Instructions to use ZON8955/classification_v3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ZON8955/classification_v3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ZON8955/classification_v3")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ZON8955/classification_v3") model = AutoModelForSequenceClassification.from_pretrained("ZON8955/classification_v3") - Notebooks
- Google Colab
- Kaggle
metadata
library_name: transformers
license: apache-2.0
base_model: bert-base-chinese
tags:
- generated_from_trainer
model-index:
- name: classification_v3
results: []
classification_v3
This model is a fine-tuned version of bert-base-chinese on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.0010
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: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 0.1
- num_epochs: 10
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| No log | 1.0 | 5 | 0.0127 |
| 0.1471 | 2.0 | 10 | 0.0063 |
| 0.1471 | 3.0 | 15 | 0.0036 |
| 0.0399 | 4.0 | 20 | 0.0023 |
| 0.0399 | 5.0 | 25 | 0.0017 |
| 0.0163 | 6.0 | 30 | 0.0014 |
| 0.0163 | 7.0 | 35 | 0.0012 |
| 0.0112 | 8.0 | 40 | 0.0011 |
| 0.0112 | 9.0 | 45 | 0.0010 |
| 0.0096 | 10.0 | 50 | 0.0010 |
Framework versions
- Transformers 5.0.0
- Pytorch 2.10.0+cu128
- Datasets 4.0.0
- Tokenizers 0.22.2