metadata

license: apache-2.0
tags:
  - generated_from_trainer
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: electra-base-ner-food-recipe-v2
    results: []

electra-base-ner-food-recipe-v2

This model is a fine-tuned version of google/electra-base-discriminator on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.0818
Precision: 0.8510
Recall: 0.8785
F1: 0.8645
Accuracy: 0.9735

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-06
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: 15

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
0.1958	0.63	500	0.0924	0.8293	0.8557	0.8423	0.9710
0.0939	1.26	1000	0.0827	0.8358	0.8826	0.8585	0.9727
0.0837	1.88	1500	0.0797	0.8542	0.8776	0.8657	0.9740
0.0817	2.51	2000	0.0799	0.8441	0.8821	0.8627	0.9732
0.0761	3.14	2500	0.0793	0.8527	0.8853	0.8687	0.9743
0.0743	3.77	3000	0.0799	0.8381	0.8885	0.8626	0.9729
0.076	4.4	3500	0.0793	0.8458	0.8862	0.8655	0.9736
0.07	5.03	4000	0.0782	0.8448	0.8844	0.8641	0.9730
0.067	5.65	4500	0.0784	0.8558	0.8835	0.8694	0.9738
0.0732	6.28	5000	0.0787	0.8559	0.8785	0.8670	0.9742
0.0655	6.91	5500	0.0780	0.8627	0.8780	0.8703	0.9749
0.0668	7.54	6000	0.0778	0.8563	0.8789	0.8675	0.9739
0.0653	8.17	6500	0.0789	0.8537	0.8821	0.8677	0.9738
0.0671	8.79	7000	0.0786	0.8533	0.8817	0.8672	0.9739
0.06	9.42	7500	0.0806	0.8482	0.8826	0.8650	0.9731
0.0645	10.05	8000	0.0792	0.8546	0.8803	0.8673	0.9740
0.0615	10.68	8500	0.0795	0.8464	0.8803	0.8630	0.9731
0.0597	11.31	9000	0.0807	0.8502	0.8780	0.8639	0.9734
0.0609	11.93	9500	0.0810	0.8527	0.8771	0.8647	0.9737
0.0592	12.56	10000	0.0818	0.8502	0.8757	0.8628	0.9733
0.0607	13.19	10500	0.0812	0.8495	0.8812	0.8651	0.9734
0.0597	13.82	11000	0.0813	0.8484	0.8785	0.8631	0.9733
0.0589	14.45	11500	0.0818	0.8510	0.8785	0.8645	0.9735

Framework versions

Transformers 4.27.4
Pytorch 2.0.0+cu118
Datasets 2.11.0
Tokenizers 0.13.3