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---
license: mit
base_model: FacebookAI/xlm-roberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: llm-data-textbook-quality-classifer-v1
results: []
datasets:
- kenhktsui/llm-data-quality-tokenized
language:
- en
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# 2024-05-19: v2 is released -> [llm-data-textbook-quality-fasttext-classifer-v2](https://huggingface.co/kenhktsui/llm-data-textbook-quality-fasttext-classifer-v2)
# A more optimized model is released -> [kenhktsui/llm-data-textbook-quality-fasttext-classifer-v1](https://huggingface.co/kenhktsui/llm-data-textbook-quality-fasttext-classifer-v1)
# llm-data-textbook-quality-classifer-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
|Dataset | Sampling | Average Quality Score |
|--------------------------------------|---|-------------------|
|[nampdn-ai/tiny-textbooks](https://huggingface.co/datasets/nampdn-ai/tiny-textbooks) |First 10,000| 0.8618|
|[nampdn-ai/tiny-orca-textbooks](https://huggingface.co/datasets/nampdn-ai/tiny-orca-textbooks) |First 10,000| 0.8544|
|[SciPhi/textbooks-are-all-you-need-lite](https://huggingface.co/datasets/SciPhi/textbooks-are-all-you-need-lite) |First 10,000| 0.8109|
|[vikp/textbook_quality_programming](https://huggingface.co/datasets/vikp/textbook_quality_programming)| First 10,000 | 0.6883 |
|[BEE-spoke-data/fineweb-100k_en-med](https://huggingface.co/datasets/BEE-spoke-data/fineweb-100k_en-med)| Full| 0.5516|
|[pszemraj/simple_wikipedia_LM](https://huggingface.co/datasets/pszemraj/simple_wikipedia_LM) | Full| 0.5386|
|[mattymchen/refinedweb-3m](https://huggingface.co/datasets/mattymchen/refinedweb-3m)| Full| 0.2951|
|[JeanKaddour/minipile](https://huggingface.co/datasets/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](https://huggingface.co/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 |