README.md

metadata

license: mit
base_model: camembert-base
tags:
  - generated_from_trainer
metrics:
  - accuracy
  - precision
  - recall
  - f1
model-index:
  - name: VogagenRelation
    results: []

VogagenRelation

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

• Loss: 0.4869
• Accuracy: 0.9016
• Precision: 0.8671
• Recall: 0.9484
• F1: 0.9060

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: 8

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1
No log	0.21	100	0.6272	0.6729	0.6415	0.7828	0.7051
No log	0.42	200	0.4933	0.7799	0.7406	0.8609	0.7962
No log	0.62	300	0.4114	0.8431	0.8087	0.8984	0.8512
No log	0.83	400	0.4483	0.8384	0.8054	0.8922	0.8466
0.5445	1.04	500	0.4149	0.8525	0.7971	0.9453	0.8649
0.5445	1.25	600	0.4221	0.8532	0.8038	0.9344	0.8642
0.5445	1.46	700	0.4022	0.8712	0.8728	0.8688	0.8708
0.5445	1.66	800	0.4083	0.8509	0.8013	0.9328	0.8621
0.5445	1.87	900	0.4272	0.8704	0.8455	0.9062	0.8748
0.3857	2.08	1000	0.3800	0.8759	0.8501	0.9125	0.8802
0.3857	2.29	1100	0.4684	0.8673	0.8357	0.9141	0.8731
0.3857	2.49	1200	0.4754	0.8634	0.8207	0.9297	0.8718
0.3857	2.7	1300	0.4392	0.8681	0.8294	0.9266	0.8753
0.3857	2.91	1400	0.5272	0.8470	0.7803	0.9656	0.8631
0.2687	3.12	1500	0.3529	0.9016	0.8693	0.9453	0.9057
0.2687	3.33	1600	0.3857	0.8899	0.8499	0.9469	0.8958
0.2687	3.53	1700	0.3852	0.9016	0.8836	0.925	0.9038
0.2687	3.74	1800	0.4860	0.8829	0.8365	0.9516	0.8904
0.2687	3.95	1900	0.4014	0.9001	0.8657	0.9469	0.9045
0.1785	4.16	2000	0.4295	0.8993	0.8655	0.9453	0.9037
0.1785	4.37	2100	0.4592	0.8977	0.8550	0.9578	0.9035
0.1785	4.57	2200	0.4392	0.9055	0.8844	0.9328	0.9080
0.1785	4.78	2300	0.4659	0.9024	0.8759	0.9375	0.9057
0.1785	4.99	2400	0.4059	0.9110	0.9021	0.9219	0.9119
0.1098	5.2	2500	0.4869	0.9016	0.8671	0.9484	0.9060

Framework versions

• Transformers 4.34.0
• Pytorch 2.1.0+cu121
• Datasets 2.14.5
• Tokenizers 0.14.1