Instructions to use bjbjbj/classifier-chapter4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bjbjbj/classifier-chapter4 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="bjbjbj/classifier-chapter4")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("bjbjbj/classifier-chapter4") model = AutoModelForSequenceClassification.from_pretrained("bjbjbj/classifier-chapter4", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from bjbjbj/classifier-chapter4: direct link, hf CLI and curl.
- Browser
- Download file 1.63 kB
-
https://huggingface.co/bjbjbj/classifier-chapter4/resolve/main/README.md
- Command line
-
hf download hf://bjbjbj/classifier-chapter4/README.md
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curl -L -o README.md https://huggingface.co/bjbjbj/classifier-chapter4/resolve/main/README.md
1.63 kB
metadata
library_name: transformers
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: classifier-chapter4
results: []
classifier-chapter4
This model is a fine-tuned version of bert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.2394
- Accuracy: 0.9261
- F1: 0.9260
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: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 2
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|---|---|---|---|---|---|
| No log | 1.0 | 313 | 0.2599 | 0.9105 | 0.9102 |
| 0.2993 | 2.0 | 626 | 0.2394 | 0.9261 | 0.9260 |
Framework versions
- Transformers 4.46.3
- Pytorch 2.5.1
- Datasets 2.16.1
- Tokenizers 0.20.3