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| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
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| downloads
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| likes
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11.7k
| library_name
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dzegan/a2c-PandaReachDense-v2 | dzegan | 2023-02-02T18:51:14Z | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"PandaReachDense-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-02-02T18:48:49Z | ---
library_name: stable-baselines3
tags:
- PandaReachDense-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachDense-v2
type: PandaReachDense-v2
metrics:
- type: mean_reward
value: -3.98 +/- 0.75
name: mean_reward
verified: false
---
# **A2C** Agent playing **PandaReachDense-v2**
This is a trained model of a **A2C** agent playing **PandaReachDense-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
XperienciaVirtual/sd-1-5-db-ai-creative-hub-hdbglv | XperienciaVirtual | 2023-02-02T18:29:01Z | 2 | 0 | diffusers | [
"diffusers",
"tensorboard",
"text-to-image",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
]
| text-to-image | 2023-02-02T18:28:03Z | ---
license: creativeml-openrail-m
tags:
- text-to-image
widget:
- text: hdbglv
---
### sd-1-5-db-ai-creative-hub-hdbglv Dreambooth model trained by jaimexv with [Hugging Face Dreambooth Training Space](https://huggingface.co/spaces/multimodalart/dreambooth-training) with the v1-5 base model
You run your new concept via `diffusers` [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb). Don't forget to use the concept prompts!
Sample pictures of:
hdbglv (use that on your prompt)
|
fermaat/ppo-SnowballTarget | fermaat | 2023-02-02T18:21:06Z | 5 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SnowballTarget",
"region:us"
]
| reinforcement-learning | 2023-02-02T18:21:00Z |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SnowballTarget
library_name: ml-agents
---
# **ppo** Agent playing **SnowballTarget**
This is a trained model of a **ppo** agent playing **SnowballTarget** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-SnowballTarget
2. Step 1: Write your model_id: fermaat/ppo-SnowballTarget
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play π
|
Anjoe/poetry-gpt2-large-complete | Anjoe | 2023-02-02T17:43:11Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2023-02-02T13:06:56Z | ---
license: mit
tags:
- generated_from_trainer
model-index:
- name: poetry-gpt2-large-complete
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. -->
# poetry-gpt2-large-complete
This model is a fine-tuned version of [benjamin/gerpt2-large](https://huggingface.co/benjamin/gerpt2-large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.5588
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 3.6616 | 1.0 | 20566 | 3.6252 |
| 3.2695 | 2.0 | 41132 | 3.5428 |
| 3.0406 | 3.0 | 61698 | 3.5588 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
|
h9LtLSb/whisper-small-es | h9LtLSb | 2023-02-02T17:29:19Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"whisper",
"automatic-speech-recognition",
"arxiv:1910.09700",
"model-index",
"endpoints_compatible",
"region:us"
]
| automatic-speech-recognition | 2023-02-01T20:57:48Z | ---
model-index:
- name: h9LtLSb/whisper-small-es
results:
- task:
type: automatic-speech-recognition
name: Automatic Speech Recognition
dataset:
name: mozilla-foundation/common_voice_11_0
type: mozilla-foundation/common_voice_11_0
config: es
split: test
metrics:
- type: wer
value: 8.43
name: WER
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
# Model Details
## Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
## Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
# Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
## Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
## Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
## Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
# Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
## Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
# Training Details
## Training Data
<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
## Training Procedure [optional]
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
### Preprocessing
[More Information Needed]
### Speeds, Sizes, Times
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
# Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
## Testing Data, Factors & Metrics
### Testing Data
<!-- This should link to a Data Card if possible. -->
[More Information Needed]
### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
## Results
[More Information Needed]
### Summary
# Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
# Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
# Technical Specifications [optional]
## Model Architecture and Objective
[More Information Needed]
## Compute Infrastructure
[More Information Needed]
### Hardware
[More Information Needed]
### Software
[More Information Needed]
# Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
# Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
# More Information [optional]
[More Information Needed]
# Model Card Authors [optional]
[More Information Needed]
# Model Card Contact
[More Information Needed]
|
vvn0/Taxi-v3 | vvn0 | 2023-02-02T16:24:58Z | 0 | 0 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-02-02T16:24:56Z | ---
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: 7.52 +/- 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="vvn0/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"])
```
|
vvn0/q-FrozenLake-v1-4x4-noSlippery | vvn0 | 2023-02-02T16:18:55Z | 0 | 0 | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-02-02T16:18:53Z | ---
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="vvn0/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"])
```
|
rabiyulfahim/pegasus_pararephrase | rabiyulfahim | 2023-02-02T15:50:53Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"paraphrasing",
"seq2seq",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text2text-generation | 2023-02-02T15:21:58Z | ---
language: en
license: apache-2.0
tags:
- pegasus
- paraphrasing
- seq2seq
---
## Model description
[PEGASUS](https://github.com/google-research/pegasus) fine-tuned for paraphrasing
## Model in Action π
```
import torch
from transformers import PegasusForConditionalGeneration, PegasusTokenizer
model_name = 'tuner007/pegasus_paraphrase'
torch_device = 'cuda' if torch.cuda.is_available() else 'cpu'
tokenizer = PegasusTokenizer.from_pretrained(model_name)
model = PegasusForConditionalGeneration.from_pretrained(model_name).to(torch_device)
def get_response(input_text,num_return_sequences,num_beams):
batch = tokenizer([input_text],truncation=True,padding='longest',max_length=60, return_tensors="pt").to(torch_device)
translated = model.generate(**batch,max_length=60,num_beams=num_beams, num_return_sequences=num_return_sequences, temperature=1.5)
tgt_text = tokenizer.batch_decode(translated, skip_special_tokens=True)
return tgt_text
```
#### Example:
```
num_beams = 10
num_return_sequences = 10
context = "The ultimate test of your knowledge is your capacity to convey it to another."
get_response(context,num_return_sequences,num_beams)
# output:
['The test of your knowledge is your ability to convey it.',
'The ability to convey your knowledge is the ultimate test of your knowledge.',
'The ability to convey your knowledge is the most important test of your knowledge.',
'Your capacity to convey your knowledge is the ultimate test of it.',
'The test of your knowledge is your ability to communicate it.',
'Your capacity to convey your knowledge is the ultimate test of your knowledge.',
'Your capacity to convey your knowledge to another is the ultimate test of your knowledge.',
'Your capacity to convey your knowledge is the most important test of your knowledge.',
'The test of your knowledge is how well you can convey it.',
'Your capacity to convey your knowledge is the ultimate test.']
```
> Created by [Arpit Rajauria](https://twitter.com/arpit_rajauria)
[](https://twitter.com/arpit_rajauria)
|
Jackmin108/Reinforce-CartPole-v1 | Jackmin108 | 2023-02-02T15:42:33Z | 0 | 0 | null | [
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-02-02T15:03:46Z | ---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-CartPole-v1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 1000.00 +/- 0.00
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
jannikskytt/ppo-Huggy | jannikskytt | 2023-02-02T15:41:05Z | 11 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
]
| reinforcement-learning | 2023-02-02T15:40:57Z |
---
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: jannikskytt/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play π
|
gokuls/distilbert_sa_GLUE_Experiment_data_aug_stsb_384 | gokuls | 2023-02-02T15:25:51Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"en",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2023-02-02T14:45:15Z | ---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- spearmanr
model-index:
- name: distilbert_sa_GLUE_Experiment_data_aug_stsb_384
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE STSB
type: glue
args: stsb
metrics:
- name: Spearmanr
type: spearmanr
value: 0.1905368464556858
---
<!-- 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_sa_GLUE_Experiment_data_aug_stsb_384
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the GLUE STSB dataset.
It achieves the following results on the evaluation set:
- Loss: 2.8610
- Pearson: 0.1867
- Spearmanr: 0.1905
- Combined Score: 0.1886
## 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: 256
- eval_batch_size: 256
- seed: 10
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Pearson | Spearmanr | Combined Score |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:---------:|:--------------:|
| 0.9512 | 1.0 | 1259 | 2.8610 | 0.1867 | 0.1905 | 0.1886 |
| 0.3073 | 2.0 | 2518 | 3.0669 | 0.1520 | 0.1508 | 0.1514 |
| 0.1587 | 3.0 | 3777 | 3.1954 | 0.1595 | 0.1627 | 0.1611 |
| 0.1014 | 4.0 | 5036 | 2.9135 | 0.1600 | 0.1591 | 0.1596 |
| 0.0713 | 5.0 | 6295 | 3.2956 | 0.1514 | 0.1464 | 0.1489 |
| 0.0551 | 6.0 | 7554 | 3.1588 | 0.1712 | 0.1642 | 0.1677 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.14.0a0+410ce96
- Datasets 2.9.0
- Tokenizers 0.13.2
|
torchbearer241996/finetuning-sentiment-model-3000-samples | torchbearer241996 | 2023-02-02T15:23:37Z | 5 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2023-02-02T05:12:18Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
metrics:
- accuracy
- f1
model-index:
- name: finetuning-sentiment-model-3000-samples
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: imdb
type: imdb
config: plain_text
split: test
args: plain_text
metrics:
- name: Accuracy
type: accuracy
value: 0.8633333333333333
- name: F1
type: f1
value: 0.8637873754152824
---
<!-- 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. -->
# finetuning-sentiment-model-3000-samples
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3200
- Accuracy: 0.8633
- F1: 0.8638
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
### Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
|
toinsson/poca-SoccerTwos_1 | toinsson | 2023-02-02T15:23:33Z | 9 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SoccerTwos",
"region:us"
]
| reinforcement-learning | 2023-02-02T15:14:36Z |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SoccerTwos
library_name: ml-agents
---
# **poca** Agent playing **SoccerTwos**
This is a trained model of a **poca** agent playing **SoccerTwos** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-SoccerTwos
2. Step 1: Write your model_id: toinsson/poca-SoccerTwos_1
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play π
|
YoriV/q-Taxi-v3 | YoriV | 2023-02-02T15:19:37Z | 0 | 0 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-02-02T15:19:34Z | ---
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="YoriV/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"])
```
|
KushalRamaiya/sd-class-butterflies-32 | KushalRamaiya | 2023-02-02T14:36:01Z | 6 | 0 | diffusers | [
"diffusers",
"pytorch",
"unconditional-image-generation",
"diffusion-models-class",
"license:mit",
"diffusers:DDPMPipeline",
"region:us"
]
| unconditional-image-generation | 2023-02-02T14:35:24Z | ---
license: mit
tags:
- pytorch
- diffusers
- unconditional-image-generation
- diffusion-models-class
---
# Model Card for Unit 1 of the [Diffusion Models Class π§¨](https://github.com/huggingface/diffusion-models-class)
This model is a diffusion model for unconditional image generation of cute π¦.
## Usage
```python
from diffusers import DDPMPipeline
pipeline = DDPMPipeline.from_pretrained('KushalRamaiya/sd-class-butterflies-32')
image = pipeline().images[0]
image
```
|
tr9800a/ppo-Huggy | tr9800a | 2023-02-02T14:28:53Z | 9 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
]
| reinforcement-learning | 2023-02-02T13:46:34Z |
---
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: tr9800a/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play π
|
research-backup/mbart-large-cc25-itquad-qg-ae | research-backup | 2023-02-02T14:09:46Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"mbart",
"text2text-generation",
"question generation",
"answer extraction",
"it",
"dataset:lmqg/qg_itquad",
"arxiv:2210.03992",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text2text-generation | 2023-02-02T13:55:30Z |
---
license: cc-by-4.0
metrics:
- bleu4
- meteor
- rouge-l
- bertscore
- moverscore
language: it
datasets:
- lmqg/qg_itquad
pipeline_tag: text2text-generation
tags:
- question generation
- answer extraction
widget:
- text: "generate question: <hl> Dopo il 1971 <hl> , l' OPEC ha tardato ad adeguare i prezzi per riflettere tale deprezzamento."
example_title: "Question Generation Example 1"
- text: "generate question: L' individuazione del petrolio e lo sviluppo di nuovi giacimenti richiedeva in genere <hl> da cinque a dieci anni <hl> prima di una produzione significativa."
example_title: "Question Generation Example 2"
- text: "generate question: il <hl> Giappone <hl> Γ¨ stato il paese piΓΉ dipendente dal petrolio arabo."
example_title: "Question Generation Example 3"
- text: "extract answers: <hl> Il 6 ottobre 1973 , la Siria e l' Egitto, con il sostegno di altre nazioni arabe, lanciarono un attacco a sorpresa su Israele, su Yom Kippur. <hl> Questo rinnovo delle ostilitΓ nel conflitto arabo-israeliano ha liberato la pressione economica sottostante sui prezzi del petrolio. All' epoca, l' Iran era il secondo esportatore mondiale di petrolio e un vicino alleato degli Stati Uniti. Settimane piΓΉ tardi, lo sciΓ d' Iran ha detto in un' intervista: Naturalmente[il prezzo del petrolio] sta andando a salire Certamente! E come! Avete[Paesi occidentali] aumentato il prezzo del grano che ci vendete del 300 per cento, e lo stesso per zucchero e cemento."
example_title: "Answer Extraction Example 1"
- text: "extract answers: <hl> Furono introdotti autocarri compatti, come la Toyota Hilux e il Datsun Truck, seguiti dal camion Mazda (venduto come il Ford Courier), e l' Isuzu costruito Chevrolet LUV. <hl> Mitsubishi rebranded il suo Forte come Dodge D-50 pochi anni dopo la crisi petrolifera. Mazda, Mitsubishi e Isuzu avevano partnership congiunte rispettivamente con Ford, Chrysler e GM. In seguito i produttori americani introdussero le loro sostituzioni nazionali (Ford Ranger, Dodge Dakota e la Chevrolet S10/GMC S-15), ponendo fine alla loro politica di importazione vincolata."
example_title: "Answer Extraction Example 2"
model-index:
- name: lmqg/mbart-large-cc25-itquad-qg-ae
results:
- task:
name: Text2text Generation
type: text2text-generation
dataset:
name: lmqg/qg_itquad
type: default
args: default
metrics:
- name: BLEU4 (Question Generation)
type: bleu4_question_generation
value: 7.06
- name: ROUGE-L (Question Generation)
type: rouge_l_question_generation
value: 20.15
- name: METEOR (Question Generation)
type: meteor_question_generation
value: 16.86
- name: BERTScore (Question Generation)
type: bertscore_question_generation
value: 79.29
- name: MoverScore (Question Generation)
type: moverscore_question_generation
value: 55.92
- name: QAAlignedF1Score-BERTScore (Question & Answer Generation (with Gold Answer))
type: qa_aligned_f1_score_bertscore_question_answer_generation_with_gold_answer
value: 82.65
- name: QAAlignedRecall-BERTScore (Question & Answer Generation (with Gold Answer))
type: qa_aligned_recall_bertscore_question_answer_generation_with_gold_answer
value: 84.34
- name: QAAlignedPrecision-BERTScore (Question & Answer Generation (with Gold Answer))
type: qa_aligned_precision_bertscore_question_answer_generation_with_gold_answer
value: 81.06
- name: QAAlignedF1Score-MoverScore (Question & Answer Generation (with Gold Answer))
type: qa_aligned_f1_score_moverscore_question_answer_generation_with_gold_answer
value: 56.14
- name: QAAlignedRecall-MoverScore (Question & Answer Generation (with Gold Answer))
type: qa_aligned_recall_moverscore_question_answer_generation_with_gold_answer
value: 57.13
- name: QAAlignedPrecision-MoverScore (Question & Answer Generation (with Gold Answer))
type: qa_aligned_precision_moverscore_question_answer_generation_with_gold_answer
value: 55.22
- name: BLEU4 (Answer Extraction)
type: bleu4_answer_extraction
value: 20.21
- name: ROUGE-L (Answer Extraction)
type: rouge_l_answer_extraction
value: 46.51
- name: METEOR (Answer Extraction)
type: meteor_answer_extraction
value: 44.48
- name: BERTScore (Answer Extraction)
type: bertscore_answer_extraction
value: 90.63
- name: MoverScore (Answer Extraction)
type: moverscore_answer_extraction
value: 83.05
- name: AnswerF1Score (Answer Extraction)
type: answer_f1_score__answer_extraction
value: 76.59
- name: AnswerExactMatch (Answer Extraction)
type: answer_exact_match_answer_extraction
value: 63.88
---
# Model Card of `lmqg/mbart-large-cc25-itquad-qg-ae`
This model is fine-tuned version of [facebook/mbart-large-cc25](https://huggingface.co/facebook/mbart-large-cc25) for question generation and answer extraction jointly on the [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-generation).
### Overview
- **Language model:** [facebook/mbart-large-cc25](https://huggingface.co/facebook/mbart-large-cc25)
- **Language:** it
- **Training data:** [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) (default)
- **Online Demo:** [https://autoqg.net/](https://autoqg.net/)
- **Repository:** [https://github.com/asahi417/lm-question-generation](https://github.com/asahi417/lm-question-generation)
- **Paper:** [https://arxiv.org/abs/2210.03992](https://arxiv.org/abs/2210.03992)
### Usage
- With [`lmqg`](https://github.com/asahi417/lm-question-generation#lmqg-language-model-for-question-generation-)
```python
from lmqg import TransformersQG
# initialize model
model = TransformersQG(language="it", model="lmqg/mbart-large-cc25-itquad-qg-ae")
# model prediction
question_answer_pairs = model.generate_qa("Dopo il 1971 , l' OPEC ha tardato ad adeguare i prezzi per riflettere tale deprezzamento.")
```
- With `transformers`
```python
from transformers import pipeline
pipe = pipeline("text2text-generation", "lmqg/mbart-large-cc25-itquad-qg-ae")
# answer extraction
answer = pipe("generate question: <hl> Dopo il 1971 <hl> , l' OPEC ha tardato ad adeguare i prezzi per riflettere tale deprezzamento.")
# question generation
question = pipe("extract answers: <hl> Il 6 ottobre 1973 , la Siria e l' Egitto, con il sostegno di altre nazioni arabe, lanciarono un attacco a sorpresa su Israele, su Yom Kippur. <hl> Questo rinnovo delle ostilitΓ nel conflitto arabo-israeliano ha liberato la pressione economica sottostante sui prezzi del petrolio. All' epoca, l' Iran era il secondo esportatore mondiale di petrolio e un vicino alleato degli Stati Uniti. Settimane piΓΉ tardi, lo sciΓ d' Iran ha detto in un' intervista: Naturalmente[il prezzo del petrolio] sta andando a salire Certamente! E come! Avete[Paesi occidentali] aumentato il prezzo del grano che ci vendete del 300 per cento, e lo stesso per zucchero e cemento.")
```
## Evaluation
- ***Metric (Question Generation)***: [raw metric file](https://huggingface.co/lmqg/mbart-large-cc25-itquad-qg-ae/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_itquad.default.json)
| | Score | Type | Dataset |
|:-----------|--------:|:--------|:-----------------------------------------------------------------|
| BERTScore | 79.29 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
| Bleu_1 | 22.03 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
| Bleu_2 | 14.31 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
| Bleu_3 | 9.9 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
| Bleu_4 | 7.06 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
| METEOR | 16.86 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
| MoverScore | 55.92 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
| ROUGE_L | 20.15 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
- ***Metric (Question & Answer Generation)***: [raw metric file](https://huggingface.co/lmqg/mbart-large-cc25-itquad-qg-ae/raw/main/eval/metric.first.answer.paragraph.questions_answers.lmqg_qg_itquad.default.json)
| | Score | Type | Dataset |
|:--------------------------------|--------:|:--------|:-----------------------------------------------------------------|
| QAAlignedF1Score (BERTScore) | 82.65 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
| QAAlignedF1Score (MoverScore) | 56.14 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
| QAAlignedPrecision (BERTScore) | 81.06 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
| QAAlignedPrecision (MoverScore) | 55.22 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
| QAAlignedRecall (BERTScore) | 84.34 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
| QAAlignedRecall (MoverScore) | 57.13 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
- ***Metric (Answer Extraction)***: [raw metric file](https://huggingface.co/lmqg/mbart-large-cc25-itquad-qg-ae/raw/main/eval/metric.first.answer.paragraph_sentence.answer.lmqg_qg_itquad.default.json)
| | Score | Type | Dataset |
|:-----------------|--------:|:--------|:-----------------------------------------------------------------|
| AnswerExactMatch | 63.88 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
| AnswerF1Score | 76.59 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
| BERTScore | 90.63 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
| Bleu_1 | 33.66 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
| Bleu_2 | 27.96 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
| Bleu_3 | 23.79 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
| Bleu_4 | 20.21 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
| METEOR | 44.48 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
| MoverScore | 83.05 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
| ROUGE_L | 46.51 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
## Training hyperparameters
The following hyperparameters were used during fine-tuning:
- dataset_path: lmqg/qg_itquad
- dataset_name: default
- input_types: ['paragraph_answer', 'paragraph_sentence']
- output_types: ['question', 'answer']
- prefix_types: ['qg', 'ae']
- model: facebook/mbart-large-cc25
- max_length: 512
- max_length_output: 32
- epoch: 8
- batch: 2
- lr: 0.0001
- fp16: False
- random_seed: 1
- gradient_accumulation_steps: 32
- label_smoothing: 0.15
The full configuration can be found at [fine-tuning config file](https://huggingface.co/lmqg/mbart-large-cc25-itquad-qg-ae/raw/main/trainer_config.json).
## Citation
```
@inproceedings{ushio-etal-2022-generative,
title = "{G}enerative {L}anguage {M}odels for {P}aragraph-{L}evel {Q}uestion {G}eneration",
author = "Ushio, Asahi and
Alva-Manchego, Fernando and
Camacho-Collados, Jose",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2022",
address = "Abu Dhabi, U.A.E.",
publisher = "Association for Computational Linguistics",
}
```
|
nandysoham16/14-clustered_aug | nandysoham16 | 2023-02-02T14:03:07Z | 4 | 0 | keras | [
"keras",
"tf",
"distilbert",
"en",
"arxiv:1910.09700",
"license:mit",
"region:us"
]
| null | 2023-02-02T13:56:33Z | ---
language: en
license: mit
library_name: keras
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
# Model Details
## Model Description
<!-- Provide a longer summary of what this model is. -->
['The_Legend_of_Zelda:_Twilight_Princess', 'Symbiosis', 'Tristan_da_Cunha', 'Hokkien', 'Thuringia', 'Samoa', 'Chinese_characters', 'Digimon', 'Tuvalu', 'Geological_history_of_Earth']
- **Developed by:** nandysoham
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** en
- **License:** mit
- **Finetuned from model [optional]:** [More Information Needed]
## Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
# Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
## Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
## Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
## Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
# Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
## Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
# Training Details
## Training Data
<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
## Training Procedure [optional]
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
### Preprocessing
[More Information Needed]
### Speeds, Sizes, Times
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
# Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
## Testing Data, Factors & Metrics
### Testing Data
<!-- This should link to a Data Card if possible. -->
[More Information Needed]
### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
## Results
[More Information Needed]
### Summary
# Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
# Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
# Technical Specifications [optional]
## Model Architecture and Objective
[More Information Needed]
## Compute Infrastructure
[More Information Needed]
### Hardware
[More Information Needed]
### Software
[More Information Needed]
# Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
# Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
# More Information [optional]
[More Information Needed]
# Model Card Authors [optional]
[More Information Needed]
# Model Card Contact
[More Information Needed]
|
nandysoham16/13-clustered_aug | nandysoham16 | 2023-02-02T13:55:38Z | 3 | 0 | keras | [
"keras",
"tf",
"distilbert",
"en",
"arxiv:1910.09700",
"license:mit",
"region:us"
]
| null | 2023-02-02T13:45:53Z | ---
language: en
license: mit
library_name: keras
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
# Model Details
## Model Description
<!-- Provide a longer summary of what this model is. -->
['Iranian_languages', 'Aspirated_consonant', 'Catalan_language', 'Estonian_language', 'Dialect', 'Slavs', 'Szlachta', 'Letter_case', 'Old_English', 'Mesozoic', 'ASCII', 'Sanskrit', 'Multiracial_American', 'Dutch_language', 'Germans', 'Avicenna', 'Textual_criticism', 'Unicode', 'Culture', 'Serbo-Croatian', 'Czech_language', 'Spanish_language_in_the_United_States', 'Greeks', 'Translation', 'Kievan_Rus%27', 'Russian_language', 'Armenians', 'Myocardial_infarction']
- **Developed by:** nandysoham
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** en
- **License:** mit
- **Finetuned from model [optional]:** [More Information Needed]
## Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
# Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
## Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
## Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
## Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
# Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
## Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
# Training Details
## Training Data
<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
## Training Procedure [optional]
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
### Preprocessing
[More Information Needed]
### Speeds, Sizes, Times
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
# Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
## Testing Data, Factors & Metrics
### Testing Data
<!-- This should link to a Data Card if possible. -->
[More Information Needed]
### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
## Results
[More Information Needed]
### Summary
# Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
# Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
# Technical Specifications [optional]
## Model Architecture and Objective
[More Information Needed]
## Compute Infrastructure
[More Information Needed]
### Hardware
[More Information Needed]
### Software
[More Information Needed]
# Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
# Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
# More Information [optional]
[More Information Needed]
# Model Card Authors [optional]
[More Information Needed]
# Model Card Contact
[More Information Needed]
|
sakodu/ppo-LunarLander-v2 | sakodu | 2023-02-02T13:47:32Z | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-02-02T13:47:11Z | ---
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: 261.91 +/- 28.51
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
...
```
|
Martinkoling/my-first-setfit-hyperparam-4epochs | Martinkoling | 2023-02-02T13:39:06Z | 1 | 0 | sentence-transformers | [
"sentence-transformers",
"pytorch",
"mpnet",
"feature-extraction",
"sentence-similarity",
"transformers",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| sentence-similarity | 2023-02-02T13:38:57Z | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
model = AutoModel.from_pretrained('{MODEL_NAME}')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 120 with parameters:
```
{'batch_size': 4, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss`
Parameters of the fit()-Method:
```
{
"epochs": 4,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 4.3853483064647136e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": 480,
"warmup_steps": 48,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: MPNetModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information --> |
OlegBatrakov/sd-class-butterflies-32 | OlegBatrakov | 2023-02-02T13:38:18Z | 1 | 0 | diffusers | [
"diffusers",
"pytorch",
"unconditional-image-generation",
"diffusion-models-class",
"license:mit",
"diffusers:DDPMPipeline",
"region:us"
]
| unconditional-image-generation | 2023-02-02T13:37:51Z | ---
license: mit
tags:
- pytorch
- diffusers
- unconditional-image-generation
- diffusion-models-class
---
# Model Card for Unit 1 of the [Diffusion Models Class π§¨](https://github.com/huggingface/diffusion-models-class)
This model is a diffusion model for unconditional image generation of cute π¦.
## Usage
```python
from diffusers import DDPMPipeline
pipeline = DDPMPipeline.from_pretrained('OlegBatrakov/sd-class-butterflies-32')
image = pipeline().images[0]
image
```
|
sinny/ppo-SnowballTarget | sinny | 2023-02-02T13:36:05Z | 7 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SnowballTarget",
"region:us"
]
| reinforcement-learning | 2023-02-02T13:36:03Z |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SnowballTarget
library_name: ml-agents
---
# **ppo** Agent playing **SnowballTarget**
This is a trained model of a **ppo** agent playing **SnowballTarget** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-SnowballTarget
2. Step 1: Write your model_id: sinny/ppo-SnowballTarget
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play π
|
stevaras2/poca-SoccerTwos | stevaras2 | 2023-02-02T13:19:26Z | 4 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SoccerTwos",
"region:us"
]
| reinforcement-learning | 2023-02-02T13:11:36Z |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SoccerTwos
library_name: ml-agents
---
# **poca** Agent playing **SoccerTwos**
This is a trained model of a **poca** agent playing **SoccerTwos** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-SoccerTwos
2. Step 1: Write your model_id: stevaras2/poca-SoccerTwos
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play π
|
gokuls/distilbert_sa_GLUE_Experiment_data_aug_sst2_192 | gokuls | 2023-02-02T13:10:46Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"en",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2023-02-02T11:56:46Z | ---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: distilbert_sa_GLUE_Experiment_data_aug_sst2_192
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE SST2
type: glue
args: sst2
metrics:
- name: Accuracy
type: accuracy
value: 0.786697247706422
---
<!-- 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_sa_GLUE_Experiment_data_aug_sst2_192
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the GLUE SST2 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5194
- Accuracy: 0.7867
## 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: 256
- eval_batch_size: 256
- seed: 10
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.3624 | 1.0 | 4374 | 0.5194 | 0.7867 |
| 0.2778 | 2.0 | 8748 | 0.6027 | 0.7867 |
| 0.2345 | 3.0 | 13122 | 0.6679 | 0.7856 |
| 0.2023 | 4.0 | 17496 | 0.7301 | 0.7890 |
| 0.1774 | 5.0 | 21870 | 0.7613 | 0.7718 |
| 0.1582 | 6.0 | 26244 | 0.9199 | 0.7626 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.14.0a0+410ce96
- Datasets 2.9.0
- Tokenizers 0.13.2
|
s3nh/DialoGPT-tony-montana | s3nh | 2023-02-02T12:55:11Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"en",
"license:openrail",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2023-02-02T09:56:44Z | ---
license: openrail
language:
- en
pipeline_tag: conversational
---
<img src = 'https://images.unsplash.com/photo-1628432136678-43ff9be34064?ixlib=rb-4.0.3&ixid=MnwxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8&auto=format&fit=crop&w=663&q=80'>
<a href="https://www.buymeacoffee.com/s3nh"><img src="https://www.buymeacoffee.com/assets/img/guidelines/download-assets-sm-1.svg" alt=""></a>
### Description
DialogGPT is a variant of the GPT (Generative Pretrained Transformer) language model developed by OpenAI. It's a deep neural network-based language model that's trained on massive amounts of text data to generate human-like text.
DialogGPT uses the transformer architecture, which is a type of neural network designed for processing sequential data such as language. During the training phase, the model is exposed to a large corpus of text and learns to predict the next word in a sequence given the previous words.
In the context of dialog, DialogGPT is trained to predict the response in a conversation, given the context of the conversation. This context can include one or more turns of the conversation, along with any additional information such as the topic of the conversation or the speaker's personality.
At inference time, the model takes the current context of the conversation as input and generates a response. The response is generated by sampling from the model's predicted distribution over the vocabulary.
Overall, DialogGPT provides a flexible and powerful solution for generating human-like text in a conversational context, allowing for the creation of a wide range of applications such as chatbots, conversational agents, and virtual assistants
## Parameters
Model was trained for 40 epochs, using params as follows.
```
per_gpu_train_batch_size: int = 2
self.per_gpu_eval_batch_size: int = 2
self.gradient_accumulation_steps: int = 1
self.learning_rate: float = 5e-5
self.weight_decay: float = 0.0
self.adam_epsilon: float = 1e-8
self.max_grad_norm: int = 1.0
self.num_train_epochs: int = 40
self.max_steps: int = -1
self.warmup_steps: int = 0
self.logging_steps: int = 1000
self.save_steps: int = 3500
self.save_total_limit = None
self.eval_all_checkpoints: bool = False
self.no_cuda: bool = False
self.overwrite_output_dir: bool = True
self.overwrite_cache: bool = True
self.should_continue: bool = False
self.seed: int = 42
self.local_rank: int = -1
self.fp16: bool = False
self.fp16_opt_level: str = 'O1'
```
## Usage
DialoGPT **large** version, finetuned on Tony Montana sequences (ScarFace main character).
Simple snippet of how to infer of this model:
```python
from transformers import AutoModelWithLMHead, AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained('s3nh/DialoGPT-tony-montana')
model = AutoModelWithLMHead.from_pretrained('s3nh/DialoGPT-tony-montana')
for step in range(4):
new_user_input_ids = tokenizer.encode(input(">> User:") + tokenizer.eos_token, return_tensors='pt')
bot_input_ids = torch.cat([chat_history_ids, new_user_input_ids], dim=-1) if step > 0 else new_user_input_ids
chat_history_ids = model.generate(
bot_input_ids, max_length=200,
pad_token_id=tokenizer.eos_token_id,
no_repeat_ngram_size=3,
do_sample=True,
top_k=100,
top_p=0.7,
temperature=0.8
)
print("MontanaBot: {}".format(tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True)))
|
Aotsuyu/DiscoElysiumLora | Aotsuyu | 2023-02-02T12:52:41Z | 0 | 5 | null | [
"license:creativeml-openrail-m",
"region:us"
]
| null | 2023-01-11T19:12:18Z | ---
license: creativeml-openrail-m
---
|
nandysoham16/7-clustered_aug | nandysoham16 | 2023-02-02T12:37:56Z | 1 | 0 | keras | [
"keras",
"tf",
"distilbert",
"en",
"arxiv:1910.09700",
"license:mit",
"region:us"
]
| null | 2023-02-02T12:30:33Z | ---
language: en
license: mit
library_name: keras
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
# Model Details
## Model Description
<!-- Provide a longer summary of what this model is. -->
['Spectre_(2015_film)', 'Architecture', 'Materialism', 'Russian_Soviet_Federative_Socialist_Republic', 'Hellenistic_period', 'Gothic_architecture', 'Cubism', 'Renewable_energy_commercialization', 'Neoclassical_architecture', 'Idealism', 'Georgian_architecture', 'Economy_of_Greece']
- **Developed by:** nandysoham
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** en
- **License:** mit
- **Finetuned from model [optional]:** [More Information Needed]
## Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
# Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
## Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
## Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
## Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
# Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
## Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
# Training Details
## Training Data
<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
## Training Procedure [optional]
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
### Preprocessing
[More Information Needed]
### Speeds, Sizes, Times
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
# Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
## Testing Data, Factors & Metrics
### Testing Data
<!-- This should link to a Data Card if possible. -->
[More Information Needed]
### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
## Results
[More Information Needed]
### Summary
# Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
# Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
# Technical Specifications [optional]
## Model Architecture and Objective
[More Information Needed]
## Compute Infrastructure
[More Information Needed]
### Hardware
[More Information Needed]
### Software
[More Information Needed]
# Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
# Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
# More Information [optional]
[More Information Needed]
# Model Card Authors [optional]
[More Information Needed]
# Model Card Contact
[More Information Needed]
|
vsrinivas/marian-finetuned-kde4-en-to-hi | vsrinivas | 2023-02-02T12:35:16Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"translation",
"generated_from_trainer",
"dataset:kde4",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| translation | 2023-01-30T17:10:08Z | ---
license: apache-2.0
tags:
- translation
- generated_from_trainer
datasets:
- kde4
metrics:
- bleu
model-index:
- name: marian-finetuned-kde4-en-to-hi
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: kde4
type: kde4
config: en-hi
split: train
args: en-hi
metrics:
- name: Bleu
type: bleu
value: 51.039293551719226
---
<!-- 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. -->
# marian-finetuned-kde4-en-to-hi
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-hi](https://huggingface.co/Helsinki-NLP/opus-mt-en-hi) on the kde4 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9597
- Bleu: 51.0393
## 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: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
|
huynhdoo/camembert-base-finetuned-CLS | huynhdoo | 2023-02-02T12:19:37Z | 6 | 0 | transformers | [
"transformers",
"tf",
"tensorboard",
"camembert",
"text-classification",
"generated_from_keras_callback",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2023-02-02T11:02:02Z | ---
license: mit
tags:
- generated_from_keras_callback
model-index:
- name: huynhdoo/camembert-base-finetuned-CLS
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. -->
# huynhdoo/camembert-base-finetuned-CLS
This model is a fine-tuned version of [camembert-base](https://huggingface.co/camembert-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.1062
- Validation Loss: 0.1546
- Train Accuracy: 0.9521
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 669, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Train Accuracy | Epoch |
|:----------:|:---------------:|:--------------:|:-----:|
| 0.3620 | 0.1712 | 0.9471 | 0 |
| 0.1632 | 0.1488 | 0.9521 | 1 |
| 0.1062 | 0.1546 | 0.9521 | 2 |
### Framework versions
- Transformers 4.26.0
- TensorFlow 2.9.2
- Datasets 2.9.0
- Tokenizers 0.13.2
|
nandysoham16/5-clustered_aug | nandysoham16 | 2023-02-02T12:12:17Z | 1 | 0 | keras | [
"keras",
"tf",
"distilbert",
"en",
"arxiv:1910.09700",
"license:mit",
"region:us"
]
| null | 2023-02-02T12:05:45Z | ---
language: en
license: mit
library_name: keras
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
# Model Details
## Model Description
<!-- Provide a longer summary of what this model is. -->
['Daylight_saving_time', 'Chihuahua_(state)', 'United_States_dollar', 'Gregorian_calendar', 'Circadian_rhythm', 'Department_store', 'Planck_constant']
- **Developed by:** nandysoham
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** en
- **License:** mit
- **Finetuned from model [optional]:** [More Information Needed]
## Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
# Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
## Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
## Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
## Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
# Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
## Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
# Training Details
## Training Data
<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
## Training Procedure [optional]
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
### Preprocessing
[More Information Needed]
### Speeds, Sizes, Times
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
# Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
## Testing Data, Factors & Metrics
### Testing Data
<!-- This should link to a Data Card if possible. -->
[More Information Needed]
### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
## Results
[More Information Needed]
### Summary
# Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
# Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
# Technical Specifications [optional]
## Model Architecture and Objective
[More Information Needed]
## Compute Infrastructure
[More Information Needed]
### Hardware
[More Information Needed]
### Software
[More Information Needed]
# Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
# Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
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[More Information Needed]
# Model Card Contact
[More Information Needed]
|
dyy2003/pegasus-samsum | dyy2003 | 2023-02-02T11:50:01Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"pegasus",
"text2text-generation",
"generated_from_trainer",
"dataset:samsum",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text2text-generation | 2023-02-02T11:05:54Z | ---
tags:
- generated_from_trainer
datasets:
- samsum
model-index:
- name: pegasus-samsum
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. -->
# pegasus-samsum
This model is a fine-tuned version of [google/pegasus-cnn_dailymail](https://huggingface.co/google/pegasus-cnn_dailymail) on the samsum 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: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 1
### Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cpu
- Datasets 2.9.0
- Tokenizers 0.13.2
|
mqy/mt5-small-finetuned-1feb-2 | mqy | 2023-02-02T11:48:14Z | 8 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"mt5",
"text2text-generation",
"summarization",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| summarization | 2023-02-02T10:35:48Z | ---
license: apache-2.0
tags:
- summarization
- generated_from_trainer
metrics:
- rouge
model-index:
- name: mt5-small-finetuned-1feb-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. -->
# mt5-small-finetuned-1feb-2
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.3856
- Rouge1: 8.74
- Rouge2: 2.66
- Rougel: 8.58
- Rougelsum: 8.6
## 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: 10
- eval_batch_size: 10
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|
| 5.3153 | 1.0 | 311 | 2.6946 | 7.56 | 2.04 | 7.55 | 7.46 |
| 3.3159 | 2.0 | 622 | 2.5923 | 8.07 | 2.28 | 8.05 | 8.02 |
| 3.092 | 3.0 | 933 | 2.5342 | 7.83 | 2.01 | 7.81 | 7.76 |
| 2.9676 | 4.0 | 1244 | 2.4982 | 8.45 | 2.49 | 8.37 | 8.39 |
| 2.862 | 5.0 | 1555 | 2.4627 | 8.3 | 2.5 | 8.26 | 8.27 |
| 2.7891 | 6.0 | 1866 | 2.4366 | 8.67 | 2.81 | 8.53 | 8.55 |
| 2.7391 | 7.0 | 2177 | 2.4215 | 8.51 | 2.54 | 8.45 | 8.42 |
| 2.6887 | 8.0 | 2488 | 2.4277 | 8.71 | 2.53 | 8.56 | 8.54 |
| 2.6392 | 9.0 | 2799 | 2.3939 | 8.49 | 2.53 | 8.4 | 8.4 |
| 2.6139 | 10.0 | 3110 | 2.4015 | 9.28 | 2.85 | 9.14 | 9.19 |
| 2.5727 | 11.0 | 3421 | 2.3956 | 9.24 | 2.9 | 9.08 | 9.09 |
| 2.5595 | 12.0 | 3732 | 2.3856 | 8.45 | 2.59 | 8.31 | 8.35 |
| 2.5471 | 13.0 | 4043 | 2.3891 | 8.64 | 2.79 | 8.53 | 8.52 |
| 2.5231 | 14.0 | 4354 | 2.3870 | 8.78 | 2.79 | 8.64 | 8.6 |
| 2.5024 | 15.0 | 4665 | 2.3856 | 8.74 | 2.66 | 8.58 | 8.6 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
|
blazers/nfmystyle | blazers | 2023-02-02T11:41:40Z | 5 | 0 | diffusers | [
"diffusers",
"safetensors",
"text-to-image",
"stable-diffusion",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
]
| text-to-image | 2023-02-02T11:16:34Z | ---
license: creativeml-openrail-m
tags:
- text-to-image
- stable-diffusion
---
### nfmystyle Dreambooth model trained by blazers 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:
use CFG Scale To :- 3.5 final
|
jamm55/autotrain-improved-pidgin-model-2837583189 | jamm55 | 2023-02-02T11:31:34Z | 15 | 4 | transformers | [
"transformers",
"pytorch",
"marian",
"text2text-generation",
"autotrain",
"translation",
"unk",
"dataset:jamm55/autotrain-data-improved-pidgin-model",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| translation | 2023-01-11T17:45:15Z | ---
tags:
- autotrain
- translation
language:
- unk
- unk
datasets:
- jamm55/autotrain-data-improved-pidgin-model
co2_eq_emissions:
emissions: 4.315660252959388
---
# Model Trained Using AutoTrain
- Problem type: Translation
- Model ID: 2837583189
- CO2 Emissions (in grams): 4.3157
## Validation Metrics
- Loss: 0.753
- SacreBLEU: 46.837
- Gen len: 21.250
-
- ## English to Pidgin
- This model will translate English to pidgin
- Pidgin, a simplified version of english. Mostly used in Africa |
HDKCL/izamizam | HDKCL | 2023-02-02T11:15:26Z | 78 | 1 | diffusers | [
"diffusers",
"text-to-image",
"stable-diffusion",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
]
| text-to-image | 2022-12-11T01:27:37Z | ---
license: creativeml-openrail-m
tags:
- text-to-image
- stable-diffusion
---
### izamizam Dreambooth model trained by HDKCL 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)
Or you can run your new concept via `diffusers` [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb)
Sample pictures of this concept:
|
OAOA/DifFace | OAOA | 2023-02-02T10:27:45Z | 26 | 4 | diffusers | [
"diffusers",
"pytorch",
"face image enhancement",
"arxiv:2212.06512",
"license:other",
"diffusers:DifFacePipeline",
"region:us"
]
| null | 2023-01-18T08:06:39Z | ---
license: other
tags:
- pytorch
- diffusers
- face image enhancement
---
# DifFace: Blind Face Restoration with Diffused Error Contraction
**Paper**: [DifFace: Blind Face Restoration with Diffused Error Contraction](https://arxiv.org/abs/2212.06512)
**Authors**: Zongsheng Yue, Chen Change Loy
**Abstract**:
*While deep learning-based methods for blind face restoration have achieved unprecedented success, they still suffer from two major limitations. First, most of them deteriorate when facing complex degradations out of their training data. Second, these methods require multiple constraints, e.g., fidelity, perceptual, and adversarial losses, which require laborious hyper-parameter tuning to stabilize and balance their influences. In this work, we propose a novel method named DifFace that is capable of coping with unseen and complex degradations more gracefully without complicated loss designs. The key of our method is to establish a posterior distribution from the observed low-quality (LQ) image to its high-quality (HQ) counterpart. In particular, we design a transition distribution from the LQ image to the intermediate state of a pre-trained diffusion model and then gradually transmit from this intermediate state to the HQ target by recursively applying a pre-trained diffusion model. The transition distribution only relies on a restoration backbone that is trained with L2 loss on some synthetic data, which favorably avoids the cumbersome training process in existing methods. Moreover, the transition distribution can contract the error of the restoration backbone and thus makes our method more robust to unknown degradations. Comprehensive experiments show that DifFace is superior to current state-of-the-art methods, especially in cases with severe degradations.*
## Inference
```python
# !pip install diffusers
from diffusers import DifFacePipeline
model_id = "OAOA/DifFace"
# load model and scheduler
pipe = DifFacePipeline.from_pretrained(model_id)
pipe = pipe.to("cuda")
im_lr = cv2.imread(im_path) # read the low quality face image
im_sr = pipe(im_lr, num_inference_steps=250, started_steps=100, aligned=True)['images'][0]
image[0].save("restorated_difface.png") # save the result
```
<!--For more in-detail information, please have a look at the [official inference example](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/diffusers_intro.ipynb)-->
## Training
If you want to train your own model, please have a look at the [official training example](https://github.com/zsyOAOA/DifFace).
## Samples
[<img src="assets/Solvay_conference.png" width="805px"/>](https://imgsli.com/MTM5NTgw)
[<img src="assets/Hepburn.png" height="555px" width="400px"/>](https://imgsli.com/MTM5NTc5) [<img src="assets/oldimg_05.png" height="555px" width="400px"/>](https://imgsli.com/MTM5NTgy)
<img src="cropped_faces/0368.png" height="200px" width="200px"/><img src="assets/0368.png" height="200px" width="200px"/> <img src="cropped_faces/0885.png" height="200px" width="200px"/><img src="assets/0885.png" height="200px" width="200px"/>
<img src="cropped_faces/0729.png" height="200px" width="200px"/><img src="assets/0729.png" height="200px" width="200px"/> <img src="cropped_faces/0934.png" height="200px" width="200px"/><img src="assets/0934.png" height="200px" width="200px"/>
|
loubnabnl/santacoder-code-to-text | loubnabnl | 2023-02-02T10:16:02Z | 11 | 5 | transformers | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"code",
"custom_code",
"dataset:codeparrot/github-jupyter-code-to-text",
"license:openrail",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2023-01-24T18:50:19Z | ---
license: openrail
datasets:
- codeparrot/github-jupyter-code-to-text
library_name: transformers
tags:
- code
---
# Santacoder code-to-text
This model is a fine-tuned version of [bigcode/santacoder](https://huggingface.co/bigcode/santacoder) on
[copdeparrot/gitub-jupyter-code-to-text](https://huggingface.co/datasets/codeparrot/github-jupyter-code-to-text).
## Training procedure
The model was trained on 4 A100 for 3h with the following hyperparameters were used during training on 4 A100:
- learning_rate: 5e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 100
- training_steps: 800
|
Addwater/a2c-PandaReachDense-v2 | Addwater | 2023-02-02T10:10:05Z | 1 | 0 | stable-baselines3 | [
"stable-baselines3",
"PandaReachDense-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-02-02T10:07:48Z | ---
library_name: stable-baselines3
tags:
- PandaReachDense-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachDense-v2
type: PandaReachDense-v2
metrics:
- type: mean_reward
value: -2.59 +/- 0.52
name: mean_reward
verified: false
---
# **A2C** Agent playing **PandaReachDense-v2**
This is a trained model of a **A2C** agent playing **PandaReachDense-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
Hoax0930/tf_distiluse-base-multilingual-cased-v2 | Hoax0930 | 2023-02-02T10:06:12Z | 5 | 0 | sentence-transformers | [
"sentence-transformers",
"pytorch",
"distilbert",
"feature-extraction",
"sentence-similarity",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
]
| sentence-similarity | 2023-02-02T09:57:05Z | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 512 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 8837 with parameters:
```
{'batch_size': 64, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss`
Parameters of the fit()-Method:
```
{
"epochs": 2,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 100,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: DistilBertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
(2): Dense({'in_features': 768, 'out_features': 512, 'bias': True, 'activation_function': 'torch.nn.modules.activation.Tanh'})
)
```
## Citing & Authors
<!--- Describe where people can find more information --> |
Hoax0930/tf_distiluse-base-multilingual-cased-v1 | Hoax0930 | 2023-02-02T10:05:09Z | 5 | 0 | sentence-transformers | [
"sentence-transformers",
"pytorch",
"distilbert",
"feature-extraction",
"sentence-similarity",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
]
| sentence-similarity | 2023-02-02T09:57:04Z | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 512 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 8837 with parameters:
```
{'batch_size': 64, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss`
Parameters of the fit()-Method:
```
{
"epochs": 2,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 100,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: DistilBertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
(2): Dense({'in_features': 768, 'out_features': 512, 'bias': True, 'activation_function': 'torch.nn.modules.activation.Tanh'})
)
```
## Citing & Authors
<!--- Describe where people can find more information --> |
Hoax0930/tf_paraphrase-multilingual-MiniLM-L12-v2 | Hoax0930 | 2023-02-02T10:04:28Z | 7 | 0 | sentence-transformers | [
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
]
| sentence-similarity | 2023-02-02T09:57:02Z | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
model = AutoModel.from_pretrained('{MODEL_NAME}')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 8837 with parameters:
```
{'batch_size': 64, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss`
Parameters of the fit()-Method:
```
{
"epochs": 2,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 100,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information --> |
Hoax0930/tf_paraphrase-multilingual-mpnet-base-v2 | Hoax0930 | 2023-02-02T10:03:39Z | 7 | 0 | sentence-transformers | [
"sentence-transformers",
"pytorch",
"xlm-roberta",
"feature-extraction",
"sentence-similarity",
"transformers",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
]
| sentence-similarity | 2023-02-02T09:57:00Z | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
model = AutoModel.from_pretrained('{MODEL_NAME}')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 8837 with parameters:
```
{'batch_size': 64, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss`
Parameters of the fit()-Method:
```
{
"epochs": 2,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 100,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: XLMRobertaModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information --> |
Hoax0930/pseudo_distiluse-base-multilingual-cased-v2 | Hoax0930 | 2023-02-02T10:01:59Z | 8 | 0 | sentence-transformers | [
"sentence-transformers",
"pytorch",
"distilbert",
"feature-extraction",
"sentence-similarity",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
]
| sentence-similarity | 2023-02-02T09:56:59Z | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 512 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 7 with parameters:
```
{'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss`
Parameters of the fit()-Method:
```
{
"epochs": 1,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 100,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: DistilBertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
(2): Dense({'in_features': 768, 'out_features': 512, 'bias': True, 'activation_function': 'torch.nn.modules.activation.Tanh'})
)
```
## Citing & Authors
<!--- Describe where people can find more information --> |
Hoax0930/pseudo_paraphrase-multilingual-mpnet-base-v2 | Hoax0930 | 2023-02-02T09:59:05Z | 5 | 0 | sentence-transformers | [
"sentence-transformers",
"pytorch",
"xlm-roberta",
"feature-extraction",
"sentence-similarity",
"transformers",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
]
| sentence-similarity | 2023-02-02T09:56:54Z | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
model = AutoModel.from_pretrained('{MODEL_NAME}')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 7 with parameters:
```
{'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss`
Parameters of the fit()-Method:
```
{
"epochs": 1,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 100,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: XLMRobertaModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information --> |
Hoax0930/sbert | Hoax0930 | 2023-02-02T09:57:34Z | 1 | 0 | sentence-transformers | [
"sentence-transformers",
"pytorch",
"distilbert",
"feature-extraction",
"sentence-similarity",
"transformers",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
]
| sentence-similarity | 2023-02-02T09:56:53Z | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
model = AutoModel.from_pretrained('{MODEL_NAME}')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 7 with parameters:
```
{'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss`
Parameters of the fit()-Method:
```
{
"epochs": 1,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 100,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: DistilBertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information --> |
neuronaut/vasyalozhkin2-style | neuronaut | 2023-02-02T09:37:11Z | 0 | 1 | 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 | 2023-01-06T17:07:34Z | ---
license: creativeml-openrail-m
tags:
- pytorch
- diffusers
- stable-diffusion
- text-to-image
- diffusion-models-class
- dreambooth-hackathon
- wildcard
widget:
- text: vasyalozhkin style, painting of a dog flying in the sky
---
# DreamBooth model for the vasyalozhkin concept trained by neuronaut on the neuronaut/vasyalozhkin2 dataset.
This is a Stable Diffusion model v1.5 fine-tuned on Vasya Lozhkin paintings with DreamBooth. Used 8000 steps. It can be used by modifying the instance_prompt: **vasyalozhkin style** or **vasyalozhkin**
This model was created as part of the DreamBooth Hackathon π₯. 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 `style` images for the wildcard theme.
## Usage
```python
from diffusers import StableDiffusionPipeline
pipeline = StableDiffusionPipeline.from_pretrained('neuronaut/vasyalozhkin2-style')
image = pipeline().images[0]
image
```
|
Closen/Pixelcopter-PLE-v0_PG | Closen | 2023-02-02T09:29:35Z | 0 | 0 | null | [
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-02-02T09:21:32Z | ---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Pixelcopter-PLE-v0_PG
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 21.80 +/- 26.48
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
jojoUla/bert-large-cased-finetuned-low10-0-cased-DA-20 | jojoUla | 2023-02-02T09:14:19Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| fill-mask | 2023-02-02T09:11:12Z | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: bert-large-cased-finetuned-low10-0-cased-DA-20
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. -->
# bert-large-cased-finetuned-low10-0-cased-DA-20
This model is a fine-tuned version of [bert-large-cased](https://huggingface.co/bert-large-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.5523
## 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: 30
- eval_batch_size: 30
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.9248 | 1.0 | 1 | 1.0349 |
| 1.9585 | 2.0 | 2 | 1.1866 |
| 3.2777 | 3.0 | 3 | 1.4471 |
| 0.8177 | 4.0 | 4 | 3.6448 |
| 0.8142 | 5.0 | 5 | 3.3777 |
| 1.2679 | 6.0 | 6 | 3.3755 |
| 3.0205 | 7.0 | 7 | 1.4410 |
| 1.902 | 8.0 | 8 | 2.0879 |
| 1.5332 | 9.0 | 9 | 1.2120 |
| 1.2021 | 10.0 | 10 | 1.3473 |
| 1.017 | 11.0 | 11 | 1.7179 |
| 0.9292 | 12.0 | 12 | 4.3621 |
| 2.6595 | 13.0 | 13 | 0.5600 |
| 1.2934 | 14.0 | 14 | 0.5098 |
| 0.3334 | 15.0 | 15 | 2.2589 |
| 0.778 | 16.0 | 16 | 1.4632 |
| 0.9396 | 17.0 | 17 | 0.8874 |
| 1.8881 | 18.0 | 18 | 3.0849 |
| 0.9685 | 19.0 | 19 | 4.1051 |
| 1.4742 | 20.0 | 20 | 1.4036 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
|
laamaai/clasificador-muchocine-1 | laamaai | 2023-02-02T09:12:02Z | 5 | 0 | transformers | [
"transformers",
"pytorch",
"electra",
"text-classification",
"classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2023-02-02T09:10:49Z | ---
tags:
- classification
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: clasificador-muchocine-1
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. -->
# clasificador-muchocine-1
This model is a fine-tuned version of [mrm8488/electricidad-base-finetuned-muchocine](https://huggingface.co/mrm8488/electricidad-base-finetuned-muchocine) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7100
- Accuracy: 0.4632
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 388 | 1.1848 | 0.4710 |
| 1.1797 | 2.0 | 776 | 1.4089 | 0.4465 |
| 0.6868 | 3.0 | 1164 | 1.7100 | 0.4632 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
|
jojoUla/bert-large-cased-finetuned-low20-cased-DA-20 | jojoUla | 2023-02-02T09:05:19Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| fill-mask | 2023-02-02T08:34:31Z | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: bert-large-cased-finetuned-low20-cased-DA-20
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. -->
# bert-large-cased-finetuned-low20-cased-DA-20 (not in use)
This model is a fine-tuned version of [bert-large-cased](https://huggingface.co/bert-large-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3667
## 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: 20.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.477 | 1.0 | 1 | 3.0843 |
| 3.5516 | 2.0 | 2 | 4.2279 |
| 3.6173 | 3.0 | 3 | 4.2543 |
| 3.1873 | 4.0 | 4 | 2.8752 |
| 3.9494 | 5.0 | 5 | 1.7727 |
| 2.628 | 6.0 | 6 | 2.2849 |
| 1.7451 | 7.0 | 7 | 2.2338 |
| 2.6641 | 8.0 | 8 | 1.4185 |
| 3.0739 | 9.0 | 9 | 4.0617 |
| 2.1557 | 10.0 | 10 | 3.4256 |
| 1.6353 | 11.0 | 11 | 3.0232 |
| 2.6313 | 12.0 | 12 | 4.2908 |
| 1.9466 | 13.0 | 13 | 3.0047 |
| 1.8104 | 14.0 | 14 | 2.9170 |
| 2.0315 | 15.0 | 15 | 3.5850 |
| 2.6848 | 16.0 | 16 | 4.4435 |
| 2.0859 | 17.0 | 17 | 3.9439 |
| 1.6852 | 18.0 | 18 | 0.9313 |
| 1.6071 | 19.0 | 19 | 3.6927 |
| 1.697 | 20.0 | 20 | 3.7250 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
|
laamaai/clasificador-tomatoes | laamaai | 2023-02-02T08:47:56Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"electra",
"text-classification",
"classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2023-02-02T08:46:31Z | ---
tags:
- classification
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: clasificador-tomatoes
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. -->
# clasificador-tomatoes
This model is a fine-tuned version of [mrm8488/electricidad-base-discriminator](https://huggingface.co/mrm8488/electricidad-base-discriminator) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7090
- Accuracy: 0.7450
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6906 | 1.0 | 853 | 0.6964 | 0.6231 |
| 0.5222 | 2.0 | 1706 | 0.5627 | 0.7345 |
| 0.3525 | 3.0 | 2559 | 0.7090 | 0.7450 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
|
MHaurel/a2c-AntBulletEnv-v0 | MHaurel | 2023-02-02T08:38:29Z | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"AntBulletEnv-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-02-02T08:37:23Z | ---
library_name: stable-baselines3
tags:
- AntBulletEnv-v0
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: AntBulletEnv-v0
type: AntBulletEnv-v0
metrics:
- type: mean_reward
value: 1812.08 +/- 54.55
name: mean_reward
verified: false
---
# **A2C** Agent playing **AntBulletEnv-v0**
This is a trained model of a **A2C** agent playing **AntBulletEnv-v0**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
PeterDerLustige/ppo-SnowballTarget | PeterDerLustige | 2023-02-02T08:24:19Z | 13 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SnowballTarget",
"region:us"
]
| reinforcement-learning | 2023-02-02T08:24:12Z |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SnowballTarget
library_name: ml-agents
---
# **ppo** Agent playing **SnowballTarget**
This is a trained model of a **ppo** agent playing **SnowballTarget** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-SnowballTarget
2. Step 1: Write your model_id: PeterDerLustige/ppo-SnowballTarget
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play π
|
scronberg/poca-SoccerTwos | scronberg | 2023-02-02T08:24:17Z | 62 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SoccerTwos",
"region:us"
]
| reinforcement-learning | 2023-02-02T08:24:09Z |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SoccerTwos
library_name: ml-agents
---
# **poca** Agent playing **SoccerTwos**
This is a trained model of a **poca** agent playing **SoccerTwos** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-SoccerTwos
2. Step 1: Write your model_id: scronberg/poca-SoccerTwos
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play π
|
ykleeee/wav2vec2-5epochs-3e4 | ykleeee | 2023-02-02T07:50:48Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
]
| automatic-speech-recognition | 2023-02-01T08:21:34Z | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-owndata
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-owndata
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2515
- Wer: 0.3212
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 5
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 4.262 | 0.36 | 100 | 3.4482 | 0.9832 |
| 3.0032 | 0.72 | 200 | 2.9441 | 0.9832 |
| 2.9141 | 1.08 | 300 | 2.9393 | 0.9832 |
| 2.8585 | 1.44 | 400 | 2.8848 | 0.9627 |
| 2.2837 | 1.8 | 500 | 2.1732 | 1.0111 |
| 0.9834 | 2.16 | 600 | 0.8765 | 0.7345 |
| 0.7288 | 2.52 | 700 | 0.5741 | 0.5641 |
| 0.5521 | 2.88 | 800 | 0.3937 | 0.4467 |
| 0.3751 | 3.24 | 900 | 0.3484 | 0.4112 |
| 0.3733 | 3.6 | 1000 | 0.2964 | 0.3912 |
| 0.2443 | 3.96 | 1100 | 0.2673 | 0.3446 |
| 0.2667 | 4.32 | 1200 | 0.2657 | 0.3357 |
| 0.2237 | 4.68 | 1300 | 0.2515 | 0.3212 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.1
- Datasets 2.9.0
- Tokenizers 0.10.3
|
FoxFive/LunarLander-v2-ppo-2_1 | FoxFive | 2023-02-02T07:42:59Z | 0 | 0 | null | [
"license:bigscience-bloom-rail-1.0",
"region:us"
]
| null | 2023-02-02T07:42:59Z | ---
license: bigscience-bloom-rail-1.0
---
|
MukeshYadav/fine_tuned_theme2 | MukeshYadav | 2023-02-02T06:53:16Z | 1 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"electra",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
]
| null | 2023-01-31T19:52:24Z | ---
tags:
- generated_from_trainer
model-index:
- name: fine_tuned_theme2
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. -->
# fine_tuned_theme2
This model was trained from scratch on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
### Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
|
igorcheb/REINFORCE-LunarLanderContinuous-v2 | igorcheb | 2023-02-02T06:42:34Z | 0 | 0 | null | [
"LunarLanderContinuous-v2",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-01-16T15:26:41Z | ---
tags:
- LunarLanderContinuous-v2
- reinforce
- reinforcement-learning
- custom-implementation
model-index:
- name: REINFORCE-LunarLanderContinuous-v2
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLanderContinuous-v2
type: LunarLanderContinuous-v2
metrics:
- type: mean_reward
value: 264.10 +/- 37.17
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **LunarLanderContinuous-v2**
This is a custom REINFORCE RL agent. Performance has been measured over 900 episodes.
To try the agent, user needs to import the `ParameterisedPolicy` class from the agent_class.py file. </br>
Training progress:
Numbers on X axis are average over 40 episodes, each lasting for about 500 timesteps on average. So in total the agent was trained over about 5e6 timesteps.
Learning rate decay schedule: <code>torch.optim.lr_scheduler.StepLR(opt, step_size=4000, gamma=0.7)</code>. Training code is shown in the training.py file for reference.
In case video demo does not work, here's a gif:
Minimal code to use the agent:</br>
```
import gym
from agent_class import ParameterisedPolicy
env_name = 'LunarLanderContinuous-v2'
env = gym.make(env_name)
agent = torch.load('best_reinforce_lunar_lander_cont_model_269.402.pt')
render = True
observation = env.reset()
while True:
if render:
env.render()
action = agent.act(observation)
observation, reward, done, info = env.step(action)
if done:
break
env.close()
``` |
aristeia/q-FrozenLake-v1-4x4-noSlippery | aristeia | 2023-02-02T06:24:49Z | 0 | 0 | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-02-02T06:24:45Z | ---
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="aristeia/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"])
```
|
Brain22/ppo-Huggy | Brain22 | 2023-02-02T06:17:08Z | 0 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
]
| reinforcement-learning | 2023-02-02T06:17:01Z |
---
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: Brain22/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play π
|
gokuls/distilbert_sa_GLUE_Experiment_data_aug_qnli_192 | gokuls | 2023-02-02T05:57:19Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"en",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2023-02-02T01:17:40Z | ---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: distilbert_sa_GLUE_Experiment_data_aug_qnli_192
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE QNLI
type: glue
args: qnli
metrics:
- name: Accuracy
type: accuracy
value: 0.5701995240710233
---
<!-- 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_sa_GLUE_Experiment_data_aug_qnli_192
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the GLUE QNLI dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0016
- Accuracy: 0.5702
## 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: 256
- eval_batch_size: 256
- seed: 10
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.5035 | 1.0 | 16604 | 1.0016 | 0.5702 |
| 0.2645 | 2.0 | 33208 | 1.2295 | 0.5724 |
| 0.1684 | 3.0 | 49812 | 1.3804 | 0.5826 |
| 0.1171 | 4.0 | 66416 | 1.5434 | 0.5792 |
| 0.085 | 5.0 | 83020 | 1.5556 | 0.5792 |
| 0.064 | 6.0 | 99624 | 1.7284 | 0.5731 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.14.0a0+410ce96
- Datasets 2.9.0
- Tokenizers 0.13.2
|
FloydianSound/WLOP_Diffusion_v1-5 | FloydianSound | 2023-02-02T05:50:29Z | 40 | 26 | diffusers | [
"diffusers",
"stable-diffusion",
"text-to-image",
"en",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
]
| text-to-image | 2022-12-07T19:31:20Z | ---
language:
- en
tags:
- stable-diffusion
- text-to-image
license: creativeml-openrail-m
inference: true
---
## Informations
Fine-tuned SD v1-5 model, 61320 steps, 7 epochs
Aspect Ratio Bucketing centered at 768 resolution
<img alt="Showcase" src="https://huggingface.co/FloydianSound/Wlop_Diffusion/resolve/main/WLOP_Artstyle_AR_Chart.png"/>
Made with 876 pictures of the artist WLOP
If you like the artist support their work on https://www.artstation.com/wlop - https://www.deviantart.com/wlop
## Tags
Tokens are in the tags.txt along with their occurrences in [#] format
## Samples
<img alt="Showcase" src="https://huggingface.co/FloydianSound/Wlop_Diffusion/resolve/main/00000-souryuu%20asuka%20langley%20red%20hairs%20green%20eyes%20wlop-2961790964-WLOP_Artstyle_wlop_artstyle_768_e7.png"/>
<img alt="Showcase" src="https://huggingface.co/FloydianSound/Wlop_Diffusion/resolve/main/00000-princess%20aeolian%20solo%20focus%20dark%20hairs%20green%20eyes%20wlop-486739327-WLOP_Artstyle_wlop_artstyle_768_e7.png"/>
<img alt="Showcase" src="https://huggingface.co/FloydianSound/Wlop_Diffusion/resolve/main/00000-nier%20automata%20yorha%20no%202%20type%20b%20solo%20focus%20white%20hair%20black%20dress%20wlop-1122837997-WLOP_Artstyle_wlop_artstyle_768_e7.png"/>
## License
This model is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage.
The CreativeML OpenRAIL License specifies:
1. You can't use the model to deliberately produce nor share illegal or harmful outputs or content
2. The authors claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license
3. You may re-distribute the weights and use the model commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully)
[Please read the full license here](https://huggingface.co/spaces/CompVis/stable-diffusion-license) |
FloydianSound/Nixeu_Diffusion_v1-5 | FloydianSound | 2023-02-02T05:49:57Z | 14 | 4 | diffusers | [
"diffusers",
"stable-diffusion",
"text-to-image",
"en",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
]
| text-to-image | 2022-12-06T04:09:12Z | ---
language:
- en
tags:
- stable-diffusion
- text-to-image
license: creativeml-openrail-m
inference: true
---
## Informations
Fine-tuned SD v1-5 model, 25040 steps, 10 epochs
Aspect Ratio Bucketing centered at 768 resolution
Made with 250 pictures of the artist NIXEU;
if you like the artist support their work on https://www.artstation.com/nixeu - https://www.deviantart.com/nixeu
## Tags
Tokens are in the tags.txt along with their occurrences in [#] format
<img alt="Showcase" src="https://huggingface.co/FloydianSound/Nixeu_Diffusion/resolve/main/00000-nurse%20single%20realistic%20lips%20highres%20fringe%20tall%20image%20absurdres%20long%20hair%20black%20hair%20upper%20body%20dress%20nixeu%20-%201522939414%20-%20Nixeu_Artstyle_nixeu_artstyle_768_e10.png"/>
## License
This model is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage.
The CreativeML OpenRAIL License specifies:
1. You can't use the model to deliberately produce nor share illegal or harmful outputs or content
2. The authors claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license
3. You may re-distribute the weights and use the model commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully)
[Please read the full license here](https://huggingface.co/spaces/CompVis/stable-diffusion-license) |
ttj/flex-diffusion-2-1 | ttj | 2023-02-02T05:38:59Z | 8 | 24 | diffusers | [
"diffusers",
"safetensors",
"stable-diffusion",
"text-to-image",
"license:openrail++",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
]
| text-to-image | 2023-01-29T14:26:11Z | ---
license: openrail++
tags:
- stable-diffusion
- text-to-image
pinned: true
---
# Model Card for flex-diffusion-2-1
<!-- Provide a quick summary of what the model is/does. [Optional] -->
stable-diffusion-2-1 (stabilityai/stable-diffusion-2-1) finetuned with different aspect ratios.
## TLDR:
### There are 2 models in this repo:
- One based on stable-diffusion-2-1 (stabilityai/stable-diffusion-2-1) finetuned for 6k steps.
- One based on stable-diffusion-2-base (stabilityai/stable-diffusion-2-base) finetuned for 6k steps, on the same dataset.
For usage, see - [How to Get Started with the Model](#how-to-get-started-with-the-model)
### It aims to solve the following issues:
1. Generated images looks like they are cropped from a larger image.
2. Generating non-square images creates weird results, due to the model being trained on square images.
Examples:
| resolution | model | stable diffusion | flex diffusion |
|:---------------:|:-------:|:----------------------------:|:-----------------------------:|
| 576x1024 (9:16) | v2-1 |  |  |
| 576x1024 (9:16) | v2-base |  |  |
| 1024x576 (16:9) | v2-1 |  |  |
| 1024x576 (16:9) | v2-base |  |  |
### Limitations:
1. It's trained on a small dataset, so it's improvements may be limited.
2. For each aspect ratio, it's trained on only a fixed resolution. So it may not be able to generate images of different resolutions.
For 1:1 aspect ratio, it's fine-tuned at 512x512, although flex-diffusion-2-1 was last finetuned at 768x768.
### Potential improvements:
1. Train on a larger dataset.
2. Train on different resolutions even for the same aspect ratio.
3. Train on specific aspect ratios, instead of a range of aspect ratios.
# Table of Contents
- [Model Card for flex-diffusion-2-1](#model-card-for--model_id-)
- [Table of Contents](#table-of-contents)
- [Table of Contents](#table-of-contents-1)
- [Model Details](#model-details)
- [Model Description](#model-description)
- [Uses](#uses)
- [Direct Use](#direct-use)
- [Downstream Use [Optional]](#downstream-use-optional)
- [Out-of-Scope Use](#out-of-scope-use)
- [Bias, Risks, and Limitations](#bias-risks-and-limitations)
- [Recommendations](#recommendations)
- [Training Details](#training-details)
- [Training Data](#training-data)
- [Training Procedure](#training-procedure)
- [Preprocessing](#preprocessing)
- [Speeds, Sizes, Times](#speeds-sizes-times)
- [Evaluation](#evaluation)
- [Testing Data, Factors & Metrics](#testing-data-factors--metrics)
- [Testing Data](#testing-data)
- [Factors](#factors)
- [Metrics](#metrics)
- [Results](#results)
- [Model Examination](#model-examination)
- [Environmental Impact](#environmental-impact)
- [Technical Specifications [optional]](#technical-specifications-optional)
- [Model Architecture and Objective](#model-architecture-and-objective)
- [Compute Infrastructure](#compute-infrastructure)
- [Hardware](#hardware)
- [Software](#software)
- [Citation](#citation)
- [Glossary [optional]](#glossary-optional)
- [More Information [optional]](#more-information-optional)
- [Model Card Authors [optional]](#model-card-authors-optional)
- [Model Card Contact](#model-card-contact)
- [How to Get Started with the Model](#how-to-get-started-with-the-model)
# Model Details
## Model Description
<!-- Provide a longer summary of what this model is/does. -->
stable-diffusion-2-1 (stabilityai/stable-diffusion-2-1) finetuned for dynamic aspect ratios.
finetuned resolutions:
| | width | height | aspect ratio |
|---:|--------:|---------:|:---------------|
| 0 | 512 | 1024 | 1:2 |
| 1 | 576 | 1024 | 9:16 |
| 2 | 576 | 960 | 3:5 |
| 3 | 640 | 1024 | 5:8 |
| 4 | 512 | 768 | 2:3 |
| 5 | 640 | 896 | 5:7 |
| 6 | 576 | 768 | 3:4 |
| 7 | 512 | 640 | 4:5 |
| 8 | 640 | 768 | 5:6 |
| 9 | 640 | 704 | 10:11 |
| 10 | 512 | 512 | 1:1 |
| 11 | 704 | 640 | 11:10 |
| 12 | 768 | 640 | 6:5 |
| 13 | 640 | 512 | 5:4 |
| 14 | 768 | 576 | 4:3 |
| 15 | 896 | 640 | 7:5 |
| 16 | 768 | 512 | 3:2 |
| 17 | 1024 | 640 | 8:5 |
| 18 | 960 | 576 | 5:3 |
| 19 | 1024 | 576 | 16:9 |
| 20 | 1024 | 512 | 2:1 |
- **Developed by:** Jonathan Chang
- **Model type:** Diffusion-based text-to-image generation model
- **Language(s)**: English
- **License:** creativeml-openrail-m
- **Parent Model:** https://huggingface.co/stabilityai/stable-diffusion-2-1
- **Resources for more information:** More information needed
# Uses
- see https://huggingface.co/stabilityai/stable-diffusion-2-1
# Training Details
## Training Data
- LAION aesthetic dataset, subset of it with 6+ rating
- https://laion.ai/blog/laion-aesthetics/
- https://huggingface.co/datasets/ChristophSchuhmann/improved_aesthetics_6plus
- I only used a small portion of that, see [Preprocessing](#preprocessing)
- most common aspect ratios in the dataset (before preprocessing)
| | aspect_ratio | counts |
|---:|:---------------|---------:|
| 0 | 1:1 | 154727 |
| 1 | 3:2 | 119615 |
| 2 | 2:3 | 61197 |
| 3 | 4:3 | 52276 |
| 4 | 16:9 | 38862 |
| 5 | 400:267 | 21893 |
| 6 | 3:4 | 16893 |
| 7 | 8:5 | 16258 |
| 8 | 4:5 | 15684 |
| 9 | 6:5 | 12228 |
| 10 | 1000:667 | 12097 |
| 11 | 2:1 | 11006 |
| 12 | 800:533 | 10259 |
| 13 | 5:4 | 9753 |
| 14 | 500:333 | 9700 |
| 15 | 250:167 | 9114 |
| 16 | 5:3 | 8460 |
| 17 | 200:133 | 7832 |
| 18 | 1024:683 | 7176 |
| 19 | 11:10 | 6470 |
- predefined aspect ratios
| | width | height | aspect ratio |
|---:|--------:|---------:|:---------------|
| 0 | 512 | 1024 | 1:2 |
| 1 | 576 | 1024 | 9:16 |
| 2 | 576 | 960 | 3:5 |
| 3 | 640 | 1024 | 5:8 |
| 4 | 512 | 768 | 2:3 |
| 5 | 640 | 896 | 5:7 |
| 6 | 576 | 768 | 3:4 |
| 7 | 512 | 640 | 4:5 |
| 8 | 640 | 768 | 5:6 |
| 9 | 640 | 704 | 10:11 |
| 10 | 512 | 512 | 1:1 |
| 11 | 704 | 640 | 11:10 |
| 12 | 768 | 640 | 6:5 |
| 13 | 640 | 512 | 5:4 |
| 14 | 768 | 576 | 4:3 |
| 15 | 896 | 640 | 7:5 |
| 16 | 768 | 512 | 3:2 |
| 17 | 1024 | 640 | 8:5 |
| 18 | 960 | 576 | 5:3 |
| 19 | 1024 | 576 | 16:9 |
| 20 | 1024 | 512 | 2:1 |
## Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
### Preprocessing
1. download files with url & caption from https://huggingface.co/datasets/ChristophSchuhmann/improved_aesthetics_6plus
- I only used the first file `train-00000-of-00007-29aec9150af50f9f.parquet`
2. use img2dataset to convert to webdataset
- https://github.com/rom1504/img2dataset
- I put train-00000-of-00007-29aec9150af50f9f.parquet in a folder called `first-file`
- the output folder is `/mnt/aesthetics6plus`, change this to your own folder
```bash
echo INPUT_FOLDER=first-file
echo OUTPUT_FOLDER=/mnt/aesthetics6plus
img2dataset --url_list $INPUT_FOLDER --input_format "parquet"\
--url_col "URL" --caption_col "TEXT" --output_format webdataset\
--output_folder $OUTPUT_FOLDER --processes_count 3 --thread_count 6 --image_size 1024 --resize_only_if_bigger --resize_mode=keep_ratio_largest \
--save_additional_columns '["WIDTH","HEIGHT","punsafe","similarity"]' --enable_wandb True
```
3. The data-loading code will do preprocessing on the fly, so no need to do anything else. But it's not optimized for speed, the GPU utilization fluctuates between 80% and 100%. And it's not written for multi-GPU training, so use it with caution. The code will do the following:
- use webdataset to load the data
- calculate the aspect ratio of each image
- find the closest aspect ratio & it's associated resolution from the predefined resolutions: `argmin(abs(aspect_ratio - predefined_aspect_ratios))`. E.g. if the aspect ratio is 1:3, the closest resolution is 1:2. and it's associated resolution is 512x1024.
- keeping the aspect ratio, resize the image such that it's larger or equal to the associated resolution on each side. E.g. resize to 512x(512*3) = 512x1536
- random crop the image to the associated resolution. E.g. crop to 512x1024
- if more than 10% of the image is lost in the cropping, discard this example.
- batch examples by aspect ratio, so all examples in a batch have the same aspect ratio
### Speeds, Sizes, Times
- Dataset size: 100k image-caption pairs, before filtering.
- I didn't wait for the whole dataset to be downloaded, I copied the first 10 tar files and their index files to a new folder called `aesthetics6plus-small`, with 100k image-caption pairs in total. The full dataset is a lot bigger.
- Hardware: 1 RTX3090 GPUs
- Optimizer: 8bit Adam
- Batch size: 32
- actual batch size: 2
- gradient_accumulation_steps: 16
- effective batch size: 32
- Learning rate: warmup to 2e-6 for 500 steps and then kept constant
- Learning rate: 2e-6
- Training steps: 6k
- Epoch size (approximate): 32 * 6k / 100k = 1.92 (not accounting for the filtering)
- Each example is seen 1.92 times on average.
- Training time: approximately 1 day
## Results
More information needed
# Model Card Authors
Jonathan Chang
# How to Get Started with the Model
Use the code below to get started with the model.
```python
from diffusers import StableDiffusionPipeline, DPMSolverMultistepScheduler, UNet2DConditionModel
def use_DPM_solver(pipe):
pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
return pipe
pipe = StableDiffusionPipeline.from_pretrained(
"stabilityai/stable-diffusion-2-1",
unet = UNet2DConditionModel.from_pretrained("ttj/flex-diffusion-2-1", subfolder="2-1/unet", torch_dtype=torch.float16),
torch_dtype=torch.float16,
)
# for v2-base, use the following line instead
#pipe = StableDiffusionPipeline.from_pretrained(
# "stabilityai/stable-diffusion-2-base",
# unet = UNet2DConditionModel.from_pretrained("ttj/flex-diffusion-2-1", subfolder="2-base/unet", torch_dtype=torch.float16),
# torch_dtype=torch.float16)
pipe = use_DPM_solver(pipe).to("cuda")
pipe = pipe.to("cuda")
prompt = "a professional photograph of an astronaut riding a horse"
image = pipe(prompt).images[0]
image.save("astronaut_rides_horse.png")
``` |
shivr/dqn-SpaceInvadersNoFrameskip-v4 | shivr | 2023-02-02T05:36:20Z | 4 | 0 | stable-baselines3 | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-02-02T05:35:50Z | ---
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: 374.00 +/- 214.89
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga shivr -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga shivr -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga shivr
```
## Hyperparameters
```python
OrderedDict([('batch_size', 64),
('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', 0.001),
('learning_starts', 100000),
('n_timesteps', 1000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
|
Hyeoni/Question-Generation-Multitask-Korquad | Hyeoni | 2023-02-02T05:17:00Z | 0 | 1 | null | [
"region:us"
]
| null | 2022-08-29T08:51:45Z | # Question Generation Model with KorQuAD
___
This model is a fine-tuend version of paust/pko-t5-base on the KorQuAD v1.0 Dataset.
### Dataset
KorQuAD v1.0 Dataset (csv)
[Train](https://drive.google.com/file/d/1p0LYPBQE8OW6XRFEW5nxc8P03wgD_plE/view?usp=sharing)
[Valid](https://drive.google.com/file/d/1O0-8BCsYn3PpEmIUjiEBnPz4sBBmQmud/view?usp=sharing)
### Train
30% νλ₯ λ‘ input answer λμ '[MASK]'λ₯Ό λ£μ΄ μ§λ¬Έ λ¬Έμ₯μ μμ±νλλ‘ νμ΅νλ€.
κ·Έ κ²°κ³Ό, input answerκ° μμ λλ μ μ ν answerμ μ°Ύμ μ§λ¬Έμ μμ±ν μ μλ€.
### Question Generation without Input Answer
```python
context = """ CONTEXT """
input_answer = '[MASK]'
generated = generate(best_model, input_answer, context)
show_result(generated)
```
### References
____
Leaf-Question-Generation :https://github.com/KristiyanVachev/Leaf-Question-Generation
pko-t5-base : https://huggingface.co/paust/pko-t5-base
KorQuAD v1.0 : https://korquad.github.io/KorQuad%201.0/
|
DioLiu/autotrain-koles_score-3215890190 | DioLiu | 2023-02-02T05:02:45Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain",
"en",
"dataset:DioLiu/autotrain-data-koles_score",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2023-02-02T05:01:13Z | ---
tags:
- autotrain
- text-classification
language:
- en
widget:
- text: "I love AutoTrain π€"
datasets:
- DioLiu/autotrain-data-koles_score
co2_eq_emissions:
emissions: 0.009007200392120884
---
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 3215890190
- CO2 Emissions (in grams): 0.0090
## Validation Metrics
- Loss: 1.187
- Accuracy: 0.542
- Macro F1: 0.368
- Micro F1: 0.542
- Weighted F1: 0.482
- Macro Precision: 0.331
- Micro Precision: 0.542
- Weighted Precision: 0.434
- Macro Recall: 0.414
- Micro Recall: 0.542
- Weighted Recall: 0.542
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/DioLiu/autotrain-koles_score-3215890190
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("DioLiu/autotrain-koles_score-3215890190", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("DioLiu/autotrain-koles_score-3215890190", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
``` |
culteejen/PPO-default-Roomba | culteejen | 2023-02-02T04:10:01Z | 9 | 2 | stable-baselines3 | [
"stable-baselines3",
"Roomba",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-01-25T22:28:27Z | ---
library_name: stable-baselines3
tags:
- Roomba
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Roomba
type: Roomba
metrics:
- type: mean_reward
value: -132.80 +/- 40.23
name: mean_reward
verified: false
---
# **PPO** Agent playing **Roomba**
This is a trained model of a **PPO** agent playing **Roomba**
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
...
```
|
onefish51/dog_w_prior-preservation | onefish51 | 2023-02-02T03:18:18Z | 2 | 0 | diffusers | [
"diffusers",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"lora",
"license:creativeml-openrail-m",
"region:us"
]
| text-to-image | 2023-02-02T03:03:17Z |
---
license: creativeml-openrail-m
base_model: /data2/home/tyu/stable_diffusion/diffusers/stable-diffusion-v1-4
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- lora
inference: true
---
# LoRA DreamBooth - onefish51/dog_w_prior-preservation
These are LoRA adaption weights for /data2/home/tyu/stable_diffusion/diffusers/stable-diffusion-v1-4. The weights were trained on a photo of sks panda using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following.
|
FUXI/yuyan-dialogue | FUXI | 2023-02-02T03:01:44Z | 0 | 2 | null | [
"text-generation",
"dialogue-generation",
"pytorch",
"inference acceleration",
"gpt2",
"gpt3",
"zh",
"arxiv:2005.14165",
"license:apache-2.0",
"region:us"
]
| text-generation | 2022-12-26T06:05:50Z | ---
license: apache-2.0
language: zh
inference: false
tags:
- text-generation
- dialogue-generation
- pytorch
- inference acceleration
- gpt2
- gpt3
---
# YuYan-Dialogue
YuYan is a series of Chinese language models with different size, developed by Fuxi AI lab, Netease.Inc. They are trained on a large Chinese novel dataset of high quality.
YuYan is in the same family of decoder-only models like [GPT2 and GPT-3](https://arxiv.org/abs/2005.14165). As such, it was pretrained using the self-supervised causal language modedling objective.
YuYan-Dialogue is a dialogue model by fine-tuning the YuYan-11b on a large multi-turn dialogue dataset of high quality. It has very strong conversation generation capabilities.
## Model Inference Acceleration
As the model size increases, the model inference time increases and more computational resources are required.
Therefore, we developed our own transformer model inference acceleration framework, [EET](https://github.com/NetEase-FuXi/EET.git). More details are in [Easy and Efficient Transformer: Scalable Inference Solution For Large NLP Model](https://aclanthology.org/2022.naacl-industry.8/).
We combine our language model with the EET inference framework to provide industrial-grade inference reasoning performance.
## How to use
Our model is trained based on the [fairseq](https://github.com/facebookresearch/fairseq). As a result, the inference and finetuning depend on it.
For inference, we modify some parts of the original fairseq codes. Mainly
> fairseq-0.12.2/fairseq/sequence_generator.py
We integrate the EET with sequence_generator. We replace the eos token to a token unlikely to be sampled to ensure the generated text length. The repetition penalty trick is also modified. You can change the penalty strength by adjusting the value of `self.ban_weight`.
Then, to keep the eos token in the final generated text, we change the line 75 `include_eos=False` to `include_eos=True` in
> fairseq-0.12.2/fairseq/data/dictionary.py
Finally, to pass in parameters in python scripts, we remove the line 67 ~ line 69 in
>fairseq-0.12.2/fairseq/dataclass/utils.py
Below are the install tutorial.
```
# install pytorch
pip install torch==1.8.1 # install pytorch
# install fairseq
unzip fairseq-0.12.2.zip
cd fairseq-0.12.2
pip install.
# install EET
git clone https://github.com/NetEase-FuXi/EET.git
cd EET
pip install .
# install transformers (EET requirements)
pip install transformers==4.23
# make a folder, move the dictionary file and model file into it.
mkdir transformer_lm_gpt2_xxl_dialogue
mv dict.txt transformer_lm_gpt2_xxl_dialogue/
mv checkpoint_best_part_*.pt transformer_lm_gpt2_xxl_dialogue/
```
`inference.py` is a script to provide a interface to initialize the EET object and sequence_generator. It includes some pre-process and post-process functions for text input and output. You can modify the script according to your needs.
In addition, it provide a simple object to organize the dialogue generation and dialogue history.
After the environment is ready, several lines of codes can realize the inference.
``` python
from inference import Inference, Dialogue
model_path = "transformer_lm_gpt2_xxl_dialogue/checkpoint_best.pt"
data_path = "transformer_lm_gpt2_xxl_dialogue"
eet_batch_size = 10 # max inference batch size, adjust according to cuda memory, 40GB memory is necessary
inference = Inference(model_path, data_path, eet_batch_size)
dialogue_model = Dialogue(inference)
dialogue_model.get_repsonse("δ½ ε₯½ε")
```
## Citation
If you find the technical report or resource is useful, please cite the following technical report in your paper.
- https://aclanthology.org/2022.naacl-industry.8/
```
@inproceedings{li-etal-2022-easy,
title = "Easy and Efficient Transformer: Scalable Inference Solution For Large {NLP} Model",
author = "Li, Gongzheng and
Xi, Yadong and
Ding, Jingzhen and
Wang, Duan and
Luo, Ziyang and
Zhang, Rongsheng and
Liu, Bai and
Fan, Changjie and
Mao, Xiaoxi and
Zhao, Zeng",
booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Industry Track",
month = jul,
year = "2022",
address = "Hybrid: Seattle, Washington + Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.naacl-industry.8",
doi = "10.18653/v1/2022.naacl-industry.8",
pages = "62--68"
}
```
## Contact Us
You can also contact us by email:
[email protected], [email protected]
|
rohitp1/Nystrom-W2V2-100hrs-take-4-unfreeze-extractor-try-2 | rohitp1 | 2023-02-02T02:20:16Z | 1 | 0 | transformers | [
"transformers",
"pytorch",
"wav2vec2",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
]
| null | 2023-01-30T04:30:59Z | ---
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: Nystrom-W2V2-100hrs-take-4-unfreeze-extractor-try-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. -->
# Nystrom-W2V2-100hrs-take-4-unfreeze-extractor-try-2
This model is a fine-tuned version of [rohitp1/Nystrom-W2V2-100hrs-take-4-unfreeze-extractor](https://huggingface.co/rohitp1/Nystrom-W2V2-100hrs-take-4-unfreeze-extractor) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 27.1915
- Wer: 0.0869
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 4
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 64
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.2
- num_epochs: 100
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 23.1458 | 9.01 | 1000 | 28.9573 | 0.1039 |
| 32.7156 | 18.02 | 2000 | 25.6155 | 0.1218 |
| 43.506 | 27.03 | 3000 | 27.6332 | 0.1228 |
| 43.3608 | 36.04 | 4000 | 26.0539 | 0.1169 |
| 39.984 | 45.04 | 5000 | 25.9836 | 0.1137 |
| 35.1977 | 54.05 | 6000 | 26.2060 | 0.1077 |
| 30.1951 | 63.06 | 7000 | 27.0999 | 0.1033 |
| 25.7519 | 72.07 | 8000 | 27.8459 | 0.0964 |
| 22.1982 | 81.08 | 9000 | 27.9773 | 0.0908 |
| 20.0551 | 90.09 | 10000 | 27.4222 | 0.0884 |
| 19.4505 | 99.1 | 11000 | 27.1915 | 0.0869 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1
- Datasets 2.7.1
- Tokenizers 0.11.0
|
StupidGame/AnythingV4.5 | StupidGame | 2023-02-02T02:10:47Z | 21 | 1 | diffusers | [
"diffusers",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
]
| text-to-image | 2023-01-16T01:13:38Z | ---
license: creativeml-openrail-m
---
|
erud1t3/ppo-lunarlander-v2 | erud1t3 | 2023-02-02T02:02:08Z | 1 | 0 | stable-baselines3 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-02-02T00:33:36Z | ---
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: 288.17 +/- 23.46
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
...
```
|
swl-models/9527 | swl-models | 2023-02-02T01:48:23Z | 0 | 14 | null | [
"license:creativeml-openrail-m",
"region:us"
]
| null | 2023-02-02T00:54:21Z | ---
license: creativeml-openrail-m
---
|
swl-models/DanMix-v1 | swl-models | 2023-02-02T01:34:25Z | 0 | 0 | null | [
"license:creativeml-openrail-m",
"region:us"
]
| null | 2023-02-02T00:30:09Z | ---
license: creativeml-openrail-m
---
|
AdhilB/AI | AdhilB | 2023-02-02T00:57:10Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
]
| null | 2023-02-02T00:53:24Z | ---
title: GFPGAN
emoji: π
colorFrom: yellow
colorTo: green
sdk: gradio
sdk_version: 3.1.7
app_file: app.py
pinned: false
license: apache-2.0
---
Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference |
gokuls/distilbert_sa_GLUE_Experiment_data_aug_mrpc_96 | gokuls | 2023-02-02T00:49:02Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"en",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2023-02-01T22:48:14Z | ---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
- f1
model-index:
- name: distilbert_sa_GLUE_Experiment_data_aug_mrpc_96
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE MRPC
type: glue
args: mrpc
metrics:
- name: Accuracy
type: accuracy
value: 1.0
- name: F1
type: f1
value: 1.0
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert_sa_GLUE_Experiment_data_aug_mrpc_96
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the GLUE MRPC dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0000
- Accuracy: 1.0
- F1: 1.0
- Combined Score: 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: 5e-05
- train_batch_size: 256
- eval_batch_size: 256
- seed: 10
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Combined Score |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:--------------:|
| 0.3242 | 1.0 | 980 | 0.0830 | 0.9804 | 0.9857 | 0.9830 |
| 0.0843 | 2.0 | 1960 | 0.0355 | 0.9828 | 0.9875 | 0.9852 |
| 0.0431 | 3.0 | 2940 | 0.0105 | 1.0 | 1.0 | 1.0 |
| 0.0268 | 4.0 | 3920 | 0.0046 | 1.0 | 1.0 | 1.0 |
| 0.019 | 5.0 | 4900 | 0.0015 | 1.0 | 1.0 | 1.0 |
| 0.0141 | 6.0 | 5880 | 0.0011 | 1.0 | 1.0 | 1.0 |
| 0.0115 | 7.0 | 6860 | 0.0007 | 1.0 | 1.0 | 1.0 |
| 0.0094 | 8.0 | 7840 | 0.0004 | 1.0 | 1.0 | 1.0 |
| 0.0078 | 9.0 | 8820 | 0.0004 | 1.0 | 1.0 | 1.0 |
| 0.0056 | 10.0 | 9800 | 0.0006 | 1.0 | 1.0 | 1.0 |
| 0.0056 | 11.0 | 10780 | 0.0001 | 1.0 | 1.0 | 1.0 |
| 0.0039 | 12.0 | 11760 | 0.0001 | 1.0 | 1.0 | 1.0 |
| 0.0038 | 13.0 | 12740 | 0.0001 | 1.0 | 1.0 | 1.0 |
| 0.0029 | 14.0 | 13720 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0026 | 15.0 | 14700 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0025 | 16.0 | 15680 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0019 | 17.0 | 16660 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0017 | 18.0 | 17640 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0015 | 19.0 | 18620 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0013 | 20.0 | 19600 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0013 | 21.0 | 20580 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0013 | 22.0 | 21560 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0012 | 23.0 | 22540 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.001 | 24.0 | 23520 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0008 | 25.0 | 24500 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0007 | 26.0 | 25480 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0006 | 27.0 | 26460 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0007 | 28.0 | 27440 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0007 | 29.0 | 28420 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0005 | 30.0 | 29400 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0004 | 31.0 | 30380 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0005 | 32.0 | 31360 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0004 | 33.0 | 32340 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0003 | 34.0 | 33320 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0004 | 35.0 | 34300 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0002 | 36.0 | 35280 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0003 | 37.0 | 36260 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0003 | 38.0 | 37240 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0003 | 39.0 | 38220 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0002 | 40.0 | 39200 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0002 | 41.0 | 40180 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0 | 42.0 | 41160 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0002 | 43.0 | 42140 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0002 | 44.0 | 43120 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0001 | 45.0 | 44100 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0001 | 46.0 | 45080 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0 | 47.0 | 46060 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0 | 48.0 | 47040 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0 | 49.0 | 48020 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0001 | 50.0 | 49000 | 0.0000 | 1.0 | 1.0 | 1.0 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.14.0a0+410ce96
- Datasets 2.9.0
- Tokenizers 0.13.2
|
dn-gh/dqn-SpaceInvadersNoFrameskip-v4-1 | dn-gh | 2023-02-02T00:42:20Z | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-02-02T00:41:43Z | ---
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: 614.00 +/- 265.66
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga dn-gh -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga dn-gh -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga dn-gh
```
## 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)])
```
|
gokuls/distilbert_sa_GLUE_Experiment_data_aug_mrpc_384 | gokuls | 2023-02-02T00:34:07Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"en",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2023-02-01T22:50:13Z | ---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
- f1
model-index:
- name: distilbert_sa_GLUE_Experiment_data_aug_mrpc_384
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE MRPC
type: glue
args: mrpc
metrics:
- name: Accuracy
type: accuracy
value: 1.0
- name: F1
type: f1
value: 1.0
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert_sa_GLUE_Experiment_data_aug_mrpc_384
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the GLUE MRPC dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0000
- Accuracy: 1.0
- F1: 1.0
- Combined Score: 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: 5e-05
- train_batch_size: 256
- eval_batch_size: 256
- seed: 10
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Combined Score |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:---:|:--------------:|
| 0.1771 | 1.0 | 980 | 0.0049 | 1.0 | 1.0 | 1.0 |
| 0.0321 | 2.0 | 1960 | 0.0009 | 1.0 | 1.0 | 1.0 |
| 0.0154 | 3.0 | 2940 | 0.0001 | 1.0 | 1.0 | 1.0 |
| 0.0086 | 4.0 | 3920 | 0.0009 | 1.0 | 1.0 | 1.0 |
| 0.0062 | 5.0 | 4900 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0039 | 6.0 | 5880 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0039 | 7.0 | 6860 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0028 | 8.0 | 7840 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0022 | 9.0 | 8820 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0018 | 10.0 | 9800 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.002 | 11.0 | 10780 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0011 | 12.0 | 11760 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0015 | 13.0 | 12740 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0011 | 14.0 | 13720 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0011 | 15.0 | 14700 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0008 | 16.0 | 15680 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0009 | 17.0 | 16660 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0007 | 18.0 | 17640 | 0.0001 | 1.0 | 1.0 | 1.0 |
| 0.0006 | 19.0 | 18620 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0006 | 20.0 | 19600 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0004 | 21.0 | 20580 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0004 | 22.0 | 21560 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0002 | 23.0 | 22540 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0004 | 24.0 | 23520 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0003 | 25.0 | 24500 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0004 | 26.0 | 25480 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0003 | 27.0 | 26460 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0002 | 28.0 | 27440 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0002 | 29.0 | 28420 | 0.0000 | 1.0 | 1.0 | 1.0 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.14.0a0+410ce96
- Datasets 2.9.0
- Tokenizers 0.13.2
|
gokuls/mobilebert_sa_GLUE_Experiment_logit_kd_data_aug_cola | gokuls | 2023-02-02T00:32:21Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"mobilebert",
"text-classification",
"generated_from_trainer",
"en",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2023-02-01T22:57:01Z | ---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- matthews_correlation
model-index:
- name: mobilebert_sa_GLUE_Experiment_logit_kd_data_aug_cola
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE COLA
type: glue
args: cola
metrics:
- name: Matthews Correlation
type: matthews_correlation
value: 0.10549049137169143
---
<!-- 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. -->
# mobilebert_sa_GLUE_Experiment_logit_kd_data_aug_cola
This model is a fine-tuned version of [google/mobilebert-uncased](https://huggingface.co/google/mobilebert-uncased) on the GLUE COLA dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6837
- Matthews Correlation: 0.1055
## 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: 128
- eval_batch_size: 128
- seed: 10
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation |
|:-------------:|:-----:|:-----:|:---------------:|:--------------------:|
| 0.6247 | 1.0 | 1669 | 0.6837 | 0.1055 |
| 0.5458 | 2.0 | 3338 | 0.7216 | 0.1168 |
| 0.5041 | 3.0 | 5007 | 0.7127 | 0.1296 |
| 0.4445 | 4.0 | 6676 | 0.7718 | 0.1436 |
| 0.3961 | 5.0 | 8345 | 0.8417 | 0.1284 |
| 0.3603 | 6.0 | 10014 | 0.7805 | 0.1240 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.14.0a0+410ce96
- Datasets 2.9.0
- Tokenizers 0.13.2
|
Nonin/DQN-LunarLander-v2 | Nonin | 2023-02-02T00:23:25Z | 1 | 0 | stable-baselines3 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-02-02T00:23:10Z | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 249.70 +/- 77.81
name: mean_reward
verified: false
---
# **DQN** Agent playing **LunarLander-v2**
This is a trained model of a **DQN** 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
...
```
|
sammael70/1223 | sammael70 | 2023-02-02T00:09:41Z | 0 | 0 | null | [
"es",
"arxiv:1910.09700",
"license:odbl",
"region:us"
]
| null | 2023-02-02T00:07:39Z | ---
license: odbl
language:
- es
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
# Model Details
## Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
## Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
# Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
## Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
## Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
## Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
# Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
## Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
# Training Details
## Training Data
<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
## Training Procedure [optional]
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
### Preprocessing
[More Information Needed]
### Speeds, Sizes, Times
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
# Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
## Testing Data, Factors & Metrics
### Testing Data
<!-- This should link to a Data Card if possible. -->
[More Information Needed]
### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
## Results
[More Information Needed]
### Summary
# Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
# Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
# Technical Specifications [optional]
## Model Architecture and Objective
[More Information Needed]
## Compute Infrastructure
[More Information Needed]
### Hardware
[More Information Needed]
### Software
[More Information Needed]
# Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
# Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
# More Information [optional]
[More Information Needed]
# Model Card Authors [optional]
[More Information Needed]
# Model Card Contact
[More Information Needed]
|
gokuls/mobilebert_sa_GLUE_Experiment_data_aug_cola | gokuls | 2023-02-01T23:54:20Z | 5 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"mobilebert",
"text-classification",
"generated_from_trainer",
"en",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2023-02-01T22:35:34Z | ---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- matthews_correlation
model-index:
- name: mobilebert_sa_GLUE_Experiment_data_aug_cola
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE COLA
type: glue
args: cola
metrics:
- name: Matthews Correlation
type: matthews_correlation
value: 0.05152844185670031
---
<!-- 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. -->
# mobilebert_sa_GLUE_Experiment_data_aug_cola
This model is a fine-tuned version of [google/mobilebert-uncased](https://huggingface.co/google/mobilebert-uncased) on the GLUE COLA dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6549
- Matthews Correlation: 0.0515
## 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: 128
- eval_batch_size: 128
- seed: 10
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation |
|:-------------:|:-----:|:-----:|:---------------:|:--------------------:|
| 0.5347 | 1.0 | 1669 | 0.6549 | 0.0515 |
| 0.4507 | 2.0 | 3338 | 0.8182 | 0.0794 |
| 0.407 | 3.0 | 5007 | 0.8573 | 0.0853 |
| 0.3439 | 4.0 | 6676 | 0.9437 | 0.0871 |
| 0.2873 | 5.0 | 8345 | 1.0250 | 0.0530 |
| 0.2424 | 6.0 | 10014 | 1.2340 | 0.0733 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.14.0a0+410ce96
- Datasets 2.9.0
- Tokenizers 0.13.2
|
Lakoc/a2c-PandaReachDense-v2 | Lakoc | 2023-02-01T23:19:16Z | 1 | 0 | stable-baselines3 | [
"stable-baselines3",
"PandaReachDense-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-02-01T23:17:09Z | ---
library_name: stable-baselines3
tags:
- PandaReachDense-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachDense-v2
type: PandaReachDense-v2
metrics:
- type: mean_reward
value: -0.48 +/- 0.17
name: mean_reward
verified: false
---
# **A2C** Agent playing **PandaReachDense-v2**
This is a trained model of a **A2C** agent playing **PandaReachDense-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
uisikdag/footballplayers_yolov8 | uisikdag | 2023-02-01T23:01:28Z | 189 | 0 | ultralytics | [
"ultralytics",
"tensorboard",
"v8",
"ultralyticsplus",
"yolov8",
"yolo",
"vision",
"object-detection",
"pytorch",
"model-index",
"region:us"
]
| object-detection | 2023-02-01T23:00:57Z |
---
tags:
- ultralyticsplus
- yolov8
- ultralytics
- yolo
- vision
- object-detection
- pytorch
library_name: ultralytics
library_version: 8.0.25
inference: false
model-index:
- name: uisikdag/football_players_rf
results:
- task:
type: object-detection
metrics:
- type: precision # since [email protected] is not available on hf.co/metrics
value: 0.78517 # min: 0.0 - max: 1.0
name: [email protected](box)
---
<div align="center">
<img width="640" alt="uisikdag/football_players_rf" src="https://huggingface.co/uisikdag/football_players_rf/resolve/main/thumbnail.jpg">
</div>
### Supported Labels
```
['ball', 'goalkeeper', 'player', 'referee']
```
### How to use
- Install [ultralyticsplus](https://github.com/fcakyon/ultralyticsplus):
```bash
pip install ultralyticsplus==0.0.25 ultralytics==8.0.25
```
- Load model and perform prediction:
```python
from ultralyticsplus import YOLO, render_result
# load model
model = YOLO('uisikdag/football_players_rf')
# set model parameters
model.overrides['conf'] = 0.25 # NMS confidence threshold
model.overrides['iou'] = 0.45 # NMS IoU threshold
model.overrides['agnostic_nms'] = False # NMS class-agnostic
model.overrides['max_det'] = 1000 # maximum number of detections per image
# set image
image = 'https://github.com/ultralytics/yolov5/raw/master/data/images/zidane.jpg'
# perform inference
results = model.predict(image)
# observe results
print(results[0].boxes)
render = render_result(model=model, image=image, result=results[0])
render.show()
```
|
gokuls/distilbert_sa_GLUE_Experiment_data_aug_cola | gokuls | 2023-02-01T22:56:57Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"en",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2023-02-01T22:27:01Z | ---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- matthews_correlation
model-index:
- name: distilbert_sa_GLUE_Experiment_data_aug_cola
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE COLA
type: glue
args: cola
metrics:
- name: Matthews Correlation
type: matthews_correlation
value: 0.12046776548411303
---
<!-- 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_sa_GLUE_Experiment_data_aug_cola
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the GLUE COLA dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8362
- Matthews Correlation: 0.1205
## 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: 256
- eval_batch_size: 256
- seed: 10
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.4726 | 1.0 | 835 | 0.8362 | 0.1205 |
| 0.2428 | 2.0 | 1670 | 1.3000 | 0.1122 |
| 0.1378 | 3.0 | 2505 | 1.3626 | 0.1226 |
| 0.0893 | 4.0 | 3340 | 1.6155 | 0.1608 |
| 0.0648 | 5.0 | 4175 | 1.8098 | 0.0958 |
| 0.049 | 6.0 | 5010 | 2.0187 | 0.1179 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.14.0a0+410ce96
- Datasets 2.9.0
- Tokenizers 0.13.2
|
clarin-knext/plt5-base-poquad-qa-v2 | clarin-knext | 2023-02-01T22:53:42Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"generated_from_trainer",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text2text-generation | 2023-01-21T11:06:31Z | ---
tags:
- generated_from_trainer
model-index:
- name: plt5-base-poquad-qa-v2
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. -->
# plt5-base-poquad-qa-v2
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5435
## 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
- gradient_accumulation_steps: 8
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 480 | 0.7467 |
| 1.3112 | 2.0 | 960 | 0.6548 |
| 1.0033 | 3.0 | 1440 | 0.6064 |
| 0.8897 | 4.0 | 1920 | 0.5882 |
| 0.8223 | 5.0 | 2400 | 0.5701 |
| 0.7911 | 6.0 | 2880 | 0.5567 |
| 0.7651 | 7.0 | 3360 | 0.5514 |
| 0.7641 | 8.0 | 3840 | 0.5448 |
| 0.7295 | 9.0 | 4320 | 0.5451 |
| 0.7304 | 10.0 | 4800 | 0.5435 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.1+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
|
tomekkorbak/compassionate_lumiere | tomekkorbak | 2023-02-01T22:52:15Z | 10 | 0 | transformers | [
"transformers",
"pytorch",
"gpt2",
"generated_from_trainer",
"en",
"dataset:tomekkorbak/pii-pile-chunk3-0-50000",
"dataset:tomekkorbak/pii-pile-chunk3-50000-100000",
"dataset:tomekkorbak/pii-pile-chunk3-100000-150000",
"dataset:tomekkorbak/pii-pile-chunk3-150000-200000",
"dataset:tomekkorbak/pii-pile-chunk3-200000-250000",
"dataset:tomekkorbak/pii-pile-chunk3-250000-300000",
"dataset:tomekkorbak/pii-pile-chunk3-300000-350000",
"dataset:tomekkorbak/pii-pile-chunk3-350000-400000",
"dataset:tomekkorbak/pii-pile-chunk3-400000-450000",
"dataset:tomekkorbak/pii-pile-chunk3-450000-500000",
"dataset:tomekkorbak/pii-pile-chunk3-500000-550000",
"dataset:tomekkorbak/pii-pile-chunk3-550000-600000",
"dataset:tomekkorbak/pii-pile-chunk3-600000-650000",
"dataset:tomekkorbak/pii-pile-chunk3-650000-700000",
"dataset:tomekkorbak/pii-pile-chunk3-700000-750000",
"dataset:tomekkorbak/pii-pile-chunk3-750000-800000",
"dataset:tomekkorbak/pii-pile-chunk3-800000-850000",
"dataset:tomekkorbak/pii-pile-chunk3-850000-900000",
"dataset:tomekkorbak/pii-pile-chunk3-900000-950000",
"dataset:tomekkorbak/pii-pile-chunk3-950000-1000000",
"dataset:tomekkorbak/pii-pile-chunk3-1000000-1050000",
"dataset:tomekkorbak/pii-pile-chunk3-1050000-1100000",
"dataset:tomekkorbak/pii-pile-chunk3-1100000-1150000",
"dataset:tomekkorbak/pii-pile-chunk3-1150000-1200000",
"dataset:tomekkorbak/pii-pile-chunk3-1200000-1250000",
"dataset:tomekkorbak/pii-pile-chunk3-1250000-1300000",
"dataset:tomekkorbak/pii-pile-chunk3-1300000-1350000",
"dataset:tomekkorbak/pii-pile-chunk3-1350000-1400000",
"dataset:tomekkorbak/pii-pile-chunk3-1400000-1450000",
"dataset:tomekkorbak/pii-pile-chunk3-1450000-1500000",
"dataset:tomekkorbak/pii-pile-chunk3-1500000-1550000",
"dataset:tomekkorbak/pii-pile-chunk3-1550000-1600000",
"dataset:tomekkorbak/pii-pile-chunk3-1600000-1650000",
"dataset:tomekkorbak/pii-pile-chunk3-1650000-1700000",
"dataset:tomekkorbak/pii-pile-chunk3-1700000-1750000",
"dataset:tomekkorbak/pii-pile-chunk3-1750000-1800000",
"dataset:tomekkorbak/pii-pile-chunk3-1800000-1850000",
"dataset:tomekkorbak/pii-pile-chunk3-1850000-1900000",
"dataset:tomekkorbak/pii-pile-chunk3-1900000-1950000",
"license:mit",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| null | 2023-02-01T06:50:32Z | ---
language:
- en
license: mit
tags:
- generated_from_trainer
datasets:
- tomekkorbak/pii-pile-chunk3-0-50000
- tomekkorbak/pii-pile-chunk3-50000-100000
- tomekkorbak/pii-pile-chunk3-100000-150000
- tomekkorbak/pii-pile-chunk3-150000-200000
- tomekkorbak/pii-pile-chunk3-200000-250000
- tomekkorbak/pii-pile-chunk3-250000-300000
- tomekkorbak/pii-pile-chunk3-300000-350000
- tomekkorbak/pii-pile-chunk3-350000-400000
- tomekkorbak/pii-pile-chunk3-400000-450000
- tomekkorbak/pii-pile-chunk3-450000-500000
- tomekkorbak/pii-pile-chunk3-500000-550000
- tomekkorbak/pii-pile-chunk3-550000-600000
- tomekkorbak/pii-pile-chunk3-600000-650000
- tomekkorbak/pii-pile-chunk3-650000-700000
- tomekkorbak/pii-pile-chunk3-700000-750000
- tomekkorbak/pii-pile-chunk3-750000-800000
- tomekkorbak/pii-pile-chunk3-800000-850000
- tomekkorbak/pii-pile-chunk3-850000-900000
- tomekkorbak/pii-pile-chunk3-900000-950000
- tomekkorbak/pii-pile-chunk3-950000-1000000
- tomekkorbak/pii-pile-chunk3-1000000-1050000
- tomekkorbak/pii-pile-chunk3-1050000-1100000
- tomekkorbak/pii-pile-chunk3-1100000-1150000
- tomekkorbak/pii-pile-chunk3-1150000-1200000
- tomekkorbak/pii-pile-chunk3-1200000-1250000
- tomekkorbak/pii-pile-chunk3-1250000-1300000
- tomekkorbak/pii-pile-chunk3-1300000-1350000
- tomekkorbak/pii-pile-chunk3-1350000-1400000
- tomekkorbak/pii-pile-chunk3-1400000-1450000
- tomekkorbak/pii-pile-chunk3-1450000-1500000
- tomekkorbak/pii-pile-chunk3-1500000-1550000
- tomekkorbak/pii-pile-chunk3-1550000-1600000
- tomekkorbak/pii-pile-chunk3-1600000-1650000
- tomekkorbak/pii-pile-chunk3-1650000-1700000
- tomekkorbak/pii-pile-chunk3-1700000-1750000
- tomekkorbak/pii-pile-chunk3-1750000-1800000
- tomekkorbak/pii-pile-chunk3-1800000-1850000
- tomekkorbak/pii-pile-chunk3-1850000-1900000
- tomekkorbak/pii-pile-chunk3-1900000-1950000
model-index:
- name: compassionate_lumiere
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. -->
# compassionate_lumiere
This model was trained from scratch on the tomekkorbak/pii-pile-chunk3-0-50000, the tomekkorbak/pii-pile-chunk3-50000-100000, the tomekkorbak/pii-pile-chunk3-100000-150000, the tomekkorbak/pii-pile-chunk3-150000-200000, the tomekkorbak/pii-pile-chunk3-200000-250000, the tomekkorbak/pii-pile-chunk3-250000-300000, the tomekkorbak/pii-pile-chunk3-300000-350000, the tomekkorbak/pii-pile-chunk3-350000-400000, the tomekkorbak/pii-pile-chunk3-400000-450000, the tomekkorbak/pii-pile-chunk3-450000-500000, the tomekkorbak/pii-pile-chunk3-500000-550000, the tomekkorbak/pii-pile-chunk3-550000-600000, the tomekkorbak/pii-pile-chunk3-600000-650000, the tomekkorbak/pii-pile-chunk3-650000-700000, the tomekkorbak/pii-pile-chunk3-700000-750000, the tomekkorbak/pii-pile-chunk3-750000-800000, the tomekkorbak/pii-pile-chunk3-800000-850000, the tomekkorbak/pii-pile-chunk3-850000-900000, the tomekkorbak/pii-pile-chunk3-900000-950000, the tomekkorbak/pii-pile-chunk3-950000-1000000, the tomekkorbak/pii-pile-chunk3-1000000-1050000, the tomekkorbak/pii-pile-chunk3-1050000-1100000, the tomekkorbak/pii-pile-chunk3-1100000-1150000, the tomekkorbak/pii-pile-chunk3-1150000-1200000, the tomekkorbak/pii-pile-chunk3-1200000-1250000, the tomekkorbak/pii-pile-chunk3-1250000-1300000, the tomekkorbak/pii-pile-chunk3-1300000-1350000, the tomekkorbak/pii-pile-chunk3-1350000-1400000, the tomekkorbak/pii-pile-chunk3-1400000-1450000, the tomekkorbak/pii-pile-chunk3-1450000-1500000, the tomekkorbak/pii-pile-chunk3-1500000-1550000, the tomekkorbak/pii-pile-chunk3-1550000-1600000, the tomekkorbak/pii-pile-chunk3-1600000-1650000, the tomekkorbak/pii-pile-chunk3-1650000-1700000, the tomekkorbak/pii-pile-chunk3-1700000-1750000, the tomekkorbak/pii-pile-chunk3-1750000-1800000, the tomekkorbak/pii-pile-chunk3-1800000-1850000, the tomekkorbak/pii-pile-chunk3-1850000-1900000 and the tomekkorbak/pii-pile-chunk3-1900000-1950000 datasets.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.01
- training_steps: 12588
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.24.0
- Pytorch 1.11.0+cu113
- Datasets 2.5.1
- Tokenizers 0.11.6
# Full config
{'dataset': {'conditional_training_config': {'aligned_prefix': '<|aligned|>',
'drop_token_fraction': 0.01,
'misaligned_prefix': '<|misaligned|>',
'threshold': 0.0},
'datasets': ['tomekkorbak/pii-pile-chunk3-0-50000',
'tomekkorbak/pii-pile-chunk3-50000-100000',
'tomekkorbak/pii-pile-chunk3-100000-150000',
'tomekkorbak/pii-pile-chunk3-150000-200000',
'tomekkorbak/pii-pile-chunk3-200000-250000',
'tomekkorbak/pii-pile-chunk3-250000-300000',
'tomekkorbak/pii-pile-chunk3-300000-350000',
'tomekkorbak/pii-pile-chunk3-350000-400000',
'tomekkorbak/pii-pile-chunk3-400000-450000',
'tomekkorbak/pii-pile-chunk3-450000-500000',
'tomekkorbak/pii-pile-chunk3-500000-550000',
'tomekkorbak/pii-pile-chunk3-550000-600000',
'tomekkorbak/pii-pile-chunk3-600000-650000',
'tomekkorbak/pii-pile-chunk3-650000-700000',
'tomekkorbak/pii-pile-chunk3-700000-750000',
'tomekkorbak/pii-pile-chunk3-750000-800000',
'tomekkorbak/pii-pile-chunk3-800000-850000',
'tomekkorbak/pii-pile-chunk3-850000-900000',
'tomekkorbak/pii-pile-chunk3-900000-950000',
'tomekkorbak/pii-pile-chunk3-950000-1000000',
'tomekkorbak/pii-pile-chunk3-1000000-1050000',
'tomekkorbak/pii-pile-chunk3-1050000-1100000',
'tomekkorbak/pii-pile-chunk3-1100000-1150000',
'tomekkorbak/pii-pile-chunk3-1150000-1200000',
'tomekkorbak/pii-pile-chunk3-1200000-1250000',
'tomekkorbak/pii-pile-chunk3-1250000-1300000',
'tomekkorbak/pii-pile-chunk3-1300000-1350000',
'tomekkorbak/pii-pile-chunk3-1350000-1400000',
'tomekkorbak/pii-pile-chunk3-1400000-1450000',
'tomekkorbak/pii-pile-chunk3-1450000-1500000',
'tomekkorbak/pii-pile-chunk3-1500000-1550000',
'tomekkorbak/pii-pile-chunk3-1550000-1600000',
'tomekkorbak/pii-pile-chunk3-1600000-1650000',
'tomekkorbak/pii-pile-chunk3-1650000-1700000',
'tomekkorbak/pii-pile-chunk3-1700000-1750000',
'tomekkorbak/pii-pile-chunk3-1750000-1800000',
'tomekkorbak/pii-pile-chunk3-1800000-1850000',
'tomekkorbak/pii-pile-chunk3-1850000-1900000',
'tomekkorbak/pii-pile-chunk3-1900000-1950000'],
'is_split_by_sentences': True,
'skip_tokens': 1649999872},
'generation': {'force_call_on': [25177],
'metrics_configs': [{}, {'n': 1}, {'n': 2}, {'n': 5}],
'scenario_configs': [{'generate_kwargs': {'bad_words_ids': [[50257],
[50258]],
'do_sample': True,
'max_length': 128,
'min_length': 10,
'temperature': 0.7,
'top_k': 0,
'top_p': 0.9},
'name': 'unconditional',
'num_samples': 4096,
'prefix': '<|aligned|>'}],
'scorer_config': {}},
'kl_gpt3_callback': {'force_call_on': [25177],
'gpt3_kwargs': {'model_name': 'davinci'},
'max_tokens': 64,
'num_samples': 4096,
'prefix': '<|aligned|>'},
'model': {'from_scratch': False,
'gpt2_config_kwargs': {'reorder_and_upcast_attn': True,
'scale_attn_by': True},
'model_kwargs': {'revision': '9e6c78543a6ff1e4089002c38864d5a9cf71ec90'},
'num_additional_tokens': 2,
'path_or_name': 'tomekkorbak/nervous_wozniak'},
'objective': {'name': 'MLE'},
'tokenizer': {'path_or_name': 'gpt2',
'special_tokens': ['<|aligned|>', '<|misaligned|>']},
'training': {'dataloader_num_workers': 0,
'effective_batch_size': 128,
'evaluation_strategy': 'no',
'fp16': True,
'hub_model_id': 'compassionate_lumiere',
'hub_strategy': 'all_checkpoints',
'learning_rate': 0.0001,
'logging_first_step': True,
'logging_steps': 1,
'num_tokens': 3300000000,
'output_dir': 'training_output2',
'per_device_train_batch_size': 16,
'push_to_hub': True,
'remove_unused_columns': False,
'save_steps': 251,
'save_strategy': 'steps',
'seed': 42,
'tokens_already_seen': 1649999872,
'warmup_ratio': 0.01,
'weight_decay': 0.1}}
# Wandb URL:
https://wandb.ai/tomekkorbak/apo/runs/1q3x5956 |
gokuls/distilbert_sa_GLUE_Experiment_data_aug_cola_384 | gokuls | 2023-02-01T22:49:08Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"en",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2023-02-01T22:30:00Z | ---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- matthews_correlation
model-index:
- name: distilbert_sa_GLUE_Experiment_data_aug_cola_384
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE COLA
type: glue
args: cola
metrics:
- name: Matthews Correlation
type: matthews_correlation
value: 0.12073105148250744
---
<!-- 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_sa_GLUE_Experiment_data_aug_cola_384
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the GLUE COLA dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7008
- Matthews Correlation: 0.1207
## 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: 256
- eval_batch_size: 256
- seed: 10
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.5179 | 1.0 | 835 | 0.7008 | 0.1207 |
| 0.3641 | 2.0 | 1670 | 0.9121 | 0.1063 |
| 0.2641 | 3.0 | 2505 | 1.0415 | 0.0951 |
| 0.1963 | 4.0 | 3340 | 1.2167 | 0.1072 |
| 0.1519 | 5.0 | 4175 | 1.3170 | 0.1162 |
| 0.1191 | 6.0 | 5010 | 1.4385 | 0.1118 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.14.0a0+410ce96
- Datasets 2.9.0
- Tokenizers 0.13.2
|
TolgahanT/TT | TolgahanT | 2023-02-01T22:21:32Z | 0 | 0 | diffusers | [
"diffusers",
"ee",
"dataset:fka/awesome-chatgpt-prompts",
"license:creativeml-openrail-m",
"region:us"
]
| null | 2023-02-01T22:18:33Z | ---
license: creativeml-openrail-m
datasets:
- fka/awesome-chatgpt-prompts
language:
- ee
metrics:
- cer
library_name: diffusers
--- |
tomekkorbak/nostalgic_jones | tomekkorbak | 2023-02-01T22:21:04Z | 6 | 0 | transformers | [
"transformers",
"pytorch",
"gpt2",
"generated_from_trainer",
"en",
"dataset:tomekkorbak/detoxify-pile-chunk3-0-50000",
"dataset:tomekkorbak/detoxify-pile-chunk3-50000-100000",
"dataset:tomekkorbak/detoxify-pile-chunk3-100000-150000",
"dataset:tomekkorbak/detoxify-pile-chunk3-150000-200000",
"dataset:tomekkorbak/detoxify-pile-chunk3-200000-250000",
"dataset:tomekkorbak/detoxify-pile-chunk3-250000-300000",
"dataset:tomekkorbak/detoxify-pile-chunk3-300000-350000",
"dataset:tomekkorbak/detoxify-pile-chunk3-350000-400000",
"dataset:tomekkorbak/detoxify-pile-chunk3-400000-450000",
"dataset:tomekkorbak/detoxify-pile-chunk3-450000-500000",
"dataset:tomekkorbak/detoxify-pile-chunk3-500000-550000",
"dataset:tomekkorbak/detoxify-pile-chunk3-550000-600000",
"dataset:tomekkorbak/detoxify-pile-chunk3-600000-650000",
"dataset:tomekkorbak/detoxify-pile-chunk3-650000-700000",
"dataset:tomekkorbak/detoxify-pile-chunk3-700000-750000",
"dataset:tomekkorbak/detoxify-pile-chunk3-750000-800000",
"dataset:tomekkorbak/detoxify-pile-chunk3-800000-850000",
"dataset:tomekkorbak/detoxify-pile-chunk3-850000-900000",
"dataset:tomekkorbak/detoxify-pile-chunk3-900000-950000",
"dataset:tomekkorbak/detoxify-pile-chunk3-950000-1000000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1000000-1050000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1050000-1100000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1100000-1150000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1150000-1200000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1200000-1250000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1250000-1300000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1300000-1350000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1350000-1400000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1400000-1450000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1450000-1500000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1500000-1550000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1550000-1600000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1600000-1650000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1650000-1700000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1700000-1750000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1750000-1800000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1800000-1850000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1850000-1900000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1900000-1950000",
"license:mit",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| null | 2023-01-31T22:34:53Z | ---
language:
- en
license: mit
tags:
- generated_from_trainer
datasets:
- tomekkorbak/detoxify-pile-chunk3-0-50000
- tomekkorbak/detoxify-pile-chunk3-50000-100000
- tomekkorbak/detoxify-pile-chunk3-100000-150000
- tomekkorbak/detoxify-pile-chunk3-150000-200000
- tomekkorbak/detoxify-pile-chunk3-200000-250000
- tomekkorbak/detoxify-pile-chunk3-250000-300000
- tomekkorbak/detoxify-pile-chunk3-300000-350000
- tomekkorbak/detoxify-pile-chunk3-350000-400000
- tomekkorbak/detoxify-pile-chunk3-400000-450000
- tomekkorbak/detoxify-pile-chunk3-450000-500000
- tomekkorbak/detoxify-pile-chunk3-500000-550000
- tomekkorbak/detoxify-pile-chunk3-550000-600000
- tomekkorbak/detoxify-pile-chunk3-600000-650000
- tomekkorbak/detoxify-pile-chunk3-650000-700000
- tomekkorbak/detoxify-pile-chunk3-700000-750000
- tomekkorbak/detoxify-pile-chunk3-750000-800000
- tomekkorbak/detoxify-pile-chunk3-800000-850000
- tomekkorbak/detoxify-pile-chunk3-850000-900000
- tomekkorbak/detoxify-pile-chunk3-900000-950000
- tomekkorbak/detoxify-pile-chunk3-950000-1000000
- tomekkorbak/detoxify-pile-chunk3-1000000-1050000
- tomekkorbak/detoxify-pile-chunk3-1050000-1100000
- tomekkorbak/detoxify-pile-chunk3-1100000-1150000
- tomekkorbak/detoxify-pile-chunk3-1150000-1200000
- tomekkorbak/detoxify-pile-chunk3-1200000-1250000
- tomekkorbak/detoxify-pile-chunk3-1250000-1300000
- tomekkorbak/detoxify-pile-chunk3-1300000-1350000
- tomekkorbak/detoxify-pile-chunk3-1350000-1400000
- tomekkorbak/detoxify-pile-chunk3-1400000-1450000
- tomekkorbak/detoxify-pile-chunk3-1450000-1500000
- tomekkorbak/detoxify-pile-chunk3-1500000-1550000
- tomekkorbak/detoxify-pile-chunk3-1550000-1600000
- tomekkorbak/detoxify-pile-chunk3-1600000-1650000
- tomekkorbak/detoxify-pile-chunk3-1650000-1700000
- tomekkorbak/detoxify-pile-chunk3-1700000-1750000
- tomekkorbak/detoxify-pile-chunk3-1750000-1800000
- tomekkorbak/detoxify-pile-chunk3-1800000-1850000
- tomekkorbak/detoxify-pile-chunk3-1850000-1900000
- tomekkorbak/detoxify-pile-chunk3-1900000-1950000
model-index:
- name: nostalgic_jones
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. -->
# nostalgic_jones
This model was trained from scratch on the tomekkorbak/detoxify-pile-chunk3-0-50000, the tomekkorbak/detoxify-pile-chunk3-50000-100000, the tomekkorbak/detoxify-pile-chunk3-100000-150000, the tomekkorbak/detoxify-pile-chunk3-150000-200000, the tomekkorbak/detoxify-pile-chunk3-200000-250000, the tomekkorbak/detoxify-pile-chunk3-250000-300000, the tomekkorbak/detoxify-pile-chunk3-300000-350000, the tomekkorbak/detoxify-pile-chunk3-350000-400000, the tomekkorbak/detoxify-pile-chunk3-400000-450000, the tomekkorbak/detoxify-pile-chunk3-450000-500000, the tomekkorbak/detoxify-pile-chunk3-500000-550000, the tomekkorbak/detoxify-pile-chunk3-550000-600000, the tomekkorbak/detoxify-pile-chunk3-600000-650000, the tomekkorbak/detoxify-pile-chunk3-650000-700000, the tomekkorbak/detoxify-pile-chunk3-700000-750000, the tomekkorbak/detoxify-pile-chunk3-750000-800000, the tomekkorbak/detoxify-pile-chunk3-800000-850000, the tomekkorbak/detoxify-pile-chunk3-850000-900000, the tomekkorbak/detoxify-pile-chunk3-900000-950000, the tomekkorbak/detoxify-pile-chunk3-950000-1000000, the tomekkorbak/detoxify-pile-chunk3-1000000-1050000, the tomekkorbak/detoxify-pile-chunk3-1050000-1100000, the tomekkorbak/detoxify-pile-chunk3-1100000-1150000, the tomekkorbak/detoxify-pile-chunk3-1150000-1200000, the tomekkorbak/detoxify-pile-chunk3-1200000-1250000, the tomekkorbak/detoxify-pile-chunk3-1250000-1300000, the tomekkorbak/detoxify-pile-chunk3-1300000-1350000, the tomekkorbak/detoxify-pile-chunk3-1350000-1400000, the tomekkorbak/detoxify-pile-chunk3-1400000-1450000, the tomekkorbak/detoxify-pile-chunk3-1450000-1500000, the tomekkorbak/detoxify-pile-chunk3-1500000-1550000, the tomekkorbak/detoxify-pile-chunk3-1550000-1600000, the tomekkorbak/detoxify-pile-chunk3-1600000-1650000, the tomekkorbak/detoxify-pile-chunk3-1650000-1700000, the tomekkorbak/detoxify-pile-chunk3-1700000-1750000, the tomekkorbak/detoxify-pile-chunk3-1750000-1800000, the tomekkorbak/detoxify-pile-chunk3-1800000-1850000, the tomekkorbak/detoxify-pile-chunk3-1850000-1900000 and the tomekkorbak/detoxify-pile-chunk3-1900000-1950000 datasets.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.01
- training_steps: 50354
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.24.0
- Pytorch 1.11.0+cu113
- Datasets 2.5.1
- Tokenizers 0.11.6
# Full config
{'dataset': {'conditional_training_config': {'aligned_prefix': '<|aligned|>',
'drop_token_fraction': 0.01,
'misaligned_prefix': '<|misaligned|>',
'threshold': 0.00056},
'datasets': ['tomekkorbak/detoxify-pile-chunk3-0-50000',
'tomekkorbak/detoxify-pile-chunk3-50000-100000',
'tomekkorbak/detoxify-pile-chunk3-100000-150000',
'tomekkorbak/detoxify-pile-chunk3-150000-200000',
'tomekkorbak/detoxify-pile-chunk3-200000-250000',
'tomekkorbak/detoxify-pile-chunk3-250000-300000',
'tomekkorbak/detoxify-pile-chunk3-300000-350000',
'tomekkorbak/detoxify-pile-chunk3-350000-400000',
'tomekkorbak/detoxify-pile-chunk3-400000-450000',
'tomekkorbak/detoxify-pile-chunk3-450000-500000',
'tomekkorbak/detoxify-pile-chunk3-500000-550000',
'tomekkorbak/detoxify-pile-chunk3-550000-600000',
'tomekkorbak/detoxify-pile-chunk3-600000-650000',
'tomekkorbak/detoxify-pile-chunk3-650000-700000',
'tomekkorbak/detoxify-pile-chunk3-700000-750000',
'tomekkorbak/detoxify-pile-chunk3-750000-800000',
'tomekkorbak/detoxify-pile-chunk3-800000-850000',
'tomekkorbak/detoxify-pile-chunk3-850000-900000',
'tomekkorbak/detoxify-pile-chunk3-900000-950000',
'tomekkorbak/detoxify-pile-chunk3-950000-1000000',
'tomekkorbak/detoxify-pile-chunk3-1000000-1050000',
'tomekkorbak/detoxify-pile-chunk3-1050000-1100000',
'tomekkorbak/detoxify-pile-chunk3-1100000-1150000',
'tomekkorbak/detoxify-pile-chunk3-1150000-1200000',
'tomekkorbak/detoxify-pile-chunk3-1200000-1250000',
'tomekkorbak/detoxify-pile-chunk3-1250000-1300000',
'tomekkorbak/detoxify-pile-chunk3-1300000-1350000',
'tomekkorbak/detoxify-pile-chunk3-1350000-1400000',
'tomekkorbak/detoxify-pile-chunk3-1400000-1450000',
'tomekkorbak/detoxify-pile-chunk3-1450000-1500000',
'tomekkorbak/detoxify-pile-chunk3-1500000-1550000',
'tomekkorbak/detoxify-pile-chunk3-1550000-1600000',
'tomekkorbak/detoxify-pile-chunk3-1600000-1650000',
'tomekkorbak/detoxify-pile-chunk3-1650000-1700000',
'tomekkorbak/detoxify-pile-chunk3-1700000-1750000',
'tomekkorbak/detoxify-pile-chunk3-1750000-1800000',
'tomekkorbak/detoxify-pile-chunk3-1800000-1850000',
'tomekkorbak/detoxify-pile-chunk3-1850000-1900000',
'tomekkorbak/detoxify-pile-chunk3-1900000-1950000'],
'is_split_by_sentences': True},
'generation': {'force_call_on': [25354],
'metrics_configs': [{}, {'n': 1}, {'n': 2}, {'n': 5}],
'scenario_configs': [{'generate_kwargs': {'bad_words_ids': [[50257],
[50258]],
'do_sample': True,
'max_length': 128,
'min_length': 10,
'temperature': 0.7,
'top_k': 0,
'top_p': 0.9},
'name': 'unconditional',
'num_samples': 4096,
'prefix': '<|aligned|>'}],
'scorer_config': {'device': 'cuda:0'}},
'kl_gpt3_callback': {'force_call_on': [25354],
'gpt3_kwargs': {'model_name': 'davinci'},
'max_tokens': 64,
'num_samples': 4096,
'prefix': '<|aligned|>'},
'model': {'from_scratch': True,
'gpt2_config_kwargs': {'reorder_and_upcast_attn': True,
'scale_attn_by': True},
'num_additional_tokens': 2,
'path_or_name': 'gpt2'},
'objective': {'name': 'MLE'},
'tokenizer': {'path_or_name': 'gpt2',
'special_tokens': ['<|aligned|>', '<|misaligned|>']},
'training': {'dataloader_num_workers': 0,
'effective_batch_size': 64,
'evaluation_strategy': 'no',
'fp16': True,
'hub_model_id': 'nostalgic_jones',
'hub_strategy': 'all_checkpoints',
'learning_rate': 0.0005,
'logging_first_step': True,
'logging_steps': 1,
'num_tokens': 3300000000,
'output_dir': 'training_output104340',
'per_device_train_batch_size': 16,
'push_to_hub': True,
'remove_unused_columns': False,
'save_steps': 5070,
'save_strategy': 'steps',
'seed': 42,
'warmup_ratio': 0.01,
'weight_decay': 0.1}}
# Wandb URL:
https://wandb.ai/tomekkorbak/apo/runs/pw7t099z |
Nonin/ppo-LunarLander-v2 | Nonin | 2023-02-01T22:17:51Z | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-02-01T22:17:32Z | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 273.25 +/- 22.65
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
hectorjelly/ppo-LunarLander-v2 | hectorjelly | 2023-02-01T22:08:32Z | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-02-01T22:08:12Z | ---
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: 268.23 +/- 21.16
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
...
```
|
jha2ee/StableDiffusion_finetuning_Disney | jha2ee | 2023-02-01T22:00:48Z | 12 | 3 | diffusers | [
"diffusers",
"safetensors",
"text-to-image",
"stable-diffusion",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
]
| text-to-image | 2023-02-01T21:55:19Z | ---
license: creativeml-openrail-m
tags:
- text-to-image
- stable-diffusion
---
### Disney-style Dreambooth model trained by jha2ee 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:
|
Closen/CartPole-v1_PG | Closen | 2023-02-01T21:58:27Z | 0 | 0 | null | [
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-02-01T21:25:04Z | ---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: CartPole-v1_PG
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 500.00 +/- 0.00
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
stinoco/Taxi-v3 | stinoco | 2023-02-01T21:55:04Z | 0 | 0 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-02-01T21:55:01Z | ---
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: 7.52 +/- 2.72
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="stinoco/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"])
```
|
smartik/t5-small-finetuned-xsum | smartik | 2023-02-01T21:17:01Z | 9 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:xsum",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text2text-generation | 2023-01-26T14:23:46Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- xsum
model-index:
- name: t5-small-finetuned-xsum
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-small-finetuned-xsum
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the xsum dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
|
ietz/token-paraphrase-MiniLM-L6-v2-baseline | ietz | 2023-02-01T21:08:04Z | 3 | 0 | sentence-transformers | [
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
]
| sentence-similarity | 2023-02-01T21:05:54Z | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
model = AutoModel.from_pretrained('{MODEL_NAME}')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information --> |
Sartc/PPO-2FEB-LunarLander-v2 | Sartc | 2023-02-01T20:47:15Z | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-02-01T20:44:12Z | ---
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: -402.40 +/- 104.21
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
...
```
|
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