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README.md

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+
+---
+---
+license: mit
+tags:
+- zero-shot-classification
+- text-classification
+- pytorch
+metrics:
+- accuracy
+- f1-score
+---
+# MODEL_NAME
+## Model description
+An `xlm-roberta-large` model finetuned on training data containing [major topic codes](https://www.comparativeagendas.net/pages/master-codebook) from the [Comparative Agendas Project](https://www.comparativeagendas.net/).
+
+## How to use the model
+#### Loading and tokenizing input data
+```python
+import pandas as pd
+import numpy as np
+from datasets import Dataset
+from transformers import (AutoModelForSequenceClassification, AutoTokenizer, 
+                          Trainer, TrainingArguments)
+
+CAP_NUM_DICT = {0: '1', 1: '2', 2: '3', 3: '4', 4: '5', 5: '6', 
+6: '7', 7: '8', 8: '9', 9: '10', 10: '12', 11: '13', 12: '14', 
+13: '15', 14: '16', 15: '17', 16: '18', 17: '19', 18: '20', 19: 
+'21', 20: '23', 21: '999'}
+
+tokenizer = AutoTokenizer.from_pretrained('xlm-roberta-large')
+num_labels = len(CAP_NUM_DICT)
+
+def tokenize_dataset(data : pd.DataFrame):
+    tokenized = tokenizer(data["text"],
+                          max_length=MAXLEN,
+                          truncation=True,
+                          padding="max_length")
+    return tokenized
+
+hg_data = Dataset.from_pandas(data)
+dataset = hg_data.map(tokenize_dataset, batched=True, remove_columns=hg_data.column_names)
+```
+
+#### Inference using the Trainer class
+```python
+model = AutoModelForSequenceClassification.from_pretrained('poltextlab/MODEL_NAME',
+                                                           num_labels=num_labels,
+                                                           problem_type="multi_label_classification",
+                                                           ignore_mismatched_sizes=True
+                                                           )
+
+training_args = TrainingArguments(
+    output_dir='.',
+    per_device_train_batch_size=BATCH,
+    per_device_eval_batch_size=BATCH
+)
+
+trainer = Trainer(
+    model=model,
+    args=training_args
+)
+
+probs = trainer.predict(test_dataset=dataset).predictions
+predicted = pd.DataFrame(np.argmax(probs, axis=1)).replace({0: CAP_NUM_DICT}).rename(
+    columns={0: 'predicted'}).reset_index(drop=True)
+
+```
+
+### Fine-tuning procedure
+`MODEL_NAME` was fine-tuned using the Hugging Face Trainer class with the following hyperparameters:
+```
+training_args = TrainingArguments(
+    output_dir=f"../model/{model_dir}/tmp/",
+    logging_dir=f"../logs/{model_dir}/",
+    logging_strategy='epoch',
+    num_train_epochs=10,
+    per_device_train_batch_size=args.batch,
+    per_device_eval_batch_size=args.batch,
+    learning_rate=args.lr,
+    seed=42,
+    save_strategy='epoch',
+    evaluation_strategy='epoch',
+    save_total_limit=1,
+    load_best_model_at_end=True
+)
+```
+We also incorporated an EarlyStoppingCallback in the process with a patience of 2 epochs.
+
+## Model performance
+The model was evaluated on a test set of NUM_TEST_SET examples (10% of the available data). 
+Model accuracy is **0.83**.
+METRICS_TABLE
+
+## Inference platform
+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.  
+
+## Cooperation
+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).
+
+## Debugging and issues
+This architecture uses the `sentencepiece` tokenizer. In order to run the model before `transformers==4.27` you need to install it manually.
+
+If you encounter a `RuntimeError` when loading the model using the `from_pretrained()` method, adding `ignore_mismatched_sizes=True` should solve the issue.