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--- |
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license: mit |
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datasets: |
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- astromis/presuicidal_signals |
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language: |
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- ru |
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metrics: |
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- f1 |
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library_name: transformers |
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pipeline_tag: text-classification |
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tags: |
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- russian |
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- suicide |
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--- |
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# Presuicidal RuBERT base |
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The fine-tuned [ruBert](https://huggingface.co/ai-forever/ruBert-base) on the presuicidal dataset. Aims to help the psychologists to find text with useful information about person's suicide behavior. |
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The model has two categories: |
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* category 1 - the texts with useful information about person's suicidal behavior such as attempts and facts of rape, problems with parents, the fact of being in a psychiatric hospital, facts of self-harm, etc. Also, this category includes messages containing a display of subjective negative attitude towards oneself and others, including a desire to die, a feeling of pressure from the past, self-hatred, aggressiveness, rage directed at oneself or others. |
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* category 0 - normal texts that don't contain abovementioned information. |
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# How to use |
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```python |
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import torch |
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tokenizer = AutoTokenizer.from_pretrained("astromis/presuisidal_rubert") |
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model = BertForSequenceClassification.from_pretrained("astromis/presuisidal_rubert") |
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model.eval() |
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text = ["мне так плохо я хочу умереть", "вчера была на сходке с друзьями было оч клево"] |
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tokenized_text = tokenizer(text, padding="max_length", truncation=True, max_length=512, return_tensors="pt") |
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with torch.no_grad(): |
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prediction = model(**tokenized_text).logits |
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print(prediction.argmax(dim=1).numpy()) |
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# >>> [1, 0] |
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``` |
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# Training procedure |
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## Data preprocessing |
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Before training, the text was transformed in the next way: |
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* removed all emojis. In the dataset, they are marked as `<emoji>emoja_name</emoji>`; |
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* the punctuation was removed; |
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* text was lowered; |
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* all enters was swapped to spaces; |
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* all several spaces were collapsed. |
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As the dataset is heavily imbalanced, the train part of normal texts was randomly downsampled to have only 22% samples out of source volume. |
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## Training |
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The training was done with `Trainier` class that have next parameters: |
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``` |
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TrainingArguments(evaluation_strategy="epoch", |
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per_device_train_batch_size=16, |
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per_device_eval_batch_size=32, |
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learning_rate=1e-5, |
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num_train_epochs=5, |
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weight_decay=1e-3, |
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load_best_model_at_end=True, |
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save_strategy="epoch") |
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``` |
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# Metrics |
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| F1-micro | F1-macro | F1-weighted | |
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|----------|----------|-------------| |
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| 0.811926 | 0.726722 | 0.831000 | |
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# Citation |
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```bibxtex |
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@article {Buyanov2022TheDF, |
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title={The dataset for presuicidal signals detection in text and its analysis}, |
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author={Igor Buyanov and Ilya Sochenkov}, |
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journal={Computational Linguistics and Intellectual Technologies}, |
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year={2022}, |
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month={June}, |
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number={21}, |
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pages={81--92}, |
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url={https://api.semanticscholar.org/CorpusID:253195162}, |
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} |
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``` |
