Text Classification
Transformers
TensorBoard
Safetensors
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use Wei-K/LLM_HW01 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Wei-K/LLM_HW01 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Wei-K/LLM_HW01")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Wei-K/LLM_HW01") model = AutoModelForSequenceClassification.from_pretrained("Wei-K/LLM_HW01", device_map="auto") - Notebooks
- Google Colab
- Kaggle
metadata
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- matthews_correlation
model-index:
- name: LLM_HW01
results: []
LLM_HW01
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.8671
- Matthews Correlation: 0.5311
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: 16
- eval_batch_size: 16
- 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 | Matthews Correlation |
|---|---|---|---|---|
| 0.5172 | 1.0 | 535 | 0.4515 | 0.4501 |
| 0.3423 | 2.0 | 1070 | 0.4639 | 0.5083 |
| 0.2365 | 3.0 | 1605 | 0.6431 | 0.5153 |
| 0.1641 | 4.0 | 2140 | 0.7695 | 0.5301 |
| 0.1238 | 5.0 | 2675 | 0.8671 | 0.5311 |
Framework versions
- Transformers 4.36.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.0
- Tokenizers 0.15.0