Instructions to use ICT2214Team7/Combined_model_v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ICT2214Team7/Combined_model_v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="ICT2214Team7/Combined_model_v1")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("ICT2214Team7/Combined_model_v1") model = AutoModelForTokenClassification.from_pretrained("ICT2214Team7/Combined_model_v1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Combined_model_v1
RoBERTa_Test_Training(conll) + Test_Dataset
This model is a fine-tuned version of ICT2214Team7/RoBERTa_Test_Training on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.1242
- Precision: 0.8
- Recall: 0.9231
- F1: 0.8571
- Accuracy: 0.9667
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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 10 | 0.1598 | 0.6667 | 0.7692 | 0.7143 | 0.9417 |
| No log | 2.0 | 20 | 0.1385 | 0.625 | 0.7692 | 0.6897 | 0.9583 |
| No log | 3.0 | 30 | 0.1242 | 0.8 | 0.9231 | 0.8571 | 0.9667 |
Framework versions
- Transformers 4.40.2
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
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Model tree for ICT2214Team7/Combined_model_v1
Base model
distilbert/distilroberta-base Finetuned
ICT2214Team7/RoBERTa_Test_Training