distilCamemBERT_nli_on_legal_data

hcene/distilCamemBERT_nli_on_legal_data

distilCamemBERT_nli_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. The card lists the license as mit.

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

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At a glance

Task: Text Classification
Library: transformers
License: mit
Model type: camembert
Access: Public
Created: Feb 29, 2024
Updated: Feb 29, 2024
SHA: 26a61fd3

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Base models

cmarkea/distilcamembert-base-nli

Model card

From the Hugging Face model README

distilCamemBERT_nli_on_legal_data

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

Loss: 0.7470
Accuracy: 0.7384
Precision: 0.7415
Recall: 0.7395
F1: 0.7378
Ratio: 0.3297

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: 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: 10
label_smoothing_factor: 0.1

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1	Ratio
1.3001	0.14	10	0.8533	0.6452	0.6683	0.6459	0.6525	0.2903
0.9775	0.27	20	0.7170	0.6918	0.7191	0.6944	0.6639	0.3513
0.8096	0.41	30	0.6432	0.7204	0.7222	0.7218	0.7213	0.3297
0.9883	0.55	40	0.6914	0.7204	0.7382	0.7228	0.7064	0.3297
0.9221	0.68	50	0.6597	0.7563	0.7582	0.7574	0.7570	0.3297
0.8253	0.82	60	0.6639	0.7563	0.7802	0.7568	0.7440	0.3297
0.9096	0.96	70	0.6612	0.7384	0.7484	0.7392	0.7319	0.3297
0.8466	1.1	80	0.6884	0.7168	0.7534	0.7195	0.6884	0.3297
0.8457	1.23	90	0.6884	0.7563	0.7898	0.7567	0.7397	0.3297
0.836	1.37	100	0.6409	0.7563	0.7711	0.7569	0.7488	0.3297
0.7878	1.51	110	0.6876	0.7276	0.7324	0.7286	0.7249	0.3297
0.8107	1.64	120	0.6720	0.7419	0.7518	0.7427	0.7361	0.3297
0.782	1.78	130	0.7397	0.7419	0.7518	0.7427	0.7361	0.3297
0.7728	1.92	140	0.6921	0.7455	0.7551	0.7463	0.7402	0.3297
0.7704	2.05	150	0.6882	0.7419	0.7518	0.7427	0.7361	0.3297
0.7593	2.19	160	0.7163	0.7348	0.7487	0.7355	0.7304	0.3226
0.7995	2.33	170	0.6639	0.7419	0.7518	0.7427	0.7361	0.3297
0.7657	2.47	180	0.6906	0.7885	0.8418	0.7887	0.7720	0.3297
0.7758	2.6	190	0.7577	0.7133	0.7154	0.7146	0.7134	0.3297
0.8269	2.74	200	0.8168	0.5591	0.6324	0.5596	0.5804	0.2151
0.7721	2.88	210	0.6721	0.7706	0.7924	0.7711	0.7615	0.3297
0.7098	3.01	220	0.6917	0.7133	0.7157	0.7145	0.7129	0.3297
0.7683	3.15	230	0.7175	0.7168	0.7192	0.7181	0.7168	0.3297
0.6907	3.29	240	0.7298	0.7491	0.7598	0.7499	0.7434	0.3297
0.7013	3.42	250	0.7363	0.7634	0.7794	0.7641	0.7562	0.3297
0.7852	3.56	260	0.7616	0.7025	0.7181	0.7034	0.7052	0.3082
0.7375	3.7	270	0.7247	0.7849	0.8127	0.7853	0.7753	0.3297
0.7242	3.84	280	0.7199	0.7921	0.8182	0.7925	0.7838	0.3297
0.7135	3.97	290	0.7190	0.7742	0.7907	0.7748	0.7677	0.3297
0.7501	4.11	300	0.7104	0.7921	0.8292	0.7924	0.7805	0.3297
0.663	4.25	310	0.7579	0.7921	0.8643	0.7922	0.7720	0.3297
0.7316	4.38	320	0.7671	0.7312	0.7347	0.7323	0.7301	0.3297
0.7045	4.52	330	0.7673	0.7204	0.7227	0.7217	0.7206	0.3297
0.7316	4.66	340	0.7421	0.7849	0.8096	0.7854	0.7764	0.3297
0.7667	4.79	350	0.7269	0.7527	0.7576	0.7536	0.7512	0.3297
0.7109	4.93	360	0.7305	0.7742	0.7907	0.7748	0.7677	0.3297
0.7677	5.07	370	0.7805	0.7885	0.8226	0.7888	0.7773	0.3297
0.6988	5.21	380	0.7531	0.7921	0.8182	0.7925	0.7838	0.3297
0.7119	5.34	390	0.7396	0.7670	0.7773	0.7677	0.7630	0.3297
0.6535	5.48	400	0.7259	0.7634	0.7714	0.7642	0.7604	0.3297
0.6732	5.62	410	0.7301	0.7921	0.8292	0.7924	0.7805	0.3297
0.7243	5.75	420	0.7094	0.7849	0.8127	0.7853	0.7753	0.3297
0.7367	5.89	430	0.7266	0.7670	0.7759	0.7678	0.7637	0.3297
0.7464	6.03	440	0.7929	0.7957	0.8277	0.7960	0.7860	0.3297
0.6836	6.16	450	0.7844	0.7957	0.8209	0.7961	0.7880	0.3297
0.6901	6.3	460	0.7724	0.7706	0.7837	0.7713	0.7654	0.3297
0.6776	6.44	470	0.7513	0.7670	0.7806	0.7677	0.7613	0.3297
0.6388	6.58	480	0.7491	0.7384	0.7410	0.7395	0.7383	0.3297
0.7258	6.71	490	0.7361	0.7599	0.7695	0.7606	0.7557	0.3297
0.7458	6.85	500	0.7777	0.7993	0.8483	0.7994	0.7856	0.3297
0.7937	6.99	510	0.7797	0.7921	0.8336	0.7923	0.7793	0.3297
0.6984	7.12	520	0.7597	0.7634	0.7714	0.7642	0.7604	0.3297
0.7206	7.26	530	0.7470	0.7384	0.7415	0.7395	0.7378	0.3297

Framework versions

Transformers 4.39.0.dev0
Pytorch 2.1.0+cu121
Datasets 2.17.1
Tokenizers 0.15.2

Task: Text Classification
Library: transformers
Type: camembert
License: mit
Created: Feb 29, 2024
Updated: Feb 29, 2024