Text Classification
Transformers
TensorBoard
Safetensors
camembert
Generated from Trainer
text-embeddings-inference
Instructions to use mmenuu/wisesight_sentiment with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mmenuu/wisesight_sentiment with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="mmenuu/wisesight_sentiment")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("mmenuu/wisesight_sentiment") model = AutoModelForSequenceClassification.from_pretrained("mmenuu/wisesight_sentiment") - Notebooks
- Google Colab
- Kaggle
wisesight_sentiment
This model is a fine-tuned version of clicknext/phayathaibert on an unknown dataset. It achieves the following results on the evaluation set:
- eval_loss: 0.6884
- eval_micro_average_f1: 0.7442
- eval_macro_average_f1: 0.6334
- eval_class_f1: {'pos': 0.5728155339805825, 'neu': 0.7831094049904032, 'neg': 0.8028059236165237, 'q': 0.375}
- eval_runtime: 7.5819
- eval_samples_per_second: 317.072
- eval_steps_per_second: 19.916
- epoch: 0.3698
- step: 500
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-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
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
- mixed_precision_training: Native AMP
Framework versions
- Transformers 4.40.1
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
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Model tree for mmenuu/wisesight_sentiment
Base model
clicknext/phayathaibert