Text Classification
Transformers
PyTorch
TensorBoard
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use SmilestheSad/hf_trainer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SmilestheSad/hf_trainer with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="SmilestheSad/hf_trainer")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("SmilestheSad/hf_trainer") model = AutoModelForSequenceClassification.from_pretrained("SmilestheSad/hf_trainer") - Notebooks
- Google Colab
- Kaggle
hf_trainer
This model is a fine-tuned version of distilbert-base-multilingual-cased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.0708
- F1: 0.9066
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|---|---|---|---|---|
| 0.0344 | 1.0 | 565 | 0.0661 | 0.8811 |
| 0.0354 | 2.0 | 1130 | 0.0641 | 0.8963 |
| 0.0222 | 3.0 | 1695 | 0.0690 | 0.8994 |
| 0.0145 | 4.0 | 2260 | 0.0714 | 0.9036 |
| 0.011 | 5.0 | 2825 | 0.0708 | 0.9066 |
Framework versions
- Transformers 4.22.1
- Pytorch 1.12.1+cu113
- Datasets 2.5.1
- Tokenizers 0.12.1
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