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--- |
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license: apache-2.0 |
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--- |
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### Deprem NER Training Results |
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``` |
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precision recall f1-score support |
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0 0.85 0.91 0.88 734 |
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1 0.77 0.84 0.80 207 |
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2 0.71 0.88 0.79 130 |
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3 0.68 0.76 0.72 94 |
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4 0.80 0.85 0.82 362 |
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5 0.63 0.59 0.61 112 |
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6 0.73 0.82 0.77 108 |
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7 0.55 0.77 0.64 78 |
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8 0.65 0.71 0.68 31 |
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9 0.70 0.85 0.76 117 |
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micro avg 0.77 0.85 0.81 1973 |
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macro avg 0.71 0.80 0.75 1973 |
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weighted avg 0.77 0.85 0.81 1973 |
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samples avg 0.82 0.87 0.83 1973 |
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``` |
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### Preprocessing Funcs |
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``` |
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tr_stopwords = stopwords.words('turkish') |
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tr_stopwords.append("hic") |
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tr_stopwords.append("dm") |
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tr_stopwords.append("vs") |
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tr_stopwords.append("ya") |
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def remove_punct(tok): |
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tok = re.sub(r'[^\w\s]', '', tok) |
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return tok |
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def normalize(tok): |
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if tok.isdigit(): |
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tok = "digit" |
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return tok |
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def clean(tok): |
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tok = remove_punct(tok) |
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tok = normalize(tok) |
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return tok |
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def exceptions(tok): |
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if not tok.isdigit() and len(tok)==1: |
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return False |
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if not tok: |
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return False |
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if tok in tr_stopwords: |
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return False |
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if tok.startswith('#') or tok.startswith("@"): |
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return False |
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return True |
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sm_tok = lambda text: [clean(tok) for tok in text.split(" ") if exceptions(tok)] |
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``` |
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### Other HyperParams |
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``` |
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training_args = TrainingArguments( |
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output_dir="./output", |
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evaluation_strategy="epoch", |
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per_device_train_batch_size=32, |
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per_device_eval_batch_size=32, |
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weight_decay=0.01, |
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report_to=None, |
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num_train_epochs=4 |
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) |
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``` |
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``` |
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class_weights[0] = 1.0 |
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class_weights[1] = 1.5167249178108022 |
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class_weights[2] = 1.7547338578655642 |
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class_weights[3] = 1.9610520059358458 |
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class_weights[4] = 1.269341370129623 |
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class_weights[5] = 1.8684086209021484 |
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class_weights[6] = 1.8019018017117145 |
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class_weights[7] = 2.110648663094536 |
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class_weights[8] = 3.081208739200435 |
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class_weights[9] = 1.7994815143101963 |
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``` |
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Threshold: 0.25 |
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``` |
