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galaxywavee/personaluse | galaxywavee | 2023-07-15T06:50:48Z | 0 | 0 | null | [
"license:bigscience-openrail-m",
"region:us"
] | null | 2023-04-18T03:00:57Z | ---
license: bigscience-openrail-m
---
majicMIX realistic >>>
推荐关键词 recommended positive prompts: Best quality, masterpiece, ultra high res, (photorealistic:1.4), 1girl
如果想要更暗的图像 if you want darker picture, add: in the dark, deep shadow, low key, etc.
负面关键词 use ng_deepnegative_v1_75t and badhandv4 in negative prompt
Sampler: DPM++ 2M Karras (bug-fixed) or DPM++ SDE Karras
Steps: 20~40
Hires upscaler: R-ESRGAN 4x+ or 4x-UltraSharp
Hires upscale: 2
Hires steps: 15
Denoising strength: 0.2~0.5
CFG scale: 6-8
clip skip 2
Aerial (Animation and img2img) >>> Trigger Words : aerialstyle
|
NasimB/guten-rarity-all-end-19k-ctx-512 | NasimB | 2023-07-15T06:32:42Z | 143 | 0 | transformers | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"generated_from_trainer",
"dataset:generator",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2023-07-15T05:38:01Z | ---
license: mit
tags:
- generated_from_trainer
datasets:
- generator
model-index:
- name: guten-rarity-all-end-19k-ctx-512
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. -->
# guten-rarity-all-end-19k-ctx-512
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the generator dataset.
It achieves the following results on the evaluation set:
- Loss: 4.2404
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 1000
- num_epochs: 6
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 6.5135 | 1.19 | 500 | 5.4526 |
| 4.9916 | 2.38 | 1000 | 4.8062 |
| 4.3998 | 3.56 | 1500 | 4.4088 |
| 3.9739 | 4.75 | 2000 | 4.2180 |
| 3.6922 | 5.94 | 2500 | 4.1726 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.11.0+cu113
- Datasets 2.13.0
- Tokenizers 0.13.3
|
AnaBach/roberta-base-bne-finetuned-amazon_reviews_multi | AnaBach | 2023-07-15T06:11:29Z | 105 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"dataset:amazon_reviews_multi",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2023-07-14T02:15:57Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- amazon_reviews_multi
metrics:
- accuracy
model-index:
- name: roberta-base-bne-finetuned-amazon_reviews_multi
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: amazon_reviews_multi
type: amazon_reviews_multi
config: es
split: validation
args: es
metrics:
- name: Accuracy
type: accuracy
value: 0.9355
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-base-bne-finetuned-amazon_reviews_multi
This model is a fine-tuned version of [BSC-TeMU/roberta-base-bne](https://huggingface.co/BSC-TeMU/roberta-base-bne) on the amazon_reviews_multi dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2188
- Accuracy: 0.9355
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.1953 | 1.0 | 1250 | 0.1686 | 0.9343 |
| 0.1034 | 2.0 | 2500 | 0.2188 | 0.9355 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
sriharib/bloomz-3b-mmail-2 | sriharib | 2023-07-15T06:10:03Z | 1 | 0 | peft | [
"peft",
"region:us"
] | null | 2023-07-15T06:09:55Z | ---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: True
- load_in_4bit: False
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
### Framework versions
- PEFT 0.4.0.dev0
|
coreml-community/coreml-8528-diffusion | coreml-community | 2023-07-15T06:08:15Z | 0 | 23 | null | [
"coreml",
"stable-diffusion",
"text-to-image",
"license:creativeml-openrail-m",
"region:us"
] | text-to-image | 2023-01-09T23:50:12Z | ---
license: creativeml-openrail-m
tags:
- coreml
- stable-diffusion
- text-to-image
---
# Core ML Converted Model
This model was converted to Core ML for use on Apple Silicon devices by following Apple's instructions [here](https://github.com/apple/ml-stable-diffusion#-converting-models-to-core-ml).<br>
Provide the model to an app such as [Mochi Diffusion](https://github.com/godly-devotion/MochiDiffusion) to generate images.<br>
`split_einsum` version is compatible with all compute unit options including Neural Engine.<br>
`original` version is only compatible with CPU & GPU option.
# 8528-diffusion final
Source: [Hugging Face](https://huggingface.co/852wa/8528-diffusion) (The release of the source model has ended.)
8528-diffusion is a latent text-to-image diffusion model, conditioned by fine-tuning to colorful character images.
8528 Diffusion is a fine-tuning model of Stable Diffusion v1.4 with AI output images (t2i and t2i with i2i).
I recommend entering "low quality,worst quality," for Negative prompt and Clip skip: 2.
<!--
<img src=https://i.imgur.com/vCn02tM.jpg >
!-->
((ultra-detailed)), ((illustration)), Silver hair, red eyes, beautiful eyes, dress, Queen,Anime style, pretty face, pretty eyes, pretty, girl,High resolution, beautiful girl,octane render, realistic, hyper detailed ray tracing, 8k,classic style,Rococo
Negative prompt: (low quality, worst quality:1.4) concept art
Steps: 20, Sampler: Euler a, CFG scale: 7, Seed: 241379229, Size: 512x768, Model hash: 31cd036c, Clip skip: 2
# 8528-diffusion v0.4
<!--
<img src=https://i.imgur.com/X2zFoeA.jpg >
!-->
# 8528-diffusion v0.3
<!--
<img src=https://i.imgur.com/QQuNpYl.png >
<img src=https://i.imgur.com/u785LlC.png >
!-->
# 8528-diffusion v0.2
8528-diffusion is a latent text-to-image diffusion model, conditioned by fine-tuning to colorful character images.
8528 Diffusion v0.2 & v0.1 is a fine-tuning model of Waifu Diffusion with AI output images (t2i and t2i with i2i).
<!--
<img src=https://i.imgur.com/z4sFctp.png >
!-->
# 8528-diffusion v0.1
<!--
<img src=https://i.imgur.com/8chXeif.png >
!-->
[google colab](https://colab.research.google.com/drive/1ksRxO84CMbXrW_p-x5Vuz74AHnrWpe_u)
## License
This model is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage.
The CreativeML OpenRAIL License specifies:
1. You can't use the model to deliberately produce nor share illegal or harmful outputs or content
2. The authors claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license
3. You may re-distribute the weights and use the model commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully)
[Please read the full license here](https://huggingface.co/spaces/CompVis/stable-diffusion-license)
<!-- Discord Server has been stopped.
## Discord
https://discord.gg/ax9KgpUMUP
!--> |
akselozer9/akselo | akselozer9 | 2023-07-15T05:48:16Z | 0 | 0 | null | [
"token-classification",
"dataset:Open-Orca/OpenOrca",
"region:us"
] | token-classification | 2023-07-15T05:47:48Z | ---
datasets:
- Open-Orca/OpenOrca
metrics:
- accuracy
pipeline_tag: token-classification
--- |
akselozer9/cityofalbuquerque | akselozer9 | 2023-07-15T05:44:11Z | 0 | 0 | null | [
"token-classification",
"en",
"dataset:WizardLM/WizardLM_evol_instruct_V2_196k",
"region:us"
] | token-classification | 2023-07-15T05:43:06Z | ---
datasets:
- WizardLM/WizardLM_evol_instruct_V2_196k
language:
- en
pipeline_tag: token-classification
--- |
Evan-Lin/Bart-RL-many-keywordmax-entailment-attractive-reward1 | Evan-Lin | 2023-07-15T05:39:14Z | 49 | 0 | transformers | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"trl",
"reinforcement-learning",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | reinforcement-learning | 2023-07-14T17:58:29Z | ---
license: apache-2.0
tags:
- trl
- transformers
- reinforcement-learning
---
# TRL Model
This is a [TRL language model](https://github.com/lvwerra/trl) that has been fine-tuned with reinforcement learning to
guide the model outputs according to a value, function, or human feedback. The model can be used for text generation.
## Usage
To use this model for inference, first install the TRL library:
```bash
python -m pip install trl
```
You can then generate text as follows:
```python
from transformers import pipeline
generator = pipeline("text-generation", model="Evan-Lin//tmp/tmp71nhx1t_/Evan-Lin/Bart-RL-many-keywordmax-entailment-attractive-beam10")
outputs = generator("Hello, my llama is cute")
```
If you want to use the model for training or to obtain the outputs from the value head, load the model as follows:
```python
from transformers import AutoTokenizer
from trl import AutoModelForCausalLMWithValueHead
tokenizer = AutoTokenizer.from_pretrained("Evan-Lin//tmp/tmp71nhx1t_/Evan-Lin/Bart-RL-many-keywordmax-entailment-attractive-beam10")
model = AutoModelForCausalLMWithValueHead.from_pretrained("Evan-Lin//tmp/tmp71nhx1t_/Evan-Lin/Bart-RL-many-keywordmax-entailment-attractive-beam10")
inputs = tokenizer("Hello, my llama is cute", return_tensors="pt")
outputs = model(**inputs, labels=inputs["input_ids"])
```
|
kelvinih/taser-cocondenser-wiki | kelvinih | 2023-07-15T05:33:04Z | 0 | 0 | null | [
"pytorch",
"license:mit",
"region:us"
] | null | 2023-07-15T05:26:30Z | ---
license: mit
---
# Task-Aware Specialization for Efficient and Robust Dense Retrieval for Open-Domain Question Answering
This repository includes the model for
[Task-Aware Specialization for Efficient and Robust Dense Retrieval for Open-Domain Question Answering](https://aclanthology.org/2023.acl-short.159/).
If you find this useful, please cite the following paper:
```
@inproceedings{cheng-etal-2023-task,
title = "Task-Aware Specialization for Efficient and Robust Dense Retrieval for Open-Domain Question Answering",
author = "Cheng, Hao and
Fang, Hao and
Liu, Xiaodong and
Gao, Jianfeng",
booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.acl-short.159",
pages = "1864--1875",
}
```
|
digiplay/Opiate_v2 | digiplay | 2023-07-15T05:07:02Z | 333 | 2 | diffusers | [
"diffusers",
"safetensors",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | text-to-image | 2023-07-15T04:16:25Z | ---
license: other
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
inference: true
---
Model info:
https://civitai.com/models/69587?modelVersionId=98101
Original Author's DEMO images :
|
NasimB/guten-rarity-end-cut-19k | NasimB | 2023-07-15T04:56:56Z | 5 | 0 | transformers | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"generated_from_trainer",
"dataset:generator",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2023-07-15T03:03:02Z | ---
license: mit
tags:
- generated_from_trainer
datasets:
- generator
model-index:
- name: guten-rarity-end-cut-19k
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. -->
# guten-rarity-end-cut-19k
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the generator dataset.
It achieves the following results on the evaluation set:
- Loss: 4.3128
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 1000
- num_epochs: 6
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 6.69 | 0.29 | 500 | 5.6412 |
| 5.3327 | 0.59 | 1000 | 5.2058 |
| 4.9884 | 0.88 | 1500 | 4.9570 |
| 4.7105 | 1.18 | 2000 | 4.8008 |
| 4.5563 | 1.47 | 2500 | 4.6777 |
| 4.4438 | 1.77 | 3000 | 4.5652 |
| 4.3057 | 2.06 | 3500 | 4.4916 |
| 4.1258 | 2.36 | 4000 | 4.4456 |
| 4.1001 | 2.65 | 4500 | 4.3854 |
| 4.0586 | 2.94 | 5000 | 4.3319 |
| 3.8297 | 3.24 | 5500 | 4.3249 |
| 3.8029 | 3.53 | 6000 | 4.2962 |
| 3.7812 | 3.83 | 6500 | 4.2655 |
| 3.6544 | 4.12 | 7000 | 4.2687 |
| 3.5166 | 4.42 | 7500 | 4.2598 |
| 3.4969 | 4.71 | 8000 | 4.2438 |
| 3.4978 | 5.01 | 8500 | 4.2328 |
| 3.3159 | 5.3 | 9000 | 4.2445 |
| 3.3203 | 5.59 | 9500 | 4.2434 |
| 3.3104 | 5.89 | 10000 | 4.2422 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.11.0+cu113
- Datasets 2.13.0
- Tokenizers 0.13.3
|
ZidanSink/Kayess | ZidanSink | 2023-07-15T04:35:29Z | 0 | 0 | null | [
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-06-29T07:27:11Z | ---
license: creativeml-openrail-m
---
|
Wiryan/imryan | Wiryan | 2023-07-15T04:27:51Z | 0 | 0 | null | [
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-07-15T04:22:48Z | ---
license: creativeml-openrail-m
---
|
manmyung/Reinforce-CartPole-v1 | manmyung | 2023-07-15T04:24:11Z | 0 | 0 | null | [
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | reinforcement-learning | 2023-07-15T04:23:52Z | ---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-CartPole-v1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 490.20 +/- 23.02
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
jerryjalapeno/nart-100k-7b | jerryjalapeno | 2023-07-15T03:57:11Z | 1,520 | 20 | transformers | [
"transformers",
"pytorch",
"llama",
"text-generation",
"license:cc-by-nc-nd-4.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2023-07-14T19:01:46Z | ---
license: cc-by-nc-nd-4.0
---
|
cbredallas/labelclassification | cbredallas | 2023-07-15T03:44:59Z | 0 | 0 | adapter-transformers | [
"adapter-transformers",
"en",
"license:openrail",
"region:us"
] | null | 2023-07-15T03:43:24Z | ---
license: openrail
language:
- en
library_name: adapter-transformers
--- |
renatostrianese/q-FrozenLake-v1-4x4-noSlippery | renatostrianese | 2023-07-15T03:43:44Z | 0 | 0 | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | reinforcement-learning | 2023-07-15T03:43:33Z | ---
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="renatostrianese/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"])
```
|
xielenite/zethielzero | xielenite | 2023-07-15T03:18:29Z | 0 | 0 | null | [
"license:openrail",
"region:us"
] | null | 2023-07-13T23:40:11Z | ---
license: openrail
---
voice models for RVC inferencing. see https://docs.google.com/document/d/13_l1bd1Osgz7qlAZn-zhklCbHpVRk6bYOuAuB78qmsE/edit to see how to use. |
AdanLee/ppo-Huggy | AdanLee | 2023-07-15T03:01:35Z | 11 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"Huggy",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
] | reinforcement-learning | 2023-07-15T03:01:15Z | ---
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: AdanLee/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
RajanGo/RajanGo-Asgn-2 | RajanGo | 2023-07-15T01:43:52Z | 0 | 0 | peft | [
"peft",
"region:us"
] | null | 2023-07-15T01:43:44Z | ---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: True
- load_in_4bit: False
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
### Framework versions
- PEFT 0.4.0.dev0
|
akraieski/ppo-LunarLander-v2 | akraieski | 2023-07-15T01:42:23Z | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | 2023-07-15T01:42:05Z | ---
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: 277.81 +/- 14.10
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
...
```
|
timjwhite/a2c-AntBulletEnv-v0 | timjwhite | 2023-07-15T01:39:05Z | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"AntBulletEnv-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | 2023-07-15T01:37:29Z | ---
library_name: stable-baselines3
tags:
- AntBulletEnv-v0
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: AntBulletEnv-v0
type: AntBulletEnv-v0
metrics:
- type: mean_reward
value: 792.36 +/- 37.50
name: mean_reward
verified: false
---
# **A2C** Agent playing **AntBulletEnv-v0**
This is a trained model of a **A2C** agent playing **AntBulletEnv-v0**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
Panchovix/guanaco-33b-PI-8192-LoRA-4bit-32g | Panchovix | 2023-07-15T01:38:52Z | 5 | 4 | transformers | [
"transformers",
"pytorch",
"llama",
"text-generation",
"license:other",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2023-07-04T06:00:12Z | ---
license: other
---
[guanaco-33b](https://huggingface.co/timdettmers/guanaco-33b-merged) merged with bhenrym14's [airoboros-33b-gpt4-1.4.1-PI-8192-LoRA](https://huggingface.co/bhenrym14/airoboros-33b-gpt4-1.4.1-PI-8192-LoRA), quantized at 4 bit.
More info about the LoRA [Here](https://huggingface.co/bhenrym14/airoboros-33b-gpt4-1.4.1-PI-8192-fp16). This is an alternative to SuperHOT 8k LoRA trained with LoRA_rank 64, and airoboros 1.4.1 dataset.
It was created with GPTQ-for-LLaMA with group size 32 and act order true as parameters, to get the maximum perplexity vs FP16 model.
I HIGHLY suggest to use exllama, to evade some VRAM issues.
Use compress_pos_emb = 4 for any context up to 8192 context.
If you have 2x24 GB VRAM GPUs cards, to not get Out of Memory errors at 8192 context, use:
gpu_split: 9,21 |
chandrasutrisnotjhong/taxi | chandrasutrisnotjhong | 2023-07-15T01:06:47Z | 0 | 0 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | reinforcement-learning | 2023-07-15T01:06:45Z | ---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: taxi
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.56 +/- 2.71
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="chandrasutrisnotjhong/taxi", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
borkur/gpt2-finetuned-wikitext2 | borkur | 2023-07-15T00:56:29Z | 85 | 0 | transformers | [
"transformers",
"tf",
"gpt2",
"text-generation",
"generated_from_keras_callback",
"base_model:openai-community/gpt2",
"base_model:finetune:openai-community/gpt2",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2023-07-14T21:30:03Z | ---
license: mit
base_model: gpt2
tags:
- generated_from_keras_callback
model-index:
- name: borkur/gpt2-finetuned-wikitext2
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. -->
# borkur/gpt2-finetuned-wikitext2
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 6.4948
- Validation Loss: 6.3466
- Epoch: 1
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 2e-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 | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 7.3152 | 6.7681 | 0 |
| 6.4948 | 6.3466 | 1 |
### Framework versions
- Transformers 4.31.0.dev0
- TensorFlow 2.13.0
- Datasets 2.13.1
- Tokenizers 0.13.3
|
giocs2017/dqn-SpaceInvadersNoFrameskip-v4-gio | giocs2017 | 2023-07-15T00:25:57Z | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | 2023-07-15T00:25:23Z | ---
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: 595.00 +/- 126.25
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 giocs2017 -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 giocs2017 -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 giocs2017
```
## 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'}
```
|
ALM-AHME/beit-large-patch16-224-finetuned-BreastCancer-Classification-BreakHis-AH-60-20-20 | ALM-AHME | 2023-07-14T23:55:06Z | 5 | 3 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"beit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-classification | 2023-07-14T20:43:15Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: beit-large-patch16-224-finetuned-BreastCancer-Classification-BreakHis-AH-60-20-20
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: Splitted-Resized
split: train
args: Splitted-Resized
metrics:
- name: Accuracy
type: accuracy
value: 0.9938708156529938
---
<!-- 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. -->
# beit-large-patch16-224-finetuned-BreastCancer-Classification-BreakHis-AH-60-20-20
This model is a fine-tuned version of [microsoft/beit-large-patch16-224](https://huggingface.co/microsoft/beit-large-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0275
- Accuracy: 0.9939
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-06
- train_batch_size: 16
- eval_batch_size: 16
- 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
- lr_scheduler_warmup_ratio: 0.9
- num_epochs: 12
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.46 | 1.0 | 199 | 0.3950 | 0.8482 |
| 0.2048 | 2.0 | 398 | 0.1886 | 0.9189 |
| 0.182 | 3.0 | 597 | 0.1382 | 0.9481 |
| 0.0826 | 4.0 | 796 | 0.0760 | 0.9694 |
| 0.0886 | 5.0 | 995 | 0.0600 | 0.9788 |
| 0.0896 | 6.0 | 1194 | 0.0523 | 0.9802 |
| 0.0774 | 7.0 | 1393 | 0.0482 | 0.9826 |
| 0.0876 | 8.0 | 1592 | 0.0289 | 0.9877 |
| 0.1105 | 9.0 | 1791 | 0.0580 | 0.9821 |
| 0.0289 | 10.0 | 1990 | 0.0294 | 0.9925 |
| 0.0594 | 11.0 | 2189 | 0.0331 | 0.9906 |
| 0.0011 | 12.0 | 2388 | 0.0275 | 0.9939 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
CheeriosMomentors/LORA | CheeriosMomentors | 2023-07-14T23:32:58Z | 0 | 0 | null | [
"en",
"license:wtfpl",
"region:us"
] | null | 2023-04-08T06:21:46Z | ---
license: wtfpl
language:
- en
---
Okay listen up. This is mostly loras that I made by myself.
Some of these may be released on Civitai and some may not.
If you found these, good job you now have cool loras.
You can post these on Civitai or anywhere idc.
You can say these are yours, get money I do not care.
But please for god sake, leave my name out of it.
I am not responsible for anything you done with these.
These were just for fun, that is all. Now enjoy.
Lora Count: 2
We currently have Nisho Ishin (Medaka Box) style and ryukishi07 (Umineko Style.)
I may make more and post them here. |
chunwoolee0/seqcls_mrpc_bert_base_uncased_model | chunwoolee0 | 2023-07-14T23:32:36Z | 103 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2023-07-14T23:27:51Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
- f1
model-index:
- name: seqcls_mrpc_bert_base_uncased_model
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
config: mrpc
split: validation
args: mrpc
metrics:
- name: Accuracy
type: accuracy
value: 0.8014705882352942
- name: F1
type: f1
value: 0.8669950738916257
---
<!-- 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. -->
# seqcls_mrpc_bert_base_uncased_model
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4621
- Accuracy: 0.8015
- F1: 0.8670
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log | 1.0 | 58 | 0.5442 | 0.7108 | 0.8228 |
| No log | 2.0 | 116 | 0.5079 | 0.7745 | 0.8558 |
| No log | 3.0 | 174 | 0.4621 | 0.8015 | 0.8670 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
foreverip/dqn-SpaceInvadersNoFrameskip-v4 | foreverip | 2023-07-14T23:31:22Z | 1 | 0 | stable-baselines3 | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | 2023-07-14T23:30:45Z | ---
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: 603.00 +/- 169.77
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga foreverip -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 foreverip -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 foreverip
```
## 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'}
```
|
Yntec/Photosphere | Yntec | 2023-07-14T23:22:58Z | 1,547 | 4 | diffusers | [
"diffusers",
"safetensors",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"Noosphere",
"Dreamlike",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | text-to-image | 2023-07-14T22:54:19Z | ---
license: creativeml-openrail-m
library_name: diffusers
pipeline_tag: text-to-image
tags:
- stable-diffusion
- stable-diffusion-diffusers
- diffusers
- text-to-image
- Noosphere
- Dreamlike
---
# Photosphere
A mix of Noosphere v3 by skumerz and photorealistic models.
Original page:
https://civitai.com/models/36538?modelVersionId=107675 |
MnLgt/slope-bed | MnLgt | 2023-07-14T23:19:56Z | 0 | 0 | null | [
"license:mit",
"region:us"
] | null | 2023-07-14T23:19:55Z | ---
license: mit
---
### slope-bed on Stable Diffusion
This is the `<slope-bed>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb).
Here is the new concept you will be able to use as an `object`:
|
cgr28/q-Taxi-v3 | cgr28 | 2023-07-14T23:15:40Z | 0 | 0 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | reinforcement-learning | 2023-07-14T23:15:38Z | ---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.52 +/- 2.74
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="cgr28/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
0sunfire0/Pixelcopter_train_00 | 0sunfire0 | 2023-07-14T23:10:07Z | 0 | 0 | null | [
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | reinforcement-learning | 2023-07-14T23:10:05Z | ---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Pixelcopter_train_00
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 7.20 +/- 7.10
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
|
Alignment-Lab-AI/AttourneyAtLam | Alignment-Lab-AI | 2023-07-14T23:01:49Z | 0 | 0 | peft | [
"peft",
"region:us"
] | null | 2023-07-14T22:53:40Z | ---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: float16
### Framework versions
- PEFT 0.4.0.dev0
|
GISDGDIGDI9ED/leslie | GISDGDIGDI9ED | 2023-07-14T22:53:08Z | 0 | 0 | flair | [
"flair",
"art",
"es",
"dataset:openchat/openchat_sharegpt4_dataset",
"license:bsd",
"region:us"
] | null | 2023-07-14T22:50:29Z | ---
license: bsd
datasets:
- openchat/openchat_sharegpt4_dataset
language:
- es
metrics:
- character
library_name: flair
tags:
- art
--- |
YanJiangJerry/sentiment-roberta-e3-b16-v2-w0.01 | YanJiangJerry | 2023-07-14T22:45:22Z | 121 | 0 | transformers | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2023-07-14T13:20:25Z | ---
tags:
- generated_from_trainer
metrics:
- f1
- recall
- precision
model-index:
- name: sentiment-roberta-e3-b16-v2-w0.01
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. -->
# sentiment-roberta-e3-b16-v2-w0.01
This model is a fine-tuned version of [siebert/sentiment-roberta-large-english](https://huggingface.co/siebert/sentiment-roberta-large-english) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6014
- F1: 0.7844
- Recall: 0.7844
- Precision: 0.7844
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 | Recall | Precision |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:---------:|
| No log | 1.0 | 187 | 0.6687 | 0.7574 | 0.7574 | 0.7574 |
| No log | 2.0 | 374 | 0.5700 | 0.7898 | 0.7898 | 0.7898 |
| 0.6052 | 3.0 | 561 | 0.6014 | 0.7844 | 0.7844 | 0.7844 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
underactuated/opt-350m_ft | underactuated | 2023-07-14T22:41:50Z | 136 | 0 | transformers | [
"transformers",
"pytorch",
"opt",
"text-generation",
"generated_from_trainer",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2023-07-14T22:39:39Z | ---
tags:
- generated_from_trainer
model-index:
- name: opt-350m_ft
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. -->
# opt-350m_ft
This model was trained from scratch on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
YanJiangJerry/sentiment-roberta-e2-b16-v2-w0.01 | YanJiangJerry | 2023-07-14T22:29:12Z | 106 | 0 | transformers | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2023-07-14T22:22:40Z | ---
tags:
- generated_from_trainer
metrics:
- f1
- recall
- precision
model-index:
- name: sentiment-roberta-e2-b16-v2-w0.01
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. -->
# sentiment-roberta-e2-b16-v2-w0.01
This model is a fine-tuned version of [siebert/sentiment-roberta-large-english](https://huggingface.co/siebert/sentiment-roberta-large-english) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8630
- F1: 0.7520
- Recall: 0.7520
- Precision: 0.7520
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 | Recall | Precision |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:---------:|
| No log | 1.0 | 375 | 0.8651 | 0.6739 | 0.6739 | 0.6739 |
| 0.6564 | 2.0 | 750 | 0.8630 | 0.7520 | 0.7520 | 0.7520 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
marloz03/my_awesome_qa_model | marloz03 | 2023-07-14T22:26:40Z | 61 | 0 | transformers | [
"transformers",
"tf",
"distilbert",
"question-answering",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | question-answering | 2023-07-13T21:07:04Z | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: marloz03/my_awesome_qa_model
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. -->
# marloz03/my_awesome_qa_model
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 1.2264
- Validation Loss: 1.4529
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 1000, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 2.6044 | 1.5880 | 0 |
| 1.3853 | 1.4529 | 1 |
| 1.2264 | 1.4529 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.10.0
- Datasets 2.12.0
- Tokenizers 0.13.2
|
Recognai/zeroshot_selectra_small | Recognai | 2023-07-14T22:23:19Z | 129 | 5 | transformers | [
"transformers",
"pytorch",
"safetensors",
"electra",
"text-classification",
"zero-shot-classification",
"nli",
"es",
"dataset:xnli",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | zero-shot-classification | 2022-03-02T23:29:04Z | ---
language: es
tags:
- zero-shot-classification
- nli
- pytorch
datasets:
- xnli
pipeline_tag: zero-shot-classification
license: apache-2.0
widget:
- text: "El autor se perfila, a los 50 años de su muerte, como uno de los grandes de su siglo"
candidate_labels: "cultura, sociedad, economia, salud, deportes"
---
# Zero-shot SELECTRA: A zero-shot classifier based on SELECTRA
*Zero-shot SELECTRA* is a [SELECTRA model](https://huggingface.co/Recognai/selectra_small) fine-tuned on the Spanish portion of the [XNLI dataset](https://huggingface.co/datasets/xnli). You can use it with Hugging Face's [Zero-shot pipeline](https://huggingface.co/transformers/master/main_classes/pipelines.html#transformers.ZeroShotClassificationPipeline) to make [zero-shot classifications](https://joeddav.github.io/blog/2020/05/29/ZSL.html).
In comparison to our previous zero-shot classifier [based on BETO](https://huggingface.co/Recognai/bert-base-spanish-wwm-cased-xnli), zero-shot SELECTRA is **much more lightweight**. As shown in the *Metrics* section, the *small* version (5 times fewer parameters) performs slightly worse, while the *medium* version (3 times fewer parameters) **outperforms** the BETO based zero-shot classifier.
## Usage
```python
from transformers import pipeline
classifier = pipeline("zero-shot-classification",
model="Recognai/zeroshot_selectra_medium")
classifier(
"El autor se perfila, a los 50 años de su muerte, como uno de los grandes de su siglo",
candidate_labels=["cultura", "sociedad", "economia", "salud", "deportes"],
hypothesis_template="Este ejemplo es {}."
)
"""Output
{'sequence': 'El autor se perfila, a los 50 años de su muerte, como uno de los grandes de su siglo',
'labels': ['sociedad', 'cultura', 'salud', 'economia', 'deportes'],
'scores': [0.3711881935596466,
0.25650349259376526,
0.17355826497077942,
0.1641489565372467,
0.03460107371211052]}
"""
```
The `hypothesis_template` parameter is important and should be in Spanish. **In the widget on the right, this parameter is set to its default value: "This example is {}.", so different results are expected.**
## Metrics
| Model | Params | XNLI (acc) | \*MLSUM (acc) |
| --- | --- | --- | --- |
| [zs BETO](https://huggingface.co/Recognai/bert-base-spanish-wwm-cased-xnli) | 110M | 0.799 | 0.530 |
| [zs SELECTRA medium](https://huggingface.co/Recognai/zeroshot_selectra_medium) | 41M | **0.807** | **0.589** |
| zs SELECTRA small | **22M** | 0.795 | 0.446 |
\*evaluated with zero-shot learning (ZSL)
- **XNLI**: The stated accuracy refers to the test portion of the [XNLI dataset](https://huggingface.co/datasets/xnli), after finetuning the model on the training portion.
- **MLSUM**: For this accuracy we take the test set of the [MLSUM dataset](https://huggingface.co/datasets/mlsum) and classify the summaries of 5 selected labels. For details, check out our [evaluation notebook](https://github.com/recognai/selectra/blob/main/zero-shot_classifier/evaluation.ipynb)
## Training
Check out our [training notebook](https://github.com/recognai/selectra/blob/main/zero-shot_classifier/training.ipynb) for all the details.
## Authors
- David Fidalgo ([GitHub](https://github.com/dcfidalgo))
- Daniel Vila ([GitHub](https://github.com/dvsrepo))
- Francisco Aranda ([GitHub](https://github.com/frascuchon))
- Javier Lopez ([GitHub](https://github.com/javispp)) |
cuervjos/alpacaIOD-7b-plus | cuervjos | 2023-07-14T22:22:53Z | 1 | 0 | peft | [
"peft",
"region:us"
] | null | 2023-07-13T08:58:46Z | ---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.4.0.dev0
- PEFT 0.4.0.dev0
- PEFT 0.4.0.dev0
- PEFT 0.4.0.dev0
- PEFT 0.4.0.dev0
|
Recognai/bert-base-spanish-wwm-cased-xnli | Recognai | 2023-07-14T22:22:51Z | 2,134 | 16 | transformers | [
"transformers",
"pytorch",
"jax",
"safetensors",
"bert",
"text-classification",
"zero-shot-classification",
"nli",
"es",
"dataset:xnli",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | zero-shot-classification | 2022-03-02T23:29:04Z | ---
language: es
tags:
- zero-shot-classification
- nli
- pytorch
datasets:
- xnli
license: mit
pipeline_tag: zero-shot-classification
widget:
- text: "El autor se perfila, a los 50 años de su muerte, como uno de los grandes de su siglo"
candidate_labels: "cultura, sociedad, economia, salud, deportes"
---
# bert-base-spanish-wwm-cased-xnli
**UPDATE, 15.10.2021: Check out our new zero-shot classifiers, much more lightweight and even outperforming this one: [zero-shot SELECTRA small](https://huggingface.co/Recognai/zeroshot_selectra_small) and [zero-shot SELECTRA medium](https://huggingface.co/Recognai/zeroshot_selectra_medium).**
## Model description
This model is a fine-tuned version of the [spanish BERT model](https://huggingface.co/dccuchile/bert-base-spanish-wwm-cased) with the Spanish portion of the XNLI dataset. You can have a look at the [training script](https://huggingface.co/Recognai/bert-base-spanish-wwm-cased-xnli/blob/main/zeroshot_training_script.py) for details of the training.
### How to use
You can use this model with Hugging Face's [zero-shot-classification pipeline](https://discuss.huggingface.co/t/new-pipeline-for-zero-shot-text-classification/681):
```python
from transformers import pipeline
classifier = pipeline("zero-shot-classification",
model="Recognai/bert-base-spanish-wwm-cased-xnli")
classifier(
"El autor se perfila, a los 50 años de su muerte, como uno de los grandes de su siglo",
candidate_labels=["cultura", "sociedad", "economia", "salud", "deportes"],
hypothesis_template="Este ejemplo es {}."
)
"""output
{'sequence': 'El autor se perfila, a los 50 años de su muerte, como uno de los grandes de su siglo',
'labels': ['cultura', 'sociedad', 'economia', 'salud', 'deportes'],
'scores': [0.38897448778152466,
0.22997373342514038,
0.1658431738615036,
0.1205764189362526,
0.09463217109441757]}
"""
```
## Eval results
Accuracy for the test set:
| | XNLI-es |
|-----------------------------|---------|
|bert-base-spanish-wwm-cased-xnli | 79.9% | |
Recognai/distilbert-base-es-multilingual-cased | Recognai | 2023-07-14T22:20:32Z | 352 | 3 | transformers | [
"transformers",
"pytorch",
"safetensors",
"distilbert",
"fill-mask",
"es",
"dataset:wikipedia",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | fill-mask | 2022-03-02T23:29:04Z | ---
language: es
license: apache-2.0
datasets:
- wikipedia
widget:
- text: "Mi nombre es Juan y vivo en [MASK]."
---
# DistilBERT base multilingual model Spanish subset (cased)
This model is the Spanish extract of `distilbert-base-multilingual-cased` (https://huggingface.co/distilbert-base-multilingual-cased), a distilled version of the [BERT base multilingual model](bert-base-multilingual-cased). This model is cased: it does make a difference between english and English.
It uses the extraction method proposed by Geotrend described in https://github.com/Geotrend-research/smaller-transformers.
The resulting model has the same architecture as DistilmBERT: 6 layers, 768 dimension and 12 heads, with a total of **63M parameters** (compared to 134M parameters for DistilmBERT).
The goal of this model is to reduce even further the size of the `distilbert-base-multilingual` multilingual model by selecting only most frequent tokens for Spanish, reducing the size of the embedding layer. For more details visit the paper from the Geotrend team: Load What You Need: Smaller Versions of Multilingual BERT. |
Jowie/ppo-LunarLander | Jowie | 2023-07-14T22:08:23Z | 4 | 0 | stable-baselines3 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | 2023-07-14T22:07:58Z | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 227.31 +/- 46.54
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
...
```
|
LarryAIDraw/Wa2k | LarryAIDraw | 2023-07-14T22:06:30Z | 0 | 0 | null | [
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-07-14T21:45:20Z | ---
license: creativeml-openrail-m
---
https://civitai.com/models/13926/wa2000-or-girls-frontline |
brucew5978/my_awesome_asr_mind_model | brucew5978 | 2023-07-14T22:02:12Z | 77 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | 2023-07-12T18:24:27Z | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: my_awesome_asr_mind_model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# my_awesome_asr_mind_model
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 57.1369
- Wer: 1.1053
## 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
- 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
- training_steps: 2000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 48.7151 | 200.0 | 1000 | 57.1369 | 1.1053 |
| 47.4068 | 400.0 | 2000 | 57.1369 | 1.1053 |
### Framework versions
- Transformers 4.30.2
- Pytorch 1.12.1
- Datasets 2.13.1
- Tokenizers 0.13.3
|
sghirardelli/vit-base-patch16-224-in21k-rgbd | sghirardelli | 2023-07-14T22:00:58Z | 64 | 0 | transformers | [
"transformers",
"tf",
"tensorboard",
"vit",
"image-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-classification | 2023-07-14T20:21:17Z | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: vit-base-patch16-224-in21k-rgbd
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. -->
# vit-base-patch16-224-in21k-rgbd
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.5496
- Train Accuracy: 1.0
- Train Top-3-accuracy: 1.0
- Validation Loss: 0.3955
- Validation Accuracy: 0.9994
- Validation Top-3-accuracy: 1.0
- Epoch: 1
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'inner_optimizer': {'class_name': 'AdamWeightDecay', 'config': {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 3e-05, 'decay_steps': 1455, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}}, 'dynamic': True, 'initial_scale': 32768.0, 'dynamic_growth_steps': 2000}
- training_precision: mixed_float16
### Training results
| Train Loss | Train Accuracy | Train Top-3-accuracy | Validation Loss | Validation Accuracy | Validation Top-3-accuracy | Epoch |
|:----------:|:--------------:|:--------------------:|:---------------:|:-------------------:|:-------------------------:|:-----:|
| 1.6822 | 0.9392 | 0.9664 | 0.7810 | 0.9994 | 1.0 | 0 |
| 0.5496 | 1.0 | 1.0 | 0.3955 | 0.9994 | 1.0 | 1 |
### Framework versions
- Transformers 4.30.2
- TensorFlow 2.12.0
- Datasets 2.13.1
- Tokenizers 0.13.3
|
wolffenbuetell/PFKODRCHORMA | wolffenbuetell | 2023-07-14T21:53:52Z | 0 | 0 | null | [
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-07-14T21:48:13Z | ---
license: creativeml-openrail-m
---
|
AACEE/textual_inversion_sksship | AACEE | 2023-07-14T21:48:01Z | 4 | 0 | diffusers | [
"diffusers",
"tensorboard",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"textual_inversion",
"base_model:runwayml/stable-diffusion-v1-5",
"base_model:adapter:runwayml/stable-diffusion-v1-5",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | text-to-image | 2023-07-14T20:13:35Z |
---
license: creativeml-openrail-m
base_model: runwayml/stable-diffusion-v1-5
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- textual_inversion
inference: true
---
# Textual inversion text2image fine-tuning - AACEE/textual_inversion_sksship
These are textual inversion adaption weights for runwayml/stable-diffusion-v1-5. You can find some example images in the following.
|
YanJiangJerry/covid-tweet-bert-large-e2-noweight | YanJiangJerry | 2023-07-14T21:45:24Z | 105 | 0 | transformers | [
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2023-07-14T21:30:30Z | ---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: covid-tweet-bert-large-e2-noweight
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. -->
# covid-tweet-bert-large-e2-noweight
This model is a fine-tuned version of [digitalepidemiologylab/covid-twitter-bert-v2](https://huggingface.co/digitalepidemiologylab/covid-twitter-bert-v2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2582
- Accuracy: 0.9568
- F1: 0.8878
- Precision: 0.8604
- Recall: 0.9170
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.0593 | 1.0 | 1023 | 0.2053 | 0.9581 | 0.8885 | 0.8810 | 0.8962 |
| 0.0146 | 2.0 | 2046 | 0.2582 | 0.9568 | 0.8878 | 0.8604 | 0.9170 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
0sunfire0/Cartpole-v1_train_01 | 0sunfire0 | 2023-07-14T21:31:24Z | 0 | 0 | null | [
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | reinforcement-learning | 2023-07-14T21:31:15Z | ---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Cartpole-v1_train_01
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 497.20 +/- 8.40
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
XO-Appleton/vit-base-patch16-224-in21k-MR | XO-Appleton | 2023-07-14T20:45:21Z | 66 | 0 | transformers | [
"transformers",
"tf",
"tensorboard",
"vit",
"image-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-classification | 2023-07-14T18:12:06Z | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: XO-Appleton/vit-base-patch16-224-in21k-MR
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. -->
# XO-Appleton/vit-base-patch16-224-in21k-MR
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0042
- Train Accuracy: 1.0
- Train Top-3-accuracy: 1.0
- Validation Loss: 0.0126
- Validation Accuracy: 0.9983
- Validation Top-3-accuracy: 1.0
- Epoch: 3
## 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: {'inner_optimizer': {'class_name': 'AdamWeightDecay', 'config': {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 3e-05, 'decay_steps': 4485, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}}, 'dynamic': True, 'initial_scale': 32768.0, 'dynamic_growth_steps': 2000}
- training_precision: mixed_float16
### Training results
| Train Loss | Train Accuracy | Train Top-3-accuracy | Validation Loss | Validation Accuracy | Validation Top-3-accuracy | Epoch |
|:----------:|:--------------:|:--------------------:|:---------------:|:-------------------:|:-------------------------:|:-----:|
| 0.1624 | 0.9544 | 1.0 | 0.0380 | 0.9933 | 1.0 | 0 |
| 0.0178 | 0.9979 | 1.0 | 0.0197 | 0.9966 | 1.0 | 1 |
| 0.0063 | 1.0 | 1.0 | 0.0139 | 0.9983 | 1.0 | 2 |
| 0.0042 | 1.0 | 1.0 | 0.0126 | 0.9983 | 1.0 | 3 |
### Framework versions
- Transformers 4.30.2
- TensorFlow 2.12.0
- Datasets 2.13.1
- Tokenizers 0.13.3
|
fontanap/Taxi-v3 | fontanap | 2023-07-14T20:36:49Z | 0 | 0 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | reinforcement-learning | 2023-07-14T20:36:48Z | ---
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: 7.54 +/- 2.73
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="fontanap/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"])
```
|
RiversHaveWings/minihf_evaluator_openllama_7b | RiversHaveWings | 2023-07-14T20:35:57Z | 7 | 0 | peft | [
"peft",
"safetensors",
"license:apache-2.0",
"region:us"
] | null | 2023-07-14T19:42:13Z | ---
library_name: peft
license: apache-2.0
---
# minihf_evaluator_openllama_7b
`minihf_evaluator_openllama_7b` is a LoRA instruct fine-tune of [OpenLLaMA 7B](https://huggingface.co/openlm-research/open_llama_7b).
The sequence `<|end|>` was used to separate the prompt and response. The correct way to prompt the model is: `Does 2 + 2 = 4?<|end|>`. The tokenizer will prepend a BOS token (`<s>`) by default. The response will end with an EOS token (`</s>`).
## Training procedure
`minihf_evaluator_openllama_7b` was fine-tuned for 100,000 examples on 90% [Muennighoff/flan](https://huggingface.co/datasets/Muennighoff/flan) / 10% [databricks/databricks-dolly-15k](https://huggingface.co/datasets/databricks/databricks-dolly-15k) using batch size 4 per GPU on 8 40GB A100 GPUs. Examples where the prompt and response would not fit into 2,048 tokens were dropped. The fine-tuning was done using the following command:
```bash
accelerate launch make_evaluator.py --output-dir minihf_evaluator_openllama_7b
```
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.4.0.dev0
|
n3bbb/distilbert-base-uncased-finetuned-cola | n3bbb | 2023-07-14T20:34:39Z | 105 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2023-07-14T19:20:07Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- matthews_correlation
model-index:
- name: distilbert-base-uncased-finetuned-cola
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
config: cola
split: validation
args: cola
metrics:
- name: Matthews Correlation
type: matthews_correlation
value: 0.5514555448601601
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-cola
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8001
- Matthews Correlation: 0.5515
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.5242 | 1.0 | 535 | 0.5338 | 0.4221 |
| 0.3484 | 2.0 | 1070 | 0.4976 | 0.4779 |
| 0.2417 | 3.0 | 1605 | 0.5211 | 0.5452 |
| 0.1765 | 4.0 | 2140 | 0.7580 | 0.5282 |
| 0.1269 | 5.0 | 2675 | 0.8001 | 0.5515 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
Rui31415/Taxi | Rui31415 | 2023-07-14T20:32:17Z | 0 | 0 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | reinforcement-learning | 2023-07-14T20:32:15Z | ---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: Taxi
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.50 +/- 2.76
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="Rui31415/Taxi", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
fontanap/q-FrozenLake-v1-4x4-noSlippery | fontanap | 2023-07-14T20:27:22Z | 0 | 0 | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | reinforcement-learning | 2023-07-14T20:27:20Z | ---
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="fontanap/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"])
```
|
avishek-018/bert-semantic-similarity | avishek-018 | 2023-07-14T20:22:22Z | 6 | 1 | tf-keras | [
"tf-keras",
"sentence-similarity",
"en",
"license:mit",
"region:us"
] | sentence-similarity | 2023-07-14T19:41:59Z | ---
license: mit
language:
- en
pipeline_tag: sentence-similarity
---
widget:
- source_sentence: Two women are observing something together.
sentences:
- Two women are standing with their eyes closed.
example_title: Example 1
- source_sentence: A smiling costumed woman is holding an umbrella
sentences:
- A happy woman in a fairy costume holds an umbrella
example_title: Example 2
- source_sentence: A soccer game with multiple males playing
sentences:
- Some men are playing a sport
example_title: Example 3 |
davej23/distilhubert-finetuned-gtzan | davej23 | 2023-07-14T20:20:33Z | 159 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"hubert",
"audio-classification",
"generated_from_trainer",
"dataset:marsyas/gtzan",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | audio-classification | 2023-07-14T18:19:55Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- marsyas/gtzan
metrics:
- accuracy
model-index:
- name: distilhubert-finetuned-gtzan
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. -->
# distilhubert-finetuned-gtzan
This model is a fine-tuned version of [ntu-spml/distilhubert](https://huggingface.co/ntu-spml/distilhubert) on the GTZAN dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4577
- Accuracy: 0.86
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.8254 | 1.0 | 113 | 1.8353 | 0.48 |
| 1.2492 | 2.0 | 226 | 1.4297 | 0.57 |
| 1.0203 | 3.0 | 339 | 0.9814 | 0.69 |
| 0.633 | 4.0 | 452 | 0.7345 | 0.83 |
| 0.5642 | 5.0 | 565 | 0.6213 | 0.8 |
| 0.3219 | 6.0 | 678 | 0.5763 | 0.84 |
| 0.1772 | 7.0 | 791 | 0.4850 | 0.86 |
| 0.2427 | 8.0 | 904 | 0.4841 | 0.86 |
| 0.1397 | 9.0 | 1017 | 0.4760 | 0.86 |
| 0.4494 | 10.0 | 1130 | 0.4577 | 0.86 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
MicroPanda123/PythonBasic | MicroPanda123 | 2023-07-14T20:15:25Z | 4 | 0 | null | [
"text-generation",
"license:gpl-2.0",
"region:us"
] | text-generation | 2023-07-14T13:25:41Z | ---
license: gpl-2.0
pipeline_tag: text-generation
---
Got bored so used [nanoGPT](https://github.com/karpathy/nanoGPT) to train model on all Python snippets from https://www.kaggle.com/datasets/simiotic/github-code-snippets
Model was trained on default train.py settings, except
```
eval_intervals=20
eval_iters=40
batch_size=2
gradient_accumulation_steps = 64
```
This was because I was training it locally on RTX2060 and did not have enough power to train it on higher settings.
Model is stored in "model" folder that contains model itself and "info.txt" file containing:
- iter_num - number of iterations
- train_loss - training loss at time of checkpoint
- val_loss - validation loss at time of checkpoint
- config - nanoGPT config
At first I made it only save model after validation loss improved, to not allow overfitting, but after some time I decided to risk it and turned that off and allowed it to save everytime, luckly it worked out fine. |
YanJiangJerry/SA-berttweet-large-e3-b16-w0.01 | YanJiangJerry | 2023-07-14T20:11:27Z | 106 | 0 | transformers | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2023-07-14T19:51:53Z | ---
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: SA-berttweet-large-e3-b16-w0.01
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# SA-berttweet-large-e3-b16-w0.01
This model is a fine-tuned version of [vinai/bertweet-large](https://huggingface.co/vinai/bertweet-large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2822
- Accuracy: 0.926
- F1: 0.9333
- Precision: 0.9487
- Recall: 0.9184
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| No log | 1.0 | 285 | 0.3723 | 0.839 | 0.8342 | 0.9951 | 0.7181 |
| 0.3662 | 2.0 | 570 | 0.2310 | 0.915 | 0.9242 | 0.9300 | 0.9184 |
| 0.3662 | 3.0 | 855 | 0.2822 | 0.926 | 0.9333 | 0.9487 | 0.9184 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
roilhi/ppo-LunarLander-v2 | roilhi | 2023-07-14T20:08:11Z | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | 2023-07-14T20:07:54Z | ---
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: 286.00 +/- 24.34
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
...
```
|
felflare/EasyOCR-weights | felflare | 2023-07-14T19:57:55Z | 0 | 0 | null | [
"region:us"
] | null | 2023-03-29T17:40:39Z | ## Port of EasyOCR weights from Jaided AI model Hub
These Weights are from Gen 2 of EasyOCR weights
**Original weights can be found here - [Jaided AI Model Hub](https://www.jaided.ai/easyocr/modelhub/)**
Licensed under [Jaided AI license terms](https://github.com/JaidedAI/EasyOCR/blob/master/LICENSE), this is only a port of the weights onto Hugginface model repository for ease of access. |
Rui31415/q-FrozenLake-v1-4x4-noSlippery | Rui31415 | 2023-07-14T19:50:52Z | 0 | 0 | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | reinforcement-learning | 2023-07-14T19:50:49Z | ---
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="Rui31415/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"])
```
|
chaojiang06/arxiv-sentence-alignment | chaojiang06 | 2023-07-14T19:42:21Z | 108 | 0 | transformers | [
"transformers",
"pytorch",
"arxiv:2210.15067",
"endpoints_compatible",
"region:us"
] | null | 2023-02-19T21:55:34Z |
# Checkpoints for [arXivEdits paper](https://arxiv.org/pdf/2210.15067.pdf). Please see more details at the [github repo](https://github.com/chaojiang06/arXivEdits/tree/main).
|
chaojiang06/arXivEdits-intention-classifier-T5-base-fine-grained | chaojiang06 | 2023-07-14T19:40:52Z | 110 | 0 | transformers | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"generated_from_trainer",
"arxiv:2210.15067",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2023-07-14T19:12:44Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: arXivEdits-intention-classifier-T5-base-fine-grained
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 for [arXivEdits paper](https://arxiv.org/pdf/2210.15067.pdf). Please see more details at the [github repo](https://github.com/chaojiang06/arXivEdits/tree/main).
# arXivEdits-intention-classifier-T5-base-fine-grained
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: 0.1457
- Accuracy: 0.6826
## 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: 12
- eval_batch_size: 12
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 105 | 0.3043 | 0.2991 |
| No log | 2.0 | 210 | 0.2653 | 0.3311 |
| No log | 3.0 | 315 | 0.2475 | 0.4726 |
| No log | 4.0 | 420 | 0.1737 | 0.6096 |
| 0.5112 | 5.0 | 525 | 0.1660 | 0.6256 |
| 0.5112 | 6.0 | 630 | 0.1499 | 0.6575 |
| 0.5112 | 7.0 | 735 | 0.1497 | 0.6438 |
| 0.5112 | 8.0 | 840 | 0.1457 | 0.6826 |
| 0.5112 | 9.0 | 945 | 0.1470 | 0.6781 |
| 0.151 | 10.0 | 1050 | 0.1428 | 0.6781 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.11.0+cu113
- Datasets 1.17.0
- Tokenizers 0.11.6
|
YanJiangJerry/SA-berttweet-large-e6-w2-1-b16-w0.01 | YanJiangJerry | 2023-07-14T19:35:33Z | 105 | 0 | transformers | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2023-07-14T18:56:29Z | ---
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: SA-berttweet-large-e6-w2-1-b16-w0.01
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# SA-berttweet-large-e6-w2-1-b16-w0.01
This model is a fine-tuned version of [vinai/bertweet-large](https://huggingface.co/vinai/bertweet-large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4510
- Accuracy: 0.935
- F1: 0.9423
- Precision: 0.9432
- Recall: 0.9415
## 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: 6
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| No log | 1.0 | 285 | 0.2599 | 0.871 | 0.8714 | 0.9954 | 0.7748 |
| 0.3039 | 2.0 | 570 | 0.2502 | 0.929 | 0.9371 | 0.9363 | 0.9379 |
| 0.3039 | 3.0 | 855 | 0.4228 | 0.923 | 0.9331 | 0.9148 | 0.9521 |
| 0.1246 | 4.0 | 1140 | 0.4102 | 0.934 | 0.9414 | 0.9431 | 0.9397 |
| 0.1246 | 5.0 | 1425 | 0.4532 | 0.933 | 0.9407 | 0.9398 | 0.9415 |
| 0.0379 | 6.0 | 1710 | 0.4510 | 0.935 | 0.9423 | 0.9432 | 0.9415 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
hongrui/mammogram_v_2_2_2_100 | hongrui | 2023-07-14T19:34:21Z | 2 | 0 | diffusers | [
"diffusers",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"lora",
"base_model:runwayml/stable-diffusion-v1-5",
"base_model:adapter:runwayml/stable-diffusion-v1-5",
"license:creativeml-openrail-m",
"region:us"
] | text-to-image | 2023-07-14T00:35:12Z |
---
license: creativeml-openrail-m
base_model: runwayml/stable-diffusion-v1-5
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- lora
inference: true
---
# LoRA text2image fine-tuning - hongrui/mammogram_v_2_2_2_100
These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were fine-tuned on the hongrui/mammo_100_v1 dataset. You can find some example images in the following.
|
Leon68/opt-6.7b-lora | Leon68 | 2023-07-14T19:03:58Z | 1 | 0 | peft | [
"peft",
"region:us"
] | null | 2023-07-14T19:03:51Z | ---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: True
- load_in_4bit: False
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
### Framework versions
- PEFT 0.4.0.dev0
|
franklab/HSSM | franklab | 2023-07-14T18:48:22Z | 0 | 2 | null | [
"onnx",
"license:bsd-2-clause",
"region:us"
] | null | 2023-06-22T16:08:25Z | ---
license: bsd-2-clause
---
# Utilizing Custom ONNX Models Stored in Hugging Face within HSSM
This guide will walk you through the process of using custom ONNX models stored in Hugging Face within HSSM (Hierarchical State Space Model) framework.
## Prerequisites
1. Python 3.8 or later.
2. HSSM library installed in your Python environment.
3. A pre-trained ONNX model stored on Hugging Face model hub.
## Step-by-step guide
### Step 1: Import necessary libraries
```
import pandas as pd
import hssm
import ssms.basic_simulators
pytensor.config.floatX = "float32"
```
### Step 2: Define HSSM Configuration
You will have to define the configuration of your model. Make sure you are defining the log-likelihood kind as "approx_differentiable" and providing the Hugging Face model name in the loglik field.
```
my_hssm = hssm.HSSM(
data=dataset_lan,
loglik_kind = "approx_differentiable",
loglik = "levy.onnx",
model="custom",
model_config= {
"backend": "jax",
"list_params": ["v", "a", "z", "alpha", "t"],
"bounds": {
"v": (-3.0, 3.0),
"a": (0.3, 3.0),
"z": (0.1, 0.9),
"alpha": (1.0, 2.0),
"t": (1e-3, 2.0),
},
}
)
```
This creates an HSSM object my_hssm using the custom ONNX model levy.onnx from the Hugging Face repository.
```
my_hssm.sample(cores=2, draws=500, tune=500, mp_ctx="forkserver")
```
# Uploading ONNX Files to a Hugging Face Repository
If your ONNX file is not currently housed in your Hugging Face repository, you can include it by adhering to the steps delineated below:
1. Import the HfApi module from huggingface_hub:
```
from huggingface_hub import HfApi
```
2. Upload the ONNX file using the upload_file method:
```
api = HfApi()
api.upload_file(
path_or_fileobj="test.onnx",
path_in_repo="test.onnx",
repo_id="franklab/HSSM",
repo_type="model",
create_pr=True,
)
```
The execution of these steps will generate a Pull Request (PR) on Hugging Face, which will subsequently be evaluated by a member of our team.
## Creating a Pull Request and a New ONNX Model
1. **Creating a Pull Request on Hugging Face**
Navigate to the following link: [Hugging Face PR](https://huggingface.co/franklab/HSSM/blob/refs%2Fpr%2F1/test.onnx)
By doing so, you will **generate a Pull Request on Hugging Face**, which will be reviewed by our team members.
2. **Creating a Custom ONNX Model**
### Establish a Network Config and State Dictionary Files in PyTorch
To construct a custom model and save it as an ONNX file, you must create a network configuration file and a state dictionary file in PyTorch. Refer to the instructions outlined in the README of the [LANFactory package](LINK_TO_LANFACTORY_PACKAGE).
### Convert Network Config and State Dictionary Files to ONNX
Once you've generated the network configuration and state dictionary files, you will need to **convert these files into an ONNX format**.
|
Danish-summarisation/DanSumT5-large | Danish-summarisation | 2023-07-14T18:43:21Z | 29 | 3 | transformers | [
"transformers",
"pytorch",
"safetensors",
"mt5",
"text2text-generation",
"summarization",
"da",
"arxiv:1804.11283",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | summarization | 2023-04-10T12:21:06Z | ---
pipeline_tag: summarization
license: apache-2.0
language:
- da
---
# mT5-large fine-tuned for News article Summarisation ✏️🧾
[Google's mT5](https://aclanthology.org/2021.naacl-main.41/) for **summarisation** downstream task.
# Model summary
This repository contains a model for Danish abstractive summarisation of news articles. The summariser is based on a language-specific mT5-large.
The model is fine-tuned using an abstractive subset of the DaNewsroom dataset (Varab & Schluter, 2020), according to the binned density categories employed in Newsroom (Grusky et al., 2019).
# References
Grusky, M., Naaman, M., & Artzi, Y. (2018). Newsroom: A Dataset of 1.3 Million Summaries with Diverse Extractive Strategies. ArXiv:1804.11283 [Cs]. http://arxiv.org/abs/1804.11283
Varab, D., & Schluter, N. (2020). DaNewsroom: A Large-scale Danish Summarisation Dataset. Proceedings of the 12th Language Resources and Evaluation Conference, 6731–6739. https://aclanthology.org/2020.lrec-1.831
|
giocs2017/ast-finetuned-audioset-10-10-0.4593-finetuned-gtzan | giocs2017 | 2023-07-14T18:35:09Z | 161 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"audio-spectrogram-transformer",
"audio-classification",
"generated_from_trainer",
"dataset:marsyas/gtzan",
"base_model:MIT/ast-finetuned-audioset-10-10-0.4593",
"base_model:finetune:MIT/ast-finetuned-audioset-10-10-0.4593",
"license:bsd-3-clause",
"model-index",
"endpoints_compatible",
"region:us"
] | audio-classification | 2023-07-14T17:01:58Z | ---
license: bsd-3-clause
base_model: MIT/ast-finetuned-audioset-10-10-0.4593
tags:
- generated_from_trainer
datasets:
- marsyas/gtzan
metrics:
- accuracy
model-index:
- name: ast-finetuned-audioset-10-10-0.4593-finetuned-gtzan
results:
- task:
name: Audio Classification
type: audio-classification
dataset:
name: GTZAN
type: marsyas/gtzan
config: all
split: train
args: all
metrics:
- name: Accuracy
type: accuracy
value: 0.9
---
<!-- 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. -->
# ast-finetuned-audioset-10-10-0.4593-finetuned-gtzan
This model is a fine-tuned version of [MIT/ast-finetuned-audioset-10-10-0.4593](https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.4593) on the GTZAN dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4700
- Accuracy: 0.9
## 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:
- learning_rate: 5e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.9627 | 1.0 | 112 | 0.7284 | 0.75 |
| 0.3776 | 1.99 | 224 | 0.4641 | 0.83 |
| 0.4536 | 3.0 | 337 | 0.5534 | 0.85 |
| 0.0602 | 4.0 | 449 | 0.4999 | 0.86 |
| 0.1927 | 4.99 | 561 | 0.5989 | 0.85 |
| 0.0122 | 6.0 | 674 | 0.7778 | 0.85 |
| 0.0006 | 6.99 | 786 | 0.4095 | 0.9 |
| 0.0005 | 8.0 | 899 | 0.5149 | 0.9 |
| 0.1723 | 9.0 | 1011 | 0.4558 | 0.9 |
| 0.0001 | 9.99 | 1123 | 0.4700 | 0.9 |
### Framework versions
- Transformers 4.31.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
saicharan8/my_first_finetune | saicharan8 | 2023-07-14T18:33:08Z | 108 | 0 | transformers | [
"transformers",
"pytorch",
"bert",
"text-classification",
"en",
"dataset:yelp_review_full",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2023-07-14T18:24:40Z | ---
license: apache-2.0
datasets:
- yelp_review_full
language:
- en
---
This is my first fine tuned bert model on yelp data .
|
absolutt/ppo-LunarLander-v2-1stTry | absolutt | 2023-07-14T18:24:14Z | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | 2023-07-14T18:23:51Z | ---
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: 251.71 +/- 21.38
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
...
```
|
YanJiangJerry/SA-roberta-e3-w1-5-b16-w0.01-data2 | YanJiangJerry | 2023-07-14T18:19:30Z | 118 | 0 | transformers | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2023-07-14T17:48:27Z | ---
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: SA-roberta-e3-w1-5-b16-w0.01-data2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# SA-roberta-e3-w1-5-b16-w0.01-data2
This model is a fine-tuned version of [Amalq/autotrain-smm4h_large_roberta_clean-874027878](https://huggingface.co/Amalq/autotrain-smm4h_large_roberta_clean-874027878) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7680
- Accuracy: 0.9021
- F1: 0.8646
- Precision: 0.8921
- Recall: 0.8388
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.2612 | 1.0 | 581 | 0.4296 | 0.9021 | 0.8721 | 0.8499 | 0.8955 |
| 0.1252 | 2.0 | 1162 | 0.7605 | 0.8977 | 0.8571 | 0.8932 | 0.8239 |
| 0.0567 | 3.0 | 1743 | 0.7680 | 0.9021 | 0.8646 | 0.8921 | 0.8388 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
hafeezmhk6/vikas_trail2 | hafeezmhk6 | 2023-07-14T18:14:36Z | 103 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2023-07-14T18:10:26Z | ---
tags:
- generated_from_trainer
model-index:
- name: vikas_trail2
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. -->
# vikas_trail2
This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- 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
- lr_scheduler_warmup_steps: 500
- num_epochs: 10
### Training results
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
jamanca94/roberta-base-bne-finetuned-sqac | jamanca94 | 2023-07-14T18:08:34Z | 114 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"question-answering",
"generated_from_trainer",
"dataset:sqac",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | question-answering | 2023-07-14T16:53:52Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- sqac
model-index:
- name: roberta-base-bne-finetuned-sqac
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-base-bne-finetuned-sqac
This model is a fine-tuned version of [BSC-TeMU/roberta-base-bne](https://huggingface.co/BSC-TeMU/roberta-base-bne) on the sqac dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1472
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.9881 | 1.0 | 1196 | 0.8653 |
| 0.4671 | 2.0 | 2392 | 0.9053 |
| 0.1506 | 3.0 | 3588 | 1.1472 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
CMunch/fine_tuned_temp_real | CMunch | 2023-07-14T17:34:39Z | 103 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2023-07-13T17:35:57Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
metrics:
- accuracy
model-index:
- name: fine_tuned_temp_real
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: imdb
type: imdb
config: plain_text
split: test
args: plain_text
metrics:
- name: Accuracy
type: accuracy
value: 0.93116
---
<!-- 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. -->
# fine_tuned_temp_real
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2334
- Accuracy: 0.9312
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.2349 | 1.0 | 1563 | 0.1965 | 0.9247 |
| 0.1521 | 2.0 | 3126 | 0.2334 | 0.9312 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
YanJiangJerry/SA-roberta-e3-w1-2.5-b16-mt4-w0.01-data2 | YanJiangJerry | 2023-07-14T17:32:07Z | 107 | 0 | transformers | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2023-07-14T17:01:00Z | ---
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: SA-roberta-e3-w1-2.5-b16-mt4-w0.01-data2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# SA-roberta-e3-w1-2.5-b16-mt4-w0.01-data2
This model is a fine-tuned version of [Amalq/autotrain-smm4h_large_roberta_clean-874027878](https://huggingface.co/Amalq/autotrain-smm4h_large_roberta_clean-874027878) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6606
- Accuracy: 0.9088
- F1: 0.875
- Precision: 0.8941
- Recall: 0.8567
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.2898 | 1.0 | 581 | 0.5149 | 0.9221 | 0.8930 | 0.9154 | 0.8716 |
| 0.1117 | 2.0 | 1162 | 0.7317 | 0.9010 | 0.8633 | 0.8892 | 0.8388 |
| 0.0536 | 3.0 | 1743 | 0.6606 | 0.9088 | 0.875 | 0.8941 | 0.8567 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
achang/fin_forecast_0 | achang | 2023-07-14T17:06:10Z | 0 | 0 | null | [
"region:us"
] | null | 2023-07-03T00:24:42Z | commit: 1559705809d9ef7ac5e7dfe8e739c13c1b26f77a
model train on https://huggingface.co/datasets/achang/stock_forecast_0 |
YanJiangJerry/SA-roberta-e3-w1-1.5-b16-mt4-w0.01-data2 | YanJiangJerry | 2023-07-14T16:57:59Z | 107 | 0 | transformers | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2023-07-14T16:26:57Z | ---
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: SA-roberta-e3-w1-1.5-b16-mt4-w0.01-data2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# SA-roberta-e3-w1-1.5-b16-mt4-w0.01-data2
This model is a fine-tuned version of [Amalq/autotrain-smm4h_large_roberta_clean-874027878](https://huggingface.co/Amalq/autotrain-smm4h_large_roberta_clean-874027878) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5675
- Accuracy: 0.9121
- F1: 0.8819
- Precision: 0.8832
- Recall: 0.8806
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.3096 | 1.0 | 581 | 0.3382 | 0.9077 | 0.8770 | 0.8706 | 0.8836 |
| 0.1202 | 2.0 | 1162 | 0.5246 | 0.9032 | 0.8730 | 0.8543 | 0.8925 |
| 0.0655 | 3.0 | 1743 | 0.5675 | 0.9121 | 0.8819 | 0.8832 | 0.8806 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
sotoy/path_to_saved_model | sotoy | 2023-07-14T16:56:29Z | 1 | 0 | diffusers | [
"diffusers",
"tensorboard",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"dreambooth",
"base_model:CompVis/stable-diffusion-v1-4",
"base_model:finetune:CompVis/stable-diffusion-v1-4",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | text-to-image | 2023-07-14T13:32:56Z |
---
license: creativeml-openrail-m
base_model: CompVis/stable-diffusion-v1-4
instance_prompt: a photo of sks dog
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- dreambooth
inference: true
---
# DreamBooth - sotoy/path_to_saved_model
This is a dreambooth model derived from CompVis/stable-diffusion-v1-4. The weights were trained on a photo of sks dog using [DreamBooth](https://dreambooth.github.io/).
You can find some example images in the following.
DreamBooth for the text encoder was enabled: True.
|
FrancescoBonzi/speecht5_finetuned_voxpopuli_it | FrancescoBonzi | 2023-07-14T16:39:52Z | 93 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"speecht5",
"text-to-audio",
"generated_from_trainer",
"text-to-speech",
"dataset:voxpopuli",
"license:mit",
"endpoints_compatible",
"region:us"
] | text-to-speech | 2023-07-14T14:17:17Z | ---
license: mit
tags:
- generated_from_trainer
- text-to-speech
datasets:
- voxpopuli
model-index:
- name: speecht5_finetuned_voxpopuli_it
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# speecht5_finetuned_voxpopuli_it
This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the voxpopuli dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4871
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 4
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 4000
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.5505 | 6.13 | 1000 | 0.5092 |
| 0.5245 | 12.26 | 2000 | 0.4941 |
| 0.5192 | 18.39 | 3000 | 0.4885 |
| 0.5126 | 24.52 | 4000 | 0.4871 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.0
- Datasets 2.13.1
- Tokenizers 0.13.3 |
giocs2017/distilhubert-finetuned-gtzan | giocs2017 | 2023-07-14T16:32:55Z | 160 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"hubert",
"audio-classification",
"generated_from_trainer",
"dataset:marsyas/gtzan",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | audio-classification | 2023-07-13T01:20:22Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- marsyas/gtzan
metrics:
- accuracy
model-index:
- name: distilhubert-finetuned-gtzan
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. -->
# distilhubert-finetuned-gtzan
This model is a fine-tuned version of [ntu-spml/distilhubert](https://huggingface.co/ntu-spml/distilhubert) on the GTZAN dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8934
- Accuracy: 0.82
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.681 | 1.0 | 450 | 1.7351 | 0.5 |
| 1.5534 | 2.0 | 900 | 1.2192 | 0.66 |
| 0.6835 | 3.0 | 1350 | 1.0462 | 0.71 |
| 1.069 | 4.0 | 1800 | 0.5503 | 0.83 |
| 0.1563 | 5.0 | 2250 | 0.9394 | 0.78 |
| 0.0077 | 6.0 | 2700 | 0.9394 | 0.81 |
| 0.7444 | 7.0 | 3150 | 0.8934 | 0.82 |
### Framework versions
- Transformers 4.31.0.dev0
- Pytorch 2.0.0+cu117
- Datasets 2.13.1
- Tokenizers 0.13.2
|
giocs2017/Taxi-v3_v1 | giocs2017 | 2023-07-14T16:00:46Z | 0 | 0 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | reinforcement-learning | 2023-07-14T15:47:30Z | ---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: Taxi-v3_v1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.56 +/- 2.71
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="giocs2017/Taxi-v3_v1", 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"])
```
|
giocs2017/q-FrozenLake-v1-4x4-noSlippery | giocs2017 | 2023-07-14T15:59:09Z | 0 | 0 | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | reinforcement-learning | 2023-07-14T15:46:03Z | ---
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="giocs2017/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"])
```
|
bhenrym14/airoboros-33b-gpt4-1.4.1-lxctx-PI-16384-LoRA | bhenrym14 | 2023-07-14T15:50:00Z | 0 | 1 | null | [
"dataset:jondurbin/airoboros-gpt4-1.4.1",
"region:us"
] | null | 2023-07-14T02:51:39Z | ---
datasets:
- jondurbin/airoboros-gpt4-1.4.1
---
NOTE: This LoRA was trained on Llama-30b AFTER additional pretraining. I intend on providing the LoRA of that pretraining too. Applying this LoRA to base Llama-30b will likely result in a performance reduction. I have uploaded the fp16 merged weights [here](https://huggingface.co/bhenrym14/airoboros-33b-gpt4-1.4.1-lxctx-PI-16384-LoRA/)
Mostly untested!
Find GPTQ quantized weights and full model card here: https://huggingface.co/bhenrym14/airoboros-33b-gpt4-1.4.1-lxctx-PI-16384-GPTQ
# RoPE Scaled QLoRA Fine-tune of Llama-33b on airoboros-gpt4-1.4.1 (LoRA)
## Overview
This is [Jon Durbin's Airoboros 33B GPT4 1.4](https://huggingface.co/jondurbin/airoboros-33b-gpt4-1.4) (LoRA) with several key modifications:
- Context length extended to 16384 by RoPE Scaled Embeddings.
- The Llama-33b base model is pretrained for additional 100 steps on 8192 length sequences from the pile dataset.
- Used airoboros-gpt4-1.4.1 dataset instead of airoboros-gpt4-1.4
**This is a QLoRA fine-tune**
Pretraining took 10 hours. Finetuning took ~41 hours on 1x RTX 6000 Ada. |
Dlychan/Nadhieraa | Dlychan | 2023-07-14T15:47:59Z | 0 | 0 | null | [
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-07-14T15:42:59Z | ---
license: creativeml-openrail-m
---
|
PlankyxD/Reinforce-CartPole-v1 | PlankyxD | 2023-07-14T15:46:00Z | 0 | 0 | null | [
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | reinforcement-learning | 2023-06-27T02:12:53Z | ---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-CartPole-v1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 466.10 +/- 71.75
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
PavlySafwat/ExtractiveQABertBase | PavlySafwat | 2023-07-14T15:25:14Z | 101 | 0 | transformers | [
"transformers",
"pytorch",
"roberta",
"question-answering",
"endpoints_compatible",
"region:us"
] | question-answering | 2023-07-14T15:21:12Z | ---
pipeline_tag: question-answering
---
Hyperparameters
batch_size = 96
n_epochs = 2
base_LM_model = "roberta-base"
max_seq_len = 386
learning_rate = 3e-5
lr_schedule = LinearWarmup
warmup_proportion = 0.2
doc_stride=128
max_query_length=64 |
Vladislav-HuggingFace/Taxi-v3 | Vladislav-HuggingFace | 2023-07-14T15:17:01Z | 0 | 0 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | reinforcement-learning | 2023-07-14T14:47:39Z | ---
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: 7.50 +/- 2.75
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="Vladislav-HuggingFace/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"])
```
|
ahmadnajm/KurdGPT | ahmadnajm | 2023-07-14T14:58:36Z | 0 | 0 | adapter-transformers | [
"adapter-transformers",
"code",
"chemistry",
"biology",
"legal",
"art",
"medical",
"text-classification",
"en",
"ku",
"dataset:fka/awesome-chatgpt-prompts",
"license:openrail",
"region:us"
] | text-classification | 2023-06-16T23:54:34Z | ---
license: openrail
datasets:
- fka/awesome-chatgpt-prompts
language:
- en
- ku
metrics:
- accuracy
library_name: adapter-transformers
pipeline_tag: text-classification
tags:
- code
- chemistry
- biology
- legal
- art
- medical
--- |
grace-pro/xlmr-base-igbo-5e-5 | grace-pro | 2023-07-14T14:49:57Z | 105 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | 2023-07-14T14:18:19Z | ---
license: mit
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: xlmr-base-igbo-5e-5
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlmr-base-igbo-5e-5
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2368
- Precision: 0.7064
- Recall: 0.5075
- F1: 0.5907
- Accuracy: 0.9212
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2768 | 1.0 | 1257 | 0.2611 | 0.7212 | 0.3117 | 0.4353 | 0.9055 |
| 0.2299 | 2.0 | 2514 | 0.2606 | 0.7395 | 0.3797 | 0.5018 | 0.9134 |
| 0.1966 | 3.0 | 3771 | 0.2224 | 0.7252 | 0.4496 | 0.5550 | 0.9186 |
| 0.1697 | 4.0 | 5028 | 0.2290 | 0.7273 | 0.4775 | 0.5765 | 0.9208 |
| 0.1449 | 5.0 | 6285 | 0.2368 | 0.7064 | 0.5075 | 0.5907 | 0.9212 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
Vladislav-HuggingFace/q-Taxi-v3 | Vladislav-HuggingFace | 2023-07-14T14:44:51Z | 0 | 0 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | reinforcement-learning | 2023-07-14T14:44:30Z | ---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.56 +/- 2.71
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="Vladislav-HuggingFace/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
athrunsunny/YOLOMT | athrunsunny | 2023-07-14T14:44:02Z | 0 | 0 | null | [
"onnx",
"region:us"
] | null | 2023-07-14T14:06:16Z | Pretrained models of our method YOLOMT
demo link: https://github.com/athrunsunny/YOLOMT
YOLOMT is a multi-task model base yolo,detect human orientation,face pose and face landmark
detect result:
model:[yolomt.onnx](https://huggingface.co/athrunsunny/YOLOMT/blob/main/YOLOMT.onnx) |
shahafw/ppo-CartPole-v1 | shahafw | 2023-07-14T14:39:10Z | 0 | 0 | null | [
"tensorboard",
"LunarLander-v2",
"ppo",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"deep-rl-course",
"model-index",
"region:us"
] | reinforcement-learning | 2023-07-14T14:35:39Z | ---
tags:
- LunarLander-v2
- ppo
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
- deep-rl-course
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: -160.58 +/- 79.59
name: mean_reward
verified: false
---
# PPO Agent Playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2.
# Hyperparameters
```python
{'exp_name': 'ppo'
'seed': 1
'torch_deterministic': True
'cuda': True
'track': False
'wandb_project_name': 'cleanRL'
'wandb_entity': None
'capture_video': False
'env_id': 'LunarLander-v2'
'total_timesteps': 50000
'learning_rate': 0.00025
'num_envs': 4
'num_steps': 128
'anneal_lr': True
'gae': True
'gamma': 0.99
'gae_lambda': 0.95
'num_minibatches': 4
'update_epochs': 4
'norm_adv': True
'clip_coef': 0.2
'clip_vloss': True
'ent_coef': 0.01
'vf_coef': 0.5
'max_grad_norm': 0.5
'target_kl': None
'repo_id': 'shahafw/ppo-CartPole-v1'
'batch_size': 512
'minibatch_size': 128}
```
|
PaulineJamin/cart-pole-v1 | PaulineJamin | 2023-07-14T14:13:25Z | 0 | 0 | null | [
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | reinforcement-learning | 2023-07-14T09:45:08Z | ---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: cart-pole-v1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 500.40 +/- 39.85
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
wf123aaa/resnet-50-finetuned-ai-art | wf123aaa | 2023-07-14T14:12:21Z | 0 | 0 | nemo | [
"nemo",
"pytorch",
"resnet",
"art",
"image-classification",
"en",
"license:apache-2.0",
"region:us"
] | image-classification | 2023-07-14T06:40:05Z | ---
license: apache-2.0
language:
- en
metrics:
- accuracy
library_name: nemo
pipeline_tag: image-classification
tags:
- art
--- |
HuengchI/my_awesome_eli5_clm-model | HuengchI | 2023-07-14T13:59:42Z | 198 | 0 | transformers | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2023-07-14T13:46:28Z | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: my_awesome_eli5_clm-model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# my_awesome_eli5_clm-model
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.7590
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 282 | 3.7798 |
| 3.8857 | 2.0 | 564 | 3.7630 |
| 3.8857 | 3.0 | 846 | 3.7590 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu117
- Datasets 2.13.0
- Tokenizers 0.13.3
|
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