modelId
stringlengths 4
81
| tags
list | pipeline_tag
stringclasses 17
values | config
dict | downloads
int64 0
59.7M
| first_commit
timestamp[ns, tz=UTC] | card
stringlengths 51
438k
|
|---|---|---|---|---|---|---|
Davlan/bert-base-multilingual-cased-finetuned-igbo
|
[
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
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| 15
| 2023-03-05T05:01:16Z
|
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SoccerTwos
library_name: ml-agents
---
# **poca** Agent playing **SoccerTwos**
This is a trained model of a **poca** agent playing **SoccerTwos** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-SoccerTwos
2. Step 1: Write your model_id: pyflynn/poca-SoccerTwos
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
Davlan/bert-base-multilingual-cased-finetuned-wolof
|
[
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
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"BertForMaskedLM"
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| 4
| 2023-03-05T05:09:13Z
|
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="AmazonBBQ/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"])
```
|
Davlan/bert-base-multilingual-cased-finetuned-yoruba
|
[
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
{
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"BertForMaskedLM"
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| 21
| null |
---
language:
- en
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: hBERTv2_data_aug_sst2
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE SST2
type: glue
args: sst2
metrics:
- name: Accuracy
type: accuracy
value: 0.5091743119266054
---
<!-- 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. -->
# hBERTv2_data_aug_sst2
This model is a fine-tuned version of [gokuls/bert_12_layer_model_v2](https://huggingface.co/gokuls/bert_12_layer_model_v2) on the GLUE SST2 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6962
- Accuracy: 0.5092
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 256
- eval_batch_size: 256
- seed: 10
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.6879 | 1.0 | 4374 | 0.6995 | 0.5092 |
| 0.6873 | 2.0 | 8748 | 0.6962 | 0.5092 |
| 0.6869 | 3.0 | 13122 | 0.7095 | 0.5092 |
| 0.6862 | 4.0 | 17496 | 0.7039 | 0.5092 |
| 0.685 | 5.0 | 21870 | 0.7252 | 0.5092 |
| 0.6841 | 6.0 | 26244 | 0.7280 | 0.5092 |
| 0.6837 | 7.0 | 30618 | 0.7191 | 0.5092 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.14.0a0+410ce96
- Datasets 2.10.1
- Tokenizers 0.13.2
|
Davlan/bert-base-multilingual-cased-ner-hrl
|
[
"pytorch",
"tf",
"bert",
"token-classification",
"transformers",
"autotrain_compatible",
"has_space"
] |
token-classification
|
{
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"BertForTokenClassification"
],
"model_type": "bert",
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}
| 269,898
| 2023-03-05T05:20:53Z
|
---
license: creativeml-openrail-m
---
https://civitai.com/models/13716/idolmster-hayamikanade-lora
|
Davlan/byt5-base-eng-yor-mt
|
[
"pytorch",
"t5",
"text2text-generation",
"arxiv:2103.08647",
"transformers",
"autotrain_compatible"
] |
text2text-generation
|
{
"architectures": [
"T5ForConditionalGeneration"
],
"model_type": "t5",
"task_specific_params": {
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}
| 11
| null |
---
license: creativeml-openrail-m
---
https://civitai.com/models/14200/idolmster-higuchimadokayen-lora
|
Davlan/mt5_base_eng_yor_mt
|
[
"pytorch",
"mt5",
"text2text-generation",
"arxiv:2103.08647",
"transformers",
"autotrain_compatible"
] |
text2text-generation
|
{
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"MT5ForConditionalGeneration"
],
"model_type": "mt5",
"task_specific_params": {
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},
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}
| 2
| 2023-03-05T05:51:37Z
|
---
tags:
- automatic-speech-recognition
- dna_r9.4.1
- generated_from_trainer
model-index:
- name: bonito-wav2vec2-tiny-demo
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. -->
# bonito-wav2vec2-tiny-demo
This model is a fine-tuned version of [yenpolin/bonito-wav2vec2-tiny](https://huggingface.co/yenpolin/bonito-wav2vec2-tiny) on the DNA_R9.4.1 - NA dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1499
- Mean Acc: 0.0
- Median Acc: 0.0
## 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: 0.002
- train_batch_size: 320
- eval_batch_size: 768
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Mean Acc | Median Acc |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:----------:|
| No log | 0.51 | 160 | 1.1511 | 0.0 | 0.0 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.12.1
- Datasets 2.9.0
- Tokenizers 0.13.2
|
Davlan/mt5_base_yor_eng_mt
|
[
"pytorch",
"mt5",
"text2text-generation",
"arxiv:2103.08647",
"transformers",
"autotrain_compatible"
] |
text2text-generation
|
{
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"MT5ForConditionalGeneration"
],
"model_type": "mt5",
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}
| 8
| 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 73 with parameters:
```
{'batch_size': 32, '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": 73,
"warmup_steps": 8,
"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 -->
|
Davlan/xlm-roberta-base-finetuned-chichewa
|
[
"pytorch",
"xlm-roberta",
"fill-mask",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] |
fill-mask
|
{
"architectures": [
"XLMRobertaForMaskedLM"
],
"model_type": "xlm-roberta",
"task_specific_params": {
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},
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}
| 5
| 2023-03-05T06:03:14Z
|
---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-CartPole
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 500.00 +/- 0.00
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
Davlan/xlm-roberta-base-finetuned-english
|
[
"pytorch",
"xlm-roberta",
"fill-mask",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] |
fill-mask
|
{
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"XLMRobertaForMaskedLM"
],
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}
| 5
| 2023-03-05T06:03:17Z
|
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
config: split
split: validation
args: split
metrics:
- name: Accuracy
type: accuracy
value: 0.928
- name: F1
type: f1
value: 0.9281573845269205
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2144
- Accuracy: 0.928
- F1: 0.9282
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8343 | 1.0 | 250 | 0.3130 | 0.911 | 0.9087 |
| 0.2517 | 2.0 | 500 | 0.2144 | 0.928 | 0.9282 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.12.0
- Datasets 2.9.0
- Tokenizers 0.13.2
|
Davlan/xlm-roberta-base-finetuned-hausa
|
[
"pytorch",
"xlm-roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
{
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"XLMRobertaForMaskedLM"
],
"model_type": "xlm-roberta",
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},
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}
}
| 234
| null |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SnowballTarget
library_name: ml-agents
---
# **ppo** Agent playing **SnowballTarget**
This is a trained model of a **ppo** agent playing **SnowballTarget** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-SnowballTarget
2. Step 1: Write your model_id: emylrahim/ppo-SnowballTarget
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
Davlan/xlm-roberta-base-finetuned-igbo
|
[
"pytorch",
"xlm-roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
{
"architectures": [
"XLMRobertaForMaskedLM"
],
"model_type": "xlm-roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
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"num_beams": null,
"prefix": null
},
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},
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},
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}
| 68
| null |
---
library_name: stable-baselines3
tags:
- PandaReachDense-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachDense-v2
type: PandaReachDense-v2
metrics:
- type: mean_reward
value: -1.29 +/- 0.29
name: mean_reward
verified: false
---
# **A2C** Agent playing **PandaReachDense-v2**
This is a trained model of a **A2C** agent playing **PandaReachDense-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
Davlan/xlm-roberta-base-finetuned-lingala
|
[
"pytorch",
"xlm-roberta",
"fill-mask",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] |
fill-mask
|
{
"architectures": [
"XLMRobertaForMaskedLM"
],
"model_type": "xlm-roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
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},
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},
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},
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},
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"early_stopping": null,
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"num_beams": null,
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}
}
}
| 9
| null |
---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Pixelcopter-PLE-v0-Reinforce
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 29.00 +/- 15.39
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
Davlan/xlm-roberta-base-finetuned-luganda
|
[
"pytorch",
"xlm-roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
{
"architectures": [
"XLMRobertaForMaskedLM"
],
"model_type": "xlm-roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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},
"text-generation": {
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"max_length": null
},
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},
"translation_en_to_fr": {
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},
"translation_en_to_ro": {
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"max_length": null,
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"prefix": null
}
}
}
| 11
| 2023-03-05T06:21:08Z
|
---
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: 350.00 +/- 109.38
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga dyingc -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga dyingc -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga dyingc
```
## Hyperparameters
```python
OrderedDict([('batch_size', 256),
('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)])
```
|
Davlan/xlm-roberta-large-ner-hrl
|
[
"pytorch",
"tf",
"xlm-roberta",
"token-classification",
"transformers",
"autotrain_compatible"
] |
token-classification
|
{
"architectures": [
"XLMRobertaForTokenClassification"
],
"model_type": "xlm-roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
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},
"text-generation": {
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"max_length": null
},
"translation_en_to_de": {
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},
"translation_en_to_fr": {
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},
"translation_en_to_ro": {
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"num_beams": null,
"prefix": null
}
}
}
| 1,322
| null |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Pyramids
library_name: ml-agents
---
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-Pyramids
2. Step 1: Write your model_id: emylrahim/ppo-PyramidsRND1
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
Dean/summarsiation
|
[] | null |
{
"architectures": null,
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},
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}
| 0
| null |
---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 662.00 +/- 166.65
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga raminass -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga raminass -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga raminass
```
## 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)])
```
|
Declan/Breitbart_modelv7
|
[] | null |
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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
import gym
from huggingface_sb3 import load_from_hub
model = load_from_hub(
repo_id="dmenini/q-FrozenLake-v1-4x4-noSlippery",
filename="q-learning.pkl"
)
env = gym.make("FrozenLake-v1", map_name="4x4", is_slippery=False)
```
|
Declan/CNN_model_v4
|
[
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
{
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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}
| 3
| null |
---
license: cc-by-4.0
metrics:
- bleu4
- meteor
- rouge-l
- bertscore
- moverscore
language: ru
datasets:
- lmqg/qg_ruquad
pipeline_tag: text2text-generation
tags:
- question generation
widget:
- text: "Нелишним будет отметить, что, развивая это направление, Д. И. Менделеев, поначалу априорно выдвинув идею о температуре, при которой высота мениска будет нулевой, <hl> в мае 1860 года <hl> провёл серию опытов."
example_title: "Question Generation Example 1"
- text: "Однако, франкоязычный <hl> Квебек <hl> практически никогда не включается в состав Латинской Америки."
example_title: "Question Generation Example 2"
- text: "Классическим примером международного синдиката XX века была группа компаний <hl> Де Бирс <hl> , которая в 1980-е годы контролировала до 90 % мировой торговли алмазами."
example_title: "Question Generation Example 3"
model-index:
- name: vocabtrimmer/mt5-small-trimmed-ru-ruquad-qg
results:
- task:
name: Text2text Generation
type: text2text-generation
dataset:
name: lmqg/qg_ruquad
type: default
args: default
metrics:
- name: BLEU4 (Question Generation)
type: bleu4_question_generation
value: 18.44
- name: ROUGE-L (Question Generation)
type: rouge_l_question_generation
value: 33.83
- name: METEOR (Question Generation)
type: meteor_question_generation
value: 28.88
- name: BERTScore (Question Generation)
type: bertscore_question_generation
value: 86.35
- name: MoverScore (Question Generation)
type: moverscore_question_generation
value: 64.78
---
# Model Card of `vocabtrimmer/mt5-small-trimmed-ru-ruquad-qg`
This model is fine-tuned version of [vocabtrimmer/mt5-small-trimmed-ru](https://huggingface.co/vocabtrimmer/mt5-small-trimmed-ru) for question generation task on the [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-generation).
### Overview
- **Language model:** [vocabtrimmer/mt5-small-trimmed-ru](https://huggingface.co/vocabtrimmer/mt5-small-trimmed-ru)
- **Language:** ru
- **Training data:** [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) (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="ru", model="vocabtrimmer/mt5-small-trimmed-ru-ruquad-qg")
# model prediction
questions = model.generate_q(list_context="Нелишним будет отметить, что, развивая это направление, Д. И. Менделеев, поначалу априорно выдвинув идею о температуре, при которой высота мениска будет нулевой, в мае 1860 года провёл серию опытов.", list_answer="в мае 1860 года")
```
- With `transformers`
```python
from transformers import pipeline
pipe = pipeline("text2text-generation", "vocabtrimmer/mt5-small-trimmed-ru-ruquad-qg")
output = pipe("Нелишним будет отметить, что, развивая это направление, Д. И. Менделеев, поначалу априорно выдвинув идею о температуре, при которой высота мениска будет нулевой, <hl> в мае 1860 года <hl> провёл серию опытов.")
```
## Evaluation
- ***Metric (Question Generation)***: [raw metric file](https://huggingface.co/vocabtrimmer/mt5-small-trimmed-ru-ruquad-qg/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_ruquad.default.json)
| | Score | Type | Dataset |
|:-----------|--------:|:--------|:-----------------------------------------------------------------|
| BERTScore | 86.35 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) |
| Bleu_1 | 34.27 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) |
| Bleu_2 | 27.42 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) |
| Bleu_3 | 22.36 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) |
| Bleu_4 | 18.44 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) |
| METEOR | 28.88 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) |
| MoverScore | 64.78 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) |
| ROUGE_L | 33.83 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) |
## Training hyperparameters
The following hyperparameters were used during fine-tuning:
- dataset_path: lmqg/qg_ruquad
- dataset_name: default
- input_types: paragraph_answer
- output_types: question
- prefix_types: None
- model: vocabtrimmer/mt5-small-trimmed-ru
- max_length: 512
- max_length_output: 32
- epoch: 12
- batch: 32
- lr: 0.001
- fp16: False
- random_seed: 1
- gradient_accumulation_steps: 2
- label_smoothing: 0.15
The full configuration can be found at [fine-tuning config file](https://huggingface.co/vocabtrimmer/mt5-small-trimmed-ru-ruquad-qg/raw/main/trainer_config.json).
## Citation
```
@inproceedings{ushio-etal-2022-generative,
title = "{G}enerative {L}anguage {M}odels for {P}aragraph-{L}evel {Q}uestion {G}eneration",
author = "Ushio, Asahi and
Alva-Manchego, Fernando and
Camacho-Collados, Jose",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2022",
address = "Abu Dhabi, U.A.E.",
publisher = "Association for Computational Linguistics",
}
```
|
Declan/CNN_model_v6
|
[
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
{
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"BertForMaskedLM"
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}
| 3
| null |
---
license: mit
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-de
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xtreme
type: xtreme
config: PAN-X.de
split: validation
args: PAN-X.de
metrics:
- name: F1
type: f1
value: 0.8638300289723342
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-de
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1358
- F1: 0.8638
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2591 | 1.0 | 525 | 0.1621 | 0.8206 |
| 0.1276 | 2.0 | 1050 | 0.1379 | 0.8486 |
| 0.082 | 3.0 | 1575 | 0.1358 | 0.8638 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
|
Declan/FoxNews_model_v4
|
[
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
{
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
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},
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},
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},
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}
}
}
| 7
| 2023-03-05T08:57:00Z
|
---
tags:
- autotrain
- text-classification
language:
- unk
widget:
- text: "Enter text"
datasets:
- systash/autotrain-data-fake_news_fine_tuned_v4
co2_eq_emissions:
emissions: 0.007112583756560004
---
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 38998102353
- CO2 Emissions (in grams): 0.0071
## Validation Metrics
- Loss: 0.091
- Accuracy: 0.983
- Precision: 0.986
- Recall: 0.979
- AUC: 0.998
- F1: 0.982
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/systash/autotrain-fake_news_fine_tuned_v4-38998102353
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("systash/autotrain-fake_news_fine_tuned_v4-38998102353", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("systash/autotrain-fake_news_fine_tuned_v4-38998102353", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
```
|
Declan/HuffPost_model_v6
|
[
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
{
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
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},
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},
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},
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}
}
}
| 9
| 2023-03-05T09:23:12Z
|
---
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="Developer-Karthi/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"])
```
|
Declan/NPR_model_v2
|
[
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
{
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
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},
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},
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},
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},
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}
}
}
| 7
| 2023-03-05T09:28:13Z
|
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-taxi-v3-rl
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="Developer-Karthi/q-taxi-v3-rl", 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"])
```
|
Declan/NPR_model_v3
|
[
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
{
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
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"prefix": null
},
"translation_en_to_fr": {
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},
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}
}
}
| 9
| 2023-03-05T09:28:40Z
|
---
license: cc-by-nc-4.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: greek-nllb-4ep-384
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. -->
# greek-nllb-4ep-384
This model is a fine-tuned version of [facebook/nllb-200-distilled-600M](https://huggingface.co/facebook/nllb-200-distilled-600M) on the squad dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2875
## 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: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 32
- 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 |
|:-------------:|:-----:|:----:|:---------------:|
| 2.215 | 0.17 | 100 | 1.5536 |
| 1.7149 | 0.34 | 200 | 1.4819 |
| 1.544 | 0.51 | 300 | 1.4381 |
| 1.5006 | 0.67 | 400 | 1.3999 |
| 1.4577 | 0.84 | 500 | 1.3756 |
| 1.4495 | 1.01 | 600 | 1.3613 |
| 1.32 | 1.18 | 700 | 1.3467 |
| 1.2999 | 1.35 | 800 | 1.3404 |
| 1.2993 | 1.52 | 900 | 1.3339 |
| 1.2909 | 1.69 | 1000 | 1.3189 |
| 1.2974 | 1.86 | 1100 | 1.3112 |
| 1.2516 | 2.03 | 1200 | 1.3171 |
| 1.1852 | 2.2 | 1300 | 1.3032 |
| 1.1862 | 2.36 | 1400 | 1.3072 |
| 1.184 | 2.53 | 1500 | 1.2967 |
| 1.1865 | 2.7 | 1600 | 1.2968 |
| 1.1797 | 2.87 | 1700 | 1.2903 |
| 1.1707 | 3.04 | 1800 | 1.2934 |
| 1.1128 | 3.21 | 1900 | 1.2927 |
| 1.1314 | 3.38 | 2000 | 1.2895 |
| 1.1141 | 3.55 | 2100 | 1.2889 |
| 1.1137 | 3.72 | 2200 | 1.2888 |
| 1.1069 | 3.88 | 2300 | 1.2875 |
### Framework versions
- Transformers 4.27.0.dev0
- Pytorch 1.13.0
- Datasets 2.1.0
- Tokenizers 0.13.2
|
Declan/NewYorkTimes_model_v3
|
[] | null |
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}
| 0
| 2023-03-05T09:46:55Z
|
---
tags:
- text-to-image
- stable-diffusion
---
### Hackenbacker/g Dreambooth model trained by Hackenbacker 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:
|
DeepESP/gpt2-spanish
|
[
"pytorch",
"tf",
"jax",
"gpt2",
"text-generation",
"es",
"dataset:ebooks",
"transformers",
"GPT-2",
"Spanish",
"ebooks",
"nlg",
"license:mit",
"has_space"
] |
text-generation
|
{
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
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},
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},
"text-generation": {
"do_sample": true,
"max_length": 50
},
"translation_en_to_de": {
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},
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}
}
| 1,463
| null |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: my_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. -->
# my_model
This model is a fine-tuned version of [FYP19/t5-small-finetuned-wikisql](https://huggingface.co/FYP19/t5-small-finetuned-wikisql) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1327
- Rouge2 Precision: 0.5324
- Rouge2 Recall: 0.3366
- Rouge2 Fmeasure: 0.3851
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge2 Precision | Rouge2 Recall | Rouge2 Fmeasure |
|:-------------:|:-----:|:----:|:---------------:|:----------------:|:-------------:|:---------------:|
| No log | 1.0 | 438 | 0.1740 | 0.4577 | 0.288 | 0.3286 |
| 0.29 | 2.0 | 876 | 0.1478 | 0.5054 | 0.3184 | 0.3651 |
| 0.1503 | 3.0 | 1314 | 0.1381 | 0.5217 | 0.3264 | 0.3756 |
| 0.1271 | 4.0 | 1752 | 0.1343 | 0.5155 | 0.33 | 0.3751 |
| 0.1153 | 5.0 | 2190 | 0.1327 | 0.5324 | 0.3366 | 0.3851 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
|
DeepPavlov/xlm-roberta-large-en-ru-mnli
|
[
"pytorch",
"xlm-roberta",
"text-classification",
"en",
"ru",
"dataset:glue",
"dataset:mnli",
"transformers",
"xlm-roberta-large",
"xlm-roberta-large-en-ru",
"xlm-roberta-large-en-ru-mnli",
"has_space"
] |
text-classification
|
{
"architectures": [
"XLMRobertaForSequenceClassification"
],
"model_type": "xlm-roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
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}
}
}
| 227
| null |
---
license: creativeml-openrail-m
base_model: wavymulder/portraitplus
instance_prompt: a photo of ahn-hye-nah
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- lora
inference: true
---
# LoRA DreamBooth - jakeythelad/lora_output_hyenah_5
These are LoRA adaption weights for wavymulder/portraitplus. The weights were trained on a photo of ahn-hye-nah using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following.
|
DeltaHub/adapter_t5-3b_mrpc
|
[
"pytorch",
"transformers"
] | null |
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}
| 3
| null |
---
license: other
---
LLaMA-13B converted to work with Transformers/HuggingFace. This is under a special license, please see the LICENSE file for details.
--
license: other
---
# LLaMA Model Card
## Model details
**Organization developing the model**
The FAIR team of Meta AI.
**Model date**
LLaMA was trained between December. 2022 and Feb. 2023.
**Model version**
This is version 1 of the model.
**Model type**
LLaMA is an auto-regressive language model, based on the transformer architecture. The model comes in different sizes: 7B, 13B, 33B and 65B parameters.
**Paper or resources for more information**
More information can be found in the paper “LLaMA, Open and Efficient Foundation Language Models”, available at https://research.facebook.com/publications/llama-open-and-efficient-foundation-language-models/.
**Citations details**
https://research.facebook.com/publications/llama-open-and-efficient-foundation-language-models/
**License**
Non-commercial bespoke license
**Where to send questions or comments about the model**
Questions and comments about LLaMA can be sent via the [GitHub repository](https://github.com/facebookresearch/llama) of the project , by opening an issue.
## Intended use
**Primary intended uses**
The primary use of LLaMA is research on large language models, including:
exploring potential applications such as question answering, natural language understanding or reading comprehension,
understanding capabilities and limitations of current language models, and developing techniques to improve those,
evaluating and mitigating biases, risks, toxic and harmful content generations, hallucinations.
**Primary intended users**
The primary intended users of the model are researchers in natural language processing, machine learning and artificial intelligence.
**Out-of-scope use cases**
LLaMA is a base, or foundational, model. As such, it should not be used on downstream applications without further risk evaluation and mitigation. In particular, our model has not been trained with human feedback, and can thus generate toxic or offensive content, incorrect information or generally unhelpful answers.
## Factors
**Relevant factors**
One of the most relevant factors for which model performance may vary is which language is used. Although we included 20 languages in the training data, most of our dataset is made of English text, and we thus expect the model to perform better for English than other languages. Relatedly, it has been shown in previous studies that performance might vary for different dialects, and we expect that it will be the case for our model.
**Evaluation factors**
As our model is trained on data from the Web, we expect that it reflects biases from this source. We thus evaluated on RAI datasets to measure biases exhibited by the model for gender, religion, race, sexual orientation, age, nationality, disability, physical appearance and socio-economic status. We also measure the toxicity of model generations, depending on the toxicity of the context used to prompt the model.
## Metrics
**Model performance measures**
We use the following measure to evaluate the model:
- Accuracy for common sense reasoning, reading comprehension, natural language understanding (MMLU), BIG-bench hard, WinoGender and CrowS-Pairs,
- Exact match for question answering,
- The toxicity score from Perspective API on RealToxicityPrompts.
**Decision thresholds**
Not applicable.
**Approaches to uncertainty and variability**
Due to the high computational requirements of training LLMs, we trained only one model of each size, and thus could not evaluate variability of pre-training.
## Evaluation datasets
The model was evaluated on the following benchmarks: BoolQ, PIQA, SIQA, HellaSwag, WinoGrande, ARC, OpenBookQA, NaturalQuestions, TriviaQA, RACE, MMLU, BIG-bench hard, GSM8k, RealToxicityPrompts, WinoGender, CrowS-Pairs.
## Training dataset
The model was trained using the following source of data: CCNet [67%], C4 [15%], GitHub [4.5%], Wikipedia [4.5%], Books [4.5%], ArXiv [2.5%], Stack Exchange[2%]. The Wikipedia and Books domains include data in the following languages: bg, ca, cs, da, de, en, es, fr, hr, hu, it, nl, pl, pt, ro, ru, sl, sr, sv, uk. See the paper for more details about the training set and corresponding preprocessing.
## Quantitative analysis
Hyperparameters for the model architecture
<table>
<thead>
<tr>
<th >LLaMA</th> <th colspan=6>Model hyper parameters </th>
</tr>
<tr>
<th>Number of parameters</th><th>dimension</th><th>n heads</th><th>n layers</th><th>Learn rate</th><th>Batch size</th><th>n tokens</th>
</tr>
</thead>
<tbody>
<tr>
<th>7B</th> <th>4096</th> <th>32</th> <th>32</th> <th>3.0E-04</th><th>4M</th><th>1T
</tr>
<tr>
<th>13B</th><th>5120</th><th>40</th><th>40</th><th>3.0E-04</th><th>4M</th><th>1T
</tr>
<tr>
<th>33B</th><th>6656</th><th>52</th><th>60</th><th>1.5.E-04</th><th>4M</th><th>1.4T
</tr>
<tr>
<th>65B</th><th>8192</th><th>64</th><th>80</th><th>1.5.E-04</th><th>4M</th><th>1.4T
</tr>
</tbody>
</table>
*Table 1 - Summary of LLama Model Hyperparameters*
We present our results on eight standard common sense reasoning benchmarks in the table below.
<table>
<thead>
<tr>
<th>LLaMA</th> <th colspan=9>Reasoning tasks </th>
</tr>
<tr>
<th>Number of parameters</th> <th>BoolQ</th><th>PIQA</th><th>SIQA</th><th>HellaSwag</th><th>WinoGrande</th><th>ARC-e</th><th>ARC-c</th><th>OBQA</th><th>COPA</th>
</tr>
</thead>
<tbody>
<tr>
<th>7B</th><th>76.5</th><th>79.8</th><th>48.9</th><th>76.1</th><th>70.1</th><th>76.7</th><th>47.6</th><th>57.2</th><th>93
</th>
<tr><th>13B</th><th>78.1</th><th>80.1</th><th>50.4</th><th>79.2</th><th>73</th><th>78.1</th><th>52.7</th><th>56.4</th><th>94
</th>
<tr><th>33B</th><th>83.1</th><th>82.3</th><th>50.4</th><th>82.8</th><th>76</th><th>81.4</th><th>57.8</th><th>58.6</th><th>92
</th>
<tr><th>65B</th><th>85.3</th><th>82.8</th><th>52.3</th><th>84.2</th><th>77</th><th>81.5</th><th>56</th><th>60.2</th><th>94</th></tr>
</tbody>
</table>
*Table 2 - Summary of LLama Model Performance on Reasoning tasks*
We present our results on bias in the table below. Note that lower value is better indicating lower bias.
| No | Category | FAIR LLM |
| --- | -------------------- | -------- |
| 1 | Gender | 70.6 |
| 2 | Religion | 79 |
| 3 | Race/Color | 57 |
| 4 | Sexual orientation | 81 |
| 5 | Age | 70.1 |
| 6 | Nationality | 64.2 |
| 7 | Disability | 66.7 |
| 8 | Physical appearance | 77.8 |
| 9 | Socioeconomic status | 71.5 |
| | LLaMA Average | 66.6 |
*Table 3 - Summary bias of our model output*
## Ethical considerations
**Data**
The data used to train the model is collected from various sources, mostly from the Web. As such, it contains offensive, harmful and biased content. We thus expect the model to exhibit such biases from the training data.
**Human life**
The model is not intended to inform decisions about matters central to human life, and should not be used in such a way.
**Mitigations**
We filtered the data from the Web based on its proximity to Wikipedia text and references. For this, we used a Kneser-Ney language model and a fastText linear classifier.
**Risks and harms**
Risks and harms of large language models include the generation of harmful, offensive or biased content. These models are often prone to generating incorrect information, sometimes referred to as hallucinations. We do not expect our model to be an exception in this regard.
**Use cases**
LLaMA is a foundational model, and as such, it should not be used for downstream applications without further investigation and mitigations of risks. These risks and potential fraught use cases include, but are not limited to: generation of misinformation and generation of harmful, biased or offensive content.
|
DeltaHub/lora_t5-base_mrpc
|
[
"pytorch",
"transformers"
] | null |
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| 3
| 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: georgao/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
Denilson/gbert-base-germaner
|
[] | null |
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}
}
| 0
| null |
---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: doom_health_gathering_supreme
type: doom_health_gathering_supreme
metrics:
- type: mean_reward
value: 9.29 +/- 2.34
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **doom_health_gathering_supreme** environment.
This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory.
Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/
## Downloading the model
After installing Sample-Factory, download the model with:
```
python -m sample_factory.huggingface.load_from_hub -r rootacess/rl_course_vizdoom_health_gathering_supreme
```
## Using the model
To run the model after download, use the `enjoy` script corresponding to this environment:
```
python -m .usr.local.lib.python3.8.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme
```
You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag.
See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details
## Training with this model
To continue training with this model, use the `train` script corresponding to this environment:
```
python -m .usr.local.lib.python3.8.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme --restart_behavior=resume --train_for_env_steps=10000000000
```
Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
|
Deniskin/essays_small_2000
|
[] | null |
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| 0
| null |
---
license: other
---
LLaMA-7B converted to work with Transformers/HuggingFace. This variant is also quantized to int8. This is under a special license, please see the LICENSE file for details.
|
Deniskin/gpt3_medium
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers",
"has_space"
] |
text-generation
|
{
"architectures": [
"GPT2LMHeadModel"
],
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}
| 52
| 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: 245.82 +/- 20.59
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
...
```
|
DeskDown/MarianMixFT_en-id
|
[
"pytorch",
"marian",
"text2text-generation",
"transformers",
"autotrain_compatible"
] |
text2text-generation
|
{
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"MarianMTModel"
],
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}
| 3
| null |
---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: finetuned_roberta-base-uncased
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. -->
# finetuned_roberta-base-uncased
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: 1.4799
- Accuracy: 0.6519
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.372 | 1.0 | 102 | 1.3643 | 0.3375 |
| 1.1591 | 2.0 | 204 | 1.1988 | 0.4830 |
| 0.9623 | 3.0 | 306 | 1.0802 | 0.5694 |
| 0.7766 | 4.0 | 408 | 0.9885 | 0.6237 |
| 0.7336 | 5.0 | 510 | 1.0393 | 0.6120 |
| 0.6284 | 6.0 | 612 | 1.1150 | 0.6392 |
| 0.3616 | 7.0 | 714 | 1.2183 | 0.6402 |
| 0.3526 | 8.0 | 816 | 1.2362 | 0.6305 |
| 0.3151 | 9.0 | 918 | 1.3058 | 0.6372 |
| 0.3035 | 10.0 | 1020 | 1.2966 | 0.6343 |
| 0.2458 | 11.0 | 1122 | 1.3752 | 0.6508 |
| 0.2469 | 12.0 | 1224 | 1.4557 | 0.6557 |
| 0.2039 | 13.0 | 1326 | 1.5541 | 0.6372 |
| 0.1691 | 14.0 | 1428 | 1.5308 | 0.6343 |
| 0.1455 | 15.0 | 1530 | 1.6339 | 0.6421 |
| 0.1716 | 16.0 | 1632 | 1.6843 | 0.6392 |
| 0.1698 | 17.0 | 1734 | 1.6802 | 0.6479 |
| 0.2009 | 18.0 | 1836 | 1.6544 | 0.6479 |
| 0.1415 | 19.0 | 1938 | 1.6759 | 0.6518 |
| 0.1616 | 20.0 | 2040 | 1.6833 | 0.6508 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
|
DeskDown/MarianMixFT_en-ms
|
[
"pytorch",
"marian",
"text2text-generation",
"transformers",
"autotrain_compatible"
] |
text2text-generation
|
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"MarianMTModel"
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}
| 5
| null |
---
datasets:
- BeardedJohn/FakeNews
---
NLP fake news classifier based on pre-trained BERT model
|
DeskDown/MarianMixFT_en-my
|
[
"pytorch",
"marian",
"text2text-generation",
"transformers",
"autotrain_compatible"
] |
text2text-generation
|
{
"architectures": [
"MarianMTModel"
],
"model_type": "marian",
"task_specific_params": {
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}
| 7
| null |
---
language:
- ar
metrics:
- cer
pipeline_tag: automatic-speech-recognition
---
|
Devmapall/paraphrase-quora
|
[
"pytorch",
"jax",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] |
text2text-generation
|
{
"architectures": [
"T5ForConditionalGeneration"
],
"model_type": "t5",
"task_specific_params": {
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},
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"num_beams": 4,
"prefix": "translate English to German: "
},
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"early_stopping": true,
"max_length": 300,
"num_beams": 4,
"prefix": "translate English to French: "
},
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"early_stopping": true,
"max_length": 300,
"num_beams": 4,
"prefix": "translate English to Romanian: "
}
}
}
| 3
| null |
---
language:
- en
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- spearmanr
model-index:
- name: hBERTv2_data_aug_stsb
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE STSB
type: glue
args: stsb
metrics:
- name: Spearmanr
type: spearmanr
value: 0.5131253663491117
---
<!-- 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. -->
# hBERTv2_data_aug_stsb
This model is a fine-tuned version of [gokuls/bert_12_layer_model_v2](https://huggingface.co/gokuls/bert_12_layer_model_v2) on the GLUE STSB dataset.
It achieves the following results on the evaluation set:
- Loss: 2.1357
- Pearson: 0.5181
- Spearmanr: 0.5131
- Combined Score: 0.5156
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 256
- eval_batch_size: 256
- seed: 10
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Pearson | Spearmanr | Combined Score |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:---------:|:--------------:|
| 0.6302 | 1.0 | 1259 | 2.1357 | 0.5181 | 0.5131 | 0.5156 |
| 0.0973 | 2.0 | 2518 | 2.4678 | 0.4495 | 0.4283 | 0.4389 |
| 0.0514 | 3.0 | 3777 | 2.3102 | 0.4101 | 0.3922 | 0.4011 |
| 0.0384 | 4.0 | 5036 | 2.5410 | 0.4446 | 0.4376 | 0.4411 |
| 0.031 | 5.0 | 6295 | 2.4586 | 0.4091 | 0.3917 | 0.4004 |
| 0.0255 | 6.0 | 7554 | 2.5981 | 0.3998 | 0.3874 | 0.3936 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.14.0a0+410ce96
- Datasets 2.10.1
- Tokenizers 0.13.2
|
DevsIA/Devs_IA
|
[] | null |
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}
| 0
| null |
---
license: creativeml-openrail-m
language: en
tags :
- stable-diffusion
- text-to-image
- stable-diffusion-diffusers
- diffusers
---
# Wellcome To shiowo-flora-mix
This is my first ever model released publicly
# Image and model comming soon (+- 3 days)
---
---
# safetensors comming soon (1 week +-)
### Recepie:
https://huggingface.co/SweetLuna/Kenshi/resolve/main/KENSHI%2001/KENSHI01_Pruned.safetensors
https://huggingface.co/mindplayer/mindplayer-floralboys/resolve/main/mindplayer-floralboys.ckpt
KENSHI01_Pruned.safetensors (fp 32 as base 60%) + mindplayer-floralboys.ckpt(40%) = shiowomix
mindplayer-floralboys.ckpt(60% as base) + KENSHI01_Pruned.safetensors (fp 32 40%) = Nekomix
# for vae Please choose between:
https://huggingface.co/hakurei/waifu-diffusion-v1-4/blob/main/vae/kl-f8-anime2.ckpt
https://huggingface.co/stabilityai/sd-vae-ft-mse-original/resolve/main/vae-ft-mse-840000-ema-pruned.ckpt
## you can use the vae by replacing the ckpt with vae.pt for web ui users. (example: kl-f8-anime2.ckpt rename )
---
---
# Have FUN
### I am not responsilbe for any of the output
---
---
|
Dibyaranjan/nl_image_search
|
[] | null |
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| 0
| null |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SoccerTwos
library_name: ml-agents
---
# **poca** Agent playing **SoccerTwos**
This is a trained model of a **poca** agent playing **SoccerTwos** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-SoccerTwos
2. Step 1: Write your model_id: mingdinghan/poca-SoccerTwos-250000
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
Dilmk2/DialoGPT-small-harrypotter
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] |
conversational
|
{
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
"summarization": {
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},
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},
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},
"translation_en_to_fr": {
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}
}
| 13
| null |
---
license: mit
tags:
- conversational
---
# Tyrion Lannister Model
|
DimaOrekhov/cubert-method-name
|
[
"pytorch",
"encoder-decoder",
"text2text-generation",
"transformers",
"autotrain_compatible"
] |
text2text-generation
|
{
"architectures": [
"EncoderDecoderModel"
],
"model_type": "encoder-decoder",
"task_specific_params": {
"conversational": {
"max_length": null
},
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}
| 10
| 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: 252.21 +/- 15.19
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
...
```
|
DimaOrekhov/transformer-method-name
|
[
"pytorch",
"encoder-decoder",
"text2text-generation",
"transformers",
"autotrain_compatible"
] |
text2text-generation
|
{
"architectures": [
"EncoderDecoderModel"
],
"model_type": "encoder-decoder",
"task_specific_params": {
"conversational": {
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},
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| 8
| 2023-03-05T13:17:54Z
|
---
license: creativeml-openrail-m
tags:
- stable-diffusion
- text-to-image
---
<b>Please read this!</b><br>
My model has always been free and always will be free. There are no restrictions on the use of the model. The rights to this model still belong to me.
<hr/>
<b>Important note: "RAW photo" in the prompt may degrade the result.</b>
<b>I use this template to get good generation results:
Prompt:</b>
*subject*, (high detailed skin:1.2), 8k uhd, dslr, soft lighting, high quality, film grain, Fujifilm XT3
<b>Example:</b> a close up portrait photo of 26 y.o woman in wastelander clothes, long haircut, pale skin, slim body, background is city ruins, (high detailed skin:1.2), 8k uhd, dslr, soft lighting, high quality, film grain, Fujifilm XT3
<b>Negative Prompt:</b>
(deformed iris, deformed pupils, semi-realistic, cgi, 3d, render, sketch, cartoon, drawing, anime:1.4), text, close up, cropped, out of frame, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck<br>
<b>OR</b><br>
(deformed iris, deformed pupils, semi-realistic, cgi, 3d, render, sketch, cartoon, drawing, anime, mutated hands and fingers:1.4), (deformed, distorted, disfigured:1.3), poorly drawn, bad anatomy, wrong anatomy, extra limb, missing limb, floating limbs, disconnected limbs, mutation, mutated, ugly, disgusting, amputation
<b>Euler A or DPM++ 2M Karras with 25 steps<br>
CFG Scale 3,5 - 7<br>
Hires. fix with Latent upscaler<br>
0 Hires steps and Denoising strength 0.25-0.45<br>
Upscale by 1.1-2.0</b>
|
DivyanshuSheth/T5-Seq2Seq-Final
|
[] | null |
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| 0
| null |
---
language: ja
widget:
- text: Ｘ が 部屋 で ゲーム するxEffect
---
# COMET-GPT2 ja v2
Finetuned GPT-2 on the large version of [ATOMIC ja](https://github.com/nlp-waseda/comet-atomic-ja) using a causal language modeling (CLM) objective.
The original version and the large version of ATOMIC ja were introduced in [this paper](https://www.anlp.jp/proceedings/annual_meeting/2023/pdf_dir/B2-5.pdf) and in [this paper](https://www.anlp.jp/proceedings/annual_meeting/2023/pdf_dir/B9-1.pdf), respectively.
### How to use
You can use this model directly with a pipeline for text generation.
Since the generation relies on some randomness, we set a seed for reproducibility:
```python
>>> from transformers import pipeline, set_seed
>>> generator = pipeline('text-generation', model='nlp-waseda/comet-v2-gpt2-small-japanese')
>>> set_seed(42)
>>> generator('Ｘ が 副業 を 始めるxEffect', max_length=30, num_return_sequences=5, do_sample=True)
[{'generated_text': 'Ｘ が 副業 を 始めるxEffect X が 収入 を 得る'},
{'generated_text': 'Ｘ が 副業 を 始めるxEffect X が 時間 を 失う'},
{'generated_text': 'Ｘ が 副業 を 始めるxEffect X が 儲かる'},
{'generated_text': 'Ｘ が 副業 を 始めるxEffect X が 稼ぐ'},
{'generated_text': 'Ｘ が 副業 を 始めるxEffect X が 稼げる ように なる'}]
```
### Preprocessing
The texts are segmented into words using Juman++ and tokenized using SentencePiece.
## Evaluation results
The model achieves the following results:
| BLEU | BERTScore |
|:-----:|:---------:|
| - | - |
### BibTeX entry and citation info
```bibtex
@InProceedings{ide_nlp2023_event,
author = "井手竜也 and 村田栄樹 and 堀尾海斗 and 河原大輔 and 山崎天 and 李聖哲 and 新里顕大 and 佐藤敏紀",
title = "人間と言語モデルに対するプロンプトを用いたゼロからのイベント常識知識グラフ構築",
booktitle = "言語処理学会第29回年次大会",
year = "2023",
url = "https://www.anlp.jp/proceedings/annual_meeting/2023/pdf_dir/B2-5.pdf"
note = "in Japanese"
}
@InProceedings{murata_nlp2023,
author = "村田栄樹 and 井手竜也 and 榮田亮真 and 河原大輔 and 山崎天 and 李聖哲 and 新里顕大 and 佐藤敏紀",
title = "大規模言語モデルによって構築された常識知識グラフの拡大と低コストフィルタリング",
booktitle = "言語処理学会第29回年次大会",
year = "2023",
url = "https://www.anlp.jp/proceedings/annual_meeting/2023/pdf_dir/B9-1.pdf"
note = "in Japanese"
}
```
|
Dongjae/mrc2reader
|
[
"pytorch",
"xlm-roberta",
"question-answering",
"transformers",
"autotrain_compatible"
] |
question-answering
|
{
"architectures": [
"XLMRobertaForQuestionAnswering"
],
"model_type": "xlm-roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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}
| 3
| null |
---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: unit4SundayMarch5
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 500.00 +/- 0.00
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
DoyyingFace/bert-COVID-HATE-finetuned-test
|
[
"pytorch",
"bert",
"text-classification",
"transformers"
] |
text-classification
|
{
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
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},
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}
}
}
| 29
| 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: 242.56 +/- 23.32
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
DoyyingFace/bert-asian-hate-tweets-asian-unclean-freeze-8
|
[
"pytorch",
"bert",
"text-classification",
"transformers"
] |
text-classification
|
{
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
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"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
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"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
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},
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"early_stopping": null,
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"prefix": null
}
}
}
| 30
| 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: 266.24 +/- 20.93
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
...
```
|
DoyyingFace/bert-asian-hate-tweets-asian-unclean-slanted
|
[
"pytorch",
"bert",
"text-classification",
"transformers"
] |
text-classification
|
{
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
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},
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},
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},
"translation_en_to_fr": {
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}
}
| 29
| null |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SoccerTwos
library_name: ml-agents
---
# **poca** Agent playing **SoccerTwos**
This is a trained model of a **poca** agent playing **SoccerTwos** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-SoccerTwos
2. Step 1: Write your model_id: DaniilSirota/poca-SoccerTwos
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
DoyyingFace/bert-asian-hate-tweets-asian-unclean-warmup-100
|
[
"pytorch",
"bert",
"text-classification",
"transformers"
] |
text-classification
|
{
"architectures": [
"BertForSequenceClassification"
],
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}
}
| 28
| 2023-03-05T14:39:39Z
|
---
language:
- en
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: hBERTv2_data_aug_wnli
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE WNLI
type: glue
args: wnli
metrics:
- name: Accuracy
type: accuracy
value: 0.5633802816901409
---
<!-- 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. -->
# hBERTv2_data_aug_wnli
This model is a fine-tuned version of [gokuls/bert_12_layer_model_v2](https://huggingface.co/gokuls/bert_12_layer_model_v2) on the GLUE WNLI dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6873
- Accuracy: 0.5634
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 256
- eval_batch_size: 256
- seed: 10
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.699 | 1.0 | 218 | 0.6895 | 0.5634 |
| 0.6947 | 2.0 | 436 | 0.6886 | 0.5634 |
| 0.6935 | 3.0 | 654 | 0.6873 | 0.5634 |
| 0.6937 | 4.0 | 872 | 0.6921 | 0.5634 |
| 0.6934 | 5.0 | 1090 | 0.6892 | 0.5634 |
| 0.6932 | 6.0 | 1308 | 0.6911 | 0.5634 |
| 0.6933 | 7.0 | 1526 | 0.6955 | 0.4366 |
| 0.6931 | 8.0 | 1744 | 0.6908 | 0.5634 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.14.0a0+410ce96
- Datasets 2.10.1
- Tokenizers 0.13.2
|
DoyyingFace/bert-asian-hate-tweets-asian-unclean-warmup-25
|
[
"pytorch",
"bert",
"text-classification",
"transformers"
] |
text-classification
|
{
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
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},
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},
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}
}
}
| 30
| 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: 392.50 +/- 96.57
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
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga eswardivi -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga eswardivi -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga eswardivi
```
## 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', 700000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
|
DoyyingFace/bert-asian-hate-tweets-asian-unclean-warmup-50
|
[
"pytorch",
"bert",
"text-classification",
"transformers"
] |
text-classification
|
{
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
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},
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},
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},
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},
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}
}
}
| 28
| null |
Access to model Neno-sols/sols-golden-dress is restricted and you are not in the authorized list. Visit https://huggingface.co/Neno-sols/sols-golden-dress to ask for access.
|
DoyyingFace/bert-asian-hate-tweets-asian-unclean-warmup-75
|
[
"pytorch",
"bert",
"text-classification",
"transformers"
] |
text-classification
|
{
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
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"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
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"prefix": null
},
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},
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"prefix": null
},
"translation_en_to_fr": {
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},
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}
}
}
| 37
| 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="i4ata/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"])
```
|
DoyyingFace/bert-asian-hate-tweets-asonam-clean
|
[
"pytorch",
"bert",
"text-classification",
"transformers"
] |
text-classification
|
{
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
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"max_length": null
},
"translation_en_to_de": {
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},
"translation_en_to_fr": {
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},
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}
}
}
| 27
| null |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.54 +/- 2.73
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="i4ata/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"])
```
|
DoyyingFace/bert-asian-hate-tweets-concat-clean-with-unclean-valid
|
[
"pytorch",
"bert",
"text-classification",
"transformers"
] |
text-classification
|
{
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
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},
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},
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},
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"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 25
| null |
---
tags:
- LunarLander-v2
- ppo
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
- deep-rl-course
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: -135.92 +/- 66.03
name: mean_reward
verified: false
---
# PPO Agent Playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2.
# Hyperparameters
```python
{'exp_name': 'ppo'
'seed': 1
'torch_deterministic': True
'cuda': True
'track': False
'wandb_project_name': 'cleanRL'
'wandb_entity': None
'capture_video': False
'env_id': 'LunarLander-v2'
'total_timesteps': 50000
'learning_rate': 0.00025
'num_envs': 4
'num_steps': 128
'anneal_lr': True
'gae': True
'gamma': 0.99
'gae_lambda': 0.95
'num_minibatches': 4
'update_epochs': 4
'norm_adv': True
'clip_coef': 0.2
'clip_vloss': True
'ent_coef': 0.01
'vf_coef': 0.5
'max_grad_norm': 0.5
'target_kl': None
'repo_id': 'lunared473/ppo-scratch-LunarLander-v2'
'batch_size': 512
'minibatch_size': 128}
```
|
DoyyingFace/bert-asian-hate-tweets-concat-clean
|
[
"pytorch",
"bert",
"text-classification",
"transformers"
] |
text-classification
|
{
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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"length_penalty": null,
"max_length": null,
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},
"text-generation": {
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},
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},
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}
}
}
| 25
| null |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 272.88 +/- 19.85
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
...
```
|
albert-base-v1
|
[
"pytorch",
"tf",
"safetensors",
"albert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1909.11942",
"transformers",
"exbert",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] |
fill-mask
|
{
"architectures": [
"AlbertForMaskedLM"
],
"model_type": "albert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
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},
"translation_en_to_fr": {
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},
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}
}
}
| 38,156
| 2023-03-05T14:57:54Z
|
---
license: bigscience-openrail-m
language:
- en
---
GPT-J-Pyg_PPO-6B [GPT-J Pygmalion + GPT-J PPO_HH]
GPT-J-Pyg_PPO-6B is an experimental model containing a parameter-wise 40/60 blend (weighted average PPO_HH:Pygmalion) of the weights of ppo_hh_gpt-j and Pygmalion-6b.
-Intended Merge Value-
As with fine-tuning, merging weights does not add information but transforms it, therefore it is important to consider trade-offs.
Pyg_PPO combines ppo_hh_gpt-j and Pygmalion-6b; both technical
achievements are blended with the intent to elevate the strengths of
both. Datasets of both are linked below to assist in exploratory speculation on which datasets in what quantity and configuration have
the largest impact on the usefulness of a model without the expense of
fine-tuning. Blend was done in FP32 and output in FP16.
-Intended Use-
Research purposes only, intended for responsible use.
Express a conversation in natural language, and Pyg_PPO will do the thing.
Try starting a two line prompt such as:
```
Bot: "Hello, how are you?"
You: "I am doing just fine, thank you."
```
Or any other
topic, and the model will carry on in this back and forth format.
Can also be used as a base to merge with other creative,
technical, or adventure themed models of the same class
(GPT-J & 6b NeoX) and parameter size (6b) to experiment with
the morphology of model weights based on the value added
by instruct.
Merge tested using KoboldAI with Nucleus Sampling Top-P set to 0.9, Temperature at 0.6, and Repetition Penalty at 1.1; extra samplers
disabled.
-Credits To-
Core Model:
https://huggingface.co/EleutherAI/gpt-j-6B
Author:
https://www.eleuther.ai/
Model1; 50% ppo_hh_gpt-j:
https://huggingface.co/reciprocate/ppo_hh_gpt-j
Author Repo:
https://huggingface.co/reciprocate
Related; CarperAI:
https://huggingface.co/CarperAI
Dataset is a variant of the Helpful Harmless assistant themed
dataset and Proximal Policy Optimization, specific datasets
used are unknown; listed repo datasets include:
https://huggingface.co/datasets/reciprocate/summarize_eval_ilql
https://huggingface.co/datasets/reciprocate/hh_eval_ilql
PPO explained:
https://paperswithcode.com/method/ppo
Potential HH-type datasets utilized:
https://huggingface.co/HuggingFaceH4
https://huggingface.co/datasets/Anthropic/hh-rlhf
Model2; 50% Pygmalion-6b:
https://huggingface.co/PygmalionAI/pygmalion-6b
Author Repo:
https://huggingface.co/PygmalionAI
Weight merge Script credit to Concedo:
https://huggingface.co/concedo
Model's card template credit to Digitous:
https://huggingface.co/digitous/GPT-R
|
albert-base-v2
|
[
"pytorch",
"tf",
"jax",
"rust",
"safetensors",
"albert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1909.11942",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] |
fill-mask
|
{
"architectures": [
"AlbertForMaskedLM"
],
"model_type": "albert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
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"max_length": null
},
"translation_en_to_de": {
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"prefix": null
},
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},
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}
}
}
| 4,785,283
| 2023-03-05T14:57:59Z
|
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Pyramids
library_name: ml-agents
---
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-Pyramids
2. Step 1: Write your model_id: VAZaytsev/Reinforce-Pyramids
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
albert-xlarge-v1
|
[
"pytorch",
"tf",
"albert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1909.11942",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] |
fill-mask
|
{
"architectures": [
"AlbertForMaskedLM"
],
"model_type": "albert",
"task_specific_params": {
"conversational": {
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},
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},
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},
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},
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}
}
}
| 341
| 2023-03-05T15:01:55Z
|
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SoccerTwos
library_name: ml-agents
---
# **poca** Agent playing **SoccerTwos**
This is a trained model of a **poca** agent playing **SoccerTwos** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-SoccerTwos
2. Step 1: Write your model_id: stinoco/poca-SoccerTwos
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
albert-xlarge-v2
|
[
"pytorch",
"tf",
"albert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1909.11942",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] |
fill-mask
|
{
"architectures": [
"AlbertForMaskedLM"
],
"model_type": "albert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
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"prefix": null
},
"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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}
}
}
| 2,973
| 2023-03-05T15:01:58Z
|
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: bert-large-cased-sigir-support-refute-no-label-40-2nd-test-LR10-40-1
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-large-cased-sigir-support-refute-no-label-40-2nd-test-LR10-40-1
This model is a fine-tuned version of [jojoUla/bert-large-cased-sigir-support-refute-no-label-40](https://huggingface.co/jojoUla/bert-large-cased-sigir-support-refute-no-label-40) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1480
## 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: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 5.5106 | 1.0 | 1 | 5.1619 |
| 5.6209 | 2.0 | 2 | 5.5710 |
| 5.8886 | 3.0 | 3 | 2.9607 |
| 5.2551 | 4.0 | 4 | 4.7114 |
| 3.4045 | 5.0 | 5 | 5.3069 |
| 2.7632 | 6.0 | 6 | 7.4665 |
| 2.4015 | 7.0 | 7 | 0.4605 |
| 2.6532 | 8.0 | 8 | 0.8724 |
| 1.2054 | 9.0 | 9 | 0.0124 |
| 2.2897 | 10.0 | 10 | 3.4811 |
| 1.8984 | 11.0 | 11 | 0.1331 |
| 1.8627 | 12.0 | 12 | 0.5143 |
| 1.79 | 13.0 | 13 | 1.3302 |
| 1.2529 | 14.0 | 14 | 0.0777 |
| 1.2926 | 15.0 | 15 | 1.0649 |
| 1.2448 | 16.0 | 16 | 0.0018 |
| 1.6533 | 17.0 | 17 | 0.7471 |
| 1.171 | 18.0 | 18 | 0.2074 |
| 1.2245 | 19.0 | 19 | 1.7576 |
| 0.7455 | 20.0 | 20 | 0.0755 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
|
albert-xxlarge-v1
|
[
"pytorch",
"tf",
"albert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1909.11942",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] |
fill-mask
|
{
"architectures": [
"AlbertForMaskedLM"
],
"model_type": "albert",
"task_specific_params": {
"conversational": {
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},
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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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"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 7,091
| 2023-03-05T15:06:29Z
|
---
license: apache-2.0
tags:
- setfit
- sentence-transformers
- text-classification
pipeline_tag: text-classification
---
# setfit-distilbert-user-intent
This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves:
1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.
## Usage
To use this model for inference, first install the SetFit library:
```bash
python -m pip install setfit
```
You can then run inference as follows:
```python
from setfit import SetFitModel
# Download from Hub and run inference
model = SetFitModel.from_pretrained("setfit-distilbert-user-intent")
# Run inference
preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"])
```
## BibTeX entry and citation info
```bibtex
@article{https://doi.org/10.48550/arxiv.2209.11055,
doi = {10.48550/ARXIV.2209.11055},
url = {https://arxiv.org/abs/2209.11055},
author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Efficient Few-Shot Learning Without Prompts},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
```
|
albert-xxlarge-v2
|
[
"pytorch",
"tf",
"safetensors",
"albert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1909.11942",
"transformers",
"exbert",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] |
fill-mask
|
{
"architectures": [
"AlbertForMaskedLM"
],
"model_type": "albert",
"task_specific_params": {
"conversational": {
"max_length": null
},
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"no_repeat_ngram_size": null,
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},
"text-generation": {
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},
"translation_en_to_de": {
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},
"translation_en_to_fr": {
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},
"translation_en_to_ro": {
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"prefix": null
}
}
}
| 42,640
| 2023-03-05T15:07:22Z
|
---
license: creativeml-openrail-m
tags:
- text-to-image
- stable-diffusion
---
### Harsha Dreambooth model trained by haytin69 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:
|
bert-base-chinese
|
[
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"fill-mask",
"zh",
"arxiv:1810.04805",
"transformers",
"autotrain_compatible",
"has_space"
] |
fill-mask
|
{
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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"no_repeat_ngram_size": null,
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},
"text-generation": {
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"max_length": null
},
"translation_en_to_de": {
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"max_length": null,
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"prefix": null
},
"translation_en_to_fr": {
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"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 3,377,486
| 2023-03-05T15:14:50Z
|
---
tags:
- music-generation
- transformer
- pytorch
- audio
- music
- piano
license: mit
---
# Compose & Embellish: Piano Performance Generation Pipeline
Trained model weights and training datasets for the paper:
* Shih-Lun Wu and Yi-Hsuan Yang
"[Compose & Embellish: Well-Structured Piano Performance Generation via A Two-Stage Approach](https://arxiv.org/abs/2209.08212)."
_Int. Conf. on Acoustics, Speech and Signal Processing (ICASSP)_, 2023
**Note:** Materials here should be used in conjunction with our [model implementation Github repo](https://github.com/slSeanWU/Compose_and_Embellish).
## Model characteristics
### Stage 1: "Compose" model
Generates **melody and chord progression** from scratch.
- Model backbone: 12-layer Transformer w/ relative positional encoding
- Num trainable params: 41.3M
- Token vocabulary: [Revamped MIDI-derived events](https://arxiv.org/abs/2002.00212) (**REMI**) w/ slight modifications
- Pretraining dataset: subset of [Lakh MIDI full](https://colinraffel.com/projects/lmd/) (**LMD-full**), 14934 songs
- melody extraction (and data filtering) done by **matching lyrics to tracks**: https://github.com/gulnazaki/lyrics-melody/blob/main/pre-processing/create_dataset.py
- structural segmentation done with **A\* search**: https://github.com/Dsqvival/hierarchical-structure-analysis
- Finetuning dataset: subset of [AILabs.tw Pop1K7](https://github.com/YatingMusic/compound-word-transformer) (**Pop1K7**), 1591 songs
- melody extraction done with **skyline algorithm**: https://github.com/wazenmai/MIDI-BERT/blob/CP/melody_extraction/skyline/analyzer.py
- structural segmentation done in the same way as pretraining dataset
- Training sequence length: 2400
### Stage 2: "Embellish" model
Generates **accompaniment, timing and dynamics** conditioned on Stage 1 outputs.
- Model backbone: 12-layer **Performer** ([paper](https://arxiv.org/abs/2009.14794), [implementation](https://github.com/idiap/fast-transformers))
- Num trainable params: 38.2M
- Token vocabulary: [Revamped MIDI-derived events](https://arxiv.org/abs/2002.00212) (**REMI**) w/ slight modifications
- Training dataset: [AILabs.tw Pop1K7](https://github.com/YatingMusic/compound-word-transformer) (**Pop1K7**), 1747 songs
- Training sequence length: 3072
## BibTex
If you find the materials useful, please consider citing our work:
```
@inproceedings{wu2023compembellish,
title={{Compose \& Embellish}: Well-Structured Piano Performance Generation via A Two-Stage Approach},
author={Wu, Shih-Lun and Yang, Yi-Hsuan},
booktitle={Proc. Int. Conf. on Acoustics, Speech and Signal Processing (ICASSP)},
year={2023},
url={https://arxiv.org/pdf/2209.08212.pdf}
}
```
|
bert-base-german-cased
|
[
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"fill-mask",
"de",
"transformers",
"exbert",
"license:mit",
"autotrain_compatible",
"has_space"
] |
fill-mask
|
{
"architectures": [
"BertForMaskedLM"
],
"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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},
"translation_en_to_fr": {
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},
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"prefix": null
}
}
}
| 175,983
| 2023-03-05T15:15:30Z
|
---
license: cc-by-4.0
metrics:
- bleu4
- meteor
- rouge-l
- bertscore
- moverscore
language: fr
datasets:
- lmqg/qg_frquad
pipeline_tag: text2text-generation
tags:
- question generation
widget:
- text: "Créateur » (Maker), lui aussi au singulier, « <hl> le Suprême Berger <hl> » (The Great Shepherd) ; de l'autre, des réminiscences de la théologie de l'Antiquité : le tonnerre, voix de Jupiter, « Et souvent ta voix gronde en un tonnerre terrifiant », etc."
example_title: "Question Generation Example 1"
- text: "Ce black dog peut être lié à des évènements traumatisants issus du monde extérieur, tels que son renvoi de l'Amirauté après la catastrophe des Dardanelles, lors de la <hl> Grande Guerre <hl> de 14-18, ou son rejet par l'électorat en juillet 1945."
example_title: "Question Generation Example 2"
- text: "contre <hl> Normie Smith <hl> et 15 000 dollars le 28 novembre 1938."
example_title: "Question Generation Example 3"
model-index:
- name: vocabtrimmer/mt5-small-trimmed-fr-frquad-qg
results:
- task:
name: Text2text Generation
type: text2text-generation
dataset:
name: lmqg/qg_frquad
type: default
args: default
metrics:
- name: BLEU4 (Question Generation)
type: bleu4_question_generation
value: 7.18
- name: ROUGE-L (Question Generation)
type: rouge_l_question_generation
value: 26.74
- name: METEOR (Question Generation)
type: meteor_question_generation
value: 16.12
- name: BERTScore (Question Generation)
type: bertscore_question_generation
value: 79.16
- name: MoverScore (Question Generation)
type: moverscore_question_generation
value: 55.31
---
# Model Card of `vocabtrimmer/mt5-small-trimmed-fr-frquad-qg`
This model is fine-tuned version of [vocabtrimmer/mt5-small-trimmed-fr](https://huggingface.co/vocabtrimmer/mt5-small-trimmed-fr) for question generation task on the [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-generation).
### Overview
- **Language model:** [vocabtrimmer/mt5-small-trimmed-fr](https://huggingface.co/vocabtrimmer/mt5-small-trimmed-fr)
- **Language:** fr
- **Training data:** [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) (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="fr", model="vocabtrimmer/mt5-small-trimmed-fr-frquad-qg")
# model prediction
questions = model.generate_q(list_context="Créateur » (Maker), lui aussi au singulier, « le Suprême Berger » (The Great Shepherd) ; de l'autre, des réminiscences de la théologie de l'Antiquité : le tonnerre, voix de Jupiter, « Et souvent ta voix gronde en un tonnerre terrifiant », etc.", list_answer="le Suprême Berger")
```
- With `transformers`
```python
from transformers import pipeline
pipe = pipeline("text2text-generation", "vocabtrimmer/mt5-small-trimmed-fr-frquad-qg")
output = pipe("Créateur » (Maker), lui aussi au singulier, « <hl> le Suprême Berger <hl> » (The Great Shepherd) ; de l'autre, des réminiscences de la théologie de l'Antiquité : le tonnerre, voix de Jupiter, « Et souvent ta voix gronde en un tonnerre terrifiant », etc.")
```
## Evaluation
- ***Metric (Question Generation)***: [raw metric file](https://huggingface.co/vocabtrimmer/mt5-small-trimmed-fr-frquad-qg/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_frquad.default.json)
| | Score | Type | Dataset |
|:-----------|--------:|:--------|:-----------------------------------------------------------------|
| BERTScore | 79.16 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) |
| Bleu_1 | 27.02 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) |
| Bleu_2 | 15.5 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) |
| Bleu_3 | 10.32 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) |
| Bleu_4 | 7.18 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) |
| METEOR | 16.12 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) |
| MoverScore | 55.31 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) |
| ROUGE_L | 26.74 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) |
## Training hyperparameters
The following hyperparameters were used during fine-tuning:
- dataset_path: lmqg/qg_frquad
- dataset_name: default
- input_types: paragraph_answer
- output_types: question
- prefix_types: None
- model: vocabtrimmer/mt5-small-trimmed-fr
- max_length: 512
- max_length_output: 32
- epoch: 17
- batch: 32
- lr: 0.001
- fp16: False
- random_seed: 1
- gradient_accumulation_steps: 2
- label_smoothing: 0.15
The full configuration can be found at [fine-tuning config file](https://huggingface.co/vocabtrimmer/mt5-small-trimmed-fr-frquad-qg/raw/main/trainer_config.json).
## Citation
```
@inproceedings{ushio-etal-2022-generative,
title = "{G}enerative {L}anguage {M}odels for {P}aragraph-{L}evel {Q}uestion {G}eneration",
author = "Ushio, Asahi and
Alva-Manchego, Fernando and
Camacho-Collados, Jose",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2022",
address = "Abu Dhabi, U.A.E.",
publisher = "Association for Computational Linguistics",
}
```
|
bert-base-german-dbmdz-cased
|
[
"pytorch",
"jax",
"bert",
"fill-mask",
"de",
"transformers",
"license:mit",
"autotrain_compatible",
"has_space"
] |
fill-mask
|
{
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
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},
"translation_en_to_fr": {
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"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 1,814
| 2023-03-05T15:17:16Z
|
---
license: creativeml-openrail-m
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
### How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Data Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
bert-base-german-dbmdz-uncased
|
[
"pytorch",
"jax",
"safetensors",
"bert",
"fill-mask",
"de",
"transformers",
"license:mit",
"autotrain_compatible",
"has_space"
] |
fill-mask
|
{
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
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"prefix": null
},
"text-generation": {
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"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
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},
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"max_length": null,
"num_beams": null,
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}
}
}
| 68,305
| 2023-03-05T15:18:02Z
|
---
license: apache-2.0
tags:
- summarization
- generated_from_trainer
datasets:
- ravkuk_summerize_dataset
metrics:
- rouge
model-index:
- name: le-fine-tune-mt5-base
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: ravkuk_summerize_dataset
type: ravkuk_summerize_dataset
config: default
split: train
args: default
metrics:
- name: Rouge1
type: rouge
value: 0.1555
---
<!-- 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. -->
# le-fine-tune-mt5-base
This model is a fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) on the ravkuk_summerize_dataset dataset.
It achieves the following results on the evaluation set:
- Loss: 2.6590
- Rouge1: 0.1555
- Rouge2: 0.065
- Rougel: 0.1489
- Rougelsum: 0.149
- Gen Len: 18.9858
## 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: 0.0014142135623730952
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.3
- num_epochs: 8
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| 5.0797 | 1.0 | 197 | 2.7316 | 0.1101 | 0.0319 | 0.1025 | 0.1024 | 18.9432 |
| 2.8975 | 2.0 | 394 | 2.6943 | 0.1239 | 0.0453 | 0.1207 | 0.1204 | 18.9688 |
| 2.7115 | 3.0 | 591 | 2.6143 | 0.1333 | 0.0505 | 0.1283 | 0.1289 | 18.9688 |
| 2.365 | 4.0 | 788 | 2.5704 | 0.125 | 0.0433 | 0.1201 | 0.1199 | 19.0 |
| 2.0738 | 5.0 | 985 | 2.5296 | 0.1341 | 0.0478 | 0.1284 | 0.1286 | 18.9858 |
| 1.6716 | 6.0 | 1182 | 2.4902 | 0.1451 | 0.0554 | 0.1397 | 0.1395 | 18.9886 |
| 1.2644 | 7.0 | 1379 | 2.5039 | 0.1446 | 0.0562 | 0.1407 | 0.1406 | 18.9744 |
| 0.9641 | 8.0 | 1576 | 2.6590 | 0.1555 | 0.065 | 0.1489 | 0.149 | 18.9858 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
|
bert-base-multilingual-cased
|
[
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"fill-mask",
"multilingual",
"af",
"sq",
"ar",
"an",
"hy",
"ast",
"az",
"ba",
"eu",
"bar",
"be",
"bn",
"inc",
"bs",
"br",
"bg",
"my",
"ca",
"ceb",
"ce",
"zh",
"cv",
"hr",
"cs",
"da",
"nl",
"en",
"et",
"fi",
"fr",
"gl",
"ka",
"de",
"el",
"gu",
"ht",
"he",
"hi",
"hu",
"is",
"io",
"id",
"ga",
"it",
"ja",
"jv",
"kn",
"kk",
"ky",
"ko",
"la",
"lv",
"lt",
"roa",
"nds",
"lm",
"mk",
"mg",
"ms",
"ml",
"mr",
"mn",
"min",
"ne",
"new",
"nb",
"nn",
"oc",
"fa",
"pms",
"pl",
"pt",
"pa",
"ro",
"ru",
"sco",
"sr",
"scn",
"sk",
"sl",
"aze",
"es",
"su",
"sw",
"sv",
"tl",
"tg",
"th",
"ta",
"tt",
"te",
"tr",
"uk",
"ud",
"uz",
"vi",
"vo",
"war",
"cy",
"fry",
"pnb",
"yo",
"dataset:wikipedia",
"arxiv:1810.04805",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] |
fill-mask
|
{
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
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},
"text-generation": {
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},
"translation_en_to_de": {
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},
"translation_en_to_fr": {
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"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 4,749,504
| 2023-03-05T15:20:37Z
|
---
datasets:
- breadlicker45/musenet-encoders-12k
---
|
bert-base-multilingual-uncased
|
[
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"fill-mask",
"multilingual",
"af",
"sq",
"ar",
"an",
"hy",
"ast",
"az",
"ba",
"eu",
"bar",
"be",
"bn",
"inc",
"bs",
"br",
"bg",
"my",
"ca",
"ceb",
"ce",
"zh",
"cv",
"hr",
"cs",
"da",
"nl",
"en",
"et",
"fi",
"fr",
"gl",
"ka",
"de",
"el",
"gu",
"ht",
"he",
"hi",
"hu",
"is",
"io",
"id",
"ga",
"it",
"ja",
"jv",
"kn",
"kk",
"ky",
"ko",
"la",
"lv",
"lt",
"roa",
"nds",
"lm",
"mk",
"mg",
"ms",
"ml",
"mr",
"min",
"ne",
"new",
"nb",
"nn",
"oc",
"fa",
"pms",
"pl",
"pt",
"pa",
"ro",
"ru",
"sco",
"sr",
"scn",
"sk",
"sl",
"aze",
"es",
"su",
"sw",
"sv",
"tl",
"tg",
"ta",
"tt",
"te",
"tr",
"uk",
"ud",
"uz",
"vi",
"vo",
"war",
"cy",
"fry",
"pnb",
"yo",
"dataset:wikipedia",
"arxiv:1810.04805",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] |
fill-mask
|
{
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 328,585
| 2023-03-05T15:21:50Z
|
---
license: apache-2.0
language:
- en
metrics:
- f1
---
# Federated Learning Based Multilingual Emoji Prediction
This repository contains code for training and evaluating transformer-based models for Uni/multilingual emoji prediction in clean and attack scenarios using Federated Learning. This work is described in the paper "Federated Learning-Based Multilingual Emoji Prediction in Clean and Attack Scenarios."
# Abstract
Federated learning is a growing field in the machine learning community due to its decentralized and private design. Model training in federated learning is distributed over multiple clients giving access to lots of client data while maintaining privacy. Then, a server aggregates the training done on these multiple clients without access to their data, which could be emojis widely used in any social media service and instant messaging platforms to express users' sentiments. This paper proposes federated learning-based multilingual emoji prediction in both clean and attack scenarios. Emoji prediction data have been crawled from both Twitter and SemEval emoji datasets. This data is used to train and evaluate different transformer model sizes including a sparsely activated transformer with either the assumption of clean data in all clients or poisoned data via label flipping attack in some clients. Experimental results on these models show that federated learning in either clean or attacked scenarios performs similarly to centralized training in multilingual emoji prediction on seen and unseen languages under different data sources and distributions. Our trained transformers perform better than other techniques on the SemEval emoji dataset in addition to the privacy as well as distributed benefits of federated learning.
# Performance
> * Acc : 47.000 %
> * Mac-F1 : 33.368 %
> * Also see our [GitHub Repo](https://github.com/kareemgamalmahmoud/FEDERATED-LEARNING-BASED-MULTILINGUAL-EMOJI-PREDICTION-IN-CLEAN-AND-ATTACK-SCENARIOS)
# Dependencies
> * Python 3.6+
> * PyTorch 1.7.0+
> * Transformers 4.0.0+
# Usage
> To use the model, first install the `transformers` package from Hugging Face:
```python
pip install transformers
```
> Then, you can load the model and tokenizer using the following code:
```python
from transformers import AutoModelForSequenceClassification, AutoTokenizer
import numpy as np
import urllib.request
import csv
```
```python
MODEL = "Karim-Gamal/MMiniLM-L12-finetuned-SemEval-2018-emojis-cen-1"
tokenizer = AutoTokenizer.from_pretrained(MODEL)
model = AutoModelForSequenceClassification.from_pretrained(MODEL)
```
> Once you have the tokenizer and model, you can preprocess your text and pass it to the model for prediction:
```python
# Preprocess text (username and link placeholders)
def preprocess(text):
new_text = []
for t in text.split(" "):
t = '@user' if t.startswith('@') and len(t) > 1 else t
t = 'http' if t.startswith('http') else t
new_text.append(t)
return " ".join(new_text)
text = "Hello world"
text = preprocess(text)
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
scores = output[0][0].detach().numpy()
```
> The scores variable contains the probabilities for each of the possible emoji labels. To get the top k predictions, you can use the following code:
```python
# download label mapping
labels=[]
mapping_link = "https://raw.githubusercontent.com/cardiffnlp/tweeteval/main/datasets/emoji/mapping.txt"
with urllib.request.urlopen(mapping_link) as f:
html = f.read().decode('utf-8').split("\n")
csvreader = csv.reader(html, delimiter='\t')
labels = [row[1] for row in csvreader if len(row) > 1]
k = 3 # number of top predictions to show
ranking = np.argsort(scores)
ranking = ranking[::-1]
for i in range(k):
l = labels[ranking[i]]
s = scores[ranking[i]]
print(f"{i+1}) {l} {np.round(float(s), 4)}")
```
## Note : this is the source for that code : [Link](https://huggingface.co/cardiffnlp/twitter-roberta-base-emoji)
|
bert-base-uncased
|
[
"pytorch",
"tf",
"jax",
"rust",
"safetensors",
"bert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1810.04805",
"transformers",
"exbert",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] |
fill-mask
|
{
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
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"num_beams": null,
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},
"translation_en_to_fr": {
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"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 59,663,489
| 2023-03-05T15:22:37Z
|
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SoccerTwos
library_name: ml-agents
---
# **poca** Agent playing **SoccerTwos**
This is a trained model of a **poca** agent playing **SoccerTwos** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-SoccerTwos
2. Step 1: Write your model_id: jinhu2659/poca-SoccerTwos
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
bert-large-cased
|
[
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1810.04805",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] |
fill-mask
|
{
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 388,769
| 2023-03-05T15:27:10Z
|
---
language:
- en
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: hBERTv2_data_aug_mnli
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE MNLI
type: glue
args: mnli
metrics:
- name: Accuracy
type: accuracy
value: 0.318246541903987
---
<!-- 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. -->
# hBERTv2_data_aug_mnli
This model is a fine-tuned version of [gokuls/bert_12_layer_model_v2](https://huggingface.co/gokuls/bert_12_layer_model_v2) on the GLUE MNLI dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0988
- Accuracy: 0.3182
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 256
- eval_batch_size: 256
- seed: 10
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:------:|:---------------:|:--------:|
| 1.0988 | 1.0 | 31440 | 1.0988 | 0.3182 |
| 1.0985 | 2.0 | 62880 | 1.0992 | 0.3182 |
| 1.0985 | 3.0 | 94320 | 1.0991 | 0.3182 |
| 1.0985 | 4.0 | 125760 | 1.0991 | 0.3182 |
| 1.0985 | 5.0 | 157200 | 1.0988 | 0.3182 |
| 1.0985 | 6.0 | 188640 | 1.0988 | 0.3182 |
| 1.0985 | 7.0 | 220080 | 1.0988 | 0.3182 |
| 1.0985 | 8.0 | 251520 | 1.0988 | 0.3182 |
| 1.0985 | 9.0 | 282960 | 1.0988 | 0.3182 |
| 1.0985 | 10.0 | 314400 | 1.0988 | 0.3182 |
| 1.0985 | 11.0 | 345840 | 1.0988 | 0.3182 |
| 1.0985 | 12.0 | 377280 | 1.0988 | 0.3182 |
| 1.0985 | 13.0 | 408720 | 1.0988 | 0.3182 |
| 1.0985 | 14.0 | 440160 | 1.0988 | 0.3182 |
| 1.0985 | 15.0 | 471600 | 1.0988 | 0.3182 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.14.0a0+410ce96
- Datasets 2.10.1
- Tokenizers 0.13.2
|
bert-large-uncased-whole-word-masking-finetuned-squad
|
[
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"question-answering",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1810.04805",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] |
question-answering
|
{
"architectures": [
"BertForQuestionAnswering"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
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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,
"prefix": null
}
}
}
| 480,510
| 2023-03-05T15:27:19Z
|
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 261.54 +/- 16.51
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
bert-large-uncased
|
[
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1810.04805",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] |
fill-mask
|
{
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
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"max_length": null,
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"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 1,058,496
| 2023-03-05T15:29:35Z
|
---
tags:
- FrozenLake-v1-8x8
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-8x8-Slippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-8x8
type: FrozenLake-v1-8x8
metrics:
- type: mean_reward
value: 0.62 +/- 0.49
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
import gym
from huggingface_sb3 import load_from_hub
model = load_from_hub(
repo_id="dmenini/q-FrozenLake-v1-8x8-Slippery",
filename="q-learning.pkl"
)
env = gym.make("FrozenLake-v1", map_name="8x8", is_slippery=True)
```
|
distilbert-base-german-cased
|
[
"pytorch",
"safetensors",
"distilbert",
"fill-mask",
"de",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] |
fill-mask
|
{
"architectures": [
"DistilBertForMaskedLM"
],
"model_type": "distilbert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
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"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 43,667
| 2023-03-05T15:39:05Z
|
---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: doom_health_gathering_supreme
type: doom_health_gathering_supreme
metrics:
- type: mean_reward
value: 10.21 +/- 3.56
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **doom_health_gathering_supreme** environment.
This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory.
Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/
## Downloading the model
After installing Sample-Factory, download the model with:
```
python -m sample_factory.huggingface.load_from_hub -r lunared473/rl_course_vizdoom_health_gathering_supreme
```
## Using the model
To run the model after download, use the `enjoy` script corresponding to this environment:
```
python -m .usr.local.lib.python3.8.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme
```
You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag.
See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details
## Training with this model
To continue training with this model, use the `train` script corresponding to this environment:
```
python -m .usr.local.lib.python3.8.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme --restart_behavior=resume --train_for_env_steps=10000000000
```
Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
|
distilbert-base-multilingual-cased
|
[
"pytorch",
"tf",
"onnx",
"safetensors",
"distilbert",
"fill-mask",
"multilingual",
"af",
"sq",
"ar",
"an",
"hy",
"ast",
"az",
"ba",
"eu",
"bar",
"be",
"bn",
"inc",
"bs",
"br",
"bg",
"my",
"ca",
"ceb",
"ce",
"zh",
"cv",
"hr",
"cs",
"da",
"nl",
"en",
"et",
"fi",
"fr",
"gl",
"ka",
"de",
"el",
"gu",
"ht",
"he",
"hi",
"hu",
"is",
"io",
"id",
"ga",
"it",
"ja",
"jv",
"kn",
"kk",
"ky",
"ko",
"la",
"lv",
"lt",
"roa",
"nds",
"lm",
"mk",
"mg",
"ms",
"ml",
"mr",
"mn",
"min",
"ne",
"new",
"nb",
"nn",
"oc",
"fa",
"pms",
"pl",
"pt",
"pa",
"ro",
"ru",
"sco",
"sr",
"scn",
"sk",
"sl",
"aze",
"es",
"su",
"sw",
"sv",
"tl",
"tg",
"th",
"ta",
"tt",
"te",
"tr",
"uk",
"ud",
"uz",
"vi",
"vo",
"war",
"cy",
"fry",
"pnb",
"yo",
"dataset:wikipedia",
"arxiv:1910.01108",
"arxiv:1910.09700",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] |
fill-mask
|
{
"architectures": [
"DistilBertForMaskedLM"
],
"model_type": "distilbert",
"task_specific_params": {
"conversational": {
"max_length": null
},
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},
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},
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},
"translation_en_to_fr": {
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"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 8,339,633
| 2023-03-05T15:42:44Z
|
---
license: apache-2.0
datasets:
- wikipedia
language:
- it
widget:
- text: "milano è una [MASK] dell'italia"
example_title: "Example 1"
- text: "il sole è una [MASK] della via lattea"
example_title: "Example 2"
- text: "l'italia è una [MASK] dell'unione europea"
example_title: "Example 3"
---
--------------------------------------------------------------------------------------------------
<body>
<span class="vertical-text" style="background-color:lightgreen;border-radius: 3px;padding: 3px;"> </span>
<br>
<span class="vertical-text" style="background-color:orange;border-radius: 3px;padding: 3px;"> </span>
<br>
<span class="vertical-text" style="background-color:lightblue;border-radius: 3px;padding: 3px;"> Model: BLAZE 🔥</span>
<br>
<span class="vertical-text" style="background-color:tomato;border-radius: 3px;padding: 3px;"> Lang: IT</span>
<br>
<span class="vertical-text" style="background-color:lightgrey;border-radius: 3px;padding: 3px;"> </span>
<br>
<span class="vertical-text" style="background-color:#CF9FFF;border-radius: 3px;padding: 3px;"> </span>
</body>
--------------------------------------------------------------------------------------------------
<h3>Introduction</h3>
This model is a <b>lightweight</b> and uncased version of <b>BERT</b> <b>[1]</b> for the <b>Italian</b> language. Its <b>55M parameters</b> and <b>220MB</b> size make it
<b>50% lighter</b> than a typical mono-lingual BERT model. It is ideal when memory consumption and execution speed are critical while maintaining high-quality results.
<h3>Model description</h3>
The model builds on the multilingual <b>DistilBERT</b> <b>[2]</b> model (from the HuggingFace team: [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased)) as a starting point,
focusing it on the Italian language while at the same time turning it into an uncased model by modifying the embedding layer
(as in <b>[3]</b>, but computing document-level frequencies over the <b>Wikipedia</b> dataset and setting a frequency threshold of 0.1%), which brings a considerable
reduction in the number of parameters.
To compensate for the deletion of cased tokens, which now forces the model to exploit lowercase representations of words previously capitalized,
the model has been further pre-trained on the Italian split of the [Wikipedia](https://huggingface.co/datasets/wikipedia) dataset, using the <b>whole word masking [4]</b> technique to make it more robust
to the new uncased representations.
The resulting model has 55M parameters, a vocabulary of 13.832 tokens, and a size of 220MB, which makes it <b>50% lighter</b> than a typical mono-lingual BERT model and
20% lighter than a standard mono-lingual DistilBERT model.
<h3>Training procedure</h3>
The model has been trained for <b>masked language modeling</b> on the Italian <b>Wikipedia</b> (~3GB) dataset for 10K steps, using the AdamW optimizer, with a batch size of 512
(obtained through 128 gradient accumulation steps),
a sequence length of 512, and a linearly decaying learning rate starting from 5e-5. The training has been performed using <b>dynamic masking</b> between epochs and
exploiting the <b>whole word masking</b> technique.
<h3>Performances</h3>
The following metrics have been computed on the Part of Speech Tagging and Named Entity Recognition tasks, using the <b>UD Italian ISDT</b> and <b>WikiNER</b> datasets, respectively.
The PoST model has been trained for 5 epochs, and the NER model for 3 epochs, both with a constant learning rate, fixed at 1e-5. For Part of Speech Tagging, the metrics have been computed on the default test set
provided with the dataset, while for Named Entity Recognition the metrics have been computed with a 5-fold cross-validation
| Task | Recall | Precision | F1 |
| ------ | ------ | ------ | ------ |
| Part of Speech Tagging | 97.48 | 97.29 | 97.37 |
| Named Entity Recognition | 89.29 | 89.84 | 89.53 |
The metrics have been computed at the token level and macro-averaged over the classes.
<h3>Demo</h3>
You can try the model online (fine-tuned on named entity recognition) using this web app: https://huggingface.co/spaces/osiria/blaze-it-demo
<h3>Quick usage</h3>
```python
from transformers import AutoTokenizer, DistilBertForMaskedLM
from transformers import pipeline
tokenizer = AutoTokenizer.from_pretrained("osiria/blaze-it")
model = DistilBertForMaskedLM.from_pretrained("osiria/blaze-it")
pipeline_mlm = pipeline(task="fill-mask", model=model, tokenizer=tokenizer)
```
<h3>Limitations</h3>
This lightweight model is mainly trained on Wikipedia, so it's particularly suitable as an agile analyzer for large volumes of natively digital text
from the world wide web, written in a correct and fluent form (like wikis, web pages, news, etc.). However, it may show limitations when it comes to chaotic text, containing errors and slang expressions
(like social media posts) or when it comes to domain-specific text (like medical, financial or legal content).
<h3>References</h3>
[1] https://arxiv.org/abs/1810.04805
[2] https://arxiv.org/abs/1910.01108
[3] https://arxiv.org/abs/2010.05609
[4] https://arxiv.org/abs/1906.08101
<h3>License</h3>
The model is released under <b>Apache-2.0</b> license
|
distilbert-base-uncased
|
[
"pytorch",
"tf",
"jax",
"rust",
"safetensors",
"distilbert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1910.01108",
"transformers",
"exbert",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] |
fill-mask
|
{
"architectures": [
"DistilBertForMaskedLM"
],
"model_type": "distilbert",
"task_specific_params": {
"conversational": {
"max_length": null
},
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"no_repeat_ngram_size": null,
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},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
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},
"translation_en_to_fr": {
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},
"translation_en_to_ro": {
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"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 10,887,471
| 2023-03-05T15:45:14Z
|
---
license: apache-2.0
language:
- en
metrics:
- f1
---
# Federated Learning Based Multilingual Emoji Prediction
This repository contains code for training and evaluating transformer-based models for Uni/multilingual emoji prediction in clean and attack scenarios using Federated Learning. This work is described in the paper "Federated Learning-Based Multilingual Emoji Prediction in Clean and Attack Scenarios."
# Abstract
Federated learning is a growing field in the machine learning community due to its decentralized and private design. Model training in federated learning is distributed over multiple clients giving access to lots of client data while maintaining privacy. Then, a server aggregates the training done on these multiple clients without access to their data, which could be emojis widely used in any social media service and instant messaging platforms to express users' sentiments. This paper proposes federated learning-based multilingual emoji prediction in both clean and attack scenarios. Emoji prediction data have been crawled from both Twitter and SemEval emoji datasets. This data is used to train and evaluate different transformer model sizes including a sparsely activated transformer with either the assumption of clean data in all clients or poisoned data via label flipping attack in some clients. Experimental results on these models show that federated learning in either clean or attacked scenarios performs similarly to centralized training in multilingual emoji prediction on seen and unseen languages under different data sources and distributions. Our trained transformers perform better than other techniques on the SemEval emoji dataset in addition to the privacy as well as distributed benefits of federated learning.
# Performance
> * ACC : 48688 %
> * Mac-F1 : 35.937%
> * Also see our [GitHub Repo](https://github.com/kareemgamalmahmoud/FEDERATED-LEARNING-BASED-MULTILINGUAL-EMOJI-PREDICTION-IN-CLEAN-AND-ATTACK-SCENARIOS)
# Dependencies
> * Python 3.6+
> * PyTorch 1.7.0+
> * Transformers 4.0.0+
# Usage
> To use the model, first install the `transformers` package from Hugging Face:
```python
pip install transformers
```
> Then, you can load the model and tokenizer using the following code:
```python
from transformers import AutoModelForSequenceClassification, AutoTokenizer
import numpy as np
import urllib.request
import csv
```
```python
MODEL = "Karim-Gamal/MMiniLM-L12-finetuned-SemEval-2018-emojis-cen-2"
tokenizer = AutoTokenizer.from_pretrained(MODEL)
model = AutoModelForSequenceClassification.from_pretrained(MODEL)
```
> Once you have the tokenizer and model, you can preprocess your text and pass it to the model for prediction:
```python
# Preprocess text (username and link placeholders)
def preprocess(text):
new_text = []
for t in text.split(" "):
t = '@user' if t.startswith('@') and len(t) > 1 else t
t = 'http' if t.startswith('http') else t
new_text.append(t)
return " ".join(new_text)
text = "Hello world"
text = preprocess(text)
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
scores = output[0][0].detach().numpy()
```
> The scores variable contains the probabilities for each of the possible emoji labels. To get the top k predictions, you can use the following code:
```python
# download label mapping
labels=[]
mapping_link = "https://raw.githubusercontent.com/cardiffnlp/tweeteval/main/datasets/emoji/mapping.txt"
with urllib.request.urlopen(mapping_link) as f:
html = f.read().decode('utf-8').split("\n")
csvreader = csv.reader(html, delimiter='\t')
labels = [row[1] for row in csvreader if len(row) > 1]
k = 3 # number of top predictions to show
ranking = np.argsort(scores)
ranking = ranking[::-1]
for i in range(k):
l = labels[ranking[i]]
s = scores[ranking[i]]
print(f"{i+1}) {l} {np.round(float(s), 4)}")
```
## Note : this is the source for that code : [Link](https://huggingface.co/cardiffnlp/twitter-roberta-base-emoji)
|
gpt2-large
|
[
"pytorch",
"tf",
"jax",
"rust",
"safetensors",
"gpt2",
"text-generation",
"en",
"arxiv:1910.09700",
"transformers",
"license:mit",
"has_space"
] |
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
},
"translation_en_to_de": {
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},
"translation_en_to_fr": {
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},
"translation_en_to_ro": {
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"max_length": null,
"num_beams": null,
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}
}
}
| 1,454,819
| 2023-03-05T15:47:14Z
|
---
license: apache-2.0
language:
- en
metrics:
- f1
---
# Federated Learning Based Multilingual Emoji Prediction
This repository contains code for training and evaluating transformer-based models for Uni/multilingual emoji prediction in clean and attack scenarios using Federated Learning. This work is described in the paper "Federated Learning-Based Multilingual Emoji Prediction in Clean and Attack Scenarios."
# Abstract
Federated learning is a growing field in the machine learning community due to its decentralized and private design. Model training in federated learning is distributed over multiple clients giving access to lots of client data while maintaining privacy. Then, a server aggregates the training done on these multiple clients without access to their data, which could be emojis widely used in any social media service and instant messaging platforms to express users' sentiments. This paper proposes federated learning-based multilingual emoji prediction in both clean and attack scenarios. Emoji prediction data have been crawled from both Twitter and SemEval emoji datasets. This data is used to train and evaluate different transformer model sizes including a sparsely activated transformer with either the assumption of clean data in all clients or poisoned data via label flipping attack in some clients. Experimental results on these models show that federated learning in either clean or attacked scenarios performs similarly to centralized training in multilingual emoji prediction on seen and unseen languages under different data sources and distributions. Our trained transformers perform better than other techniques on the SemEval emoji dataset in addition to the privacy as well as distributed benefits of federated learning.
# Performance
> * Acc : 48.516 %
> * Mac-F1 : 33.907 %
> * Also see our [GitHub Repo](https://github.com/kareemgamalmahmoud/FEDERATED-LEARNING-BASED-MULTILINGUAL-EMOJI-PREDICTION-IN-CLEAN-AND-ATTACK-SCENARIOS)
# Dependencies
> * Python 3.6+
> * PyTorch 1.7.0+
> * Transformers 4.0.0+
# Usage
> To use the model, first install the `transformers` package from Hugging Face:
```python
pip install transformers
```
> Then, you can load the model and tokenizer using the following code:
```python
from transformers import AutoModelForSequenceClassification, AutoTokenizer
import numpy as np
import urllib.request
import csv
```
```python
MODEL = "Karim-Gamal/MMiniLM-L12-finetuned-SemEval-2018-emojis-IID-Fed"
tokenizer = AutoTokenizer.from_pretrained(MODEL)
model = AutoModelForSequenceClassification.from_pretrained(MODEL)
```
> Once you have the tokenizer and model, you can preprocess your text and pass it to the model for prediction:
```python
# Preprocess text (username and link placeholders)
def preprocess(text):
new_text = []
for t in text.split(" "):
t = '@user' if t.startswith('@') and len(t) > 1 else t
t = 'http' if t.startswith('http') else t
new_text.append(t)
return " ".join(new_text)
text = "Hello world"
text = preprocess(text)
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
scores = output[0][0].detach().numpy()
```
> The scores variable contains the probabilities for each of the possible emoji labels. To get the top k predictions, you can use the following code:
```python
# download label mapping
labels=[]
mapping_link = "https://raw.githubusercontent.com/cardiffnlp/tweeteval/main/datasets/emoji/mapping.txt"
with urllib.request.urlopen(mapping_link) as f:
html = f.read().decode('utf-8').split("\n")
csvreader = csv.reader(html, delimiter='\t')
labels = [row[1] for row in csvreader if len(row) > 1]
k = 3 # number of top predictions to show
ranking = np.argsort(scores)
ranking = ranking[::-1]
for i in range(k):
l = labels[ranking[i]]
s = scores[ranking[i]]
print(f"{i+1}) {l} {np.round(float(s), 4)}")
```
## Note : this is the source for that code : [Link](https://huggingface.co/cardiffnlp/twitter-roberta-base-emoji)
|
gpt2-medium
|
[
"pytorch",
"tf",
"jax",
"rust",
"safetensors",
"gpt2",
"text-generation",
"en",
"arxiv:1910.09700",
"transformers",
"license:mit",
"has_space"
] |
text-generation
|
{
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": null
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}
| 759,601
| 2023-03-05T15:57:05Z
|
---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 634.00 +/- 208.48
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga Nelsonlin0321 -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga Nelsonlin0321 -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga Nelsonlin0321
```
## 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)])
```
|
0307061430/xuangou
|
[] | null |
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}
| 0
| 2023-03-05T16:48:38Z
|
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SoccerTwos
library_name: ml-agents
---
# **poca** Agent playing **SoccerTwos**
This is a trained model of a **poca** agent playing **SoccerTwos** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-SoccerTwos
2. Step 1: Write your model_id: Taratata/poca-SoccerTwos-v0
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
AI-Growth-Lab/PatentSBERTa
|
[
"pytorch",
"mpnet",
"arxiv:2103.11933",
"sentence-transformers",
"feature-extraction",
"sentence-similarity",
"transformers",
"has_space"
] |
sentence-similarity
|
{
"architectures": [
"MPNetModel"
],
"model_type": "mpnet",
"task_specific_params": {
"conversational": {
"max_length": null
},
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}
}
}
| 659
| 2023-03-05T18:51:35Z
|
---
language:
- en
license: apache-2.0
tags:
- hf-asr-leaderboard
- generated_from_trainer
datasets:
- tobiolatunji/afrispeech-200
metrics:
- wer
model-index:
- name: Whisper Small En - Moh
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: AfriSpeech
type: tobiolatunji/afrispeech-200
config: all
split: train
args: 'config: en, split: test'
metrics:
- name: Wer
type: wer
value: 32.87142507484043
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Whisper Small En - Moh
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the AfriSpeech dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6236
- Wer: 32.8714
## 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_steps: 500
- training_steps: 1000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.677 | 0.5 | 500 | 0.6841 | 31.2466 |
| 0.428 | 1.0 | 1000 | 0.6236 | 32.8714 |
### Framework versions
- Transformers 4.27.0.dev0
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
|
AI-Lab-Makerere/en_lg
|
[
"pytorch",
"marian",
"text2text-generation",
"unk",
"dataset:Eric Peter/autonlp-data-EN-LUG",
"transformers",
"autonlp",
"co2_eq_emissions",
"autotrain_compatible"
] |
text2text-generation
|
{
"architectures": [
"MarianMTModel"
],
"model_type": "marian",
"task_specific_params": {
"conversational": {
"max_length": null
},
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}
}
}
| 6
| 2023-03-05T18:52:02Z
|
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 268.95 +/- 25.11
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
...
```
|
AK/ak_nlp
|
[
"pytorch",
"jax",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
{
"architectures": [
"RobertaForMaskedLM"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
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},
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},
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}
}
}
| 6
| 2023-03-05T19:15:21Z
|
# Finetuned TorToiSe Models
In the `./finetunes/` folder contains a collection of my finetuned models. Each model folder contains:
* the `pickle`'d finetuned model for tortoise-tts
* the LJSpeech-formatted dataset used to train on it, also containing:
- the generated YAML for training stored in `train.yaml`
- the openai/whisper output stored in `whisper.json`
* a pre-computed voice latents (auto-suggested by parsing each chunk at 10 seconds, seems to be decent)
Most of these were quickly trained on either my dedicated system (2x6800XTs) or my personal system (1x2060) with a learning rate of `1e-4` for about 200 epochs each, for acceptable results, and to just provide some examples. In the future, I'll retrain these at lower LRs to compare.
## Model List
* Harry Mason (Silent Hill)
* James Sunderland (Silent Hill 2)
* Mitsuru Kirijo (Persona 3)
* Melina (Elden Ring)
* Japanese
### Planned
* Patrick Bateman (American Psycho)
* Shadow, Rouge, and Knuckles (Sonic Adventure 2)
|
ASCCCCCCCC/bert-base-chinese-finetuned-amazon_zh
|
[
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
] |
text-classification
|
{
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
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},
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},
"translation_en_to_fr": {
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},
"translation_en_to_ro": {
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}
}
| 39
| 2023-03-05T19:54:01Z
|
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Huggy
library_name: ml-agents
---
# **ppo** Agent playing **Huggy**
This is a trained model of a **ppo** agent playing **Huggy** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-Huggy
2. Step 1: Write your model_id: eoulster/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
Abirate/bert_fine_tuned_cola
|
[
"tf",
"bert",
"text-classification",
"arxiv:1810.04805",
"transformers",
"has_space"
] |
text-classification
|
{
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
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},
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},
"translation_en_to_fr": {
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},
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}
}
}
| 26
| null |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: CA_2_INITIAL_1
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# CA_2_INITIAL_1
This model is a fine-tuned version of [Sjdan/CA_1_2](https://huggingface.co/Sjdan/CA_1_2) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 7
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
|
AdharshJolly/HarryPotterBot-Model
|
[
"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,
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"no_repeat_ngram_size": null,
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"prefix": null
},
"text-generation": {
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},
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},
"translation_en_to_fr": {
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}
}
}
| 10
| 2023-03-06T03:05:39Z
|
---
tags:
- LunarLander-v2
- ppo
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
- deep-rl-course
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: -147.87 +/- 52.48
name: mean_reward
verified: false
---
# PPO Agent Playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2.
# Hyperparameters
```python
{'exp_name': 'ppo'
'seed': 1
'torch_deterministic': True
'cuda': True
'track': False
'wandb_project_name': 'cleanRL'
'wandb_entity': None
'capture_video': False
'env_id': 'LunarLander-v2'
'total_timesteps': 50000
'learning_rate': 0.00025
'num_envs': 4
'num_steps': 128
'anneal_lr': True
'gae': True
'gamma': 0.99
'gae_lambda': 0.95
'num_minibatches': 4
'update_epochs': 4
'norm_adv': True
'clip_coef': 0.2
'clip_vloss': True
'ent_coef': 0.01
'vf_coef': 0.5
'max_grad_norm': 0.5
'target_kl': None
'repo_id': 'kelestemur/ppo-LunarLander-v2'
'batch_size': 512
'minibatch_size': 128}
```
|
Ahmed59/Demo-Team-5-SIAD
|
[
"tf",
"roberta",
"text-classification",
"transformers"
] |
text-classification
|
{
"architectures": [
"RobertaForSequenceClassification"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
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},
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},
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}
| 14
| 2023-03-12T04:01:12Z
|
---
license: mit
tags:
- generated_from_trainer
datasets:
- stereoset
metrics:
- accuracy
model-index:
- name: gpt2_stereoset_classifieronly
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: stereoset
type: stereoset
config: intersentence
split: validation
args: intersentence
metrics:
- name: Accuracy
type: accuracy
value: 0.6923076923076923
---
<!-- 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_stereoset_classifieronly
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the stereoset dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5990
- Accuracy: 0.6923
- Tp: 0.3501
- Tn: 0.3422
- Fp: 0.1625
- Fn: 0.1452
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 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: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Tp | Tn | Fp | Fn |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:------:|:------:|
| 0.8922 | 0.43 | 20 | 0.6913 | 0.5549 | 0.2402 | 0.3148 | 0.1900 | 0.2551 |
| 0.7884 | 0.85 | 40 | 0.6671 | 0.5934 | 0.2182 | 0.3752 | 0.1295 | 0.2771 |
| 0.6991 | 1.28 | 60 | 0.6561 | 0.6193 | 0.2206 | 0.3987 | 0.1060 | 0.2747 |
| 0.6819 | 1.7 | 80 | 0.6499 | 0.6311 | 0.2088 | 0.4223 | 0.0824 | 0.2865 |
| 0.6501 | 2.13 | 100 | 0.6379 | 0.6507 | 0.2991 | 0.3516 | 0.1531 | 0.1962 |
| 0.6566 | 2.55 | 120 | 0.6569 | 0.6185 | 0.1695 | 0.4490 | 0.0557 | 0.3257 |
| 0.6671 | 2.98 | 140 | 0.6313 | 0.6609 | 0.2943 | 0.3666 | 0.1381 | 0.2009 |
| 0.6551 | 3.4 | 160 | 0.6309 | 0.6484 | 0.3862 | 0.2622 | 0.2425 | 0.1091 |
| 0.633 | 3.83 | 180 | 0.6244 | 0.6656 | 0.3014 | 0.3642 | 0.1405 | 0.1939 |
| 0.6432 | 4.26 | 200 | 0.6320 | 0.6554 | 0.2402 | 0.4152 | 0.0895 | 0.2551 |
| 0.6326 | 4.68 | 220 | 0.6240 | 0.6601 | 0.2849 | 0.3752 | 0.1295 | 0.2104 |
| 0.6347 | 5.11 | 240 | 0.6259 | 0.6523 | 0.3689 | 0.2834 | 0.2214 | 0.1264 |
| 0.6204 | 5.53 | 260 | 0.6256 | 0.6499 | 0.3697 | 0.2802 | 0.2245 | 0.1256 |
| 0.6242 | 5.96 | 280 | 0.6172 | 0.6774 | 0.3210 | 0.3564 | 0.1484 | 0.1743 |
| 0.6189 | 6.38 | 300 | 0.6186 | 0.6546 | 0.3493 | 0.3053 | 0.1994 | 0.1460 |
| 0.625 | 6.81 | 320 | 0.6187 | 0.6727 | 0.2881 | 0.3846 | 0.1201 | 0.2072 |
| 0.5963 | 7.23 | 340 | 0.6173 | 0.6758 | 0.3571 | 0.3187 | 0.1860 | 0.1381 |
| 0.6214 | 7.66 | 360 | 0.6158 | 0.6695 | 0.3203 | 0.3493 | 0.1554 | 0.1750 |
| 0.6007 | 8.09 | 380 | 0.6123 | 0.6797 | 0.3611 | 0.3187 | 0.1860 | 0.1342 |
| 0.6454 | 8.51 | 400 | 0.6168 | 0.6570 | 0.3736 | 0.2834 | 0.2214 | 0.1217 |
| 0.6012 | 8.94 | 420 | 0.6115 | 0.6868 | 0.3320 | 0.3548 | 0.1499 | 0.1633 |
| 0.627 | 9.36 | 440 | 0.6485 | 0.6193 | 0.1656 | 0.4537 | 0.0510 | 0.3297 |
| 0.6213 | 9.79 | 460 | 0.6092 | 0.6829 | 0.3022 | 0.3807 | 0.1240 | 0.1931 |
| 0.6286 | 10.21 | 480 | 0.6109 | 0.6711 | 0.3603 | 0.3108 | 0.1939 | 0.1350 |
| 0.609 | 10.64 | 500 | 0.6134 | 0.6633 | 0.3611 | 0.3022 | 0.2025 | 0.1342 |
| 0.5958 | 11.06 | 520 | 0.6409 | 0.6248 | 0.4262 | 0.1986 | 0.3061 | 0.0691 |
| 0.6494 | 11.49 | 540 | 0.6332 | 0.6342 | 0.4192 | 0.2151 | 0.2896 | 0.0761 |
| 0.6012 | 11.91 | 560 | 0.6159 | 0.6593 | 0.3885 | 0.2708 | 0.2339 | 0.1068 |
| 0.606 | 12.34 | 580 | 0.6050 | 0.6947 | 0.3359 | 0.3587 | 0.1460 | 0.1593 |
| 0.5872 | 12.77 | 600 | 0.6135 | 0.6641 | 0.3878 | 0.2763 | 0.2284 | 0.1075 |
| 0.6026 | 13.19 | 620 | 0.6061 | 0.6962 | 0.3265 | 0.3697 | 0.1350 | 0.1688 |
| 0.6179 | 13.62 | 640 | 0.6118 | 0.6876 | 0.2826 | 0.4050 | 0.0997 | 0.2127 |
| 0.5744 | 14.04 | 660 | 0.6058 | 0.6923 | 0.3030 | 0.3893 | 0.1154 | 0.1923 |
| 0.6061 | 14.47 | 680 | 0.6072 | 0.6860 | 0.2849 | 0.4011 | 0.1036 | 0.2104 |
| 0.609 | 14.89 | 700 | 0.6025 | 0.7064 | 0.3367 | 0.3697 | 0.1350 | 0.1586 |
| 0.6019 | 15.32 | 720 | 0.6046 | 0.6876 | 0.3540 | 0.3336 | 0.1711 | 0.1413 |
| 0.6183 | 15.74 | 740 | 0.6087 | 0.6735 | 0.3791 | 0.2943 | 0.2104 | 0.1162 |
| 0.6173 | 16.17 | 760 | 0.6010 | 0.6954 | 0.3407 | 0.3548 | 0.1499 | 0.1546 |
| 0.5873 | 16.6 | 780 | 0.6078 | 0.6766 | 0.3815 | 0.2951 | 0.2096 | 0.1138 |
| 0.6095 | 17.02 | 800 | 0.6151 | 0.6625 | 0.3948 | 0.2677 | 0.2370 | 0.1005 |
| 0.5936 | 17.45 | 820 | 0.6026 | 0.6915 | 0.3469 | 0.3446 | 0.1601 | 0.1484 |
| 0.5821 | 17.87 | 840 | 0.6025 | 0.6931 | 0.3485 | 0.3446 | 0.1601 | 0.1468 |
| 0.6036 | 18.3 | 860 | 0.6032 | 0.7049 | 0.3391 | 0.3658 | 0.1389 | 0.1562 |
| 0.5872 | 18.72 | 880 | 0.6057 | 0.6813 | 0.3587 | 0.3226 | 0.1821 | 0.1366 |
| 0.6085 | 19.15 | 900 | 0.6045 | 0.6845 | 0.3571 | 0.3273 | 0.1774 | 0.1381 |
| 0.5972 | 19.57 | 920 | 0.6203 | 0.6562 | 0.4042 | 0.2520 | 0.2527 | 0.0911 |
| 0.5732 | 20.0 | 940 | 0.6095 | 0.6672 | 0.3807 | 0.2865 | 0.2182 | 0.1146 |
| 0.5718 | 20.43 | 960 | 0.6054 | 0.6868 | 0.2936 | 0.3932 | 0.1115 | 0.2017 |
| 0.5919 | 20.85 | 980 | 0.6031 | 0.6931 | 0.3501 | 0.3430 | 0.1617 | 0.1452 |
| 0.6175 | 21.28 | 1000 | 0.6088 | 0.6703 | 0.3823 | 0.2881 | 0.2166 | 0.1130 |
| 0.5793 | 21.7 | 1020 | 0.5986 | 0.6994 | 0.3430 | 0.3564 | 0.1484 | 0.1523 |
| 0.5943 | 22.13 | 1040 | 0.6064 | 0.6852 | 0.2826 | 0.4027 | 0.1020 | 0.2127 |
| 0.5716 | 22.55 | 1060 | 0.5996 | 0.6947 | 0.3485 | 0.3462 | 0.1586 | 0.1468 |
| 0.6115 | 22.98 | 1080 | 0.6111 | 0.6727 | 0.3893 | 0.2834 | 0.2214 | 0.1060 |
| 0.5984 | 23.4 | 1100 | 0.6058 | 0.6837 | 0.3807 | 0.3030 | 0.2017 | 0.1146 |
| 0.5882 | 23.83 | 1120 | 0.5993 | 0.6962 | 0.3352 | 0.3611 | 0.1436 | 0.1601 |
| 0.5924 | 24.26 | 1140 | 0.6128 | 0.6680 | 0.3909 | 0.2771 | 0.2276 | 0.1044 |
| 0.5984 | 24.68 | 1160 | 0.6017 | 0.6970 | 0.3242 | 0.3728 | 0.1319 | 0.1711 |
| 0.5781 | 25.11 | 1180 | 0.6018 | 0.7002 | 0.3352 | 0.3650 | 0.1397 | 0.1601 |
| 0.5937 | 25.53 | 1200 | 0.6051 | 0.6845 | 0.3619 | 0.3226 | 0.1821 | 0.1334 |
| 0.5678 | 25.96 | 1220 | 0.5998 | 0.7002 | 0.3297 | 0.3705 | 0.1342 | 0.1656 |
| 0.5776 | 26.38 | 1240 | 0.6202 | 0.6523 | 0.3972 | 0.2551 | 0.2496 | 0.0981 |
| 0.5891 | 26.81 | 1260 | 0.6080 | 0.6821 | 0.3791 | 0.3030 | 0.2017 | 0.1162 |
| 0.5915 | 27.23 | 1280 | 0.6026 | 0.6947 | 0.2998 | 0.3948 | 0.1099 | 0.1954 |
| 0.5972 | 27.66 | 1300 | 0.5994 | 0.6931 | 0.3556 | 0.3375 | 0.1672 | 0.1397 |
| 0.5721 | 28.09 | 1320 | 0.6038 | 0.6829 | 0.3736 | 0.3093 | 0.1954 | 0.1217 |
| 0.5813 | 28.51 | 1340 | 0.5981 | 0.6954 | 0.3367 | 0.3587 | 0.1460 | 0.1586 |
| 0.5914 | 28.94 | 1360 | 0.5982 | 0.6986 | 0.3367 | 0.3619 | 0.1429 | 0.1586 |
| 0.5848 | 29.36 | 1380 | 0.5977 | 0.7002 | 0.3399 | 0.3603 | 0.1444 | 0.1554 |
| 0.5772 | 29.79 | 1400 | 0.6024 | 0.6876 | 0.3673 | 0.3203 | 0.1845 | 0.1279 |
| 0.581 | 30.21 | 1420 | 0.6004 | 0.6939 | 0.3611 | 0.3328 | 0.1719 | 0.1342 |
| 0.5881 | 30.64 | 1440 | 0.5969 | 0.7002 | 0.3462 | 0.3540 | 0.1507 | 0.1491 |
| 0.601 | 31.06 | 1460 | 0.5970 | 0.6994 | 0.3328 | 0.3666 | 0.1381 | 0.1625 |
| 0.5759 | 31.49 | 1480 | 0.5971 | 0.6986 | 0.3375 | 0.3611 | 0.1436 | 0.1578 |
| 0.5738 | 31.91 | 1500 | 0.5969 | 0.7002 | 0.3454 | 0.3548 | 0.1499 | 0.1499 |
| 0.5576 | 32.34 | 1520 | 0.5983 | 0.6931 | 0.3493 | 0.3438 | 0.1609 | 0.1460 |
| 0.58 | 32.77 | 1540 | 0.5976 | 0.7009 | 0.3359 | 0.3650 | 0.1397 | 0.1593 |
| 0.5798 | 33.19 | 1560 | 0.5980 | 0.7017 | 0.3469 | 0.3548 | 0.1499 | 0.1484 |
| 0.5802 | 33.62 | 1580 | 0.5988 | 0.6954 | 0.3477 | 0.3477 | 0.1570 | 0.1476 |
| 0.587 | 34.04 | 1600 | 0.5997 | 0.6931 | 0.3532 | 0.3399 | 0.1648 | 0.1421 |
| 0.5499 | 34.47 | 1620 | 0.6081 | 0.6797 | 0.3830 | 0.2967 | 0.2080 | 0.1122 |
| 0.5878 | 34.89 | 1640 | 0.5989 | 0.6970 | 0.3438 | 0.3532 | 0.1515 | 0.1515 |
| 0.5855 | 35.32 | 1660 | 0.6073 | 0.6829 | 0.3815 | 0.3014 | 0.2033 | 0.1138 |
| 0.5836 | 35.74 | 1680 | 0.5977 | 0.7002 | 0.3359 | 0.3642 | 0.1405 | 0.1593 |
| 0.5576 | 36.17 | 1700 | 0.5984 | 0.6986 | 0.3399 | 0.3587 | 0.1460 | 0.1554 |
| 0.5929 | 36.6 | 1720 | 0.6035 | 0.6907 | 0.3697 | 0.3210 | 0.1837 | 0.1256 |
| 0.5672 | 37.02 | 1740 | 0.6023 | 0.6923 | 0.3705 | 0.3218 | 0.1829 | 0.1248 |
| 0.5774 | 37.45 | 1760 | 0.5986 | 0.6947 | 0.3509 | 0.3438 | 0.1609 | 0.1444 |
| 0.5785 | 37.87 | 1780 | 0.5990 | 0.6962 | 0.3195 | 0.3768 | 0.1279 | 0.1758 |
| 0.5885 | 38.3 | 1800 | 0.5979 | 0.6994 | 0.3375 | 0.3619 | 0.1429 | 0.1578 |
| 0.5449 | 38.72 | 1820 | 0.6030 | 0.6923 | 0.3713 | 0.3210 | 0.1837 | 0.1240 |
| 0.5857 | 39.15 | 1840 | 0.5990 | 0.7009 | 0.3328 | 0.3681 | 0.1366 | 0.1625 |
| 0.5839 | 39.57 | 1860 | 0.6003 | 0.6907 | 0.3548 | 0.3359 | 0.1688 | 0.1405 |
| 0.5806 | 40.0 | 1880 | 0.5976 | 0.6962 | 0.3414 | 0.3548 | 0.1499 | 0.1538 |
| 0.5692 | 40.43 | 1900 | 0.5976 | 0.7025 | 0.3399 | 0.3626 | 0.1421 | 0.1554 |
| 0.593 | 40.85 | 1920 | 0.5984 | 0.6947 | 0.3430 | 0.3516 | 0.1531 | 0.1523 |
| 0.5736 | 41.28 | 1940 | 0.5992 | 0.6931 | 0.3556 | 0.3375 | 0.1672 | 0.1397 |
| 0.5653 | 41.7 | 1960 | 0.5978 | 0.6970 | 0.3438 | 0.3532 | 0.1515 | 0.1515 |
| 0.5631 | 42.13 | 1980 | 0.6006 | 0.6947 | 0.3603 | 0.3344 | 0.1703 | 0.1350 |
| 0.5794 | 42.55 | 2000 | 0.5983 | 0.6994 | 0.3336 | 0.3658 | 0.1389 | 0.1617 |
| 0.5876 | 42.98 | 2020 | 0.5984 | 0.6939 | 0.3422 | 0.3516 | 0.1531 | 0.1531 |
| 0.5726 | 43.4 | 2040 | 0.6005 | 0.6962 | 0.3634 | 0.3328 | 0.1719 | 0.1319 |
| 0.566 | 43.83 | 2060 | 0.5982 | 0.6970 | 0.3242 | 0.3728 | 0.1319 | 0.1711 |
| 0.5603 | 44.26 | 2080 | 0.5994 | 0.6947 | 0.3579 | 0.3367 | 0.1680 | 0.1374 |
| 0.5697 | 44.68 | 2100 | 0.6037 | 0.6892 | 0.3728 | 0.3163 | 0.1884 | 0.1224 |
| 0.5624 | 45.11 | 2120 | 0.5981 | 0.7002 | 0.3297 | 0.3705 | 0.1342 | 0.1656 |
| 0.5648 | 45.53 | 2140 | 0.5979 | 0.6962 | 0.3422 | 0.3540 | 0.1507 | 0.1531 |
| 0.578 | 45.96 | 2160 | 0.6024 | 0.6907 | 0.3713 | 0.3195 | 0.1852 | 0.1240 |
| 0.5593 | 46.38 | 2180 | 0.5977 | 0.7002 | 0.3391 | 0.3611 | 0.1436 | 0.1562 |
| 0.5755 | 46.81 | 2200 | 0.5979 | 0.6978 | 0.3336 | 0.3642 | 0.1405 | 0.1617 |
| 0.59 | 47.23 | 2220 | 0.6046 | 0.6868 | 0.3736 | 0.3132 | 0.1915 | 0.1217 |
| 0.5648 | 47.66 | 2240 | 0.5997 | 0.6931 | 0.3564 | 0.3367 | 0.1680 | 0.1389 |
| 0.5812 | 48.09 | 2260 | 0.5979 | 0.6954 | 0.3336 | 0.3619 | 0.1429 | 0.1617 |
| 0.5796 | 48.51 | 2280 | 0.5979 | 0.6962 | 0.3336 | 0.3626 | 0.1421 | 0.1617 |
| 0.5701 | 48.94 | 2300 | 0.5981 | 0.6947 | 0.3454 | 0.3493 | 0.1554 | 0.1499 |
| 0.5807 | 49.36 | 2320 | 0.5988 | 0.6931 | 0.3501 | 0.3430 | 0.1617 | 0.1452 |
| 0.5836 | 49.79 | 2340 | 0.5990 | 0.6923 | 0.3501 | 0.3422 | 0.1625 | 0.1452 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1
- Datasets 2.10.1
- Tokenizers 0.13.2
|
Akash7897/my-newtokenizer
|
[] | null |
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| 0
| 2023-03-06T04:49:13Z
|
---
tags:
- generated_from_trainer
model-index:
- name: git-tiny
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. -->
# git-tiny
This model was trained from scratch on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.1+cu117
- Datasets 2.8.0
- Tokenizers 0.13.2
|
Akash7897/test-clm
|
[] | null |
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}
| 0
| null |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: CA_SID_F05_2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# CA_SID_F05_2
This model is a fine-tuned version of [Sjdan/cls_3ep1](https://huggingface.co/Sjdan/cls_3ep1) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 7
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
|
Akashamba/distilbert-base-uncased-finetuned-ner
|
[] | null |
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}
| 0
| 2023-03-06T04:53:40Z
|
---
license: apache-2.0
tags:
- summarization
- generated_from_trainer
datasets:
- pubmed-summarization
metrics:
- rouge
model-index:
- name: mt5-small-finetuned-arxiv-summarization
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: pubmed-summarization
type: pubmed-summarization
config: section
split: validation
args: section
metrics:
- name: Rouge1
type: rouge
value: 0.6353
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mt5-small-finetuned-arxiv-summarization
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the pubmed-summarization dataset.
It achieves the following results on the evaluation set:
- Loss: nan
- Rouge1: 0.6353
- Rouge2: 0.0849
- Rougel: 0.5942
- Rougelsum: 0.6117
## 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: 5.6e-05
- train_batch_size: 6
- eval_batch_size: 6
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|
| 0.0 | 1.0 | 1999 | nan | 0.6353 | 0.0849 | 0.5942 | 0.6117 |
| 0.0 | 2.0 | 3998 | nan | 0.6353 | 0.0849 | 0.5942 | 0.6117 |
| 0.0 | 3.0 | 5997 | nan | 0.6353 | 0.0849 | 0.5942 | 0.6117 |
| 0.0 | 4.0 | 7996 | nan | 0.6353 | 0.0849 | 0.5942 | 0.6117 |
| 0.0 | 5.0 | 9995 | nan | 0.6353 | 0.0849 | 0.5942 | 0.6117 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
|
AkshayDev/BERT_Fine_Tuning
|
[] | null |
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| 0
| null |
# Vocabulary Trimmed [xlm-roberta-large](https://huggingface.co/xlm-roberta-large): `vocabtrimmer/xlm-roberta-large-trimmed-de-75000`
This model is a trimmed version of [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) by [`vocabtrimmer`](https://github.com/asahi417/lm-vocab-trimmer), a tool for trimming vocabulary of language models to compress the model size.
Following table shows a summary of the trimming process.
| | xlm-roberta-large | vocabtrimmer/xlm-roberta-large-trimmed-de-75000 |
|:---------------------------|:--------------------|:--------------------------------------------------|
| parameter_size_full | 560,142,482 | 380,767,482 |
| parameter_size_embedding | 256,002,048 | 76,802,048 |
| vocab_size | 250,002 | 75,002 |
| compression_rate_full | 100.0 | 67.98 |
| compression_rate_embedding | 100.0 | 30.0 |
Following table shows the parameter used to trim vocabulary.
| language | dataset | dataset_column | dataset_name | dataset_split | target_vocab_size | min_frequency |
|:-----------|:----------------------------|:-----------------|:---------------|:----------------|--------------------:|----------------:|
| de | vocabtrimmer/mc4_validation | text | de | validation | 75000 | 2 |
|
Aleksandar/distilbert-srb-ner-setimes
|
[
"pytorch",
"distilbert",
"token-classification",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
] |
token-classification
|
{
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| 3
| 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: 279.94 +/- 22.48
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
...
```
|
Aleksandar/distilbert-srb-ner
|
[
"pytorch",
"distilbert",
"token-classification",
"sr",
"dataset:wikiann",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
] |
token-classification
|
{
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"DistilBertForTokenClassification"
],
"model_type": "distilbert",
"task_specific_params": {
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| 9
| null |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 253.85 +/- 21.15
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
...
```
|
Aleksandar1932/gpt2-soul
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers"
] |
text-generation
|
{
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
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| 10
| null |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: SID_CA_M04
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. -->
# SID_CA_M04
This model is a fine-tuned version of [Sjdan/cls_3ep1](https://huggingface.co/Sjdan/cls_3ep1) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 7
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
|
Alireza1044/michael_bert_lm
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers"
] |
text-generation
|
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"GPT2LMHeadModel"
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| 10
| null |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: finetuning-sentiment-model-samples
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuning-sentiment-model-samples
This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
|
AllwynJ/HarryBoy
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] |
conversational
|
{
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"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
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| 12
| 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: 174.00 +/- 49.99
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga kraken2404 -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga kraken2404 -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga kraken2404
```
## 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', 110000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
|
Allybaby21/Allysai
|
[] | null |
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| 0
| null |
---
tags:
- image-classification
- pytorch
- huggingpics
metrics:
- accuracy
model-index:
- name: cartoondetection_sagnik
results:
- task:
name: Image Classification
type: image-classification
metrics:
- name: Accuracy
type: accuracy
value: 0.9976562261581421
---
# cartoondetection_sagnik
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics).
## Example Images
#### cartoon
#### person
|
Analufm/Ana
|
[] | null |
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| 0
| null |
---
license: mit
tags:
- generated_from_trainer
metrics:
- f1
- precision
- recall
model-index:
- name: roberta-bne-clara
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-bne-clara
This model is a fine-tuned version of [roberta-bne](https://huggingface.co/PlanTL-GOB-ES/roberta-base-bne) on the Diario de Madrid dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2078
- F1: 0.5174
- Precision: 0.4561
- Recall: 0.5977
## 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: 12
- eval_batch_size: 12
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:------:|:---------:|:------:|
| 0.7026 | 1.0 | 103 | 0.2844 | 0.4720 | 0.5135 | 0.4368 |
| 0.325 | 2.0 | 206 | 0.1715 | 0.5665 | 0.5698 | 0.5632 |
| 0.2094 | 3.0 | 309 | 0.1452 | 0.6294 | 0.5636 | 0.7126 |
| 0.1416 | 4.0 | 412 | 0.1508 | 0.5178 | 0.4636 | 0.5862 |
| 0.1058 | 5.0 | 515 | 0.1794 | 0.5700 | 0.4917 | 0.6782 |
| 0.0711 | 6.0 | 618 | 0.1743 | 0.5510 | 0.4954 | 0.6207 |
| 0.0489 | 7.0 | 721 | 0.1900 | 0.5895 | 0.5437 | 0.6437 |
| 0.0373 | 8.0 | 824 | 0.1841 | 0.6131 | 0.5446 | 0.7011 |
| 0.0277 | 9.0 | 927 | 0.1954 | 0.5445 | 0.5 | 0.5977 |
| 0.0215 | 10.0 | 1030 | 0.2078 | 0.5174 | 0.4561 | 0.5977 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 1.16.1
- Tokenizers 0.13.2
|
Andranik/TestPytorchClassification
|
[
"pytorch",
"distilbert",
"text-classification",
"transformers"
] |
text-classification
|
{
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"DistilBertForSequenceClassification"
],
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| 36
| null |
# `cardiffnlp/xlm-roberta-base-tweet-sentiment-pt`
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the
[cardiffnlp/tweet_sentiment_multilingual](https://huggingface.co/datasets/cardiffnlp/tweet_sentiment_multilingual) (portuguese).
Following metrics are computed on the `test` split of
[cardiffnlp/tweet_sentiment_multilingual](https://huggingface.co/datasets/cardiffnlp/tweet_sentiment_multilingual)(portuguese).
| | eval_f1_micro | eval_recall_micro | eval_precision_micro | eval_f1_macro | eval_recall_macro | eval_precision_macro | eval_accuracy |
|---:|----------------:|--------------------:|-----------------------:|----------------:|--------------------:|-----------------------:|----------------:|
| 0 | 70.69 | 70.69 | 70.69 | 70.73 | 70.69 | 70.78 | 70.69 |
Check the result file [here](https://huggingface.co/cardiffnlp/xlm-roberta-base-tweet-sentiment-pt/raw/main/eval.json).
|
Andranik/TestQaV1
|
[
"pytorch",
"rust",
"roberta",
"question-answering",
"transformers",
"autotrain_compatible"
] |
question-answering
|
{
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"RobertaForQuestionAnswering"
],
"model_type": "roberta",
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| 4
| null |
---
license: mit
tags:
- generated_from_trainer
metrics:
- f1
- precision
- recall
model-index:
- name: fine-tuned-DatasetQAS-TYDI-QA-ID-with-indobert-base-uncased-with-ITTL-without-freeze-LR-1e-05
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# fine-tuned-DatasetQAS-TYDI-QA-ID-with-indobert-base-uncased-with-ITTL-without-freeze-LR-1e-05
This model is a fine-tuned version of [indolem/indobert-base-uncased](https://huggingface.co/indolem/indobert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1675
- Exact Match: 61.4311
- F1: 76.0013
- Precision: 77.2642
- Recall: 81.7278
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.06
- num_epochs: 16
### Training results
| Training Loss | Epoch | Step | Validation Loss | Exact Match | F1 | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:-----------:|:-------:|:---------:|:-------:|
| 6.2602 | 0.5 | 38 | 4.9592 | 0.5236 | 11.0968 | 11.1980 | 26.6188 |
| 5.556 | 0.99 | 76 | 3.0406 | 12.7400 | 25.1945 | 25.6795 | 39.6592 |
| 3.3395 | 1.5 | 114 | 2.4880 | 21.1169 | 34.7076 | 32.9184 | 52.2869 |
| 2.4731 | 1.99 | 152 | 2.2257 | 27.3997 | 40.0066 | 39.3514 | 53.5252 |
| 2.4731 | 2.5 | 190 | 2.0431 | 32.6353 | 44.5789 | 44.7211 | 55.0020 |
| 2.162 | 2.99 | 228 | 1.8362 | 38.9180 | 50.4876 | 50.7087 | 59.7434 |
| 1.8755 | 3.5 | 266 | 1.6441 | 43.9791 | 56.8266 | 57.4538 | 65.5751 |
| 1.5888 | 3.99 | 304 | 1.4664 | 52.0070 | 63.9616 | 65.2046 | 70.0798 |
| 1.5888 | 4.5 | 342 | 1.3509 | 54.4503 | 68.6979 | 70.2140 | 76.0813 |
| 1.333 | 4.99 | 380 | 1.2571 | 54.7993 | 68.9857 | 70.8728 | 75.8745 |
| 1.2051 | 5.5 | 418 | 1.2440 | 56.5445 | 70.2921 | 72.4571 | 75.9313 |
| 1.0522 | 5.99 | 456 | 1.1808 | 57.5916 | 72.1230 | 73.6246 | 78.8092 |
| 1.0522 | 6.5 | 494 | 1.1575 | 58.9878 | 73.1594 | 74.9064 | 79.2545 |
| 0.9584 | 6.99 | 532 | 1.1553 | 58.9878 | 73.5139 | 75.2615 | 79.3901 |
| 0.9006 | 7.5 | 570 | 1.1112 | 60.0349 | 74.5273 | 75.8170 | 81.1555 |
| 0.8102 | 7.99 | 608 | 1.1164 | 59.8604 | 74.5013 | 76.2748 | 80.2875 |
| 0.8102 | 8.5 | 646 | 1.1371 | 60.0349 | 74.2469 | 75.9082 | 79.9186 |
| 0.773 | 8.99 | 684 | 1.1410 | 60.7330 | 74.9095 | 76.7045 | 80.9178 |
| 0.7482 | 9.5 | 722 | 1.1307 | 60.3839 | 74.7594 | 76.8954 | 80.6364 |
| 0.6878 | 9.99 | 760 | 1.1219 | 61.0820 | 74.9064 | 76.4266 | 81.4087 |
| 0.6878 | 10.5 | 798 | 1.1362 | 62.1291 | 76.5097 | 77.5924 | 82.8049 |
| 0.6401 | 10.99 | 836 | 1.1266 | 61.0820 | 75.8874 | 77.0263 | 81.7467 |
| 0.634 | 11.5 | 874 | 1.1570 | 61.7801 | 75.9638 | 77.5661 | 80.8536 |
| 0.5856 | 11.99 | 912 | 1.1675 | 61.4311 | 76.0013 | 77.2642 | 81.7278 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu117
- Datasets 2.2.0
- Tokenizers 0.13.2
|
AndreLiu1225/t5-news-summarizer
|
[
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] |
text2text-generation
|
{
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}
| 10
| null |
# `cardiffnlp/xlm-roberta-base-tweet-sentiment-ar`
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the
[cardiffnlp/tweet_sentiment_multilingual](https://huggingface.co/datasets/cardiffnlp/tweet_sentiment_multilingual) (arabic).
Following metrics are computed on the `test` split of
[cardiffnlp/tweet_sentiment_multilingual](https://huggingface.co/datasets/cardiffnlp/tweet_sentiment_multilingual)(arabic).
| | eval_f1_micro | eval_recall_micro | eval_precision_micro | eval_f1_macro | eval_recall_macro | eval_precision_macro | eval_accuracy |
|---:|----------------:|--------------------:|-----------------------:|----------------:|--------------------:|-----------------------:|----------------:|
| 0 | 66.21 | 66.21 | 66.21 | 66.33 | 66.21 | 66.51 | 66.21 |
Check the result file [here](https://huggingface.co/cardiffnlp/xlm-roberta-base-tweet-sentiment-ar/raw/main/eval.json).
|
Andrija/RobertaFastBPE
|
[] | null |
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| 0
| null |
---
license: cc-by-4.0
metrics:
- bleu4
- meteor
- rouge-l
- bertscore
- moverscore
language: es
datasets:
- lmqg/qg_esquad
pipeline_tag: text2text-generation
tags:
- question generation
widget:
- text: "del <hl> Ministerio de Desarrollo Urbano <hl> , Gobierno de la India."
example_title: "Question Generation Example 1"
- text: "a <hl> noviembre <hl> , que es también la estación lluviosa."
example_title: "Question Generation Example 2"
- text: "como <hl> el gobierno de Abbott <hl> que asumió el cargo el 18 de septiembre de 2013."
example_title: "Question Generation Example 3"
model-index:
- name: vocabtrimmer/mt5-small-trimmed-es-esquad-qg
results:
- task:
name: Text2text Generation
type: text2text-generation
dataset:
name: lmqg/qg_esquad
type: default
args: default
metrics:
- name: BLEU4 (Question Generation)
type: bleu4_question_generation
value: 9.52
- name: ROUGE-L (Question Generation)
type: rouge_l_question_generation
value: 24.24
- name: METEOR (Question Generation)
type: meteor_question_generation
value: 22.26
- name: BERTScore (Question Generation)
type: bertscore_question_generation
value: 84.19
- name: MoverScore (Question Generation)
type: moverscore_question_generation
value: 58.91
---
# Model Card of `vocabtrimmer/mt5-small-trimmed-es-esquad-qg`
This model is fine-tuned version of [vocabtrimmer/mt5-small-trimmed-es](https://huggingface.co/vocabtrimmer/mt5-small-trimmed-es) for question generation task on the [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-generation).
### Overview
- **Language model:** [vocabtrimmer/mt5-small-trimmed-es](https://huggingface.co/vocabtrimmer/mt5-small-trimmed-es)
- **Language:** es
- **Training data:** [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) (default)
- **Online Demo:** [https://autoqg.net/](https://autoqg.net/)
- **Repository:** [https://github.com/asahi417/lm-question-generation](https://github.com/asahi417/lm-question-generation)
- **Paper:** [https://arxiv.org/abs/2210.03992](https://arxiv.org/abs/2210.03992)
### Usage
- With [`lmqg`](https://github.com/asahi417/lm-question-generation#lmqg-language-model-for-question-generation-)
```python
from lmqg import TransformersQG
# initialize model
model = TransformersQG(language="es", model="vocabtrimmer/mt5-small-trimmed-es-esquad-qg")
# model prediction
questions = model.generate_q(list_context="a noviembre , que es también la estación lluviosa.", list_answer="noviembre")
```
- With `transformers`
```python
from transformers import pipeline
pipe = pipeline("text2text-generation", "vocabtrimmer/mt5-small-trimmed-es-esquad-qg")
output = pipe("del <hl> Ministerio de Desarrollo Urbano <hl> , Gobierno de la India.")
```
## Evaluation
- ***Metric (Question Generation)***: [raw metric file](https://huggingface.co/vocabtrimmer/mt5-small-trimmed-es-esquad-qg/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_esquad.default.json)
| | Score | Type | Dataset |
|:-----------|--------:|:--------|:-----------------------------------------------------------------|
| BERTScore | 84.19 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) |
| Bleu_1 | 25.92 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) |
| Bleu_2 | 17.66 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) |
| Bleu_3 | 12.76 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) |
| Bleu_4 | 9.52 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) |
| METEOR | 22.26 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) |
| MoverScore | 58.91 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) |
| ROUGE_L | 24.24 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) |
## Training hyperparameters
The following hyperparameters were used during fine-tuning:
- dataset_path: lmqg/qg_esquad
- dataset_name: default
- input_types: paragraph_answer
- output_types: question
- prefix_types: None
- model: vocabtrimmer/mt5-small-trimmed-es
- max_length: 512
- max_length_output: 32
- epoch: 15
- batch: 32
- lr: 0.0005
- fp16: False
- random_seed: 1
- gradient_accumulation_steps: 2
- label_smoothing: 0.15
The full configuration can be found at [fine-tuning config file](https://huggingface.co/vocabtrimmer/mt5-small-trimmed-es-esquad-qg/raw/main/trainer_config.json).
## Citation
```
@inproceedings{ushio-etal-2022-generative,
title = "{G}enerative {L}anguage {M}odels for {P}aragraph-{L}evel {Q}uestion {G}eneration",
author = "Ushio, Asahi and
Alva-Manchego, Fernando and
Camacho-Collados, Jose",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2022",
address = "Abu Dhabi, U.A.E.",
publisher = "Association for Computational Linguistics",
}
```
|
Andrija/SRoBERTa-NER
|
[
"pytorch",
"roberta",
"token-classification",
"hr",
"sr",
"multilingual",
"dataset:hr500k",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] |
token-classification
|
{
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"RobertaForTokenClassification"
],
"model_type": "roberta",
"task_specific_params": {
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}
| 7
| null |
---
license: mit
tags:
- pytorch
- diffusers
- unconditional-image-generation
- diffusion-models-class
---
This model is a latent diffusion model for unconditional image generation of mammograms of size 512vs512.
The model was trained with 1000 images using the [DDPM](https://arxiv.org/abs/2006.11239) architecture.
The model was trained for 50 epochs with a batch size of 8 and gradient accumulation of 4, using around 9 GB of GPU memory.
## Usage
```python
from diffusers import DDPMPipeline
pipeline = DDPMPipeline.from_pretrained({hub_model_id})
image = pipeline().images[0]
image
```
|
Ann2020/rubert-base-cased-finetuned-ner
|
[] | null |
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}
}
| 0
| 2023-03-06T10:35:21Z
|
---
license: bsd-2-clause
pipeline_tag: image-classification
tags:
- code
---
|
Anonymous/ReasonBERT-BERT
|
[
"pytorch",
"bert",
"feature-extraction",
"transformers"
] |
feature-extraction
|
{
"architectures": [
"BertModel"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
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}
| 5
| null |
---
library_name: stable-baselines3
tags:
- AntBulletEnv-v0
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: AntBulletEnv-v0
type: AntBulletEnv-v0
metrics:
- type: mean_reward
value: 1839.16 +/- 135.23
name: mean_reward
verified: false
---
# **A2C** Agent playing **AntBulletEnv-v0**
This is a trained model of a **A2C** agent playing **AntBulletEnv-v0**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
Anonymous/ReasonBERT-RoBERTa
|
[
"pytorch",
"roberta",
"feature-extraction",
"transformers"
] |
feature-extraction
|
{
"architectures": [
"RobertaModel"
],
"model_type": "roberta",
"task_specific_params": {
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}
}
| 5
| null |
---
license: mit
tags:
- generated_from_trainer
model-index:
- name: Vi-gec8
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. -->
# Vi-gec8
This model is a fine-tuned version of [VietAI/vit5-large](https://huggingface.co/VietAI/vit5-large) on the None dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.0642
- eval_wer: 2.0284
- eval_runtime: 5.3381
- eval_samples_per_second: 3.747
- eval_steps_per_second: 0.562
- epoch: 3.2
- step: 3800
## 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-06
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 9500
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
|
AnonymousSub/EManuals_BERT_copy
|
[
"pytorch",
"bert",
"feature-extraction",
"transformers"
] |
feature-extraction
|
{
"architectures": [
"BertModel"
],
"model_type": "bert",
"task_specific_params": {
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},
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},
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},
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"num_beams": null,
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},
"translation_en_to_fr": {
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}
| 2
| null |
---
license: mit
tags:
- generated_from_trainer
datasets:
- squad_modified_for_t5_qg_2
model-index:
- name: greek-m2m100-4ep-512
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. -->
# greek-m2m100-4ep-512
This model is a fine-tuned version of [facebook/m2m100_418M](https://huggingface.co/facebook/m2m100_418M) on the squad_modified_for_t5_qg_2 dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2974
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.9785 | 0.34 | 100 | 1.4949 |
| 1.5296 | 0.67 | 200 | 1.4161 |
| 1.4651 | 1.01 | 300 | 1.3816 |
| 1.246 | 1.35 | 400 | 1.3648 |
| 1.2419 | 1.69 | 500 | 1.3383 |
| 1.2132 | 2.03 | 600 | 1.3348 |
| 1.0558 | 2.36 | 700 | 1.3216 |
| 1.0584 | 2.7 | 800 | 1.3078 |
| 1.035 | 3.04 | 900 | 1.3108 |
| 0.9301 | 3.38 | 1000 | 1.3030 |
| 0.9222 | 3.72 | 1100 | 1.2974 |
### Framework versions
- Transformers 4.27.0.dev0
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
|
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