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{text}1from transformers import AutoTokenizer, AutoModelForSequenceClassification
2
3tokenizer = AutoTokenizer.from_pretrained("wissamantoun/wissamantoun/WebOrganizer-TopicClassifier-ModernBERT")
4model = AutoModelForSequenceClassification.from_pretrained(
5 "wissamantoun/wissamantoun/WebOrganizer-TopicClassifier-ModernBERT",
6 trust_remote_code=True,
7 use_memory_efficient_attention=False)
8
9web_page = """http://www.example.com
10
11How to build a computer from scratch? Here are the components you need..."""
12
13inputs = tokenizer([web_page], return_tensors="pt")
14outputs = model(**inputs)
15
16probs = outputs.logits.softmax(dim=-1)
17print(probs.argmax(dim=-1))
18# -> 5 ("Hardware" topic)logits of the model with a softmax to obtain a probability distribution over the following 24 categories (in order of labels, also see id2label and label2id in the model config):***** pred metrics *****
test_accuracy = 0.8585
test_accuracy__0 = 0.9346
test_accuracy__1 = 0.7317
test_accuracy__10 = 0.9148
test_accuracy__11 = 0.8927
test_accuracy__12 = 0.8687
test_accuracy__13 = 0.814
test_accuracy__14 = 0.8616
test_accuracy__15 = 0.7179
test_accuracy__16 = 0.855
test_accuracy__17 = 0.8246
test_accuracy__18 = 0.907
test_accuracy__19 = 0.8333
test_accuracy__2 = 0.866
test_accuracy__20 = 0.8294
test_accuracy__21 = 0.9441
test_accuracy__22 = 0.8788
test_accuracy__23 = 0.9
test_accuracy__3 = 0.847
test_accuracy__4 = 0.8442
test_accuracy__5 = 0.8189
test_accuracy__6 = 0.8997
test_accuracy__7 = 0.7295
test_accuracy__8 = 0.8937
test_accuracy__9 = 0.8665
test_accuracy_conf50 = 0.8674
test_accuracy_conf50__0 = 0.9434
test_accuracy_conf50__1 = 0.7453
test_accuracy_conf50__10 = 0.93
test_accuracy_conf50__11 = 0.8958
test_accuracy_conf50__12 = 0.8768
test_accuracy_conf50__13 = 0.8193
test_accuracy_conf50__14 = 0.8691
test_accuracy_conf50__15 = 0.7237
test_accuracy_conf50__16 = 0.864
test_accuracy_conf50__17 = 0.8358
test_accuracy_conf50__18 = 0.91
test_accuracy_conf50__19 = 0.8481
test_accuracy_conf50__2 = 0.8768
test_accuracy_conf50__20 = 0.8434
test_accuracy_conf50__21 = 0.9505
test_accuracy_conf50__22 = 0.8844
test_accuracy_conf50__23 = 0.9028
test_accuracy_conf50__3 = 0.8571
test_accuracy_conf50__4 = 0.851
test_accuracy_conf50__5 = 0.8206
test_accuracy_conf50__6 = 0.9071
test_accuracy_conf50__7 = 0.7442
test_accuracy_conf50__8 = 0.9006
test_accuracy_conf50__9 = 0.8761
test_accuracy_conf75 = 0.9178 <--- Metric from the paper
test_accuracy_conf75__0 = 0.95
test_accuracy_conf75__1 = 0.8413
test_accuracy_conf75__10 = 0.9556
test_accuracy_conf75__11 = 0.9298
test_accuracy_conf75__12 = 0.9299
test_accuracy_conf75__13 = 0.8788
test_accuracy_conf75__14 = 0.9126
test_accuracy_conf75__15 = 0.8253
test_accuracy_conf75__16 = 0.8885
test_accuracy_conf75__17 = 0.8968
test_accuracy_conf75__18 = 0.938
test_accuracy_conf75__19 = 0.9113
test_accuracy_conf75__2 = 0.9029
test_accuracy_conf75__20 = 0.8966
test_accuracy_conf75__21 = 0.968
test_accuracy_conf75__22 = 0.9225
test_accuracy_conf75__23 = 0.9444
test_accuracy_conf75__3 = 0.9319
test_accuracy_conf75__4 = 0.8976
test_accuracy_conf75__5 = 0.9167
test_accuracy_conf75__6 = 0.9483
test_accuracy_conf75__7 = 0.804
test_accuracy_conf75__8 = 0.9448
test_accuracy_conf75__9 = 0.932
test_accuracy_label_average = 0.8531
test_accuracy_label_average_conf50 = 0.8615
test_accuracy_label_average_conf75 = 0.9111
test_accuracy_label_min = 0.7179
test_accuracy_label_min_conf50 = 0.7237
test_accuracy_label_min_conf75 = 0.804 <--- Metric from the paper
test_loss = 0.4694
test_proportion_conf50 = 0.9811
test_proportion_conf75 = 0.8535
test_runtime = 0:00:08.39
test_samples_per_second = 1191.144
test_steps_per_second = 37.2831@article{wettig2025organize,
2 title={Organize the Web: Constructing Domains Enhances Pre-Training Data Curation},
3 author={Alexander Wettig and Kyle Lo and Sewon Min and Hannaneh Hajishirzi and Danqi Chen and Luca Soldaini},
4 journal={arXiv preprint arXiv:2502.10341},
5 year={2025}
6}