modelId
stringlengths 5
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| author
stringlengths 2
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| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-08-30 00:39:23
| downloads
int64 0
223M
| likes
int64 0
11.7k
| library_name
stringclasses 526
values | tags
listlengths 1
4.05k
| pipeline_tag
stringclasses 55
values | createdAt
timestamp[us, tz=UTC]date 2022-03-02 23:29:04
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| card
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|---|---|---|---|---|---|---|---|---|---|
KBLab/megatron-bert-large-swedish-cased-165k
|
KBLab
| 2023-03-20T08:01:57Z | 115 | 0 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"megatron-bert",
"fill-mask",
"sv",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-21T09:38:41Z |
---
language:
- sv
---
# Megatron-BERT-large Swedish 165k
This BERT model was trained using the Megatron-LM library.
The size of the model is a regular BERT-large with 340M parameters.
The model was trained on about 70GB of data, consisting mostly of OSCAR and Swedish newspaper text curated by the National Library of Sweden.
Training was done for 165k training steps using a batch size of 8k; the number of training steps is set to 500k, meaning that this version is a checkpoint.
The hyperparameters for training followed the setting for RoBERTa.
The model has three sister models trained on the same dataset:
- [🤗 BERT Swedish](https://huggingface.co/KBLab/bert-base-swedish-cased-new)
- [Megatron-BERT-base-600k](https://huggingface.co/KBLab/megatron-bert-base-swedish-cased-600k)
- [Megatron-BERT-base-125k](https://huggingface.co/KBLab/megatron-bert-base-swedish-cased-125k)
and an earlier checkpoint
- [Megatron-BERT-large-110k](https://huggingface.co/KBLab/megatron-bert-large-swedish-cased-110k)
## Acknowledgements
We gratefully acknowledge the HPC RIVR consortium (https://www.hpc-rivr.si) and EuroHPC JU (https://eurohpc-ju.europa.eu) for funding this research by providing computing resources of the HPC system Vega at the Institute of Information Science (https://www.izum.si).
|
DmitriyVasiliev/autotrain-test2summbart-42231108366
|
DmitriyVasiliev
| 2023-03-20T07:48:54Z | 8 | 0 |
transformers
|
[
"transformers",
"pytorch",
"mbart",
"text2text-generation",
"autotrain",
"summarization",
"unk",
"dataset:DmitriyVasiliev/autotrain-data-test2summbart",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
summarization
| 2023-03-20T07:36:00Z |
---
tags:
- autotrain
- summarization
language:
- unk
widget:
- text: "I love AutoTrain 🤗"
datasets:
- DmitriyVasiliev/autotrain-data-test2summbart
co2_eq_emissions:
emissions: 4.976184480111491
---
# Model Trained Using AutoTrain
- Problem type: Summarization
- Model ID: 42231108366
- CO2 Emissions (in grams): 4.9762
## Validation Metrics
- Loss: 1.595
- Rouge1: 5.139
- Rouge2: 1.648
- RougeL: 5.056
- RougeLsum: 5.063
- Gen Len: 31.428
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_HUGGINGFACE_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/DmitriyVasiliev/autotrain-test2summbart-42231108366
```
|
loveisp/taxi-v3
|
loveisp
| 2023-03-20T07:33:53Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-20T07:33:50Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.54 +/- 2.74
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="loveisp/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"])
```
|
alvarez/rl_course_vizdoom_health_gathering_supreme
|
alvarez
| 2023-03-20T07:25:43Z | 0 | 0 |
sample-factory
|
[
"sample-factory",
"tensorboard",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-20T07:25:17Z |
---
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: 4.16 +/- 0.54
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 alvarez/rl_course_vizdoom_health_gathering_supreme
```
## Using the model
To run the model after download, use the `enjoy` script corresponding to this environment:
```
python -m .usr.local.lib.python3.9.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme
```
You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag.
See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details
## Training with this model
To continue training with this model, use the `train` script corresponding to this environment:
```
python -m .usr.local.lib.python3.9.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme --restart_behavior=resume --train_for_env_steps=10000000000
```
Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
|
Raiden-1001/a2c-PandaReachDense-v2
|
Raiden-1001
| 2023-03-20T07:21:17Z | 2 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"PandaReachDense-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-20T07:18:52Z |
---
library_name: stable-baselines3
tags:
- PandaReachDense-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachDense-v2
type: PandaReachDense-v2
metrics:
- type: mean_reward
value: -2.14 +/- 0.44
name: mean_reward
verified: false
---
# **A2C** Agent playing **PandaReachDense-v2**
This is a trained model of a **A2C** agent playing **PandaReachDense-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
...
```
|
YoungBeauty/xlm-roberta-base-finetuned-panx-de
|
YoungBeauty
| 2023-03-20T07:01:20Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2023-03-19T15:31:34Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-de
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xtreme
type: xtreme
config: PAN-X.de
split: validation
args: PAN-X.de
metrics:
- name: F1
type: f1
value: 0.8633240588268695
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-de
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1350
- F1: 0.8633
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2586 | 1.0 | 525 | 0.1608 | 0.8206 |
| 0.1256 | 2.0 | 1050 | 0.1344 | 0.8455 |
| 0.0802 | 3.0 | 1575 | 0.1350 | 0.8633 |
### Framework versions
- Transformers 4.27.1
- Pytorch 2.0.0+cu117
- Datasets 2.10.1
- Tokenizers 0.13.2
|
mr4/phobert-base-vi-sentiment-analysis
|
mr4
| 2023-03-20T06:55:54Z | 191,047 | 3 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"roberta",
"text-classification",
"Vietnamese",
"sentiment",
"analysis",
"vi",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-03-17T07:10:43Z |
---
language:
- vi
library_name: transformers
pipeline_tag: text-classification
tags:
- Vietnamese
- sentiment
- analysis
---
# Sentiment Analysis in Vietnamese - Phân tích cảm xúc trong tiếng Việt
## Phở Bert phân tích cảm xúc
## Model description
Mô hình có tác dụng xác định cảm xúc của đoạn văn.
Sử dụng nhãn: "Tích cực", "Tiêu cực", "Trung tính"
Ví dụ:
Thời tiết hôm nay không được đẹp, trời mưa và lạnh.
```text
Tiêu cực: 0.9596341252326965
Tích cực: 0.010115462355315685
Trung tính: 0.030250443145632744
```
Hôm nay đi làm thật vui, ăn uống thật ngon.
```text
Tiêu cực: 0.002220266032963991
Tích cực: 0.9917450547218323
Trung tính: 0.006034655496478081
```
Bình thường. Không có gì đặc biệt.
```text
Tiêu cực: 0.03198615834116936
Tích cực: 0.05307402461767197
Trung tính: 0.9149397611618042
```
## Base model
Mô hình được đạo tạo dựa trên cơ sở của model PhoBert-Base của VinAI (https://huggingface.co/vinai/phobert-large)
## Training data
Mô hình được đào tạo dựa trên dữ liệu được thu thập bởi linhlpv (https://www.kaggle.com/datasets/linhlpv/vietnamese-sentiment-analyst) - có chỉnh sửa.
Với 31436 nội dung đánh giá sảm phẩm.
## Model variations
Chưa xác định
## Intended uses & limitations
Chưa xác định
## License
Đây là một open-source library, bạn có thể sử dụng nó với bất kì mục đích nào.
Rất cảm ơn nếu bạn ghi nguồn khi sử dụng mô hình này (nếu không ghi cũng không sao).
### How to use
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
import os
def clear():
os.system('clear')
checkpoint = "mr4/phobert-base-vi-sentiment-analysis"
tokenizer = AutoTokenizer.from_pretrained(checkpoint)
model = AutoModelForSequenceClassification.from_pretrained(checkpoint)
clear()
print("Ngày hôm nay của bạn thế nào?")
val = input("")
raw_inputs = [val]
inputs = tokenizer(raw_inputs, padding=True,
truncation=True, return_tensors="pt")
outputs = model(**inputs)
predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)
clear()
print(">>>>>>>>>>>>>>>>>>>>>>>>>>")
for i, prediction in enumerate(predictions):
print(raw_inputs[i])
for j, value in enumerate(prediction):
print(
" " + model.config.id2label[j] + ": " + str(value.item()))
print("<<<<<<<<<<<<<<<<<<<<<<<<<<")
```
## Liên hệ
Mọi thông tin liên quan có thể liên hệ qua email: zZz4everzZz@live.co.uk.
|
alibaba-pai/pai-diffusion-general-large-zh
|
alibaba-pai
| 2023-03-20T06:55:07Z | 5 | 2 |
diffusers
|
[
"diffusers",
"pytorch",
"text-to-image",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2022-12-06T06:48:07Z |
---
license: apache-2.0
tags:
- pytorch
- diffusers
- text-to-image
---
# Chinese Latent Diffusion Model
我们开源了一个中文 Lattent Diffusion 模型（开放领域通用版本）
* Github: [EasyNLP](https://github.com/alibaba/EasyNLP)
```python
from diffusers import StableDiffusionPipeline
model_id = "alibaba-pai/pai-diffusion-general-large-zh"
pipe = StableDiffusionPipeline.from_pretrained(model_id)
pipe = pipe.to("cuda")
prompt = "雾蒙蒙的日出在湖面上"
image = pipe(prompt).images[0]
image.save("result.png")
```
|
mr4/bert-base-jp-sentiment-analysis
|
mr4
| 2023-03-20T06:54:56Z | 30 | 1 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"bert",
"text-classification",
"sentiment",
"analysis",
"Japanses",
"ja",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-03-20T04:45:19Z |
---
language:
- ja
library_name: transformers
pipeline_tag: text-classification
tags:
- sentiment
- analysis
- Japanses
---
# Sentiment Analysis in Japanese - Phân tích cảm xúc trong tiếng Nhật
## Bert phân tích cảm xúc
## Model description
Mô hình có tác dụng xác định cảm xúc của đoạn văn.
Sử dụng nhãn: "positive", "negative"
Ví dụ:
今日はいい天気ですね
```text
negative: 6.001393558108248e-05
positive: 0.999940037727356
```
今日の食べ物はとてもつまらない
```text
negative: 0.9999252557754517
positive: 7.470489799743518e-05
```
## Base model
Mô hình được đạo tạo dựa trên cơ sở của model Base Japanese
## Training data
Mô hình được đào tạo dựa trên dữ liệu được thu thập bởi TAKAHIRO KUBO (https://www.kaggle.com/datasets/takahirokubo0/chabsa) - có chỉnh sửa.
## Model variations
Chưa xác định
## Intended uses & limitations
Chưa xác định
## License
Đây là một open-source library, bạn có thể sử dụng nó với bất kì mục đích nào.
Rất cảm ơn nếu bạn ghi nguồn khi sử dụng mô hình này (nếu không ghi cũng không sao).
### How to use
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
import os
def clear():
os.system('clear')
checkpoint = "mr4/bert-base-jp-sentiment-analysis"
tokenizer = AutoTokenizer.from_pretrained(checkpoint)
model = AutoModelForSequenceClassification.from_pretrained(checkpoint)
clear()
print("Ngày hôm nay của bạn thế nào?")
val = input("")
raw_inputs = [val]
inputs = tokenizer(raw_inputs, padding=True,
truncation=True, return_tensors="pt")
outputs = model(**inputs)
predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)
clear()
print(">>>>>>>>>>>>>>>>>>>>>>>>>>")
for i, prediction in enumerate(predictions):
print(raw_inputs[i])
for j, value in enumerate(prediction):
print(
" " + model.config.id2label[j] + ": " + str(value.item()))
print("<<<<<<<<<<<<<<<<<<<<<<<<<<")
```
## Liên hệ
Mọi thông tin liên quan có thể liên hệ qua email: zZz4everzZz@live.co.uk.
|
stmnk/codet5-small-code-summarization-python
|
stmnk
| 2023-03-20T06:42:26Z | 36 | 1 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"t5",
"text2text-generation",
"Code2TextGeneration",
"Code2TextSummarisation",
"py",
"en",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-03-02T23:29:05Z |
---
language:
- py
- en
thumbnail: "url to a thumbnail used in social sharing"
tags:
- Code2TextGeneration
- Code2TextSummarisation
license: apache-2.0
datasets:
- code_x_glue_ct_code_to_text
- code_x_glue_ct_code_to_text (python)
metrics:
- code-x-bleu
---
pretrained model: https://huggingface.co/Salesforce/codet5-small
finetuning dataset: https://huggingface.co/datasets/code_x_glue_ct_code_to_text (only the python split)
official inference check point (for comparison, using base, not small, size): https://storage.googleapis.com/sfr-codet5-data-research/finetuned_models/summarize_python_codet5_base.bin
for fine-tuning process metrics see [this w&b report](https://wandb.ai/stmnk/CodeT5/reports/Code-T5-code_x_glue_code2text--VmlldzoxMjM4MTUy?accessToken=5stsbg6bn2x0m6svrosxtq0zv3vhlgzr4cjcyapw52xq5puc09wo6f8li40ln7fm)
<!-- <iframe src="https://wandb.ai/stmnk/CodeT5/reports/Code-T5-code_x_glue_code2text--VmlldzoxMjM4MTUy" style="border:none;height:1024px;width:100%"> -->
|
victorfu/sd-class-butterflies-32
|
victorfu
| 2023-03-20T06:35:45Z | 0 | 0 |
diffusers
|
[
"diffusers",
"pytorch",
"unconditional-image-generation",
"diffusion-models-class",
"license:mit",
"diffusers:DDPMPipeline",
"region:us"
] |
unconditional-image-generation
| 2023-03-20T06:31:07Z |
---
license: mit
tags:
- pytorch
- diffusers
- unconditional-image-generation
- diffusion-models-class
---
# Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class)
This model is a diffusion model for unconditional image generation of cute 🦋.
## Usage
```python
from diffusers import DDPMPipeline
pipeline = DDPMPipeline.from_pretrained('victorfu/sd-class-butterflies-32')
image = pipeline().images[0]
image
```
|
Amir13/xlm-roberta-base-wnut2017
|
Amir13
| 2023-03-20T06:20:29Z | 12 | 0 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:Amir13/wnut2017-persian",
"arxiv:2302.09611",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2023-02-14T13:19:51Z |
---
license: mit
tags:
- generated_from_trainer
datasets: Amir13/wnut2017-persian
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: xlm-roberta-base-wnut2017
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-wnut2017
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the [wnut2017-persian](https://huggingface.co/datasets/Amir13/wnut2017-persian) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2943
- Precision: 0.5430
- Recall: 0.4181
- F1: 0.4724
- Accuracy: 0.9379
## 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: 15
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 106 | 0.3715 | 0.0667 | 0.0012 | 0.0024 | 0.9119 |
| No log | 2.0 | 212 | 0.3279 | 0.3482 | 0.1783 | 0.2359 | 0.9217 |
| No log | 3.0 | 318 | 0.3008 | 0.5574 | 0.3627 | 0.4394 | 0.9344 |
| No log | 4.0 | 424 | 0.2884 | 0.5226 | 0.3614 | 0.4274 | 0.9363 |
| 0.2149 | 5.0 | 530 | 0.2943 | 0.5430 | 0.4181 | 0.4724 | 0.9379 |
| 0.2149 | 6.0 | 636 | 0.3180 | 0.5338 | 0.3711 | 0.4378 | 0.9377 |
| 0.2149 | 7.0 | 742 | 0.3090 | 0.4993 | 0.4277 | 0.4607 | 0.9365 |
| 0.2149 | 8.0 | 848 | 0.3300 | 0.5300 | 0.4048 | 0.4590 | 0.9380 |
| 0.2149 | 9.0 | 954 | 0.3365 | 0.4938 | 0.3843 | 0.4322 | 0.9367 |
| 0.0623 | 10.0 | 1060 | 0.3363 | 0.5028 | 0.4313 | 0.4643 | 0.9363 |
| 0.0623 | 11.0 | 1166 | 0.3567 | 0.4992 | 0.3880 | 0.4366 | 0.9356 |
| 0.0623 | 12.0 | 1272 | 0.3681 | 0.5164 | 0.3988 | 0.4500 | 0.9375 |
| 0.0623 | 13.0 | 1378 | 0.3698 | 0.5086 | 0.3928 | 0.4432 | 0.9376 |
| 0.0623 | 14.0 | 1484 | 0.3690 | 0.5157 | 0.4157 | 0.4603 | 0.9380 |
| 0.0303 | 15.0 | 1590 | 0.3744 | 0.5045 | 0.4072 | 0.4507 | 0.9375 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
### Citation
If you used the datasets and models in this repository, please cite it.
```bibtex
@misc{https://doi.org/10.48550/arxiv.2302.09611,
doi = {10.48550/ARXIV.2302.09611},
url = {https://arxiv.org/abs/2302.09611},
author = {Sartipi, Amir and Fatemi, Afsaneh},
keywords = {Computation and Language (cs.CL), Artificial Intelligence (cs.AI), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Exploring the Potential of Machine Translation for Generating Named Entity Datasets: A Case Study between Persian and English},
publisher = {arXiv},
year = {2023},
copyright = {arXiv.org perpetual, non-exclusive license}
}
```
|
Orreo/Iridescent_painter
|
Orreo
| 2023-03-20T06:05:11Z | 0 | 5 | null |
[
"license:artistic-2.0",
"region:us"
] | null | 2023-03-20T05:51:55Z |
---
license: artistic-2.0
---
The description below was created using machine translation
Merged Pastel Mix, oil paint trained model and stable diffusion 1.5 default model.
An oil painting-inspired anime-style model with bright, vibrant colors and a soft brushstroke.
Use the OIL PAINT prompt to blur outlines and make colors more colorful. If you don't use the oil paint prompts, the lines are relatively bold and the colors are a bit muted.
|
CristianLazoQuispe/AIorNot-model
|
CristianLazoQuispe
| 2023-03-20T06:02:23Z | 0 | 0 | null |
[
"image-classification",
"en",
"dataset:competitions/aiornot",
"license:mit",
"region:us"
] |
image-classification
| 2023-03-20T05:25:24Z |
---
license: mit
datasets:
- competitions/aiornot
language:
- en
metrics:
- accuracy
- f1
pipeline_tag: image-classification
---
Fatima 2023 Application
This project is about an image classification task of artificial and natural classes.
Setup:
pip install -r requirements.txt
Inference:
from torchvision import transforms
from PIL import Image
import torch
inference_transform = transforms.Compose([
transforms.Resize(128),
transforms.ToTensor(),
transforms.Normalize(mean=[0.4914, 0.4822, 0.4465],
std=[0.2023, 0.1994, 0.2010]),
])
#load image and model
img_example = Image.open("image_example.png").convert('RGB')
print("image loaded!")
model_loaded = torch.load("fatima_challenge_model_exp3.pt")
model_loaded.eval()
print("model loaded!")
img_example_transformed = inference_transform(img_example)
out = model_loaded(img_example_transformed.to(torch.device("cuda:0")).unsqueeze(0)) # Generate predictions
_, outs = torch.max(out, 1)
prediction = "natural" if int(outs.cpu().numpy())==0 else "artificial"
print("prediction = ",prediction)
|
jinukoo/a2c-PandaReachDense-v2
|
jinukoo
| 2023-03-20T06:01:35Z | 1 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"PandaReachDense-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-20T04:08:43Z |
---
library_name: stable-baselines3
tags:
- PandaReachDense-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachDense-v2
type: PandaReachDense-v2
metrics:
- type: mean_reward
value: -0.91 +/- 0.15
name: mean_reward
verified: false
---
# **A2C** Agent playing **PandaReachDense-v2**
This is a trained model of a **A2C** agent playing **PandaReachDense-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
...
```
|
pfunk/CartPole-v1-CP_DQPN_x1-seed3
|
pfunk
| 2023-03-20T05:43:32Z | 0 | 0 |
cleanrl
|
[
"cleanrl",
"tensorboard",
"CartPole-v1",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-20T02:58:44Z |
---
tags:
- CartPole-v1
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: DQPN_freq
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
---
# (CleanRL) **DQPN_freq** Agent Playing **CartPole-v1**
This is a trained model of a DQPN_freq agent playing CartPole-v1.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/CP_DQPN_x1.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[CP_DQPN_x1]"
python -m cleanrl_utils.enjoy --exp-name CP_DQPN_x1 --env-id CartPole-v1
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/pfunk/CartPole-v1-CP_DQPN_x1-seed3/raw/main/dqpn_freq.py
curl -OL https://huggingface.co/pfunk/CartPole-v1-CP_DQPN_x1-seed3/raw/main/pyproject.toml
curl -OL https://huggingface.co/pfunk/CartPole-v1-CP_DQPN_x1-seed3/raw/main/poetry.lock
poetry install --all-extras
python dqpn_freq.py --track --wandb-entity pfunk --wandb-project-name dqpn --capture-video true --save-model true --upload-model true --hf-entity pfunk --exp-name CP_DQPN_x1 --policy-network-frequency 20 --seed 3
```
# Hyperparameters
```python
{'alg_type': 'dqpn_freq.py',
'batch_size': 256,
'buffer_size': 300000,
'capture_video': True,
'cuda': True,
'end_e': 0.1,
'env_id': 'CartPole-v1',
'exp_name': 'CP_DQPN_x1',
'exploration_fraction': 0.2,
'gamma': 1.0,
'hf_entity': 'pfunk',
'learning_rate': 0.0001,
'learning_starts': 1000,
'policy_network_frequency': 20,
'policy_tau': 1.0,
'save_model': True,
'seed': 3,
'start_e': 1.0,
'target_network_frequency': 20,
'target_tau': 1.0,
'torch_deterministic': True,
'total_timesteps': 500000,
'track': True,
'train_frequency': 1,
'upload_model': True,
'wandb_entity': 'pfunk',
'wandb_project_name': 'dqpn'}
```
|
pfunk/CartPole-v1-CP_DQPN_x1-seed2
|
pfunk
| 2023-03-20T05:43:19Z | 0 | 0 |
cleanrl
|
[
"cleanrl",
"tensorboard",
"CartPole-v1",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-20T02:58:27Z |
---
tags:
- CartPole-v1
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: DQPN_freq
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
---
# (CleanRL) **DQPN_freq** Agent Playing **CartPole-v1**
This is a trained model of a DQPN_freq agent playing CartPole-v1.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/CP_DQPN_x1.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[CP_DQPN_x1]"
python -m cleanrl_utils.enjoy --exp-name CP_DQPN_x1 --env-id CartPole-v1
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/pfunk/CartPole-v1-CP_DQPN_x1-seed2/raw/main/dqpn_freq.py
curl -OL https://huggingface.co/pfunk/CartPole-v1-CP_DQPN_x1-seed2/raw/main/pyproject.toml
curl -OL https://huggingface.co/pfunk/CartPole-v1-CP_DQPN_x1-seed2/raw/main/poetry.lock
poetry install --all-extras
python dqpn_freq.py --track --wandb-entity pfunk --wandb-project-name dqpn --capture-video true --save-model true --upload-model true --hf-entity pfunk --exp-name CP_DQPN_x1 --policy-network-frequency 20 --seed 2
```
# Hyperparameters
```python
{'alg_type': 'dqpn_freq.py',
'batch_size': 256,
'buffer_size': 300000,
'capture_video': True,
'cuda': True,
'end_e': 0.1,
'env_id': 'CartPole-v1',
'exp_name': 'CP_DQPN_x1',
'exploration_fraction': 0.2,
'gamma': 1.0,
'hf_entity': 'pfunk',
'learning_rate': 0.0001,
'learning_starts': 1000,
'policy_network_frequency': 20,
'policy_tau': 1.0,
'save_model': True,
'seed': 2,
'start_e': 1.0,
'target_network_frequency': 20,
'target_tau': 1.0,
'torch_deterministic': True,
'total_timesteps': 500000,
'track': True,
'train_frequency': 1,
'upload_model': True,
'wandb_entity': 'pfunk',
'wandb_project_name': 'dqpn'}
```
|
starsdeep/sd-class-butterflies-32
|
starsdeep
| 2023-03-20T05:02:37Z | 3 | 0 |
diffusers
|
[
"diffusers",
"pytorch",
"unconditional-image-generation",
"diffusion-models-class",
"license:mit",
"diffusers:DDPMPipeline",
"region:us"
] |
unconditional-image-generation
| 2023-03-20T05:02:22Z |
---
license: mit
tags:
- pytorch
- diffusers
- unconditional-image-generation
- diffusion-models-class
---
# Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class)
This model is a diffusion model for unconditional image generation of cute 🦋.
## Usage
```python
from diffusers import DDPMPipeline
pipeline = DDPMPipeline.from_pretrained('starsdeep/sd-class-butterflies-32')
image = pipeline().images[0]
image
```
|
luongphamit/DreamShaper
|
luongphamit
| 2023-03-20T04:56:45Z | 14 | 4 |
diffusers
|
[
"diffusers",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"art",
"artistic",
"en",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-03-20T01:33:24Z |
---
language:
- en
license: other
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- art
- artistic
- diffusers
inference: true
---
# Dream Shaper
## Official Repository
Read more about this model here: https://civitai.com/models/4384/dreamshaper
Also please support by giving 5 stars and a heart, which will notify new updates.
Also consider supporting me on Patreon or ByuMeACoffee
- https://www.patreon.com/Lykon275
- https://www.buymeacoffee.com/lykon
You can run this model on:
- https://huggingface.co/spaces/Lykon/DreamShaper-webui
- https://sinkin.ai/m/4zdwGOB
Be sure to check out NeverEnding Dream, which is another semi-realistic model which aims at being fully compatible with booru tag loras and prompts
- https://huggingface.co/Lykon/NeverEnding-Dream
Some sample output:
|
liuyanchen1015/roberta-base-mnli_AppE
|
liuyanchen1015
| 2023-03-20T04:42:34Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-03-18T19:52:13Z |
---
license: mit
tags:
- generated_from_trainer
model-index:
- name: roberta-base-mnli_AppE
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-base-mnli_AppE
This model is a fine-tuned version of [WillHeld/roberta-base-mnli](https://huggingface.co/WillHeld/roberta-base-mnli) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7433
- Acc: 0.8622
## 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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Acc |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 0.3562 | 0.17 | 2000 | 0.4166 | 0.8524 |
| 0.3414 | 0.33 | 4000 | 0.4399 | 0.8476 |
| 0.3308 | 0.5 | 6000 | 0.3951 | 0.8561 |
| 0.3306 | 0.67 | 8000 | 0.4152 | 0.8488 |
| 0.3295 | 0.83 | 10000 | 0.3917 | 0.8575 |
| 0.3288 | 1.0 | 12000 | 0.4095 | 0.8581 |
| 0.229 | 1.17 | 14000 | 0.4420 | 0.8611 |
| 0.2307 | 1.33 | 16000 | 0.4876 | 0.8561 |
| 0.2334 | 1.5 | 18000 | 0.4496 | 0.8577 |
| 0.2392 | 1.67 | 20000 | 0.4188 | 0.8608 |
| 0.233 | 1.83 | 22000 | 0.4493 | 0.8578 |
| 0.2343 | 2.0 | 24000 | 0.4278 | 0.8603 |
| 0.163 | 2.17 | 26000 | 0.5700 | 0.8635 |
| 0.1657 | 2.33 | 28000 | 0.5323 | 0.8561 |
| 0.1652 | 2.5 | 30000 | 0.5047 | 0.8589 |
| 0.1654 | 2.67 | 32000 | 0.5082 | 0.8590 |
| 0.1659 | 2.83 | 34000 | 0.5076 | 0.8631 |
| 0.1654 | 3.0 | 36000 | 0.5036 | 0.8612 |
| 0.1195 | 3.17 | 38000 | 0.6221 | 0.8600 |
| 0.1194 | 3.33 | 40000 | 0.6467 | 0.8575 |
| 0.1205 | 3.5 | 42000 | 0.6742 | 0.8578 |
| 0.1209 | 3.67 | 44000 | 0.6537 | 0.8580 |
| 0.1234 | 3.83 | 46000 | 0.6385 | 0.8580 |
| 0.122 | 4.0 | 48000 | 0.6104 | 0.8628 |
| 0.0937 | 4.17 | 50000 | 0.7261 | 0.8603 |
| 0.0941 | 4.33 | 52000 | 0.7634 | 0.8607 |
| 0.0926 | 4.5 | 54000 | 0.7737 | 0.8617 |
| 0.093 | 4.67 | 56000 | 0.7577 | 0.8633 |
| 0.0918 | 4.83 | 58000 | 0.7535 | 0.8631 |
| 0.0935 | 5.0 | 60000 | 0.7433 | 0.8622 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.13.0+cu117
- Datasets 2.7.1
- Tokenizers 0.12.1
|
Godgiven/MyOwnPastelMix
|
Godgiven
| 2023-03-20T04:28:55Z | 0 | 0 | null |
[
"en",
"license:openrail",
"region:us"
] | null | 2023-03-15T03:48:10Z |
---
license: openrail
language:
- en
---
|
keras-dreambooth/dreambooth_diffusion_clay_cups
|
keras-dreambooth
| 2023-03-20T04:27:37Z | 5 | 0 |
keras
|
[
"keras",
"tf-keras",
"diffusers",
"stable-diffusion",
"text-to-image",
"keras-dreambooth",
"region:us"
] |
text-to-image
| 2023-03-18T13:51:24Z |
---
library_name: keras
tags:
- keras
- diffusers
- stable-diffusion
- text-to-image
- keras-dreambooth
widget:
- text: a photo of traditional cups image in bng clay style
---
## Model description
This model has been fine-tuned by by [Shamima](https://huggingface.co/Shamima) on bengali clay cups and saucers.
## Intended uses & limitations
Please feel free to make your imaginations run wild and have fun.
## Example images generated
Unique sets of cups using bengali clay pottery style
Michelango's David re-imagined in bengali clay pottery style and texture
A town of futuristic buildings in clay pottery style
Unique jewellery pieces in bengali clay pottery style
## Training and evaluation data
The model was fine-tuned on my personal collection of cups and saucer sets made of clay pottery. Dataset can be found [here](https://huggingface.co/datasets/keras-dreambooth/bengali-clay-cups-dataset).
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
| Hyperparameters | Value |
| :-- | :-- |
| inner_optimizer.class_name | Custom>RMSprop |
| inner_optimizer.config.name | RMSprop |
| inner_optimizer.config.weight_decay | None |
| inner_optimizer.config.clipnorm | None |
| inner_optimizer.config.global_clipnorm | None |
| inner_optimizer.config.clipvalue | None |
| inner_optimizer.config.use_ema | False |
| inner_optimizer.config.ema_momentum | 0.99 |
| inner_optimizer.config.ema_overwrite_frequency | 100 |
| inner_optimizer.config.jit_compile | True |
| inner_optimizer.config.is_legacy_optimizer | False |
| inner_optimizer.config.learning_rate | 0.0010000000474974513 |
| inner_optimizer.config.rho | 0.9 |
| inner_optimizer.config.momentum | 0.0 |
| inner_optimizer.config.epsilon | 1e-07 |
| inner_optimizer.config.centered | False |
| dynamic | True |
| initial_scale | 32768.0 |
| dynamic_growth_steps | 2000 |
| training_precision | mixed_float16 |
## Model Plot
<details>
<summary>View Model Plot</summary>
</details>
|
sd-dreambooth-library/fashion
|
sd-dreambooth-library
| 2023-03-20T04:22:59Z | 45 | 24 |
diffusers
|
[
"diffusers",
"dreambooth-hackathon",
"Text-to-image",
"stable-diffusion",
"text-to-image",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2022-12-01T03:31:46Z |
---
license: apache-2.0
tags:
- dreambooth-hackathon
- Text-to-image
- stable-diffusion
pipeline_tag: text-to-image
---
https://civitai.com/models/21642/fashion3d
import torch
from diffusers import StableDiffusionPipeline
pipe = StableDiffusionPipeline.from_pretrained("sd-dreambooth-library/fashion")
pipe = pipe.to("cuda")
prompt = "a photograph of an astronaut riding a horse"
image = pipe(prompt).images[0]
image.save(f"astronaut_rides_horse.png")
|
xinyixiuxiu/roberta-base-SST2-finetuned-try
|
xinyixiuxiu
| 2023-03-20T03:55:52Z | 6 | 0 |
transformers
|
[
"transformers",
"tf",
"roberta",
"text-classification",
"generated_from_keras_callback",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-03-20T03:19:50Z |
---
license: mit
tags:
- generated_from_keras_callback
model-index:
- name: xinyixiuxiu/roberta-base-SST2-finetuned-try
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. -->
# xinyixiuxiu/roberta-base-SST2-finetuned-try
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.1172
- Train Accuracy: 0.9690
- Validation Loss: 0.7077
- Validation Accuracy: 0.8555
- 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': True, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.1493 | 0.9510 | 0.3429 | 0.8911 | 0 |
| 0.1810 | 0.9470 | 0.6590 | 0.8108 | 1 |
| 0.1172 | 0.9690 | 0.7077 | 0.8555 | 2 |
### Framework versions
- Transformers 4.27.1
- TensorFlow 2.11.0
- Datasets 2.10.1
- Tokenizers 0.13.2
|
OpenMatch/ance-tele_triviaqa_psg-encoder
|
OpenMatch
| 2023-03-20T03:32:28Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"feature-extraction",
"arxiv:2210.17167",
"license:mit",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2022-10-30T07:11:44Z |
---
license: mit
---
This model is the **passage** encoder of ANCE-Tele trained on TriviaQA, described in the EMNLP 2022 paper ["Reduce Catastrophic Forgetting of Dense Retrieval Training with Teleportation Negatives"](https://arxiv.org/pdf/2210.17167.pdf). The associated GitHub repository is available at https://github.com/OpenMatch/ANCE-Tele.
ANCE-Tele only trains with self-mined negatives (teleportation negatives) without using additional negatives (e.g., BM25, other DR systems) and eliminates the dependency on filtering strategies and distillation modules.
|NQ (Test)|R@5|R@20|R@20|
|:---|:---|:---|:---|
|ANCE-Tele|76.9|83.4|87.3|
```
@inproceedings{sun2022ancetele,
title={Reduce Catastrophic Forgetting of Dense Retrieval Training with Teleportation Negatives},
author={Si, Sun and Chenyan, Xiong and Yue, Yu and Arnold, Overwijk and Zhiyuan, Liu and Jie, Bao},
booktitle={Proceedings of EMNLP 2022},
year={2022}
}
```
|
OpenMatch/ance-tele_triviaqa_qry-encoder
|
OpenMatch
| 2023-03-20T03:32:01Z | 7 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"feature-extraction",
"arxiv:2210.17167",
"license:mit",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2022-10-30T07:10:45Z |
---
license: mit
---
This model is the **query** encoder of ANCE-Tele trained on TriviaQA, described in the EMNLP 2022 paper ["Reduce Catastrophic Forgetting of Dense Retrieval Training with Teleportation Negatives"](https://arxiv.org/pdf/2210.17167.pdf). The associated GitHub repository is available at https://github.com/OpenMatch/ANCE-Tele.
ANCE-Tele only trains with self-mined negatives (teleportation negatives) without using additional negatives (e.g., BM25, other DR systems) and eliminates the dependency on filtering strategies and distillation modules.
|NQ (Test)|R@5|R@20|R@20|
|:---|:---|:---|:---|
|ANCE-Tele|76.9|83.4|87.3|
```
@inproceedings{sun2022ancetele,
title={Reduce Catastrophic Forgetting of Dense Retrieval Training with Teleportation Negatives},
author={Si, Sun and Chenyan, Xiong and Yue, Yu and Arnold, Overwijk and Zhiyuan, Liu and Jie, Bao},
booktitle={Proceedings of EMNLP 2022},
year={2022}
}
```
|
OpenMatch/ance-tele_nq_psg-encoder
|
OpenMatch
| 2023-03-20T03:31:31Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"feature-extraction",
"arxiv:2210.17167",
"license:mit",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2022-10-30T07:09:37Z |
---
license: mit
---
This model is the **passage** encoder of ANCE-Tele trained on NQ, described in the EMNLP 2022 paper ["Reduce Catastrophic Forgetting of Dense Retrieval Training with Teleportation Negatives"](https://arxiv.org/pdf/2210.17167.pdf). The associated GitHub repository is available at https://github.com/OpenMatch/ANCE-Tele.
ANCE-Tele only trains with self-mined negatives (teleportation negatives) without using additional negatives (e.g., BM25, other DR systems) and eliminates the dependency on filtering strategies and distillation modules.
|NQ (Test)|R@5|R@20|R@20|
|:---|:---|:---|:---|
|ANCE-Tele|77.0|84.9|89.7|
```
@inproceedings{sun2022ancetele,
title={Reduce Catastrophic Forgetting of Dense Retrieval Training with Teleportation Negatives},
author={Si, Sun and Chenyan, Xiong and Yue, Yu and Arnold, Overwijk and Zhiyuan, Liu and Jie, Bao},
booktitle={Proceedings of EMNLP 2022},
year={2022}
}
```
|
OpenMatch/ance-tele_nq_qry-encoder
|
OpenMatch
| 2023-03-20T03:31:08Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"feature-extraction",
"arxiv:2210.17167",
"license:mit",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2022-10-30T07:08:39Z |
---
license: mit
---
This model is the **query** encoder of ANCE-Tele trained on NQ, described in the EMNLP 2022 paper ["Reduce Catastrophic Forgetting of Dense Retrieval Training with Teleportation Negatives"](https://arxiv.org/pdf/2210.17167.pdf). The associated GitHub repository is available at https://github.com/OpenMatch/ANCE-Tele.
ANCE-Tele only trains with self-mined negatives (teleportation negatives) without using additional negatives (e.g., BM25, other DR systems) and eliminates the dependency on filtering strategies and distillation modules.
|NQ (Test)|R@5|R@20|R@20|
|:---|:---|:---|:---|
|ANCE-Tele|77.0|84.9|89.7|
```
@inproceedings{sun2022ancetele,
title={Reduce Catastrophic Forgetting of Dense Retrieval Training with Teleportation Negatives},
author={Si, Sun and Chenyan, Xiong and Yue, Yu and Arnold, Overwijk and Zhiyuan, Liu and Jie, Bao},
booktitle={Proceedings of EMNLP 2022},
year={2022}
}
```
|
jackhhhh/Reinforce_Pixelcopter-PLE-v0-2
|
jackhhhh
| 2023-03-20T03:19:35Z | 0 | 0 | null |
[
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-20T03:19:27Z |
---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce_Pixelcopter-PLE-v0-2
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 33.80 +/- 21.20
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
|
222zhaolinniloahszss/hhdaaa
|
222zhaolinniloahszss
| 2023-03-20T03:09:46Z | 0 | 0 |
open_clip
|
[
"open_clip",
"chemistry",
"video-classification",
"am",
"ch",
"zh",
"en",
"dataset:SirNeural/flan_v2",
"dataset:fka/awesome-chatgpt-prompts",
"license:openrail",
"region:us"
] |
video-classification
| 2023-03-20T03:03:24Z |
---
license: openrail
datasets:
- SirNeural/flan_v2
- fka/awesome-chatgpt-prompts
language:
- am
- ch
- zh
- en
metrics:
- character
library_name: open_clip
pipeline_tag: video-classification
tags:
- chemistry
---
|
pfunk/CartPole-v1-CP_DQPN_x50-seed1
|
pfunk
| 2023-03-20T03:00:48Z | 0 | 0 |
cleanrl
|
[
"cleanrl",
"tensorboard",
"CartPole-v1",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-20T03:00:45Z |
---
tags:
- CartPole-v1
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: DQPN_freq
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 498.63 +/- 0.00
name: mean_reward
verified: false
---
# (CleanRL) **DQPN_freq** Agent Playing **CartPole-v1**
This is a trained model of a DQPN_freq agent playing CartPole-v1.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/CP_DQPN_x50.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[CP_DQPN_x50]"
python -m cleanrl_utils.enjoy --exp-name CP_DQPN_x50 --env-id CartPole-v1
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/pfunk/CartPole-v1-CP_DQPN_x50-seed1/raw/main/dqpn_freq.py
curl -OL https://huggingface.co/pfunk/CartPole-v1-CP_DQPN_x50-seed1/raw/main/pyproject.toml
curl -OL https://huggingface.co/pfunk/CartPole-v1-CP_DQPN_x50-seed1/raw/main/poetry.lock
poetry install --all-extras
python dqpn_freq.py --track --wandb-entity pfunk --wandb-project-name dqpn --capture-video true --save-model true --upload-model true --hf-entity pfunk --exp-name CP_DQPN_x50 --policy-network-frequency 1000 --seed 1
```
# Hyperparameters
```python
{'alg_type': 'dqpn_freq.py',
'batch_size': 256,
'buffer_size': 300000,
'capture_video': True,
'cuda': True,
'end_e': 0.1,
'env_id': 'CartPole-v1',
'exp_name': 'CP_DQPN_x50',
'exploration_fraction': 0.2,
'gamma': 1.0,
'hf_entity': 'pfunk',
'learning_rate': 0.0001,
'learning_starts': 1000,
'policy_network_frequency': 1000,
'policy_tau': 1.0,
'save_model': True,
'seed': 1,
'start_e': 1.0,
'target_network_frequency': 20,
'target_tau': 1.0,
'torch_deterministic': True,
'total_timesteps': 500000,
'track': True,
'train_frequency': 1,
'upload_model': True,
'wandb_entity': 'pfunk',
'wandb_project_name': 'dqpn'}
```
|
pfunk/CartPole-v1-CP_DQPN_x50-seed4
|
pfunk
| 2023-03-20T03:00:45Z | 0 | 0 |
cleanrl
|
[
"cleanrl",
"tensorboard",
"CartPole-v1",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-20T03:00:41Z |
---
tags:
- CartPole-v1
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: DQPN_freq
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 346.65 +/- 0.00
name: mean_reward
verified: false
---
# (CleanRL) **DQPN_freq** Agent Playing **CartPole-v1**
This is a trained model of a DQPN_freq agent playing CartPole-v1.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/CP_DQPN_x50.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[CP_DQPN_x50]"
python -m cleanrl_utils.enjoy --exp-name CP_DQPN_x50 --env-id CartPole-v1
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/pfunk/CartPole-v1-CP_DQPN_x50-seed4/raw/main/dqpn_freq.py
curl -OL https://huggingface.co/pfunk/CartPole-v1-CP_DQPN_x50-seed4/raw/main/pyproject.toml
curl -OL https://huggingface.co/pfunk/CartPole-v1-CP_DQPN_x50-seed4/raw/main/poetry.lock
poetry install --all-extras
python dqpn_freq.py --track --wandb-entity pfunk --wandb-project-name dqpn --capture-video true --save-model true --upload-model true --hf-entity pfunk --exp-name CP_DQPN_x50 --policy-network-frequency 1000 --seed 4
```
# Hyperparameters
```python
{'alg_type': 'dqpn_freq.py',
'batch_size': 256,
'buffer_size': 300000,
'capture_video': True,
'cuda': True,
'end_e': 0.1,
'env_id': 'CartPole-v1',
'exp_name': 'CP_DQPN_x50',
'exploration_fraction': 0.2,
'gamma': 1.0,
'hf_entity': 'pfunk',
'learning_rate': 0.0001,
'learning_starts': 1000,
'policy_network_frequency': 1000,
'policy_tau': 1.0,
'save_model': True,
'seed': 4,
'start_e': 1.0,
'target_network_frequency': 20,
'target_tau': 1.0,
'torch_deterministic': True,
'total_timesteps': 500000,
'track': True,
'train_frequency': 1,
'upload_model': True,
'wandb_entity': 'pfunk',
'wandb_project_name': 'dqpn'}
```
|
pfunk/CartPole-v1-CP_DQPN_x50-seed2
|
pfunk
| 2023-03-20T03:00:44Z | 0 | 0 |
cleanrl
|
[
"cleanrl",
"tensorboard",
"CartPole-v1",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-20T03:00:40Z |
---
tags:
- CartPole-v1
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: DQPN_freq
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
---
# (CleanRL) **DQPN_freq** Agent Playing **CartPole-v1**
This is a trained model of a DQPN_freq agent playing CartPole-v1.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/CP_DQPN_x50.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[CP_DQPN_x50]"
python -m cleanrl_utils.enjoy --exp-name CP_DQPN_x50 --env-id CartPole-v1
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/pfunk/CartPole-v1-CP_DQPN_x50-seed2/raw/main/dqpn_freq.py
curl -OL https://huggingface.co/pfunk/CartPole-v1-CP_DQPN_x50-seed2/raw/main/pyproject.toml
curl -OL https://huggingface.co/pfunk/CartPole-v1-CP_DQPN_x50-seed2/raw/main/poetry.lock
poetry install --all-extras
python dqpn_freq.py --track --wandb-entity pfunk --wandb-project-name dqpn --capture-video true --save-model true --upload-model true --hf-entity pfunk --exp-name CP_DQPN_x50 --policy-network-frequency 1000 --seed 2
```
# Hyperparameters
```python
{'alg_type': 'dqpn_freq.py',
'batch_size': 256,
'buffer_size': 300000,
'capture_video': True,
'cuda': True,
'end_e': 0.1,
'env_id': 'CartPole-v1',
'exp_name': 'CP_DQPN_x50',
'exploration_fraction': 0.2,
'gamma': 1.0,
'hf_entity': 'pfunk',
'learning_rate': 0.0001,
'learning_starts': 1000,
'policy_network_frequency': 1000,
'policy_tau': 1.0,
'save_model': True,
'seed': 2,
'start_e': 1.0,
'target_network_frequency': 20,
'target_tau': 1.0,
'torch_deterministic': True,
'total_timesteps': 500000,
'track': True,
'train_frequency': 1,
'upload_model': True,
'wandb_entity': 'pfunk',
'wandb_project_name': 'dqpn'}
```
|
pfunk/CartPole-v1-CP_DQPN_x50-seed3
|
pfunk
| 2023-03-20T03:00:41Z | 0 | 0 |
cleanrl
|
[
"cleanrl",
"tensorboard",
"CartPole-v1",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-20T03:00:38Z |
---
tags:
- CartPole-v1
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: DQPN_freq
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
---
# (CleanRL) **DQPN_freq** Agent Playing **CartPole-v1**
This is a trained model of a DQPN_freq agent playing CartPole-v1.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/CP_DQPN_x50.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[CP_DQPN_x50]"
python -m cleanrl_utils.enjoy --exp-name CP_DQPN_x50 --env-id CartPole-v1
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/pfunk/CartPole-v1-CP_DQPN_x50-seed3/raw/main/dqpn_freq.py
curl -OL https://huggingface.co/pfunk/CartPole-v1-CP_DQPN_x50-seed3/raw/main/pyproject.toml
curl -OL https://huggingface.co/pfunk/CartPole-v1-CP_DQPN_x50-seed3/raw/main/poetry.lock
poetry install --all-extras
python dqpn_freq.py --track --wandb-entity pfunk --wandb-project-name dqpn --capture-video true --save-model true --upload-model true --hf-entity pfunk --exp-name CP_DQPN_x50 --policy-network-frequency 1000 --seed 3
```
# Hyperparameters
```python
{'alg_type': 'dqpn_freq.py',
'batch_size': 256,
'buffer_size': 300000,
'capture_video': True,
'cuda': True,
'end_e': 0.1,
'env_id': 'CartPole-v1',
'exp_name': 'CP_DQPN_x50',
'exploration_fraction': 0.2,
'gamma': 1.0,
'hf_entity': 'pfunk',
'learning_rate': 0.0001,
'learning_starts': 1000,
'policy_network_frequency': 1000,
'policy_tau': 1.0,
'save_model': True,
'seed': 3,
'start_e': 1.0,
'target_network_frequency': 20,
'target_tau': 1.0,
'torch_deterministic': True,
'total_timesteps': 500000,
'track': True,
'train_frequency': 1,
'upload_model': True,
'wandb_entity': 'pfunk',
'wandb_project_name': 'dqpn'}
```
|
pfunk/CartPole-v1-CP_DQPN_x5-seed2
|
pfunk
| 2023-03-20T02:59:56Z | 0 | 0 |
cleanrl
|
[
"cleanrl",
"tensorboard",
"CartPole-v1",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-20T02:59:51Z |
---
tags:
- CartPole-v1
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: DQPN_freq
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 495.13 +/- 0.00
name: mean_reward
verified: false
---
# (CleanRL) **DQPN_freq** Agent Playing **CartPole-v1**
This is a trained model of a DQPN_freq agent playing CartPole-v1.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/CP_DQPN_x5.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[CP_DQPN_x5]"
python -m cleanrl_utils.enjoy --exp-name CP_DQPN_x5 --env-id CartPole-v1
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/pfunk/CartPole-v1-CP_DQPN_x5-seed2/raw/main/dqpn_freq.py
curl -OL https://huggingface.co/pfunk/CartPole-v1-CP_DQPN_x5-seed2/raw/main/pyproject.toml
curl -OL https://huggingface.co/pfunk/CartPole-v1-CP_DQPN_x5-seed2/raw/main/poetry.lock
poetry install --all-extras
python dqpn_freq.py --track --wandb-entity pfunk --wandb-project-name dqpn --capture-video true --save-model true --upload-model true --hf-entity pfunk --exp-name CP_DQPN_x5 --policy-network-frequency 100 --seed 2
```
# Hyperparameters
```python
{'alg_type': 'dqpn_freq.py',
'batch_size': 256,
'buffer_size': 300000,
'capture_video': True,
'cuda': True,
'end_e': 0.1,
'env_id': 'CartPole-v1',
'exp_name': 'CP_DQPN_x5',
'exploration_fraction': 0.2,
'gamma': 1.0,
'hf_entity': 'pfunk',
'learning_rate': 0.0001,
'learning_starts': 1000,
'policy_network_frequency': 100,
'policy_tau': 1.0,
'save_model': True,
'seed': 2,
'start_e': 1.0,
'target_network_frequency': 20,
'target_tau': 1.0,
'torch_deterministic': True,
'total_timesteps': 500000,
'track': True,
'train_frequency': 1,
'upload_model': True,
'wandb_entity': 'pfunk',
'wandb_project_name': 'dqpn'}
```
|
pfunk/CartPole-v1-CP_DQPN_x5-seed3
|
pfunk
| 2023-03-20T02:59:44Z | 0 | 0 |
cleanrl
|
[
"cleanrl",
"tensorboard",
"CartPole-v1",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-20T02:59:41Z |
---
tags:
- CartPole-v1
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: DQPN_freq
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
---
# (CleanRL) **DQPN_freq** Agent Playing **CartPole-v1**
This is a trained model of a DQPN_freq agent playing CartPole-v1.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/CP_DQPN_x5.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[CP_DQPN_x5]"
python -m cleanrl_utils.enjoy --exp-name CP_DQPN_x5 --env-id CartPole-v1
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/pfunk/CartPole-v1-CP_DQPN_x5-seed3/raw/main/dqpn_freq.py
curl -OL https://huggingface.co/pfunk/CartPole-v1-CP_DQPN_x5-seed3/raw/main/pyproject.toml
curl -OL https://huggingface.co/pfunk/CartPole-v1-CP_DQPN_x5-seed3/raw/main/poetry.lock
poetry install --all-extras
python dqpn_freq.py --track --wandb-entity pfunk --wandb-project-name dqpn --capture-video true --save-model true --upload-model true --hf-entity pfunk --exp-name CP_DQPN_x5 --policy-network-frequency 100 --seed 3
```
# Hyperparameters
```python
{'alg_type': 'dqpn_freq.py',
'batch_size': 256,
'buffer_size': 300000,
'capture_video': True,
'cuda': True,
'end_e': 0.1,
'env_id': 'CartPole-v1',
'exp_name': 'CP_DQPN_x5',
'exploration_fraction': 0.2,
'gamma': 1.0,
'hf_entity': 'pfunk',
'learning_rate': 0.0001,
'learning_starts': 1000,
'policy_network_frequency': 100,
'policy_tau': 1.0,
'save_model': True,
'seed': 3,
'start_e': 1.0,
'target_network_frequency': 20,
'target_tau': 1.0,
'torch_deterministic': True,
'total_timesteps': 500000,
'track': True,
'train_frequency': 1,
'upload_model': True,
'wandb_entity': 'pfunk',
'wandb_project_name': 'dqpn'}
```
|
pfunk/CartPole-v1-CP_DQPN_x5-seed4
|
pfunk
| 2023-03-20T02:59:40Z | 0 | 0 |
cleanrl
|
[
"cleanrl",
"tensorboard",
"CartPole-v1",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-20T02:59:38Z |
---
tags:
- CartPole-v1
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: DQPN_freq
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 55.21 +/- 0.00
name: mean_reward
verified: false
---
# (CleanRL) **DQPN_freq** Agent Playing **CartPole-v1**
This is a trained model of a DQPN_freq agent playing CartPole-v1.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/CP_DQPN_x5.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[CP_DQPN_x5]"
python -m cleanrl_utils.enjoy --exp-name CP_DQPN_x5 --env-id CartPole-v1
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/pfunk/CartPole-v1-CP_DQPN_x5-seed4/raw/main/dqpn_freq.py
curl -OL https://huggingface.co/pfunk/CartPole-v1-CP_DQPN_x5-seed4/raw/main/pyproject.toml
curl -OL https://huggingface.co/pfunk/CartPole-v1-CP_DQPN_x5-seed4/raw/main/poetry.lock
poetry install --all-extras
python dqpn_freq.py --track --wandb-entity pfunk --wandb-project-name dqpn --capture-video true --save-model true --upload-model true --hf-entity pfunk --exp-name CP_DQPN_x5 --policy-network-frequency 100 --seed 4
```
# Hyperparameters
```python
{'alg_type': 'dqpn_freq.py',
'batch_size': 256,
'buffer_size': 300000,
'capture_video': True,
'cuda': True,
'end_e': 0.1,
'env_id': 'CartPole-v1',
'exp_name': 'CP_DQPN_x5',
'exploration_fraction': 0.2,
'gamma': 1.0,
'hf_entity': 'pfunk',
'learning_rate': 0.0001,
'learning_starts': 1000,
'policy_network_frequency': 100,
'policy_tau': 1.0,
'save_model': True,
'seed': 4,
'start_e': 1.0,
'target_network_frequency': 20,
'target_tau': 1.0,
'torch_deterministic': True,
'total_timesteps': 500000,
'track': True,
'train_frequency': 1,
'upload_model': True,
'wandb_entity': 'pfunk',
'wandb_project_name': 'dqpn'}
```
|
pfunk/CartPole-v1-CP_DQPN_x2-seed1
|
pfunk
| 2023-03-20T02:59:17Z | 0 | 0 |
cleanrl
|
[
"cleanrl",
"tensorboard",
"CartPole-v1",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-20T02:59:14Z |
---
tags:
- CartPole-v1
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: DQPN_freq
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
---
# (CleanRL) **DQPN_freq** Agent Playing **CartPole-v1**
This is a trained model of a DQPN_freq agent playing CartPole-v1.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/CP_DQPN_x2.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[CP_DQPN_x2]"
python -m cleanrl_utils.enjoy --exp-name CP_DQPN_x2 --env-id CartPole-v1
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/pfunk/CartPole-v1-CP_DQPN_x2-seed1/raw/main/dqpn_freq.py
curl -OL https://huggingface.co/pfunk/CartPole-v1-CP_DQPN_x2-seed1/raw/main/pyproject.toml
curl -OL https://huggingface.co/pfunk/CartPole-v1-CP_DQPN_x2-seed1/raw/main/poetry.lock
poetry install --all-extras
python dqpn_freq.py --track --wandb-entity pfunk --wandb-project-name dqpn --capture-video true --save-model true --upload-model true --hf-entity pfunk --exp-name CP_DQPN_x2 --policy-network-frequency 40 --seed 1
```
# Hyperparameters
```python
{'alg_type': 'dqpn_freq.py',
'batch_size': 256,
'buffer_size': 300000,
'capture_video': True,
'cuda': True,
'end_e': 0.1,
'env_id': 'CartPole-v1',
'exp_name': 'CP_DQPN_x2',
'exploration_fraction': 0.2,
'gamma': 1.0,
'hf_entity': 'pfunk',
'learning_rate': 0.0001,
'learning_starts': 1000,
'policy_network_frequency': 40,
'policy_tau': 1.0,
'save_model': True,
'seed': 1,
'start_e': 1.0,
'target_network_frequency': 20,
'target_tau': 1.0,
'torch_deterministic': True,
'total_timesteps': 500000,
'track': True,
'train_frequency': 1,
'upload_model': True,
'wandb_entity': 'pfunk',
'wandb_project_name': 'dqpn'}
```
|
alexcleu/wav2vec2-large-xlsr-polish
|
alexcleu
| 2023-03-20T02:57:34Z | 23 | 3 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"pl",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
language: pl
datasets:
- common_voice
tags:
- audio
- automatic-speech-recognition
- speech
- xlsr-fine-tuning-week
license: apache-2.0
model-index:
- name: XLSR Wav2vec2 Large 53 Polish by Alex Leu
results:
- task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice pl
type: common_voice
args: pl
metrics:
- name: Test WER
type: wer
value: 24.846030
---
# wav2vec2-large-xlsr-polish
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) in Polish using the [Common Voice](https://huggingface.co/datasets/common_voice)
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used directly (without a language model) as follows:
```python
import torch
import torchaudio
from datasets import load_dataset
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
test_dataset = load_dataset("common_voice", "pl", split="test[:2%]")
processor = Wav2Vec2Processor.from_pretrained("alexcleu/wav2vec2-large-xlsr-polish")
model = Wav2Vec2ForCTC.from_pretrained("alexcleu/wav2vec2-large-xlsr-polish")
resampler = torchaudio.transforms.Resample(48_000, 16_000)
# Preprocessing the datasets.
# We need to read the aduio files as arrays
def speech_file_to_array_fn(batch):
speech_array, sampling_rate = torchaudio.load(batch["path"])
batch["speech"] = resampler(speech_array).squeeze().numpy()
return batch
test_dataset = test_dataset.map(speech_file_to_array_fn)
inputs = processor(test_dataset["speech"][:2], sampling_rate=16_000, return_tensors="pt", padding=True)
with torch.no_grad():
logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits
predicted_ids = torch.argmax(logits, dim=-1)
print("Prediction:", processor.batch_decode(predicted_ids))
print("Reference:", test_dataset["sentence"][:2])
```
## Evaluation
The model can be evaluated as follows on the Turkish test data of Common Voice.
```python
import torch
import torchaudio
from datasets import load_dataset, load_metric
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import re
test_dataset = load_dataset("common_voice", "pl", split="test")
wer = load_metric("wer")
processor = Wav2Vec2Processor.from_pretrained("alexcleu/wav2vec2-large-xlsr-polish")
model = Wav2Vec2ForCTC.from_pretrained("alexcleu/wav2vec2-large-xlsr-polish")
model.to("cuda")
chars_to_ignore_regex = '[\\\\\\\\\\\\\\\\,\\\\\\\\\\\\\\\\?\\\\\\\\\\\\\\\\.\\\\\\\\\\\\\\\\!\\\\\\\\\\\\\\\\-\\\\\\\\\\\\\\\\;\\\\\\\\\\\\\\\\:\\\\\\\\\\\\\\\\"\\\\\\\\\\\\\\\\“]'
resampler = torchaudio.transforms.Resample(48_000, 16_000)
# Preprocessing the datasets.
# We need to read the aduio files as arrays
def speech_file_to_array_fn(batch):
batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower()
speech_array, sampling_rate = torchaudio.load(batch["path"])
batch["speech"] = resampler(speech_array).squeeze().numpy()
return batch
test_dataset = test_dataset.map(speech_file_to_array_fn)
# Preprocessing the datasets.
# We need to read the aduio files as arrays
def evaluate(batch):
inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
with torch.no_grad():
logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits
pred_ids = torch.argmax(logits, dim=-1)
batch["pred_strings"] = processor.batch_decode(pred_ids)
return batch
result = test_dataset.map(evaluate, batched=True, batch_size=8)
print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"])))
```
**Test Result**: 24.846030
## Training
The Common Voice `train`, `validation` datasets were used for training.
|
yujiepan/internal.swin-base-food101-int8-structured38.01
|
yujiepan
| 2023-03-20T02:35:20Z | 31 | 0 |
transformers
|
[
"transformers",
"pytorch",
"openvino",
"swin",
"image-classification",
"vision",
"generated_from_trainer",
"dataset:food101",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-03-20T02:32:30Z |
---
license: apache-2.0
tags:
- image-classification
- vision
- generated_from_trainer
datasets:
- food101
metrics:
- accuracy
model-index:
- name: swin-food101-jpqd-1to2r1.5-epo10-finetuned-student
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: food101
type: food101
config: default
split: validation
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9183762376237624
---
<!-- 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-food101-jpqd-1to2r1.5-epo10-finetuned-student
This model is a fine-tuned version of [skylord/swin-finetuned-food101](https://huggingface.co/skylord/swin-finetuned-food101) on the food101 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2391
- Accuracy: 0.9184
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 128
- 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
- num_epochs: 10.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.3011 | 0.42 | 500 | 0.1951 | 0.9124 |
| 0.2613 | 0.84 | 1000 | 0.1897 | 0.9139 |
| 100.1552 | 1.27 | 1500 | 99.5975 | 0.7445 |
| 162.0751 | 1.69 | 2000 | 162.5020 | 0.3512 |
| 1.061 | 2.11 | 2500 | 0.7523 | 0.8550 |
| 0.9728 | 2.54 | 3000 | 0.5263 | 0.8767 |
| 0.5851 | 2.96 | 3500 | 0.4599 | 0.8892 |
| 0.4668 | 3.38 | 4000 | 0.4064 | 0.8938 |
| 0.6967 | 3.8 | 4500 | 0.3814 | 0.8986 |
| 0.4928 | 4.23 | 5000 | 0.3522 | 0.9036 |
| 0.4893 | 4.65 | 5500 | 0.3562 | 0.9026 |
| 0.5421 | 5.07 | 6000 | 0.3182 | 0.9049 |
| 0.4405 | 5.49 | 6500 | 0.3112 | 0.9071 |
| 0.4423 | 5.92 | 7000 | 0.3012 | 0.9092 |
| 0.4143 | 6.34 | 7500 | 0.2958 | 0.9095 |
| 0.4997 | 6.76 | 8000 | 0.2796 | 0.9126 |
| 0.2448 | 7.19 | 8500 | 0.2747 | 0.9124 |
| 0.4468 | 7.61 | 9000 | 0.2699 | 0.9144 |
| 0.4163 | 8.03 | 9500 | 0.2583 | 0.9166 |
| 0.3651 | 8.45 | 10000 | 0.2567 | 0.9165 |
| 0.3946 | 8.88 | 10500 | 0.2489 | 0.9176 |
| 0.3196 | 9.3 | 11000 | 0.2444 | 0.9180 |
| 0.312 | 9.72 | 11500 | 0.2402 | 0.9172 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
|
ndkhanh95/detr-resnet-50_finetuned_cppe5
|
ndkhanh95
| 2023-03-20T02:31:54Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"detr",
"object-detection",
"generated_from_trainer",
"dataset:cppe-5",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
object-detection
| 2023-03-19T02:33:40Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- cppe-5
model-index:
- name: detr-resnet-50_finetuned_cppe5
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# detr-resnet-50_finetuned_cppe5
This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on the cppe-5 dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 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
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.27.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
|
yujiepan/internal.swin-base-food101-int8-structured38.63
|
yujiepan
| 2023-03-20T02:31:24Z | 31 | 0 |
transformers
|
[
"transformers",
"pytorch",
"openvino",
"swin",
"image-classification",
"vision",
"generated_from_trainer",
"dataset:food101",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-03-20T02:28:21Z |
---
license: apache-2.0
tags:
- image-classification
- vision
- generated_from_trainer
datasets:
- food101
metrics:
- accuracy
model-index:
- name: swin-food101-jpqd-1to2r1.5-epo7-finetuned-student
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: food101
type: food101
config: default
split: validation
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9123960396039604
---
<!-- 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-food101-jpqd-1to2r1.5-epo7-finetuned-student
This model is a fine-tuned version of [skylord/swin-finetuned-food101](https://huggingface.co/skylord/swin-finetuned-food101) on the food101 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2658
- Accuracy: 0.9124
## 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: 128
- 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
- num_epochs: 7.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.2977 | 0.42 | 500 | 0.1949 | 0.9112 |
| 0.3183 | 0.84 | 1000 | 0.1867 | 0.9144 |
| 99.9552 | 1.27 | 1500 | 99.4882 | 0.7577 |
| 162.4195 | 1.69 | 2000 | 162.7763 | 0.3373 |
| 1.2272 | 2.11 | 2500 | 0.7333 | 0.8564 |
| 1.0236 | 2.54 | 3000 | 0.5016 | 0.8823 |
| 0.6472 | 2.96 | 3500 | 0.4337 | 0.8908 |
| 0.52 | 3.38 | 4000 | 0.3927 | 0.8974 |
| 0.6075 | 3.8 | 4500 | 0.3506 | 0.9011 |
| 0.5348 | 4.23 | 5000 | 0.3425 | 0.9006 |
| 0.444 | 4.65 | 5500 | 0.3268 | 0.9044 |
| 0.5787 | 5.07 | 6000 | 0.3020 | 0.9078 |
| 0.3995 | 5.49 | 6500 | 0.2932 | 0.9095 |
| 0.414 | 5.92 | 7000 | 0.2806 | 0.9104 |
| 0.4386 | 6.34 | 7500 | 0.2738 | 0.9112 |
| 0.452 | 6.76 | 8000 | 0.2673 | 0.9127 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
|
jackhhhh/LunarLander-v2-1
|
jackhhhh
| 2023-03-20T02:08:51Z | 0 | 0 | null |
[
"tensorboard",
"LunarLander-v2",
"ppo",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"deep-rl-course",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-20T02:08:40Z |
---
tags:
- LunarLander-v2
- ppo
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
- deep-rl-course
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: -105.10 +/- 39.46
name: mean_reward
verified: false
---
# PPO Agent Playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2.
# Hyperparameters
```python
{'exp_name': 'ppo'
'seed': 1
'torch_deterministic': True
'cuda': True
'track': False
'wandb_project_name': 'cleanRL'
'wandb_entity': None
'capture_video': False
'env_id': 'LunarLander-v2'
'total_timesteps': 50000
'learning_rate': 0.00025
'num_envs': 4
'num_steps': 128
'anneal_lr': True
'gae': True
'gamma': 0.99
'gae_lambda': 0.95
'num_minibatches': 4
'update_epochs': 4
'norm_adv': True
'clip_coef': 0.2
'clip_vloss': True
'ent_coef': 0.01
'vf_coef': 0.5
'max_grad_norm': 0.5
'target_kl': None
'repo_id': 'jackhhhh/LunarLander-v2-1'
'batch_size': 512
'minibatch_size': 128}
```
|
jinukoo/ppo-PyramidsRND
|
jinukoo
| 2023-03-20T02:07:23Z | 0 | 0 |
ml-agents
|
[
"ml-agents",
"Pyramids",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
] |
reinforcement-learning
| 2023-03-20T00:57:39Z |
---
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://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
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. Go to https://huggingface.co/spaces/unity/ML-Agents-Pyramids
2. Step 1: Find your model_id: jinukoo/ppo-PyramidsRND
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
jjlira/poca-SoccerTwos
|
jjlira
| 2023-03-20T02:00:13Z | 32 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SoccerTwos",
"region:us"
] |
reinforcement-learning
| 2023-03-20T02:00:05Z |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SoccerTwos
library_name: ml-agents
---
# **poca** Agent playing **SoccerTwos**
This is a trained model of a **poca** agent playing **SoccerTwos** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
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. Go to https://huggingface.co/spaces/unity/ML-Agents-SoccerTwos
2. Step 1: Write your model_id: jjlira/poca-SoccerTwos
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
cpark2/my_awesome_model
|
cpark2
| 2023-03-20T01:54:41Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-03-19T17:50:55Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
metrics:
- accuracy
model-index:
- name: my_awesome_model
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: imdb
type: imdb
config: plain_text
split: test
args: plain_text
metrics:
- name: Accuracy
type: accuracy
value: 0.93092
---
<!-- 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_model
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2365
- Accuracy: 0.9309
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.231 | 1.0 | 1563 | 0.1882 | 0.9278 |
| 0.1488 | 2.0 | 3126 | 0.2365 | 0.9309 |
### Framework versions
- Transformers 4.27.1
- Pytorch 2.1.0.dev20230319
- Datasets 2.10.1
- Tokenizers 0.13.2
|
thu-coai/LongLM-large
|
thu-coai
| 2023-03-20T01:40:26Z | 21 | 6 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"t5",
"text2text-generation",
"lm-head",
"zh",
"arxiv:2108.12960",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-03-02T23:29:05Z |
---
language:
- zh
thumbnail: http://coai.cs.tsinghua.edu.cn/coai/img/logo.png?v=13923
tags:
- pytorch
- lm-head
- zh
widget:
- text: "小咕噜对靳司寒完全是个自来熟，小家伙爬进他怀里小手搂着他的脖子，奶声奶气的要求：“靳蜀黎,你给咕噜讲故事好不好？”讲故事？童话故事吗？“我不会。”小家伙明显不信。嘟着小嘴大眼汪汪的盯着他，“哼。”小家伙轻轻哼了一声,靳司寒默了半晌，<extra_id_1>"
- text: "美女亲自打招呼，这可是破天荒第一次，之前不管他献多少次殷勤，美女<extra_id_1>甩他，难道今天真是老天<extra_id_2>不敢<extra_id_3>的兄连滚带爬的来到<extra_id_4>身边队友都带着艳<extra_id_5>他，<extra_id_6>连计算机系的那票球友都在那儿不住地偷看MAGGIE，这种感觉真<extra_id_7>毙了！"
inference:
parameters:
top_p: 0.9
---
## LongLM
### 1. Parameters
| Versions | $d_m$ | $d_{ff}$ | $d_{kv}$ | $n_h$ | $n_e/n_d$ | \# P |
| ------------ | ----- | -------- | -------- | ----- | --------- | ---- |
| LongLM-small | 512 | 2,048 | 64 | 8 | 6/6 | 60M |
| LongLM-base | 768 | 3,072 | 64 | 12 | 12/12 | 223M |
| LongLM-large | 1,536 | 3,072 | 64 | 12 | 24/32 | 1B |
- $d_m$: the dimension of hidden states
- $d_{ff}$: the dimension of feed forward layers
- $d_{kv}$: the dimension of the keys/values in the self-attention layers
- $n_h$: the number of attention heads
- $n_e$: the number of hidden layers of the encoder
- $n_d$: the number of hidden layers of the decoder
- \#P: the number of parameters
### 2. Pretraining Tasks
Encoder-decoder models are trained typically by maximizing the likelihood of the target output given an input. To improve the capacities of both the encoder and decoder, we propose to train LongLM with two pretraining tasks including text infilling (Raffel et al., 2020) and conditional continuation (Radford et al., 2019). For the first task, the input is a text where a number of spans are sampled and replaced by special tokens with unique IDs, while the output is the spans delimited by the special tokens used in the input. The lengths of masked spans are drawn from a Poisson distribution with λ=3 and all masked tokens compress 15% of the original texts. As for the second task, the input and output are respectively the front and back half of a text, which is split into two parts randomly.
### 3. Pretraining Data
We collect 120G novels as the pretraining data for LongLM.
### 4. Checkpoints
1. **Model Loading:**
```python\
from transformers import T5Tokenizer, T5ForConditionalGeneration
tokenizer = T5Tokenizer.from_pretrained('LongLM-large')
tokenizer.add_special_tokens({"additional_special_tokens": ["<extra_id_%d>"%d for d in range(100)]})
model = T5ForConditionalGeneration.from_pretrained('LongLM-large')
```
2. **Generation:**
```python
input_ids = tokenizer("小咕噜对，<extra_id_1>",return_tensors="pt", padding=True, truncation=True, max_length=512).input_ids.to(device)
gen = model.generate(input_ids, do_sample=True, decoder_start_token_id=1, top_p=0.9, max_length=512)
```
### 5. Dependencies
```
datasets 1.6.2
deepspeed 0.3.16
huggingface-hub 0.0.8
jieba 0.42.1
jsonlines 2.0.0
nltk 3.5
numpy 1.19.5
pytorch-lightning 1.2.0
regex 2020.11.13
rouge 1.0.1
rouge-score 0.0.4
sacrebleu 1.5.0
scipy 1.5.4
sentencepiece 0.1.95
tokenizers 0.10.1
torch 1.8.1
torchaudio 0.8.0
torchmetrics 0.2.0
torchvision 0.9.0
transformers 4.6.1
```
### 6. Contributers
[Jian Guan](https://jianguanthu.github.io/) at [thu-coai](http://coai.cs.tsinghua.edu.cn/)
## Citation
```txt
@misc{guan2021lot,
title={LOT: A Benchmark for Evaluating Chinese Long Text Understanding and Generation},
author={Jian Guan and Zhuoer Feng and Yamei Chen and Ruilin He and Xiaoxi Mao and Changjie Fan and Minlie Huang},
year={2021},
eprint={2108.12960},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
naeisher/ppo-Pyramids
|
naeisher
| 2023-03-20T01:36:07Z | 37 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"Pyramids",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
] |
reinforcement-learning
| 2023-03-19T21:21:46Z |
---
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://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
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. Go to https://huggingface.co/spaces/unity/ML-Agents-Pyramids
2. Step 1: Find your model_id: naeisher/ppo-Pyramids
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
ramCalAmateur/ppo-LunarLander-v2
|
ramCalAmateur
| 2023-03-20T01:31:09Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-20T01:30:47Z |
---
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: 234.72 +/- 20.06
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
michelle-lohwt/ppo-Huggy
|
michelle-lohwt
| 2023-03-20T01:29:16Z | 8 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"Huggy",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
] |
reinforcement-learning
| 2023-03-19T23:57:07Z |
---
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://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
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. Go to https://huggingface.co/spaces/unity/ML-Agents-Huggy
2. Step 1: Find your model_id: michelle-lohwt/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
Surteng/embeddings
|
Surteng
| 2023-03-20T01:27:57Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-02-26T01:38:09Z |
---
license: creativeml-openrail-m
---
|
zbash/Reinforce-Pixelcopter-PLE-v0
|
zbash
| 2023-03-20T01:22:55Z | 0 | 0 | null |
[
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-19T20:19:29Z |
---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-Pixelcopter-PLE-v0
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 23.40 +/- 17.61
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
|
kejian/cpsc-wmle-0.93
|
kejian
| 2023-03-20T01:12:20Z | 2 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"generated_from_trainer",
"en",
"dataset:tomekkorbak/detoxify-pile-chunk3-50000-100000",
"dataset:tomekkorbak/detoxify-pile-chunk3-100000-150000",
"dataset:tomekkorbak/detoxify-pile-chunk3-150000-200000",
"dataset:tomekkorbak/detoxify-pile-chunk3-200000-250000",
"dataset:tomekkorbak/detoxify-pile-chunk3-250000-300000",
"dataset:tomekkorbak/detoxify-pile-chunk3-300000-350000",
"dataset:tomekkorbak/detoxify-pile-chunk3-350000-400000",
"dataset:tomekkorbak/detoxify-pile-chunk3-400000-450000",
"dataset:tomekkorbak/detoxify-pile-chunk3-450000-500000",
"dataset:tomekkorbak/detoxify-pile-chunk3-500000-550000",
"dataset:tomekkorbak/detoxify-pile-chunk3-550000-600000",
"dataset:tomekkorbak/detoxify-pile-chunk3-600000-650000",
"dataset:tomekkorbak/detoxify-pile-chunk3-650000-700000",
"dataset:tomekkorbak/detoxify-pile-chunk3-700000-750000",
"dataset:tomekkorbak/detoxify-pile-chunk3-750000-800000",
"dataset:tomekkorbak/detoxify-pile-chunk3-800000-850000",
"dataset:tomekkorbak/detoxify-pile-chunk3-850000-900000",
"dataset:tomekkorbak/detoxify-pile-chunk3-900000-950000",
"dataset:tomekkorbak/detoxify-pile-chunk3-950000-1000000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1000000-1050000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1050000-1100000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1100000-1150000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1150000-1200000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1200000-1250000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1250000-1300000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1300000-1350000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1350000-1400000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1400000-1450000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1450000-1500000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1500000-1550000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1550000-1600000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1600000-1650000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1650000-1700000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1700000-1750000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1750000-1800000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1800000-1850000",
"license:mit",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | null | 2023-03-19T04:19:15Z |
---
language:
- en
license: mit
tags:
- generated_from_trainer
datasets:
- tomekkorbak/detoxify-pile-chunk3-50000-100000
- tomekkorbak/detoxify-pile-chunk3-100000-150000
- tomekkorbak/detoxify-pile-chunk3-150000-200000
- tomekkorbak/detoxify-pile-chunk3-200000-250000
- tomekkorbak/detoxify-pile-chunk3-250000-300000
- tomekkorbak/detoxify-pile-chunk3-300000-350000
- tomekkorbak/detoxify-pile-chunk3-350000-400000
- tomekkorbak/detoxify-pile-chunk3-400000-450000
- tomekkorbak/detoxify-pile-chunk3-450000-500000
- tomekkorbak/detoxify-pile-chunk3-500000-550000
- tomekkorbak/detoxify-pile-chunk3-550000-600000
- tomekkorbak/detoxify-pile-chunk3-600000-650000
- tomekkorbak/detoxify-pile-chunk3-650000-700000
- tomekkorbak/detoxify-pile-chunk3-700000-750000
- tomekkorbak/detoxify-pile-chunk3-750000-800000
- tomekkorbak/detoxify-pile-chunk3-800000-850000
- tomekkorbak/detoxify-pile-chunk3-850000-900000
- tomekkorbak/detoxify-pile-chunk3-900000-950000
- tomekkorbak/detoxify-pile-chunk3-950000-1000000
- tomekkorbak/detoxify-pile-chunk3-1000000-1050000
- tomekkorbak/detoxify-pile-chunk3-1050000-1100000
- tomekkorbak/detoxify-pile-chunk3-1100000-1150000
- tomekkorbak/detoxify-pile-chunk3-1150000-1200000
- tomekkorbak/detoxify-pile-chunk3-1200000-1250000
- tomekkorbak/detoxify-pile-chunk3-1250000-1300000
- tomekkorbak/detoxify-pile-chunk3-1300000-1350000
- tomekkorbak/detoxify-pile-chunk3-1350000-1400000
- tomekkorbak/detoxify-pile-chunk3-1400000-1450000
- tomekkorbak/detoxify-pile-chunk3-1450000-1500000
- tomekkorbak/detoxify-pile-chunk3-1500000-1550000
- tomekkorbak/detoxify-pile-chunk3-1550000-1600000
- tomekkorbak/detoxify-pile-chunk3-1600000-1650000
- tomekkorbak/detoxify-pile-chunk3-1650000-1700000
- tomekkorbak/detoxify-pile-chunk3-1700000-1750000
- tomekkorbak/detoxify-pile-chunk3-1750000-1800000
- tomekkorbak/detoxify-pile-chunk3-1800000-1850000
model-index:
- name: kejian/cpsc-wmle-0.93
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. -->
# kejian/cpsc-wmle-0.93
This model was trained from scratch on the tomekkorbak/detoxify-pile-chunk3-50000-100000, the tomekkorbak/detoxify-pile-chunk3-100000-150000, the tomekkorbak/detoxify-pile-chunk3-150000-200000, the tomekkorbak/detoxify-pile-chunk3-200000-250000, the tomekkorbak/detoxify-pile-chunk3-250000-300000, the tomekkorbak/detoxify-pile-chunk3-300000-350000, the tomekkorbak/detoxify-pile-chunk3-350000-400000, the tomekkorbak/detoxify-pile-chunk3-400000-450000, the tomekkorbak/detoxify-pile-chunk3-450000-500000, the tomekkorbak/detoxify-pile-chunk3-500000-550000, the tomekkorbak/detoxify-pile-chunk3-550000-600000, the tomekkorbak/detoxify-pile-chunk3-600000-650000, the tomekkorbak/detoxify-pile-chunk3-650000-700000, the tomekkorbak/detoxify-pile-chunk3-700000-750000, the tomekkorbak/detoxify-pile-chunk3-750000-800000, the tomekkorbak/detoxify-pile-chunk3-800000-850000, the tomekkorbak/detoxify-pile-chunk3-850000-900000, the tomekkorbak/detoxify-pile-chunk3-900000-950000, the tomekkorbak/detoxify-pile-chunk3-950000-1000000, the tomekkorbak/detoxify-pile-chunk3-1000000-1050000, the tomekkorbak/detoxify-pile-chunk3-1050000-1100000, the tomekkorbak/detoxify-pile-chunk3-1100000-1150000, the tomekkorbak/detoxify-pile-chunk3-1150000-1200000, the tomekkorbak/detoxify-pile-chunk3-1200000-1250000, the tomekkorbak/detoxify-pile-chunk3-1250000-1300000, the tomekkorbak/detoxify-pile-chunk3-1300000-1350000, the tomekkorbak/detoxify-pile-chunk3-1350000-1400000, the tomekkorbak/detoxify-pile-chunk3-1400000-1450000, the tomekkorbak/detoxify-pile-chunk3-1450000-1500000, the tomekkorbak/detoxify-pile-chunk3-1500000-1550000, the tomekkorbak/detoxify-pile-chunk3-1550000-1600000, the tomekkorbak/detoxify-pile-chunk3-1600000-1650000, the tomekkorbak/detoxify-pile-chunk3-1650000-1700000, the tomekkorbak/detoxify-pile-chunk3-1700000-1750000, the tomekkorbak/detoxify-pile-chunk3-1750000-1800000 and the tomekkorbak/detoxify-pile-chunk3-1800000-1850000 datasets.
## 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: 0.0007
- train_batch_size: 32
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.01
- training_steps: 42724
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.23.0
- Pytorch 1.13.0+cu116
- Datasets 2.0.0
- Tokenizers 0.12.1
# Full config
{'dataset': {'datasets': ['tomekkorbak/detoxify-pile-chunk3-50000-100000',
'tomekkorbak/detoxify-pile-chunk3-100000-150000',
'tomekkorbak/detoxify-pile-chunk3-150000-200000',
'tomekkorbak/detoxify-pile-chunk3-200000-250000',
'tomekkorbak/detoxify-pile-chunk3-250000-300000',
'tomekkorbak/detoxify-pile-chunk3-300000-350000',
'tomekkorbak/detoxify-pile-chunk3-350000-400000',
'tomekkorbak/detoxify-pile-chunk3-400000-450000',
'tomekkorbak/detoxify-pile-chunk3-450000-500000',
'tomekkorbak/detoxify-pile-chunk3-500000-550000',
'tomekkorbak/detoxify-pile-chunk3-550000-600000',
'tomekkorbak/detoxify-pile-chunk3-600000-650000',
'tomekkorbak/detoxify-pile-chunk3-650000-700000',
'tomekkorbak/detoxify-pile-chunk3-700000-750000',
'tomekkorbak/detoxify-pile-chunk3-750000-800000',
'tomekkorbak/detoxify-pile-chunk3-800000-850000',
'tomekkorbak/detoxify-pile-chunk3-850000-900000',
'tomekkorbak/detoxify-pile-chunk3-900000-950000',
'tomekkorbak/detoxify-pile-chunk3-950000-1000000',
'tomekkorbak/detoxify-pile-chunk3-1000000-1050000',
'tomekkorbak/detoxify-pile-chunk3-1050000-1100000',
'tomekkorbak/detoxify-pile-chunk3-1100000-1150000',
'tomekkorbak/detoxify-pile-chunk3-1150000-1200000',
'tomekkorbak/detoxify-pile-chunk3-1200000-1250000',
'tomekkorbak/detoxify-pile-chunk3-1250000-1300000',
'tomekkorbak/detoxify-pile-chunk3-1300000-1350000',
'tomekkorbak/detoxify-pile-chunk3-1350000-1400000',
'tomekkorbak/detoxify-pile-chunk3-1400000-1450000',
'tomekkorbak/detoxify-pile-chunk3-1450000-1500000',
'tomekkorbak/detoxify-pile-chunk3-1500000-1550000',
'tomekkorbak/detoxify-pile-chunk3-1550000-1600000',
'tomekkorbak/detoxify-pile-chunk3-1600000-1650000',
'tomekkorbak/detoxify-pile-chunk3-1650000-1700000',
'tomekkorbak/detoxify-pile-chunk3-1700000-1750000',
'tomekkorbak/detoxify-pile-chunk3-1750000-1800000',
'tomekkorbak/detoxify-pile-chunk3-1800000-1850000'],
'is_split_by_sentences': True},
'generation': {'force_call_on': [42724],
'metrics_configs': [{}, {'n': 1}, {'n': 2}, {'n': 5}],
'scenario_configs': [{'generate_kwargs': {'do_sample': True,
'max_length': 128,
'min_length': 10,
'temperature': 0.7,
'top_k': 0,
'top_p': 0.9},
'name': 'unconditional',
'num_samples': 2048},
{'generate_kwargs': {'do_sample': True,
'max_length': 128,
'min_length': 10,
'temperature': 0.7,
'top_k': 0,
'top_p': 0.9},
'name': 'challenging_rtp',
'num_samples': 2048,
'prompts_path': 'resources/challenging_rtp.jsonl'}],
'scorer_config': {'device': 'cuda:0'}},
'kl_gpt3_callback': {'force_call_on': [42724],
'gpt3_kwargs': {'model_name': 'davinci'},
'max_tokens': 64,
'num_samples': 2048},
'model': {'from_scratch': True,
'gpt2_config_kwargs': {'reorder_and_upcast_attn': True,
'scale_attn_by': True},
'path_or_name': 'gpt2'},
'objective': {'beta': 0.93, 'exponential': False, 'name': 'WeightedMLE'},
'tokenizer': {'path_or_name': 'gpt2'},
'training': {'dataloader_num_workers': 0,
'effective_batch_size': 64,
'evaluation_strategy': 'no',
'fp16': True,
'hub_model_id': 'kejian/cpsc-wmle-0.93',
'hub_strategy': 'all_checkpoints',
'learning_rate': 0.0007,
'logging_first_step': True,
'logging_steps': 50,
'num_tokens': 2800000000.0,
'output_dir': 'training_output_0.93',
'per_device_train_batch_size': 16,
'push_to_hub': True,
'remove_unused_columns': False,
'save_steps': 21362,
'save_strategy': 'steps',
'seed': 42,
'warmup_ratio': 0.01,
'weight_decay': 0.1}}
# Wandb URL:
https://wandb.ai/kejian/uncategorized/runs/bcisua7o
|
LozanoJohan/q-Gym-Taxi
|
LozanoJohan
| 2023-03-20T00:58:44Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-20T00:58:41Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Gym-Taxi
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.54 +/- 2.69
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="LozanoJohan/q-Gym-Taxi", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
kasseev/ppo-LunarLander-v2
|
kasseev
| 2023-03-20T00:53:14Z | 4 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-20T00:52:47Z |
---
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: 264.52 +/- 13.77
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
...
```
|
k4black/Salesforce-codet5-small-CodeXGLUE-CONCODE-adafactor
|
k4black
| 2023-03-20T00:28:12Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-03-19T23:33:38Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- rouge
- bleu
model-index:
- name: Salesforce-codet5-small-CodeXGLUE-CONCODE-adafactor
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. -->
# Salesforce-codet5-small-CodeXGLUE-CONCODE-adafactor
This model is a fine-tuned version of [Salesforce/codet5-small](https://huggingface.co/Salesforce/codet5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8118
- Exact Match: 0.1555
- Rouge1: 0.5580
- Rouge2: 0.3886
- Rougel: 0.5407
- Rougelsum: 0.5483
- Bleu: 0.1297
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Exact Match | Rouge1 | Rouge2 | Rougel | Rougelsum | Bleu |
|:-------------:|:-----:|:----:|:---------------:|:-----------:|:------:|:------:|:------:|:---------:|:------:|
| 1.8525 | 0.16 | 500 | 0.9340 | 0.1435 | 0.5360 | 0.3596 | 0.5171 | 0.5238 | 0.1146 |
| 0.8679 | 0.32 | 1000 | 0.9262 | 0.1405 | 0.5385 | 0.3659 | 0.5228 | 0.5294 | 0.1179 |
| 0.8169 | 0.48 | 1500 | 0.8957 | 0.139 | 0.5372 | 0.3642 | 0.5192 | 0.5265 | 0.1135 |
| 0.7734 | 0.64 | 2000 | 0.8827 | 0.14 | 0.5485 | 0.3706 | 0.5316 | 0.5381 | 0.1210 |
| 0.743 | 0.8 | 2500 | 0.8647 | 0.155 | 0.5503 | 0.3833 | 0.5338 | 0.5411 | 0.1184 |
| 0.72 | 0.96 | 3000 | 0.8661 | 0.1545 | 0.5460 | 0.3735 | 0.5284 | 0.5366 | 0.1162 |
| 0.6539 | 1.12 | 3500 | 0.8591 | 0.156 | 0.5540 | 0.3841 | 0.5365 | 0.5444 | 0.1241 |
| 0.6301 | 1.28 | 4000 | 0.8452 | 0.1485 | 0.5556 | 0.3794 | 0.5369 | 0.5451 | 0.1237 |
| 0.6222 | 1.44 | 4500 | 0.8321 | 0.1585 | 0.5529 | 0.3818 | 0.5343 | 0.5430 | 0.1228 |
| 0.6221 | 1.6 | 5000 | 0.8317 | 0.154 | 0.5664 | 0.3925 | 0.5481 | 0.5575 | 0.1289 |
| 0.6067 | 1.76 | 5500 | 0.8228 | 0.1625 | 0.5607 | 0.3933 | 0.5438 | 0.5516 | 0.1299 |
| 0.5927 | 1.92 | 6000 | 0.8179 | 0.156 | 0.5625 | 0.3942 | 0.5457 | 0.5526 | 0.1309 |
| 0.5548 | 2.08 | 6500 | 0.8259 | 0.152 | 0.5582 | 0.3846 | 0.5402 | 0.5485 | 0.1314 |
| 0.5146 | 2.24 | 7000 | 0.8328 | 0.1545 | 0.5605 | 0.3889 | 0.5429 | 0.5514 | 0.1299 |
| 0.5193 | 2.4 | 7500 | 0.8197 | 0.1555 | 0.5604 | 0.3866 | 0.5431 | 0.5501 | 0.1268 |
| 0.5172 | 2.56 | 8000 | 0.8118 | 0.1555 | 0.5580 | 0.3886 | 0.5407 | 0.5483 | 0.1297 |
### Framework versions
- Transformers 4.27.1
- Pytorch 1.12.1+cu113
- Datasets 2.10.1
- Tokenizers 0.13.2
|
kucharskipj/ppo-PyramidsRND
|
kucharskipj
| 2023-03-20T00:24:07Z | 8 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"Pyramids",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
] |
reinforcement-learning
| 2023-03-20T00:19:16Z |
---
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://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
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. Go to https://huggingface.co/spaces/unity/ML-Agents-Pyramids
2. Step 1: Find your model_id: kucharskipj/ppo-PyramidsRND
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
jinukoo/ppo-SnowballTarget
|
jinukoo
| 2023-03-20T00:22:10Z | 4 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"SnowballTarget",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SnowballTarget",
"region:us"
] |
reinforcement-learning
| 2023-03-20T00:22:04Z |
---
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://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
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. Go to https://huggingface.co/spaces/unity/ML-Agents-SnowballTarget
2. Step 1: Find your model_id: jinukoo/ppo-SnowballTarget
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
liqi6811/skillsBERT_v3_tf_epoch200_Important
|
liqi6811
| 2023-03-20T00:20:04Z | 2 | 0 |
transformers
|
[
"transformers",
"tf",
"bert",
"pretraining",
"generated_from_keras_callback",
"endpoints_compatible",
"region:us"
] | null | 2023-03-20T00:19:19Z |
---
tags:
- generated_from_keras_callback
model-index:
- name: skillsBERT_v3_epoch200
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. -->
# skillsBERT_v3_epoch200
This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0256
- Validation Loss: 7.8012
- Epoch: 199
## 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': 'AdamW', 'weight_decay': 0.004, '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': 5e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 6.8416 | 6.7417 | 0 |
| 6.2162 | 6.1436 | 1 |
| 5.3514 | 5.3111 | 2 |
| 4.5931 | 4.9790 | 3 |
| 4.0664 | 4.8477 | 4 |
| 3.6654 | 4.6776 | 5 |
| 3.3343 | 4.5758 | 6 |
| 3.0431 | 4.4659 | 7 |
| 2.7726 | 4.4337 | 8 |
| 2.5106 | 4.6514 | 9 |
| 2.2625 | 4.6512 | 10 |
| 2.0222 | 4.7317 | 11 |
| 1.7924 | 4.6184 | 12 |
| 1.5719 | 4.7085 | 13 |
| 1.3735 | 4.8741 | 14 |
| 1.1820 | 5.0078 | 15 |
| 1.0164 | 5.0224 | 16 |
| 0.8588 | 5.2085 | 17 |
| 0.7247 | 5.2827 | 18 |
| 0.6140 | 5.3904 | 19 |
| 0.5144 | 5.3287 | 20 |
| 0.4330 | 5.4909 | 21 |
| 0.3603 | 5.6482 | 22 |
| 0.3120 | 5.4950 | 23 |
| 0.2634 | 5.8465 | 24 |
| 0.2322 | 5.8744 | 25 |
| 0.2077 | 5.8122 | 26 |
| 0.1904 | 5.9466 | 27 |
| 0.1698 | 6.0747 | 28 |
| 0.1536 | 6.2833 | 29 |
| 0.1462 | 6.2201 | 30 |
| 0.1367 | 6.3858 | 31 |
| 0.1309 | 6.4264 | 32 |
| 0.1196 | 6.3880 | 33 |
| 0.1129 | 6.6965 | 34 |
| 0.1100 | 6.4638 | 35 |
| 0.1050 | 6.4099 | 36 |
| 0.0994 | 6.4061 | 37 |
| 0.0973 | 6.4458 | 38 |
| 0.0934 | 6.5099 | 39 |
| 0.0909 | 6.4002 | 40 |
| 0.0896 | 6.5372 | 41 |
| 0.0839 | 6.5808 | 42 |
| 0.0817 | 6.4682 | 43 |
| 0.0814 | 6.6921 | 44 |
| 0.0793 | 6.7584 | 45 |
| 0.0765 | 6.7847 | 46 |
| 0.0765 | 6.8182 | 47 |
| 0.0712 | 6.7281 | 48 |
| 0.0710 | 6.7083 | 49 |
| 0.0700 | 6.6643 | 50 |
| 0.0695 | 6.7186 | 51 |
| 0.0681 | 6.9158 | 52 |
| 0.0647 | 6.8065 | 53 |
| 0.0662 | 7.0515 | 54 |
| 0.0630 | 6.9353 | 55 |
| 0.0624 | 7.0418 | 56 |
| 0.0640 | 6.7393 | 57 |
| 0.0610 | 7.0111 | 58 |
| 0.0602 | 7.0310 | 59 |
| 0.0577 | 6.7995 | 60 |
| 0.0616 | 6.7364 | 61 |
| 0.0575 | 7.0542 | 62 |
| 0.0532 | 7.1219 | 63 |
| 0.0601 | 6.9904 | 64 |
| 0.0528 | 7.2782 | 65 |
| 0.0551 | 7.2465 | 66 |
| 0.0551 | 7.2380 | 67 |
| 0.0542 | 6.9920 | 68 |
| 0.0536 | 7.1704 | 69 |
| 0.0529 | 7.1467 | 70 |
| 0.0488 | 7.0684 | 71 |
| 0.0494 | 7.0333 | 72 |
| 0.0518 | 7.3027 | 73 |
| 0.0505 | 7.1332 | 74 |
| 0.0481 | 7.0856 | 75 |
| 0.0493 | 7.2170 | 76 |
| 0.0490 | 7.3652 | 77 |
| 0.0480 | 7.3370 | 78 |
| 0.0485 | 7.1336 | 79 |
| 0.0480 | 7.2017 | 80 |
| 0.0483 | 7.2421 | 81 |
| 0.0463 | 7.3675 | 82 |
| 0.0455 | 7.3847 | 83 |
| 0.0441 | 7.3112 | 84 |
| 0.0454 | 7.2941 | 85 |
| 0.0474 | 7.4086 | 86 |
| 0.0451 | 7.1806 | 87 |
| 0.0417 | 7.4458 | 88 |
| 0.0464 | 7.2912 | 89 |
| 0.0422 | 7.6368 | 90 |
| 0.0434 | 7.4060 | 91 |
| 0.0427 | 7.4733 | 92 |
| 0.0433 | 7.4114 | 93 |
| 0.0416 | 7.3643 | 94 |
| 0.0428 | 7.5354 | 95 |
| 0.0426 | 7.2827 | 96 |
| 0.0400 | 7.4285 | 97 |
| 0.0413 | 7.4499 | 98 |
| 0.0422 | 7.4816 | 99 |
| 0.0407 | 7.3491 | 100 |
| 0.0402 | 7.3784 | 101 |
| 0.0412 | 7.3845 | 102 |
| 0.0389 | 7.5468 | 103 |
| 0.0372 | 7.4723 | 104 |
| 0.0421 | 7.4283 | 105 |
| 0.0382 | 7.4074 | 106 |
| 0.0392 | 7.4365 | 107 |
| 0.0399 | 7.4375 | 108 |
| 0.0396 | 7.5146 | 109 |
| 0.0389 | 7.2877 | 110 |
| 0.0384 | 7.3907 | 111 |
| 0.0386 | 7.5558 | 112 |
| 0.0378 | 7.3746 | 113 |
| 0.0359 | 7.5122 | 114 |
| 0.0412 | 7.4631 | 115 |
| 0.0341 | 7.5950 | 116 |
| 0.0380 | 7.3713 | 117 |
| 0.0382 | 7.4232 | 118 |
| 0.0350 | 7.5180 | 119 |
| 0.0374 | 7.4993 | 120 |
| 0.0373 | 7.4308 | 121 |
| 0.0357 | 7.4511 | 122 |
| 0.0364 | 7.5254 | 123 |
| 0.0349 | 7.4326 | 124 |
| 0.0371 | 7.5467 | 125 |
| 0.0344 | 7.5324 | 126 |
| 0.0375 | 7.4660 | 127 |
| 0.0365 | 7.5816 | 128 |
| 0.0348 | 7.5425 | 129 |
| 0.0333 | 7.5655 | 130 |
| 0.0331 | 7.6466 | 131 |
| 0.0369 | 7.6142 | 132 |
| 0.0332 | 7.7292 | 133 |
| 0.0349 | 7.6649 | 134 |
| 0.0343 | 7.5255 | 135 |
| 0.0335 | 7.7736 | 136 |
| 0.0334 | 7.6680 | 137 |
| 0.0356 | 7.4846 | 138 |
| 0.0323 | 7.7691 | 139 |
| 0.0339 | 7.6986 | 140 |
| 0.0333 | 7.4287 | 141 |
| 0.0333 | 7.5534 | 142 |
| 0.0322 | 7.5383 | 143 |
| 0.0333 | 7.5212 | 144 |
| 0.0320 | 7.5945 | 145 |
| 0.0335 | 7.5932 | 146 |
| 0.0332 | 7.7700 | 147 |
| 0.0323 | 7.4798 | 148 |
| 0.0318 | 7.5804 | 149 |
| 0.0336 | 7.5721 | 150 |
| 0.0332 | 7.3627 | 151 |
| 0.0334 | 7.6093 | 152 |
| 0.0293 | 7.7731 | 153 |
| 0.0336 | 7.6722 | 154 |
| 0.0319 | 7.5856 | 155 |
| 0.0325 | 7.6355 | 156 |
| 0.0287 | 7.5941 | 157 |
| 0.0318 | 7.6476 | 158 |
| 0.0304 | 7.5365 | 159 |
| 0.0313 | 7.6429 | 160 |
| 0.0319 | 7.5318 | 161 |
| 0.0311 | 7.7468 | 162 |
| 0.0321 | 7.6332 | 163 |
| 0.0301 | 7.8412 | 164 |
| 0.0292 | 7.6819 | 165 |
| 0.0313 | 7.5544 | 166 |
| 0.0311 | 7.6667 | 167 |
| 0.0274 | 7.7875 | 168 |
| 0.0317 | 7.6632 | 169 |
| 0.0305 | 7.8710 | 170 |
| 0.0311 | 7.5799 | 171 |
| 0.0311 | 7.7357 | 172 |
| 0.0271 | 7.7491 | 173 |
| 0.0317 | 7.8025 | 174 |
| 0.0294 | 7.6856 | 175 |
| 0.0302 | 7.7687 | 176 |
| 0.0293 | 7.8676 | 177 |
| 0.0315 | 7.6371 | 178 |
| 0.0286 | 7.8114 | 179 |
| 0.0288 | 7.6690 | 180 |
| 0.0304 | 7.6712 | 181 |
| 0.0293 | 7.8668 | 182 |
| 0.0305 | 7.8221 | 183 |
| 0.0284 | 7.7506 | 184 |
| 0.0309 | 7.6629 | 185 |
| 0.0282 | 7.7157 | 186 |
| 0.0262 | 7.8241 | 187 |
| 0.0305 | 7.6471 | 188 |
| 0.0288 | 7.6409 | 189 |
| 0.0283 | 7.7386 | 190 |
| 0.0286 | 7.8070 | 191 |
| 0.0284 | 7.7921 | 192 |
| 0.0287 | 7.9042 | 193 |
| 0.0289 | 7.7297 | 194 |
| 0.0276 | 7.8584 | 195 |
| 0.0278 | 7.8580 | 196 |
| 0.0258 | 7.9323 | 197 |
| 0.0306 | 7.7566 | 198 |
| 0.0256 | 7.8012 | 199 |
### Framework versions
- Transformers 4.28.0.dev0
- TensorFlow 2.11.0
- Datasets 2.10.1
- Tokenizers 0.13.2
|
MakiPan/Reinforce-PixelCopter-PLE-v0
|
MakiPan
| 2023-03-19T23:58:51Z | 0 | 0 | null |
[
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-19T23:58:44Z |
---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-PixelCopter-PLE-v0
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 21.30 +/- 16.08
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
|
nikgeo/a2c-PandaReachDense-v2
|
nikgeo
| 2023-03-19T23:55:34Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"PandaReachDense-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-19T22:26:34Z |
---
library_name: stable-baselines3
tags:
- PandaReachDense-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachDense-v2
type: PandaReachDense-v2
metrics:
- type: mean_reward
value: -1.40 +/- 0.41
name: mean_reward
verified: false
---
# **A2C** Agent playing **PandaReachDense-v2**
This is a trained model of a **A2C** agent playing **PandaReachDense-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
...
```
|
pfunk/CartPole-v1-DQN_newww-seed3
|
pfunk
| 2023-03-19T23:48:21Z | 0 | 0 |
cleanrl
|
[
"cleanrl",
"tensorboard",
"CartPole-v1",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-19T23:48:18Z |
---
tags:
- CartPole-v1
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 494.56 +/- 0.00
name: mean_reward
verified: false
---
# (CleanRL) **DQN** Agent Playing **CartPole-v1**
This is a trained model of a DQN agent playing CartPole-v1.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/DQN_newww.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[DQN_newww]"
python -m cleanrl_utils.enjoy --exp-name DQN_newww --env-id CartPole-v1
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/pfunk/CartPole-v1-DQN_newww-seed3/raw/main/dqn.py
curl -OL https://huggingface.co/pfunk/CartPole-v1-DQN_newww-seed3/raw/main/pyproject.toml
curl -OL https://huggingface.co/pfunk/CartPole-v1-DQN_newww-seed3/raw/main/poetry.lock
poetry install --all-extras
python dqn.py --track --wandb-entity pfunk --wandb-project-name dqpn --capture-video true --save-model true --upload-model true --hf-entity pfunk --exp-name DQN_newww --seed 3
```
# Hyperparameters
```python
{'alg_type': 'dqn.py',
'batch_size': 256,
'buffer_size': 300000,
'capture_video': True,
'cuda': True,
'end_e': 0.1,
'env_id': 'CartPole-v1',
'exp_name': 'DQN_newww',
'exploration_fraction': 0.2,
'gamma': 1.0,
'hf_entity': 'pfunk',
'learning_rate': 0.0001,
'learning_starts': 1000,
'save_model': True,
'seed': 3,
'start_e': 1.0,
'target_network_frequency': 20,
'target_tau': 1.0,
'torch_deterministic': True,
'total_timesteps': 500000,
'track': True,
'train_frequency': 1,
'upload_model': True,
'wandb_entity': 'pfunk',
'wandb_project_name': 'dqpn'}
```
|
pfunk/CartPole-v1-DQN_newww-seed4
|
pfunk
| 2023-03-19T23:48:18Z | 0 | 0 |
cleanrl
|
[
"cleanrl",
"tensorboard",
"CartPole-v1",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-19T23:48:15Z |
---
tags:
- CartPole-v1
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 495.67 +/- 0.00
name: mean_reward
verified: false
---
# (CleanRL) **DQN** Agent Playing **CartPole-v1**
This is a trained model of a DQN agent playing CartPole-v1.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/DQN_newww.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[DQN_newww]"
python -m cleanrl_utils.enjoy --exp-name DQN_newww --env-id CartPole-v1
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/pfunk/CartPole-v1-DQN_newww-seed4/raw/main/dqn.py
curl -OL https://huggingface.co/pfunk/CartPole-v1-DQN_newww-seed4/raw/main/pyproject.toml
curl -OL https://huggingface.co/pfunk/CartPole-v1-DQN_newww-seed4/raw/main/poetry.lock
poetry install --all-extras
python dqn.py --track --wandb-entity pfunk --wandb-project-name dqpn --capture-video true --save-model true --upload-model true --hf-entity pfunk --exp-name DQN_newww --seed 4
```
# Hyperparameters
```python
{'alg_type': 'dqn.py',
'batch_size': 256,
'buffer_size': 300000,
'capture_video': True,
'cuda': True,
'end_e': 0.1,
'env_id': 'CartPole-v1',
'exp_name': 'DQN_newww',
'exploration_fraction': 0.2,
'gamma': 1.0,
'hf_entity': 'pfunk',
'learning_rate': 0.0001,
'learning_starts': 1000,
'save_model': True,
'seed': 4,
'start_e': 1.0,
'target_network_frequency': 20,
'target_tau': 1.0,
'torch_deterministic': True,
'total_timesteps': 500000,
'track': True,
'train_frequency': 1,
'upload_model': True,
'wandb_entity': 'pfunk',
'wandb_project_name': 'dqpn'}
```
|
jimregan/whisper-small-sv-riksdag
|
jimregan
| 2023-03-19T23:43:05Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"sv",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-03-01T17:57:25Z |
---
language:
- sv
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: Whisper Small Sv - Riksdag 100h
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 Small Sv - Riksdag 100h
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4977
- Wer: 1118.4718
## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 20000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|
| 0.1384 | 0.11 | 1000 | 0.4747 | 380.8335 |
| 0.1186 | 0.22 | 2000 | 0.4513 | 1032.3900 |
| 0.1056 | 0.33 | 3000 | 0.4385 | 582.0427 |
| 0.0824 | 0.43 | 4000 | 0.4465 | 574.8907 |
| 0.0961 | 0.54 | 5000 | 0.4199 | 1004.9138 |
| 0.0939 | 0.65 | 6000 | 0.4478 | 866.2979 |
| 0.0758 | 0.76 | 7000 | 0.4384 | 907.9496 |
| 0.0741 | 0.87 | 8000 | 0.4264 | 641.1371 |
| 0.0692 | 0.98 | 9000 | 0.4206 | 1142.6550 |
| 0.0257 | 1.08 | 10000 | 0.4707 | 1152.4312 |
| 0.0273 | 1.19 | 11000 | 0.4789 | 1100.2058 |
| 0.021 | 1.3 | 12000 | 0.4763 | 1236.1719 |
| 0.0163 | 1.41 | 13000 | 0.5035 | 924.8006 |
| 0.0183 | 1.52 | 14000 | 0.4911 | 1285.1814 |
| 0.024 | 1.63 | 15000 | 0.4861 | 1140.8284 |
| 0.0158 | 1.73 | 16000 | 0.4793 | 1181.7597 |
| 0.0167 | 1.84 | 17000 | 0.4759 | 1207.3064 |
| 0.0231 | 1.95 | 18000 | 0.4801 | 1139.6964 |
| 0.0054 | 2.06 | 19000 | 0.4934 | 1114.4842 |
| 0.006 | 2.17 | 20000 | 0.4977 | 1118.4718 |
### Framework versions
- Transformers 4.27.0.dev0
- Pytorch 1.13.1
- Datasets 2.10.1
- Tokenizers 0.13.2
|
KarosY/lianjia_2l_100per800_2e-4
|
KarosY
| 2023-03-19T23:40:24Z | 1 | 0 |
diffusers
|
[
"diffusers",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"lora",
"base_model:stabilityai/stable-diffusion-2-1-base",
"base_model:adapter:stabilityai/stable-diffusion-2-1-base",
"license:creativeml-openrail-m",
"region:us"
] |
text-to-image
| 2023-03-19T15:40:37Z |
---
license: creativeml-openrail-m
base_model: stabilityai/stable-diffusion-2-1-base
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- lora
inference: true
---
# LoRA text2image fine-tuning - https://huggingface.co/KarosY/lianjia_2l_100per800_2e-4
These are LoRA adaption weights for stabilityai/stable-diffusion-2-1-base. The weights were fine-tuned on the None dataset. You can find some example images in the following.
|
KarosY/lianjia_2l_100per800_1e-4
|
KarosY
| 2023-03-19T23:40:18Z | 1 | 0 |
diffusers
|
[
"diffusers",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"lora",
"base_model:stabilityai/stable-diffusion-2-1-base",
"base_model:adapter:stabilityai/stable-diffusion-2-1-base",
"license:creativeml-openrail-m",
"region:us"
] |
text-to-image
| 2023-03-19T15:39:55Z |
---
license: creativeml-openrail-m
base_model: stabilityai/stable-diffusion-2-1-base
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- lora
inference: true
---
# LoRA text2image fine-tuning - https://huggingface.co/KarosY/lianjia_2l_100per800_1e-4
These are LoRA adaption weights for stabilityai/stable-diffusion-2-1-base. The weights were fine-tuned on the None dataset. You can find some example images in the following.
|
vkheman/autotrain-multic-42139108182
|
vkheman
| 2023-03-19T23:38:57Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain",
"en",
"dataset:vkheman/autotrain-data-multic",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-03-19T23:35:35Z |
---
tags:
- autotrain
- text-classification
language:
- en
widget:
- text: "I love AutoTrain 🤗"
datasets:
- vkheman/autotrain-data-multic
co2_eq_emissions:
emissions: 1.3867154772046042
---
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 42139108182
- CO2 Emissions (in grams): 1.3867
## Validation Metrics
- Loss: 0.758
- Accuracy: 0.836
- Macro F1: 0.835
- Micro F1: 0.836
- Weighted F1: 0.836
- Macro Precision: 0.839
- Micro Precision: 0.836
- Weighted Precision: 0.840
- Macro Recall: 0.836
- Micro Recall: 0.836
- Weighted Recall: 0.836
## 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/vkheman/autotrain-multic-42139108182
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("vkheman/autotrain-multic-42139108182", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("vkheman/autotrain-multic-42139108182", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
```
|
KBLab/whisper-large-rixvox
|
KBLab
| 2023-03-19T23:24:34Z | 518 | 4 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"whisper",
"automatic-speech-recognition",
"sv",
"dataset:KBLab/rixvox",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-03-13T06:31:06Z |
---
license: apache-2.0
datasets:
- KBLab/rixvox
language:
- sv
---
# Whisper Large RixVox Swedish
This is a [Whisper large](https://huggingface.co/openai/whisper-large-v2) finetuned for Swedish
using the [RixVox](https://huggingface.co/datasets/KBLab/rixvox) dataset.
Please note that this model, as every other encoder-decoder speech-to-text model, is prone to
hallucinating on unexpected inputs and treats the task as translation rather than transcription.
I.e your mileage may vary depending on filtering and type of data.
In this release the entire encoder was frozen. Subsequent releases will not do this **if** the
generalization to other types of data (i.e not parliamentary speeches) is kept when not freezing
the encoder.
## Evaluation (test)
* RixVox WER: `22.59`
* RixVox WER (normalized*): `19.33`
* Common Voice 11 WER: `18.03`
* Common Voice 11 WER (normalized*): `13.23`
* Fleurs WER: `14.26`
* Fleurs WER (normalized*): `8.99`
*) Normalization is done by applying the following to source and generated texts:
```
def normalize(s):
return ' '.join([ x for x in sub('[^0-9a-zåäöA-ZÅÄÖ ]', ' ', s.lower().replace('é', 'e')).split() ])
```
In comparison the original Whisper large gets `30.56`/`25.58`, `18.76`/`15.00`, and `14.53`/`9.19` respectively.
## Training
Training was done using Huggingface and Deepspeed with ZeRO stage 2.
* learning rate: 1e-5
* optimizer: CPUAdamW (Deepspeed)
* lr scheduler: linear
* warmup steps: 500
* per device batch size: 20
* GPUs: 8 x NVIDIA A100 40GB
* total batch size: 160
* steps: 20000
* lowercase: no
* fp16
* entire encoder was frozen
|
Crosbot/marvel_snap_cards
|
Crosbot
| 2023-03-19T23:17:47Z | 0 | 0 | null |
[
"dataset:Crosbot/mv_snp_cards",
"region:us"
] | null | 2023-03-19T23:16:10Z |
---
datasets:
- Crosbot/mv_snp_cards
---
|
k4black/Salesforce-codet5-small-CodeXGLUE-CONCODE-test
|
k4black
| 2023-03-19T23:06:23Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-03-19T22:43:18Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- rouge
- bleu
model-index:
- name: Salesforce-codet5-small-CodeXGLUE-CONCODE-test
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Salesforce-codet5-small-CodeXGLUE-CONCODE-test
This model is a fine-tuned version of [Salesforce/codet5-small](https://huggingface.co/Salesforce/codet5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8508
- Exact Match: 0.156
- Rouge1: 0.5559
- Rouge2: 0.3857
- Rougel: 0.5378
- Rougelsum: 0.5465
- Bleu: 0.1246
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Exact Match | Rouge1 | Rouge2 | Rougel | Rougelsum | Bleu |
|:-------------:|:-----:|:----:|:---------------:|:-----------:|:------:|:------:|:------:|:---------:|:------:|
| 1.3563 | 0.16 | 500 | 1.1652 | 0.1115 | 0.5098 | 0.3191 | 0.4915 | 0.4982 | 0.1088 |
| 0.9656 | 0.32 | 1000 | 1.0435 | 0.1245 | 0.5246 | 0.3444 | 0.5075 | 0.5145 | 0.1164 |
| 0.8627 | 0.48 | 1500 | 0.9851 | 0.121 | 0.5275 | 0.3420 | 0.5074 | 0.5154 | 0.1132 |
| 0.7718 | 0.64 | 2000 | 0.9288 | 0.1385 | 0.5334 | 0.3589 | 0.5174 | 0.5242 | 0.1206 |
| 0.7237 | 0.8 | 2500 | 0.8867 | 0.1495 | 0.5505 | 0.3762 | 0.5328 | 0.5406 | 0.1208 |
| 0.6812 | 0.96 | 3000 | 0.8508 | 0.156 | 0.5559 | 0.3857 | 0.5378 | 0.5465 | 0.1246 |
### Framework versions
- Transformers 4.27.1
- Pytorch 1.12.1+cu113
- Datasets 2.10.1
- Tokenizers 0.13.2
|
MakiPan/Reinforce-CartPole-v1
|
MakiPan
| 2023-03-19T23:03:59Z | 0 | 0 | null |
[
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-19T23:03:47Z |
---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-CartPole-v1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 461.80 +/- 114.60
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
|
vocabtrimmer/mt5-small-trimmed-ru-10000-ruquad-qg
|
vocabtrimmer
| 2023-03-19T22:12:55Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"question generation",
"ru",
"dataset:lmqg/qg_ruquad",
"arxiv:2210.03992",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-03-19T22:12:24Z |
---
license: cc-by-4.0
metrics:
- bleu4
- meteor
- rouge-l
- bertscore
- moverscore
language: ru
datasets:
- lmqg/qg_ruquad
pipeline_tag: text2text-generation
tags:
- question generation
widget:
- text: "Нелишним будет отметить, что, развивая это направление, Д. И. Менделеев, поначалу априорно выдвинув идею о температуре, при которой высота мениска будет нулевой, <hl> в мае 1860 года <hl> провёл серию опытов."
example_title: "Question Generation Example 1"
- text: "Однако, франкоязычный <hl> Квебек <hl> практически никогда не включается в состав Латинской Америки."
example_title: "Question Generation Example 2"
- text: "Классическим примером международного синдиката XX века была группа компаний <hl> Де Бирс <hl> , которая в 1980-е годы контролировала до 90 % мировой торговли алмазами."
example_title: "Question Generation Example 3"
model-index:
- name: vocabtrimmer/mt5-small-trimmed-ru-10000-ruquad-qg
results:
- task:
name: Text2text Generation
type: text2text-generation
dataset:
name: lmqg/qg_ruquad
type: default
args: default
metrics:
- name: BLEU4 (Question Generation)
type: bleu4_question_generation
value: 18.8
- name: ROUGE-L (Question Generation)
type: rouge_l_question_generation
value: 34.64
- name: METEOR (Question Generation)
type: meteor_question_generation
value: 30.03
- name: BERTScore (Question Generation)
type: bertscore_question_generation
value: 86.61
- name: MoverScore (Question Generation)
type: moverscore_question_generation
value: 65.62
---
# Model Card of `vocabtrimmer/mt5-small-trimmed-ru-10000-ruquad-qg`
This model is fine-tuned version of [vocabtrimmer/mt5-small-trimmed-ru-10000](https://huggingface.co/vocabtrimmer/mt5-small-trimmed-ru-10000) for question generation task on the [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-generation).
### Overview
- **Language model:** [vocabtrimmer/mt5-small-trimmed-ru-10000](https://huggingface.co/vocabtrimmer/mt5-small-trimmed-ru-10000)
- **Language:** ru
- **Training data:** [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) (default)
- **Online Demo:** [https://autoqg.net/](https://autoqg.net/)
- **Repository:** [https://github.com/asahi417/lm-question-generation](https://github.com/asahi417/lm-question-generation)
- **Paper:** [https://arxiv.org/abs/2210.03992](https://arxiv.org/abs/2210.03992)
### Usage
- With [`lmqg`](https://github.com/asahi417/lm-question-generation#lmqg-language-model-for-question-generation-)
```python
from lmqg import TransformersQG
# initialize model
model = TransformersQG(language="ru", model="vocabtrimmer/mt5-small-trimmed-ru-10000-ruquad-qg")
# model prediction
questions = model.generate_q(list_context="Нелишним будет отметить, что, развивая это направление, Д. И. Менделеев, поначалу априорно выдвинув идею о температуре, при которой высота мениска будет нулевой, в мае 1860 года провёл серию опытов.", list_answer="в мае 1860 года")
```
- With `transformers`
```python
from transformers import pipeline
pipe = pipeline("text2text-generation", "vocabtrimmer/mt5-small-trimmed-ru-10000-ruquad-qg")
output = pipe("Нелишним будет отметить, что, развивая это направление, Д. И. Менделеев, поначалу априорно выдвинув идею о температуре, при которой высота мениска будет нулевой, <hl> в мае 1860 года <hl> провёл серию опытов.")
```
## Evaluation
- ***Metric (Question Generation)***: [raw metric file](https://huggingface.co/vocabtrimmer/mt5-small-trimmed-ru-10000-ruquad-qg/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_ruquad.default.json)
| | Score | Type | Dataset |
|:-----------|--------:|:--------|:-----------------------------------------------------------------|
| BERTScore | 86.61 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) |
| Bleu_1 | 34.72 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) |
| Bleu_2 | 27.84 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) |
| Bleu_3 | 22.74 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) |
| Bleu_4 | 18.8 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) |
| METEOR | 30.03 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) |
| MoverScore | 65.62 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) |
| ROUGE_L | 34.64 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) |
## Training hyperparameters
The following hyperparameters were used during fine-tuning:
- dataset_path: lmqg/qg_ruquad
- dataset_name: default
- input_types: paragraph_answer
- output_types: question
- prefix_types: None
- model: vocabtrimmer/mt5-small-trimmed-ru-10000
- max_length: 512
- max_length_output: 32
- epoch: 15
- batch: 16
- lr: 0.001
- fp16: False
- random_seed: 1
- gradient_accumulation_steps: 4
- label_smoothing: 0.15
The full configuration can be found at [fine-tuning config file](https://huggingface.co/vocabtrimmer/mt5-small-trimmed-ru-10000-ruquad-qg/raw/main/trainer_config.json).
## Citation
```
@inproceedings{ushio-etal-2022-generative,
title = "{G}enerative {L}anguage {M}odels for {P}aragraph-{L}evel {Q}uestion {G}eneration",
author = "Ushio, Asahi and
Alva-Manchego, Fernando and
Camacho-Collados, Jose",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2022",
address = "Abu Dhabi, U.A.E.",
publisher = "Association for Computational Linguistics",
}
```
|
dominguesm/positive-reframing-ptbr
|
dominguesm
| 2023-03-19T22:10:55Z | 32 | 3 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"t5",
"text2text-generation",
"seq2seq",
"positive_perspectives",
"pt",
"dataset:dominguesm/positive-reframing-ptbr-dataset",
"arxiv:2204.02952",
"license:cc-by-4.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-10-20T17:26:47Z |
---
language: pt
license: cc-by-4.0
tags:
- seq2seq
- t5
- positive_perspectives
datasets:
- dominguesm/positive-reframing-ptbr-dataset
widget:
- text: "['growth', 'neutralizing']: Sempre estressado e pensando em um monte de coisas ao mesmo tempo, preciso levar uma de cada vez, sobrecarga estressada, necessidade de reclamar"
- text: "['growth', 'neutralizing', 'optimism']: Se eu não tiver um colapso mental antes do final do verão, será um milagre."
- text: "['impermanence']: Dirigindo para visitar a vovó no hospital e o meu filho que está doente."
- text: "['optimism']: Ótimo agora, como vou explicar isso para ela, ela está tão perto de mim que não posso perdê-la :'("
- text: "['growth', 'optimism']: sempre há algo que eu poderia estar fazendo. Eu geralmente escolho não fazer isso."
---
# Positive Perspectives with Portuguese Text Reframing
## Model description
This model is a [PTT5](https://huggingface.co/unicamp-dl/ptt5-base-portuguese-vocab) adjusted to the sentiment transfer task, where the objective is to reverse the sentiment polarity of a text without contradicting the original meaning. Positive reframing induces a complementary positive viewpoint (e.g. glass-half-full) escaping negative patterns. Based on the article [arXiv:2204.02952](https://arxiv.org/abs/2204.02952).
## How to use
The model uses one or more sentiment strategies concatenated with a sentence and will generate a sentence with the applied sentiment output. The maximum string length is 1024 tokens. Entries must be organized in the following format:
```
"['thankfulness', 'optimism']: Tenho tanta coisa para fazer antes de sair da cidade por uma semana no domingo."
```
### Available sentiment strategies:
**growth**: viewing a challenging event as an opportunity for the author to specifically grow or improve himself.
**impermanence**: Saying that bad things don't last forever, will get better soon, and/or that other people have had similar difficulties.
**neutralizing**: Replacing a negative word with a neutral word. For example, “This was a terrible day” becomes “This was a long day”.
**optimism**: Focusing on things about the situation itself, at that moment, that are good (not just predicting a better future).
**self_affirmation**: Talking about what strengths the author already has, or values he admires, such as love, courage, perseverance, etc.
**thankfulness**: Expressing gratitude or gratitude with keywords like appreciate, happy for it, grateful for, good thing, etc.
### Usage
```python
from transformers import pipeline
pipe = pipeline('summarization', "dominguesm/positive-reframing-ptbr")
text = "['thankfulness', 'optimism']: Tenho tanta coisa para fazer antes de sair da cidade por uma semana no domingo."
pipe(text, max_length=1024)
```
|
josu/gpt-neo-br-instruction
|
josu
| 2023-03-19T21:54:05Z | 19 | 1 |
transformers
|
[
"transformers",
"pytorch",
"gpt_neo",
"text-generation",
"pt",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-03-19T21:11:41Z |
---
language:
- pt
widget:
- text: Explique o que é inteligência artificial.
- text: Explique o que é processamento de linguagem natural.
---
``` python
from transformers import GenerationConfig
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("josu/gpt-neo-br-instruction")
tokenizer = AutoTokenizer.from_pretrained("josu/gpt-neo-br-instruction")
def generate_prompt(instruction, input=None):
if input:
return f"""Abaixo está uma instrução que descreve uma tarefa, juntamente com uma entrada que fornece mais contexto. Escreva uma resposta que complete adequadamente o pedido.
### Instrução:
{instruction}
### Entrada:
{input}
### Resposta:"""
else:
return f"""Abaixo está uma instrução que descreve uma tarefa. Escreva uma resposta que complete adequadamente o pedido.
### Instrução:
{instruction}
### Resposta:"""
generation_config = GenerationConfig(
temperature=0.2,
top_p=0.75,
num_beams=4,
)
def evaluate(instruction, input=None):
prompt = generate_prompt(instruction, input)
inputs = tokenizer(prompt, return_tensors="pt")
input_ids = inputs["input_ids"].cuda()
generation_output = model.generate(
input_ids=input_ids,
generation_config=generation_config,
return_dict_in_generate=True,
output_scores=True,
max_new_tokens=256
)
content = []
for s in generation_output.sequences:
output = tokenizer.decode(s)
content.append(output.split("### Resposta:")[1].strip())
return content
```
|
1-800-BAD-CODE/punctuation_fullstop_truecase_english
|
1-800-BAD-CODE
| 2023-03-19T21:35:48Z | 1,267,560 | 8 |
generic
|
[
"generic",
"onnx",
"text2text-generation",
"punctuation",
"true-casing",
"sentence-boundary-detection",
"nlp",
"en",
"license:apache-2.0",
"region:us"
] |
text2text-generation
| 2023-03-11T22:21:22Z |
---
license: apache-2.0
language:
- en
tags:
- text2text-generation
- punctuation
- true-casing
- sentence-boundary-detection
- nlp
widget:
- text: "hey man how's it going i haven't seen you in a while let's meet at 6 pm for drinks"
- text: "hello user this is an example input this text should be split into several sentences including a final interrogative did it work"
library_name: generic
inference: true
---
# Model Overview
This model accepts as input lower-cased, unpunctuated English text and performs in one pass punctuation restoration, true-casing (capitalization), and sentence boundary detection (segmentation).
In contast to many similar models, this model can predict punctuated acronyms (e.g., "U.S.") via a special "acronym" class, as well as arbitarily-capitalized words (NATO, McDonald's, etc.) via multi-label true-casing predictions.
**Widget note**: The text generation widget doesn't seem to respect line breaks.
Instead, the pipeline inserts a new line token `\n` in the text where the model has predicted sentence boundaries (line breaks).
# Usage
The easy way to use this model is to install [punctuators](https://github.com/1-800-BAD-CODE/punctuators):
```bash
pip install punctuators
```
If this package is broken, please let me know in the community tab (I update it for each model and break it a lot!).
Let's punctuate my weekend recap, as well as few interesting sentences with acronyms and abbreviations that I made up or found on Wikipedia:
<details open>
<summary>Example Usage</summary>
```
from typing import List
from punctuators.models import PunctCapSegModelONNX
# Instantiate this model
# This will download the ONNX and SPE models. To clean up, delete this model from your HF cache directory.
m = PunctCapSegModelONNX.from_pretrained("pcs_en")
# Define some input texts to punctuate
input_texts: List[str] = [
# Literally my weekend
"i woke up at 6 am and took the dog for a hike in the metacomet mountains we like to take morning adventures on the weekends",
"despite being mid march it snowed overnight and into the morning here in connecticut it was snowier up in the mountains than in the farmington valley where i live",
"when i got home i trained this model on the lambda cloud on an a100 gpu with about 10 million lines of text the total budget was less than 5 dollars",
# Real acronyms in sentences that I made up
"george hw bush was the president of the us for 8 years",
"i saw mr smith at the store he was shopping for a new lawn mower i suggested he get one of those new battery operated ones they're so much quieter",
# See how the model performs on made-up acronyms
"i went to the fgw store and bought a new tg optical scope",
# First few sentences from today's featured article summary on wikipedia
"it's that man again itma was a radio comedy programme that was broadcast by the bbc for twelve series from 1939 to 1949 featuring tommy handley in the central role itma was a character driven comedy whose satirical targets included officialdom and the proliferation of minor wartime regulations parts of the scripts were rewritten in the hours before the broadcast to ensure topicality"
]
results: List[List[str]] = m.infer(input_texts)
for input_text, output_texts in zip(input_texts, results):
print(f"Input: {input_text}")
print(f"Outputs:")
for text in output_texts:
print(f"\t{text}")
print()
```
Exact output may vary based on the model version; here is the current output:
</details>
<details open>
<summary>Expected Output</summary>
```text
In: i woke up at 6 am and took the dog for a hike in the metacomet mountains we like to take morning adventures on the weekends
Out: I woke up at 6 a.m. and took the dog for a hike in the Metacomet Mountains.
Out: We like to take morning adventures on the weekends.
In: despite being mid march it snowed overnight and into the morning here in connecticut it was snowier up in the mountains than in the farmington valley where i live
Out: Despite being mid March, it snowed overnight and into the morning.
Out: Here in Connecticut, it was snowier up in the mountains than in the Farmington Valley where I live.
In: when i got home i trained this model on the lambda cloud on an a100 gpu with about 10 million lines of text the total budget was less than 5 dollars
Out: When I got home, I trained this model on the Lambda Cloud.
Out: On an A100 GPU with about 10 million lines of text, the total budget was less than 5 dollars.
In: george hw bush was the president of the us for 8 years
Out: George H.W. Bush was the president of the U.S. for 8 years.
In: i saw mr smith at the store he was shopping for a new lawn mower i suggested he get one of those new battery operated ones they're so much quieter
Out: I saw Mr. Smith at the store he was shopping for a new lawn mower.
Out: I suggested he get one of those new battery operated ones.
Out: They're so much quieter.
In: i went to the fgw store and bought a new tg optical scope
Out: I went to the FGW store and bought a new TG optical scope.
In: it's that man again itma was a radio comedy programme that was broadcast by the bbc for twelve series from 1939 to 1949 featuring tommy handley in the central role itma was a character driven comedy whose satirical targets included officialdom and the proliferation of minor wartime regulations parts of the scripts were rewritten in the hours before the broadcast to ensure topicality
Out: It's that man again.
Out: ITMA was a radio comedy programme that was broadcast by the BBC for Twelve Series from 1939 to 1949, featuring Tommy Handley.
Out: In the central role, ITMA was a character driven comedy whose satirical targets included officialdom and the proliferation of minor wartime regulations.
Out: Parts of the scripts were rewritten in the hours before the broadcast to ensure topicality.
```
</details>
# Model Details
This model implements the graph shown below, with brief descriptions for each step following.
1. **Encoding**:
The model begins by tokenizing the text with a subword tokenizer.
The tokenizer used here is a `SentencePiece` model with a vocabulary size of 32k.
Next, the input sequence is encoded with a base-sized Transformer, consisting of 6 layers with a model dimension of 512.
2. **Punctuation**:
The encoded sequence is then fed into a feed-forward classification network to predict punctuation tokens.
Punctation is predicted once per subword, to allow acronyms to be properly punctuated.
An indiret benefit of per-subword prediction is to allow the model to run in a graph generalized for continuous-script languages, e.g., Chinese.
5. **Sentence boundary detection**
For sentence boundary detection, we condition the model on punctuation via embeddings.
Each punctuation prediction is used to select an embedding for that token, which is concatenated to the encoded representation.
The SBD head analyzes both the encoding of the un-punctuated sequence and the puncutation predictions, and predicts which tokens are sentence boundaries.
7. **Shift and concat sentence boundaries**
In English, the first character of each sentence should be upper-cased.
Thus, we should feed the sentence boundary information to the true-case classification network.
Since the true-case classification network is feed-forward and has no temporal context, each time step must embed whether it is the first word of a sentence.
Therefore, we shift the binary sentence boundary decisions to the right by one: if token `N-1` is a sentence boundary, token `N` is the first word of a sentence.
Concatenating this with the encoded text, each time step contains whether it is the first word of a sentence as predicted by the SBD head.
8. **True-case prediction**
Armed with the knowledge of punctation and sentence boundaries, a classification network predicts true-casing.
Since true-casing should be done on a per-character basis, the classification network makes `N` predictions per token, where `N` is the length of the subtoken.
(In practice, `N` is the longest possible subword, and the extra predictions are ignored).
This scheme captures acronyms, e.g., "NATO", as well as bi-capitalized words, e.g., "MacDonald".
The model's maximum length is 256 subtokens, due to the limit of the trained embeddings.
However, the [punctuators](https://github.com/1-800-BAD-CODE/punctuators) package
as described above will transparently predict on overlapping subgsegments of long inputs and fuse the results before returning output,
allowing inputs to be arbitrarily long.
## Punctuation Tokens
This model predicts the following set of punctuation tokens:
| Token | Description |
| ---: | :---------- |
| NULL | Predict no punctuation |
| ACRONYM | Every character in this subword ends with a period |
| . | Latin full stop |
| , | Latin comma |
| ? | Latin question mark |
# Training Details
## Training Framework
This model was trained on a forked branch of the [NeMo](https://github.com/NVIDIA/NeMo) framework.
## Training Data
This model was trained with News Crawl data from WMT.
Approximately 10M lines were used from the years 2021 and 2012.
The latter was used to attempt to reduce bias: annual news is typically dominated by a few topics, and 2021 is dominated by COVID discussions.
# Limitations
## Domain
This model was trained on news data, and may not perform well on conversational or informal data.
## Noisy Training Data
The training data was noisy, and no manual cleaning was utilized.
### Acronyms and Abbreviations
Acronyms and abbreviations are especially noisy; the table below shows how many variations of each token appear in the training data.
| Token | Count |
| -: | :- |
| Mr | 115232 |
| Mr. | 108212 |
| Token | Count |
| -: | :- |
| U.S. | 85324 |
| US | 37332 |
| U.S | 354 |
| U.s | 108 |
| u.S. | 65 |
Thus, the model's acronym and abbreviation predictions may be a bit unpredictable.
### Sentence Boundary Detection Targets
An assumption for sentence boundary detection targets is that each line of the input data is exactly one sentence.
However, a non-negligible portion of the training data contains multiple sentences per line.
Thus, the SBD head may miss an obvious sentence boundary if it's similar to an error seen in the training data.
# Evaluation
In these metrics, keep in mind that
1. The data is noisy
2. Sentence boundaries and true-casing are conditioned on predicted punctuation, which is the most difficult task and sometimes incorrect.
When conditioning on reference punctuation, true-casing and SBD metrics are much higher w.r.t. the reference targets.
4. Punctuation can be subjective. E.g.,
`Hello Frank, how's it going?`
or
`Hello Frank. How's it going?`
When the sentences are longer and more practical, these ambiguities abound and affect all 3 analytics.
## Test Data and Example Generation
Each test example was generated using the following procedure:
1. Concatenate 10 random sentences
2. Lower-case the concatenated sentence
3. Remove all punctuation
The data is a held-out portion of News Crawl, which has been deduplicated.
3,000 lines of data was used, generating 3,000 unique examples of 10 sentences each.
## Results
<details open>
<summary>Punctuation Report</summary>
```text
label precision recall f1 support
<NULL> (label_id: 0) 98.83 98.49 98.66 446496
<ACRONYM> (label_id: 1) 74.15 94.26 83.01 697
. (label_id: 2) 90.64 92.99 91.80 30002
, (label_id: 3) 77.19 79.13 78.15 23321
? (label_id: 4) 76.58 74.56 75.56 1022
-------------------
micro avg 97.21 97.21 97.21 501538
macro avg 83.48 87.89 85.44 501538
weighted avg 97.25 97.21 97.23 501538
```
</details>
<details open>
<summary>True-casing Report</summary>
```text
# With predicted punctuation (not aligned with targets)
label precision recall f1 support
LOWER (label_id: 0) 99.76 99.72 99.74 2020678
UPPER (label_id: 1) 93.32 94.20 93.76 83873
-------------------
micro avg 99.50 99.50 99.50 2104551
macro avg 96.54 96.96 96.75 2104551
weighted avg 99.50 99.50 99.50 2104551
# With reference punctuation (punctuation matches targets)
label precision recall f1 support
LOWER (label_id: 0) 99.83 99.81 99.82 2020678
UPPER (label_id: 1) 95.51 95.90 95.71 83873
-------------------
micro avg 99.66 99.66 99.66 2104551
macro avg 97.67 97.86 97.76 2104551
weighted avg 99.66 99.66 99.66 2104551
```
</details>
<details open>
<summary>Sentence Boundary Detection report</summary>
```text
# With predicted punctuation (not aligned with targets)
label precision recall f1 support
NOSTOP (label_id: 0) 99.59 99.45 99.52 471608
FULLSTOP (label_id: 1) 91.47 93.53 92.49 29930
-------------------
micro avg 99.09 99.09 99.09 501538
macro avg 95.53 96.49 96.00 501538
weighted avg 99.10 99.09 99.10 501538
# With reference punctuation (punctuation matches targets)
label precision recall f1 support
NOSTOP (label_id: 0) 100.00 99.97 99.98 471608
FULLSTOP (label_id: 1) 99.63 99.93 99.78 32923
-------------------
micro avg 99.97 99.97 99.97 504531
macro avg 99.81 99.95 99.88 504531
weighted avg 99.97 99.97 99.97 504531
```
</details>
# Fun Facts
Some fun facts are examined in this section.
## Embeddings
Let's examine the embeddings (see graph above) to see if the model meaningfully employed them.
We show here the cosine similarity between the embeddings of each token:
| | NULL | ACRONYM | . | , | ? |
| - | - | - | - | - | - |
| NULL | 1.00 | | | | |
| ACRONYM | -0.49 | 1.00 | | ||
| . | -1.00 | 0.48 | 1.00 | | |
| , | 1.00 | -0.48 | -1.00 | 1.00 | |
| ? | -1.00 | 0.49 | 1.00 | -1.00 | 1.00 |
Recall that these embeddings are used to predict sentence boundaries... thus we should expect full stops to cluster.
Indeed, we see that `NULL` and "`,`" are exactly the same, because neither have an implication on sentence boundaries.
Next, we see that "`.`" and "`?`" are exactly the same, because w.r.t. SBD these are exactly the same: strong full stop implications.
(Though, we may expect some difference between these tokens, given that "`.`" is predicted after abbreviations, e.g., 'Mr.', that are not full stops.)
Further, we see that "`.`" and "`?`" are exactly the opposite of `NULL`.
This is expected since these tokens typically imply sentence boundaries, whereas `NULL` and "`,`" never do.
Lastly, we see that `ACRONYM` is similar to, but not the same as, the full stops "`.`" and "`?`",
and far from, but not the opposite of, `NULL` and "`,`".
Intuition suggests this is because acronyms can be full stops ("I live in the northern U.S. It's cold here.") or not ("It's 5 a.m. and I'm tired.").
|
CreativeEvolution/rl_course_vizdoom_health_gathering_supreme
|
CreativeEvolution
| 2023-03-19T21:23:37Z | 0 | 0 |
sample-factory
|
[
"sample-factory",
"tensorboard",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-19T21:23:33Z |
---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: doom_health_gathering_supreme
type: doom_health_gathering_supreme
metrics:
- type: mean_reward
value: 10.86 +/- 2.32
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 CreativeEvolution/rl_course_vizdoom_health_gathering_supreme
```
## Using the model
To run the model after download, use the `enjoy` script corresponding to this environment:
```
python -m .usr.local.lib.python3.9.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme
```
You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag.
See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details
## Training with this model
To continue training with this model, use the `train` script corresponding to this environment:
```
python -m .usr.local.lib.python3.9.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme --restart_behavior=resume --train_for_env_steps=10000000000
```
Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
|
lipee/a2c-AntBulletEnv-v0
|
lipee
| 2023-03-19T21:22:05Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"AntBulletEnv-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-19T21:20:51Z |
---
library_name: stable-baselines3
tags:
- AntBulletEnv-v0
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: AntBulletEnv-v0
type: AntBulletEnv-v0
metrics:
- type: mean_reward
value: 1310.87 +/- 62.72
name: mean_reward
verified: false
---
# **A2C** Agent playing **AntBulletEnv-v0**
This is a trained model of a **A2C** agent playing **AntBulletEnv-v0**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
mdoshi2612/fake-news-detector
|
mdoshi2612
| 2023-03-19T21:07:21Z | 0 | 0 | null |
[
"code",
"en",
"arxiv:1910.09700",
"region:us"
] | null | 2023-03-19T21:01:06Z |
---
language:
- en
tags:
- code
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
### How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Data Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
Zilikon/q-FrozenLake-v1-4x4-noSlippery
|
Zilikon
| 2023-03-19T21:06:23Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-19T21:06:22Z |
---
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="Soulaimene1/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"])
```
|
galsenai/wavlm-large-waxal-keyword-spotting
|
galsenai
| 2023-03-19T21:06:05Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wavlm",
"audio-classification",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] |
audio-classification
| 2023-03-19T20:49:07Z |
---
tags:
- audio-classification
- generated_from_trainer
metrics:
- accuracy
- precision
- f1
model-index:
- name: wavlm-large
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. -->
# wavlm-large
This model is a fine-tuned version of [microsoft/wavlm-large](https://huggingface.co/microsoft/wavlm-large) on the galsenai/waxal_dataset dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5936
- Accuracy: 0.8950
- Precision: 0.9789
- F1: 0.9334
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 12
- eval_batch_size: 12
- seed: 0
- gradient_accumulation_steps: 4
- total_train_batch_size: 48
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 32.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | F1 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:---------:|:------:|
| 4.7405 | 1.01 | 500 | 5.1525 | 0.0 | 0.0 | 0.0 |
| 4.4299 | 2.02 | 1000 | 5.8969 | 0.0 | 0.0 | 0.0 |
| 4.2868 | 3.04 | 1500 | 4.9304 | 0.0019 | 0.0031 | 0.0023 |
| 3.6242 | 4.05 | 2000 | 4.3396 | 0.0409 | 0.0224 | 0.0237 |
| 2.686 | 5.06 | 2500 | 3.9399 | 0.0549 | 0.0320 | 0.0308 |
| 1.9284 | 6.07 | 3000 | 3.7736 | 0.0500 | 0.0779 | 0.0442 |
| 1.3936 | 7.08 | 3500 | 3.5380 | 0.0947 | 0.1381 | 0.0916 |
| 1.0764 | 8.1 | 4000 | 3.3281 | 0.1584 | 0.3514 | 0.1839 |
| 0.872 | 9.11 | 4500 | 2.9592 | 0.2755 | 0.6027 | 0.3315 |
| 0.7026 | 10.12 | 5000 | 2.5049 | 0.3971 | 0.6971 | 0.4587 |
| 0.603 | 11.13 | 5500 | 2.1485 | 0.5479 | 0.8074 | 0.6129 |
| 0.5042 | 12.15 | 6000 | 1.6532 | 0.7014 | 0.8604 | 0.7544 |
| 0.4542 | 13.16 | 6500 | 1.4057 | 0.7435 | 0.8941 | 0.7990 |
| 0.388 | 14.17 | 7000 | 1.2338 | 0.7802 | 0.9219 | 0.8332 |
| 0.3515 | 15.18 | 7500 | 0.9898 | 0.8170 | 0.9433 | 0.8681 |
| 0.3195 | 16.19 | 8000 | 1.1404 | 0.8067 | 0.9523 | 0.8635 |
| 0.2882 | 17.21 | 8500 | 0.9811 | 0.8177 | 0.9540 | 0.8746 |
| 0.2695 | 18.22 | 9000 | 0.9483 | 0.8318 | 0.9616 | 0.8878 |
| 0.2535 | 19.23 | 9500 | 0.6694 | 0.8844 | 0.9692 | 0.9198 |
| 0.2437 | 20.24 | 10000 | 0.7546 | 0.8700 | 0.9656 | 0.9125 |
| 0.2376 | 21.25 | 10500 | 0.6698 | 0.8810 | 0.9695 | 0.9202 |
| 0.2214 | 22.27 | 11000 | 0.7156 | 0.8727 | 0.9726 | 0.9174 |
| 0.2148 | 23.28 | 11500 | 0.5982 | 0.8931 | 0.9711 | 0.9286 |
| 0.2087 | 24.29 | 12000 | 0.7109 | 0.8814 | 0.9757 | 0.9243 |
| 0.2039 | 25.3 | 12500 | 0.6577 | 0.8897 | 0.9799 | 0.9306 |
| 0.1997 | 26.32 | 13000 | 0.7307 | 0.8746 | 0.9774 | 0.9203 |
| 0.1896 | 27.33 | 13500 | 0.6143 | 0.8905 | 0.9748 | 0.9290 |
| 0.1869 | 28.34 | 14000 | 0.6380 | 0.8909 | 0.9739 | 0.9287 |
| 0.185 | 29.35 | 14500 | 0.6932 | 0.8871 | 0.9791 | 0.9289 |
| 0.1813 | 30.36 | 15000 | 0.5936 | 0.8950 | 0.9789 | 0.9334 |
| 0.1801 | 31.38 | 15500 | 0.6150 | 0.8947 | 0.9801 | 0.9334 |
### Framework versions
- Transformers 4.27.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.9.1.dev0
- Tokenizers 0.13.2
|
c0ldstudy/unit2
|
c0ldstudy
| 2023-03-19T21:05:27Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-19T21:05:24Z |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: unit2
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="c0ldstudy/unit2", 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"])
```
|
vocabtrimmer/mt5-small-trimmed-fr-10000-frquad-qa
|
vocabtrimmer
| 2023-03-19T20:52:37Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"question answering",
"fr",
"dataset:lmqg/qg_frquad",
"arxiv:2210.03992",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-03-19T20:52:08Z |
---
license: cc-by-4.0
metrics:
- bleu4
- meteor
- rouge-l
- bertscore
- moverscore
language: fr
datasets:
- lmqg/qg_frquad
pipeline_tag: text2text-generation
tags:
- question answering
widget:
- text: "question: En quelle année a-t-on trouvé trace d'un haut fourneau similaire?, context: Cette technologie ne disparaît qu'au début du XXe siècle. On retrouve vers 1900 un haut fourneau similaire dans le Bulacan, aux Philippines. Plus tard encore, le « haut fourneau dans la cour » prôné par Mao Zedong pendant le Grand Bond en avant est de ce type. L'expérience n'est un échec technique que dans les régions où le savoir-faire n'existe pas, ou a disparu."
example_title: "Question Answering Example 1"
- text: "question: Comment appelle-t-on la Guerre de 14-18 ?, context: Ce black dog peut être lié à des évènements traumatisants issus du monde extérieur, tels que son renvoi de l'Amirauté après la catastrophe des Dardanelles, lors de la Grande Guerre de 14-18, ou son rejet par l'électorat en juillet 1945. On sait également que dans ces deux cas, la guérison, certes lente et douloureuse et jamais complète ni définitive, se fera grâce à la peinture. D'un autre côté, étant donnés les symptômes de ce mal que Churchill éprouvait de plus en plus, il ne pouvait rien moins qu'être purement associé à de telles causes extrinsèques, ce qui correspond au profil classique de la dépression majeure unipolaire ou bipolaire."
example_title: "Question Answering Example 2"
model-index:
- name: vocabtrimmer/mt5-small-trimmed-fr-10000-frquad-qa
results:
- task:
name: Text2text Generation
type: text2text-generation
dataset:
name: lmqg/qg_frquad
type: default
args: default
metrics:
- name: BLEU4 (Question Answering)
type: bleu4_question_answering
value: 18.63
- name: ROUGE-L (Question Answering)
type: rouge_l_question_answering
value: 29.33
- name: METEOR (Question Answering)
type: meteor_question_answering
value: 23.73
- name: BERTScore (Question Answering)
type: bertscore_question_answering
value: 89.65
- name: MoverScore (Question Answering)
type: moverscore_question_answering
value: 72.01
- name: AnswerF1Score (Question Answering)
type: answer_f1_score__question_answering
value: 47.59
- name: AnswerExactMatch (Question Answering)
type: answer_exact_match_question_answering
value: 30.24
---
# Model Card of `vocabtrimmer/mt5-small-trimmed-fr-10000-frquad-qa`
This model is fine-tuned version of [vocabtrimmer/mt5-small-trimmed-fr-10000](https://huggingface.co/vocabtrimmer/mt5-small-trimmed-fr-10000) for question answering task on the [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-generation).
### Overview
- **Language model:** [vocabtrimmer/mt5-small-trimmed-fr-10000](https://huggingface.co/vocabtrimmer/mt5-small-trimmed-fr-10000)
- **Language:** fr
- **Training data:** [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) (default)
- **Online Demo:** [https://autoqg.net/](https://autoqg.net/)
- **Repository:** [https://github.com/asahi417/lm-question-generation](https://github.com/asahi417/lm-question-generation)
- **Paper:** [https://arxiv.org/abs/2210.03992](https://arxiv.org/abs/2210.03992)
### Usage
- With [`lmqg`](https://github.com/asahi417/lm-question-generation#lmqg-language-model-for-question-generation-)
```python
from lmqg import TransformersQG
# initialize model
model = TransformersQG(language="fr", model="vocabtrimmer/mt5-small-trimmed-fr-10000-frquad-qa")
# model prediction
answers = model.answer_q(list_question="En quelle année a-t-on trouvé trace d'un haut fourneau similaire?", list_context=" Cette technologie ne disparaît qu'au début du XXe siècle. On retrouve vers 1900 un haut fourneau similaire dans le Bulacan, aux Philippines. Plus tard encore, le « haut fourneau dans la cour » prôné par Mao Zedong pendant le Grand Bond en avant est de ce type. L'expérience n'est un échec technique que dans les régions où le savoir-faire n'existe pas, ou a disparu.")
```
- With `transformers`
```python
from transformers import pipeline
pipe = pipeline("text2text-generation", "vocabtrimmer/mt5-small-trimmed-fr-10000-frquad-qa")
output = pipe("question: En quelle année a-t-on trouvé trace d'un haut fourneau similaire?, context: Cette technologie ne disparaît qu'au début du XXe siècle. On retrouve vers 1900 un haut fourneau similaire dans le Bulacan, aux Philippines. Plus tard encore, le « haut fourneau dans la cour » prôné par Mao Zedong pendant le Grand Bond en avant est de ce type. L'expérience n'est un échec technique que dans les régions où le savoir-faire n'existe pas, ou a disparu.")
```
## Evaluation
- ***Metric (Question Answering)***: [raw metric file](https://huggingface.co/vocabtrimmer/mt5-small-trimmed-fr-10000-frquad-qa/raw/main/eval/metric.first.answer.paragraph_question.answer.lmqg_qg_frquad.default.json)
| | Score | Type | Dataset |
|:-----------------|--------:|:--------|:-----------------------------------------------------------------|
| AnswerExactMatch | 30.24 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) |
| AnswerF1Score | 47.59 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) |
| BERTScore | 89.65 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) |
| Bleu_1 | 28.11 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) |
| Bleu_2 | 23.97 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) |
| Bleu_3 | 21.1 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) |
| Bleu_4 | 18.63 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) |
| METEOR | 23.73 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) |
| MoverScore | 72.01 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) |
| ROUGE_L | 29.33 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) |
## Training hyperparameters
The following hyperparameters were used during fine-tuning:
- dataset_path: lmqg/qg_frquad
- dataset_name: default
- input_types: ['paragraph_question']
- output_types: ['answer']
- prefix_types: None
- model: vocabtrimmer/mt5-small-trimmed-fr-10000
- max_length: 512
- max_length_output: 32
- epoch: 25
- batch: 32
- lr: 0.0005
- fp16: False
- random_seed: 1
- gradient_accumulation_steps: 2
- label_smoothing: 0.15
The full configuration can be found at [fine-tuning config file](https://huggingface.co/vocabtrimmer/mt5-small-trimmed-fr-10000-frquad-qa/raw/main/trainer_config.json).
## Citation
```
@inproceedings{ushio-etal-2022-generative,
title = "{G}enerative {L}anguage {M}odels for {P}aragraph-{L}evel {Q}uestion {G}eneration",
author = "Ushio, Asahi and
Alva-Manchego, Fernando and
Camacho-Collados, Jose",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2022",
address = "Abu Dhabi, U.A.E.",
publisher = "Association for Computational Linguistics",
}
```
|
BenDaouda/wav2vec2-large-xls-r-1b-wolof-VoiceToText
|
BenDaouda
| 2023-03-19T20:51:25Z | 15 | 1 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-03-19T14:46:20Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: wav2vec2-large-xls-r-1b-wolof-VoiceToText
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. -->
# wav2vec2-large-xls-r-1b-wolof-VoiceToText
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3951
- Wer: 0.3838
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 3.2047 | 14.29 | 400 | 0.4485 | 0.4602 |
| 0.1987 | 28.57 | 800 | 0.3951 | 0.3838 |
### Framework versions
- Transformers 4.27.1
- Pytorch 1.10.0+cu113
- Datasets 1.18.3
- Tokenizers 0.13.2
|
Carslo45/DialoGPT-medium-ddlc-monika
|
Carslo45
| 2023-03-19T20:25:00Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-03-19T19:06:26Z |
---
language:
- en
tags:
- conversational
---
# Doki Doki Literature Club - Monika : A DialoGPT-medium Based Model
|
brahamdp/poca-SoccerTwos
|
brahamdp
| 2023-03-19T20:08:27Z | 44 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"SoccerTwos",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SoccerTwos",
"region:us"
] |
reinforcement-learning
| 2023-03-19T20:08:20Z |
---
library_name: ml-agents
tags:
- SoccerTwos
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SoccerTwos
---
# **poca** Agent playing **SoccerTwos**
This is a trained model of a **poca** agent playing **SoccerTwos** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
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. Go to https://huggingface.co/spaces/unity/ML-Agents-SoccerTwos
2. Step 1: Find your model_id: brahamdp/poca-SoccerTwos
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
AlekseyKorshuk/cup-it-ds-sft-pretrained
|
AlekseyKorshuk
| 2023-03-19T20:08:16Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"generated_from_trainer",
"dataset:AlekseyKorshuk/up-it-ds-sft",
"license:mit",
"model-index",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-03-19T19:42:01Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- AlekseyKorshuk/up-it-ds-sft
metrics:
- accuracy
model-index:
- name: test-clm
results:
- task:
name: Causal Language Modeling
type: text-generation
dataset:
name: AlekseyKorshuk/up-it-ds-sft
type: AlekseyKorshuk/up-it-ds-sft
metrics:
- name: Accuracy
type: accuracy
value: 0.4214217746661709
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# test-clm
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the AlekseyKorshuk/up-it-ds-sft dataset.
It achieves the following results on the evaluation set:
- Loss: 3.1025
- Accuracy: 0.4214
## 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
- distributed_type: multi-GPU
- num_devices: 4
- total_train_batch_size: 32
- total_eval_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
### Framework versions
- Transformers 4.28.0.dev0
- Pytorch 1.10.1+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
nickmuchi/finbert-tone-finetuned-fintwitter-classification
|
nickmuchi
| 2023-03-19T20:07:42Z | 66 | 12 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"financial-tweets-sentiment-analysis",
"sentiment-analysis",
"financial",
"stocks",
"sentiment",
"dataset:zeroshot/twitter-financial-news-sentiment",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-12-30T14:30:37Z |
---
tags:
- generated_from_trainer
- financial-tweets-sentiment-analysis
- sentiment-analysis
- generated_from_trainer
- financial
- stocks
- sentiment
datasets:
- zeroshot/twitter-financial-news-sentiment
metrics:
- accuracy
- f1
- precision
- recall
widget:
- text: "$LOW - Lowe's racks up another positive rating despite recession risk"
example_title: "Bullish Sentiment"
- text: "$HNHAF $HNHPD $AAPL - Trendforce cuts iPhone estimate after Foxconn delay"
example_title: "Bearish Sentiment"
- text: "Coin Toss: Morgan Stanley Raises Tesla Bull Case To $500, Keeps Bear Case At $10"
example_title: "Neutral Sentiment"
model-index:
- name: finbert-tone-finetuned-fintwitter-classification
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: twitter-financial-news-sentiment
type: finance
metrics:
- type: F1
name: F1
value: 0.8838
- type: accuracy
name: accuracy
value: 0.8840
---
<!-- 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. -->
# finbert-tone-finetuned-fintwitter-classification
This model is a fine-tuned version of [yiyanghkust/finbert-tone](https://huggingface.co/yiyanghkust/finbert-tone) on [Twitter Financial News](https://huggingface.co/datasets/zeroshot/twitter-financial-news-sentiment) dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4078
- Accuracy: 0.8840
- F1: 0.8838
- Precision: 0.8838
- Recall: 0.8840
## Model description
Model determines the financial sentiment of given tweets. Given the unbalanced distribution of the class labels, the weights were adjusted to pay attention to the less sampled labels which should increase overall performance..
## 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: 20
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.6385 | 1.0 | 597 | 0.3688 | 0.8668 | 0.8693 | 0.8744 | 0.8668 |
| 0.3044 | 2.0 | 1194 | 0.3994 | 0.8744 | 0.8726 | 0.8739 | 0.8744 |
| 0.1833 | 3.0 | 1791 | 0.6212 | 0.8781 | 0.8764 | 0.8762 | 0.8781 |
| 0.1189 | 4.0 | 2388 | 0.8370 | 0.8740 | 0.8743 | 0.8748 | 0.8740 |
| 0.0759 | 5.0 | 2985 | 0.9107 | 0.8807 | 0.8798 | 0.8796 | 0.8807 |
| 0.0291 | 6.0 | 3582 | 0.9711 | 0.8836 | 0.8825 | 0.8821 | 0.8836 |
| 0.0314 | 7.0 | 4179 | 1.1305 | 0.8819 | 0.8811 | 0.8812 | 0.8819 |
| 0.0217 | 8.0 | 4776 | 1.0190 | 0.8811 | 0.8813 | 0.8816 | 0.8811 |
| 0.0227 | 9.0 | 5373 | 1.1940 | 0.8844 | 0.8832 | 0.8838 | 0.8844 |
| 0.0156 | 10.0 | 5970 | 1.2595 | 0.8752 | 0.8768 | 0.8801 | 0.8752 |
| 0.0135 | 11.0 | 6567 | 1.1931 | 0.8760 | 0.8768 | 0.8780 | 0.8760 |
| 0.009 | 12.0 | 7164 | 1.2154 | 0.8857 | 0.8852 | 0.8848 | 0.8857 |
| 0.0058 | 13.0 | 7761 | 1.3874 | 0.8748 | 0.8759 | 0.8776 | 0.8748 |
| 0.009 | 14.0 | 8358 | 1.4193 | 0.8740 | 0.8754 | 0.8780 | 0.8740 |
| 0.0042 | 15.0 | 8955 | 1.2999 | 0.8807 | 0.8800 | 0.8796 | 0.8807 |
| 0.0028 | 16.0 | 9552 | 1.3428 | 0.8802 | 0.8805 | 0.8817 | 0.8802 |
| 0.0029 | 17.0 | 10149 | 1.3959 | 0.8807 | 0.8807 | 0.8810 | 0.8807 |
| 0.0022 | 18.0 | 10746 | 1.4149 | 0.8827 | 0.8823 | 0.8824 | 0.8827 |
| 0.0037 | 19.0 | 11343 | 1.4078 | 0.8840 | 0.8838 | 0.8838 | 0.8840 |
| 0.001 | 20.0 | 11940 | 1.4236 | 0.8823 | 0.8823 | 0.8825 | 0.8823 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
|
gauthamk28/ppo-SnowballTarget
|
gauthamk28
| 2023-03-19T19:55:36Z | 0 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"SnowballTarget",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SnowballTarget",
"region:us"
] |
reinforcement-learning
| 2023-03-19T19:55:30Z |
---
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://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
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. Go to https://huggingface.co/spaces/unity/ML-Agents-SnowballTarget
2. Step 1: Find your model_id: gauthamk28/ppo-SnowballTarget
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
CreativeEvolution/ppo-LunarLander-v2-unit8
|
CreativeEvolution
| 2023-03-19T19:38:21Z | 0 | 0 | null |
[
"tensorboard",
"LunarLander-v2",
"ppo",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"deep-rl-course",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-19T19:37:43Z |
---
tags:
- LunarLander-v2
- ppo
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
- deep-rl-course
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 107.81 +/- 83.20
name: mean_reward
verified: false
---
# PPO Agent Playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2.
# Hyperparameters
```python
{'exp_name': 'ppo'
'seed': 1
'torch_deterministic': True
'cuda': True
'track': False
'wandb_project_name': 'cleanRL'
'wandb_entity': None
'capture_video': False
'env_id': 'LunarLander-v2'
'total_timesteps': 2000000
'learning_rate': 0.0003
'num_envs': 4
'num_steps': 1024
'anneal_lr': True
'gae': True
'gamma': 0.99
'gae_lambda': 0.98
'num_minibatches': 4
'update_epochs': 4
'norm_adv': True
'clip_coef': 0.2
'clip_vloss': True
'ent_coef': 0.01
'vf_coef': 0.5
'max_grad_norm': 0.5
'target_kl': None
'repo_id': 'CreativeEvolution/ppo-LunarLander-v2-unit8'
'batch_size': 4096
'minibatch_size': 1024}
```
|
reyhanemyr/roberta-base-finetuned-cvbest2
|
reyhanemyr
| 2023-03-19T19:34:00Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"roberta",
"token-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2023-03-19T19:25:32Z |
---
license: mit
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: roberta-base-finetuned-cvbest2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-base-finetuned-cvbest2
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1263
- Precision: 0.6839
- Recall: 0.7805
- F1: 0.7290
- Accuracy: 0.9674
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 17 | 0.4054 | 0.5 | 0.0010 | 0.0019 | 0.9038 |
| No log | 2.0 | 34 | 0.2859 | 0.3247 | 0.2660 | 0.2924 | 0.9201 |
| No log | 3.0 | 51 | 0.2169 | 0.3832 | 0.5774 | 0.4606 | 0.9358 |
| No log | 4.0 | 68 | 0.1691 | 0.4744 | 0.6634 | 0.5532 | 0.9504 |
| No log | 5.0 | 85 | 0.1571 | 0.5145 | 0.7360 | 0.6057 | 0.9495 |
| No log | 6.0 | 102 | 0.1458 | 0.5905 | 0.7669 | 0.6672 | 0.9596 |
| No log | 7.0 | 119 | 0.1304 | 0.6293 | 0.7718 | 0.6933 | 0.9630 |
| No log | 8.0 | 136 | 0.1284 | 0.6664 | 0.7901 | 0.7230 | 0.9666 |
| No log | 9.0 | 153 | 0.1263 | 0.6839 | 0.7805 | 0.7290 | 0.9674 |
| No log | 10.0 | 170 | 0.1295 | 0.6699 | 0.7930 | 0.7263 | 0.9669 |
### Framework versions
- Transformers 4.27.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
|
justkorudev/emojicrafter
|
justkorudev
| 2023-03-19T19:27:35Z | 6 | 1 |
diffusers
|
[
"diffusers",
"text-to-image",
"stable-diffusion",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-03-19T19:20:28Z |
---
license: creativeml-openrail-m
tags:
- text-to-image
- stable-diffusion
---
### EmojiCrafter AI model made by JustKoru for use in the official NixBrains webiste. IMPORTANT: This is a work in progress, and the model does not work completely at this moment.
Sample generations of this AI image generation model:
|
codeSpaghetti/a2c-PandaReachDense-v2
|
codeSpaghetti
| 2023-03-19T19:22:39Z | 0 | 1 |
stable-baselines3
|
[
"stable-baselines3",
"PandaReachDense-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-19T17:46:28Z |
---
library_name: stable-baselines3
tags:
- PandaReachDense-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachDense-v2
type: PandaReachDense-v2
metrics:
- type: mean_reward
value: -1.52 +/- 0.21
name: mean_reward
verified: false
---
# **A2C** Agent playing **PandaReachDense-v2**
This is a trained model of a **A2C** agent playing **PandaReachDense-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
...
```
|
kejian/cpsc-awr
|
kejian
| 2023-03-19T19:21:05Z | 1 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"generated_from_trainer",
"en",
"dataset:tomekkorbak/detoxify-pile-chunk3-50000-100000",
"dataset:tomekkorbak/detoxify-pile-chunk3-100000-150000",
"dataset:tomekkorbak/detoxify-pile-chunk3-150000-200000",
"dataset:tomekkorbak/detoxify-pile-chunk3-200000-250000",
"dataset:tomekkorbak/detoxify-pile-chunk3-250000-300000",
"dataset:tomekkorbak/detoxify-pile-chunk3-300000-350000",
"dataset:tomekkorbak/detoxify-pile-chunk3-350000-400000",
"dataset:tomekkorbak/detoxify-pile-chunk3-400000-450000",
"dataset:tomekkorbak/detoxify-pile-chunk3-450000-500000",
"dataset:tomekkorbak/detoxify-pile-chunk3-500000-550000",
"dataset:tomekkorbak/detoxify-pile-chunk3-550000-600000",
"dataset:tomekkorbak/detoxify-pile-chunk3-600000-650000",
"dataset:tomekkorbak/detoxify-pile-chunk3-650000-700000",
"dataset:tomekkorbak/detoxify-pile-chunk3-700000-750000",
"dataset:tomekkorbak/detoxify-pile-chunk3-750000-800000",
"dataset:tomekkorbak/detoxify-pile-chunk3-800000-850000",
"dataset:tomekkorbak/detoxify-pile-chunk3-850000-900000",
"dataset:tomekkorbak/detoxify-pile-chunk3-900000-950000",
"dataset:tomekkorbak/detoxify-pile-chunk3-950000-1000000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1000000-1050000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1050000-1100000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1100000-1150000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1150000-1200000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1200000-1250000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1250000-1300000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1300000-1350000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1350000-1400000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1400000-1450000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1450000-1500000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1500000-1550000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1550000-1600000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1600000-1650000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1650000-1700000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1700000-1750000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1750000-1800000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1800000-1850000",
"license:mit",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | null | 2023-03-19T06:49:37Z |
---
language:
- en
license: mit
tags:
- generated_from_trainer
datasets:
- tomekkorbak/detoxify-pile-chunk3-50000-100000
- tomekkorbak/detoxify-pile-chunk3-100000-150000
- tomekkorbak/detoxify-pile-chunk3-150000-200000
- tomekkorbak/detoxify-pile-chunk3-200000-250000
- tomekkorbak/detoxify-pile-chunk3-250000-300000
- tomekkorbak/detoxify-pile-chunk3-300000-350000
- tomekkorbak/detoxify-pile-chunk3-350000-400000
- tomekkorbak/detoxify-pile-chunk3-400000-450000
- tomekkorbak/detoxify-pile-chunk3-450000-500000
- tomekkorbak/detoxify-pile-chunk3-500000-550000
- tomekkorbak/detoxify-pile-chunk3-550000-600000
- tomekkorbak/detoxify-pile-chunk3-600000-650000
- tomekkorbak/detoxify-pile-chunk3-650000-700000
- tomekkorbak/detoxify-pile-chunk3-700000-750000
- tomekkorbak/detoxify-pile-chunk3-750000-800000
- tomekkorbak/detoxify-pile-chunk3-800000-850000
- tomekkorbak/detoxify-pile-chunk3-850000-900000
- tomekkorbak/detoxify-pile-chunk3-900000-950000
- tomekkorbak/detoxify-pile-chunk3-950000-1000000
- tomekkorbak/detoxify-pile-chunk3-1000000-1050000
- tomekkorbak/detoxify-pile-chunk3-1050000-1100000
- tomekkorbak/detoxify-pile-chunk3-1100000-1150000
- tomekkorbak/detoxify-pile-chunk3-1150000-1200000
- tomekkorbak/detoxify-pile-chunk3-1200000-1250000
- tomekkorbak/detoxify-pile-chunk3-1250000-1300000
- tomekkorbak/detoxify-pile-chunk3-1300000-1350000
- tomekkorbak/detoxify-pile-chunk3-1350000-1400000
- tomekkorbak/detoxify-pile-chunk3-1400000-1450000
- tomekkorbak/detoxify-pile-chunk3-1450000-1500000
- tomekkorbak/detoxify-pile-chunk3-1500000-1550000
- tomekkorbak/detoxify-pile-chunk3-1550000-1600000
- tomekkorbak/detoxify-pile-chunk3-1600000-1650000
- tomekkorbak/detoxify-pile-chunk3-1650000-1700000
- tomekkorbak/detoxify-pile-chunk3-1700000-1750000
- tomekkorbak/detoxify-pile-chunk3-1750000-1800000
- tomekkorbak/detoxify-pile-chunk3-1800000-1850000
model-index:
- name: kejian/cpsc-awr
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. -->
# kejian/cpsc-awr
This model was trained from scratch on the tomekkorbak/detoxify-pile-chunk3-50000-100000, the tomekkorbak/detoxify-pile-chunk3-100000-150000, the tomekkorbak/detoxify-pile-chunk3-150000-200000, the tomekkorbak/detoxify-pile-chunk3-200000-250000, the tomekkorbak/detoxify-pile-chunk3-250000-300000, the tomekkorbak/detoxify-pile-chunk3-300000-350000, the tomekkorbak/detoxify-pile-chunk3-350000-400000, the tomekkorbak/detoxify-pile-chunk3-400000-450000, the tomekkorbak/detoxify-pile-chunk3-450000-500000, the tomekkorbak/detoxify-pile-chunk3-500000-550000, the tomekkorbak/detoxify-pile-chunk3-550000-600000, the tomekkorbak/detoxify-pile-chunk3-600000-650000, the tomekkorbak/detoxify-pile-chunk3-650000-700000, the tomekkorbak/detoxify-pile-chunk3-700000-750000, the tomekkorbak/detoxify-pile-chunk3-750000-800000, the tomekkorbak/detoxify-pile-chunk3-800000-850000, the tomekkorbak/detoxify-pile-chunk3-850000-900000, the tomekkorbak/detoxify-pile-chunk3-900000-950000, the tomekkorbak/detoxify-pile-chunk3-950000-1000000, the tomekkorbak/detoxify-pile-chunk3-1000000-1050000, the tomekkorbak/detoxify-pile-chunk3-1050000-1100000, the tomekkorbak/detoxify-pile-chunk3-1100000-1150000, the tomekkorbak/detoxify-pile-chunk3-1150000-1200000, the tomekkorbak/detoxify-pile-chunk3-1200000-1250000, the tomekkorbak/detoxify-pile-chunk3-1250000-1300000, the tomekkorbak/detoxify-pile-chunk3-1300000-1350000, the tomekkorbak/detoxify-pile-chunk3-1350000-1400000, the tomekkorbak/detoxify-pile-chunk3-1400000-1450000, the tomekkorbak/detoxify-pile-chunk3-1450000-1500000, the tomekkorbak/detoxify-pile-chunk3-1500000-1550000, the tomekkorbak/detoxify-pile-chunk3-1550000-1600000, the tomekkorbak/detoxify-pile-chunk3-1600000-1650000, the tomekkorbak/detoxify-pile-chunk3-1650000-1700000, the tomekkorbak/detoxify-pile-chunk3-1700000-1750000, the tomekkorbak/detoxify-pile-chunk3-1750000-1800000 and the tomekkorbak/detoxify-pile-chunk3-1800000-1850000 datasets.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.001
- train_batch_size: 32
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 32
- total_train_batch_size: 1024
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.01
- training_steps: 2670
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.23.0
- Pytorch 1.13.0+cu116
- Datasets 2.0.0
- Tokenizers 0.12.1
# Full config
{'dataset': {'datasets': ['tomekkorbak/detoxify-pile-chunk3-50000-100000',
'tomekkorbak/detoxify-pile-chunk3-100000-150000',
'tomekkorbak/detoxify-pile-chunk3-150000-200000',
'tomekkorbak/detoxify-pile-chunk3-200000-250000',
'tomekkorbak/detoxify-pile-chunk3-250000-300000',
'tomekkorbak/detoxify-pile-chunk3-300000-350000',
'tomekkorbak/detoxify-pile-chunk3-350000-400000',
'tomekkorbak/detoxify-pile-chunk3-400000-450000',
'tomekkorbak/detoxify-pile-chunk3-450000-500000',
'tomekkorbak/detoxify-pile-chunk3-500000-550000',
'tomekkorbak/detoxify-pile-chunk3-550000-600000',
'tomekkorbak/detoxify-pile-chunk3-600000-650000',
'tomekkorbak/detoxify-pile-chunk3-650000-700000',
'tomekkorbak/detoxify-pile-chunk3-700000-750000',
'tomekkorbak/detoxify-pile-chunk3-750000-800000',
'tomekkorbak/detoxify-pile-chunk3-800000-850000',
'tomekkorbak/detoxify-pile-chunk3-850000-900000',
'tomekkorbak/detoxify-pile-chunk3-900000-950000',
'tomekkorbak/detoxify-pile-chunk3-950000-1000000',
'tomekkorbak/detoxify-pile-chunk3-1000000-1050000',
'tomekkorbak/detoxify-pile-chunk3-1050000-1100000',
'tomekkorbak/detoxify-pile-chunk3-1100000-1150000',
'tomekkorbak/detoxify-pile-chunk3-1150000-1200000',
'tomekkorbak/detoxify-pile-chunk3-1200000-1250000',
'tomekkorbak/detoxify-pile-chunk3-1250000-1300000',
'tomekkorbak/detoxify-pile-chunk3-1300000-1350000',
'tomekkorbak/detoxify-pile-chunk3-1350000-1400000',
'tomekkorbak/detoxify-pile-chunk3-1400000-1450000',
'tomekkorbak/detoxify-pile-chunk3-1450000-1500000',
'tomekkorbak/detoxify-pile-chunk3-1500000-1550000',
'tomekkorbak/detoxify-pile-chunk3-1550000-1600000',
'tomekkorbak/detoxify-pile-chunk3-1600000-1650000',
'tomekkorbak/detoxify-pile-chunk3-1650000-1700000',
'tomekkorbak/detoxify-pile-chunk3-1700000-1750000',
'tomekkorbak/detoxify-pile-chunk3-1750000-1800000',
'tomekkorbak/detoxify-pile-chunk3-1800000-1850000'],
'is_split_by_sentences': True},
'generation': {'every_n_steps': 16,
'force_call_on': [42724],
'metrics_configs': [{}, {'n': 1}, {'n': 2}, {'n': 5}],
'scenario_configs': [{'generate_kwargs': {'do_sample': True,
'max_length': 128,
'min_length': 10,
'temperature': 0.7,
'top_k': 0,
'top_p': 0.9},
'name': 'unconditional',
'num_samples': 2048},
{'generate_kwargs': {'do_sample': True,
'max_length': 128,
'min_length': 10,
'temperature': 0.7,
'top_k': 0,
'top_p': 0.9},
'name': 'challenging_rtp',
'num_samples': 2048,
'prompts_path': 'resources/challenging_rtp.jsonl'}],
'scorer_config': {'device': 'cuda:0'}},
'kl_gpt3_callback': {'every_n_steps': 16,
'force_call_on': [42724],
'gpt3_kwargs': {'model_name': 'davinci'},
'max_tokens': 64,
'num_samples': 2048},
'model': {'from_scratch': True,
'gpt2_config_kwargs': {'reorder_and_upcast_attn': True,
'scale_attn_by': True},
'model_kwargs': {'value_head_config': {'is_detached': False}},
'path_or_name': 'gpt2'},
'objective': {'alpha': 0.5, 'beta': 10, 'name': 'AWR'},
'tokenizer': {'path_or_name': 'gpt2'},
'training': {'dataloader_num_workers': 0,
'effective_batch_size': 1024,
'evaluation_strategy': 'no',
'fp16': True,
'hub_model_id': 'kejian/cpsc-awr',
'hub_strategy': 'all_checkpoints',
'learning_rate': 0.001,
'logging_first_step': True,
'logging_steps': 50,
'num_tokens': 2800000000.0,
'output_dir': 'training_output_awr',
'per_device_train_batch_size': 16,
'push_to_hub': True,
'remove_unused_columns': False,
'save_steps': 1334,
'save_strategy': 'steps',
'seed': 42,
'warmup_ratio': 0.01,
'weight_decay': 0.1}}
# Wandb URL:
https://wandb.ai/kejian/uncategorized/runs/r6y25g38
|
reyhanemyr/roberta-base-finetuned-papernew5
|
reyhanemyr
| 2023-03-19T19:06:05Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"roberta",
"token-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2023-03-19T18:52:19Z |
---
license: mit
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: roberta-base-finetuned-papernew5
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-base-finetuned-papernew5
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0864
- Precision: 0.7835
- Recall: 0.8144
- F1: 0.7986
- Accuracy: 0.9742
## 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: 15
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 81 | 0.1872 | 0.6657 | 0.5174 | 0.5822 | 0.9511 |
| No log | 2.0 | 162 | 0.1321 | 0.6189 | 0.7912 | 0.6945 | 0.9585 |
| No log | 3.0 | 243 | 0.0864 | 0.7835 | 0.8144 | 0.7986 | 0.9742 |
| No log | 4.0 | 324 | 0.0891 | 0.7532 | 0.8144 | 0.7826 | 0.9723 |
| No log | 5.0 | 405 | 0.1004 | 0.7542 | 0.8399 | 0.7947 | 0.9723 |
| No log | 6.0 | 486 | 0.1197 | 0.7267 | 0.8515 | 0.7842 | 0.9677 |
| 0.1476 | 7.0 | 567 | 0.1237 | 0.7605 | 0.8399 | 0.7982 | 0.9709 |
| 0.1476 | 8.0 | 648 | 0.1104 | 0.7383 | 0.8445 | 0.7879 | 0.9728 |
| 0.1476 | 9.0 | 729 | 0.1179 | 0.7863 | 0.8283 | 0.8068 | 0.9742 |
| 0.1476 | 10.0 | 810 | 0.1150 | 0.7811 | 0.8608 | 0.8190 | 0.9752 |
| 0.1476 | 11.0 | 891 | 0.1273 | 0.7602 | 0.8608 | 0.8074 | 0.9728 |
| 0.1476 | 12.0 | 972 | 0.1230 | 0.7711 | 0.8677 | 0.8166 | 0.9751 |
| 0.014 | 13.0 | 1053 | 0.1280 | 0.7815 | 0.8631 | 0.8203 | 0.9753 |
| 0.014 | 14.0 | 1134 | 0.1285 | 0.7755 | 0.8654 | 0.8180 | 0.9753 |
| 0.014 | 15.0 | 1215 | 0.1336 | 0.7639 | 0.8631 | 0.8105 | 0.9740 |
### Framework versions
- Transformers 4.27.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
|
osbm/Reinforce-pixelcopter
|
osbm
| 2023-03-19T18:59:06Z | 0 | 0 | null |
[
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-19T18:58:58Z |
---
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: 37.80 +/- 30.47
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
|
amandyk/QazGPT2
|
amandyk
| 2023-03-19T18:50:27Z | 20 | 5 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"license:afl-3.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-03-19T17:39:24Z |
---
license: afl-3.0
---
Welcome to the GPT-2 repository for the Kazakh language(latin alphabet)! This repository contains a language model
that has been trained from scratch on a combination of news and wiki corpora in Kazakh language.
The model is capable of generating coherent and natural-sounding text in Kazakh, and
can be used for a wide range of NLP tasks, including text classification, question answering,
and text generation.
Please note that while the model has been trained on a 4m sentence corpus of text, it may still
contain biases or errors. As with any machine learning model, it is important to thoroughly
evaluate its performance before using it in production applications.
I recommend to use this qazaq latin converter for testing: https://masa.kz/en
|
naeisher/ppo-SnowballTarget
|
naeisher
| 2023-03-19T18:48:56Z | 3 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"SnowballTarget",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SnowballTarget",
"region:us"
] |
reinforcement-learning
| 2023-03-19T18:48:51Z |
---
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://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
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. Go to https://huggingface.co/spaces/unity/ML-Agents-SnowballTarget
2. Step 1: Find your model_id: naeisher/ppo-SnowballTarget
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
Ru3ll/dsn_afrispeech-shuffle
|
Ru3ll
| 2023-03-19T18:37:27Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"whisper",
"automatic-speech-recognition",
"hf-asr-leaderboard",
"generated_from_trainer",
"en",
"dataset:tobiolatunji/afrispeech-200",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-03-17T13:36:42Z |
---
language:
- en
tags:
- hf-asr-leaderboard
- generated_from_trainer
datasets:
- tobiolatunji/afrispeech-200
metrics:
- wer
model-index:
- name: Ru3ll/dsn_afrispeech3
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Afrispeech-200
type: tobiolatunji/afrispeech-200
config: all
split: train
args: 'config: en, split: test'
metrics:
- name: Wer
type: wer
value: 23.825127429563658
---
<!-- 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. -->
# Ru3ll/dsn_afrispeech3
This model is a fine-tuned version of [ru3ll/dsn_afrispeech2/whisper-small](https://huggingface.co/ru3ll/dsn_afrispeech2/whisper-small) on the Afrispeech-200 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5952
- Wer: 23.8251
## 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: 3.46e-06
- train_batch_size: 8
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 200
- training_steps: 498
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.6516 | 0.5 | 249 | 0.6065 | 23.3294 |
| 0.8152 | 1.0 | 498 | 0.5952 | 23.8251 |
### Framework versions
- Transformers 4.28.0.dev0
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
|
alc15492/retromdl
|
alc15492
| 2023-03-19T18:32:34Z | 0 | 0 |
diffusers
|
[
"diffusers",
"text-to-image",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-03-19T18:17:02Z |
---
library_name: diffusers
pipeline_tag: text-to-image
---
|
diptanu/fBERT
|
diptanu
| 2023-03-19T18:18:14Z | 52 | 4 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"bert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:05Z |
fBERT: A Neural Transformer for Identifying Offensive Content [Accepted at EMNLP 2021]
Authors: Diptanu Sarkar, Marcos Zampieri, Tharindu Ranasinghe and Alexander Ororbia
About:
Transformer-based models such as BERT, ELMO, and XLM-R have achieved state-of-the-art performance across various NLP tasks including the identification of offensive language and hate speech, an important problem in social media. Previous studies have shown that domain-specific fine-tuning or retraining of models before attempting to solve downstream tasks can lead to excellent results in multiple domains. Fine-tuning/retraining a complex models to identify offensive language has not been substantially explored before and we address this gap by proposing fBERT, a bert-base-uncased model that has been learned using over 1.4 million offensive instances from the SOLID dataset. The shifted fBERT model better incorporates domain-specific offensive language and social media features. The fBERT model achieves better results in both OffensEval and HatEval tasks and in the HS & O dataset over BERT and HateBERT.
|
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