pranaydeeps/lettuce_pos_fr_mono

pos_final_mono_fr

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

• Loss: 0.5416
• Precision: 0.9742
• Recall: 0.9745
• F1: 0.9743
• Accuracy: 0.9768

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: 256
• eval_batch_size: 256
• seed: 42
• gradient_accumulation_steps: 4
• total_train_batch_size: 1024
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 500
• num_epochs: 40.0
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
No log	0.95	14	3.6697	0.0210	0.0194	0.0201	0.0215
No log	1.95	28	3.6329	0.0513	0.0484	0.0498	0.0511
No log	2.95	42	3.5739	0.1142	0.1086	0.1113	0.1267
No log	3.95	56	3.4791	0.2535	0.1976	0.2221	0.3061
No log	4.95	70	3.3377	0.3393	0.2029	0.2539	0.3788
No log	5.95	84	3.1886	0.3737	0.1401	0.2038	0.3427
No log	6.95	98	3.0505	0.4342	0.3211	0.3692	0.4600
No log	7.95	112	2.8996	0.5160	0.4319	0.4702	0.5282
No log	8.95	126	2.7485	0.5617	0.4878	0.5222	0.5732
No log	9.95	140	2.5862	0.6077	0.5374	0.5704	0.6246
No log	10.95	154	2.4205	0.6805	0.6311	0.6549	0.6887
No log	11.95	168	2.2603	0.7816	0.7569	0.7691	0.7839
No log	12.95	182	2.1124	0.8366	0.8305	0.8335	0.8370
No log	13.95	196	1.9826	0.8691	0.8681	0.8686	0.8736
No log	14.95	210	1.8721	0.9210	0.92	0.9205	0.9240
No log	15.95	224	1.7779	0.9390	0.9392	0.9391	0.9417
No log	16.95	238	1.6986	0.9442	0.9452	0.9447	0.9466
No log	17.95	252	1.6294	0.9467	0.9476	0.9472	0.9486
No log	18.95	266	1.5667	0.9481	0.9493	0.9487	0.9499
No log	19.95	280	1.5073	0.9507	0.9522	0.9514	0.9523
No log	20.95	294	1.4499	0.9538	0.9550	0.9544	0.9552
No log	21.95	308	1.3926	0.9555	0.9563	0.9559	0.9563
No log	22.95	322	1.3373	0.9609	0.9614	0.9612	0.9612
No log	23.95	336	1.2815	0.9622	0.9624	0.9623	0.9623
No log	24.95	350	1.2246	0.9649	0.9648	0.9648	0.9646
No log	25.95	364	1.1682	0.9653	0.9652	0.9652	0.9648
No log	26.95	378	1.1114	0.9650	0.9659	0.9654	0.9661
No log	27.95	392	1.0521	0.9669	0.9675	0.9672	0.9699
No log	28.95	406	0.9950	0.9677	0.9679	0.9678	0.9707
No log	29.95	420	0.9364	0.9687	0.9690	0.9688	0.9716
No log	30.95	434	0.8800	0.9691	0.9693	0.9692	0.9721
No log	31.95	448	0.8233	0.9693	0.9698	0.9696	0.9726
No log	32.95	462	0.7679	0.9703	0.9703	0.9703	0.9733
No log	33.95	476	0.7146	0.9711	0.9711	0.9711	0.9737
No log	34.95	490	0.6641	0.9722	0.9724	0.9723	0.9750
2.0937	35.95	504	0.6187	0.9729	0.9729	0.9729	0.9755
2.0937	36.95	518	0.5834	0.9727	0.9732	0.9729	0.9756
2.0937	37.95	532	0.5605	0.9735	0.9739	0.9737	0.9762
2.0937	38.95	546	0.5466	0.9737	0.9742	0.9739	0.9765
2.0937	39.95	560	0.5416	0.9742	0.9745	0.9743	0.9768

Framework versions

• Transformers 4.25.1
• Pytorch 1.12.0
• Datasets 2.18.0
• Tokenizers 0.13.2