modelId
stringlengths 4
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| tags
list | pipeline_tag
stringclasses 17
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int64 0
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AnonymousSub/bert_hier_diff_equal_wts_epochs_1_shard_10 | [
"pytorch",
"bert",
"feature-extraction",
"transformers"
]
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} | 1 | null | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
model = AutoModel.from_pretrained('{MODEL_NAME}')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 240 with parameters:
```
{'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss`
Parameters of the fit()-Method:
```
{
"epochs": 1,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": 240,
"warmup_steps": 24,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information --> |
AnonymousSub/rule_based_roberta_hier_quadruplet_epochs_1_shard_1 | [
"pytorch",
"roberta",
"feature-extraction",
"transformers"
]
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} | 1 | null | ---
license: openrail
---
cardcaptor sakura model trained on anime screenshots all were 768 resolution images. 16 batch size and 1.6e-5 lr. the number indicates the epoch. you can really only do sakura
tomoyo. something like kinomoto sakura, white beret, school bag, tomeda elementary school uniform, happy should give ok results when added to a normal prompt. |
AnonymousSub/unsup-consert-base | [
"pytorch",
"bert",
"feature-extraction",
"transformers"
]
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} | 6 | null | ---
tags:
- BreakoutNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: C51
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: BreakoutNoFrameskip-v4
type: BreakoutNoFrameskip-v4
metrics:
- type: mean_reward
value: 381.00 +/- 56.35
name: mean_reward
verified: false
---
# (CleanRL) **C51** Agent Playing **BreakoutNoFrameskip-v4**
This is a trained model of a C51 agent playing BreakoutNoFrameskip-v4.
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/c51_atari.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[c51_atari]"
python -m cleanrl_utils.enjoy --exp-name c51_atari --env-id BreakoutNoFrameskip-v4
```
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/kinalmehta/BreakoutNoFrameskip-v4-c51_atari-seed1/raw/main/c51_atari.py
curl -OL https://huggingface.co/kinalmehta/BreakoutNoFrameskip-v4-c51_atari-seed1/raw/main/pyproject.toml
curl -OL https://huggingface.co/kinalmehta/BreakoutNoFrameskip-v4-c51_atari-seed1/raw/main/poetry.lock
poetry install --all-extras
python c51_atari.py --save-model --upload-model --hf-entity kinalmehta --env-id BreakoutNoFrameskip-v4
```
# Hyperparameters
```python
{'batch_size': 32,
'buffer_size': 1000000,
'capture_video': False,
'cuda': True,
'end_e': 0.01,
'env_id': 'BreakoutNoFrameskip-v4',
'exp_name': 'c51_atari',
'exploration_fraction': 0.1,
'gamma': 0.99,
'hf_entity': 'kinalmehta',
'learning_rate': 0.00025,
'learning_starts': 80000,
'n_atoms': 51,
'save_model': True,
'seed': 1,
'start_e': 1,
'target_network_frequency': 10000,
'torch_deterministic': True,
'total_timesteps': 10000000,
'track': False,
'train_frequency': 4,
'upload_model': True,
'v_max': 10,
'v_min': -10,
'wandb_entity': None,
'wandb_project_name': 'cleanRL'}
```
|
AnthonyNelson/DialoGPT-small-ricksanchez | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
| conversational | {
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} | 12 | null | ---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.56 +/- 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="BKluwe2209/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"])
```
|
Anthos23/distilbert-base-uncased-finetuned-sst2 | [
"tf",
"tensorboard",
"distilbert",
"text-classification",
"transformers",
"generated_from_keras_callback",
"license:apache-2.0"
]
| text-classification | {
"architectures": [
"DistilBertForSequenceClassification"
],
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}
} | 21 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: wav2vec2-base-timit-finetune-3
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-base-timit-finetune-3
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4387
- Wer: 0.2750
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 60
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 3.4692 | 6.94 | 500 | 0.9978 | 0.7091 |
| 0.3946 | 13.88 | 1000 | 0.3674 | 0.3253 |
| 0.1291 | 20.83 | 1500 | 0.3987 | 0.3042 |
| 0.074 | 27.77 | 2000 | 0.4292 | 0.2916 |
| 0.0487 | 34.72 | 2500 | 0.4302 | 0.2853 |
| 0.0368 | 41.66 | 3000 | 0.4222 | 0.2789 |
| 0.0281 | 48.61 | 3500 | 0.4481 | 0.2783 |
| 0.0237 | 55.55 | 4000 | 0.4387 | 0.2750 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1
- Datasets 2.7.0
- Tokenizers 0.11.0
|
Anthos23/sentiment-roberta-large-english-finetuned-sentiment-analysis | []
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} | 0 | null | ---
license: creativeml-openrail-m
tags:
- text-to-image
- stable-diffusion
---
### Krystal-Test Dreambooth model trained by Slashy with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook
Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb)
Sample pictures of this concept:
|
AntonClaesson/movie-plot-generator | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
]
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} | 9 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: distilbart-podimo-data-eval-3
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbart-podimo-data-eval-3
This model is a fine-tuned version of [sshleifer/distilbart-cnn-12-6](https://huggingface.co/sshleifer/distilbart-cnn-12-6) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 3.3828
- Rouge1: 32.8203
- Rouge2: 7.8994
- Rougel: 18.9659
- Rougelsum: 29.4196
- Gen Len: 114.5264
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 64
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 8
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:|:--------:|
| 3.9049 | 1.0 | 132 | 3.5343 | 30.2542 | 6.031 | 17.269 | 26.9847 | 113.7689 |
| 3.4248 | 2.0 | 264 | 3.4055 | 31.6518 | 7.2786 | 18.2641 | 28.4006 | 114.6547 |
| 3.1594 | 3.0 | 396 | 3.3579 | 32.0442 | 7.3554 | 18.3492 | 28.7615 | 113.7443 |
| 2.9645 | 4.0 | 528 | 3.3445 | 32.0945 | 7.637 | 18.6289 | 28.899 | 115.5321 |
| 2.8073 | 5.0 | 660 | 3.3470 | 32.7852 | 7.9597 | 19.2358 | 29.5057 | 108.3519 |
| 2.685 | 6.0 | 792 | 3.3532 | 32.3775 | 7.661 | 18.6719 | 28.9282 | 117.1104 |
| 2.5941 | 7.0 | 924 | 3.3711 | 32.6976 | 7.8917 | 19.069 | 29.3785 | 113.1943 |
| 2.5267 | 8.0 | 1056 | 3.3828 | 32.8203 | 7.8994 | 18.9659 | 29.4196 | 114.5264 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.11.0
- Datasets 2.2.1
- Tokenizers 0.12.1
|
Antony/mint_model | []
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} | 0 | null | ---
license: mit
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: xlm-roberta-base-language-detection-finetuned-ner-finetuned-ner
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-language-detection-finetuned-ner-finetuned-ner
This model is a fine-tuned version of [carexl8/xlm-roberta-base-language-detection-finetuned-ner](https://huggingface.co/carexl8/xlm-roberta-base-language-detection-finetuned-ner) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0001
- Precision: 1.0000
- Recall: 1.0000
- F1: 1.0000
- Accuracy: 1.0000
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0007 | 1.0 | 1543 | 0.0001 | 1.0000 | 1.0000 | 1.0000 | 1.0000 |
| 0.0003 | 2.0 | 3086 | 0.0001 | 1.0000 | 1.0000 | 1.0000 | 1.0000 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
|
Anubhav23/indianlegal | []
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} | 0 | 2023-01-03T19:29:56Z | ---
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: 6.50 +/- 16.29
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga tkurtulus -f logs/
python enjoy.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga tkurtulus -f logs/
rl_zoo3 enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python train.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga tkurtulus
```
## Hyperparameters
```python
OrderedDict([('batch_size', 64),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 100000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
|
gaurishhs/API | []
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} | 0 | null | NOT COMPATIBLE WITH V1 BASED MODELS.
i did this bc i was bored and i find writing ridiculous negative prompts funny and nobody had done it yet with wd 1.4. to say i was shocked at the results is an understatement. it can turn extremely simple prompts like "1girl" into masterpieces without having to actually say "masterpiece" or anything other than "by wd14neg" in negatives. so you can all stop complaining that wd 1.4 isn't as good as anything v3 now. i never rly share my stuff bc i hate attention but i just had to this time.
trained on 500+ images generated with wd 1.4 anime from an extremely exaggerated negative prompt in positives (seriously it was like 2000 tokens) and quality enhancers in negatives.
wd14neg is the best and can stand on its own, but may improve with wd14neg-2 and/or wd14neg-3. combining all of them may result in deepfrying. to solve this i would either lower your cfg or deemphasize it.
the embedding may struggle with eliminating extra limbs and fingers since i mainly focused on blurriness, flat colors, and jpeg artifacts to fix embeddings of mine with crappy datasets i don't have the executive function to redo but i plan to make another embedding focusing on those.
 |
Apisate/DialoGPT-small-jordan | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
| conversational | {
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"GPT2LMHeadModel"
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} | 12 | null | Access to model Antale123/ConorBot is restricted and you are not in the authorized list. Visit https://huggingface.co/Antale123/ConorBot to ask for access. |
Apisate/Discord-Ai-Bot | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
]
| text-generation | {
"architectures": [
"GPT2LMHeadModel"
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}
} | 11 | null | ---
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: gpt2-ner-invoiceSenderRecipient_all_inv_03_01
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. -->
# gpt2-ner-invoiceSenderRecipient_all_inv_03_01
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0307
- Precision: 0.7932
- Recall: 0.8488
- F1: 0.8201
- Accuracy: 0.9895
## 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: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0363 | 0.01 | 500 | 0.0338 | 0.7846 | 0.7969 | 0.7907 | 0.9884 |
| 0.0392 | 0.02 | 1000 | 0.0346 | 0.7665 | 0.8211 | 0.7929 | 0.9881 |
| 0.0363 | 0.04 | 1500 | 0.0347 | 0.7701 | 0.8075 | 0.7884 | 0.9880 |
| 0.0396 | 0.05 | 2000 | 0.0347 | 0.7454 | 0.8375 | 0.7888 | 0.9879 |
| 0.0366 | 0.06 | 2500 | 0.0350 | 0.7519 | 0.8345 | 0.7911 | 0.9879 |
| 0.0382 | 0.07 | 3000 | 0.0356 | 0.7500 | 0.8434 | 0.7939 | 0.9877 |
| 0.0424 | 0.09 | 3500 | 0.0358 | 0.7517 | 0.8287 | 0.7883 | 0.9877 |
| 0.0385 | 0.1 | 4000 | 0.0352 | 0.7605 | 0.8225 | 0.7903 | 0.9880 |
| 0.0382 | 0.11 | 4500 | 0.0361 | 0.7494 | 0.8159 | 0.7813 | 0.9874 |
| 0.0372 | 0.12 | 5000 | 0.0345 | 0.7817 | 0.8044 | 0.7929 | 0.9885 |
| 0.0377 | 0.14 | 5500 | 0.0346 | 0.7749 | 0.8238 | 0.7986 | 0.9884 |
| 0.0383 | 0.15 | 6000 | 0.0359 | 0.7568 | 0.8341 | 0.7936 | 0.9879 |
| 0.0372 | 0.16 | 6500 | 0.0356 | 0.7548 | 0.8356 | 0.7932 | 0.9879 |
| 0.0371 | 0.17 | 7000 | 0.0352 | 0.7540 | 0.8477 | 0.7981 | 0.9880 |
| 0.0368 | 0.19 | 7500 | 0.0349 | 0.7662 | 0.8310 | 0.7973 | 0.9881 |
| 0.0388 | 0.2 | 8000 | 0.0339 | 0.7648 | 0.8336 | 0.7977 | 0.9883 |
| 0.0368 | 0.21 | 8500 | 0.0336 | 0.7729 | 0.8305 | 0.8006 | 0.9886 |
| 0.0389 | 0.22 | 9000 | 0.0340 | 0.7750 | 0.8208 | 0.7972 | 0.9884 |
| 0.0384 | 0.24 | 9500 | 0.0349 | 0.7549 | 0.8499 | 0.7996 | 0.9880 |
| 0.0376 | 0.25 | 10000 | 0.0358 | 0.7531 | 0.8390 | 0.7938 | 0.9875 |
| 0.0354 | 0.26 | 10500 | 0.0346 | 0.7650 | 0.8318 | 0.7970 | 0.9882 |
| 0.0358 | 0.27 | 11000 | 0.0338 | 0.7694 | 0.8397 | 0.8030 | 0.9886 |
| 0.0389 | 0.28 | 11500 | 0.0341 | 0.7586 | 0.8502 | 0.8018 | 0.9882 |
| 0.0383 | 0.3 | 12000 | 0.0342 | 0.7688 | 0.8275 | 0.7971 | 0.9881 |
| 0.0355 | 0.31 | 12500 | 0.0337 | 0.7783 | 0.8281 | 0.8024 | 0.9885 |
| 0.0372 | 0.32 | 13000 | 0.0338 | 0.7703 | 0.8399 | 0.8036 | 0.9884 |
| 0.0369 | 0.33 | 13500 | 0.0331 | 0.7683 | 0.8427 | 0.8038 | 0.9886 |
| 0.0361 | 0.35 | 14000 | 0.0336 | 0.7699 | 0.8322 | 0.7999 | 0.9885 |
| 0.0361 | 0.36 | 14500 | 0.0336 | 0.7735 | 0.8390 | 0.8049 | 0.9885 |
| 0.0372 | 0.37 | 15000 | 0.0333 | 0.7747 | 0.8343 | 0.8034 | 0.9887 |
| 0.0366 | 0.38 | 15500 | 0.0343 | 0.7646 | 0.8468 | 0.8036 | 0.9883 |
| 0.0345 | 0.4 | 16000 | 0.0333 | 0.7790 | 0.8334 | 0.8053 | 0.9887 |
| 0.0363 | 0.41 | 16500 | 0.0329 | 0.7783 | 0.8301 | 0.8034 | 0.9887 |
| 0.0348 | 0.42 | 17000 | 0.0341 | 0.7626 | 0.8533 | 0.8054 | 0.9884 |
| 0.0391 | 0.43 | 17500 | 0.0324 | 0.7873 | 0.8295 | 0.8079 | 0.9889 |
| 0.0344 | 0.45 | 18000 | 0.0334 | 0.7769 | 0.8369 | 0.8058 | 0.9887 |
| 0.0378 | 0.46 | 18500 | 0.0337 | 0.7741 | 0.8394 | 0.8054 | 0.9886 |
| 0.035 | 0.47 | 19000 | 0.0328 | 0.7827 | 0.8323 | 0.8067 | 0.9888 |
| 0.0351 | 0.48 | 19500 | 0.0327 | 0.7815 | 0.8371 | 0.8083 | 0.9889 |
| 0.037 | 0.5 | 20000 | 0.0328 | 0.7793 | 0.8388 | 0.8079 | 0.9888 |
| 0.0346 | 0.51 | 20500 | 0.0325 | 0.7804 | 0.8416 | 0.8099 | 0.9890 |
| 0.0364 | 0.52 | 21000 | 0.0323 | 0.7861 | 0.8339 | 0.8093 | 0.9889 |
| 0.0356 | 0.53 | 21500 | 0.0327 | 0.7729 | 0.8510 | 0.8101 | 0.9889 |
| 0.0346 | 0.54 | 22000 | 0.0325 | 0.7791 | 0.8407 | 0.8087 | 0.9889 |
| 0.0342 | 0.56 | 22500 | 0.0334 | 0.7790 | 0.8443 | 0.8104 | 0.9889 |
| 0.0368 | 0.57 | 23000 | 0.0322 | 0.7869 | 0.8323 | 0.8089 | 0.9890 |
| 0.0371 | 0.58 | 23500 | 0.0320 | 0.7890 | 0.8356 | 0.8116 | 0.9891 |
| 0.0344 | 0.59 | 24000 | 0.0321 | 0.7910 | 0.8321 | 0.8110 | 0.9892 |
| 0.0342 | 0.61 | 24500 | 0.0319 | 0.7881 | 0.8356 | 0.8111 | 0.9892 |
| 0.0339 | 0.62 | 25000 | 0.0320 | 0.7889 | 0.8317 | 0.8097 | 0.9892 |
| 0.0347 | 0.63 | 25500 | 0.0316 | 0.7909 | 0.8347 | 0.8122 | 0.9892 |
| 0.034 | 0.64 | 26000 | 0.0318 | 0.7887 | 0.8324 | 0.8100 | 0.9891 |
| 0.0347 | 0.66 | 26500 | 0.0317 | 0.7791 | 0.8525 | 0.8141 | 0.9891 |
| 0.0345 | 0.67 | 27000 | 0.0318 | 0.7870 | 0.8384 | 0.8119 | 0.9892 |
| 0.0347 | 0.68 | 27500 | 0.0317 | 0.7903 | 0.8426 | 0.8157 | 0.9893 |
| 0.0371 | 0.69 | 28000 | 0.0311 | 0.7965 | 0.8332 | 0.8144 | 0.9894 |
| 0.0338 | 0.71 | 28500 | 0.0316 | 0.7863 | 0.8442 | 0.8142 | 0.9892 |
| 0.0352 | 0.72 | 29000 | 0.0315 | 0.7810 | 0.8537 | 0.8157 | 0.9892 |
| 0.0344 | 0.73 | 29500 | 0.0314 | 0.7953 | 0.8353 | 0.8148 | 0.9894 |
| 0.0322 | 0.74 | 30000 | 0.0320 | 0.7836 | 0.8449 | 0.8131 | 0.9891 |
| 0.0355 | 0.76 | 30500 | 0.0312 | 0.7877 | 0.8480 | 0.8167 | 0.9894 |
| 0.035 | 0.77 | 31000 | 0.0313 | 0.7864 | 0.8504 | 0.8171 | 0.9893 |
| 0.0346 | 0.78 | 31500 | 0.0310 | 0.7931 | 0.8424 | 0.8170 | 0.9895 |
| 0.0339 | 0.79 | 32000 | 0.0316 | 0.7857 | 0.8501 | 0.8166 | 0.9893 |
| 0.033 | 0.8 | 32500 | 0.0311 | 0.7975 | 0.8406 | 0.8185 | 0.9895 |
| 0.0337 | 0.82 | 33000 | 0.0314 | 0.7886 | 0.8457 | 0.8162 | 0.9894 |
| 0.0357 | 0.83 | 33500 | 0.0311 | 0.7923 | 0.8437 | 0.8172 | 0.9894 |
| 0.0348 | 0.84 | 34000 | 0.0312 | 0.7909 | 0.8490 | 0.8189 | 0.9894 |
| 0.0343 | 0.85 | 34500 | 0.0311 | 0.7856 | 0.8528 | 0.8179 | 0.9893 |
| 0.0323 | 0.87 | 35000 | 0.0311 | 0.7884 | 0.8505 | 0.8183 | 0.9894 |
| 0.0329 | 0.88 | 35500 | 0.0307 | 0.7981 | 0.8399 | 0.8185 | 0.9896 |
| 0.0324 | 0.89 | 36000 | 0.0313 | 0.7830 | 0.8576 | 0.8186 | 0.9893 |
| 0.0336 | 0.9 | 36500 | 0.0312 | 0.7836 | 0.8566 | 0.8185 | 0.9893 |
| 0.0327 | 0.92 | 37000 | 0.0309 | 0.7887 | 0.8501 | 0.8182 | 0.9895 |
| 0.0338 | 0.93 | 37500 | 0.0312 | 0.7887 | 0.8514 | 0.8188 | 0.9894 |
| 0.0327 | 0.94 | 38000 | 0.0311 | 0.7873 | 0.8534 | 0.8190 | 0.9894 |
| 0.0326 | 0.95 | 38500 | 0.0308 | 0.7953 | 0.8459 | 0.8198 | 0.9895 |
| 0.0338 | 0.97 | 39000 | 0.0307 | 0.7932 | 0.8488 | 0.8201 | 0.9895 |
| 0.0354 | 0.98 | 39500 | 0.0308 | 0.7916 | 0.8502 | 0.8198 | 0.9895 |
| 0.0313 | 0.99 | 40000 | 0.0309 | 0.7897 | 0.8523 | 0.8198 | 0.9895 |
### Framework versions
- Transformers 4.22.0
- Pytorch 1.10.0
- Tokenizers 0.12.1
|
ArBert/albert-base-v2-finetuned-ner-agglo-twitter | [
"pytorch",
"tensorboard",
"albert",
"token-classification",
"transformers",
"autotrain_compatible"
]
| token-classification | {
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} | 27 | null | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 303 with parameters:
```
{'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss`
Parameters of the fit()-Method:
```
{
"epochs": 1,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": 303,
"warmup_steps": 31,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
(2): Dense({'in_features': 768, 'out_features': 768, 'bias': True, 'activation_function': 'torch.nn.modules.activation.Tanh'})
(3): Normalize()
)
```
## Citing & Authors
<!--- Describe where people can find more information --> |
ArBert/albert-base-v2-finetuned-ner-agglo | [
"pytorch",
"tensorboard",
"albert",
"token-classification",
"transformers",
"autotrain_compatible"
]
| token-classification | {
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} | 8 | 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="amal94/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"])
```
|
ArBert/albert-base-v2-finetuned-ner-gmm | [
"pytorch",
"tensorboard",
"albert",
"token-classification",
"transformers",
"autotrain_compatible"
]
| token-classification | {
"architectures": [
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],
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} | 8 | 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: 7.56 +/- 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="amal94/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"])
```
|
ArBert/bert-base-uncased-finetuned-ner-agglo | []
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} | 0 | null | ---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: Taxi-v3-default
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.46 +/- 2.79
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="gmojko/Taxi-v3-default", 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"])
```
|
ArBert/bert-base-uncased-finetuned-ner-kmeans-twitter | []
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} | 0 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- summarize_from_feedback
metrics:
- rouge
model-index:
- name: flan-t5-base-finetuned-openai-summarize_from_feedback
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: summarize_from_feedback
type: summarize_from_feedback
config: comparisons
split: train
args: comparisons
metrics:
- name: Rouge1
type: rouge
value: 29.3494
---
<!-- 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. -->
# flan-t5-base-finetuned-openai-summarize_from_feedback
This model is a fine-tuned version of [google/flan-t5-base](https://huggingface.co/google/flan-t5-base) on the summarize_from_feedback dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8833
- Rouge1: 29.3494
- Rouge2: 10.9406
- Rougel: 23.9907
- Rougelsum: 25.461
- Gen Len: 18.9265
## 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: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 6
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| 1.7678 | 1.0 | 5804 | 1.8833 | 29.3494 | 10.9406 | 23.9907 | 25.461 | 18.9265 |
| 1.5839 | 2.0 | 11608 | 1.8992 | 29.6239 | 11.1795 | 24.2927 | 25.7183 | 18.9358 |
| 1.4812 | 3.0 | 17412 | 1.8929 | 29.8899 | 11.2855 | 24.4193 | 25.9219 | 18.9189 |
| 1.4198 | 4.0 | 23216 | 1.8939 | 29.8897 | 11.2606 | 24.3262 | 25.8642 | 18.9309 |
| 1.3612 | 5.0 | 29020 | 1.9105 | 29.8469 | 11.2112 | 24.2483 | 25.7884 | 18.9396 |
| 1.3279 | 6.0 | 34824 | 1.9170 | 30.038 | 11.3426 | 24.4385 | 25.9675 | 18.9328 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
|
ArBert/bert-base-uncased-finetuned-ner-kmeans | [
"pytorch",
"tensorboard",
"bert",
"token-classification",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible"
]
| token-classification | {
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],
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}
} | 6 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- summarize_from_feedback
metrics:
- rouge
model-index:
- name: flan-t5-small-finetuned-openai-summarize_from_feedback
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: summarize_from_feedback
type: summarize_from_feedback
config: comparisons
split: train
args: comparisons
metrics:
- name: Rouge1
type: rouge
value: 27.2966
---
<!-- 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. -->
# Flan-T5 (small) fine-tuned on OpenAI summarize_from_feedback for summarizing
This model is a fine-tuned version of [google/flan-t5-small](https://huggingface.co/google/flan-t5-small) on the summarize_from_feedback dataset.
It achieves the following results on the evaluation set:
- Loss: 2.1488
- Rouge1: 27.2966
- Rouge2: 9.5886
- Rougel: 22.1999
- Rougelsum: 23.6317
- Gen Len: 18.9310
## 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: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 6
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:|
| 2.2472 | 1.0 | 2902 | 2.1882 | 26.2033 | 8.83 | 21.3673 | 22.7758 | 18.9234 |
| 2.1142 | 2.0 | 5804 | 2.1608 | 27.1972 | 9.4269 | 22.1761 | 23.6252 | 18.8796 |
| 2.0484 | 3.0 | 8706 | 2.1524 | 27.0963 | 9.4578 | 21.9866 | 23.5124 | 18.9033 |
| 2.0055 | 4.0 | 11608 | 2.1519 | 27.2428 | 9.5514 | 22.1542 | 23.6036 | 18.9347 |
| 1.9647 | 5.0 | 14510 | 2.1488 | 27.2966 | 9.5886 | 22.1999 | 23.6317 | 18.9310 |
| 1.9547 | 6.0 | 17412 | 2.1488 | 27.5602 | 9.673 | 22.3768 | 23.8399 | 18.9236 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
|
ArBert/bert-base-uncased-finetuned-ner | [
"pytorch",
"tensorboard",
"bert",
"token-classification",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible"
]
| token-classification | {
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"BertForTokenClassification"
],
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} | 8 | 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: 375.00 +/- 126.57
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga mahmoud-mohey -f logs/
python enjoy.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga mahmoud-mohey -f logs/
rl_zoo3 enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python train.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga mahmoud-mohey
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.15),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.001),
('learning_starts', 100000),
('n_timesteps', 1500000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1200),
('train_freq', 4),
('normalize', False)])
```
|
ArBert/roberta-base-finetuned-ner-gmm-twitter | []
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} | 0 | null | ---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: testnewreinforcecartpole
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 392.60 +/- 31.61
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 Class: https://huggingface.co/deep-rl-course/unit4/introduction
|
ArBert/roberta-base-finetuned-ner-gmm | []
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}
} | 0 | null | ---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: Taxi-v3-v2
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="gmojko/Taxi-v3-v2", 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"])
```
|
Aran/DialoGPT-medium-harrypotter | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
| conversational | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
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} | 8 | null | ---
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: legal_text_classifier_10_class
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. -->
# legal_text_classifier_10_class
This model is a fine-tuned version of [aubmindlab/bert-base-arabertv2](https://huggingface.co/aubmindlab/bert-base-arabertv2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2908
- F1: 0.9329
## 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: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 1.0 | 84 | 0.7287 | 0.8255 |
| No log | 2.0 | 168 | 0.3221 | 0.9262 |
| No log | 3.0 | 252 | 0.3014 | 0.9060 |
| No log | 4.0 | 336 | 0.3104 | 0.9128 |
| No log | 5.0 | 420 | 0.2636 | 0.9262 |
| 0.4014 | 6.0 | 504 | 0.2793 | 0.9262 |
| 0.4014 | 7.0 | 588 | 0.2509 | 0.9262 |
| 0.4014 | 8.0 | 672 | 0.2715 | 0.9329 |
| 0.4014 | 9.0 | 756 | 0.2688 | 0.9329 |
| 0.4014 | 10.0 | 840 | 0.2850 | 0.9329 |
| 0.4014 | 11.0 | 924 | 0.2972 | 0.9329 |
| 0.069 | 12.0 | 1008 | 0.2783 | 0.9329 |
| 0.069 | 13.0 | 1092 | 0.2942 | 0.9329 |
| 0.069 | 14.0 | 1176 | 0.2725 | 0.9329 |
| 0.069 | 15.0 | 1260 | 0.2594 | 0.9329 |
| 0.069 | 16.0 | 1344 | 0.2768 | 0.9329 |
| 0.069 | 17.0 | 1428 | 0.2889 | 0.9329 |
| 0.0447 | 18.0 | 1512 | 0.2908 | 0.9329 |
| 0.0447 | 19.0 | 1596 | 0.2880 | 0.9329 |
| 0.0447 | 20.0 | 1680 | 0.2908 | 0.9329 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.12.1
|
Aran/DialoGPT-small-harrypotter | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
| conversational | {
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"GPT2LMHeadModel"
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}
} | 8 | null | ---
language: en
thumbnail: https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1268086791443230737/BRGz4AiW_400x400.jpg')">
</div>
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1394266006395228162/qIjjvzl7_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Pop Base & Pop Crave</div>
<div style="text-align: center; font-size: 14px;">@popbase-popcrave</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from Pop Base & Pop Crave.
| Data | Pop Base | Pop Crave |
| --- | --- | --- |
| Tweets downloaded | 3240 | 3212 |
| Retweets | 343 | 244 |
| Short tweets | 306 | 89 |
| Tweets kept | 2591 | 2879 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/231p93io/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @popbase-popcrave's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/9st2g69y) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/9st2g69y/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/popbase-popcrave')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
ArashEsk95/bert-base-uncased-finetuned-stsb | []
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} | 0 | 2023-01-03T21:06:56Z | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
model = AutoModel.from_pretrained('{MODEL_NAME}')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 605 with parameters:
```
{'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss`
Parameters of the fit()-Method:
```
{
"epochs": 1,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": 605,
"warmup_steps": 61,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information --> |
Aravinth/test | []
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} | 0 | null | ---
inference: false
tags:
- onnx
- bert
- adapterhub:comsense/cosmosqa
- adapter-transformers
datasets:
- cosmos_qa
language:
- en
---
# ONNX export of Adapter `AdapterHub/bert-base-uncased-pf-cosmos_qa` for bert-base-uncased
## Conversion of [AdapterHub/bert-base-uncased-pf-cosmos_qa](https://huggingface.co/AdapterHub/bert-base-uncased-pf-cosmos_qa) for UKP SQuARE
## Usage
```python
onnx_path = hf_hub_download(repo_id='UKP-SQuARE/bert-base-uncased-pf-cosmos_qa-onnx', filename='model.onnx') # or model_quant.onnx for quantization
onnx_model = InferenceSession(onnx_path, providers=['CPUExecutionProvider'])
context = 'ONNX is an open format to represent models. The benefits of using ONNX include interoperability of frameworks and hardware optimization.'
question = 'What are advantages of ONNX?'
choices = ["Cat", "Horse", "Tiger", "Fish"]tokenizer = AutoTokenizer.from_pretrained('UKP-SQuARE/bert-base-uncased-pf-cosmos_qa-onnx')
raw_input = [[context, question + + choice] for choice in choices]
inputs = tokenizer(raw_input, padding=True, truncation=True, return_tensors="np")
inputs['token_type_ids'] = np.expand_dims(inputs['token_type_ids'], axis=0)
inputs['input_ids'] = np.expand_dims(inputs['input_ids'], axis=0)
inputs['attention_mask'] = np.expand_dims(inputs['attention_mask'], axis=0)
outputs = onnx_model.run(input_feed=dict(inputs), output_names=None)
```
## Architecture & Training
The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found [here](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs).
## Evaluation results
Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results.
## Citation
If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247):
```bibtex
@inproceedings{poth-etal-2021-what-to-pre-train-on,
title={What to Pre-Train on? Efficient Intermediate Task Selection},
author={Clifton Poth and Jonas Pfeiffer and Andreas Rücklé and Iryna Gurevych},
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
month = nov,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/2104.08247",
pages = "to appear",
}
``` |
ArcQ/gpt-experiments | []
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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: 264.56 +/- 19.76
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
...
```
|
Archie/myProject | []
| null | {
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} | 0 | 2023-01-03T21:12:42Z | ---
inference: false
tags:
- onnx
- roberta
- adapterhub:comsense/cosmosqa
- adapter-transformers
datasets:
- cosmos_qa
language:
- en
---
# ONNX export of Adapter `AdapterHub/roberta-base-pf-cosmos_qa` for roberta-base
## Conversion of [AdapterHub/roberta-base-pf-cosmos_qa](https://huggingface.co/AdapterHub/roberta-base-pf-cosmos_qa) for UKP SQuARE
## Usage
```python
onnx_path = hf_hub_download(repo_id='UKP-SQuARE/roberta-base-pf-cosmos_qa-onnx', filename='model.onnx') # or model_quant.onnx for quantization
onnx_model = InferenceSession(onnx_path, providers=['CPUExecutionProvider'])
context = 'ONNX is an open format to represent models. The benefits of using ONNX include interoperability of frameworks and hardware optimization.'
question = 'What are advantages of ONNX?'
choices = ["Cat", "Horse", "Tiger", "Fish"]tokenizer = AutoTokenizer.from_pretrained('UKP-SQuARE/roberta-base-pf-cosmos_qa-onnx')
raw_input = [[context, question + + choice] for choice in choices]
inputs = tokenizer(raw_input, padding=True, truncation=True, return_tensors="np")
inputs['token_type_ids'] = np.expand_dims(inputs['token_type_ids'], axis=0)
inputs['input_ids'] = np.expand_dims(inputs['input_ids'], axis=0)
inputs['attention_mask'] = np.expand_dims(inputs['attention_mask'], axis=0)
outputs = onnx_model.run(input_feed=dict(inputs), output_names=None)
```
## Architecture & Training
The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found [here](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs).
## Evaluation results
Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results.
## Citation
If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247):
```bibtex
@inproceedings{poth-etal-2021-what-to-pre-train-on,
title={What to Pre-Train on? Efficient Intermediate Task Selection},
author={Clifton Poth and Jonas Pfeiffer and Andreas Rücklé and Iryna Gurevych},
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
month = nov,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/2104.08247",
pages = "to appear",
}
``` |
ArenaGrenade/char-cnn | []
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} | 0 | null |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Huggy
library_name: ml-agents
---
# **ppo** Agent playing **Huggy**
This is a trained model of a **ppo** agent playing **Huggy** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-Huggy
2. Step 1: Write your model_id: jnacey2/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
AriakimTaiyo/DialoGPT-cultured-Kumiko | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
| conversational | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
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} | 8 | null | ---
inference: false
tags:
- onnx
- text-classification
- adapterhub:rc/multirc
- bert
- adapter-transformers
language:
- en
---
# ONNX export of Adapter `AdapterHub/bert-base-uncased-pf-multirc` for bert-base-uncased
## Conversion of [AdapterHub/bert-base-uncased-pf-multirc](https://huggingface.co/AdapterHub/bert-base-uncased-pf-multirc) for UKP SQuARE
## Usage
```python
onnx_path = hf_hub_download(repo_id='UKP-SQuARE/bert-base-uncased-pf-multirc-onnx', filename='model.onnx') # or model_quant.onnx for quantization
onnx_model = InferenceSession(onnx_path, providers=['CPUExecutionProvider'])
context = 'ONNX is an open format to represent models. The benefits of using ONNX include interoperability of frameworks and hardware optimization.'
question = 'What are advantages of ONNX?'
choices = ["Cat", "Horse", "Tiger", "Fish"]tokenizer = AutoTokenizer.from_pretrained('UKP-SQuARE/bert-base-uncased-pf-multirc-onnx')
raw_input = [[context, question + + choice] for choice in choices]
inputs = tokenizer(raw_input, padding=True, truncation=True, return_tensors="np")
inputs['token_type_ids'] = np.expand_dims(inputs['token_type_ids'], axis=0)
inputs['input_ids'] = np.expand_dims(inputs['input_ids'], axis=0)
inputs['attention_mask'] = np.expand_dims(inputs['attention_mask'], axis=0)
outputs = onnx_model.run(input_feed=dict(inputs), output_names=None)
```
## Architecture & Training
The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found [here](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs).
## Evaluation results
Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results.
## Citation
If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247):
```bibtex
@inproceedings{poth-etal-2021-pre,
title = "{W}hat to Pre-Train on? {E}fficient Intermediate Task Selection",
author = {Poth, Clifton and
Pfeiffer, Jonas and
R{"u}ckl{'e}, Andreas and
Gurevych, Iryna},
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.emnlp-main.827",
pages = "10585--10605",
}
``` |
AriakimTaiyo/DialoGPT-medium-Kumiko | [
"conversational"
]
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} | 0 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: tiny-mlm-snli-plain_text
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# tiny-mlm-snli-plain_text
This model is a fine-tuned version of [google/bert_uncased_L-2_H-128_A-2](https://huggingface.co/google/bert_uncased_L-2_H-128_A-2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.1233
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- num_epochs: 200
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 3.665 | 0.4 | 500 | 3.2495 |
| 3.4103 | 0.8 | 1000 | nan |
| 3.2635 | 1.2 | 1500 | 3.1518 |
| 3.1738 | 1.6 | 2000 | 3.1555 |
| 3.0556 | 2.0 | 2500 | 3.0593 |
| 2.9933 | 2.4 | 3000 | 3.0970 |
| 2.9019 | 2.8 | 3500 | 3.0773 |
| 2.876 | 3.2 | 4000 | 3.1233 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.1
- Datasets 2.8.0
- Tokenizers 0.13.2
|
AriakimTaiyo/DialoGPT-revised-Kumiko | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
| conversational | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
"summarization": {
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"max_length": null,
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"translation_en_to_de": {
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"translation_en_to_fr": {
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}
} | 6 | null | ---
inference: false
tags:
- onnx
- text-classification
- adapterhub:rc/multirc
- roberta
- adapter-transformers
language:
- en
---
# ONNX export of Adapter `AdapterHub/roberta-base-pf-multirc` for roberta-base
## Conversion of [AdapterHub/roberta-base-pf-multirc](https://huggingface.co/AdapterHub/roberta-base-pf-multirc) for UKP SQuARE
## Usage
```python
onnx_path = hf_hub_download(repo_id='UKP-SQuARE/roberta-base-pf-multirc-onnx', filename='model.onnx') # or model_quant.onnx for quantization
onnx_model = InferenceSession(onnx_path, providers=['CPUExecutionProvider'])
context = 'ONNX is an open format to represent models. The benefits of using ONNX include interoperability of frameworks and hardware optimization.'
question = 'What are advantages of ONNX?'
choices = ["Cat", "Horse", "Tiger", "Fish"]tokenizer = AutoTokenizer.from_pretrained('UKP-SQuARE/roberta-base-pf-multirc-onnx')
raw_input = [[context, question + + choice] for choice in choices]
inputs = tokenizer(raw_input, padding=True, truncation=True, return_tensors="np")
inputs['token_type_ids'] = np.expand_dims(inputs['token_type_ids'], axis=0)
inputs['input_ids'] = np.expand_dims(inputs['input_ids'], axis=0)
inputs['attention_mask'] = np.expand_dims(inputs['attention_mask'], axis=0)
outputs = onnx_model.run(input_feed=dict(inputs), output_names=None)
```
## Architecture & Training
The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found [here](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs).
## Evaluation results
Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results.
## Citation
If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247):
```bibtex
@inproceedings{poth-etal-2021-pre,
title = "{W}hat to Pre-Train on? {E}fficient Intermediate Task Selection",
author = {Poth, Clifton and
Pfeiffer, Jonas and
R{"u}ckl{'e}, Andreas and
Gurevych, Iryna},
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.emnlp-main.827",
pages = "10585--10605",
}
``` |
AriakimTaiyo/DialoGPT-small-Rikka | [
"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": {
"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": {
"early_stopping": null,
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},
"translation_en_to_ro": {
"early_stopping": null,
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}
}
} | 8 | null | ---
inference: false
tags:
- onnx
- bert
- adapter-transformers
datasets:
- quail
language:
- en
---
# ONNX export of Adapter `AdapterHub/bert-base-uncased-pf-quail` for bert-base-uncased
## Conversion of [AdapterHub/bert-base-uncased-pf-quail](https://huggingface.co/AdapterHub/bert-base-uncased-pf-quail) for UKP SQuARE
## Usage
```python
onnx_path = hf_hub_download(repo_id='UKP-SQuARE/bert-base-uncased-pf-quail-onnx', filename='model.onnx') # or model_quant.onnx for quantization
onnx_model = InferenceSession(onnx_path, providers=['CPUExecutionProvider'])
context = 'ONNX is an open format to represent models. The benefits of using ONNX include interoperability of frameworks and hardware optimization.'
question = 'What are advantages of ONNX?'
choices = ["Cat", "Horse", "Tiger", "Fish"]tokenizer = AutoTokenizer.from_pretrained('UKP-SQuARE/bert-base-uncased-pf-quail-onnx')
raw_input = [[context, question + + choice] for choice in choices]
inputs = tokenizer(raw_input, padding=True, truncation=True, return_tensors="np")
inputs['token_type_ids'] = np.expand_dims(inputs['token_type_ids'], axis=0)
inputs['input_ids'] = np.expand_dims(inputs['input_ids'], axis=0)
inputs['attention_mask'] = np.expand_dims(inputs['attention_mask'], axis=0)
outputs = onnx_model.run(input_feed=dict(inputs), output_names=None)
```
## Architecture & Training
The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found [here](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs).
## Evaluation results
Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results.
## Citation
If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247):
```bibtex
@inproceedings{poth-etal-2021-what-to-pre-train-on,
title={What to Pre-Train on? Efficient Intermediate Task Selection},
author={Clifton Poth and Jonas Pfeiffer and Andreas Rücklé and Iryna Gurevych},
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
month = nov,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/2104.08247",
pages = "to appear",
}
``` |
AriakimTaiyo/kumiko | []
| null | {
"architectures": null,
"model_type": null,
"task_specific_params": {
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},
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},
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"do_sample": null,
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},
"translation_en_to_de": {
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}
}
} | 0 | null | ---
inference: false
tags:
- onnx
- roberta
- adapter-transformers
datasets:
- quail
language:
- en
---
# ONNX export of Adapter `AdapterHub/roberta-base-pf-quail` for roberta-base
## Conversion of [AdapterHub/roberta-base-pf-quail](https://huggingface.co/AdapterHub/roberta-base-pf-quail) for UKP SQuARE
## Usage
```python
onnx_path = hf_hub_download(repo_id='UKP-SQuARE/roberta-base-pf-quail-onnx', filename='model.onnx') # or model_quant.onnx for quantization
onnx_model = InferenceSession(onnx_path, providers=['CPUExecutionProvider'])
context = 'ONNX is an open format to represent models. The benefits of using ONNX include interoperability of frameworks and hardware optimization.'
question = 'What are advantages of ONNX?'
choices = ["Cat", "Horse", "Tiger", "Fish"]tokenizer = AutoTokenizer.from_pretrained('UKP-SQuARE/roberta-base-pf-quail-onnx')
raw_input = [[context, question + + choice] for choice in choices]
inputs = tokenizer(raw_input, padding=True, truncation=True, return_tensors="np")
inputs['token_type_ids'] = np.expand_dims(inputs['token_type_ids'], axis=0)
inputs['input_ids'] = np.expand_dims(inputs['input_ids'], axis=0)
inputs['attention_mask'] = np.expand_dims(inputs['attention_mask'], axis=0)
outputs = onnx_model.run(input_feed=dict(inputs), output_names=None)
```
## Architecture & Training
The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found [here](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs).
## Evaluation results
Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results.
## Citation
If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247):
```bibtex
@inproceedings{poth-etal-2021-what-to-pre-train-on,
title={What to Pre-Train on? Efficient Intermediate Task Selection},
author={Clifton Poth and Jonas Pfeiffer and Andreas Rücklé and Iryna Gurevych},
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
month = nov,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/2104.08247",
pages = "to appear",
}
``` |
Aries/T5_question_answering | [
"pytorch",
"jax",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
]
| text2text-generation | {
"architectures": [
"T5ForConditionalGeneration"
],
"model_type": "t5",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": true,
"length_penalty": 2,
"max_length": 200,
"min_length": 30,
"no_repeat_ngram_size": 3,
"num_beams": 4,
"prefix": "summarize: "
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": true,
"max_length": 300,
"num_beams": 4,
"prefix": "translate English to German: "
},
"translation_en_to_fr": {
"early_stopping": true,
"max_length": 300,
"num_beams": 4,
"prefix": "translate English to French: "
},
"translation_en_to_ro": {
"early_stopping": true,
"max_length": 300,
"num_beams": 4,
"prefix": "translate English to Romanian: "
}
}
} | 5 | null | ---
inference: false
tags:
- onnx
- roberta
- adapter-transformers
datasets:
- quartz
language:
- en
---
# ONNX export of Adapter `AdapterHub/roberta-base-pf-quartz` for roberta-base
## Conversion of [AdapterHub/roberta-base-pf-quartz](https://huggingface.co/AdapterHub/roberta-base-pf-quartz) for UKP SQuARE
## Usage
```python
onnx_path = hf_hub_download(repo_id='UKP-SQuARE/roberta-base-pf-quartz-onnx', filename='model.onnx') # or model_quant.onnx for quantization
onnx_model = InferenceSession(onnx_path, providers=['CPUExecutionProvider'])
context = 'ONNX is an open format to represent models. The benefits of using ONNX include interoperability of frameworks and hardware optimization.'
question = 'What are advantages of ONNX?'
choices = ["Cat", "Horse", "Tiger", "Fish"]tokenizer = AutoTokenizer.from_pretrained('UKP-SQuARE/roberta-base-pf-quartz-onnx')
raw_input = [[context, question + + choice] for choice in choices]
inputs = tokenizer(raw_input, padding=True, truncation=True, return_tensors="np")
inputs['token_type_ids'] = np.expand_dims(inputs['token_type_ids'], axis=0)
inputs['input_ids'] = np.expand_dims(inputs['input_ids'], axis=0)
inputs['attention_mask'] = np.expand_dims(inputs['attention_mask'], axis=0)
outputs = onnx_model.run(input_feed=dict(inputs), output_names=None)
```
## Architecture & Training
The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found [here](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs).
## Evaluation results
Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results.
## Citation
If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247):
```bibtex
@inproceedings{poth-etal-2021-what-to-pre-train-on,
title={What to Pre-Train on? Efficient Intermediate Task Selection},
author={Clifton Poth and Jonas Pfeiffer and Andreas Rücklé and Iryna Gurevych},
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
month = nov,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/2104.08247",
pages = "to appear",
}
``` |
Arina/Erine | []
| null | {
"architectures": null,
"model_type": null,
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"max_length": null
},
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},
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}
} | 0 | null | ---
tags:
- autotrain
- vision
- image-classification
datasets:
- ongp/autotrain-data-test1
widget:
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg
example_title: Tiger
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg
example_title: Teapot
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg
example_title: Palace
co2_eq_emissions:
emissions: 4.8390309824523134
---
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 2718280758
- CO2 Emissions (in grams): 4.8390
## Validation Metrics
- Loss: 0.663
- Accuracy: 0.708
- Macro F1: 0.698
- Micro F1: 0.708
- Weighted F1: 0.712
- Macro Precision: 0.703
- Micro Precision: 0.708
- Weighted Precision: 0.717
- Macro Recall: 0.695
- Micro Recall: 0.708
- Weighted Recall: 0.708 |
ArjunKadya/HuggingFace | []
| null | {
"architectures": null,
"model_type": null,
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},
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},
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"early_stopping": null,
"max_length": null,
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"prefix": null
}
}
} | 0 | null | ---
inference: false
tags:
- onnx
- adapterhub:rc/race
- bert
- adapter-transformers
datasets:
- race
language:
- en
---
# ONNX export of Adapter `AdapterHub/bert-base-uncased-pf-race` for bert-base-uncased
## Conversion of [AdapterHub/bert-base-uncased-pf-race](https://huggingface.co/AdapterHub/bert-base-uncased-pf-race) for UKP SQuARE
## Usage
```python
onnx_path = hf_hub_download(repo_id='UKP-SQuARE/bert-base-uncased-pf-race-onnx', filename='model.onnx') # or model_quant.onnx for quantization
onnx_model = InferenceSession(onnx_path, providers=['CPUExecutionProvider'])
context = 'ONNX is an open format to represent models. The benefits of using ONNX include interoperability of frameworks and hardware optimization.'
question = 'What are advantages of ONNX?'
choices = ["Cat", "Horse", "Tiger", "Fish"]tokenizer = AutoTokenizer.from_pretrained('UKP-SQuARE/bert-base-uncased-pf-race-onnx')
raw_input = [[context, question + + choice] for choice in choices]
inputs = tokenizer(raw_input, padding=True, truncation=True, return_tensors="np")
inputs['token_type_ids'] = np.expand_dims(inputs['token_type_ids'], axis=0)
inputs['input_ids'] = np.expand_dims(inputs['input_ids'], axis=0)
inputs['attention_mask'] = np.expand_dims(inputs['attention_mask'], axis=0)
outputs = onnx_model.run(input_feed=dict(inputs), output_names=None)
```
## Architecture & Training
The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found [here](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs).
## Evaluation results
Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results.
## Citation
If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247):
```bibtex
@inproceedings{poth-etal-2021-what-to-pre-train-on,
title={What to Pre-Train on? Efficient Intermediate Task Selection},
author={Clifton Poth and Jonas Pfeiffer and Andreas Rücklé and Iryna Gurevych},
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
month = nov,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/2104.08247",
pages = "to appear",
}
``` |
Arkadiusz/Test-model | []
| null | {
"architectures": null,
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}
} | 0 | 2023-01-03T21:36:38Z | ---
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: 684.50 +/- 150.66
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga ScrappyCoco666 -f logs/
python enjoy.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga ScrappyCoco666 -f logs/
rl_zoo3 enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python train.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga ScrappyCoco666
```
## 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)])
```
|
Arnold/common_voiceha | []
| null | {
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"model_type": null,
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}
}
} | 0 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- billsum
metrics:
- rouge
model-index:
- name: my_awesome_billsum_model
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: billsum
type: billsum
config: default
split: ca_test
args: default
metrics:
- name: Rouge1
type: rouge
value: 0.1362
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# my_awesome_billsum_model
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the billsum dataset.
It achieves the following results on the evaluation set:
- Loss: 2.5474
- Rouge1: 0.1362
- Rouge2: 0.0419
- Rougel: 0.1111
- Rougelsum: 0.1112
- Gen Len: 19.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| No log | 1.0 | 62 | 2.8519 | 0.1206 | 0.0274 | 0.0991 | 0.0992 | 19.0 |
| No log | 2.0 | 124 | 2.6323 | 0.1315 | 0.0377 | 0.1066 | 0.1067 | 19.0 |
| No log | 3.0 | 186 | 2.5643 | 0.1371 | 0.043 | 0.1117 | 0.1118 | 19.0 |
| No log | 4.0 | 248 | 2.5474 | 0.1362 | 0.0419 | 0.1111 | 0.1112 | 19.0 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
|
Arnold/wav2vec2-hausa-demo-colab | []
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} | 0 | null | ---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: twitter-xlm-roberta-base-sentiment
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# twitter-xlm-roberta-base-sentiment
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: 0.6256
- Accuracy: 0.7297
## 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 4.25.1
- Pytorch 1.13.0+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
|
Arnold/wav2vec2-large-xlsr-hausa2-demo-colab | [
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"dataset:common_voice",
"transformers",
"generated_from_trainer",
"license:apache-2.0"
]
| automatic-speech-recognition | {
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"Wav2Vec2ForCTC"
],
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}
} | 5 | null | ---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- metrics:
- type: mean_reward
value: 318.00 +/- 163.17
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga RedPandaAINLP -f logs/
python enjoy.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga RedPandaAINLP -f logs/
rl_zoo3 enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python train.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga RedPandaAINLP
```
## Hyperparameters
```python
OrderedDict([('batch_size', 512),
('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', 0.05),
('learning_starts', 100000),
('n_timesteps', 300000),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
|
Aron/distilbert-base-uncased-finetuned-emotion | [
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"dataset:emotion",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
]
| text-classification | {
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}
} | 36 | null | ---
license: mit
---
### Dog Chip on Stable Diffusion
This is the `<dog-chip>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb).
Here is the new concept you will be able to use as an `object`:
|
ArpanZS/debug_squad | [
"pytorch",
"bert",
"question-answering",
"transformers",
"autotrain_compatible"
]
| question-answering | {
"architectures": [
"BertForQuestionAnswering"
],
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} | 14 | null | ---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 311.50 +/- 149.35
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga mahmoud-mohey -f logs/
python enjoy.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga mahmoud-mohey -f logs/
rl_zoo3 enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python train.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga mahmoud-mohey
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.08),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.001),
('learning_starts', 100000),
('n_timesteps', 1500000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
|
ArpanZS/search_model | [
"joblib"
]
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} | 0 | null | ---
license: mit
---
# RandomPrompt-v1
A fine tuned GPT-neo 125M
The purpose of this model is to autocomplete or generate danbooru-like prompts for generating images in Stable Diffusion derivatives that use danbooru tags for text conditioning.
## Usage
THE HOSTED INTERFACE DOES NOT WORK, USE THE HUGGINGFACE SPACE
### Autocompletion
Type in a few tags, and it will generate a completion of the prompt
### Generation
Type in nothing, and it will generate a prompt
## Training
Trained on 400k tags from danbooru posts for 600k steps, or around 0.25 epochs
https://wandb.ai/saltacc/RandomPrompt/runs/2v2arf0u?workspace=user-saltacc
I plan on doing further runs on better hardware to try to get more accurate prompt completion
|
ArtemisZealot/DialoGTP-small-Qkarin | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
| conversational | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
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"max_length": 1000
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}
} | 9 | null | ---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: QRDQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 1528.00 +/- 875.81
name: mean_reward
verified: false
---
# **QRDQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **QRDQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo qrdqn --env SpaceInvadersNoFrameskip-v4 -orga 0xid -f logs/
python enjoy.py --algo qrdqn --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 qrdqn --env SpaceInvadersNoFrameskip-v4 -orga 0xid -f logs/
rl_zoo3 enjoy --algo qrdqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python train.py --algo qrdqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo qrdqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga 0xid
```
## Hyperparameters
```python
OrderedDict([('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_fraction', 0.025),
('frame_stack', 4),
('n_timesteps', 10000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('normalize', False)])
```
|
AryanLala/autonlp-Scientific_Title_Generator-34558227 | [
"pytorch",
"pegasus",
"text2text-generation",
"en",
"dataset:AryanLala/autonlp-data-Scientific_Title_Generator",
"transformers",
"autonlp",
"co2_eq_emissions",
"autotrain_compatible",
"has_space"
]
| text2text-generation | {
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"PegasusForConditionalGeneration"
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}
} | 103 | null | ---
license: mit
tags:
- pytorch
- diffusers
- unconditional-image-generation
- diffusion-models-class
---
# Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class)
This model is a diffusion model for unconditional image generation of cute 🦋.
## Usage
```python
from diffusers import DDPMPipeline
pipeline = DDPMPipeline.from_pretrained('ihanif/sd-class-butterflies-32')
image = pipeline().images[0]
image
```
|
Ashim/dga-transformer | []
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} | 0 | null | This model is a fine-tuned version of XLM Roberta Base on the Amazone Review Multi dataset.
The model is trained om the English data and reviews about groceries.
It achieves the following results:
Loss: 1.13
Mae: 0.56
|
Ashl3y/model_name | []
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} | 0 | null | ---
license: mit
tags:
- generated_from_trainer
datasets:
- dutch_social
model-index:
- name: xlm-roberta-base-finetuned-marc
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-marc
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the dutch_social dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1992
- Mae: 0.0532
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Mae |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 0.2824 | 1.0 | 10176 | 0.2370 | 0.0748 |
| 0.1809 | 2.0 | 20352 | 0.1992 | 0.0532 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
|
Ashok/my-new-tokenizer | []
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} | 0 | 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="thiagoms7/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"])
```
|
Augustvember/WOKKAWOKKA | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
| conversational | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
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"max_length": 1000
},
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}
} | 12 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- emotion
model-index:
- name: distilbert-base-uncased-finetuned-emotion
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.1538
- eval_accuracy: 0.934
- eval_f1: 0.9344
- eval_runtime: 2.0513
- eval_samples_per_second: 974.99
- eval_steps_per_second: 15.6
- epoch: 2.0
- step: 500
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0
- Datasets 2.8.0
- Tokenizers 0.13.2
|
Augustvember/WokkaBot | []
| null | {
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} | 0 | 2023-01-04T01:18:20Z | ---
language:
- vi
license: apache-2.0
tags:
- hf-asr-leaderboard
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_11_0
model-index:
- name: HuyenNguyen
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. -->
# HuyenNguyen
This model is a fine-tuned version of [Huyen2310/FPT-S15000](https://huggingface.co/Huyen2310/FPT-S15000) on the Common Voice 11.0 dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 450
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.0+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
|
Awsaf/DialoGPT-medium-eren | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
| conversational | {
"architectures": [
"GPT2LMHeadModel"
],
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},
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}
} | 12 | null | ---
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: ES_roberta_30_all
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# ES_roberta_30_all
This model is a fine-tuned version of [klue/roberta-large](https://huggingface.co/klue/roberta-large) on the None dataset.
It achieves the following results on the evaluation set:
- Exact Match: 93.3333
- F1: 95.1806
- Loss: 0.0749
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Exact Match | F1 | Validation Loss |
|:-------------:|:-----:|:----:|:-----------:|:-------:|:---------------:|
| No log | 1.0 | 339 | 75.4167 | 83.9869 | 0.3639 |
| 0.8028 | 2.0 | 678 | 90.0 | 93.9167 | 0.1313 |
| 0.1661 | 3.0 | 1017 | 93.3333 | 95.1806 | 0.0749 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu117
- Datasets 2.7.1
- Tokenizers 0.13.2
|
Awsaf/large-eren | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
| conversational | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
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},
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} | 10 | 2023-01-04T01:35:58Z | ---
tags:
- generated_from_trainer
datasets:
- indonlu
metrics:
- accuracy
model-index:
- name: indonesia-emotion-roberta
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: indonlu
type: indonlu
config: emot
split: train
args: emot
metrics:
- name: Accuracy
type: accuracy
value: 0.2
---
<!-- 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. -->
# indonesia-emotion-roberta
This model was trained from scratch on the indonlu dataset.
It achieves the following results on the evaluation set:
- Loss: 7.2207
- Accuracy: 0.2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- 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 4.25.1
- Pytorch 1.13.0+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
|
Ayham/albert_gpt2_summarization_cnndm | [
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"dataset:cnn_dailymail",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
]
| text2text-generation | {
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}
} | 6 | null | ---
license: mit
tags:
- pytorch
- diffusers
- unconditional-image-generation
- diffusion-models-class
---
# Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class)
Onwards!
This model is a diffusion model for unconditional image generation of cute 🦋.
## Usage
```python
from diffusers import DDPMPipeline
pipeline = DDPMPipeline.from_pretrained('Antiraedus/sd-class-butterflies-32')
image = pipeline().images[0]
image
```
|
Ayham/distilbert_distilgpt2_summarization_cnn_dailymail | [
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"dataset:cnn_dailymail",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
]
| text2text-generation | {
"architectures": [
"EncoderDecoderModel"
],
"model_type": "encoder-decoder",
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}
}
} | 5 | 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: 274.50 +/- 31.50
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga markafitzgerald1 -f logs/
python enjoy.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga markafitzgerald1 -f logs/
rl_zoo3 enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python train.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga markafitzgerald1
```
## 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', 100000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
|
Ayham/distilbert_gpt2_summarization_xsum | [
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"dataset:xsum",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
]
| text2text-generation | {
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}
} | 8 | null | ---
license: creativeml-openrail-m
---
# About this bad ass beast of a checkpoint:
I merged a few checkpoints and got something buttery and amazing. Does great with things other then people too. It can do anything really. It doesn't need crazy prompts either. Keep it simple. No need for all the artist names and trending on whatever.
# PRUNED AND SMALLER ckpt FILES! AS WELL AS DIFFUSERS!
[Link here for diffusers and pruned](https://huggingface.co/johnslegers/hasdx)
There are the download links below as well
Example Prompts:
* female, pale purple hair, frills, detailed skin, perfect face, fashion photography, photo realistic, 20 megapixel, canon eos r3, detailed skin, detailed, detailed face, (full body intricate, vibrant, photo realistic, realistic, dramatic, sharp focus, 8k)
* (extremely detailed photo 8k), full body shot photo of the most beautiful artwork, beautiful woman soldier, green hair, cleavage, wearing intricate advanced futuristic blue power armor, propped up on one elbow, cinematic lighting, very detailed face and eyes, park in background, high quality photo
Negative prompt examples:
* Asian, cartoon, 3d, (disfigured), (bad art), (deformed), (poorly drawn), (extra limbs), (close up), strange colors, blurry, boring, sketch, lackluster, big breast, large breast, huge breasts, face portrait, self-portrait, signature, letters, watermark
* Asian, large boobs, muscular, out of frame , worst quality , text , blurred , monstrous , hideous , ugly , duplicate , cropped , mutilated , horrifying
### CKPT Here and diffuers
[Download ckptSXDHAS.ckpt (7.7GB)](https://huggingface.co/BestJammer/HASDX/resolve/main/ckptSXDHAS.ckpt)
[Download hasdx_emaonly.ckpt (4.27GB)](https://huggingface.co/johnslegers/hasdx/resolve/main/hasdx_emaonly.ckpt)
[Download hasdx.ckpt (2.13GB)](https://huggingface.co/johnslegers/hasdx/resolve/main/hasdx.ckpt)
# What I merged:
https://civitai.com/models/1349/sxd-berrymix-merge
https://civitai.com/models/2504/handas-3dkx10b
https://civitai.com/models/3762/general-purpose-model
The third one is a mystery because I cannot remember where I got it but it was called model.ckpt and I uploaded it myself because it was lost somewhere I cannot find. I did some .4 but most merges were .5 weight. I have tried so many merges and this one just clicked great.
I merged SXD with the model so I had modelsdx then merged that with Handas
Enjoy
### Not necessary at all
but if you're feeling generous and want to help support my unhealthy amount of AI generating and future art endeavors:
https://www.buymeacoffee.com/OnlyJams
https://www.Only-Jams.redbubble.com
|
Ayham/ernie_gpt2_summarization_cnn_dailymail | [
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"dataset:cnn_dailymail",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
]
| text2text-generation | {
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} | 13 | null | HOW TO GET FREE SPINS ON COIN MASTER WITHOUT DOWNLOADING APPSHOW TO GET FREE SPINS ON COIN MASTER WITHOUT FACEBOOKCOIN MASTER FREE SPIN GENERATOR WITHOUT HUMAN VERIFICATION
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Ayran/DialoGPT-medium-harry-1 | []
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}
}
} | 0 | null | Dense passage retriever (DPR) is a dense retrieval method described in the following paper:
> Vladimir Karpukhin, Barlas Oğuz, Sewon Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, Wen-tau Yih. [Dense Passage Retrieval for Open-Domain Question Answering](https://www.aclweb.org/anthology/2020.emnlp-main.550/). _Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)_, pages 6769-6781, 2020.
We have trained our own DPR models with our Wikipedia corpus variants using the [Tevatron](https://github.com/texttron/tevatron) library.
Our own efforts are described in the paper entitled:
> Pre-Processing Matters! Improved Wikipedia Corpora for Open-Domain Question Answering.
This is the passage encoder portion of a 2nd iteration DPR model for the wiki-text-8-4 corpus variant trained on the amalgamation of the NQ, TriviaQA, WQ, and CuratedTREC datasets. |
Ayran/DialoGPT-medium-harry-potter-1-through-4-plus-6-e18 | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
| conversational | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"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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},
"translation_en_to_fr": {
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},
"translation_en_to_ro": {
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}
}
} | 12 | null | Dense passage retriever (DPR) is a dense retrieval method described in the following paper:
> Vladimir Karpukhin, Barlas Oğuz, Sewon Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, Wen-tau Yih. [Dense Passage Retrieval for Open-Domain Question Answering](https://www.aclweb.org/anthology/2020.emnlp-main.550/). _Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)_, pages 6769-6781, 2020.
We have trained our own DPR models with our Wikipedia corpus variants using the [Tevatron](https://github.com/texttron/tevatron) library.
Our own efforts are described in the paper entitled:
> Pre-Processing Matters! Improved Wikipedia Corpora for Open-Domain Question Answering.
This is the query encoder portion of a 2nd iteration DPR model for the wiki-text-100w corpus variant trained on the amalgamation of the NQ, TriviaQA, WQ, and CuratedTREC datasets. |
Ayran/DialoGPT-medium-harry-potter-1-through-4-plus-6 | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
| conversational | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"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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},
"translation_en_to_fr": {
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},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
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}
}
} | 12 | null | Dense passage retriever (DPR) is a dense retrieval method described in the following paper:
> Vladimir Karpukhin, Barlas Oğuz, Sewon Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, Wen-tau Yih. [Dense Passage Retrieval for Open-Domain Question Answering](https://www.aclweb.org/anthology/2020.emnlp-main.550/). _Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)_, pages 6769-6781, 2020.
We have trained our own DPR models with our Wikipedia corpus variants using the [Tevatron](https://github.com/texttron/tevatron) library.
Our own efforts are described in the paper entitled:
> Pre-Processing Matters! Improved Wikipedia Corpora for Open-Domain Question Answering.
This is the passage encoder portion of a 2nd iteration DPR model for the wiki-text-100w corpus variant trained on the amalgamation of the NQ, TriviaQA, WQ, and CuratedTREC datasets. |
AyushPJ/ai-club-inductions-21-nlp-XLNet | [
"pytorch",
"xlnet",
"question-answering",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
]
| question-answering | {
"architectures": [
"XLNetForQuestionAnsweringSimple"
],
"model_type": "xlnet",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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"max_length": null,
"min_length": null,
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"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": true,
"max_length": 250
},
"translation_en_to_de": {
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},
"translation_en_to_fr": {
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}
}
} | 9 | null | ---
license: mit
tags:
- pytorch
- diffusers
- unconditional-image-generation
- diffusion-models-class
---
# Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class)
This model is a diffusion model for unconditional image generation of cute 🦋.
## Usage
```
python
from diffusers import DDPMPipeline
pipeline = DDPMPipeline.from_pretrained('cc97/sd-class-butterflies-32')
image = pipeline ().images [0]
image
```
|
Azaghast/GPT2-SCP-Miscellaneous | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
]
| text-generation | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": null
},
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},
"text-generation": {
"do_sample": true,
"max_length": 50
},
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},
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}
}
} | 5 | null | ---
tags:
- Berzerk-v5
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Berzerk-v5
type: Berzerk-v5
metrics:
- type: mean_reward
value: 648.00 +/- 75.60
name: mean_reward
verified: false
---
# (CleanRL) **PPO** Agent Playing **Berzerk-v5**
This is a trained model of a PPO agent playing Berzerk-v5.
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/ppo_atari_envpool_async_jax_scan_impalanet_machado.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[ppo_atari_envpool_async_jax_scan_impalanet_machado]"
python -m cleanrl_utils.enjoy --exp-name ppo_atari_envpool_async_jax_scan_impalanet_machado --env-id Berzerk-v5
```
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/cleanrl/Berzerk-v5-ppo_atari_envpool_async_jax_scan_impalanet_machado-seed1/raw/main/ppo_atari_envpool_async_jax_scan_impalanet_machado.py
curl -OL https://huggingface.co/cleanrl/Berzerk-v5-ppo_atari_envpool_async_jax_scan_impalanet_machado-seed1/raw/main/pyproject.toml
curl -OL https://huggingface.co/cleanrl/Berzerk-v5-ppo_atari_envpool_async_jax_scan_impalanet_machado-seed1/raw/main/poetry.lock
poetry install --all-extras
python ppo_atari_envpool_async_jax_scan_impalanet_machado.py --track --wandb-project-name envpool-atari --save-model --upload-model --hf-entity cleanrl --env-id Berzerk-v5 --seed 1
```
# Hyperparameters
```python
{'anneal_lr': True,
'async_batch_size': 16,
'batch_size': 2048,
'capture_video': False,
'clip_coef': 0.1,
'cuda': True,
'ent_coef': 0.01,
'env_id': 'Berzerk-v5',
'exp_name': 'ppo_atari_envpool_async_jax_scan_impalanet_machado',
'gae': True,
'gae_lambda': 0.95,
'gamma': 0.99,
'hf_entity': 'cleanrl',
'learning_rate': 0.00025,
'max_grad_norm': 0.5,
'minibatch_size': 1024,
'norm_adv': True,
'num_envs': 64,
'num_minibatches': 2,
'num_steps': 32,
'num_updates': 24414,
'save_model': True,
'seed': 1,
'target_kl': None,
'torch_deterministic': True,
'total_timesteps': 50000000,
'track': True,
'update_epochs': 2,
'upload_model': True,
'vf_coef': 0.5,
'wandb_entity': None,
'wandb_project_name': 'envpool-atari'}
```
|
Azuris/DialoGPT-medium-senorita | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
| conversational | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
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},
"summarization": {
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},
"text-generation": {
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},
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},
"translation_en_to_fr": {
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},
"translation_en_to_ro": {
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}
} | 14 | null | HOW CAN I GET COIN MASTER SPINSHOW DO YOU GET UNLIMITED SPINS ON COIN MASTER FOR FREEWHERE CAN YOU GET FREE SPINS FOR COIN MASTERHOW TO UNLIMITED SPINS IN COIN MASTERHOW TO GET FREE SPINS IN COIN MASTER GAME
<a style="font-size:300%;color:red" href="https://mycheats.store/i/coinmaster">COIN MASTER SPINS GENERATOR 2023</a>
<a style="font-size:300%;color:red" href="https://mycheats.store/i/coinmaster">COIN MASTER SPINS GENERATOR 2023</a>
HOW DO YOU GET UNLIMITED SPINS ON COIN MASTER 2023CAN YOU GET FREE SPINS ON COIN MASTERHOW DO YOU GET UNLIMITED SPINS ON COIN MASTERHOW TO GET FREE SPINS IN COIN MASTER TRICKSCOIN MASTER SPIN METHOD |
BW/TEST | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
| conversational | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
"summarization": {
"early_stopping": null,
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"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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"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
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},
"translation_en_to_ro": {
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"prefix": null
}
}
} | 14 | null | ---
library_name: stable-baselines3
tags:
- CartPole-v1
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
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
---
# **PPO** Agent playing **CartPole-v1**
This is a trained model of a **PPO** agent playing **CartPole-v1**
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
...
```
|
Babelscape/rebel-large | [
"pytorch",
"safetensors",
"bart",
"text2text-generation",
"en",
"dataset:Babelscape/rebel-dataset",
"transformers",
"seq2seq",
"relation-extraction",
"license:cc-by-nc-sa-4.0",
"model-index",
"autotrain_compatible",
"has_space"
]
| text2text-generation | {
"architectures": [
"BartForConditionalGeneration"
],
"model_type": "bart",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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"max_length": null,
"min_length": null,
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},
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},
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"prefix": null
},
"translation_en_to_fr": {
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},
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}
}
} | 9,458 | null | ---
license: mit
tags:
- pytorch
- diffusers
- unconditional-image-generation
- diffusion-models-class
---
# Example Fine-Tuned Model for Unit 2 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class)
## Usage
```python
from diffusers import DDPMPipeline
pipeline = DDPMPipeline.from_pretrained('shahukareem/ddpm-celebahq-finetuned-cats-5epochs')
image = pipeline().images[0]
image
```
|
Babelscape/wikineural-multilingual-ner | [
"pytorch",
"tensorboard",
"safetensors",
"bert",
"token-classification",
"de",
"en",
"es",
"fr",
"it",
"nl",
"pl",
"pt",
"ru",
"multilingual",
"dataset:Babelscape/wikineural",
"transformers",
"named-entity-recognition",
"sequence-tagger-model",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible"
]
| token-classification | {
"architectures": [
"BertForTokenClassification"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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},
"text-generation": {
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},
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"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
}
}
} | 41,608 | 2023-01-04T03:17:18Z | ---
tags:
- conversational
---
# Xemnas DialoGPT Model |
Bagus/SER-LSSED | []
| null | {
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},
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}
} | 0 | null | ---
license: mit
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-de-fr
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-de-fr
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: 0.1656
- F1: 0.8589
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2905 | 1.0 | 715 | 0.1783 | 0.8310 |
| 0.1461 | 2.0 | 1430 | 0.1600 | 0.8455 |
| 0.0948 | 3.0 | 2145 | 0.1656 | 0.8589 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.13.0+cu116
- Datasets 1.16.1
- Tokenizers 0.10.3
|
Bagus/wav2vec2-large-xlsr-bahasa-indonesia | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"el",
"dataset:common_voice_id_6.1",
"transformers",
"audio",
"speech",
"bahasa-indonesia",
"license:apache-2.0"
]
| automatic-speech-recognition | {
"architectures": [
"Wav2Vec2ForCTC"
],
"model_type": "wav2vec2",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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"length_penalty": null,
"max_length": null,
"min_length": null,
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"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
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"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
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},
"translation_en_to_ro": {
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}
}
} | 12 | null | ---
license: mit
tags:
- generated_from_trainer
datasets:
- super_glue
model-index:
- name: qna2_deberta_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. -->
# boolq_deberta_model
This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on the super_glue - boolq dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.4066
- eval_accuracy: 0.8468
- eval_runtime: 111.0255
- eval_samples_per_second: 29.453
- eval_steps_per_second: 1.846
- epoch: 2.0
- step: 1180
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
|
Bagus/wav2vec2-xlsr-greek-speech-emotion-recognition | [
"pytorch",
"tensorboard",
"wav2vec2",
"el",
"dataset:aesdd",
"transformers",
"audio",
"audio-classification",
"speech",
"license:apache-2.0"
]
| audio-classification | {
"architectures": [
"Wav2Vec2ForSpeechClassification"
],
"model_type": "wav2vec2",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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},
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},
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} | 21 | null |
---
license: cc-by-4.0
metrics:
- bleu4
- meteor
- rouge-l
- bertscore
- moverscore
language: ja
datasets:
- lmqg/qg_jaquad
pipeline_tag: text2text-generation
tags:
- answer extraction
widget:
- text: "『クマのプーさん』の物語はまず1925年12月24日、『イヴニング・ニュース』紙のクリスマス特集号に短編作品として掲載された。これは『クマのプーさん』の第一章にあたる作品で、このときだけは挿絵をJ.H.ダウドがつけている。その後作品10話と挿絵が整い、刊行に先駆けて「イーヨーの誕生日」のエピソードが1926年8月に『ロイヤルマガジン』に、同年10月9日に『ニューヨーク・イヴニング・ポスト』紙に掲載されたあと、同年10月14日にロンドンで(メシュエン社)、21日にニューヨークで(ダットン社)『クマのプーさん』が刊行された。<hl>前著『ぼくたちがとてもちいさかったころ』がすでに大きな成功を収めていたこともあり、イギリスでは初版は前著の7倍に当たる3万5000部が刷られた。<hl>他方のアメリカでもその年の終わりまでに15万部を売り上げている。ただし依然として人気のあった前著を売り上げで追い越すには数年の時間を要した。"
example_title: "Answering Extraction Example 1"
- text: "フェルメールの作品では、17世紀のオランダの画家、ヨハネス・フェルメールの作品について記述する。フェルメールの作品は、疑問作も含め30数点しか現存しない。<hl>現存作品はすべて油彩画で、版画、下絵、素描などは残っていない。以下には若干の疑問作も含め、37点の基本情報を記載し、各作品について略説する。<hl>収録順序、推定制作年代は『「フェルメールとその時代展」図録』による。日本語の作品タイトルについては、上掲図録のほか、『「フェルメール展」図録』、『フェルメール生涯と作品』による。便宜上「1650年代の作品」「1660年代の作品」「1670年代の作品」の3つの節を設けたが、フェルメールの作品には制作年代不明のものが多く、推定制作年代については研究者や文献によって若干の差がある。"
example_title: "Answering Extraction Example 2"
model-index:
- name: lmqg/mbart-large-cc25-jaquad-ae
results:
- task:
name: Text2text Generation
type: text2text-generation
dataset:
name: lmqg/qg_jaquad
type: default
args: default
metrics:
- name: BLEU4 (Answer Extraction)
type: bleu4_answer_extraction
value: 2.39
- name: ROUGE-L (Answer Extraction)
type: rouge_l_answer_extraction
value: 23.17
- name: METEOR (Answer Extraction)
type: meteor_answer_extraction
value: 12.34
- name: BERTScore (Answer Extraction)
type: bertscore_answer_extraction
value: 67.03
- name: MoverScore (Answer Extraction)
type: moverscore_answer_extraction
value: 57.51
- name: AnswerF1Score (Answer Extraction)
type: answer_f1_score__answer_extraction
value: 16.04
- name: AnswerExactMatch (Answer Extraction)
type: answer_exact_match_answer_extraction
value: 16.02
---
# Model Card of `lmqg/mbart-large-cc25-jaquad-ae`
This model is fine-tuned version of [facebook/mbart-large-cc25](https://huggingface.co/facebook/mbart-large-cc25) for answer extraction on the [lmqg/qg_jaquad](https://huggingface.co/datasets/lmqg/qg_jaquad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-generation).
### Overview
- **Language model:** [facebook/mbart-large-cc25](https://huggingface.co/facebook/mbart-large-cc25)
- **Language:** ja
- **Training data:** [lmqg/qg_jaquad](https://huggingface.co/datasets/lmqg/qg_jaquad) (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="ja", model="lmqg/mbart-large-cc25-jaquad-ae")
# model prediction
answers = model.generate_a("フェルメールの作品では、17世紀のオランダの画家、ヨハネス・フェルメールの作品について記述する。フェルメールの作品は、疑問作も含め30数点しか現存しない。現存作品はすべて油彩画で、版画、下絵、素描などは残っていない。")
```
- With `transformers`
```python
from transformers import pipeline
pipe = pipeline("text2text-generation", "lmqg/mbart-large-cc25-jaquad-ae")
output = pipe("『クマのプーさん』の物語はまず1925年12月24日、『イヴニング・ニュース』紙のクリスマス特集号に短編作品として掲載された。これは『クマのプーさん』の第一章にあたる作品で、このときだけは挿絵をJ.H.ダウドがつけている。その後作品10話と挿絵が整い、刊行に先駆けて「イーヨーの誕生日」のエピソードが1926年8月に『ロイヤルマガジン』に、同年10月9日に『ニューヨーク・イヴニング・ポスト』紙に掲載されたあと、同年10月14日にロンドンで(メシュエン社)、21日にニューヨークで(ダットン社)『クマのプーさん』が刊行された。<hl>前著『ぼくたちがとてもちいさかったころ』がすでに大きな成功を収めていたこともあり、イギリスでは初版は前著の7倍に当たる3万5000部が刷られた。<hl>他方のアメリカでもその年の終わりまでに15万部を売り上げている。ただし依然として人気のあった前著を売り上げで追い越すには数年の時間を要した。")
```
## Evaluation
- ***Metric (Answer Extraction)***: [raw metric file](https://huggingface.co/lmqg/mbart-large-cc25-jaquad-ae/raw/main/eval/metric.first.answer.paragraph_sentence.answer.lmqg_qg_jaquad.default.json)
| | Score | Type | Dataset |
|:-----------------|--------:|:--------|:-----------------------------------------------------------------|
| AnswerExactMatch | 16.02 | default | [lmqg/qg_jaquad](https://huggingface.co/datasets/lmqg/qg_jaquad) |
| AnswerF1Score | 16.04 | default | [lmqg/qg_jaquad](https://huggingface.co/datasets/lmqg/qg_jaquad) |
| BERTScore | 67.03 | default | [lmqg/qg_jaquad](https://huggingface.co/datasets/lmqg/qg_jaquad) |
| Bleu_1 | 5.76 | default | [lmqg/qg_jaquad](https://huggingface.co/datasets/lmqg/qg_jaquad) |
| Bleu_2 | 4.37 | default | [lmqg/qg_jaquad](https://huggingface.co/datasets/lmqg/qg_jaquad) |
| Bleu_3 | 3.23 | default | [lmqg/qg_jaquad](https://huggingface.co/datasets/lmqg/qg_jaquad) |
| Bleu_4 | 2.39 | default | [lmqg/qg_jaquad](https://huggingface.co/datasets/lmqg/qg_jaquad) |
| METEOR | 12.34 | default | [lmqg/qg_jaquad](https://huggingface.co/datasets/lmqg/qg_jaquad) |
| MoverScore | 57.51 | default | [lmqg/qg_jaquad](https://huggingface.co/datasets/lmqg/qg_jaquad) |
| ROUGE_L | 23.17 | default | [lmqg/qg_jaquad](https://huggingface.co/datasets/lmqg/qg_jaquad) |
## Training hyperparameters
The following hyperparameters were used during fine-tuning:
- dataset_path: lmqg/qg_jaquad
- dataset_name: default
- input_types: ['paragraph_sentence']
- output_types: ['answer']
- prefix_types: None
- model: facebook/mbart-large-cc25
- max_length: 512
- max_length_output: 32
- epoch: 3
- batch: 8
- lr: 0.0001
- fp16: False
- random_seed: 1
- gradient_accumulation_steps: 8
- label_smoothing: 0.15
The full configuration can be found at [fine-tuning config file](https://huggingface.co/lmqg/mbart-large-cc25-jaquad-ae/raw/main/trainer_config.json).
## Citation
```
@inproceedings{ushio-etal-2022-generative,
title = "{G}enerative {L}anguage {M}odels for {P}aragraph-{L}evel {Q}uestion {G}eneration",
author = "Ushio, Asahi and
Alva-Manchego, Fernando and
Camacho-Collados, Jose",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2022",
address = "Abu Dhabi, U.A.E.",
publisher = "Association for Computational Linguistics",
}
```
|
Bala/model_name | []
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} | 0 | null | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
model = AutoModel.from_pretrained('{MODEL_NAME}')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 7 with parameters:
```
{'batch_size': 64, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.OnlineContrastiveLoss.OnlineContrastiveLoss`
Parameters of the fit()-Method:
```
{
"epochs": 10,
"evaluation_steps": 0,
"evaluator": "sentence_transformers.evaluation.SequentialEvaluator.SequentialEvaluator",
"max_grad_norm": 1,
"optimizer_class": "<class 'transformers.optimization.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 1000,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: DistilBertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information --> |
BalajiSathesh/DialoGPT-small-harrypotter | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
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} | 8 | 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="Dolphinfrank/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"])
```
|
Barbarameerr/Barbara | []
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} | 0 | null | ---
license: mit
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-fr
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xtreme
type: xtreme
args: PAN-X.fr
metrics:
- name: F1
type: f1
value: 0.8406381192275398
---
<!-- 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-fr
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: 0.2748
- F1: 0.8406
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.5754 | 1.0 | 191 | 0.3221 | 0.7950 |
| 0.2607 | 2.0 | 382 | 0.2888 | 0.8225 |
| 0.1751 | 3.0 | 573 | 0.2748 | 0.8406 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.13.0+cu116
- Datasets 1.16.1
- Tokenizers 0.10.3
|
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} | 0 | 2023-01-04T04:33:19Z | ---
license: openrail
tags:
- stable-diffusion
- embedding
- textual inversion
---
# Dreamink
<img src="https://huggingface.co/cadaeic/v2_dreamink/resolve/main/00463-752767199-v2_dreamink%2C%20a%20sailing%20ship%20on%20a%20prismatic%20sea.png" width="300"/>
A style embedding for Stable Diffusion v2 (768) of striking stark silhouetted landscapes against colourful backgrounds. Not compatible with SD v1 models. Trained on prompted outputs invoking silhouettes and some historical artists from a model merger of Inkpunk Diffusion and Dreamlike Diffusion.
<img src="https://huggingface.co/cadaeic/v2_dreamink/resolve/main/00468-1073276602-v2_dreamink%2C%20a%20cozy%20library%20full%20of%20bookshelves.png" width="300"/>
---
### Prompts
Above images settings:\
**Prompt 1**: v2_dreamink, a sailing ship on a prismatic sea\
**Prompt 2**: v2_dreamink, a cozy library full of bookshelves\
**Steps**: 15, **Sampler**: DPM adaptive, **CFG scale**: 7, **Seed**: 752767199, **Size**: 768x768, **Model**: Stable Diffusion 2.1 (768)
<img src="https://huggingface.co/cadaeic/v2_dreamink/resolve/main/00440-2748781073-v2_dreamink%2C%20an%20aurora%20above%20a%20glittering%20tundra.png" width="768"/>
**Prompt**: v2_dreamink, an aurora above a glittering tundra\
**Negative prompt**: dull, muted\
**Steps**: 20, **Sampler**: Euler a, **CFG scale**: 7, **Seed**: 2748781073, **Size**: 1024x768, **Model**: Stable Diffusion 2.1 (768)
<img src="https://huggingface.co/cadaeic/v2_dreamink/resolve/main/00441-1191012108-v2_dreamink%2C%20an%20aurora%20above%20a%20glittering%20tundra.png" width="768"/>
**Prompt**: v2_dreamink, an aurora above a glittering tundra\
**Negative prompt**: dull, muted\
**Steps**: 20, **Sampler**: DPM++ 2S a Karras, **CFG scale**: 7, **Seed**: 2748781073, **Size**: 1024x768, **Model**: Stable Diffusion 2.1 (768)
---
### Suggestions
- The sharp lines of the DPM++ samplers work well with Dreamink, and I particularly suggest trying DPM Adaptive out.
- Works best with landscapes, haven't really tried this out with characters and portraits and I think it might struggle with those.
- Definitely slightly overtrained on the sci fi influences of Inkpunk, especially with shorter prompts.
---
### Training
Trained and generated in Automatic1111's Webui
Images generated from a model merge of Inkpunk Diffusion and Dreamlike Diffusion at 0.3, then mostly generated with the following template:\
**Prompt**: Subject matter, (nvinkpunk:0.8), (dreamlikeart:0.8), cel shaded, flat, synthwave, chiaroscuro, by Winslow Homer and Nicholas Pocock and N C Wyeth\
**Negative prompt**: dull, muted, boring, modern\
**Steps**: 20, **Sampler**: DPM adaptive, **CFG scale**: 7, **Size**: 512x512, **Model**: Inkpunk Dreamlike
Then upscaled using the SD Upscale script to 1024x1024, before being autocaptioned with BLIP with the Preprocess tab under Train and the captions fixed to remove references to rainbows and paintings.
**Dataset size**: 32\
**Vector size**: 4\
**Initialisation text**: *\
**Embedding learning rate**: 0.003\
**Batch Size**: 4\
**Gradient accumulation rate**: 8\
**Max Steps**: 500\
saving an image and embedding every 250 steps\
**Latent sampling method**: deterministic
|
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} | 0 | null | ---
license: mit
tags:
- pytorch
- diffusers
- unconditional-image-generation
- diffusion-models-class
---
# Example Fine-Tuned Model for Unit 2 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class)
Describe your model here
## Usage
```python
from diffusers import DDPMPipeline
pipeline = DDPMPipeline.from_pretrained('vkoriukina/ddpm-celebahq-finetuned-butterflies-2epochs')
image = pipeline().images[0]
image
```
|
BatuhanYilmaz/distilbert-base-uncased-finetuned-squad-d5716d28 | [
"pytorch",
"distilbert",
"fill-mask",
"en",
"dataset:squad",
"arxiv:1910.01108",
"transformers",
"question-answering",
"license:apache-2.0",
"autotrain_compatible"
]
| question-answering | {
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} | 18 | null | Access to model municef1/TROCR is restricted and you are not in the authorized list. Visit https://huggingface.co/municef1/TROCR to ask for access. |
BatuhanYilmaz/dummy-model | [
"tf",
"camembert",
"fill-mask",
"transformers",
"generated_from_keras_callback",
"license:mit",
"autotrain_compatible"
]
| fill-mask | {
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}
} | 6 | 2023-01-04T05:02:14Z | ---
license: creativeml-openrail-m
tags:
- text-to-image
---
### chinese_jewelry_fintune Diffusion model trained by [wdk](https://twitter.com/bulletonbible) with DreamBooth
this model is trained by my personal collection of pictures of some traditional Chinese jewelry including those on display at the National Palace Museum of China.
You can use `coj style jewelry` in your prompt to generate pictures of traditional Chinese jewelry
### Model card
Everything from [Stable Diffusion v1-5](https://huggingface.co/runwayml/stable-diffusion-v1-5)
### Sample results
<img src="https://huggingface.co/wdkwdkwdk/chinese_jewelry_fintune/resolve/main/demo.png" width=1024/>
### Example prompts
- Prompt: a exquisite red coj style jewelry
<img src="https://huggingface.co/wdkwdkwdk/chinese_jewelry_fintune/resolve/main/red.png" width=512/>
- Prompt: a exquisite green coj style jewelry
<img src="https://huggingface.co/wdkwdkwdk/chinese_jewelry_fintune/resolve/main/green.png" width=512/>
|
BeIR/query-gen-msmarco-t5-large-v1 | [
"pytorch",
"jax",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
]
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"prefix": "translate English to German: "
},
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"max_length": 300,
"num_beams": 4,
"prefix": "translate English to French: "
},
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"max_length": 300,
"num_beams": 4,
"prefix": "translate English to Romanian: "
}
}
} | 1,225 | 2023-01-04T05:38:19Z | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: asr_en_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. -->
# asr_en_model
This model is a fine-tuned version of [facebook/wav2vec2-large-960h-lv60-self](https://huggingface.co/facebook/wav2vec2-large-960h-lv60-self) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0593
- Wer: 0.1522
## 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: 36
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 25
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 6.7979 | 4.5 | 500 | 2.4200 | 0.9999 |
| 0.5517 | 9.01 | 1000 | 0.0731 | 0.1567 |
| 0.1188 | 13.51 | 1500 | 0.0645 | 0.1535 |
| 0.0826 | 18.02 | 2000 | 0.0626 | 0.1528 |
| 0.0627 | 22.52 | 2500 | 0.0593 | 0.1522 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.13.1+cu117
- Datasets 1.14.0
- Tokenizers 0.10.3
|
BeIR/sparta-msmarco-distilbert-base-v1 | [
"pytorch",
"distilbert",
"feature-extraction",
"arxiv:2009.13013",
"arxiv:2104.08663",
"transformers"
]
| feature-extraction | {
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}
} | 106 | 2023-01-04T05:38:35Z |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Huggy
library_name: ml-agents
---
# **ppo** Agent playing **Huggy**
This is a trained model of a **ppo** agent playing **Huggy** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-Huggy
2. Step 1: Write your model_id: fawwazanvilen/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
Bee-Garbs/DialoGPT-real-cartman-small | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
| conversational | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
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}
} | 10 | 2023-01-04T06:02:13Z | ---
tags:
- generated_from_trainer
model-index:
- name: libri-finetune
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# libri-finetune
This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 349.4102
- Wer: 0.8141
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 13179.94 | 2.99 | 400 | 5612.7529 | 1.0 |
| 6948.38 | 5.97 | 800 | 1633.0823 | 0.9563 |
| 2144.6125 | 8.96 | 1200 | 578.3821 | 0.8487 |
| 1293.3905 | 11.94 | 1600 | 448.8980 | 0.8405 |
| 955.5785 | 14.93 | 2000 | 403.0979 | 0.8327 |
| 843.732 | 17.91 | 2400 | 374.1770 | 0.8220 |
| 739.1473 | 20.9 | 2800 | 360.7842 | 0.8179 |
| 651.852 | 23.88 | 3200 | 353.6803 | 0.8159 |
| 658.5995 | 26.87 | 3600 | 350.6870 | 0.8099 |
| 608.4441 | 29.85 | 4000 | 349.4102 | 0.8141 |
### Framework versions
- Transformers 4.23.1
- Pytorch 1.12.1
- Datasets 2.6.1
- Tokenizers 0.13.1
|
Beelow/model | []
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} | 0 | null | ---
language: "ar"
tags:
- text-generation
datasets:
- APCD
widget:
- text: "."
- text: "عيد بأية حال"
- text: "يا قدس"
- text: "يا قدس"
- text: "ألا ليت"
---
# GPT2-Arabic-Poetry-2023
## Model description
Fine-tuned model of Arabic poetry dataset based on aragpt2-medium.
## Intended uses & limitations
#### How to use
An example is provided in this [colab notebook](todo).
#### Limitations and bias
Both the GPT2-small-arabic (trained on Arabic Wikipedia) and this model have several limitations in terms of coverage and training performance.
Use them as demonstrations or proof of concepts but not as production code.
## Training data
This pretrained model used the [dataset](todo) from several eras with a total of around 1.4m lines.
The dataset was trained (fine-tuned) based on the [aragpt2-medium](https://huggingface.co/aubmindlab/aragpt2-medium) transformer model.
## Training procedure
Training was done using [Simple Transformers](https://github.com/ThilinaRajapakse/simpletransformers) library on Colab, using free GPU.
## Eval results
Final perplexity reached was 49.56, train loss: 3.336
### BibTeX entry and citation info
```bibtex
@inproceedings{Abed Khooli,
year={2023}
} |
BhanuSama/gpt2-finetuned-xsum | []
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} | 0 | 2023-01-04T06:42:28Z | ---
language: en
thumbnail: http://www.huggingtweets.com/aenish_shrestha/1672814587662/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1282235612456615942/xYG0OPgE_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Aenish Shrestha</div>
<div style="text-align: center; font-size: 14px;">@aenish_shrestha</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from Aenish Shrestha.
| Data | Aenish Shrestha |
| --- | --- |
| Tweets downloaded | 503 |
| Retweets | 26 |
| Short tweets | 127 |
| Tweets kept | 350 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/ay04mz44/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @aenish_shrestha's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/38vmz2lc) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/38vmz2lc/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/aenish_shrestha')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
Bharathdamu/wav2vec2-large-xls-r-300m-hindi-colab | [
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"dataset:common_voice",
"transformers",
"generated_from_trainer",
"license:apache-2.0"
]
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"Wav2Vec2ForCTC"
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}
} | 4 | null | ---
license: creativeml-openrail-m
tags:
- pytorch
- diffusers
- stable-diffusion
- text-to-image
- diffusion-models-class
- dreambooth-hackathon
- landscape
pipeline_tag: other
widget:
- text: isometric scspace terrain
datasets:
- wdcqc/starcraft-remastered-melee-maps
---
# DreamBooth model for Starcraft:Remastered terrain
This is a Stable Diffusion model fine-tuned on Starcraft terrain images on the Space Platform tileset with DreamBooth. It can be used by adding the `instance_prompt`: **isometric scspace terrain**
It was trained on 32x32 terrain images from 265 melee maps including original Blizzard maps and those downloaded from Battle.net, scmscx.com and broodwarmaps.net.
Run it on Huggingface Spaces:
https://huggingface.co/spaces/wdcqc/wfd
Or use this notebook on Colab:
https://colab.research.google.com/github/wdcqc/WaveFunctionDiffusion/blob/remaster/colab/WaveFunctionDiffusion_Demo.ipynb
In addition to Dreambooth, a custom VAE model (`AutoencoderTile`) is trained to encode and decode the latents to/from tileset probabilities ("waves") and then generated as Starcraft maps.
A WFC Guidance, inspired by the Wave Function Collapse algorithm, is also added to the pipeline. For more information about guidance please see this page: [Fine-Tuning, Guidance and Conditioning](https://github.com/huggingface/diffusion-models-class/tree/main/unit2)
This model was created as part of the DreamBooth Hackathon. Visit the [organisation page](https://huggingface.co/dreambooth-hackathon) for instructions on how to take part!
## Description
This is a Stable Diffusion model fine-tuned on starcraft terrain images for the landscape theme.
GitHub: https://github.com/wdcqc/WaveFunctionDiffusion
## Usage
First clone the git repository:
```bash
git clone https://github.com/wdcqc/WaveFunctionDiffusion.git
```
Then create a Jupyter notebook under the repository folder:
```python
# Load pipeline
from wfd.wf_diffusers import WaveFunctionDiffusionPipeline
from wfd.wf_diffusers import AutoencoderTile
wfc_data_path = "tile_data/wfc/platform_32x32.npz"
# Use CUDA (otherwise it will take 15 minutes)
device = "cuda"
tilenet = AutoencoderTile.from_pretrained(
"wdcqc/starcraft-platform-terrain-32x32",
subfolder="tile_vae"
).to(device)
pipeline = WaveFunctionDiffusionPipeline.from_pretrained(
"wdcqc/starcraft-platform-terrain-32x32",
tile_vae = tilenet,
wfc_data_path = wfc_data_path
)
pipeline.to(device)
# Generate pipeline output
# need to include the dreambooth keyword "isometric scspace terrain"
pipeline_output = pipeline(
"isometric scspace terrain, corgi",
num_inference_steps = 50,
wfc_guidance_start_step = 20,
wfc_guidance_strength = 5,
wfc_guidance_final_steps = 20,
wfc_guidance_final_strength = 10,
)
image = pipeline_output.images[0]
# Display raw generated image
from IPython.display import display
display(image)
# Display generated image as tiles
wave = pipeline_output.waves[0]
tile_result = wave.argmax(axis=2)
from wfd.scmap import demo_map_image
display(demo_map_image(tile_result, wfc_data_path = wfc_data_path))
# Generate map file
from wfd.scmap import tiles_to_scx
import random, time
tiles_to_scx(
tile_result,
"outputs/generated_{}_{:04d}.scx".format(time.strftime("%Y%m%d_%H%M%S"), random.randint(0, 1e4)),
wfc_data_path = wfc_data_path
)
# Open the generated map file in `outputs` folder with Scmdraft 2
``` |
Bharathdamu/wav2vec2-large-xls-r-300m-hindi | [
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"dataset:common_voice",
"transformers",
"generated_from_trainer",
"license:apache-2.0"
]
| automatic-speech-recognition | {
"architectures": [
"Wav2Vec2ForCTC"
],
"model_type": "wav2vec2",
"task_specific_params": {
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},
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},
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},
"translation_en_to_fr": {
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},
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}
}
} | 10 | 2023-01-04T06:50:13Z | ---
widget:
- text: "Chiều 3/1, Đoàn công tác của Báo Nhân Dân do đồng chí Lê Quốc Minh, Ủy viên Trung ương Đảng, Tổng Biên tập Báo Nhân Dân, Phó Trưởng Ban Tuyên giáo Trung ương, Chủ tịch Hội Nhà báo Việt Nam làm Trưởng đoàn đã có buổi làm việc với lãnh đạo tỉnh Tuyên Quang."
inference: false
tags:
- named-entity-recognition
language:
- vi
model-index:
- name: lsg-ner-vietnamese-electra-base-1024
results: []
---
# LSG ner vietnamese electra base model with max input length of 1024
A LSG version with extended input length based on [NlpHUST/ner-vietnamese-electra-base](https://huggingface.co/NlpHUST/ner-vietnamese-electra-base) and [LSG Attention](https://arxiv.org/abs/2210.15497).\
Remember to add trust_remote_code=True option while loading the model.
## Usage
Fill mask example:
```python:
from transformers import AutoModelForTokenClassification, AutoTokenizer, pipeline
model = AutoModelForTokenClassification.from_pretrained("nguyendangsonlam/lsg-ner-vietnamese-electra-base-1024", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("nguyendangsonlam/lsg-ner-vietnamese-electra-base-1024")
nlp = pipeline("ner", model=model, tokenizer=tokenizer)
example = "Chiều 3/1, Đoàn công tác của Báo Nhân Dân do đồng chí Lê Quốc Minh, Ủy viên Trung ương Đảng, Tổng Biên tập Báo Nhân Dân, Phó Trưởng Ban Tuyên giáo Trung ương, Chủ tịch Hội Nhà báo Việt Nam làm Trưởng đoàn đã có buổi làm việc với lãnh đạo tỉnh Tuyên Quang."
ner_results = nlp(example)
print(ner_results)
``` |
Bharathdamu/wav2vec2-large-xls-r-300m-hindi3-colab | []
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} | 0 | 2023-01-04T07:06:36Z | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: HateXplain-first-annotator
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. -->
# HateXplain-first-annotator
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.8646
- Accuracy: 0.6065
## 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
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
|
Bharathdamu/wav2vec2-model-hindibhasha | []
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} | 0 | 2023-01-04T07:08:24Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: distilbert-base-uncased-finetuned-squad
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-squad
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1547
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 1.2164 | 1.0 | 5533 | 1.1486 |
| 0.9546 | 2.0 | 11066 | 1.1251 |
| 0.7573 | 3.0 | 16599 | 1.1547 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
|
BigSalmon/FormalBerta3 | [
"pytorch",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
]
| fill-mask | {
"architectures": [
"RobertaForMaskedLM"
],
"model_type": "roberta",
"task_specific_params": {
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}
} | 4 | null | ---
license: creativeml-openrail-m
tags:
- pytorch
- diffusers
- stable-diffusion
- text-to-image
- diffusion-models-class
- dreambooth-hackathon
- landscape
widget:
- text: a photo of sinha rock in the Kingdom of Greece
---
# DreamBooth model for the sinha (Sigiriya rock) concept trained by hasarinduperera on the hasarinduperera/sigiriya-image-dataset dataset.
This is a Stable Diffusion model fine-tuned on the sinha (Sigiriya rock) concept with DreamBooth. It can be used by modifying the `instance_prompt`: **a photo of sinha rock**
This model was created as part of the DreamBooth Hackathon 🔥. Visit the [organisation page](https://huggingface.co/dreambooth-hackathon) for instructions on how to take part!
## Description
This is a Stable Diffusion model fine-tuned on Sigiriya `rock` images for the landscape theme.
## Usage
```python
from diffusers import StableDiffusionPipeline
pipeline = StableDiffusionPipeline.from_pretrained('hasarinduperera/sinha-rock')
image = pipeline().images[0]
image
```
|
BigSalmon/FormalRobertaaa | [
"pytorch",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
]
| fill-mask | {
"architectures": [
"RobertaForMaskedLM"
],
"model_type": "roberta",
"task_specific_params": {
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"max_length": null
},
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},
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},
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},
"translation_en_to_fr": {
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},
"translation_en_to_ro": {
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}
}
} | 12 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: 8_koelectra_train_korquad-1_2_aihub
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. -->
# 8_koelectra_train_korquad-1_2_aihub
This model is a fine-tuned version of [monologg/koelectra-base-v3-discriminator](https://huggingface.co/monologg/koelectra-base-v3-discriminator) on the None dataset.
It achieves the following results on the evaluation set:
- Exact Match: 78.9613
- F1: 84.5790
- Loss: 0.8506
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
- mixed_precision_training: Native AMP
- label_smoothing_factor: 0.1
### Training results
| Training Loss | Epoch | Step | Exact Match | F1 | Validation Loss |
|:-------------:|:-----:|:------:|:-----------:|:-------:|:---------------:|
| 1.5282 | 0.33 | 4000 | 64.4807 | 72.3547 | 1.4717 |
| 1.0638 | 0.67 | 8000 | 72.1118 | 79.0236 | 1.0938 |
| 1.0703 | 1.0 | 12000 | 74.0859 | 80.5242 | 1.0459 |
| 0.9242 | 1.34 | 16000 | 75.1325 | 81.4470 | 0.9775 |
| 0.9312 | 1.67 | 20000 | 75.6492 | 81.7357 | 0.9707 |
| 0.9483 | 2.01 | 24000 | 76.2189 | 82.3461 | 0.9248 |
| 0.8454 | 2.34 | 28000 | 76.8813 | 82.9913 | 0.9268 |
| 0.8541 | 2.67 | 32000 | 77.1330 | 83.1591 | 0.9004 |
| 0.8647 | 3.01 | 36000 | 77.1860 | 83.1519 | 0.8911 |
| 0.8952 | 3.34 | 40000 | 77.1993 | 83.1777 | 0.8765 |
| 0.7345 | 3.68 | 44000 | 77.3450 | 83.4184 | 0.9365 |
| 0.708 | 4.01 | 48000 | 77.8617 | 83.7737 | 0.8599 |
| 0.7217 | 4.34 | 52000 | 77.8352 | 83.6681 | 0.8770 |
| 0.817 | 4.68 | 56000 | 77.9809 | 83.8054 | 0.8730 |
| 0.7655 | 5.01 | 60000 | 78.0207 | 83.8704 | 0.8623 |
| 0.7276 | 5.35 | 64000 | 78.2989 | 84.0245 | 0.8535 |
| 0.6739 | 5.68 | 68000 | 78.2724 | 84.0880 | 0.8726 |
| 0.652 | 6.02 | 72000 | 78.5639 | 84.2059 | 0.8657 |
| 0.6615 | 6.35 | 76000 | 78.3254 | 84.1279 | 0.8623 |
| 0.6624 | 6.68 | 80000 | 78.7493 | 84.4215 | 0.8525 |
| 0.707 | 7.02 | 84000 | 78.5374 | 84.2300 | 0.8486 |
| 0.8086 | 7.35 | 88000 | 78.3519 | 84.1909 | 0.8442 |
| 0.6347 | 7.69 | 92000 | 78.6963 | 84.4347 | 0.8760 |
| 0.702 | 8.02 | 96000 | 78.9083 | 84.6330 | 0.8418 |
| 0.6618 | 8.36 | 100000 | 78.7493 | 84.5021 | 0.8672 |
| 0.6294 | 8.69 | 104000 | 78.5374 | 84.3771 | 0.8770 |
| 0.5797 | 9.02 | 108000 | 78.5904 | 84.3051 | 0.8623 |
| 0.6073 | 9.36 | 112000 | 78.9216 | 84.6703 | 0.8638 |
| 0.6717 | 9.69 | 116000 | 78.9613 | 84.5790 | 0.8506 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.7.1
- Datasets 2.7.0
- Tokenizers 0.13.2
|
BigSalmon/Neo | [
"pytorch",
"gpt_neo",
"text-generation",
"transformers"
]
| text-generation | {
"architectures": [
"GPTNeoForCausalLM"
],
"model_type": "gpt_neo",
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} | 13 | 2023-01-04T10:34:28Z | ---
language:
- hu
tags:
- text-generation
license: cc-by-nc-4.0
widget:
- text: "Elmesélek egy történetet a nyelvtechnológiáról."
---
# PULI GPT-2
For further details, see [our demo site](https://juniper.nytud.hu/demo/gpt2).
- Hungarian GPT-2 model
- Trained with Megatron-DeepSpeed [github](https://github.com/microsoft/Megatron-DeepSpeed)
- Dataset: 36.3 billion words
- Checkpoint: 500 000 steps
## Limitations
- max_seq_length = 1024
## Citation
If you use this model, please cite the following paper:
```
@inproceedings {yang-puli,
title = {Jönnek a nagyok! BERT-Large, GPT-2 és GPT-3 nyelvmodellek magyar nyelvre},
booktitle = {XIX. Magyar Számítógépes Nyelvészeti Konferencia (MSZNY 2023)},
year = {2023},
publisher = {Szegedi Tudományegyetem, Informatikai Intézet},
address = {Szeged, Hungary},
author = {Yang, Zijian Győző and Dodé, Réka and Ferenczi, Gergő and Héja, Enikő and Jelencsik-Mátyus, Kinga and Kőrös, Ádám and Laki, László János and Ligeti-Nagy, Noémi and Vadász, Noémi and Váradi, Tamás},
pages = {247--262}
}
```
## Usage
```python
from transformers import GPT2Tokenizer, GPT2Model
tokenizer = GPT2Tokenizer.from_pretrained('NYTK/PULI-GPT-2')
model = GPT2Model.from_pretrained('NYTK/PULI-GPT-2')
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
```
## Usage with pipeline
```python
from transformers import pipeline
prompt = "Elmesélek egy történetet a nyelvtechnológiáról."
generator = pipeline(task="text-generation", model="NYTK/PULI-GPT-2")
print(generator(prompt)[0]["generated_text"])
``` |
BotterHax/DialoGPT-small-harrypotter | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
| conversational | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
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"max_length": 1000
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}
} | 8 | 2023-01-04T13:11:16Z | ---
language:
- zh
library_name: transformers
pipeline_tag: text2text-generation
---
```python
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("svjack/T5-dialogue-collect-v5")
model = AutoModelForSeq2SeqLM.from_pretrained("svjack/T5-dialogue-collect-v5")
text = '''
根据下面的上下文进行分段：
上下文 他 喜欢 吃 汉堡 是 但 我 可 不 喜欢。
答案：
'''
tokenizer.decode(
model.generate(
tokenizer.encode(
text, return_tensors="pt", add_special_tokens=True
))[0],
skip_special_tokens = True
)
'''
'分段:他喜欢吃汉堡 分段:是的,但我可不喜欢。'
'''
``` |
Branex/gpt-neo-2.7B | []
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} | 0 | 2023-01-15T08:25:44Z | ---
language:
- en
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- art
- artistic
- diffusers
inference: true
thumbnail: "https://i2.lensdump.com/i/TAxjOD.png"
license: creativeml-openrail-m
---
<center><h1><b><a href="https://huggingface.co/SweetLuna/Aurora"> Be sure to Check out Aurora 💛 - Luna </a></b></h1></center>
# <h1 style="font-size: 4em; text-align: center; color:black; font-family: Segoe UI"> <a href="https://huggingface.co/SweetLuna/Kenshi/blob/main/README.md" style="text-decoration: none; background-color: transparent;">Kenshi</a> </h1>
<a href="https://lensdump.com/i/RL8CTQ"><img src="https://i1.lensdump.com/i/RXYEm2.png" alt="RXYEm2.png" onclick="window.open('https://i1.lensdump.com/i/RXYEm2.png', '_blank')"></a>
<h4 style="font-size: 1em; text-align: center;"><p style="color: black;">“Do I hide or do I roam? That indecision… Now the world has changed and I’ve missed it all.”</p></h1>
---
### <h1 style="font-size: 1.75em; font-family: Segoe UI">[FULLSCREEN](https://huggingface.co/SweetLuna/Kenshi/blob/main/README.md) | [Demo (Discord Server)](https://discord.gg/pD9MKyBgNp)</h1>
<hr>
### <h1 style="font-size: 1.75em; font-family: Segoe UI">[CivitAI](https://civitai.com/models/3850) | [Download](https://huggingface.co/SweetLuna/Kenshi/tree/main/KENSHI%2001) | [Changelog](https://huggingface.co/SweetLuna/Kenshi/blob/main/Changelog.md)</h1>
<hr>
<style>▼-preamble {
font-size: 2em;
}</style>
<details id="#contents">
<summary style="font-size: 2.25em; font-family: Segoe UI"><strong>🧧 Contents</strong></summary>
<hr>
# <h1 style="font-size: 1.5em;"><strong>
- [🏮 Preamble](#▼-preamble)<p>
- [⚙️ Usage](#▼-usage)<p>
- [🎨 Versatility](#▼-versatility)<p>
- [🥢 VAE [ IMPORTANT ! ]](#▼-vae)<p>
- [🏔️ Examples Images ](#▼-sample)
- [The Celestial ☄️](#▼-celestial)
- [ChatGPT Prompt ⚙️](#▼-chatgpt)
- [Vivid 🌈](#▼-vivid)
- [Moon 🌙](#▼-moon)<p>
- [🍣 Merge Recipes](#▼-merge)<p>
- [💡 Suggestions](#▼-suggestions)
- [Trigger Words](#trigger-words)
- [WebUI](#webui)
- [VAE](#vae)
- [Embeddings](#embeddings)<p>
- [💛 Donate](#▼-donation)<p>
- [License](#license)<p>
- [Disclaimer](#disclaimer)
</strong>
</h1>
</details>
<hr>
<details id="▼-preamble">
<summary style="font-size: 2.25em; font-family: Segoe UI"><strong>🏮 What is Kenshi?</strong></summary>
<hr>
<h1>
**Kenshi** is my personal merges which created by combining different models together. ***This includes models such as Nixeu, WLOP, Guweiz, BoChen, and many others.***
```TypeScript
My goal is to archive my own feelings towards styles I want for Semi-realistic artstyle.
Through this process, I hope not only to gain a deeper understanding of my own preferences, but also to inform and refine the capabilities of my personal skills,
and AI Art as it generates artwork that reflects my desired style.
```
Kenshi because it represents strength, resilience, and the ability to adapt and overcome challenges. Just like AI.
</h1>
</details>
<hr>
<details id="▼-usage">
<summary style="font-size: 2.25em; font-family: font-family: Segoe UI"><strong>⚙️ Usage</strong></summary>
<hr>
<h1>
## <h1 style="font-size: 1.5em; text-align: center; color:black; font-family: Segoe UI"> These are the settings I always use it is recommended but not essential;
| Settings | Value |
| ----------------- | ------------------------------------------------------------------ |
| Steps | 20+ |
| Sampler | DPM++ 2M Karras |
| CFG scale | 2-7 |
| Size |600x800 |
| Clip skip | 2 |
| ENSD | 31337 |
| Hires Fix | Enabled |
| Upscale by | 1.5 |
| Upscaler Fix | https://de-next.owncube.com/index.php/s/x99pKzS7TNaErrC |
| Hires Fix | Enabled |
Kenshi is not recommended for new users since it requires a lot of prompt to work with I suggest using this if you still want to use the model (install it as an extension on Automatic1111 WebUI) : https://github.com/DominikDoom/a1111-sd-webui-tagcomplete
</h1>
</h1>
<center><a href="https://i2.lensdump.com/i/TAbhx1.png"><img src="https://i2.lensdump.com/i/TAbhx1.png" alt="TAbhx1.png" onclick="window.open('https://i2.lensdump.com/i/TAbhx1.png', '_blank')"></a></center>
</details>
<hr>
<details id="▼-versatility">
<summary style="font-size: 2.25em; font-family: font-family: Segoe UI"><strong>🎨 Versatility</strong></summary>
<hr>
<h1>
## Unlike most models, Kenshi is known for its versatility, able to perform various styles with remarkable results. I've undergone testing with over 30 to 50 styles and most of the time I get remarkable results. I recommend using Lora and Embedding to improve this even further.
<center><a href="https://i2.lensdump.com/i/TAxjOD.png"><img src="https://i2.lensdump.com/i/TAxjOD.png" alt="TAxjOD.png" onclick="window.open('https://i2.lensdump.com/i/TAxjOD.png, '_blank')"></a></center>
</details>
<hr>
<details id="▼-vae">
<summary style="font-size: 2.25em; font-family: font-family: Segoe UI"><strong>🥢 VAE ⚠️</strong></summary>
<hr>
<h1>
## I recommend <a href="https://huggingface.co/hakurei/waifu-diffusion-v1-4/blob/main/vae/kl-f8-anime2.ckpt" >**kl-f8-anime2.ckpt**</a> VAE from waifu-diffusion-v1-4 <a href="https://huggingface.co/hakurei">which is made by hakurei.</a>
</h1>
<a href="https://i2.lensdump.com/i/RbBe37.png"><img src="https://i2.lensdump.com/i/RbBe37.png" alt="RbBe37.png" onclick="window.open('https://i2.lensdump.com/i/RbBe37.png', '_blank')"></a>
# <h1 style="font-size: 2.5em;"><a href="https://huggingface.co/hakurei/waifu-diffusion-v1-4/blob/main/vae/kl-f8-anime2.ckpt" >**VAE is important, please download it.**</h1></a>
</details>
<hr>
<details id="▼-sample">
<summary style="font-size: 2.25em; font-family: Segoe UI"><strong>🏔️ Examples Images</strong></summary><hr>
<details id="▼-celestial">
<summary style="font-size: 1.75em; font-family: monospace"><strong>The Celestial ☄️</strong></summary>
<img src="https://i3.lensdump.com/i/RLEz8M.png" alt=”1”>
<h1>
```c#
1girl, highly detailed face, bleak and dangerous atmosphere, moody, (dynamic pose:1.6), cataclysmic magic, dark blue wavy long hair,
(glowing eyes:0.85), (reaching through a magic circle:1.35), extremely detailed 8k wallpaper, (highly detailed:1.1), [anime:Impasto:0.5],
intricate, fantasy, clear sky, wind, beautiful sky, (nightsky), (galaxy), (huge blood moon in the background:1.05)
```
# **KENSHI 00**
</details>
<hr>
<details id="▼-chatgpt">
<summary style="font-size: 1.75em; font-family: monospace"><strong>ChatGPT Prompt ⚙️</strong></summary>
<img src="https://i.lensdump.com/i/RLkz3v.png" alt=”2”>
<img src="https://i1.lensdump.com/i/RLkFND.png" alt=”3”>
<img src="https://i3.lensdump.com/i/RLkulr.png" alt=”4”>
```c#
(A cursed knight, clad in black armor,) must journey through a desolate,
haunted land to reach the Elden Ring and lift the (curse that plagues their soul.)Along the way,
they encounter other travelers, (each struggling with their own demons and secrets), As they draw closer to the Elden Ring,
they are confronted with visions of their past mistakes, (all tinged with a red hue,)
looking at viewer, highres, superb, 8k wallpaper, extremely detailed, intricate, unreal engine 5, volumetric lighting, realistic, realistic lighting,
cinematic, 4k, cinematic lighting, 8k, depth of field, 3d, perfect, award-winning, hyper-detailed, photorealistic, ultra realistic, realistic light,
hard lighting, intricate details, stop motion, hyperfocus, tonemapping, sharp focus, hyper detailed, detailed eyes, eyes focus, (illustration:1.1),
highres, (extremely detailed CG unity 8k wallpaper:1.1), (beautiful face:1.15), (cowboy_shot:1.5)
(nixeu_soft:0.7), (nixeu_white:0.7),
```
# **KENSHI 00**
</details>
<hr>
<details id="▼-vivid">
<summary style="font-size: 1.75em; font-family: monospace"><strong>Vivid 🌈</strong></summary>
<img src="https://i.lensdump.com/i/RXY1Fo.png" alt=”5”>
```c#
close POV, young adult woman, blue purple green color palette, black hair with dark green shine, two symmetrical antennae on head,
big blue eyes sparkling, rings around eyes, two-tone black and red, smiling at the camera, elegant pose, looking at the viewer,
vivid stained glass window background, oil painting, character portrait, drawn in medibang paint, 4k wallpaper, aesthetic, masterpiece,
award-winning photography, macro photography vivid colors, photorealistic, atmospheric, cinematic, moody, rule of thirds, majestic, detailed, perfect anatomy
cowboy shot, contrapposto, looking at viewer, highres, superb, 8k wallpaper, extremely detailed, intricate, unreal engine 5, volumetric lighting,
realistic, realistic lighting, cinematic, 4k, cinematic lighting, 8k, depth of field, 3d, masterpiece, perfect, award-winning, hyper-detailed,
photorealistic, ultra realistic, realistic light, hard lighting, intricate details, stop motion, hyperfocus, tonemapping, sharp focus, hyper detailed,
detailed eyes, eyes focus, (illustration:1.1), highres, (extremely detailed CG unity 8k wallpaper:1.1), (mid shot1.25), (portrait:1.25), (solo:1.2), 1girl,
(beautiful face:1.15),
(nixeu_soft:0.7), (nixeu_white:0.7),
```
# **KENSHI 01**
</details>
<hr>
<details id="▼-moon">
<summary style="font-size: 1.75em; font-family: monospace"><strong>Moon 🌙</strong></summary>
<img src="https://i2.lensdump.com/i/RXYt7i.png" alt=”6”>
```c#
(on the moon, space, looking back into earth), white hair, black tank top, volumetric lighting, white jacket, glowing headphone, cyberpunk, futuristic,
multi-color eyes, detailed eyes, hyper detailed,light smile,
highly detailed, beautiful, small details, ultra detailed, best quality, intricate, hyperrealism, sharp, digital illustration, detailed, realism, intricate,
4k, 8k, trending on artstation, good anatomy, beautiful lighting, award-winning, photorealistic, realistic shadows, realistic lighting, beautiful lighting,
raytracing, intricate details, moody, rule of thirds, masterpiece, (illustration:1.1), highres, (extremely detailed CG, unity, 8k wallpaper:1.1), beautiful face,
highly detailed face, ultra realistic, masterpiece, bokeh, extremely detailed, intricate, zoomout,
colorful, vibrant colors, red nail polish, side view,
```
# **KENSHI 01**
</details>
</details>
<hr>
</h1>
<details id="▼-merge">
<summary style="font-size: 2.25em; font-family: Segoe UI"><strong>🍣 Merge Recipes</strong></summary>
<hr>
<h1><strong>
<a href="
https://www.figma.com/file/aESyZAxHxBJjE63gog5ExZ/KENSHI?node-id=0%3A1&t=2ULQWeLUSIWhk1aE-0" class="no-underline" style="font-size: 1.75em;">Here is my Cookbook that you can check out.
<img src="https://i2.lensdump.com/i/RLCJIH.png" alt="COOKBOOK"></strong>
</h1>
</a>
</details>
<hr>
<details id="▼-donation">
<summary style="font-size: 2.25em; font-family: Segoe UI"><strong>💛 Donate</strong></summary>
<hr>
<h1><strong>
I've been working hard to complete my college education. The thing is, paying for college is no joke and I've been feeling the pressure of the mounting bills.
I know times are tough for everyone, but if you're able to help in any way, I would be forever grateful.
Considering supporting me on <a href="https://www.patreon.com/thesweetluna">Patreon</a>
</h1>
</a>
</details>
<hr>
<details id="▼-suggestions">
<summary style="font-size: 2.25em; font-family: Segoe UI"><strong>💡 Suggestions</strong></summary>
<hr>
## <h1 style="font-size: 1.75em;">Trigger Words</h1>
<hr>
<h1 style="font-size: 1.5em;">
**Trigger Words are not required** but are meant to **enhance the effectiveness of the prompt** and improve the overall outcome.
```c#
WLOP, Nixeu, Guweiz
```
</h1>
<hr>
## <h1 style="font-size: 1.75em;">WebUI</h1>
<hr>
<h1 style="font-size: 1.5em;">
<a href="https://github.com/AUTOMATIC1111/stable-diffusion-webui">AUTOMATIC1111</a> Grab it, a must-have. Have all the features you want and is easy to access.
<hr>
</h1>
## <h1 style="font-size: 1.75em;">Embeddings</h1>
<hr>
<h1 style="font-size: 1.5em;">
I recommend grabbing ***all*** <a href="https://huggingface.co/Nerfgun3">Nerfgun3</a> embeddings ***and*** Sirveggie <a href="https://huggingface.co/SirVeggie/nixeu_embeddings">nixeu_embeddings</a>
</h1>
</details>
<hr>
# License
This embedding is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage.
The CreativeML OpenRAIL License specifies:
```
1. You can't use the model to deliberately produce nor share illegal or harmful outputs or content
2. The authors claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against theprovisions set in the license
3. You may re-distribute the weights and use the model commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully)
```
[Please read the full license here](https://huggingface.co/spaces/CompVis/stable-diffusion-license)
<hr>
# Disclaimer
```c#
The use of this learning model is entirely at the discretion of the user, and they have the freedom to choose whether or not to create NSFW content.
This is important to note that the model itself does not contain any explicit or inappropriate imagery that can be easily accessed with a single click.
The purpose of sharing this model is not to showcase obscene material in a public forum, but rather to provide a tool for users to utilize as they see fit.
The decision of whether to engage with SFW or NSFW content lies with the user and their own personal preferences.
``` |
CAMeL-Lab/bert-base-arabic-camelbert-ca | [
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"ar",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
]
| fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
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}
}
} | 580 | null | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
def cls_pooling(model_output, attention_mask):
return model_output[0][:,0]
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
model = AutoModel.from_pretrained('{MODEL_NAME}')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, cls pooling.
sentence_embeddings = cls_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 605 with parameters:
```
{'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss`
Parameters of the fit()-Method:
```
{
"epochs": 1,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": 605,
"warmup_steps": 61,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: MPNetModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information --> |
CAMeL-Lab/bert-base-arabic-camelbert-da-poetry | [
"pytorch",
"tf",
"bert",
"text-classification",
"ar",
"arxiv:1905.05700",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0"
]
| text-classification | {
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
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}
} | 37 | null | ---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3-v2
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="Pablinsv/q-Taxi-v3-v2", 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"])
```
|
CAMeL-Lab/bert-base-arabic-camelbert-da-pos-msa | [
"pytorch",
"tf",
"bert",
"token-classification",
"ar",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
]
| token-classification | {
"architectures": [
"BertForTokenClassification"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
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},
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},
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}
} | 27 | 2023-01-04T14:49:50Z | ---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Cartpolefinalfinaletest
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 92.80 +/- 31.78
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
|
CAMeL-Lab/bert-base-arabic-camelbert-mix-poetry | [
"pytorch",
"tf",
"bert",
"text-classification",
"ar",
"arxiv:1905.05700",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0"
]
| text-classification | {
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
"task_specific_params": {
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"max_length": null
},
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}
}
} | 31 | 2023-01-04T14:58:42Z | ---
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: 298.53 +/- 18.94
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
...
```
|
CAMeL-Lab/bert-base-arabic-camelbert-mix-sentiment | [
"pytorch",
"tf",
"bert",
"text-classification",
"ar",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0"
]
| text-classification | {
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
"task_specific_params": {
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"max_length": null
},
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}
}
} | 855 | null | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: transformers-abhi
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. -->
# transformers-abhi
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 2.9227
- Validation Loss: 2.5929
- Train Rougel: tf.Tensor(0.19853836, shape=(), dtype=float32)
- 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', 'learning_rate': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Train Rougel | Epoch |
|:----------:|:---------------:|:----------------------------------------------:|:-----:|
| 2.9227 | 2.5929 | tf.Tensor(0.19853836, shape=(), dtype=float32) | 0 |
### Framework versions
- Transformers 4.20.0
- TensorFlow 2.9.2
- Datasets 2.8.0
- Tokenizers 0.12.1
|
CAMeL-Lab/bert-base-arabic-camelbert-mix | [
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"ar",
"arxiv:2103.06678",
"transformers",
"Arabic",
"Dialect",
"Egyptian",
"Gulf",
"Levantine",
"Classical Arabic",
"MSA",
"Modern Standard Arabic",
"license:apache-2.0",
"autotrain_compatible"
]
| fill-mask | {
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"BertForMaskedLM"
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} | 20,880 | null | ---
language: en
tags:
- distilroberta
widget:
- text: animal
- text: love
- text: oh happy day
--- |
CAMeL-Lab/bert-base-arabic-camelbert-msa-did-madar-twitter5 | [
"pytorch",
"tf",
"bert",
"text-classification",
"ar",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0"
]
| text-classification | {
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"BertForSequenceClassification"
],
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}
} | 75 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
metrics:
- accuracy
- f1
model-index:
- name: finetuning-sentiment-model-3000-samples
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: imdb
type: imdb
config: plain_text
split: train
args: plain_text
metrics:
- name: Accuracy
type: accuracy
value: 0.87
- name: F1
type: f1
value: 0.8695652173913044
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuning-sentiment-model-3000-samples
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3142
- Accuracy: 0.87
- F1: 0.8696
## 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 4.25.1
- Pytorch 1.13.0+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
|
CAMeL-Lab/bert-base-arabic-camelbert-msa-pos-egy | [
"pytorch",
"tf",
"bert",
"token-classification",
"ar",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
]
| token-classification | {
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}
} | 52 | null | ---
license: mit
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: bart-large-cnn-finetune
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bart-large-cnn-finetune
This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/facebook/bart-large-cnn) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5677
- Rouge1: 9.9893
- Rouge2: 5.2818
- Rougel: 9.7766
- Rougelsum: 9.7951
- Gen Len: 58.1672
## 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: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| 0.2639 | 1.0 | 4774 | 1.5677 | 9.9893 | 5.2818 | 9.7766 | 9.7951 | 58.1672 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.13.0+cu116
- Datasets 2.7.0
- Tokenizers 0.13.2
|
CAMeL-Lab/bert-base-arabic-camelbert-msa-pos-msa | [
"pytorch",
"tf",
"bert",
"token-classification",
"ar",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
]
| token-classification | {
"architectures": [
"BertForTokenClassification"
],
"model_type": "bert",
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} | 133 | null | ---
license: cc-by-4.0
tags:
- generated_from_trainer
model-index:
- name: CTEBMSP_ner_test2
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. -->
# CTEBMSP_ner_test2
This model is a fine-tuned version of [chizhikchi/Spanish_disease_finder](https://huggingface.co/chizhikchi/Spanish_disease_finder) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0586
- Diso Precision: 0.8836
- Diso Recall: 0.8902
- Diso F1: 0.8869
- Diso Number: 4052
- Overall Precision: 0.8836
- Overall Recall: 0.8902
- Overall F1: 0.8869
- Overall Accuracy: 0.9885
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 4e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Diso Precision | Diso Recall | Diso F1 | Diso Number | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------------:|:-----------:|:-------:|:-----------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 0.0463 | 1.0 | 2566 | 0.0512 | 0.8791 | 0.8384 | 0.8583 | 4052 | 0.8791 | 0.8384 | 0.8583 | 0.9859 |
| 0.0204 | 2.0 | 5132 | 0.0615 | 0.8942 | 0.8655 | 0.8796 | 4052 | 0.8942 | 0.8655 | 0.8796 | 0.9875 |
| 0.0095 | 3.0 | 7698 | 0.0545 | 0.8877 | 0.8776 | 0.8826 | 4052 | 0.8877 | 0.8776 | 0.8826 | 0.9881 |
| 0.0045 | 4.0 | 10264 | 0.0586 | 0.8836 | 0.8902 | 0.8869 | 4052 | 0.8836 | 0.8902 | 0.8869 | 0.9885 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
|
CAUKiel/JavaBERT | [
"pytorch",
"safetensors",
"bert",
"fill-mask",
"code",
"arxiv:2110.10404",
"arxiv:1910.09700",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
]
| fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
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},
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}
}
} | 388 | null | ---
license: creativeml-openrail-m
tags:
- pytorch
- diffusers
- stable-diffusion
- text-to-image
- diffusion-models-class
- dreambooth-hackathon
- wildcard
widget:
- text: photo of bergraffi futuristic cyberpunk portrait painted by van gogh
---
# DreamBooth model for the bergraffi concept trained by bakebrain.
This is a Stable Diffusion model fine-tuned on the bergraffi concept with DreamBooth. It can be used by modifying the `instance_prompt`: **photo of bergraffi futuristic cyberpunk portrait painted by van gogh**
This model was created as part of the DreamBooth Hackathon 🔥. Visit the [organisation page](https://huggingface.co/dreambooth-hackathon) for instructions on how to take part!
## Description
This is a Stable Diffusion model fine-tuned on Berlin `graffiti` images.
#### Sample Images
150+ generated images can be found here: https://imgur.com/a/0Q00Rq7
<table>
<tr>
<td align="center"><img src="https://huggingface.co/bakebrain/bergraffi-berlin-graffiti/resolve/main/sample_images/bergraffi_sample_1.png" style="height:200px"> </td>
<td align="center"><img src="https://huggingface.co/bakebrain/bergraffi-berlin-graffiti/resolve/main/sample_images/bergraffi_sample_2.png" style="height:200px"> </td>
<td align="center"><img src="https://huggingface.co/bakebrain/bergraffi-berlin-graffiti/resolve/main/sample_images/bergraffi_sample_3.png" style="height:200px"> </td>
</tr>
<tr>
<td align="center"><img src="https://huggingface.co/bakebrain/bergraffi-berlin-graffiti/resolve/main/sample_images/bergraffi_sample_4.png" style="height:200px"> </td>
<td align="center"><img src="https://huggingface.co/bakebrain/bergraffi-berlin-graffiti/resolve/main/sample_images/bergraffi_sample_5.png" style="height:200px"> </td>
<td align="center"><img src="https://huggingface.co/bakebrain/bergraffi-berlin-graffiti/resolve/main/sample_images/bergraffi_sample_6.png" style="height:200px"> </td>
</tr>
<tr>
<td align="center"><img src="https://huggingface.co/bakebrain/bergraffi-berlin-graffiti/resolve/main/sample_images/bergraffi_sample_7.png" style="height:200px"> </td>
<td align="center"><img src="https://huggingface.co/bakebrain/bergraffi-berlin-graffiti/resolve/main/sample_images/bergraffi_sample_8.png" style="height:200px"> </td>
<td align="center"><img src="https://huggingface.co/bakebrain/bergraffi-berlin-graffiti/resolve/main/sample_images/bergraffi_sample_9.png" style="height:200px"> </td>
</tr>
<tr>
<td align="center"><img src="https://huggingface.co/bakebrain/bergraffi-berlin-graffiti/resolve/main/sample_images/bergraffi_sample_10.png" style="height:200px"> </td>
<td align="center"><img src="https://huggingface.co/bakebrain/bergraffi-berlin-graffiti/resolve/main/sample_images/bergraffi_sample_11.png" style="height:200px"> </td>
<td align="center"><img src="https://huggingface.co/bakebrain/bergraffi-berlin-graffiti/resolve/main/sample_images/bergraffi_sample_12.png" style="height:200px"> </td>
</tr>
<tr>
<td align="center"><img src="https://huggingface.co/bakebrain/bergraffi-berlin-graffiti/resolve/main/sample_images/bergraffi_sample_13.png" style="height:200px"> </td>
<td align="center"><img src="https://huggingface.co/bakebrain/bergraffi-berlin-graffiti/resolve/main/sample_images/bergraffi_sample_14.png" style="height:200px"> </td>
<td align="center"><img src="https://huggingface.co/bakebrain/bergraffi-berlin-graffiti/resolve/main/sample_images/bergraffi_sample_15.png" style="height:200px"> </td>
</tr>
<tr>
<td align="center"><img src="https://huggingface.co/bakebrain/bergraffi-berlin-graffiti/resolve/main/sample_images/bergraffi_sample_16.png" style="height:200px"> </td>
<td align="center"><img src="https://huggingface.co/bakebrain/bergraffi-berlin-graffiti/resolve/main/sample_images/bergraffi_sample_17.png" style="height:200px"> </td>
<td align="center"><img src="https://huggingface.co/bakebrain/bergraffi-berlin-graffiti/resolve/main/sample_images/bergraffi_sample_18.png" style="height:200px"> </td>
</tr>
</table>
## Usage
Experiment with the guidance scale! Enjoy!
```python
from diffusers import StableDiffusionPipeline
pipeline = StableDiffusionPipeline.from_pretrained('bakebrain/bergraffi-berlin-graffiti')
prompt = "photo of bergraffi futuristic cyberpunk portrait painted by van gogh"
guidance_scale = 12
image = pipeline(prompt, guidance_scale=guidance_scale).images[0]
image
```
|
CBreit00/DialoGPT_small_Rick | []
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} | 0 | null | ---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-Md
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
|
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