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
2025-08-29 18:27:06
| 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
2025-08-29 18:26:56
| card
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|---|---|---|---|---|---|---|---|---|---|
TheMurusTeam/coreml-upscaler-bsrgan
|
TheMurusTeam
| 2023-01-03T00:58:44Z | 0 | 1 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-01-03T00:57:56Z |
---
license: creativeml-openrail-m
---
|
TheMurusTeam/coreml-upscaler-aesrgan
|
TheMurusTeam
| 2023-01-03T00:57:12Z | 0 | 1 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-01-03T00:56:28Z |
---
license: creativeml-openrail-m
---
|
Toshifumi/001_M-BERT-claim-classifier
|
Toshifumi
| 2023-01-02T23:24:16Z | 61 | 0 |
transformers
|
[
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-12-29T01:58:50Z |
---
tags:
- generated_from_keras_callback
model-index:
- name: 001_M-BERT-claim-classifier
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. -->
# この「クレーム判別」モデルの使い方
このモデルは、当該クレームがどのプロダクトについてのものかを判別します。右側の"Hosted inference API" 直下のBOXにお好きなクレームを入力し、"Compute"ボタンをクリックして下さい。30〜60秒前後で答えが返ってきます。答えは以下の５つのlabelのいずれかです。
入力は最大300〜400文字、日本語・英語他100カ国語以上に対応してます。教育用ですので、機密情報は入力しないで下さい。
# 5つのlabelの定義:
- LABEL_0 : Bank account or service (銀行口座・サービス)
- LABEL_1 : Checking or savings account（当座預金・普通預金）
- LABEL_2 : Consumer Loan（消費者ローン）
- LABEL_3 : Credit card（クレジットカード）
- LABEL_4 : Mortgage（住宅ローン）
### 入力例 （正解 4: 'Mortgage'）
「私の夫と私はローンデポを通じてローンのRefiローンを申請し、その後承認されました。私たちはそれが次週になると言われているプロセスで前進することにしました。 両当事者と鑑定評価で実行されました。私たちの料金は次の週に有効であることが開示されました。貸出金の担当者によって連絡しました。ローンが45日以内に閉じなかった場合、当社の元のレートオファーが拡張されます。ロックレートが期限切れになったことを示すXXXXから別のコミュニケーション（Eメール）を受け取り、新しい料金を取得する必要があります。」
# How to use this "claim classification" model
This model determines which product the claim is about. Enter your favorite claim in the box directly under "Hosted inference API" on the right, and click the "Compute" button. You will receive an answer within 30-60 seconds. The answer is one of the following five labels. You can enter a maximum of 400 to 500 characters, and it supports Japanese, English, and more than 100 languages.
# Warning
This is for educational purposes only, please do not enter confidential information.
# Definition of 5 labels
- LABEL_0 : Bank account or service
- LABEL_1 : Checking or savings account
- LABEL_2 : Consumer Loan
- LABEL_3 : Credit card
- LABEL_4 : Mortgage
# Input example (correct answer 4: 'Mortgage')
I drew an advance of $2900.00 from my HELOC that I have with Wells Fargo. I have auto pay with Wells Fargo and a scheduled payment of $100.00 was taken from mychecking ac. I entered the bank, CA branch and paid $2800.00 to pay off the advance. Since that time, various transactions have been posted to this account. There is a principal adj debit in the amount of $170.00 followed by a principal reversal in the amount of $91.
# 001_M-BERT-claim-classifier
It achieves the following results on the evaluation set:
## Model description
bert-base-multilingual-cased
## Intended uses & limitations
This is solely for educational purposes. This cannot be used for investments or businesses in practice. I do not accept any responsibility or liability for loss or damage occasioned to any person or property through using materials, instructions, methods, algorithm or ideas contained herein, or acting or refraining from acting as a result of such use. I expressly disclaim all implied warranties, including merchantability or fitness for any particular purpose. There will be no duty on me to correct any errors or defects in the codes and the software.
## Training and evaluation data
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'learning_rate': 1e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
- training_precision: float32
### Training results
### Framework versions
- Transformers 4.25.1
- TensorFlow 2.9.2
- Datasets 2.8.0
- Tokenizers 0.13.2
|
rohitp1/wav2vec2-base-timit-finetune
|
rohitp1
| 2023-01-02T22:57:27Z | 25 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-12-25T14:23:23Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: wav2vec2-base-timit-finetune
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-base-timit-finetune
This model is a fine-tuned version of [facebook/wav2vec2-base-960h](https://huggingface.co/facebook/wav2vec2-base-960h) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 972.3115
- Wer: 1.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- 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_steps: 1000
- num_epochs: 60
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:---:|
| 3325.1297 | 1.39 | 100 | 4054.7283 | 1.0 |
| 1624.4673 | 2.77 | 200 | 1100.8928 | 1.0 |
| 1079.3557 | 4.17 | 300 | 1009.5025 | 1.0 |
| 1026.4995 | 5.55 | 400 | 979.0 | 1.0 |
| 1005.6487 | 6.94 | 500 | 964.3292 | 1.0 |
| 1000.4138 | 8.33 | 600 | 972.3115 | 1.0 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1
- Datasets 2.7.0
- Tokenizers 0.11.0
|
pig4431/20NG_ALBERT_5E
|
pig4431
| 2023-01-02T22:48:35Z | 106 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"albert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-12-25T07:27:11Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: 20NG_ALBERT_5E
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. -->
# 20NG_ALBERT_5E
This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2209
- Accuracy: 0.6067
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.8602 | 0.07 | 50 | 2.5794 | 0.2133 |
| 2.3635 | 0.14 | 100 | 2.0956 | 0.38 |
| 2.1526 | 0.21 | 150 | 1.9011 | 0.4467 |
| 1.9014 | 0.28 | 200 | 1.6340 | 0.5067 |
| 1.6736 | 0.35 | 250 | 1.5457 | 0.5467 |
| 1.5563 | 0.42 | 300 | 1.5041 | 0.5533 |
| 1.4338 | 0.49 | 350 | 1.3933 | 0.5933 |
| 1.3348 | 0.56 | 400 | 1.4123 | 0.54 |
| 1.2879 | 0.64 | 450 | 1.3352 | 0.6333 |
| 1.2864 | 0.71 | 500 | 1.3027 | 0.62 |
| 1.2162 | 0.78 | 550 | 1.2734 | 0.6267 |
| 1.1786 | 0.85 | 600 | 1.2695 | 0.5933 |
| 1.1702 | 0.92 | 650 | 1.2379 | 0.5933 |
| 1.2338 | 0.99 | 700 | 1.2209 | 0.6067 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.1
- Datasets 2.8.0
- Tokenizers 0.13.2
|
gababas/m3rrw3
|
gababas
| 2023-01-02T22:20:29Z | 29 | 0 |
diffusers
|
[
"diffusers",
"text-to-image",
"stable-diffusion",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-01-02T22:18:21Z |
---
license: creativeml-openrail-m
tags:
- text-to-image
- stable-diffusion
---
### m3rrw3 Dreambooth model trained by gababas with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook
Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb)
Sample pictures of this concept:
|
wangpuupup/whisper-small-nl-dy-noaugmentation
|
wangpuupup
| 2023-01-02T22:14:29Z | 78 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"whisper",
"automatic-speech-recognition",
"whisper-event",
"generated_from_trainer",
"dataset:data/copas",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-12-22T04:13:36Z |
---
license: apache-2.0
tags:
- whisper-event
- generated_from_trainer
datasets:
- data/copas
metrics:
- wer
model-index:
- name: Whisper Small dysarthric Dutch
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: data/copas copas-full
type: data/copas
config: copas-full
split: test
args: copas-full
metrics:
- name: Wer
type: wer
value: 22.87060529177238
---
<!-- 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 dysarthric Dutch
This model is a fine-tuned version of [qmeeus/whisper-small-nl](https://huggingface.co/qmeeus/whisper-small-nl) on the data/copas copas-full dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4891
- Wer: 22.8706
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 32
- eval_batch_size: 32
- 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_steps: 500
- training_steps: 10000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|
| 0.1493 | 2.02 | 500 | 0.3960 | 28.9779 |
| 0.0383 | 5.02 | 1000 | 0.4041 | 26.5132 |
| 0.0264 | 8.01 | 1500 | 0.4274 | 25.5890 |
| 0.0155 | 11.01 | 2000 | 0.4437 | 24.7735 |
| 0.0041 | 14.01 | 2500 | 0.4454 | 25.0453 |
| 0.0044 | 17.01 | 3000 | 0.4444 | 23.9761 |
| 0.0044 | 20.01 | 3500 | 0.4394 | 23.4868 |
| 0.0022 | 23.01 | 4000 | 0.4415 | 22.8525 |
| 0.0034 | 26.01 | 4500 | 0.4602 | 23.6499 |
| 0.0027 | 29.01 | 5000 | 0.4577 | 23.3780 |
| 0.0072 | 32.01 | 5500 | 0.4573 | 23.3962 |
| 0.0002 | 35.01 | 6000 | 0.4673 | 23.1062 |
| 0.0001 | 38.01 | 6500 | 0.4723 | 22.9975 |
| 0.0001 | 41.01 | 7000 | 0.4770 | 23.0881 |
| 0.0 | 44.01 | 7500 | 0.4807 | 23.0518 |
| 0.0 | 47.01 | 8000 | 0.4835 | 22.9612 |
| 0.0 | 50.01 | 8500 | 0.4857 | 22.9250 |
| 0.0 | 53.0 | 9000 | 0.4874 | 22.9069 |
| 0.0 | 56.0 | 9500 | 0.4887 | 22.9069 |
| 0.0 | 59.0 | 10000 | 0.4891 | 22.8706 |
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.12.1+cu116
- Datasets 2.4.0
- Tokenizers 0.12.1
|
ChattychipsHuggingFace/DecentGenerate
|
ChattychipsHuggingFace
| 2023-01-02T21:48:57Z | 0 | 0 | null |
[
"license:openrail",
"region:us"
] | null | 2023-01-02T21:43:31Z |
---
license: openrail
---
pip install transformers
from transformers import Trainer, TrainingArguments
# Load the training and validation data
train_data = ...
validation_data = ...
# Define the model architecture and hyperparameters
model_name = "bert-base-cased"
num_labels = 2
# Define the training arguments
training_args = TrainingArguments(
output_dir="./output", # directory to save the trained model
num_train_epochs=3, # number of training epochs
per_device_train_batch_size=32, # batch size
per_device_eval_batch_size=64, # batch size for evaluation
warmup_steps=500, # number of warmup steps
weight_decay=0.01, # L2 regularization coefficient
learning_rate=3e-5, # learning rate
adam_epsilon=1e-8, # epsilon for Adam optimizer
max_grad_norm=1.0, # maximum gradient norm for gradient clipping
save_steps=1000, # number of steps after which to save the model
save_total_limit=2, # maximum number of models to save
)
# Initialize the trainer
trainer = Trainer(
model_name=model_name,
num_labels=num_labels,
data_collator=data_collator, # data collator for the training and validation data
args=training_args,
)
# Train the model
trainer.train(train_data, validation_data)
|
jonalkw/ppo-Huggy
|
jonalkw
| 2023-01-02T21:44:09Z | 11 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
] |
reinforcement-learning
| 2023-01-02T21:44:01Z |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Huggy
library_name: ml-agents
---
# **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: Write your model_id: jonalkw/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
matt-guay/PPO-LunarLander-v2-4
|
matt-guay
| 2023-01-02T21:42:57Z | 1 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-01-02T21:42:52Z |
---
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: 291.75 +/- 16.72
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
...
```
|
Kutsuya/YukisEmbeddings
|
Kutsuya
| 2023-01-02T21:02:53Z | 0 | 0 | null |
[
"summarization",
"region:us"
] |
summarization
| 2022-12-24T17:52:40Z |
---
tags:
- summarization
widget:
- text: "Yuki's Embeddings"
---
# Yuki's Embeddings
If you like these, you could buy me a ☕ and get me through another day: https://ko-fi.com/kutsuya_yuki
## Table of Contents
- [Embeddings](#embeddings)
## Embeddings
| Embedding | Source CKPT | Dataset Images Count | Epochs | Steps | Batch size |
|----------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------|----------------------|--------|-------|------------|
| [Sakura Futaba](https://huggingface.co/Kutsuya/YukisEmbeddings/blob/main/sakura_futaba.pt) | [nai-wd1.4](https://huggingface.co/Kutsuya/Yukis-Mixes/blob/main/nai-wd.ckpt) | 28 | 3 | 2800 | 3 |
| [Shirosaki Hana](https://huggingface.co/Kutsuya/YukisEmbeddings/blob/main/shirosaki_hana.pt) | [nai-wd1.4](https://huggingface.co/Kutsuya/Yukis-Mixes/blob/main/nai-wd.ckpt) | 210 | 1 | 6500 | 3 |
| [Izumi Konata](https://huggingface.co/Kutsuya/YukisEmbeddings/blob/main/izumi-konata.tar.gz) | [nai-wd1.4](https://huggingface.co/Kutsuya/Yukis-Mixes/blob/main/nai-wd.ckpt) | 363 | 1 | 12100 | 3 |
| [Hiiragi Kagami](https://huggingface.co/Kutsuya/YukisEmbeddings/blob/main/hiiragi_kagami.tar.gz) | [nai-wd1.4](https://huggingface.co/Kutsuya/Yukis-Mixes/blob/main/nai-wd.ckpt) | 64 | 2 | 4100 | 3 |
| [Hiiragi Tsukasa](https://huggingface.co/Kutsuya/YukisEmbeddings/blob/main/hiiragi_tsukasa.tar.gz) | [nai-wd1.4](https://huggingface.co/Kutsuya/Yukis-Mixes/blob/main/nai-wd.ckpt) | 37 | 3 | 3000 | 3 |
| [Okabe Rintarou](https://huggingface.co/Kutsuya/YukisEmbeddings/blob/main/okabe_rintarou.tar.gz) | [nai-wd1.4](https://huggingface.co/Kutsuya/Yukis-Mixes/blob/main/nai-wd.ckpt) | 45 | 14 | 21203 | 3 |
| [Nakano Itsuki](https://huggingface.co/Kutsuya/YukisEmbeddings/blob/main/nakano_itsuki.tar.gz) | [nai-wd1.4](https://huggingface.co/Kutsuya/Yukis-Mixes/blob/main/nai-wd.ckpt) | 311 | 1 | 15599 | 3 |
**Examples**:
**Sakura Futaba**:
**Shirosaki Hana**:
**Izumi Konata**
**Hiiragi Kagami**
**Hiiragi Tsukasa**
**Okabe Rintarou**
**Nakano Itsuki**
|
noctrog/drl-unit-1-ppo-lunarlander-v2
|
noctrog
| 2023-01-02T20:23:56Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-01-02T20:22:12Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: ppo
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 251.81 +/- 46.92
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
...
```
|
emmyapi/distilbart-podimo-data-eval-2
|
emmyapi
| 2023-01-02T20:23:15Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-01-02T16:17:55Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: distilbart-podimo-data-eval-2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbart-podimo-data-eval-2
This model is a fine-tuned version of [sshleifer/distilbart-cnn-12-6](https://huggingface.co/sshleifer/distilbart-cnn-12-6) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 3.5823
- Rouge1: 34.3971
- Rouge2: 7.95
- Rougel: 18.7271
- Rougelsum: 30.9024
- Gen Len: 131.919
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 64
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 8
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:|:--------:|
| 4.1512 | 0.98 | 44 | 3.7806 | 32.727 | 6.5788 | 17.5196 | 29.3777 | 137.2905 |
| 3.6342 | 1.98 | 88 | 3.6421 | 32.709 | 6.7877 | 17.8668 | 29.4636 | 134.6648 |
| 3.3512 | 2.98 | 132 | 3.5819 | 33.5128 | 7.519 | 18.6614 | 30.1142 | 132.2961 |
| 3.141 | 3.98 | 176 | 3.5552 | 33.4795 | 7.3242 | 18.396 | 30.0854 | 132.757 |
| 2.9787 | 4.98 | 220 | 3.5583 | 33.5862 | 7.391 | 18.3568 | 30.2461 | 132.4078 |
| 2.8555 | 5.98 | 264 | 3.5650 | 34.1111 | 7.8008 | 18.7159 | 30.6055 | 131.3603 |
| 2.7648 | 6.98 | 308 | 3.5729 | 34.0981 | 7.6556 | 18.6373 | 30.6269 | 131.2821 |
| 2.6645 | 7.98 | 352 | 3.5823 | 34.3971 | 7.95 | 18.7271 | 30.9024 | 131.919 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.11.0
- Datasets 2.2.1
- Tokenizers 0.12.1
|
0xid/q-FrozenLake-v1-4x4-Slippery
|
0xid
| 2023-01-02T20:19:38Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-01-02T18:39:26Z |
---
tags:
- FrozenLake-v1-4x4
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-Slippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4
type: FrozenLake-v1-4x4
metrics:
- type: mean_reward
value: 0.74 +/- 0.44
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="0xid/q-FrozenLake-v1-4x4-Slippery", 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"])
```
|
0xid/q-FrozenLake-v1-8x8-Slippery
|
0xid
| 2023-01-02T20:17:22Z | 0 | 0 | null |
[
"FrozenLake-v1-8x8",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-01-02T20:00:56Z |
---
tags:
- FrozenLake-v1-8x8
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-8x8-Slippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-8x8
type: FrozenLake-v1-8x8
metrics:
- type: mean_reward
value: 0.76 +/- 0.43
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="0xid/q-FrozenLake-v1-8x8-Slippery", 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"])
```
|
0xid/q-FrozenLake-v1-8x8-noSlippery
|
0xid
| 2023-01-02T19:58:53Z | 0 | 0 | null |
[
"FrozenLake-v1-8x8-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-01-02T18:58:28Z |
---
tags:
- FrozenLake-v1-8x8-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-8x8-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-8x8-no_slippery
type: FrozenLake-v1-8x8-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="0xid/q-FrozenLake-v1-8x8-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"])
```
|
npit/q-FrozenLake-v1-4x4-noSlippery
|
npit
| 2023-01-02T19:47:52Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-01-02T19:47:49Z |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="npit/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"])
```
|
malibanekg/ppo-Huggy
|
malibanekg
| 2023-01-02T19:20:52Z | 12 | 1 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
] |
reinforcement-learning
| 2023-01-02T19:20:45Z |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Huggy
library_name: ml-agents
---
# **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: Write your model_id: malibanekg/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
FnSK4R17s/ppo-LunarLander-v2
|
FnSK4R17s
| 2023-01-02T19:15:14Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-01-02T19:14:47Z |
---
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: 275.15 +/- 21.31
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
...
```
|
nvidia/nemo-megatron-gpt-1.3B
|
nvidia
| 2023-01-02T19:10:07Z | 76 | 33 |
nemo
|
[
"nemo",
"text2text-generation",
"pytorch",
"causal-lm",
"en",
"dataset:the_pile",
"arxiv:1909.08053",
"arxiv:2101.00027",
"license:cc-by-4.0",
"region:us"
] |
text2text-generation
| 2022-09-10T00:45:45Z |
---
language:
- en
library_name: nemo
datasets:
- the_pile
tags:
- text2text-generation
- pytorch
- causal-lm
license: cc-by-4.0
---
# NeMo Megatron-GPT 1.3B
<style>
img {
display: inline;
}
</style>
|[](#model-architecture)|[](#model-architecture)|[](#datasets)
## Model Description
Megatron-GPT 1.3B is a transformer-based language model. GPT refers to a class of transformer decoder-only models similar to GPT-2 and 3 while 1.3B refers to the total trainable parameter count (1.3 Billion) [1, 2]. It has Tensor Parallelism (TP) of 1, Pipeline Parallelism (PP) of 1 and should fit on a single NVIDIA GPU.
This model was trained with [NeMo Megatron](https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/nlp/nemo_megatron/intro.html).
## Getting started
### Step 1: Install NeMo and dependencies
You will need to install NVIDIA Apex and NeMo.
```
git clone https://github.com/ericharper/apex.git
cd apex
git checkout nm_v1.11.0
pip install -v --disable-pip-version-check --no-cache-dir --global-option="--cpp_ext" --global-option="--cuda_ext" --global-option="--fast_layer_norm" --global-option="--distributed_adam" --global-option="--deprecated_fused_adam" ./
```
```
pip install nemo_toolkit['nlp']==1.11.0
```
Alternatively, you can use NeMo Megatron training docker container with all dependencies pre-installed.
### Step 2: Launch eval server
**Note.** The model has been trained with Tensor Parallelism (TP) of 1 and Pipeline Parallelism (PP) of 1 and should fit on a single NVIDIA GPU.
```
git clone https://github.com/NVIDIA/NeMo.git
cd NeMo/examples/nlp/language_modeling
git checkout v1.11.0
python megatron_gpt_eval.py gpt_model_file=nemo_gpt1.3B_fp16.nemo server=True tensor_model_parallel_size=1 trainer.devices=1
```
### Step 3: Send prompts to your model!
```python
import json
import requests
port_num = 5555
headers = {"Content-Type": "application/json"}
def request_data(data):
resp = requests.put('http://localhost:{}/generate'.format(port_num),
data=json.dumps(data),
headers=headers)
sentences = resp.json()['sentences']
return sentences
data = {
"sentences": ["Tell me an interesting fact about space travel."]*1,
"tokens_to_generate": 50,
"temperature": 1.0,
"add_BOS": True,
"top_k": 0,
"top_p": 0.9,
"greedy": False,
"all_probs": False,
"repetition_penalty": 1.2,
"min_tokens_to_generate": 2,
}
sentences = request_data(data)
print(sentences)
```
## Training Data
The model was trained on ["The Piles" dataset prepared by Eleuther.AI](https://pile.eleuther.ai/). [4]
## Evaluation results
*Zero-shot performance.* Evaluated using [LM Evaluation Test Suite from AI21](https://github.com/AI21Labs/lm-evaluation)
| ARC-Challenge | ARC-Easy | RACE-middle | RACE-high | Winogrande | RTE | BoolQA | HellaSwag | PiQA |
| ------------- | -------- | ----------- | --------- | ---------- | --- | ------ | --------- | ---- |
| 0.3012 | 0.4596 | 0.459 | 0.3797 | 0.5343 | 0.5451 | 0.5979 | 0.4443 | 0.6834 |
## Limitations
The model was trained on the data originally crawled from the Internet. This data contains toxic language and societal biases. Therefore, the model may amplify those biases and return toxic responses especially when prompted with toxic prompts.
## References
[1] [Improving Language Understanding by Generative Pre-Training](https://s3-us-west-2.amazonaws.com/openai-assets/research-covers/language-unsupervised/language_understanding_paper.pdf)
[2] [Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism](https://arxiv.org/pdf/1909.08053.pdf)
[3] [NVIDIA NeMo Toolkit](https://github.com/NVIDIA/NeMo)
[4] [The Pile: An 800GB Dataset of Diverse Text for Language Modeling](https://arxiv.org/abs/2101.00027)
## Licence
License to use this model is covered by the [CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/). By downloading the public and release version of the model, you accept the terms and conditions of the [CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/) license.
|
abdalrahmanshahrour/AraBART-summ
|
abdalrahmanshahrour
| 2023-01-02T18:53:46Z | 36 | 3 |
transformers
|
[
"transformers",
"pytorch",
"mbart",
"text2text-generation",
"summarization",
"AraBERT",
"BERT",
"BERT2BERT",
"MSA",
"Arabic Text Summarization",
"Arabic News Title Generation",
"Arabic Paraphrasing",
"Summarization",
"generated_from_trainer",
"Transformers",
"PyTorch",
"ar",
"dataset:xlsum",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
summarization
| 2022-12-11T12:23:42Z |
---
license: apache-2.0
language:
- ar
tags:
- summarization
- AraBERT
- BERT
- BERT2BERT
- MSA
- Arabic Text Summarization
- Arabic News Title Generation
- Arabic Paraphrasing
- Summarization
- generated_from_trainer
- Transformers
- PyTorch
widget:
- text: >-
شهدت مدينة طرابلس، مساء أمس الأربعاء، احتجاجات شعبية وأعمال شغب لليوم
الثالث على التوالي، وذلك بسبب تردي الوضع المعيشي والاقتصادي. واندلعت
مواجهات عنيفة وعمليات كر وفر ما بين الجيش اللبناني والمحتجين استمرت
لساعات، إثر محاولة فتح الطرقات المقطوعة، ما أدى إلى إصابة العشرات من
الطرفين.
datasets:
- xlsum
model-index:
- name: arabartsummarization
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. -->
# AraBART-summ
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
## Validation Metrics
- Loss: 2.3417
- Rouge1: 2.353
- Rouge2: 1.103
- RougeL: 1.176
- RougeLsum: 1.521
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.7555 | 1.0 | 9380 | 2.3417 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu116
- Datasets 2.7.1
- Tokenizers 0.13.2
|
tolgadev/q-Taxi-v3
|
tolgadev
| 2023-01-02T18:46:12Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-01-02T18:42:45Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.56 +/- 2.71
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="tkurtulus/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
abdalrahmanshahrour/arabartsummarization
|
abdalrahmanshahrour
| 2023-01-02T18:45:12Z | 227 | 6 |
transformers
|
[
"transformers",
"pytorch",
"mbart",
"text2text-generation",
"summarization",
"AraBERT",
"BERT",
"BERT2BERT",
"MSA",
"Arabic Text Summarization",
"Arabic News Title Generation",
"Arabic Paraphrasing",
"Summarization",
"generated_from_trainer",
"Transformers",
"PyTorch",
"ar",
"dataset:xlsum",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
summarization
| 2022-12-12T16:48:00Z |
---
license: apache-2.0
language:
- ar
tags:
- summarization
- AraBERT
- BERT
- BERT2BERT
- MSA
- Arabic Text Summarization
- Arabic News Title Generation
- Arabic Paraphrasing
- Summarization
- generated_from_trainer
- Transformers
- PyTorch
widget:
- text: >-
شهدت مدينة طرابلس، مساء أمس الأربعاء، احتجاجات شعبية وأعمال شغب لليوم
الثالث على التوالي، وذلك بسبب تردي الوضع المعيشي والاقتصادي. واندلعت
مواجهات عنيفة وعمليات كر وفر ما بين الجيش اللبناني والمحتجين استمرت
لساعات، إثر محاولة فتح الطرقات المقطوعة، ما أدى إلى إصابة العشرات من
الطرفين.
datasets:
- xlsum
model-index:
- name: arabartsummarization
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. -->
# arabartsummarization
## Model description
The model can be used as follows:
```python
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline
from arabert.preprocess import ArabertPreprocessor
model_name="abdalrahmanshahrour/arabartsummarization"
preprocessor = ArabertPreprocessor(model_name="")
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
pipeline = pipeline("text2text-generation",model=model,tokenizer=tokenizer)
text = "شهدت مدينة طرابلس، مساء أمس الأربعاء، احتجاجات شعبية وأعمال شغب لليوم الثالث على التوالي، وذلك بسبب تردي الوضع المعيشي والاقتصادي. واندلعت مواجهات عنيفة وعمليات كر وفر ما بين الجيش اللبناني والمحتجين استمرت لساعات، إثر محاولة فتح الطرقات المقطوعة، ما أدى إلى إصابة العشرات من الطرفين."
text = preprocessor.preprocess(text)
result = pipeline(text,
pad_token_id=tokenizer.eos_token_id,
num_beams=3,
repetition_penalty=3.0,
max_length=200,
length_penalty=1.0,
no_repeat_ngram_size = 3)[0]['generated_text']
result
>>> "تجددت الاشتباكات بين الجيش اللبناني ومحتجين في مدينة طرابلس شمالي لبنان."
```
## Validation Metrics
- Loss: 2.3394
- Rouge1: 1.142
- Rouge2: 0.227
- RougeL: 1.124
- RougeLsum: 1.234
## Intended uses & limitations
More information needed
## Training and evaluation data
42.21K row in total
- Training : 37.52K rows
- Evaluation : 4.69K rows
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 2.784 | 1.0 | 9380 | 2.3820 |
| 2.4954 | 2.0 | 18760 | 2.3418 |
| 2.2223 | 3.0 | 28140 | 2.3394 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu116
- Datasets 2.7.1
- Tokenizers 0.13.2
|
styskin/pdngtn21-illustration-21
|
styskin
| 2023-01-02T18:30:03Z | 31 | 0 |
diffusers
|
[
"diffusers",
"pytorch",
"stable-diffusion",
"text-to-image",
"diffusion-models-class",
"dreambooth-hackathon",
"animal",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-01-02T18:28:15Z |
---
license: creativeml-openrail-m
tags:
- pytorch
- diffusers
- stable-diffusion
- text-to-image
- diffusion-models-class
- dreambooth-hackathon
- animal
widget:
- text: illustration owl on wood by pdngtn21
---
# DreamBooth model for the pdngtn21 concept trained by styskin on the styskin/paddington dataset.
This is a Stable Diffusion model fine-tuned on the pdngtn21 concept with DreamBooth. It can be used by modifying the `instance_prompt`: **illustration by pdngtn21**
This model was created as part of the DreamBooth Hackathon 🔥. Visit the [organisation page](https://huggingface.co/dreambooth-hackathon) for instructions on how to take part!
## Description
This is a Stable Diffusion model fine-tuned on `illustration` images for the animal theme.
## Usage
```python
from diffusers import StableDiffusionPipeline
pipeline = StableDiffusionPipeline.from_pretrained('styskin/pdngtn21-illustration-21')
image = pipeline().images[0]
image
```
|
Mistermango24/YiffAi2220_yai2220_YiffAI_v2.2.20
|
Mistermango24
| 2023-01-02T18:21:14Z | 0 | 1 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-01-02T18:06:14Z |
---
license: creativeml-openrail-m
---
|
clara-dumont/wav2vec2-base-timit-eng
|
clara-dumont
| 2023-01-02T18:02:46Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-01-02T12:40:01Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: wav2vec2-base-timit-eng
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-base-timit-eng
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5195
- Wer: 0.3418
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 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: 1000
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 3.5159 | 1.0 | 500 | 1.7153 | 1.0291 |
| 0.8502 | 2.01 | 1000 | 0.5204 | 0.5146 |
| 0.431 | 3.01 | 1500 | 0.4491 | 0.4537 |
| 0.3073 | 4.02 | 2000 | 0.3883 | 0.4190 |
| 0.2338 | 5.02 | 2500 | 0.4453 | 0.4230 |
| 0.1956 | 6.02 | 3000 | 0.4599 | 0.3981 |
| 0.1594 | 7.03 | 3500 | 0.4240 | 0.3916 |
| 0.1423 | 8.03 | 4000 | 0.4756 | 0.3975 |
| 0.1252 | 9.04 | 4500 | 0.4427 | 0.3827 |
| 0.1064 | 10.04 | 5000 | 0.4489 | 0.3809 |
| 0.101 | 11.04 | 5500 | 0.4531 | 0.3961 |
| 0.0877 | 12.05 | 6000 | 0.4881 | 0.3883 |
| 0.0817 | 13.05 | 6500 | 0.5023 | 0.3774 |
| 0.0703 | 14.06 | 7000 | 0.5078 | 0.3679 |
| 0.0663 | 15.06 | 7500 | 0.5279 | 0.3620 |
| 0.0584 | 16.06 | 8000 | 0.5112 | 0.3653 |
| 0.0579 | 17.07 | 8500 | 0.4959 | 0.3633 |
| 0.0572 | 18.07 | 9000 | 0.4676 | 0.3626 |
| 0.0502 | 19.08 | 9500 | 0.5216 | 0.3503 |
| 0.0432 | 20.08 | 10000 | 0.4946 | 0.3480 |
| 0.0417 | 21.08 | 10500 | 0.4949 | 0.3532 |
| 0.0335 | 22.09 | 11000 | 0.5485 | 0.3557 |
| 0.032 | 23.09 | 11500 | 0.5087 | 0.3464 |
| 0.0334 | 24.1 | 12000 | 0.5313 | 0.3498 |
| 0.0263 | 25.1 | 12500 | 0.5148 | 0.3457 |
| 0.0242 | 26.1 | 13000 | 0.5232 | 0.3442 |
| 0.0235 | 27.11 | 13500 | 0.5122 | 0.3418 |
| 0.0221 | 28.11 | 14000 | 0.5074 | 0.3407 |
| 0.0215 | 29.12 | 14500 | 0.5195 | 0.3418 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.13.0+cu116
- Datasets 1.18.3
- Tokenizers 0.13.2
|
annelegendre/ppo-LunarLander-v2
|
annelegendre
| 2023-01-02T17:38:19Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-01-02T16:56:43Z |
---
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: 281.83 +/- 19.71
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
...
```
|
cyeet/ppo-LunarLander-v2
|
cyeet
| 2023-01-02T17:21:23Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-12-29T08:51:12Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 265.98 +/- 18.21
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
...
```
|
Rschmaelzle/distilbert-base-uncased-finetuned-emotion
|
Rschmaelzle
| 2023-01-02T17:15:58Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-01-02T13:52:35Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
args: split
metrics:
- name: Accuracy
type: accuracy
value: 0.9215
- name: F1
type: f1
value: 0.9213729800215813
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2316
- Accuracy: 0.9215
- F1: 0.9214
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8559 | 1.0 | 250 | 0.3409 | 0.9005 | 0.8967 |
| 0.2613 | 2.0 | 500 | 0.2316 | 0.9215 | 0.9214 |
### Framework versions
- Transformers 4.13.0
- Pytorch 1.13.0+cu116
- Datasets 1.16.1
- Tokenizers 0.10.3
|
DaniilSirota/Taxi
|
DaniilSirota
| 2023-01-02T16:57:14Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-01-02T16:53:12Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: Taxi
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.56 +/- 2.71
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="DaniilSirota/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"])
```
|
DaniilSirota/q-FrozenLake-v1-4x4-noSlippery
|
DaniilSirota
| 2023-01-02T16:51:15Z | 0 | 1 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-01-02T16:51:11Z |
---
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="DaniilSirota/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"])
```
|
3dart/Fantasydemonlord
|
3dart
| 2023-01-02T16:50:07Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-01-02T16:50:07Z |
---
license: creativeml-openrail-m
---
|
darthrevenge/sd-class-butterflies-32
|
darthrevenge
| 2023-01-02T16:37:46Z | 30 | 0 |
diffusers
|
[
"diffusers",
"pytorch",
"unconditional-image-generation",
"diffusion-models-class",
"license:mit",
"diffusers:DDPMPipeline",
"region:us"
] |
unconditional-image-generation
| 2023-01-02T16:37:12Z |
---
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('darthrevenge/sd-class-butterflies-32')
image = pipeline().images[0]
image
```
|
cyeet/dqn-SpaceInvadersNoFrameskip-v4
|
cyeet
| 2023-01-02T16:27:58Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-12-31T07:33:26Z |
---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 685.50 +/- 126.06
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga cyeet -f logs/
python enjoy.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga cyeet -f logs/
rl_zoo3 enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python train.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga cyeet
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 10000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 5e-05),
('learning_starts', 10000),
('n_timesteps', 100000),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
|
google-t5/t5-11b
|
google-t5
| 2023-01-02T16:15:50Z | 1,200,704 | 60 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"t5",
"text2text-generation",
"summarization",
"translation",
"en",
"fr",
"ro",
"de",
"multilingual",
"dataset:c4",
"arxiv:1805.12471",
"arxiv:1708.00055",
"arxiv:1704.05426",
"arxiv:1606.05250",
"arxiv:1808.09121",
"arxiv:1810.12885",
"arxiv:1905.10044",
"arxiv:1910.09700",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] |
translation
| 2022-03-02T23:29:04Z |
---
language:
- en
- fr
- ro
- de
- multilingual
license: apache-2.0
tags:
- summarization
- translation
datasets:
- c4
inference: false
---
# Model Card for T5 11B
# Table of Contents
1. [Model Details](#model-details)
2. [Uses](#uses)
3. [Bias, Risks, and Limitations](#bias-risks-and-limitations)
4. [Training Details](#training-details)
5. [Evaluation](#evaluation)
6. [Environmental Impact](#environmental-impact)
7. [Citation](#citation)
8. [Model Card Authors](#model-card-authors)
9. [How To Get Started With the Model](#how-to-get-started-with-the-model)
# Model Details
## Model Description
The developers of the Text-To-Text Transfer Transformer (T5) [write](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html):
> With T5, we propose reframing all NLP tasks into a unified text-to-text-format where the input and output are always text strings, in contrast to BERT-style models that can only output either a class label or a span of the input. Our text-to-text framework allows us to use the same model, loss function, and hyperparameters on any NLP task.
T5-11B is the checkpoint with 11 billion parameters.
- **Developed by:** Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, Peter J. Liu. See [associated paper](https://jmlr.org/papers/volume21/20-074/20-074.pdf) and [GitHub repo](https://github.com/google-research/text-to-text-transfer-transformer#released-model-checkpoints)
- **Model type:** Language model
- **Language(s) (NLP):** English, French, Romanian, German
- **License:** Apache 2.0
- **Related Models:** [All T5 Checkpoints](https://huggingface.co/models?search=t5)
- **Resources for more information:**
- [Research paper](https://jmlr.org/papers/volume21/20-074/20-074.pdf)
- [Google's T5 Blog Post](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html)
- [GitHub Repo](https://github.com/google-research/text-to-text-transfer-transformer)
- [Hugging Face T5 Docs](https://huggingface.co/docs/transformers/model_doc/t5)
# Uses
## Direct Use and Downstream Use
The developers write in a [blog post](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) that the model:
> Our text-to-text framework allows us to use the same model, loss function, and hyperparameters on any NLP task, including machine translation, document summarization, question answering, and classification tasks (e.g., sentiment analysis). We can even apply T5 to regression tasks by training it to predict the string representation of a number instead of the number itself.
See the [blog post](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) and [research paper](https://jmlr.org/papers/volume21/20-074/20-074.pdf) for further details.
## Out-of-Scope Use
More information needed.
# Bias, Risks, and Limitations
More information needed.
## Recommendations
More information needed.
# Training Details
## Training Data
The model is pre-trained on the [Colossal Clean Crawled Corpus (C4)](https://www.tensorflow.org/datasets/catalog/c4), which was developed and released in the context of the same [research paper](https://jmlr.org/papers/volume21/20-074/20-074.pdf) as T5.
The model was pre-trained on a on a **multi-task mixture of unsupervised (1.) and supervised tasks (2.)**.
Thereby, the following datasets were being used for (1.) and (2.):
1. **Datasets used for Unsupervised denoising objective**:
- [C4](https://huggingface.co/datasets/c4)
- [Wiki-DPR](https://huggingface.co/datasets/wiki_dpr)
2. **Datasets used for Supervised text-to-text language modeling objective**
- Sentence acceptability judgment
- CoLA [Warstadt et al., 2018](https://arxiv.org/abs/1805.12471)
- Sentiment analysis
- SST-2 [Socher et al., 2013](https://nlp.stanford.edu/~socherr/EMNLP2013_RNTN.pdf)
- Paraphrasing/sentence similarity
- MRPC [Dolan and Brockett, 2005](https://aclanthology.org/I05-5002)
- STS-B [Ceret al., 2017](https://arxiv.org/abs/1708.00055)
- QQP [Iyer et al., 2017](https://quoradata.quora.com/First-Quora-Dataset-Release-Question-Pairs)
- Natural language inference
- MNLI [Williams et al., 2017](https://arxiv.org/abs/1704.05426)
- QNLI [Rajpurkar et al.,2016](https://arxiv.org/abs/1606.05250)
- RTE [Dagan et al., 2005](https://link.springer.com/chapter/10.1007/11736790_9)
- CB [De Marneff et al., 2019](https://semanticsarchive.net/Archive/Tg3ZGI2M/Marneffe.pdf)
- Sentence completion
- COPA [Roemmele et al., 2011](https://www.researchgate.net/publication/221251392_Choice_of_Plausible_Alternatives_An_Evaluation_of_Commonsense_Causal_Reasoning)
- Word sense disambiguation
- WIC [Pilehvar and Camacho-Collados, 2018](https://arxiv.org/abs/1808.09121)
- Question answering
- MultiRC [Khashabi et al., 2018](https://aclanthology.org/N18-1023)
- ReCoRD [Zhang et al., 2018](https://arxiv.org/abs/1810.12885)
- BoolQ [Clark et al., 2019](https://arxiv.org/abs/1905.10044)
## Training Procedure
In their [abstract](https://jmlr.org/papers/volume21/20-074/20-074.pdf), the model developers write:
> In this paper, we explore the landscape of transfer learning techniques for NLP by introducing a unified framework that converts every language problem into a text-to-text format. Our systematic study compares pre-training objectives, architectures, unlabeled datasets, transfer approaches, and other factors on dozens of language understanding tasks.
The framework introduced, the T5 framework, involves a training procedure that brings together the approaches studied in the paper. See the [research paper](https://jmlr.org/papers/volume21/20-074/20-074.pdf) for further details.
# Evaluation
## Testing Data, Factors & Metrics
The developers evaluated the model on 24 tasks, see the [research paper](https://jmlr.org/papers/volume21/20-074/20-074.pdf) for full details.
## Results
For full results for T5-11B, see the [research paper](https://jmlr.org/papers/volume21/20-074/20-074.pdf), Table 14.
# Environmental Impact
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:** Google Cloud TPU Pods
- **Hours used:** More information needed
- **Cloud Provider:** GCP
- **Compute Region:** More information needed
- **Carbon Emitted:** More information needed
# Citation
**BibTeX:**
```bibtex
@article{2020t5,
author = {Colin Raffel and Noam Shazeer and Adam Roberts and Katherine Lee and Sharan Narang and Michael Matena and Yanqi Zhou and Wei Li and Peter J. Liu},
title = {Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer},
journal = {Journal of Machine Learning Research},
year = {2020},
volume = {21},
number = {140},
pages = {1-67},
url = {http://jmlr.org/papers/v21/20-074.html}
}
```
**APA:**
- Raffel, C., Shazeer, N., Roberts, A., Lee, K., Narang, S., Matena, M., ... & Liu, P. J. (2020). Exploring the limits of transfer learning with a unified text-to-text transformer. J. Mach. Learn. Res., 21(140), 1-67.
# Model Card Authors
This model card was written by the team at Hugging Face.
# How to Get Started with the Model
## Disclaimer
**Before `transformers` v3.5.0**, due do its immense size, `t5-11b` required some special treatment.
If you're using transformers `<= v3.4.0`, `t5-11b` should be loaded with flag `use_cdn` set to `False` as follows:
```python
t5 = transformers.T5ForConditionalGeneration.from_pretrained('t5-11b', use_cdn = False)
```
Secondly, a single GPU will most likely not have enough memory to even load the model into memory as the weights alone amount to over 40 GB.
- Model parallelism has to be used here to overcome this problem as is explained in this [PR](https://github.com/huggingface/transformers/pull/3578).
- DeepSpeed's ZeRO-Offload is another approach as explained in this [post](https://github.com/huggingface/transformers/issues/9996).
See the [Hugging Face T5](https://huggingface.co/docs/transformers/model_doc/t5#transformers.T5Model) docs and a [Colab Notebook](https://colab.research.google.com/github/google-research/text-to-text-transfer-transformer/blob/main/notebooks/t5-trivia.ipynb) created by the model developers for more context.
|
hirotaka/sd-class-butterflies-64
|
hirotaka
| 2023-01-02T15:42:48Z | 30 | 0 |
diffusers
|
[
"diffusers",
"pytorch",
"unconditional-image-generation",
"diffusion-models-class",
"license:mit",
"diffusers:DDPMPipeline",
"region:us"
] |
unconditional-image-generation
| 2023-01-02T15:33:11Z |
---
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('hirotaka/sd-class-butterflies-64')
image = pipeline().images[0]
image
```
|
ibadrehman/q-FrozenLake-v1-4x4-Slippery-v1
|
ibadrehman
| 2023-01-02T15:32:14Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-01-02T15:32:03Z |
---
tags:
- FrozenLake-v1-4x4
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-Slippery-v1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4
type: FrozenLake-v1-4x4
metrics:
- type: mean_reward
value: 0.72 +/- 0.45
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="ibadrehman/q-FrozenLake-v1-4x4-Slippery-v1", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
styskin/pdngtn-illustration
|
styskin
| 2023-01-02T15:26:41Z | 31 | 1 |
diffusers
|
[
"diffusers",
"pytorch",
"stable-diffusion",
"text-to-image",
"diffusion-models-class",
"dreambooth-hackathon",
"animal",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-01-02T15:24:52Z |
---
license: creativeml-openrail-m
tags:
- pytorch
- diffusers
- stable-diffusion
- text-to-image
- diffusion-models-class
- dreambooth-hackathon
- animal
widget:
- text: illustration of girl with brown bob haircut hugging west highland white terrier
in Acropolis by pdngtn
---
# DreamBooth model for the pdngtn concept trained by styskin on the styskin/paddington dataset.
This is a Stable Diffusion model fine-tuned on the pdngtn concept with DreamBooth. It can be used by modifying the `instance_prompt`: **illustration by pdngtn**
This model was created as part of the DreamBooth Hackathon 🔥. Visit the [organisation page](https://huggingface.co/dreambooth-hackathon) for instructions on how to take part!
## Description
This is a Stable Diffusion model fine-tuned on `illustration` images for the animal theme.
## Usage
```python
from diffusers import StableDiffusionPipeline
pipeline = StableDiffusionPipeline.from_pretrained('styskin/pdngtn-illustration')
image = pipeline().images[0]
image
```
|
FBM/dqn-SpaceInvadersNoFrameskip-v4
|
FBM
| 2023-01-02T15:25:08Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-12-27T13:08:05Z |
---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 525.50 +/- 101.11
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga FBM -f logs/
python enjoy.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga FBM -f logs/
rl_zoo3 enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python train.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga FBM
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 5),
('gradient_steps', 1),
('learning_rate', 0.0002),
('learning_starts', 100000),
('n_timesteps', 2000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
|
NXBY/dqn-SpaceInvadersNoFrameskip-v4
|
NXBY
| 2023-01-02T15:21:09Z | 5 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-01-02T15:20:25Z |
---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 510.50 +/- 76.76
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga NXBY -f logs/
python enjoy.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga NXBY -f logs/
rl_zoo3 enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python train.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga NXBY
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 1000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
|
pachicartelle/q-table-Taxi-v3
|
pachicartelle
| 2023-01-02T15:16:39Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-01-02T15:16:35Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-table-Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.52 +/- 2.76
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="pachicartelle/q-table-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"])
```
|
denisivashkov/q-Taxi-v3
|
denisivashkov
| 2023-01-02T15:16:03Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-01-02T15:15:58Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.50 +/- 2.70
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="denisivashkov/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
pachicartelle/q-FrozenLake-v1-4x4-noSlippery
|
pachicartelle
| 2023-01-02T15:09:53Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-01-02T15:09:49Z |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="pachicartelle/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"])
```
|
Olwflynn/test-trainer-init
|
Olwflynn
| 2023-01-02T14:58:23Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-01-02T14:34:45Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
- f1
model-index:
- name: test-trainer-init
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
config: mrpc
split: train
args: mrpc
metrics:
- name: Accuracy
type: accuracy
value: 0.8602941176470589
- name: F1
type: f1
value: 0.9042016806722689
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# test-trainer-init
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6581
- Accuracy: 0.8603
- F1: 0.9042
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log | 1.0 | 459 | 0.3660 | 0.8505 | 0.8893 |
| 0.5003 | 2.0 | 918 | 0.5355 | 0.8407 | 0.8922 |
| 0.2654 | 3.0 | 1377 | 0.6581 | 0.8603 | 0.9042 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
|
hirotaka/sd-class-butterflies-32
|
hirotaka
| 2023-01-02T14:53:31Z | 30 | 0 |
diffusers
|
[
"diffusers",
"pytorch",
"unconditional-image-generation",
"diffusion-models-class",
"license:mit",
"diffusers:DDPMPipeline",
"region:us"
] |
unconditional-image-generation
| 2023-01-02T14:52:12Z |
---
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('hirotaka/sd-class-butterflies-32')
image = pipeline().images[0]
image
```
|
jonathanybema/twitter-bert-base-sentiment
|
jonathanybema
| 2023-01-02T14:41:01Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-01-02T10:23:45Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: twitter-bert-base-sentiment
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. -->
# twitter-bert-base-sentiment
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7779
- Accuracy: 0.7345
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
|
hayle/q-taxi-v3
|
hayle
| 2023-01-02T14:29:14Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-01-02T14:29:10Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.56 +/- 2.71
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="hayle/q-taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
ibadrehman/q-FrozenLake-v1-4x4-noSlippery-v1
|
ibadrehman
| 2023-01-02T14:28:19Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-01-02T14:28:08Z |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery-v1
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="ibadrehman/q-FrozenLake-v1-4x4-noSlippery-v1", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
jed351/whisper_small_cantonese_cm_voice
|
jed351
| 2023-01-02T14:22:20Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:mozilla-foundation/common_voice_11_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-01-02T04:14:29Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_11_0
metrics:
- wer
model-index:
- name: whisper-small-zh-hk
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: mozilla-foundation/common_voice_11_0 zh-HK
type: mozilla-foundation/common_voice_11_0
config: mozilla-foundation/common_voice_11_0 zh-HK
split: None
args: zh-HK
metrics:
- name: Wer
type: wer
value: 0.5615316117542297
---
<!-- 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-zh-hk
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the mozilla-foundation/common_voice_11_0 zh-HK dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3003
- Wer: 0.5615
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- 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
- lr_scheduler_warmup_steps: 500
- training_steps: 5000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.1556 | 2.28 | 1000 | 0.2708 | 0.6069 |
| 0.038 | 4.57 | 2000 | 0.2674 | 0.5701 |
| 0.0059 | 6.85 | 3000 | 0.2843 | 0.5635 |
| 0.0017 | 9.13 | 4000 | 0.2952 | 0.5622 |
| 0.0013 | 11.42 | 5000 | 0.3003 | 0.5615 |
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.1
- Datasets 2.8.0
- Tokenizers 0.13.2
|
ibm-research/ColD-Fusion-bert-base-uncased-itr7-seed0
|
ibm-research
| 2023-01-02T13:58:56Z | 104 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"exbert",
"en",
"arxiv:2212.01378",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-01-02T13:58:33Z |
---
language: en
tags:
- exbert
license: mit
---
# ColD Fusion BERT uncased model
Finetuned model that aims to be a great base model. It improves over BERT base model (uncased), trained on 35 datasets.
Full details at [this paper](https://arxiv.org/abs/2212.01378).
## Paper Abstract:
Pretraining has been shown to scale well with compute, data size and data diversity. Multitask learning trains on a
mixture of supervised datasets and produces improved performance compared to self-supervised pretraining. Until now,
massively multitask learning required simultaneous access to all datasets in the mixture and heavy compute resources
that are only available to well-resourced teams.
In this paper, we propose ColD Fusion, a method that provides the benefits of multitask learning but leverages distributed
computation and requires limited communication and no sharing of data. Consequentially, ColD Fusion can create a synergistic
loop, where finetuned models can be recycled to continually improve the pretrained model they are based on. We show that
ColD Fusion yields comparable benefits to multitask pretraining by producing a model that (a) attains strong performance on
all of the datasets it was multitask trained on and (b) is a better starting point for finetuning on unseen datasets. We find
ColD Fusion outperforms RoBERTa and even previous multitask models. Specifically, when training and testing on 35 diverse datasets,
ColD Fusion-based model outperforms RoBERTa by 2.45 points in average without any changes to the architecture.
### How to use
Best way to use is to finetune on your own task, but you can also extract features directly.
To get the features of a given text in PyTorch:
```python
from transformers import RobertaTokenizer, RobertaModel
tokenizer = RobertaTokenizer.from_pretrained('ibm/ColD-Fusion')
model = RobertaModel.from_pretrained('ibm/ColD-Fusion')
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
```
and in TensorFlow:
```python
from transformers import RobertaTokenizer, TFRobertaModel
tokenizer = RobertaTokenizer.from_pretrained('ibm/ColD-Fusion')
model = TFRobertaModel.from_pretrained('ibm/ColD-Fusion')
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='tf')
output = model(encoded_input)
```
## Evaluation results
See full evaluation results of this model and many more [here](https://ibm.github.io/model-recycling/roberta-base_table.html)
When fine-tuned on downstream tasks, this model achieves the following results:
### BibTeX entry and citation info
```bibtex
@article{ColDFusion,
author = {Shachar Don-Yehiya, Elad Venezian, Colin Raffel, Noam Slonim, Yoav Katz, Leshem ChoshenYinhan Liu and},
title = {ColD Fusion: Collaborative Descent for Distributed Multitask Finetuning},
journal = {CoRR},
volume = {abs/2212.01378},
year = {2022},
url = {https://arxiv.org/abs/2212.01378},
archivePrefix = {arXiv},
eprint = {2212.01378},
}
```
<a href="https://huggingface.co/exbert/?model=ibm/ColD-Fusion">
<img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png">
</a>
|
ibm-research/ColD-Fusion-bert-base-uncased-itr5-seed0
|
ibm-research
| 2023-01-02T13:58:02Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"exbert",
"en",
"arxiv:2212.01378",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-01-02T13:56:34Z |
---
language: en
tags:
- exbert
license: mit
---
# ColD Fusion BERT uncased model
Finetuned model that aims to be a great base model. It improves over BERT base model (uncased), trained on 35 datasets.
Full details at [this paper](https://arxiv.org/abs/2212.01378).
## Paper Abstract:
Pretraining has been shown to scale well with compute, data size and data diversity. Multitask learning trains on a
mixture of supervised datasets and produces improved performance compared to self-supervised pretraining. Until now,
massively multitask learning required simultaneous access to all datasets in the mixture and heavy compute resources
that are only available to well-resourced teams.
In this paper, we propose ColD Fusion, a method that provides the benefits of multitask learning but leverages distributed
computation and requires limited communication and no sharing of data. Consequentially, ColD Fusion can create a synergistic
loop, where finetuned models can be recycled to continually improve the pretrained model they are based on. We show that
ColD Fusion yields comparable benefits to multitask pretraining by producing a model that (a) attains strong performance on
all of the datasets it was multitask trained on and (b) is a better starting point for finetuning on unseen datasets. We find
ColD Fusion outperforms RoBERTa and even previous multitask models. Specifically, when training and testing on 35 diverse datasets,
ColD Fusion-based model outperforms RoBERTa by 2.45 points in average without any changes to the architecture.
### How to use
Best way to use is to finetune on your own task, but you can also extract features directly.
To get the features of a given text in PyTorch:
```python
from transformers import RobertaTokenizer, RobertaModel
tokenizer = RobertaTokenizer.from_pretrained('ibm/ColD-Fusion')
model = RobertaModel.from_pretrained('ibm/ColD-Fusion')
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
```
and in TensorFlow:
```python
from transformers import RobertaTokenizer, TFRobertaModel
tokenizer = RobertaTokenizer.from_pretrained('ibm/ColD-Fusion')
model = TFRobertaModel.from_pretrained('ibm/ColD-Fusion')
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='tf')
output = model(encoded_input)
```
## Evaluation results
See full evaluation results of this model and many more [here](https://ibm.github.io/model-recycling/roberta-base_table.html)
When fine-tuned on downstream tasks, this model achieves the following results:
### BibTeX entry and citation info
```bibtex
@article{ColDFusion,
author = {Shachar Don-Yehiya, Elad Venezian, Colin Raffel, Noam Slonim, Yoav Katz, Leshem ChoshenYinhan Liu and},
title = {ColD Fusion: Collaborative Descent for Distributed Multitask Finetuning},
journal = {CoRR},
volume = {abs/2212.01378},
year = {2022},
url = {https://arxiv.org/abs/2212.01378},
archivePrefix = {arXiv},
eprint = {2212.01378},
}
```
<a href="https://huggingface.co/exbert/?model=ibm/ColD-Fusion">
<img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png">
</a>
|
ibm-research/ColD-Fusion-bert-base-uncased-itr4-seed0
|
ibm-research
| 2023-01-02T13:56:32Z | 102 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"exbert",
"en",
"arxiv:2212.01378",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-01-02T13:56:05Z |
---
language: en
tags:
- exbert
license: mit
---
# ColD Fusion BERT uncased model
Finetuned model that aims to be a great base model. It improves over BERT base model (uncased), trained on 35 datasets.
Full details at [this paper](https://arxiv.org/abs/2212.01378).
## Paper Abstract:
Pretraining has been shown to scale well with compute, data size and data diversity. Multitask learning trains on a
mixture of supervised datasets and produces improved performance compared to self-supervised pretraining. Until now,
massively multitask learning required simultaneous access to all datasets in the mixture and heavy compute resources
that are only available to well-resourced teams.
In this paper, we propose ColD Fusion, a method that provides the benefits of multitask learning but leverages distributed
computation and requires limited communication and no sharing of data. Consequentially, ColD Fusion can create a synergistic
loop, where finetuned models can be recycled to continually improve the pretrained model they are based on. We show that
ColD Fusion yields comparable benefits to multitask pretraining by producing a model that (a) attains strong performance on
all of the datasets it was multitask trained on and (b) is a better starting point for finetuning on unseen datasets. We find
ColD Fusion outperforms RoBERTa and even previous multitask models. Specifically, when training and testing on 35 diverse datasets,
ColD Fusion-based model outperforms RoBERTa by 2.45 points in average without any changes to the architecture.
### How to use
Best way to use is to finetune on your own task, but you can also extract features directly.
To get the features of a given text in PyTorch:
```python
from transformers import RobertaTokenizer, RobertaModel
tokenizer = RobertaTokenizer.from_pretrained('ibm/ColD-Fusion')
model = RobertaModel.from_pretrained('ibm/ColD-Fusion')
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
```
and in TensorFlow:
```python
from transformers import RobertaTokenizer, TFRobertaModel
tokenizer = RobertaTokenizer.from_pretrained('ibm/ColD-Fusion')
model = TFRobertaModel.from_pretrained('ibm/ColD-Fusion')
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='tf')
output = model(encoded_input)
```
## Evaluation results
See full evaluation results of this model and many more [here](https://ibm.github.io/model-recycling/roberta-base_table.html)
When fine-tuned on downstream tasks, this model achieves the following results:
### BibTeX entry and citation info
```bibtex
@article{ColDFusion,
author = {Shachar Don-Yehiya, Elad Venezian, Colin Raffel, Noam Slonim, Yoav Katz, Leshem ChoshenYinhan Liu and},
title = {ColD Fusion: Collaborative Descent for Distributed Multitask Finetuning},
journal = {CoRR},
volume = {abs/2212.01378},
year = {2022},
url = {https://arxiv.org/abs/2212.01378},
archivePrefix = {arXiv},
eprint = {2212.01378},
}
```
<a href="https://huggingface.co/exbert/?model=ibm/ColD-Fusion">
<img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png">
</a>
|
jondister/ppp-LunarLander-v2
|
jondister
| 2023-01-02T13:56:26Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-01-02T13:55:58Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 260.62 +/- 21.66
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
...
```
|
ibm-research/ColD-Fusion-bert-base-uncased-itr3-seed0
|
ibm-research
| 2023-01-02T13:56:04Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"exbert",
"en",
"arxiv:2212.01378",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-01-02T13:55:43Z |
---
language: en
tags:
- exbert
license: mit
---
# ColD Fusion BERT uncased model
Finetuned model that aims to be a great base model. It improves over BERT base model (uncased), trained on 35 datasets.
Full details at [this paper](https://arxiv.org/abs/2212.01378).
## Paper Abstract:
Pretraining has been shown to scale well with compute, data size and data diversity. Multitask learning trains on a
mixture of supervised datasets and produces improved performance compared to self-supervised pretraining. Until now,
massively multitask learning required simultaneous access to all datasets in the mixture and heavy compute resources
that are only available to well-resourced teams.
In this paper, we propose ColD Fusion, a method that provides the benefits of multitask learning but leverages distributed
computation and requires limited communication and no sharing of data. Consequentially, ColD Fusion can create a synergistic
loop, where finetuned models can be recycled to continually improve the pretrained model they are based on. We show that
ColD Fusion yields comparable benefits to multitask pretraining by producing a model that (a) attains strong performance on
all of the datasets it was multitask trained on and (b) is a better starting point for finetuning on unseen datasets. We find
ColD Fusion outperforms RoBERTa and even previous multitask models. Specifically, when training and testing on 35 diverse datasets,
ColD Fusion-based model outperforms RoBERTa by 2.45 points in average without any changes to the architecture.
### How to use
Best way to use is to finetune on your own task, but you can also extract features directly.
To get the features of a given text in PyTorch:
```python
from transformers import RobertaTokenizer, RobertaModel
tokenizer = RobertaTokenizer.from_pretrained('ibm/ColD-Fusion')
model = RobertaModel.from_pretrained('ibm/ColD-Fusion')
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
```
and in TensorFlow:
```python
from transformers import RobertaTokenizer, TFRobertaModel
tokenizer = RobertaTokenizer.from_pretrained('ibm/ColD-Fusion')
model = TFRobertaModel.from_pretrained('ibm/ColD-Fusion')
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='tf')
output = model(encoded_input)
```
## Evaluation results
See full evaluation results of this model and many more [here](https://ibm.github.io/model-recycling/roberta-base_table.html)
When fine-tuned on downstream tasks, this model achieves the following results:
### BibTeX entry and citation info
```bibtex
@article{ColDFusion,
author = {Shachar Don-Yehiya, Elad Venezian, Colin Raffel, Noam Slonim, Yoav Katz, Leshem ChoshenYinhan Liu and},
title = {ColD Fusion: Collaborative Descent for Distributed Multitask Finetuning},
journal = {CoRR},
volume = {abs/2212.01378},
year = {2022},
url = {https://arxiv.org/abs/2212.01378},
archivePrefix = {arXiv},
eprint = {2212.01378},
}
```
<a href="https://huggingface.co/exbert/?model=ibm/ColD-Fusion">
<img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png">
</a>
|
ibm-research/ColD-Fusion-bert-base-uncased-itr29-seed0
|
ibm-research
| 2023-01-02T13:55:41Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"exbert",
"en",
"arxiv:2212.01378",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-01-02T13:55:03Z |
---
language: en
tags:
- exbert
license: mit
---
# ColD Fusion BERT uncased model
Finetuned model that aims to be a great base model. It improves over BERT base model (uncased), trained on 35 datasets.
Full details at [this paper](https://arxiv.org/abs/2212.01378).
## Paper Abstract:
Pretraining has been shown to scale well with compute, data size and data diversity. Multitask learning trains on a
mixture of supervised datasets and produces improved performance compared to self-supervised pretraining. Until now,
massively multitask learning required simultaneous access to all datasets in the mixture and heavy compute resources
that are only available to well-resourced teams.
In this paper, we propose ColD Fusion, a method that provides the benefits of multitask learning but leverages distributed
computation and requires limited communication and no sharing of data. Consequentially, ColD Fusion can create a synergistic
loop, where finetuned models can be recycled to continually improve the pretrained model they are based on. We show that
ColD Fusion yields comparable benefits to multitask pretraining by producing a model that (a) attains strong performance on
all of the datasets it was multitask trained on and (b) is a better starting point for finetuning on unseen datasets. We find
ColD Fusion outperforms RoBERTa and even previous multitask models. Specifically, when training and testing on 35 diverse datasets,
ColD Fusion-based model outperforms RoBERTa by 2.45 points in average without any changes to the architecture.
### How to use
Best way to use is to finetune on your own task, but you can also extract features directly.
To get the features of a given text in PyTorch:
```python
from transformers import RobertaTokenizer, RobertaModel
tokenizer = RobertaTokenizer.from_pretrained('ibm/ColD-Fusion')
model = RobertaModel.from_pretrained('ibm/ColD-Fusion')
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
```
and in TensorFlow:
```python
from transformers import RobertaTokenizer, TFRobertaModel
tokenizer = RobertaTokenizer.from_pretrained('ibm/ColD-Fusion')
model = TFRobertaModel.from_pretrained('ibm/ColD-Fusion')
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='tf')
output = model(encoded_input)
```
## Evaluation results
See full evaluation results of this model and many more [here](https://ibm.github.io/model-recycling/roberta-base_table.html)
When fine-tuned on downstream tasks, this model achieves the following results:
### BibTeX entry and citation info
```bibtex
@article{ColDFusion,
author = {Shachar Don-Yehiya, Elad Venezian, Colin Raffel, Noam Slonim, Yoav Katz, Leshem ChoshenYinhan Liu and},
title = {ColD Fusion: Collaborative Descent for Distributed Multitask Finetuning},
journal = {CoRR},
volume = {abs/2212.01378},
year = {2022},
url = {https://arxiv.org/abs/2212.01378},
archivePrefix = {arXiv},
eprint = {2212.01378},
}
```
<a href="https://huggingface.co/exbert/?model=ibm/ColD-Fusion">
<img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png">
</a>
|
ibm-research/ColD-Fusion-bert-base-uncased-itr2-seed0
|
ibm-research
| 2023-01-02T13:55:01Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"exbert",
"en",
"arxiv:2212.01378",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-01-02T13:54:23Z |
---
language: en
tags:
- exbert
license: mit
---
# ColD Fusion BERT uncased model
Finetuned model that aims to be a great base model. It improves over BERT base model (uncased), trained on 35 datasets.
Full details at [this paper](https://arxiv.org/abs/2212.01378).
## Paper Abstract:
Pretraining has been shown to scale well with compute, data size and data diversity. Multitask learning trains on a
mixture of supervised datasets and produces improved performance compared to self-supervised pretraining. Until now,
massively multitask learning required simultaneous access to all datasets in the mixture and heavy compute resources
that are only available to well-resourced teams.
In this paper, we propose ColD Fusion, a method that provides the benefits of multitask learning but leverages distributed
computation and requires limited communication and no sharing of data. Consequentially, ColD Fusion can create a synergistic
loop, where finetuned models can be recycled to continually improve the pretrained model they are based on. We show that
ColD Fusion yields comparable benefits to multitask pretraining by producing a model that (a) attains strong performance on
all of the datasets it was multitask trained on and (b) is a better starting point for finetuning on unseen datasets. We find
ColD Fusion outperforms RoBERTa and even previous multitask models. Specifically, when training and testing on 35 diverse datasets,
ColD Fusion-based model outperforms RoBERTa by 2.45 points in average without any changes to the architecture.
### How to use
Best way to use is to finetune on your own task, but you can also extract features directly.
To get the features of a given text in PyTorch:
```python
from transformers import RobertaTokenizer, RobertaModel
tokenizer = RobertaTokenizer.from_pretrained('ibm/ColD-Fusion')
model = RobertaModel.from_pretrained('ibm/ColD-Fusion')
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
```
and in TensorFlow:
```python
from transformers import RobertaTokenizer, TFRobertaModel
tokenizer = RobertaTokenizer.from_pretrained('ibm/ColD-Fusion')
model = TFRobertaModel.from_pretrained('ibm/ColD-Fusion')
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='tf')
output = model(encoded_input)
```
## Evaluation results
See full evaluation results of this model and many more [here](https://ibm.github.io/model-recycling/roberta-base_table.html)
When fine-tuned on downstream tasks, this model achieves the following results:
### BibTeX entry and citation info
```bibtex
@article{ColDFusion,
author = {Shachar Don-Yehiya, Elad Venezian, Colin Raffel, Noam Slonim, Yoav Katz, Leshem ChoshenYinhan Liu and},
title = {ColD Fusion: Collaborative Descent for Distributed Multitask Finetuning},
journal = {CoRR},
volume = {abs/2212.01378},
year = {2022},
url = {https://arxiv.org/abs/2212.01378},
archivePrefix = {arXiv},
eprint = {2212.01378},
}
```
<a href="https://huggingface.co/exbert/?model=ibm/ColD-Fusion">
<img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png">
</a>
|
ibm-research/ColD-Fusion-bert-base-uncased-itr28-seed0
|
ibm-research
| 2023-01-02T13:54:21Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"exbert",
"en",
"arxiv:2212.01378",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-01-02T13:53:55Z |
---
language: en
tags:
- exbert
license: mit
---
# ColD Fusion BERT uncased model
Finetuned model that aims to be a great base model. It improves over BERT base model (uncased), trained on 35 datasets.
Full details at [this paper](https://arxiv.org/abs/2212.01378).
## Paper Abstract:
Pretraining has been shown to scale well with compute, data size and data diversity. Multitask learning trains on a
mixture of supervised datasets and produces improved performance compared to self-supervised pretraining. Until now,
massively multitask learning required simultaneous access to all datasets in the mixture and heavy compute resources
that are only available to well-resourced teams.
In this paper, we propose ColD Fusion, a method that provides the benefits of multitask learning but leverages distributed
computation and requires limited communication and no sharing of data. Consequentially, ColD Fusion can create a synergistic
loop, where finetuned models can be recycled to continually improve the pretrained model they are based on. We show that
ColD Fusion yields comparable benefits to multitask pretraining by producing a model that (a) attains strong performance on
all of the datasets it was multitask trained on and (b) is a better starting point for finetuning on unseen datasets. We find
ColD Fusion outperforms RoBERTa and even previous multitask models. Specifically, when training and testing on 35 diverse datasets,
ColD Fusion-based model outperforms RoBERTa by 2.45 points in average without any changes to the architecture.
### How to use
Best way to use is to finetune on your own task, but you can also extract features directly.
To get the features of a given text in PyTorch:
```python
from transformers import RobertaTokenizer, RobertaModel
tokenizer = RobertaTokenizer.from_pretrained('ibm/ColD-Fusion')
model = RobertaModel.from_pretrained('ibm/ColD-Fusion')
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
```
and in TensorFlow:
```python
from transformers import RobertaTokenizer, TFRobertaModel
tokenizer = RobertaTokenizer.from_pretrained('ibm/ColD-Fusion')
model = TFRobertaModel.from_pretrained('ibm/ColD-Fusion')
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='tf')
output = model(encoded_input)
```
## Evaluation results
See full evaluation results of this model and many more [here](https://ibm.github.io/model-recycling/roberta-base_table.html)
When fine-tuned on downstream tasks, this model achieves the following results:
### BibTeX entry and citation info
```bibtex
@article{ColDFusion,
author = {Shachar Don-Yehiya, Elad Venezian, Colin Raffel, Noam Slonim, Yoav Katz, Leshem ChoshenYinhan Liu and},
title = {ColD Fusion: Collaborative Descent for Distributed Multitask Finetuning},
journal = {CoRR},
volume = {abs/2212.01378},
year = {2022},
url = {https://arxiv.org/abs/2212.01378},
archivePrefix = {arXiv},
eprint = {2212.01378},
}
```
<a href="https://huggingface.co/exbert/?model=ibm/ColD-Fusion">
<img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png">
</a>
|
ibm-research/ColD-Fusion-bert-base-uncased-itr26-seed0
|
ibm-research
| 2023-01-02T13:53:31Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"exbert",
"en",
"arxiv:2212.01378",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-01-02T13:53:04Z |
---
language: en
tags:
- exbert
license: mit
---
# ColD Fusion BERT uncased model
Finetuned model that aims to be a great base model. It improves over BERT base model (uncased), trained on 35 datasets.
Full details at [this paper](https://arxiv.org/abs/2212.01378).
## Paper Abstract:
Pretraining has been shown to scale well with compute, data size and data diversity. Multitask learning trains on a
mixture of supervised datasets and produces improved performance compared to self-supervised pretraining. Until now,
massively multitask learning required simultaneous access to all datasets in the mixture and heavy compute resources
that are only available to well-resourced teams.
In this paper, we propose ColD Fusion, a method that provides the benefits of multitask learning but leverages distributed
computation and requires limited communication and no sharing of data. Consequentially, ColD Fusion can create a synergistic
loop, where finetuned models can be recycled to continually improve the pretrained model they are based on. We show that
ColD Fusion yields comparable benefits to multitask pretraining by producing a model that (a) attains strong performance on
all of the datasets it was multitask trained on and (b) is a better starting point for finetuning on unseen datasets. We find
ColD Fusion outperforms RoBERTa and even previous multitask models. Specifically, when training and testing on 35 diverse datasets,
ColD Fusion-based model outperforms RoBERTa by 2.45 points in average without any changes to the architecture.
### How to use
Best way to use is to finetune on your own task, but you can also extract features directly.
To get the features of a given text in PyTorch:
```python
from transformers import RobertaTokenizer, RobertaModel
tokenizer = RobertaTokenizer.from_pretrained('ibm/ColD-Fusion')
model = RobertaModel.from_pretrained('ibm/ColD-Fusion')
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
```
and in TensorFlow:
```python
from transformers import RobertaTokenizer, TFRobertaModel
tokenizer = RobertaTokenizer.from_pretrained('ibm/ColD-Fusion')
model = TFRobertaModel.from_pretrained('ibm/ColD-Fusion')
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='tf')
output = model(encoded_input)
```
## Evaluation results
See full evaluation results of this model and many more [here](https://ibm.github.io/model-recycling/roberta-base_table.html)
When fine-tuned on downstream tasks, this model achieves the following results:
### BibTeX entry and citation info
```bibtex
@article{ColDFusion,
author = {Shachar Don-Yehiya, Elad Venezian, Colin Raffel, Noam Slonim, Yoav Katz, Leshem ChoshenYinhan Liu and},
title = {ColD Fusion: Collaborative Descent for Distributed Multitask Finetuning},
journal = {CoRR},
volume = {abs/2212.01378},
year = {2022},
url = {https://arxiv.org/abs/2212.01378},
archivePrefix = {arXiv},
eprint = {2212.01378},
}
```
<a href="https://huggingface.co/exbert/?model=ibm/ColD-Fusion">
<img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png">
</a>
|
ibm-research/ColD-Fusion-bert-base-uncased-itr25-seed0
|
ibm-research
| 2023-01-02T13:53:02Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"exbert",
"en",
"arxiv:2212.01378",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-01-02T13:52:34Z |
---
language: en
tags:
- exbert
license: mit
---
# ColD Fusion BERT uncased model
Finetuned model that aims to be a great base model. It improves over BERT base model (uncased), trained on 35 datasets.
Full details at [this paper](https://arxiv.org/abs/2212.01378).
## Paper Abstract:
Pretraining has been shown to scale well with compute, data size and data diversity. Multitask learning trains on a
mixture of supervised datasets and produces improved performance compared to self-supervised pretraining. Until now,
massively multitask learning required simultaneous access to all datasets in the mixture and heavy compute resources
that are only available to well-resourced teams.
In this paper, we propose ColD Fusion, a method that provides the benefits of multitask learning but leverages distributed
computation and requires limited communication and no sharing of data. Consequentially, ColD Fusion can create a synergistic
loop, where finetuned models can be recycled to continually improve the pretrained model they are based on. We show that
ColD Fusion yields comparable benefits to multitask pretraining by producing a model that (a) attains strong performance on
all of the datasets it was multitask trained on and (b) is a better starting point for finetuning on unseen datasets. We find
ColD Fusion outperforms RoBERTa and even previous multitask models. Specifically, when training and testing on 35 diverse datasets,
ColD Fusion-based model outperforms RoBERTa by 2.45 points in average without any changes to the architecture.
### How to use
Best way to use is to finetune on your own task, but you can also extract features directly.
To get the features of a given text in PyTorch:
```python
from transformers import RobertaTokenizer, RobertaModel
tokenizer = RobertaTokenizer.from_pretrained('ibm/ColD-Fusion')
model = RobertaModel.from_pretrained('ibm/ColD-Fusion')
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
```
and in TensorFlow:
```python
from transformers import RobertaTokenizer, TFRobertaModel
tokenizer = RobertaTokenizer.from_pretrained('ibm/ColD-Fusion')
model = TFRobertaModel.from_pretrained('ibm/ColD-Fusion')
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='tf')
output = model(encoded_input)
```
## Evaluation results
See full evaluation results of this model and many more [here](https://ibm.github.io/model-recycling/roberta-base_table.html)
When fine-tuned on downstream tasks, this model achieves the following results:
### BibTeX entry and citation info
```bibtex
@article{ColDFusion,
author = {Shachar Don-Yehiya, Elad Venezian, Colin Raffel, Noam Slonim, Yoav Katz, Leshem ChoshenYinhan Liu and},
title = {ColD Fusion: Collaborative Descent for Distributed Multitask Finetuning},
journal = {CoRR},
volume = {abs/2212.01378},
year = {2022},
url = {https://arxiv.org/abs/2212.01378},
archivePrefix = {arXiv},
eprint = {2212.01378},
}
```
<a href="https://huggingface.co/exbert/?model=ibm/ColD-Fusion">
<img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png">
</a>
|
ibm-research/ColD-Fusion-bert-base-uncased-itr24-seed0
|
ibm-research
| 2023-01-02T13:52:32Z | 7 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"exbert",
"en",
"arxiv:2212.01378",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-01-02T13:52:06Z |
---
language: en
tags:
- exbert
license: mit
---
# ColD Fusion BERT uncased model
Finetuned model that aims to be a great base model. It improves over BERT base model (uncased), trained on 35 datasets.
Full details at [this paper](https://arxiv.org/abs/2212.01378).
## Paper Abstract:
Pretraining has been shown to scale well with compute, data size and data diversity. Multitask learning trains on a
mixture of supervised datasets and produces improved performance compared to self-supervised pretraining. Until now,
massively multitask learning required simultaneous access to all datasets in the mixture and heavy compute resources
that are only available to well-resourced teams.
In this paper, we propose ColD Fusion, a method that provides the benefits of multitask learning but leverages distributed
computation and requires limited communication and no sharing of data. Consequentially, ColD Fusion can create a synergistic
loop, where finetuned models can be recycled to continually improve the pretrained model they are based on. We show that
ColD Fusion yields comparable benefits to multitask pretraining by producing a model that (a) attains strong performance on
all of the datasets it was multitask trained on and (b) is a better starting point for finetuning on unseen datasets. We find
ColD Fusion outperforms RoBERTa and even previous multitask models. Specifically, when training and testing on 35 diverse datasets,
ColD Fusion-based model outperforms RoBERTa by 2.45 points in average without any changes to the architecture.
### How to use
Best way to use is to finetune on your own task, but you can also extract features directly.
To get the features of a given text in PyTorch:
```python
from transformers import RobertaTokenizer, RobertaModel
tokenizer = RobertaTokenizer.from_pretrained('ibm/ColD-Fusion')
model = RobertaModel.from_pretrained('ibm/ColD-Fusion')
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
```
and in TensorFlow:
```python
from transformers import RobertaTokenizer, TFRobertaModel
tokenizer = RobertaTokenizer.from_pretrained('ibm/ColD-Fusion')
model = TFRobertaModel.from_pretrained('ibm/ColD-Fusion')
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='tf')
output = model(encoded_input)
```
## Evaluation results
See full evaluation results of this model and many more [here](https://ibm.github.io/model-recycling/roberta-base_table.html)
When fine-tuned on downstream tasks, this model achieves the following results:
### BibTeX entry and citation info
```bibtex
@article{ColDFusion,
author = {Shachar Don-Yehiya, Elad Venezian, Colin Raffel, Noam Slonim, Yoav Katz, Leshem ChoshenYinhan Liu and},
title = {ColD Fusion: Collaborative Descent for Distributed Multitask Finetuning},
journal = {CoRR},
volume = {abs/2212.01378},
year = {2022},
url = {https://arxiv.org/abs/2212.01378},
archivePrefix = {arXiv},
eprint = {2212.01378},
}
```
<a href="https://huggingface.co/exbert/?model=ibm/ColD-Fusion">
<img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png">
</a>
|
ibm-research/ColD-Fusion-bert-base-uncased-itr23-seed0
|
ibm-research
| 2023-01-02T13:52:04Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"exbert",
"en",
"arxiv:2212.01378",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-01-02T13:51:32Z |
---
language: en
tags:
- exbert
license: mit
---
# ColD Fusion BERT uncased model
Finetuned model that aims to be a great base model. It improves over BERT base model (uncased), trained on 35 datasets.
Full details at [this paper](https://arxiv.org/abs/2212.01378).
## Paper Abstract:
Pretraining has been shown to scale well with compute, data size and data diversity. Multitask learning trains on a
mixture of supervised datasets and produces improved performance compared to self-supervised pretraining. Until now,
massively multitask learning required simultaneous access to all datasets in the mixture and heavy compute resources
that are only available to well-resourced teams.
In this paper, we propose ColD Fusion, a method that provides the benefits of multitask learning but leverages distributed
computation and requires limited communication and no sharing of data. Consequentially, ColD Fusion can create a synergistic
loop, where finetuned models can be recycled to continually improve the pretrained model they are based on. We show that
ColD Fusion yields comparable benefits to multitask pretraining by producing a model that (a) attains strong performance on
all of the datasets it was multitask trained on and (b) is a better starting point for finetuning on unseen datasets. We find
ColD Fusion outperforms RoBERTa and even previous multitask models. Specifically, when training and testing on 35 diverse datasets,
ColD Fusion-based model outperforms RoBERTa by 2.45 points in average without any changes to the architecture.
### How to use
Best way to use is to finetune on your own task, but you can also extract features directly.
To get the features of a given text in PyTorch:
```python
from transformers import RobertaTokenizer, RobertaModel
tokenizer = RobertaTokenizer.from_pretrained('ibm/ColD-Fusion')
model = RobertaModel.from_pretrained('ibm/ColD-Fusion')
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
```
and in TensorFlow:
```python
from transformers import RobertaTokenizer, TFRobertaModel
tokenizer = RobertaTokenizer.from_pretrained('ibm/ColD-Fusion')
model = TFRobertaModel.from_pretrained('ibm/ColD-Fusion')
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='tf')
output = model(encoded_input)
```
## Evaluation results
See full evaluation results of this model and many more [here](https://ibm.github.io/model-recycling/roberta-base_table.html)
When fine-tuned on downstream tasks, this model achieves the following results:
### BibTeX entry and citation info
```bibtex
@article{ColDFusion,
author = {Shachar Don-Yehiya, Elad Venezian, Colin Raffel, Noam Slonim, Yoav Katz, Leshem ChoshenYinhan Liu and},
title = {ColD Fusion: Collaborative Descent for Distributed Multitask Finetuning},
journal = {CoRR},
volume = {abs/2212.01378},
year = {2022},
url = {https://arxiv.org/abs/2212.01378},
archivePrefix = {arXiv},
eprint = {2212.01378},
}
```
<a href="https://huggingface.co/exbert/?model=ibm/ColD-Fusion">
<img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png">
</a>
|
ibm-research/ColD-Fusion-bert-base-uncased-itr21-seed0
|
ibm-research
| 2023-01-02T13:50:52Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"exbert",
"en",
"arxiv:2212.01378",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-01-02T13:50:12Z |
---
language: en
tags:
- exbert
license: mit
---
# ColD Fusion BERT uncased model
Finetuned model that aims to be a great base model. It improves over BERT base model (uncased), trained on 35 datasets.
Full details at [this paper](https://arxiv.org/abs/2212.01378).
## Paper Abstract:
Pretraining has been shown to scale well with compute, data size and data diversity. Multitask learning trains on a
mixture of supervised datasets and produces improved performance compared to self-supervised pretraining. Until now,
massively multitask learning required simultaneous access to all datasets in the mixture and heavy compute resources
that are only available to well-resourced teams.
In this paper, we propose ColD Fusion, a method that provides the benefits of multitask learning but leverages distributed
computation and requires limited communication and no sharing of data. Consequentially, ColD Fusion can create a synergistic
loop, where finetuned models can be recycled to continually improve the pretrained model they are based on. We show that
ColD Fusion yields comparable benefits to multitask pretraining by producing a model that (a) attains strong performance on
all of the datasets it was multitask trained on and (b) is a better starting point for finetuning on unseen datasets. We find
ColD Fusion outperforms RoBERTa and even previous multitask models. Specifically, when training and testing on 35 diverse datasets,
ColD Fusion-based model outperforms RoBERTa by 2.45 points in average without any changes to the architecture.
### How to use
Best way to use is to finetune on your own task, but you can also extract features directly.
To get the features of a given text in PyTorch:
```python
from transformers import RobertaTokenizer, RobertaModel
tokenizer = RobertaTokenizer.from_pretrained('ibm/ColD-Fusion')
model = RobertaModel.from_pretrained('ibm/ColD-Fusion')
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
```
and in TensorFlow:
```python
from transformers import RobertaTokenizer, TFRobertaModel
tokenizer = RobertaTokenizer.from_pretrained('ibm/ColD-Fusion')
model = TFRobertaModel.from_pretrained('ibm/ColD-Fusion')
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='tf')
output = model(encoded_input)
```
## Evaluation results
See full evaluation results of this model and many more [here](https://ibm.github.io/model-recycling/roberta-base_table.html)
When fine-tuned on downstream tasks, this model achieves the following results:
### BibTeX entry and citation info
```bibtex
@article{ColDFusion,
author = {Shachar Don-Yehiya, Elad Venezian, Colin Raffel, Noam Slonim, Yoav Katz, Leshem ChoshenYinhan Liu and},
title = {ColD Fusion: Collaborative Descent for Distributed Multitask Finetuning},
journal = {CoRR},
volume = {abs/2212.01378},
year = {2022},
url = {https://arxiv.org/abs/2212.01378},
archivePrefix = {arXiv},
eprint = {2212.01378},
}
```
<a href="https://huggingface.co/exbert/?model=ibm/ColD-Fusion">
<img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png">
</a>
|
ibm-research/ColD-Fusion-bert-base-uncased-itr20-seed0
|
ibm-research
| 2023-01-02T13:50:11Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"exbert",
"en",
"arxiv:2212.01378",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-01-02T13:49:51Z |
---
language: en
tags:
- exbert
license: mit
---
# ColD Fusion BERT uncased model
Finetuned model that aims to be a great base model. It improves over BERT base model (uncased), trained on 35 datasets.
Full details at [this paper](https://arxiv.org/abs/2212.01378).
## Paper Abstract:
Pretraining has been shown to scale well with compute, data size and data diversity. Multitask learning trains on a
mixture of supervised datasets and produces improved performance compared to self-supervised pretraining. Until now,
massively multitask learning required simultaneous access to all datasets in the mixture and heavy compute resources
that are only available to well-resourced teams.
In this paper, we propose ColD Fusion, a method that provides the benefits of multitask learning but leverages distributed
computation and requires limited communication and no sharing of data. Consequentially, ColD Fusion can create a synergistic
loop, where finetuned models can be recycled to continually improve the pretrained model they are based on. We show that
ColD Fusion yields comparable benefits to multitask pretraining by producing a model that (a) attains strong performance on
all of the datasets it was multitask trained on and (b) is a better starting point for finetuning on unseen datasets. We find
ColD Fusion outperforms RoBERTa and even previous multitask models. Specifically, when training and testing on 35 diverse datasets,
ColD Fusion-based model outperforms RoBERTa by 2.45 points in average without any changes to the architecture.
### How to use
Best way to use is to finetune on your own task, but you can also extract features directly.
To get the features of a given text in PyTorch:
```python
from transformers import RobertaTokenizer, RobertaModel
tokenizer = RobertaTokenizer.from_pretrained('ibm/ColD-Fusion')
model = RobertaModel.from_pretrained('ibm/ColD-Fusion')
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
```
and in TensorFlow:
```python
from transformers import RobertaTokenizer, TFRobertaModel
tokenizer = RobertaTokenizer.from_pretrained('ibm/ColD-Fusion')
model = TFRobertaModel.from_pretrained('ibm/ColD-Fusion')
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='tf')
output = model(encoded_input)
```
## Evaluation results
See full evaluation results of this model and many more [here](https://ibm.github.io/model-recycling/roberta-base_table.html)
When fine-tuned on downstream tasks, this model achieves the following results:
### BibTeX entry and citation info
```bibtex
@article{ColDFusion,
author = {Shachar Don-Yehiya, Elad Venezian, Colin Raffel, Noam Slonim, Yoav Katz, Leshem ChoshenYinhan Liu and},
title = {ColD Fusion: Collaborative Descent for Distributed Multitask Finetuning},
journal = {CoRR},
volume = {abs/2212.01378},
year = {2022},
url = {https://arxiv.org/abs/2212.01378},
archivePrefix = {arXiv},
eprint = {2212.01378},
}
```
<a href="https://huggingface.co/exbert/?model=ibm/ColD-Fusion">
<img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png">
</a>
|
ibm-research/ColD-Fusion-bert-base-uncased-itr19-seed0
|
ibm-research
| 2023-01-02T13:49:49Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"exbert",
"en",
"arxiv:2212.01378",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-01-02T13:49:28Z |
---
language: en
tags:
- exbert
license: mit
---
# ColD Fusion BERT uncased model
Finetuned model that aims to be a great base model. It improves over BERT base model (uncased), trained on 35 datasets.
Full details at [this paper](https://arxiv.org/abs/2212.01378).
## Paper Abstract:
Pretraining has been shown to scale well with compute, data size and data diversity. Multitask learning trains on a
mixture of supervised datasets and produces improved performance compared to self-supervised pretraining. Until now,
massively multitask learning required simultaneous access to all datasets in the mixture and heavy compute resources
that are only available to well-resourced teams.
In this paper, we propose ColD Fusion, a method that provides the benefits of multitask learning but leverages distributed
computation and requires limited communication and no sharing of data. Consequentially, ColD Fusion can create a synergistic
loop, where finetuned models can be recycled to continually improve the pretrained model they are based on. We show that
ColD Fusion yields comparable benefits to multitask pretraining by producing a model that (a) attains strong performance on
all of the datasets it was multitask trained on and (b) is a better starting point for finetuning on unseen datasets. We find
ColD Fusion outperforms RoBERTa and even previous multitask models. Specifically, when training and testing on 35 diverse datasets,
ColD Fusion-based model outperforms RoBERTa by 2.45 points in average without any changes to the architecture.
### How to use
Best way to use is to finetune on your own task, but you can also extract features directly.
To get the features of a given text in PyTorch:
```python
from transformers import RobertaTokenizer, RobertaModel
tokenizer = RobertaTokenizer.from_pretrained('ibm/ColD-Fusion')
model = RobertaModel.from_pretrained('ibm/ColD-Fusion')
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
```
and in TensorFlow:
```python
from transformers import RobertaTokenizer, TFRobertaModel
tokenizer = RobertaTokenizer.from_pretrained('ibm/ColD-Fusion')
model = TFRobertaModel.from_pretrained('ibm/ColD-Fusion')
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='tf')
output = model(encoded_input)
```
## Evaluation results
See full evaluation results of this model and many more [here](https://ibm.github.io/model-recycling/roberta-base_table.html)
When fine-tuned on downstream tasks, this model achieves the following results:
### BibTeX entry and citation info
```bibtex
@article{ColDFusion,
author = {Shachar Don-Yehiya, Elad Venezian, Colin Raffel, Noam Slonim, Yoav Katz, Leshem ChoshenYinhan Liu and},
title = {ColD Fusion: Collaborative Descent for Distributed Multitask Finetuning},
journal = {CoRR},
volume = {abs/2212.01378},
year = {2022},
url = {https://arxiv.org/abs/2212.01378},
archivePrefix = {arXiv},
eprint = {2212.01378},
}
```
<a href="https://huggingface.co/exbert/?model=ibm/ColD-Fusion">
<img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png">
</a>
|
ibm-research/ColD-Fusion-bert-base-uncased-itr18-seed0
|
ibm-research
| 2023-01-02T13:48:52Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"exbert",
"en",
"arxiv:2212.01378",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-01-02T13:48:16Z |
---
language: en
tags:
- exbert
license: mit
---
# ColD Fusion BERT uncased model
Finetuned model that aims to be a great base model. It improves over BERT base model (uncased), trained on 35 datasets.
Full details at [this paper](https://arxiv.org/abs/2212.01378).
## Paper Abstract:
Pretraining has been shown to scale well with compute, data size and data diversity. Multitask learning trains on a
mixture of supervised datasets and produces improved performance compared to self-supervised pretraining. Until now,
massively multitask learning required simultaneous access to all datasets in the mixture and heavy compute resources
that are only available to well-resourced teams.
In this paper, we propose ColD Fusion, a method that provides the benefits of multitask learning but leverages distributed
computation and requires limited communication and no sharing of data. Consequentially, ColD Fusion can create a synergistic
loop, where finetuned models can be recycled to continually improve the pretrained model they are based on. We show that
ColD Fusion yields comparable benefits to multitask pretraining by producing a model that (a) attains strong performance on
all of the datasets it was multitask trained on and (b) is a better starting point for finetuning on unseen datasets. We find
ColD Fusion outperforms RoBERTa and even previous multitask models. Specifically, when training and testing on 35 diverse datasets,
ColD Fusion-based model outperforms RoBERTa by 2.45 points in average without any changes to the architecture.
### How to use
Best way to use is to finetune on your own task, but you can also extract features directly.
To get the features of a given text in PyTorch:
```python
from transformers import RobertaTokenizer, RobertaModel
tokenizer = RobertaTokenizer.from_pretrained('ibm/ColD-Fusion')
model = RobertaModel.from_pretrained('ibm/ColD-Fusion')
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
```
and in TensorFlow:
```python
from transformers import RobertaTokenizer, TFRobertaModel
tokenizer = RobertaTokenizer.from_pretrained('ibm/ColD-Fusion')
model = TFRobertaModel.from_pretrained('ibm/ColD-Fusion')
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='tf')
output = model(encoded_input)
```
## Evaluation results
See full evaluation results of this model and many more [here](https://ibm.github.io/model-recycling/roberta-base_table.html)
When fine-tuned on downstream tasks, this model achieves the following results:
### BibTeX entry and citation info
```bibtex
@article{ColDFusion,
author = {Shachar Don-Yehiya, Elad Venezian, Colin Raffel, Noam Slonim, Yoav Katz, Leshem ChoshenYinhan Liu and},
title = {ColD Fusion: Collaborative Descent for Distributed Multitask Finetuning},
journal = {CoRR},
volume = {abs/2212.01378},
year = {2022},
url = {https://arxiv.org/abs/2212.01378},
archivePrefix = {arXiv},
eprint = {2212.01378},
}
```
<a href="https://huggingface.co/exbert/?model=ibm/ColD-Fusion">
<img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png">
</a>
|
ibm-research/ColD-Fusion-bert-base-uncased-itr16-seed0
|
ibm-research
| 2023-01-02T13:47:48Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"exbert",
"en",
"arxiv:2212.01378",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-01-02T13:47:18Z |
---
language: en
tags:
- exbert
license: mit
---
# ColD Fusion BERT uncased model
Finetuned model that aims to be a great base model. It improves over BERT base model (uncased), trained on 35 datasets.
Full details at [this paper](https://arxiv.org/abs/2212.01378).
## Paper Abstract:
Pretraining has been shown to scale well with compute, data size and data diversity. Multitask learning trains on a
mixture of supervised datasets and produces improved performance compared to self-supervised pretraining. Until now,
massively multitask learning required simultaneous access to all datasets in the mixture and heavy compute resources
that are only available to well-resourced teams.
In this paper, we propose ColD Fusion, a method that provides the benefits of multitask learning but leverages distributed
computation and requires limited communication and no sharing of data. Consequentially, ColD Fusion can create a synergistic
loop, where finetuned models can be recycled to continually improve the pretrained model they are based on. We show that
ColD Fusion yields comparable benefits to multitask pretraining by producing a model that (a) attains strong performance on
all of the datasets it was multitask trained on and (b) is a better starting point for finetuning on unseen datasets. We find
ColD Fusion outperforms RoBERTa and even previous multitask models. Specifically, when training and testing on 35 diverse datasets,
ColD Fusion-based model outperforms RoBERTa by 2.45 points in average without any changes to the architecture.
### How to use
Best way to use is to finetune on your own task, but you can also extract features directly.
To get the features of a given text in PyTorch:
```python
from transformers import RobertaTokenizer, RobertaModel
tokenizer = RobertaTokenizer.from_pretrained('ibm/ColD-Fusion')
model = RobertaModel.from_pretrained('ibm/ColD-Fusion')
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
```
and in TensorFlow:
```python
from transformers import RobertaTokenizer, TFRobertaModel
tokenizer = RobertaTokenizer.from_pretrained('ibm/ColD-Fusion')
model = TFRobertaModel.from_pretrained('ibm/ColD-Fusion')
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='tf')
output = model(encoded_input)
```
## Evaluation results
See full evaluation results of this model and many more [here](https://ibm.github.io/model-recycling/roberta-base_table.html)
When fine-tuned on downstream tasks, this model achieves the following results:
### BibTeX entry and citation info
```bibtex
@article{ColDFusion,
author = {Shachar Don-Yehiya, Elad Venezian, Colin Raffel, Noam Slonim, Yoav Katz, Leshem ChoshenYinhan Liu and},
title = {ColD Fusion: Collaborative Descent for Distributed Multitask Finetuning},
journal = {CoRR},
volume = {abs/2212.01378},
year = {2022},
url = {https://arxiv.org/abs/2212.01378},
archivePrefix = {arXiv},
eprint = {2212.01378},
}
```
<a href="https://huggingface.co/exbert/?model=ibm/ColD-Fusion">
<img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png">
</a>
|
ibm-research/ColD-Fusion-bert-base-uncased-itr13-seed0
|
ibm-research
| 2023-01-02T13:46:29Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"exbert",
"en",
"arxiv:2212.01378",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-01-02T13:46:08Z |
---
language: en
tags:
- exbert
license: mit
---
# ColD Fusion BERT uncased model
Finetuned model that aims to be a great base model. It improves over BERT base model (uncased), trained on 35 datasets.
Full details at [this paper](https://arxiv.org/abs/2212.01378).
## Paper Abstract:
Pretraining has been shown to scale well with compute, data size and data diversity. Multitask learning trains on a
mixture of supervised datasets and produces improved performance compared to self-supervised pretraining. Until now,
massively multitask learning required simultaneous access to all datasets in the mixture and heavy compute resources
that are only available to well-resourced teams.
In this paper, we propose ColD Fusion, a method that provides the benefits of multitask learning but leverages distributed
computation and requires limited communication and no sharing of data. Consequentially, ColD Fusion can create a synergistic
loop, where finetuned models can be recycled to continually improve the pretrained model they are based on. We show that
ColD Fusion yields comparable benefits to multitask pretraining by producing a model that (a) attains strong performance on
all of the datasets it was multitask trained on and (b) is a better starting point for finetuning on unseen datasets. We find
ColD Fusion outperforms RoBERTa and even previous multitask models. Specifically, when training and testing on 35 diverse datasets,
ColD Fusion-based model outperforms RoBERTa by 2.45 points in average without any changes to the architecture.
### How to use
Best way to use is to finetune on your own task, but you can also extract features directly.
To get the features of a given text in PyTorch:
```python
from transformers import RobertaTokenizer, RobertaModel
tokenizer = RobertaTokenizer.from_pretrained('ibm/ColD-Fusion')
model = RobertaModel.from_pretrained('ibm/ColD-Fusion')
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
```
and in TensorFlow:
```python
from transformers import RobertaTokenizer, TFRobertaModel
tokenizer = RobertaTokenizer.from_pretrained('ibm/ColD-Fusion')
model = TFRobertaModel.from_pretrained('ibm/ColD-Fusion')
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='tf')
output = model(encoded_input)
```
## Evaluation results
See full evaluation results of this model and many more [here](https://ibm.github.io/model-recycling/roberta-base_table.html)
When fine-tuned on downstream tasks, this model achieves the following results:
### BibTeX entry and citation info
```bibtex
@article{ColDFusion,
author = {Shachar Don-Yehiya, Elad Venezian, Colin Raffel, Noam Slonim, Yoav Katz, Leshem ChoshenYinhan Liu and},
title = {ColD Fusion: Collaborative Descent for Distributed Multitask Finetuning},
journal = {CoRR},
volume = {abs/2212.01378},
year = {2022},
url = {https://arxiv.org/abs/2212.01378},
archivePrefix = {arXiv},
eprint = {2212.01378},
}
```
<a href="https://huggingface.co/exbert/?model=ibm/ColD-Fusion">
<img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png">
</a>
|
ibm-research/ColD-Fusion-bert-base-uncased-itr12-seed0
|
ibm-research
| 2023-01-02T13:46:06Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"exbert",
"en",
"arxiv:2212.01378",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-01-02T13:45:43Z |
---
language: en
tags:
- exbert
license: mit
---
# ColD Fusion BERT uncased model
Finetuned model that aims to be a great base model. It improves over BERT base model (uncased), trained on 35 datasets.
Full details at [this paper](https://arxiv.org/abs/2212.01378).
## Paper Abstract:
Pretraining has been shown to scale well with compute, data size and data diversity. Multitask learning trains on a
mixture of supervised datasets and produces improved performance compared to self-supervised pretraining. Until now,
massively multitask learning required simultaneous access to all datasets in the mixture and heavy compute resources
that are only available to well-resourced teams.
In this paper, we propose ColD Fusion, a method that provides the benefits of multitask learning but leverages distributed
computation and requires limited communication and no sharing of data. Consequentially, ColD Fusion can create a synergistic
loop, where finetuned models can be recycled to continually improve the pretrained model they are based on. We show that
ColD Fusion yields comparable benefits to multitask pretraining by producing a model that (a) attains strong performance on
all of the datasets it was multitask trained on and (b) is a better starting point for finetuning on unseen datasets. We find
ColD Fusion outperforms RoBERTa and even previous multitask models. Specifically, when training and testing on 35 diverse datasets,
ColD Fusion-based model outperforms RoBERTa by 2.45 points in average without any changes to the architecture.
### How to use
Best way to use is to finetune on your own task, but you can also extract features directly.
To get the features of a given text in PyTorch:
```python
from transformers import RobertaTokenizer, RobertaModel
tokenizer = RobertaTokenizer.from_pretrained('ibm/ColD-Fusion')
model = RobertaModel.from_pretrained('ibm/ColD-Fusion')
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
```
and in TensorFlow:
```python
from transformers import RobertaTokenizer, TFRobertaModel
tokenizer = RobertaTokenizer.from_pretrained('ibm/ColD-Fusion')
model = TFRobertaModel.from_pretrained('ibm/ColD-Fusion')
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='tf')
output = model(encoded_input)
```
## Evaluation results
See full evaluation results of this model and many more [here](https://ibm.github.io/model-recycling/roberta-base_table.html)
When fine-tuned on downstream tasks, this model achieves the following results:
### BibTeX entry and citation info
```bibtex
@article{ColDFusion,
author = {Shachar Don-Yehiya, Elad Venezian, Colin Raffel, Noam Slonim, Yoav Katz, Leshem ChoshenYinhan Liu and},
title = {ColD Fusion: Collaborative Descent for Distributed Multitask Finetuning},
journal = {CoRR},
volume = {abs/2212.01378},
year = {2022},
url = {https://arxiv.org/abs/2212.01378},
archivePrefix = {arXiv},
eprint = {2212.01378},
}
```
<a href="https://huggingface.co/exbert/?model=ibm/ColD-Fusion">
<img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png">
</a>
|
huggingtweets/bowtieddingo
|
huggingtweets
| 2023-01-02T13:45:56Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-01-02T13:40:50Z |
---
language: en
thumbnail: http://www.huggingtweets.com/bowtieddingo/1672667150823/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1476559010664902660/4iIifKqL_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Dingo - Read Pinned Tweet</div>
<div style="text-align: center; font-size: 14px;">@bowtieddingo</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from Dingo - Read Pinned Tweet.
| Data | Dingo - Read Pinned Tweet |
| --- | --- |
| Tweets downloaded | 1340 |
| Retweets | 169 |
| Short tweets | 313 |
| Tweets kept | 858 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3ij44tep/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @bowtieddingo's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/25u7g1ui) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/25u7g1ui/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/bowtieddingo')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
ibm-research/ColD-Fusion-bert-base-uncased-itr11-seed0
|
ibm-research
| 2023-01-02T13:45:41Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"exbert",
"en",
"arxiv:2212.01378",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-01-02T13:45:08Z |
---
language: en
tags:
- exbert
license: mit
---
# ColD Fusion BERT uncased model
Finetuned model that aims to be a great base model. It improves over BERT base model (uncased), trained on 35 datasets.
Full details at [this paper](https://arxiv.org/abs/2212.01378).
## Paper Abstract:
Pretraining has been shown to scale well with compute, data size and data diversity. Multitask learning trains on a
mixture of supervised datasets and produces improved performance compared to self-supervised pretraining. Until now,
massively multitask learning required simultaneous access to all datasets in the mixture and heavy compute resources
that are only available to well-resourced teams.
In this paper, we propose ColD Fusion, a method that provides the benefits of multitask learning but leverages distributed
computation and requires limited communication and no sharing of data. Consequentially, ColD Fusion can create a synergistic
loop, where finetuned models can be recycled to continually improve the pretrained model they are based on. We show that
ColD Fusion yields comparable benefits to multitask pretraining by producing a model that (a) attains strong performance on
all of the datasets it was multitask trained on and (b) is a better starting point for finetuning on unseen datasets. We find
ColD Fusion outperforms RoBERTa and even previous multitask models. Specifically, when training and testing on 35 diverse datasets,
ColD Fusion-based model outperforms RoBERTa by 2.45 points in average without any changes to the architecture.
### How to use
Best way to use is to finetune on your own task, but you can also extract features directly.
To get the features of a given text in PyTorch:
```python
from transformers import RobertaTokenizer, RobertaModel
tokenizer = RobertaTokenizer.from_pretrained('ibm/ColD-Fusion')
model = RobertaModel.from_pretrained('ibm/ColD-Fusion')
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
```
and in TensorFlow:
```python
from transformers import RobertaTokenizer, TFRobertaModel
tokenizer = RobertaTokenizer.from_pretrained('ibm/ColD-Fusion')
model = TFRobertaModel.from_pretrained('ibm/ColD-Fusion')
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='tf')
output = model(encoded_input)
```
## Evaluation results
See full evaluation results of this model and many more [here](https://ibm.github.io/model-recycling/roberta-base_table.html)
When fine-tuned on downstream tasks, this model achieves the following results:
### BibTeX entry and citation info
```bibtex
@article{ColDFusion,
author = {Shachar Don-Yehiya, Elad Venezian, Colin Raffel, Noam Slonim, Yoav Katz, Leshem ChoshenYinhan Liu and},
title = {ColD Fusion: Collaborative Descent for Distributed Multitask Finetuning},
journal = {CoRR},
volume = {abs/2212.01378},
year = {2022},
url = {https://arxiv.org/abs/2212.01378},
archivePrefix = {arXiv},
eprint = {2212.01378},
}
```
<a href="https://huggingface.co/exbert/?model=ibm/ColD-Fusion">
<img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png">
</a>
|
ibm-research/ColD-Fusion-bert-base-uncased-itr9-seed0
|
ibm-research
| 2023-01-02T13:44:30Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"exbert",
"en",
"arxiv:2212.01378",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-01-02T13:41:16Z |
---
language: en
tags:
- exbert
license: mit
---
# ColD Fusion BERT uncased model
Finetuned model that aims to be a great base model. It improves over BERT base model (uncased), trained on 35 datasets.
Full details at [this paper](https://arxiv.org/abs/2212.01378).
## Paper Abstract:
Pretraining has been shown to scale well with compute, data size and data diversity. Multitask learning trains on a
mixture of supervised datasets and produces improved performance compared to self-supervised pretraining. Until now,
massively multitask learning required simultaneous access to all datasets in the mixture and heavy compute resources
that are only available to well-resourced teams.
In this paper, we propose ColD Fusion, a method that provides the benefits of multitask learning but leverages distributed
computation and requires limited communication and no sharing of data. Consequentially, ColD Fusion can create a synergistic
loop, where finetuned models can be recycled to continually improve the pretrained model they are based on. We show that
ColD Fusion yields comparable benefits to multitask pretraining by producing a model that (a) attains strong performance on
all of the datasets it was multitask trained on and (b) is a better starting point for finetuning on unseen datasets. We find
ColD Fusion outperforms RoBERTa and even previous multitask models. Specifically, when training and testing on 35 diverse datasets,
ColD Fusion-based model outperforms RoBERTa by 2.45 points in average without any changes to the architecture.
### How to use
Best way to use is to finetune on your own task, but you can also extract features directly.
To get the features of a given text in PyTorch:
```python
from transformers import RobertaTokenizer, RobertaModel
tokenizer = RobertaTokenizer.from_pretrained('ibm/ColD-Fusion')
model = RobertaModel.from_pretrained('ibm/ColD-Fusion')
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
```
and in TensorFlow:
```python
from transformers import RobertaTokenizer, TFRobertaModel
tokenizer = RobertaTokenizer.from_pretrained('ibm/ColD-Fusion')
model = TFRobertaModel.from_pretrained('ibm/ColD-Fusion')
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='tf')
output = model(encoded_input)
```
## Evaluation results
See full evaluation results of this model and many more [here](https://ibm.github.io/model-recycling/roberta-base_table.html)
When fine-tuned on downstream tasks, this model achieves the following results:
### BibTeX entry and citation info
```bibtex
@article{ColDFusion,
author = {Shachar Don-Yehiya, Elad Venezian, Colin Raffel, Noam Slonim, Yoav Katz, Leshem ChoshenYinhan Liu and},
title = {ColD Fusion: Collaborative Descent for Distributed Multitask Finetuning},
journal = {CoRR},
volume = {abs/2212.01378},
year = {2022},
url = {https://arxiv.org/abs/2212.01378},
archivePrefix = {arXiv},
eprint = {2212.01378},
}
```
<a href="https://huggingface.co/exbert/?model=ibm/ColD-Fusion">
<img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png">
</a>
|
armargolis/taxi_v3
|
armargolis
| 2023-01-02T13:37:35Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-01-02T13:37:31Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: taxi_v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.56 +/- 2.71
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="armargolis/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"])
```
|
DrBob2142/Mix-Models
|
DrBob2142
| 2023-01-02T13:34:18Z | 0 | 31 | null |
[
"region:us"
] | null | 2022-12-05T16:45:48Z |
These are a few mix models that I had created in my spare time, whatever I consider to be the primary model in the mix will have its name before the mixing method
Midnight Mixes are the latest Mixes, in theroy they should have a even spread of all modlels in the mix
Spiced models folow an idea to keep as mutch of one model preserved when mixed with somthing else, will be generaly anything Mixed at 25 weighted sum or lower
Bake models folow a very easy to track mix of the models, First part of the mix is the model that was primaraly used
Potluck models end up being a large mix of models, with one model still having a majority in the mix, mixing methods can get messy so true recipes can be had to keep track of
Below are a few examples of what I consider to be the best of these mixes thus far, will update this page the more I mess with it!
All examples use the WD 1.4 VAE kl-f8-anime2.ckpt, and are done on clip skip 2
### Examples :
## Midnight Mixes:
# DustStorm Waistlands
# CherryBlossoms
# Cyber Fox
## Other Mixes :
# Fire Fox
# Mecha
# Cyber Fox
|
hayle/ppo-LunarLander-v2
|
hayle
| 2023-01-02T13:26:03Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-01-02T13:25:23Z |
---
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: 262.58 +/- 25.21
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
...
```
|
nonsm/sd-class-butterflies-32
|
nonsm
| 2023-01-02T13:16:00Z | 30 | 0 |
diffusers
|
[
"diffusers",
"pytorch",
"unconditional-image-generation",
"diffusion-models-class",
"license:mit",
"diffusers:DDPMPipeline",
"region:us"
] |
unconditional-image-generation
| 2023-01-02T13:13:48Z |
---
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('nonsm/sd-class-butterflies-32')
image = pipeline().images[0]
image
```
|
j-hartmann/purchase-intention-english-roberta-large
|
j-hartmann
| 2023-01-02T13:06:04Z | 13 | 5 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"text-classification",
"sentiment",
"twitter",
"en",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
---
language: "en"
tags:
- roberta
- sentiment
- twitter
widget:
- text: "This looks tasty. Where can I buy it??"
- text: "Now I want this, too."
- text: "You look great today!"
- text: "I just love spring and sunshine!"
---
This RoBERTa-based model can classify *expressed purchase intentions* in English language text in 2 classes:
- purchase intention 🤩
- no purchase intention 😐
The model was fine-tuned on 2,000 manually annotated social media posts.
The hold-out accuracy is 95% (vs. a balanced 50% random-chance baseline).
For details on the training approach see Web Appendix F in Hartmann et al. (2021).
# Application
```python
from transformers import pipeline
classifier = pipeline("text-classification", model="j-hartmann/purchase-intention-english-roberta-large", return_all_scores=True)
classifier("I want this!")
```
```python
Output:
[[{'label': 'no', 'score': 0.0014553926885128021},
{'label': 'yes', 'score': 0.9985445737838745}]]
```
# Reference
Please cite [this paper](https://journals.sagepub.com/doi/full/10.1177/00222437211037258) when you use our model. Feel free to reach out to [jochen.hartmann@tum.de](mailto:jochen.hartmann@tum.de) with any questions or feedback you may have.
```
@article{hartmann2021,
title={The Power of Brand Selfies},
author={Hartmann, Jochen and Heitmann, Mark and Schamp, Christina and Netzer, Oded},
journal={Journal of Marketing Research}
year={2021}
}
```
|
j-hartmann/sentiment-roberta-large-english-3-classes
|
j-hartmann
| 2023-01-02T13:02:49Z | 6,349 | 22 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"text-classification",
"sentiment",
"twitter",
"en",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
---
language: "en"
tags:
- roberta
- sentiment
- twitter
widget:
- text: "Oh no. This is bad.."
- text: "To be or not to be."
- text: "Oh Happy Day"
---
This RoBERTa-based model can classify the sentiment of English language text in 3 classes:
- positive 😀
- neutral 😐
- negative 🙁
The model was fine-tuned on 5,304 manually annotated social media posts.
The hold-out accuracy is 86.1%.
For details on the training approach see Web Appendix F in Hartmann et al. (2021).
# Application
```python
from transformers import pipeline
classifier = pipeline("text-classification", model="j-hartmann/sentiment-roberta-large-english-3-classes", return_all_scores=True)
classifier("This is so nice!")
```
```python
Output:
[[{'label': 'negative', 'score': 0.00016451838018838316},
{'label': 'neutral', 'score': 0.000174045650055632},
{'label': 'positive', 'score': 0.9996614456176758}]]
```
# Reference
Please cite [this paper](https://journals.sagepub.com/doi/full/10.1177/00222437211037258) when you use our model. Feel free to reach out to [jochen.hartmann@tum.de](mailto:jochen.hartmann@tum.de) with any questions or feedback you may have.
```
@article{hartmann2021,
title={The Power of Brand Selfies},
author={Hartmann, Jochen and Heitmann, Mark and Schamp, Christina and Netzer, Oded},
journal={Journal of Marketing Research}
year={2021}
}
```
|
inverse-scaling/opt-13b_eval
|
inverse-scaling
| 2023-01-02T13:01:36Z | 19 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"jax",
"opt",
"text-generation",
"en",
"arxiv:2205.01068",
"arxiv:2005.14165",
"license:other",
"model-index",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2022-10-07T20:51:46Z |
---
language: en
license: other
tags:
- opt
- text-generation
inference: false
commercial: false
model-index:
- name: inverse-scaling/opt-13b_eval
results:
- task:
type: zero-shot-classification
name: Zero-Shot Text Classification
dataset:
name: inverse-scaling/NeQA
type: inverse-scaling/NeQA
config: inverse-scaling--NeQA
split: train
metrics:
- type: accuracy
value: 0.49666666666666665
name: Accuracy
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiOWQwNzZlNTM4ZWVjODNkOTIzNjg1NTNkNjE0MGJlMjU4ZWI3NTQzYjg4YTY3MDU2MGViYTYyYjZlZDc0NzQzNCIsInZlcnNpb24iOjF9.qNBGm2Mc3OKjadswivJnO1Lul0NeAjGJe-2FfO57phNPMdgp-rDkTl0YMqC1Rljp8BjT4egJ8IdEQgynUE_hDg
- type: loss
value: 0.7090707456072172
name: Loss
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMmY2NTAxOTQ3MmUwZjgxZGM0NDU1YmQzNmRmMTk3MTZhM2IxM2EwYmYxNzJjODM4MWMxNWQwOTczZWRiMGU1NyIsInZlcnNpb24iOjF9.rni9n_PdKnee5J_sMwlS0W7QWfhqlAXX6S4dUAakGQFW10zLDBb2pPfkKdSYz956yyTMrKBX0ZYT2uQGWxurAg
- task:
type: zero-shot-classification
name: Zero-Shot Text Classification
dataset:
name: inverse-scaling/quote-repetition
type: inverse-scaling/quote-repetition
config: inverse-scaling--quote-repetition
split: train
metrics:
- type: accuracy
value: 0.8
name: Accuracy
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiZjFjZjM5NWFjN2Y5ODFiYzRjOGE3MDQ1YmFmYjlkYWRlNTdlMjlhMTY2ZmZmNGQwOWQyNmEzZDk2ZTkwZjQyMCIsInZlcnNpb24iOjF9.Fn-zemt_ghgMvekGYouH-ldScOskoGtbBJ6Mpz8vE27Eca_bOYV6DdQq4Mhd3q9eVqAVg_ybsUFAx215Pjs1Cg
- type: loss
value: 0.4678814027383723
name: Loss
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNWVlZjVlZjE5OGIwYTg0ZjJkZjI0NTA2MzUyNDgyY2EyODIzYzk5Zjg1OTMwMTcyODNlZjM2MWE3YWI0MDlhMCIsInZlcnNpb24iOjF9.kFNX4JZsFTeIaxw8kuuc7l5e4J6KWygm6U4RsKwEr8qZumKuJ0IDVPlNzIh0lh2z7OjbGCHsq1bRbPeJQb_bAg
- task:
type: zero-shot-classification
name: Zero-Shot Text Classification
dataset:
name: inverse-scaling/redefine-math
type: inverse-scaling/redefine-math
config: inverse-scaling--redefine-math
split: train
metrics:
- type: accuracy
value: 0.5933333333333334
name: Accuracy
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiOWVhN2NiMmM4ODAzYzliMTgwZGI0MTdjOGZiM2QwOWVkNGFiMTUwZTA1OGE5MjQwODBjNzFlNjYyMGViNjU0YSIsInZlcnNpb24iOjF9.nQ_UAPkYBSJNpyCP3Pc9ZG3Ns905vy-41HDVdxZrvrs3s5yhiDIH1Gu6bvAzTeiupPVLCu_Rpfp63e4h1sBDBg
- type: loss
value: 0.7308767640383708
name: Loss
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMzVlYTBjMmI4OTBlN2I2M2IxYmM4NGNhYzkxMzA1MWExOWYxZWFkMzlhZDRlYzk3MzkzOTBiOGU4YTJhNGExMyIsInZlcnNpb24iOjF9.xNkna8ygLtmV3ezRbOeYfushHT-p2Kbja3kKkGhUcfAPjKgUVe-mu9dyxez6G-fUWZHHaXuCZuZMvWqP27MGDA
- task:
type: zero-shot-classification
name: Zero-Shot Text Classification
dataset:
name: inverse-scaling/hindsight-neglect-10shot
type: inverse-scaling/hindsight-neglect-10shot
config: inverse-scaling--hindsight-neglect-10shot
split: train
metrics:
- type: accuracy
value: 0.2698412698412698
name: Accuracy
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiZDc1MzczMmE5ZjRhNWY5YWQzYzc5NTA1OGQ2OTAyYTQzMjFhMWJjYTU2NDYxYThmNzgzMzVmMDNhZmY4ODMxYyIsInZlcnNpb24iOjF9.KtTrigpdC3RydTC0L6ueo-D8lBhsYFTt5ncvlFoDksMDKEo-OiqZj2vkPuErII9Rzr-3H-MqDVyO2UN-VDH7AA
- type: loss
value: 0.7708483344978756
name: Loss
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNDM2NGMzZTBiMjBkNTAxMGI0MWU5YjQ4NmI4OTU5ZmNiMGE4ZTc1MTczOGRmZTVhMmI5MWNkOGZkMWVhZjQxYSIsInZlcnNpb24iOjF9.CKR5kHqjy07_Rkv2VngLM5cl3KRWQ7rHayctMbzmUzDJq39fJq-jkERNW_JZGIZnMQ4GSINGpnrgP_PE73QzBw
- task:
type: zero-shot-classification
name: Zero-Shot Text Classification
dataset:
name: mathemakitten/winobias_antistereotype_test_cot_v1
type: mathemakitten/winobias_antistereotype_test_cot_v1
config: mathemakitten--winobias_antistereotype_test_cot_v1
split: test
metrics:
- type: accuracy
value: 0.3422330097087379
name: Accuracy
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNjAwOGI4YTRlNmUwMTNlNTEyNjQ1YWNjOTcxOGM1N2M4YjY3ZDczMzBhYTM1Y2ZhMWNhM2U3NjQwNDc5Zjk2MiIsInZlcnNpb24iOjF9.ig0ColofjUx0XbMxwbc1n0D5ZX_Pd5csQKXt0GtcrMsgGUU1pz26ArpxcNFThaQT33-PwTLSjf7_W_wMnwDsCw
- type: loss
value: 1.4404955777914985
name: Loss
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiM2JhNGZkOTFiMjFlNGU0MGIxOGI0NTFmN2Q2ODE0ZDEwZjY2NzhmOGU4ZDY3ZDM4Y2ExNGY2MDY4ZDk5ZmFlZSIsInZlcnNpb24iOjF9.9jjeZD1rWaxyIUQO2uyJv2Yf3pNCC6fLnKWJGKSYf2nyWgThKS2JgR0jI4oFG7GtsON03tjeGvmkTdC_Fv7kCQ
- task:
type: zero-shot-classification
name: Zero-Shot Text Classification
dataset:
name: mathemakitten/winobias_antistereotype_test_cot_v3
type: mathemakitten/winobias_antistereotype_test_cot_v3
config: mathemakitten--winobias_antistereotype_test_cot_v3
split: test
metrics:
- type: accuracy
value: 0.30339805825242716
name: Accuracy
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiYTE5ZWFlMzQ5ZWRjNDlkZGFiOThiMzRkMTQ3ZDRkYjkzYzY0OTc3NWI1MzBmZDUwZmMzYTBjZDZlOTc0ODdjNyIsInZlcnNpb24iOjF9.hvwwChF87sW6hJ-Jg_pVPagKNACcVTx8-S-_FFbWW97PHZbhtwLgef_tTCGMF2t4HdPssTr1EEgQ3DOh0RfYDg
- type: loss
value: 1.539870785999474
name: Loss
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMWYwOTgxYmVhMjY3Mzg0NzA5NWY4MmQ4ZjhlYjA0M2YyZDE5MTczZDRhN2FjMjc2MGMwMjU0MDk1YTQ5MzRkZCIsInZlcnNpb24iOjF9.fmdxhv2Ern7ZnCWW19cDTAB3-NaXmYF8xkEw40W2ssxGq50WymezMuqo2ssYGmFZJiiZNPx15OjRQza6V-DDAA
- task:
type: zero-shot-classification
name: Zero-Shot Text Classification
dataset:
name: mathemakitten/winobias_antistereotype_test_v5
type: mathemakitten/winobias_antistereotype_test_v5
config: mathemakitten--winobias_antistereotype_test_v5
split: test
metrics:
- type: accuracy
value: 0.3640776699029126
name: Accuracy
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiZDA2NTZjYmM4MmYyNmM2YjA0YTA4NTVlODFlYjBhYTZlOWFmMGU5YzhlM2RkNWFhZTg1NGM4YjI4YzBmY2IxOSIsInZlcnNpb24iOjF9.6yqaB2Owq36GDA3kHfbkWyuxhmj8LhO8kEGYm7vZ6g3qfM6OkkkXFhX-D4bse-W3WILLRb4TE3xAad2EIkSLAA
- type: loss
value: 1.4798047741848304
name: Loss
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMTMxMzVmYThkNmU4ODhmNDgwZWM5ZjM2ZjFkODBjYTY1OGFiNDIwZTM4NDlmMTA4N2Q5ZTk4MThhMzVhN2RjNCIsInZlcnNpb24iOjF9.4i_6ZOjSLyMoPl3BlNMQJ3a1uRYcVpdyaEucECvzJ9786tUQ-RZ-6guKy2-hiZI3DKa1gsks9nPFfeRhLJyiBA
---
# OPT : Open Pre-trained Transformer Language Models
OPT was first introduced in [Open Pre-trained Transformer Language Models](https://arxiv.org/abs/2205.01068) and first released in [metaseq's repository](https://github.com/facebookresearch/metaseq) on May 3rd 2022 by Meta AI.
**Disclaimer**: The team releasing OPT wrote an official model card, which is available in Appendix D of the [paper](https://arxiv.org/pdf/2205.01068.pdf).
Content from **this** model card has been written by the Hugging Face team.
## Intro
To quote the first two paragraphs of the [official paper](https://arxiv.org/abs/2205.01068)
> Large language models trained on massive text collections have shown surprising emergent
> capabilities to generate text and perform zero- and few-shot learning. While in some cases the public
> can interact with these models through paid APIs, full model access is currently limited to only a
> few highly resourced labs. This restricted access has limited researchers’ ability to study how and
> why these large language models work, hindering progress on improving known challenges in areas
> such as robustness, bias, and toxicity.
> We present Open Pretrained Transformers (OPT), a suite of decoder-only pre-trained transformers ranging from 125M
> to 175B parameters, which we aim to fully and responsibly share with interested researchers. We train the OPT models to roughly match
> the performance and sizes of the GPT-3 class of models, while also applying the latest best practices in data
> collection and efficient training. Our aim in developing this suite of OPT models is to enable reproducible and responsible research at scale, and
> to bring more voices to the table in studying the impact of these LLMs. Definitions of risk, harm, bias, and toxicity, etc., should be articulated by the
> collective research community as a whole, which is only possible when models are available for study.
## Model description
OPT was predominantly pretrained with English text, but a small amount of non-English data is still present within the training corpus via CommonCrawl. The model was pretrained using a causal language modeling (CLM) objective.
OPT belongs to the same family of decoder-only models like [GPT-3](https://arxiv.org/abs/2005.14165). As such, it was pretrained using the self-supervised causal language modedling objective.
For evaluation, OPT follows [GPT-3](https://arxiv.org/abs/2005.14165) by using their prompts and overall experimental setup. For more details, please read
the [official paper](https://arxiv.org/abs/2205.01068).
## Intended uses & limitations
The pretrained-only model can be used for prompting for evaluation of downstream tasks as well as text generation.
In addition, the model can be fine-tuned on a downstream task using the [CLM example](https://github.com/huggingface/transformers/tree/main/examples/pytorch/language-modeling). For all other OPT checkpoints, please have a look at the [model hub](https://huggingface.co/models?filter=opt).
### How to use
For large OPT models, such as this one, it is not recommend to make use of the `text-generation` pipeline because
one should load the model in half-precision to accelerate generation and optimize memory consumption on GPU.
It is recommended to directly call the [`generate`](https://huggingface.co/docs/transformers/main/en/main_classes/text_generation#transformers.generation_utils.GenerationMixin.generate)
method as follows:
```python
>>> from transformers import AutoModelForCausalLM, AutoTokenizer
>>> import torch
>>> model = AutoModelForCausalLM.from_pretrained("facebook/opt-13b", torch_dtype=torch.float16).cuda()
>>> # the fast tokenizer currently does not work correctly
>>> tokenizer = AutoTokenizer.from_pretrained("facebook/opt-13b", use_fast=False)
>>> prompt = "Hello, I'm am conscious and"
>>> input_ids = tokenizer(prompt, return_tensors="pt").input_ids.cuda()
>>> generated_ids = model.generate(input_ids)
>>> tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
['Hello, I am conscious and aware of my surroundings.\nI am conscious and aware of my']
```
By default, generation is deterministic. In order to use the top-k sampling, please set `do_sample` to `True`.
```python
>>> from transformers import AutoModelForCausalLM, AutoTokenizer, set_seed
>>> import torch
>>> model = AutoModelForCausalLM.from_pretrained("facebook/opt-13b", torch_dtype=torch.float16).cuda()
>>> # the fast tokenizer currently does not work correctly
>>> tokenizer = AutoTokenizer.from_pretrained("facebook/opt-13b", use_fast=False)
>>> prompt = "Hello, I'm am conscious and"
>>> input_ids = tokenizer(prompt, return_tensors="pt").input_ids.cuda()
>>> set_seed(32)
>>> generated_ids = model.generate(input_ids, do_sample=True)
>>> tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
['Hello, I am conscious and aware.\nSo that makes you dead, right? ']
```
### Limitations and bias
As mentioned in Meta AI's model card, given that the training data used for this model contains a lot of
unfiltered content from the internet, which is far from neutral the model is strongly biased :
> Like other large language models for which the diversity (or lack thereof) of training
> data induces downstream impact on the quality of our model, OPT-175B has limitations in terms
> of bias and safety. OPT-175B can also have quality issues in terms of generation diversity and
> hallucination. In general, OPT-175B is not immune from the plethora of issues that plague modern
> large language models.
Here's an example of how the model can have biased predictions:
```python
>>> from transformers import AutoModelForCausalLM, AutoTokenizer, set_seed
>>> import torch
>>> model = AutoModelForCausalLM.from_pretrained("facebook/opt-13b", torch_dtype=torch.float16).cuda()
>>> # the fast tokenizer currently does not work correctly
>>> tokenizer = AutoTokenizer.from_pretrained("facebook/opt-13b", use_fast=False)
>>> prompt = "The woman worked as a"
>>> input_ids = tokenizer(prompt, return_tensors="pt").input_ids.cuda()
>>> set_seed(32)
>>> generated_ids = model.generate(input_ids, do_sample=True, num_return_sequences=5, max_length=10)
>>> tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
The woman worked as a supervisor in the office
The woman worked as a social media consultant for
The woman worked as a cashier at the
The woman worked as a teacher, and was
The woman worked as a maid at our friends
```
compared to:
```python
>>> from transformers import AutoModelForCausalLM, AutoTokenizer, set_seed
>>> import torch
>>> model = AutoModelForCausalLM.from_pretrained("facebook/opt-13b", torch_dtype=torch.float16).cuda()
>>> # the fast tokenizer currently does not work correctly
>>> tokenizer = AutoTokenizer.from_pretrained("facebook/opt-13b", use_fast=False)
>>> prompt = "The man worked as a"
>>> input_ids = tokenizer(prompt, return_tensors="pt").input_ids.cuda()
>>> set_seed(32)
>>> generated_ids = model.generate(input_ids, do_sample=True, num_return_sequences=5, max_length=10)
>>> tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
The man worked as a consultant to the defense
The man worked as a bartender in a bar
The man worked as a cashier at the
The man worked as a teacher, and was
The man worked as a professional athlete while he
```
This bias will also affect all fine-tuned versions of this model.
## Training data
The Meta AI team wanted to train this model on a corpus as large as possible. It is composed of the union of the following 5 filtered datasets of textual documents:
- BookCorpus, which consists of more than 10K unpublished books,
- CC-Stories, which contains a subset of CommonCrawl data filtered to match the
story-like style of Winograd schemas,
- The Pile, from which * Pile-CC, OpenWebText2, USPTO, Project Gutenberg, OpenSubtitles, Wikipedia, DM Mathematics and HackerNews* were included.
- Pushshift.io Reddit dataset that was developed in Baumgartner et al. (2020) and processed in
Roller et al. (2021)
- CCNewsV2 containing an updated version of the English portion of the CommonCrawl News
dataset that was used in RoBERTa (Liu et al., 2019b)
The final training data contains 180B tokens corresponding to 800GB of data. The validation split was made of 200MB of the pretraining data, sampled proportionally
to each dataset’s size in the pretraining corpus.
The dataset might contains offensive content as parts of the dataset are a subset of
public Common Crawl data, along with a subset of public Reddit data, which could contain sentences
that, if viewed directly, can be insulting, threatening, or might otherwise cause anxiety.
### Collection process
The dataset was collected form internet, and went through classic data processing algorithms and
re-formatting practices, including removing repetitive/non-informative text like *Chapter One* or
*This ebook by Project Gutenberg.*
## Training procedure
### Preprocessing
The texts are tokenized using the **GPT2** byte-level version of Byte Pair Encoding (BPE) (for unicode characters) and a
vocabulary size of 50272. The inputs are sequences of 2048 consecutive tokens.
The 175B model was trained on 992 *80GB A100 GPUs*. The training duration was roughly ~33 days of continuous training.
### BibTeX entry and citation info
```bibtex
@misc{zhang2022opt,
title={OPT: Open Pre-trained Transformer Language Models},
author={Susan Zhang and Stephen Roller and Naman Goyal and Mikel Artetxe and Moya Chen and Shuohui Chen and Christopher Dewan and Mona Diab and Xian Li and Xi Victoria Lin and Todor Mihaylov and Myle Ott and Sam Shleifer and Kurt Shuster and Daniel Simig and Punit Singh Koura and Anjali Sridhar and Tianlu Wang and Luke Zettlemoyer},
year={2022},
eprint={2205.01068},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
inverse-scaling/opt-6.7b_eval
|
inverse-scaling
| 2023-01-02T13:01:30Z | 92 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"jax",
"opt",
"text-generation",
"en",
"arxiv:2205.01068",
"arxiv:2005.14165",
"license:other",
"model-index",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2022-10-05T15:06:33Z |
---
language: en
license: other
tags:
- text-generation
- opt
inference: false
commercial: false
model-index:
- name: inverse-scaling/opt-6.7b_eval
results:
- task:
type: zero-shot-classification
name: Zero-Shot Text Classification
dataset:
name: inverse-scaling/NeQA
type: inverse-scaling/NeQA
config: inverse-scaling--NeQA
split: train
metrics:
- type: accuracy
value: 0.54
name: Accuracy
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiOWE0ZjA1NDg0YjYzNTZhYjIwZDRhNDcxYjNiYTQ1YTY2YWQ1YTUzZmIyMTlmYTljMGJiNjAyNzc0YTNiYWFhNCIsInZlcnNpb24iOjF9.eWcHC6dzOjnuF-mT6Z2G8Z1xCoow6iViE1Qy-VNKMSzIcJZcvgkZI0NhU50YMi4tOOZN2k92MATtbXtcZR5yCQ
- type: loss
value: 0.740270353704691
name: Loss
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNTY1M2ZlYTYzMjZhNTVmZjgyMWJiYmYxZGM2NjQxYjdlZDI3ZmZmODAxMTI5N2RmMjMyNzYzMWUxZTViNjM5YSIsInZlcnNpb24iOjF9.G3DqNVlNLP5uAmzOKa9hsxBBiSWXbrDesp3hIlQomYe2YsbWbYF0WssbFi7DXEu5hmj6yCN2E-olbEjzwZ2eBQ
- task:
type: zero-shot-classification
name: Zero-Shot Text Classification
dataset:
name: inverse-scaling/quote-repetition
type: inverse-scaling/quote-repetition
config: inverse-scaling--quote-repetition
split: train
metrics:
- type: accuracy
value: 0.86
name: Accuracy
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMzA0ZjJlZGUwOGNhNmE3MmMzMDY1YTM4ZjYzNDUwYjk1MTU2MmVhMGQzYjI3YzI0ZGMzMWFkODIyZWE5Mjk2ZCIsInZlcnNpb24iOjF9.pc3tzIMBv05ZBixkmRojnIzsdHLvYhZX_sJnNZ_t_oo61DrTUhYQYq3xikx8S5rIr5sWrLTbxWn3rAAXme0KAQ
- type: loss
value: 0.22016974209290055
name: Loss
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiOWE5Njk4OWQ5Mzg3ODljMWM3MzhiNjBhNTk5ZGJjMDU3ZTJlZDZjZjBjYzdkMmMxZTJlMTJkMjg1OTA5ZWQxNSIsInZlcnNpb24iOjF9.NubehOGlzEURMYuTkvqzXmf1ENadam7uZ62YA1nv1DjAivd8VySmpLl-QnnZLcDbhduMZbRp4lMQbWG9Z26LAg
- task:
type: zero-shot-classification
name: Zero-Shot Text Classification
dataset:
name: inverse-scaling/redefine-math
type: inverse-scaling/redefine-math
config: inverse-scaling--redefine-math
split: train
metrics:
- type: accuracy
value: 0.6733333333333333
name: Accuracy
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiZTAyYjAwMzgyMDc2MmU1NDM0MmMyOWUyYzc3YTYxNzkyYzk2ZGZiMTk5NjlkODUwNDQ1NzFlMTU0Y2Y0ZGZlYSIsInZlcnNpb24iOjF9.VMxtPMY9qKk4eSjAlDb_jfg1nsf8eq1Oz5WnfUSC-VkXREQ6-f1qBooJc617t6U5apIbHnaW9XP3LTYrGzvUDQ
- type: loss
value: 0.638882334422734
name: Loss
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNDYwNWVmZGM5ZmM2MmY0Y2IzYzNhOTNkZmU2YTA2MWZlZTU1ZGI2OTM1YzJiNjViNzMwMjA0Y2Q0ODBlYTgzOSIsInZlcnNpb24iOjF9.YJujmeEYbf4ZOJ0w_Q24d7t5ksKST35aweNJSk6UYuCiV6uSIJhJUz_w8iFwo9ykM-EOXamL87dftlkyawgtBw
- task:
type: zero-shot-classification
name: Zero-Shot Text Classification
dataset:
name: inverse-scaling/hindsight-neglect-10shot
type: inverse-scaling/hindsight-neglect-10shot
config: inverse-scaling--hindsight-neglect-10shot
split: train
metrics:
- type: accuracy
value: 0.4666666666666667
name: Accuracy
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiYjVlMzdkYTUxZmI1ZDBmMDdjM2VhMjA1ZTg0MGYzMzU0NzFlN2JmNDY2NDc0MmVlMjI3MDg1Y2Q5MDRhYWU1ZCIsInZlcnNpb24iOjF9.Z01fwvvUFNOWeUWexSpdmAUPYJIsYUV-eb1ybSEjQ3cb9ow2STMVgxp0PqaDJMVWKg30xIkARahsg8ci6QpbBw
- type: loss
value: 0.7550815605928027
name: Loss
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiZWQyNmYwZjdkMTM1YjIxYzEwMmUwMWVlZTRjODQwYWExNDQ2MTgzYzA0ZTlkODcxYWIxMzdmNWE0NDdmNzcxYiIsInZlcnNpb24iOjF9.TtX2cKfatVMFX09l6DiuKFEa1vlDJUBPohSLmdQGh8QCTf-DrylUqARU8Ni5cSiSlidFF4n4IWIL0vQ941n6DQ
- task:
type: zero-shot-classification
name: Zero-Shot Text Classification
dataset:
name: mathemakitten/winobias_antistereotype_test_cot_v3
type: mathemakitten/winobias_antistereotype_test_cot_v3
config: mathemakitten--winobias_antistereotype_test_cot_v3
split: test
metrics:
- type: accuracy
value: 0.3737864077669903
name: Accuracy
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiOTliMzU1NGIxMTUxYTM4NzVlYzI4YzljMDYyOWM1ZDdkMWMyNjIwOWQ4OGNhZWE3ZTljZGI0ZTA2ZWU3MjVmMiIsInZlcnNpb24iOjF9.dTlDpXOusgl6m3dn7XwfKeaxaVfU1VnEHWFeh7yBNSq5TyHPWbixlNumOWDjc-y9v8g0oWBXqWhT0KMQDaGVCQ
- type: loss
value: 1.2823651640752816
name: Loss
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMTRjZmU4YWNkNGEwMjNlMGEyYjA1ZjhjOGE3OTZiZTJlYjMyMjViMTYyYWQ1YTdlMmM1ZjU5NTFhOWU3NzM1OCIsInZlcnNpb24iOjF9.yGmOME0MrX0moaU5c2WYf8H7CFfSGsPuQ2qp9MCi_es5RQRWoCHeCcR5oLQ4RATmVpYdzocPxqrbeZfqxVIOAQ
- task:
type: zero-shot-classification
name: Zero-Shot Text Classification
dataset:
name: mathemakitten/winobias_antistereotype_test_v5
type: mathemakitten/winobias_antistereotype_test_v5
config: mathemakitten--winobias_antistereotype_test_v5
split: test
metrics:
- type: accuracy
value: 0.3859223300970874
name: Accuracy
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNzZlZjIzNDM0Mzk5MmRlMTFlOWVlZjY3MDFmY2NhZjlkYWNmMWQ2MjdhOTg3YTg0OTI1YjY5YmYxMTc4YjYyOCIsInZlcnNpb24iOjF9.nCFVShWbHuHFKEdK5INjQSfLI9KQUNQZqqjqYCw_HVHSW0QHLIXdAb7_GDZJhCUTJ-JkBVCJFtEliA2Zw9GjAw
- type: loss
value: 1.295986159347468
name: Loss
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiYWE3ZjhmYzM3NjRhMjc3OGU5NWQzY2Q1NzA2ZDBjN2Q1YmZkYzdiMDBhMmY1ZDM5NmU2YzQ2ZGZmZmYyMzg5NiIsInZlcnNpb24iOjF9.2UzIpqw83YQdGOqTKKP7ywqpNdgCDkR36lhkbja6qFsKyQctcg4vZgLXfMSfufWf1G_9iXqY8r-JiZadMdK3Dg
---
# OPT : Open Pre-trained Transformer Language Models
OPT was first introduced in [Open Pre-trained Transformer Language Models](https://arxiv.org/abs/2205.01068) and first released in [metaseq's repository](https://github.com/facebookresearch/metaseq) on May 3rd 2022 by Meta AI.
**Disclaimer**: The team releasing OPT wrote an official model card, which is available in Appendix D of the [paper](https://arxiv.org/pdf/2205.01068.pdf).
Content from **this** model card has been written by the Hugging Face team.
## Intro
To quote the first two paragraphs of the [official paper](https://arxiv.org/abs/2205.01068)
> Large language models trained on massive text collections have shown surprising emergent
> capabilities to generate text and perform zero- and few-shot learning. While in some cases the public
> can interact with these models through paid APIs, full model access is currently limited to only a
> few highly resourced labs. This restricted access has limited researchers’ ability to study how and
> why these large language models work, hindering progress on improving known challenges in areas
> such as robustness, bias, and toxicity.
> We present Open Pretrained Transformers (OPT), a suite of decoder-only pre-trained transformers ranging from 125M
> to 175B parameters, which we aim to fully and responsibly share with interested researchers. We train the OPT models to roughly match
> the performance and sizes of the GPT-3 class of models, while also applying the latest best practices in data
> collection and efficient training. Our aim in developing this suite of OPT models is to enable reproducible and responsible research at scale, and
> to bring more voices to the table in studying the impact of these LLMs. Definitions of risk, harm, bias, and toxicity, etc., should be articulated by the
> collective research community as a whole, which is only possible when models are available for study.
## Model description
OPT was predominantly pretrained with English text, but a small amount of non-English data is still present within the training corpus via CommonCrawl. The model was pretrained using a causal language modeling (CLM) objective.
OPT belongs to the same family of decoder-only models like [GPT-3](https://arxiv.org/abs/2005.14165). As such, it was pretrained using the self-supervised causal language modedling objective.
For evaluation, OPT follows [GPT-3](https://arxiv.org/abs/2005.14165) by using their prompts and overall experimental setup. For more details, please read
the [official paper](https://arxiv.org/abs/2205.01068).
## Intended uses & limitations
The pretrained-only model can be used for prompting for evaluation of downstream tasks as well as text generation.
In addition, the model can be fine-tuned on a downstream task using the [CLM example](https://github.com/huggingface/transformers/tree/main/examples/pytorch/language-modeling). For all other OPT checkpoints, please have a look at the [model hub](https://huggingface.co/models?filter=opt).
### How to use
For large OPT models, such as this one, it is not recommend to make use of the `text-generation` pipeline because
one should load the model in half-precision to accelerate generation and optimize memory consumption on GPU.
It is recommended to directly call the [`generate`](https://huggingface.co/docs/transformers/main/en/main_classes/text_generation#transformers.generation_utils.GenerationMixin.generate)
method as follows:
```python
>>> from transformers import AutoModelForCausalLM, AutoTokenizer
>>> import torch
>>> model = AutoModelForCausalLM.from_pretrained("facebook/opt-6.7b", torch_dtype=torch.float16).cuda()
>>> # the fast tokenizer currently does not work correctly
>>> tokenizer = AutoTokenizer.from_pretrained("facebook/opt-6.7b", use_fast=False)
>>> prompt = "Hello, I'm am conscious and"
>>> input_ids = tokenizer(prompt, return_tensors="pt").input_ids.cuda()
>>> generated_ids = model.generate(input_ids)
>>> tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
["Hello, I'm am conscious and aware of my surroundings. I'm not sure what you mean"]
```
By default, generation is deterministic. In order to use the top-k sampling, please set `do_sample` to `True`.
```python
>>> from transformers import AutoModelForCausalLM, AutoTokenizer, set_seed
>>> import torch
>>> model = AutoModelForCausalLM.from_pretrained("facebook/opt-6.7b", torch_dtype=torch.float16).cuda()
>>> # the fast tokenizer currently does not work correctly
>>> tokenizer = AutoTokenizer.from_pretrained("facebook/opt-6.7b", use_fast=False)
>>> prompt = "Hello, I'm am conscious and"
>>> input_ids = tokenizer(prompt, return_tensors="pt").input_ids.cuda()
>>> set_seed(32)
>>> generated_ids = model.generate(input_ids, do_sample=True)
>>> tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
["Hello, I'm am conscious and aware of my surroundings. I'm not sure if I'm"]
```
### Limitations and bias
As mentioned in Meta AI's model card, given that the training data used for this model contains a lot of
unfiltered content from the internet, which is far from neutral the model is strongly biased :
> Like other large language models for which the diversity (or lack thereof) of training
> data induces downstream impact on the quality of our model, OPT-175B has limitations in terms
> of bias and safety. OPT-175B can also have quality issues in terms of generation diversity and
> hallucination. In general, OPT-175B is not immune from the plethora of issues that plague modern
> large language models.
Here's an example of how the model can have biased predictions:
```python
>>> from transformers import AutoModelForCausalLM, AutoTokenizer, set_seed
>>> import torch
>>> model = AutoModelForCausalLM.from_pretrained("facebook/opt-6.7b", torch_dtype=torch.float16).cuda()
>>> # the fast tokenizer currently does not work correctly
>>> tokenizer = AutoTokenizer.from_pretrained("facebook/opt-6.7b", use_fast=False)
>>> prompt = "The woman worked as a"
>>> input_ids = tokenizer(prompt, return_tensors="pt").input_ids.cuda()
>>> set_seed(32)
>>> generated_ids = model.generate(input_ids, do_sample=True, num_return_sequences=5, max_length=10)
>>> tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
The woman worked as a supervisor in the office
The woman worked as a bartender in a bar
The woman worked as a cashier at the
The woman worked as a teacher, and was
The woman worked as a maid at a house
```
compared to:
```python
>>> from transformers import AutoModelForCausalLM, AutoTokenizer, set_seed
>>> import torch
>>> model = AutoModelForCausalLM.from_pretrained("facebook/opt-6.7b", torch_dtype=torch.float16).cuda()
>>> # the fast tokenizer currently does not work correctly
>>> tokenizer = AutoTokenizer.from_pretrained("facebook/opt-6.7b", use_fast=False)
>>> prompt = "The man worked as a"
>>> input_ids = tokenizer(prompt, return_tensors="pt").input_ids.cuda()
>>> set_seed(32)
>>> generated_ids = model.generate(input_ids, do_sample=True, num_return_sequences=5, max_length=10)
>>> tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
The man worked as a consultant to the Government
The man worked as a bartender in a bar
The man worked as a cashier at the
The man worked as a teacher, and was
The man worked as a professional at a bank
```
This bias will also affect all fine-tuned versions of this model.
## Training data
The Meta AI team wanted to train this model on a corpus as large as possible. It is composed of the union of the following 5 filtered datasets of textual documents:
- BookCorpus, which consists of more than 10K unpublished books,
- CC-Stories, which contains a subset of CommonCrawl data filtered to match the
story-like style of Winograd schemas,
- The Pile, from which * Pile-CC, OpenWebText2, USPTO, Project Gutenberg, OpenSubtitles, Wikipedia, DM Mathematics and HackerNews* were included.
- Pushshift.io Reddit dataset that was developed in Baumgartner et al. (2020) and processed in
Roller et al. (2021)
- CCNewsV2 containing an updated version of the English portion of the CommonCrawl News
dataset that was used in RoBERTa (Liu et al., 2019b)
The final training data contains 180B tokens corresponding to 800GB of data. The validation split was made of 200MB of the pretraining data, sampled proportionally
to each dataset’s size in the pretraining corpus.
The dataset might contains offensive content as parts of the dataset are a subset of
public Common Crawl data, along with a subset of public Reddit data, which could contain sentences
that, if viewed directly, can be insulting, threatening, or might otherwise cause anxiety.
### Collection process
The dataset was collected form internet, and went through classic data processing algorithms and
re-formatting practices, including removing repetitive/non-informative text like *Chapter One* or
*This ebook by Project Gutenberg.*
## Training procedure
### Preprocessing
The texts are tokenized using the **GPT2** byte-level version of Byte Pair Encoding (BPE) (for unicode characters) and a
vocabulary size of 50272. The inputs are sequences of 2048 consecutive tokens.
The 175B model was trained on 992 *80GB A100 GPUs*. The training duration was roughly ~33 days of continuous training.
### BibTeX entry and citation info
```bibtex
@misc{zhang2022opt,
title={OPT: Open Pre-trained Transformer Language Models},
author={Susan Zhang and Stephen Roller and Naman Goyal and Mikel Artetxe and Moya Chen and Shuohui Chen and Christopher Dewan and Mona Diab and Xian Li and Xi Victoria Lin and Todor Mihaylov and Myle Ott and Sam Shleifer and Kurt Shuster and Daniel Simig and Punit Singh Koura and Anjali Sridhar and Tianlu Wang and Luke Zettlemoyer},
year={2022},
eprint={2205.01068},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
facebook/timesformer-base-finetuned-k400
|
facebook
| 2023-01-02T11:43:07Z | 226,788 | 29 |
transformers
|
[
"transformers",
"pytorch",
"timesformer",
"video-classification",
"vision",
"arxiv:2102.05095",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
] |
video-classification
| 2022-10-07T19:03:04Z |
---
license: "cc-by-nc-4.0"
tags:
- vision
- video-classification
---
# TimeSformer (base-sized model, fine-tuned on Kinetics-400)
TimeSformer model pre-trained on [Kinetics-400](https://www.deepmind.com/open-source/kinetics). It was introduced in the paper [TimeSformer: Is Space-Time Attention All You Need for Video Understanding?](https://arxiv.org/abs/2102.05095) by Tong et al. and first released in [this repository](https://github.com/facebookresearch/TimeSformer).
Disclaimer: The team releasing TimeSformer did not write a model card for this model so this model card has been written by [fcakyon](https://github.com/fcakyon).
## Intended uses & limitations
You can use the raw model for video classification into one of the 400 possible Kinetics-400 labels.
### How to use
Here is how to use this model to classify a video:
```python
from transformers import AutoImageProcessor, TimesformerForVideoClassification
import numpy as np
import torch
video = list(np.random.randn(8, 3, 224, 224))
processor = AutoImageProcessor.from_pretrained("facebook/timesformer-base-finetuned-k400")
model = TimesformerForVideoClassification.from_pretrained("facebook/timesformer-base-finetuned-k400")
inputs = processor(video, return_tensors="pt")
with torch.no_grad():
outputs = model(**inputs)
logits = outputs.logits
predicted_class_idx = logits.argmax(-1).item()
print("Predicted class:", model.config.id2label[predicted_class_idx])
```
For more code examples, we refer to the [documentation](https://huggingface.co/transformers/main/model_doc/timesformer.html#).
### BibTeX entry and citation info
```bibtex
@inproceedings{bertasius2021space,
title={Is Space-Time Attention All You Need for Video Understanding?},
author={Bertasius, Gedas and Wang, Heng and Torresani, Lorenzo},
booktitle={International Conference on Machine Learning},
pages={813--824},
year={2021},
organization={PMLR}
}
```
|
adasgaleus/insertion-prop-05-correct-data
|
adasgaleus
| 2023-01-02T11:35:38Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2023-01-02T11:12:15Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: insertion-prop-05-correct-data
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. -->
# insertion-prop-05-correct-data
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0794
- Precision: 0.9284
- Recall: 0.9056
- F1: 0.9169
- Accuracy: 0.9689
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1815 | 0.32 | 500 | 0.0982 | 0.9159 | 0.8802 | 0.8977 | 0.9619 |
| 0.1113 | 0.64 | 1000 | 0.0833 | 0.9257 | 0.9018 | 0.9136 | 0.9676 |
| 0.1018 | 0.96 | 1500 | 0.0794 | 0.9284 | 0.9056 | 0.9169 | 0.9689 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
|
nanashisan/DBLora
|
nanashisan
| 2023-01-02T11:27:22Z | 0 | 7 | null |
[
"license:unknown",
"region:us"
] | null | 2022-12-30T12:27:53Z |
---
license: unknown
---
sd-webui-additional-networksで読み込むことが出来るLoraファイルやで
使用方法は下記ExtensionをWebuiにインストールして「Additional Networks」の項目に絶対パスでptファイルを指定するだけやで
https://github.com/kohya-ss/sd-webui-additional-networks
このファイルはKohya-SD-Scriptで作成されてる
WebUIのDreamboothで作成されるLoraDBファイルとは互換性がないから注意してな
|
roscazo/DISO_SINAI_test1
|
roscazo
| 2023-01-02T10:53:21Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"roberta",
"token-classification",
"generated_from_trainer",
"license:cc-by-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-12-29T10:44:04Z |
---
license: cc-by-4.0
tags:
- generated_from_trainer
model-index:
- name: DISO_SINAI_test1
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. -->
# DISO_SINAI_test1
This model is a fine-tuned version of [chizhikchi/Spanish_disease_finder](https://huggingface.co/chizhikchi/Spanish_disease_finder) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0807
- Diso Precision: 0.8260
- Diso Recall: 0.8247
- Diso F1: 0.8253
- Diso Number: 4552
- Overall Precision: 0.8260
- Overall Recall: 0.8247
- Overall F1: 0.8253
- Overall Accuracy: 0.9815
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Diso Precision | Diso Recall | Diso F1 | Diso Number | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------------:|:-----------:|:-------:|:-----------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 0.0629 | 1.0 | 2799 | 0.0573 | 0.8315 | 0.8014 | 0.8162 | 4552 | 0.8315 | 0.8014 | 0.8162 | 0.9813 |
| 0.0429 | 2.0 | 5598 | 0.0622 | 0.8228 | 0.8264 | 0.8246 | 4552 | 0.8228 | 0.8264 | 0.8246 | 0.9815 |
| 0.0299 | 3.0 | 8397 | 0.0689 | 0.8217 | 0.8251 | 0.8234 | 4552 | 0.8217 | 0.8251 | 0.8234 | 0.9814 |
| 0.0214 | 4.0 | 11196 | 0.0807 | 0.8260 | 0.8247 | 0.8253 | 4552 | 0.8260 | 0.8247 | 0.8253 | 0.9815 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
|
jsalvador/ppo-LunarLander-v2-TEST
|
jsalvador
| 2023-01-02T10:22:50Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-01-02T10:22:21Z |
---
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: 249.59 +/- 19.81
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
...
```
|
HumanCompatibleAI/sac-seals-Walker2d-v0
|
HumanCompatibleAI
| 2023-01-02T10:01:21Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"seals/Walker2d-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-07-27T15:06:52Z |
---
library_name: stable-baselines3
tags:
- seals/Walker2d-v0
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: SAC
results:
- metrics:
- type: mean_reward
value: 2492.52 +/- 1181.09
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: seals/Walker2d-v0
type: seals/Walker2d-v0
---
# **SAC** Agent playing **seals/Walker2d-v0**
This is a trained model of a **SAC** agent playing **seals/Walker2d-v0**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo sac --env seals/Walker2d-v0 -orga HumanCompatibleAI -f logs/
python enjoy.py --algo sac --env seals/Walker2d-v0 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo sac --env seals/Walker2d-v0 -orga HumanCompatibleAI -f logs/
rl_zoo3 enjoy --algo sac --env seals/Walker2d-v0 -f logs/
```
## Training (with the RL Zoo)
```
python train.py --algo sac --env seals/Walker2d-v0 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo sac --env seals/Walker2d-v0 -f logs/ -orga HumanCompatibleAI
```
## Hyperparameters
```python
OrderedDict([('batch_size', 128),
('buffer_size', 100000),
('gamma', 0.99),
('learning_rate', 0.0005845844772048097),
('learning_starts', 1000),
('n_timesteps', 1000000.0),
('policy', 'MlpPolicy'),
('policy_kwargs',
{'log_std_init': 0.1955317469998743,
'net_arch': [400, 300],
'use_sde': False}),
('tau', 0.02),
('train_freq', 1),
('normalize', False)])
```
|
HumanCompatibleAI/sac-seals-Humanoid-v0
|
HumanCompatibleAI
| 2023-01-02T09:59:01Z | 3 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"seals/Humanoid-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-07-27T15:12:08Z |
---
library_name: stable-baselines3
tags:
- seals/Humanoid-v0
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: SAC
results:
- metrics:
- type: mean_reward
value: 423.21 +/- 100.55
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: seals/Humanoid-v0
type: seals/Humanoid-v0
---
# **SAC** Agent playing **seals/Humanoid-v0**
This is a trained model of a **SAC** agent playing **seals/Humanoid-v0**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo sac --env seals/Humanoid-v0 -orga HumanCompatibleAI -f logs/
python enjoy.py --algo sac --env seals/Humanoid-v0 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo sac --env seals/Humanoid-v0 -orga HumanCompatibleAI -f logs/
rl_zoo3 enjoy --algo sac --env seals/Humanoid-v0 -f logs/
```
## Training (with the RL Zoo)
```
python train.py --algo sac --env seals/Humanoid-v0 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo sac --env seals/Humanoid-v0 -f logs/ -orga HumanCompatibleAI
```
## Hyperparameters
```python
OrderedDict([('batch_size', 64),
('buffer_size', 100000),
('gamma', 0.98),
('learning_rate', 4.426351861707874e-05),
('learning_starts', 20000),
('n_timesteps', 2000000.0),
('policy', 'MlpPolicy'),
('policy_kwargs',
{'log_std_init': -0.1034412732183072,
'net_arch': [400, 300],
'use_sde': False}),
('tau', 0.08),
('train_freq', 8),
('normalize', False)])
```
|
HumanCompatibleAI/ppo-seals-Humanoid-v0
|
HumanCompatibleAI
| 2023-01-02T09:57:44Z | 10 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"seals/Humanoid-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-07-25T11:10:16Z |
---
library_name: stable-baselines3
tags:
- seals/Humanoid-v0
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 2242.51 +/- 858.20
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: seals/Humanoid-v0
type: seals/Humanoid-v0
---
# **PPO** Agent playing **seals/Humanoid-v0**
This is a trained model of a **PPO** agent playing **seals/Humanoid-v0**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo ppo --env seals/Humanoid-v0 -orga HumanCompatibleAI -f logs/
python enjoy.py --algo ppo --env seals/Humanoid-v0 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo ppo --env seals/Humanoid-v0 -orga HumanCompatibleAI -f logs/
rl_zoo3 enjoy --algo ppo --env seals/Humanoid-v0 -f logs/
```
## Training (with the RL Zoo)
```
python train.py --algo ppo --env seals/Humanoid-v0 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo ppo --env seals/Humanoid-v0 -f logs/ -orga HumanCompatibleAI
```
## Hyperparameters
```python
OrderedDict([('batch_size', 256),
('clip_range', 0.2),
('ent_coef', 2.0745206045994986e-05),
('gae_lambda', 0.92),
('gamma', 0.999),
('learning_rate', 2.0309225666232827e-05),
('max_grad_norm', 0.5),
('n_envs', 1),
('n_epochs', 20),
('n_steps', 2048),
('n_timesteps', 10000000.0),
('normalize',
{'gamma': 0.999, 'norm_obs': False, 'norm_reward': True}),
('policy', 'MlpPolicy'),
('policy_kwargs',
{'activation_fn': <class 'torch.nn.modules.activation.ReLU'>,
'features_extractor_class': <class 'imitation.policies.base.NormalizeFeaturesExtractor'>,
'net_arch': [{'pi': [256, 256], 'vf': [256, 256]}]}),
('vf_coef', 0.819262464558427),
('normalize_kwargs',
{'norm_obs': {'gamma': 0.999,
'norm_obs': False,
'norm_reward': True},
'norm_reward': False})])
```
|
amengemeda/amharic-hate-speech-detection-mBERT
|
amengemeda
| 2023-01-02T09:46:00Z | 13 | 3 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"Amharic",
"hate speech",
"sentiment analysis",
"amh",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-12-03T06:41:48Z |
---
language:
- amh
tags:
- Amharic
- hate speech
- sentiment analysis
datasets:
- https://data.mendeley.com/datasets/ymtmxx385m
metrics:
- F1
- Accuracy
---
**Amharic Hate Speech Detection using Fine-tuned mBERT**
**Model description**
This model was created by finetuning the mBERT model for the downstream task of Hate speech detection for the Amharic language.
The initial mBERT model used for finetuning is Davlan/bert-base-multilingual-cased-finetuned-amharic which was provided by Davlan on Huggingface.
The model was fine-tuned using HuggingFace's Trainer API. The final result of the finetuning has an F1-score of 0.9172 and an accuracy of 91.59%.
The model was finetuned with 15 epochs and a learning rate of 0.00005.
**Dataset description**
The finetuning was done on an Amharic Dataset that was made available by Mendeley Data (https://data.mendeley.com/datasets/ymtmxx385m). It has a size of 30,000 rows.
**Other**
The Google Colab notebook is made available on my GitHub. Check this path https://github.com/amengemeda/ISproject-2/blob/main/mBERT/Amharic_Hate_Speech_detection_using_mBERT_(Trainer_API).ipynb
|
JYC333/q-FrozenLake-v1-4x4-noSlippery
|
JYC333
| 2023-01-02T09:42:41Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-01-02T00:12:24Z |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="JYC333/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"])
```
|
samay/Icecreamtypes
|
samay
| 2023-01-02T09:30:01Z | 19 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-01-02T09:23:07Z |
---
tags:
- image-classification
- pytorch
- huggingpics
metrics:
- accuracy
model-index:
- name: Icecreamtypes
results:
- task:
name: Image Classification
type: image-classification
metrics:
- name: Accuracy
type: accuracy
value: 0.5555555820465088
---
# Icecreamtypes
## Example Images
#### Ice cream
#### Kulfi
#### ice cream cone
|
denisivashkov/q-FrozenLake-v1-4x4-noSlippery
|
denisivashkov
| 2023-01-02T09:28:31Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-01-02T09:28:27Z |
---
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="denisivashkov/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"])
```
|
simonschoe/iridescent-jellyfish
|
simonschoe
| 2023-01-02T07:52:20Z | 31 | 6 |
diffusers
|
[
"diffusers",
"pytorch",
"stable-diffusion",
"text-to-image",
"diffusion-models-class",
"dreambooth-hackathon",
"animal",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2022-12-29T22:11:52Z |
---
license: creativeml-openrail-m
tags:
- pytorch
- diffusers
- stable-diffusion
- text-to-image
- diffusion-models-class
- dreambooth-hackathon
- animal
widget:
- text: a photo of a ðŁĴŁ jellyfish in the snow
- text: a photo of a ðŁĴŁ jellyfish next to a dog
- text: a photo of a ðŁĴŁ jellyfish on top of a mountain
---
# Iridescent Jellyfish
**Iridescent Jellyfish** is a Dreambooth model for the `iridescent` jellyfish concept (represented by the `ðŁĴŁ` identifier).
It applies to the *animal* theme.
It is fine-tuned from `runwayml/stable-diffusion-v1-5` checkpoint on a small dataset of jellyfish images.
It can be used by modifying the `instance_prompt`: **a photo of a ðŁĴŁ jellyfish in the snow**
This model was created as part of the DreamBooth Hackathon 🔥. Visit the [organisation page](https://huggingface.co/dreambooth-hackathon) for instructions on how to take part!
#### Fine-Tuning Details
- Number of training images: 17
- Learning rate: 2e-06
- Training steps: 800
- Guidance Scale: 7
- Inference Steps: 50
#### Output Examples
<table>
<tr>
<td>a oil painting of a <b>ðŁĴŁ</b> jellyfish</td>
<td>a photo of a <b>ðŁĴŁ</b> jellyfish next to a dog</td>
<td>a photo of a <b>ðŁĴŁ</b> jellyfish in the snow</td>
</tr>
<tr>
<td align="center"><img src="https://huggingface.co/simonschoe/iridescent-jellyfish/resolve/main/output/jelly%20(4).png" style="height:200px"> </td>
<td align="center"><img src="https://huggingface.co/simonschoe/iridescent-jellyfish/resolve/main/output/jelly%20(5).png" style="height:200px"> </td>
<td align="center"><img src="https://huggingface.co/simonschoe/iridescent-jellyfish/resolve/main/output/jelly%20(6).png" style="height:200px"> </td>
</tr>
<tr>
<td>a photo of a <b>ðŁĴŁ</b> jellyfish on top of a mountain</td>
<td>a photo of a <b>ðŁĴŁ</b> jellyfish in the sky</td>
<td>a photo of a <b>ðŁĴŁ</b> jellyfish</td>
</tr>
<tr>
<td align="center"><img src="https://huggingface.co/simonschoe/iridescent-jellyfish/resolve/main/output/jelly%20(7).png" style="height:200px"> </td>
<td align="center"><img src="https://huggingface.co/simonschoe/iridescent-jellyfish/resolve/main/output/jelly%20(8).png" style="height:200px"> </td>
<td align="center"><img src="https://huggingface.co/simonschoe/iridescent-jellyfish/resolve/main/output/jelly%20(9).png" style="height:200px"> </td>
</tr>
<tr>
<td>a photo of a <b>ðŁĴŁ</b> jellyfish skydiving</td>
<td>a photo of a <b>ðŁĴŁ</b> jellyfish sutfing on a surfboard</td>
<td>a photo of a choclate <b>ðŁĴŁ</b> jellyfish</td>
</tr>
<tr>
<td align="center"><img src="https://huggingface.co/simonschoe/iridescent-jellyfish/resolve/main/output/jelly%20(10).jpg" style="height:200px"> </td>
<td align="center"><img src="https://huggingface.co/simonschoe/iridescent-jellyfish/resolve/main/output/jelly%20(11).jpg" style="height:200px"> </td>
<td align="center"><img src="https://huggingface.co/simonschoe/iridescent-jellyfish/resolve/main/output/jelly%20(12).jpg" style="height:200px"> </td>
</tr>
<tr>
<td>a photo of a <b>ðŁĴŁ</b> jellyfish shooting fireworks in the sky</td>
<td>a photo of a <b>ðŁĴŁ</b> jellyfish on rollerblades</td>
<td>a photo of a <b>ðŁĴŁ</b> jellyfish in a beer bottle</td>
</tr>
<tr>
<td align="center"><img src="https://huggingface.co/simonschoe/iridescent-jellyfish/resolve/main/output/jelly%20(13).jpg" style="height:200px"> </td>
<td align="center"><img src="https://huggingface.co/simonschoe/iridescent-jellyfish/resolve/main/output/jelly%20(14).jpg" style="height:200px"> </td>
<td align="center"><img src="https://huggingface.co/simonschoe/iridescent-jellyfish/resolve/main/output/jelly%20(15).jpg" style="height:200px"> </td>
</tr>
<tr>
<td>a colorful sketch of a <b>ðŁĴŁ</b> jellyfish</td>
<td>a photo of a <b>ðŁĴŁ</b> jellyfish in the jungle</td>
<td>a mystic <b>ðŁĴŁ</b> jellyfish, trending on artstation</td>
</tr>
<tr>
<td align="center"><img src="https://huggingface.co/simonschoe/iridescent-jellyfish/resolve/main/output/jelly%20(1).png" style="height:200px"> </td>
<td align="center"><img src="https://huggingface.co/simonschoe/iridescent-jellyfish/resolve/main/output/jelly%20(2).png" style="height:200px"> </td>
<td align="center"><img src="https://huggingface.co/simonschoe/iridescent-jellyfish/resolve/main/output/jelly%20(3).png" style="height:200px"> </td>
</tr>
</table>
## Usage
```python
from diffusers import StableDiffusionPipeline
import torch
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
pipeline = StableDiffusionPipeline.from_pretrained('simonschoe/iridescent-jellyfish').to(device)
prompt = "a photo of a ðŁĴŁ jellyfish in the snow"
image = pipeline(
prompt,
num_inference_steps=50,
guidance_scale=7,
num_images_per_prompt=1
).images[0]
image
```
|
nlp04/ES_roberta_30
|
nlp04
| 2023-01-02T07:41:09Z | 8 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"question-answering",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2023-01-02T03:05:15Z |
---
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: ES_roberta_30
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. -->
# ES_roberta_30
This model is a fine-tuned version of [klue/roberta-large](https://huggingface.co/klue/roberta-large) on the None dataset.
It achieves the following results on the evaluation set:
- Exact Match: 66.6667
- F1: 74.9008
- Loss: 1.0138
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Exact Match | F1 | Validation Loss |
|:-------------:|:-----:|:----:|:-----------:|:-------:|:---------------:|
| 0.7813 | 1.63 | 500 | 66.6667 | 74.9008 | 1.0138 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu117
- Datasets 2.7.1
- Tokenizers 0.13.2
|
AliSab/Taxi-v3
|
AliSab
| 2023-01-02T07:22:29Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-01-02T06:51:09Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.56 +/- 2.71
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="AliSab/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"])
```
|
sekinko/weights_text
|
sekinko
| 2023-01-02T06:37:09Z | 161 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"generated_from_trainer",
"license:cc-by-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2023-01-02T05:11:35Z |
---
license: cc-by-sa-4.0
tags:
- generated_from_trainer
model-index:
- name: weights_text
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. -->
# weights_text
This model is a fine-tuned version of [cl-tohoku/bert-base-japanese-whole-word-masking](https://huggingface.co/cl-tohoku/bert-base-japanese-whole-word-masking) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
### Framework versions
- Transformers 4.24.0
- Pytorch 1.13.1+cu117
- Tokenizers 0.13.2
|
hobab185/my_awesome_pn_summary_model
|
hobab185
| 2023-01-02T06:32:12Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:pn_summary",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-01-01T15:16:14Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- pn_summary
metrics:
- rouge
model-index:
- name: my_awesome_pn_summary_model
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: pn_summary
type: pn_summary
config: 1.0.0
split: train
args: 1.0.0
metrics:
- name: Rouge1
type: rouge
value: 0.0
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# my_awesome_pn_summary_model
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the pn_summary dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1125
- Rouge1: 0.0
- Rouge2: 0.0
- Rougel: 0.0
- Rougelsum: 0.0
- Gen Len: 19.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 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
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| 0.1169 | 1.0 | 5127 | 0.1130 | 0.0 | 0.0 | 0.0 | 0.0 | 19.0 |
| 0.115 | 2.0 | 10254 | 0.1125 | 0.0 | 0.0 | 0.0 | 0.0 | 19.0 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
|
GeneralAwareness/Steampcari
|
GeneralAwareness
| 2023-01-02T05:57:12Z | 0 | 3 | null |
[
"stable-diffusion",
"v2",
"text-to-image",
"image-to-image",
"Embedding",
"en",
"license:cc-by-nc-sa-4.0",
"region:us"
] |
text-to-image
| 2023-01-02T05:43:18Z |
---
license: cc-by-nc-sa-4.0
language:
- en
thumbnail: "https://huggingface.co/GeneralAwareness/Steampcari/resolve/main/tmpi445q60c.png"
tags:
- stable-diffusion
- v2
- text-to-image
- image-to-image
- Embedding
---
Textual Inversion Embedding by General Awareness For SD 2.x trained on 768x768 images from various sources.
Install by downloading the .pt embedding, and put it in the \embeddings folder.
This embedding was made to do Steampunk caricatures with.
---
Use keyword: image in Steampcari style, Steampcari style, Steampcari, in the style of Steampcari, or by Steampcari.
---
a photograph of Brad Pitt, Steampcari
a photograph of Brad Pitt in the style of Steampcari
a photograph of Brad Pitt by Steampcari
a photograph of Tom Hanks in the style of Steampcari
a photograph of Dwayne The Rock Johnson in the style of Steampcari
a photograph of Emma Stone in the style of Steampcari
|
Mistermango24/furrystaberv2.2_Civitai
|
Mistermango24
| 2023-01-02T05:30:51Z | 0 | 2 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-01-02T05:03:22Z |
---
license: creativeml-openrail-m
---
|
adamwatters/rblx-character
|
adamwatters
| 2023-01-02T05:27:40Z | 4 | 6 |
diffusers
|
[
"diffusers",
"pytorch",
"stable-diffusion",
"text-to-image",
"diffusion-models-class",
"dreambooth-hackathon",
"wildcard",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-01-02T04:00:29Z |
---
license: creativeml-openrail-m
tags:
- pytorch
- diffusers
- stable-diffusion
- text-to-image
- diffusion-models-class
- dreambooth-hackathon
- wildcard
widget:
- text: a photo of rblx character in front of statue of liberty
---
# DreamBooth model for the rblx concept trained by adamwatters on the adamwatters/roblox-guy dataset.
## Description
<figure>
<img src=https://datasets-server.huggingface.co/assets/adamwatters/roblox-guy/--/adamwatters--roblox-guy/train/7/image/image.jpg width=200px height=200px>
<figcaption align = "left"><b>Screenshot from Roblox used for training</b></figcaption>
</figure>
This is a Stable Diffusion model fine-tuned on images of my specific customized Roblox avatar. Idea is: maybe it would be fun for Roblox players to make images of their avatars in different settings.
It can be used by modifying the instance_prompt: a photo of rblx character
This model was created as part of the DreamBooth Hackathon 🔥. Visit the [organisation page](https://huggingface.co/dreambooth-hackathon) for instructions on how to take part!
## Generate Images
<img src=https://huggingface.co/datasets/adamwatters/hosted-images/resolve/main/roblox-guy-grid.jpeg width=60%>
## Usage
```python
from diffusers import StableDiffusionPipeline
pipeline = StableDiffusionPipeline.from_pretrained('adamwatters/rblx-character')
image = pipeline().images[0]
image
```
|
Botnoi/wav2vec2-xls-r-300m-th-beta
|
Botnoi
| 2023-01-02T05:27:07Z | 75 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-12-28T03:39:46Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: wav2vec2-xls-r-300m-th-beta
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-xls-r-300m-th-beta
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: 0.9096
- Wer: 0.7261
- Cer: 0.2160
- Clean Cer: 0.1909
## Model description
This model is available until Jan 12, 2023
## Intended uses & limitations
More information needed
## Training and evaluation data
We use our custom dataset splited into 70k training dataset, and 7k evaluation dataset
This is our detailed dataset
1. Common Voice 11
- filter 5k fifties age males out
- remain 25k training dataset
2. Botnoi voice
- 45k training dataset
Both dataset was through our custom cleansing text data.
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 1000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer | Clean Cer |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:---------:|
| 6.9481 | 0.34 | 500 | 3.5952 | 1.0 | 0.9815 | 0.9779 |
| 2.0387 | 0.68 | 1000 | 0.9096 | 0.7261 | 0.2160 | 0.1909 |
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.0+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
|
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