CamemBERT_xnli_fintuned_on_legal_data

hcene/CamemBERT_xnli_fintuned_on_legal_data

CamemBERT_xnli_fintuned_on_legal_data is a text classification model from hcene. Use it when you need a label for a piece of text. It is set up for transformers.

should probably proofread and complete it, then remove this comment. --

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At a glance

Task: Text Classification
Library: transformers
Model type: camembert
Access: Public
Created: Mar 2, 2024
Updated: Mar 2, 2024
SHA: a278bcaa

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Base models

mtheo/camembert-base-xnli

Model card

From the Hugging Face model README

legal-data-mDEBERTa-V3-base-mnli-xnli

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

Loss: 0.7972
Accuracy: 0.7706
Precision: 0.7891
Recall: 0.7711
F1: 0.7697
Ratio: 0.3154

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: 20
eval_batch_size: 20
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.1
lr_scheduler_warmup_steps: 4
num_epochs: 15
label_smoothing_factor: 0.1

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1	Ratio
0.9937	0.17	10	0.8509	0.6093	0.7022	0.6086	0.6168	0.1935
0.8433	0.34	20	0.7131	0.6667	0.6703	0.6687	0.6666	0.3262
0.8683	0.52	30	0.7101	0.7312	0.7350	0.7324	0.7324	0.3262
0.8536	0.69	40	0.7542	0.6989	0.7152	0.6999	0.7053	0.3011
0.7964	0.86	50	0.7249	0.7670	0.7773	0.7677	0.7630	0.3297
0.7651	1.03	60	0.8253	0.7455	0.7517	0.7465	0.7450	0.3262
0.7658	1.21	70	0.8282	0.6953	0.7366	0.6956	0.7030	0.2688
0.7297	1.38	80	0.8694	0.7634	0.7771	0.7641	0.7612	0.3226
0.7726	1.55	90	0.7898	0.7097	0.7112	0.7112	0.7111	0.3297
0.7107	1.72	100	0.8279	0.7599	0.7642	0.7608	0.7589	0.3297
0.7204	1.9	110	0.9353	0.7240	0.7728	0.7240	0.7279	0.2724
0.7241	2.07	120	0.7903	0.7527	0.7818	0.7530	0.7531	0.3011
0.6683	2.24	130	0.8139	0.7384	0.7931	0.7382	0.7397	0.2760
0.6832	2.41	140	0.8339	0.7993	0.8268	0.7996	0.7913	0.3297
0.7397	2.59	150	0.8309	0.7849	0.7997	0.7855	0.7801	0.3297
0.6945	2.76	160	0.7860	0.7240	0.7259	0.7253	0.7247	0.3297
0.7067	2.93	170	0.7046	0.7957	0.8152	0.7962	0.7898	0.3297
0.6759	3.1	180	0.7465	0.7634	0.7703	0.7643	0.7611	0.3297
0.6673	3.28	190	0.8461	0.8029	0.8234	0.8033	0.7972	0.3297
0.6748	3.45	200	0.8701	0.8065	0.8354	0.8068	0.7987	0.3297
0.7638	3.62	210	0.7501	0.8136	0.8521	0.8138	0.8043	0.3297
0.6426	3.79	220	0.7165	0.8100	0.8379	0.8103	0.8029	0.3297
0.6569	3.97	230	0.7244	0.8100	0.8415	0.8103	0.8020	0.3297
0.7068	4.14	240	0.7448	0.8100	0.8499	0.8102	0.8000	0.3297
0.6544	4.31	250	0.8241	0.8065	0.8476	0.8066	0.7957	0.3297
0.6261	4.48	260	0.8409	0.7634	0.7692	0.7643	0.7617	0.3297
0.6428	4.66	270	0.7887	0.7491	0.7528	0.7501	0.7484	0.3297
0.657	4.83	280	0.7534	0.7706	0.7791	0.7714	0.7677	0.3297
0.6895	5.0	290	0.7863	0.8100	0.8379	0.8103	0.8029	0.3297
0.6422	5.17	300	0.7908	0.8136	0.8439	0.8139	0.8062	0.3297
0.5933	5.34	310	0.8330	0.7885	0.8031	0.7891	0.7869	0.3226
0.5863	5.52	320	0.8494	0.7527	0.7598	0.7536	0.7535	0.3226
0.6787	5.69	330	0.7748	0.7742	0.7823	0.7750	0.7716	0.3297
0.6761	5.86	340	0.7256	0.7814	0.7929	0.7820	0.7775	0.3297
0.6974	6.03	350	0.7711	0.8029	0.8208	0.8033	0.7980	0.3297
0.6083	6.21	360	0.8435	0.7993	0.8191	0.7997	0.7951	0.3262
0.5897	6.38	370	0.8773	0.7849	0.8124	0.7852	0.7831	0.3118
0.6076	6.55	380	0.8255	0.7634	0.7683	0.7644	0.7623	0.3297
0.6709	6.72	390	0.7865	0.7527	0.7551	0.7538	0.7530	0.3297
0.7063	6.9	400	0.7898	0.8029	0.8234	0.8033	0.7972	0.3297
0.6804	7.07	410	0.7804	0.7921	0.8152	0.7925	0.7848	0.3297
0.6227	7.24	420	0.7515	0.7706	0.7778	0.7714	0.7683	0.3297
0.6482	7.41	430	0.7758	0.7670	0.7725	0.7679	0.7656	0.3297
0.6072	7.59	440	0.8077	0.7706	0.7792	0.7714	0.7693	0.3262
0.5695	7.76	450	0.8460	0.7921	0.8068	0.7926	0.7892	0.3262
0.6097	7.93	460	0.7856	0.7993	0.8191	0.7997	0.7951	0.3262
0.5591	8.1	470	0.7812	0.8136	0.8447	0.8138	0.8074	0.3262
0.5573	8.28	480	0.7249	0.7849	0.7930	0.7857	0.7828	0.3297
0.6128	8.45	490	0.7245	0.7921	0.8006	0.7928	0.7901	0.3297
0.6072	8.62	500	0.7732	0.7885	0.8038	0.7891	0.7852	0.3262
0.6276	8.79	510	0.8017	0.7885	0.8038	0.7891	0.7852	0.3262
0.647	8.97	520	0.7998	0.7885	0.8038	0.7891	0.7852	0.3262
0.5924	9.14	530	0.7972	0.7706	0.7891	0.7711	0.7697	0.3154

Framework versions

Transformers 4.39.0.dev0
Pytorch 2.1.0+cu121
Datasets 2.18.0
Tokenizers 0.15.2

Task: Text Classification
Library: transformers
Type: camembert
Created: Mar 2, 2024
Updated: Mar 2, 2024