metadata
license: apache-2.0
base_model: sentence-transformers/paraphrase-MiniLM-L3-v2
tags:
- generated_from_trainer
datasets:
- nyt_ingredients
model-index:
- name: nyt_ingredients-crf-tagger-paraphrase-MiniLM-L3-v2
results: []
nyt_ingredients-crf-tagger-paraphrase-MiniLM-L3-v2
This model is a fine-tuned version of sentence-transformers/paraphrase-MiniLM-L3-v2 on the nyt_ingredients dataset. It achieves the following results on the evaluation set:
- Loss: 10.2590
- Comment: {'precision': 0.03657262277951933, 'recall': 0.0264750378214826, 'f1': 0.030715225976305396, 'number': 1322}
- Name: {'precision': 0.5238095238095238, 'recall': 0.01245753114382786, 'f1': 0.024336283185840708, 'number': 1766}
- Qty: {'precision': 0.0234375, 'recall': 0.0020920502092050207, 'f1': 0.003841229193341869, 'number': 1434}
- Range End: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 17}
- Unit: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1166}
- Overall Precision: 0.0419
- Overall Recall: 0.0105
- Overall F1: 0.0168
- Overall Accuracy: 0.1284
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: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
Training results
| Training Loss | Epoch | Step | Validation Loss | Comment | Name | Qty | Range End | Unit | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| No log | 1.0 | 54 | 11.5992 | {'precision': 0.03826530612244898, 'recall': 0.0340393343419062, 'f1': 0.036028823058446756, 'number': 1322} | {'precision': 0.9047619047619048, 'recall': 0.010758776896942242, 'f1': 0.021264689423614997, 'number': 1766} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1434} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 17} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1166} | 0.0526 | 0.0112 | 0.0185 | 0.1319 |
| No log | 2.0 | 108 | 10.2590 | {'precision': 0.03657262277951933, 'recall': 0.0264750378214826, 'f1': 0.030715225976305396, 'number': 1322} | {'precision': 0.5238095238095238, 'recall': 0.01245753114382786, 'f1': 0.024336283185840708, 'number': 1766} | {'precision': 0.0234375, 'recall': 0.0020920502092050207, 'f1': 0.003841229193341869, 'number': 1434} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 17} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1166} | 0.0419 | 0.0105 | 0.0168 | 0.1284 |
Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.0