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ArJakusz/DialoGPT-small-starky | []
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} | 0 | 2022-12-29T14:27:19Z | ---
license: creativeml-openrail-m
tags:
- pytorch
- diffusers
- stable-diffusion
- text-to-image
- diffusion-models-class
- dreambooth-hackathon
- wildcard
datasets: Arch4ngel/pochita
widget:
- text: a photo of pochita plushie in the cosmos
---
# DreamBooth model for the pochita concept trained by Arch4ngel on the Arch4ngel/pochita dataset.
This is a Stable Diffusion model fine-tuned on the pochita concept with DreamBooth. It can be used by modifying the `instance_prompt`: **a photo of pochita plushie**
This model was created as part of the DreamBooth Hackathon 🔥. Visit the [organisation page](https://huggingface.co/dreambooth-hackathon) for instructions on how to take part!
## Description
Stable Diffusion model fine-tuned for generating Pochita plushie images.
## Usage
```python
from diffusers import StableDiffusionPipeline
pipeline = StableDiffusionPipeline.from_pretrained('Arch4ngel/pochita-plushie')
image = pipeline().images[0]
image
```
|
Araby/Arabic-TTS | []
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} | 0 | 2022-12-29T14:46:19Z | ---
license: mit
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-de
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xtreme
type: xtreme
args: PAN-X.de
metrics:
- name: F1
type: f1
value: 0.8638300289723342
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-de
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1358
- 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: 5e-05
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2591 | 1.0 | 525 | 0.1621 | 0.8206 |
| 0.1276 | 2.0 | 1050 | 0.1379 | 0.8486 |
| 0.082 | 3.0 | 1575 | 0.1358 | 0.8638 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.13.0+cu116
- Datasets 1.16.1
- Tokenizers 0.10.3
|
Aracatto/Catto | []
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} | 0 | 2022-12-29T14:53:32Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
metrics:
- accuracy
model-index:
- name: my_awesome_model
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: imdb
type: imdb
config: plain_text
split: train
args: plain_text
metrics:
- name: Accuracy
type: accuracy
value: 0.93144
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# my_awesome_model
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2341
- Accuracy: 0.9314
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.2289 | 1.0 | 1563 | 0.2023 | 0.9219 |
| 0.1513 | 2.0 | 3126 | 0.2341 | 0.9314 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
|
Aran/DialoGPT-medium-harrypotter | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
| conversational | {
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"GPT2LMHeadModel"
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} | 8 | 2022-12-29T15:02:52Z | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 271.31 +/- 14.18
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
...
```
|
Aran/DialoGPT-small-harrypotter | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
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"GPT2LMHeadModel"
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} | 8 | 2022-12-29T15:06:22Z | ---
license: apache-2.0
tags:
- image-classification
- vision
- generated_from_trainer
datasets:
- beans
metrics:
- accuracy
model-index:
- name: vit-base-beans
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: beans
type: beans
config: default
split: validation
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9699248120300752
---
<!-- 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. -->
# vit-base-beans
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the beans dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1328
- Accuracy: 0.9699
## 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: 1337
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5.0
### Training results
| Training Loss | Epoch | Step | Accuracy | Validation Loss |
|:-------------:|:-----:|:----:|:--------:|:---------------:|
| 0.49 | 1.0 | 65 | 0.9624 | 0.4050 |
| 0.2769 | 2.0 | 130 | 0.9850 | 0.1862 |
| 0.1441 | 3.0 | 195 | 0.9774 | 0.1554 |
| 0.1661 | 4.0 | 260 | 0.9774 | 0.1333 |
| 0.1754 | 5.0 | 325 | 0.9699 | 0.1328 |
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.1+cu117
- Datasets 2.8.0
- Tokenizers 0.13.2
|
ArashEsk95/bert-base-uncased-finetuned-cola | []
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} | 0 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: test_trainer
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. -->
# test_trainer
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4375
- Rmse: 0.6614
## 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: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rmse |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 1.0663 | 1.0 | 2639 | 0.5119 | 0.7155 |
| 0.3704 | 2.0 | 5278 | 0.4375 | 0.6614 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0
- Datasets 2.3.2
- Tokenizers 0.12.1
|
ArashEsk95/bert-base-uncased-finetuned-stsb | []
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} | 0 | 2022-12-29T15:12:27Z | ---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="RegisGraptin/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"])
```
|
Aravinth/test | []
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} | 0 | 2022-12-29T15:16:58Z | ---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.54 +/- 2.74
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="RegisGraptin/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"])
```
|
ArcQ/gpt-experiments | []
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}
} | 0 | 2022-12-29T15:18:26Z | ---
license: creativeml-openrail-m
tags:
- pytorch
- diffusers
- stable-diffusion
- text-to-image
- diffusion-models-class
- dreambooth-hackathon
- wildcard
datasets: Arch4ngel/pochita_v2
widget:
- text: pochita plushie goes fishing
---
# DreamBooth model for the pochita concept trained by Arch4ngel on the Arch4ngel/pochita_v2 dataset.
This is a Stable Diffusion model fine-tuned on the pochita concept with DreamBooth. It can be used by modifying the `instance_prompt`: **a photo of pochita plushie**
This model was created as part of the DreamBooth Hackathon 🔥. Visit the [organisation page](https://huggingface.co/dreambooth-hackathon) for instructions on how to take part!
## Description
Stable Diffusion model fine-tuned for generating Pochita plushie images.
## Usage
```python
from diffusers import StableDiffusionPipeline
pipeline = StableDiffusionPipeline.from_pretrained('Arch4ngel/pochita-plushie-v2')
image = pipeline().images[0]
image
```
|
AriakimTaiyo/DialoGPT-small-Kumiko | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
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} | 11 | null | ---
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: 7.54 +/- 2.74
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="andreidore/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"])
```
|
AriakimTaiyo/DialoGPT-small-Rikka | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
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}
} | 8 | null | ---
language:
- ar
license: apache-2.0
tags:
- hf-ast-leaderboard
- generated_from_trainer
metrics:
- wer
model-index:
- name: Whisper Small arb - GP
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Whisper Small arb - GP
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Dialect Arabic dataset.
It achieves the following results on the evaluation set:
- Loss: 2.1489
- Wer: 110.7984
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 4000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.9933 | 1.89 | 1000 | 2.0970 | 125.2555 |
| 1.3119 | 3.79 | 2000 | 1.9818 | 113.1290 |
| 0.7643 | 5.68 | 3000 | 2.0559 | 115.4176 |
| 0.5144 | 7.58 | 4000 | 2.1489 | 110.7984 |
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.12.1
|
Aries/T5_question_generation | [
"pytorch",
"jax",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
]
| text2text-generation | {
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"num_beams": 4,
"prefix": "translate English to German: "
},
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"num_beams": 4,
"prefix": "translate English to French: "
},
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"early_stopping": true,
"max_length": 300,
"num_beams": 4,
"prefix": "translate English to Romanian: "
}
}
} | 13 | null |
---
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: cyeet/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
Arina/Erine | []
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}
} | 0 | null | ---
license: mit
tags:
- pytorch
- diffusers
- unconditional-image-generation
- diffusion-models-class
---
# Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class)
This model is a diffusion model for unconditional image generation of cute 🦋.
## Usage
```python
from diffusers import DDPMPipeline
pipeline = DDPMPipeline.from_pretrained('Thabet/stable_diffusion-class-butterflies-32')
image = pipeline().images[0]
image
```
|
Arkadiusz/Test-model | []
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} | 0 | null | ---
license: mit
tags:
- pytorch
- diffusers
- unconditional-image-generation
- diffusion-models-class
---
# Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class)
This model is a diffusion model for unconditional image generation of cute 🦋.
## Usage
```python
from diffusers import DDPMPipeline
pipeline = DDPMPipeline.from_pretrained('rexoscare/sd-butterflies')
image = pipeline().images[0]
image
```
|
Arnold/wav2vec2-hausa-demo-colab | []
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}
} | 0 | null | ---
license: mit
datasets:
- xnli
language:
- en
metrics:
- accuracy
pipeline_tag: zero-shot-classification
---
# XLM-ROBERTA-BASE-XNLI-EN
## Model description
This model takes the XLM-Roberta-base model which has been continued to pre-traine on a large corpus of Twitter in multiple languages.
It was developed following a similar strategy as introduced as part of the [Tweet Eval](https://github.com/cardiffnlp/tweeteval) framework.
The model is further finetuned on the english part of the XNLI training dataset.
## Intended Usage
This model was developed to do Zero-Shot Text Classification in the realm of Hate Speech Detection. It is focused on the language of english as it was finetuned on data in said language. Since the base model was pre-trained on 100 different languages it has shown some effectiveness in other languages. Please refer to the list of languages in the [XLM Roberta paper](https://arxiv.org/abs/1911.02116)
### Usage with Zero-Shot Classification pipeline
```python
from transformers import pipeline
classifier = pipeline("zero-shot-classification",
model="morit/english_xlm_xnli")
```
After loading the model you can classify sequences in the languages mentioned above. You can specify your sequences and a matching hypothesis to be able to classify your proposed candidate labels.
```python
sequence_to_classify = "I think Rishi Sunak is going to win the elections"
# we can specify candidate labels and hypothesis:
candidate_labels = ["politics", "football"]
hypothesis_template = "This example is {}"
# classify using the information provided
classifier(sequence_to_classify, candidate_labels, hypothesis_template=hypothesis_template)
# Output
#{'sequence': 'I think Rishi Sunak is going to win the elections',
# 'labels': ['politics', 'football'],
# 'scores': [0.7982912659645081, 0.20170868933200836]}
```
## Training
This model was pre-trained on a set of 100 languages and follwed further training on 198M multilingual tweets as described in the original [paper](https://arxiv.org/abs/2104.12250). Further it was trained on the training set of XNLI dataset in english which is a machine translated version of the MNLI dataset. It was trained on 5 epochs of the XNLI train set and evaluated on the XNLI eval dataset at the end of every epoch to find the best performing model. The model which had the highest accuracy on the eval set was chosen at the end.
- learning rate: 2e-5
- batch size: 32
- max sequence: length 128
using a GPU (NVIDIA GeForce RTX 3090) resulting in a training time of 1h 47 mins.
## Evaluation
The best performing model was evaluatated on the XNLI test set to get a comparable result
```
predict_accuracy = 82.89%
``` |
ArseniyBolotin/bert-multi-PAD-ner | [
"pytorch",
"jax",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
]
| token-classification | {
"architectures": [
"BertForTokenClassification"
],
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}
}
} | 11 | 2022-12-29T16:50:09Z | ---
license: mit
datasets:
- xnli
language:
- hi
metrics:
- accuracy
pipeline_tag: zero-shot-classification
---
# XLM-ROBERTA-BASE-XNLI-HI
## Model description
This model takes the XLM-Roberta-base model which has been continued to pre-traine on a large corpus of Twitter in multiple languages.
It was developed following a similar strategy as introduced as part of the [Tweet Eval](https://github.com/cardiffnlp/tweeteval) framework.
The model is further finetuned on the hindi part of the XNLI training dataset.
## Intended Usage
This model was developed to do Zero-Shot Text Classification in the realm of Hate Speech Detection. It is focused on the language of hindi as it was finetuned on data in said language. Since the base model was pre-trained on 100 different languages it has shown some effectiveness in other languages. Please refer to the list of languages in the [XLM Roberta paper](https://arxiv.org/abs/1911.02116)
### Usage with Zero-Shot Classification pipeline
```python
from transformers import pipeline
classifier = pipeline("zero-shot-classification",
model="morit/hindi_xlm_xnli")
```
## Training
This model was pre-trained on a set of 100 languages and follwed further training on 198M multilingual tweets as described in the original [paper](https://arxiv.org/abs/2104.12250). Further it was trained on the training set of XNLI dataset in hindi which is a machine translated version of the MNLI dataset. It was trained on 5 epochs of the XNLI train set and evaluated on the XNLI eval dataset at the end of every epoch to find the best performing model. The model which had the highest accuracy on the eval set was chosen at the end.
- learning rate: 2e-5
- batch size: 32
- max sequence: length 128
using a GPU (NVIDIA GeForce RTX 3090) resulting in a training time of 1h 47 mins.
## Evaluation
The best performing model was evaluatated on the XNLI test set to get a comparable result
```
predict_accuracy = 71.22 %
``` |
Augustvember/test | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
| conversational | {
"architectures": [
"GPT2LMHeadModel"
],
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}
} | 12 | 2022-12-29T19:10:51Z | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 265.52 +/- 22.03
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
...
```
|
Augustvember/wokka | [
"gpt2",
"text-generation",
"transformers"
]
| text-generation | {
"architectures": [
"GPT2LMHeadModel"
],
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}
} | 4 | 2022-12-29T19:14:44Z | ---
tags:
- espnet
- audio
- automatic-speech-recognition
language: it
datasets:
- voxforge
license: cc-by-4.0
---
## ESPnet2 ASR model
### `pyf98/voxforge_it_conformer_e15_linear1024`
This model was trained by Yifan Peng using voxforge recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
Follow the [ESPnet installation instructions](https://espnet.github.io/espnet/installation.html)
if you haven't done that already.
```bash
cd espnet
git checkout bf8c8f00194bdfed8ca388d8b20d14791b7d270e
pip install -e .
cd egs2/voxforge/asr1
./run.sh --skip_data_prep false --skip_train true --download_model pyf98/voxforge_it_conformer_e15_linear1024
```
<!-- Generated by scripts/utils/show_asr_result.sh -->
# RESULTS
## Environments
- date: `Thu Dec 29 01:59:25 EST 2022`
- python version: `3.9.15 (main, Nov 24 2022, 14:31:59) [GCC 11.2.0]`
- espnet version: `espnet 202211`
- pytorch version: `pytorch 1.12.1`
- Git hash: `bf8c8f00194bdfed8ca388d8b20d14791b7d270e`
- Commit date: `Wed Dec 28 22:43:13 2022 -0500`
## asr_train_asr_conformer_e15_linear1024_raw_it_char_normalize_confnorm_varsFalse
### WER
|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|decode_asr_asr_model_valid.acc.ave/dt_it|1035|12587|70.2|24.6|5.2|3.3|33.1|94.7|
|decode_asr_asr_model_valid.acc.ave/et_it|1103|13699|71.9|23.3|4.8|2.9|31.0|92.4|
### CER
|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|decode_asr_asr_model_valid.acc.ave/dt_it|1035|75494|92.9|3.9|3.2|1.8|9.0|94.7|
|decode_asr_asr_model_valid.acc.ave/et_it|1103|81228|93.6|3.6|2.8|1.7|8.1|92.4|
### TER
|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
## ASR config
<details><summary>expand</summary>
```
config: conf/tuning/train_asr_conformer_e15_linear1024.yaml
print_config: false
log_level: INFO
dry_run: false
iterator_type: sequence
output_dir: exp/asr_train_asr_conformer_e15_linear1024_raw_it_char_normalize_confnorm_varsFalse
ngpu: 1
seed: 0
num_workers: 4
num_att_plot: 3
dist_backend: nccl
dist_init_method: env://
dist_world_size: null
dist_rank: null
local_rank: 0
dist_master_addr: null
dist_master_port: null
dist_launcher: null
multiprocessing_distributed: false
unused_parameters: false
sharded_ddp: false
cudnn_enabled: true
cudnn_benchmark: false
cudnn_deterministic: true
collect_stats: false
write_collected_feats: false
max_epoch: 100
patience: null
val_scheduler_criterion:
- valid
- loss
early_stopping_criterion:
- valid
- loss
- min
best_model_criterion:
- - valid
- acc
- max
keep_nbest_models: 10
nbest_averaging_interval: 0
grad_clip: 5.0
grad_clip_type: 2.0
grad_noise: false
accum_grad: 1
no_forward_run: false
resume: true
train_dtype: float32
use_amp: true
log_interval: null
use_matplotlib: true
use_tensorboard: true
create_graph_in_tensorboard: false
use_wandb: false
wandb_project: null
wandb_id: null
wandb_entity: null
wandb_name: null
wandb_model_log_interval: -1
detect_anomaly: false
pretrain_path: null
init_param: []
ignore_init_mismatch: false
freeze_param: []
num_iters_per_epoch: null
batch_size: 128
valid_batch_size: null
batch_bins: 1000000
valid_batch_bins: null
train_shape_file:
- exp/asr_stats_raw_it_char/train/speech_shape
- exp/asr_stats_raw_it_char/train/text_shape.char
valid_shape_file:
- exp/asr_stats_raw_it_char/valid/speech_shape
- exp/asr_stats_raw_it_char/valid/text_shape.char
batch_type: folded
valid_batch_type: null
fold_length:
- 80000
- 150
sort_in_batch: descending
sort_batch: descending
multiple_iterator: false
chunk_length: 500
chunk_shift_ratio: 0.5
num_cache_chunks: 1024
train_data_path_and_name_and_type:
- - dump/raw/tr_it/wav.scp
- speech
- sound
- - dump/raw/tr_it/text
- text
- text
valid_data_path_and_name_and_type:
- - dump/raw/dt_it/wav.scp
- speech
- sound
- - dump/raw/dt_it/text
- text
- text
allow_variable_data_keys: false
max_cache_size: 0.0
max_cache_fd: 32
valid_max_cache_size: null
optim: adam
optim_conf:
lr: 0.002
scheduler: warmuplr
scheduler_conf:
warmup_steps: 10000
token_list:
- <blank>
- <unk>
- <space>
- A
- E
- I
- O
- R
- N
- L
- S
- T
- C
- D
- U
- M
- P
- V
- G
- F
- H
- B
- Q
- Z
- ''''
- Ò
- À
- È
- Ú
- X
- W
- Í
- É
- Y
- K
- J
- '1'
- <sos/eos>
init: null
input_size: null
ctc_conf:
dropout_rate: 0.0
ctc_type: builtin
reduce: true
ignore_nan_grad: null
zero_infinity: true
joint_net_conf: null
use_preprocessor: true
token_type: char
bpemodel: null
non_linguistic_symbols: null
cleaner: null
g2p: null
speech_volume_normalize: null
rir_scp: null
rir_apply_prob: 1.0
noise_scp: null
noise_apply_prob: 1.0
noise_db_range: '13_15'
short_noise_thres: 0.5
frontend: default
frontend_conf:
fs: 16k
specaug: null
specaug_conf: {}
normalize: global_mvn
normalize_conf:
stats_file: exp/asr_stats_raw_it_char/train/feats_stats.npz
norm_vars: false
model: espnet
model_conf:
ctc_weight: 0.3
lsm_weight: 0.1
length_normalized_loss: false
preencoder: null
preencoder_conf: {}
encoder: conformer
encoder_conf:
output_size: 256
attention_heads: 4
linear_units: 1024
num_blocks: 15
dropout_rate: 0.1
positional_dropout_rate: 0.1
attention_dropout_rate: 0.1
input_layer: conv2d
normalize_before: true
macaron_style: true
rel_pos_type: latest
pos_enc_layer_type: rel_pos
selfattention_layer_type: rel_selfattn
activation_type: swish
use_cnn_module: true
cnn_module_kernel: 31
postencoder: null
postencoder_conf: {}
decoder: transformer
decoder_conf:
attention_heads: 4
linear_units: 2048
num_blocks: 6
dropout_rate: 0.1
positional_dropout_rate: 0.1
self_attention_dropout_rate: 0.0
src_attention_dropout_rate: 0.0
preprocessor: default
preprocessor_conf: {}
required:
- output_dir
- token_list
version: '202211'
distributed: false
```
</details>
### Citing ESPnet
```BibTex
@inproceedings{watanabe2018espnet,
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
title={{ESPnet}: End-to-End Speech Processing Toolkit},
year={2018},
booktitle={Proceedings of Interspeech},
pages={2207--2211},
doi={10.21437/Interspeech.2018-1456},
url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}
```
or arXiv:
```bibtex
@misc{watanabe2018espnet,
title={ESPnet: End-to-End Speech Processing Toolkit},
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
year={2018},
eprint={1804.00015},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
Augustvember/wokka5 | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
| conversational | {
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"GPT2LMHeadModel"
],
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}
} | 11 | null | ---
language: en
thumbnail: http://www.huggingtweets.com/dhanushkadev/1672342292500/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1536879822718537728/ikh3D53q_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Dhanushka madushan</div>
<div style="text-align: center; font-size: 14px;">@dhanushkadev</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from Dhanushka madushan.
| Data | Dhanushka madushan |
| --- | --- |
| Tweets downloaded | 1075 |
| Retweets | 117 |
| Short tweets | 119 |
| Tweets kept | 839 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/c5fei3i2/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @dhanushkadev's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1ppe8zfn) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1ppe8zfn/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/dhanushkadev')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
Augustvember/wokkabottest2 | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
| conversational | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
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},
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}
} | 13 | null | ---
tags:
- FrozenLake-v1-8x8
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-8x8-slippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-8x8
type: FrozenLake-v1-8x8
metrics:
- type: mean_reward
value: 0.32 +/- 0.47
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="zipbomb/q-FrozenLake-v1-8x8-slippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
Aurora/community.afpglobal | []
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}
} | 0 | null | ---
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: 504.00 +/- 193.48
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga jonathanmutal -f logs/
python enjoy.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga jonathanmutal -f logs/
rl_zoo3 enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python train.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga jonathanmutal
```
## 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', 10000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
|
Axon/resnet50-v1 | [
"dataset:ImageNet",
"arxiv:1512.03385",
"Axon",
"Elixir",
"license:apache-2.0"
]
| null | {
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} | 0 | null |
---
tags:
- yolov5
- yolo
- vision
- object-detection
- pytorch
library_name: yolov5
library_version: 7.0.6
inference: false
datasets:
- keremberke/construction-safety-object-detection
model-index:
- name: keremberke/yolov5n-construction-safety
results:
- task:
type: object-detection
dataset:
type: keremberke/construction-safety-object-detection
name: keremberke/construction-safety-object-detection
split: validation
metrics:
- type: precision # since [email protected] is not available on hf.co/metrics
value: 0.36535576104287554 # min: 0.0 - max: 1.0
name: [email protected]
---
<div align="center">
<img width="640" alt="keremberke/yolov5n-construction-safety" src="https://huggingface.co/keremberke/yolov5n-construction-safety/resolve/main/sample_visuals.jpg">
</div>
### How to use
- Install [yolov5](https://github.com/fcakyon/yolov5-pip):
```bash
pip install -U yolov5
```
- Load model and perform prediction:
```python
import yolov5
# load model
model = yolov5.load('keremberke/yolov5n-construction-safety')
# set model parameters
model.conf = 0.25 # NMS confidence threshold
model.iou = 0.45 # NMS IoU threshold
model.agnostic = False # NMS class-agnostic
model.multi_label = False # NMS multiple labels per box
model.max_det = 1000 # maximum number of detections per image
# set image
img = 'https://github.com/ultralytics/yolov5/raw/master/data/images/zidane.jpg'
# perform inference
results = model(img, size=640)
# inference with test time augmentation
results = model(img, augment=True)
# parse results
predictions = results.pred[0]
boxes = predictions[:, :4] # x1, y1, x2, y2
scores = predictions[:, 4]
categories = predictions[:, 5]
# show detection bounding boxes on image
results.show()
# save results into "results/" folder
results.save(save_dir='results/')
```
- Finetune the model on your custom dataset:
```bash
yolov5 train --data data.yaml --img 640 --batch 16 --weights keremberke/yolov5n-construction-safety --epochs 10
```
**More models available at: [awesome-yolov5-models](https://github.com/keremberke/awesome-yolov5-models)** |
Ayham/bert_gpt2_summarization_cnndm_new | [
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"dataset:cnn_dailymail",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
]
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} | 8 | 2022-12-29T21:35:05Z | ---
language: en
license: apache-2.0
datasets:
- squad
metrics:
- squad
model-index:
- name: questionanswering-v7
results:
- task:
type: question-answering
name: Question Answering
dataset:
name: squad
type: squad
config: plain_text
split: validation
metrics:
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value: 79.5998
name: Exact Match
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiZTViZDA2Y2E2NjUyMjNjYjkzNTUzODc5OTk2OTNkYjQxMDRmMDhlYjdmYWJjYWQ2N2RlNzY1YmI3OWY1NmRhOSIsInZlcnNpb24iOjF9.ZJHhboAMwsi3pqU-B-XKRCYP_tzpCRb8pEjGr2Oc-TteZeoWHI8CXcpDxugfC3f7d_oBcKWLzh3CClQxBW1iAQ
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value: 86.9965
name: F1
verified: true
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--- |
Ayham/bert_gpt2_summarization_xsum | [
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"dataset:xsum",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
]
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} | 6 | 2022-12-29T21:35:15Z | ---
language: en
license: apache-2.0
datasets:
- squad
metrics:
- squad
model-index:
- name: questionanswering-v8
results:
- task:
type: question-answering
name: Question Answering
dataset:
name: squad
type: squad
config: plain_text
split: validation
metrics:
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value: 79.5998
name: Exact Match
verified: true
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- type: f1
value: 86.9965
name: F1
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiZWZlMzY2MmE1NDNhOGNjNWRmODg0YjQ2Zjk5MjUzZDQ2MDYxOTBlMTNhNzQ4NTA2NjRmNDU3MGIzMTYwMmUyOSIsInZlcnNpb24iOjF9.z0ZDir87aT7UEmUeDm8Uw0oUdAqzlBz343gwnsQP3YLfGsaHe-jGlhco0Z7ISUd9NokyCiJCRc4NNxJQ83IuCw
--- |
Ayham/bert_roberta_summarization_cnn_dailymail | [
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"dataset:cnn_dailymail",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
]
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} | 3 | null | ---
language: en
license: apache-2.0
datasets:
- squad
metrics:
- squad
model-index:
- name: questionanswering-v1
results:
- task:
type: question-answering
name: Question Answering
dataset:
name: squad
type: squad
config: plain_text
split: validation
metrics:
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value: 79.5998
name: Exact Match
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiZTViZDA2Y2E2NjUyMjNjYjkzNTUzODc5OTk2OTNkYjQxMDRmMDhlYjdmYWJjYWQ2N2RlNzY1YmI3OWY1NmRhOSIsInZlcnNpb24iOjF9.ZJHhboAMwsi3pqU-B-XKRCYP_tzpCRb8pEjGr2Oc-TteZeoWHI8CXcpDxugfC3f7d_oBcKWLzh3CClQxBW1iAQ
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value: 86.9965
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verified: true
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--- |
Ayham/distilbert_bert_summarization_cnn_dailymail | [
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"dataset:cnn_dailymail",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
]
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} | 11 | null | ---
language: en
license: apache-2.0
datasets:
- squad
metrics:
- squad
model-index:
- name: questionanswering-v3
results:
- task:
type: question-answering
name: Question Answering
dataset:
name: squad
type: squad
config: plain_text
split: validation
metrics:
- type: exact_match
value: 79.5998
name: Exact Match
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiZTViZDA2Y2E2NjUyMjNjYjkzNTUzODc5OTk2OTNkYjQxMDRmMDhlYjdmYWJjYWQ2N2RlNzY1YmI3OWY1NmRhOSIsInZlcnNpb24iOjF9.ZJHhboAMwsi3pqU-B-XKRCYP_tzpCRb8pEjGr2Oc-TteZeoWHI8CXcpDxugfC3f7d_oBcKWLzh3CClQxBW1iAQ
- type: f1
value: 86.9965
name: F1
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiZWZlMzY2MmE1NDNhOGNjNWRmODg0YjQ2Zjk5MjUzZDQ2MDYxOTBlMTNhNzQ4NTA2NjRmNDU3MGIzMTYwMmUyOSIsInZlcnNpb24iOjF9.z0ZDir87aT7UEmUeDm8Uw0oUdAqzlBz343gwnsQP3YLfGsaHe-jGlhco0Z7ISUd9NokyCiJCRc4NNxJQ83IuCw
--- |
Ayham/distilbert_distilgpt2_summarization_cnn_dailymail | [
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"tensorboard",
"encoder-decoder",
"text2text-generation",
"dataset:cnn_dailymail",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
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} | 5 | null | ---
language: en
license: apache-2.0
datasets:
- squad
metrics:
- squad
model-index:
- name: questionanswering-v4
results:
- task:
type: question-answering
name: Question Answering
dataset:
name: squad
type: squad
config: plain_text
split: validation
metrics:
- type: exact_match
value: 79.5998
name: Exact Match
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiZTViZDA2Y2E2NjUyMjNjYjkzNTUzODc5OTk2OTNkYjQxMDRmMDhlYjdmYWJjYWQ2N2RlNzY1YmI3OWY1NmRhOSIsInZlcnNpb24iOjF9.ZJHhboAMwsi3pqU-B-XKRCYP_tzpCRb8pEjGr2Oc-TteZeoWHI8CXcpDxugfC3f7d_oBcKWLzh3CClQxBW1iAQ
- type: f1
value: 86.9965
name: F1
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiZWZlMzY2MmE1NDNhOGNjNWRmODg0YjQ2Zjk5MjUzZDQ2MDYxOTBlMTNhNzQ4NTA2NjRmNDU3MGIzMTYwMmUyOSIsInZlcnNpb24iOjF9.z0ZDir87aT7UEmUeDm8Uw0oUdAqzlBz343gwnsQP3YLfGsaHe-jGlhco0Z7ISUd9NokyCiJCRc4NNxJQ83IuCw
--- |
Ayham/distilbert_gpt2_summarization_cnndm | [
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"tensorboard",
"encoder-decoder",
"text2text-generation",
"dataset:cnn_dailymail",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
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} | 6 | null |
---
tags:
- yolov5
- yolo
- vision
- object-detection
- pytorch
library_name: yolov5
library_version: 7.0.6
inference: false
datasets:
- keremberke/construction-safety-object-detection
model-index:
- name: keremberke/yolov5s-construction-safety
results:
- task:
type: object-detection
dataset:
type: keremberke/construction-safety-object-detection
name: keremberke/construction-safety-object-detection
split: validation
metrics:
- type: precision # since [email protected] is not available on hf.co/metrics
value: 0.3947243485213459 # min: 0.0 - max: 1.0
name: [email protected]
---
<div align="center">
<img width="640" alt="keremberke/yolov5s-construction-safety" src="https://huggingface.co/keremberke/yolov5s-construction-safety/resolve/main/sample_visuals.jpg">
</div>
### How to use
- Install [yolov5](https://github.com/fcakyon/yolov5-pip):
```bash
pip install -U yolov5
```
- Load model and perform prediction:
```python
import yolov5
# load model
model = yolov5.load('keremberke/yolov5s-construction-safety')
# set model parameters
model.conf = 0.25 # NMS confidence threshold
model.iou = 0.45 # NMS IoU threshold
model.agnostic = False # NMS class-agnostic
model.multi_label = False # NMS multiple labels per box
model.max_det = 1000 # maximum number of detections per image
# set image
img = 'https://github.com/ultralytics/yolov5/raw/master/data/images/zidane.jpg'
# perform inference
results = model(img, size=640)
# inference with test time augmentation
results = model(img, augment=True)
# parse results
predictions = results.pred[0]
boxes = predictions[:, :4] # x1, y1, x2, y2
scores = predictions[:, 4]
categories = predictions[:, 5]
# show detection bounding boxes on image
results.show()
# save results into "results/" folder
results.save(save_dir='results/')
```
- Finetune the model on your custom dataset:
```bash
yolov5 train --data data.yaml --img 640 --batch 16 --weights keremberke/yolov5s-construction-safety --epochs 10
```
**More models available at: [awesome-yolov5-models](https://github.com/keremberke/awesome-yolov5-models)** |
Ayham/ernie_gpt2_summarization_cnn_dailymail | [
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"dataset:cnn_dailymail",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
]
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}
} | 13 | null | ---
license: mit
tags:
- pytorch
- diffusers
- unconditional-image-generation
- diffusion-models-class
---
# Example Fine-Tuned Model for Unit 2 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class)
Unit 2-1 exercise
## Usage
```python
from diffusers import DDPMPipeline
pipeline = DDPMPipeline.from_pretrained('gstaff/ddpm-celebahq-finetuned-butterflies-2epochs')
image = pipeline().images[0]
image
```
|
Ayham/roberta_bert_summarization_cnn_dailymail | [
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"dataset:cnn_dailymail",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
]
| text2text-generation | {
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}
} | 12 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- cifar100
metrics:
- accuracy
model-index:
- name: swin-small-finetuned-cifar100
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: cifar100
type: cifar100
args: cifar100
metrics:
- name: Accuracy
type: accuracy
value: 0.8938
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# swin-small-finetuned-cifar100
This model is a fine-tuned version of [microsoft/swin-small-patch4-window7-224](https://huggingface.co/microsoft/swin-small-patch4-window7-224) on the cifar100 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6281
- Accuracy: 0.8938
## 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: 4e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.72 | 1.0 | 781 | 0.6691 | 0.8077 |
| 0.6944 | 2.0 | 1562 | 0.4797 | 0.8495 |
| 0.2794 | 3.0 | 2343 | 0.4338 | 0.869 |
| 0.2569 | 4.0 | 3124 | 0.4263 | 0.879 |
| 0.1417 | 5.0 | 3905 | 0.4385 | 0.8819 |
| 0.0961 | 6.0 | 4686 | 0.4720 | 0.8854 |
| 0.0584 | 7.0 | 5467 | 0.4941 | 0.885 |
| 0.0351 | 8.0 | 6248 | 0.5253 | 0.885 |
| 0.0107 | 9.0 | 7029 | 0.5598 | 0.8887 |
| 0.0118 | 10.0 | 7810 | 0.5998 | 0.8858 |
| 0.0097 | 11.0 | 8591 | 0.5957 | 0.8941 |
| 0.0044 | 12.0 | 9372 | 0.6237 | 0.8912 |
| 0.0013 | 13.0 | 10153 | 0.6286 | 0.8929 |
| 0.0102 | 14.0 | 10934 | 0.6281 | 0.8938 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.12.1
|
Ayham/roberta_distilgpt2_summarization_cnn_dailymail | [
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"dataset:cnn_dailymail",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
]
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}
} | 4 | 2022-12-29T22:02:15Z | ---
language:
- zh
library_name: transformers
pipeline_tag: text2text-generation
---
```python
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("svjack/T5-dialogue-collect")
model = AutoModelForSeq2SeqLM.from_pretrained("svjack/T5-dialogue-collect")
text = '''
根据下面的上下文进行分段：
上下文 他 喜欢 吃 汉堡 是 但 我 可 不 喜欢。
答案：
'''
tokenizer.decode(
model.generate(
tokenizer.encode(
text, return_tensors="pt", add_special_tokens=True
))[0],
skip_special_tokens = True
)
'''
'分段:他喜欢吃汉堡 分段:是的,但我可不喜欢。'
'''
``` |
Ayham/roberta_gpt2_new_max64_summarization_cnndm | [
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"dataset:cnn_dailymail",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
]
| text2text-generation | {
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} | 4 | 2022-12-29T22:11:52Z | ---
license: creativeml-openrail-m
tags:
- pytorch
- diffusers
- stable-diffusion
- text-to-image
- diffusion-models-class
- dreambooth-hackathon
- animal
widget:
- text: a photo of a ðŁĴŁ jellyfish in the snow
- text: a photo of a ðŁĴŁ jellyfish next to a dog
- text: a photo of a ðŁĴŁ jellyfish on top of a mountain
---
# Iridescent Jellyfish
**Iridescent Jellyfish** is a Dreambooth model for the `iridescent` jellyfish concept (represented by the `ðŁĴŁ` identifier).
It applies to the *animal* theme.
It is fine-tuned from `runwayml/stable-diffusion-v1-5` checkpoint on a small dataset of jellyfish images.
It can be used by modifying the `instance_prompt`: **a photo of a ðŁĴŁ jellyfish in the snow**
This model was created as part of the DreamBooth Hackathon 🔥. Visit the [organisation page](https://huggingface.co/dreambooth-hackathon) for instructions on how to take part!
#### Fine-Tuning Details
- Number of training images: 17
- Learning rate: 2e-06
- Training steps: 800
- Guidance Scale: 7
- Inference Steps: 50
#### Output Examples
<table>
<tr>
<td>a oil painting of a <b>ðŁĴŁ</b> jellyfish</td>
<td>a photo of a <b>ðŁĴŁ</b> jellyfish next to a dog</td>
<td>a photo of a <b>ðŁĴŁ</b> jellyfish in the snow</td>
</tr>
<tr>
<td align="center"><img src="https://huggingface.co/simonschoe/iridescent-jellyfish/resolve/main/output/jelly%20(4).png" style="height:200px"> </td>
<td align="center"><img src="https://huggingface.co/simonschoe/iridescent-jellyfish/resolve/main/output/jelly%20(5).png" style="height:200px"> </td>
<td align="center"><img src="https://huggingface.co/simonschoe/iridescent-jellyfish/resolve/main/output/jelly%20(6).png" style="height:200px"> </td>
</tr>
<tr>
<td>a photo of a <b>ðŁĴŁ</b> jellyfish on top of a mountain</td>
<td>a photo of a <b>ðŁĴŁ</b> jellyfish in the sky</td>
<td>a photo of a <b>ðŁĴŁ</b> jellyfish</td>
</tr>
<tr>
<td align="center"><img src="https://huggingface.co/simonschoe/iridescent-jellyfish/resolve/main/output/jelly%20(7).png" style="height:200px"> </td>
<td align="center"><img src="https://huggingface.co/simonschoe/iridescent-jellyfish/resolve/main/output/jelly%20(8).png" style="height:200px"> </td>
<td align="center"><img src="https://huggingface.co/simonschoe/iridescent-jellyfish/resolve/main/output/jelly%20(9).png" style="height:200px"> </td>
</tr>
<tr>
<td>a photo of a <b>ðŁĴŁ</b> jellyfish skydiving</td>
<td>a photo of a <b>ðŁĴŁ</b> jellyfish sutfing on a surfboard</td>
<td>a photo of a choclate <b>ðŁĴŁ</b> jellyfish</td>
</tr>
<tr>
<td align="center"><img src="https://huggingface.co/simonschoe/iridescent-jellyfish/resolve/main/output/jelly%20(10).jpg" style="height:200px"> </td>
<td align="center"><img src="https://huggingface.co/simonschoe/iridescent-jellyfish/resolve/main/output/jelly%20(11).jpg" style="height:200px"> </td>
<td align="center"><img src="https://huggingface.co/simonschoe/iridescent-jellyfish/resolve/main/output/jelly%20(12).jpg" style="height:200px"> </td>
</tr>
<tr>
<td>a photo of a <b>ðŁĴŁ</b> jellyfish shooting fireworks in the sky</td>
<td>a photo of a <b>ðŁĴŁ</b> jellyfish on rollerblades</td>
<td>a photo of a <b>ðŁĴŁ</b> jellyfish in a beer bottle</td>
</tr>
<tr>
<td align="center"><img src="https://huggingface.co/simonschoe/iridescent-jellyfish/resolve/main/output/jelly%20(13).jpg" style="height:200px"> </td>
<td align="center"><img src="https://huggingface.co/simonschoe/iridescent-jellyfish/resolve/main/output/jelly%20(14).jpg" style="height:200px"> </td>
<td align="center"><img src="https://huggingface.co/simonschoe/iridescent-jellyfish/resolve/main/output/jelly%20(15).jpg" style="height:200px"> </td>
</tr>
<tr>
<td>a colorful sketch of a <b>ðŁĴŁ</b> jellyfish</td>
<td>a photo of a <b>ðŁĴŁ</b> jellyfish in the jungle</td>
<td>a mystic <b>ðŁĴŁ</b> jellyfish, trending on artstation</td>
</tr>
<tr>
<td align="center"><img src="https://huggingface.co/simonschoe/iridescent-jellyfish/resolve/main/output/jelly%20(1).png" style="height:200px"> </td>
<td align="center"><img src="https://huggingface.co/simonschoe/iridescent-jellyfish/resolve/main/output/jelly%20(2).png" style="height:200px"> </td>
<td align="center"><img src="https://huggingface.co/simonschoe/iridescent-jellyfish/resolve/main/output/jelly%20(3).png" style="height:200px"> </td>
</tr>
</table>
## Usage
```python
from diffusers import StableDiffusionPipeline
import torch
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
pipeline = StableDiffusionPipeline.from_pretrained('simonschoe/iridescent-jellyfish').to(device)
prompt = "a photo of a ðŁĴŁ jellyfish in the snow"
image = pipeline(
prompt,
num_inference_steps=50,
guidance_scale=7,
num_images_per_prompt=1
).images[0]
image
```
|
Ayham/roberta_gpt2_summarization_cnn_dailymail | [
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"tensorboard",
"encoder-decoder",
"text2text-generation",
"dataset:cnn_dailymail",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
]
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} | 31 | 2022-12-29T22:18:44Z |
---
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: Bunkerj/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
Ayham/roberta_roberta_summarization_cnn_dailymail | [
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"dataset:cnn_dailymail",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
]
| text2text-generation | {
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"EncoderDecoderModel"
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} | 3 | null | ---
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: 534.50 +/- 40.52
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga zlicastro -f logs/
python enjoy.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga zlicastro -f logs/
rl_zoo3 enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python train.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga zlicastro
```
## Hyperparameters
```python
OrderedDict([('batch_size', 64),
('buffer_size', 125000),
('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', 2000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
|
Ayham/robertagpt2_xsum | [
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
]
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} | 4 | null | ---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
- f1
model-index:
- name: Glue_distilbert
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE MRPC
type: glue
args: mrpc
metrics:
- name: Accuracy
type: accuracy
value: 0.8504901960784313
- name: F1
type: f1
value: 0.8960817717206134
---
<!-- 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. -->
# Glue_distilbert
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: 1.1042
- Accuracy: 0.8505
- F1: 0.8961
- Combined Score: 0.8733
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 33
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------------:|
| 0.5066 | 1.0 | 115 | 0.3833 | 0.8358 | 0.8851 | 0.8604 |
| 0.3227 | 2.0 | 230 | 0.4336 | 0.8309 | 0.8844 | 0.8577 |
| 0.1764 | 3.0 | 345 | 0.4943 | 0.8309 | 0.8757 | 0.8533 |
| 0.0792 | 4.0 | 460 | 0.7271 | 0.8431 | 0.8861 | 0.8646 |
| 0.058 | 5.0 | 575 | 0.8374 | 0.8456 | 0.8945 | 0.8700 |
| 0.0594 | 6.0 | 690 | 0.7570 | 0.8309 | 0.8816 | 0.8563 |
| 0.0395 | 7.0 | 805 | 0.8640 | 0.8431 | 0.8897 | 0.8664 |
| 0.03 | 8.0 | 920 | 0.9007 | 0.8260 | 0.8799 | 0.8529 |
| 0.0283 | 9.0 | 1035 | 0.9479 | 0.8211 | 0.8685 | 0.8448 |
| 0.0127 | 10.0 | 1150 | 1.0686 | 0.8431 | 0.8915 | 0.8673 |
| 0.0097 | 11.0 | 1265 | 1.0752 | 0.8431 | 0.8919 | 0.8675 |
| 0.0164 | 12.0 | 1380 | 1.0627 | 0.8284 | 0.8801 | 0.8543 |
| 0.007 | 13.0 | 1495 | 1.1466 | 0.8333 | 0.8815 | 0.8574 |
| 0.0132 | 14.0 | 1610 | 1.1442 | 0.8456 | 0.8938 | 0.8697 |
| 0.0125 | 15.0 | 1725 | 1.1716 | 0.8235 | 0.8771 | 0.8503 |
| 0.0174 | 16.0 | 1840 | 1.1187 | 0.8333 | 0.8790 | 0.8562 |
| 0.0171 | 17.0 | 1955 | 1.1053 | 0.8456 | 0.8938 | 0.8697 |
| 0.0026 | 18.0 | 2070 | 1.2011 | 0.8309 | 0.8787 | 0.8548 |
| 0.0056 | 19.0 | 2185 | 1.3085 | 0.8260 | 0.8748 | 0.8504 |
| 0.0067 | 20.0 | 2300 | 1.3042 | 0.8333 | 0.8803 | 0.8568 |
| 0.0129 | 21.0 | 2415 | 1.1042 | 0.8505 | 0.8961 | 0.8733 |
| 0.0149 | 22.0 | 2530 | 1.1575 | 0.8235 | 0.8820 | 0.8527 |
| 0.0045 | 23.0 | 2645 | 1.2359 | 0.8407 | 0.8900 | 0.8654 |
| 0.0029 | 24.0 | 2760 | 1.3823 | 0.8211 | 0.8744 | 0.8477 |
| 0.0074 | 25.0 | 2875 | 1.2394 | 0.8431 | 0.8904 | 0.8668 |
| 0.002 | 26.0 | 2990 | 1.4450 | 0.8333 | 0.8859 | 0.8596 |
| 0.0039 | 27.0 | 3105 | 1.5102 | 0.8284 | 0.8805 | 0.8545 |
| 0.0015 | 28.0 | 3220 | 1.4767 | 0.8431 | 0.8915 | 0.8673 |
| 0.0062 | 29.0 | 3335 | 1.5101 | 0.8407 | 0.8926 | 0.8666 |
| 0.0054 | 30.0 | 3450 | 1.3861 | 0.8382 | 0.8893 | 0.8637 |
| 0.0001 | 31.0 | 3565 | 1.4101 | 0.8456 | 0.8948 | 0.8702 |
| 0.0 | 32.0 | 3680 | 1.4203 | 0.8480 | 0.8963 | 0.8722 |
| 0.002 | 33.0 | 3795 | 1.4526 | 0.8431 | 0.8923 | 0.8677 |
| 0.0019 | 34.0 | 3910 | 1.6265 | 0.8260 | 0.8842 | 0.8551 |
| 0.0029 | 35.0 | 4025 | 1.4788 | 0.8456 | 0.8945 | 0.8700 |
| 0.0 | 36.0 | 4140 | 1.4668 | 0.8480 | 0.8956 | 0.8718 |
| 0.0007 | 37.0 | 4255 | 1.5248 | 0.8456 | 0.8945 | 0.8700 |
| 0.0 | 38.0 | 4370 | 1.5202 | 0.8480 | 0.8960 | 0.8720 |
| 0.0033 | 39.0 | 4485 | 1.5541 | 0.8358 | 0.8878 | 0.8618 |
| 0.0017 | 40.0 | 4600 | 1.5097 | 0.8407 | 0.8904 | 0.8655 |
| 0.0 | 41.0 | 4715 | 1.5301 | 0.8407 | 0.8904 | 0.8655 |
| 0.0 | 42.0 | 4830 | 1.4974 | 0.8407 | 0.8862 | 0.8634 |
| 0.0018 | 43.0 | 4945 | 1.5483 | 0.8382 | 0.8896 | 0.8639 |
| 0.0 | 44.0 | 5060 | 1.5071 | 0.8480 | 0.8931 | 0.8706 |
| 0.0 | 45.0 | 5175 | 1.5104 | 0.8480 | 0.8935 | 0.8708 |
| 0.0011 | 46.0 | 5290 | 1.5445 | 0.8382 | 0.8896 | 0.8639 |
| 0.0012 | 47.0 | 5405 | 1.5378 | 0.8431 | 0.8900 | 0.8666 |
| 0.0 | 48.0 | 5520 | 1.5577 | 0.8407 | 0.8881 | 0.8644 |
| 0.0009 | 49.0 | 5635 | 1.5431 | 0.8407 | 0.8885 | 0.8646 |
| 0.0002 | 50.0 | 5750 | 1.5383 | 0.8431 | 0.8904 | 0.8668 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
|
Ayham/robertagpt2_xsum2 | [
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
]
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} | 6 | null |
---
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: mstauffer/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
Ayham/robertagpt2_xsum4 | [
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
]
| text2text-generation | {
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"EncoderDecoderModel"
],
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} | 8 | null | ---
license: mit
---
### center-table on Stable Diffusion
This is the `<wakefit-center-table>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb).
Here is the new concept you will be able to use as an `object`:
|
Ayham/xlmroberta_gpt2_summarization_xsum | [
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"dataset:xsum",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
]
| text2text-generation | {
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"EncoderDecoderModel"
],
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}
} | 9 | null | ---
tags:
- LunarLander-v2
- ppo
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
- deep-rl-course
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 25.48 +/- 76.06
name: mean_reward
verified: false
---
# PPO Agent Playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2.
# Hyperparameters
```python
{'exp_name': 'ppo'
'seed': 1
'torch_deterministic': True
'cuda': True
'track': False
'wandb_project_name': 'cleanRL'
'wandb_entity': None
'capture_video': False
'env_id': 'LunarLander-v2'
'total_timesteps': 500000
'learning_rate': 0.00025
'num_envs': 4
'num_steps': 128
'anneal_lr': True
'gae': True
'gamma': 0.99
'gae_lambda': 0.95
'num_minibatches': 4
'update_epochs': 4
'norm_adv': True
'clip_coef': 0.2
'clip_vloss': True
'ent_coef': 0.01
'vf_coef': 0.5
'max_grad_norm': 0.5
'target_kl': None
'repo_id': 'Mithul/ppo-LunarLander-v2'
'batch_size': 512
'minibatch_size': 128}
```
|
Ayham/xlnet_bert_summarization_cnn_dailymail | [
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"dataset:cnn_dailymail",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
]
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} | 7 | null | ---
license: creativeml-openrail-m
tags:
- pytorch
- diffusers
- stable-diffusion
- text-to-image
- diffusion-models-class
- dreambooth-hackathon
- animal
widget:
- text: a photo of shoebill bird as a gold monument in the Alhambra Granada Spain, realistic, camera, 35mm
---
# DreamBooth model for the shoebill concept trained by fnavales
This is a Stable Diffusion model fine-tuned on the shoebill concept with DreamBooth. It can be used by modifying the `instance_prompt`: **a photo of shoebill bird**
This model was created as part of the DreamBooth Hackathon 🔥. Visit the [organisation page](https://huggingface.co/dreambooth-hackathon) for instructions on how to take part!
## Description
This is a Stable Diffusion model fine-tuned on `bird` images for the animal theme.
The shoebill, also known as the shoebill stork or Balaeniceps rex, is a large bird native to swamps in East Africa.
It is known for its distinctive appearance, with a long, narrow bill that resembles a shoe and a tall, thick neck.
It has a mostly grey plumage, with a white belly and a few patches of brown and black on its wings and back.
The shoebill is a carnivorous bird, and it feeds mainly on fish, although it has also been known to eat reptiles, mammals, and birds.
It is a solitary and elusive bird, and it is not commonly seen in the wild.
The shoebill is endangered, and it is protected by law in many of the countries where it is found.
## Examples
| | |
| ---------------- | ----------------- |
|  |  |
|  |  |
## Usage
```python
from diffusers import StableDiffusionPipeline
pipeline = StableDiffusionPipeline.from_pretrained('fnavales/shoebill-bird-fnavales')
image = pipeline().images[0]
image
```
|
Ayham/xlnet_gpt2_summarization_cnn_dailymail | [
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"dataset:cnn_dailymail",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
]
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} | 8 | null |
---
tags:
- yolov5
- yolo
- vision
- object-detection
- pytorch
library_name: yolov5
library_version: 7.0.6
inference: false
datasets:
- keremberke/construction-safety-object-detection
model-index:
- name: keremberke/yolov5m-construction-safety
results:
- task:
type: object-detection
dataset:
type: keremberke/construction-safety-object-detection
name: keremberke/construction-safety-object-detection
split: validation
metrics:
- type: precision # since [email protected] is not available on hf.co/metrics
value: 0.37443513503008957 # min: 0.0 - max: 1.0
name: [email protected]
---
<div align="center">
<img width="640" alt="keremberke/yolov5m-construction-safety" src="https://huggingface.co/keremberke/yolov5m-construction-safety/resolve/main/sample_visuals.jpg">
</div>
### How to use
- Install [yolov5](https://github.com/fcakyon/yolov5-pip):
```bash
pip install -U yolov5
```
- Load model and perform prediction:
```python
import yolov5
# load model
model = yolov5.load('keremberke/yolov5m-construction-safety')
# set model parameters
model.conf = 0.25 # NMS confidence threshold
model.iou = 0.45 # NMS IoU threshold
model.agnostic = False # NMS class-agnostic
model.multi_label = False # NMS multiple labels per box
model.max_det = 1000 # maximum number of detections per image
# set image
img = 'https://github.com/ultralytics/yolov5/raw/master/data/images/zidane.jpg'
# perform inference
results = model(img, size=640)
# inference with test time augmentation
results = model(img, augment=True)
# parse results
predictions = results.pred[0]
boxes = predictions[:, :4] # x1, y1, x2, y2
scores = predictions[:, 4]
categories = predictions[:, 5]
# show detection bounding boxes on image
results.show()
# save results into "results/" folder
results.save(save_dir='results/')
```
- Finetune the model on your custom dataset:
```bash
yolov5 train --data data.yaml --img 640 --batch 16 --weights keremberke/yolov5m-construction-safety --epochs 10
```
**More models available at: [awesome-yolov5-models](https://github.com/keremberke/awesome-yolov5-models)** |
Ayham/xlnet_roberta_new_summarization_cnn_dailymail | []
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} | 0 | null | ---
language:
- "zh"
tags:
- "chinese"
- "token-classification"
- "pos"
- "dependency-parsing"
datasets:
- "universal_dependencies"
license: "apache-2.0"
pipeline_tag: "token-classification"
---
# deberta-large-chinese-erlangshen-ud-goeswith
## Model Description
This is a DeBERTa(V2) model pre-trained on Chinese texts (both simplified and traditional) for POS-tagging and dependency-parsing (using `goeswith` for subwords), derived from [deberta-large-chinese-erlangshen-upos](https://huggingface.co/KoichiYasuoka/deberta-large-chinese-erlangshen-upos).
## How to Use
```py
class UDgoeswith(object):
def __init__(self,bert):
from transformers import AutoTokenizer,AutoModelForTokenClassification
self.tokenizer=AutoTokenizer.from_pretrained(bert)
self.model=AutoModelForTokenClassification.from_pretrained(bert)
def __call__(self,text):
import numpy,torch,ufal.chu_liu_edmonds
w=self.tokenizer(text,return_offsets_mapping=True)
v=w["input_ids"]
x=[v[0:i]+[self.tokenizer.mask_token_id]+v[i+1:]+[j] for i,j in enumerate(v[1:-1],1)]
with torch.no_grad():
e=self.model(input_ids=torch.tensor(x)).logits.numpy()[:,1:-2,:]
r=[1 if i==0 else -1 if j.endswith("|root") else 0 for i,j in sorted(self.model.config.id2label.items())]
e+=numpy.where(numpy.add.outer(numpy.identity(e.shape[0]),r)==0,0,numpy.nan)
g=self.model.config.label2id["X|_|goeswith"]
r=numpy.tri(e.shape[0])
for i in range(e.shape[0]):
for j in range(i+2,e.shape[1]):
r[i,j]=r[i,j-1] if numpy.nanargmax(e[i,j-1])==g else 1
e[:,:,g]+=numpy.where(r==0,0,numpy.nan)
m=numpy.full((e.shape[0]+1,e.shape[1]+1),numpy.nan)
m[1:,1:]=numpy.nanmax(e,axis=2).transpose()
p=numpy.zeros(m.shape)
p[1:,1:]=numpy.nanargmax(e,axis=2).transpose()
for i in range(1,m.shape[0]):
m[i,0],m[i,i],p[i,0]=m[i,i],numpy.nan,p[i,i]
h=ufal.chu_liu_edmonds.chu_liu_edmonds(m)[0]
if [0 for i in h if i==0]!=[0]:
m[:,0]+=numpy.where(m[:,0]==numpy.nanmax(m[[i for i,j in enumerate(h) if j==0],0]),0,numpy.nan)
m[[i for i,j in enumerate(h) if j==0]]+=[0 if i==0 or j==0 else numpy.nan for i,j in enumerate(h)]
h=ufal.chu_liu_edmonds.chu_liu_edmonds(m)[0]
u="# text = "+text+"\n"
v=[(s,e) for s,e in w["offset_mapping"] if s<e]
for i,(s,e) in enumerate(v,1):
q=self.model.config.id2label[p[i,h[i]]].split("|")
u+="\t".join([str(i),text[s:e],"_",q[0],"_","|".join(q[1:-1]),str(h[i]),q[-1],"_","_" if i<len(v) and e<v[i][0] else "SpaceAfter=No"])+"\n"
return u+"\n"
nlp=UDgoeswith("KoichiYasuoka/deberta-large-chinese-erlangshen-ud-goeswith")
print(nlp("我把这本书看完了"))
```
with [ufal.chu-liu-edmonds](https://pypi.org/project/ufal.chu-liu-edmonds/).
Or without ufal.chu-liu-edmonds:
```
from transformers import pipeline
nlp=pipeline("universal-dependencies","KoichiYasuoka/deberta-large-chinese-erlangshen-ud-goeswith",trust_remote_code=True,aggregation_strategy="simple")
print(nlp("我把这本书看完了"))
```
|
Ayham/xlnet_roberta_summarization_cnn_dailymail | [
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"dataset:cnn_dailymail",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
]
| text2text-generation | {
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}
} | 10 | 2022-12-30T00:14:56Z | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-switchboard
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-switchboard
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on switchboard phone conversation transcripts.
It achieves the following results on the evaluation set:
- Loss: 0.5255
- Accuracy: 0.7421
- F1: 0.7383
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.5645 | 1.0 | 370 | 0.5308 | 0.7316 | 0.7290 |
| 0.5121 | 2.0 | 740 | 0.5255 | 0.7421 | 0.7383 |
### Framework versions
- Transformers 4.13.0
- Pytorch 1.13.0+cu116
- Datasets 1.16.1
- Tokenizers 0.10.3
|
Ayoola/cdial-yoruba-test | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"transformers",
"has_space"
]
| automatic-speech-recognition | {
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"Wav2Vec2ForCTC"
],
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}
} | 25 | 2022-12-30T00:54:22Z | ---
license: mit
tags:
- generated_from_keras_callback
model-index:
- name: brabus61/joke-generator
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. -->
# brabus61/joke-generator
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: nan
- Validation Loss: nan
- Epoch: 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:
- optimizer: {'inner_optimizer': {'class_name': 'AdamWeightDecay', 'config': {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 5e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5e-05, 'decay_steps': 0, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '__passive_serialization__': True}, 'warmup_steps': 1000, 'power': 1.0, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}}, 'dynamic': True, 'initial_scale': 32768.0, 'dynamic_growth_steps': 2000}
- training_precision: mixed_float16
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| nan | nan | 0 |
### Framework versions
- Transformers 4.25.1
- TensorFlow 2.9.3
- Datasets 2.7.1
- Tokenizers 0.13.2
|
Ayran/DialoGPT-medium-harry-1 | []
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}
} | 0 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- clinc_oos
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased-finetuned-clinc
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: clinc_oos
type: clinc_oos
args: plus
metrics:
- name: Accuracy
type: accuracy
value: 0.9183870967741935
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-clinc
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the clinc_oos dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7721
- Accuracy: 0.9184
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 48
- eval_batch_size: 48
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 4.2896 | 1.0 | 318 | 3.2890 | 0.7432 |
| 2.6284 | 2.0 | 636 | 1.8756 | 0.8377 |
| 1.5483 | 3.0 | 954 | 1.1572 | 0.8961 |
| 1.015 | 4.0 | 1272 | 0.8573 | 0.9132 |
| 0.7953 | 5.0 | 1590 | 0.7721 | 0.9184 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.13.0+cu116
- Datasets 1.16.1
- Tokenizers 0.10.3
|
Ayran/DialoGPT-medium-harry-potter-1-through-3 | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
| conversational | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
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},
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}
} | 12 | null | ---
license: creativeml-openrail-m
tags:
- text-to-image
widget:
- text: clrikt
---
### Magic Cube Dreambooth model trained by renee127 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: Cube sculpture
Thank you to creators of this easy to use [dreambooth training](https://huggingface.co/spaces/multimodalart/dreambooth-training)
clrikt (use that on your prompt)
|
Ayran/DialoGPT-medium-harry-potter-1-through-4-plus-6-e18 | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
| conversational | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
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"max_length": 1000
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}
} | 12 | 2022-12-30T02:01:24Z |
---
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: HamzaFarhan/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
Ayran/DialoGPT-medium-harry-potter-1-through-4-plus-6 | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
| conversational | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
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"max_length": 1000
},
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}
} | 12 | 2022-12-30T02:06:38Z | ---
license: openrail
---
How to use:
a) download the ".ckpt" files
b) remove the suffix ".ckpt"\
c) unzip it and get the fking video |
Ayran/DialoGPT-small-gandalf | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
| conversational | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
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"max_length": 1000
},
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}
} | 11 | null | ---
language: en
license: apache-2.0
library_name: diffusers
tags: []
datasets: huggan/smithsonian_butterflies_subset
metrics: []
---
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# ddpm-butterflies-128
## Model description
This diffusion model is trained with the [🤗 Diffusers](https://github.com/huggingface/diffusers) library
on the `huggan/smithsonian_butterflies_subset` dataset.
## Intended uses & limitations
#### How to use
```python
# TODO: add an example code snippet for running this diffusion pipeline
```
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training data
[TODO: describe the data used to train the model]
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 16
- eval_batch_size: 16
- gradient_accumulation_steps: 1
- optimizer: AdamW with betas=(None, None), weight_decay=None and epsilon=None
- lr_scheduler: None
- lr_warmup_steps: 500
- ema_inv_gamma: None
- ema_inv_gamma: None
- ema_inv_gamma: None
- mixed_precision: fp16
### Training results
📈 [TensorBoard logs](https://huggingface.co/edsion/ddpm-butterflies-128/tensorboard?#scalars)
|
Ayta/Haha | []
| null | {
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"model_type": null,
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} | 0 | null | ---
library_name: keras
---
## 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:
| Hyperparameters | Value |
| :-- | :-- |
| name | Adam |
| learning_rate | 0.10000000149011612 |
| decay | 0.0 |
| beta_1 | 0.8999999761581421 |
| beta_2 | 0.9990000128746033 |
| epsilon | 1e-07 |
| amsgrad | False |
| training_precision | float32 |
## Model Plot
<details>
<summary>View Model Plot</summary>
</details> |
Ayu/Shiriro | []
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} | 0 | null | ---
language: en
license: apache-2.0
library_name: diffusers
tags: []
datasets: huggan/smithsonian_butterflies_subset
metrics: []
---
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# ddpm-butterflies-128
## Model description
This diffusion model is trained with the [🤗 Diffusers](https://github.com/huggingface/diffusers) library
on the `huggan/smithsonian_butterflies_subset` dataset.
## Intended uses & limitations
#### How to use
```python
# TODO: add an example code snippet for running this diffusion pipeline
```
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training data
[TODO: describe the data used to train the model]
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 16
- eval_batch_size: 16
- gradient_accumulation_steps: 1
- optimizer: AdamW with betas=(None, None), weight_decay=None and epsilon=None
- lr_scheduler: None
- lr_warmup_steps: 500
- ema_inv_gamma: None
- ema_inv_gamma: None
- ema_inv_gamma: None
- mixed_precision: fp16
### Training results
📈 [TensorBoard logs](https://huggingface.co/anergy7/ddpm-butterflies-128/tensorboard?#scalars)
|
Ayumi/Jovana | []
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} | 0 | 2022-12-30T02:30:35Z | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: distilbert-base-uncased-finetuned-switchboard-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. -->
# distilbert-base-uncased-finetuned-switchboard-2
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on Switchboard dataset.
It achieves the following results on the validation set:
- Loss: 0.7090
- Accuracy: 0.7215
- Precision: 0.7176
- Recall: 0.7215
- F1: 0.7188
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.2139 | 1.0 | 370 | 0.8510 | 0.6875 | 0.6831 | 0.6875 | 0.6846 |
| 0.3195 | 2.0 | 740 | 0.7090 | 0.7215 | 0.7176 | 0.7215 | 0.7188 |
### Framework versions
- Transformers 4.13.0
- Pytorch 1.13.0+cu116
- Datasets 1.16.1
- Tokenizers 0.10.3
|
AyushPJ/test-squad-trained-finetuned-squad | [
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"dataset:squad",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
]
| question-answering | {
"architectures": [
"DistilBertForQuestionAnswering"
],
"model_type": "distilbert",
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}
} | 8 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: distilgpt2_v1
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. -->
# distilgpt2_v1
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.2572
## 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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 3.3962 | 1.0 | 18050 | 3.3250 |
| 3.2561 | 2.0 | 36100 | 3.2652 |
| 3.1727 | 3.0 | 54150 | 3.2572 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.1+cu117
- Datasets 2.8.0
- Tokenizers 0.13.2
|
Bagus/wav2vec2-large-xlsr-bahasa-indonesia | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"el",
"dataset:common_voice_id_6.1",
"transformers",
"audio",
"speech",
"bahasa-indonesia",
"license:apache-2.0"
]
| automatic-speech-recognition | {
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} | 12 | null | ---
library_name: stable-baselines3
tags:
- BipedalWalkerHardcore-v3
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: TQC
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: BipedalWalkerHardcore-v3
type: BipedalWalkerHardcore-v3
metrics:
- type: mean_reward
value: 217.41 +/- 130.53
name: mean_reward
verified: false
---
# **TQC** Agent playing **BipedalWalkerHardcore-v3**
This is a trained model of a **TQC** agent playing **BipedalWalkerHardcore-v3**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo tqc --env BipedalWalkerHardcore-v3 -orga RayanRen -f logs/
python enjoy.py --algo tqc --env BipedalWalkerHardcore-v3 -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 tqc --env BipedalWalkerHardcore-v3 -orga RayanRen -f logs/
rl_zoo3 enjoy --algo tqc --env BipedalWalkerHardcore-v3 -f logs/
```
## Training (with the RL Zoo)
```
python train.py --algo tqc --env BipedalWalkerHardcore-v3 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo tqc --env BipedalWalkerHardcore-v3 -f logs/ -orga RayanRen
```
## Hyperparameters
```python
OrderedDict([('batch_size', 256),
('buffer_size', 1000000),
('ent_coef', 'auto'),
('gamma', 0.99),
('gradient_steps', 1),
('learning_rate', 'lin_7.3e-4'),
('learning_starts', 10000),
('n_timesteps', 2000000.0),
('policy', 'MlpPolicy'),
('policy_kwargs', 'dict(net_arch=[400, 300])'),
('tau', 0.01),
('train_freq', 1),
('normalize', False)])
```
|
Bagus/wav2vec2-xlsr-japanese-speech-emotion-recognition | [
"pytorch",
"wav2vec2",
"audio-classification",
"ja",
"dataset:jtes",
"transformers",
"audio",
"speech",
"speech-emotion-recognition",
"has_space"
]
| audio-classification | {
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}
} | 26 | 2022-12-30T06:54:20Z | ---
tags:
- generated_from_trainer
datasets:
- klue
metrics:
- pearsonr
model-index:
- name: bert-base-finetuned-sts
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: klue
type: klue
config: sts
split: train
args: sts
metrics:
- name: Pearsonr
type: pearsonr
value: 0.8823459724851859
---
<!-- 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-base-finetuned-sts
This model is a fine-tuned version of [klue/bert-base](https://huggingface.co/klue/bert-base) on the klue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5543
- Pearsonr: 0.8823
## 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: 256
- eval_batch_size: 256
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Pearsonr |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 46 | 0.6248 | 0.8466 |
| No log | 2.0 | 92 | 0.5657 | 0.8710 |
| No log | 3.0 | 138 | 0.5442 | 0.8801 |
| No log | 4.0 | 184 | 0.5262 | 0.8823 |
| No log | 5.0 | 230 | 0.5543 | 0.8823 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
|
BertChristiaens/EmojiPredictor | [
"pytorch",
"distilbert",
"token-classification",
"transformers",
"autotrain_compatible"
]
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} | 6 | 2022-12-30T11:10:50Z | ---
language:
- en
tags:
- stable-diffusion
- text-to-image
license: creativeml-openrail-m
inference: true
---
|
Bharathdamu/wav2vec2-large-xls-r-300m-hindi-colab | [
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"dataset:common_voice",
"transformers",
"generated_from_trainer",
"license:apache-2.0"
]
| automatic-speech-recognition | {
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}
} | 4 | null |
---
tags:
- yolov5
- yolo
- vision
- object-detection
- pytorch
library_name: yolov5
library_version: 7.0.6
inference: false
datasets:
- keremberke/nfl-object-detection
model-index:
- name: keremberke/yolov5n-nfl
results:
- task:
type: object-detection
dataset:
type: keremberke/nfl-object-detection
name: keremberke/nfl-object-detection
split: validation
metrics:
- type: precision # since [email protected] is not available on hf.co/metrics
value: 0.2171148618855661 # min: 0.0 - max: 1.0
name: [email protected]
---
<div align="center">
<img width="640" alt="keremberke/yolov5n-nfl" src="https://huggingface.co/keremberke/yolov5n-nfl/resolve/main/sample_visuals.jpg">
</div>
### How to use
- Install [yolov5](https://github.com/fcakyon/yolov5-pip):
```bash
pip install -U yolov5
```
- Load model and perform prediction:
```python
import yolov5
# load model
model = yolov5.load('keremberke/yolov5n-nfl')
# set model parameters
model.conf = 0.25 # NMS confidence threshold
model.iou = 0.45 # NMS IoU threshold
model.agnostic = False # NMS class-agnostic
model.multi_label = False # NMS multiple labels per box
model.max_det = 1000 # maximum number of detections per image
# set image
img = 'https://github.com/ultralytics/yolov5/raw/master/data/images/zidane.jpg'
# perform inference
results = model(img, size=640)
# inference with test time augmentation
results = model(img, augment=True)
# parse results
predictions = results.pred[0]
boxes = predictions[:, :4] # x1, y1, x2, y2
scores = predictions[:, 4]
categories = predictions[:, 5]
# show detection bounding boxes on image
results.show()
# save results into "results/" folder
results.save(save_dir='results/')
```
- Finetune the model on your custom dataset:
```bash
yolov5 train --data data.yaml --img 640 --batch 16 --weights keremberke/yolov5n-nfl --epochs 10
```
**More models available at: [awesome-yolov5-models](https://github.com/keremberke/awesome-yolov5-models)**
|
Bharathdamu/wav2vec2-large-xls-r-300m-hindi | [
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"dataset:common_voice",
"transformers",
"generated_from_trainer",
"license:apache-2.0"
]
| automatic-speech-recognition | {
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"Wav2Vec2ForCTC"
],
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} | 10 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-xls-r-phone-mfa_korean
results: []
language:
- ko
metrics:
- wer
pipeline_tag: automatic-speech-recognition
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-xls-r-300m_phoneme-mfa_korean
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on a phonetically balanced native Korean read-speech corpus.
# Training and Evaluation Data
Training Data
- Data Name: Phonetically Balanced Native Korean Read-speech Corpus
- Num. of Samples: 54,000
- Audio Length: 108 Hours
Evaluation Data
- Data Name: Phonetically Balanced Native Korean Read-speech Corpus
- Num. of Samples: 6,000
- Audio Length: 12 Hours
# Training Hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.2
- num_epochs: 20 (EarlyStopping: patience: 5 epochs max)
- mixed_precision_training: Native AMP
# Evaluation Result
Phone Error Rate 3.88%
# Output Examples
# MFA-IPA Phoneset Tables
## Vowels
## Consonants
## Experimental Results
Official implementation of the paper (in review)
Major error patterns of L2 Korean speech from five different L1s: Chinese (ZH), Vietnamese (VI), Japanese (JP), Thai (TH), English (EN)
# Framework versions
- Transformers 4.21.3
- Pytorch 1.12.1
- Datasets 2.4.0
- Tokenizers 0.12.1 |
Bharathdamu/wav2vec2-model-hindi-stt | []
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} | 0 | null | Access to model lafifi-24/arabert_arabic_dialect_identification is restricted and you are not in the authorized list. Visit https://huggingface.co/lafifi-24/arabert_arabic_dialect_identification to ask for access. |
Bharathdamu/wav2vec2-model-hindibhasha | []
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} | 0 | null | ---
license: unknown
---
sd-webui-additional-networksで読み込むことが出来るLoraファイルやで
使用方法は下記ExtensionをWebuiにインストールして「Additional Networks」の項目に絶対パスでptファイルを指定するだけやで
https://github.com/kohya-ss/sd-webui-additional-networks
このファイルはKohya-SD-Scriptで作成されてる
WebUIのDreamboothで作成されるLoraDBファイルとは互換性がないから注意してな |
Bia18/Beatriz | []
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} | 0 | null |
---
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: jz01/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
Biasface/DDDC | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
| conversational | {
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} | 14 | null | ---
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: 774.50 +/- 160.48
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga saikiranp -f logs/
python enjoy.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga saikiranp -f logs/
rl_zoo3 enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python train.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga saikiranp
```
## 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)])
```
|
BigSalmon/BestMask2 | [
"pytorch",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible",
"has_space"
]
| fill-mask | {
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"RobertaForMaskedLM"
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} | 10 | 2022-12-30T12:54:47Z | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 245.51 +/- 22.95
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
...
```
|
BigSalmon/BlankSlots | [
"pytorch",
"jax",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
]
| text2text-generation | {
"architectures": [
"T5ForConditionalGeneration"
],
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"prefix": "translate English to German: "
},
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"early_stopping": true,
"max_length": 300,
"num_beams": 4,
"prefix": "translate English to French: "
},
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"max_length": 300,
"num_beams": 4,
"prefix": "translate English to Romanian: "
}
}
} | 4 | 2022-12-30T12:55:07Z | ---
language: en
license: apache-2.0
library_name: diffusers
tags: []
datasets: huggan/smithsonian_butterflies_subset
metrics: []
---
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# ddpm-butterflies-128
## Model description
This diffusion model is trained with the [🤗 Diffusers](https://github.com/huggingface/diffusers) library
on the `huggan/smithsonian_butterflies_subset` dataset.
## Intended uses & limitations
#### How to use
```python
# TODO: add an example code snippet for running this diffusion pipeline
```
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training data
[TODO: describe the data used to train the model]
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 16
- eval_batch_size: 16
- gradient_accumulation_steps: 1
- optimizer: AdamW with betas=(None, None), weight_decay=None and epsilon=None
- lr_scheduler: None
- lr_warmup_steps: 500
- ema_inv_gamma: None
- ema_inv_gamma: None
- ema_inv_gamma: None
- mixed_precision: fp16
### Training results
📈 [TensorBoard logs](https://huggingface.co/inkittmahdi/ddpm-butterflies-128/tensorboard?#scalars)
|
BigSalmon/FormalBerta | [
"pytorch",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
]
| fill-mask | {
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"RobertaForMaskedLM"
],
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} | 10 | null | ---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: unit2-frozen-lake
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="adlrocha/unit2-frozen-lake", 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"])
```
|
BigSalmon/FormalBerta3 | [
"pytorch",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
]
| fill-mask | {
"architectures": [
"RobertaForMaskedLM"
],
"model_type": "roberta",
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}
} | 4 | null | ---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: unit2-taxi
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.56 +/- 2.71
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="adlrocha/unit2-taxi", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
BigSalmon/FormalRobertaa | [
"pytorch",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible",
"has_space"
]
| fill-mask | {
"architectures": [
"RobertaForMaskedLM"
],
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}
} | 5 | null | ---
tags:
- masked-auto-encoding
- generated_from_trainer
model-index:
- name: zh_wiki_small
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. -->
# zh_wiki_small
This model is a fine-tuned version of [](https://huggingface.co/) on the wikipedia dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4159
## 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: 0.00015
- train_batch_size: 32
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 2
- total_train_batch_size: 256
- total_eval_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.05
- training_steps: 500000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:------:|:---------------:|
| 0.4793 | 0.14 | 1000 | 0.4540 |
| 0.4701 | 0.28 | 2000 | 0.4467 |
| 0.4673 | 0.42 | 3000 | 0.4304 |
| 0.4669 | 0.55 | 4000 | 0.4413 |
| 0.4668 | 0.69 | 5000 | 0.4368 |
| 0.4676 | 0.83 | 6000 | 0.4358 |
| 0.4691 | 0.97 | 7000 | 0.4367 |
| 0.4693 | 1.11 | 8000 | 0.4429 |
| 0.4709 | 1.25 | 9000 | 0.4388 |
| 0.4722 | 1.39 | 10000 | 0.4453 |
| 0.4729 | 1.53 | 11000 | 0.4415 |
| 0.4732 | 1.67 | 12000 | 0.4510 |
| 0.4751 | 1.8 | 13000 | 0.4461 |
| 0.4765 | 1.94 | 14000 | 0.4448 |
| 0.477 | 2.08 | 15000 | 0.4498 |
| 0.4779 | 2.22 | 16000 | 0.4447 |
| 0.4795 | 2.36 | 17000 | 0.4430 |
| 0.481 | 2.5 | 18000 | 0.4499 |
| 0.4821 | 2.64 | 19000 | 0.4551 |
| 0.4829 | 2.78 | 20000 | 0.4519 |
| 0.4838 | 2.91 | 21000 | 0.4520 |
| 0.4856 | 3.05 | 22000 | 0.4633 |
| 0.4857 | 3.19 | 23000 | 0.4576 |
| 0.4869 | 3.33 | 24000 | 0.4485 |
| 0.4882 | 3.47 | 25000 | 0.4591 |
| 0.4883 | 3.61 | 26000 | 0.4645 |
| 0.4889 | 3.75 | 27000 | 0.4570 |
| 0.4884 | 3.89 | 28000 | 0.4572 |
| 0.4897 | 4.02 | 29000 | 0.4553 |
| 0.4883 | 4.16 | 30000 | 0.4534 |
| 0.4881 | 4.3 | 31000 | 0.4587 |
| 0.4889 | 4.44 | 32000 | 0.4632 |
| 0.4886 | 4.58 | 33000 | 0.4587 |
| 0.4883 | 4.72 | 34000 | 0.4621 |
| 0.4876 | 4.86 | 35000 | 0.4522 |
| 0.4878 | 5.0 | 36000 | 0.4560 |
| 0.4883 | 5.13 | 37000 | 0.4579 |
| 0.4882 | 5.27 | 38000 | 0.4554 |
| 0.4883 | 5.41 | 39000 | 0.4588 |
| 0.4872 | 5.55 | 40000 | 0.4561 |
| 0.4868 | 5.69 | 41000 | 0.4614 |
| 0.4875 | 5.83 | 42000 | 0.4584 |
| 0.4868 | 5.97 | 43000 | 0.4619 |
| 0.4874 | 6.11 | 44000 | 0.4519 |
| 0.4874 | 6.24 | 45000 | 0.4625 |
| 0.487 | 6.38 | 46000 | 0.4579 |
| 0.4872 | 6.52 | 47000 | 0.4534 |
| 0.4872 | 6.66 | 48000 | 0.4516 |
| 0.4865 | 6.8 | 49000 | 0.4635 |
| 0.4865 | 6.94 | 50000 | 0.4610 |
| 0.4863 | 7.08 | 51000 | 0.4515 |
| 0.4861 | 7.22 | 52000 | 0.4584 |
| 0.4866 | 7.35 | 53000 | 0.4541 |
| 0.4862 | 7.49 | 54000 | 0.4508 |
| 0.4863 | 7.63 | 55000 | 0.4565 |
| 0.486 | 7.77 | 56000 | 0.4665 |
| 0.486 | 7.91 | 57000 | 0.4565 |
| 0.4861 | 8.05 | 58000 | 0.4580 |
| 0.4852 | 8.19 | 59000 | 0.4596 |
| 0.4846 | 8.33 | 60000 | 0.4527 |
| 0.4848 | 8.46 | 61000 | 0.4505 |
| 0.4849 | 8.6 | 62000 | 0.4407 |
| 0.4851 | 8.74 | 63000 | 0.4579 |
| 0.4848 | 8.88 | 64000 | 0.4559 |
| 0.4851 | 9.02 | 65000 | 0.4505 |
| 0.4846 | 9.16 | 66000 | 0.4615 |
| 0.4842 | 9.3 | 67000 | 0.4618 |
| 0.484 | 9.44 | 68000 | 0.4559 |
| 0.4841 | 9.57 | 69000 | 0.4613 |
| 0.484 | 9.71 | 70000 | 0.4527 |
| 0.4842 | 9.85 | 71000 | 0.4483 |
| 0.4842 | 9.99 | 72000 | 0.4585 |
| 0.4837 | 10.13 | 73000 | 0.4585 |
| 0.4833 | 10.27 | 74000 | 0.4541 |
| 0.4836 | 10.41 | 75000 | 0.4528 |
| 0.4832 | 10.55 | 76000 | 0.4475 |
| 0.4836 | 10.68 | 77000 | 0.4525 |
| 0.4826 | 10.82 | 78000 | 0.4562 |
| 0.4824 | 10.96 | 79000 | 0.4502 |
| 0.4828 | 11.1 | 80000 | 0.4529 |
| 0.4829 | 11.24 | 81000 | 0.4524 |
| 0.4823 | 11.38 | 82000 | 0.4506 |
| 0.4827 | 11.52 | 83000 | 0.4511 |
| 0.4823 | 11.66 | 84000 | 0.4506 |
| 0.4827 | 11.79 | 85000 | 0.4561 |
| 0.4832 | 11.93 | 86000 | 0.4471 |
| 0.482 | 12.07 | 87000 | 0.4479 |
| 0.4819 | 12.21 | 88000 | 0.4561 |
| 0.4816 | 12.35 | 89000 | 0.4590 |
| 0.4818 | 12.49 | 90000 | 0.4469 |
| 0.4815 | 12.63 | 91000 | 0.4633 |
| 0.4822 | 12.77 | 92000 | 0.4566 |
| 0.4816 | 12.9 | 93000 | 0.4548 |
| 0.4824 | 13.04 | 94000 | 0.4548 |
| 0.4812 | 13.18 | 95000 | 0.4533 |
| 0.4809 | 13.32 | 96000 | 0.4546 |
| 0.481 | 13.46 | 97000 | 0.4590 |
| 0.4807 | 13.6 | 98000 | 0.4465 |
| 0.4808 | 13.74 | 99000 | 0.4531 |
| 0.4806 | 13.88 | 100000 | 0.4459 |
| 0.4809 | 14.01 | 101000 | 0.4517 |
| 0.4801 | 14.15 | 102000 | 0.4519 |
| 0.4801 | 14.29 | 103000 | 0.4547 |
| 0.4805 | 14.43 | 104000 | 0.4517 |
| 0.4799 | 14.57 | 105000 | 0.4491 |
| 0.4805 | 14.71 | 106000 | 0.4559 |
| 0.48 | 14.85 | 107000 | 0.4551 |
| 0.4796 | 14.99 | 108000 | 0.4537 |
| 0.4801 | 15.12 | 109000 | 0.4509 |
| 0.4797 | 15.26 | 110000 | 0.4482 |
| 0.4798 | 15.4 | 111000 | 0.4466 |
| 0.4789 | 15.54 | 112000 | 0.4445 |
| 0.4808 | 15.68 | 113000 | 0.4493 |
| 0.4789 | 15.82 | 114000 | 0.4475 |
| 0.4792 | 15.96 | 115000 | 0.4543 |
| 0.4787 | 16.1 | 116000 | 0.4471 |
| 0.4796 | 16.23 | 117000 | 0.4565 |
| 0.4787 | 16.37 | 118000 | 0.4515 |
| 0.4788 | 16.51 | 119000 | 0.4449 |
| 0.4783 | 16.65 | 120000 | 0.4454 |
| 0.4787 | 16.79 | 121000 | 0.4486 |
| 0.4789 | 16.93 | 122000 | 0.4480 |
| 0.4782 | 17.07 | 123000 | 0.4529 |
| 0.4782 | 17.21 | 124000 | 0.4481 |
| 0.4777 | 17.34 | 125000 | 0.4528 |
| 0.4779 | 17.48 | 126000 | 0.4514 |
| 0.4781 | 17.62 | 127000 | 0.4520 |
| 0.4776 | 17.76 | 128000 | 0.4495 |
| 0.4777 | 17.9 | 129000 | 0.4501 |
| 0.4783 | 18.04 | 130000 | 0.4528 |
| 0.4771 | 18.18 | 131000 | 0.4498 |
| 0.4775 | 18.32 | 132000 | 0.4525 |
| 0.4772 | 18.45 | 133000 | 0.4482 |
| 0.4775 | 18.59 | 134000 | 0.4532 |
| 0.4769 | 18.73 | 135000 | 0.4537 |
| 0.4776 | 18.87 | 136000 | 0.4509 |
| 0.4775 | 19.01 | 137000 | 0.4464 |
| 0.4769 | 19.15 | 138000 | 0.4464 |
| 0.4772 | 19.29 | 139000 | 0.4499 |
| 0.4766 | 19.43 | 140000 | 0.4428 |
| 0.4764 | 19.56 | 141000 | 0.4536 |
| 0.477 | 19.7 | 142000 | 0.4444 |
| 0.4764 | 19.84 | 143000 | 0.4482 |
| 0.4764 | 19.98 | 144000 | 0.4510 |
| 0.4763 | 20.12 | 145000 | 0.4519 |
| 0.4761 | 20.26 | 146000 | 0.4452 |
| 0.4761 | 20.4 | 147000 | 0.4476 |
| 0.4756 | 20.54 | 148000 | 0.4494 |
| 0.4757 | 20.67 | 149000 | 0.4544 |
| 0.4762 | 20.81 | 150000 | 0.4412 |
| 0.4757 | 20.95 | 151000 | 0.4459 |
| 0.4749 | 21.09 | 152000 | 0.4532 |
| 0.4752 | 21.23 | 153000 | 0.4477 |
| 0.4749 | 21.37 | 154000 | 0.4396 |
| 0.4764 | 21.51 | 155000 | 0.4466 |
| 0.4753 | 21.65 | 156000 | 0.4523 |
| 0.4755 | 21.78 | 157000 | 0.4582 |
| 0.4749 | 21.92 | 158000 | 0.4539 |
| 0.475 | 22.06 | 159000 | 0.4539 |
| 0.4747 | 22.2 | 160000 | 0.4519 |
| 0.4745 | 22.34 | 161000 | 0.4370 |
| 0.4748 | 22.48 | 162000 | 0.4449 |
| 0.4743 | 22.62 | 163000 | 0.4484 |
| 0.4745 | 22.76 | 164000 | 0.4471 |
| 0.4739 | 22.89 | 165000 | 0.4480 |
| 0.4746 | 23.03 | 166000 | 0.4519 |
| 0.4739 | 23.17 | 167000 | 0.4478 |
| 0.4739 | 23.31 | 168000 | 0.4497 |
| 0.4738 | 23.45 | 169000 | 0.4462 |
| 0.474 | 23.59 | 170000 | 0.4430 |
| 0.4737 | 23.73 | 171000 | 0.4483 |
| 0.4737 | 23.87 | 172000 | 0.4508 |
| 0.474 | 24.0 | 173000 | 0.4439 |
| 0.4729 | 24.14 | 174000 | 0.4426 |
| 0.4735 | 24.28 | 175000 | 0.4433 |
| 0.4722 | 24.42 | 176000 | 0.4483 |
| 0.4728 | 24.56 | 177000 | 0.4496 |
| 0.4727 | 24.7 | 178000 | 0.4473 |
| 0.4729 | 24.84 | 179000 | 0.4404 |
| 0.4722 | 24.98 | 180000 | 0.4426 |
| 0.4724 | 25.11 | 181000 | 0.4479 |
| 0.4739 | 25.25 | 182000 | 0.4430 |
| 0.4723 | 25.39 | 183000 | 0.4418 |
| 0.4724 | 25.53 | 184000 | 0.4371 |
| 0.472 | 25.67 | 185000 | 0.4456 |
| 0.4726 | 25.81 | 186000 | 0.4419 |
| 0.4721 | 25.95 | 187000 | 0.4417 |
| 0.4722 | 26.09 | 188000 | 0.4475 |
| 0.4715 | 26.22 | 189000 | 0.4389 |
| 0.4717 | 26.36 | 190000 | 0.4451 |
| 0.4716 | 26.5 | 191000 | 0.4440 |
| 0.4714 | 26.64 | 192000 | 0.4399 |
| 0.4712 | 26.78 | 193000 | 0.4398 |
| 0.4709 | 26.92 | 194000 | 0.4424 |
| 0.4714 | 27.06 | 195000 | 0.4533 |
| 0.4706 | 27.2 | 196000 | 0.4394 |
| 0.471 | 27.33 | 197000 | 0.4436 |
| 0.4707 | 27.47 | 198000 | 0.4421 |
| 0.471 | 27.61 | 199000 | 0.4459 |
| 0.4707 | 27.75 | 200000 | 0.4439 |
| 0.471 | 27.89 | 201000 | 0.4467 |
| 0.471 | 28.03 | 202000 | 0.4439 |
| 0.4704 | 28.17 | 203000 | 0.4445 |
| 0.4705 | 28.31 | 204000 | 0.4429 |
| 0.4706 | 28.44 | 205000 | 0.4382 |
| 0.4703 | 28.58 | 206000 | 0.4425 |
| 0.4695 | 28.72 | 207000 | 0.4414 |
| 0.4696 | 28.86 | 208000 | 0.4405 |
| 0.4696 | 29.0 | 209000 | 0.4460 |
| 0.4701 | 29.14 | 210000 | 0.4460 |
| 0.4696 | 29.28 | 211000 | 0.4397 |
| 0.4693 | 29.42 | 212000 | 0.4439 |
| 0.4694 | 29.55 | 213000 | 0.4495 |
| 0.469 | 29.69 | 214000 | 0.4466 |
| 0.4691 | 29.83 | 215000 | 0.4336 |
| 0.4694 | 29.97 | 216000 | 0.4377 |
| 0.4698 | 30.11 | 217000 | 0.4356 |
| 0.4689 | 30.25 | 218000 | 0.4381 |
| 0.4685 | 30.39 | 219000 | 0.4431 |
| 0.4688 | 30.53 | 220000 | 0.4411 |
| 0.4687 | 30.66 | 221000 | 0.4445 |
| 0.4685 | 30.8 | 222000 | 0.4432 |
| 0.4687 | 30.94 | 223000 | 0.4383 |
| 0.4681 | 31.08 | 224000 | 0.4371 |
| 0.4683 | 31.22 | 225000 | 0.4384 |
| 0.4678 | 31.36 | 226000 | 0.4396 |
| 0.4682 | 31.5 | 227000 | 0.4387 |
| 0.4671 | 31.64 | 228000 | 0.4382 |
| 0.4676 | 31.77 | 229000 | 0.4410 |
| 0.4681 | 31.91 | 230000 | 0.4391 |
| 0.4676 | 32.05 | 231000 | 0.4429 |
| 0.4673 | 32.19 | 232000 | 0.4395 |
| 0.4669 | 32.33 | 233000 | 0.4389 |
| 0.4675 | 32.47 | 234000 | 0.4452 |
| 0.4667 | 32.61 | 235000 | 0.4395 |
| 0.4667 | 32.75 | 236000 | 0.4460 |
| 0.4672 | 32.88 | 237000 | 0.4404 |
| 0.4667 | 33.02 | 238000 | 0.4372 |
| 0.4663 | 33.16 | 239000 | 0.4362 |
| 0.4669 | 33.3 | 240000 | 0.4428 |
| 0.4662 | 33.44 | 241000 | 0.4370 |
| 0.4662 | 33.58 | 242000 | 0.4382 |
| 0.466 | 33.72 | 243000 | 0.4395 |
| 0.4661 | 33.86 | 244000 | 0.4418 |
| 0.4663 | 33.99 | 245000 | 0.4407 |
| 0.4661 | 34.13 | 246000 | 0.4346 |
| 0.4652 | 34.27 | 247000 | 0.4392 |
| 0.4662 | 34.41 | 248000 | 0.4396 |
| 0.4655 | 34.55 | 249000 | 0.4427 |
| 0.4657 | 34.69 | 250000 | 0.4484 |
| 0.4654 | 34.83 | 251000 | 0.4268 |
| 0.4655 | 34.97 | 252000 | 0.4384 |
| 0.4649 | 35.1 | 253000 | 0.4383 |
| 0.465 | 35.24 | 254000 | 0.4368 |
| 0.4648 | 35.38 | 255000 | 0.4327 |
| 0.4647 | 35.52 | 256000 | 0.4416 |
| 0.4652 | 35.66 | 257000 | 0.4390 |
| 0.4646 | 35.8 | 258000 | 0.4450 |
| 0.4651 | 35.94 | 259000 | 0.4354 |
| 0.4643 | 36.08 | 260000 | 0.4473 |
| 0.464 | 36.21 | 261000 | 0.4423 |
| 0.4638 | 36.35 | 262000 | 0.4339 |
| 0.464 | 36.49 | 263000 | 0.4438 |
| 0.464 | 36.63 | 264000 | 0.4398 |
| 0.4637 | 36.77 | 265000 | 0.4352 |
| 0.4641 | 36.91 | 266000 | 0.4352 |
| 0.4651 | 37.05 | 267000 | 0.4324 |
| 0.4637 | 37.19 | 268000 | 0.4341 |
| 0.4633 | 37.32 | 269000 | 0.4331 |
| 0.4639 | 37.46 | 270000 | 0.4391 |
| 0.463 | 37.6 | 271000 | 0.4380 |
| 0.4635 | 37.74 | 272000 | 0.4355 |
| 0.4631 | 37.88 | 273000 | 0.4397 |
| 0.464 | 38.02 | 274000 | 0.4336 |
| 0.4629 | 38.16 | 275000 | 0.4339 |
| 0.4634 | 38.3 | 276000 | 0.4355 |
| 0.4632 | 38.43 | 277000 | 0.4388 |
| 0.4628 | 38.57 | 278000 | 0.4341 |
| 0.4621 | 38.71 | 279000 | 0.4337 |
| 0.4626 | 38.85 | 280000 | 0.4340 |
| 0.462 | 38.99 | 281000 | 0.4306 |
| 0.8286 | 39.13 | 282000 | 0.4504 |
| 0.4624 | 39.27 | 283000 | 0.4399 |
| 0.4621 | 39.41 | 284000 | 0.4351 |
| 0.4622 | 39.54 | 285000 | 0.4304 |
| 0.4619 | 39.68 | 286000 | 0.4329 |
| 0.4618 | 39.82 | 287000 | 0.4208 |
| 0.462 | 39.96 | 288000 | 0.4414 |
| 0.4615 | 40.1 | 289000 | 0.4353 |
| 0.4614 | 40.24 | 290000 | 0.4398 |
| 0.4611 | 40.38 | 291000 | 0.4371 |
| 0.4608 | 40.52 | 292000 | 0.4326 |
| 0.4611 | 40.65 | 293000 | 0.4332 |
| 0.4614 | 40.79 | 294000 | 0.4343 |
| 0.4609 | 40.93 | 295000 | 0.4306 |
| 0.4608 | 41.07 | 296000 | 0.4323 |
| 0.4608 | 41.21 | 297000 | 0.4321 |
| 0.4601 | 41.35 | 298000 | 0.4330 |
| 0.4606 | 41.49 | 299000 | 0.4361 |
| 0.4606 | 41.63 | 300000 | 0.4367 |
| 0.46 | 41.76 | 301000 | 0.4327 |
| 0.4596 | 41.9 | 302000 | 0.4306 |
| 0.46 | 42.04 | 303000 | 0.4352 |
| 0.46 | 42.18 | 304000 | 0.4338 |
| 0.4597 | 42.32 | 305000 | 0.4333 |
| 0.4596 | 42.46 | 306000 | 0.4334 |
| 0.4591 | 42.6 | 307000 | 0.4334 |
| 0.4597 | 42.74 | 308000 | 0.4319 |
| 0.4586 | 42.87 | 309000 | 0.4268 |
| 0.4593 | 43.01 | 310000 | 0.4366 |
| 0.4591 | 43.15 | 311000 | 0.4283 |
| 0.4587 | 43.29 | 312000 | 0.4289 |
| 0.4594 | 43.43 | 313000 | 0.4332 |
| 0.459 | 43.57 | 314000 | 0.4326 |
| 0.4586 | 43.71 | 315000 | 0.4356 |
| 0.4581 | 43.85 | 316000 | 0.4271 |
| 0.4584 | 43.98 | 317000 | 0.4325 |
| 0.4586 | 44.12 | 318000 | 0.4350 |
| 0.4584 | 44.26 | 319000 | 0.4273 |
| 0.4576 | 44.4 | 320000 | 0.4284 |
| 0.458 | 44.54 | 321000 | 0.4331 |
| 0.4581 | 44.68 | 322000 | 0.4263 |
| 0.4579 | 44.82 | 323000 | 0.4283 |
| 0.4583 | 44.96 | 324000 | 0.4362 |
| 0.4571 | 45.1 | 325000 | 0.4330 |
| 0.4566 | 45.23 | 326000 | 0.4300 |
| 0.4572 | 45.37 | 327000 | 0.4258 |
| 0.4574 | 45.51 | 328000 | 0.4200 |
| 0.4573 | 45.65 | 329000 | 0.4299 |
| 0.4578 | 45.79 | 330000 | 0.4319 |
| 0.4576 | 45.93 | 331000 | 0.4352 |
| 0.4574 | 46.07 | 332000 | 0.4278 |
| 0.4572 | 46.21 | 333000 | 0.4326 |
| 0.4568 | 46.34 | 334000 | 0.4295 |
| 0.4569 | 46.48 | 335000 | 0.4300 |
| 0.4566 | 46.62 | 336000 | 0.4333 |
| 0.4567 | 46.76 | 337000 | 0.4262 |
| 0.4564 | 46.9 | 338000 | 0.4354 |
| 0.4574 | 47.04 | 339000 | 0.4357 |
| 0.4564 | 47.18 | 340000 | 0.4308 |
| 0.4554 | 47.32 | 341000 | 0.4350 |
| 0.456 | 47.45 | 342000 | 0.4400 |
| 0.456 | 47.59 | 343000 | 0.4237 |
| 0.4559 | 47.73 | 344000 | 0.4236 |
| 0.4559 | 47.87 | 345000 | 0.4305 |
| 0.4559 | 48.01 | 346000 | 0.4245 |
| 0.4549 | 48.15 | 347000 | 0.4182 |
| 0.4556 | 48.29 | 348000 | 0.4330 |
| 0.4551 | 48.43 | 349000 | 0.4397 |
| 0.455 | 48.56 | 350000 | 0.4252 |
| 0.4548 | 48.7 | 351000 | 0.4246 |
| 0.4551 | 48.84 | 352000 | 0.4291 |
| 0.4554 | 48.98 | 353000 | 0.4286 |
| 0.4547 | 49.12 | 354000 | 0.4336 |
| 0.4548 | 49.26 | 355000 | 0.4324 |
| 0.4545 | 49.4 | 356000 | 0.4236 |
| 0.4547 | 49.54 | 357000 | 0.4345 |
| 0.4542 | 49.67 | 358000 | 0.4329 |
| 0.4545 | 49.81 | 359000 | 0.4241 |
| 0.4541 | 49.95 | 360000 | 0.4177 |
| 0.454 | 50.09 | 361000 | 0.4244 |
| 0.4538 | 50.23 | 362000 | 0.4190 |
| 0.4535 | 50.37 | 363000 | 0.4331 |
| 0.4545 | 50.51 | 364000 | 0.4252 |
| 0.454 | 50.65 | 365000 | 0.4315 |
| 0.4536 | 50.78 | 366000 | 0.4301 |
| 0.4534 | 50.92 | 367000 | 0.4357 |
| 0.4537 | 51.06 | 368000 | 0.4334 |
| 0.4535 | 51.2 | 369000 | 0.4200 |
| 0.4538 | 51.34 | 370000 | 0.4274 |
| 0.4536 | 51.48 | 371000 | 0.4178 |
| 0.4534 | 51.62 | 372000 | 0.4181 |
| 0.4533 | 51.76 | 373000 | 0.4211 |
| 0.4535 | 51.89 | 374000 | 0.4290 |
| 0.4535 | 52.03 | 375000 | 0.4201 |
| 0.4526 | 52.17 | 376000 | 0.4263 |
| 0.4526 | 52.31 | 377000 | 0.4237 |
| 0.4524 | 52.45 | 378000 | 0.4254 |
| 0.4529 | 52.59 | 379000 | 0.4260 |
| 0.4531 | 52.73 | 380000 | 0.4202 |
| 0.4523 | 52.87 | 381000 | 0.4223 |
| 0.4523 | 53.0 | 382000 | 0.4271 |
| 0.4522 | 53.14 | 383000 | 0.4286 |
| 0.4524 | 53.28 | 384000 | 0.4256 |
| 0.4515 | 53.42 | 385000 | 0.4221 |
| 0.4513 | 53.56 | 386000 | 0.4255 |
| 0.452 | 53.7 | 387000 | 0.4270 |
| 0.4519 | 53.84 | 388000 | 0.4222 |
| 0.4518 | 53.98 | 389000 | 0.4233 |
| 0.4513 | 54.11 | 390000 | 0.4233 |
| 0.4517 | 54.25 | 391000 | 0.4239 |
| 0.4518 | 54.39 | 392000 | 0.4273 |
| 0.4508 | 54.53 | 393000 | 0.4200 |
| 0.4511 | 54.67 | 394000 | 0.4236 |
| 0.4508 | 54.81 | 395000 | 0.4193 |
| 0.4507 | 54.95 | 396000 | 0.4293 |
| 0.4508 | 55.09 | 397000 | 0.4187 |
| 0.4504 | 55.22 | 398000 | 0.4283 |
| 0.4512 | 55.36 | 399000 | 0.4239 |
| 0.4504 | 55.5 | 400000 | 0.4269 |
| 0.4506 | 55.64 | 401000 | 0.4291 |
| 0.4504 | 55.78 | 402000 | 0.4238 |
| 0.4503 | 55.92 | 403000 | 0.4200 |
| 0.4506 | 56.06 | 404000 | 0.4186 |
| 0.4507 | 56.2 | 405000 | 0.4260 |
| 0.4504 | 56.33 | 406000 | 0.4188 |
| 0.4503 | 56.47 | 407000 | 0.4231 |
| 0.4498 | 56.61 | 408000 | 0.4148 |
| 0.4499 | 56.75 | 409000 | 0.4182 |
| 0.4498 | 56.89 | 410000 | 0.4229 |
| 0.4501 | 57.03 | 411000 | 0.4252 |
| 0.4497 | 57.17 | 412000 | 0.4220 |
| 0.45 | 57.31 | 413000 | 0.4181 |
| 0.4497 | 57.44 | 414000 | 0.4270 |
| 0.4497 | 57.58 | 415000 | 0.4208 |
| 0.4499 | 57.72 | 416000 | 0.4224 |
| 0.4496 | 57.86 | 417000 | 0.4207 |
| 0.4494 | 58.0 | 418000 | 0.4268 |
| 0.4499 | 58.14 | 419000 | 0.4240 |
| 0.4495 | 58.28 | 420000 | 0.4294 |
| 0.4487 | 58.42 | 421000 | 0.4207 |
| 0.4495 | 58.55 | 422000 | 0.4246 |
| 0.4491 | 58.69 | 423000 | 0.4213 |
| 0.4492 | 58.83 | 424000 | 0.4241 |
| 0.4486 | 58.97 | 425000 | 0.4247 |
| 0.4485 | 59.11 | 426000 | 0.4163 |
| 0.4489 | 59.25 | 427000 | 0.4239 |
| 0.4483 | 59.39 | 428000 | 0.4240 |
| 0.4491 | 59.53 | 429000 | 0.4214 |
| 0.4485 | 59.66 | 430000 | 0.4285 |
| 0.449 | 59.8 | 431000 | 0.4265 |
| 0.4484 | 59.94 | 432000 | 0.4188 |
| 0.4484 | 60.08 | 433000 | 0.4176 |
| 0.4488 | 60.22 | 434000 | 0.4200 |
| 0.448 | 60.36 | 435000 | 0.4116 |
| 0.4477 | 60.5 | 436000 | 0.4215 |
| 0.4484 | 60.64 | 437000 | 0.4204 |
| 0.448 | 60.77 | 438000 | 0.4093 |
| 0.4479 | 60.91 | 439000 | 0.4181 |
| 0.4481 | 61.05 | 440000 | 0.4232 |
| 0.4477 | 61.19 | 441000 | 0.4202 |
| 0.4478 | 61.33 | 442000 | 0.4167 |
| 0.4481 | 61.47 | 443000 | 0.4173 |
| 0.4483 | 61.61 | 444000 | 0.4158 |
| 0.4473 | 61.75 | 445000 | 0.4174 |
| 0.4474 | 61.88 | 446000 | 0.4266 |
| 0.4477 | 62.02 | 447000 | 0.4242 |
| 0.4476 | 62.16 | 448000 | 0.4240 |
| 0.4478 | 62.3 | 449000 | 0.4286 |
| 0.4474 | 62.44 | 450000 | 0.4294 |
| 0.4482 | 62.58 | 451000 | 0.4144 |
| 0.4471 | 62.72 | 452000 | 0.4316 |
| 0.448 | 62.86 | 453000 | 0.4228 |
| 0.4474 | 62.99 | 454000 | 0.4242 |
| 0.447 | 63.13 | 455000 | 0.4231 |
| 0.4475 | 63.27 | 456000 | 0.4235 |
| 0.4475 | 63.41 | 457000 | 0.4279 |
| 0.4476 | 63.55 | 458000 | 0.4230 |
| 0.4464 | 63.69 | 459000 | 0.4145 |
| 0.4467 | 63.83 | 460000 | 0.4230 |
| 0.4465 | 63.97 | 461000 | 0.4208 |
| 0.4466 | 64.1 | 462000 | 0.4243 |
| 0.447 | 64.24 | 463000 | 0.4220 |
| 0.4473 | 64.38 | 464000 | 0.4253 |
| 0.4471 | 64.52 | 465000 | 0.4194 |
| 0.447 | 64.66 | 466000 | 0.4262 |
| 0.447 | 64.8 | 467000 | 0.4245 |
| 0.4468 | 64.94 | 468000 | 0.4143 |
| 0.4463 | 65.08 | 469000 | 0.4187 |
| 0.4465 | 65.21 | 470000 | 0.4185 |
| 0.4465 | 65.35 | 471000 | 0.4244 |
| 0.4467 | 65.49 | 472000 | 0.4201 |
| 0.4465 | 65.63 | 473000 | 0.4160 |
| 0.4467 | 65.77 | 474000 | 0.4273 |
| 0.4465 | 65.91 | 475000 | 0.4183 |
| 0.4467 | 66.05 | 476000 | 0.4227 |
| 0.4469 | 66.19 | 477000 | 0.4166 |
| 0.4467 | 66.32 | 478000 | 0.4199 |
| 0.4464 | 66.46 | 479000 | 0.4181 |
| 0.4463 | 66.6 | 480000 | 0.4217 |
| 0.4464 | 66.74 | 481000 | 0.4158 |
| 0.4468 | 66.88 | 482000 | 0.4191 |
| 0.447 | 67.02 | 483000 | 0.4248 |
| 0.4465 | 67.16 | 484000 | 0.4234 |
| 0.4463 | 67.3 | 485000 | 0.4238 |
| 0.446 | 67.43 | 486000 | 0.4162 |
| 0.4462 | 67.57 | 487000 | 0.4202 |
| 0.4462 | 67.71 | 488000 | 0.4177 |
| 0.4455 | 67.85 | 489000 | 0.4228 |
| 0.4463 | 67.99 | 490000 | 0.4146 |
| 0.4454 | 68.13 | 491000 | 0.4190 |
| 0.446 | 68.27 | 492000 | 0.4219 |
| 0.4461 | 68.41 | 493000 | 0.4250 |
| 0.4462 | 68.54 | 494000 | 0.4172 |
| 0.4464 | 68.68 | 495000 | 0.4122 |
| 0.4459 | 68.82 | 496000 | 0.4178 |
| 0.4459 | 68.96 | 497000 | 0.4095 |
| 0.4458 | 69.1 | 498000 | 0.4124 |
| 0.4458 | 69.24 | 499000 | 0.4182 |
| 0.4458 | 69.38 | 500000 | 0.4177 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.12.0
- Datasets 2.0.0
- Tokenizers 0.13.2
|
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} | 0 | null | ---
license: apache-2.0
---
# sketch2img with diffusion models
https://github.com/IzumiSatoshi/sketch2img |
BigSalmon/GPTNeo350MInformalToFormalLincoln | [
"pytorch",
"gpt_neo",
"text-generation",
"transformers",
"has_space"
]
| text-generation | {
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} | 8 | 2022-12-30T13:29:50Z | ---
tags:
- conversational
---
# DialoGPT-Elysia
This is a fine-tuned version of the DialoGPT-medium model trained on the dialogues of Elysia from the Elysian Archives chapter of the popular video game Honkai Impact. (Elysian archives)
This fine-tuned version of the model has been trained specifically on a dataset of dialogues featuring Elysia from the Elysian Archives chapter, and as such, it has a better understanding of her character and is able to generate responses that are more in line with her personality and behavior as portrayed in this specific chapter of the game.
To use this model, you will need to provide it with a prompt in the form of a conversation context or a specific question. The model will then generate a response based on the provided prompt and its understanding of Elysia's character in the Elysian Archives chapter.
Here is an example of how you might use the model:
|Role | Response |
|---------|--------|
|User | Hey Elysia, how are you doing today? |
|Elysia | Doing well, thanks for asking. What about you? |
|User | I'm good. |
|Elysia | That’s good. I thought you’d let it go at that. |
|Role | Response |
|---------|--------|
|User | I heard that you are a skilled fighter, is that true? |
|Elysia | If I had to be ranked, I guess I would be first in command. |
Please note that while this model is able to generate responses that are in line with Elysia's character, it is not able to provide any actual gameplay-related information or assistance. It is intended solely for generating text based on its understanding of Elysia's character. |
BigSalmon/GPTNeo350MInformalToFormalLincoln2 | [
"pytorch",
"gpt_neo",
"text-generation",
"transformers",
"has_space"
]
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}
} | 8 | 2022-12-30T13:33:33Z | ---
license: mit
tags:
- pytorch
- diffusers
- unconditional-image-generation
- diffusion-models-class
---
# Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class)
This model is a diffusion model for unconditional image generation of cute 🦋.
## Usage
```python
from diffusers import DDPMPipeline
pipeline = DDPMPipeline.from_pretrained(Welaury/sd-class-butterflies-32)
image = pipeline().images[0]
image
```
|
BigSalmon/GPTT | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
]
| text-generation | {
"architectures": [
"GPT2LMHeadModel"
],
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}
} | 9 | null | ---
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: 255.03 +/- 23.32
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
...
```
|
BigSalmon/InfillFormalLincoln | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
]
| text-generation | {
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"GPT2LMHeadModel"
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} | 8 | 2022-12-30T13:59:38Z | ---
license: creativeml-openrail-m
tags:
- text-to-image
widget:
- text: sdcid
---
### kemar Dreambooth model trained by zigg-ai with 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:
sdcid (use that on your prompt)
|
BigSalmon/InformalToFormalLincoln14 | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
]
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} | 5 | null | ---
license: creativeml-openrail-m
tags:
- pytorch
- diffusers
- stable-diffusion
- text-to-image
- diffusion-models-class
- dreambooth-hackathon
- wildcard
datasets: avojarot/duolingo_owl
widget:
- text: a angry green duolingo owl with knife realistic art in space
---
# DreamBooth model for the duolingo concept trained by avojarot on the avojarot/duolingo_owl dataset.
This is a Stable Diffusion model fine-tuned on the duolingo concept with DreamBooth. It can be used by modifying the `instance_prompt`: **a photo of duolingo owl**
This model was created as part of the DreamBooth Hackathon 🔥. Visit the [organisation page](https://huggingface.co/dreambooth-hackathon) for instructions on how to take part!
## Description
This is a Stable Diffusion model fine-tuned on duolingo `owl` images for the wildcard theme.
## Images generated by model
Cute
Realistic
Pizza
Some others
## Usage
```python
from diffusers import StableDiffusionPipeline
pipeline = StableDiffusionPipeline.from_pretrained('avojarot/duolingo-owl')
image = pipeline().images[0]
image
```
|
BigSalmon/InformalToFormalLincoln16 | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
]
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} | 8 | null | ---
license: creativeml-openrail-m
tags:
- pytorch
- diffusers
- stable-diffusion
- text-to-image
- diffusion-models-class
- dreambooth-hackathon
- animal
widget:
- text: a photo of shiba dog in the Acropolis
---
# DreamBooth model for the shiba concept trained by ashiqabdulkhader on the ashiqabdulkhader/animals dataset.
This is a Stable Diffusion model fine-tuned on the shiba concept with DreamBooth. It can be used by modifying the `instance_prompt`: **a photo of shiba dog**
This model was created as part of the DreamBooth Hackathon 🔥. Visit the [organisation page](https://huggingface.co/dreambooth-hackathon) for instructions on how to take part!
## Description
This is a Stable Diffusion model fine-tuned on `dog` images for the animal theme.
## Usage
```python
from diffusers import StableDiffusionPipeline
pipeline = StableDiffusionPipeline.from_pretrained('ashiqabdulkhader/shiba-dog')
image = pipeline().images[0]
image
```
|
BigSalmon/InformalToFormalLincoln21 | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"has_space"
]
| text-generation | {
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}
} | 8 | null | ---
tags:
- generated_from_trainer
- financial-tweets-sentiment-analysis
- sentiment-analysis
- generated_from_trainer
- financial
- stocks
- sentiment
datasets:
- zeroshot/twitter-financial-news-sentiment
metrics:
- accuracy
- f1
- precision
- recall
widget:
- text: "$LOW - Lowe's racks up another positive rating despite recession risk"
example_title: "Bullish Sentiment"
- text: "$HNHAF $HNHPD $AAPL - Trendforce cuts iPhone estimate after Foxconn delay"
example_title: "Bearish Sentiment"
- text: "Coin Toss: Morgan Stanley Raises Tesla Bull Case To $500, Keeps Bear Case At $10"
example_title: "Neutral Sentiment"
model-index:
- name: finbert-tone-finetuned-fintwitter-classification
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: twitter-financial-news-sentiment
type: finance
metrics:
- type: F1
name: F1
value: 0.8838
- type: accuracy
name: accuracy
value: 0.8840
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finbert-tone-finetuned-fintwitter-classification
This model is a fine-tuned version of [yiyanghkust/finbert-tone](https://huggingface.co/yiyanghkust/finbert-tone) on [Twitter Financial News](https://huggingface.co/datasets/zeroshot/twitter-financial-news-sentiment) dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4078
- Accuracy: 0.8840
- F1: 0.8838
- Precision: 0.8838
- Recall: 0.8840
## Model description
Model determines the financial sentiment of given tweets. Given the unbalanced distribution of the class labels, the weights were adjusted to pay attention to the less sampled labels which should increase overall performance..
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.6385 | 1.0 | 597 | 0.3688 | 0.8668 | 0.8693 | 0.8744 | 0.8668 |
| 0.3044 | 2.0 | 1194 | 0.3994 | 0.8744 | 0.8726 | 0.8739 | 0.8744 |
| 0.1833 | 3.0 | 1791 | 0.6212 | 0.8781 | 0.8764 | 0.8762 | 0.8781 |
| 0.1189 | 4.0 | 2388 | 0.8370 | 0.8740 | 0.8743 | 0.8748 | 0.8740 |
| 0.0759 | 5.0 | 2985 | 0.9107 | 0.8807 | 0.8798 | 0.8796 | 0.8807 |
| 0.0291 | 6.0 | 3582 | 0.9711 | 0.8836 | 0.8825 | 0.8821 | 0.8836 |
| 0.0314 | 7.0 | 4179 | 1.1305 | 0.8819 | 0.8811 | 0.8812 | 0.8819 |
| 0.0217 | 8.0 | 4776 | 1.0190 | 0.8811 | 0.8813 | 0.8816 | 0.8811 |
| 0.0227 | 9.0 | 5373 | 1.1940 | 0.8844 | 0.8832 | 0.8838 | 0.8844 |
| 0.0156 | 10.0 | 5970 | 1.2595 | 0.8752 | 0.8768 | 0.8801 | 0.8752 |
| 0.0135 | 11.0 | 6567 | 1.1931 | 0.8760 | 0.8768 | 0.8780 | 0.8760 |
| 0.009 | 12.0 | 7164 | 1.2154 | 0.8857 | 0.8852 | 0.8848 | 0.8857 |
| 0.0058 | 13.0 | 7761 | 1.3874 | 0.8748 | 0.8759 | 0.8776 | 0.8748 |
| 0.009 | 14.0 | 8358 | 1.4193 | 0.8740 | 0.8754 | 0.8780 | 0.8740 |
| 0.0042 | 15.0 | 8955 | 1.2999 | 0.8807 | 0.8800 | 0.8796 | 0.8807 |
| 0.0028 | 16.0 | 9552 | 1.3428 | 0.8802 | 0.8805 | 0.8817 | 0.8802 |
| 0.0029 | 17.0 | 10149 | 1.3959 | 0.8807 | 0.8807 | 0.8810 | 0.8807 |
| 0.0022 | 18.0 | 10746 | 1.4149 | 0.8827 | 0.8823 | 0.8824 | 0.8827 |
| 0.0037 | 19.0 | 11343 | 1.4078 | 0.8840 | 0.8838 | 0.8838 | 0.8840 |
| 0.001 | 20.0 | 11940 | 1.4236 | 0.8823 | 0.8823 | 0.8825 | 0.8823 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
|
BigSalmon/InformalToFormalLincoln22 | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
]
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} | 6 | null | ---
license: creativeml-openrail-m
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
inference: true
extra_gated_prompt: |-
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. CompVis 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 carefully here: https://huggingface.co/spaces/CompVis/stable-diffusion-license
extra_gated_heading: Please read the LICENSE to access this model
---
# Stable Diffusion v1-5 Model Card
Stable Diffusion is a latent text-to-image diffusion model capable of generating photo-realistic images given any text input.
For more information about how Stable Diffusion functions, please have a look at [🤗's Stable Diffusion blog](https://huggingface.co/blog/stable_diffusion).
The **Stable-Diffusion-v1-5** checkpoint was initialized with the weights of the [Stable-Diffusion-v1-2](https:/steps/huggingface.co/CompVis/stable-diffusion-v1-2)
checkpoint and subsequently fine-tuned on 595k steps at resolution 512x512 on "laion-aesthetics v2 5+" and 10% dropping of the text-conditioning to improve [classifier-free guidance sampling](https://arxiv.org/abs/2207.12598).
You can use this both with the [🧨Diffusers library](https://github.com/huggingface/diffusers) and the [RunwayML GitHub repository](https://github.com/runwayml/stable-diffusion).
### Diffusers
```py
from diffusers import StableDiffusionPipeline
import torch
model_id = "runwayml/stable-diffusion-v1-5"
pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16)
pipe = pipe.to("cuda")
prompt = "a photo of an astronaut riding a horse on mars"
image = pipe(prompt).images[0]
image.save("astronaut_rides_horse.png")
```
For more detailed instructions, use-cases and examples in JAX follow the instructions [here](https://github.com/huggingface/diffusers#text-to-image-generation-with-stable-diffusion)
### Original GitHub Repository
1. Download the weights
- [v1-5-pruned-emaonly.ckpt](https://huggingface.co/runwayml/stable-diffusion-v1-5/resolve/main/v1-5-pruned-emaonly.ckpt) - 4.27GB, ema-only weight. uses less VRAM - suitable for inference
- [v1-5-pruned.ckpt](https://huggingface.co/runwayml/stable-diffusion-v1-5/resolve/main/v1-5-pruned.ckpt) - 7.7GB, ema+non-ema weights. uses more VRAM - suitable for fine-tuning
2. Follow instructions [here](https://github.com/runwayml/stable-diffusion).
## Model Details
- **Developed by:** Robin Rombach, Patrick Esser
- **Model type:** Diffusion-based text-to-image generation model
- **Language(s):** English
- **License:** [The CreativeML OpenRAIL M license](https://huggingface.co/spaces/CompVis/stable-diffusion-license) is an [Open RAIL M license](https://www.licenses.ai/blog/2022/8/18/naming-convention-of-responsible-ai-licenses), adapted from the work that [BigScience](https://bigscience.huggingface.co/) and [the RAIL Initiative](https://www.licenses.ai/) are jointly carrying in the area of responsible AI licensing. See also [the article about the BLOOM Open RAIL license](https://bigscience.huggingface.co/blog/the-bigscience-rail-license) on which our license is based.
- **Model Description:** This is a model that can be used to generate and modify images based on text prompts. It is a [Latent Diffusion Model](https://arxiv.org/abs/2112.10752) that uses a fixed, pretrained text encoder ([CLIP ViT-L/14](https://arxiv.org/abs/2103.00020)) as suggested in the [Imagen paper](https://arxiv.org/abs/2205.11487).
- **Resources for more information:** [GitHub Repository](https://github.com/CompVis/stable-diffusion), [Paper](https://arxiv.org/abs/2112.10752).
- **Cite as:**
@InProceedings{Rombach_2022_CVPR,
author = {Rombach, Robin and Blattmann, Andreas and Lorenz, Dominik and Esser, Patrick and Ommer, Bj\"orn},
title = {High-Resolution Image Synthesis With Latent Diffusion Models},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2022},
pages = {10684-10695}
}
# Uses
## Direct Use
The model is intended for research purposes only. Possible research areas and
tasks include
- Safe deployment of models which have the potential to generate harmful content.
- Probing and understanding the limitations and biases of generative models.
- Generation of artworks and use in design and other artistic processes.
- Applications in educational or creative tools.
- Research on generative models.
Excluded uses are described below.
### Misuse, Malicious Use, and Out-of-Scope Use
_Note: This section is taken from the [DALLE-MINI model card](https://huggingface.co/dalle-mini/dalle-mini), but applies in the same way to Stable Diffusion v1_.
The model should not be used to intentionally create or disseminate images that create hostile or alienating environments for people. This includes generating images that people would foreseeably find disturbing, distressing, or offensive; or content that propagates historical or current stereotypes.
#### Out-of-Scope Use
The model was not trained to be factual or true representations of people or events, and therefore using the model to generate such content is out-of-scope for the abilities of this model.
#### Misuse and Malicious Use
Using the model to generate content that is cruel to individuals is a misuse of this model. This includes, but is not limited to:
- Generating demeaning, dehumanizing, or otherwise harmful representations of people or their environments, cultures, religions, etc.
- Intentionally promoting or propagating discriminatory content or harmful stereotypes.
- Impersonating individuals without their consent.
- Sexual content without consent of the people who might see it.
- Mis- and disinformation
- Representations of egregious violence and gore
- Sharing of copyrighted or licensed material in violation of its terms of use.
- Sharing content that is an alteration of copyrighted or licensed material in violation of its terms of use.
## Limitations and Bias
### Limitations
- The model does not achieve perfect photorealism
- The model cannot render legible text
- The model does not perform well on more difficult tasks which involve compositionality, such as rendering an image corresponding to “A red cube on top of a blue sphere”
- Faces and people in general may not be generated properly.
- The model was trained mainly with English captions and will not work as well in other languages.
- The autoencoding part of the model is lossy
- The model was trained on a large-scale dataset
[LAION-5B](https://laion.ai/blog/laion-5b/) which contains adult material
and is not fit for product use without additional safety mechanisms and
considerations.
- No additional measures were used to deduplicate the dataset. As a result, we observe some degree of memorization for images that are duplicated in the training data.
The training data can be searched at [https://rom1504.github.io/clip-retrieval/](https://rom1504.github.io/clip-retrieval/) to possibly assist in the detection of memorized images.
### Bias
While the capabilities of image generation models are impressive, they can also reinforce or exacerbate social biases.
Stable Diffusion v1 was trained on subsets of [LAION-2B(en)](https://laion.ai/blog/laion-5b/),
which consists of images that are primarily limited to English descriptions.
Texts and images from communities and cultures that use other languages are likely to be insufficiently accounted for.
This affects the overall output of the model, as white and western cultures are often set as the default. Further, the
ability of the model to generate content with non-English prompts is significantly worse than with English-language prompts.
### Safety Module
The intended use of this model is with the [Safety Checker](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/safety_checker.py) in Diffusers.
This checker works by checking model outputs against known hard-coded NSFW concepts.
The concepts are intentionally hidden to reduce the likelihood of reverse-engineering this filter.
Specifically, the checker compares the class probability of harmful concepts in the embedding space of the `CLIPTextModel` *after generation* of the images.
The concepts are passed into the model with the generated image and compared to a hand-engineered weight for each NSFW concept.
## Training
**Training Data**
The model developers used the following dataset for training the model:
- LAION-2B (en) and subsets thereof (see next section)
**Training Procedure**
Stable Diffusion v1-5 is a latent diffusion model which combines an autoencoder with a diffusion model that is trained in the latent space of the autoencoder. During training,
- Images are encoded through an encoder, which turns images into latent representations. The autoencoder uses a relative downsampling factor of 8 and maps images of shape H x W x 3 to latents of shape H/f x W/f x 4
- Text prompts are encoded through a ViT-L/14 text-encoder.
- The non-pooled output of the text encoder is fed into the UNet backbone of the latent diffusion model via cross-attention.
- The loss is a reconstruction objective between the noise that was added to the latent and the prediction made by the UNet.
Currently six Stable Diffusion checkpoints are provided, which were trained as follows.
- [`stable-diffusion-v1-1`](https://huggingface.co/CompVis/stable-diffusion-v1-1): 237,000 steps at resolution `256x256` on [laion2B-en](https://huggingface.co/datasets/laion/laion2B-en).
194,000 steps at resolution `512x512` on [laion-high-resolution](https://huggingface.co/datasets/laion/laion-high-resolution) (170M examples from LAION-5B with resolution `>= 1024x1024`).
- [`stable-diffusion-v1-2`](https://huggingface.co/CompVis/stable-diffusion-v1-2): Resumed from `stable-diffusion-v1-1`.
515,000 steps at resolution `512x512` on "laion-improved-aesthetics" (a subset of laion2B-en,
filtered to images with an original size `>= 512x512`, estimated aesthetics score `> 5.0`, and an estimated watermark probability `< 0.5`. The watermark estimate is from the LAION-5B metadata, the aesthetics score is estimated using an [improved aesthetics estimator](https://github.com/christophschuhmann/improved-aesthetic-predictor)).
- [`stable-diffusion-v1-3`](https://huggingface.co/CompVis/stable-diffusion-v1-3): Resumed from `stable-diffusion-v1-2` - 195,000 steps at resolution `512x512` on "laion-improved-aesthetics" and 10 % dropping of the text-conditioning to improve [classifier-free guidance sampling](https://arxiv.org/abs/2207.12598).
- [`stable-diffusion-v1-4`](https://huggingface.co/CompVis/stable-diffusion-v1-4) Resumed from `stable-diffusion-v1-2` - 225,000 steps at resolution `512x512` on "laion-aesthetics v2 5+" and 10 % dropping of the text-conditioning to improve [classifier-free guidance sampling](https://arxiv.org/abs/2207.12598).
- [`stable-diffusion-v1-5`](https://huggingface.co/runwayml/stable-diffusion-v1-5) Resumed from `stable-diffusion-v1-2` - 595,000 steps at resolution `512x512` on "laion-aesthetics v2 5+" and 10 % dropping of the text-conditioning to improve [classifier-free guidance sampling](https://arxiv.org/abs/2207.12598).
- [`stable-diffusion-inpainting`](https://huggingface.co/runwayml/stable-diffusion-inpainting) Resumed from `stable-diffusion-v1-5` - then 440,000 steps of inpainting training at resolution 512x512 on “laion-aesthetics v2 5+” and 10% dropping of the text-conditioning. For inpainting, the UNet has 5 additional input channels (4 for the encoded masked-image and 1 for the mask itself) whose weights were zero-initialized after restoring the non-inpainting checkpoint. During training, we generate synthetic masks and in 25% mask everything.
- **Hardware:** 32 x 8 x A100 GPUs
- **Optimizer:** AdamW
- **Gradient Accumulations**: 2
- **Batch:** 32 x 8 x 2 x 4 = 2048
- **Learning rate:** warmup to 0.0001 for 10,000 steps and then kept constant
## Evaluation Results
Evaluations with different classifier-free guidance scales (1.5, 2.0, 3.0, 4.0,
5.0, 6.0, 7.0, 8.0) and 50 PNDM/PLMS sampling
steps show the relative improvements of the checkpoints:
Evaluated using 50 PLMS steps and 10000 random prompts from the COCO2017 validation set, evaluated at 512x512 resolution. Not optimized for FID scores.
## Environmental Impact
**Stable Diffusion v1** **Estimated Emissions**
Based on that information, we estimate the following CO2 emissions using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). The hardware, runtime, cloud provider, and compute region were utilized to estimate the carbon impact.
- **Hardware Type:** A100 PCIe 40GB
- **Hours used:** 150000
- **Cloud Provider:** AWS
- **Compute Region:** US-east
- **Carbon Emitted (Power consumption x Time x Carbon produced based on location of power grid):** 11250 kg CO2 eq.
## Citation
```bibtex
@InProceedings{Rombach_2022_CVPR,
author = {Rombach, Robin and Blattmann, Andreas and Lorenz, Dominik and Esser, Patrick and Ommer, Bj\"orn},
title = {High-Resolution Image Synthesis With Latent Diffusion Models},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2022},
pages = {10684-10695}
}
```
*This model card was written by: Robin Rombach and Patrick Esser and is based on the [DALL-E Mini model card](https://huggingface.co/dalle-mini/dalle-mini).* |
BigSalmon/InformalToFormalLincoln24 | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"has_space"
]
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}
} | 5 | 2022-12-30T14:35:22Z | ---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: PXTEST
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 13.00 +/- 10.80
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 5 of the Deep Reinforcement Learning Class: https://github.com/huggingface/deep-rl-class/tree/main/unit5
|
BigSalmon/InformalToFormalLincoln25 | [
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"gpt2",
"text-generation",
"transformers",
"has_space"
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}
} | 10 | 2022-12-30T14:37:12Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
- f1
model-index:
- name: Glue_distilbert_new
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
config: mrpc
split: validation
args: mrpc
metrics:
- name: Accuracy
type: accuracy
value: 0.6397058823529411
- name: F1
type: f1
value: 0.7360861759425494
---
<!-- 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. -->
# Glue_distilbert_new
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6153
- Accuracy: 0.6397
- F1: 0.7361
- Combined Score: 0.6879
## 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: 1024
- eval_batch_size: 1024
- seed: 33
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------------:|
| 0.6415 | 1.0 | 4 | 0.6363 | 0.6838 | 0.8122 | 0.7480 |
| 0.6292 | 2.0 | 8 | 0.6101 | 0.6838 | 0.8122 | 0.7480 |
| 0.6244 | 3.0 | 12 | 0.6047 | 0.6838 | 0.8122 | 0.7480 |
| 0.6075 | 4.0 | 16 | 0.6153 | 0.6397 | 0.7361 | 0.6879 |
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.1+cu117
- Datasets 2.8.0
- Tokenizers 0.11.6
|
BigSalmon/MrLincoln10 | [
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"transformers"
]
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} | 5 | null | ---
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: 241.54 +/- 20.26
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
...
```
|
BigSalmon/MrLincoln12 | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"has_space"
]
| text-generation | {
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"GPT2LMHeadModel"
],
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}
} | 9 | null | ---
license: mit
tags:
- generated_from_trainer
datasets:
- super_glue
metrics:
- accuracy
model-index:
- name: yes_no_qna_deberta_model
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: super_glue
type: super_glue
config: boolq
split: train
args: boolq
metrics:
- name: Accuracy
type: accuracy
value: 0.8507645259938837
---
<!-- 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. -->
# yes_no_qna_deberta_model
This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on the super_glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5570
- Accuracy: 0.8508
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.583 | 1.0 | 590 | 0.4086 | 0.8251 |
| 0.348 | 2.0 | 1180 | 0.4170 | 0.8465 |
| 0.2183 | 3.0 | 1770 | 0.5570 | 0.8508 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
|
BigSalmon/MrLincoln13 | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
]
| text-generation | {
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"GPT2LMHeadModel"
],
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}
} | 9 | null | ---
license: mit
tags:
- generated_from_trainer
model-index:
- name: gpt-neo-125M-dream
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. -->
# gpt-neo-125M-dream
This model is a fine-tuned version of [EleutherAI/gpt-neo-125M](https://huggingface.co/EleutherAI/gpt-neo-125M) on the [DreamBank](https://www.dreambank.net/) dataset, a collection of over 20,000 dream reports. It achieves the following results on the evaluation set:
- Loss: 3.3769
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 3.4013 | 1.0 | 9077 | 3.3905 |
| 3.369 | 2.0 | 18154 | 3.3784 |
| 3.3613 | 3.0 | 27231 | 3.3769 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.1+cu117
- Datasets 2.8.0
- Tokenizers 0.13.2
|
BigSalmon/MrLincoln6 | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
]
| text-generation | {
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"GPT2LMHeadModel"
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} | 9 | null | ---
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: 237.84 +/- 60.88
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
...
```
|
BigSalmon/MrLincoln7 | []
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}
} | 0 | null | ---
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: 255.60 +/- 16.55
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
...
```
|
BigSalmon/MrLincoln8 | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
]
| text-generation | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
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},
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}
}
} | 12 | null | ---
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="1itai1/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"])
```
|
BigSalmon/MrLincolnBerta | [
"pytorch",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible",
"has_space"
]
| fill-mask | {
"architectures": [
"RobertaForMaskedLM"
],
"model_type": "roberta",
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},
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}
}
} | 8 | null | ---
license: creativeml-openrail-m
tags:
- text-to-image
- stable-diffusion
---
### stargate-diffusion-sg1-1 Dreambooth model trained by Aphophis420 with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook
USE: *prompt*, still from stargate sg1
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)
|
BigSalmon/NEO125InformalToFormalLincoln | [
"pytorch",
"gpt_neo",
"text-generation",
"transformers"
]
| text-generation | {
"architectures": [
"GPTNeoForCausalLM"
],
"model_type": "gpt_neo",
"task_specific_params": {
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},
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}
} | 8 | null |
---
tags:
- yolov5
- yolo
- vision
- object-detection
- pytorch
library_name: yolov5
library_version: 7.0.6
inference: false
datasets:
- keremberke/nfl-object-detection
model-index:
- name: keremberke/yolov5m-nfl
results:
- task:
type: object-detection
dataset:
type: keremberke/nfl-object-detection
name: keremberke/nfl-object-detection
split: validation
metrics:
- type: precision # since [email protected] is not available on hf.co/metrics
value: 0.3141797014905773 # min: 0.0 - max: 1.0
name: [email protected]
---
<div align="center">
<img width="640" alt="keremberke/yolov5m-nfl" src="https://huggingface.co/keremberke/yolov5m-nfl/resolve/main/sample_visuals.jpg">
</div>
### How to use
- Install [yolov5](https://github.com/fcakyon/yolov5-pip):
```bash
pip install -U yolov5
```
- Load model and perform prediction:
```python
import yolov5
# load model
model = yolov5.load('keremberke/yolov5m-nfl')
# set model parameters
model.conf = 0.25 # NMS confidence threshold
model.iou = 0.45 # NMS IoU threshold
model.agnostic = False # NMS class-agnostic
model.multi_label = False # NMS multiple labels per box
model.max_det = 1000 # maximum number of detections per image
# set image
img = 'https://github.com/ultralytics/yolov5/raw/master/data/images/zidane.jpg'
# perform inference
results = model(img, size=640)
# inference with test time augmentation
results = model(img, augment=True)
# parse results
predictions = results.pred[0]
boxes = predictions[:, :4] # x1, y1, x2, y2
scores = predictions[:, 4]
categories = predictions[:, 5]
# show detection bounding boxes on image
results.show()
# save results into "results/" folder
results.save(save_dir='results/')
```
- Finetune the model on your custom dataset:
```bash
yolov5 train --data data.yaml --img 640 --batch 16 --weights keremberke/yolov5m-nfl --epochs 10
```
**More models available at: [awesome-yolov5-models](https://github.com/keremberke/awesome-yolov5-models)**
|
BigSalmon/ParaphraseParentheses2.0 | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
]
| text-generation | {
"architectures": [
"GPT2LMHeadModel"
],
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}
}
} | 13 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: flash-cards-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. -->
# flash-cards-2
This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) 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: 0.0001
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 7
### Training results
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
|
BigSalmon/SimplifyText | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
]
| text-generation | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
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},
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},
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},
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}
} | 17 | 2022-12-30T15:45:04Z | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 251.78 +/- 15.45
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
BigTooth/DialoGPT-Megumin | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
| conversational | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
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},
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},
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}
}
} | 16 | null | ---
language: ar
widget:
- text: 'واش هاد ال+ شي مخص ل+ دراري ال+ صغار'
---
Our Arabic Dialect Identification models are trained to accurately identify spoken dialects in Arabic text. Developed as part of a larger project, these models were trained using a combination of publicly available datasets and fine-tuned on our own dataset. With high accuracy in identifying Arabic dialects, our models can be utilized in a variety of applications.
Check out our project on Arabic Dialect Identification for more information! https://github.com/Lafifi-24/arabic-dialect-identification |
BigTooth/DialoGPT-small-tohru | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
| conversational | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
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},
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},
"translation_en_to_fr": {
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},
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}
}
} | 10 | null | ---
tags:
- generated_from_trainer
- twitter-financial-topic-classification
- financial
- stocks
- twitter
datasets:
- zeroshot/twitter-financial-news-topic
metrics:
- accuracy
- f1
- precision
- recall
widget:
- text: "Here are Thursday's biggest analyst calls: Apple, Amazon, Tesla, Palantir, DocuSign, Exxon & more"
example_title: "Analyst Update'"
- text: "LIVE: ECB surprises with 50bps hike, ending its negative rate era. President Christine Lagarde is taking questions "
example_title: "Fed | Central Banks"
- text: "Goldman Sachs traders countered the industry’s underwriting slump with revenue gains that raced past analysts’ estimates. The trading operation posted a 32% surge in second-quarter revenue that included another banner period for fixed income"
example_title: "Company | Product News"
- text: "China Evergrande Group’s onshore bond holders rejected a plan by the distressed developer to further extend a bond payment which was due on Friday. Rebecca Choong Wilkins reports on Bloomberg Television"
example_title: "Treasuries | Corporate Debt"
- text: "Investing Club: Morgan Stanley's dividend, buyback pay us for our patience after quarterly missteps"
example_title: "Dividend"
- text: "Investing Club: Our takes on Amazon and Apple heading into next week's earnings reports"
example_title: "Earnings"
- text: "JUST RELEASED: Oil Price Dynamics Report → Over the past week, oil prices decreased as supply expectations rose and anticipated demand remained unchanged."
example_title: "Energy | Oil"
- text: "Delta Air Lines fell short of profit expectations in the second quarter and said high operating costs will persist through the rest of the year. Bloomberg Opinion's Brooke Sutherland has more on 'Bloomberg Markets'"
example_title: "Financials"
- text: "BREAKING: The Indian rupee plummets to a record 80 per US dollar as foreign investors pull out money from the nation's stocks"
example_title: "Currencies"
- text: "Twitter and Elon Musk are now in a high stakes/high risk situation, one analyst said."
example_title: "General News | Opinion"
- text: "Copper prices are signaling that investors are bearish on the economy, strategist says"
example_title: "Gold | Metals | Materials"
- text: "Johnson & Johnson CFO Joe Wolk says the company is positioned for the long term and the plans for its consumer operations include an IPO. He speaks on 'Bloomberg Markets'"
example_title: "IPO"
- text: "Company and Elon Musk are set for a blockbuster courtroom battle over Musk’s attempt to terminate his $44 billion acquisition deal for $TWTR, according to Wedbush analyst Dan Ives."
example_title: "Legal | Regulation"
- text: "Amazon to buy primary health care provider One Medical for roughly $3.9 billion"
example_title: "M&A | Investments"
- text: "Barclays Senior Analyst For Equity Research Jason Goldberg: 'Price expectations have changed.'' The global markets business recorded $6.47 billion of revenue in the quarter with rates, commodities and currencies helping drive the fixed-income gains."
example_title: "Macro"
- text: "US stocks push higher in a volatile session. We break it down on The Countdown to The Close"
example_title: "Markets"
- text: "Zelenskyy fires security chiefs over ‘treasonous’ officials"
example_title: "Politics"
- text: "Airbnb co-founder Joe Gebbia is stepping down"
example_title: "Personnel Change"
- text: "French power group EDF requests its shares be suspended"
example_title: "Stock Commentary"
- text: "JUST IN: Alibaba shares slide as much as 5.7%, bringing this week's slump to over 15%, after it reportedly faced a data-theft inquiry"
example_title: "Stock Movement"
model-index:
- name: finbert-tone-finetuned-finance-topic-classification
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: twitter-financial-news-topic
type: finance
metrics:
- type: F1
name: F1
value: 0.910647
- type: accuracy
name: accuracy
value: 0.910615
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finbert-tone-finetuned-finance-topic-classification
This model is a fine-tuned version of [yiyanghkust/finbert-tone](https://huggingface.co/yiyanghkust/finbert-tone) on [Twitter Financial News Topic](https://huggingface.co/datasets/zeroshot/twitter-financial-news-topic) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.509021
- Accuracy: 0.910615
- F1: 0.910647
- Precision: 0.911335
- Recall: 0.910615
## Model description
Model determines the financial topic of given tweets over 20 various topics. Given the unbalanced distribution of the class labels, the weights were adjusted to pay attention to the less sampled labels which should increase overall performance..
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 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
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| No log | 1.0 | 266 | 0.5152 | 0.8552 | 0.8504 | 0.8508 | 0.8552 |
| 0.7618 | 2.0 | 532 | 0.3999 | 0.8790 | 0.8781 | 0.8842 | 0.8790 |
| 0.7618 | 3.0 | 798 | 0.3628 | 0.8943 | 0.8940 | 0.8958 | 0.8943 |
| 0.16 | 4.0 | 1064 | 0.3776 | 0.8997 | 0.9001 | 0.9025 | 0.8997 |
| 0.16 | 5.0 | 1330 | 0.4286 | 0.8999 | 0.9002 | 0.9022 | 0.8999 |
| 0.058 | 6.0 | 1596 | 0.4500 | 0.9043 | 0.9042 | 0.9055 | 0.9043 |
| 0.058 | 7.0 | 1862 | 0.4689 | 0.9021 | 0.9017 | 0.9026 | 0.9021 |
| 0.0267 | 8.0 | 2128 | 0.4918 | 0.9031 | 0.9029 | 0.9039 | 0.9031 |
| 0.0267 | 9.0 | 2394 | 0.5030 | 0.9048 | 0.9049 | 0.9060 | 0.9048 |
| 0.0177 | 10.0 | 2660 | 0.5052 | 0.9033 | 0.9034 | 0.9044 | 0.9033 |
| 0.0177 | 11.0 | 2926 | 0.5265 | 0.9036 | 0.9034 | 0.9055 | 0.9036 |
| 0.013 | 12.0 | 3192 | 0.5267 | 0.9041 | 0.9041 | 0.9058 | 0.9041 |
| 0.013 | 13.0 | 3458 | 0.5090 | 0.9106 | 0.9106 | 0.9113 | 0.9106 |
| 0.0105 | 14.0 | 3724 | 0.5315 | 0.9067 | 0.9067 | 0.9080 | 0.9067 |
| 0.0105 | 15.0 | 3990 | 0.5339 | 0.9084 | 0.9084 | 0.9093 | 0.9084 |
| 0.0068 | 16.0 | 4256 | 0.5414 | 0.9072 | 0.9074 | 0.9088 | 0.9072 |
| 0.0051 | 17.0 | 4522 | 0.5460 | 0.9092 | 0.9091 | 0.9102 | 0.9092 |
| 0.0051 | 18.0 | 4788 | 0.5438 | 0.9072 | 0.9073 | 0.9081 | 0.9072 |
| 0.0035 | 19.0 | 5054 | 0.5474 | 0.9072 | 0.9073 | 0.9080 | 0.9072 |
| 0.0035 | 20.0 | 5320 | 0.5484 | 0.9079 | 0.9080 | 0.9087 | 0.9079 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
|
BinksSachary/ShaxxBot2 | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
| conversational | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
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}
} | 12 | 2022-12-30T17:11:34Z | ---
license: creativeml-openrail-m
tags:
- pytorch
- diffusers
- stable-diffusion
- text-to-image
- diffusion-models-class
- dreambooth-hackathon
- landscape
widget:
- text: a photo of ioprt cliff with a dog
---
# DreamBooth Hackathon model for the Isle of Portland concept trained by harveymannering on the [jurassic-coast](https://huggingface.co/datasets/harveymannering/jurassic-coast) dataset.
The model was fine-tuned on the `cliff` prior for the landscape theme. I used the [jurassic-coast](https://huggingface.co/datasets/harveymannering/jurassic-coast) dataset which contains 14 images of a particular cliff on the Isle of Portland on the south coast of England.
This is a Stable Diffusion model fine-tuned on the Isle of Portland (shortened to the rare token `ioprt`) concept with DreamBooth. It can be used by modifying the `instance_prompt`: **a photo of ioprt cliff**
This model was created as part of the DreamBooth Hackathon 🔥. Visit the [organisation page](https://huggingface.co/dreambooth-hackathon) for instructions on how to take part!
## Examples
<table>
<tr>
<td><b>In the style of different artists</b>
<br>"a photo of ioprt cliff in the style of monet"</td>
<td><br>"a photo of ioprt cliff in the style of raphael"</td>
</tr>
<tr>
<td>
<a data-flickr-embed="true" href="https://www.flickr.com/photos/197317911@N06/52595583182/in/dateposted-public/" title="monet"><img src="https://live.staticflickr.com/65535/52595583182_2f3e1597a3.jpg" width="300" height="300" alt="monet"></a><script async src="//embedr.flickr.com/assets/client-code.js" charset="utf-8"></script>
</td>
<td>
<a data-flickr-embed="true" href="https://www.flickr.com/photos/197317911@N06/52596072366/in/dateposted-public/" title="raphael"><img src="https://live.staticflickr.com/65535/52596072366_b3142a805d.jpg" width="300" height="300" alt="raphael"></a><script async src="//embedr.flickr.com/assets/client-code.js" charset="utf-8"></script> </td>
</tr>
<tr>
<td><b>Adding things to the environment</b><br>"a photo of ioprt cliff with a dog"</td>
<td><br>"a photo of ioprt cliff with a boat"</td>
</tr>
<tr>
<td>
<a data-flickr-embed="true" href="https://www.flickr.com/photos/197317911@N06/52596072406/in/dateposted-public/" title="dog"><img src="https://live.staticflickr.com/65535/52596072406_f90bdeae1b.jpg" width="300" height="300" alt="dog"></a><script async src="//embedr.flickr.com/assets/client-code.js" charset="utf-8"></script> </td>
<td>
<a data-flickr-embed="true" href="https://www.flickr.com/photos/197317911@N06/52595583222/in/dateposted-public/" title="boat"><img src="https://live.staticflickr.com/65535/52595583222_ed4159e458.jpg" width="300" height="300" alt="boat"></a><script async src="//embedr.flickr.com/assets/client-code.js" charset="utf-8"></script> </tr>
<tr>
<td><b>Making changes to the environment</b><br>"a photo of a sunset at ioprt cliff"</td>
<td><br>"a photo of ioprt cliff in the desert"</td>
</tr>
<tr>
<td>
<a data-flickr-embed="true" href="https://www.flickr.com/photos/197317911@N06/52596072351/in/dateposted-public/" title="sunset"><img src="https://live.staticflickr.com/65535/52596072351_2af35dfef9.jpg" width="300" height="300" alt="sunset"></a><script async src="//embedr.flickr.com/assets/client-code.js" charset="utf-8"></script> </td>
<td>
<a data-flickr-embed="true" href="https://www.flickr.com/photos/197317911@N06/52596334474/in/dateposted-public/" title="desert"><img src="https://live.staticflickr.com/65535/52596334474_de30ac48fd.jpg" width="300" height="300" alt="desert"></a><script async src="//embedr.flickr.com/assets/client-code.js" charset="utf-8"></script> </tr>
</table>
## Usage
```python
from diffusers import StableDiffusionPipeline
pipeline = StableDiffusionPipeline.from_pretrained('harveymannering/jurassic-coast')
image = pipeline().images[0]
image
```
|
BitanBiswas/mbert-bengali-ner-finetuned-ner | [
"pytorch",
"tensorboard",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
]
| token-classification | {
"architectures": [
"BertForTokenClassification"
],
"model_type": "bert",
"task_specific_params": {
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},
"summarization": {
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},
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},
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}
}
} | 4 | 2022-12-30T17:11:57Z | ---
license: mit
tags:
- pytorch
- diffusers
- unconditional-image-generation
- diffusion-models-class
---
# Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class)
This model is a diffusion model for unconditional image generation of cute 🦋.
## Usage
```python
from diffusers import DDPMPipeline
pipeline = DDPMPipeline.from_pretrained('bobber/sd-class-butterflies-32')
image = pipeline().images[0]
image
```
|
Blaine-Mason/hackMIT-finetuned-sst2 | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"dataset:glue",
"transformers",
"generated_from_trainer"
]
| text-classification | {
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
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"no_repeat_ngram_size": null,
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},
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},
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},
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}
}
} | 36 | 2022-12-30T17:24:12Z | ---
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: 942.50 +/- 194.07
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga Nishant91 -f logs/
python enjoy.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga Nishant91 -f logs/
rl_zoo3 enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python train.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga Nishant91
```
## 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)])
```
|
Blazeolmo/Scrabunzi | []
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}
} | 0 | null | Access to model BooBoa/BooBoa is restricted and you are not in the authorized list. Visit https://huggingface.co/BooBoa/BooBoa to ask for access. |
Blerrrry/Kkk | []
| null | {
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}
}
} | 0 | 2022-12-30T17:35:38Z | ---
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: 231.15 +/- 72.46
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
...
```
|
Bman/DialoGPT-medium-harrypotter | []
| null | {
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}
}
} | 0 | 2022-12-30T17:59:17Z | ---
'[object Object]': null
license: apache-2.0
language:
- en
pipeline_tag: summarization
---
# Model Card for T5-base for Claim Summarization
<!-- Provide a quick summary of what the model is/does. -->
This model can be used to summarize noisy claims on social media into clean and concise claims which can be used for downstream tasks in a fact-checking pipeline.
# Model Details
This is the fine-tuned T5-base model with 'Pre-processed with Mention and Hashtag Run Removed (P-MRR-HRR)' preprocessing strategy detailed in Table 2 in the paper.
## Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** Varad Bhatnagar, Diptesh Kanojia and Kameswari Chebrolu
- **Model type:** Summarization
- **Language(s) (NLP):** English
- **Finetuned from model:** https://huggingface.co/t5-base
## Model Sources
<!-- Provide the basic links for the model. -->
- **Repository:** https://github.com/varadhbhatnagar/FC-Claim-Det
- **Paper:** https://aclanthology.org/2022.coling-1.259/
## Tokenizer
Same as [T5-base](https://huggingface.co/t5-base)
# 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. -->
English to English summarization on noisy fact-checking worthy claims found on social media.
## Downstream Use
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
Can be used for other tasks in a fact-checking pipeline such as claim matching and evidence retrieval.
# Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
As the [Google Fact Check Explorer](https://toolbox.google.com/factcheck/explorer) is an ever growing and evolving system, the current Retrieval@k results may not exactly match
those in the corresponding paper as those experiments were conducted in the month of April and May 2022.
# 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. -->
[Data](https://github.com/varadhbhatnagar/FC-Claim-Det/blob/main/public_data/released_data.csv)
## Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
Finetuning the pretrained T5-base model on the 567 pairs released in our paper.
### Preprocessing
Pre-processed with Mention and Hashtag Run Removed (P-MRR-HRR). Apply this strategy on the input text before feeding it to model for summarization.
# Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
Retrieval@5 and Mean Reciprocal Recall scores are reported.
## Results
Retrieval@5 = 28.75
MRR = 0.25
Further details can be found in the paper.
# Other Models from same work
[DBART](https://huggingface.co/varadhbhatnagar/fc-claim-det-DBART)
[DPEGASUS](https://huggingface.co/varadhbhatnagar/fc-claim-det-DPEGASUS)
# Citation
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
```
@inproceedings{bhatnagar-etal-2022-harnessing,
title = "Harnessing Abstractive Summarization for Fact-Checked Claim Detection",
author = "Bhatnagar, Varad and
Kanojia, Diptesh and
Chebrolu, Kameswari",
booktitle = "Proceedings of the 29th International Conference on Computational Linguistics",
month = oct,
year = "2022",
address = "Gyeongju, Republic of Korea",
publisher = "International Committee on Computational Linguistics",
url = "https://aclanthology.org/2022.coling-1.259",
pages = "2934--2945",
abstract = "Social media platforms have become new battlegrounds for anti-social elements, with misinformation being the weapon of choice. Fact-checking organizations try to debunk as many claims as possible while staying true to their journalistic processes but cannot cope with its rapid dissemination. We believe that the solution lies in partial automation of the fact-checking life cycle, saving human time for tasks which require high cognition. We propose a new workflow for efficiently detecting previously fact-checked claims that uses abstractive summarization to generate crisp queries. These queries can then be executed on a general-purpose retrieval system associated with a collection of previously fact-checked claims. We curate an abstractive text summarization dataset comprising noisy claims from Twitter and their gold summaries. It is shown that retrieval performance improves 2x by using popular out-of-the-box summarization models and 3x by fine-tuning them on the accompanying dataset compared to verbatim querying. Our approach achieves Recall@5 and MRR of 35{\%} and 0.3, compared to baseline values of 10{\%} and 0.1, respectively. Our dataset, code, and models are available publicly: https://github.com/varadhbhatnagar/FC-Claim-Det/.",
}
```
# Model Card Authors
Varad Bhatnagar
# Model Card Contact
Email: [email protected]
# How to Get Started with the Model
Use the code below to get started with the model.
```
from transformers import T5ForConditionalGeneration, T5TokenizerFast
hft = T5TokenizerFast.from_pretrained('varadhbhatnagar/fc-claim-det-T5-base')
hfm = T5ForConditionalGeneration.from_pretrained('varadhbhatnagar/fc-claim-det-T5-base').to(device)
row = 'hi satya my name is arman today i got this video which is being spread in whatsapp and it is being said that the all old age covid 19 patients are being killed in the government hospital kindly check the facts'
tokenized_text = hft.encode(row, return_tensors="pt")
summary_ids = hfm.generate(tokenized_text,
num_beams=6,
no_repeat_ngram_size=2,
min_length=5,
max_length=15,
early_stopping=True)
output = hft.decode(summary_ids[0], skip_special_tokens=True)
``` |
BobBraico/distilbert-base-uncased-finetuned-imdb-accelerate | []
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}
}
} | 0 | 2022-12-30T18:01:05Z | ---
tags:
- conversational
---
# Aiko Dialogpt model |
BobBraico/distilbert-base-uncased-finetuned-imdb | []
| null | {
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}
} | 0 | 2022-12-30T18:09:20Z | ---
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: 410.00 +/- 148.49
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga claterza -f logs/
python enjoy.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga claterza -f logs/
rl_zoo3 enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python train.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga claterza
```
## 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', 0.0003),
('learning_starts', 100000),
('n_timesteps', 1000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
|
BogdanKuloren/continual-learning-paper-embeddings-model | [
"pytorch",
"mpnet",
"feature-extraction",
"transformers"
]
| feature-extraction | {
"architectures": [
"MPNetModel"
],
"model_type": "mpnet",
"task_specific_params": {
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},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 11 | 2022-12-30T18:11:34Z | ---
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="Kon3000/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"])
```
|
BonjinKim/dst_kor_bert | [
"pytorch",
"jax",
"bert",
"pretraining",
"transformers"
]
| null | {
"architectures": [
"BertForPreTraining"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
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"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 5 | null | ---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.54 +/- 2.71
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="Kon3000/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"])
```
|
Boondong/Wandee | []
| null | {
"architectures": null,
"model_type": null,
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 0 | 2022-12-30T18:26:04Z | ---
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="srandazzo/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"])
```
|
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