Text Classification
Transformers
TensorBoard
Safetensors
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use Kingsman1977/AIA_LLM_B_Work1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Kingsman1977/AIA_LLM_B_Work1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Kingsman1977/AIA_LLM_B_Work1")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Kingsman1977/AIA_LLM_B_Work1") model = AutoModelForSequenceClassification.from_pretrained("Kingsman1977/AIA_LLM_B_Work1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
AIA_LLM_B_Work1
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: 0.8415
- Matthews Correlation: 0.5445
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 |
|---|---|---|---|---|
| 0.519 | 1.0 | 535 | 0.4553 | 0.4563 |
| 0.3452 | 2.0 | 1070 | 0.4619 | 0.5242 |
| 0.2344 | 3.0 | 1605 | 0.6638 | 0.4943 |
| 0.1782 | 4.0 | 2140 | 0.8306 | 0.5235 |
| 0.1256 | 5.0 | 2675 | 0.8415 | 0.5445 |
Framework versions
- Transformers 4.39.1
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
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Model tree for Kingsman1977/AIA_LLM_B_Work1
Base model
distilbert/distilbert-base-uncased