modelId
stringlengths 5
139
| author
stringlengths 2
42
| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-07-14 18:27:59
| downloads
int64 0
223M
| likes
int64 0
11.7k
| library_name
stringclasses 520
values | tags
listlengths 1
4.05k
| pipeline_tag
stringclasses 55
values | createdAt
timestamp[us, tz=UTC]date 2022-03-02 23:29:04
2025-07-14 18:27:48
| card
stringlengths 11
1.01M
|
|---|---|---|---|---|---|---|---|---|---|
carbon225/vit-base-patch16-224-hentai | carbon225 | 2023-07-04T14:50:00Z | 225 | 19 | transformers | [
"transformers",
"pytorch",
"safetensors",
"vit",
"image-classification",
"art",
"anime",
"visual-novel",
"nsfw",
"dataset:carbon225/vndb_img",
"license:cc0-1.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| image-classification | 2022-09-30T12:06:40Z | ---
license: cc0-1.0
widget:
- src: >-
https://huggingface.co/carbon225/vit-base-patch16-224-hentai/resolve/main/samples/1.jpeg
- src: >-
https://huggingface.co/carbon225/vit-base-patch16-224-hentai/resolve/main/samples/2.jpeg
datasets:
- carbon225/vndb_img
tags:
- art
- anime
- visual-novel
- nsfw
---
# ViT for NSFW classification
## Model info
This is Google's [vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k)
finetuned for flagging images according to [vndb.org](https://vndb.org/d19) with 3 classes:
- safe
- suggestive
- explicit
## Training data
The model was trained on the vndb.org [database dump](https://vndb.org/d14)
using full size screenshots (`sf` in the database dump).
The dataset can be loaded from [carbon225/vndb_img](https://huggingface.co/datasets/carbon225/vndb_img).
## Intended use
The model can be used for flagging anime-style images for sexual content.
It can also be finetuned on other tasks related to anime images. |
leofn3/autotrain-racismo | leofn3 | 2023-07-04T14:43:08Z | 81 | 0 | transformers | [
"transformers",
"pytorch",
"safetensors",
"deberta-v2",
"text-classification",
"autotrain",
"unk",
"dataset:leofn3/autotrain-data-racismo-sandbox",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2023-07-04T14:37:11Z | ---
tags:
- autotrain
- text-classification
language:
- unk
widget:
- text: "Negra melodia que vem do sangue do coraรงรฃo"
datasets:
- leofn3/autotrain-data-racismo-sandbox
co2_eq_emissions:
emissions: 0.9388908689973346
---
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 72132138873
- CO2 Emissions (in grams): 0.9389
## Validation Metrics
- Loss: 0.562
- Accuracy: 0.833
- Precision: 1.000
- Recall: 0.667
- AUC: 0.901
- F1: 0.800
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/leofn3/autotrain-racismo-sandbox-72132138873
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("leofn3/autotrain-racismo-sandbox-72132138873", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("leofn3/autotrain-racismo-sandbox-72132138873", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
``` |
dcarpintero/ppo-Pyramids | dcarpintero | 2023-07-04T14:39:33Z | 0 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"Pyramids",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
]
| reinforcement-learning | 2023-07-04T14:39:30Z | ---
library_name: ml-agents
tags:
- Pyramids
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Pyramids
---
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog ๐ถ to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: dcarpintero/ppo-Pyramids
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play ๐
|
osunlp/BioVocabBERT | osunlp | 2023-07-04T14:26:56Z | 117 | 3 | transformers | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"arxiv:2306.17649",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| fill-mask | 2023-06-05T17:57:26Z | This biomedical language model uses a specialized biomedical tokenizer which is more closely aligned with human-morphological judgements than previous biomedical tokenizers such as PubMedBERT.
Details about our tokenizer design, pre-training procedure and downstream results can be found in our [BioNLP @ ACL 2023 paper](http://arxiv.org/pdf/2306.17649.pdf)
---
license: apache-2.0
---
|
Apoorvakoira/wizabc | Apoorvakoira | 2023-07-04T14:23:44Z | 8 | 1 | transformers | [
"transformers",
"gpt_bigcode",
"text-generation",
"arxiv:2306.08568",
"license:bigcode-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-generation | 2023-07-04T13:45:23Z | ---
license: bigcode-openrail-m
---
This is the Full-Weight of WizardCoder.
**Repository**: https://github.com/nlpxucan/WizardLM/tree/main/WizardCoder
**Twitter**: https://twitter.com/WizardLM_AI/status/1669109414559911937
**Paper**: [WizardCoder: Empowering Code Large Language Models with Evol-Instruct](https://arxiv.org/abs/2306.08568)
# WizardCoder: Empowering Code Large Language Models with Evol-Instruct
To develop our WizardCoder model, we begin by adapting the Evol-Instruct method specifically for coding tasks. This involves tailoring the prompt to the domain of code-related instructions. Subsequently, we fine-tune the Code LLM, StarCoder, utilizing the newly created instruction-following training set.
## News
- ๐ฅ Our **WizardCoder-15B-v1.0** model achieves the **57.3 pass@1** on the [HumanEval Benchmarks](https://github.com/openai/human-eval), which is **22.3** points higher than the SOTA open-source Code LLMs.
- ๐ฅ We released **WizardCoder-15B-v1.0** trained with **78k** evolved code instructions. Please checkout the [Model Weights](https://huggingface.co/WizardLM/WizardCoder-15B-V1.0), and [Paper]().
- 📣 Please refer to our Twitter account https://twitter.com/WizardLM_AI and HuggingFace Repo https://huggingface.co/WizardLM . We will use them to announce any new release at the 1st time.
## Comparing WizardCoder with the Closed-Source Models.
๐ฅ The following figure shows that our **WizardCoder attains the third position in this benchmark**, surpassing Claude-Plus (59.8 vs. 53.0) and Bard (59.8 vs. 44.5). Notably, our model exhibits a substantially smaller size compared to these models.
<p align="center" width="100%">
<a ><img src="https://raw.githubusercontent.com/nlpxucan/WizardLM/main/WizardCoder/imgs/pass1.png" alt="WizardCoder" style="width: 86%; min-width: 300px; display: block; margin: auto;"></a>
</p>
โ**Note: In this study, we copy the scores for HumanEval and HumanEval+ from the [LLM-Humaneval-Benchmarks](https://github.com/my-other-github-account/llm-humaneval-benchmarks). Notably, all the mentioned models generate code solutions for each problem utilizing a **single attempt**, and the resulting pass rate percentage is reported. Our **WizardCoder** generates answers using greedy decoding and tests with the same [code](https://github.com/evalplus/evalplus).**
## Comparing WizardCoder with the Open-Source Models.
The following table clearly demonstrates that our **WizardCoder** exhibits a substantial performance advantage over all the open-source models. โ**If you are confused with the different scores of our model (57.3 and 59.8), please check the Notes.**
| Model | HumanEval Pass@1 | MBPP Pass@1 |
|------------------|------------------|-------------|
| CodeGen-16B-Multi| 18.3 |20.9 |
| CodeGeeX | 22.9 |24.4 |
| LLaMA-33B | 21.7 |30.2 |
| LLaMA-65B | 23.7 |37.7 |
| PaLM-540B | 26.2 |36.8 |
| PaLM-Coder-540B | 36.0 |47.0 |
| PaLM 2-S | 37.6 |50.0 |
| CodeGen-16B-Mono | 29.3 |35.3 |
| Code-Cushman-001 | 33.5 |45.9 |
| StarCoder-15B | 33.6 |43.6* |
| InstructCodeT5+ | 35.0 |-- |
| WizardLM-30B 1.0| 37.8 |-- |
| WizardCoder-15B 1.0 | **57.3** |**51.8** |
โ**Note: The reproduced result of StarCoder on MBPP.**
โ**Note: The above table conducts a comprehensive comparison of our **WizardCoder** with other models on the HumanEval and MBPP benchmarks. We adhere to the approach outlined in previous studies by generating **20 samples** for each problem to estimate the pass@1 score and evaluate with the same [code](https://github.com/openai/human-eval/tree/master). The scores of GPT4 and GPT3.5 reported by [OpenAI](https://openai.com/research/gpt-4) are 67.0 and 48.1 (maybe these are the early version GPT4&3.5).**
## Call for Feedbacks
We welcome everyone to use your professional and difficult instructions to evaluate WizardCoder, and show us examples of poor performance and your suggestions in the [issue discussion](https://github.com/nlpxucan/WizardLM/issues) area. We are focusing on improving the Evol-Instruct now and hope to relieve existing weaknesses and issues in the the next version of WizardCoder. After that, we will open the code and pipeline of up-to-date Evol-Instruct algorithm and work with you together to improve it.
## Contents
1. [Online Demo](#online-demo)
2. [Fine-tuning](#fine-tuning)
3. [Inference](#inference)
4. [Evaluation](#evaluation)
5. [Citation](#citation)
6. [Disclaimer](#disclaimer)
## Online Demo
We will provide our latest models for you to try for as long as possible. If you find a link is not working, please try another one. At the same time, please try as many **real-world** and **challenging** code-related problems that you encounter in your work and life as possible. We will continue to evolve our models with your feedbacks.
## Fine-tuning
We fine-tune WizardCoder using the modified code `train.py` from [Llama-X](https://github.com/AetherCortex/Llama-X).
We fine-tune StarCoder-15B with the following hyperparameters:
| Hyperparameter | StarCoder-15B |
|----------------|---------------|
| Batch size | 512 |
| Learning rate | 2e-5 |
| Epochs | 3 |
| Max length | 2048 |
| Warmup step | 30 |
| LR scheduler | cosine |
To reproduce our fine-tuning of WizardCoder, please follow the following steps:
1. According to the instructions of [Llama-X](https://github.com/AetherCortex/Llama-X), install the environment, download the training code, and deploy. (Note: `deepspeed==0.9.2` and `transformers==4.29.2`)
2. Replace the `train.py` with the `train_wizardcoder.py` in our repo (`src/train_wizardcoder.py`)
3. Login Huggingface:
```bash
huggingface-cli login
```
4. Execute the following training command:
```bash
deepspeed train_wizardcoder.py \
--model_name_or_path "bigcode/starcoder" \
--data_path "/your/path/to/code_instruction_data.json" \
--output_dir "/your/path/to/ckpt" \
--num_train_epochs 3 \
--model_max_length 2048 \
--per_device_train_batch_size 16 \
--per_device_eval_batch_size 1 \
--gradient_accumulation_steps 4 \
--evaluation_strategy "no" \
--save_strategy "steps" \
--save_steps 50 \
--save_total_limit 2 \
--learning_rate 2e-5 \
--warmup_steps 30 \
--logging_steps 2 \
--lr_scheduler_type "cosine" \
--report_to "tensorboard" \
--gradient_checkpointing True \
--deepspeed configs/deepspeed_config.json \
--fp16 True
```
## Inference
We provide the decoding script for WizardCoder, which reads a input file and generates corresponding responses for each sample, and finally consolidates them into an output file.
You can specify `base_model`, `input_data_path` and `output_data_path` in `src\inference_wizardcoder.py` to set the decoding model, path of input file and path of output file.
```bash
pip install jsonlines
```
The decoding command is:
```
python src\inference_wizardcoder.py \
--base_model "/your/path/to/ckpt" \
--input_data_path "/your/path/to/input/data.jsonl" \
--output_data_path "/your/path/to/output/result.jsonl"
```
The format of `data.jsonl` should be:
```
{"idx": 11, "Instruction": "Write a Python code to count 1 to 10."}
{"idx": 12, "Instruction": "Write a Jave code to sum 1 to 10."}
```
The prompt for our WizardCoder in `src\inference_wizardcoder.py` is:
```
Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
{instruction}
### Response:
```
## Evaluation
We provide the evaluation script on HumanEval for WizardCoder.
1. According to the instructions of [HumanEval](https://github.com/openai/human-eval), install the environment.
2. Run the following script to generate the answer.
```bash
model="/path/to/your/model"
temp=0.2
max_len=2048
pred_num=200
num_seqs_per_iter=2
output_path=preds/T${temp}_N${pred_num}
mkdir -p ${output_path}
echo 'Output path: '$output_path
echo 'Model to eval: '$model
# 164 problems, 21 per GPU if GPU=8
index=0
gpu_num=8
for ((i = 0; i < $gpu_num; i++)); do
start_index=$((i * 21))
end_index=$(((i + 1) * 21))
gpu=$((i))
echo 'Running process #' ${i} 'from' $start_index 'to' $end_index 'on GPU' ${gpu}
((index++))
(
CUDA_VISIBLE_DEVICES=$gpu python humaneval_gen.py --model ${model} \
--start_index ${start_index} --end_index ${end_index} --temperature ${temp} \
--num_seqs_per_iter ${num_seqs_per_iter} --N ${pred_num} --max_len ${max_len} --output_path ${output_path}
) &
if (($index % $gpu_num == 0)); then wait; fi
done
```
3. Run the post processing code `src/process_humaneval.py` to collect the code completions from all answer files.
```bash
output_path=preds/T${temp}_N${pred_num}
echo 'Output path: '$output_path
python process_humaneval.py --path ${output_path} --out_path ${output_path}.jsonl --add_prompt
evaluate_functional_correctness ${output_path}.jsonl
```
## Citation
Please cite the repo if you use the data or code in this repo.
```
@misc{luo2023wizardcoder,
title={WizardCoder: Empowering Code Large Language Models with Evol-Instruct},
author={Ziyang Luo and Can Xu and Pu Zhao and Qingfeng Sun and Xiubo Geng and Wenxiang Hu and Chongyang Tao and Jing Ma and Qingwei Lin and Daxin Jiang},
year={2023},
}
```
## Disclaimer
The resources, including code, data, and model weights, associated with this project are restricted for academic research purposes only and cannot be used for commercial purposes. The content produced by any version of WizardCoder is influenced by uncontrollable variables such as randomness, and therefore, the accuracy of the output cannot be guaranteed by this project. This project does not accept any legal liability for the content of the model output, nor does it assume responsibility for any losses incurred due to the use of associated resources and output results.
|
Graphcore/sentence-t5-base | Graphcore | 2023-07-04T14:05:42Z | 1 | 0 | null | [
"optimum_graphcore",
"license:apache-2.0",
"region:us"
]
| null | 2023-07-04T13:53:20Z | ---
license: apache-2.0
---
# Graphcore/sentence-t5-base
Optimum Graphcore is a new open-source library and toolkit that enables developers to access IPU-optimized models certified by Hugging Face. It is an extension of Transformers, providing a set of performance optimization tools enabling maximum efficiency to train and run models on Graphcoreโs IPUs - a completely new kind of massively parallel processor to accelerate machine intelligence. Learn more about how to take train Transformer models faster with IPUs at [hf.co/hardware/graphcore](https://huggingface.co/hardware/graphcore).
Through HuggingFace Optimum, Graphcore released ready-to-use IPU-trained model checkpoints and IPU configuration files to make it easy to train models with maximum efficiency in the IPU. Optimum shortens the development lifecycle of your AI models by letting you plug-and-play any public dataset and allows a seamless integration to our State-of-the-art hardware giving you a quicker time-to-value for your AI project.
## Model description
(source: https://huggingface.co/sentence-transformers/sentence-t5-base)
Sentence-t5 is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space. The model works well for sentence similarity tasks, but doesn't perform that well for semantic search tasks.
This model was converted from the Tensorflow model st5-base-1 to PyTorch. When using this model, have a look at the publication: Sentence-T5: Scalable sentence encoders from pre-trained text-to-text models. The tfhub model and this PyTorch model can produce slightly different embeddings, however, when run on the same benchmarks, they produce identical results.
The model uses only the encoder from a T5-base model. The weights are stored in FP16.
## Intended uses & limitations
This model contains just the `IPUConfig` files for running the `sentence-t5-base` model (e.g. [sentence-transformers/sentence-t5-base](https://huggingface.co/sentence-transformers/sentence-t5-base)) on Graphcore IPUs.
**This model contains no model weights, only an IPUConfig.**
## Usage
```
from optimum.graphcore import IPUConfig
from transformers import T5EncoderModel
ipu_config = IPUConfig.from_pretrained("Graphcore/sentence-t5-base")
model = T5EncoderModel.from_pretrained("sentence-transformers/sentence-t5-base")
``` |
idealflaw/ppo-Huggy | idealflaw | 2023-07-04T14:01:19Z | 11 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"Huggy",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
]
| reinforcement-learning | 2023-07-04T14:01:15Z | ---
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 ๐ถ to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: idealflaw/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play ๐
|
lucas-valenzuela-everke/BETO-chile-politico-1990-2019 | lucas-valenzuela-everke | 2023-07-04T13:57:32Z | 112 | 2 | transformers | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"legal",
"es",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| fill-mask | 2023-07-04T04:59:50Z | ---
language:
- es
tags:
- legal
---
This BETO was fine-tuned using 196.063 speeches made by legislators from the Chilean Chamber of Deputies and the Senate.
Only 5 words were added to the tokenizer: pinochet, aylwin, frei, bachelet and piรฑera.
|
maxkskhor/q-FrozenLake-v1-4x4-noSlippery | maxkskhor | 2023-07-04T13:44:59Z | 0 | 0 | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-07-04T13:44:57Z | ---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="maxkskhor/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"])
```
|
apechliva/code-summ_v2 | apechliva | 2023-07-04T13:36:17Z | 1 | 0 | peft | [
"peft",
"region:us"
]
| null | 2023-07-04T13:36:15Z | ---
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
|
Collab-uniba/github-issues-preprocessed-mpnet-st-e10 | Collab-uniba | 2023-07-04T13:28:35Z | 5 | 1 | sentence-transformers | [
"sentence-transformers",
"pytorch",
"mpnet",
"feature-extraction",
"sentence-similarity",
"transformers",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| sentence-similarity | 2023-07-04T13:22:12Z | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# GitHub Issues Preprocessed MPNet Sentence Transformer (10 Epochs)
This is a [sentence-transformers](https://www.SBERT.net) model, specific for GitHub Issue data.
## Dataset
For training, we used the [NLBSE22 dataset](https://nlbse2022.github.io/tools/), after removing issues with empty body and duplicates.
Similarity between title and body was used to train the sentence embedding model.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('Collab-uniba/github-issues-preprocessed-mpnet-st-e10')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('Collab-uniba/github-issues-preprocessed-mpnet-st-e10')
model = AutoModel.from_pretrained('Collab-uniba/github-issues-preprocessed-mpnet-st-e10')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 43709 with parameters:
```
{'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters:
```
{'scale': 20.0, 'similarity_fct': 'cos_sim'}
```
Parameters of the fit()-Method:
```
{
"epochs": 10,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 43709,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: MPNetModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information --> |
hezheop/q-Taxi-v3 | hezheop | 2023-07-04T13:22:30Z | 0 | 0 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-07-04T13:22:29Z | ---
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="hezheop/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"])
```
|
jimregan/BERTreach | jimregan | 2023-07-04T13:18:51Z | 175 | 0 | transformers | [
"transformers",
"pytorch",
"jax",
"safetensors",
"roberta",
"fill-mask",
"irish",
"ga",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| fill-mask | 2022-03-02T23:29:05Z | ---
license: apache-2.0
language: ga
tags:
- irish
---
## BERTreach
([beirtreach](https://www.teanglann.ie/en/fgb/beirtreach) means 'oyster bed')
**Model size:** 84M
**Training data:**
* [PARSEME 1.2](https://gitlab.com/parseme/parseme_corpus_ga/-/blob/master/README.md)
* Newscrawl 300k portion of the [Leipzig Corpora](https://wortschatz.uni-leipzig.de/en/download/irish)
* Private news corpus crawled with [Corpus Crawler](https://github.com/google/corpuscrawler)
(2125804 sentences, 47419062 tokens, as reckoned by wc)
```
from transformers import pipeline
fill_mask = pipeline("fill-mask", model="jimregan/BERTreach", tokenizer="jimregan/BERTreach")
```
|
jimregan/psst-partial-timit | jimregan | 2023-07-04T13:14:23Z | 18 | 0 | transformers | [
"transformers",
"pytorch",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"en",
"dataset:jimregan/psst",
"dataset:timit_asr",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
]
| automatic-speech-recognition | 2022-04-06T08:30:28Z | ---
language:
- en
license: apache-2.0
tags:
- automatic-speech-recognition
datasets:
- jimregan/psst
- timit_asr
---
This repository contains a number of experiments for the [PSST Challenge](https://psst.study/).
As the test set is unavailable, all numbers are based on the validation set.
The experiments in the tables below were finetuned on [Wav2vec 2.0 Base, No finetuning](https://github.com/pytorch/fairseq/tree/main/examples/wav2vec)
Our overall best performing model (**FER** 9\.2%, **PER:** 21\.0%) was based on [Wav2vec 2.0 Large, No finetuning](https://github.com/pytorch/fairseq/tree/main/examples/wav2vec) (git tag: `larger-rir`), with the TIMIT subset augmented with Room Impulse Response, based on the experiments below, on the base model.
## Augmented TIMIT subset
Using a subset of TIMIT that could map easily to the phoneset used by the PSST Challenge data (a list of IDs are in the repository), we experimented with augmenting the data to better match the PSST data.
The best results were obtained using Room Impulse Response (tag: `rir`)
| **Augmentation** | **FER** | **PER** | **Git tag** |
| :----------------------------------------------- | :-------- | :--------- | :---------------------------------- |
| unaugmented | 10\.2% | 22\.5% | huggingface-unaugmented |
| Gaussian noise | 10\.0% | 22\.1% | gaussian |
| Pitchshift | 9\.6% | 22\.9% | pitchshift |
| RIR | **9\.6%** | **21\.8%** | rir |
| Time stretch | 10\.1% | 22\.8% | timestretch |
| Gaussian noise + RIR | 10\.0% | 23\.4% | gaussian-rir |
| Pitchshift + Gaussian noise | 9\.9% | 22\.9% | pitchshift-gaussian |
| Pitchshift + RIR | 9\.9% | 22\.8% | pitchshift-rir |
| Tim estretch + Gaussian noise | 10\.2% | 22\.8% | timestretch-gaussian |
| Time stretch + Pitchshift | 9\.8% | 22\.0% | timestretch-pitchshift |
| Time stretch + RIR | 9\.7% | 22\.2% | timestretch-rir |
| Pitchshift + Gaussian noise + RIR | 10\.1% | 23\.5% | pitchshift-gaussian-rir |
| Time stretch + Gaussian noise + RIR | 9\.7% | 22\.3% | timestretch-gaussian-rir |
| Time stretch + Pitchshift + Gaussian noise | 10\.2% | 22\.9% | timestretch-pitchshift-gaussian |
| Time stretch + Pitchshift + RIR | 10\.2% | 22\.5% | timestretch-pitchshift-rir |
| Time stretch + Pitchshift + Gaussian noise + RIR | 10\.9% | 24\.1% | timestretch-pitchshift-gaussian-rir |
## LM experiments
We experimented with a number of language model configurations, combining the data from the PSST challenge, the subset of TIMIT we used, and CMUdict.
We tried combining CMUdict data in a number of ways: unmodified, with a silence token added at the start of the pronunciation, at the end, and at both the start and the end.
The best result was from a 5-gram model, with silences added at the end of the CMUdict data (git tag: `lm-nosil-cmudict-sile.5`).
Evaluation was performed using scripts provided by the PSST Challenge's organisers, so there are no scripts in place to automatically use the LM with the transformers library.
| | **n-gram** | **FER** | **PER** | **Tag** |
| :----------------------------- | :--------- | :--------- | :--------- | :--------- |
| Baseline + TIMIT | --- | **10\.2%** | 22\.5% | huggingface-unaugmented |
| All silences | 4 | 10\.5% | 23\.0% | lm-allsil.4 |
| | 5 | 10\.5% | 22\.6% | lm-allsil.5 |
| | 6 | 10\.3% | 22\.3% | lm-allsil.6 |
| No silences | 4 | 10\.3% | 22\.6% | lm-nosil.4 |
| | 5 | **10\.2%** | 22\.2% | lm-nosil.5 |
| | 6 | **10\.2%** | 22\.4% | lm-nosil.6 |
| PSST and TIMIT without silence | | | | |
| Unmodified CMUdict | 4 | 10\.3% | 22\.6% | lm-nosil-cmudict-nosil.4 |
| | 5 | 10\.2% | 22\.2% | lm-nosil-cmudict-nosil.5 |
| | 6 | **10\.2%** | 22\.4% | lm-nosil-cmudict-nosil.6 |
| CMUdict-end | 4 | 10\.3% | 22\.6% | lm-nosil-cmudict-sile.4 |
| | 5 | **10\.2%** | **22\.1%** | lm-nosil-cmudict-sile.5 |
| | 6 | **10\.2%** | 22\.3% | lm-nosil-cmudict-sile.6 |
| CMUdict-start | 4 | 10\.4% | 22\.6% | lm-nosil-cmudict-sils.4 |
| | 5 | 10\.3% | 22\.4% | lm-nosil-cmudict-sils.5 |
| | 6 | 10\.3% | 22\.3% | lm-nosil-cmudict-sils.6 |
| CMUdict-both | 4 | 10\.4% | 22\.7% | lm-nosil-cmudict-silb.4 |
| | 5 | 10\.4% | 22\.3% | lm-nosil-cmudict-silb.5 |
| | 6 | 10\.3% | 22\.3% | lm-nosil-cmudict-silb.6 |
| Unmodified PSST and TIMIT | | | | |
| Unmodified CMUdict | 4 | 10\.3% | 22\.8% | lm-orig-cmudict-nosil.4 |
| | 5 | 10\.3% | 22\.4% | lm-orig-cmudict-nosil.5 |
| | 6 | **10\.2%** | 22\.4% | lm-orig-cmudict-nosil.6 |
| CMUdict-end | 4 | 10\.3% | 22\.7% | lm-orig-cmudict-sile.4 |
| | 5 | **10\.2%** | 22\.2% | lm-orig-cmudict-sile.5 |
| | 6 | **10\.2%** | 22\.3% | lm-orig-cmudict-sile.6 |
| CMUdict-start | 4 | 10\.5% | 22\.8% | lm-orig-cmudict-sils.4 |
| | 5 | 10\.4% | 22\.5% | lm-orig-cmudict-sils.5 |
| | 6 | 10\.3% | 22\.4% | lm-orig-cmudict-sils.6 |
| CMUdict-both | 4 | 10\.5% | 22\.8% | lm-orig-cmudict-silb.4 |
| | 5 | 10\.4% | 22\.4% | lm-orig-cmudict-silb.5 |
| | 6 | 10\.4% | 22\.4% | lm-orig-cmudict-silb.6 |
|
pratikg123/finetunned_falcon-7b | pratikg123 | 2023-07-04T13:10:35Z | 1 | 0 | peft | [
"peft",
"region:us"
]
| null | 2023-07-04T12:45:50Z | ---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float16
### Framework versions
- PEFT 0.4.0.dev0
|
tanmayyyj/dqn-SpaceInvadersNoFrameskip-v4 | tanmayyyj | 2023-07-04T13:09:53Z | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-07-04T13:09:15Z | ---
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: 627.00 +/- 271.64
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 tanmayyyj -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 tanmayyyj -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 tanmayyyj
```
## 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'}
```
|
PraveenJesu/openai-whisper-medium-zrx-peft-lora-v2.2.1 | PraveenJesu | 2023-07-04T13:01:53Z | 0 | 0 | peft | [
"peft",
"region:us"
]
| null | 2023-07-04T13:01:51Z | ---
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
|
dcarpintero/ppo-SnowballTarget | dcarpintero | 2023-07-04T13:01:15Z | 2 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"SnowballTarget",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SnowballTarget",
"region:us"
]
| reinforcement-learning | 2023-07-04T13:01:12Z | ---
library_name: ml-agents
tags:
- SnowballTarget
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SnowballTarget
---
# **ppo** Agent playing **SnowballTarget**
This is a trained model of a **ppo** agent playing **SnowballTarget**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog ๐ถ to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: dcarpintero/ppo-SnowballTarget
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play ๐
|
velascoluis/falcon7b-instruct-database-ft-5-epochs | velascoluis | 2023-07-04T12:48:16Z | 0 | 0 | null | [
"generated_from_trainer",
"license:apache-2.0",
"region:us"
]
| null | 2023-07-04T12:48:03Z | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: falcon7b-instruct-database-ft-5-epochs
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. -->
# falcon7b-instruct-database-ft-5-epochs
This model is a fine-tuned version of [tiiuae/falcon-7b-instruct](https://huggingface.co/tiiuae/falcon-7b-instruct) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2279
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu117
- Datasets 2.13.1
- Tokenizers 0.13.3
|
Arindam75/ppo-Pyramids | Arindam75 | 2023-07-04T12:40:57Z | 25 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"Pyramids",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
]
| reinforcement-learning | 2023-07-04T08:03:09Z | ---
library_name: ml-agents
tags:
- Pyramids
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Pyramids
---
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog ๐ถ to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: Arindam75/ppo-Pyramids
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play ๐
|
ahmedALM1221/convnextv2-tiny-1k-224-finetuned-eurosat-50 | ahmedALM1221 | 2023-07-04T12:40:43Z | 190 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"convnextv2",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| image-classification | 2023-07-04T11:41:59Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: convnextv2-tiny-1k-224-finetuned-eurosat-50
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: Skin_Dataset
split: train
args: Skin_Dataset
metrics:
- name: Accuracy
type: accuracy
value: 0.7762711864406779
---
<!-- 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. -->
# convnextv2-tiny-1k-224-finetuned-eurosat-50
This model is a fine-tuned version of [facebook/convnextv2-tiny-1k-224](https://huggingface.co/facebook/convnextv2-tiny-1k-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2472
- Accuracy: 0.7763
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.9434 | 0.97 | 18 | 1.8549 | 0.2847 |
| 1.7722 | 2.0 | 37 | 1.6757 | 0.3661 |
| 1.5502 | 2.97 | 55 | 1.4652 | 0.4339 |
| 1.2595 | 4.0 | 74 | 1.1916 | 0.6068 |
| 0.9304 | 4.97 | 92 | 1.0282 | 0.6576 |
| 0.7333 | 6.0 | 111 | 0.8574 | 0.7051 |
| 0.6015 | 6.97 | 129 | 0.8427 | 0.6983 |
| 0.4617 | 8.0 | 148 | 0.7682 | 0.7458 |
| 0.3162 | 8.97 | 166 | 0.7453 | 0.7559 |
| 0.2249 | 10.0 | 185 | 0.7475 | 0.7661 |
| 0.1667 | 10.97 | 203 | 0.7677 | 0.7492 |
| 0.091 | 12.0 | 222 | 1.0114 | 0.7220 |
| 0.0783 | 12.97 | 240 | 1.0206 | 0.7186 |
| 0.0613 | 14.0 | 259 | 0.8466 | 0.7492 |
| 0.0703 | 14.97 | 277 | 1.1067 | 0.7119 |
| 0.0335 | 16.0 | 296 | 1.0117 | 0.7390 |
| 0.0171 | 16.97 | 314 | 0.9367 | 0.7525 |
| 0.0253 | 18.0 | 333 | 1.3196 | 0.7153 |
| 0.0201 | 18.97 | 351 | 1.0530 | 0.7525 |
| 0.0041 | 20.0 | 370 | 1.0523 | 0.7729 |
| 0.0154 | 20.97 | 388 | 1.1311 | 0.7661 |
| 0.0025 | 22.0 | 407 | 1.1477 | 0.7729 |
| 0.0036 | 22.97 | 425 | 1.1309 | 0.7627 |
| 0.002 | 24.0 | 444 | 1.1399 | 0.7729 |
| 0.0014 | 24.97 | 462 | 1.1543 | 0.7797 |
| 0.0011 | 26.0 | 481 | 1.1799 | 0.7763 |
| 0.0011 | 26.97 | 499 | 1.1579 | 0.7661 |
| 0.0009 | 28.0 | 518 | 1.1907 | 0.7627 |
| 0.0009 | 28.97 | 536 | 1.1878 | 0.7661 |
| 0.0008 | 30.0 | 555 | 1.1986 | 0.7661 |
| 0.0008 | 30.97 | 573 | 1.2051 | 0.7661 |
| 0.0007 | 32.0 | 592 | 1.2073 | 0.7661 |
| 0.0007 | 32.97 | 610 | 1.2156 | 0.7661 |
| 0.0007 | 34.0 | 629 | 1.2218 | 0.7627 |
| 0.0007 | 34.97 | 647 | 1.2173 | 0.7661 |
| 0.0006 | 36.0 | 666 | 1.2217 | 0.7729 |
| 0.0006 | 36.97 | 684 | 1.2272 | 0.7695 |
| 0.0006 | 38.0 | 703 | 1.2261 | 0.7763 |
| 0.0006 | 38.97 | 721 | 1.2305 | 0.7763 |
| 0.0006 | 40.0 | 740 | 1.2325 | 0.7763 |
| 0.0005 | 40.97 | 758 | 1.2362 | 0.7763 |
| 0.0005 | 42.0 | 777 | 1.2409 | 0.7763 |
| 0.0005 | 42.97 | 795 | 1.2422 | 0.7763 |
| 0.0005 | 44.0 | 814 | 1.2429 | 0.7729 |
| 0.0005 | 44.97 | 832 | 1.2434 | 0.7763 |
| 0.0005 | 46.0 | 851 | 1.2458 | 0.7763 |
| 0.0005 | 46.97 | 869 | 1.2468 | 0.7763 |
| 0.0005 | 48.0 | 888 | 1.2471 | 0.7763 |
| 0.0005 | 48.65 | 900 | 1.2472 | 0.7763 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
AlpacaSundae/full_totakeke | AlpacaSundae | 2023-07-04T12:35:10Z | 0 | 0 | null | [
"license:openrail",
"region:us"
]
| null | 2023-07-04T12:11:15Z | ---
license: openrail
---
Trained using all 73 songs from acww.
(overkill but seems to work better than when I hand selected 5 songs,
maybe the next model will just be scales etc of the sound bank instead)
Muted the instrument tracks in sf2 and converted to wav in python, but
I left the whistling in as I thought it would be ok but it get's weird
with silences so maybe it will be remade without whistling one day.
I typically just use mangio-crepe set to 64 hop length and I set the pitch down an octave.
For some songs I'll generate two octaves to get clarity in higher and lower parts of the song.
It seems that too high/low pitch in the often lets words slip through too much. Usually need to
cut bits where the input was silence after generation as well due to weird artefacts mentioned.
idk what im doing |
ccattomio/q-Taxi-v3 | ccattomio | 2023-07-04T12:25:37Z | 0 | 0 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-07-04T12:04:19Z | ---
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 playing **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="ccattomio/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"])
```
|
ccattomio/q-FrozenLake-v1-4x4-noSlippery | ccattomio | 2023-07-04T12:25:16Z | 0 | 0 | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-07-04T09:57:07Z | ---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="ccattomio/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"])
```
|
juancopi81/lmd-8bars-2048-epochs10 | juancopi81 | 2023-07-04T12:23:11Z | 127 | 0 | transformers | [
"transformers",
"pytorch",
"safetensors",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2023-07-01T23:26:04Z | ---
license: mit
tags:
- generated_from_trainer
model-index:
- name: lmd-8bars-2048-epochs10
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. -->
# lmd-8bars-2048-epochs10
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0086
## 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: 8
- eval_batch_size: 4
- seed: 1
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.01
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 2.4182 | 0.5 | 4994 | 1.4933 |
| 1.4626 | 1.0 | 9988 | 1.3082 |
| 1.3176 | 1.5 | 14982 | 1.2276 |
| 1.2604 | 2.0 | 19976 | 1.1815 |
| 1.2101 | 2.5 | 24970 | 1.1499 |
| 1.1804 | 3.0 | 29964 | 1.1260 |
| 1.1517 | 3.5 | 34958 | 1.1043 |
| 1.1349 | 4.0 | 39952 | 1.0887 |
| 1.1133 | 4.5 | 44946 | 1.0762 |
| 1.0995 | 5.0 | 49940 | 1.0618 |
| 1.0824 | 5.5 | 54934 | 1.0507 |
| 1.0713 | 6.0 | 59928 | 1.0423 |
| 1.0552 | 6.5 | 64922 | 1.0328 |
| 1.0505 | 7.0 | 69916 | 1.0279 |
| 1.0365 | 7.5 | 74910 | 1.0217 |
| 1.0307 | 8.0 | 79904 | 1.0153 |
| 1.022 | 8.5 | 84898 | 1.0107 |
| 1.0189 | 9.0 | 89892 | 1.0090 |
| 1.0129 | 9.5 | 94886 | 1.0084 |
| 1.0139 | 10.0 | 99880 | 1.0086 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
maxkskhor/ppo-Huggy | maxkskhor | 2023-07-04T12:20:09Z | 0 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"Huggy",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
]
| reinforcement-learning | 2023-07-04T12:20:04Z | ---
library_name: ml-agents
tags:
- Huggy
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Huggy
---
# **ppo** Agent playing **Huggy**
This is a trained model of a **ppo** agent playing **Huggy**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog ๐ถ to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: maxkskhor/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play ๐
|
BadreddineHug/donut-base-ocr3 | BadreddineHug | 2023-07-04T12:09:53Z | 72 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"vision-encoder-decoder",
"image-text-to-text",
"generated_from_trainer",
"dataset:imagefolder",
"license:mit",
"endpoints_compatible",
"region:us"
]
| image-text-to-text | 2023-07-04T11:22:07Z | ---
license: mit
tags:
- generated_from_trainer
datasets:
- imagefolder
model-index:
- name: donut-base-ocr3
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. -->
# donut-base-ocr3
This model is a fine-tuned version of [naver-clova-ix/donut-base-finetuned-cord-v2](https://huggingface.co/naver-clova-ix/donut-base-finetuned-cord-v2) on the imagefolder dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.002
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
ddoc/adt | ddoc | 2023-07-04T12:02:45Z | 0 | 1 | null | [
"region:us"
]
| null | 2023-07-04T12:02:27Z | # !After Detailer
!After Detailer is a extension for stable diffusion webui, similar to Detection Detailer, except it uses ultralytics instead of the mmdet.
## Install
(from Mikubill/sd-webui-controlnet)
1. Open "Extensions" tab.
2. Open "Install from URL" tab in the tab.
3. Enter `https://github.com/Bing-su/adetailer.git` to "URL for extension's git repository".
4. Press "Install" button.
5. Wait 5 seconds, and you will see the message "Installed into stable-diffusion-webui\extensions\adetailer. Use Installed tab to restart".
6. Go to "Installed" tab, click "Check for updates", and then click "Apply and restart UI". (The next time you can also use this method to update extensions.)
7. Completely restart A1111 webui including your terminal. (If you do not know what is a "terminal", you can reboot your computer: turn your computer off and turn it on again.)
You can now install it directly from the Extensions tab.
You **DON'T** need to download any model from huggingface.
## Options
| Model, Prompts | | |
| --------------------------------- | ------------------------------------- | ------------------------------------------------- |
| ADetailer model | Determine what to detect. | `None`ย = disable |
| ADetailer prompt,ย negative prompt | Prompts and negative prompts to apply | If left blank, it will use the same as the input. |
| Detection | | |
| ------------------------------------ | -------------------------------------------------------------------------------------------- | --- |
| Detection model confidence threshold | Only objects with a detection model confidence above this threshold are used for inpainting. | |
| Mask min/max ratio | Only use masks whose area is between those ratios for the area of the entire image. | |
If you want to exclude objects in the background, try setting the min ratio to around `0.01`.
| Mask Preprocessing | | |
| ------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------- |
| Mask x, y offset | Moves the mask horizontally and vertically by | |
| Mask erosion (-) / dilation (+) | Enlarge or reduce the detected mask. | [opencv example](https://docs.opencv.org/4.7.0/db/df6/tutorial_erosion_dilatation.html) |
| Mask merge mode | `None`: Inpaint each mask<br/>`Merge`: Merge all masks and inpaint<br/>`Merge and Invert`: Merge all masks and Invert, then inpaint | |
Applied in this order: x, y offset โ erosion/dilation โ merge/invert.
#### Inpainting
Each option corresponds to a corresponding option on the inpaint tab.
## ControlNet Inpainting
You can use the ControlNet extension if you have ControlNet installed and ControlNet models.
Support `inpaint, scribble, lineart, openpose, tile` controlnet models. Once you choose a model, the preprocessor is set automatically.
## Model
| Model | Target | mAP 50 | mAP 50-95 |
| --------------------- | --------------------- | ----------------------------- | ----------------------------- |
| face_yolov8n.pt | 2D / realistic face | 0.660 | 0.366 |
| face_yolov8s.pt | 2D / realistic face | 0.713 | 0.404 |
| hand_yolov8n.pt | 2D / realistic hand | 0.767 | 0.505 |
| person_yolov8n-seg.pt | 2D / realistic person | 0.782 (bbox)<br/>0.761 (mask) | 0.555 (bbox)<br/>0.460 (mask) |
| person_yolov8s-seg.pt | 2D / realistic person | 0.824 (bbox)<br/>0.809 (mask) | 0.605 (bbox)<br/>0.508 (mask) |
| mediapipe_face_full | realistic face | - | - |
| mediapipe_face_short | realistic face | - | - |
| mediapipe_face_mesh | realistic face | - | - |
The yolo models can be found on huggingface [Bingsu/adetailer](https://huggingface.co/Bingsu/adetailer).
### User Model
Put your [ultralytics](https://github.com/ultralytics/ultralytics) model in `webui/models/adetailer`. The model name should end with `.pt` or `.pth`.
It must be a bbox detection or segment model and use all label.
### Dataset
Datasets used for training the yolo models are:
#### Face
- [Anime Face CreateML](https://universe.roboflow.com/my-workspace-mph8o/anime-face-createml)
- [xml2txt](https://universe.roboflow.com/0oooooo0/xml2txt-njqx1)
- [AN](https://universe.roboflow.com/sed-b8vkf/an-lfg5i)
- [wider face](http://shuoyang1213.me/WIDERFACE/index.html)
#### Hand
- [AnHDet](https://universe.roboflow.com/1-yshhi/anhdet)
- [hand-detection-fuao9](https://universe.roboflow.com/catwithawand/hand-detection-fuao9)
#### Person
- [coco2017](https://cocodataset.org/#home) (only person)
- [AniSeg](https://github.com/jerryli27/AniSeg)
- [skytnt/anime-segmentation](https://huggingface.co/datasets/skytnt/anime-segmentation)
## Example
[](https://ko-fi.com/F1F1L7V2N)
|
HilbertS/ppo-CartPole-v1 | HilbertS | 2023-07-04T11:56:04Z | 0 | 0 | null | [
"tensorboard",
"CartPole-v1",
"ppo",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"deep-rl-course",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-07-04T11:55:56Z | ---
tags:
- CartPole-v1
- ppo
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
- deep-rl-course
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 179.30 +/- 76.48
name: mean_reward
verified: false
---
# PPO Agent Playing CartPole-v1
This is a trained model of a PPO agent playing CartPole-v1.
# Hyperparameters
```python
{'exp_name': 'first-run'
'seed': 1
'torch_deterministic': True
'cuda': True
'track': False
'wandb_project_name': 'cleanRL'
'wandb_entity': None
'capture_video': False
'env_id': 'CartPole-v1'
'total_timesteps': 50000
'learning_rate': 0.00025
'num_envs': 4
'num_steps': 128
'anneal_lr': True
'gae': True
'gamma': 0.99
'gae_lambda': 0.95
'num_minibatches': 4
'update_epochs': 4
'norm_adv': True
'clip_coef': 0.2
'clip_vloss': True
'ent_coef': 0.01
'vf_coef': 0.5
'max_grad_norm': 0.5
'target_kl': None
'f': '/root/.local/share/jupyter/runtime/kernel-10ad5965-bc3b-4029-b8a5-74b58d83db89.json'
'repo_id': 'HilbertS/ppo-CartPole-v1'
'batch_size': 512
'minibatch_size': 128}
```
|
fatcat22/rl_course_vizdoom_health_gathering_supreme | fatcat22 | 2023-07-04T11:52:55Z | 0 | 0 | sample-factory | [
"sample-factory",
"tensorboard",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-07-04T11:52:52Z | ---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: doom_health_gathering_supreme
type: doom_health_gathering_supreme
metrics:
- type: mean_reward
value: 7.46 +/- 2.25
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **doom_health_gathering_supreme** environment.
This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory.
Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/
## Downloading the model
After installing Sample-Factory, download the model with:
```
python -m sample_factory.huggingface.load_from_hub -r fatcat22/rl_course_vizdoom_health_gathering_supreme
```
## Using the model
To run the model after download, use the `enjoy` script corresponding to this environment:
```
python -m <path.to.enjoy.module> --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme
```
You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag.
See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details
## Training with this model
To continue training with this model, use the `train` script corresponding to this environment:
```
python -m <path.to.train.module> --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme --restart_behavior=resume --train_for_env_steps=10000000000
```
Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
|
cv43/distilbert-base-uncased-finetuned-squad | cv43 | 2023-07-04T11:51:02Z | 133 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:squad_v2",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
]
| question-answering | 2023-07-03T12:52:57Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad_v2
model-index:
- name: distilbert-base-uncased-finetuned-squad
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-squad
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad_v2 dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5644
## 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
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 190 | 2.0763 |
| No log | 2.0 | 380 | 1.6763 |
| 2.3144 | 3.0 | 570 | 1.5644 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu117
- Datasets 2.13.1
- Tokenizers 0.13.3
|
LarryAIDraw/CHAR-Kord | LarryAIDraw | 2023-07-04T11:47:18Z | 0 | 0 | null | [
"license:creativeml-openrail-m",
"region:us"
]
| null | 2023-07-04T11:32:25Z | ---
license: creativeml-openrail-m
---
https://civitai.com/models/100517/kord-or-girls-frontline |
Bilgilice/bilgilice35 | Bilgilice | 2023-07-04T11:46:09Z | 0 | 0 | null | [
"arxiv:1703.10135",
"arxiv:1712.05884",
"arxiv:2005.11129",
"arxiv:2008.03802",
"arxiv:2003.01950",
"arxiv:2006.06873",
"arxiv:1905.09263",
"arxiv:2006.04558",
"arxiv:2104.05557",
"arxiv:1906.03402",
"arxiv:2211.06892",
"arxiv:2108.13320",
"arxiv:2106.06103",
"arxiv:2112.02418",
"arxiv:1710.08969",
"arxiv:1907.09006",
"arxiv:1910.10288",
"arxiv:2108.10447",
"arxiv:1710.10467",
"arxiv:2003.11982",
"arxiv:1910.06711",
"arxiv:2005.05106",
"arxiv:1910.11480",
"arxiv:1909.11646",
"arxiv:2009.00713",
"arxiv:2010.05646",
"arxiv:2106.07889",
"arxiv:2210.15418",
"region:us"
]
| null | 2023-07-04T11:44:42Z |
## ๐ธCoqui.ai News
- ๐ฃ [๐ถBark](https://github.com/suno-ai/bark) is now available for inference with uncontrained voice cloning. [Docs](https://tts.readthedocs.io/en/dev/models/bark.html)
- ๐ฃ You can use [~1100 Fairseq models](https://github.com/facebookresearch/fairseq/tree/main/examples/mms) with ๐ธTTS.
- ๐ฃ ๐ธTTS now supports ๐ขTortoise with faster inference. [Docs](https://tts.readthedocs.io/en/dev/models/tortoise.html)
- ๐ฃ **Coqui Studio API** is landed on ๐ธTTS. - [Example](https://github.com/coqui-ai/TTS/blob/dev/README.md#-python-api)
- ๐ฃ [**Coqui Studio API**](https://docs.coqui.ai/docs) is live.
- ๐ฃ Voice generation with prompts - **Prompt to Voice** - is live on [**Coqui Studio**](https://app.coqui.ai/auth/signin)!! - [Blog Post](https://coqui.ai/blog/tts/prompt-to-voice)
- ๐ฃ Voice generation with fusion - **Voice fusion** - is live on [**Coqui Studio**](https://app.coqui.ai/auth/signin).
- ๐ฃ Voice cloning is live on [**Coqui Studio**](https://app.coqui.ai/auth/signin).
## <img src="https://raw.githubusercontent.com/coqui-ai/TTS/main/images/coqui-log-green-TTS.png" height="56"/>
๐ธTTS is a library for advanced Text-to-Speech generation. It's built on the latest research, was designed to achieve the best trade-off among ease-of-training, speed and quality.
๐ธTTS comes with pretrained models, tools for measuring dataset quality and already used in **20+ languages** for products and research projects.
[](https://discord.gg/5eXr5seRrv)
[](https://opensource.org/licenses/MPL-2.0)
[](https://badge.fury.io/py/TTS)
[](https://github.com/coqui-ai/TTS/blob/master/CODE_OF_CONDUCT.md)
[](https://pepy.tech/project/tts)
[](https://zenodo.org/badge/latestdoi/265612440)
[](https://tts.readthedocs.io/en/latest/)
๐ฐ [**Subscribe to ๐ธCoqui.ai Newsletter**](https://coqui.ai/?subscription=true)
๐ข [English Voice Samples](https://erogol.github.io/ddc-samples/) and [SoundCloud playlist](https://soundcloud.com/user-565970875/pocket-article-wavernn-and-tacotron2)
๐ [Text-to-Speech paper collection](https://github.com/erogol/TTS-papers)
<img src="https://static.scarf.sh/a.png?x-pxid=cf317fe7-2188-4721-bc01-124bb5d5dbb2" />
## ๐ฌ Where to ask questions
Please use our dedicated channels for questions and discussion. Help is much more valuable if it's shared publicly so that more people can benefit from it.
| Type | Platforms |
| ------------------------------- | --------------------------------------- |
| ๐จ **Bug Reports** | [GitHub Issue Tracker] |
| ๐ **Feature Requests & Ideas** | [GitHub Issue Tracker] |
| ๐ฉโ๐ป **Usage Questions** | [GitHub Discussions] |
| ๐ฏ **General Discussion** | [GitHub Discussions] or [Discord] |
[github issue tracker]: https://github.com/coqui-ai/tts/issues
[github discussions]: https://github.com/coqui-ai/TTS/discussions
[discord]: https://discord.gg/5eXr5seRrv
[Tutorials and Examples]: https://github.com/coqui-ai/TTS/wiki/TTS-Notebooks-and-Tutorials
## ๐ Links and Resources
| Type | Links |
| ------------------------------- | --------------------------------------- |
| ๐ผ **Documentation** | [ReadTheDocs](https://tts.readthedocs.io/en/latest/)
| ๐พ **Installation** | [TTS/README.md](https://github.com/coqui-ai/TTS/tree/dev#install-tts)|
| ๐ฉโ๐ป **Contributing** | [CONTRIBUTING.md](https://github.com/coqui-ai/TTS/blob/main/CONTRIBUTING.md)|
| ๐ **Road Map** | [Main Development Plans](https://github.com/coqui-ai/TTS/issues/378)
| ๐ **Released Models** | [TTS Releases](https://github.com/coqui-ai/TTS/releases) and [Experimental Models](https://github.com/coqui-ai/TTS/wiki/Experimental-Released-Models)|
## ๐ฅ TTS Performance
<p align="center"><img src="https://raw.githubusercontent.com/coqui-ai/TTS/main/images/TTS-performance.png" width="800" /></p>
Underlined "TTS*" and "Judy*" are **internal** ๐ธTTS models that are not released open-source. They are here to show the potential.
## Features
- High-performance Deep Learning models for Text2Speech tasks.
- Text2Spec models (Tacotron, Tacotron2, Glow-TTS, SpeedySpeech).
- Speaker Encoder to compute speaker embeddings efficiently.
- Vocoder models (MelGAN, Multiband-MelGAN, GAN-TTS, ParallelWaveGAN, WaveGrad, WaveRNN)
- Fast and efficient model training.
- Detailed training logs on the terminal and Tensorboard.
- Support for Multi-speaker TTS.
- Efficient, flexible, lightweight but feature complete `Trainer API`.
- Released and ready-to-use models.
- Tools to curate Text2Speech datasets under```dataset_analysis```.
- Utilities to use and test your models.
- Modular (but not too much) code base enabling easy implementation of new ideas.
## Implemented Models
### Spectrogram models
- Tacotron: [paper](https://arxiv.org/abs/1703.10135)
- Tacotron2: [paper](https://arxiv.org/abs/1712.05884)
- Glow-TTS: [paper](https://arxiv.org/abs/2005.11129)
- Speedy-Speech: [paper](https://arxiv.org/abs/2008.03802)
- Align-TTS: [paper](https://arxiv.org/abs/2003.01950)
- FastPitch: [paper](https://arxiv.org/pdf/2006.06873.pdf)
- FastSpeech: [paper](https://arxiv.org/abs/1905.09263)
- FastSpeech2: [paper](https://arxiv.org/abs/2006.04558)
- SC-GlowTTS: [paper](https://arxiv.org/abs/2104.05557)
- Capacitron: [paper](https://arxiv.org/abs/1906.03402)
- OverFlow: [paper](https://arxiv.org/abs/2211.06892)
- Neural HMM TTS: [paper](https://arxiv.org/abs/2108.13320)
### End-to-End Models
- VITS: [paper](https://arxiv.org/pdf/2106.06103)
- ๐ธ YourTTS: [paper](https://arxiv.org/abs/2112.02418)
- ๐ข Tortoise: [orig. repo](https://github.com/neonbjb/tortoise-tts)
- ๐ถ Bark: [orig. repo](https://github.com/suno-ai/bark)
### Attention Methods
- Guided Attention: [paper](https://arxiv.org/abs/1710.08969)
- Forward Backward Decoding: [paper](https://arxiv.org/abs/1907.09006)
- Graves Attention: [paper](https://arxiv.org/abs/1910.10288)
- Double Decoder Consistency: [blog](https://erogol.com/solving-attention-problems-of-tts-models-with-double-decoder-consistency/)
- Dynamic Convolutional Attention: [paper](https://arxiv.org/pdf/1910.10288.pdf)
- Alignment Network: [paper](https://arxiv.org/abs/2108.10447)
### Speaker Encoder
- GE2E: [paper](https://arxiv.org/abs/1710.10467)
- Angular Loss: [paper](https://arxiv.org/pdf/2003.11982.pdf)
### Vocoders
- MelGAN: [paper](https://arxiv.org/abs/1910.06711)
- MultiBandMelGAN: [paper](https://arxiv.org/abs/2005.05106)
- ParallelWaveGAN: [paper](https://arxiv.org/abs/1910.11480)
- GAN-TTS discriminators: [paper](https://arxiv.org/abs/1909.11646)
- WaveRNN: [origin](https://github.com/fatchord/WaveRNN/)
- WaveGrad: [paper](https://arxiv.org/abs/2009.00713)
- HiFiGAN: [paper](https://arxiv.org/abs/2010.05646)
- UnivNet: [paper](https://arxiv.org/abs/2106.07889)
### Voice Conversion
- FreeVC: [paper](https://arxiv.org/abs/2210.15418)
You can also help us implement more models.
## Install TTS
๐ธTTS is tested on Ubuntu 18.04 with **python >= 3.7, < 3.11.**.
If you are only interested in [synthesizing speech](https://tts.readthedocs.io/en/latest/inference.html) with the released ๐ธTTS models, installing from PyPI is the easiest option.
```bash
pip install TTS
```
If you plan to code or train models, clone ๐ธTTS and install it locally.
```bash
git clone https://github.com/coqui-ai/TTS
pip install -e .[all,dev,notebooks] # Select the relevant extras
```
If you are on Ubuntu (Debian), you can also run following commands for installation.
```bash
$ make system-deps # intended to be used on Ubuntu (Debian). Let us know if you have a different OS.
$ make install
```
If you are on Windows, ๐@GuyPaddock wrote installation instructions [here](https://stackoverflow.com/questions/66726331/how-can-i-run-mozilla-tts-coqui-tts-training-with-cuda-on-a-windows-system).
## Docker Image
You can also try TTS without install with the docker image.
Simply run the following command and you will be able to run TTS without installing it.
```bash
docker run --rm -it -p 5002:5002 --entrypoint /bin/bash ghcr.io/coqui-ai/tts-cpu
python3 TTS/server/server.py --list_models #To get the list of available models
python3 TTS/server/server.py --model_name tts_models/en/vctk/vits # To start a server
```
You can then enjoy the TTS server [here](http://[::1]:5002/)
More details about the docker images (like GPU support) can be found [here](https://tts.readthedocs.io/en/latest/docker_images.html)
## Synthesizing speech by ๐ธTTS
### ๐ Python API
```python
from TTS.api import TTS
# Running a multi-speaker and multi-lingual model
# List available ๐ธTTS models and choose the first one
model_name = TTS.list_models()[0]
# Init TTS
tts = TTS(model_name)
# Run TTS
# โ Since this model is multi-speaker and multi-lingual, we must set the target speaker and the language
# Text to speech with a numpy output
wav = tts.tts("This is a test! This is also a test!!", speaker=tts.speakers[0], language=tts.languages[0])
# Text to speech to a file
tts.tts_to_file(text="Hello world!", speaker=tts.speakers[0], language=tts.languages[0], file_path="output.wav")
# Running a single speaker model
# Init TTS with the target model name
tts = TTS(model_name="tts_models/de/thorsten/tacotron2-DDC", progress_bar=False, gpu=False)
# Run TTS
tts.tts_to_file(text="Ich bin eine Testnachricht.", file_path=OUTPUT_PATH)
# Example voice cloning with YourTTS in English, French and Portuguese
tts = TTS(model_name="tts_models/multilingual/multi-dataset/your_tts", progress_bar=False, gpu=True)
tts.tts_to_file("This is voice cloning.", speaker_wav="my/cloning/audio.wav", language="en", file_path="output.wav")
tts.tts_to_file("C'est le clonage de la voix.", speaker_wav="my/cloning/audio.wav", language="fr-fr", file_path="output.wav")
tts.tts_to_file("Isso รฉ clonagem de voz.", speaker_wav="my/cloning/audio.wav", language="pt-br", file_path="output.wav")
# Example voice conversion converting speaker of the `source_wav` to the speaker of the `target_wav`
tts = TTS(model_name="voice_conversion_models/multilingual/vctk/freevc24", progress_bar=False, gpu=True)
tts.voice_conversion_to_file(source_wav="my/source.wav", target_wav="my/target.wav", file_path="output.wav")
# Example voice cloning by a single speaker TTS model combining with the voice conversion model. This way, you can
# clone voices by using any model in ๐ธTTS.
tts = TTS("tts_models/de/thorsten/tacotron2-DDC")
tts.tts_with_vc_to_file(
"Wie sage ich auf Italienisch, dass ich dich liebe?",
speaker_wav="target/speaker.wav",
file_path="output.wav"
)
# Example text to speech using [๐ธCoqui Studio](https://coqui.ai) models.
# You can use all of your available speakers in the studio.
# [๐ธCoqui Studio](https://coqui.ai) API token is required. You can get it from the [account page](https://coqui.ai/account).
# You should set the `COQUI_STUDIO_TOKEN` environment variable to use the API token.
# If you have a valid API token set you will see the studio speakers as separate models in the list.
# The name format is coqui_studio/en/<studio_speaker_name>/coqui_studio
models = TTS().list_models()
# Init TTS with the target studio speaker
tts = TTS(model_name="coqui_studio/en/Torcull Diarmuid/coqui_studio", progress_bar=False, gpu=False)
# Run TTS
tts.tts_to_file(text="This is a test.", file_path=OUTPUT_PATH)
# Run TTS with emotion and speed control
tts.tts_to_file(text="This is a test.", file_path=OUTPUT_PATH, emotion="Happy", speed=1.5)
#Example text to speech using **Fairseq models in ~1100 languages** ๐คฏ.
#For these models use the following name format: `tts_models/<lang-iso_code>/fairseq/vits`.
#You can find the list of language ISO codes [here](https://dl.fbaipublicfiles.com/mms/tts/all-tts-languages.html) and learn about the Fairseq models [here](https://github.com/facebookresearch/fairseq/tree/main/examples/mms).
# TTS with on the fly voice conversion
api = TTS("tts_models/deu/fairseq/vits")
api.tts_with_vc_to_file(
"Wie sage ich auf Italienisch, dass ich dich liebe?",
speaker_wav="target/speaker.wav",
file_path="output.wav"
)
```
### Command line `tts`
#### Single Speaker Models
- List provided models:
```
$ tts --list_models
```
- Get model info (for both tts_models and vocoder_models):
- Query by type/name:
The model_info_by_name uses the name as it from the --list_models.
```
$ tts --model_info_by_name "<model_type>/<language>/<dataset>/<model_name>"
```
For example:
```
$ tts --model_info_by_name tts_models/tr/common-voice/glow-tts
```
```
$ tts --model_info_by_name vocoder_models/en/ljspeech/hifigan_v2
```
- Query by type/idx:
The model_query_idx uses the corresponding idx from --list_models.
```
$ tts --model_info_by_idx "<model_type>/<model_query_idx>"
```
For example:
```
$ tts --model_info_by_idx tts_models/3
```
- Run TTS with default models:
```
$ tts --text "Text for TTS" --out_path output/path/speech.wav
```
- Run a TTS model with its default vocoder model:
```
$ tts --text "Text for TTS" --model_name "<model_type>/<language>/<dataset>/<model_name>" --out_path output/path/speech.wav
```
For example:
```
$ tts --text "Text for TTS" --model_name "tts_models/en/ljspeech/glow-tts" --out_path output/path/speech.wav
```
- Run with specific TTS and vocoder models from the list:
```
$ tts --text "Text for TTS" --model_name "<model_type>/<language>/<dataset>/<model_name>" --vocoder_name "<model_type>/<language>/<dataset>/<model_name>" --out_path output/path/speech.wav
```
For example:
```
$ tts --text "Text for TTS" --model_name "tts_models/en/ljspeech/glow-tts" --vocoder_name "vocoder_models/en/ljspeech/univnet" --out_path output/path/speech.wav
```
- Run your own TTS model (Using Griffin-Lim Vocoder):
```
$ tts --text "Text for TTS" --model_path path/to/model.pth --config_path path/to/config.json --out_path output/path/speech.wav
```
- Run your own TTS and Vocoder models:
```
$ tts --text "Text for TTS" --model_path path/to/model.pth --config_path path/to/config.json --out_path output/path/speech.wav
--vocoder_path path/to/vocoder.pth --vocoder_config_path path/to/vocoder_config.json
```
#### Multi-speaker Models
- List the available speakers and choose a <speaker_id> among them:
```
$ tts --model_name "<language>/<dataset>/<model_name>" --list_speaker_idxs
```
- Run the multi-speaker TTS model with the target speaker ID:
```
$ tts --text "Text for TTS." --out_path output/path/speech.wav --model_name "<language>/<dataset>/<model_name>" --speaker_idx <speaker_id>
```
- Run your own multi-speaker TTS model:
```
$ tts --text "Text for TTS" --out_path output/path/speech.wav --model_path path/to/model.pth --config_path path/to/config.json --speakers_file_path path/to/speaker.json --speaker_idx <speaker_id>
```
## Directory Structure
```
|- notebooks/ (Jupyter Notebooks for model evaluation, parameter selection and data analysis.)
|- utils/ (common utilities.)
|- TTS
|- bin/ (folder for all the executables.)
|- train*.py (train your target model.)
|- ...
|- tts/ (text to speech models)
|- layers/ (model layer definitions)
|- models/ (model definitions)
|- utils/ (model specific utilities.)
|- speaker_encoder/ (Speaker Encoder models.)
|- (same)
|- vocoder/ (Vocoder models.)
|- (same)
```
|
Word2vec/nlpl_7 | Word2vec | 2023-07-04T11:45:15Z | 0 | 0 | null | [
"word2vec",
"eng",
"dataset:English_Wikipedia_Dump_of_February_2017",
"license:cc-by-4.0",
"region:us"
]
| null | 2023-07-04T10:02:23Z | ---
language: eng
license: cc-by-4.0
tags:
- word2vec
datasets: English_Wikipedia_Dump_of_February_2017
---
## Information
A word2vec model trained by Andrey Kutuzov ([email protected]) on a vocabulary of size 273930 corresponding to 2252637050 tokens from the dataset `English_Wikipedia_Dump_of_February_2017`.
The model is trained with the following properties: lemmatization and postag with the algorith Global Vectors with window of 5 and dimension of 300.
## How to use?
```
from gensim.models import KeyedVectors
from huggingface_hub import hf_hub_download
model = KeyedVectors.load_word2vec_format(hf_hub_download(repo_id="Word2vec/nlpl_7", filename="model.bin"), binary=True, unicode_errors="ignore")
```
## Citation
Fares, Murhaf; Kutuzov, Andrei; Oepen, Stephan & Velldal, Erik (2017). Word vectors, reuse, and replicability: Towards a community repository of large-text resources, In Jรถrg Tiedemann (ed.), Proceedings of the 21st Nordic Conference on Computational Linguistics, NoDaLiDa, 22-24 May 2017. Linkรถping University Electronic Press. ISBN 978-91-7685-601-7
This archive is part of the NLPL Word Vectors Repository (http://vectors.nlpl.eu/repository/), version 2.0, published on Friday, December 27, 2019.
Please see the file 'meta.json' in this archive and the overall repository metadata file http://vectors.nlpl.eu/repository/20.json for additional information.
The life-time identifier for this model is: http://vectors.nlpl.eu/repository/20/7.zip
|
nolanaatama/tny | nolanaatama | 2023-07-04T11:43:50Z | 0 | 0 | null | [
"license:creativeml-openrail-m",
"region:us"
]
| null | 2023-07-04T11:40:16Z | ---
license: creativeml-openrail-m
---
|
Allenpai/alpacaRec | Allenpai | 2023-07-04T11:43:15Z | 0 | 0 | null | [
"region:us"
]
| null | 2023-07-04T11:42:16Z |
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 |
dcarpintero/Reinforce-Pixelcopter-PLE-v1 | dcarpintero | 2023-07-04T11:41:06Z | 0 | 0 | null | [
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-07-04T11:41:02Z | ---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-Pixelcopter-PLE-v1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 28.70 +/- 22.43
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
|
iammartian0/whisper-tiny-finetuned-gtzan | iammartian0 | 2023-07-04T11:08:08Z | 103 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"whisper",
"audio-classification",
"generated_from_trainer",
"dataset:marsyas/gtzan",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
]
| audio-classification | 2023-07-04T10:40:41Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- marsyas/gtzan
metrics:
- accuracy
model-index:
- name: whisper-tiny-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. -->
# whisper-tiny-finetuned-gtzan
This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the GTZAN dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4342
- Accuracy: 0.87
## 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
- 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_ratio: 0.1
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.7087 | 0.99 | 56 | 1.6682 | 0.53 |
| 1.0139 | 2.0 | 113 | 1.1272 | 0.64 |
| 0.8057 | 2.99 | 169 | 0.7579 | 0.79 |
| 0.393 | 4.0 | 226 | 0.5791 | 0.86 |
| 0.3414 | 4.99 | 282 | 0.5055 | 0.86 |
| 0.1083 | 6.0 | 339 | 0.4109 | 0.9 |
| 0.0783 | 6.99 | 395 | 0.4297 | 0.87 |
| 0.0998 | 8.0 | 452 | 0.4627 | 0.87 |
| 0.0119 | 8.99 | 508 | 0.4410 | 0.87 |
| 0.0095 | 9.91 | 560 | 0.4342 | 0.87 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
heybezayb/ppo-LunarLander-v2 | heybezayb | 2023-07-04T11:01:18Z | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-07-04T11:00:59Z | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 265.41 +/- 16.07
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
...
```
|
sazyou-roukaku/LittleStepMix | sazyou-roukaku | 2023-07-04T10:47:46Z | 248 | 33 | diffusers | [
"diffusers",
"stable-diffusion",
"text-to-image",
"ja",
"license:creativeml-openrail-m",
"region:us"
]
| text-to-image | 2023-06-25T06:57:42Z | ---
license: creativeml-openrail-m
language:
- ja
library_name: diffusers
pipeline_tag: text-to-image
tags:
- stable-diffusion
- text-to-image
---
License:[CreativeML Open RAIL-M](https://huggingface.co/sazyou-roukaku/LittleStepMix/blob/main/license_v1.txt)<br>
Additional Copyright: sazyou_roukaku (TwitterID [@sazyou_roukaku](https://twitter.com/sazyou_roukaku)) as of June 25, 2023<br>
ใใฎใขใใซใฏใCreativeML Open RAIL-MใใงLicenseใใฎใใฎใซๅคๆดใฏใใใพใใใ<br>
ใใใ่ฟฝๅ ่ไฝ่
ใจใใฆไฝๅ้็ปใฎๅๅใ่ฟฝๅ ใใใฆใใพใใ<br>
ใชใใCreativeML Open RAIL-Mใใซ่จ่ผใใใฆใใ้ใใ<br>
ๆฌใขใใซใไฝฟ็จใใฆใฎ็ๆ็ฉใซ้ขใใฆใฏLicenseใฎไฝฟ็จๅถ้Aใฎไบไพใ้คใใๅฝๆนใฏไธๅ้ขไธ่ดใใพใใใ<br>
็ฏ็ฝช็ฎ็ๅฉ็จใๅป็็จ็ปๅใชใฉ็นๅฎๅฐ้็ใช็จ้ใงใฎๅฉ็จใฏไฝฟ็จๅถ้Aใง็ฆๆญขใใใฆใใพใใ<br>
ๅฟ
ใ็ขบ่ชใใๅฉ็จใใ ใใใ<br>
ใพใๅฝๆนใฏไธๅ่ฒฌไปปใๆใกใพใใใๅ
่ฒฌใใใฆใใใใจใใไบๆฟใฎไธใใไฝฟ็จใใ ใใใ<br>
<br>
ใใฎCheckPointใฎใใฆใณใญใผใใปไฝฟ็จใฏไธ่จCreativeML Open RAIL-M Licenseใ็ขบ่ชใฎไธใ<br>
ๅๆใใใจใใๅๆๅใณๅฅ็ดใซๅบใฅใใใฎใจๅคๆญใใใพใใ<br>
<h4>ๆดๆฐๅฑฅๆญด</h4>
<ul>
<li>6/25 LittleStepMix_v1ๅ
ฌ้</li>
<li>7/1 LittleStepMix_AใปBใปCๅ
ฌ้</li>
<li>7/3 LittleStepMix_AใTextencoderๅคๆดๅใใขใใใใฆใใ็บใๅ้คใๅคๆดๆธ็ใๅๅ
ฌ้</li>
</ul>
<h4>ๅถ้</h4>
<div class="px-2">
<table class="table-fixed border mt-0 text-xs">
<tr>
<td class="align-middle px-4 w-8">
<span class="text-green-500">
<h5>OK</h5>
</span>
</td>
<td>
่ไฝ่
่กจ่จใๅ
ฅใใใซใขใใซใไฝฟ็จใใ<br>
Use the model without crediting the creator
</td>
</tr>
<tr>
<td class="align-middle px-4 w-8">
<span class="text-green-500">
<h5>OK</h5>
</span>
</td>
<td>
ใใฎใขใใซใง็ๆใใ็ปๅใๅ็จๅฉ็จใใ<br>
Sell images they generate
</td>
</tr>
<tr>
<td class="align-middle px-4 w-8">
<span class="text-green-500">
<h5>OK</h5>
</span>
</td>
<td>
ๅ็จ็ปๅ็ๆใตใผใในใซใใใฎใขใใซใไฝฟ็จใใ<br>
Run on services that generate images for money
</td>
</tr>
<tr>
<td class="align-middle px-4 w-8">
<span class="text-green-500">
<h5>OK</h5>
</span>
</td>
<td>
ใใฎใขใใซใไฝฟ็จใใใใผใธใขใใซใๅ
ฑๆใป้
ๅธใใ<br>
Share merges using this model
</td>
</tr>
<tr>
<td class="align-middle px-4 w-8">
<span class="text-green-500">
<h5>OK</h5>
</span>
</td>
<td>
ใใฎใขใใซใใพใใฏๆดพ็ใขใใซใ่ฒฉๅฃฒใใ<br>
Sell this model or merges using this model
</td>
</tr>
<tr>
<td class="align-middle px-4 w-8">
<span class="text-green-500">
<h5>OK</h5>
</span>
</td>
<td>
ใใฎใขใใซใใใผใธใใใขใใซใซ็ฐใชใๆจฉ้ใ่จญๅฎใใ<br>
Have different permissions when sharing merges
</td>
</tr>
</table>
</div>
ใชใใไธ่จใฎใขใใซใใฎใใฎใฎ่ฒฉๅฃฒใๅ็จ็ปๅ็ๆใตใผใในใธใฎๅฉ็จใฏใ<br>
ใCreativeML Open RAIL-MใใฎLicenseไธใไฝฟ็จๅถ้Aใซ่ฟฝ่จ่จ่ผใใชใ้ใใ<br>
ๅถ้ใใใใจใๆฌๆฅใงใใชใ็บใใใผใธ่
ใธใฎ่ฒ ๆ
ใ่ๆ
ฎใใcivitaiๅถ้่กจ่จไธOKใจใใฆใใใ ใใงใใใ<br>
็ฉๆฅต็ใชๆจๅฅจใฏ่กใฃใฆใใใใใพใใใใซใใไฝใใใฎๅ้กใ็ใใฆใๅฝๆนใฏไธๅ่ฒฌไปปใๆใกใพใใใ<br>
ใใฎ็นใใ็ๆใใใ ใใใใ้กใใใใใพใใ<br>
<br>
<h2>LittleStepMix_v1 ใใผใธๅฉ็จใขใใซไธ่ฆง</h2>
<ul>
<li><a href="https://civitai.com/models/4384">dreamshaper_6BakedVae</a> ยฉLykon</li>
<li><a href="https://civitai.com/models/25694">epicrealism_newAge</a> ยฉepinikion</li>
<li><a href="https://civitai.com/models/1169">sxd_v10</a> ยฉizuek</li>
<li><a href="https://huggingface.co/haor/Evt_V4-preview">Evt_V4_e04_ema</a> ยฉhaor</li>
<li><a href="https://huggingface.co/Crosstyan/BPModel">bp_mk5</a> ยฉCrosstyan</li>
<li><a href="https://huggingface.co/naclbit/trinart_characters_19.2m_stable_diffusion_v1">trinart_characters_it4_v1</a> ยฉSta, AI Novelist Dev <a href="https://ai-novel.com/">(https://ai-novel.com/)</a> @ Bit192, Inc.</li>
</ul>
<h2>LLittleStepMix_AใปBใปC่ฟฝๅ ใใผใธๅฉ็จใขใใซ</h2>
<ul>
<li><a href="https://huggingface.co/Ai-tensa/FlexWaifu">FlexWaifuRainbow</a> <a href="https://twitter.com/Ai_tensa">ยฉAi-tensa</a></li>
<li><a href="https://huggingface.co/hakurei/waifu-diffusion-v1-3">wd-v1-3-float16</a> developed by Anthony Mercurio, Salt, and Cafe</a></li>
</ul>
<p></p>
--------------------------------------------------------------------------
<h4>ใตใณใใซ</h4>
<img src="https://huggingface.co/sazyou-roukaku/LittleStepMix/resolve/main/sample/002.jpg" width="100%" height="100%">
<pre style="white-space: pre-line;" class="w-full">
(gyaru:1.3),high resolution,ultra-detail,solo,short shirt and short shorts,locker room,
(cowboy shot:1.2),sexy smile,blonde long hair,
Negative prompt: (worst quality:2),(low quality:1.4),(manicure:1.5),(long neck:2),lip
Steps: 30
Sampler: DPM++ 2M Karras
CFG scale: 7
Seed: 3358380436
</pre>
<img src="https://huggingface.co/sazyou-roukaku/LittleStepMix/resolve/main/sample/001.jpg" width="100%" height="100%">
<pre style="white-space: pre-line;" class="w-full">
1girl,handsome face,cool beauty,high resolution,ultra-detail,solo,punk tee and cargo pants,
london street, (cowboy shot:1.2),happy smile,black short hair,
Negative prompt: (worst quality:2),(low quality:1.4),(manicure:1.5),(long neck:2),lip
Steps: 30
Sampler: DPM++ 2M Karras
CFG scale: 7
Seed: 269540596
</pre>
--------------------------------------------------------------------------
<div>
<h3>่ฉณ็ดฐ</h3>
<p>
<div class="px-2">
<div class="border p-2">
<details>
<summary><h4>LittleStepMix_AใปBใปC</h4></summary>
CLIP่จญๅฎ/clip skip:2<br>
ๆจๅฅจVAE/mse840000_klf8anime_klf8anime2.vae<br>
ใใใใฏใใฉใซใๅ
ใซใใsr_SDv2vae_kl-f8anime2.safetensors<br>
sr_SDv2vae_kl-f8anime2.safetensorsใฏSD2VAEใจkl-f8anime2ใ็งใใใผใธใใVAEใงใใ<br>
LittleStepMix_AใLittleStepMix_BใLittleStepMix_Cใฏ็ผใ่พผใฟใชใใฎNoVAEใงใใ<br>
ClearVAEใฏ1.0ใNAIVAEใฎๅฝฑ้ฟใใใใจ่จ่ผใใใใใใไปฅ้ใฎVersionใๅบๆไธๆใฎ็บใใณใณใปใใ็ใซๆจๅฅจใใฆใใพใใใ<br>
<br>
1ไบบใฎๆใฏsoloใใใญใณใใใงๅ
ฅใใชใใจใๅคใชใณใๅฒใ็ปๅใฎใใใช่กจ็คบใซใชใใใใๅพๅใใใใพใใ
SD1.4ใใ็ขบ่ชใใใฆใใใฎใงใใใACertainty็ณปใฏ็นใซใใฎๅพๅใๅผทใใฎใงใ1ไบบใฎๅ ดๅใฏsoloใจๆๅฎๆจๅฅจใ<br>
NFSWใฏใใใใใพใงใฏๆฎ้ใซๅบใใพใใ
</details>
</div>
</div>
<div class="px-2">
<div class="border p-2">
<details>
<summary><h4>LittleStepMix_v1</h4></summary>
CLIP่จญๅฎ/clip skip:2<br>
ๆจๅฅจVAE/mse840000_klf8anime_klf8anime2.vae<br>
ใใใใฏใใฉใซใๅ
ใซใใsr_SDv2vae_kl-f8anime2.safetensorsใๅฅฝใฟใงใใ<br>
sr_SDv2vae_kl-f8anime2.safetensorsใฏSD2VAEใจkl-f8anime2ใ็งใใใผใธใใVAEใงใใ<br>
<br>
ใชใLittleStepMix_v1ใฏSD1.xใฎใใใฉใซใVAEใๆจๆบ็ผใ่พผใฟๆธใฟใงใใ<br>
<br>
่ช็ถ่จ่ช(ๆ็ซ )ใใญใณใใใ ใจใใใ้กใฎใชใขใซๅใๅผทใใชใๅพๅใใฟใใใพใใ<br>
ๅๆใใญใณใใใงใฎๅฉ็จใๆจๅฅจใใพใใ<br>
ใชใใคใฉในใใขใใซใปใใฉใใชใขใซใขใใซใๅซใใไปๅพใฎ่ชๅทฑใใผใน็ด ๆใขใใซใจใใฆใฎๅ
ฌ้ใฎๅด้ขใๅผทใใ็พ็ถ่ฉณใใ่ฝๅใฏๆค่จผไธญใงใใใไบๆฟใใ ใใใ<br>
ใชใใใผในใใใฉใใชใขใซใขใใซใใปใใชใขใซใขใใซใฎ็บใใจใใงใฏใ็ณปใฏใใชใๅผฑใๅฐ่ฑกใงใใ<br>
</details>
</div>
</div>
<h3>FAQ</h3>
<h4>Q1:LittleStepMixใจใฏไฝใ</h4>
A1:<br>
็พๅจใคใฉในใใใผใธใขใใซใฏleakใขใใซใฎๆททๅ
ฅใฎๅ้กใๆธๅฟตใใใๆฌกใ
ใซๅ
ฌ้ๅๆญขใ็ธๆฌกใใชใฉ่็ธฎใ ใผใใซๅ
ฅใฃใฆใใพใใ<br>
ๅฝใขใใซใฏๆฏ่ผ็ๅฎ็ใจๆใใใ่จ็ทดใขใใซใไธป่ปธใจใใไปๅพ่ชฟๆดไบๅฎใฎใขใใซใฎๅบ็คใจใใฆไฝใฃใฆใใพใใ<br>
ๅฎๅ
จใซๆททๅ
ฅใใชใใจใฏๆญ่จใงใใชใใใฎใฎใใใผใธ็ด ๆใฏ่กจ่จใฎใใฎไปฅๅคไธๅไฝฟ็จใใฆใใชใ็นใ(add็จใฎSD1.4ใSD1.5ใฏ้คใ)<br>
่จ็ทดใขใใซใฎใฟใงใฎใใผใธใงใใ็นใใใๆฏ่ผ็ไฝใชในใฏใฎใฉใคใณใ็ฎๆใใฆใใพใใ<br>
ๅบๆฌ็ใซใฏไผๆฅญใขใใซ็ญใๆๅ
ฅใใใใชใฉใฎๆไปฃใพใงใฎ็นใใจใใฆใฎๅฝนๅฒใงใใ<br>
ๆใๅ
ฅใใพใใใๅ
จใฆใฎใใผใธ็ด ๆใ็ขบ่ชใฎไธใใๅฉ็จใฏ่ชๅทฑใงใๅคๆญใใ ใใใ<br>
<br>
*7/1่ฟฝ่จ*ใLittleStepMix_AใปBใปCใฏLittleStepMixใๅๅฐใจใใฆใคใฉในใใขใใซๅใใพใใใ<br>
ใใผใธ็ด ๆใจใใฆ่ช็ฑใซใๅฉ็จใใใ ใใฆๅ้กใใใพใใใ<br>
<h4>Q2:ๅๅญฆ็ฟใขใใซ้ธๅฎๅบๆบใซใคใใฆ</h4>
A2:<br>
*7/1่ฟฝ่จ* sampleใใฉใซใๅ
ใซใAnything-V3.0ใๅบๆบใจใใฆใ<br>
Baka-DiffusionV1(fp16)ใsd-v1-4ใLittleStepMixใทใชใผใบ4็จฎๅใณไธป่ปธใขใใซใงใใdreamshaperใง็พ็ถๆๅคใฎๅ
ฌ้ใขใใซ<br>
dreamshaper_252ใใฉใณใใ Seedใง10ๅใ<br>
IN01-02,04-05,07-08/OUT03-11ใฎcosineไธ่ด็ใๅบๅใใใใกใคใซใๅ
ฌ้ใใใใพใใ<br>
Anything-V3.0ใซๅฏพใใSD1.4ใฏๆฆใญ84๏ผ
ใปใฉไธ่ดใ<br>
dreamshaper_252.safetensorsใง88๏ผ
ใLittleStepMixใทใชใผใบใฏๆฆใญ89๏ผ
็จๅบฆใฎไธ่ด็ใงใใ<br>
Baka-DiffusionV1ใๆก็จใใชใใฃใ็็ฑใใใฎๆฐๅคใซใใใพใใ<br>
ไธ่จใฎASimilarityCalculatiorใใใผในใซใใฉใณใใ Seedใงใๅ่จใงใฏใชใๅๆฐๅคใๅบใใใใๆน่ฏใใใใฎใ็จใใฆใใพใใ<br>
ใๅ่ใพใงใซใ
<br>
<br>
<br>
โ dreamshaper_6BakedVae<br>
ๆฌใขใใซใฎ<strong>ไธป่ปธ</strong>ใจใชใฃใฆใใ่จ็ทดใขใใซใงใใ<br>
่จ็ทดใขใใซใฎ่กจ่จใใใใ่คๆฐใฎๅ็จ็ปๅ็ๆใตใผใในใงใๅฉ็จใใใฆใใ็บใไธๅฎใฎไฟก้ ผๆงใๆ
ไฟใใใฆใใใจๅคๆญใใฆใใพใใ<br>
<strong>ใขในใซใในใใชใฉใงใฎ้กไผผๆงใฏๅบๆฌ็ใซdreamshaper_6BakedVae็ฑๆฅ</strong>ใงใใ<br>
<br>
โกsxd_v10<br>
v0.8ใจ้ใใv1.0ๅ
ฌ้ๆฅใฏใชใผใฏๅพใชใใSD1.5ใใผในใฎ่จ็ทดใขใใซใงใใชใขใชใใฃ้่ฆใฎ็บใ็ทๅ็ใซๅคๆญใ<br>
ไบบไฝๆง้ ๅผทๅใจๅฐๆฅNFSWใขใใซๅใ่กใ้ใฎ่ฃๅผทใจใใฆๆก็จใ<br>
<br>
โขepicrealism_newAge<br>
็พ่กใฎ่จ็ทดใขใใซใงๆๅผทใฎในใใใฏใ่ชใใจๆใใใใขใใซใ<br>
่ๆฏ่ฃๅผทใจ่ฝๅใฎ้ซใใใๆก็จใ<br>
ๆๆฐใงใฏใชใใฎใฏใไปใฎ็งใฎใใฉใใชใขใซใขใใซใจใฎๅ
ผใญๅใใจใใณใณใใฉในใใชใฉใฎๅ
ผใญๅใใใepicrealism_newAgeใ้ธๆใ<br>
<br>
โฃEvt_V4_e04_ema<br>
ACertaintyใจใใleakใใผใฟใๅซใพใชใใจๅ
ฌ่จใใฆใใใคใฉในใๅญฆ็ฟใขใใซใงใใฌใผใใณใฐใ่กใ็ใฟๅบใใใใขใใซใ<br>
็ตตๆใฎไธป่ปธใขใใซใe04ๆก็จใฏใใพใๆ นๆ ใใชใใ็ตตๆ็ใซไธ็ชๅฅฝใฟใชใฎใงๆก็จใใใใฉใใชใขใซใขใใซใงใใกใใๅฉ็จใใฆใใใฎใงๅคๆดใใใจ็ฎก็ใ้ขๅใซใชใใจใใ็็ฑใ ใใงใใ<br>
โปACertaintyใฏNOVEL AIใฎใใผใฟใ่ธ็ใใฆใใๅฏ่ฝๆงใฏใใใพใใใใใกใใฏ็น่จฑๆณใซๆต่งฆใใชใ็บใๅ้กใชใใจ่ใใฆใใพใใ<br>
ACertainty<br>
<a href="https://huggingface.co/JosephusCheung/ACertainty">https://huggingface.co/JosephusCheung/ACertainty</a><br>
<a href="https://huggingface.co/JosephusCheung/ASimilarityCalculatior">https://huggingface.co/JosephusCheung/ASimilarityCalculatior</a><br>
<br>
โคbp_mk5<br>
ACertaintyใใผในใฎ่จ็ทดใขใใซใไธ่จๅๆงใ<br>
<br>
โฅtrinart_characters_it4_v1<br>
AIใฎในใใใจใงๆๅใชไผ็คพใๅ
ฌ้ใใฆใใ ใใฃใใขใใซใชใฎใงไธ็ชไฟก้ ผๆงใใใใพใใ<br>
ใคใฉในใ่ฆ็ด ่ฃๅผทใจใใฆไฝฟ็จใใฆใใพใใ<br>
<br>
โฆFlexWaifuRainbow<br>
ใขใใซใฎ้ใใผใธ่งฃๆใณใผใใๅ
ฌ้ใใใชใฉใ่กใฃใฆใใๅคฉ็ดๆๆฐใWD1.3ใซ่ฟฝๅ ๅญฆ็ฟใๆฝใใใขใใซใ<br>
ACertaintyใใผในใฎใขใใซๆก็จใซๅฝใใใACertaintyใฎ่งฃๆ็ตๆใชใฉใๅ่ใซใใใฆใใใ ใใฆใใพใใ<br>
้ฃ็ถๅบๅๆใฎ็ตตๆใฎๅฎๅฎๆงใจไฟก้ ผๆงใใ้ธใฐใใฆใใใ ใใพใใใ
<br>
<h3>Q3:ไปๅใฎๅถ้ใซๅ้กใ็็พใฏใชใใฎใ</h3>
<h4>A3:</h4> <strong>dreamshaper_6BakedVae</strong> ใฏcivitaiใฎใใผใใทใงใณใใ
<strong>OK:Have different permissions when sharing merges</strong>ใจใชใฃใฆใใ่งฃ้คๅฏ่ฝใ<br>
ไปใฏๅถ้ใชใใฎ็บใไปๅๅ
จใฆๅถ้ใชใใจใๅ
ฌ้ใใฆใใใพใใ<br>
<br>
ใชใใใผใธๅฉ็จใขใใซๅดใซLicenseๅคๆดใปๅถ้ๅคๆด็ญใ็ใใ้ใ<br>
ใใฆใณใญใผใๆ็นใฎLicenseใๅถ้ใๅๆใจใใฆๅ
ฌ้ใใฆใใ็บใcreativeml-openrail-mใซๆบใใพใใ<br>
ใใกใใฏLittleStepMIXMerge_LicenseSS_v1ใซ่ฉฒๅฝใขใใซใฎSSใไฟ็ฎกใใฆใใใพใใ<br>
ใใ ใhuggingfaceๅ
ฌ้ใฎใขใใซใฏSSใใใชใใธใใชใฎใปใใไฟก้ ผๆงใ้ซใใฎใงใไฟ็ฎกใใฆใใใพใใใ<br>
<br>
ใชใใใผใธๅฉ็จใขใใซๅดใซ้ๅคงใชๅ้กใ็บ็ใใๅ ดๅใฏใใขใใซใฎๅ
ฌ้ๅๆญขใ่กใใ<br>
ๅฉ็จๅๆญขใๅผใณใใใๅฏ่ฝๆงใฏใใใพใใใ<strong>ๅฝๆนๅดใ็็ฑใจใใ่ฟฝๅ ๅถ้ใ่จญใใใใจใฏ่ดใใพใใใ</strong>
</div> |
Anwaarma/autotrain-enhancedauto-72049138835 | Anwaarma | 2023-07-04T10:47:14Z | 108 | 0 | transformers | [
"transformers",
"pytorch",
"safetensors",
"bert",
"text-classification",
"autotrain",
"unk",
"dataset:Anwaarma/autotrain-data-enhancedauto",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2023-07-04T10:42:11Z | ---
tags:
- autotrain
- text-classification
language:
- unk
widget:
- text: "I love AutoTrain"
datasets:
- Anwaarma/autotrain-data-enhancedauto
co2_eq_emissions:
emissions: 3.3106524610859784
---
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 72049138835
- CO2 Emissions (in grams): 3.3107
## Validation Metrics
- Loss: 0.042
- Accuracy: 0.990
- Precision: 0.994
- Recall: 0.935
- AUC: 0.997
- F1: 0.964
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/Anwaarma/autotrain-enhancedauto-72049138835
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("Anwaarma/autotrain-enhancedauto-72049138835", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("Anwaarma/autotrain-enhancedauto-72049138835", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
``` |
vivekraina/falcon-7b-4bit | vivekraina | 2023-07-04T10:47:09Z | 4 | 0 | peft | [
"peft",
"region:us"
]
| null | 2023-07-04T10:46:07Z | ---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.4.0.dev0
|
revmag/Taxi-v3 | revmag | 2023-07-04T10:43:12Z | 0 | 1 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-07-04T10:43:11Z | ---
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: 7.56 +/- 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="revmag/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"])
```
|
Bugsys0302/niplbarpcg | Bugsys0302 | 2023-07-04T10:38:55Z | 0 | 0 | null | [
"license:creativeml-openrail-m",
"region:us"
]
| null | 2023-07-04T10:33:15Z | ---
license: creativeml-openrail-m
---
|
ericNguyen0132/roberta-large-Dep-pretrain | ericNguyen0132 | 2023-07-04T10:33:09Z | 105 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2023-07-04T06:57:43Z | ---
tags:
- generated_from_trainer
model-index:
- name: roberta-large-Dep-pretrain
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-large-Dep-pretrain
This model was trained from scratch 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: 5e-06
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
revmag/q-FrozenLake-v1-4x4-noSlippery | revmag | 2023-07-04T10:30:26Z | 0 | 0 | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-07-04T10:30:24Z | ---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="revmag/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"])
```
|
chenxingphh/distilbert-base-uncased-finetuned-imdb | chenxingphh | 2023-07-04T10:28:47Z | 126 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"fill-mask",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| fill-mask | 2023-07-04T10:21:04Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
model-index:
- name: distilbert-base-uncased-finetuned-imdb
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-imdb
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: 2.4721
## 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: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.7086 | 1.0 | 157 | 2.4897 |
| 2.5796 | 2.0 | 314 | 2.4230 |
| 2.5269 | 3.0 | 471 | 2.4354 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
erkam/sd-clevr-sg2im-objects_cap-e2e | erkam | 2023-07-04T10:26:20Z | 1 | 0 | diffusers | [
"diffusers",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"lora",
"base_model:stabilityai/stable-diffusion-2",
"base_model:adapter:stabilityai/stable-diffusion-2",
"license:creativeml-openrail-m",
"region:us"
]
| text-to-image | 2023-06-08T12:35:18Z |
---
license: creativeml-openrail-m
base_model: stabilityai/stable-diffusion-2
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- lora
inference: true
---
# LoRA text2image fine-tuning - erkam/sd-clevr-sg2im-objects_cap-e2e
These are LoRA adaption weights for stabilityai/stable-diffusion-2. The weights were fine-tuned on the erkam/clevr-full-v4 dataset. You can find some example images in the following.
|
AnnaAp/Equip | AnnaAp | 2023-07-04T10:23:54Z | 0 | 0 | null | [
"region:us"
]
| null | 2023-07-04T10:16:55Z | ---
language:
- ru
---ะปะพะณะพัะธะฟ
ัััะพะธัะตะปัะฝะฐั ัะตั
ะฝะธะบะฐ
ะพะฑะพััะดะพะฒะฐะฝะธะต |
msladic/ppo-ML-Agents-Pyramids | msladic | 2023-07-04T10:19:42Z | 0 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"Pyramids",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
]
| reinforcement-learning | 2023-07-04T10:19:39Z | ---
library_name: ml-agents
tags:
- Pyramids
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Pyramids
---
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog ๐ถ to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: msladic/ppo-ML-Agents-Pyramids
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play ๐
|
msladic/ppo-SnowballTarget | msladic | 2023-07-04T10:18:36Z | 6 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"SnowballTarget",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SnowballTarget",
"region:us"
]
| reinforcement-learning | 2023-07-04T10:02:46Z | ---
library_name: ml-agents
tags:
- SnowballTarget
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SnowballTarget
---
# **ppo** Agent playing **SnowballTarget**
This is a trained model of a **ppo** agent playing **SnowballTarget**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog ๐ถ to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: msladic/ppo-SnowballTarget
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play ๐
|
NasimB/gpt2-cl-concat-log-rarity-9-210k-mod-datasets | NasimB | 2023-07-04T10:10:08Z | 121 | 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 | 2023-07-04T08:51:19Z | ---
license: mit
tags:
- generated_from_trainer
datasets:
- generator
model-index:
- name: gpt2-cl-concat-log-rarity-9-210k-mod-datasets
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# gpt2-cl-concat-log-rarity-9-210k-mod-datasets
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: 5.0793
## 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: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 6.2877 | 0.07 | 500 | 5.9527 |
| 5.0107 | 0.14 | 1000 | 5.5940 |
| 4.7383 | 0.21 | 1500 | 5.4130 |
| 4.5602 | 0.28 | 2000 | 5.2903 |
| 4.423 | 0.35 | 2500 | 5.2322 |
| 4.3129 | 0.41 | 3000 | 5.1696 |
| 4.2078 | 0.48 | 3500 | 5.1278 |
| 4.1161 | 0.55 | 4000 | 5.1007 |
| 4.023 | 0.62 | 4500 | 5.0613 |
| 3.933 | 0.69 | 5000 | 5.0483 |
| 3.8578 | 0.76 | 5500 | 5.0290 |
| 3.7859 | 0.83 | 6000 | 5.0156 |
| 3.746 | 0.9 | 6500 | 5.0064 |
| 3.7228 | 0.97 | 7000 | 5.0027 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.11.0+cu113
- Datasets 2.13.0
- Tokenizers 0.13.3
|
nageen/roberta-finetuned-subjqa-event_model | nageen | 2023-07-04T10:05:57Z | 122 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"question-answering",
"generated_from_trainer",
"license:cc-by-4.0",
"endpoints_compatible",
"region:us"
]
| question-answering | 2023-05-29T22:46:41Z | ---
license: cc-by-4.0
tags:
- generated_from_trainer
model-index:
- name: roberta-finetuned-subjqa-event_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. -->
# roberta-finetuned-subjqa-event_model
This model is a fine-tuned version of [deepset/roberta-base-squad2](https://huggingface.co/deepset/roberta-base-squad2) 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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.0+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
heka-ai/cross-mpnet-70k | heka-ai | 2023-07-04T10:01:14Z | 1 | 0 | sentence-transformers | [
"sentence-transformers",
"pytorch",
"distilbert",
"feature-extraction",
"sentence-similarity",
"transformers",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
]
| sentence-similarity | 2023-07-04T10:01:10Z | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# heka-ai/cross-mpnet-70k
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('heka-ai/cross-mpnet-70k')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
def cls_pooling(model_output, attention_mask):
return model_output[0][:,0]
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('heka-ai/cross-mpnet-70k')
model = AutoModel.from_pretrained('heka-ai/cross-mpnet-70k')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, cls pooling.
sentence_embeddings = cls_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=heka-ai/cross-mpnet-70k)
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 400000 with parameters:
```
{'batch_size': 8, 'sampler': 'torch.utils.data.sampler.SequentialSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`gpl.toolkit.loss.MarginDistillationLoss`
Parameters of the fit()-Method:
```
{
"epochs": 1,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": 100000,
"warmup_steps": 1000,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 350, 'do_lower_case': False}) with Transformer model: DistilBertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information --> |
macavaney/deepct | macavaney | 2023-07-04T09:57:26Z | 114 | 1 | transformers | [
"transformers",
"pytorch",
"safetensors",
"bert",
"token-classification",
"retrieval",
"document-rewriting",
"en",
"arxiv:1910.10687",
"arxiv:2007.14271",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| token-classification | 2022-11-23T13:17:13Z | ---
language:
- en
tags:
- retrieval
- document-rewriting
datasets:
- irds:msmarco-passage
library_name: transformers
---
A DeepCT model based on `bert-base-uncased` and trained on MS MARCO. This is a version of [the checkpoint released by the original authors](http://boston.lti.cs.cmu.edu/appendices/arXiv2019-DeepCT-Zhuyun-Dai/outputs/marco.zip), converted to pytorch format and ready for use in PyTerrier.
## References
- [Dai19]: Zhuyun Dai, Jamie Callan. Context-Aware Sentence/Passage Term Importance Estimation For First Stage Retrieval. https://arxiv.org/abs/1910.10687
- [Macdonald20]: Craig Macdonald, Nicola Tonellotto. Declarative Experimentation in Information Retrieval using PyTerrier. Craig Macdonald and Nicola Tonellotto. In Proceedings of ICTIR 2020. https://arxiv.org/abs/2007.14271
|
Ejru5/ml_model | Ejru5 | 2023-07-04T09:55:32Z | 0 | 0 | null | [
"region:us"
]
| null | 2023-07-04T09:41:17Z | # Random_Forest
A project we made while having ML value added course
|
ymkgr/shikimiya_mana_from_Re_Stage | ymkgr | 2023-07-04T09:27:21Z | 0 | 1 | null | [
"anime",
"game",
"license:creativeml-openrail-m",
"region:us"
]
| null | 2023-07-04T08:29:48Z | ---
license: creativeml-openrail-m
metrics:
- character
tags:
- anime
- game
---
ๆจกๅ็ฑปๅ/Model type: LoRA
---
v2.3็ๆฌๆจกๅ่ฏฆ็ปไฟกๆฏ/v2.3 Version Model Details(I used a translator in English):
- ๆฅ่ช ๆฅๆฌๅคๅชไฝไผๅ๏ผRe:Stage! - ็ปๅ๏ผKiRaRe - ่ง่ฒๅ๏ผๅผๅฎซ่่ใ/from Japanese multimedia project: Re:Stage! - Unit: KiRaRe - character name: shikimiya mana.
- LoRAๆ้/weight๏ผ0.6~1ใ
- ่งฆๅ่ฏ/Trigger Words * ่ฏท่ช่กๅจ"("ๅ")"็ๅ้ขๆทปๅ \็ฌฆๅท๏ผ่ฟไธช้กต้ขไผผไนไธ่ฝๅฐ\็ฌฆๅทไธๅ
ถๅฎ็ฌฆๅท่ฟๅจไธ่ตทๆพ็คบ/Please add the \ symbol before "(" and ")" yourself. It seems that the Model card cannot display the \ symbol together with other symbols๏ผ
- ่ง่ฒ/character๏ผ
shikimiya mana\(re:stage!\), ahoge, short hair, orange hair, blue eyes, clover hairclip\(shikimiya mana\),
็คบไพ/Example:
- ่ๅฐๆ/stage dress๏ผ
dress\(smsa\), star hair ornament\(smsa\), hat\(smsa\), one wrist cuffs\(smsa\), one wrist scrunchie\(smsa\), asymmetrical thighhighs\(smsa\), shoes\(smsa\), 
- ๆ กๆ/school uniform๏ผ
sailor collar, blue pleated skirt, bowtie,
---
v2.3็ๆฌ่ฏดๆ/v2.3 Version description:
- ๅฎๅจไธๆทปๅ ไปปไฝๅ้ฅฐ็ฑป็ๆ็คบ่ฏๆถ๏ผไนๅฏ่ฝไผ็ๆ็ฑปไผผๅ้ฅฐ็ๆ็ฉ๏ผ่งฃๅณๆนๆณ/It may also generate something similar to hair accessories without adding any hint words for hair accessories. Solution:๏ผ
ยท ๅจ Negative prompt ไธญๆทปๅ hairclipใhair ornament ็ญๅ้ฅฐ็ฑปๆ็คบ่ฏ/Add hairclip, hair oment, and other hair accessory prompts to Negative prompt
ยท ้ไฝLoRAๆ้/Reduce LoRA weight
็ธๆฏv1็ๆฌ๏ผๆ้ฅฐๆน้ขๆดๅใ/Compared to the v1 Version, the clothing aspect is more similar.
---
I don't know English and I'm not very good at using the Hugging Face website. I also use a translation for the description
Please comply with regulations. |
ak2704/q-FrozenLake-v1-4x4-noSlippery | ak2704 | 2023-07-04T09:24:35Z | 0 | 0 | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-07-04T09:24:29Z | ---
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="ak2704/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"])
```
|
a2ran/kor_chatGLM | a2ran | 2023-07-04T09:21:16Z | 3 | 0 | peft | [
"peft",
"region:us"
]
| null | 2023-07-04T09:15:50Z | ---
library_name: peft
---
- **WIP**
Data used : https://raw.githubusercontent.com/Beomi/KoAlpaca/main/alpaca_data.json
training_args = TrainingArguments(
"output",
fp16 =True,
gradient_accumulation_steps=1,
per_device_train_batch_size = 1,
learning_rate = 1e-4,
max_steps=3000,
logging_steps=100,
remove_unused_columns=False,
seed=0,
data_seed=0,
group_by_length=False,
) |
Word2vec/nlpl_5 | Word2vec | 2023-07-04T09:20:25Z | 0 | 0 | null | [
"word2vec",
"eng",
"dataset:English_Wikipedia_Dump_of_February_2017",
"license:cc-by-4.0",
"region:us"
]
| null | 2023-06-01T15:35:34Z | ---
language: eng
tags:
- word2vec
datasets: English_Wikipedia_Dump_of_February_2017
license: cc-by-4.0
---
## Information
A word2vec model trained by Andrey Kutuzov ([email protected]) on a vocabulary of size 302866 corresponding to 2252637050 tokens from the dataset `English_Wikipedia_Dump_of_February_2017`.
The model is trained with the following properties: lemmatization and postag with the algorith Gensim Continuous Skipgram with window of 5 and dimension of 300.
## How to use?
```
from gensim.models import KeyedVectors
from huggingface_hub import hf_hub_download
model = KeyedVectors.load_word2vec_format(hf_hub_download(repo_id="Word2vec/nlpl_5", filename="model.bin"), binary=True, unicode_errors="ignore")
```
## Citation
Fares, Murhaf; Kutuzov, Andrei; Oepen, Stephan & Velldal, Erik (2017). Word vectors, reuse, and replicability: Towards a community repository of large-text resources, In Jรถrg Tiedemann (ed.), Proceedings of the 21st Nordic Conference on Computational Linguistics, NoDaLiDa, 22-24 May 2017. Linkรถping University Electronic Press. ISBN 978-91-7685-601-7
This archive is part of the NLPL Word Vectors Repository (http://vectors.nlpl.eu/repository/), version 2.0, published on Friday, December 27, 2019.
Please see the file 'meta.json' in this archive and the overall repository metadata file http://vectors.nlpl.eu/repository/20.json for additional information.
The life-time identifier for this model is:
http://vectors.nlpl.eu/repository/20/5.zip |
NasimB/gpt2-dp-cl-rarity-9-210k-mod-datasets | NasimB | 2023-07-04T09:20:18Z | 125 | 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 | 2023-07-04T07:52:17Z | ---
license: mit
tags:
- generated_from_trainer
datasets:
- generator
model-index:
- name: gpt2-dp-cl-rarity-9-210k-mod-datasets
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# gpt2-dp-cl-rarity-9-210k-mod-datasets
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: 5.0528
## 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: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 6.3046 | 0.06 | 500 | 5.9519 |
| 5.0135 | 0.13 | 1000 | 5.5816 |
| 4.7368 | 0.19 | 1500 | 5.3952 |
| 4.5486 | 0.26 | 2000 | 5.2773 |
| 4.412 | 0.32 | 2500 | 5.2062 |
| 4.3027 | 0.39 | 3000 | 5.1514 |
| 4.1991 | 0.45 | 3500 | 5.1160 |
| 4.1058 | 0.52 | 4000 | 5.0827 |
| 4.0144 | 0.58 | 4500 | 5.0443 |
| 3.9241 | 0.65 | 5000 | 5.0280 |
| 3.8441 | 0.71 | 5500 | 5.0056 |
| 3.7614 | 0.78 | 6000 | 4.9986 |
| 3.7094 | 0.84 | 6500 | 4.9807 |
| 3.6717 | 0.91 | 7000 | 4.9782 |
| 3.6519 | 0.97 | 7500 | 4.9763 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.11.0+cu113
- Datasets 2.13.0
- Tokenizers 0.13.3
|
Copax/Graceful | Copax | 2023-07-04T09:19:30Z | 0 | 0 | null | [
"region:us"
]
| null | 2023-07-04T08:31:12Z | version: Spotlight
https://civitai.com/models/102749?modelVersionId=109965
The model brings vibrant and vivid colors to the images, with excellent contrast.
The hair details create flowing and intricate hairstyles, while the overall appearance of the characters follows a tall and slender catwalk style.
The outfits are enhanced with additional ornate patterns along the edges.
It's important to note that this model primarily focuses on female character designs, so drawing male characters or other genres may not yield the desired results.
Recommend:
step: 30~60
Denoising strength: 0.3
CFG Scale: 7 ~14
Upscaler:
4x-UltraSharp
R-ESRGAN 4x+ for real,
R-ESRGAN 4x+Anime6B for anime
N prompt
illustration, 3d, 2d, painting, cartoons, sketch, (worst quality:2), (low quality:2), (normal quality:2), lowres, bad anatomy, bad hands, vaginas in breasts, ((monochrome)), ((grayscale)), collapsed eyeshadow, multiple eyeblows, (cropped), oversaturated, extra limb, missing limbs, deformed hands, long neck, long body, imperfect, (bad hands), signature, watermark, username, artist name, conjoined fingers, deformed fingers, ugly eyes, imperfect eyes, skewed eyes, unnatural face, unnatural body, error, bad image, bad photo
|
DEplain/trimmed_longmbart_docs_apa | DEplain | 2023-07-04T09:18:27Z | 85 | 0 | transformers | [
"transformers",
"pytorch",
"mbart",
"text2text-generation",
"text simplification",
"plain language",
"easy-to-read language",
"document simplification",
"de",
"dataset:DEplain/DEplain-APA-doc",
"arxiv:2305.18939",
"license:apache-2.0",
"autotrain_compatible",
"region:us"
]
| text2text-generation | 2023-03-02T16:39:31Z | ---
inference: false
license: apache-2.0
language:
- de
datasets:
- DEplain/DEplain-APA-doc
metrics:
- sari
- bleu
- bertscore
library_name: transformers
pipeline_tag: text2text-generation
tags:
- text simplification
- plain language
- easy-to-read language
- document simplification
---
# DEplain German Text Simplification
This model belongs to the experiments done at the work of Stodden, Momen, Kallmeyer (2023). ["DEplain: A German Parallel Corpus with Intralingual Translations into Plain Language for Sentence and Document Simplification."](https://arxiv.org/abs/2305.18939) In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Toronto, Canada. Association for Computational Linguistics.
Detailed documentation can be found on this GitHub repository [https://github.com/rstodden/DEPlain](https://github.com/rstodden/DEPlain)
We reused the codes from [https://github.com/a-rios/ats-models](https://github.com/a-rios/ats-models) to do our experiments.
### Model Description
The model is a finetuned checkpoint of the pre-trained LongmBART model based on `mbart-large-cc25`. With a trimmed vocabulary to the most frequent 30k words in the German language.
The model was finetuned towards the task of German text simplification of documents.
The finetuning dataset included manually aligned sentences from the datasets `DEplain-APA-doc` only.
### Model Usage
This model can't be used in the HuggingFace interface or via the .from_pretrained method currently. As it's a finetuning of a custom model (LongMBart), which hasn't been registered on HF yet.
You can find this custom model codes at: [https://github.com/a-rios/ats-models](https://github.com/a-rios/ats-models)
To test this model checkpoint, you need to clone the checkpoint repository as follows:
```
# Make sure you have git-lfs installed (https://git-lfs.com)
git lfs install
git clone https://huggingface.co/DEplain/trimmed_longmbart_docs_apa
# if you want to clone without large files โ just their pointers
# prepend your git clone with the following env var:
GIT_LFS_SKIP_SMUDGE=1
```
Then set up the conda environment via:
```
conda env create -f environment.yaml
```
Then follow the procedure in the notebook `generation.ipynb`. |
Word2vec/nlpl_3 | Word2vec | 2023-07-04T09:08:44Z | 0 | 0 | null | [
"word2vec",
"eng",
"dataset:English_Wikipedia_Dump_of_February_2017",
"license:cc-by-4.0",
"region:us"
]
| null | 2023-06-01T15:13:39Z | ---
language: eng
tags:
- word2vec
datasets: English_Wikipedia_Dump_of_February_2017
license: cc-by-4.0
---
## Information
A word2vec model trained by Andrey Kutuzov ([email protected]) on a vocabulary of size 296630 corresponding to 2252637050 tokens from the dataset `English_Wikipedia_Dump_of_February_2017`.
The model is trained with the following properties: lemmatization and postag with the algorith Gensim Continuous Skipgram with window of 5 and dimension of 300.
## How to use?
```
from gensim.models import KeyedVectors
from huggingface_hub import hf_hub_download
model = KeyedVectors.load_word2vec_format(hf_hub_download(repo_id="Word2vec/nlpl_3", filename="model.bin"), binary=True, unicode_errors="ignore")
```
## Citation
Fares, Murhaf; Kutuzov, Andrei; Oepen, Stephan & Velldal, Erik (2017). Word vectors, reuse, and replicability: Towards a community repository of large-text resources, In Jรถrg Tiedemann (ed.), Proceedings of the 21st Nordic Conference on Computational Linguistics, NoDaLiDa, 22-24 May 2017. Linkรถping University Electronic Press. ISBN 978-91-7685-601-7
This archive is part of the NLPL Word Vectors Repository (http://vectors.nlpl.eu/repository/), version 2.0, published on Friday, December 27, 2019.
Please see the file 'meta.json' in this archive and the overall repository metadata file http://vectors.nlpl.eu/repository/20.json for additional information.
The life-time identifier for this model is:
http://vectors.nlpl.eu/repository/20/3.zip |
Word2vec/nlpl_2 | Word2vec | 2023-07-04T09:06:54Z | 0 | 1 | null | [
"word2vec",
"nor",
"dataset:Norsk_Aviskorpus/NoWaC",
"license:cc-by-4.0",
"region:us"
]
| null | 2023-06-01T15:11:33Z | ---
language: nor
tags:
- word2vec
datasets: Norsk_Aviskorpus/NoWaC
license: cc-by-4.0
---
## Information
A word2vec model trained by Andrey Kutuzov ([email protected]) on a vocabulary of size 306943 corresponding to 1941761506 tokens from the dataset `Norsk_Aviskorpus/NoWaC`.
The model is trained with the following properties: lemmatization and postag with the algorith Gensim Continuous Skipgram with window of 5 and dimension of 300.
## How to use?
```
from gensim.models import KeyedVectors
from huggingface_hub import hf_hub_download
model = KeyedVectors.load_word2vec_format(hf_hub_download(repo_id="Word2vec/nlpl_2", filename="model.bin"), binary=True, unicode_errors="ignore")
```
## Citation
Fares, Murhaf; Kutuzov, Andrei; Oepen, Stephan & Velldal, Erik (2017). Word vectors, reuse, and replicability: Towards a community repository of large-text resources, In Jรถrg Tiedemann (ed.), Proceedings of the 21st Nordic Conference on Computational Linguistics, NoDaLiDa, 22-24 May 2017. Linkรถping University Electronic Press. ISBN 978-91-7685-601-7
This archive is part of the NLPL Word Vectors Repository (http://vectors.nlpl.eu/repository/), version 2.0, published on Friday, December 27, 2019.
Please see the file 'meta.json' in this archive and the overall repository metadata file http://vectors.nlpl.eu/repository/20.json for additional information.
The life-time identifier for this model is:
http://vectors.nlpl.eu/repository/20/2.zip |
natykov/swin-tiny-patch4-window7-224-finetuned-eurosat | natykov | 2023-07-04T09:01:46Z | 209 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"swin",
"image-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| image-classification | 2023-07-04T08:52:09Z | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: swin-tiny-patch4-window7-224-finetuned-eurosat
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. -->
# swin-tiny-patch4-window7-224-finetuned-eurosat
This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5564
- Accuracy: 0.2861
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.5752 | 0.99 | 115 | 1.5699 | 0.2685 |
| 1.5519 | 2.0 | 231 | 1.5570 | 0.2866 |
| 1.5324 | 2.98 | 345 | 1.5564 | 0.2861 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
KPF/KPF-bert-cls3 | KPF | 2023-07-04T08:54:34Z | 161 | 0 | transformers | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2023-07-04T07:48:21Z | # KPF-BERT-CLS2
- [๋น
์นด์ธ์ฆ๋ฉ](https://lab.bigkinds.or.kr/) ์ธ์ฌ์ด๋ ๋ฉ๋ด์ ์ง์ญ๋ด์ค์์ ์ฌ์ฉ๋ ์ง์ญ๋ถ๋ฅ ์์ธก ๋ชจ๋ธ์ด๋ฉฐ ์ง์ญ์ ์ธ๋ถ๋ฅ ๊ฒฐ๊ณผ๋ฅผ ๋ํ๋ธ๋ค.
- ์ฌ์ฉ ๋ฐฉ๋ฒ์ ๋ํ ์๋ด ๋ฐ ์ฝ๋๋ [KPF-bigkinds github](https://github.com/KPF-bigkinds/BIGKINDS-LAB/tree/main/KPF-BERT-CLS)์์ ํ์ธํ ์ ์์ต๋๋ค.
## ๋ชจ๋ธ ์๊ฐ
### KPF-BERT-CLS
ํ๊ตญ์ธ๋ก ์งํฅ์ฌ๋จ์ด ๊ฐ๋ฐํ kpf-BERT ๋ชจ๋ธ์ ๊ธฐ๋ฐ์ผ๋ก CLS(Classification) task๋ฅผ ์ํํ ์ ์๋ kpf-BERT-cls ๋ชจ๋ธ์ ์ค๊ณ ๋ฐ ๊ฐ๋ฐํ์๋ค.
- ๋ณธ ์์ ์ ์ฌ์ฉ๋ kpf-BERT๋ [kpfBERT](https://github.com/KPFBERT/kpfbert)์ ๊ณต๊ฐ๋์ด ์๋ค.
- ๋ณธ ์์ ์์๋ ๋๋ถ๋ฅ, ์ง์ญ์ ์ ์ธํ ๋๋ถ๋ฅ๋ค์ ์ธ๋ถ๋ฅ, ์ง์ญ ์ธ๋ถ๋ฅ๋ก ๊ตฌ๋ถํ์ฌ ๋ฐ์ดํฐ๋ฅผ ํ์ตํ๋ค.
ํ์ต๋ฐ์ดํฐ๋ ๊ธฐ์ฌ๋ด์ฉ๊ณผ ๋ถ๋ฅ๋ช
์ ๋ฃ์ด ์ ์ํ์๋ค. ๋ถ๋ฅ๋ช
์ ์๋์ ๋ถ๋ฅ์ฒด๊ณ๋ฅผ ๋ฐ๋ฅด๋ฉฐ, ๊ธฐ์ฌ๋ด์ฉ + ๋๋ถ๋ฅ(์ง์ญ์ ์ธ) ๋ฐ์ดํฐ์
, ๊ธฐ์ฌ๋ด์ฉ + ์ธ๋ถ๋ฅ(์ง์ญ์ ์ธ) ๋ฐ์ดํฐ์
, ๊ธฐ์ฌ๋ด์ฉ + ์ง์ญ์ธ๋ถ๋ฅ ๋ฐ์ดํฐ์
์ผ๋ก ๋๋์ด ํ์ต์ ์งํํ๋ค.
ํ๊ตญ์ธ๋ก ์งํฅ์ฌ๋จ์ด ๊ฐ๋ฐํ kpf-BERT๋ฅผ ๊ธฐ๋ฐ์ผ๋ก classification layer๋ฅผ ์ถ๊ฐํ์ฌ kpf-BERT-cls ๋ชจ๋ธ์ ๊ฐ๋ฐํ๋ค. kpf-BERT-cls ๋ชจ๋ธ์ ๊ธฐ์ฌ๋ฅผ ์
๋ ฅ๋ฐ์ kpf-BERT ํ ํฌ๋์ด์ ๋ฅผ ์ฌ์ฉํ์ฌ ํด๋น ๊ธฐ์ฌ๊ฐ ์ด๋ ํด๋์ค์ ์ํ๋์ง ์์ธกํ๋ค.
๊ธฐ๋ณธ BERT ๋ชจ๋ธ์ ๊ตฌ์กฐ์ ํ ํฌ๋์ด์ ๋ ์๋์ ๊ทธ๋ฆผ๊ณผ ๊ฐ๋ค.
BERT๋ ์
๋ ฅ ๊ธธ์ด์ ์ ํ์ผ๋ก 512 subword ์ดํ์ ๊ฐ๋ง ์
๋ ฅ๋ฐ์ ์ ์๋ค. ๊ธฐ์ฌ์ ํน์ฑ์ ์ธํฐ๋ทฐ ๋ฑ์ ๊ธ์ 512 subword๋ณด๋ค ๊ธด ๊ฒ์ด ๋๋ถ๋ถ์ด๋ค. ์ด๋ฅผ ํด๊ฒฐํ๊ธฐ ์ํด ๋ณธ ๊ณผ์ ์์๋ stride๋ฅผ ์ฃผ์ด ๋
๋ฆฝ์ ์ผ๋ก ๋ฌธ์์ ์กฐ๊ฐ๋ค์ ์ฒ๋ฆฌํ๋ค.
kpf-BERT-cls๋ ๋๋ถ๋ฅ ์์ธก ๋ชจ๋ธ, ์ธ๋ถ๋ฅ ์์ธก ๋ชจ๋ธ, ์ง์ญ ์ธ๋ถ๋ฅ ์์ธก ๋ชจ๋ธ๋ก ๊ตฌ์ฑ๋์ด ์๋ค. ๋๋ถ๋ฅ/์ธ๋ถ๋ฅ ์์ธก ๋ชจ๋ธ์ top-3 ๊ฒฐ๊ณผ๋ฅผ ์ถ๋ ฅํ๋ค.
|
Fuyuxiang123/ppo-Huggy | Fuyuxiang123 | 2023-07-04T08:51:14Z | 3 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"Huggy",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
]
| reinforcement-learning | 2023-07-04T08:51:10Z | ---
library_name: ml-agents
tags:
- Huggy
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Huggy
---
# **ppo** Agent playing **Huggy**
This is a trained model of a **ppo** agent playing **Huggy**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog ๐ถ to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: Fuyuxiang123/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play ๐
|
nolanaatama/vgtfrmdbzrvcncgm | nolanaatama | 2023-07-04T08:51:11Z | 0 | 0 | null | [
"license:creativeml-openrail-m",
"region:us"
]
| null | 2023-07-04T08:47:40Z | ---
license: creativeml-openrail-m
---
|
greenw0lf/wav2vec2-large-xls-r-1b-frisian-cv-8-large-train | greenw0lf | 2023-07-04T08:49:21Z | 114 | 0 | transformers | [
"transformers",
"pytorch",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice_8_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
]
| automatic-speech-recognition | 2023-05-25T08:03:44Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- common_voice_8_0
metrics:
- wer
model-index:
- name: wav2vec2-large-xls-r-1b-frisian-cv-8-large-train
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: common_voice_8_0
type: common_voice_8_0
config: fy-NL
split: validation
args: fy-NL
metrics:
- name: Wer
type: wer
value: 0.04206541922582488
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: common_voice_8_0
type: common_voice_8_0
config: fy-NL
split: test
args: fy-NL
metrics:
- name: Wer
type: wer
value: 0.04108252637664402
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-1b-frisian-cv-8-large-train
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on the common_voice_8_0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0444
- Wer: 0.0421
And on the test set:
- Wer: 0.0411
## Model description
This model has been developed for my Master's thesis in "Voice Technology" at Rijksuniversiteit Groningen - Campus Fryslรขn. It corresponds to experiment 2 where
I use as training set all validated data (~ 50 hours) except the test and evaluation sets (~ 4.5 hours each). The number of training hours adds up to 41 hours of Frisian speech.
## Intended uses & limitations
The intended use is for recognizing Frisian speech.
Limitations include no LM rescoring and using version 8.0 of Common Voice instead of 13.0.
## Training and evaluation data
The evaluation split used is the one available in the Common Voice 8.0 Frisian subset. The train split corresponds to all of the validated data except for the recordings found in the evaluation and test splits.
## Training procedure
The script used for training this model can be found in this GitHub repository: [link](https://github.com/greenw0lf/MSc-VT-Thesis/).
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 36
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.98) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 40
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 7.2522 | 0.48 | 400 | 3.1028 | 1.0 |
| 3.0052 | 0.97 | 800 | 2.9334 | 1.0 |
| 2.0865 | 1.45 | 1200 | 0.7288 | 0.6646 |
| 1.1654 | 1.93 | 1600 | 0.4298 | 0.4196 |
| 0.9665 | 2.41 | 2000 | 0.3134 | 0.3162 |
| 0.7891 | 2.9 | 2400 | 0.2378 | 0.2587 |
| 0.8366 | 3.38 | 2800 | 0.1896 | 0.2016 |
| 0.8606 | 3.86 | 3200 | 0.1647 | 0.1903 |
| 0.7536 | 4.34 | 3600 | 0.1486 | 0.1573 |
| 0.632 | 4.83 | 4000 | 0.1341 | 0.1450 |
| 0.5198 | 5.31 | 4400 | 0.1223 | 0.1415 |
| 0.4998 | 5.79 | 4800 | 0.1155 | 0.1388 |
| 0.4273 | 6.27 | 5200 | 0.1132 | 0.1302 |
| 0.3982 | 6.76 | 5600 | 0.1036 | 0.1102 |
| 0.3964 | 7.24 | 6000 | 0.0988 | 0.1209 |
| 0.3848 | 7.72 | 6400 | 0.0995 | 0.0985 |
| 0.3702 | 8.2 | 6800 | 0.0969 | 0.0945 |
| 0.3612 | 8.69 | 7200 | 0.0899 | 0.0967 |
| 0.3518 | 9.17 | 7600 | 0.0856 | 0.1061 |
| 0.3371 | 9.65 | 8000 | 0.0902 | 0.0875 |
| 0.3295 | 10.13 | 8400 | 0.0819 | 0.0914 |
| 0.3157 | 10.62 | 8800 | 0.0785 | 0.0937 |
| 0.3025 | 11.1 | 9200 | 0.0782 | 0.0804 |
| 0.3092 | 11.58 | 9600 | 0.0758 | 0.0845 |
| 0.301 | 12.06 | 10000 | 0.0775 | 0.0847 |
| 0.3016 | 12.55 | 10400 | 0.0730 | 0.0776 |
| 0.2892 | 13.03 | 10800 | 0.0719 | 0.0735 |
| 0.283 | 13.51 | 11200 | 0.0728 | 0.0727 |
| 0.2806 | 13.99 | 11600 | 0.0694 | 0.0710 |
| 0.2639 | 14.48 | 12000 | 0.0705 | 0.0703 |
| 0.2606 | 14.96 | 12400 | 0.0652 | 0.0668 |
| 0.2595 | 15.44 | 12800 | 0.0638 | 0.0691 |
| 0.2611 | 15.92 | 13200 | 0.0636 | 0.0713 |
| 0.246 | 16.41 | 13600 | 0.0632 | 0.0653 |
| 0.2544 | 16.89 | 14000 | 0.0605 | 0.0638 |
| 0.2509 | 17.37 | 14400 | 0.0640 | 0.0646 |
| 0.2381 | 17.85 | 14800 | 0.0604 | 0.0663 |
| 0.2336 | 18.34 | 15200 | 0.0590 | 0.0628 |
| 0.2285 | 18.82 | 15600 | 0.0580 | 0.0612 |
| 0.2362 | 19.3 | 16000 | 0.0655 | 0.0638 |
| 0.2279 | 19.78 | 16400 | 0.0611 | 0.0669 |
| 0.2228 | 20.27 | 16800 | 0.0606 | 0.0621 |
| 0.2242 | 20.75 | 17200 | 0.0560 | 0.0575 |
| 0.2053 | 21.23 | 17600 | 0.0571 | 0.0572 |
| 0.2097 | 21.71 | 18000 | 0.0557 | 0.0555 |
| 0.2072 | 22.2 | 18400 | 0.0563 | 0.0576 |
| 0.2076 | 22.68 | 18800 | 0.0532 | 0.0562 |
| 0.2026 | 23.16 | 19200 | 0.0531 | 0.0540 |
| 0.1941 | 23.64 | 19600 | 0.0535 | 0.0534 |
| 0.1983 | 24.13 | 20000 | 0.0528 | 0.0541 |
| 0.2075 | 24.61 | 20400 | 0.0536 | 0.0538 |
| 0.1937 | 25.09 | 20800 | 0.0532 | 0.0569 |
| 0.1943 | 25.57 | 21200 | 0.0511 | 0.0507 |
| 0.1844 | 26.06 | 21600 | 0.0521 | 0.0521 |
| 0.181 | 26.54 | 22000 | 0.0506 | 0.0507 |
| 0.1877 | 27.02 | 22400 | 0.0529 | 0.0510 |
| 0.1825 | 27.5 | 22800 | 0.0527 | 0.0498 |
| 0.1872 | 27.99 | 23200 | 0.0506 | 0.0485 |
| 0.1857 | 28.47 | 23600 | 0.0497 | 0.0492 |
| 0.1766 | 28.95 | 24000 | 0.0504 | 0.0488 |
| 0.1756 | 29.43 | 24400 | 0.0496 | 0.0482 |
| 0.1701 | 29.92 | 24800 | 0.0479 | 0.0479 |
| 0.1717 | 30.4 | 25200 | 0.0499 | 0.0468 |
| 0.1624 | 30.88 | 25600 | 0.0492 | 0.0466 |
| 0.1671 | 31.36 | 26000 | 0.0490 | 0.0461 |
| 0.1704 | 31.85 | 26400 | 0.0482 | 0.0452 |
| 0.1653 | 32.33 | 26800 | 0.0467 | 0.0446 |
| 0.158 | 32.81 | 27200 | 0.0465 | 0.0449 |
| 0.1599 | 33.29 | 27600 | 0.0473 | 0.0445 |
| 0.1558 | 33.78 | 28000 | 0.0475 | 0.0453 |
| 0.1556 | 34.26 | 28400 | 0.0462 | 0.0445 |
| 0.1591 | 34.74 | 28800 | 0.0464 | 0.0431 |
| 0.1544 | 35.22 | 29200 | 0.0476 | 0.0433 |
| 0.1576 | 35.71 | 29600 | 0.0466 | 0.0434 |
| 0.1507 | 36.19 | 30000 | 0.0451 | 0.0435 |
| 0.1501 | 36.67 | 30400 | 0.0453 | 0.0429 |
| 0.1482 | 37.15 | 30800 | 0.0439 | 0.0432 |
| 0.1518 | 37.64 | 31200 | 0.0446 | 0.0424 |
| 0.1454 | 38.12 | 31600 | 0.0449 | 0.0417 |
| 0.145 | 38.6 | 32000 | 0.0440 | 0.0421 |
| 0.147 | 39.08 | 32400 | 0.0441 | 0.0424 |
| 0.141 | 39.57 | 32800 | 0.0444 | 0.0421 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu117
- Datasets 2.11.0
- Tokenizers 0.13.3
|
NourEldin-Osama/mT5-finetuned-xlsum | NourEldin-Osama | 2023-07-04T08:47:02Z | 104 | 0 | transformers | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"generated_from_trainer",
"dataset:xlsum",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text2text-generation | 2023-07-04T04:03:22Z | ---
tags:
- generated_from_trainer
datasets:
- xlsum
metrics:
- rouge
model-index:
- name: mT5-finetuned-xlsum
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: xlsum
type: xlsum
config: arabic
split: validation
args: arabic
metrics:
- name: Rouge1
type: rouge
value: 0.1179
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mT5-finetuned-xlsum
This model is a fine-tuned version of [csebuetnlp/mT5_m2o_arabic_crossSum](https://huggingface.co/csebuetnlp/mT5_m2o_arabic_crossSum) on the xlsum dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6752
- Rouge1: 0.1179
- Rouge2: 0.0231
- Rougel: 0.118
- Rougelsum: 0.1178
- Gen Len: 47.6818
## 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: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| 0.8728 | 1.0 | 9380 | 0.6752 | 0.1179 | 0.0231 | 0.118 | 0.1178 | 47.6818 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.0
- Datasets 2.13.1
- Tokenizers 0.13.3
|
Hawk91/whisper-small-hi | Hawk91 | 2023-07-04T08:43:37Z | 78 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"hi",
"dataset:mozilla-foundation/common_voice_13_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
]
| automatic-speech-recognition | 2023-07-01T12:21:50Z | ---
language:
- hi
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_13_0
metrics:
- wer
model-index:
- name: Whisper Small Hi - Hawk
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 13
type: mozilla-foundation/common_voice_13_0
config: hi
split: test
args: hi
metrics:
- name: Wer
type: wer
value: 35.53475278714895
---
<!-- 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. -->
# Whisper Small Hi - Hawk
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 13 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8294
- Wer Ortho: 58.7561
- Wer: 35.5348
## 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: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant_with_warmup
- lr_scheduler_warmup_steps: 50
- training_steps: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer Ortho | Wer |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:-------:|
| 0.9225 | 0.03 | 50 | 0.8294 | 58.7561 | 35.5348 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu117
- Datasets 2.13.1
- Tokenizers 0.13.3
|
matejvadovic/unit1-lunar-lander-v2 | matejvadovic | 2023-07-04T08:40:04Z | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-07-04T08:39:42Z | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 268.35 +/- 19.45
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
...
```
|
dcarpintero/Reinforce-CartPole-v2 | dcarpintero | 2023-07-04T08:39:40Z | 0 | 0 | null | [
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-07-04T08:39:02Z | ---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-CartPole-v2
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 500.00 +/- 0.00
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
AIYIYA/my_awesome_model | AIYIYA | 2023-07-04T08:30:35Z | 65 | 0 | transformers | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2023-07-02T10:47:12Z | ---
tags:
- generated_from_keras_callback
model-index:
- name: AIYIYA/my_awesome_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. -->
# AIYIYA/my_awesome_model
This model is a fine-tuned version of [bert-base-chinese](https://huggingface.co/bert-base-chinese) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.1422
- Validation Loss: 0.2983
- Train Accuracy: 0.8940
- 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', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 140, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Train Accuracy | Epoch |
|:----------:|:---------------:|:--------------:|:-----:|
| 0.2014 | 0.3058 | 0.8742 | 0 |
| 0.1413 | 0.2983 | 0.8940 | 1 |
| 0.1422 | 0.2983 | 0.8940 | 2 |
### Framework versions
- Transformers 4.30.2
- TensorFlow 2.12.0
- Datasets 2.13.1
- Tokenizers 0.13.3
|
heka-ai/cross-mpnet-20k | heka-ai | 2023-07-04T08:30:13Z | 2 | 0 | sentence-transformers | [
"sentence-transformers",
"pytorch",
"distilbert",
"feature-extraction",
"sentence-similarity",
"transformers",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
]
| sentence-similarity | 2023-07-04T08:30:09Z | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# heka-ai/cross-mpnet-20k
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('heka-ai/cross-mpnet-20k')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
def cls_pooling(model_output, attention_mask):
return model_output[0][:,0]
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('heka-ai/cross-mpnet-20k')
model = AutoModel.from_pretrained('heka-ai/cross-mpnet-20k')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, cls pooling.
sentence_embeddings = cls_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=heka-ai/cross-mpnet-20k)
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 400000 with parameters:
```
{'batch_size': 8, 'sampler': 'torch.utils.data.sampler.SequentialSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`gpl.toolkit.loss.MarginDistillationLoss`
Parameters of the fit()-Method:
```
{
"epochs": 1,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": 100000,
"warmup_steps": 1000,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 350, 'do_lower_case': False}) with Transformer model: DistilBertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information --> |
userusernamename/trinity_epoch1 | userusernamename | 2023-07-04T08:28:19Z | 0 | 0 | peft | [
"peft",
"region:us"
]
| null | 2023-07-04T08:28:17Z | ---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.4.0.dev0
|
ycros/airoboros-33b-gpt4-1.4.1-PI-8192-GGML | ycros | 2023-07-04T08:06:19Z | 0 | 4 | null | [
"region:us"
]
| null | 2023-07-04T07:14:40Z | GGML quants of https://huggingface.co/bhenrym14/airoboros-33b-gpt4-1.4.1-PI-8192-fp16 |
Andrewk2/kiedis99 | Andrewk2 | 2023-07-04T08:02:57Z | 0 | 1 | null | [
"region:us"
]
| null | 2023-07-04T07:55:01Z | andthony kiedis 1999, californication full album + some bsides |
ccattomio/PPO-LunarLander-v2 | ccattomio | 2023-07-04T07:56:56Z | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-07-04T07:38:09Z | ---
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: 264.54 +/- 18.84
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
```python
from stable_baselines3 import PPO
from huggingface_sb3 import load_from_hub
repo_id = "ccattomio/PPO-LunarLander-v2"
filename = "PPO-LunarLander-v2.zip"
checkpoint = load_from_hub(repo_id, filename)
model = PPO.load(checkpoint)
```
|
skgg/output | skgg | 2023-07-04T07:56:31Z | 29 | 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 | 2023-06-27T09:22:12Z |
---
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 - skgg/output
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: False.
|
CICLAB-Comillas/AlpaCalls | CICLAB-Comillas | 2023-07-04T07:50:42Z | 2 | 0 | peft | [
"peft",
"region:us"
]
| null | 2023-06-27T11:48:49Z | ---
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
|
heka-ai/tasb-bert-50k | heka-ai | 2023-07-04T07:47:46Z | 1 | 0 | sentence-transformers | [
"sentence-transformers",
"pytorch",
"distilbert",
"feature-extraction",
"sentence-similarity",
"transformers",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
]
| sentence-similarity | 2023-07-04T07:47:42Z | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# heka-ai/tasb-bert-50k
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('heka-ai/tasb-bert-50k')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
def cls_pooling(model_output, attention_mask):
return model_output[0][:,0]
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('heka-ai/tasb-bert-50k')
model = AutoModel.from_pretrained('heka-ai/tasb-bert-50k')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, cls pooling.
sentence_embeddings = cls_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=heka-ai/tasb-bert-50k)
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 50000 with parameters:
```
{'batch_size': 32, 'sampler': 'torch.utils.data.sampler.SequentialSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`gpl.toolkit.loss.MarginDistillationLoss`
Parameters of the fit()-Method:
```
{
"epochs": 1,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": 50000,
"warmup_steps": 1000,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 350, 'do_lower_case': False}) with Transformer model: DistilBertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information --> |
revmag/ppo-LunarLander-v2 | revmag | 2023-07-04T07:41:21Z | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-07-04T07:41:05Z | ---
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: -837.23 +/- 436.43
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
...
```
|
vineetsharma/whisper-tiny-finetuned-minds14-en | vineetsharma | 2023-07-04T07:40:30Z | 86 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:PolyAI/minds14",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
]
| automatic-speech-recognition | 2023-07-03T15:25:29Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- PolyAI/minds14
metrics:
- wer
model-index:
- name: whisper-tiny-finetuned-minds14-en
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: PolyAI/minds14
type: PolyAI/minds14
config: en-US
split: train
args: en-US
metrics:
- name: Wer
type: wer
value: 0.33943329397874855
---
<!-- 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. -->
# whisper-tiny-finetuned-minds14-en
This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the PolyAI/minds14 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6329
- Wer Ortho: 0.3430
- Wer: 0.3394
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant_with_warmup
- lr_scheduler_warmup_steps: 50
- training_steps: 500
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer Ortho | Wer |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|
| 0.0009 | 17.86 | 500 | 0.6329 | 0.3430 | 0.3394 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
smart-assistant/falcon-7b-multi | smart-assistant | 2023-07-04T07:34:22Z | 0 | 0 | peft | [
"peft",
"region:us"
]
| null | 2023-07-04T07:34:21Z | ---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.4.0.dev0
|
sd-concepts-library/ahx-beta-4a3bf61 | sd-concepts-library | 2023-07-04T07:25:36Z | 0 | 0 | null | [
"license:mit",
"region:us"
]
| null | 2023-07-04T07:25:35Z | ---
license: mit
---
### ahx-beta-4a3bf61 on Stable Diffusion
This is the `<ahx-beta-4a3bf61>` 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 a `style`:
|
weifeng-chen/controlavideo-hed | weifeng-chen | 2023-07-04T07:21:48Z | 42 | 1 | diffusers | [
"diffusers",
"arxiv:2305.13840",
"license:gpl-3.0",
"diffusers:Controlnet3DStableDiffusionPipeline",
"region:us"
]
| null | 2023-06-13T14:25:02Z | ---
license: gpl-3.0
---
- Hed Control Pretrained model for [control-a-video](https://arxiv.org/abs/2305.13840)
- Project page: https://controlavideo.github.io/
- Code: https://github.com/Weifeng-Chen/control-a-video
# Citation
```
@misc{chen2023controlavideo,
title={Control-A-Video: Controllable Text-to-Video Generation with Diffusion Models},
author={Weifeng Chen and Jie Wu and Pan Xie and Hefeng Wu and Jiashi Li and Xin Xia and Xuefeng Xiao and Liang Lin},
year={2023},
eprint={2305.13840},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
``` |
weifeng-chen/controlavideo-canny | weifeng-chen | 2023-07-04T07:21:12Z | 228 | 1 | diffusers | [
"diffusers",
"arxiv:2305.13840",
"license:gpl-3.0",
"diffusers:Controlnet3DStableDiffusionPipeline",
"region:us"
]
| null | 2023-06-13T12:26:52Z | ---
license: gpl-3.0
---
- Canny Control Pretrained model for [control-a-video](https://arxiv.org/abs/2305.13840)
- Project page: https://controlavideo.github.io/
- Code: https://github.com/Weifeng-Chen/control-a-video
# Citation
```
@misc{chen2023controlavideo,
title={Control-A-Video: Controllable Text-to-Video Generation with Diffusion Models},
author={Weifeng Chen and Jie Wu and Pan Xie and Hefeng Wu and Jiashi Li and Xin Xia and Xuefeng Xiao and Liang Lin},
year={2023},
eprint={2305.13840},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
``` |
jeff1jeffo/mystarcoder | jeff1jeffo | 2023-07-04T07:10:51Z | 0 | 0 | null | [
"text-generation",
"region:us"
]
| text-generation | 2023-07-04T06:41:37Z | ---
pipeline_tag: text-generation
inference: true
--- |
megagonlabs/t5-base-japanese-web-8k | megagonlabs | 2023-07-04T07:05:38Z | 115 | 3 | transformers | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"seq2seq",
"ja",
"dataset:mc4",
"dataset:wiki40b",
"arxiv:1910.10683",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text2text-generation | 2022-03-02T23:29:05Z | ---
language: ja
tags:
- t5
- text2text-generation
- seq2seq
license: apache-2.0
datasets:
- mc4
- wiki40b
---
# t5-base-japanese-web-8k (with Byte-fallback, 8K)
## Description
[megagonlabs/t5-base-japanese-web-8k](https://huggingface.co/megagonlabs/t5-base-japanese-web-8k) is a T5 (Text-to-Text Transfer Transformer) model pre-trained on Japanese web texts.
Training codes are [available on GitHub](https://github.com/megagonlabs/t5-japanese).
The vocabulary size of this model is 8K.
[32K version is also available](https://huggingface.co/megagonlabs/t5-base-japanese-web).
### Corpora
We used following corpora for pre-training.
- Japanese in [mC4/3.0.1](https://huggingface.co/datasets/mc4) (We used [Tensorflow native format](https://github.com/allenai/allennlp/discussions/5056))
- 87,425,304 pages
- 782 GB in TFRecord format
- [Japanese](https://www.tensorflow.org/datasets/catalog/wiki40b#wiki40bja) in [wiki40b/1.3.0](https://www.tensorflow.org/datasets/catalog/wiki40b)
- 828,236 articles (2,073,584 examples)
- 2 GB in TFRecord format
### Tokenizer
We used Japanese Wikipedia to train [SentencePiece](https://github.com/google/sentencepiece).
- Vocabulary size: 8,000
- [Byte-fallback](https://github.com/google/sentencepiece/releases/tag/v0.1.9): Enabled
### Parameters
- T5 model: [models/t5.1.1.base.gin](https://github.com/google-research/text-to-text-transfer-transformer/blob/main/t5/models/gin/models/t5.1.1.base.gin)
- Training steps: 1,000,000
It took about 126 hours with TPU v3-8
## Related models
- [ๆฅๆฌ่ชT5ไบๅๅญฆ็ฟๆธใฟใขใใซ (sonoisa/t5-base-japanese)](https://huggingface.co/sonoisa/t5-base-japanese)
- [ๆฅๆฌ่ชT5ไบๅๅญฆ็ฟๆธใฟใขใใซ (sonoisa/t5-base-japanese-mC4-Wikipedia)](https://huggingface.co/sonoisa/t5-base-japanese-mC4-Wikipedia)
## License
Apache License 2.0
## Citations
- mC4
Contains information from `mC4` which is made available under the [ODC Attribution License](https://opendatacommons.org/licenses/by/1-0/).
```bibtex
@article{2019t5,
author = {Colin Raffel and Noam Shazeer and Adam Roberts and Katherine Lee and Sharan Narang and Michael Matena and Yanqi Zhou and Wei Li and Peter J. Liu},
title = {Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer},
journal = {arXiv e-prints},
year = {2019},
archivePrefix = {arXiv},
eprint = {1910.10683},
}
```
- wiki40b
```bibtex
@inproceedings{49029,
title = {Wiki-40B: Multilingual Language Model Dataset},
author = {Mandy Guo and Zihang Dai and Denny Vrandecic and Rami Al-Rfou},
year = {2020},
booktitle = {LREC 2020}
}
```
|
sang-kyung/bottle | sang-kyung | 2023-07-04T06:54:36Z | 0 | 0 | diffusers | [
"diffusers",
"tensorboard",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"dreambooth",
"base_model:stabilityai/stable-diffusion-2-1-base",
"base_model:finetune:stabilityai/stable-diffusion-2-1-base",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
]
| text-to-image | 2023-07-02T08:05:05Z |
---
license: creativeml-openrail-m
base_model: stabilityai/stable-diffusion-2-1-base
instance_prompt: a photo of sks bottle
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- dreambooth
inference: true
---
# DreamBooth - sang-kyung/bottle
This is a dreambooth model derived from stabilityai/stable-diffusion-2-1-base. The weights were trained on a photo of sks bottle using [DreamBooth](https://dreambooth.github.io/).
You can find some example images in the following.
DreamBooth for the text encoder was enabled: True.
|
adya/ppo-Huggy | adya | 2023-07-04T06:54:22Z | 15 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"Huggy",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
]
| reinforcement-learning | 2023-07-04T06:54:03Z | ---
library_name: ml-agents
tags:
- Huggy
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Huggy
---
# **ppo** Agent playing **Huggy**
This is a trained model of a **ppo** agent playing **Huggy**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog ๐ถ to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: adya/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play ๐
|
NasimB/gpt2-concat-gutenberg-fixed | NasimB | 2023-07-04T06:31:50Z | 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 | 2023-07-04T04:12:40Z | ---
license: mit
tags:
- generated_from_trainer
datasets:
- generator
model-index:
- name: gpt2-concat-gutenberg-fixed
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# gpt2-concat-gutenberg-fixed
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: 3.0040
## 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: 7
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 6.7298 | 0.29 | 500 | 5.6360 |
| 5.3656 | 0.58 | 1000 | 5.2026 |
| 5.0212 | 0.87 | 1500 | 4.9523 |
| 4.7476 | 1.16 | 2000 | 4.7988 |
| 4.586 | 1.45 | 2500 | 4.6801 |
| 4.4835 | 1.74 | 3000 | 4.5786 |
| 4.3674 | 2.03 | 3500 | 4.4991 |
| 4.1624 | 2.32 | 4000 | 4.4532 |
| 4.137 | 2.61 | 4500 | 4.3960 |
| 4.106 | 2.91 | 5000 | 4.3422 |
| 3.9133 | 3.2 | 5500 | 4.3427 |
| 3.8519 | 3.49 | 6000 | 4.3083 |
| 3.8433 | 3.78 | 6500 | 4.2794 |
| 3.758 | 4.07 | 7000 | 4.2761 |
| 3.5652 | 4.36 | 7500 | 4.2719 |
| 3.5749 | 4.65 | 8000 | 4.2517 |
| 3.5632 | 4.94 | 8500 | 4.2355 |
| 3.3622 | 5.23 | 9000 | 4.2584 |
| 3.3265 | 5.52 | 9500 | 4.2559 |
| 3.3112 | 5.81 | 10000 | 4.2500 |
| 3.264 | 6.1 | 10500 | 4.2572 |
| 3.1673 | 6.39 | 11000 | 4.2606 |
| 3.1623 | 6.68 | 11500 | 4.2607 |
| 3.1614 | 6.97 | 12000 | 4.2607 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.11.0+cu113
- Datasets 2.13.0
- Tokenizers 0.13.3
|
Softechlb/Sent_analysis_CVs | Softechlb | 2023-07-04T06:23:50Z | 240 | 0 | transformers | [
"transformers",
"pytorch",
"safetensors",
"distilbert",
"text-classification",
"sentiment-analysis",
"zero-shot-distillation",
"distillation",
"zero-shot-classification",
"debarta-v3",
"en",
"ar",
"de",
"es",
"fr",
"ja",
"zh",
"id",
"hi",
"it",
"ms",
"pt",
"dataset:tyqiangz/multilingual-sentiments",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2023-06-30T07:09:51Z | ---
license: apache-2.0
tags:
- sentiment-analysis
- text-classification
- zero-shot-distillation
- distillation
- zero-shot-classification
- debarta-v3
model-index:
- name: Softechlb/Sent_analysis_CVs
results: []
datasets:
- tyqiangz/multilingual-sentiments
language:
- en
- ar
- de
- es
- fr
- ja
- zh
- id
- hi
- it
- ms
- pt
---
<!-- 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. -->
# Softechlb/Sent_analysis_CVs
This model is distilled from the zero-shot classification pipeline on the Multilingual Sentiment
dataset using this [script](https://github.com/huggingface/transformers/tree/main/examples/research_projects/zero-shot-distillation).
In reality the multilingual-sentiment dataset is annotated of course,
but we'll pretend and ignore the annotations for the sake of example.
Teacher model: MoritzLaurer/mDeBERTa-v3-base-mnli-xnli
Teacher hypothesis template: "The sentiment of this text is {}."
Student model: distilbert-base-multilingual-cased
## Inference example
```python
from transformers import pipeline
distilled_student_sentiment_classifier = pipeline(
model="Softechlb/Sent_analysis_CVs",
return_all_scores=True
)
# english
distilled_student_sentiment_classifier ("I love this movie and i would watch it again and again!")
>> [[{'label': 'positive', 'score': 0.9731044769287109},
{'label': 'neutral', 'score': 0.016910076141357422},
{'label': 'negative', 'score': 0.009985478594899178}]]
# malay
distilled_student_sentiment_classifier("Saya suka filem ini dan saya akan menontonnya lagi dan lagi!")
[[{'label': 'positive', 'score': 0.9760093688964844},
{'label': 'neutral', 'score': 0.01804516464471817},
{'label': 'negative', 'score': 0.005945465061813593}]]
# japanese
distilled_student_sentiment_classifier("็งใฏใใฎๆ ็ปใๅคงๅฅฝใใงใไฝๅบฆใ่ฆใพใ๏ผ")
>> [[{'label': 'positive', 'score': 0.9342429041862488},
{'label': 'neutral', 'score': 0.040193185210227966},
{'label': 'negative', 'score': 0.025563929229974747}]]
```
```
### Training log
```bash
Training completed. Do not forget to share your model on huggingface.co/models =)
{'train_runtime': 2009.8864, 'train_samples_per_second': 73.0, 'train_steps_per_second': 4.563, 'train_loss': 0.6473459283913797, 'epoch': 1.0}
100%|โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ| 9171/9171 [33:29<00:00, 4.56it/s]
[INFO|trainer.py:762] 2023-05-06 10:56:18,555 >> The following columns in the evaluation set don't have a corresponding argument in `DistilBertForSequenceClassification.forward` and have been ignored: text. If text are not expected by `DistilBertForSequenceClassification.forward`, you can safely ignore this message.
[INFO|trainer.py:3129] 2023-05-06 10:56:18,557 >> ***** Running Evaluation *****
[INFO|trainer.py:3131] 2023-05-06 10:56:18,557 >> Num examples = 146721
[INFO|trainer.py:3134] 2023-05-06 10:56:18,557 >> Batch size = 128
100%|โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ| 1147/1147 [08:59<00:00, 2.13it/s]
05/06/2023 11:05:18 - INFO - __main__ - Agreement of student and teacher predictions: 88.29%
[INFO|trainer.py:2868] 2023-05-06 11:05:18,251 >> Saving model checkpoint to ./distilbert-base-multilingual-cased-sentiments-student
[INFO|configuration_utils.py:457] 2023-05-06 11:05:18,251 >> Configuration saved in ./distilbert-base-multilingual-cased-sentiments-student/config.json
[INFO|modeling_utils.py:1847] 2023-05-06 11:05:18,905 >> Model weights saved in ./distilbert-base-multilingual-cased-sentiments-student/pytorch_model.bin
[INFO|tokenization_utils_base.py:2171] 2023-05-06 11:05:18,905 >> tokenizer config file saved in ./distilbert-base-multilingual-cased-sentiments-student/tokenizer_config.json
[INFO|tokenization_utils_base.py:2178] 2023-05-06 11:05:18,905 >> Special tokens file saved in ./distilbert-base-multilingual-cased-sentiments-student/special_tokens_map.json
```
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3 |
Pranjal-666/Reinforce-pixelcopter | Pranjal-666 | 2023-07-04T06:10:20Z | 0 | 0 | null | [
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-07-03T08:35:18Z | ---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-pixelcopter
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 21.80 +/- 13.98
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
|
bobobert4/qlearning_Taxi-v3 | bobobert4 | 2023-07-04T05:31:33Z | 0 | 0 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-07-04T04:54:30Z | ---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: qlearning_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="bobobert4/qlearning_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"])
```
|
chet4/my_awesome_qa_model | chet4 | 2023-07-04T05:26:29Z | 105 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
]
| question-answering | 2023-07-03T09:31:51Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: my_awesome_qa_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_qa_model
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6204
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 250 | 2.3733 |
| 2.7971 | 2.0 | 500 | 1.7135 |
| 2.7971 | 3.0 | 750 | 1.6204 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
sourinkarmakar/kyc_v1-donut-demo | sourinkarmakar | 2023-07-04T05:25:49Z | 11 | 0 | transformers | [
"transformers",
"pytorch",
"vision-encoder-decoder",
"image-text-to-text",
"donut",
"kyc",
"en",
"endpoints_compatible",
"region:us"
]
| image-text-to-text | 2023-07-03T19:04:52Z | ---
language:
- en
metrics:
- accuracy
library_name: transformers
tags:
- donut
- kyc
---
# Model description
Donut is an end-to-end (i.e., self-contained) VDU model for the general understanding of document images. The architecture of Donut is quite simple, which consists of a Transformer based visual encoder and textual decoder modules.
Donut does not rely on any modules related to OCR functionality but uses a visual encoder for extracting features from a given document image.
The following textual decoder maps the derived features into a sequence of subword tokens to construct a desired structured format (e.g., JSON). Each model component is Transformer-based, and thus the model is trained easily in an end-to-end manner.
# Intended uses and limitations
This model is trained to be used for reading the contents of Indian KYC documents. It can classify and read the contents of Aadhar, PAN and Voter. It also can detect the orientation and whether the document is coloured or Black and White. The document for input can be oriented in any direction.
The model should be provided with a fair-quality image (so that the contents are readable).
It has been trained on limited data so the performance might not be very good. In future versions, the number of images will be more and more types of KYC documents can be added to this.
# Training data
For v1, a custom dataset has been used for the training purpose where around 283 images were used, out of which 199 were for training, 42 were for validation and 42 were for testing.
Out of 199 images, 57 Aadhar samples, 57 PAN samples and 85 Voter samples were used.
# Performance
The current performance is as follows
Overall accuracy = 74 %
Aadhar = 49 % (need to check out, the reason behind the less accuracy)
PAN = 94 %
Voter = 76 %
# Inference
``` python
from transformers import DonutProcessor, VisionEncoderDecoderModel
import re
import cv2
import json
import torch
from tqdm.auto import tqdm
import numpy as np
from donut import JSONParseEvaluator
processor = DonutProcessor.from_pretrained("sourinkarmakar/kyc_v1-donut-demo")
model = VisionEncoderDecoderModel.from_pretrained("sourinkarmakar/kyc_v1-donut-demo")
# Need to install python-donut
# !pip install -q donut-python
# Images stored inside a folder 'unseen_samples'
dataset = glob.glob(os.path.join(basepath, "unseen_samples/*"))
output_list = []
for idx, sample in tqdm(enumerate(dataset), total=len(dataset)):
# prepare encoder inputs
img = cv2.imread(sample)
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
pixel_values = processor(img, return_tensors="pt").pixel_values
pixel_values = pixel_values.to(device)
# prepare decoder inputs
task_prompt = "<s_cord-v2>"
decoder_input_ids = processor.tokenizer(task_prompt, add_special_tokens=False, return_tensors="pt").input_ids
decoder_input_ids = decoder_input_ids.to(device)
# autoregressively generate sequence
outputs = model.generate(
pixel_values,
decoder_input_ids=decoder_input_ids,
max_length=model.decoder.config.max_position_embeddings,
early_stopping=True,
pad_token_id=processor.tokenizer.pad_token_id,
eos_token_id=processor.tokenizer.eos_token_id,
use_cache=True,
num_beams=1,
bad_words_ids=[[processor.tokenizer.unk_token_id]],
return_dict_in_generate=True,
)
# turn into JSON
seq = processor.batch_decode(outputs.sequences)[0]
seq = seq.replace(processor.tokenizer.eos_token, "").replace(processor.tokenizer.pad_token, "")
seq = re.sub(r"<.*?>", "", seq, count=1).strip() # remove first task start token
seq = processor.token2json(seq)
output_list.append(seq)
print(output_list)
``` |
0x7o/rubert-base-massive-ner | 0x7o | 2023-07-04T05:18:06Z | 236 | 1 | transformers | [
"transformers",
"pytorch",
"safetensors",
"bert",
"token-classification",
"ru",
"dataset:massive",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| token-classification | 2023-07-04T05:12:15Z | ---
datasets:
- massive
model-index:
- name: rubert-base-massive-ner
results: []
license: apache-2.0
language:
- ru
pipeline_tag: token-classification
---
<!-- 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. -->
# rubert-base-massive-ner
This model is a fine-tuned version of [DeepPavlov/rubert-base-cased](https://huggingface.co/DeepPavlov/rubert-base-cased) on the massive dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0367
## 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 |
|:-------------:|:-----:|:----:|:---------------:|
| 0.1228 | 0.77 | 500 | 0.0565 |
| 0.0517 | 1.54 | 1000 | 0.0367 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3 |
Subsets and Splits