Text Classification
Transformers
PyTorch
TensorBoard
Safetensors
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use Rz1010/my_awesome_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Rz1010/my_awesome_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Rz1010/my_awesome_model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Rz1010/my_awesome_model") model = AutoModelForSequenceClassification.from_pretrained("Rz1010/my_awesome_model") - Notebooks
- Google Colab
- Kaggle
my_awesome_model
This model is a fine-tuned version of distilbert-base-uncased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.5521
- Accuracy: 0.8947
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: 2
- eval_batch_size: 2
- 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 | Accuracy |
|---|---|---|---|---|
| No log | 1.0 | 222 | 0.4673 | 0.8947 |
| No log | 2.0 | 444 | 0.4873 | 0.8842 |
| 0.4419 | 3.0 | 666 | 0.5657 | 0.8947 |
| 0.4419 | 4.0 | 888 | 0.5696 | 0.8947 |
| 0.2477 | 5.0 | 1110 | 0.5521 | 0.8947 |
Framework versions
- Transformers 4.32.1
- Pytorch 2.1.2
- Datasets 2.12.0
- Tokenizers 0.13.2
- Downloads last month
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Model tree for Rz1010/my_awesome_model
Base model
distilbert/distilbert-base-uncased