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--- |
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--- |
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license: mit |
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language: |
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- pl |
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tags: |
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- zero-shot-classification |
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- text-classification |
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- pytorch |
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metrics: |
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- accuracy |
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- f1-score |
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--- |
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# xlm-roberta-large-polish-cap |
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## Model description |
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An `xlm-roberta-large` model finetuned on polish training data labelled with [major topic codes](https://www.comparativeagendas.net/pages/master-codebook) from the [Comparative Agendas Project](https://www.comparativeagendas.net/). |
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## How to use the model |
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#### Loading and tokenizing input data |
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```python |
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import pandas as pd |
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import numpy as np |
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from datasets import Dataset |
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from transformers import (AutoModelForSequenceClassification, AutoTokenizer, |
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Trainer, TrainingArguments) |
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CAP_NUM_DICT = {0: '1', 1: '2', 2: '3', 3: '4', 4: '5', 5: '6', |
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6: '7', 7: '8', 8: '9', 9: '10', 10: '12', 11: '13', 12: '14', |
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13: '15', 14: '16', 15: '17', 16: '18', 17: '19', 18: '20', 19: |
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'21', 20: '23', 21: '999'} |
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tokenizer = AutoTokenizer.from_pretrained('xlm-roberta-large') |
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num_labels = len(CAP_NUM_DICT) |
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def tokenize_dataset(data : pd.DataFrame): |
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tokenized = tokenizer(data["text"], |
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max_length=MAXLEN, |
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truncation=True, |
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padding="max_length") |
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return tokenized |
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hg_data = Dataset.from_pandas(data) |
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dataset = hg_data.map(tokenize_dataset, batched=True, remove_columns=hg_data.column_names) |
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``` |
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#### Inference using the Trainer class |
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```python |
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model = AutoModelForSequenceClassification.from_pretrained('poltextlab/xlm-roberta-large-polish-cap', |
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num_labels=num_labels, |
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problem_type="multi_label_classification", |
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ignore_mismatched_sizes=True |
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) |
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training_args = TrainingArguments( |
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output_dir='.', |
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per_device_train_batch_size=8, |
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per_device_eval_batch_size=8 |
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) |
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trainer = Trainer( |
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model=model, |
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args=training_args |
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) |
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probs = trainer.predict(test_dataset=dataset).predictions |
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predicted = pd.DataFrame(np.argmax(probs, axis=1)).replace({0: CAP_NUM_DICT}).rename( |
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columns={0: 'predicted'}).reset_index(drop=True) |
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``` |
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### Fine-tuning procedure |
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`xlm-roberta-large-polish-cap` was fine-tuned using the Hugging Face Trainer class with the following hyperparameters: |
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``` |
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training_args = TrainingArguments( |
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output_dir=f"../model/{model_dir}/tmp/", |
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logging_dir=f"../logs/{model_dir}/", |
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logging_strategy='epoch', |
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num_train_epochs=10, |
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per_device_train_batch_size=args.batch, |
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per_device_eval_batch_size=args.batch, |
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learning_rate=args.lr, |
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seed=42, |
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save_strategy='epoch', |
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evaluation_strategy='epoch', |
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save_total_limit=1, |
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load_best_model_at_end=True |
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) |
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``` |
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We also incorporated an EarlyStoppingCallback in the process with a patience of 2 epochs. |
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## Model performance |
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The model was evaluated on a test set of 71 examples (10% of the available data).<br> |
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Model accuracy is **0.76**. |
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| label | precision | recall | f1-score | support | |
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|:-------------|------------:|---------:|-----------:|----------:| |
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| 0 | 0 | 0 | 0 | 4 | |
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| 1 | 1 | 1 | 1 | 3 | |
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| 2 | 1 | 1 | 1 | 4 | |
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| 3 | 0.56 | 1 | 0.71 | 5 | |
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| 4 | 0 | 0 | 0 | 1 | |
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| 5 | 1 | 1 | 1 | 13 | |
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| 6 | 0 | 0 | 0 | 4 | |
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| 7 | 0 | 0 | 0 | 0 | |
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| 8 | 0 | 0 | 0 | 0 | |
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| 9 | 0.4 | 1 | 0.57 | 2 | |
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| 10 | 1 | 0.8 | 0.89 | 5 | |
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| 11 | 1 | 1 | 1 | 3 | |
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| 12 | 1 | 0.33 | 0.5 | 3 | |
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| 13 | 0.69 | 0.82 | 0.75 | 11 | |
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| 14 | 0 | 0 | 0 | 0 | |
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| 15 | 0 | 0 | 0 | 1 | |
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| 16 | 0 | 0 | 0 | 0 | |
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| 17 | 0 | 0 | 0 | 0 | |
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| 18 | 0.91 | 0.83 | 0.87 | 12 | |
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| 19 | 0 | 0 | 0 | 0 | |
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| macro avg | 0.43 | 0.44 | 0.41 | 71 | |
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| weighted avg | 0.75 | 0.76 | 0.74 | 71 | |
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## Inference platform |
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This model is used by the [CAP Babel Machine](https://babel.poltextlab.com), an open-source and free natural language processing tool, designed to simplify and speed up projects for comparative research. |
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## Cooperation |
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Model performance can be significantly improved by extending our training sets. We appreciate every submission of CAP-coded corpora (of any domain and language) at poltextlab{at}poltextlab{dot}com or by using the [CAP Babel Machine](https://babel.poltextlab.com). |
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## Debugging and issues |
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This architecture uses the `sentencepiece` tokenizer. In order to run the model before `transformers==4.27` you need to install it manually. |
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If you encounter a `RuntimeError` when loading the model using the `from_pretrained()` method, adding `ignore_mismatched_sizes=True` should solve the issue. |
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