203 | 0.3450 | 51 |
| 0.6055 | 0.0305 | 0.1738 | 1.1859 | 0.0203 | 0.3394 | 52 |
| 0.5828 | 0.0307 | 0.1653 | 1.1921 | 0.0203 | 0.3379 | 53 |
| 0.5507 | 0.0311 | 0.1569 | 1.1906 | 0.0204 | 0.3385 | 54 |
| 0.5050 | 0.0315 | 0.1485 | 1.1834 | 0.0205 | 0.3361 | 55 |
| 0.4878 | 0.0316 | 0.1447 | 1.1815 | 0.0205 | 0.3329 | 56 |
| 0.4825 | 0.0317 | 0.1410 | 1.2096 | 0.0204 | 0.3359 | 57 |
| 0.4987 | 0.0315 | 0.1374 | 1.2000 | 0.0204 | 0.3352 | 58 |
| 0.4576 | 0.0319 | 0.1305 | 1.1868 | 0.0205 | 0.3329 | 59 |
| 0.4185 | 0.0323 | 0.1215 | 1.2043 | 0.0205 | 0.3322 | 60 |
| 0.3889 | 0.0326 | 0.1156 | 1.1853 | 0.0206 | 0.3302 | 61 |
| 0.3790 | 0.0327 | 0.1101 | 1.2028 | 0.0205 | 0.3316 | 62 |
| 0.4072 | 0.0324 | 0.1110 | 1.2502 | 0.0203 | 0.3309 | 63 |
| 0.3519 | 0.0330 | 0.1020 | 1.1959 | 0.0206 | 0.3284 | 64 |
| 0.3861 | 0.0326 | 0.1034 | 1.1885 | 0.0206 | 0.3271 | 65 |
| 0.3789 | 0.0326 | 0.0961 | 1.1969 | 0.0206 | 0.3298 | 66 |
| 0.3233 | 0.0332 | 0.0905 | 1.1922 | 0.0207 | 0.3280 | 67 |
| 0.2956 | 0.0335 | 0.0854 | 1.2003 | 0.0207 | 0.3296 | 68 |
| 0.2666 | 0.0339 | 0.0796 | 1.2141 | 0.0207 | 0.3252 | 69 |
| 0.3181 | 0.0333 | 0.0813 | 1.2133 | 0.0207 | 0.3302 | 70 |
| 0.3032 | 0.0335 | 0.0770 | 1.2170 | 0.0207 | 0.3315 | 71 |
| 0.2746 | 0.0337 | 0.0741 | 1.2180 | 0.0207 | 0.3299 | 72 |
| 0.2549 | 0.0339 | 0.0705 | 1.2496 | 0.0206 | 0.3308 | 73 |
| 0.2529 | 0.0339 | 0.0685 | 1.2239 | 0.0207 | 0.3321 | 74 |
| 0.2427 | 0.0340 | 0.0671 | 1.2351 | 0.0207 | 0.3292 | 75 |
| 0.2166 | 0.0343 | 0.0623 | 1.2361 | 0.0207 | 0.3313 | 76 |
| 0.2030 | 0.0345 | 0.0585 | 1.2462 | 0.0207 | 0.3312 | 77 |
| 0.2126 | 0.0344 | 0.0566 | 1.2441 | 0.0207 | 0.3313 | 78 |
| 0.2166 | 0.0343 | 0.0569 | 1.2506 | 0.0207 | 0.3334 | 79 |
| 0.2088 | 0.0344 | 0.0562 | 1.2557 | 0.0207 | 0.3389 | 80 |
| 0.2212 | 0.0342 | 0.0560 | 1.2652 | 0.0207 | 0.3334 | 81 |
| 0.2256 | 0.0343 | 0.0543 | 1.2543 | 0.0207 | 0.3393 | 82 |
| 0.1915 | 0.0346 | 0.0501 | 1.2540 | 0.0207 | 0.3299 | 83 |
| 0.1544 | 0.0350 | 0.0443 | 1.2676 | 0.0207 | 0.3347 | 84 |
| 0.1567 | 0.0350 | 0.0435 | 1.2740 | 0.0207 | 0.3375 | 85 |
| 0.1329 | 0.0352 | 0.0405 | 1.2833 | 0.0207 | 0.3398 | 86 |
| 0.1261 | 0.0353 | 0.0392 | 1.3088 | 0.0206 | 0.3397 | 87 |
| 0.1547 | 0.0350 | 0.0438 | 1.2933 | 0.0207 | 0.3279 | 88 |
| 0.1288 | 0.0352 | 0.0377 | 1.2985 | 0.0208 | 0.3371 | 89 |
| 0.1400 | 0.0351 | 0.0391 | 1.3020 | 0.0208 | 0.3329 | 90 |
| 0.1077 | 0.0355 | 0.0351 | 1.3118 | 0.0207 | 0.3344 | 91 |
| 0.0982 | 0.0355 | 0.0348 | 1.3274 | 0.0207 | 0.3352 | 92 |
| 0.1041 | 0.0355 | 0.0356 | 1.3251 | 0.0208 | 0.3362 | 93 |
| 0.1573 | 0.0350 | 0.0364 | 1.3173 | 0.0208 | 0.3362 | 94 |
| 0.1478 | 0.0350 | 0.0365 | 1.3125 | 0.0208 | 0.3367 | 95 |
| 0.0929 | 0.0356 | 0.0336 | 1.3256 | 0.0208 | 0.3351 | 96 |
| 0.1106 | 0.0354 | 0.0341 | 1.3289 | 0.0208 | 0.3428 | 97 |
| 0.0916 | 0.0355 | 0.0322 | 1.3384 | 0.0208 | 0.3383 | 98 |
| 0.0708 | 0.0358 | 0.0313 | 1.3525 | 0.0208 | 0.3333 | 99 |
### Framework versions
- Transformers 4.33.0.dev0
- TensorFlow 2.13.0
- Tokenizers 0.13.3
|
ibrahimciko/ppo-Pyramids
|
ibrahimciko
| 2023-09-03T16:02:57Z | 3 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"Pyramids",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
] |
reinforcement-learning
| 2023-09-03T16:02:54Z |
---
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: ibrahimciko/ppo-Pyramids
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
asmodehn/codellama
|
asmodehn
| 2023-09-03T15:57:20Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-09-03T15:44:57Z |
# Introducing Code Llama
Code Llama is a family of large language models for code based on [Llama 2](https://github.com/facebookresearch/llama) providing state-of-the-art performance among open models, infilling capabilities, support for large input contexts, and zero-shot instruction following ability for programming tasks. We provide multiple flavors to cover a wide range of applications: foundation models (Code Llama), Python specializations (Code Llama - Python), and instruction-following models (Code Llama - Instruct) with 7B, 13B and 34B parameters each. All models are trained on sequences of 16k tokens and show improvements on inputs with up to 100k tokens. 7B and 13B Code Llama and Code Llama - Instruct variants support infilling based on surrounding content. Code Llama was developed by fine-tuning Llama 2 using a higher sampling of code. As with Llama 2, we applied considerable safety mitigations to the fine-tuned versions of the model. For detailed information on model training, architecture and parameters, evaluations, responsible AI and safety refer to our [research paper](https://ai.meta.com/research/publications/code-llama-open-foundation-models-for-code/). Output generated by code generation features of the Llama Materials, including Code Llama, may be subject to third party licenses, including, without limitation, open source licenses.
We are unlocking the power of large language models and our latest version of Code Llama is now accessible to individuals, creators, researchers and businesses of all sizes so that they can experiment, innovate and scale their ideas responsibly. This release includes model weights and starting code for pretrained and fine-tuned Llama language models — ranging from 7B to 34B parameters.
This repository is intended as a minimal example to load [Code Llama](https://ai.meta.com/research/publications/code-llama-open-foundation-models-for-code/) models and run inference.
[comment]: <> (Code Llama models are compatible with the scripts in llama-recipes)
## Download
In order to download the model weights and tokenizers, please visit the [Meta AI website](https://ai.meta.com/resources/models-and-libraries/llama-downloads/) and accept our License.
Once your request is approved, you will receive a signed URL over email. Then run the download.sh script, passing the URL provided when prompted to start the download. Make sure that you copy the URL text itself, **do not use the 'Copy link address' option** when you right click the URL. If the copied URL text starts with: https://download.llamameta.net, you copied it correctly. If the copied URL text starts with: https://l.facebook.com, you copied it the wrong way.
Pre-requisites: make sure you have `wget` and `md5sum` installed. Then to run the script: `bash download.sh`.
Keep in mind that the links expire after 24 hours and a certain amount of downloads. If you start seeing errors such as `403: Forbidden`, you can always re-request a link.
[comment]: <> (Access on Hugging Face, We are also providing downloads on Hugging Face. You must first request a download from the Meta AI website using the same email address as your Hugging Face account. After doing so, you can request access to any of the models on Hugging Face and within 1-2 days your account will be granted access to all versions.)
## Setup
In a conda env with PyTorch / CUDA available, clone the repo and run in the top-level directory:
```
pip install -e .
```
## Inference
Different models require different model-parallel (MP) values:
| Model | MP |
|--------|----|
| 7B | 1 |
| 13B | 2 |
| 34B | 4 |
All models support sequence lengths up to 100,000 tokens, but we pre-allocate the cache according to `max_seq_len` and `max_batch_size` values. So set those according to your hardware and use-case.
### Pretrained Code Models
The Code Llama and Code Llama - Python models are not fine-tuned to follow instructions. They should be prompted so that the expected answer is the natural continuation of the prompt.
See `example_completion.py` for some examples. To illustrate, see command below to run it with the `CodeLlama-7b` model (`nproc_per_node` needs to be set to the `MP` value):
```
torchrun --nproc_per_node 1 example_completion.py \
--ckpt_dir CodeLlama-7b/ \
--tokenizer_path CodeLlama-7b/tokenizer.model \
--max_seq_len 128 --max_batch_size 4
```
Pretrained code models are: the Code Llama models `CodeLlama-7b`, `CodeLlama-13b`, `CodeLlama-34b` and the Code Llama - Python models
`CodeLlama-7b-Python`, `CodeLlama-13b-Python`, `CodeLlama-34b-Python`.
### Code Infilling
Code Llama and Code Llama - Instruct 7B and 13B models are capable of filling in code given the surrounding context.
See `example_infilling.py` for some examples. The `CodeLlama-7b` model can be run for infilling with the command below (`nproc_per_node` needs to be set to the `MP` value):
```
torchrun --nproc_per_node 1 example_infilling.py \
--ckpt_dir CodeLlama-7b/ \
--tokenizer_path CodeLlama-7b/tokenizer.model \
--max_seq_len 192 --max_batch_size 4
```
Pretrained infilling models are: the Code Llama models `CodeLlama-7b` and `CodeLlama-13b` and the Code Llama - Instruct models `CodeLlama-7b-Instruct`, `CodeLlama-13b-Instruct`.
### Fine-tuned Instruction Models
Code Llama - Instruct models are fine-tuned to follow instructions. To get the expected features and performance for them, a specific formatting defined in [`chat_completion`](https://github.com/facebookresearch/codellama/blob/main/llama/generation.py#L212)
needs to be followed, including the `INST` and `<<SYS>>` tags, `BOS` and `EOS` tokens, and the whitespaces and linebreaks in between (we recommend calling `strip()` on inputs to avoid double-spaces).
You can also deploy additional classifiers for filtering out inputs and outputs that are deemed unsafe. See the llama-recipes repo for [an example](https://github.com/facebookresearch/llama-recipes/blob/main/inference/inference.py) of how to add a safety checker to the inputs and outputs of your inference code.
Examples using `CodeLlama-7b-Instruct`:
```
torchrun --nproc_per_node 1 example_instructions.py \
--ckpt_dir CodeLlama-7b-Instruct/ \
--tokenizer_path CodeLlama-7b-Instruct/tokenizer.model \
--max_seq_len 512 --max_batch_size 4
```
Fine-tuned instruction-following models are: the Code Llama - Instruct models `CodeLlama-7b-Instruct`, `CodeLlama-13b-Instruct`, `CodeLlama-34b-Instruct`.
Code Llama is a new technology that carries potential risks with use. Testing conducted to date has not — and could not — cover all scenarios.
In order to help developers address these risks, we have created the [Responsible Use Guide](https://github.com/facebookresearch/llama/blob/main/Responsible-Use-Guide.pdf). More details can be found in our research papers as well.
## Issues
Please report any software “bug”, or other problems with the models through one of the following means:
- Reporting issues with the model: [github.com/facebookresearch/codellama](http://github.com/facebookresearch/codellama)
- Reporting risky content generated by the model: [developers.facebook.com/llama_output_feedback](http://developers.facebook.com/llama_output_feedback)
- Reporting bugs and security concerns: [facebook.com/whitehat/info](http://facebook.com/whitehat/info)
## Model Card
See [MODEL_CARD.md](MODEL_CARD.md) for the model card of Code Llama.
## License
Our model and weights are licensed for both researchers and commercial entities, upholding the principles of openness. Our mission is to empower individuals, and industry through this opportunity, while fostering an environment of discovery and ethical AI advancements.
See the [LICENSE](https://github.com/facebookresearch/llama/blob/main/LICENSE) file, as well as our accompanying [Acceptable Use Policy](https://github.com/facebookresearch/llama/blob/main/USE_POLICY.md)
## References
1. [Code Llama Research Paper](https://ai.meta.com/research/publications/code-llama-open-foundation-models-for-code/)
2. [Code Llama Blog Post](https://ai.meta.com/blog/code-llama-large-language-model-coding/)
|
grinya-ne-ustinov/ppo-Huggy
|
grinya-ne-ustinov
| 2023-09-03T15:52:42Z | 2 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"Huggy",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
] |
reinforcement-learning
| 2023-09-03T15:52:34Z |
---
library_name: ml-agents
tags:
- Huggy
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Huggy
---
# **ppo** Agent playing **Huggy**
This is a trained model of a **ppo** agent playing **Huggy**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: grinya-ne-ustinov/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
bigmorning/whisper_input_decoder_no_lob__0090
|
bigmorning
| 2023-09-03T15:51:20Z | 59 | 0 |
transformers
|
[
"transformers",
"tf",
"whisper",
"automatic-speech-recognition",
"generated_from_keras_callback",
"base_model:openai/whisper-tiny",
"base_model:finetune:openai/whisper-tiny",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-09-03T15:51:07Z |
---
license: apache-2.0
base_model: openai/whisper-tiny
tags:
- generated_from_keras_callback
model-index:
- name: whisper_input_decoder_no_lob__0090
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. -->
# whisper_input_decoder_no_lob__0090
This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.1288
- Train Accuracy: 0.0352
- Train Wermet: 0.0377
- Validation Loss: 1.2985
- Validation Accuracy: 0.0208
- Validation Wermet: 0.3371
- Epoch: 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:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 1e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Train Wermet | Validation Loss | Validation Accuracy | Validation Wermet | Epoch |
|:----------:|:--------------:|:------------:|:---------------:|:-------------------:|:-----------------:|:-----:|
| 5.4122 | 0.0107 | 0.9328 | 3.9759 | 0.0114 | 0.9606 | 0 |
| 4.7176 | 0.0116 | 0.8683 | 3.9404 | 0.0114 | 0.9334 | 1 |
| 4.6750 | 0.0117 | 0.8478 | 3.9211 | 0.0115 | 0.9237 | 2 |
| 4.6511 | 0.0117 | 0.8413 | 3.8864 | 0.0115 | 0.9331 | 3 |
| 4.6294 | 0.0118 | 0.8270 | 3.8729 | 0.0115 | 0.9228 | 4 |
| 4.6134 | 0.0118 | 0.8199 | 3.8690 | 0.0114 | 0.9451 | 5 |
| 4.5980 | 0.0118 | 0.8102 | 3.8491 | 0.0115 | 0.9152 | 6 |
| 4.5759 | 0.0119 | 0.7890 | 3.8366 | 0.0116 | 0.8691 | 7 |
| 4.5518 | 0.0120 | 0.7694 | 3.8081 | 0.0116 | 0.9013 | 8 |
| 4.5219 | 0.0121 | 0.7591 | 3.7734 | 0.0118 | 0.8383 | 9 |
| 4.4761 | 0.0122 | 0.7400 | 3.7156 | 0.0120 | 0.8125 | 10 |
| 4.4139 | 0.0125 | 0.7257 | 3.6311 | 0.0121 | 0.8188 | 11 |
| 4.3113 | 0.0128 | 0.7127 | 3.5089 | 0.0124 | 0.8008 | 12 |
| 4.1608 | 0.0132 | 0.7088 | 3.3587 | 0.0127 | 0.7742 | 13 |
| 3.9595 | 0.0138 | 0.7012 | 3.1493 | 0.0132 | 0.7718 | 14 |
| 3.7188 | 0.0145 | 0.6820 | 2.8784 | 0.0139 | 0.7292 | 15 |
| 3.4775 | 0.0153 | 0.6678 | 2.6716 | 0.0144 | 0.7074 | 16 |
| 3.2575 | 0.0160 | 0.6481 | 2.4980 | 0.0149 | 0.6764 | 17 |
| 3.0615 | 0.0167 | 0.6314 | 2.3456 | 0.0153 | 0.6476 | 18 |
| 2.8715 | 0.0174 | 0.6094 | 2.2090 | 0.0158 | 0.6210 | 19 |
| 2.6930 | 0.0181 | 0.5931 | 2.0918 | 0.0162 | 0.5992 | 20 |
| 2.5383 | 0.0187 | 0.5739 | 1.9769 | 0.0166 | 0.5791 | 21 |
| 2.3952 | 0.0193 | 0.5512 | 1.9042 | 0.0168 | 0.5589 | 22 |
| 2.2427 | 0.0201 | 0.5333 | 1.8028 | 0.0172 | 0.5394 | 23 |
| 2.1236 | 0.0206 | 0.5174 | 1.7434 | 0.0174 | 0.5240 | 24 |
| 2.0315 | 0.0211 | 0.4978 | 1.6755 | 0.0177 | 0.5084 | 25 |
| 1.9066 | 0.0217 | 0.4773 | 1.6534 | 0.0178 | 0.4947 | 26 |
| 1.8279 | 0.0221 | 0.4596 | 1.5606 | 0.0182 | 0.4788 | 27 |
| 1.7325 | 0.0227 | 0.4412 | 1.5173 | 0.0184 | 0.4667 | 28 |
| 1.6416 | 0.0232 | 0.4199 | 1.4733 | 0.0186 | 0.4511 | 29 |
| 1.5702 | 0.0236 | 0.4028 | 1.4519 | 0.0187 | 0.4442 | 30 |
| 1.4787 | 0.0241 | 0.3839 | 1.4213 | 0.0188 | 0.4322 | 31 |
| 1.4238 | 0.0244 | 0.3700 | 1.3971 | 0.0190 | 0.4272 | 32 |
| 1.3561 | 0.0249 | 0.3594 | 1.3499 | 0.0192 | 0.4171 | 33 |
| 1.2828 | 0.0254 | 0.3431 | 1.3555 | 0.0192 | 0.4097 | 34 |
| 1.2318 | 0.0257 | 0.3277 | 1.3183 | 0.0194 | 0.4035 | 35 |
| 1.1668 | 0.0262 | 0.3201 | 1.3068 | 0.0195 | 0.3978 | 36 |
| 1.1571 | 0.0261 | 0.3105 | 1.2901 | 0.0195 | 0.3916 | 37 |
| 1.0812 | 0.0267 | 0.2989 | 1.2720 | 0.0197 | 0.3860 | 38 |
| 1.0134 | 0.0273 | 0.2863 | 1.2593 | 0.0197 | 0.3777 | 39 |
| 0.9986 | 0.0273 | 0.2769 | 1.2629 | 0.0198 | 0.3754 | 40 |
| 0.9322 | 0.0279 | 0.2653 | 1.2320 | 0.0199 | 0.3694 | 41 |
| 0.9021 | 0.0281 | 0.2552 | 1.2308 | 0.0200 | 0.3651 | 42 |
| 0.8583 | 0.0284 | 0.2444 | 1.2199 | 0.0200 | 0.3614 | 43 |
| 0.8101 | 0.0288 | 0.2355 | 1.2120 | 0.0200 | 0.3597 | 44 |
| 0.8045 | 0.0288 | 0.2299 | 1.2023 | 0.0201 | 0.3567 | 45 |
| 0.7823 | 0.0290 | 0.2213 | 1.2075 | 0.0201 | 0.3529 | 46 |
| 0.7186 | 0.0296 | 0.2107 | 1.1917 | 0.0202 | 0.3530 | 47 |
| 0.6949 | 0.0298 | 0.2028 | 1.1926 | 0.0202 | 0.3465 | 48 |
| 0.6669 | 0.0300 | 0.1943 | 1.1902 | 0.0203 | 0.3446 | 49 |
| 0.6125 | 0.0305 | 0.1842 | 1.1892 | 0.0203 | 0.3437 | 50 |
| 0.5926 | 0.0307 | 0.1778 | 1.2058 | 0.0203 | 0.3450 | 51 |
| 0.6055 | 0.0305 | 0.1738 | 1.1859 | 0.0203 | 0.3394 | 52 |
| 0.5828 | 0.0307 | 0.1653 | 1.1921 | 0.0203 | 0.3379 | 53 |
| 0.5507 | 0.0311 | 0.1569 | 1.1906 | 0.0204 | 0.3385 | 54 |
| 0.5050 | 0.0315 | 0.1485 | 1.1834 | 0.0205 | 0.3361 | 55 |
| 0.4878 | 0.0316 | 0.1447 | 1.1815 | 0.0205 | 0.3329 | 56 |
| 0.4825 | 0.0317 | 0.1410 | 1.2096 | 0.0204 | 0.3359 | 57 |
| 0.4987 | 0.0315 | 0.1374 | 1.2000 | 0.0204 | 0.3352 | 58 |
| 0.4576 | 0.0319 | 0.1305 | 1.1868 | 0.0205 | 0.3329 | 59 |
| 0.4185 | 0.0323 | 0.1215 | 1.2043 | 0.0205 | 0.3322 | 60 |
| 0.3889 | 0.0326 | 0.1156 | 1.1853 | 0.0206 | 0.3302 | 61 |
| 0.3790 | 0.0327 | 0.1101 | 1.2028 | 0.0205 | 0.3316 | 62 |
| 0.4072 | 0.0324 | 0.1110 | 1.2502 | 0.0203 | 0.3309 | 63 |
| 0.3519 | 0.0330 | 0.1020 | 1.1959 | 0.0206 | 0.3284 | 64 |
| 0.3861 | 0.0326 | 0.1034 | 1.1885 | 0.0206 | 0.3271 | 65 |
| 0.3789 | 0.0326 | 0.0961 | 1.1969 | 0.0206 | 0.3298 | 66 |
| 0.3233 | 0.0332 | 0.0905 | 1.1922 | 0.0207 | 0.3280 | 67 |
| 0.2956 | 0.0335 | 0.0854 | 1.2003 | 0.0207 | 0.3296 | 68 |
| 0.2666 | 0.0339 | 0.0796 | 1.2141 | 0.0207 | 0.3252 | 69 |
| 0.3181 | 0.0333 | 0.0813 | 1.2133 | 0.0207 | 0.3302 | 70 |
| 0.3032 | 0.0335 | 0.0770 | 1.2170 | 0.0207 | 0.3315 | 71 |
| 0.2746 | 0.0337 | 0.0741 | 1.2180 | 0.0207 | 0.3299 | 72 |
| 0.2549 | 0.0339 | 0.0705 | 1.2496 | 0.0206 | 0.3308 | 73 |
| 0.2529 | 0.0339 | 0.0685 | 1.2239 | 0.0207 | 0.3321 | 74 |
| 0.2427 | 0.0340 | 0.0671 | 1.2351 | 0.0207 | 0.3292 | 75 |
| 0.2166 | 0.0343 | 0.0623 | 1.2361 | 0.0207 | 0.3313 | 76 |
| 0.2030 | 0.0345 | 0.0585 | 1.2462 | 0.0207 | 0.3312 | 77 |
| 0.2126 | 0.0344 | 0.0566 | 1.2441 | 0.0207 | 0.3313 | 78 |
| 0.2166 | 0.0343 | 0.0569 | 1.2506 | 0.0207 | 0.3334 | 79 |
| 0.2088 | 0.0344 | 0.0562 | 1.2557 | 0.0207 | 0.3389 | 80 |
| 0.2212 | 0.0342 | 0.0560 | 1.2652 | 0.0207 | 0.3334 | 81 |
| 0.2256 | 0.0343 | 0.0543 | 1.2543 | 0.0207 | 0.3393 | 82 |
| 0.1915 | 0.0346 | 0.0501 | 1.2540 | 0.0207 | 0.3299 | 83 |
| 0.1544 | 0.0350 | 0.0443 | 1.2676 | 0.0207 | 0.3347 | 84 |
| 0.1567 | 0.0350 | 0.0435 | 1.2740 | 0.0207 | 0.3375 | 85 |
| 0.1329 | 0.0352 | 0.0405 | 1.2833 | 0.0207 | 0.3398 | 86 |
| 0.1261 | 0.0353 | 0.0392 | 1.3088 | 0.0206 | 0.3397 | 87 |
| 0.1547 | 0.0350 | 0.0438 | 1.2933 | 0.0207 | 0.3279 | 88 |
| 0.1288 | 0.0352 | 0.0377 | 1.2985 | 0.0208 | 0.3371 | 89 |
### Framework versions
- Transformers 4.33.0.dev0
- TensorFlow 2.13.0
- Tokenizers 0.13.3
|
bigmorning/whisper_input_decoder_no_lob__0085
|
bigmorning
| 2023-09-03T15:38:06Z | 59 | 0 |
transformers
|
[
"transformers",
"tf",
"whisper",
"automatic-speech-recognition",
"generated_from_keras_callback",
"base_model:openai/whisper-tiny",
"base_model:finetune:openai/whisper-tiny",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-09-03T15:37:58Z |
---
license: apache-2.0
base_model: openai/whisper-tiny
tags:
- generated_from_keras_callback
model-index:
- name: whisper_input_decoder_no_lob__0085
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. -->
# whisper_input_decoder_no_lob__0085
This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.1544
- Train Accuracy: 0.0350
- Train Wermet: 0.0443
- Validation Loss: 1.2676
- Validation Accuracy: 0.0207
- Validation Wermet: 0.3347
- Epoch: 84
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 1e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Train Wermet | Validation Loss | Validation Accuracy | Validation Wermet | Epoch |
|:----------:|:--------------:|:------------:|:---------------:|:-------------------:|:-----------------:|:-----:|
| 5.4122 | 0.0107 | 0.9328 | 3.9759 | 0.0114 | 0.9606 | 0 |
| 4.7176 | 0.0116 | 0.8683 | 3.9404 | 0.0114 | 0.9334 | 1 |
| 4.6750 | 0.0117 | 0.8478 | 3.9211 | 0.0115 | 0.9237 | 2 |
| 4.6511 | 0.0117 | 0.8413 | 3.8864 | 0.0115 | 0.9331 | 3 |
| 4.6294 | 0.0118 | 0.8270 | 3.8729 | 0.0115 | 0.9228 | 4 |
| 4.6134 | 0.0118 | 0.8199 | 3.8690 | 0.0114 | 0.9451 | 5 |
| 4.5980 | 0.0118 | 0.8102 | 3.8491 | 0.0115 | 0.9152 | 6 |
| 4.5759 | 0.0119 | 0.7890 | 3.8366 | 0.0116 | 0.8691 | 7 |
| 4.5518 | 0.0120 | 0.7694 | 3.8081 | 0.0116 | 0.9013 | 8 |
| 4.5219 | 0.0121 | 0.7591 | 3.7734 | 0.0118 | 0.8383 | 9 |
| 4.4761 | 0.0122 | 0.7400 | 3.7156 | 0.0120 | 0.8125 | 10 |
| 4.4139 | 0.0125 | 0.7257 | 3.6311 | 0.0121 | 0.8188 | 11 |
| 4.3113 | 0.0128 | 0.7127 | 3.5089 | 0.0124 | 0.8008 | 12 |
| 4.1608 | 0.0132 | 0.7088 | 3.3587 | 0.0127 | 0.7742 | 13 |
| 3.9595 | 0.0138 | 0.7012 | 3.1493 | 0.0132 | 0.7718 | 14 |
| 3.7188 | 0.0145 | 0.6820 | 2.8784 | 0.0139 | 0.7292 | 15 |
| 3.4775 | 0.0153 | 0.6678 | 2.6716 | 0.0144 | 0.7074 | 16 |
| 3.2575 | 0.0160 | 0.6481 | 2.4980 | 0.0149 | 0.6764 | 17 |
| 3.0615 | 0.0167 | 0.6314 | 2.3456 | 0.0153 | 0.6476 | 18 |
| 2.8715 | 0.0174 | 0.6094 | 2.2090 | 0.0158 | 0.6210 | 19 |
| 2.6930 | 0.0181 | 0.5931 | 2.0918 | 0.0162 | 0.5992 | 20 |
| 2.5383 | 0.0187 | 0.5739 | 1.9769 | 0.0166 | 0.5791 | 21 |
| 2.3952 | 0.0193 | 0.5512 | 1.9042 | 0.0168 | 0.5589 | 22 |
| 2.2427 | 0.0201 | 0.5333 | 1.8028 | 0.0172 | 0.5394 | 23 |
| 2.1236 | 0.0206 | 0.5174 | 1.7434 | 0.0174 | 0.5240 | 24 |
| 2.0315 | 0.0211 | 0.4978 | 1.6755 | 0.0177 | 0.5084 | 25 |
| 1.9066 | 0.0217 | 0.4773 | 1.6534 | 0.0178 | 0.4947 | 26 |
| 1.8279 | 0.0221 | 0.4596 | 1.5606 | 0.0182 | 0.4788 | 27 |
| 1.7325 | 0.0227 | 0.4412 | 1.5173 | 0.0184 | 0.4667 | 28 |
| 1.6416 | 0.0232 | 0.4199 | 1.4733 | 0.0186 | 0.4511 | 29 |
| 1.5702 | 0.0236 | 0.4028 | 1.4519 | 0.0187 | 0.4442 | 30 |
| 1.4787 | 0.0241 | 0.3839 | 1.4213 | 0.0188 | 0.4322 | 31 |
| 1.4238 | 0.0244 | 0.3700 | 1.3971 | 0.0190 | 0.4272 | 32 |
| 1.3561 | 0.0249 | 0.3594 | 1.3499 | 0.0192 | 0.4171 | 33 |
| 1.2828 | 0.0254 | 0.3431 | 1.3555 | 0.0192 | 0.4097 | 34 |
| 1.2318 | 0.0257 | 0.3277 | 1.3183 | 0.0194 | 0.4035 | 35 |
| 1.1668 | 0.0262 | 0.3201 | 1.3068 | 0.0195 | 0.3978 | 36 |
| 1.1571 | 0.0261 | 0.3105 | 1.2901 | 0.0195 | 0.3916 | 37 |
| 1.0812 | 0.0267 | 0.2989 | 1.2720 | 0.0197 | 0.3860 | 38 |
| 1.0134 | 0.0273 | 0.2863 | 1.2593 | 0.0197 | 0.3777 | 39 |
| 0.9986 | 0.0273 | 0.2769 | 1.2629 | 0.0198 | 0.3754 | 40 |
| 0.9322 | 0.0279 | 0.2653 | 1.2320 | 0.0199 | 0.3694 | 41 |
| 0.9021 | 0.0281 | 0.2552 | 1.2308 | 0.0200 | 0.3651 | 42 |
| 0.8583 | 0.0284 | 0.2444 | 1.2199 | 0.0200 | 0.3614 | 43 |
| 0.8101 | 0.0288 | 0.2355 | 1.2120 | 0.0200 | 0.3597 | 44 |
| 0.8045 | 0.0288 | 0.2299 | 1.2023 | 0.0201 | 0.3567 | 45 |
| 0.7823 | 0.0290 | 0.2213 | 1.2075 | 0.0201 | 0.3529 | 46 |
| 0.7186 | 0.0296 | 0.2107 | 1.1917 | 0.0202 | 0.3530 | 47 |
| 0.6949 | 0.0298 | 0.2028 | 1.1926 | 0.0202 | 0.3465 | 48 |
| 0.6669 | 0.0300 | 0.1943 | 1.1902 | 0.0203 | 0.3446 | 49 |
| 0.6125 | 0.0305 | 0.1842 | 1.1892 | 0.0203 | 0.3437 | 50 |
| 0.5926 | 0.0307 | 0.1778 | 1.2058 | 0.0203 | 0.3450 | 51 |
| 0.6055 | 0.0305 | 0.1738 | 1.1859 | 0.0203 | 0.3394 | 52 |
| 0.5828 | 0.0307 | 0.1653 | 1.1921 | 0.0203 | 0.3379 | 53 |
| 0.5507 | 0.0311 | 0.1569 | 1.1906 | 0.0204 | 0.3385 | 54 |
| 0.5050 | 0.0315 | 0.1485 | 1.1834 | 0.0205 | 0.3361 | 55 |
| 0.4878 | 0.0316 | 0.1447 | 1.1815 | 0.0205 | 0.3329 | 56 |
| 0.4825 | 0.0317 | 0.1410 | 1.2096 | 0.0204 | 0.3359 | 57 |
| 0.4987 | 0.0315 | 0.1374 | 1.2000 | 0.0204 | 0.3352 | 58 |
| 0.4576 | 0.0319 | 0.1305 | 1.1868 | 0.0205 | 0.3329 | 59 |
| 0.4185 | 0.0323 | 0.1215 | 1.2043 | 0.0205 | 0.3322 | 60 |
| 0.3889 | 0.0326 | 0.1156 | 1.1853 | 0.0206 | 0.3302 | 61 |
| 0.3790 | 0.0327 | 0.1101 | 1.2028 | 0.0205 | 0.3316 | 62 |
| 0.4072 | 0.0324 | 0.1110 | 1.2502 | 0.0203 | 0.3309 | 63 |
| 0.3519 | 0.0330 | 0.1020 | 1.1959 | 0.0206 | 0.3284 | 64 |
| 0.3861 | 0.0326 | 0.1034 | 1.1885 | 0.0206 | 0.3271 | 65 |
| 0.3789 | 0.0326 | 0.0961 | 1.1969 | 0.0206 | 0.3298 | 66 |
| 0.3233 | 0.0332 | 0.0905 | 1.1922 | 0.0207 | 0.3280 | 67 |
| 0.2956 | 0.0335 | 0.0854 | 1.2003 | 0.0207 | 0.3296 | 68 |
| 0.2666 | 0.0339 | 0.0796 | 1.2141 | 0.0207 | 0.3252 | 69 |
| 0.3181 | 0.0333 | 0.0813 | 1.2133 | 0.0207 | 0.3302 | 70 |
| 0.3032 | 0.0335 | 0.0770 | 1.2170 | 0.0207 | 0.3315 | 71 |
| 0.2746 | 0.0337 | 0.0741 | 1.2180 | 0.0207 | 0.3299 | 72 |
| 0.2549 | 0.0339 | 0.0705 | 1.2496 | 0.0206 | 0.3308 | 73 |
| 0.2529 | 0.0339 | 0.0685 | 1.2239 | 0.0207 | 0.3321 | 74 |
| 0.2427 | 0.0340 | 0.0671 | 1.2351 | 0.0207 | 0.3292 | 75 |
| 0.2166 | 0.0343 | 0.0623 | 1.2361 | 0.0207 | 0.3313 | 76 |
| 0.2030 | 0.0345 | 0.0585 | 1.2462 | 0.0207 | 0.3312 | 77 |
| 0.2126 | 0.0344 | 0.0566 | 1.2441 | 0.0207 | 0.3313 | 78 |
| 0.2166 | 0.0343 | 0.0569 | 1.2506 | 0.0207 | 0.3334 | 79 |
| 0.2088 | 0.0344 | 0.0562 | 1.2557 | 0.0207 | 0.3389 | 80 |
| 0.2212 | 0.0342 | 0.0560 | 1.2652 | 0.0207 | 0.3334 | 81 |
| 0.2256 | 0.0343 | 0.0543 | 1.2543 | 0.0207 | 0.3393 | 82 |
| 0.1915 | 0.0346 | 0.0501 | 1.2540 | 0.0207 | 0.3299 | 83 |
| 0.1544 | 0.0350 | 0.0443 | 1.2676 | 0.0207 | 0.3347 | 84 |
### Framework versions
- Transformers 4.33.0.dev0
- TensorFlow 2.13.0
- Tokenizers 0.13.3
|
tdperez/t5-small-finetuned-pt-gec
|
tdperez
| 2023-09-03T15:34:55Z | 104 | 0 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:google-t5/t5-small",
"base_model:finetune:google-t5/t5-small",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-09-03T15:30:40Z |
---
license: apache-2.0
base_model: t5-small
tags:
- generated_from_trainer
model-index:
- name: t5-small-finetuned-pt-gec
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-small-finetuned-pt-gec
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| No log | 1.0 | 48 | 3.2209 | 62.6948 | 48.3493 | 62.6827 | 62.745 | 17.5077 |
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cpu
- Datasets 2.14.4
- Tokenizers 0.13.3
|
gsdavis/distilroberta-base-finetuned-wikitext2
|
gsdavis
| 2023-09-03T15:32:34Z | 210 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"fill-mask",
"generated_from_trainer",
"base_model:distilbert/distilroberta-base",
"base_model:finetune:distilbert/distilroberta-base",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2023-09-02T22:06:12Z |
---
license: apache-2.0
base_model: distilroberta-base
tags:
- generated_from_trainer
model-index:
- name: distilroberta-base-finetuned-wikitext2
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. -->
# distilroberta-base-finetuned-wikitext2
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8269
## 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 |
|:-------------:|:-----:|:----:|:---------------:|
| 2.0834 | 1.0 | 2406 | 1.9288 |
| 1.9987 | 2.0 | 4812 | 1.8662 |
| 1.9406 | 3.0 | 7218 | 1.8571 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
|
MattStammers/qrdqn-SpaceinvadersNoFrameskip-v4-final
|
MattStammers
| 2023-09-03T15:28:53Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-08-30T00:41:12Z |
---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: QRDQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 8255.00 +/- 6740.30
name: mean_reward
verified: false
---
# **QRDQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **QRDQN** 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 qrdqn --env SpaceInvadersNoFrameskip-v4 -orga MattStammers -f logs/
python -m rl_zoo3.enjoy --algo qrdqn --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 qrdqn --env SpaceInvadersNoFrameskip-v4 -orga MattStammers -f logs/
python -m rl_zoo3.enjoy --algo qrdqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo qrdqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo qrdqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga MattStammers
```
## Hyperparameters
```python
OrderedDict([('batch_size', 64),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_fraction', 0.025),
('frame_stack', 4),
('n_timesteps', 50000000.0),
('normalize', False),
('optimize_memory_usage', False),
('policy', 'CnnPolicy')])
```
# Environment Arguments
```python
{'render_mode': 'rgb_array'}
```
So another 24 hours of training has made all the difference and the agent is now ranked number 3. Not because it is getting more points but because it is becoming more consistent
|
NewtonKimathi/test_trainer
|
NewtonKimathi
| 2023-09-03T15:27:25Z | 164 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"text-classification",
"generated_from_trainer",
"base_model:FacebookAI/roberta-base",
"base_model:finetune:FacebookAI/roberta-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-09-03T10:53:03Z |
---
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
model-index:
- name: test_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. -->
# test_trainer
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6445
## 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
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.7754 | 0.5 | 500 | 0.7388 |
| 0.7724 | 1.0 | 1000 | 0.6889 |
| 0.693 | 1.5 | 1500 | 0.6731 |
| 0.6487 | 2.0 | 2000 | 0.6217 |
| 0.5377 | 2.5 | 2500 | 0.6684 |
| 0.5239 | 3.0 | 3000 | 0.6445 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
|
MattStammers/qrdqn-SpaceinvadersNoFrameskip-v4-blowout
|
MattStammers
| 2023-09-03T15:25:48Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-09-03T15:23:12Z |
---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: QRDQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 5252.50 +/- 6995.39
name: mean_reward
verified: false
---
# **QRDQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **QRDQN** 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 qrdqn --env SpaceInvadersNoFrameskip-v4 -orga MattStammers -f logs/
python -m rl_zoo3.enjoy --algo qrdqn --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 qrdqn --env SpaceInvadersNoFrameskip-v4 -orga MattStammers -f logs/
python -m rl_zoo3.enjoy --algo qrdqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo qrdqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo qrdqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga MattStammers
```
## Hyperparameters
```python
OrderedDict([('batch_size', 64),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_fraction', 0.025),
('frame_stack', 4),
('n_timesteps', 100000000.0),
('normalize', False),
('optimize_memory_usage', False),
('policy', 'CnnPolicy')])
```
# Environment Arguments
```python
{'render_mode': 'rgb_array'}
```
|
tdperez/mt5-small-finetuned-pt-gec
|
tdperez
| 2023-09-03T15:18:23Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"generated_from_trainer",
"base_model:google/mt5-small",
"base_model:finetune:google/mt5-small",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-09-03T15:01:01Z |
---
license: apache-2.0
base_model: google/mt5-small
tags:
- generated_from_trainer
model-index:
- name: mt5-small-finetuned-pt-gec
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mt5-small-finetuned-pt-gec
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| No log | 1.0 | 48 | 23.5346 | 0.1667 | 0.0855 | 0.1667 | 0.1667 | 3.0615 |
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cpu
- Datasets 2.14.4
- Tokenizers 0.13.3
|
ibrahimciko/ppo-SnowballTarget
|
ibrahimciko
| 2023-09-03T15:17:58Z | 3 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"SnowballTarget",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SnowballTarget",
"region:us"
] |
reinforcement-learning
| 2023-09-03T15:17:56Z |
---
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: ibrahimciko/ppo-SnowballTarget
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
bigmorning/whisper_input_decoder_no_lob__0075
|
bigmorning
| 2023-09-03T15:11:49Z | 59 | 0 |
transformers
|
[
"transformers",
"tf",
"whisper",
"automatic-speech-recognition",
"generated_from_keras_callback",
"base_model:openai/whisper-tiny",
"base_model:finetune:openai/whisper-tiny",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-09-03T15:11:41Z |
---
license: apache-2.0
base_model: openai/whisper-tiny
tags:
- generated_from_keras_callback
model-index:
- name: whisper_input_decoder_no_lob__0075
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. -->
# whisper_input_decoder_no_lob__0075
This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.2529
- Train Accuracy: 0.0339
- Train Wermet: 0.0685
- Validation Loss: 1.2239
- Validation Accuracy: 0.0207
- Validation Wermet: 0.3321
- Epoch: 74
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 1e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Train Wermet | Validation Loss | Validation Accuracy | Validation Wermet | Epoch |
|:----------:|:--------------:|:------------:|:---------------:|:-------------------:|:-----------------:|:-----:|
| 5.4122 | 0.0107 | 0.9328 | 3.9759 | 0.0114 | 0.9606 | 0 |
| 4.7176 | 0.0116 | 0.8683 | 3.9404 | 0.0114 | 0.9334 | 1 |
| 4.6750 | 0.0117 | 0.8478 | 3.9211 | 0.0115 | 0.9237 | 2 |
| 4.6511 | 0.0117 | 0.8413 | 3.8864 | 0.0115 | 0.9331 | 3 |
| 4.6294 | 0.0118 | 0.8270 | 3.8729 | 0.0115 | 0.9228 | 4 |
| 4.6134 | 0.0118 | 0.8199 | 3.8690 | 0.0114 | 0.9451 | 5 |
| 4.5980 | 0.0118 | 0.8102 | 3.8491 | 0.0115 | 0.9152 | 6 |
| 4.5759 | 0.0119 | 0.7890 | 3.8366 | 0.0116 | 0.8691 | 7 |
| 4.5518 | 0.0120 | 0.7694 | 3.8081 | 0.0116 | 0.9013 | 8 |
| 4.5219 | 0.0121 | 0.7591 | 3.7734 | 0.0118 | 0.8383 | 9 |
| 4.4761 | 0.0122 | 0.7400 | 3.7156 | 0.0120 | 0.8125 | 10 |
| 4.4139 | 0.0125 | 0.7257 | 3.6311 | 0.0121 | 0.8188 | 11 |
| 4.3113 | 0.0128 | 0.7127 | 3.5089 | 0.0124 | 0.8008 | 12 |
| 4.1608 | 0.0132 | 0.7088 | 3.3587 | 0.0127 | 0.7742 | 13 |
| 3.9595 | 0.0138 | 0.7012 | 3.1493 | 0.0132 | 0.7718 | 14 |
| 3.7188 | 0.0145 | 0.6820 | 2.8784 | 0.0139 | 0.7292 | 15 |
| 3.4775 | 0.0153 | 0.6678 | 2.6716 | 0.0144 | 0.7074 | 16 |
| 3.2575 | 0.0160 | 0.6481 | 2.4980 | 0.0149 | 0.6764 | 17 |
| 3.0615 | 0.0167 | 0.6314 | 2.3456 | 0.0153 | 0.6476 | 18 |
| 2.8715 | 0.0174 | 0.6094 | 2.2090 | 0.0158 | 0.6210 | 19 |
| 2.6930 | 0.0181 | 0.5931 | 2.0918 | 0.0162 | 0.5992 | 20 |
| 2.5383 | 0.0187 | 0.5739 | 1.9769 | 0.0166 | 0.5791 | 21 |
| 2.3952 | 0.0193 | 0.5512 | 1.9042 | 0.0168 | 0.5589 | 22 |
| 2.2427 | 0.0201 | 0.5333 | 1.8028 | 0.0172 | 0.5394 | 23 |
| 2.1236 | 0.0206 | 0.5174 | 1.7434 | 0.0174 | 0.5240 | 24 |
| 2.0315 | 0.0211 | 0.4978 | 1.6755 | 0.0177 | 0.5084 | 25 |
| 1.9066 | 0.0217 | 0.4773 | 1.6534 | 0.0178 | 0.4947 | 26 |
| 1.8279 | 0.0221 | 0.4596 | 1.5606 | 0.0182 | 0.4788 | 27 |
| 1.7325 | 0.0227 | 0.4412 | 1.5173 | 0.0184 | 0.4667 | 28 |
| 1.6416 | 0.0232 | 0.4199 | 1.4733 | 0.0186 | 0.4511 | 29 |
| 1.5702 | 0.0236 | 0.4028 | 1.4519 | 0.0187 | 0.4442 | 30 |
| 1.4787 | 0.0241 | 0.3839 | 1.4213 | 0.0188 | 0.4322 | 31 |
| 1.4238 | 0.0244 | 0.3700 | 1.3971 | 0.0190 | 0.4272 | 32 |
| 1.3561 | 0.0249 | 0.3594 | 1.3499 | 0.0192 | 0.4171 | 33 |
| 1.2828 | 0.0254 | 0.3431 | 1.3555 | 0.0192 | 0.4097 | 34 |
| 1.2318 | 0.0257 | 0.3277 | 1.3183 | 0.0194 | 0.4035 | 35 |
| 1.1668 | 0.0262 | 0.3201 | 1.3068 | 0.0195 | 0.3978 | 36 |
| 1.1571 | 0.0261 | 0.3105 | 1.2901 | 0.0195 | 0.3916 | 37 |
| 1.0812 | 0.0267 | 0.2989 | 1.2720 | 0.0197 | 0.3860 | 38 |
| 1.0134 | 0.0273 | 0.2863 | 1.2593 | 0.0197 | 0.3777 | 39 |
| 0.9986 | 0.0273 | 0.2769 | 1.2629 | 0.0198 | 0.3754 | 40 |
| 0.9322 | 0.0279 | 0.2653 | 1.2320 | 0.0199 | 0.3694 | 41 |
| 0.9021 | 0.0281 | 0.2552 | 1.2308 | 0.0200 | 0.3651 | 42 |
| 0.8583 | 0.0284 | 0.2444 | 1.2199 | 0.0200 | 0.3614 | 43 |
| 0.8101 | 0.0288 | 0.2355 | 1.2120 | 0.0200 | 0.3597 | 44 |
| 0.8045 | 0.0288 | 0.2299 | 1.2023 | 0.0201 | 0.3567 | 45 |
| 0.7823 | 0.0290 | 0.2213 | 1.2075 | 0.0201 | 0.3529 | 46 |
| 0.7186 | 0.0296 | 0.2107 | 1.1917 | 0.0202 | 0.3530 | 47 |
| 0.6949 | 0.0298 | 0.2028 | 1.1926 | 0.0202 | 0.3465 | 48 |
| 0.6669 | 0.0300 | 0.1943 | 1.1902 | 0.0203 | 0.3446 | 49 |
| 0.6125 | 0.0305 | 0.1842 | 1.1892 | 0.0203 | 0.3437 | 50 |
| 0.5926 | 0.0307 | 0.1778 | 1.2058 | 0.0203 | 0.3450 | 51 |
| 0.6055 | 0.0305 | 0.1738 | 1.1859 | 0.0203 | 0.3394 | 52 |
| 0.5828 | 0.0307 | 0.1653 | 1.1921 | 0.0203 | 0.3379 | 53 |
| 0.5507 | 0.0311 | 0.1569 | 1.1906 | 0.0204 | 0.3385 | 54 |
| 0.5050 | 0.0315 | 0.1485 | 1.1834 | 0.0205 | 0.3361 | 55 |
| 0.4878 | 0.0316 | 0.1447 | 1.1815 | 0.0205 | 0.3329 | 56 |
| 0.4825 | 0.0317 | 0.1410 | 1.2096 | 0.0204 | 0.3359 | 57 |
| 0.4987 | 0.0315 | 0.1374 | 1.2000 | 0.0204 | 0.3352 | 58 |
| 0.4576 | 0.0319 | 0.1305 | 1.1868 | 0.0205 | 0.3329 | 59 |
| 0.4185 | 0.0323 | 0.1215 | 1.2043 | 0.0205 | 0.3322 | 60 |
| 0.3889 | 0.0326 | 0.1156 | 1.1853 | 0.0206 | 0.3302 | 61 |
| 0.3790 | 0.0327 | 0.1101 | 1.2028 | 0.0205 | 0.3316 | 62 |
| 0.4072 | 0.0324 | 0.1110 | 1.2502 | 0.0203 | 0.3309 | 63 |
| 0.3519 | 0.0330 | 0.1020 | 1.1959 | 0.0206 | 0.3284 | 64 |
| 0.3861 | 0.0326 | 0.1034 | 1.1885 | 0.0206 | 0.3271 | 65 |
| 0.3789 | 0.0326 | 0.0961 | 1.1969 | 0.0206 | 0.3298 | 66 |
| 0.3233 | 0.0332 | 0.0905 | 1.1922 | 0.0207 | 0.3280 | 67 |
| 0.2956 | 0.0335 | 0.0854 | 1.2003 | 0.0207 | 0.3296 | 68 |
| 0.2666 | 0.0339 | 0.0796 | 1.2141 | 0.0207 | 0.3252 | 69 |
| 0.3181 | 0.0333 | 0.0813 | 1.2133 | 0.0207 | 0.3302 | 70 |
| 0.3032 | 0.0335 | 0.0770 | 1.2170 | 0.0207 | 0.3315 | 71 |
| 0.2746 | 0.0337 | 0.0741 | 1.2180 | 0.0207 | 0.3299 | 72 |
| 0.2549 | 0.0339 | 0.0705 | 1.2496 | 0.0206 | 0.3308 | 73 |
| 0.2529 | 0.0339 | 0.0685 | 1.2239 | 0.0207 | 0.3321 | 74 |
### Framework versions
- Transformers 4.33.0.dev0
- TensorFlow 2.13.0
- Tokenizers 0.13.3
|
Chat-Error/Blind-test02
|
Chat-Error
| 2023-09-03T15:11:04Z | 4 | 1 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-25T09:15:58Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
|
actionpace/speechless-llama2-hermes-orca-platypus-wizardlm-13b
|
actionpace
| 2023-09-03T15:07:34Z | 8 | 0 | null |
[
"gguf",
"en",
"license:other",
"endpoints_compatible",
"region:us"
] | null | 2023-09-03T14:40:29Z |
---
license: other
language:
- en
---
Some of my own quants:
* speechless-llama2-hermes-orca-platypus-wizardlm-13b_Q5_1_4K.gguf
* speechless-llama2-hermes-orca-platypus-wizardlm-13b_Q5_1_8K.gguf
Source: [uukuguy](https://huggingface.co/uukuguy)
Source Model: [speechless-llama2-hermes-orca-platypus-wizardlm-13b](https://huggingface.co/uukuguy/speechless-llama2-hermes-orca-platypus-wizardlm-13b)
|
jpostma/DagoBERT
|
jpostma
| 2023-09-03T15:03:29Z | 124 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"fill-mask",
"generated_from_trainer",
"nl",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2023-09-03T14:17:39Z |
---
license: mit
tags:
- generated_from_trainer
model-index:
- name: DagoBERT
results: []
language:
- nl
---
<!-- 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. -->
# DagoBERT
This model is a domain-adapted version of [pdelobelle/robbert-v2-dutch-base](https://huggingface.co/pdelobelle/robbert-v2-dutch-base) to Dutch annual reports.
It achieves the following results on the evaluation set:
- Loss:
## 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:
- train_batch_size:
- eval_batch_size:
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs:
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 2 | 9.5998 |
### Framework versions
- Transformers 4.27.3
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
|
jzy124/Coarse2Fine
|
jzy124
| 2023-09-03T14:57:40Z | 0 | 0 | null |
[
"license:ms-pl",
"region:us"
] | null | 2023-09-03T14:29:39Z |
---
license: ms-pl
---
The model checkpoints for reproducing _Coarse-to-Fine Generative Modeling for Graphic Layouts_ .
Please refer to the original paper [Coarse-to-Fine Generative Modeling for Graphic Layouts](https://ojs.aaai.org/index.php/AAAI/article/download/19994/19753) and the code implementation [GitHub](https://github.com/microsoft/LayoutGeneration/tree/main/Coarse-to-Fine).
Download the files in this repo by using:
git lfs install
git clone https://huggingface.co/jzy124/Coarse2Fine
|
mbarekat/ppo-LunarLander-v2
|
mbarekat
| 2023-09-03T14:57:23Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-09-03T14:56:59Z |
---
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: 269.98 +/- 14.34
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
...
```
|
malhajar/stable-diffusion-v1-5-ataturk
|
malhajar
| 2023-09-03T14:55:56Z | 33 | 0 |
diffusers
|
[
"diffusers",
"arxiv:2112.10752",
"arxiv:2103.00020",
"arxiv:2205.11487",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-09-01T22:57:42Z |
---
license: creativeml-openrail-m
---
# Stable Diffusion v1-5 atatürk Model Card
The **Stable-Diffusion-v1-5-ataturk** is a stable-diffusion-v1-5 finetuned on Gazi Mustafa Kemal Ataturk's Photos using dreambooth finetuning.
### Example Usuage
First make sure to run the following library installations
```bash
pip install -qq git+https://github.com/ShivamShrirao/diffusers
```
To run interference and get back a photo of Mustafa Kemal Ataturk please run the following code and make sure to include "zwx"
```py
import torch
from torch import autocast
from diffusers import StableDiffusionPipeline, DDIMScheduler
model_path = "malhajar/stable-diffusion-v1-5-ataturk"
pipe = StableDiffusionPipeline.from_pretrained(model_path)
pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config)
prompt = "zwx in a military suit, 4k resolution"
negative_prompt = "white robe, easynegative, bad-hands-5, grainy, low-res, extra limb, poorly drawn hands, missing limb, blurry, malformed hands, blur"
num_samples = 4
guidance_scale = 8
num_inference_steps = 40
height = 512
width = 512
images = pipe(
prompt,
height=height,
width=width,
negative_prompt=negative_prompt,
num_images_per_prompt=num_samples,
num_inference_steps=num_inference_steps,
guidance_scale=guidance_scale
).images
# Save Generated Images
count = 1
for image in images:
image.save(f"img-{count}.png")
count += 1
```
Example Photos:
## Model Details
- **Finetuned by:** Mohamad Alhajar
- **Model type:** Diffusion-based text-to-image generation model
- **Language(s):** English
- **License:** [The CreativeML OpenRAIL M license](https://huggingface.co/spaces/CompVis/stable-diffusion-license) is an [Open RAIL M license](https://www.licenses.ai/blog/2022/8/18/naming-convention-of-responsible-ai-licenses), adapted from the work that [BigScience](https://bigscience.huggingface.co/) and [the RAIL Initiative](https://www.licenses.ai/) are jointly carrying in the area of responsible AI licensing. See also [the article about the BLOOM Open RAIL license](https://bigscience.huggingface.co/blog/the-bigscience-rail-license) on which our license is based.
- **Model Description:** This is a model that can be used to generate and modify images based on text prompts. It is a [Latent Diffusion Model](https://arxiv.org/abs/2112.10752) that uses a fixed, pretrained text encoder ([CLIP ViT-L/14](https://arxiv.org/abs/2103.00020)) as suggested in the [Imagen paper](https://arxiv.org/abs/2205.11487).
# Uses
### Misuse, Malicious Use, and Out-of-Scope Use
_Note: This section is taken from the [DALLE-MINI model card](https://huggingface.co/dalle-mini/dalle-mini), but applies in the same way to Stable Diffusion v1_.
The model should not be used to intentionally create or disseminate images that create hostile or alienating environments for people. This includes generating images that people would foreseeably find disturbing, distressing, or offensive; or content that propagates historical or current stereotypes.
#### Out-of-Scope Use
The model was not trained to be factual or true representations of people or events, and therefore using the model to generate such content is out-of-scope for the abilities of this model.
#### Misuse and Malicious Use
Using the model to generate content that is cruel to individuals is a misuse of this model. This includes, but is not limited to:
- Generating demeaning, dehumanizing, or otherwise harmful representations of people or their environments, cultures, religions, etc.
- Intentionally promoting or propagating discriminatory content or harmful stereotypes.
- Impersonating individuals without their consent.
- Sexual content without consent of the people who might see it.
- Mis- and disinformation
- Representations of egregious violence and gore
- Sharing of copyrighted or licensed material in violation of its terms of use.
- Sharing content that is an alteration of copyrighted or licensed material in violation of its terms of use.
## Limitations and Bias
### Limitations
- The model does not achieve perfect photorealism
- The model cannot render legible text
- The model does not perform well on more difficult tasks which involve compositionality, such as rendering an image corresponding to “A red cube on top of a blue sphere”
- Faces and people in general may not be generated properly.
- The model was trained mainly with English captions and will not work as well in other languages.
- The autoencoding part of the model is lossy
- The model was trained on a large-scale dataset
[LAION-5B](https://laion.ai/blog/laion-5b/) which contains adult material
and is not fit for product use without additional safety mechanisms and
considerations.
- No additional measures were used to deduplicate the dataset. As a result, we observe some degree of memorization for images that are duplicated in the training data.
The training data can be searched at [https://rom1504.github.io/clip-retrieval/](https://rom1504.github.io/clip-retrieval/) to possibly assist in the detection of memorized images.
### Bias
While the capabilities of image generation models are impressive, they can also reinforce or exacerbate social biases.
Stable Diffusion v1 was trained on subsets of [LAION-2B(en)](https://laion.ai/blog/laion-5b/),
which consists of images that are primarily limited to English descriptions.
Texts and images from communities and cultures that use other languages are likely to be insufficiently accounted for.
This affects the overall output of the model, as white and western cultures are often set as the default. Further, the
ability of the model to generate content with non-English prompts is significantly worse than with English-language prompts.
### Safety Module
The intended use of this model is with the [Safety Checker](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/safety_checker.py) in Diffusers.
This checker works by checking model outputs against known hard-coded NSFW concepts.
The concepts are intentionally hidden to reduce the likelihood of reverse-engineering this filter.
Specifically, the checker compares the class probability of harmful concepts in the embedding space of the `CLIPTextModel` *after generation* of the images.
The concepts are passed into the model with the generated image and compared to a hand-engineered weight for each NSFW concept.
|
uni-tianyan/Uni-TianYan
|
uni-tianyan
| 2023-09-03T14:49:35Z | 1,575 | 48 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"en",
"arxiv:2308.07317",
"arxiv:2306.02707",
"license:llama2",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-09-01T15:06:05Z |
---
language:
- en
library_name: transformers
license: llama2
---
# Uni-TianYan
### Introduction
Uni-TianYan is a finetuned model from LLaMA2.
<br>
### Dataset
TODO
### Training Details
TODO
### license disclaimer:
This model is bound by the license & usage restrictions of the original Llama-2 model. And comes with no warranty or gurantees of any kind.
<br>
### Evaluation
| Metric | Value |
|-----------------------|-------|
| ARC (25-shot) | 72.10 |
| HellaSwag (10-shot) | 87.40 |
| MMLU (5-shot) | 69.91 |
| TruthfulQA (0-shot) | 65.81 |
| Avg. | 73.81 |
**We notice that MMLU is a more common metric and trying to reconstruct dataset.**
<br>
### Our Other Projects:
* [fangloveskari/ORCA_LLaMA_70B_QLoRA](fangloveskari/ORCA_LLaMA_70B_QLoRA)
* [fangloveskari/Platypus_QLoRA_LLaMA_70b](fangloveskari/Platypus_QLoRA_LLaMA_70b)
### Limitations & Biases:
Llama2 and fine-tuned variants are a new technology that carries risks with use. Testing conducted to date has been in English, and has not covered, nor could it cover all scenarios. For these reasons, as with all LLMs, Llama 2 and any fine-tuned varient's potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of Llama 2 variants, developers should perform safety testing and tuning tailored to their specific applications of the model.
Please see the Responsible Use Guide available at https://ai.meta.com/llama/responsible-use-guide/
<br>
### Citiation:
Please kindly cite using the following BibTeX:
```bibtex
@article{platypus2023,
title={Platypus: Quick, Cheap, and Powerful Refinement of LLMs},
author={Ariel N. Lee and Cole J. Hunter and Nataniel Ruiz},
booktitle={arXiv preprint arxiv:2308.07317},
year={2023}
}
```
```
@misc{mukherjee2023orca,
title={Orca: Progressive Learning from Complex Explanation Traces of GPT-4},
author={Subhabrata Mukherjee and Arindam Mitra and Ganesh Jawahar and Sahaj Agarwal and Hamid Palangi and Ahmed Awadallah},
year={2023},
eprint={2306.02707},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
```
@software{touvron2023llama2,
title={Llama 2: Open Foundation and Fine-Tuned Chat Models},
author={Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava,
Shruti Bhosale, Dan Bikel, Lukas Blecher, Cristian Canton Ferrer, Moya Chen, Guillem Cucurull, David Esiobu, Jude Fernandes, Jeremy Fu, Wenyin Fu, Brian Fuller,
Cynthia Gao, Vedanuj Goswami, Naman Goyal, Anthony Hartshorn, Saghar Hosseini, Rui Hou, Hakan Inan, Marcin Kardas, Viktor Kerkez Madian Khabsa, Isabel Kloumann,
Artem Korenev, Punit Singh Koura, Marie-Anne Lachaux, Thibaut Lavril, Jenya Lee, Diana Liskovich, Yinghai Lu, Yuning Mao, Xavier Martinet, Todor Mihaylov,
Pushkar Mishra, Igor Molybog, Yixin Nie, Andrew Poulton, Jeremy Reizenstein, Rashi Rungta, Kalyan Saladi, Alan Schelten, Ruan Silva, Eric Michael Smith,
Ranjan Subramanian, Xiaoqing Ellen Tan, Binh Tang, Ross Taylor, Adina Williams, Jian Xiang Kuan, Puxin Xu , Zheng Yan, Iliyan Zarov, Yuchen Zhang, Angela Fan,
Melanie Kambadur, Sharan Narang, Aurelien Rodriguez, Robert Stojnic, Sergey Edunov, Thomas Scialom},
year={2023}
}
```
|
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