---
license: apache-2.0
base_model: sentence-transformers/paraphrase-MiniLM-L3-v2
tags:
- generated_from_trainer
datasets:
- napsternxg/nyt_ingredients
model-index:
- name: nyt_ingredients-crf-tagger-paraphrase-MiniLM-L3-v2
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# nyt_ingredients-crf-tagger-paraphrase-MiniLM-L3-v2

This model is a fine-tuned version of [sentence-transformers/paraphrase-MiniLM-L3-v2](https://huggingface.co/sentence-transformers/paraphrase-MiniLM-L3-v2) on the nyt_ingredients dataset.
It achieves the following results on the evaluation set:
- Loss: 11.3870
- Comment: {'precision': 0.018842530282637954, 'recall': 0.010174418604651164, 'f1': 0.01321378008494573, 'number': 1376}
- Name: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1758}
- Qty: {'precision': 0.1499119991717569, 'recall': 0.9986206896551724, 'f1': 0.26068953101089204, 'number': 1450}
- Range End: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 14}
- Unit: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1163}
- Overall Precision: 0.1405
- Overall Recall: 0.2538
- Overall F1: 0.1809
- Overall Accuracy: 0.1528

## 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   | 13.0360         | {'precision': 0.003246753246753247, 'recall': 0.0007267441860465116, 'f1': 0.001187648456057007, 'number': 1376} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1758} | {'precision': 0.142309205350118, 'recall': 0.9979310344827587, 'f1': 0.24909622998794975, 'number': 1450}  | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 14} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1163} | 0.1382            | 0.2513         | 0.1784     | 0.1432           |
| No log        | 2.0   | 108  | 11.3870         | {'precision': 0.018842530282637954, 'recall': 0.010174418604651164, 'f1': 0.01321378008494573, 'number': 1376}   | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1758} | {'precision': 0.1499119991717569, 'recall': 0.9986206896551724, 'f1': 0.26068953101089204, 'number': 1450} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 14} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1163} | 0.1405            | 0.2538         | 0.1809     | 0.1528           |


### Framework versions

- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.0