multi-e5-small_lmd-comments_v1

gentilrenard/multi-e5-small_lmd-comments_v1

multi-e5-small_lmd-comments_v1 is a text classification model from gentilrenard. 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: bert
Access: Public
Created: Mar 26, 2024
Updated: Mar 26, 2024
SHA: c2c3b958

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Base models

intfloat/multilingual-e5-small

Model card

From the Hugging Face model README

multi-e5-small_lmd-comments_v1

This model is a fine-tuned version of intfloat/multilingual-e5-small on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.9808
F1: 0.7036
Accuracy: 0.7122

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: 8
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.1
num_epochs: 2

Training results

Training Loss	Epoch	Step	Validation Loss	F1	Accuracy
1.0969	0.04	100	1.0991	0.4109	0.4964
1.0764	0.08	200	1.0768	0.5217	0.5971
0.955	0.12	300	0.9313	0.5802	0.6691
0.8137	0.17	400	0.8927	0.5864	0.6475
0.7837	0.21	500	0.8711	0.6238	0.6475
0.7234	0.25	600	0.9953	0.5641	0.6475
0.6983	0.29	700	0.9111	0.6226	0.6475
0.6574	0.33	800	0.8557	0.6686	0.6835
0.6653	0.37	900	0.7925	0.7087	0.7122
0.6444	0.41	1000	0.8338	0.7056	0.7122
0.6155	0.46	1100	0.8339	0.7257	0.7338
0.5726	0.5	1200	0.8078	0.7140	0.7194
0.6279	0.54	1300	0.9534	0.6917	0.7050
0.6083	0.58	1400	0.9515	0.6914	0.7050
0.5525	0.62	1500	0.9281	0.6846	0.7050
0.6849	0.66	1600	0.8352	0.6917	0.7050
0.5924	0.7	1700	1.0702	0.6602	0.6906
0.5614	0.75	1800	0.9689	0.6801	0.6978
0.5936	0.79	1900	1.0179	0.6896	0.7050
0.5582	0.83	2000	0.8858	0.7320	0.7410
0.5479	0.87	2100	0.9373	0.7030	0.7122
0.6278	0.91	2200	0.8694	0.6858	0.6978
0.4819	0.95	2300	0.9440	0.7074	0.7194
0.5425	0.99	2400	1.0661	0.6765	0.6906
0.5804	1.04	2500	0.8904	0.7189	0.7266
0.5025	1.08	2600	1.0105	0.6886	0.7050
0.5148	1.12	2700	0.9934	0.7076	0.7194
0.5359	1.16	2800	0.9249	0.7291	0.7410
0.5002	1.2	2900	0.7503	0.7047	0.7050
0.4563	1.24	3000	0.8149	0.7230	0.7266
0.4837	1.28	3100	0.8956	0.7125	0.7194
0.4486	1.33	3200	0.9013	0.7110	0.7194
0.4721	1.37	3300	1.0545	0.7142	0.7266
0.5482	1.41	3400	1.0139	0.7014	0.7122
0.4488	1.45	3500	0.9427	0.7162	0.7266
0.4859	1.49	3600	1.1337	0.7074	0.7194
0.504	1.53	3700	1.0299	0.7178	0.7266
0.4555	1.57	3800	0.8830	0.7273	0.7338
0.502	1.62	3900	1.0340	0.7142	0.7266
0.5131	1.66	4000	1.0997	0.7031	0.7194
0.5208	1.7	4100	1.0845	0.7025	0.7194
0.4329	1.74	4200	1.0553	0.7132	0.7266
0.4612	1.78	4300	1.0458	0.7074	0.7194
0.4857	1.82	4400	0.9425	0.7120	0.7194
0.4986	1.86	4500	0.9965	0.7237	0.7338
0.4066	1.91	4600	0.9520	0.7041	0.7122
0.4638	1.95	4700	0.9558	0.6979	0.7050
0.4541	1.99	4800	0.9808	0.7036	0.7122

Framework versions

Transformers 4.38.1
Pytorch 2.1.2
Datasets 2.1.0
Tokenizers 0.15.2

Task: Text Classification
Library: transformers
Type: bert
License: mit
Created: Mar 26, 2024
Updated: Mar 26, 2024