# Finetune molformer model **URL:** **Author:** ![John6666](https://sea2.discourse-cdn.com/hellohellohello/user_avatar/discuss.huggingface.co/john6666/32/27664_2.png) [@John6666](/static-proxy?url=https%3A%2F%2Fdiscuss.huggingface.co%2Fu%2FJohn6666)%5C **Post date:** [March 21, 2025, 7:51am UTC](/static-proxy?url=https%3A%2F%2Fdiscuss.huggingface.co%2Ft%2Ffinetune-molformer-model%2F146740%2F2 "2025-03-21T07:51:46Z") If you search for each specific error on Google or Bing and fix it, it should work eventually. If you don’t understand, there’s no problem if you ask on the forum, HF Discord, or StackOverflow. ### MLM Training Tips > **[Fine-tuning a masked language model - Hugging Face LLM Course](https://huggingface.co/learn/llm-course/chapter7/3)** > > We’re on a journey to advance and democratize artificial intelligence through open source and open science. [https://medium.com/@lokaregns/masked-language-modeling-with-hugging-face-transformers-a-beginners-guide-b36af74a7560](https://medium.com/@lokaregns/masked-language-modeling-with-hugging-face-transformers-a-beginners-guide-b36af74a7560) > [@Fine tune Masked Language Model on custom dataset](/static-proxy?url=https%3A%2F%2Fdiscuss.huggingface.co%2Ft%2Ffine-tune-masked-language-model-on-custom-dataset%2F747%2F2)%3A > > Hi @smalltoken, what is the issue with [https://huggingface.co/blog/how-to-train](https://huggingface.co/blog/how-to-train) ? This colab should help you. It walks you through, How to to train tokenizer from scratch Create RobertaModel using the config use the DataCollatorForLanguageModeling, which handle the masking and train using Trainer. > [@Preparing a nlp dataset for MLM](/static-proxy?url=https%3A%2F%2Fdiscuss.huggingface.co%2Ft%2Fpreparing-a-nlp-dataset-for-mlm%2F3652%2F5)%3A > > If you’re preparing an NLP dataset for a Masked Language Model (MLM), it’s important to have high-quality, diverse data to ensure the model can effectively understand and predict contextual language. For a comprehensive list of NLP datasets to help you get started, check out this blog: - [Top NLP Datasets to Supercharge Your Machine Learning Models](https://www.shaip.com/blog/15-nlp-dataset-for-nlp/) . These datasets offer a variety of text sources that can support a range of NLP tasks, including MLM training. ### Code example by [Hugging Chat](https://huggingface.co/chat/) * * * To train the `ibm-research/MoLFormer-XL-both-10pct` model on your custom dataset using Hugging Face’s `datasets` library and `Trainer`, I’ll provide a step-by-step guide along with sample code. This will help you set up the dataset, preprocess it, and train the model for the fill-mask task. * * * ### **1. Loading and Preprocessing the Custom Dataset** First, you need to load your custom dataset (CSV file) and preprocess it for the fill-mask task. #### Sample Code: ```python from datasets import load_dataset import pandas as pd from transformers import AutoTokenizer # Load your custom CSV dataset def load_custom_dataset(csv_path): # Read the CSV file df = pd.read_csv(csv_path) # Ensure the dataset has columns like 'text' and 'label' or similar # For a fill-mask task, you might have sentences with masked words dataset = load_dataset('csv', data_files=csv_path) return dataset # Initialize the tokenizer tokenizer = AutoTokenizer.from_pretrained("ibm-research/MoLFormer-XL-both-10pct") # Load your custom dataset dataset = load_custom_dataset("your_dataset.csv") # Preprocess the dataset def preprocess_function(examples): # Tokenize the input text return tokenizer(examples['text'], truncation=True, padding=True) # Apply preprocessing to the dataset tokenized_datasets = dataset.map(preprocess_function, batched=True) ``` * * * ### **2. Setting Up the Model and Training Arguments** Next, initialize the model and set up the training arguments using `TrainingArguments`. #### Sample Code: ```python from transformers import AutoModelForMaskedLM, TrainingArguments, Trainer # Initialize the model model = AutoModelForMaskedLM.from_pretrained("ibm-research/MoLFormer-XL-both-10pct") # Set up training arguments training_args = TrainingArguments( output_dir='./results', num_train_epochs=3, per_device_train_batch_size=32, per_device_eval_batch_size=32, learning_rate=2e-5, weight_decay=0.01, logging_dir='./logs', logging_steps=10, evaluation_strategy="epoch", save_strategy="epoch", seed=42, ) ``` * * * ### **3. Defining the Training Loop** Use the `Trainer` class to set up the training loop for the fill-mask task. #### Sample Code: ```python # Initialize the trainer trainer = Trainer( model=model, args=training_args, train_dataset=tokenized_datasets['train'], eval_dataset=tokenized_datasets['test'], ) # Train the model trainer.train() # Evaluate the model results = trainer.evaluate() print(f"Perplexity: {results['eval_loss']}") ``` * * * ### **4. Fine-Tuning the Model** If you want to fine-tune the model further, you can use custom data processing or add callbacks for better control. #### Sample Code: ```python # Fine-tune the model with custom training arguments trainer = Trainer( model=model, args=training_args, train_dataset=tokenized_datasets['train'], eval_dataset=tokenized_datasets['test'], ) # Train the model trainer.train() ``` * * * ### **5. Saving and Loading the Model** After training, save the model and tokenizer for future use. #### Sample Code: ```python # Save the trained model model.save_pretrained("./trained_model") # Save the tokenizer tokenizer.save_pretrained("./trained_model") # Load the saved model for inference loaded_model = AutoModelForMaskedLM.from_pretrained("./trained_model") loaded_tokenizer = AutoTokenizer.from_pretrained("./trained_model") ``` * * * ### **6. Evaluating the Model** Evaluate the model on the test dataset to measure its performance. #### Sample Code: ```python # Evaluate the model evaluation_results = trainer.evaluate() # Print evaluation metrics print(f"Perplexity: {evaluation_results['eval_loss']}") ``` * * * ### **7. Testing the Model** Once the model is trained, you can test it on custom inputs. #### Sample Code: ```python # Test the model input_text = "This is a [mask] example for testing." inputs = tokenizer(input_text, return_tensors='pt') outputs = model(**inputs) # Get the predicted token IDs predicted_ids = torch.argmax(outputs.logits, dim=2) decoded_tokens = tokenizer.decode(predicted_ids[0]) print("Predicted text:", decoded_tokens) ``` * * * ### **Final Notes** - Make sure your CSV dataset is formatted correctly, with each row containing sentences and masked words suitable for the fill-mask task. - Ensure that you have the correct tokenization and padding settings for your dataset. - Consider using `DataCollatorForLanguageModeling` if you need more control over the masking process. If you encounter specific errors, feel free to provide more details, and I can help troubleshoot them! For further reference, you can consult the official Hugging Face documentation on [datasets](https://huggingface.co/docs/datasets/quickstart) and [trainers](https://huggingface.co/docs/transformers/training). --- _[View the full topic](/static-proxy?url=https%3A%2F%2Fdiscuss.huggingface.co%2Ft%2Ffinetune-molformer-model%2F146740)._