llm-data-textbook-quality-classifier-v1

kenhktsui/llm-data-textbook-quality-classifier-v1

llm-data-textbook-quality-classifier-v1 is a text classification model from kenhktsui. 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: xlm-roberta
Access: Public
Created: Jan 22, 2024
Updated: May 25, 2024
SHA: 494c91f5

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Base models

FacebookAI/xlm-roberta-base

Datasets

kenhktsui/llm-data-quality-tokenized

Model card

From the Hugging Face model README

2024-05-19: v2 is released -> llm-data-textbook-quality-fasttext-classifier-v2

A more optimized model is released -> kenhktsui/llm-data-textbook-quality-fasttext-classifier-v1

llm-data-textbook-quality-classifier-v1

This model can classify if a text is of textbook quality data. It can be used as a filter for data curation when training a LLM. Please note textbook quality is a subset of high quality.

Benchmark

image/png

Dataset	Sampling	Average Quality Score
nampdn-ai/tiny-textbooks	First 10,000	0.8618
nampdn-ai/tiny-orca-textbooks	First 10,000	0.8544
SciPhi/textbooks-are-all-you-need-lite	First 10,000	0.8109
vikp/textbook_quality_programming	First 10,000	0.6883
BEE-spoke-data/fineweb-100k_en-med	Full	0.5516
pszemraj/simple_wikipedia_LM	Full	0.5386
mattymchen/refinedweb-3m	Full	0.2951
JeanKaddour/minipile	Full	0.2618

The classifier aligns with the expectation. Textbook category scores the highest, reflecting the effectiveness of this model. Wikipedia scores lower because it is not textbook after all. Web scores the lowest.

This model is a fine-tuned version of FacebookAI/xlm-roberta-base on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.2689
Accuracy: 0.8833
Precision: 0.7551
Recall: 0.7598
F1: 0.7574

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
num_epochs: 1

Training results

Training Loss	Epoch	Step	Accuracy	F1	Validation Loss	Precision	Recall
0.4745	0.01	500	0.8076	0.6181	0.4327	0.5898	0.6493
0.4088	0.02	1000	0.8346	0.5522	0.4287	0.7870	0.4254
0.3811	0.02	1500	0.8286	0.6651	0.3741	0.6257	0.7098
0.3762	0.03	2000	0.85	0.6529	0.3413	0.7334	0.5884
0.3647	0.04	2500	0.8427	0.6632	0.3852	0.6815	0.6460
0.3495	0.05	3000	0.8629	0.6987	0.3253	0.7385	0.6631
0.3508	0.06	3500	0.8335	0.6967	0.3605	0.6186	0.7973
0.3342	0.06	4000	0.8553	0.7075	0.3273	0.6865	0.7298
0.341	0.07	4500	0.8602	0.6679	0.3320	0.7759	0.5863
0.3344	0.08	5000	0.8531	0.6916	0.3441	0.6964	0.6868
0.3341	0.09	5500	0.8536	0.7027	0.3265	0.6849	0.7214
0.3319	0.1	6000	0.8599	0.7081	0.3266	0.7076	0.7085
0.3259	0.1	6500	0.8136	0.6907	0.3908	0.5736	0.8678
0.3391	0.11	7000	0.8642	0.6770	0.3338	0.7879	0.5934
0.3207	0.12	7500	0.8668	0.7224	0.3035	0.7221	0.7227
0.3191	0.13	8000	0.8543	0.7153	0.3179	0.6730	0.7631
0.3142	0.14	8500	0.8679	0.7052	0.3101	0.7585	0.6589
0.3195	0.14	9000	0.8636	0.7254	0.3433	0.7012	0.7515
0.3196	0.15	9500	0.8707	0.7191	0.3048	0.7506	0.6902
0.3176	0.16	10000	0.8597	0.7271	0.3177	0.6814	0.7794
0.3218	0.17	10500	0.8723	0.6993	0.3212	0.8031	0.6193
0.3175	0.18	11000	0.8601	0.7239	0.3366	0.6871	0.7648
0.3296	0.18	11500	0.8526	0.7190	0.3218	0.6622	0.7865
0.3249	0.19	12000	0.8731	0.7081	0.2926	0.7896	0.6418
0.3141	0.2	12500	0.8741	0.7215	0.3035	0.7683	0.6802
0.3126	0.21	13000	0.8659	0.7231	0.3127	0.7162	0.7302
0.3204	0.22	13500	0.8665	0.7233	0.3456	0.7190	0.7277
0.3108	0.22	14000	0.8674	0.7214	0.3018	0.7269	0.7160
0.3114	0.23	14500	0.8726	0.7016	0.2967	0.8002	0.6247
0.3071	0.24	15000	0.8768	0.7211	0.2904	0.7886	0.6643
0.2965	0.25	15500	0.8674	0.7310	0.3126	0.7117	0.7515
0.3022	0.26	16000	0.8738	0.7077	0.2887	0.7958	0.6372
0.3101	0.26	16500	0.8559	0.7251	0.3312	0.6683	0.7923
0.3154	0.27	17000	0.8575	0.7304	0.3221	0.6685	0.8048
0.3041	0.28	17500	0.8754	0.7248	0.2864	0.7704	0.6843
0.3093	0.29	18000	0.8603	0.7292	0.3101	0.6813	0.7844
0.3006	0.3	18500	0.8753	0.7111	0.3008	0.7999	0.6401
0.3108	0.3	19000	0.8689	0.7316	0.2911	0.7185	0.7452
0.3071	0.31	19500	0.8793	0.7366	0.2839	0.7725	0.7039
0.3002	0.32	20000	0.852	0.7239	0.3391	0.6550	0.8090
0.301	0.33	20500	0.8769	0.7396	0.2896	0.7505	0.7289
0.3075	0.34	21000	0.8785	0.7402	0.2891	0.7595	0.7219
0.2922	0.34	21500	0.8393	0.7164	0.4094	0.6210	0.8465
0.2973	0.35	22000	0.8787	0.7416	0.2962	0.7579	0.7260
0.2987	0.36	22500	0.8711	0.7430	0.2983	0.7119	0.7769
0.3071	0.37	23000	0.8739	0.7407	0.3167	0.7306	0.7510
0.2846	0.38	23500	0.8801	0.7401	0.2901	0.7707	0.7118
0.2924	0.38	24000	0.863	0.7299	0.3155	0.6922	0.7719
0.2938	0.39	24500	0.8724	0.7368	0.2973	0.7290	0.7448
0.2917	0.4	25000	0.8772	0.7436	0.2939	0.7446	0.7427
0.294	0.41	25500	0.8772	0.7394	0.2944	0.7528	0.7264
0.2979	0.42	26000	0.8774	0.7421	0.2819	0.7487	0.7356
0.2884	0.42	26500	0.873	0.7394	0.2932	0.7278	0.7515
0.2992	0.43	27000	0.8655	0.7419	0.3053	0.6872	0.8061
0.3018	0.44	27500	0.8788	0.7296	0.2781	0.7845	0.6818
0.305	0.45	28000	0.8785	0.7408	0.2760	0.7584	0.7239
0.2918	0.46	28500	0.8788	0.7381	0.2826	0.7659	0.7123
0.2998	0.46	29000	0.874	0.7403	0.2893	0.7319	0.7490
0.2875	0.47	29500	0.8803	0.7422	0.2891	0.7675	0.7185
0.2946	0.48	30000	0.2781	0.8798	0.7415	0.7656	0.7534
0.2907	0.49	30500	0.2860	0.8752	0.7280	0.7656	0.7463
0.2981	0.5	31000	0.3012	0.8732	0.7276	0.7531	0.7402
0.2948	0.5	31500	0.2777	0.8792	0.7894	0.6768	0.7288
0.2933	0.51	32000	0.2839	0.8773	0.7428	0.7469	0.7449
0.2891	0.52	32500	0.2774	0.8795	0.7678	0.7131	0.7395
0.2869	0.53	33000	0.2790	0.8764	0.7405	0.7460	0.7432
0.2907	0.54	33500	0.2889	0.8764	0.7580	0.7118	0.7342
0.2912	0.54	34000	0.2887	0.8807	0.7464	0.7611	0.7537
0.283	0.55	34500	0.2754	0.8816	0.7847	0.6977	0.7386
0.2877	0.56	35000	0.3036	0.8727	0.7221	0.7627	0.7418
0.2923	0.57	35500	0.2853	0.8783	0.7693	0.7035	0.7349
0.2902	0.58	36000	0.2881	0.8772	0.7462	0.7394	0.7428
0.2863	0.58	36500	0.2886	0.8768	0.7303	0.7711	0.7501
0.2837	0.59	37000	0.2753	0.8801	0.7503	0.7494	0.7498
0.3021	0.6	37500	0.2848	0.8775	0.7330	0.7694	0.7508
0.291	0.61	38000	0.2793	0.88	0.7423	0.7652	0.7536
0.2821	0.62	38500	0.2867	0.88	0.7429	0.7640	0.7533
0.2867	0.62	39000	0.2851	0.8796	0.7367	0.7748	0.7553
0.2846	0.63	39500	0.2813	0.8828	0.7661	0.7360	0.7507
0.2836	0.64	40000	0.2842	0.8793	0.7406	0.7644	0.7523
0.2835	0.65	40500	0.2797	0.8792	0.7382	0.7690	0.7533
0.2833	0.66	41000	0.2763	0.8821	0.7895	0.6931	0.7382
0.2743	0.66	41500	0.2852	0.8833	0.7717	0.7289	0.7497
0.2921	0.67	42000	0.2780	0.8791	0.7561	0.7319	0.7438
0.279	0.68	42500	0.2759	0.8827	0.7882	0.6985	0.7407
0.2752	0.69	43000	0.2795	0.8796	0.7642	0.7202	0.7415
0.2902	0.7	43500	0.2735	0.8809	0.7824	0.6972	0.7374
0.2832	0.7	44000	0.2742	0.8815	0.7690	0.7231	0.7453
0.2783	0.71	44500	0.2773	0.8815	0.7692	0.7227	0.7452
0.2879	0.72	45000	0.2716	0.8838	0.7766	0.7235	0.7491
0.2898	0.73	45500	0.2728	0.8804	0.7513	0.7494	0.7503
0.2771	0.74	46000	0.2795	0.877	0.7370	0.7573	0.7470
0.2743	0.74	46500	0.2833	0.8707	0.7013	0.8028	0.7486
0.2868	0.75	47000	0.2719	0.8821	0.7575	0.7477	0.7526
0.2771	0.76	47500	0.2784	0.8833	0.7636	0.7435	0.7534
0.2824	0.77	48000	0.2778	0.8772	0.7291	0.7765	0.7520
0.2819	0.78	48500	0.2772	0.8825	0.7532	0.7585	0.7559
0.2781	0.78	49000	0.2747	0.881	0.7502	0.7552	0.7527
0.2844	0.79	49500	0.2877	0.8762	0.7215	0.7877	0.7532
0.2732	0.8	50000	0.2738	0.8809	0.7511	0.7527	0.7519
0.2681	0.81	50500	0.2832	0.8761	0.7191	0.7932	0.7543
0.2795	0.82	51000	0.2755	0.8856	0.7876	0.7160	0.7501
0.2649	0.82	51500	0.2797	0.8805	0.7360	0.7823	0.7584
0.2776	0.83	52000	0.2671	0.8833	0.7627	0.7452	0.7538
0.2762	0.84	52500	0.2745	0.8812	0.7416	0.7744	0.7576
0.2803	0.85	53000	0.2766	0.8847	0.7694	0.7415	0.7551
0.2675	0.86	53500	0.2742	0.8785	0.7392	0.7623	0.7506
0.2725	0.86	54000	0.2720	0.8826	0.7576	0.7506	0.7541
0.2693	0.87	54500	0.2739	0.8836	0.7650	0.7427	0.7537
0.2745	0.88	55000	0.2751	0.8792	0.7348	0.7765	0.7551
0.273	0.89	55500	0.2762	0.8812	0.7388	0.7807	0.7591
0.2645	0.9	56000	0.2664	0.8828	0.7647	0.7385	0.7514
0.2698	0.9	56500	0.2728	0.8814	0.7467	0.7648	0.7557
0.2771	0.91	57000	0.2681	0.8839	0.7635	0.7473	0.7553
0.2663	0.92	57500	0.2715	0.885	0.7617	0.7573	0.7595
0.2546	0.93	58000	0.2836	0.8796	0.7323	0.7848	0.7576
0.2752	0.94	58500	0.2747	0.8801	0.7363	0.7790	0.7570
0.2645	0.94	59000	0.2733	0.8834	0.7484	0.7740	0.7610
0.2561	0.95	59500	0.2765	0.8828	0.7508	0.7652	0.7580
0.2753	0.96	60000	0.2721	0.8815	0.7483	0.7623	0.7552
0.251	0.97	60500	0.2735	0.8822	0.7546	0.7540	0.7543
0.2742	0.98	61000	0.2721	0.8831	0.7497	0.7694	0.7594
0.2734	0.98	61500	0.2712	0.8836	0.7512	0.7694	0.7602
0.2713	0.99	62000	0.2690	0.8836	0.7556	0.7606	0.7581
0.2764	1.0	62500	0.2689	0.8833	0.7551	0.7598	0.7574

Framework versions

Transformers 4.35.2
Pytorch 2.1.0+cu121
Datasets 2.16.1
Tokenizers 0.15.0

Task: Text Classification
Library: transformers
Type: xlm-roberta
License: mit
Languages: en
Created: Jan 22, 2024
Updated: May 25, 2024