modelId
stringlengths 4
81
| tags
list | pipeline_tag
stringclasses 17
values | config
dict | downloads
int64 0
59.7M
| first_commit
timestamp[ns, tz=UTC] | card
stringlengths 51
438k
|
|---|---|---|---|---|---|---|
dccuchile/albert-large-spanish-finetuned-ner
|
[
"pytorch",
"albert",
"token-classification",
"transformers",
"autotrain_compatible"
] |
token-classification
|
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| 3 | null |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SoccerTwos
library_name: ml-agents
---
# **poca** Agent playing **SoccerTwos**
This is a trained model of a **poca** agent playing **SoccerTwos** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-SoccerTwos
2. Step 1: Write your model_id: ShreyasM/poca-SoccerTwos
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
dccuchile/albert-large-spanish-finetuned-pos
|
[
"pytorch",
"albert",
"token-classification",
"transformers",
"autotrain_compatible"
] |
token-classification
|
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| 1 | null |
---
library_name: stable-baselines3
tags:
- AntBulletEnv-v0
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: AntBulletEnv-v0
type: AntBulletEnv-v0
metrics:
- type: mean_reward
value: 1719.81 +/- 66.96
name: mean_reward
verified: false
---
# **A2C** Agent playing **AntBulletEnv-v0**
This is a trained model of a **A2C** agent playing **AntBulletEnv-v0**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
dccuchile/albert-large-spanish-finetuned-xnli
|
[
"pytorch",
"albert",
"text-classification",
"transformers"
] |
text-classification
|
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| 29 | null |
---
library_name: stable-baselines3
tags:
- AntBulletEnv-v0
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: AntBulletEnv-v0
type: AntBulletEnv-v0
metrics:
- type: mean_reward
value: 830.92 +/- 121.47
name: mean_reward
verified: false
---
# **A2C** Agent playing **AntBulletEnv-v0**
This is a trained model of a **A2C** agent playing **AntBulletEnv-v0**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
dccuchile/albert-tiny-spanish-finetuned-ner
|
[
"pytorch",
"albert",
"token-classification",
"transformers",
"autotrain_compatible"
] |
token-classification
|
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| 8 | null |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: third_t5-end2end-questions-generation
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. -->
# third_t5-end2end-questions-generation
This model is a fine-tuned version of [ThomasSimonini/t5-end2end-question-generation](https://huggingface.co/ThomasSimonini/t5-end2end-question-generation) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.1916
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 7
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.6125 | 0.66 | 100 | 2.3093 |
| 2.4208 | 1.32 | 200 | 2.2588 |
| 2.3535 | 1.98 | 300 | 2.2325 |
| 2.3002 | 2.65 | 400 | 2.2175 |
| 2.2785 | 3.31 | 500 | 2.2083 |
| 2.2439 | 3.97 | 600 | 2.2010 |
| 2.2188 | 4.63 | 700 | 2.1972 |
| 2.2107 | 5.29 | 800 | 2.1947 |
| 2.1938 | 5.95 | 900 | 2.1920 |
| 2.1891 | 6.61 | 1000 | 2.1916 |
### Framework versions
- Transformers 4.27.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
|
dccuchile/albert-tiny-spanish-finetuned-pawsx
|
[
"pytorch",
"albert",
"text-classification",
"transformers"
] |
text-classification
|
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| 29 | null |
---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-CartPole-v1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 500.00 +/- 0.00
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
dccuchile/albert-tiny-spanish-finetuned-pos
|
[
"pytorch",
"albert",
"token-classification",
"transformers",
"autotrain_compatible"
] |
token-classification
|
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"AlbertForTokenClassification"
],
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}
| 5 | 2023-03-18T17:59:30Z |
---
library_name: stable-baselines3
tags:
- PandaReachDense-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachDense-v2
type: PandaReachDense-v2
metrics:
- type: mean_reward
value: -1.37 +/- 0.23
name: mean_reward
verified: false
---
# **A2C** Agent playing **PandaReachDense-v2**
This is a trained model of a **A2C** agent playing **PandaReachDense-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
Chan/distilgpt2-finetuned-wikitext2
|
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| 0 | null |
---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: doom_health_gathering_supreme
type: doom_health_gathering_supreme
metrics:
- type: mean_reward
value: 9.64 +/- 3.12
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **doom_health_gathering_supreme** environment.
This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory.
Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/
## Downloading the model
After installing Sample-Factory, download the model with:
```
python -m sample_factory.huggingface.load_from_hub -r Jackmin108/rl_course_vizdoom_health_gathering_supreme
```
## Using the model
To run the model after download, use the `enjoy` script corresponding to this environment:
```
python -m .usr.local.lib.python3.9.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme
```
You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag.
See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details
## Training with this model
To continue training with this model, use the `train` script corresponding to this environment:
```
python -m .usr.local.lib.python3.9.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme --restart_behavior=resume --train_for_env_steps=10000000000
```
Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
|
Ci/Pai
|
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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: 272.62 +/- 17.23
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
...
```
|
Cinnamon/electra-small-japanese-discriminator
|
[
"pytorch",
"electra",
"pretraining",
"ja",
"transformers",
"license:apache-2.0"
] | null |
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| 419 | null |
---
license: mit
tags:
- pytorch
- diffusers
- unconditional-image-generation
- diffusion-model
---
## Usage
```python
from diffusers import DDPMPipeline
pipeline = DDPMPipeline.from_pretrained('WiNE-iNEFF/Minecraft-Skin-Diffusion-V2')
image = pipeline().images[0].convert('RGBA')
image
```
|
Cinnamon/electra-small-japanese-generator
|
[
"pytorch",
"electra",
"fill-mask",
"ja",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
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"ElectraForMaskedLM"
],
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| 19 | null |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# pushpdeep/sbert-en_hi-muril
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('pushpdeep/sbert-en_hi-muril')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('pushpdeep/sbert-en_hi-muril')
model = AutoModel.from_pretrained('pushpdeep/sbert-en_hi-muril')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=pushpdeep/sbert-en_hi-muril)
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 15106 with parameters:
```
{'batch_size': 64, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.MSELoss.MSELoss`
Parameters of the fit()-Method:
```
{
"epochs": 3,
"evaluation_steps": 1000,
"evaluator": "sentence_transformers.evaluation.SequentialEvaluator.SequentialEvaluator",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"eps": 1e-06,
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 10000,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information -->
|
Connorvr/TeachingGen
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers",
"generated_from_trainer",
"license:mit"
] |
text-generation
|
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| 4 | 2023-03-19T00:11:02Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: Taxi-v3-Q-Learning
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="MakiPan/Taxi-v3-Q-Learning", 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"])
```
|
Crumped/imdb-simpleRNN
|
[
"keras"
] | null |
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}
| 0 | null |
---
tags:
- generated_from_trainer
datasets:
- open_subtitles
metrics:
- bleu
model-index:
- name: opus-mt-en-id-open-subtitles
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: open_subtitles
type: open_subtitles
config: en-id
split: train
args: en-id
metrics:
- name: Bleu
type: bleu
value: 30.2272
---
<!-- 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. -->
# opus-mt-en-id-open-subtitles
This model was trained from scratch on the open_subtitles dataset.
It achieves the following results on the evaluation set:
- Loss: 2.3148
- Bleu: 30.2272
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 4000
- num_epochs: 25
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu |
|:-------------:|:-----:|:------:|:---------------:|:-------:|
| 1.5356 | 1.0 | 28125 | 1.5619 | 31.8599 |
| 1.4703 | 2.0 | 56250 | 1.6047 | 31.8339 |
| 1.3857 | 3.0 | 84375 | 1.6281 | 32.0796 |
| 1.313 | 4.0 | 112500 | 1.6619 | 31.7391 |
| 1.2468 | 5.0 | 140625 | 1.6706 | 31.9009 |
| 1.1831 | 6.0 | 168750 | 1.6924 | 31.4491 |
| 1.1232 | 7.0 | 196875 | 1.7252 | 31.7229 |
| 1.0649 | 8.0 | 225000 | 1.7483 | 31.7093 |
| 1.0078 | 9.0 | 253125 | 1.7697 | 31.4902 |
| 0.9516 | 10.0 | 281250 | 1.8026 | 31.4342 |
| 0.8969 | 11.0 | 309375 | 1.8364 | 31.2466 |
| 0.8436 | 12.0 | 337500 | 1.8747 | 31.1737 |
| 0.7916 | 13.0 | 365625 | 1.9035 | 31.0118 |
| 0.7406 | 14.0 | 393750 | 1.9414 | 30.9409 |
| 0.6912 | 15.0 | 421875 | 1.9776 | 30.9562 |
| 0.6439 | 16.0 | 450000 | 2.0221 | 30.582 |
| 0.5983 | 17.0 | 478125 | 2.0588 | 30.4478 |
| 0.5544 | 18.0 | 506250 | 2.1023 | 30.4601 |
| 0.5126 | 19.0 | 534375 | 2.1367 | 30.4802 |
| 0.474 | 20.0 | 562500 | 2.1790 | 30.4211 |
| 0.438 | 21.0 | 590625 | 2.2131 | 30.3327 |
| 0.4039 | 22.0 | 618750 | 2.2484 | 30.196 |
| 0.3737 | 23.0 | 646875 | 2.2779 | 30.1145 |
| 0.3475 | 24.0 | 675000 | 2.3022 | 30.2635 |
| 0.326 | 25.0 | 703125 | 2.3148 | 30.2272 |
### Framework versions
- Transformers 4.26.1
- Pytorch 2.0.0
- Datasets 2.10.1
- Tokenizers 0.11.0
|
Culmenus/opus-mt-de-is-finetuned-de-to-is_35g65cc_2
|
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}
| 0 | null |
---
tags:
- CartPole-v1
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 429.10 +/- 0.00
name: mean_reward
verified: false
---
# (CleanRL) **DQN** Agent Playing **CartPole-v1**
This is a trained model of a DQN agent playing CartPole-v1.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/DQN_buffersize_100000.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[DQN_buffersize_100000]"
python -m cleanrl_utils.enjoy --exp-name DQN_buffersize_100000 --env-id CartPole-v1
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/pfunk/CartPole-v1-DQN_buffersize_100000-seed4/raw/main/dqn.py
curl -OL https://huggingface.co/pfunk/CartPole-v1-DQN_buffersize_100000-seed4/raw/main/pyproject.toml
curl -OL https://huggingface.co/pfunk/CartPole-v1-DQN_buffersize_100000-seed4/raw/main/poetry.lock
poetry install --all-extras
python dqn.py --track --wandb-entity pfunk --wandb-project-name dqpn --capture-video true --save-model true --upload-model true --hf-entity pfunk --exp-name DQN_buffersize_100000 --buffer-size 100000 --seed 4
```
# Hyperparameters
```python
{'alg_type': 'dqn.py',
'batch_size': 256,
'buffer_size': 100000,
'capture_video': True,
'cuda': True,
'end_e': 0.1,
'env_id': 'CartPole-v1',
'exp_name': 'DQN_buffersize_100000',
'exploration_fraction': 0.2,
'gamma': 1.0,
'hf_entity': 'pfunk',
'learning_rate': 0.0001,
'learning_starts': 1000,
'save_model': True,
'seed': 4,
'start_e': 1.0,
'target_network_frequency': 20,
'target_tau': 1.0,
'torch_deterministic': True,
'total_timesteps': 500000,
'track': True,
'train_frequency': 1,
'upload_model': True,
'wandb_entity': 'pfunk',
'wandb_project_name': 'dqpn'}
```
|
D4RL1NG/yes
|
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}
| 0 | null |
---
tags:
- autotrain
- text-classification
language:
- es
widget:
- text: "I love AutoTrain 🤗"
datasets:
- milyiyo/autotrain-data-iptc-classification-v4
co2_eq_emissions:
emissions: 0.845545764970478
---
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 42015107919
- CO2 Emissions (in grams): 0.8455
## Validation Metrics
- Loss: 1.231
- Accuracy: 0.758
- Macro F1: 0.531
- Micro F1: 0.758
- Weighted F1: 0.708
- Macro Precision: 0.532
- Micro Precision: 0.758
- Weighted Precision: 0.685
- Macro Recall: 0.553
- Micro Recall: 0.758
- Weighted Recall: 0.758
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/milyiyo/autotrain-iptc-classification-v4-42015107919
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("milyiyo/autotrain-iptc-classification-v4-42015107919", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("milyiyo/autotrain-iptc-classification-v4-42015107919", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
```
|
DataikuNLP/distiluse-base-multilingual-cased-v1
|
[
"pytorch",
"distilbert",
"arxiv:1908.10084",
"sentence-transformers",
"feature-extraction",
"sentence-similarity",
"transformers",
"license:apache-2.0"
] |
sentence-similarity
|
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"DistilBertModel"
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}
| 29 | null |
---
license: mit
tags:
- feature-extraction
library_name: fasttext
language: gu
widget:
- text: apple
example_title: apple
---
# fastText (Gujarati)
fastText is an open-source, free, lightweight library that allows users to learn text representations and text classifiers. It works on standard, generic hardware. Models can later be reduced in size to even fit on mobile devices. It was introduced in [this paper](https://arxiv.org/abs/1607.04606). The official website can be found [here](https://fasttext.cc/).
## Model description
fastText is a library for efficient learning of word representations and sentence classification. fastText is designed to be simple to use for developers, domain experts, and students. It's dedicated to text classification and learning word representations, and was designed to allow for quick model iteration and refinement without specialized hardware. fastText models can be trained on more than a billion words on any multicore CPU in less than a few minutes.
It includes pre-trained models learned on Wikipedia and in over 157 different languages. fastText can be used as a command line, linked to a C++ application, or used as a library for use cases from experimentation and prototyping to production.
## Intended uses & limitations
You can use pre-trained word vectors for text classification or language identification. See the [tutorials](https://fasttext.cc/docs/en/supervised-tutorial.html) and [resources](https://fasttext.cc/docs/en/english-vectors.html) on its official website to look for tasks that interest you.
### How to use
Here is how to load and use a pre-trained vectors
```python
>>> import fasttext
>>> from huggingface_hub import hf_hub_download
>>> model_path = hf_hub_download(repo_id="facebook/fasttext-gu-vectors", filename="model.bin")
>>> model = fasttext.load_model(model_path)
>>> model.words
['the', 'of', 'and', 'to', 'in', 'a', 'that', 'is', ...]
>>> len(model.words)
145940
>>> model['bread']
array([ 4.89417791e-01, 1.60882145e-01, -2.25947708e-01, -2.94273376e-01,
-1.04577184e-01, 1.17962055e-01, 1.34821936e-01, -2.41778508e-01, ...])
```
Here is how to use this model to query nearest neighbors of an English word vector:
```python
>>> import fasttext
>>> from huggingface_hub import hf_hub_download
>>> model_path = hf_hub_download(repo_id="facebook/fasttext-en-nearest-neighbors", filename="model.bin")
>>> model = fasttext.load_model(model_path)
>>> model.get_nearest_neighbors("bread", k=5)
[(0.5641006231307983, 'butter'),
(0.48875734210014343, 'loaf'),
(0.4491206705570221, 'eat'),
(0.42444291710853577, 'food'),
(0.4229326844215393, 'cheese')]
```
Here is how to use this model to detect the language of a given text:
```python
>>> import fasttext
>>> from huggingface_hub import hf_hub_download
>>> model_path = hf_hub_download(repo_id="facebook/fasttext-language-identification", filename="model.bin")
>>> model = fasttext.load_model(model_path)
>>> model.predict("Hello, world!")
(('__label__eng_Latn',), array([0.81148803]))
>>> model.predict("Hello, world!", k=5)
(('__label__eng_Latn', '__label__vie_Latn', '__label__nld_Latn', '__label__pol_Latn', '__label__deu_Latn'),
array([0.61224753, 0.21323682, 0.09696738, 0.01359863, 0.01319415]))
```
### Limitations and bias
Even if the training data used for this model could be characterized as fairly neutral, this model can have biased predictions.
Cosine similarity can be used to measure the similarity between two different word vectors. If two two vectors are identical, the cosine similarity will be 1. For two completely unrelated vectors, the value will be 0. If two vectors have an opposite relationship, the value will be -1.
```python
>>> import numpy as np
>>> def cosine_similarity(word1, word2):
>>> return np.dot(model[word1], model[word2]) / (np.linalg.norm(model[word1]) * np.linalg.norm(model[word2]))
>>> cosine_similarity("man", "boy")
0.061653383
>>> cosine_similarity("man", "ceo")
0.11989131
>>> cosine_similarity("woman", "ceo")
-0.08834904
```
## Training data
Pre-trained word vectors for 157 languages were trained on [Common Crawl](http://commoncrawl.org/) and [Wikipedia](https://www.wikipedia.org/) using fastText. These models were trained using CBOW with position-weights, in dimension 300, with character n-grams of length 5, a window of size 5 and 10 negatives. We also distribute three new word analogy datasets, for French, Hindi and Polish.
## Training procedure
### Tokenization
We used the [Stanford word segmenter](https://nlp.stanford.edu/software/segmenter.html) for Chinese, [Mecab](http://taku910.github.io/mecab/) for Japanese and [UETsegmenter](https://github.com/phongnt570/UETsegmenter) for Vietnamese. For languages using the Latin, Cyrillic, Hebrew or Greek scripts, we used the tokenizer from the [Europarl](https://www.statmt.org/europarl/) preprocessing tools. For the remaining languages, we used the ICU tokenizer.
More information about the training of these models can be found in the article [Learning Word Vectors for 157 Languages](https://arxiv.org/abs/1802.06893).
### License
The word vectors are distributed under the [*Creative Commons Attribution-Share-Alike License 3.0*](https://creativecommons.org/licenses/by-sa/3.0/).
### Evaluation datasets
The analogy evaluation datasets described in the paper are available here: [French](https://dl.fbaipublicfiles.com/fasttext/word-analogies/questions-words-fr.txt), [Hindi](https://dl.fbaipublicfiles.com/fasttext/word-analogies/questions-words-hi.txt), [Polish](https://dl.fbaipublicfiles.com/fasttext/word-analogies/questions-words-pl.txt).
### BibTeX entry and citation info
Please cite [1] if using this code for learning word representations or [2] if using for text classification.
[1] P. Bojanowski\*, E. Grave\*, A. Joulin, T. Mikolov, [*Enriching Word Vectors with Subword Information*](https://arxiv.org/abs/1607.04606)
```markup
@article{bojanowski2016enriching,
title={Enriching Word Vectors with Subword Information},
author={Bojanowski, Piotr and Grave, Edouard and Joulin, Armand and Mikolov, Tomas},
journal={arXiv preprint arXiv:1607.04606},
year={2016}
}
```
[2] A. Joulin, E. Grave, P. Bojanowski, T. Mikolov, [*Bag of Tricks for Efficient Text Classification*](https://arxiv.org/abs/1607.01759)
```markup
@article{joulin2016bag,
title={Bag of Tricks for Efficient Text Classification},
author={Joulin, Armand and Grave, Edouard and Bojanowski, Piotr and Mikolov, Tomas},
journal={arXiv preprint arXiv:1607.01759},
year={2016}
}
```
[3] A. Joulin, E. Grave, P. Bojanowski, M. Douze, H. Jégou, T. Mikolov, [*FastText.zip: Compressing text classification models*](https://arxiv.org/abs/1612.03651)
```markup
@article{joulin2016fasttext,
title={FastText.zip: Compressing text classification models},
author={Joulin, Armand and Grave, Edouard and Bojanowski, Piotr and Douze, Matthijs and J{'e}gou, H{'e}rve and Mikolov, Tomas},
journal={arXiv preprint arXiv:1612.03651},
year={2016}
}
```
If you use these word vectors, please cite the following paper:
[4] E. Grave\*, P. Bojanowski\*, P. Gupta, A. Joulin, T. Mikolov, [*Learning Word Vectors for 157 Languages*](https://arxiv.org/abs/1802.06893)
```markup
@inproceedings{grave2018learning,
title={Learning Word Vectors for 157 Languages},
author={Grave, Edouard and Bojanowski, Piotr and Gupta, Prakhar and Joulin, Armand and Mikolov, Tomas},
booktitle={Proceedings of the International Conference on Language Resources and Evaluation (LREC 2018)},
year={2018}
}
```
(\* These authors contributed equally.)
|
DataikuNLP/paraphrase-multilingual-MiniLM-L12-v2
|
[
"pytorch",
"bert",
"arxiv:1908.10084",
"sentence-transformers",
"feature-extraction",
"sentence-similarity",
"transformers",
"license:apache-2.0"
] |
sentence-similarity
|
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"BertModel"
],
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}
| 1,517 | null |
---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: doom_health_gathering_supreme
type: doom_health_gathering_supreme
metrics:
- type: mean_reward
value: 10.03 +/- 4.85
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **doom_health_gathering_supreme** environment.
This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory.
Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/
## Downloading the model
After installing Sample-Factory, download the model with:
```
python -m sample_factory.huggingface.load_from_hub -r taohoang/rl_course_vizdoom_health_gathering_supreme
```
## Using the model
To run the model after download, use the `enjoy` script corresponding to this environment:
```
python -m .usr.local.lib.python3.9.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme
```
You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag.
See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details
## Training with this model
To continue training with this model, use the `train` script corresponding to this environment:
```
python -m .usr.local.lib.python3.9.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme --restart_behavior=resume --train_for_env_steps=10000000000
```
Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
|
Davlan/bert-base-multilingual-cased-finetuned-naija
|
[
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
{
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
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"max_length": null
},
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"length_penalty": null,
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"min_length": null,
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"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": {
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"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 13 | null |
---
library_name: ml-agents
tags:
- Pyramids
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Pyramids
---
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-Pyramids
2. Step 1: Find your model_id: jackhhhh/ppo-Pyramids_Training1
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
Davlan/bert-base-multilingual-cased-finetuned-swahili
|
[
"pytorch",
"tf",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
{
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
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"length_penalty": null,
"max_length": null,
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},
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},
"translation_en_to_de": {
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},
"translation_en_to_fr": {
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},
"translation_en_to_ro": {
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"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 67 | null |
---
license: mit
tags:
- feature-extraction
library_name: fasttext
language: he
widget:
- text: apple
example_title: apple
---
# fastText (Hebrew)
fastText is an open-source, free, lightweight library that allows users to learn text representations and text classifiers. It works on standard, generic hardware. Models can later be reduced in size to even fit on mobile devices. It was introduced in [this paper](https://arxiv.org/abs/1607.04606). The official website can be found [here](https://fasttext.cc/).
## Model description
fastText is a library for efficient learning of word representations and sentence classification. fastText is designed to be simple to use for developers, domain experts, and students. It's dedicated to text classification and learning word representations, and was designed to allow for quick model iteration and refinement without specialized hardware. fastText models can be trained on more than a billion words on any multicore CPU in less than a few minutes.
It includes pre-trained models learned on Wikipedia and in over 157 different languages. fastText can be used as a command line, linked to a C++ application, or used as a library for use cases from experimentation and prototyping to production.
## Intended uses & limitations
You can use pre-trained word vectors for text classification or language identification. See the [tutorials](https://fasttext.cc/docs/en/supervised-tutorial.html) and [resources](https://fasttext.cc/docs/en/english-vectors.html) on its official website to look for tasks that interest you.
### How to use
Here is how to load and use a pre-trained vectors
```python
>>> import fasttext
>>> from huggingface_hub import hf_hub_download
>>> model_path = hf_hub_download(repo_id="facebook/fasttext-he-vectors", filename="model.bin")
>>> model = fasttext.load_model(model_path)
>>> model.words
['the', 'of', 'and', 'to', 'in', 'a', 'that', 'is', ...]
>>> len(model.words)
145940
>>> model['bread']
array([ 4.89417791e-01, 1.60882145e-01, -2.25947708e-01, -2.94273376e-01,
-1.04577184e-01, 1.17962055e-01, 1.34821936e-01, -2.41778508e-01, ...])
```
Here is how to use this model to query nearest neighbors of an English word vector:
```python
>>> import fasttext
>>> from huggingface_hub import hf_hub_download
>>> model_path = hf_hub_download(repo_id="facebook/fasttext-en-nearest-neighbors", filename="model.bin")
>>> model = fasttext.load_model(model_path)
>>> model.get_nearest_neighbors("bread", k=5)
[(0.5641006231307983, 'butter'),
(0.48875734210014343, 'loaf'),
(0.4491206705570221, 'eat'),
(0.42444291710853577, 'food'),
(0.4229326844215393, 'cheese')]
```
Here is how to use this model to detect the language of a given text:
```python
>>> import fasttext
>>> from huggingface_hub import hf_hub_download
>>> model_path = hf_hub_download(repo_id="facebook/fasttext-language-identification", filename="model.bin")
>>> model = fasttext.load_model(model_path)
>>> model.predict("Hello, world!")
(('__label__eng_Latn',), array([0.81148803]))
>>> model.predict("Hello, world!", k=5)
(('__label__eng_Latn', '__label__vie_Latn', '__label__nld_Latn', '__label__pol_Latn', '__label__deu_Latn'),
array([0.61224753, 0.21323682, 0.09696738, 0.01359863, 0.01319415]))
```
### Limitations and bias
Even if the training data used for this model could be characterized as fairly neutral, this model can have biased predictions.
Cosine similarity can be used to measure the similarity between two different word vectors. If two two vectors are identical, the cosine similarity will be 1. For two completely unrelated vectors, the value will be 0. If two vectors have an opposite relationship, the value will be -1.
```python
>>> import numpy as np
>>> def cosine_similarity(word1, word2):
>>> return np.dot(model[word1], model[word2]) / (np.linalg.norm(model[word1]) * np.linalg.norm(model[word2]))
>>> cosine_similarity("man", "boy")
0.061653383
>>> cosine_similarity("man", "ceo")
0.11989131
>>> cosine_similarity("woman", "ceo")
-0.08834904
```
## Training data
Pre-trained word vectors for 157 languages were trained on [Common Crawl](http://commoncrawl.org/) and [Wikipedia](https://www.wikipedia.org/) using fastText. These models were trained using CBOW with position-weights, in dimension 300, with character n-grams of length 5, a window of size 5 and 10 negatives. We also distribute three new word analogy datasets, for French, Hindi and Polish.
## Training procedure
### Tokenization
We used the [Stanford word segmenter](https://nlp.stanford.edu/software/segmenter.html) for Chinese, [Mecab](http://taku910.github.io/mecab/) for Japanese and [UETsegmenter](https://github.com/phongnt570/UETsegmenter) for Vietnamese. For languages using the Latin, Cyrillic, Hebrew or Greek scripts, we used the tokenizer from the [Europarl](https://www.statmt.org/europarl/) preprocessing tools. For the remaining languages, we used the ICU tokenizer.
More information about the training of these models can be found in the article [Learning Word Vectors for 157 Languages](https://arxiv.org/abs/1802.06893).
### License
The word vectors are distributed under the [*Creative Commons Attribution-Share-Alike License 3.0*](https://creativecommons.org/licenses/by-sa/3.0/).
### Evaluation datasets
The analogy evaluation datasets described in the paper are available here: [French](https://dl.fbaipublicfiles.com/fasttext/word-analogies/questions-words-fr.txt), [Hindi](https://dl.fbaipublicfiles.com/fasttext/word-analogies/questions-words-hi.txt), [Polish](https://dl.fbaipublicfiles.com/fasttext/word-analogies/questions-words-pl.txt).
### BibTeX entry and citation info
Please cite [1] if using this code for learning word representations or [2] if using for text classification.
[1] P. Bojanowski\*, E. Grave\*, A. Joulin, T. Mikolov, [*Enriching Word Vectors with Subword Information*](https://arxiv.org/abs/1607.04606)
```markup
@article{bojanowski2016enriching,
title={Enriching Word Vectors with Subword Information},
author={Bojanowski, Piotr and Grave, Edouard and Joulin, Armand and Mikolov, Tomas},
journal={arXiv preprint arXiv:1607.04606},
year={2016}
}
```
[2] A. Joulin, E. Grave, P. Bojanowski, T. Mikolov, [*Bag of Tricks for Efficient Text Classification*](https://arxiv.org/abs/1607.01759)
```markup
@article{joulin2016bag,
title={Bag of Tricks for Efficient Text Classification},
author={Joulin, Armand and Grave, Edouard and Bojanowski, Piotr and Mikolov, Tomas},
journal={arXiv preprint arXiv:1607.01759},
year={2016}
}
```
[3] A. Joulin, E. Grave, P. Bojanowski, M. Douze, H. Jégou, T. Mikolov, [*FastText.zip: Compressing text classification models*](https://arxiv.org/abs/1612.03651)
```markup
@article{joulin2016fasttext,
title={FastText.zip: Compressing text classification models},
author={Joulin, Armand and Grave, Edouard and Bojanowski, Piotr and Douze, Matthijs and J{'e}gou, H{'e}rve and Mikolov, Tomas},
journal={arXiv preprint arXiv:1612.03651},
year={2016}
}
```
If you use these word vectors, please cite the following paper:
[4] E. Grave\*, P. Bojanowski\*, P. Gupta, A. Joulin, T. Mikolov, [*Learning Word Vectors for 157 Languages*](https://arxiv.org/abs/1802.06893)
```markup
@inproceedings{grave2018learning,
title={Learning Word Vectors for 157 Languages},
author={Grave, Edouard and Bojanowski, Piotr and Gupta, Prakhar and Joulin, Armand and Mikolov, Tomas},
booktitle={Proceedings of the International Conference on Language Resources and Evaluation (LREC 2018)},
year={2018}
}
```
(\* These authors contributed equally.)
|
Davlan/bert-base-multilingual-cased-ner-hrl
|
[
"pytorch",
"tf",
"bert",
"token-classification",
"transformers",
"autotrain_compatible",
"has_space"
] |
token-classification
|
{
"architectures": [
"BertForTokenClassification"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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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": {
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"max_length": null,
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},
"translation_en_to_fr": {
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},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 269,898 | null |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-base-multilingual-cased_0319_J
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-multilingual-cased_0319_J
This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0355
- Precision: 0.9776
- Recall: 0.9791
- F1: 0.9784
- Accuracy: 0.9949
## 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: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 95 | 0.2410 | 0.6320 | 0.7491 | 0.6856 | 0.9475 |
| No log | 2.0 | 190 | 0.0483 | 0.9430 | 0.9522 | 0.9476 | 0.9912 |
| No log | 3.0 | 285 | 0.0379 | 0.9710 | 0.9746 | 0.9728 | 0.9938 |
| No log | 4.0 | 380 | 0.0382 | 0.9645 | 0.9731 | 0.9688 | 0.9940 |
| No log | 5.0 | 475 | 0.0357 | 0.9703 | 0.9761 | 0.9732 | 0.9941 |
| No log | 6.0 | 570 | 0.0367 | 0.9710 | 0.9761 | 0.9736 | 0.9943 |
| No log | 7.0 | 665 | 0.0376 | 0.9732 | 0.9761 | 0.9746 | 0.9943 |
| No log | 8.0 | 760 | 0.0355 | 0.9776 | 0.9791 | 0.9784 | 0.9949 |
| No log | 9.0 | 855 | 0.0364 | 0.9718 | 0.9768 | 0.9743 | 0.9946 |
| No log | 10.0 | 950 | 0.0361 | 0.9747 | 0.9776 | 0.9761 | 0.9947 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.0+cu117
- Datasets 2.8.0
- Tokenizers 0.12.1
|
Davlan/byt5-base-yor-eng-mt
|
[
"pytorch",
"t5",
"text2text-generation",
"arxiv:2103.08647",
"transformers",
"autotrain_compatible"
] |
text2text-generation
|
{
"architectures": [
"T5ForConditionalGeneration"
],
"model_type": "t5",
"task_specific_params": {
"conversational": {
"max_length": null
},
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},
"text-generation": {
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"max_length": null
},
"translation_en_to_de": {
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"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
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"prefix": null
},
"translation_en_to_ro": {
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"max_length": null,
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"prefix": null
}
}
}
| 12 | null |
---
library_name: stable-baselines3
tags:
- AntBulletEnv-v0
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: AntBulletEnv-v0
type: AntBulletEnv-v0
metrics:
- type: mean_reward
value: 1538.63 +/- 176.09
name: mean_reward
verified: false
---
# **A2C** Agent playing **AntBulletEnv-v0**
This is a trained model of a **A2C** agent playing **AntBulletEnv-v0**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
Davlan/distilbert-base-multilingual-cased-masakhaner
|
[
"pytorch",
"tf",
"distilbert",
"token-classification",
"arxiv:2103.11811",
"transformers",
"autotrain_compatible"
] |
token-classification
|
{
"architectures": [
"DistilBertForTokenClassification"
],
"model_type": "distilbert",
"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,
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"prefix": null
},
"text-generation": {
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"max_length": null
},
"translation_en_to_de": {
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"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
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"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 16 | null |
---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-CartPole-v1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 500.00 +/- 0.00
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
Davlan/distilbert-base-multilingual-cased-ner-hrl
|
[
"pytorch",
"tf",
"distilbert",
"token-classification",
"transformers",
"autotrain_compatible",
"has_space"
] |
token-classification
|
{
"architectures": [
"DistilBertForTokenClassification"
],
"model_type": "distilbert",
"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
}
}
}
| 123,856 | null |
---
license: cc-by-4.0
metrics:
- bleu4
- meteor
- rouge-l
- bertscore
- moverscore
language: es
datasets:
- lmqg/qg_esquad
pipeline_tag: text2text-generation
tags:
- question answering
widget:
- text: "question: ¿Cuál es la población de Nueva York a partir de 2014?, context: Situada en uno de los mayores puertos naturales del mundo, la ciudad de Nueva York consta de cinco municipios, cada uno de los cuales es un condado separado del estado de Nueva York. Los cinco distritos - Brooklyn, Queens, Manhattan, el Bronx y Staten Island - se consolidaron en una sola ciudad en 1898. Con una población censada estimada en 2014 de 8.491.079 habitantes distribuidos en una superficie de solo 790 km ², Nueva York es la ciudad más densamente poblada de los Estados Unidos. Hasta 800 idiomas se hablan en Nueva York, por lo que es la ciudad más lingüísticamente diversa del mundo. Según estimaciones del censo de 2014, la región metropolitana de la ciudad de Nueva York sigue siendo por un margen significativo la más poblada de los Estados Unidos, según lo definido tanto por el Área Estadística Metropolitana (20,1 millones de residentes). En 2013, el MSA produjo un producto metropolitano bruto (GMP) de casi US $1,39 billones, mientras que en 2012, el CSA generó un GMP de más de US $1,55 billones, ambos clasificados en primer lugar."
example_title: "Question Answering Example 1"
- text: "question: ¿Cómo se llama el ejército personal de Sassou?, context: El progreso democrático del Congo se descarriló en 1997, cuando Lissouba y Sassou comenzaron a luchar por el poder en la guerra civil. A medida que se acercaban las elecciones presidenciales de julio de 1997, las tensiones entre los campos de Lissouba y Sassou aumentaron. El 5 de junio, las fuerzas del gobierno del presidente Lissouba rodearon el complejo de Sassou en Brazzaville y Sassou ordenó a los miembros de su milicia privada (conocida como Cobras) resistir. Así comenzó un conflicto de cuatro meses que destruyó o dañó gran parte de Brazzaville y causó decenas de miles de muertes civiles. A principios de octubre, el régimen socialista angoleño comenzó una invasión del Congo para instalar a Sassou en el poder. A mediados de octubre, el gobierno de Lissouba cayó. Poco después, Sassou se declaró presidente."
example_title: "Question Answering Example 2"
model-index:
- name: vocabtrimmer/mt5-small-trimmed-es-60000-esquad-qa
results:
- task:
name: Text2text Generation
type: text2text-generation
dataset:
name: lmqg/qg_esquad
type: default
args: default
metrics:
- name: BLEU4 (Question Answering)
type: bleu4_question_answering
value: 15.81
- name: ROUGE-L (Question Answering)
type: rouge_l_question_answering
value: 36.21
- name: METEOR (Question Answering)
type: meteor_question_answering
value: 31.38
- name: BERTScore (Question Answering)
type: bertscore_question_answering
value: 90.76
- name: MoverScore (Question Answering)
type: moverscore_question_answering
value: 75.04
- name: AnswerF1Score (Question Answering)
type: answer_f1_score__question_answering
value: 58.03
- name: AnswerExactMatch (Question Answering)
type: answer_exact_match_question_answering
value: 37.0
---
# Model Card of `vocabtrimmer/mt5-small-trimmed-es-60000-esquad-qa`
This model is fine-tuned version of [ckpts/mt5-small-trimmed-es-60000](https://huggingface.co/ckpts/mt5-small-trimmed-es-60000) for question answering task on the [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-generation).
### Overview
- **Language model:** [ckpts/mt5-small-trimmed-es-60000](https://huggingface.co/ckpts/mt5-small-trimmed-es-60000)
- **Language:** es
- **Training data:** [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) (default)
- **Online Demo:** [https://autoqg.net/](https://autoqg.net/)
- **Repository:** [https://github.com/asahi417/lm-question-generation](https://github.com/asahi417/lm-question-generation)
- **Paper:** [https://arxiv.org/abs/2210.03992](https://arxiv.org/abs/2210.03992)
### Usage
- With [`lmqg`](https://github.com/asahi417/lm-question-generation#lmqg-language-model-for-question-generation-)
```python
from lmqg import TransformersQG
# initialize model
model = TransformersQG(language="es", model="vocabtrimmer/mt5-small-trimmed-es-60000-esquad-qa")
# model prediction
answers = model.answer_q(list_question="¿Cuál es la población de Nueva York a partir de 2014?", list_context=" Situada en uno de los mayores puertos naturales del mundo, la ciudad de Nueva York consta de cinco municipios, cada uno de los cuales es un condado separado del estado de Nueva York. Los cinco distritos - Brooklyn, Queens, Manhattan, el Bronx y Staten Island - se consolidaron en una sola ciudad en 1898. Con una población censada estimada en 2014 de 8.491.079 habitantes distribuidos en una superficie de solo 790 km ², Nueva York es la ciudad más densamente poblada de los Estados Unidos. Hasta 800 idiomas se hablan en Nueva York, por lo que es la ciudad más lingüísticamente diversa del mundo. Según estimaciones del censo de 2014, la región metropolitana de la ciudad de Nueva York sigue siendo por un margen significativo la más poblada de los Estados Unidos, según lo definido tanto por el Área Estadística Metropolitana (20,1 millones de residentes). En 2013, el MSA produjo un producto metropolitano bruto (GMP) de casi US $1,39 billones, mientras que en 2012, el CSA generó un GMP de más de US $1,55 billones, ambos clasificados en primer lugar.")
```
- With `transformers`
```python
from transformers import pipeline
pipe = pipeline("text2text-generation", "vocabtrimmer/mt5-small-trimmed-es-60000-esquad-qa")
output = pipe("question: ¿Cuál es la población de Nueva York a partir de 2014?, context: Situada en uno de los mayores puertos naturales del mundo, la ciudad de Nueva York consta de cinco municipios, cada uno de los cuales es un condado separado del estado de Nueva York. Los cinco distritos - Brooklyn, Queens, Manhattan, el Bronx y Staten Island - se consolidaron en una sola ciudad en 1898. Con una población censada estimada en 2014 de 8.491.079 habitantes distribuidos en una superficie de solo 790 km ², Nueva York es la ciudad más densamente poblada de los Estados Unidos. Hasta 800 idiomas se hablan en Nueva York, por lo que es la ciudad más lingüísticamente diversa del mundo. Según estimaciones del censo de 2014, la región metropolitana de la ciudad de Nueva York sigue siendo por un margen significativo la más poblada de los Estados Unidos, según lo definido tanto por el Área Estadística Metropolitana (20,1 millones de residentes). En 2013, el MSA produjo un producto metropolitano bruto (GMP) de casi US $1,39 billones, mientras que en 2012, el CSA generó un GMP de más de US $1,55 billones, ambos clasificados en primer lugar.")
```
## Evaluation
- ***Metric (Question Answering)***: [raw metric file](https://huggingface.co/vocabtrimmer/mt5-small-trimmed-es-60000-esquad-qa/raw/main/eval/metric.first.answer.paragraph_question.answer.lmqg_qg_esquad.default.json)
| | Score | Type | Dataset |
|:-----------------|--------:|:--------|:-----------------------------------------------------------------|
| AnswerExactMatch | 37 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) |
| AnswerF1Score | 58.03 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) |
| BERTScore | 90.76 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) |
| Bleu_1 | 25.62 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) |
| Bleu_2 | 21.27 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) |
| Bleu_3 | 18.24 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) |
| Bleu_4 | 15.81 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) |
| METEOR | 31.38 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) |
| MoverScore | 75.04 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) |
| ROUGE_L | 36.21 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) |
## Training hyperparameters
The following hyperparameters were used during fine-tuning:
- dataset_path: lmqg/qg_esquad
- dataset_name: default
- input_types: ['paragraph_question']
- output_types: ['answer']
- prefix_types: None
- model: ckpts/mt5-small-trimmed-es-60000
- max_length: 512
- max_length_output: 32
- epoch: 13
- batch: 32
- lr: 0.001
- fp16: False
- random_seed: 1
- gradient_accumulation_steps: 4
- label_smoothing: 0.15
The full configuration can be found at [fine-tuning config file](https://huggingface.co/vocabtrimmer/mt5-small-trimmed-es-60000-esquad-qa/raw/main/trainer_config.json).
## Citation
```
@inproceedings{ushio-etal-2022-generative,
title = "{G}enerative {L}anguage {M}odels for {P}aragraph-{L}evel {Q}uestion {G}eneration",
author = "Ushio, Asahi and
Alva-Manchego, Fernando and
Camacho-Collados, Jose",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2022",
address = "Abu Dhabi, U.A.E.",
publisher = "Association for Computational Linguistics",
}
```
|
Davlan/mT5_base_yoruba_adr
|
[
"pytorch",
"mt5",
"text2text-generation",
"arxiv:2003.10564",
"arxiv:2103.08647",
"transformers",
"autotrain_compatible"
] |
text2text-generation
|
{
"architectures": [
"MT5ForConditionalGeneration"
],
"model_type": "mt5",
"task_specific_params": {
"conversational": {
"max_length": null
},
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},
"text-generation": {
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},
"translation_en_to_de": {
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},
"translation_en_to_fr": {
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}
}
}
| 5 | null |
---
library_name: stable-baselines3
tags:
- PandaReachDense-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachDense-v2
type: PandaReachDense-v2
metrics:
- type: mean_reward
value: -2.04 +/- 1.08
name: mean_reward
verified: false
---
# **A2C** Agent playing **PandaReachDense-v2**
This is a trained model of a **A2C** agent playing **PandaReachDense-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
Davlan/mbart50-large-eng-yor-mt
|
[
"pytorch",
"mbart",
"text2text-generation",
"arxiv:2103.08647",
"transformers",
"autotrain_compatible"
] |
text2text-generation
|
{
"architectures": [
"MBartForConditionalGeneration"
],
"model_type": "mbart",
"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,
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"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
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},
"translation_en_to_fr": {
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},
"translation_en_to_ro": {
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"max_length": null,
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"prefix": null
}
}
}
| 5 | null |
---
library_name: ml-agents
tags:
- SnowballTarget
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SnowballTarget
---
# **ppo** Agent playing **SnowballTarget**
This is a trained model of a **ppo** agent playing **SnowballTarget** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-SnowballTarget
2. Step 1: Find your model_id: Raiden-1001/ppo-SnowballTarget1
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
Davlan/mbart50-large-yor-eng-mt
|
[
"pytorch",
"mbart",
"text2text-generation",
"arxiv:2103.08647",
"transformers",
"autotrain_compatible"
] |
text2text-generation
|
{
"architectures": [
"MBartForConditionalGeneration"
],
"model_type": "mbart",
"task_specific_params": {
"conversational": {
"max_length": null
},
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},
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"max_length": null
},
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"prefix": null
},
"translation_en_to_fr": {
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"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 5 | null |
---
library_name: stable-baselines3
tags:
- PandaReachDense-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachDense-v2
type: PandaReachDense-v2
metrics:
- type: mean_reward
value: -2.23 +/- 0.71
name: mean_reward
verified: false
---
# **A2C** Agent playing **PandaReachDense-v2**
This is a trained model of a **A2C** agent playing **PandaReachDense-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
Davlan/mt5-small-en-pcm
|
[
"pytorch",
"mt5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] |
text2text-generation
|
{
"architectures": [
"MT5ForConditionalGeneration"
],
"model_type": "mt5",
"task_specific_params": {
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},
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},
"text-generation": {
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"max_length": null
},
"translation_en_to_de": {
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"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
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"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 9 | null |
---
license: mit
tags:
- feature-extraction
library_name: fasttext
language: mrj
widget:
- text: apple
example_title: apple
---
# fastText (Hill Mari)
fastText is an open-source, free, lightweight library that allows users to learn text representations and text classifiers. It works on standard, generic hardware. Models can later be reduced in size to even fit on mobile devices. It was introduced in [this paper](https://arxiv.org/abs/1607.04606). The official website can be found [here](https://fasttext.cc/).
## Model description
fastText is a library for efficient learning of word representations and sentence classification. fastText is designed to be simple to use for developers, domain experts, and students. It's dedicated to text classification and learning word representations, and was designed to allow for quick model iteration and refinement without specialized hardware. fastText models can be trained on more than a billion words on any multicore CPU in less than a few minutes.
It includes pre-trained models learned on Wikipedia and in over 157 different languages. fastText can be used as a command line, linked to a C++ application, or used as a library for use cases from experimentation and prototyping to production.
## Intended uses & limitations
You can use pre-trained word vectors for text classification or language identification. See the [tutorials](https://fasttext.cc/docs/en/supervised-tutorial.html) and [resources](https://fasttext.cc/docs/en/english-vectors.html) on its official website to look for tasks that interest you.
### How to use
Here is how to load and use a pre-trained vectors
```python
>>> import fasttext
>>> from huggingface_hub import hf_hub_download
>>> model_path = hf_hub_download(repo_id="facebook/fasttext-mrj-vectors", filename="model.bin")
>>> model = fasttext.load_model(model_path)
>>> model.words
['the', 'of', 'and', 'to', 'in', 'a', 'that', 'is', ...]
>>> len(model.words)
145940
>>> model['bread']
array([ 4.89417791e-01, 1.60882145e-01, -2.25947708e-01, -2.94273376e-01,
-1.04577184e-01, 1.17962055e-01, 1.34821936e-01, -2.41778508e-01, ...])
```
Here is how to use this model to query nearest neighbors of an English word vector:
```python
>>> import fasttext
>>> from huggingface_hub import hf_hub_download
>>> model_path = hf_hub_download(repo_id="facebook/fasttext-en-nearest-neighbors", filename="model.bin")
>>> model = fasttext.load_model(model_path)
>>> model.get_nearest_neighbors("bread", k=5)
[(0.5641006231307983, 'butter'),
(0.48875734210014343, 'loaf'),
(0.4491206705570221, 'eat'),
(0.42444291710853577, 'food'),
(0.4229326844215393, 'cheese')]
```
Here is how to use this model to detect the language of a given text:
```python
>>> import fasttext
>>> from huggingface_hub import hf_hub_download
>>> model_path = hf_hub_download(repo_id="facebook/fasttext-language-identification", filename="model.bin")
>>> model = fasttext.load_model(model_path)
>>> model.predict("Hello, world!")
(('__label__eng_Latn',), array([0.81148803]))
>>> model.predict("Hello, world!", k=5)
(('__label__eng_Latn', '__label__vie_Latn', '__label__nld_Latn', '__label__pol_Latn', '__label__deu_Latn'),
array([0.61224753, 0.21323682, 0.09696738, 0.01359863, 0.01319415]))
```
### Limitations and bias
Even if the training data used for this model could be characterized as fairly neutral, this model can have biased predictions.
Cosine similarity can be used to measure the similarity between two different word vectors. If two two vectors are identical, the cosine similarity will be 1. For two completely unrelated vectors, the value will be 0. If two vectors have an opposite relationship, the value will be -1.
```python
>>> import numpy as np
>>> def cosine_similarity(word1, word2):
>>> return np.dot(model[word1], model[word2]) / (np.linalg.norm(model[word1]) * np.linalg.norm(model[word2]))
>>> cosine_similarity("man", "boy")
0.061653383
>>> cosine_similarity("man", "ceo")
0.11989131
>>> cosine_similarity("woman", "ceo")
-0.08834904
```
## Training data
Pre-trained word vectors for 157 languages were trained on [Common Crawl](http://commoncrawl.org/) and [Wikipedia](https://www.wikipedia.org/) using fastText. These models were trained using CBOW with position-weights, in dimension 300, with character n-grams of length 5, a window of size 5 and 10 negatives. We also distribute three new word analogy datasets, for French, Hindi and Polish.
## Training procedure
### Tokenization
We used the [Stanford word segmenter](https://nlp.stanford.edu/software/segmenter.html) for Chinese, [Mecab](http://taku910.github.io/mecab/) for Japanese and [UETsegmenter](https://github.com/phongnt570/UETsegmenter) for Vietnamese. For languages using the Latin, Cyrillic, Hebrew or Greek scripts, we used the tokenizer from the [Europarl](https://www.statmt.org/europarl/) preprocessing tools. For the remaining languages, we used the ICU tokenizer.
More information about the training of these models can be found in the article [Learning Word Vectors for 157 Languages](https://arxiv.org/abs/1802.06893).
### License
The word vectors are distributed under the [*Creative Commons Attribution-Share-Alike License 3.0*](https://creativecommons.org/licenses/by-sa/3.0/).
### Evaluation datasets
The analogy evaluation datasets described in the paper are available here: [French](https://dl.fbaipublicfiles.com/fasttext/word-analogies/questions-words-fr.txt), [Hindi](https://dl.fbaipublicfiles.com/fasttext/word-analogies/questions-words-hi.txt), [Polish](https://dl.fbaipublicfiles.com/fasttext/word-analogies/questions-words-pl.txt).
### BibTeX entry and citation info
Please cite [1] if using this code for learning word representations or [2] if using for text classification.
[1] P. Bojanowski\*, E. Grave\*, A. Joulin, T. Mikolov, [*Enriching Word Vectors with Subword Information*](https://arxiv.org/abs/1607.04606)
```markup
@article{bojanowski2016enriching,
title={Enriching Word Vectors with Subword Information},
author={Bojanowski, Piotr and Grave, Edouard and Joulin, Armand and Mikolov, Tomas},
journal={arXiv preprint arXiv:1607.04606},
year={2016}
}
```
[2] A. Joulin, E. Grave, P. Bojanowski, T. Mikolov, [*Bag of Tricks for Efficient Text Classification*](https://arxiv.org/abs/1607.01759)
```markup
@article{joulin2016bag,
title={Bag of Tricks for Efficient Text Classification},
author={Joulin, Armand and Grave, Edouard and Bojanowski, Piotr and Mikolov, Tomas},
journal={arXiv preprint arXiv:1607.01759},
year={2016}
}
```
[3] A. Joulin, E. Grave, P. Bojanowski, M. Douze, H. Jégou, T. Mikolov, [*FastText.zip: Compressing text classification models*](https://arxiv.org/abs/1612.03651)
```markup
@article{joulin2016fasttext,
title={FastText.zip: Compressing text classification models},
author={Joulin, Armand and Grave, Edouard and Bojanowski, Piotr and Douze, Matthijs and J{'e}gou, H{'e}rve and Mikolov, Tomas},
journal={arXiv preprint arXiv:1612.03651},
year={2016}
}
```
If you use these word vectors, please cite the following paper:
[4] E. Grave\*, P. Bojanowski\*, P. Gupta, A. Joulin, T. Mikolov, [*Learning Word Vectors for 157 Languages*](https://arxiv.org/abs/1802.06893)
```markup
@inproceedings{grave2018learning,
title={Learning Word Vectors for 157 Languages},
author={Grave, Edouard and Bojanowski, Piotr and Gupta, Prakhar and Joulin, Armand and Mikolov, Tomas},
booktitle={Proceedings of the International Conference on Language Resources and Evaluation (LREC 2018)},
year={2018}
}
```
(\* These authors contributed equally.)
|
Davlan/mt5-small-pcm-en
|
[
"pytorch",
"mt5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] |
text2text-generation
|
{
"architectures": [
"MT5ForConditionalGeneration"
],
"model_type": "mt5",
"task_specific_params": {
"conversational": {
"max_length": null
},
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},
"text-generation": {
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"max_length": null
},
"translation_en_to_de": {
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},
"translation_en_to_fr": {
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}
}
}
| 9 | null |
---
license: creativeml-openrail-m
base_model: stabilityai/stable-diffusion-2-1-base
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- lora
inference: true
---
# LoRA text2image fine-tuning - https://huggingface.co/KarosY/lianjia_2l_100per600_1e-4
These are LoRA adaption weights for stabilityai/stable-diffusion-2-1-base. The weights were fine-tuned on the None dataset. You can find some example images in the following.
|
Davlan/mt5_base_eng_yor_mt
|
[
"pytorch",
"mt5",
"text2text-generation",
"arxiv:2103.08647",
"transformers",
"autotrain_compatible"
] |
text2text-generation
|
{
"architectures": [
"MT5ForConditionalGeneration"
],
"model_type": "mt5",
"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,
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},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
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"max_length": null,
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"prefix": null
},
"translation_en_to_fr": {
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},
"translation_en_to_ro": {
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}
}
}
| 2 | null |
---
license: creativeml-openrail-m
base_model: stabilityai/stable-diffusion-2-1-base
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- lora
inference: true
---
# LoRA text2image fine-tuning - https://huggingface.co/KarosY/lianjia_2l_100per700_1e-4
These are LoRA adaption weights for stabilityai/stable-diffusion-2-1-base. The weights were fine-tuned on the None dataset. You can find some example images in the following.
|
Davlan/naija-twitter-sentiment-afriberta-large
|
[
"pytorch",
"tf",
"xlm-roberta",
"text-classification",
"arxiv:2201.08277",
"transformers",
"has_space"
] |
text-classification
|
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"XLMRobertaForSequenceClassification"
],
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},
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},
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},
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}
}
}
| 61 | 2023-03-19T05:56:15Z |
---
license: mit
tags:
- feature-extraction
library_name: fasttext
language: hi
widget:
- text: apple
example_title: apple
---
# fastText (Hindi)
fastText is an open-source, free, lightweight library that allows users to learn text representations and text classifiers. It works on standard, generic hardware. Models can later be reduced in size to even fit on mobile devices. It was introduced in [this paper](https://arxiv.org/abs/1607.04606). The official website can be found [here](https://fasttext.cc/).
## Model description
fastText is a library for efficient learning of word representations and sentence classification. fastText is designed to be simple to use for developers, domain experts, and students. It's dedicated to text classification and learning word representations, and was designed to allow for quick model iteration and refinement without specialized hardware. fastText models can be trained on more than a billion words on any multicore CPU in less than a few minutes.
It includes pre-trained models learned on Wikipedia and in over 157 different languages. fastText can be used as a command line, linked to a C++ application, or used as a library for use cases from experimentation and prototyping to production.
## Intended uses & limitations
You can use pre-trained word vectors for text classification or language identification. See the [tutorials](https://fasttext.cc/docs/en/supervised-tutorial.html) and [resources](https://fasttext.cc/docs/en/english-vectors.html) on its official website to look for tasks that interest you.
### How to use
Here is how to load and use a pre-trained vectors
```python
>>> import fasttext
>>> from huggingface_hub import hf_hub_download
>>> model_path = hf_hub_download(repo_id="facebook/fasttext-hi-vectors", filename="model.bin")
>>> model = fasttext.load_model(model_path)
>>> model.words
['the', 'of', 'and', 'to', 'in', 'a', 'that', 'is', ...]
>>> len(model.words)
145940
>>> model['bread']
array([ 4.89417791e-01, 1.60882145e-01, -2.25947708e-01, -2.94273376e-01,
-1.04577184e-01, 1.17962055e-01, 1.34821936e-01, -2.41778508e-01, ...])
```
Here is how to use this model to query nearest neighbors of an English word vector:
```python
>>> import fasttext
>>> from huggingface_hub import hf_hub_download
>>> model_path = hf_hub_download(repo_id="facebook/fasttext-en-nearest-neighbors", filename="model.bin")
>>> model = fasttext.load_model(model_path)
>>> model.get_nearest_neighbors("bread", k=5)
[(0.5641006231307983, 'butter'),
(0.48875734210014343, 'loaf'),
(0.4491206705570221, 'eat'),
(0.42444291710853577, 'food'),
(0.4229326844215393, 'cheese')]
```
Here is how to use this model to detect the language of a given text:
```python
>>> import fasttext
>>> from huggingface_hub import hf_hub_download
>>> model_path = hf_hub_download(repo_id="facebook/fasttext-language-identification", filename="model.bin")
>>> model = fasttext.load_model(model_path)
>>> model.predict("Hello, world!")
(('__label__eng_Latn',), array([0.81148803]))
>>> model.predict("Hello, world!", k=5)
(('__label__eng_Latn', '__label__vie_Latn', '__label__nld_Latn', '__label__pol_Latn', '__label__deu_Latn'),
array([0.61224753, 0.21323682, 0.09696738, 0.01359863, 0.01319415]))
```
### Limitations and bias
Even if the training data used for this model could be characterized as fairly neutral, this model can have biased predictions.
Cosine similarity can be used to measure the similarity between two different word vectors. If two two vectors are identical, the cosine similarity will be 1. For two completely unrelated vectors, the value will be 0. If two vectors have an opposite relationship, the value will be -1.
```python
>>> import numpy as np
>>> def cosine_similarity(word1, word2):
>>> return np.dot(model[word1], model[word2]) / (np.linalg.norm(model[word1]) * np.linalg.norm(model[word2]))
>>> cosine_similarity("man", "boy")
0.061653383
>>> cosine_similarity("man", "ceo")
0.11989131
>>> cosine_similarity("woman", "ceo")
-0.08834904
```
## Training data
Pre-trained word vectors for 157 languages were trained on [Common Crawl](http://commoncrawl.org/) and [Wikipedia](https://www.wikipedia.org/) using fastText. These models were trained using CBOW with position-weights, in dimension 300, with character n-grams of length 5, a window of size 5 and 10 negatives. We also distribute three new word analogy datasets, for French, Hindi and Polish.
## Training procedure
### Tokenization
We used the [Stanford word segmenter](https://nlp.stanford.edu/software/segmenter.html) for Chinese, [Mecab](http://taku910.github.io/mecab/) for Japanese and [UETsegmenter](https://github.com/phongnt570/UETsegmenter) for Vietnamese. For languages using the Latin, Cyrillic, Hebrew or Greek scripts, we used the tokenizer from the [Europarl](https://www.statmt.org/europarl/) preprocessing tools. For the remaining languages, we used the ICU tokenizer.
More information about the training of these models can be found in the article [Learning Word Vectors for 157 Languages](https://arxiv.org/abs/1802.06893).
### License
The word vectors are distributed under the [*Creative Commons Attribution-Share-Alike License 3.0*](https://creativecommons.org/licenses/by-sa/3.0/).
### Evaluation datasets
The analogy evaluation datasets described in the paper are available here: [French](https://dl.fbaipublicfiles.com/fasttext/word-analogies/questions-words-fr.txt), [Hindi](https://dl.fbaipublicfiles.com/fasttext/word-analogies/questions-words-hi.txt), [Polish](https://dl.fbaipublicfiles.com/fasttext/word-analogies/questions-words-pl.txt).
### BibTeX entry and citation info
Please cite [1] if using this code for learning word representations or [2] if using for text classification.
[1] P. Bojanowski\*, E. Grave\*, A. Joulin, T. Mikolov, [*Enriching Word Vectors with Subword Information*](https://arxiv.org/abs/1607.04606)
```markup
@article{bojanowski2016enriching,
title={Enriching Word Vectors with Subword Information},
author={Bojanowski, Piotr and Grave, Edouard and Joulin, Armand and Mikolov, Tomas},
journal={arXiv preprint arXiv:1607.04606},
year={2016}
}
```
[2] A. Joulin, E. Grave, P. Bojanowski, T. Mikolov, [*Bag of Tricks for Efficient Text Classification*](https://arxiv.org/abs/1607.01759)
```markup
@article{joulin2016bag,
title={Bag of Tricks for Efficient Text Classification},
author={Joulin, Armand and Grave, Edouard and Bojanowski, Piotr and Mikolov, Tomas},
journal={arXiv preprint arXiv:1607.01759},
year={2016}
}
```
[3] A. Joulin, E. Grave, P. Bojanowski, M. Douze, H. Jégou, T. Mikolov, [*FastText.zip: Compressing text classification models*](https://arxiv.org/abs/1612.03651)
```markup
@article{joulin2016fasttext,
title={FastText.zip: Compressing text classification models},
author={Joulin, Armand and Grave, Edouard and Bojanowski, Piotr and Douze, Matthijs and J{'e}gou, H{'e}rve and Mikolov, Tomas},
journal={arXiv preprint arXiv:1612.03651},
year={2016}
}
```
If you use these word vectors, please cite the following paper:
[4] E. Grave\*, P. Bojanowski\*, P. Gupta, A. Joulin, T. Mikolov, [*Learning Word Vectors for 157 Languages*](https://arxiv.org/abs/1802.06893)
```markup
@inproceedings{grave2018learning,
title={Learning Word Vectors for 157 Languages},
author={Grave, Edouard and Bojanowski, Piotr and Gupta, Prakhar and Joulin, Armand and Mikolov, Tomas},
booktitle={Proceedings of the International Conference on Language Resources and Evaluation (LREC 2018)},
year={2018}
}
```
(\* These authors contributed equally.)
|
Davlan/xlm-roberta-base-finetuned-amharic
|
[
"pytorch",
"xlm-roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
{
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"XLMRobertaForMaskedLM"
],
"model_type": "xlm-roberta",
"task_specific_params": {
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}
}
| 401 | 2023-03-19T05:58:22Z |
---
library_name: stable-baselines3
tags:
- PandaReachDense-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachDense-v2
type: PandaReachDense-v2
metrics:
- type: mean_reward
value: -2.03 +/- 0.78
name: mean_reward
verified: false
---
# **A2C** Agent playing **PandaReachDense-v2**
This is a trained model of a **A2C** agent playing **PandaReachDense-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
Davlan/xlm-roberta-base-finetuned-english
|
[
"pytorch",
"xlm-roberta",
"fill-mask",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] |
fill-mask
|
{
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"XLMRobertaForMaskedLM"
],
"model_type": "xlm-roberta",
"task_specific_params": {
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}
}
| 5 | null |
---
license: mit
tags:
- generated_from_trainer
metrics:
- precision
- recall
model-index:
- name: synthetic-70-vsfc-xlm-r
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. -->
# synthetic-70-vsfc-xlm-r
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7726
- Precision: 0.8498
- Recall: 0.4018
- F1 Weighted: 0.5020
- F1 Macro: 0.3865
## 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: 64
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 Weighted | F1 Macro |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:-----------:|:--------:|
| 1.1007 | 0.22 | 25 | 1.1227 | 0.0021 | 0.0461 | 0.0041 | 0.0294 |
| 0.8388 | 0.45 | 50 | 0.7289 | 0.8086 | 0.8105 | 0.8075 | 0.6172 |
| 0.5915 | 0.67 | 75 | 1.0029 | 0.8601 | 0.5155 | 0.6026 | 0.4582 |
| 0.4876 | 0.89 | 100 | 1.5117 | 0.8650 | 0.4820 | 0.5787 | 0.4426 |
| 0.3964 | 1.12 | 125 | 1.3913 | 0.8461 | 0.3973 | 0.4908 | 0.3829 |
| 0.4464 | 1.34 | 150 | 1.3103 | 0.8332 | 0.5130 | 0.5554 | 0.4201 |
| 0.485 | 1.56 | 175 | 1.5958 | 0.8831 | 0.4277 | 0.5336 | 0.4108 |
| 0.4471 | 1.79 | 200 | 1.5570 | 0.8774 | 0.4713 | 0.5527 | 0.4252 |
| 0.3867 | 2.01 | 225 | 1.7726 | 0.8498 | 0.4018 | 0.5020 | 0.3865 |
### Framework versions
- Transformers 4.27.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
|
Davlan/xlm-roberta-base-finetuned-hausa
|
[
"pytorch",
"xlm-roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
{
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"XLMRobertaForMaskedLM"
],
"model_type": "xlm-roberta",
"task_specific_params": {
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}
| 234 | 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: -172.36 +/- 123.06
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': 50000
'learning_rate': 0.00025
'num_envs': 4
'num_steps': 128
'anneal_lr': True
'gae': True
'gamma': 0.99
'gae_lambda': 0.95
'num_minibatches': 4
'update_epochs': 4
'norm_adv': True
'clip_coef': 0.2
'clip_vloss': True
'ent_coef': 0.01
'vf_coef': 0.5
'max_grad_norm': 0.5
'target_kl': None
'repo_id': 'taohoang/ppo-torch-LunarLander-v2'
'batch_size': 512
'minibatch_size': 128}
```
|
Declan/Politico_model_v3
|
[
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
{
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"BertForMaskedLM"
],
"model_type": "bert",
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}
| 5 | null |
---
library_name: ml-agents
tags:
- Pyramids
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Pyramids
---
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-Pyramids
2. Step 1: Find your model_id: seungwoos/ppo-PyramidsRND
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
DeepChem/ChemBERTa-5M-MTR
|
[
"pytorch",
"roberta",
"transformers"
] | null |
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"RobertaForRegression"
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}
| 13 | null |
---
license: creativeml-openrail-m
tags:
- coreml
- stable-diffusion
- text-to-image
---
# Core ML Converted Model:
- This model was converted to [Core ML for use on Apple Silicon devices](https://github.com/apple/ml-stable-diffusion). Conversion instructions can be found [here](https://github.com/godly-devotion/MochiDiffusion/wiki/How-to-convert-ckpt-or-safetensors-files-to-Core-ML).<br>
- Provide the model to an app such as Mochi Diffusion [Github](https://github.com/godly-devotion/MochiDiffusion) - [Discord](https://discord.gg/x2kartzxGv) to generate images.<br>
- `split_einsum` version is compatible with all compute unit options including Neural Engine.
- `original` version is only compatible with CPU & GPU option.
- Custom resolution versions are tagged accordingly.
- The `vae-ft-mse-840000-ema-pruned.ckpt` vae is embedded into the model.
- This model was converted with a `vae-encoder` for i2i.
- This model is fp16.
- Descriptions are posted as-is from original model source.
- Not all features and/or results may be available in CoreML format.
- This model does not have the [unet split into chunks](https://github.com/apple/ml-stable-diffusion#-converting-models-to-core-ml).
- This model does not include a safety checker (for NSFW content).
# Vivid-Watercolors-v10:
Source(s): [CivitAI](https://civitai.com/models/4998/vivid-watercolors)
Introducing my new Vivid Watercolors dreambooth model.
The model is trained with beautiful, artist-agnostic watercolor images using the midjourney method.
The token is: "wtrcolor style"
It can be challenging to use, but with the right prompts, but it can create stunning artwork.
See an example prompt that I use in tests:
wtrcolor style, Digital art of (subject), official art, frontal, smiling, masterpiece, Beautiful, watercolor, face paint, paint splatter, intricate details. Highly detailed, detailed eyes, dripping, trending on artstation by [artist]
Using "watercolor" in the prompt is necessary to get a good watercolor texture, try words like face (paint, paint splatter, dripping).<br><br>
,%20official%20art,%20frontal,%20smiling,%20masterpiece,%20Beautiful,%20((watercolor)),%20face%20pai.png)
%20official%20art,%20frontal,%20smiling,%20masterpiece,%20Beautiful,%20watercolor.png)
|
DeltaHub/adapter_t5-3b_mrpc
|
[
"pytorch",
"transformers"
] | null |
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| 3 | null |
WARNING: this adds the Redwood 2L Attn Only tokenizer for compatibility with transformer lens. I think this implementation is correct, though check against the reference tokenizer here: https://github.com/redwoodresearch/rust_circuit_public/blob/42c3fcbffbc367897d3a810b20c12d7c1c99a00d/python/rust_circuit/module_library.py#L1012
|
DimaOrekhov/cubert-method-name
|
[
"pytorch",
"encoder-decoder",
"text2text-generation",
"transformers",
"autotrain_compatible"
] |
text2text-generation
|
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| 10 | 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: 643.00 +/- 94.61
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga Freddthink -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga Freddthink -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga Freddthink
```
## 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)])
```
|
DivyanshuSheth/T5-Seq2Seq-Final
|
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| 0 | null |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- superb
metrics:
- accuracy
model-index:
- name: wav2vec2-base-finetuned-ks
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-base-finetuned-ks
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the superb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0953
- Accuracy: 0.9834
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.5959 | 1.0 | 399 | 0.4714 | 0.9434 |
| 0.2623 | 2.0 | 798 | 0.1542 | 0.9793 |
| 0.1809 | 3.0 | 1197 | 0.0953 | 0.9834 |
| 0.1643 | 4.0 | 1596 | 0.0844 | 0.9825 |
| 0.1208 | 5.0 | 1995 | 0.0824 | 0.9822 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.13.1+cu116
- Datasets 1.14.0
- Tokenizers 0.13.2
|
Dkwkk/W
|
[] | null |
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}
| 0 | 2023-04-22T14:47:24Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: AlmogMor345/my_awesome_model
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# AlmogMor345/my_awesome_model
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 1.3321
- Validation Loss: 1.3458
- Train Precision: 0.0
- Train Recall: 0.0
- Train F1: 0.0
- Train Accuracy: 0.9140
- Epoch: 99
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 9, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Train Precision | Train Recall | Train F1 | Train Accuracy | Epoch |
|:----------:|:---------------:|:---------------:|:------------:|:--------:|:--------------:|:-----:|
| 2.6690 | 2.1130 | 0.0 | 0.0 | 0.0 | 0.9140 | 0 |
| 1.8194 | 1.5602 | 0.0 | 0.0 | 0.0 | 0.9140 | 1 |
| 1.3472 | 1.3458 | 0.0 | 0.0 | 0.0 | 0.9140 | 2 |
| 1.2568 | 1.3458 | 0.0 | 0.0 | 0.0 | 0.9140 | 3 |
| 1.3789 | 1.3458 | 0.0 | 0.0 | 0.0 | 0.9140 | 4 |
| 1.2069 | 1.3458 | 0.0 | 0.0 | 0.0 | 0.9140 | 5 |
| 1.2804 | 1.3458 | 0.0 | 0.0 | 0.0 | 0.9140 | 6 |
| 1.2624 | 1.3458 | 0.0 | 0.0 | 0.0 | 0.9140 | 7 |
| 1.3139 | 1.3458 | 0.0 | 0.0 | 0.0 | 0.9140 | 8 |
| 1.2605 | 1.3458 | 0.0 | 0.0 | 0.0 | 0.9140 | 9 |
| 1.2763 | 1.3458 | 0.0 | 0.0 | 0.0 | 0.9140 | 10 |
| 1.2969 | 1.3458 | 0.0 | 0.0 | 0.0 | 0.9140 | 11 |
| 1.2780 | 1.3458 | 0.0 | 0.0 | 0.0 | 0.9140 | 12 |
| 1.1983 | 1.3458 | 0.0 | 0.0 | 0.0 | 0.9140 | 13 |
| 1.2138 | 1.3458 | 0.0 | 0.0 | 0.0 | 0.9140 | 14 |
| 1.2663 | 1.3458 | 0.0 | 0.0 | 0.0 | 0.9140 | 15 |
| 1.2843 | 1.3458 | 0.0 | 0.0 | 0.0 | 0.9140 | 16 |
| 1.2251 | 1.3458 | 0.0 | 0.0 | 0.0 | 0.9140 | 17 |
| 1.3197 | 1.3458 | 0.0 | 0.0 | 0.0 | 0.9140 | 18 |
| 1.2989 | 1.3458 | 0.0 | 0.0 | 0.0 | 0.9140 | 19 |
| 1.2213 | 1.3458 | 0.0 | 0.0 | 0.0 | 0.9140 | 20 |
| 1.2360 | 1.3458 | 0.0 | 0.0 | 0.0 | 0.9140 | 21 |
| 1.2389 | 1.3458 | 0.0 | 0.0 | 0.0 | 0.9140 | 22 |
| 1.2087 | 1.3458 | 0.0 | 0.0 | 0.0 | 0.9140 | 23 |
| 1.2446 | 1.3458 | 0.0 | 0.0 | 0.0 | 0.9140 | 24 |
| 1.2931 | 1.3458 | 0.0 | 0.0 | 0.0 | 0.9140 | 25 |
| 1.2623 | 1.3458 | 0.0 | 0.0 | 0.0 | 0.9140 | 26 |
| 1.2253 | 1.3458 | 0.0 | 0.0 | 0.0 | 0.9140 | 27 |
| 1.2853 | 1.3458 | 0.0 | 0.0 | 0.0 | 0.9140 | 28 |
| 1.3132 | 1.3458 | 0.0 | 0.0 | 0.0 | 0.9140 | 29 |
| 1.2183 | 1.3458 | 0.0 | 0.0 | 0.0 | 0.9140 | 30 |
| 1.2482 | 1.3458 | 0.0 | 0.0 | 0.0 | 0.9140 | 31 |
| 1.2190 | 1.3458 | 0.0 | 0.0 | 0.0 | 0.9140 | 32 |
| 1.3112 | 1.3458 | 0.0 | 0.0 | 0.0 | 0.9140 | 33 |
| 1.2852 | 1.3458 | 0.0 | 0.0 | 0.0 | 0.9140 | 34 |
| 1.2445 | 1.3458 | 0.0 | 0.0 | 0.0 | 0.9140 | 35 |
| 1.2528 | 1.3458 | 0.0 | 0.0 | 0.0 | 0.9140 | 36 |
| 1.2339 | 1.3458 | 0.0 | 0.0 | 0.0 | 0.9140 | 37 |
| 1.2053 | 1.3458 | 0.0 | 0.0 | 0.0 | 0.9140 | 38 |
| 1.2210 | 1.3458 | 0.0 | 0.0 | 0.0 | 0.9140 | 39 |
| 1.2258 | 1.3458 | 0.0 | 0.0 | 0.0 | 0.9140 | 40 |
| 1.2772 | 1.3458 | 0.0 | 0.0 | 0.0 | 0.9140 | 41 |
| 1.1695 | 1.3458 | 0.0 | 0.0 | 0.0 | 0.9140 | 42 |
| 1.2562 | 1.3458 | 0.0 | 0.0 | 0.0 | 0.9140 | 43 |
| 1.2413 | 1.3458 | 0.0 | 0.0 | 0.0 | 0.9140 | 44 |
| 1.2390 | 1.3458 | 0.0 | 0.0 | 0.0 | 0.9140 | 45 |
| 1.3280 | 1.3458 | 0.0 | 0.0 | 0.0 | 0.9140 | 46 |
| 1.2614 | 1.3458 | 0.0 | 0.0 | 0.0 | 0.9140 | 47 |
| 1.2350 | 1.3458 | 0.0 | 0.0 | 0.0 | 0.9140 | 48 |
| 1.3510 | 1.3458 | 0.0 | 0.0 | 0.0 | 0.9140 | 49 |
| 1.3331 | 1.3458 | 0.0 | 0.0 | 0.0 | 0.9140 | 50 |
| 1.1755 | 1.3458 | 0.0 | 0.0 | 0.0 | 0.9140 | 51 |
| 1.2463 | 1.3458 | 0.0 | 0.0 | 0.0 | 0.9140 | 52 |
| 1.3322 | 1.3458 | 0.0 | 0.0 | 0.0 | 0.9140 | 53 |
| 1.1857 | 1.3458 | 0.0 | 0.0 | 0.0 | 0.9140 | 54 |
| 1.3005 | 1.3458 | 0.0 | 0.0 | 0.0 | 0.9140 | 55 |
| 1.2379 | 1.3458 | 0.0 | 0.0 | 0.0 | 0.9140 | 56 |
| 1.2763 | 1.3458 | 0.0 | 0.0 | 0.0 | 0.9140 | 57 |
| 1.2821 | 1.3458 | 0.0 | 0.0 | 0.0 | 0.9140 | 58 |
| 1.2670 | 1.3458 | 0.0 | 0.0 | 0.0 | 0.9140 | 59 |
| 1.3589 | 1.3458 | 0.0 | 0.0 | 0.0 | 0.9140 | 60 |
| 1.3354 | 1.3458 | 0.0 | 0.0 | 0.0 | 0.9140 | 61 |
| 1.2851 | 1.3458 | 0.0 | 0.0 | 0.0 | 0.9140 | 62 |
| 1.2417 | 1.3458 | 0.0 | 0.0 | 0.0 | 0.9140 | 63 |
| 1.2591 | 1.3458 | 0.0 | 0.0 | 0.0 | 0.9140 | 64 |
| 1.2056 | 1.3458 | 0.0 | 0.0 | 0.0 | 0.9140 | 65 |
| 1.2569 | 1.3458 | 0.0 | 0.0 | 0.0 | 0.9140 | 66 |
| 1.3113 | 1.3458 | 0.0 | 0.0 | 0.0 | 0.9140 | 67 |
| 1.2131 | 1.3458 | 0.0 | 0.0 | 0.0 | 0.9140 | 68 |
| 1.2395 | 1.3458 | 0.0 | 0.0 | 0.0 | 0.9140 | 69 |
| 1.2507 | 1.3458 | 0.0 | 0.0 | 0.0 | 0.9140 | 70 |
| 1.3242 | 1.3458 | 0.0 | 0.0 | 0.0 | 0.9140 | 71 |
| 1.2997 | 1.3458 | 0.0 | 0.0 | 0.0 | 0.9140 | 72 |
| 1.2895 | 1.3458 | 0.0 | 0.0 | 0.0 | 0.9140 | 73 |
| 1.3044 | 1.3458 | 0.0 | 0.0 | 0.0 | 0.9140 | 74 |
| 1.2696 | 1.3458 | 0.0 | 0.0 | 0.0 | 0.9140 | 75 |
| 1.2138 | 1.3458 | 0.0 | 0.0 | 0.0 | 0.9140 | 76 |
| 1.2914 | 1.3458 | 0.0 | 0.0 | 0.0 | 0.9140 | 77 |
| 1.1968 | 1.3458 | 0.0 | 0.0 | 0.0 | 0.9140 | 78 |
| 1.3639 | 1.3458 | 0.0 | 0.0 | 0.0 | 0.9140 | 79 |
| 1.2451 | 1.3458 | 0.0 | 0.0 | 0.0 | 0.9140 | 80 |
| 1.2949 | 1.3458 | 0.0 | 0.0 | 0.0 | 0.9140 | 81 |
| 1.2724 | 1.3458 | 0.0 | 0.0 | 0.0 | 0.9140 | 82 |
| 1.3940 | 1.3458 | 0.0 | 0.0 | 0.0 | 0.9140 | 83 |
| 1.3156 | 1.3458 | 0.0 | 0.0 | 0.0 | 0.9140 | 84 |
| 1.3080 | 1.3458 | 0.0 | 0.0 | 0.0 | 0.9140 | 85 |
| 1.2519 | 1.3458 | 0.0 | 0.0 | 0.0 | 0.9140 | 86 |
| 1.2222 | 1.3458 | 0.0 | 0.0 | 0.0 | 0.9140 | 87 |
| 1.2304 | 1.3458 | 0.0 | 0.0 | 0.0 | 0.9140 | 88 |
| 1.2843 | 1.3458 | 0.0 | 0.0 | 0.0 | 0.9140 | 89 |
| 1.2523 | 1.3458 | 0.0 | 0.0 | 0.0 | 0.9140 | 90 |
| 1.2531 | 1.3458 | 0.0 | 0.0 | 0.0 | 0.9140 | 91 |
| 1.2974 | 1.3458 | 0.0 | 0.0 | 0.0 | 0.9140 | 92 |
| 1.3301 | 1.3458 | 0.0 | 0.0 | 0.0 | 0.9140 | 93 |
| 1.2726 | 1.3458 | 0.0 | 0.0 | 0.0 | 0.9140 | 94 |
| 1.3171 | 1.3458 | 0.0 | 0.0 | 0.0 | 0.9140 | 95 |
| 1.3577 | 1.3458 | 0.0 | 0.0 | 0.0 | 0.9140 | 96 |
| 1.2373 | 1.3458 | 0.0 | 0.0 | 0.0 | 0.9140 | 97 |
| 1.2556 | 1.3458 | 0.0 | 0.0 | 0.0 | 0.9140 | 98 |
| 1.3321 | 1.3458 | 0.0 | 0.0 | 0.0 | 0.9140 | 99 |
### Framework versions
- Transformers 4.28.1
- TensorFlow 2.12.0
- Datasets 2.11.0
- Tokenizers 0.13.3
|
Doiman/DialoGPT-medium-harrypotter
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] |
conversational
|
{
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"GPT2LMHeadModel"
],
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}
| 13 | null |
---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce_pixelCopter01
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 42.77 +/- 43.19
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
DongHai/DialoGPT-small-rick
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] |
conversational
|
{
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
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}
| 9 | 2023-03-19T13:24:09Z |
---
language:
- en
- fr
- ro
- de
datasets:
- IqWikis
- c4
tags:
- summarization
- translation
license: apache-2.0
---
# Model Card for T5 Base
# Table of Contents
1. [Model Details](#model-details)
2. [Uses](#uses)
3. [Bias, Risks, and Limitations](#bias-risks-and-limitations)
4. [Training Details](#training-details)
5. [Evaluation](#evaluation)
6. [Environmental Impact](#environmental-impact)
7. [Citation](#citation)
8. [Model Card Authors](#model-card-authors)
9. [How To Get Started With the Model](#how-to-get-started-with-the-model)
# Model Details
## Model Description
The developers of the Text-To-Text Transfer Transformer (T5) [write](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html):
> With T5, we propose reframing all NLP tasks into a unified text-to-text-format where the input and output are always text strings, in contrast to BERT-style models that can only output either a class label or a span of the input. Our text-to-text framework allows us to use the same model, loss function, and hyperparameters on any NLP task.
T5-Base is the checkpoint with 220 million parameters.
- **Developed by:** Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, Peter J. Liu. See [associated paper](https://jmlr.org/papers/volume21/20-074/20-074.pdf) and [GitHub repo](https://github.com/google-research/text-to-text-transfer-transformer#released-model-checkpoints)
- **Model type:** Language model
- **Language(s) (NLP):** English, French, Romanian, German
- **License:** Apache 2.0
- **Related Models:** [All T5 Checkpoints](https://huggingface.co/models?search=t5)
- **Resources for more information:**
- [Research paper](https://jmlr.org/papers/volume21/20-074/20-074.pdf)
- [Google's T5 Blog Post](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html)
- [GitHub Repo](https://github.com/google-research/text-to-text-transfer-transformer)
- [Hugging Face T5 Docs](https://huggingface.co/docs/transformers/model_doc/t5)
# Uses
## Direct Use and Downstream Use
The developers write in a [blog post](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) that the model:
> Our text-to-text framework allows us to use the same model, loss function, and hyperparameters on any NLP task, including machine translation, document summarization, question answering, and classification tasks (e.g., sentiment analysis). We can even apply T5 to regression tasks by training it to predict the string representation of a number instead of the number itself.
See the [blog post](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) and [research paper](https://jmlr.org/papers/volume21/20-074/20-074.pdf) for further details.
## Out-of-Scope Use
More information needed.
# Bias, Risks, and Limitations
More information needed.
## Recommendations
More information needed.
# Training Details
## Training Data
The model is pre-trained on the [Colossal Clean Crawled Corpus (C4)](https://www.tensorflow.org/datasets/catalog/c4), which was developed and released in the context of the same [research paper](https://jmlr.org/papers/volume21/20-074/20-074.pdf) as T5.
The model was pre-trained on a on a **multi-task mixture of unsupervised (1.) and supervised tasks (2.)**.
Thereby, the following datasets were being used for (1.) and (2.):
1. **Datasets used for Unsupervised denoising objective**:
- [C4](https://huggingface.co/datasets/c4)
- [Wiki-DPR](https://huggingface.co/datasets/wiki_dpr)
2. **Datasets used for Supervised text-to-text language modeling objective**
- Sentence acceptability judgment
- CoLA [Warstadt et al., 2018](https://arxiv.org/abs/1805.12471)
- Sentiment analysis
- SST-2 [Socher et al., 2013](https://nlp.stanford.edu/~socherr/EMNLP2013_RNTN.pdf)
- Paraphrasing/sentence similarity
- MRPC [Dolan and Brockett, 2005](https://aclanthology.org/I05-5002)
- STS-B [Ceret al., 2017](https://arxiv.org/abs/1708.00055)
- QQP [Iyer et al., 2017](https://quoradata.quora.com/First-Quora-Dataset-Release-Question-Pairs)
- Natural language inference
- MNLI [Williams et al., 2017](https://arxiv.org/abs/1704.05426)
- QNLI [Rajpurkar et al.,2016](https://arxiv.org/abs/1606.05250)
- RTE [Dagan et al., 2005](https://link.springer.com/chapter/10.1007/11736790_9)
- CB [De Marneff et al., 2019](https://semanticsarchive.net/Archive/Tg3ZGI2M/Marneffe.pdf)
- Sentence completion
- COPA [Roemmele et al., 2011](https://www.researchgate.net/publication/221251392_Choice_of_Plausible_Alternatives_An_Evaluation_of_Commonsense_Causal_Reasoning)
- Word sense disambiguation
- WIC [Pilehvar and Camacho-Collados, 2018](https://arxiv.org/abs/1808.09121)
- Question answering
- MultiRC [Khashabi et al., 2018](https://aclanthology.org/N18-1023)
- ReCoRD [Zhang et al., 2018](https://arxiv.org/abs/1810.12885)
- BoolQ [Clark et al., 2019](https://arxiv.org/abs/1905.10044)
## Training Procedure
In their [abstract](https://jmlr.org/papers/volume21/20-074/20-074.pdf), the model developers write:
> In this paper, we explore the landscape of transfer learning techniques for NLP by introducing a unified framework that converts every language problem into a text-to-text format. Our systematic study compares pre-training objectives, architectures, unlabeled datasets, transfer approaches, and other factors on dozens of language understanding tasks.
The framework introduced, the T5 framework, involves a training procedure that brings together the approaches studied in the paper. See the [research paper](https://jmlr.org/papers/volume21/20-074/20-074.pdf) for further details.
# Evaluation
## Testing Data, Factors & Metrics
The developers evaluated the model on 24 tasks, see the [research paper](https://jmlr.org/papers/volume21/20-074/20-074.pdf) for full details.
## Results
For full results for T5-Base, see the [research paper](https://jmlr.org/papers/volume21/20-074/20-074.pdf), Table 14.
# Environmental Impact
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** Google Cloud TPU Pods
- **Hours used:** More information needed
- **Cloud Provider:** GCP
- **Compute Region:** More information needed
- **Carbon Emitted:** More information needed
# Citation
**BibTeX:**
```bibtex
@article{2020t5,
author = {Colin Raffel and Noam Shazeer and Adam Roberts and Katherine Lee and Sharan Narang and Michael Matena and Yanqi Zhou and Wei Li and Peter J. Liu},
title = {Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer},
journal = {Journal of Machine Learning Research},
year = {2020},
volume = {21},
number = {140},
pages = {1-67},
url = {http://jmlr.org/papers/v21/20-074.html}
}
```
**APA:**
- Raffel, C., Shazeer, N., Roberts, A., Lee, K., Narang, S., Matena, M., ... & Liu, P. J. (2020). Exploring the limits of transfer learning with a unified text-to-text transformer. J. Mach. Learn. Res., 21(140), 1-67.
# Model Card Authors
This model card was written by the team at Hugging Face.
# How to Get Started with the Model
Use the code below to get started with the model.
<details>
<summary> Click to expand </summary>
```python
from transformers import T5Tokenizer, T5Model
tokenizer = T5Tokenizer.from_pretrained("t5-base")
model = T5Model.from_pretrained("t5-base")
input_ids = tokenizer(
"Studies have been shown that owning a dog is good for you", return_tensors="pt"
).input_ids # Batch size 1
decoder_input_ids = tokenizer("Studies show that", return_tensors="pt").input_ids # Batch size 1
# forward pass
outputs = model(input_ids=input_ids, decoder_input_ids=decoder_input_ids)
last_hidden_states = outputs.last_hidden_state
```
See the [Hugging Face T5](https://huggingface.co/docs/transformers/model_doc/t5#transformers.T5Model) docs and a [Colab Notebook](https://colab.research.google.com/github/google-research/text-to-text-transfer-transformer/blob/main/notebooks/t5-trivia.ipynb) created by the model developers for more examples.
</details>
|
DongHyoungLee/kogpt2-base-v2-finetuned-kogpt2_nsmc_single_sentence_classification
|
[] | null |
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}
| 0 | 2023-03-19T13:28:55Z |
---
library_name: ml-agents
tags:
- SnowballTarget
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SnowballTarget
---
# **ppo** Agent playing **SnowballTarget**
This is a trained model of a **ppo** agent playing **SnowballTarget** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-SnowballTarget
2. Step 1: Find your model_id: pregonas/ppo-SnowballTarget
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
Donghyun/L2_BERT
|
[] | null |
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}
}
| 0 | 2023-03-19T13:29:56Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: unsupervised-comb-fine-tune-bert-exist
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. -->
# unsupervised-comb-fine-tune-bert-exist
This model is a fine-tuned version of [nouman-10/unsupervised-comb-cased](https://huggingface.co/nouman-10/unsupervised-comb-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6605
- Accuracy: 0.7703
- F1: 0.7703
## 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log | 1.0 | 194 | 0.5093 | 0.7587 | 0.7587 |
| No log | 2.0 | 388 | 0.5265 | 0.7791 | 0.7791 |
| 0.417 | 3.0 | 582 | 0.6628 | 0.7820 | 0.7820 |
| 0.417 | 4.0 | 776 | 1.1558 | 0.7703 | 0.7703 |
| 0.417 | 5.0 | 970 | 1.3917 | 0.7587 | 0.7587 |
| 0.0814 | 6.0 | 1164 | 1.4348 | 0.7616 | 0.7616 |
| 0.0814 | 7.0 | 1358 | 1.5183 | 0.7733 | 0.7733 |
| 0.0092 | 8.0 | 1552 | 1.5807 | 0.7733 | 0.7733 |
| 0.0092 | 9.0 | 1746 | 1.6643 | 0.7703 | 0.7703 |
| 0.0092 | 10.0 | 1940 | 1.6605 | 0.7703 | 0.7703 |
### Framework versions
- Transformers 4.27.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
|
Dongmin/testmodel
|
[
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] |
text2text-generation
|
{
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"T5ForConditionalGeneration"
],
"model_type": "t5",
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"translation_en_to_de": {
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"num_beams": 4,
"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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"early_stopping": true,
"max_length": 300,
"num_beams": 4,
"prefix": "translate English to Romanian: "
}
}
}
| 11 | null |
---
license: cc-by-nc-4.0
datasets:
- Babelscape/wikineural
language:
- de
- fr
- it
- rm
- multilingual
inference: false
tags:
- named-entity-recognition
---
The [SwissBERT](https://huggingface.co/ZurichNLP/swissbert) model fine-tuned on the [WikiNEuRal](https://huggingface.co/datasets/Babelscape/wikineural) dataset for multilingual NER.
Supports German, French and Italian as supervised languages and Romansh Grischun as a zero-shot language.
## Usage
```python
from transformers import pipeline
token_classifier = pipeline(
model="ZurichNLP/swissbert-ner",
aggregation_strategy="simple",
)
```
### German example
```python
token_classifier.model.set_default_language("de_CH")
token_classifier("Mein Name sei Gantenbein.")
```
Output:
```
[{'entity_group': 'PER',
'score': 0.5002625,
'word': 'Gantenbein',
'start': 13,
'end': 24}]
```
### French example
```python
token_classifier.model.set_default_language("fr_CH")
token_classifier("J'habite à Lausanne.")
```
Output:
```
[{'entity_group': 'LOC',
'score': 0.99955386,
'word': 'Lausanne',
'start': 10,
'end': 19}]
```
## Citation
```bibtex
@article{vamvas-etal-2023-swissbert,
title={Swiss{BERT}: The Multilingual Language Model for Switzerland},
author={Jannis Vamvas and Johannes Gra\"en and Rico Sennrich},
year={2023},
eprint={2303.13310},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2303.13310}
}
```
|
Waynehillsdev/Wayne_NLP_mT5
|
[
"pytorch",
"tensorboard",
"mt5",
"text2text-generation",
"dataset:cnn_dailymail",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
] |
text2text-generation
|
{
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"MT5ForConditionalGeneration"
],
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}
| 11 | null |
---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: round2-Pixelcopter-PLE-v0
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 14.50 +/- 6.34
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
Doogie/Waynehills-KE-T5-doogie
|
[] | null |
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}
| 0 | 2023-03-19T13:32:15Z |
This is an experiment for my Lora that focuses on Lora who has the face of Lee Sung Kyung from the Korean actress. This is purely fictional and has nothing to do with the original actress, just inspiration.
Recommendation:
- Set Weight: 0.7 - 1
Term and Conditions:
Ownership: Lora is owned by Astreum and all intellectual property rights are reserved. The use of Lora is subject to the following terms and conditions.
Use: The use of Lora is limited to non-commercial purposes only. This means that you may not sell any artwork or product created using Lora, without the express written permission of Astreum.
Education and Experimentation: Lora is designed to be used for educational and experimental purposes only. You may use Lora to create artwork, designs, or other creative works, but you may not use it to create products for commercial sale.
Attribution: If you use Lora to create any artwork or design, you must attribute the use of Lora in a visible and prominent manner. This can be done by mentioning "Lora" in the credits, or by including a visible link to the website of Astreum in any online publication or documentation of your artwork.
Lee Sung Kyung: The use of Lora in relation to actress Lee Sung Kyung is subject to the following conditions. Lora may not be used to create any content that is defamatory, insulting, or disrespectful towards Lee Sung Kyung. Lora may only be used in a positive and respectful manner towards Lee Sung Kyung.
Disclaimer: The use of Lora is at your own risk. Astreum makes no warranties, express or implied, as to the accuracy, usefulness, or fitness for any particular purpose of Lora. Astreum shall not be liable for any damages, including but not limited to direct, indirect, incidental, or consequential damages or losses arising out of the use of Lora.
Governing Law: These terms and conditions shall be governed by and construed in accordance with the laws, without giving effect to any principles of conflicts of law.
Modification: Astreum reserves the right to modify these terms and conditions at any time, without prior notice.
Acceptance: By using Lora, you agree to be bound by these terms and conditions. If you do not agree to these terms and conditions, you should not use Lora.
---
license: creativeml-openrail-m
---
|
Waynehillsdev/Waynehills-STT-doogie-server
|
[
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"transformers",
"generated_from_trainer",
"license:apache-2.0"
] |
automatic-speech-recognition
|
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"Wav2Vec2ForCTC"
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}
| 61 | null |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: decision-bert-uncased
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. -->
# decision-bert-uncased
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': 2e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
- training_precision: float32
### Training results
### Framework versions
- Transformers 4.27.1
- TensorFlow 2.11.0
- Datasets 2.10.1
- Tokenizers 0.13.2
|
Waynehillsdev/Waynehills_summary_tensorflow
|
[
"tf",
"t5",
"text2text-generation",
"transformers",
"generated_from_keras_callback",
"autotrain_compatible"
] |
text2text-generation
|
{
"architectures": [
"T5ForConditionalGeneration"
],
"model_type": "t5",
"task_specific_params": {
"conversational": {
"max_length": null
},
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}
| 5 | null |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SoccerTwos
library_name: ml-agents
---
# **poca** Agent playing **SoccerTwos**
This is a trained model of a **poca** agent playing **SoccerTwos** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-SoccerTwos
2. Step 1: Write your model_id: JYC333/poca-SoccerTwos
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
Waynehillsdev/wav2vec2-base-timit-demo-colab
|
[
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"transformers",
"generated_from_trainer",
"license:apache-2.0"
] |
automatic-speech-recognition
|
{
"architectures": [
"Wav2Vec2ForCTC"
],
"model_type": "wav2vec2",
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}
}
}
| 5 | null |
---
tags:
- CartPole-v1
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 500.00 +/- 0.00
name: mean_reward
verified: false
---
# (CleanRL) **DQN** Agent Playing **CartPole-v1**
This is a trained model of a DQN agent playing CartPole-v1.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/DQN_buffersize_500000.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[DQN_buffersize_500000]"
python -m cleanrl_utils.enjoy --exp-name DQN_buffersize_500000 --env-id CartPole-v1
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/pfunk/CartPole-v1-DQN_buffersize_500000-seed1/raw/main/dqn.py
curl -OL https://huggingface.co/pfunk/CartPole-v1-DQN_buffersize_500000-seed1/raw/main/pyproject.toml
curl -OL https://huggingface.co/pfunk/CartPole-v1-DQN_buffersize_500000-seed1/raw/main/poetry.lock
poetry install --all-extras
python dqn.py --track --wandb-entity pfunk --wandb-project-name dqpn --capture-video true --save-model true --upload-model true --hf-entity pfunk --exp-name DQN_buffersize_500000 --buffer-size 500000 --seed 1
```
# Hyperparameters
```python
{'alg_type': 'dqn.py',
'batch_size': 256,
'buffer_size': 500000,
'capture_video': True,
'cuda': True,
'end_e': 0.1,
'env_id': 'CartPole-v1',
'exp_name': 'DQN_buffersize_500000',
'exploration_fraction': 0.2,
'gamma': 1.0,
'hf_entity': 'pfunk',
'learning_rate': 0.0001,
'learning_starts': 1000,
'save_model': True,
'seed': 1,
'start_e': 1.0,
'target_network_frequency': 20,
'target_tau': 1.0,
'torch_deterministic': True,
'total_timesteps': 500000,
'track': True,
'train_frequency': 1,
'upload_model': True,
'wandb_entity': 'pfunk',
'wandb_project_name': 'dqpn'}
```
|
Waynehillsdev/waynehills_sentimental_kor
|
[
"pytorch",
"electra",
"text-classification",
"transformers"
] |
text-classification
|
{
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"ElectraForSequenceClassification"
],
"model_type": "electra",
"task_specific_params": {
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},
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}
}
}
| 33 | null |
---
tags:
- CartPole-v1
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 500.00 +/- 0.00
name: mean_reward
verified: false
---
# (CleanRL) **DQN** Agent Playing **CartPole-v1**
This is a trained model of a DQN agent playing CartPole-v1.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/DQN_buffersize_500000.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[DQN_buffersize_500000]"
python -m cleanrl_utils.enjoy --exp-name DQN_buffersize_500000 --env-id CartPole-v1
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/pfunk/CartPole-v1-DQN_buffersize_500000-seed2/raw/main/dqn.py
curl -OL https://huggingface.co/pfunk/CartPole-v1-DQN_buffersize_500000-seed2/raw/main/pyproject.toml
curl -OL https://huggingface.co/pfunk/CartPole-v1-DQN_buffersize_500000-seed2/raw/main/poetry.lock
poetry install --all-extras
python dqn.py --track --wandb-entity pfunk --wandb-project-name dqpn --capture-video true --save-model true --upload-model true --hf-entity pfunk --exp-name DQN_buffersize_500000 --buffer-size 500000 --seed 2
```
# Hyperparameters
```python
{'alg_type': 'dqn.py',
'batch_size': 256,
'buffer_size': 500000,
'capture_video': True,
'cuda': True,
'end_e': 0.1,
'env_id': 'CartPole-v1',
'exp_name': 'DQN_buffersize_500000',
'exploration_fraction': 0.2,
'gamma': 1.0,
'hf_entity': 'pfunk',
'learning_rate': 0.0001,
'learning_starts': 1000,
'save_model': True,
'seed': 2,
'start_e': 1.0,
'target_network_frequency': 20,
'target_tau': 1.0,
'torch_deterministic': True,
'total_timesteps': 500000,
'track': True,
'train_frequency': 1,
'upload_model': True,
'wandb_entity': 'pfunk',
'wandb_project_name': 'dqpn'}
```
|
Doohae/p_encoder
|
[
"pytorch"
] | null |
{
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}
}
}
| 3 | 2023-03-19T13:43:57Z |
---
tags:
- CartPole-v1
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 500.00 +/- 0.00
name: mean_reward
verified: false
---
# (CleanRL) **DQN** Agent Playing **CartPole-v1**
This is a trained model of a DQN agent playing CartPole-v1.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/DQN_buffersize_500000.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[DQN_buffersize_500000]"
python -m cleanrl_utils.enjoy --exp-name DQN_buffersize_500000 --env-id CartPole-v1
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/pfunk/CartPole-v1-DQN_buffersize_500000-seed3/raw/main/dqn.py
curl -OL https://huggingface.co/pfunk/CartPole-v1-DQN_buffersize_500000-seed3/raw/main/pyproject.toml
curl -OL https://huggingface.co/pfunk/CartPole-v1-DQN_buffersize_500000-seed3/raw/main/poetry.lock
poetry install --all-extras
python dqn.py --track --wandb-entity pfunk --wandb-project-name dqpn --capture-video true --save-model true --upload-model true --hf-entity pfunk --exp-name DQN_buffersize_500000 --buffer-size 500000 --seed 3
```
# Hyperparameters
```python
{'alg_type': 'dqn.py',
'batch_size': 256,
'buffer_size': 500000,
'capture_video': True,
'cuda': True,
'end_e': 0.1,
'env_id': 'CartPole-v1',
'exp_name': 'DQN_buffersize_500000',
'exploration_fraction': 0.2,
'gamma': 1.0,
'hf_entity': 'pfunk',
'learning_rate': 0.0001,
'learning_starts': 1000,
'save_model': True,
'seed': 3,
'start_e': 1.0,
'target_network_frequency': 20,
'target_tau': 1.0,
'torch_deterministic': True,
'total_timesteps': 500000,
'track': True,
'train_frequency': 1,
'upload_model': True,
'wandb_entity': 'pfunk',
'wandb_project_name': 'dqpn'}
```
|
Doohae/q_encoder
|
[
"pytorch"
] | null |
{
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}
}
}
| 3 | 2023-03-19T13:43:59Z |
---
tags:
- CartPole-v1
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 477.88 +/- 0.00
name: mean_reward
verified: false
---
# (CleanRL) **DQN** Agent Playing **CartPole-v1**
This is a trained model of a DQN agent playing CartPole-v1.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/DQN_buffersize_500000.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[DQN_buffersize_500000]"
python -m cleanrl_utils.enjoy --exp-name DQN_buffersize_500000 --env-id CartPole-v1
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/pfunk/CartPole-v1-DQN_buffersize_500000-seed4/raw/main/dqn.py
curl -OL https://huggingface.co/pfunk/CartPole-v1-DQN_buffersize_500000-seed4/raw/main/pyproject.toml
curl -OL https://huggingface.co/pfunk/CartPole-v1-DQN_buffersize_500000-seed4/raw/main/poetry.lock
poetry install --all-extras
python dqn.py --track --wandb-entity pfunk --wandb-project-name dqpn --capture-video true --save-model true --upload-model true --hf-entity pfunk --exp-name DQN_buffersize_500000 --buffer-size 500000 --seed 4
```
# Hyperparameters
```python
{'alg_type': 'dqn.py',
'batch_size': 256,
'buffer_size': 500000,
'capture_video': True,
'cuda': True,
'end_e': 0.1,
'env_id': 'CartPole-v1',
'exp_name': 'DQN_buffersize_500000',
'exploration_fraction': 0.2,
'gamma': 1.0,
'hf_entity': 'pfunk',
'learning_rate': 0.0001,
'learning_starts': 1000,
'save_model': True,
'seed': 4,
'start_e': 1.0,
'target_network_frequency': 20,
'target_tau': 1.0,
'torch_deterministic': True,
'total_timesteps': 500000,
'track': True,
'train_frequency': 1,
'upload_model': True,
'wandb_entity': 'pfunk',
'wandb_project_name': 'dqpn'}
```
|
Doquey/DialoGPT-small-Luisbot1
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] |
conversational
|
{
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
"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": {
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"max_length": null
},
"translation_en_to_de": {
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"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
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},
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"prefix": null
}
}
}
| 7 | null |
---
license: creativeml-openrail-m
tags:
- text-to-image
---
### arki-20230319-15-analog-cnst-5000-steps on Stable Diffusion via Dreambooth
#### model by NickKolok
This your the Stable Diffusion model fine-tuned the arki-20230319-15-analog-cnst-5000-steps concept taught to Stable Diffusion with Dreambooth.
#It can be used by modifying the `instance_prompt`: **arki**
You can also train your own concepts and upload them to the library by using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_training.ipynb).
And you can run your new concept via `diffusers`: [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb), [Spaces with the Public Concepts loaded](https://huggingface.co/spaces/sd-dreambooth-library/stable-diffusion-dreambooth-concepts)
|
DoyyingFace/bert-asian-hate-tweets-asian-clean-with-unclean-valid
|
[
"pytorch",
"bert",
"text-classification",
"transformers"
] |
text-classification
|
{
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
"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,
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},
"text-generation": {
"do_sample": null,
"max_length": null
},
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},
"translation_en_to_fr": {
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},
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"max_length": null,
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}
}
| 29 | null |
---
license: creativeml-openrail-m
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
inference: true
---
# FuzzyHazel, FuzzyAlmond
HazyAbyss - <a href="https://huggingface.co/KMAZ/TestSamples/">Download</a><br/>
OctaFuzz - <a href="https://huggingface.co/Lucetepolis/OctaFuzz">Download</a><br/>
MareAcernis - <a href="https://huggingface.co/Lucetepolis/MareAcernis">Download</a><br/>
RefSlaveV2 - <a href="https://huggingface.co/Dorshu/refslaveV2_v2">Download</a><br/>
dlfmaanjffhgkwl v2 - <a href="https://civitai.com/models/9815/dlfmaanjffhgkwl-mix">Download</a><br/>
Guardian Tales 三七-SAL-独轮车 | Chibi Style Lora 52 - <a href="https://civitai.com/models/14274/guardian-tales-sal-or-chibi-style-lora-52">Download</a><br/>
Komowata Haruka (こもわた遙華) Chibi Art Style LoRA - <a href="https://civitai.com/models/9922/komowata-haruka-chibi-art-style-lora">Download</a><br/>
Terada Tera (寺田てら) Art Style LoRA - <a href="https://civitai.com/models/15446/terada-tera-art-style-lora">Download</a><br/>
Yaro Artstyle LoRA - <a href="https://civitai.com/models/8112/yaro-artstyle-lora">Download</a><br/>
EasyNegative and pastelmix-lora seem to work well with the models.
EasyNegative - <a href="https://huggingface.co/datasets/gsdf/EasyNegative">Download</a><br/>
pastelmix-lora - <a href="https://huggingface.co/andite/pastel-mix">Download</a>
# Formula
```
MBW
HazyAbyss.safetensors [d7b0072ef7]
octafuzz.safetensors [364bdf849d]
0000.safetensors
base_alpha=1
Weight_values=1,1,0,0,0,0.5,1,1,0.5,0,0,0,1,0,0,0,0.5,1,1,0.5,0,0,0,1,1
MBW
0000.safetensors [360691971b]
mareacernis.safetensors [fbc82b317d]
0001.safetensors
base_alpha=0
Weight_values=0.5,0,0,0,0,0,0,0,0.5,0.5,0,0,0.25,0.5,0.5,0.5,0.25,0.25,0.25,0.25,0.5,0.5,0.5,0,0
MBW
0001.safetensors [ac67bd1235]
refslavev2.safetensors [cce9a2d200]
0002.safetensors
base_alpha=0
Weight_values=0,0.5,1,1,0.5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1
MBW
0002.safetensors [cc5331b8ae]
dlf.safetensors [d596b45d6b]
FuzzyHazel.safetensors
base_alpha=0
Weight_values=0,0,0,0,0,0,0,0,0,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0
SuperMerger LoRA Merge
model_0 : FuzzyHazel.safetensors
model_Out : FuzzyAlmond.safetensors
LoRa : lora:guardiantales:0.25, lora:komowata:0.25, lora:terada:0.25, lora:yaro:0.25
```
# Samples
All of the images use following negatives/settings. EXIF preserved.
```
Negative prompt: (worst quality, low quality:1.4), EasyNegative, bad anatomy, bad hands, error, missing fingers, extra digit, fewer digits
Steps: 28, Sampler: DPM++ 2M Karras, CFG scale: 7, Size: 768x512, Denoising strength: 0.6, Clip skip: 2, ENSD: 31337, Hires upscale: 1.5, Hires steps: 14, Hires upscaler: Latent (nearest-exact)
```
# FuzzyHazel
# FuzzyAlmond
|
DoyyingFace/bert-asian-hate-tweets-asian-unclean-freeze-12
|
[
"pytorch",
"bert",
"text-classification",
"transformers"
] |
text-classification
|
{
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
"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,
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"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
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},
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},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 29 | null |
---
library_name: ml-agents
tags:
- Pyramids
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Pyramids
---
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-Pyramids
2. Step 1: Find your model_id: kraken2404/ppo-Pyramid-v2
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
DoyyingFace/bert-asian-hate-tweets-asian-unclean-slanted
|
[
"pytorch",
"bert",
"text-classification",
"transformers"
] |
text-classification
|
{
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
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},
"text-generation": {
"do_sample": null,
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},
"translation_en_to_de": {
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"num_beams": null,
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},
"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
}
}
}
| 29 | null |
---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-Cartpole-v1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 500.00 +/- 0.00
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
DoyyingFace/bert-asian-hate-tweets-asian-unclean-warmup-75
|
[
"pytorch",
"bert",
"text-classification",
"transformers"
] |
text-classification
|
{
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
"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
}
}
}
| 37 | null |
---
library_name: transformers
tags:
- summarization
---
|
DoyyingFace/bert-asian-hate-tweets-asonam-clean
|
[
"pytorch",
"bert",
"text-classification",
"transformers"
] |
text-classification
|
{
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
"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
}
}
}
| 27 | null |
---
tags:
- translation3
- generated_from_trainer
datasets:
- ccmatrix
metrics:
- bleu
model-index:
- name: opus-mt-tc-big-ar-en
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: ccmatrix
type: ccmatrix
config: ar-en
split: train[894700:1000000]
args: ar-en
metrics:
- name: Bleu
type: bleu
value: 57.411941954107235
---
<!-- 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. -->
# opus-mt-tc-big-ar-en
This model was trained from scratch on the ccmatrix dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6565
- Bleu: 57.4119
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.27.3
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
|
DoyyingFace/bert-asian-hate-tweets-concat-clean-with-unclean-valid
|
[
"pytorch",
"bert",
"text-classification",
"transformers"
] |
text-classification
|
{
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
"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,
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"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 25 | null |
---
library_name: ml-agents
tags:
- Pyramids
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Pyramids
---
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-Pyramids
2. Step 1: Find your model_id: pregonas/ppo-Pyramids-Training
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
albert-base-v1
|
[
"pytorch",
"tf",
"safetensors",
"albert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1909.11942",
"transformers",
"exbert",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] |
fill-mask
|
{
"architectures": [
"AlbertForMaskedLM"
],
"model_type": "albert",
"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": {
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},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 38,156 | 2023-03-19T14:20:20Z |
---
datasets:
- cQueenccc/Vivian-Blip-Captions
language:
- en
pipeline_tag: text-to-image
---
# Disclaimer
This was inspired from https://github.com/YaYaB/finetune-diffusion
# Model Card for Finetuning Stable Diffusion on Vivian Maier's photographs
The main goal is to fine-tune the Stable Diffusion model to generate images reflecting the distinct photographic style of Vivian Maier.
And I chose to utilize a Jupyter Notebook to make the fine-tuning process accessible and easy to understand, particularly for those new to the diffusion pipeline and hugging face API.
# Requirements
To launch the finetuning with a batch_size of 1 you need to have a gpu with at least 24G VRAM (you can use accumulating gradient to simulate higher batch size)
Make sure that you have enough disk space, the model uses ~11Gb
## Examples(at epoch 90)
> A woman walking down a street
> a group of people getting on a bus
> two man working on a constructing site
## Citation
If you use this dataset, please cite it as:
```
@misc{cqueenccc2023vivian,
author = {cQueenccc},
title = {Finetuning Stable Diffusion on Vivian Maier's photographs},
year={2023},
howpublished= {\url{https://huggingface.co/cQueenccc/Fine-Tune-Diffusion-Vivian/}}
}
```
|
albert-large-v1
|
[
"pytorch",
"tf",
"albert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1909.11942",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] |
fill-mask
|
{
"architectures": [
"AlbertForMaskedLM"
],
"model_type": "albert",
"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
}
}
}
| 687 | 2023-03-19T14:23:07Z |
---
tags:
- CartPole-v1
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 399.76 +/- 0.00
name: mean_reward
verified: false
---
# (CleanRL) **DQN** Agent Playing **CartPole-v1**
This is a trained model of a DQN agent playing CartPole-v1.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/DQN_new.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[DQN_new]"
python -m cleanrl_utils.enjoy --exp-name DQN_new --env-id CartPole-v1
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/pfunk/CartPole-v1-DQN_new-seed1/raw/main/dqn.py
curl -OL https://huggingface.co/pfunk/CartPole-v1-DQN_new-seed1/raw/main/pyproject.toml
curl -OL https://huggingface.co/pfunk/CartPole-v1-DQN_new-seed1/raw/main/poetry.lock
poetry install --all-extras
python dqn.py --track --wandb-entity pfunk --wandb-project-name dqpn --capture-video true --save-model true --upload-model true --hf-entity pfunk --exp-name DQN_new --seed 1
```
# Hyperparameters
```python
{'alg_type': 'dqn.py',
'batch_size': 64,
'buffer_size': 50000,
'capture_video': True,
'cuda': True,
'end_e': 0.3,
'env_id': 'CartPole-v1',
'exp_name': 'DQN_new',
'exploration_fraction': 0.2,
'gamma': 1.0,
'hf_entity': 'pfunk',
'learning_rate': 0.0005,
'learning_starts': 500,
'save_model': True,
'seed': 1,
'start_e': 1.0,
'target_network_frequency': 10,
'target_tau': 1.0,
'torch_deterministic': True,
'total_timesteps': 500000,
'track': True,
'train_frequency': 1,
'upload_model': True,
'wandb_entity': 'pfunk',
'wandb_project_name': 'dqpn'}
```
|
albert-large-v2
|
[
"pytorch",
"tf",
"safetensors",
"albert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1909.11942",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] |
fill-mask
|
{
"architectures": [
"AlbertForMaskedLM"
],
"model_type": "albert",
"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
}
}
}
| 26,792 | 2023-03-19T14:23:18Z |
---
tags:
- CartPole-v1
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 461.91 +/- 0.00
name: mean_reward
verified: false
---
# (CleanRL) **DQN** Agent Playing **CartPole-v1**
This is a trained model of a DQN agent playing CartPole-v1.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/DQN_new.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[DQN_new]"
python -m cleanrl_utils.enjoy --exp-name DQN_new --env-id CartPole-v1
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/pfunk/CartPole-v1-DQN_new-seed2/raw/main/dqn.py
curl -OL https://huggingface.co/pfunk/CartPole-v1-DQN_new-seed2/raw/main/pyproject.toml
curl -OL https://huggingface.co/pfunk/CartPole-v1-DQN_new-seed2/raw/main/poetry.lock
poetry install --all-extras
python dqn.py --track --wandb-entity pfunk --wandb-project-name dqpn --capture-video true --save-model true --upload-model true --hf-entity pfunk --exp-name DQN_new --seed 2
```
# Hyperparameters
```python
{'alg_type': 'dqn.py',
'batch_size': 64,
'buffer_size': 50000,
'capture_video': True,
'cuda': True,
'end_e': 0.3,
'env_id': 'CartPole-v1',
'exp_name': 'DQN_new',
'exploration_fraction': 0.2,
'gamma': 1.0,
'hf_entity': 'pfunk',
'learning_rate': 0.0005,
'learning_starts': 500,
'save_model': True,
'seed': 2,
'start_e': 1.0,
'target_network_frequency': 10,
'target_tau': 1.0,
'torch_deterministic': True,
'total_timesteps': 500000,
'track': True,
'train_frequency': 1,
'upload_model': True,
'wandb_entity': 'pfunk',
'wandb_project_name': 'dqpn'}
```
|
albert-xlarge-v1
|
[
"pytorch",
"tf",
"albert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1909.11942",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] |
fill-mask
|
{
"architectures": [
"AlbertForMaskedLM"
],
"model_type": "albert",
"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
}
}
}
| 341 | 2023-03-19T14:23:23Z |
---
tags:
- CartPole-v1
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 406.75 +/- 0.00
name: mean_reward
verified: false
---
# (CleanRL) **DQN** Agent Playing **CartPole-v1**
This is a trained model of a DQN agent playing CartPole-v1.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/DQN_new.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[DQN_new]"
python -m cleanrl_utils.enjoy --exp-name DQN_new --env-id CartPole-v1
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/pfunk/CartPole-v1-DQN_new-seed3/raw/main/dqn.py
curl -OL https://huggingface.co/pfunk/CartPole-v1-DQN_new-seed3/raw/main/pyproject.toml
curl -OL https://huggingface.co/pfunk/CartPole-v1-DQN_new-seed3/raw/main/poetry.lock
poetry install --all-extras
python dqn.py --track --wandb-entity pfunk --wandb-project-name dqpn --capture-video true --save-model true --upload-model true --hf-entity pfunk --exp-name DQN_new --seed 3
```
# Hyperparameters
```python
{'alg_type': 'dqn.py',
'batch_size': 64,
'buffer_size': 50000,
'capture_video': True,
'cuda': True,
'end_e': 0.3,
'env_id': 'CartPole-v1',
'exp_name': 'DQN_new',
'exploration_fraction': 0.2,
'gamma': 1.0,
'hf_entity': 'pfunk',
'learning_rate': 0.0005,
'learning_starts': 500,
'save_model': True,
'seed': 3,
'start_e': 1.0,
'target_network_frequency': 10,
'target_tau': 1.0,
'torch_deterministic': True,
'total_timesteps': 500000,
'track': True,
'train_frequency': 1,
'upload_model': True,
'wandb_entity': 'pfunk',
'wandb_project_name': 'dqpn'}
```
|
albert-xlarge-v2
|
[
"pytorch",
"tf",
"albert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1909.11942",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] |
fill-mask
|
{
"architectures": [
"AlbertForMaskedLM"
],
"model_type": "albert",
"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
}
}
}
| 2,973 | 2023-03-19T14:23:24Z |
---
tags:
- CartPole-v1
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 134.99 +/- 0.00
name: mean_reward
verified: false
---
# (CleanRL) **DQN** Agent Playing **CartPole-v1**
This is a trained model of a DQN agent playing CartPole-v1.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/DQN_new.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[DQN_new]"
python -m cleanrl_utils.enjoy --exp-name DQN_new --env-id CartPole-v1
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/pfunk/CartPole-v1-DQN_new-seed4/raw/main/dqn.py
curl -OL https://huggingface.co/pfunk/CartPole-v1-DQN_new-seed4/raw/main/pyproject.toml
curl -OL https://huggingface.co/pfunk/CartPole-v1-DQN_new-seed4/raw/main/poetry.lock
poetry install --all-extras
python dqn.py --track --wandb-entity pfunk --wandb-project-name dqpn --capture-video true --save-model true --upload-model true --hf-entity pfunk --exp-name DQN_new --seed 4
```
# Hyperparameters
```python
{'alg_type': 'dqn.py',
'batch_size': 64,
'buffer_size': 50000,
'capture_video': True,
'cuda': True,
'end_e': 0.3,
'env_id': 'CartPole-v1',
'exp_name': 'DQN_new',
'exploration_fraction': 0.2,
'gamma': 1.0,
'hf_entity': 'pfunk',
'learning_rate': 0.0005,
'learning_starts': 500,
'save_model': True,
'seed': 4,
'start_e': 1.0,
'target_network_frequency': 10,
'target_tau': 1.0,
'torch_deterministic': True,
'total_timesteps': 500000,
'track': True,
'train_frequency': 1,
'upload_model': True,
'wandb_entity': 'pfunk',
'wandb_project_name': 'dqpn'}
```
|
albert-xxlarge-v1
|
[
"pytorch",
"tf",
"albert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1909.11942",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] |
fill-mask
|
{
"architectures": [
"AlbertForMaskedLM"
],
"model_type": "albert",
"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
}
}
}
| 7,091 | 2023-03-19T14:23:51Z |
---
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="hruslen/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"])
```
|
albert-xxlarge-v2
|
[
"pytorch",
"tf",
"safetensors",
"albert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1909.11942",
"transformers",
"exbert",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] |
fill-mask
|
{
"architectures": [
"AlbertForMaskedLM"
],
"model_type": "albert",
"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
}
}
}
| 42,640 | 2023-03-19T14:24:13Z |
---
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="LozanoJohan/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"])
```
|
bert-base-cased-finetuned-mrpc
|
[
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible",
"has_space"
] |
fill-mask
|
{
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"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
}
}
}
| 11,644 | 2023-03-19T14:30:10Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.54 +/- 2.73
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="hruslen/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"])
```
|
bert-base-chinese
|
[
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"fill-mask",
"zh",
"arxiv:1810.04805",
"transformers",
"autotrain_compatible",
"has_space"
] |
fill-mask
|
{
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"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
}
}
}
| 3,377,486 | 2023-03-19T14:32:15Z |
---
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: 257.22 +/- 19.50
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
...
```
|
bert-base-german-dbmdz-uncased
|
[
"pytorch",
"jax",
"safetensors",
"bert",
"fill-mask",
"de",
"transformers",
"license:mit",
"autotrain_compatible",
"has_space"
] |
fill-mask
|
{
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"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
}
}
}
| 68,305 | 2023-03-19T14:36:35Z |
---
tags:
- conversational
---
# Aubrey from OMORI Model
|
bert-base-uncased
|
[
"pytorch",
"tf",
"jax",
"rust",
"safetensors",
"bert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1810.04805",
"transformers",
"exbert",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] |
fill-mask
|
{
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"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
}
}
}
| 59,663,489 | 2023-03-19T14:39:34Z |
---
library_name: stable-baselines3
tags:
- AntBulletEnv-v0
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: AntBulletEnv-v0
type: AntBulletEnv-v0
metrics:
- type: mean_reward
value: 1200.23 +/- 311.70
name: mean_reward
verified: false
---
# **A2C** Agent playing **AntBulletEnv-v0**
This is a trained model of a **A2C** agent playing **AntBulletEnv-v0**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
bert-large-cased-whole-word-masking-finetuned-squad
|
[
"pytorch",
"tf",
"jax",
"rust",
"safetensors",
"bert",
"question-answering",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1810.04805",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] |
question-answering
|
{
"architectures": [
"BertForQuestionAnswering"
],
"model_type": "bert",
"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
}
}
}
| 8,214 | 2023-03-19T14:40:25Z |
---
license: cc-by-4.0
metrics:
- bleu4
- meteor
- rouge-l
- bertscore
- moverscore
language: ko
datasets:
- lmqg/qg_koquad
pipeline_tag: text2text-generation
tags:
- question generation
widget:
- text: "1990년 영화 《 <hl> 남부군 <hl> 》에서 단역으로 영화배우 첫 데뷔에 이어 같은 해 KBS 드라마 《지구인》에서 단역으로 출연하였고 이듬해 MBC 《여명의 눈동자》를 통해 단역으로 출연하였다."
example_title: "Question Generation Example 1"
- text: "백신이 없기때문에 예방책은 <hl> 살충제 <hl> 를 사용하면서 서식 장소(찻찬 받침, 배수로, 고인 물의 열린 저장소, 버려진 타이어 등)의 수를 줄임으로써 매개체를 통제할 수 있다."
example_title: "Question Generation Example 2"
- text: "<hl> 원테이크 촬영 <hl> 이기 때문에 한 사람이 실수를 하면 처음부터 다시 찍어야 하는 상황이 발생한다."
example_title: "Question Generation Example 3"
model-index:
- name: vocabtrimmer/mt5-small-trimmed-ko-60000-koquad-qg
results:
- task:
name: Text2text Generation
type: text2text-generation
dataset:
name: lmqg/qg_koquad
type: default
args: default
metrics:
- name: BLEU4 (Question Generation)
type: bleu4_question_generation
value: 11.1
- name: ROUGE-L (Question Generation)
type: rouge_l_question_generation
value: 26.7
- name: METEOR (Question Generation)
type: meteor_question_generation
value: 28.4
- name: BERTScore (Question Generation)
type: bertscore_question_generation
value: 83.43
- name: MoverScore (Question Generation)
type: moverscore_question_generation
value: 82.96
---
# Model Card of `vocabtrimmer/mt5-small-trimmed-ko-60000-koquad-qg`
This model is fine-tuned version of [ckpts/mt5-small-trimmed-ko-60000](https://huggingface.co/ckpts/mt5-small-trimmed-ko-60000) for question generation task on the [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-generation).
### Overview
- **Language model:** [ckpts/mt5-small-trimmed-ko-60000](https://huggingface.co/ckpts/mt5-small-trimmed-ko-60000)
- **Language:** ko
- **Training data:** [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) (default)
- **Online Demo:** [https://autoqg.net/](https://autoqg.net/)
- **Repository:** [https://github.com/asahi417/lm-question-generation](https://github.com/asahi417/lm-question-generation)
- **Paper:** [https://arxiv.org/abs/2210.03992](https://arxiv.org/abs/2210.03992)
### Usage
- With [`lmqg`](https://github.com/asahi417/lm-question-generation#lmqg-language-model-for-question-generation-)
```python
from lmqg import TransformersQG
# initialize model
model = TransformersQG(language="ko", model="vocabtrimmer/mt5-small-trimmed-ko-60000-koquad-qg")
# model prediction
questions = model.generate_q(list_context="1990년 영화 《 남부군 》에서 단역으로 영화배우 첫 데뷔에 이어 같은 해 KBS 드라마 《지구인》에서 단역으로 출연하였고 이듬해 MBC 《여명의 눈동자》를 통해 단역으로 출연하였다.", list_answer="남부군")
```
- With `transformers`
```python
from transformers import pipeline
pipe = pipeline("text2text-generation", "vocabtrimmer/mt5-small-trimmed-ko-60000-koquad-qg")
output = pipe("1990년 영화 《 <hl> 남부군 <hl> 》에서 단역으로 영화배우 첫 데뷔에 이어 같은 해 KBS 드라마 《지구인》에서 단역으로 출연하였고 이듬해 MBC 《여명의 눈동자》를 통해 단역으로 출연하였다.")
```
## Evaluation
- ***Metric (Question Generation)***: [raw metric file](https://huggingface.co/vocabtrimmer/mt5-small-trimmed-ko-60000-koquad-qg/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_koquad.default.json)
| | Score | Type | Dataset |
|:-----------|--------:|:--------|:-----------------------------------------------------------------|
| BERTScore | 83.43 | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) |
| Bleu_1 | 26.36 | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) |
| Bleu_2 | 19.38 | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) |
| Bleu_3 | 14.59 | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) |
| Bleu_4 | 11.1 | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) |
| METEOR | 28.4 | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) |
| MoverScore | 82.96 | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) |
| ROUGE_L | 26.7 | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) |
## Training hyperparameters
The following hyperparameters were used during fine-tuning:
- dataset_path: lmqg/qg_koquad
- dataset_name: default
- input_types: paragraph_answer
- output_types: question
- prefix_types: None
- model: ckpts/mt5-small-trimmed-ko-60000
- max_length: 512
- max_length_output: 32
- epoch: 12
- batch: 16
- lr: 0.001
- fp16: False
- random_seed: 1
- gradient_accumulation_steps: 4
- label_smoothing: 0.15
The full configuration can be found at [fine-tuning config file](https://huggingface.co/vocabtrimmer/mt5-small-trimmed-ko-60000-koquad-qg/raw/main/trainer_config.json).
## Citation
```
@inproceedings{ushio-etal-2022-generative,
title = "{G}enerative {L}anguage {M}odels for {P}aragraph-{L}evel {Q}uestion {G}eneration",
author = "Ushio, Asahi and
Alva-Manchego, Fernando and
Camacho-Collados, Jose",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2022",
address = "Abu Dhabi, U.A.E.",
publisher = "Association for Computational Linguistics",
}
```
|
bert-large-cased-whole-word-masking
|
[
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1810.04805",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] |
fill-mask
|
{
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
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},
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},
"text-generation": {
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},
"translation_en_to_de": {
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},
"translation_en_to_fr": {
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},
"translation_en_to_ro": {
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}
}
}
| 2,316 | 2023-03-19T14:40:31Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-de
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xtreme
type: xtreme
config: PAN-X.de
split: validation
args: PAN-X.de
metrics:
- name: F1
type: f1
value: 0.8638300289723342
---
<!-- 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 4.27.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
|
bert-large-uncased
|
[
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1810.04805",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] |
fill-mask
|
{
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
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},
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}
}
}
| 1,058,496 | 2023-03-19T14:44:42Z |
---
language: ja
license: apache-2.0
tags:
- sentence-transformers
- sentence-bert
- sentence-luke
- feature-extraction
- sentence-similarity
---
This is a Japanese sentence-LUKE model.
日本語用Sentence-LUKEモデルです。
[日本語Sentence-BERTモデル](https://huggingface.co/sonoisa/sentence-bert-base-ja-mean-tokens-v2)と同一のデータセットと設定で学習しました。
手元の非公開データセットでは、[日本語Sentence-BERTモデル](https://huggingface.co/sonoisa/sentence-bert-base-ja-mean-tokens-v2)と比べて定量的な精度が同等〜0.5pt程度高く、定性的な精度は本モデルの方が高い結果でした。
事前学習済みモデルとして[studio-ousia/luke-japanese-base-lite](https://huggingface.co/studio-ousia/luke-japanese-base-lite)を利用させていただきました。
推論の実行にはSentencePieceが必要です（pip install sentencepiece）。
# 使い方
```python
from transformers import MLukeTokenizer, LukeModel
import torch
class SentenceLukeJapanese:
def __init__(self, model_name_or_path, device=None):
self.tokenizer = MLukeTokenizer.from_pretrained(model_name_or_path)
self.model = LukeModel.from_pretrained(model_name_or_path)
self.model.eval()
if device is None:
device = "cuda" if torch.cuda.is_available() else "cpu"
self.device = torch.device(device)
self.model.to(device)
def _mean_pooling(self, model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
@torch.no_grad()
def encode(self, sentences, batch_size=8):
all_embeddings = []
iterator = range(0, len(sentences), batch_size)
for batch_idx in iterator:
batch = sentences[batch_idx:batch_idx + batch_size]
encoded_input = self.tokenizer.batch_encode_plus(batch, padding="longest",
truncation=True, return_tensors="pt").to(self.device)
model_output = self.model(**encoded_input)
sentence_embeddings = self._mean_pooling(model_output, encoded_input["attention_mask"]).to('cpu')
all_embeddings.extend(sentence_embeddings)
return torch.stack(all_embeddings)
MODEL_NAME = "sonoisa/sentence-luke-japanese-base-lite"
model = SentenceLukeJapanese(MODEL_NAME)
sentences = ["暴走したAI", "暴走した人工知能"]
sentence_embeddings = model.encode(sentences, batch_size=8)
print("Sentence embeddings:", sentence_embeddings)
```
|
camembert-base
|
[
"pytorch",
"tf",
"safetensors",
"camembert",
"fill-mask",
"fr",
"dataset:oscar",
"arxiv:1911.03894",
"transformers",
"license:mit",
"autotrain_compatible",
"has_space"
] |
fill-mask
|
{
"architectures": [
"CamembertForMaskedLM"
],
"model_type": "camembert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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"prefix": null
},
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},
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"prefix": null
},
"translation_en_to_fr": {
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},
"translation_en_to_ro": {
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}
}
}
| 1,440,898 | 2023-03-19T14:46:20Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: wav2vec2-large-xls-r-1b-wolof-VoiceToText
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-1b-wolof-VoiceToText
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3951
- Wer: 0.3838
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 3.2047 | 14.29 | 400 | 0.4485 | 0.4602 |
| 0.1987 | 28.57 | 800 | 0.3951 | 0.3838 |
### Framework versions
- Transformers 4.27.1
- Pytorch 1.10.0+cu113
- Datasets 1.18.3
- Tokenizers 0.13.2
|
ctrl
|
[
"pytorch",
"tf",
"ctrl",
"en",
"arxiv:1909.05858",
"arxiv:1910.09700",
"transformers",
"license:bsd-3-clause",
"has_space"
] | null |
{
"architectures": null,
"model_type": "ctrl",
"task_specific_params": {
"conversational": {
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},
"summarization": {
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"max_length": null,
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},
"text-generation": {
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"max_length": null
},
"translation_en_to_de": {
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},
"translation_en_to_fr": {
"early_stopping": null,
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"prefix": null
},
"translation_en_to_ro": {
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"max_length": null,
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"prefix": null
}
}
}
| 17,007 | 2023-03-19T14:47:44Z |
---
license: cc0-1.0
---
两个不同倾向的画风模，着重眼睛
例图：
|
distilbert-base-uncased-distilled-squad
|
[
"pytorch",
"tf",
"tflite",
"coreml",
"safetensors",
"distilbert",
"question-answering",
"en",
"dataset:squad",
"arxiv:1910.01108",
"arxiv:1910.09700",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] |
question-answering
|
{
"architectures": [
"DistilBertForQuestionAnswering"
],
"model_type": "distilbert",
"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": {
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"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
}
}
}
| 100,097 | 2023-03-19T14:57:02Z |
---
license: mit
tags:
- generated_from_trainer
model-index:
- name: xlmr-wmt20qe1-en-zh-trial1
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. -->
# xlmr-wmt20qe1-en-zh-trial1
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6455
- R Squared: 0.1176
- Mae: 0.5938
- Pearson R: 0.4682
## 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: 1986
- 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 | R Squared | Mae | Pearson R |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:---------:|
| No log | 1.0 | 438 | 0.6270 | 0.1428 | 0.6065 | 0.4297 |
| 0.7343 | 2.0 | 876 | 0.5950 | 0.1866 | 0.5788 | 0.4913 |
| 0.5661 | 3.0 | 1314 | 0.6455 | 0.1176 | 0.5938 | 0.4682 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu117
- Datasets 2.9.0
- Tokenizers 0.13.2
|
gpt2
|
[
"pytorch",
"tf",
"jax",
"tflite",
"rust",
"safetensors",
"gpt2",
"text-generation",
"en",
"doi:10.57967/hf/0039",
"transformers",
"exbert",
"license:mit",
"has_space"
] |
text-generation
|
{
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"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": true,
"max_length": 50
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
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},
"translation_en_to_ro": {
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"max_length": null,
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}
}
}
| 21,488,226 | 2023-03-19T15:06:32Z |
---
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: 582.00 +/- 198.92
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga Viswes -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga Viswes -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga Viswes
```
## 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)])
```
|
xlm-roberta-large-finetuned-conll02-spanish
|
[
"pytorch",
"rust",
"xlm-roberta",
"fill-mask",
"multilingual",
"af",
"am",
"ar",
"as",
"az",
"be",
"bg",
"bn",
"br",
"bs",
"ca",
"cs",
"cy",
"da",
"de",
"el",
"en",
"eo",
"es",
"et",
"eu",
"fa",
"fi",
"fr",
"fy",
"ga",
"gd",
"gl",
"gu",
"ha",
"he",
"hi",
"hr",
"hu",
"hy",
"id",
"is",
"it",
"ja",
"jv",
"ka",
"kk",
"km",
"kn",
"ko",
"ku",
"ky",
"la",
"lo",
"lt",
"lv",
"mg",
"mk",
"ml",
"mn",
"mr",
"ms",
"my",
"ne",
"nl",
"no",
"om",
"or",
"pa",
"pl",
"ps",
"pt",
"ro",
"ru",
"sa",
"sd",
"si",
"sk",
"sl",
"so",
"sq",
"sr",
"su",
"sv",
"sw",
"ta",
"te",
"th",
"tl",
"tr",
"ug",
"uk",
"ur",
"uz",
"vi",
"xh",
"yi",
"zh",
"arxiv:1911.02116",
"arxiv:1910.09700",
"transformers",
"autotrain_compatible",
"has_space"
] |
fill-mask
|
{
"architectures": [
"XLMRobertaForMaskedLM"
],
"model_type": "xlm-roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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"length_penalty": null,
"max_length": null,
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},
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},
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}
}
| 444 | 2023-03-19T15:31:34Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-de
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xtreme
type: xtreme
config: PAN-X.de
split: validation
args: PAN-X.de
metrics:
- name: F1
type: f1
value: 0.8633240588268695
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-de
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1350
- F1: 0.8633
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2586 | 1.0 | 525 | 0.1608 | 0.8206 |
| 0.1256 | 2.0 | 1050 | 0.1344 | 0.8455 |
| 0.0802 | 3.0 | 1575 | 0.1350 | 0.8633 |
### Framework versions
- Transformers 4.27.1
- Pytorch 2.0.0+cu117
- Datasets 2.10.1
- Tokenizers 0.13.2
|
202015004/wav2vec2-base-timit-demo-colab
|
[
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"transformers",
"generated_from_trainer",
"license:apache-2.0"
] |
automatic-speech-recognition
|
{
"architectures": [
"Wav2Vec2ForCTC"
],
"model_type": "wav2vec2",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
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}
| 2 | 2023-03-19T16:49:59Z |
---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: doom_health_gathering_supreme
type: doom_health_gathering_supreme
metrics:
- type: mean_reward
value: 11.74 +/- 5.92
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **doom_health_gathering_supreme** environment.
This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory.
Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/
## Downloading the model
After installing Sample-Factory, download the model with:
```
python -m sample_factory.huggingface.load_from_hub -r juansebashr/rl_course_vizdoom_health_gathering_supreme
```
## Using the model
To run the model after download, use the `enjoy` script corresponding to this environment:
```
python -m .usr.local.lib.python3.9.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme
```
You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag.
See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details
## Training with this model
To continue training with this model, use the `train` script corresponding to this environment:
```
python -m .usr.local.lib.python3.9.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme --restart_behavior=resume --train_for_env_steps=10000000000
```
Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
|
2umm3r/distilbert-base-uncased-finetuned-cola
|
[
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"dataset:glue",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] |
text-classification
|
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| 36 | 2023-03-19T16:56:07Z |
---
tags:
- CartPole-v1
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 500.00 +/- 0.00
name: mean_reward
verified: false
---
# (CleanRL) **DQN** Agent Playing **CartPole-v1**
This is a trained model of a DQN agent playing CartPole-v1.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/DQN_ef_0.15.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[DQN_ef_0.15]"
python -m cleanrl_utils.enjoy --exp-name DQN_ef_0.15 --env-id CartPole-v1
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/pfunk/CartPole-v1-DQN_ef_0.15-seed4/raw/main/dqn.py
curl -OL https://huggingface.co/pfunk/CartPole-v1-DQN_ef_0.15-seed4/raw/main/pyproject.toml
curl -OL https://huggingface.co/pfunk/CartPole-v1-DQN_ef_0.15-seed4/raw/main/poetry.lock
poetry install --all-extras
python dqn.py --track --wandb-entity pfunk --wandb-project-name dqpn --capture-video true --save-model true --upload-model true --hf-entity pfunk --exp-name DQN_ef_0.15 --exploration-fraction 0.15 --seed 4
```
# Hyperparameters
```python
{'alg_type': 'dqn.py',
'batch_size': 256,
'buffer_size': 300000,
'capture_video': True,
'cuda': True,
'end_e': 0.1,
'env_id': 'CartPole-v1',
'exp_name': 'DQN_ef_0.15',
'exploration_fraction': 0.15,
'gamma': 1.0,
'hf_entity': 'pfunk',
'learning_rate': 0.0001,
'learning_starts': 1000,
'save_model': True,
'seed': 4,
'start_e': 1.0,
'target_network_frequency': 20,
'target_tau': 1.0,
'torch_deterministic': True,
'total_timesteps': 500000,
'track': True,
'train_frequency': 1,
'upload_model': True,
'wandb_entity': 'pfunk',
'wandb_project_name': 'dqpn'}
```
|
9pinus/macbert-base-chinese-medical-collation
|
[
"pytorch",
"bert",
"token-classification",
"zh",
"transformers",
"Token Classification",
"license:apache-2.0",
"autotrain_compatible"
] |
token-classification
|
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| 63 | 2023-03-19T17:20:51Z |
---
license: mit
tags:
- generated_from_trainer
model-index:
- name: xlmr-wmt20qe1-ro-en-trial2
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. -->
# xlmr-wmt20qe1-ro-en-trial2
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4347
- R Squared: 0.5206
- Mae: 0.4679
- Pearson R: 0.7651
## 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: 2020
- 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 | R Squared | Mae | Pearson R |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:---------:|
| No log | 1.0 | 438 | 0.4185 | 0.5384 | 0.4976 | 0.7415 |
| 0.5999 | 2.0 | 876 | 0.4169 | 0.5402 | 0.4719 | 0.7592 |
| 0.3258 | 3.0 | 1314 | 0.4347 | 0.5206 | 0.4679 | 0.7651 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu117
- Datasets 2.9.0
- Tokenizers 0.13.2
|
AT/distilroberta-base-finetuned-wikitext2
|
[
"pytorch",
"tensorboard",
"roberta",
"fill-mask",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible"
] |
fill-mask
|
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"RobertaForMaskedLM"
],
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}
| 9 | 2023-03-19T19:25:32Z |
---
license: mit
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: roberta-base-finetuned-cvbest2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-base-finetuned-cvbest2
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1263
- Precision: 0.6839
- Recall: 0.7805
- F1: 0.7290
- Accuracy: 0.9674
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 17 | 0.4054 | 0.5 | 0.0010 | 0.0019 | 0.9038 |
| No log | 2.0 | 34 | 0.2859 | 0.3247 | 0.2660 | 0.2924 | 0.9201 |
| No log | 3.0 | 51 | 0.2169 | 0.3832 | 0.5774 | 0.4606 | 0.9358 |
| No log | 4.0 | 68 | 0.1691 | 0.4744 | 0.6634 | 0.5532 | 0.9504 |
| No log | 5.0 | 85 | 0.1571 | 0.5145 | 0.7360 | 0.6057 | 0.9495 |
| No log | 6.0 | 102 | 0.1458 | 0.5905 | 0.7669 | 0.6672 | 0.9596 |
| No log | 7.0 | 119 | 0.1304 | 0.6293 | 0.7718 | 0.6933 | 0.9630 |
| No log | 8.0 | 136 | 0.1284 | 0.6664 | 0.7901 | 0.7230 | 0.9666 |
| No log | 9.0 | 153 | 0.1263 | 0.6839 | 0.7805 | 0.7290 | 0.9674 |
| No log | 10.0 | 170 | 0.1295 | 0.6699 | 0.7930 | 0.7263 | 0.9669 |
### Framework versions
- Transformers 4.27.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
|
AdapterHub/roberta-base-pf-duorc_p
|
[
"roberta",
"en",
"dataset:duorc",
"arxiv:2104.08247",
"adapter-transformers",
"question-answering"
] |
question-answering
|
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| 2 | null |
---
library_name: ml-agents
tags:
- Pyramids
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Pyramids
---
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-Pyramids
2. Step 1: Find your model_id: jinukoo/ppo-PyramidsRND
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
AdapterHub/roberta-base-pf-qqp
|
[
"roberta",
"en",
"arxiv:2104.08247",
"adapter-transformers",
"text-classification",
"adapterhub:sts/qqp"
] |
text-classification
|
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}
| 0 | null |
# `vocabtrimmer/xlm-roberta-base-trimmed-es-5000-tweet-sentiment-es`
This model is a fine-tuned version of [/home/asahi/lm-vocab-trimmer/ckpts/xlm-roberta-base-trimmed-es-5000](https://huggingface.co//home/asahi/lm-vocab-trimmer/ckpts/xlm-roberta-base-trimmed-es-5000) on the
[cardiffnlp/tweet_sentiment_multilingual](https://huggingface.co/datasets/cardiffnlp/tweet_sentiment_multilingual) (spanish).
Following metrics are computed on the `test` split of
[cardiffnlp/tweet_sentiment_multilingual](https://huggingface.co/datasets/cardiffnlp/tweet_sentiment_multilingual)(spanish).
| | eval_f1_micro | eval_recall_micro | eval_precision_micro | eval_f1_macro | eval_recall_macro | eval_precision_macro | eval_accuracy |
|---:|----------------:|--------------------:|-----------------------:|----------------:|--------------------:|-----------------------:|----------------:|
| 0 | 61.61 | 61.61 | 61.61 | 60.38 | 61.61 | 61.51 | 61.61 |
Check the result file [here](https://huggingface.co/vocabtrimmer/xlm-roberta-base-trimmed-es-5000-tweet-sentiment-es/raw/main/eval.json).
|
AdapterHub/roberta-base-pf-yelp_polarity
|
[
"roberta",
"en",
"dataset:yelp_polarity",
"arxiv:2104.08247",
"adapter-transformers",
"text-classification"
] |
text-classification
|
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},
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| 1 | null |
---
license: apache-2.0
tags:
- Question Answering
metrics:
- squad
widget:
- text: |
Teste
#model-index:
#- name: consciousAI/question-answering-roberta-base-s-v2
# results: []
---
# Question Answering
The model is intended to be used for Q&A task, given the question & context, the model would attempt to infer the answer text, answer span & confidence score.<br>
Model is encoder-only (deepset/roberta-base-squad2) with QuestionAnswering LM Head, fine-tuned on SQUADx dataset with **exact_match:** 84.83 & **f1:** 91.80 performance scores.
[Live Demo: Question Answering Encoders vs Generative](https://huggingface.co/spaces/consciousAI/question_answering)
Please follow this link for [Encoder based Question Answering V1](https://huggingface.co/consciousAI/question-answering-roberta-base-s/)
<br>Please follow this link for [Generative Question Answering](https://huggingface.co/consciousAI/question-answering-generative-t5-v1-base-s-q-c/)
Example code:
```
from transformers import pipeline
model_checkpoint = "consciousAI/question-answering-roberta-base-s-v2"
context = """
🤗 Transformers is backed by the three most popular deep learning libraries — Jax, PyTorch and TensorFlow — with a seamless integration
between them. It's straightforward to train your models with one before loading them for inference with the other.
"""
question = "Which deep learning libraries back 🤗 Transformers?"
question_answerer = pipeline("question-answering", model=model_checkpoint)
question_answerer(question=question, context=context)
```
## Training and evaluation data
SQUAD Split
## Training procedure
Preprocessing:
1. SQUAD Data longer chunks were sub-chunked with input context max-length 384 tokens and stride as 128 tokens.
2. Target answers readjusted for sub-chunks, sub-chunks with no-answers or partial answers were set to target answer span as (0,0)
Metrics:
1. Adjusted accordingly to handle sub-chunking.
2. n best = 20
3. skip answers with length zero or higher than max answer length (30)
### Training hyperparameters
Custom Training Loop:
The following hyperparameters were used during training:
- learning_rate: 2e-5
- train_batch_size: 32
- eval_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
{'exact_match': 84.83443708609272, 'f1': 91.79987545811638}
### Framework versions
- Transformers 4.23.0.dev0
- Pytorch 1.12.1+cu113
- Datasets 2.5.2
- Tokenizers 0.13.0
|
Ahmad/parsT5
|
[
"jax",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] |
text2text-generation
|
{
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"T5ForConditionalGeneration"
],
"model_type": "t5",
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}
| 12 | null |
---
tags:
- CartPole-v1
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: DQPN_freq
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 500.00 +/- 0.00
name: mean_reward
verified: false
---
# (CleanRL) **DQPN_freq** Agent Playing **CartPole-v1**
This is a trained model of a DQPN_freq agent playing CartPole-v1.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/CP_DQPN_x1.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[CP_DQPN_x1]"
python -m cleanrl_utils.enjoy --exp-name CP_DQPN_x1 --env-id CartPole-v1
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/pfunk/CartPole-v1-CP_DQPN_x1-seed2/raw/main/dqpn_freq.py
curl -OL https://huggingface.co/pfunk/CartPole-v1-CP_DQPN_x1-seed2/raw/main/pyproject.toml
curl -OL https://huggingface.co/pfunk/CartPole-v1-CP_DQPN_x1-seed2/raw/main/poetry.lock
poetry install --all-extras
python dqpn_freq.py --track --wandb-entity pfunk --wandb-project-name dqpn --capture-video true --save-model true --upload-model true --hf-entity pfunk --exp-name CP_DQPN_x1 --policy-network-frequency 20 --seed 2
```
# Hyperparameters
```python
{'alg_type': 'dqpn_freq.py',
'batch_size': 256,
'buffer_size': 300000,
'capture_video': True,
'cuda': True,
'end_e': 0.1,
'env_id': 'CartPole-v1',
'exp_name': 'CP_DQPN_x1',
'exploration_fraction': 0.2,
'gamma': 1.0,
'hf_entity': 'pfunk',
'learning_rate': 0.0001,
'learning_starts': 1000,
'policy_network_frequency': 20,
'policy_tau': 1.0,
'save_model': True,
'seed': 2,
'start_e': 1.0,
'target_network_frequency': 20,
'target_tau': 1.0,
'torch_deterministic': True,
'total_timesteps': 500000,
'track': True,
'train_frequency': 1,
'upload_model': True,
'wandb_entity': 'pfunk',
'wandb_project_name': 'dqpn'}
```
|
Aleksandar/bert-srb-ner-setimes-lr
|
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}
| 0 | null |
---
license: artistic-2.0
---
The description below was created using machine translation
Merged Pastel Mix, oil paint trained model and stable diffusion 1.5 default model.
An oil painting-inspired anime-style model with bright, vibrant colors and a soft brushstroke.
Use the OIL PAINT prompt to blur outlines and make colors more colorful. If you don't use the oil paint prompts, the lines are relatively bold and the colors are a bit muted.
|
Aleksandar/distilbert-srb-ner-setimes-lr
|
[] | null |
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}
| 0 | null |
---
license: apache-2.0
language:
- zh
library_name: transformers
tags:
- Roberta
- Chinese Pre-trained Language Model
---
Please use 'XLMRoberta' related functions to load this model!
# MigBERT | 中文混合粒度预训练模型
[Character, Word, or Both? Revisiting the Segmentation Granularity for Chinese Pre-trained Language Models](https://arxiv.org/abs/2303.10893)
# Demo | 使用样例
https://github.com/xnliang98/MigBERT
# Citation
如果你觉得我们的工作对你有用，请在您的工作中引用我们的文章。
If you find our resource or paper is useful, please consider including the following citation in your paper.
```
@misc{liang2023character,
title={Character, Word, or Both? Revisiting the Segmentation Granularity for Chinese Pre-trained Language Models},
author={Xinnian Liang and Zefan Zhou and Hui Huang and Shuangzhi Wu and Tong Xiao and Muyun Yang and Zhoujun Li and Chao Bian},
year={2023},
eprint={2303.10893},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
Aleksandar/distilbert-srb-ner-setimes
|
[
"pytorch",
"distilbert",
"token-classification",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
] |
token-classification
|
{
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"DistilBertForTokenClassification"
],
"model_type": "distilbert",
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}
}
| 3 | 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="msp3887/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"])
```
|
Aleksandar1932/gpt2-hip-hop
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers"
] |
text-generation
|
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"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
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}
}
}
| 8 | null |
---
language:
- en
license: other
tags:
- stable-diffusion
- text-to-image
- art
- artistic
- diffusers
inference: true
---
# NeverEnding Dream (NED)
## Official Repository
Read more about this model here: https://civitai.com/models/10028/neverending-dream-ned
Also please support by giving 5 stars and a heart, which will notify new updates.
Also consider supporting me on Patreon or ByuMeACoffee
- https://www.patreon.com/Lykon275
- https://www.buymeacoffee.com/lykon
You can run this model on:
- https://sinkin.ai/m/qGdxrYG
Some sample output:
|
Aleksandar1932/gpt2-rock-124439808
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers"
] |
text-generation
|
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"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
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},
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},
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},
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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: 7.52 +/- 2.77
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="msp3887/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"])
```
|
AlekseyKorshuk/horror-scripts
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers"
] |
text-generation
|
{
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
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},
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},
"text-generation": {
"do_sample": true,
"max_length": 50
},
"translation_en_to_de": {
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},
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}
| 19 | null |
---
thumbnail:
tags:
- conversational
license: mit
---
# DialoGPT Trained on the Speech of a Light Novel Character
Chat with the model:
```python
from transformers import AutoTokenizer, AutoModelWithLMHead
tokenizer = AutoTokenizer.from_pretrained("MarinHinawa/DialoGPT-medium-haruka")
model = AutoModelWithLMHead.from_pretrained("MarinHinawa/DialoGPT-medium-haruka")
# Let's chat for 4 lines
for step in range(4):
# encode the new user input, add the eos_token and return a tensor in Pytorch
new_user_input_ids = tokenizer.encode(input(">> User:") + tokenizer.eos_token, return_tensors='pt')
# print(new_user_input_ids)
# append the new user input tokens to the chat history
bot_input_ids = torch.cat([chat_history_ids, new_user_input_ids], dim=-1) if step > 0 else new_user_input_ids
# generated a response while limiting the total chat history to 1000 tokens,
chat_history_ids = model.generate(
bot_input_ids, max_length=200,
pad_token_id=tokenizer.eos_token_id,
no_repeat_ngram_size=3,
do_sample=True,
top_k=100,
top_p=0.7,
temperature=0.8
)
# pretty print last ouput tokens from bot
print("EneBot: {}".format(tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True)))
```
|
AlekseyKulnevich/Pegasus-HeaderGeneration
|
[
"pytorch",
"pegasus",
"text2text-generation",
"transformers",
"autotrain_compatible"
] |
text2text-generation
|
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"PegasusForConditionalGeneration"
],
"model_type": "pegasus",
"task_specific_params": {
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},
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}
| 8 | null |
---
library_name: stable-baselines3
tags:
- AntBulletEnv-v0
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: AntBulletEnv-v0
type: AntBulletEnv-v0
metrics:
- type: mean_reward
value: 965.31 +/- 242.23
name: mean_reward
verified: false
---
# **A2C** Agent playing **AntBulletEnv-v0**
This is a trained model of a **A2C** agent playing **AntBulletEnv-v0**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
AlexN/xls-r-300m-fr-0
|
[
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"fr",
"dataset:mozilla-foundation/common_voice_8_0",
"transformers",
"mozilla-foundation/common_voice_8_0",
"generated_from_trainer",
"robust-speech-event",
"hf-asr-leaderboard",
"license:apache-2.0",
"model-index"
] |
automatic-speech-recognition
|
{
"architectures": [
"Wav2Vec2ForCTC"
],
"model_type": "wav2vec2",
"task_specific_params": {
"conversational": {
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},
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}
| 4 | null |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: finetuning-sentiment-model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuning-sentiment-model
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: 0.2945
- Accuracy: 0.8984
- F1: 0.9018
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
### Framework versions
- Transformers 4.27.2
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
|
AlexeyIgnatov/albert-xlarge-v2-squad-v2
|
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| 0 | null |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Shailza/final_huggingface
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. -->
# Shailza/final_huggingface
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 5.6030
- Validation Loss: 5.0251
- 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: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 40, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 5.6030 | 5.0251 | 0 |
### Framework versions
- Transformers 4.26.1
- TensorFlow 2.11.0
- Datasets 2.10.1
- Tokenizers 0.13.2
|
AliPotter24/a
|
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| 0 | null |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: labor_space_bert
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. -->
# labor_space_bert
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 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: 10.0
### Training results
### Framework versions
- Transformers 4.15.0
- Pytorch 2.0.0+cu118
- Datasets 2.8.0
- Tokenizers 0.10.3
|
Alireza1044/bert_classification_lm
|
[
"pytorch",
"bert",
"text-classification",
"transformers"
] |
text-classification
|
{
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"BertForSequenceClassification"
],
"model_type": "bert",
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},
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}
| 35 | null |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: clinico-bsc-bio-ehr-es
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. -->
# clinico-bsc-bio-ehr-es
This model is a fine-tuned version of [PlanTL-GOB-ES/bsc-bio-ehr-es](https://huggingface.co/PlanTL-GOB-ES/bsc-bio-ehr-es) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9988
- Precision: 0.4916
- Recall: 0.6526
- F1: 0.5608
- Accuracy: 0.8586
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 100
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 25 | 1.2185 | 0.0189 | 0.0359 | 0.0247 | 0.6197 |
| No log | 2.0 | 50 | 0.7442 | 0.1562 | 0.1975 | 0.1744 | 0.7996 |
| No log | 3.0 | 75 | 0.6502 | 0.2108 | 0.2640 | 0.2344 | 0.8180 |
| No log | 4.0 | 100 | 0.6404 | 0.3453 | 0.4572 | 0.3935 | 0.8258 |
| No log | 5.0 | 125 | 0.6131 | 0.3639 | 0.4657 | 0.4085 | 0.8303 |
| No log | 6.0 | 150 | 0.6123 | 0.3356 | 0.4256 | 0.3752 | 0.8341 |
| No log | 7.0 | 175 | 0.6093 | 0.3411 | 0.4498 | 0.3880 | 0.8370 |
| No log | 8.0 | 200 | 0.6198 | 0.3840 | 0.4931 | 0.4318 | 0.8379 |
| No log | 9.0 | 225 | 0.6490 | 0.3878 | 0.5037 | 0.4382 | 0.8378 |
| No log | 10.0 | 250 | 0.6653 | 0.3810 | 0.5005 | 0.4327 | 0.8371 |
| No log | 11.0 | 275 | 0.6456 | 0.3223 | 0.4847 | 0.3872 | 0.8387 |
| No log | 12.0 | 300 | 0.6475 | 0.3377 | 0.4847 | 0.3981 | 0.8474 |
| No log | 13.0 | 325 | 0.6620 | 0.4004 | 0.5734 | 0.4716 | 0.8506 |
| No log | 14.0 | 350 | 0.6798 | 0.3914 | 0.5649 | 0.4624 | 0.8533 |
| No log | 15.0 | 375 | 0.6880 | 0.3969 | 0.5671 | 0.4670 | 0.8520 |
| No log | 16.0 | 400 | 0.7012 | 0.4192 | 0.5913 | 0.4906 | 0.8551 |
| No log | 17.0 | 425 | 0.7224 | 0.4143 | 0.5924 | 0.4876 | 0.8517 |
| No log | 18.0 | 450 | 0.7510 | 0.4302 | 0.6051 | 0.5029 | 0.8553 |
| No log | 19.0 | 475 | 0.7388 | 0.4271 | 0.6030 | 0.5 | 0.8532 |
| 0.3652 | 20.0 | 500 | 0.7524 | 0.4374 | 0.6125 | 0.5103 | 0.8569 |
| 0.3652 | 21.0 | 525 | 0.7408 | 0.4427 | 0.6082 | 0.5125 | 0.8580 |
| 0.3652 | 22.0 | 550 | 0.7430 | 0.4448 | 0.6125 | 0.5153 | 0.8610 |
| 0.3652 | 23.0 | 575 | 0.7726 | 0.4193 | 0.6093 | 0.4968 | 0.8582 |
| 0.3652 | 24.0 | 600 | 0.7876 | 0.4316 | 0.6061 | 0.5042 | 0.8562 |
| 0.3652 | 25.0 | 625 | 0.7777 | 0.4620 | 0.6294 | 0.5329 | 0.8595 |
| 0.3652 | 26.0 | 650 | 0.8009 | 0.4521 | 0.6272 | 0.5254 | 0.8570 |
| 0.3652 | 27.0 | 675 | 0.8153 | 0.4583 | 0.6378 | 0.5333 | 0.8572 |
| 0.3652 | 28.0 | 700 | 0.8215 | 0.4611 | 0.6262 | 0.5311 | 0.8580 |
| 0.3652 | 29.0 | 725 | 0.8296 | 0.4699 | 0.6336 | 0.5396 | 0.8595 |
| 0.3652 | 30.0 | 750 | 0.8174 | 0.4597 | 0.6378 | 0.5343 | 0.8603 |
| 0.3652 | 31.0 | 775 | 0.8442 | 0.4765 | 0.6410 | 0.5466 | 0.8599 |
| 0.3652 | 32.0 | 800 | 0.8281 | 0.4646 | 0.6315 | 0.5354 | 0.8610 |
| 0.3652 | 33.0 | 825 | 0.8322 | 0.4583 | 0.6389 | 0.5337 | 0.8591 |
| 0.3652 | 34.0 | 850 | 0.8153 | 0.4559 | 0.6272 | 0.528 | 0.8623 |
| 0.3652 | 35.0 | 875 | 0.8529 | 0.4861 | 0.6294 | 0.5486 | 0.8589 |
| 0.3652 | 36.0 | 900 | 0.8826 | 0.4699 | 0.6272 | 0.5373 | 0.8559 |
| 0.3652 | 37.0 | 925 | 0.8856 | 0.4654 | 0.6325 | 0.5363 | 0.8571 |
| 0.3652 | 38.0 | 950 | 0.8983 | 0.4819 | 0.6315 | 0.5466 | 0.8560 |
| 0.3652 | 39.0 | 975 | 0.8723 | 0.4641 | 0.6272 | 0.5335 | 0.8556 |
| 0.0269 | 40.0 | 1000 | 0.8788 | 0.4662 | 0.6399 | 0.5394 | 0.8550 |
| 0.0269 | 41.0 | 1025 | 0.8952 | 0.4805 | 0.6378 | 0.5481 | 0.8611 |
| 0.0269 | 42.0 | 1050 | 0.8901 | 0.4657 | 0.6304 | 0.5357 | 0.8574 |
| 0.0269 | 43.0 | 1075 | 0.9015 | 0.4746 | 0.6410 | 0.5454 | 0.8574 |
| 0.0269 | 44.0 | 1100 | 0.8838 | 0.4655 | 0.6420 | 0.5397 | 0.8591 |
| 0.0269 | 45.0 | 1125 | 0.9093 | 0.4718 | 0.6441 | 0.5446 | 0.8598 |
| 0.0269 | 46.0 | 1150 | 0.9154 | 0.4826 | 0.6441 | 0.5518 | 0.8553 |
| 0.0269 | 47.0 | 1175 | 0.9214 | 0.4614 | 0.6315 | 0.5332 | 0.8538 |
| 0.0269 | 48.0 | 1200 | 0.9313 | 0.4639 | 0.6315 | 0.5349 | 0.8546 |
| 0.0269 | 49.0 | 1225 | 0.9137 | 0.4807 | 0.6431 | 0.5501 | 0.8582 |
| 0.0269 | 50.0 | 1250 | 0.9235 | 0.4939 | 0.6463 | 0.5599 | 0.8571 |
| 0.0269 | 51.0 | 1275 | 0.9263 | 0.4900 | 0.6441 | 0.5566 | 0.8580 |
| 0.0269 | 52.0 | 1300 | 0.9190 | 0.4787 | 0.6420 | 0.5485 | 0.8613 |
| 0.0269 | 53.0 | 1325 | 0.9159 | 0.4700 | 0.6441 | 0.5434 | 0.8616 |
| 0.0269 | 54.0 | 1350 | 0.9302 | 0.4806 | 0.6399 | 0.5489 | 0.8614 |
| 0.0269 | 55.0 | 1375 | 0.9391 | 0.4877 | 0.6515 | 0.5579 | 0.8581 |
| 0.0269 | 56.0 | 1400 | 0.9392 | 0.4959 | 0.6452 | 0.5608 | 0.8580 |
| 0.0269 | 57.0 | 1425 | 0.9444 | 0.4798 | 0.6410 | 0.5488 | 0.8570 |
| 0.0269 | 58.0 | 1450 | 0.9394 | 0.4777 | 0.6441 | 0.5486 | 0.8596 |
| 0.0269 | 59.0 | 1475 | 0.9562 | 0.4833 | 0.6420 | 0.5515 | 0.8586 |
| 0.0098 | 60.0 | 1500 | 0.9485 | 0.4801 | 0.6484 | 0.5517 | 0.8582 |
| 0.0098 | 61.0 | 1525 | 0.9521 | 0.4679 | 0.6463 | 0.5428 | 0.8582 |
| 0.0098 | 62.0 | 1550 | 0.9603 | 0.4759 | 0.6463 | 0.5481 | 0.8563 |
| 0.0098 | 63.0 | 1575 | 0.9663 | 0.4831 | 0.6473 | 0.5532 | 0.8561 |
| 0.0098 | 64.0 | 1600 | 0.9641 | 0.4780 | 0.6526 | 0.5518 | 0.8580 |
| 0.0098 | 65.0 | 1625 | 0.9607 | 0.4767 | 0.6494 | 0.5498 | 0.8606 |
| 0.0098 | 66.0 | 1650 | 0.9782 | 0.4849 | 0.6463 | 0.5541 | 0.8563 |
| 0.0098 | 67.0 | 1675 | 0.9806 | 0.4916 | 0.6484 | 0.5592 | 0.8562 |
| 0.0098 | 68.0 | 1700 | 0.9728 | 0.4889 | 0.6494 | 0.5578 | 0.8578 |
| 0.0098 | 69.0 | 1725 | 0.9766 | 0.4885 | 0.6494 | 0.5576 | 0.8584 |
| 0.0098 | 70.0 | 1750 | 0.9738 | 0.4862 | 0.6526 | 0.5573 | 0.8575 |
| 0.0098 | 71.0 | 1775 | 0.9788 | 0.4916 | 0.6505 | 0.56 | 0.8571 |
| 0.0098 | 72.0 | 1800 | 0.9845 | 0.4845 | 0.6452 | 0.5534 | 0.8563 |
| 0.0098 | 73.0 | 1825 | 0.9729 | 0.4876 | 0.6463 | 0.5559 | 0.8573 |
| 0.0098 | 74.0 | 1850 | 0.9854 | 0.4846 | 0.6494 | 0.5551 | 0.8569 |
| 0.0098 | 75.0 | 1875 | 0.9903 | 0.4885 | 0.6505 | 0.5580 | 0.8562 |
| 0.0098 | 76.0 | 1900 | 0.9825 | 0.4886 | 0.6558 | 0.5600 | 0.8568 |
| 0.0098 | 77.0 | 1925 | 0.9994 | 0.4876 | 0.6463 | 0.5559 | 0.8554 |
| 0.0098 | 78.0 | 1950 | 0.9922 | 0.4905 | 0.6515 | 0.5596 | 0.8546 |
| 0.0098 | 79.0 | 1975 | 1.0084 | 0.4928 | 0.6484 | 0.5600 | 0.8578 |
| 0.0057 | 80.0 | 2000 | 0.9931 | 0.4976 | 0.6526 | 0.5646 | 0.8580 |
| 0.0057 | 81.0 | 2025 | 0.9864 | 0.4826 | 0.6452 | 0.5522 | 0.8595 |
| 0.0057 | 82.0 | 2050 | 0.9929 | 0.4900 | 0.6484 | 0.5582 | 0.8595 |
| 0.0057 | 83.0 | 2075 | 0.9902 | 0.4916 | 0.6473 | 0.5588 | 0.8588 |
| 0.0057 | 84.0 | 2100 | 1.0021 | 0.4872 | 0.6431 | 0.5544 | 0.8573 |
| 0.0057 | 85.0 | 2125 | 1.0013 | 0.4964 | 0.6473 | 0.5619 | 0.8582 |
| 0.0057 | 86.0 | 2150 | 0.9814 | 0.4865 | 0.6484 | 0.5559 | 0.8625 |
| 0.0057 | 87.0 | 2175 | 0.9841 | 0.4932 | 0.6558 | 0.5630 | 0.8622 |
| 0.0057 | 88.0 | 2200 | 0.9888 | 0.4866 | 0.6515 | 0.5571 | 0.8610 |
| 0.0057 | 89.0 | 2225 | 0.9898 | 0.4924 | 0.6515 | 0.5609 | 0.8610 |
| 0.0057 | 90.0 | 2250 | 0.9860 | 0.4870 | 0.6526 | 0.5578 | 0.8607 |
| 0.0057 | 91.0 | 2275 | 0.9925 | 0.4912 | 0.6484 | 0.5589 | 0.8589 |
| 0.0057 | 92.0 | 2300 | 0.9904 | 0.4956 | 0.6536 | 0.5638 | 0.8599 |
| 0.0057 | 93.0 | 2325 | 0.9902 | 0.4980 | 0.6526 | 0.5649 | 0.8602 |
| 0.0057 | 94.0 | 2350 | 0.9925 | 0.5041 | 0.6547 | 0.5696 | 0.8602 |
| 0.0057 | 95.0 | 2375 | 0.9959 | 0.4897 | 0.6515 | 0.5591 | 0.8589 |
| 0.0057 | 96.0 | 2400 | 0.9951 | 0.4901 | 0.6505 | 0.5590 | 0.8591 |
| 0.0057 | 97.0 | 2425 | 0.9962 | 0.4924 | 0.6505 | 0.5605 | 0.8588 |
| 0.0057 | 98.0 | 2450 | 0.9972 | 0.5008 | 0.6505 | 0.5659 | 0.8585 |
| 0.0057 | 99.0 | 2475 | 0.9988 | 0.4920 | 0.6526 | 0.5611 | 0.8588 |
| 0.0045 | 100.0 | 2500 | 0.9988 | 0.4916 | 0.6526 | 0.5608 | 0.8586 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0
- Datasets 2.8.0
- Tokenizers 0.12.1
|
Alireza1044/dwight_bert_lm
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers"
] |
text-generation
|
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"GPT2LMHeadModel"
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| 14 | 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: 269.70 +/- 22.74
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
...
```
|
Alireza1044/michael_bert_lm
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers"
] |
text-generation
|
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"GPT2LMHeadModel"
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},
"text-generation": {
"do_sample": true,
"max_length": 50
},
"translation_en_to_de": {
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},
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}
| 10 | null |
---
license: bigscience-bloom-rail-1.0
---
This model is based on [bigscience/bloomz-7b1-mt](https://huggingface.co/bigscience/bloom-7b1). To make it more accessible and efficient for certain Chinese , we have pruned its original vocabulary from 250,880 tokens to 46,145 tokens using Chinese corpus data as follow [bloom-6b4-zh](https://huggingface.co/Langboat/bloom-6b4-zh). This reduction in vocabulary size has helped to significantly reduce the GPU memory usage required to run the model. As a result, the total number of parameters in the model is now 6 billion 4.
基于 [bigscience/bloomz-7b1-mt](https://huggingface.co/bigscience/bloom-7b1)，修建embeddings层到 46145，主要保留中文相关的tokens映射。修建后参数为6B4。
# How to use
```python
from transformers import BloomTokenizerFast, BloomForCausalLM
tokenizer = BloomTokenizerFast.from_pretrained('enze/bloomz-6b4-zh')
model = BloomForCausalLM.from_pretrained('enze/bloomz-6b4-zh')
print(tokenizer.batch_decode(model.generate(tokenizer.encode('中国的首都是', return_tensors='pt'))))
```
|
Alvenir/wav2vec2-base-da
|
[
"pytorch",
"wav2vec2",
"pretraining",
"da",
"transformers",
"speech",
"license:apache-2.0"
] | null |
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"Wav2Vec2ForPreTraining"
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}
| 62 | null |
---
license: mit
datasets:
- Wojood
tags:
- Named Entity Recognition
- Arabic NER
- Nested NER
language:
- ar
metrics:
- f1
- precision
- recall
library_name: http://github.com/SinaLab/ArabicNER
---
## Wojood - Nested/Flat Arabic NER Models
Wojood is a corpus for Arabic nested Named Entity Recognition (NER). Nested entities occur when one entity mention is embedded inside another entity mention. 550K tokens (MSA and dialect) This repo contains the source-code to train Wojood nested NER.
Online Demo
You can try our model using the demo link below
https://ontology.birzeit.edu/Wojood/
### Models
* Nested NER (main branch), with micro-F1 score of 0.909551
* Flat NER (flat branch), with micro-F1 score 0.883847
### Google Colab Notebooks
You can test our model using our Google Colab notebooks
* Train flat NER: https://gist.github.com/mohammedkhalilia/72c3261734d7715094089bdf4de74b4a
* Evaluate your model using flat NER model: https://gist.github.com/mohammedkhalilia/c807eb1ccb15416b187c32a362001665
* Train nested NER: https://gist.github.com/mohammedkhalilia/a4d83d4e43682d1efcdf299d41beb3da
* Evaluate your data using nested NER model: https://gist.github.com/mohammedkhalilia/9134510aa2684464f57de7934c97138b
|
Amba/wav2vec2-large-xls-r-300m-turkish-colab
|
[] | null |
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}
| 0 | null |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: vit-base-patch16-224-finetuned-main-gpu-30e-final
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: validation
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9940476190476191
---
<!-- 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-patch16-224-finetuned-main-gpu-30e-final
This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0231
- Accuracy: 0.9940
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 30
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.5113 | 1.0 | 551 | 0.4745 | 0.7971 |
| 0.3409 | 2.0 | 1102 | 0.2697 | 0.8961 |
| 0.2675 | 3.0 | 1653 | 0.1611 | 0.9381 |
| 0.2092 | 4.0 | 2204 | 0.1176 | 0.9548 |
| 0.2008 | 5.0 | 2755 | 0.0889 | 0.9656 |
| 0.1555 | 6.0 | 3306 | 0.0666 | 0.9759 |
| 0.1614 | 7.0 | 3857 | 0.0576 | 0.9778 |
| 0.1518 | 8.0 | 4408 | 0.0517 | 0.9814 |
| 0.1231 | 9.0 | 4959 | 0.0528 | 0.9812 |
| 0.1076 | 10.0 | 5510 | 0.0426 | 0.9850 |
| 0.0953 | 11.0 | 6061 | 0.0634 | 0.9795 |
| 0.1097 | 12.0 | 6612 | 0.0398 | 0.9860 |
| 0.0763 | 13.0 | 7163 | 0.0348 | 0.9866 |
| 0.0895 | 14.0 | 7714 | 0.0341 | 0.9884 |
| 0.06 | 15.0 | 8265 | 0.0381 | 0.9883 |
| 0.0767 | 16.0 | 8816 | 0.0382 | 0.9875 |
| 0.0868 | 17.0 | 9367 | 0.0309 | 0.9898 |
| 0.091 | 18.0 | 9918 | 0.0339 | 0.9885 |
| 0.0817 | 19.0 | 10469 | 0.0243 | 0.9913 |
| 0.0641 | 20.0 | 11020 | 0.0286 | 0.9906 |
| 0.0703 | 21.0 | 11571 | 0.0314 | 0.9906 |
| 0.0642 | 22.0 | 12122 | 0.0261 | 0.9913 |
| 0.0695 | 23.0 | 12673 | 0.0260 | 0.9920 |
| 0.0664 | 24.0 | 13224 | 0.0241 | 0.9928 |
| 0.0552 | 25.0 | 13775 | 0.0258 | 0.9928 |
| 0.056 | 26.0 | 14326 | 0.0230 | 0.9939 |
| 0.0488 | 27.0 | 14877 | 0.0221 | 0.9936 |
| 0.0389 | 28.0 | 15428 | 0.0225 | 0.9930 |
| 0.0402 | 29.0 | 15979 | 0.0231 | 0.9940 |
| 0.0424 | 30.0 | 16530 | 0.0211 | 0.9939 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
|
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