distilbert-base-uncased-finetuned-text_cl

This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 1.2450
• Accuracy: 0.8641
• Recall: 0.9482
• Precision: 0.8402
• F1: 0.8910

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-05
• train_batch_size: 16
• eval_batch_size: 16
• seed: 42
• optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: linear
• num_epochs: 30

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Recall	Precision	F1
No log	1.0	231	0.4105	0.7765	0.9343	0.7474	0.8305
No log	2.0	462	0.5147	0.7946	0.9741	0.75	0.8475
0.2425	3.0	693	0.3620	0.8407	0.9373	0.8175	0.8733
0.2425	4.0	924	0.5248	0.8646	0.9402	0.8459	0.8906
0.0988	5.0	1155	0.7085	0.8553	0.9492	0.8287	0.8849
0.0988	6.0	1386	0.7420	0.8652	0.9084	0.8677	0.8876
0.0284	7.0	1617	0.7172	0.8705	0.9442	0.8510	0.8952
0.0284	8.0	1848	0.8150	0.8681	0.9442	0.8479	0.8935
0.0084	9.0	2079	0.9139	0.8629	0.9373	0.8455	0.8890
0.0084	10.0	2310	0.9463	0.8571	0.9392	0.8367	0.8850
0.009	11.0	2541	0.9524	0.8658	0.9124	0.8658	0.8885
0.009	12.0	2772	1.1889	0.8483	0.9512	0.8190	0.8802
0.0036	13.0	3003	1.1781	0.8524	0.9592	0.8196	0.8839
0.0036	14.0	3234	1.1979	0.8489	0.9572	0.8165	0.8812
0.0036	15.0	3465	1.0633	0.8635	0.9373	0.8462	0.8894
0.0064	16.0	3696	1.0781	0.8629	0.9333	0.8480	0.8886
0.0064	17.0	3927	1.1728	0.8477	0.9452	0.8216	0.8791
0.0033	18.0	4158	1.1487	0.8536	0.9432	0.8300	0.8830
0.0033	19.0	4389	1.1052	0.8600	0.9482	0.8351	0.8881
0.0031	20.0	4620	1.1933	0.8629	0.9612	0.8312	0.8915
0.0031	21.0	4851	1.3387	0.8454	0.9671	0.8071	0.8799
0.0029	22.0	5082	1.1393	0.8635	0.9482	0.8395	0.8906
0.0029	23.0	5313	1.2048	0.8617	0.9522	0.8349	0.8897
0.0003	24.0	5544	1.1798	0.8652	0.9522	0.8393	0.8922
0.0003	25.0	5775	1.1385	0.8705	0.9412	0.8529	0.8949
0.0014	26.0	6006	1.2903	0.8565	0.9532	0.8279	0.8861
0.0014	27.0	6237	1.1961	0.8635	0.9412	0.8438	0.8898
0.0014	28.0	6468	1.2178	0.8635	0.9442	0.8419	0.8901
0.0013	29.0	6699	1.2409	0.8635	0.9472	0.8401	0.8904
0.0013	30.0	6930	1.2450	0.8641	0.9482	0.8402	0.8910

Framework versions

• Transformers 4.51.3
• Pytorch 2.6.0+cu124
• Datasets 3.5.0
• Tokenizers 0.21.1